From 311abb7b377292bd94895a5d6088ac4f4b9e2981 Mon Sep 17 00:00:00 2001 From: Takuya Narihira Date: Wed, 25 Feb 2015 12:10:49 -0800 Subject: [PATCH 001/446] Fix incorrectly storing empty param_name --- src/caffe/net.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index c359be9b575..49b53956858 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -402,7 +402,7 @@ void Net::AppendParam(const NetParameter& param, const int layer_id, // (i.e., not given a param_name) or explicitly given a name that we // haven't already seen. param_owners_.push_back(-1); - if (param_size) { + if (param_name.size()) { param_names_index_[param_name] = net_param_id; } } else { From 724af109a84a7daa9c721e2091f0e8b68fb156fd Mon Sep 17 00:00:00 2001 From: Rohit Durvasula Date: Sat, 7 Mar 2015 11:00:40 -0500 Subject: [PATCH 002/446] Update readme.md In the resume training option.. the name of the solver state changed to 'caffenet_train_iter_10000.solverstate' --- examples/imagenet/readme.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md index a6bdf49ca4d..b1ebfafbf46 100644 --- a/examples/imagenet/readme.md +++ b/examples/imagenet/readme.md @@ -91,9 +91,9 @@ Resume Training? We all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does anyone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as: - ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate + ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_iter_10000.solverstate -where in the script `caffenet_train_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc). +where in the script `caffenet_train_iter_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc). Parting Words ------------- From ba933d3f9714d7aa6836842158829aba7c279c60 Mon Sep 17 00:00:00 2001 From: Zifei Tong Date: Thu, 12 Mar 2015 20:09:36 +0800 Subject: [PATCH 003/446] [docs] Add missing command in OS X installation guide --- docs/install_osx.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/install_osx.md b/docs/install_osx.md index 0373a417847..e1a1d569171 100644 --- a/docs/install_osx.md +++ b/docs/install_osx.md @@ -18,7 +18,7 @@ In other `ENV` settings, things may not work as expected. brew install --fresh -vd snappy leveldb gflags glog szip lmdb # need the homebrew science source for OpenCV and hdf5 brew tap homebrew/science - hdf5 opencv + brew install hdf5 opencv If using Anaconda Python, a modification to the OpenCV formula might be needed Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below. From 58deb58084a61f70986e2ba6c2e98709febd6e35 Mon Sep 17 00:00:00 2001 From: Zifei Tong Date: Thu, 12 Mar 2015 20:11:52 +0800 Subject: [PATCH 004/446] [docs] Remove `--fresh` Homebrew option Since it's removed from Homebrew [1]. [1]: Homebrew/homebrew@8cdf4d8ebf439eb9a9ffcaa0e455ced9459e1e41 --- docs/install_osx.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/install_osx.md b/docs/install_osx.md index e1a1d569171..fbfc71e8f39 100644 --- a/docs/install_osx.md +++ b/docs/install_osx.md @@ -15,7 +15,7 @@ In other `ENV` settings, things may not work as expected. **General dependencies** - brew install --fresh -vd snappy leveldb gflags glog szip lmdb + brew install -vd snappy leveldb gflags glog szip lmdb # need the homebrew science source for OpenCV and hdf5 brew tap homebrew/science brew install hdf5 opencv @@ -31,8 +31,8 @@ If using Anaconda Python, HDF5 is bundled and the `hdf5` formula can be skipped. **Remaining dependencies, with / without Python** # with Python pycaffe needs dependencies built from source - brew install --build-from-source --with-python --fresh -vd protobuf - brew install --build-from-source --fresh -vd boost boost-python + brew install --build-from-source --with-python -vd protobuf + brew install --build-from-source -vd boost boost-python # without Python the usual installation suffices brew install protobuf boost @@ -78,9 +78,9 @@ To edit the formulae in turn, run After this, run - for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done - brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf - brew install --build-from-source --fresh -vd boost boost-python + for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source -vd $x; done + brew uninstall protobuf; brew install --build-from-source --with-python -vd protobuf + brew install --build-from-source -vd boost boost-python If this is not done exactly right then linking errors will trouble you. From 40ca4e68097589a6143a6754328a1f94786c118a Mon Sep 17 00:00:00 2001 From: tishibas67 Date: Wed, 18 Mar 2015 00:11:01 +0900 Subject: [PATCH 005/446] improved to load RGB image as grayscale image --- python/caffe/io.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/caffe/io.py b/python/caffe/io.py index 6ae2cf13cc0..acd8a1427c8 100644 --- a/python/caffe/io.py +++ b/python/caffe/io.py @@ -285,7 +285,7 @@ def load_image(filename, color=True): of size (H x W x 3) in RGB or of size (H x W x 1) in grayscale. """ - img = skimage.img_as_float(skimage.io.imread(filename)).astype(np.float32) + img = skimage.img_as_float(skimage.io.imread(filename, as_grey=not color)).astype(np.float32) if img.ndim == 2: img = img[:, :, np.newaxis] if color: From dfdf2bbe296fa42361b3984c112d7fcffde6df78 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Thu, 19 Mar 2015 11:31:17 -0700 Subject: [PATCH 006/446] CUDA kernels for {Slice,Concat}Layer --- src/caffe/layers/concat_layer.cu | 44 ++++++++++++++++++++++-------- src/caffe/layers/slice_layer.cu | 47 ++++++++++++++++++++++---------- 2 files changed, 66 insertions(+), 25 deletions(-) diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu index dbadb5aeb30..8f2e85d8f52 100644 --- a/src/caffe/layers/concat_layer.cu +++ b/src/caffe/layers/concat_layer.cu @@ -6,21 +6,41 @@ namespace caffe { +template +__global__ void Concat(const int nthreads, const Dtype* in_data, + const bool forward, const int num_concats, const int concat_size, + const int top_concat_axis, const int bottom_concat_axis, + const int offset_concat_axis, Dtype* out_data) { + CUDA_KERNEL_LOOP(index, nthreads) { + const int total_concat_size = concat_size * bottom_concat_axis; + const int concat_num = index / total_concat_size; + const int concat_index = index % total_concat_size; + const int top_index = concat_index + + (concat_num * top_concat_axis + offset_concat_axis) * concat_size; + if (forward) { + out_data[top_index] = in_data[index]; + } else { + out_data[index] = in_data[top_index]; + } + } +} + template void ConcatLayer::Forward_gpu(const vector*>& bottom, const vector*>& top) { Dtype* top_data = top[0]->mutable_gpu_data(); int offset_concat_axis = 0; const int top_concat_axis = top[0]->shape(concat_axis_); + const bool kForward = true; for (int i = 0; i < bottom.size(); ++i) { const Dtype* bottom_data = bottom[i]->gpu_data(); const int bottom_concat_axis = bottom[i]->shape(concat_axis_); - for (int n = 0; n < num_concats_; ++n) { - caffe_copy(bottom_concat_axis * concat_input_size_, - bottom_data + n * bottom_concat_axis * concat_input_size_, - top_data + (n * top_concat_axis + offset_concat_axis) - * concat_input_size_); - } + const int bottom_concat_size = bottom_concat_axis * concat_input_size_; + const int nthreads = bottom_concat_size * num_concats_; + Concat // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + nthreads, bottom_data, kForward, num_concats_, concat_input_size_, + top_concat_axis, bottom_concat_axis, offset_concat_axis, top_data); offset_concat_axis += bottom_concat_axis; } } @@ -31,15 +51,17 @@ void ConcatLayer::Backward_gpu(const vector*>& top, const Dtype* top_diff = top[0]->gpu_diff(); int offset_concat_axis = 0; const int top_concat_axis = top[0]->shape(concat_axis_); + const bool kForward = false; for (int i = 0; i < bottom.size(); ++i) { if (!propagate_down[i]) { continue; } Dtype* bottom_diff = bottom[i]->mutable_gpu_diff(); const int bottom_concat_axis = bottom[i]->shape(concat_axis_); - for (int n = 0; n < num_concats_; ++n) { - caffe_copy(bottom_concat_axis * concat_input_size_, top_diff + - (n * top_concat_axis + offset_concat_axis) * concat_input_size_, - bottom_diff + n * bottom_concat_axis * concat_input_size_); - } + const int bottom_concat_size = bottom_concat_axis * concat_input_size_; + const int nthreads = bottom_concat_size * num_concats_; + Concat // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + nthreads, top_diff, kForward, num_concats_, concat_input_size_, + top_concat_axis, bottom_concat_axis, offset_concat_axis, bottom_diff); offset_concat_axis += bottom_concat_axis; } } diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu index e6e65677bd8..796841d3f52 100644 --- a/src/caffe/layers/slice_layer.cu +++ b/src/caffe/layers/slice_layer.cu @@ -6,22 +6,41 @@ namespace caffe { +template +__global__ void Slice(const int nthreads, const Dtype* in_data, + const bool forward, const int num_slices, const int slice_size, + const int bottom_slice_axis, const int top_slice_axis, + const int offset_slice_axis, Dtype* out_data) { + CUDA_KERNEL_LOOP(index, nthreads) { + const int total_slice_size = slice_size * top_slice_axis; + const int slice_num = index / total_slice_size; + const int slice_index = index % total_slice_size; + const int bottom_index = slice_index + + (slice_num * bottom_slice_axis + offset_slice_axis) * slice_size; + if (forward) { + out_data[index] = in_data[bottom_index]; + } else { + out_data[bottom_index] = in_data[index]; + } + } +} + template void SliceLayer::Forward_gpu(const vector*>& bottom, const vector*>& top) { int offset_slice_axis = 0; const Dtype* bottom_data = bottom[0]->gpu_data(); const int bottom_slice_axis = bottom[0]->shape(slice_axis_); + const bool kForward = true; for (int i = 0; i < top.size(); ++i) { Dtype* top_data = top[i]->mutable_gpu_data(); const int top_slice_axis = top[i]->shape(slice_axis_); - for (int n = 0; n < num_slices_; ++n) { - const int top_offset = n * top_slice_axis * slice_size_; - const int bottom_offset = - (n * bottom_slice_axis + offset_slice_axis) * slice_size_; - caffe_copy(top_slice_axis * slice_size_, - bottom_data + bottom_offset, top_data + top_offset); - } + const int top_slice_size = top_slice_axis * slice_size_; + const int nthreads = top_slice_size * num_slices_; + Slice // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + nthreads, bottom_data, kForward, num_slices_, slice_size_, + bottom_slice_axis, top_slice_axis, offset_slice_axis, top_data); offset_slice_axis += top_slice_axis; } } @@ -33,16 +52,16 @@ void SliceLayer::Backward_gpu(const vector*>& top, int offset_slice_axis = 0; Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); const int bottom_slice_axis = bottom[0]->shape(slice_axis_); + const bool kForward = false; for (int i = 0; i < top.size(); ++i) { const Dtype* top_diff = top[i]->gpu_diff(); const int top_slice_axis = top[i]->shape(slice_axis_); - for (int n = 0; n < num_slices_; ++n) { - const int top_offset = n * top_slice_axis * slice_size_; - const int bottom_offset = - (n * bottom_slice_axis + offset_slice_axis) * slice_size_; - caffe_copy(top_slice_axis * slice_size_, - top_diff + top_offset, bottom_diff + bottom_offset); - } + const int top_slice_size = top_slice_axis * slice_size_; + const int nthreads = top_slice_size * num_slices_; + Slice // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + nthreads, top_diff, kForward, num_slices_, slice_size_, + bottom_slice_axis, top_slice_axis, offset_slice_axis, bottom_diff); offset_slice_axis += top_slice_axis; } } From 1a1ce5a4ef3897a0e3b40ebf243786b2f8a37667 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Wed, 4 Mar 2015 16:18:17 -0800 Subject: [PATCH 007/446] remove spurious net.hpp includes --- include/caffe/data_layers.hpp | 1 - include/caffe/neuron_layers.hpp | 1 - src/caffe/layers/base_data_layer.cpp | 1 - 3 files changed, 3 deletions(-) diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp index 2bb9d948169..24dfe723636 100644 --- a/include/caffe/data_layers.hpp +++ b/include/caffe/data_layers.hpp @@ -14,7 +14,6 @@ #include "caffe/filler.hpp" #include "caffe/internal_thread.hpp" #include "caffe/layer.hpp" -#include "caffe/net.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/db.hpp" diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp index 8669923d1fa..37553b9ee71 100644 --- a/include/caffe/neuron_layers.hpp +++ b/include/caffe/neuron_layers.hpp @@ -8,7 +8,6 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/layer.hpp" -#include "caffe/net.hpp" #include "caffe/proto/caffe.pb.h" #define HDF5_DATA_DATASET_NAME "data" diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp index 352200915d7..931e4a9c0ab 100644 --- a/src/caffe/layers/base_data_layer.cpp +++ b/src/caffe/layers/base_data_layer.cpp @@ -2,7 +2,6 @@ #include #include "caffe/data_layers.hpp" -#include "caffe/net.hpp" #include "caffe/util/io.hpp" namespace caffe { From 10ddfd32cb5ca4132902e9453fb05242ef52ec07 Mon Sep 17 00:00:00 2001 From: Nuno Subtil Date: Wed, 1 Apr 2015 19:10:55 -0700 Subject: [PATCH 008/446] Build gflags and glog through CMake if not found in the system This adds functionality to fetch gflags and glog from GitHub and build them during the Caffe build. This happens only if a system-wide installed version is not found (i.e., when find_package() fails). If built as part of the Caffe build, gflags and glog are compiled as position-independent static libraries. This avoids doing a system-wide install of these libraries during the Caffe install target. --- CMakeLists.txt | 2 ++ cmake/Dependencies.cmake | 4 +-- cmake/External/gflags.cmake | 56 +++++++++++++++++++++++++++++++++++++ cmake/External/glog.cmake | 56 +++++++++++++++++++++++++++++++++++++ cmake/Targets.cmake | 4 +++ 5 files changed, 120 insertions(+), 2 deletions(-) create mode 100644 cmake/External/gflags.cmake create mode 100644 cmake/External/glog.cmake diff --git a/CMakeLists.txt b/CMakeLists.txt index 74fa70c9d20..f22aa5763a3 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -6,6 +6,8 @@ project(Caffe C CXX) # ---[ Using cmake scripts and modules list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules) +include(ExternalProject) + include(cmake/Utils.cmake) include(cmake/Targets.cmake) include(cmake/Misc.cmake) diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake index f328e8246ab..7cae5c9da25 100644 --- a/cmake/Dependencies.cmake +++ b/cmake/Dependencies.cmake @@ -11,12 +11,12 @@ find_package(Threads REQUIRED) list(APPEND Caffe_LINKER_LIBS ${CMAKE_THREAD_LIBS_INIT}) # ---[ Google-glog -find_package(Glog REQUIRED) +include("cmake/External/glog.cmake") include_directories(SYSTEM ${GLOG_INCLUDE_DIRS}) list(APPEND Caffe_LINKER_LIBS ${GLOG_LIBRARIES}) # ---[ Google-gflags -find_package(GFlags REQUIRED) +include("cmake/External/gflags.cmake") include_directories(SYSTEM ${GFLAGS_INCLUDE_DIRS}) list(APPEND Caffe_LINKER_LIBS ${GFLAGS_LIBRARIES}) diff --git a/cmake/External/gflags.cmake b/cmake/External/gflags.cmake new file mode 100644 index 00000000000..a50a0925086 --- /dev/null +++ b/cmake/External/gflags.cmake @@ -0,0 +1,56 @@ +if (NOT __GFLAGS_INCLUDED) # guard against multiple includes + set(__GFLAGS_INCLUDED TRUE) + + # use the system-wide gflags if present + find_package(GFlags) + if (GFLAGS_FOUND) + set(GFLAGS_EXTERNAL FALSE) + else() + # gflags will use pthreads if it's available in the system, so we must link with it + find_package(Threads) + + # build directory + set(gflags_PREFIX ${CMAKE_BINARY_DIR}/external/gflags-prefix) + # install directory + set(gflags_INSTALL ${CMAKE_BINARY_DIR}/external/gflags-install) + + # we build gflags statically, but want to link it into the caffe shared library + # this requires position-independent code + if (UNIX) + set(GFLAGS_EXTRA_COMPILER_FLAGS "-fPIC") + endif() + + set(GFLAGS_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS}) + set(GFLAGS_C_FLAGS ${CMAKE_C_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS}) + + ExternalProject_Add(gflags + PREFIX ${gflags_PREFIX} + GIT_REPOSITORY "https://github.com/gflags/gflags.git" + GIT_TAG "v2.1.2" + UPDATE_COMMAND "" + INSTALL_DIR ${gflags_INSTALL} + CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} + -DCMAKE_INSTALL_PREFIX=${gflags_INSTALL} + -DBUILD_SHARED_LIBS=OFF + -DBUILD_STATIC_LIBS=ON + -DBUILD_PACKAGING=OFF + -DBUILD_TESTING=OFF + -DBUILD_NC_TESTS=OFF + -BUILD_CONFIG_TESTS=OFF + -DINSTALL_HEADERS=ON + -DCMAKE_C_FLAGS=${GFLAGS_C_FLAGS} + -DCMAKE_CXX_FLAGS=${GFLAGS_CXX_FLAGS} + LOG_DOWNLOAD 1 + LOG_INSTALL 1 + ) + + set(GFLAGS_FOUND TRUE) + set(GFLAGS_INCLUDE_DIRS ${gflags_INSTALL}/include) + set(GFLAGS_LIBRARIES ${gflags_INSTALL}/lib/libgflags.a ${CMAKE_THREAD_LIBS_INIT}) + set(GFLAGS_LIBRARY_DIRS ${gflags_INSTALL}/lib) + set(GFLAGS_EXTERNAL TRUE) + + list(APPEND external_project_dependencies gflags) + endif() + +endif() diff --git a/cmake/External/glog.cmake b/cmake/External/glog.cmake new file mode 100644 index 00000000000..02b39dde676 --- /dev/null +++ b/cmake/External/glog.cmake @@ -0,0 +1,56 @@ +# glog depends on gflags +include("cmake/External/gflags.cmake") + +if (NOT __GLOG_INCLUDED) + set(__GLOG_INCLUDED TRUE) + + # try the system-wide glog first + find_package(Glog) + if (GLOG_FOUND) + set(GLOG_EXTERNAL FALSE) + else() + # fetch and build glog from github + + # build directory + set(glog_PREFIX ${CMAKE_BINARY_DIR}/external/glog-prefix) + # install directory + set(glog_INSTALL ${CMAKE_BINARY_DIR}/external/glog-install) + + # we build glog statically, but want to link it into the caffe shared library + # this requires position-independent code + if (UNIX) + set(GLOG_EXTRA_COMPILER_FLAGS "-fPIC") + endif() + + set(GLOG_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS}) + set(GLOG_C_FLAGS ${CMAKE_C_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS}) + + # depend on gflags if we're also building it + if (GFLAGS_EXTERNAL) + set(GLOG_DEPENDS gflags) + endif() + + ExternalProject_Add(glog + DEPENDS ${GLOG_DEPENDS} + PREFIX ${glog_PREFIX} + GIT_REPOSITORY "https://github.com/google/glog" + GIT_TAG "v0.3.4" + UPDATE_COMMAND "" + INSTALL_DIR ${gflags_INSTALL} + CONFIGURE_COMMAND env "CFLAGS=${GLOG_C_FLAGS}" "CXXFLAGS=${GLOG_CXX_FLAGS}" ${glog_PREFIX}/src/glog/configure --prefix=${glog_INSTALL} --enable-shared=no --enable-static=yes --with-gflags=${GFLAGS_LIBRARY_DIRS}/.. + LOG_DOWNLOAD 1 + LOG_CONFIGURE 1 + LOG_INSTALL 1 + ) + + set(GLOG_FOUND TRUE) + set(GLOG_INCLUDE_DIRS ${glog_INSTALL}/include) + set(GLOG_LIBRARIES ${GFLAGS_LIBRARIES} ${glog_INSTALL}/lib/libglog.a) + set(GLOG_LIBRARY_DIRS ${glog_INSTALL}/lib) + set(GLOG_EXTERNAL TRUE) + + list(APPEND external_project_dependencies glog) + endif() + +endif() + diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake index e3ad872313b..ed0ff9660fd 100644 --- a/cmake/Targets.cmake +++ b/cmake/Targets.cmake @@ -110,6 +110,10 @@ function(caffe_default_properties target) ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib" LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib" RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin") + # make sure we build all external depepdencies first + if (DEFINED external_project_dependencies) + add_dependencies(${target} ${external_project_dependencies}) + endif() endfunction() ################################################################################################ From b45afa216deb99eecf838d1e927ee517a85b898f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Martin=20Ha=CC=88cker?= Date: Thu, 2 Apr 2015 22:50:55 +0200 Subject: [PATCH 009/446] Add commented out helpers for homebrew users --- Makefile.config.example | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/Makefile.config.example b/Makefile.config.example index 7a8aafd7c9f..a873502559f 100644 --- a/Makefile.config.example +++ b/Makefile.config.example @@ -37,6 +37,10 @@ BLAS := atlas # BLAS_INCLUDE := /path/to/your/blas # BLAS_LIB := /path/to/your/blas +# Homebrew puts openblas in a directory that is not on the standard search path +# BLAS_INCLUDE := $(shell brew --prefix openblas)/include +# BLAS_LIB := $(shell brew --prefix openblas)/lib + # This is required only if you will compile the matlab interface. # MATLAB directory should contain the mex binary in /bin. # MATLAB_DIR := /usr/local @@ -57,6 +61,10 @@ PYTHON_INCLUDE := /usr/include/python2.7 \ PYTHON_LIB := /usr/lib # PYTHON_LIB := $(ANACONDA_HOME)/lib +# Homebrew installs numpy in a non standard path (keg only) +# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include +# PYTHON_LIB += $(shell brew --prefix numpy)/lib + # Uncomment to support layers written in Python (will link against Python libs) # WITH_PYTHON_LAYER := 1 @@ -64,6 +72,10 @@ PYTHON_LIB := /usr/lib INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib +# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies +# INCLUDE_DIRS += $(shell brew --prefix)/include +# LIBRARY_DIRS += $(shell brew --prefix)/lib + # Uncomment to use `pkg-config` to specify OpenCV library paths. # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.) # USE_PKG_CONFIG := 1 From cf5a306cf17f12f4dc1b9de6876182b320d75592 Mon Sep 17 00:00:00 2001 From: Ashwani001 Date: Tue, 7 Apr 2015 17:44:01 +0800 Subject: [PATCH 010/446] Update generate_sample_data.py Changed os.path.dirname(__file__) as it was returning empty... --- src/caffe/test/test_data/generate_sample_data.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py index e5dbc3406d8..3b49773c3fe 100644 --- a/src/caffe/test/test_data/generate_sample_data.py +++ b/src/caffe/test/test_data/generate_sample_data.py @@ -27,12 +27,12 @@ print data print label -with h5py.File(os.path.dirname(__file__) + '/sample_data.h5', 'w') as f: +with h5py.File(os.path.dirname(os.path.abspath(__file__)) + '/sample_data.h5', 'w') as f: f['data'] = data f['label'] = label f['label2'] = label2 -with h5py.File(os.path.dirname(__file__) + '/sample_data_2_gzip.h5', 'w') as f: +with h5py.File(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_2_gzip.h5', 'w') as f: f.create_dataset( 'data', data=data + total_size, compression='gzip', compression_opts=1 @@ -46,6 +46,6 @@ compression='gzip', compression_opts=1 ) -with open(os.path.dirname(__file__) + '/sample_data_list.txt', 'w') as f: - f.write(os.path.dirname(__file__) + '/sample_data.h5\n') - f.write(os.path.dirname(__file__) + '/sample_data_2_gzip.h5\n') +with open(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_list.txt', 'w') as f: + f.write(os.path.dirname(os.path.abspath(__file__)) + '/sample_data.h5\n') + f.write(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_2_gzip.h5\n') From c8323103758fc492771e07f143357fc403d9cc0e Mon Sep 17 00:00:00 2001 From: e3 Date: Wed, 8 Apr 2015 15:00:36 -0700 Subject: [PATCH 011/446] Changing Image import to be imported from PIL. --- examples/web_demo/app.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py index bbeff5eb362..c667ea94c11 100644 --- a/examples/web_demo/app.py +++ b/examples/web_demo/app.py @@ -10,7 +10,7 @@ import tornado.httpserver import numpy as np import pandas as pd -import Image +from PIL import Image import cStringIO as StringIO import urllib import exifutil From b963008a6591600e60ed6746d208e82e107f6a89 Mon Sep 17 00:00:00 2001 From: Sergio Guadarrama Date: Tue, 7 Apr 2015 17:51:22 -0700 Subject: [PATCH 012/446] Allow Transform of encoded datum. Allow initialize transformed_blob from datum or transform params. Allow force_color and force_gray as transform params. --- src/caffe/data_transformer.cpp | 59 +++++++++++++++++++++++++++++++--- src/caffe/proto/caffe.proto | 4 +++ 2 files changed, 59 insertions(+), 4 deletions(-) diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp index b0b98e478c1..454dabbebfd 100644 --- a/src/caffe/data_transformer.cpp +++ b/src/caffe/data_transformer.cpp @@ -125,10 +125,40 @@ void DataTransformer::Transform(const Datum& datum, template void DataTransformer::Transform(const Datum& datum, Blob* transformed_blob) { + // If datum is encoded, decoded and transform the cv::image. + if (datum.encoded()) { + CHECK(!param_.force_color() && !param_.force_gray()) + << "cannot set both force_color and force_gray"; + cv::Mat cv_img; + if (param_.force_color() || param_.force_gray()) { + // If force_color then decode in color otherwise decode in gray. + cv_img = DecodeDatumToCVMat(datum, param_.force_color()); + } else { + cv_img = DecodeDatumToCVMatNative(datum); + } + // Transform the cv::image into blob. + return Transform(cv_img, transformed_blob); + } else { + if (param_.force_color() || param_.force_gray()) { + LOG(ERROR) << "force_color and force_gray only for encoded datum"; + } + } + const int datum_channels = datum.channels(); const int datum_height = datum.height(); const int datum_width = datum.width(); + const int crop_size = param_.crop_size(); + + if (transformed_blob->count() == 0) { + // Initialize it. + if (crop_size) { + transformed_blob->Reshape(1, datum_channels, crop_size, crop_size); + } else { + transformed_blob->Reshape(1, datum_channels, datum_height, datum_width); + } + } + // Check dimensions. const int channels = transformed_blob->channels(); const int height = transformed_blob->height(); const int width = transformed_blob->width(); @@ -139,8 +169,6 @@ void DataTransformer::Transform(const Datum& datum, CHECK_LE(width, datum_width); CHECK_GE(num, 1); - const int crop_size = param_.crop_size(); - if (crop_size) { CHECK_EQ(crop_size, height); CHECK_EQ(crop_size, width); @@ -200,6 +228,17 @@ void DataTransformer::Transform(const cv::Mat& cv_img, const int img_height = cv_img.rows; const int img_width = cv_img.cols; + const int crop_size = param_.crop_size(); + + if (transformed_blob->count() == 0) { + // Initialize it. + if (crop_size) { + transformed_blob->Reshape(1, img_channels, crop_size, crop_size); + } else { + transformed_blob->Reshape(1, img_channels, img_height, img_width); + } + } + const int channels = transformed_blob->channels(); const int height = transformed_blob->height(); const int width = transformed_blob->width(); @@ -212,7 +251,6 @@ void DataTransformer::Transform(const cv::Mat& cv_img, CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte"; - const int crop_size = param_.crop_size(); const Dtype scale = param_.scale(); const bool do_mirror = param_.mirror() && Rand(2); const bool has_mean_file = param_.has_mean_file(); @@ -302,6 +340,19 @@ void DataTransformer::Transform(Blob* input_blob, const int input_height = input_blob->height(); const int input_width = input_blob->width(); + const int crop_size = param_.crop_size(); + + if (transformed_blob->count() == 0) { + // Initialize it. + if (crop_size) { + transformed_blob->Reshape(input_num, input_channels, + crop_size, crop_size); + } else { + transformed_blob->Reshape(input_num, input_channels, + input_height, input_width); + } + } + const int num = transformed_blob->num(); const int channels = transformed_blob->channels(); const int height = transformed_blob->height(); @@ -313,7 +364,7 @@ void DataTransformer::Transform(Blob* input_blob, CHECK_GE(input_height, height); CHECK_GE(input_width, width); - const int crop_size = param_.crop_size(); + const Dtype scale = param_.scale(); const bool do_mirror = param_.mirror() && Rand(2); const bool has_mean_file = param_.has_mean_file(); diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 5b21cf20028..d66167eefc8 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -351,6 +351,10 @@ message TransformationParameter { // or can be repeated the same number of times as channels // (would subtract them from the corresponding channel) repeated float mean_value = 5; + // Force the decoded image to have 3 color channels. + optional bool force_color = 6 [default = false]; + // Force the decoded image to have 1 color channels. + optional bool force_gray = 7 [default = false]; } // Message that stores parameters shared by loss layers From c3cd3c4d8c25c7b227e15aff0fe93e4151ffd8f6 Mon Sep 17 00:00:00 2001 From: Sergio Guadarrama Date: Wed, 8 Apr 2015 12:08:56 -0700 Subject: [PATCH 013/446] Added InferBlobShape to data_transformer. --- include/caffe/data_transformer.hpp | 36 ++++++++++ src/caffe/data_transformer.cpp | 105 ++++++++++++++++++++++------- 2 files changed, 117 insertions(+), 24 deletions(-) diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp index 880356601a4..0ad68c80216 100644 --- a/include/caffe/data_transformer.hpp +++ b/include/caffe/data_transformer.hpp @@ -62,6 +62,7 @@ class DataTransformer { */ void Transform(const vector & mat_vector, Blob* transformed_blob); + /** * @brief Applies the transformation defined in the data layer's * transform_param block to a cv::Mat @@ -87,6 +88,41 @@ class DataTransformer { */ void Transform(Blob* input_blob, Blob* transformed_blob); + /** + * @brief Infers the shape of transformed_blob will have when + * the transformation is applied to the data. + * + * @param datum + * Datum containing the data to be transformed. + */ + vector InferBlobShape(const Datum& datum); + /** + * @brief Infers the shape of transformed_blob will have when + * the transformation is applied to the data. + * It uses the first element to infer the shape of the blob. + * + * @param datum_vector + * A vector of Datum containing the data to be transformed. + */ + vector InferBlobShape(const vector & datum_vector); + /** + * @brief Infers the shape of transformed_blob will have when + * the transformation is applied to the data. + * It uses the first element to infer the shape of the blob. + * + * @param mat_vector + * A vector of Mat containing the data to be transformed. + */ + vector InferBlobShape(const vector & mat_vector); + /** + * @brief Infers the shape of transformed_blob will have when + * the transformation is applied to the data. + * + * @param cv_img + * cv::Mat containing the data to be transformed. + */ + vector InferBlobShape(const cv::Mat& cv_img); + protected: /** * @brief Generates a random integer from Uniform({0, 1, ..., n-1}). diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp index 454dabbebfd..6f75bdb3852 100644 --- a/src/caffe/data_transformer.cpp +++ b/src/caffe/data_transformer.cpp @@ -144,20 +144,11 @@ void DataTransformer::Transform(const Datum& datum, } } + const int crop_size = param_.crop_size(); const int datum_channels = datum.channels(); const int datum_height = datum.height(); const int datum_width = datum.width(); - const int crop_size = param_.crop_size(); - - if (transformed_blob->count() == 0) { - // Initialize it. - if (crop_size) { - transformed_blob->Reshape(1, datum_channels, crop_size, crop_size); - } else { - transformed_blob->Reshape(1, datum_channels, datum_height, datum_width); - } - } // Check dimensions. const int channels = transformed_blob->channels(); const int height = transformed_blob->height(); @@ -224,21 +215,12 @@ void DataTransformer::Transform(const vector & mat_vector, template void DataTransformer::Transform(const cv::Mat& cv_img, Blob* transformed_blob) { + const int crop_size = param_.crop_size(); const int img_channels = cv_img.channels(); const int img_height = cv_img.rows; const int img_width = cv_img.cols; - const int crop_size = param_.crop_size(); - - if (transformed_blob->count() == 0) { - // Initialize it. - if (crop_size) { - transformed_blob->Reshape(1, img_channels, crop_size, crop_size); - } else { - transformed_blob->Reshape(1, img_channels, img_height, img_width); - } - } - + // Check dimensions. const int channels = transformed_blob->channels(); const int height = transformed_blob->height(); const int width = transformed_blob->width(); @@ -335,15 +317,14 @@ void DataTransformer::Transform(const cv::Mat& cv_img, template void DataTransformer::Transform(Blob* input_blob, Blob* transformed_blob) { + const int crop_size = param_.crop_size(); const int input_num = input_blob->num(); const int input_channels = input_blob->channels(); const int input_height = input_blob->height(); const int input_width = input_blob->width(); - const int crop_size = param_.crop_size(); - if (transformed_blob->count() == 0) { - // Initialize it. + // Initialize transformed_blob with the right shape. if (crop_size) { transformed_blob->Reshape(input_num, input_channels, crop_size, crop_size); @@ -446,6 +427,82 @@ void DataTransformer::Transform(Blob* input_blob, } } +template +vector DataTransformer::InferBlobShape(const Datum& datum) { + if (datum.encoded()) { + CHECK(!param_.force_color() && !param_.force_gray()) + << "cannot set both force_color and force_gray"; + cv::Mat cv_img; + if (param_.force_color() || param_.force_gray()) { + // If force_color then decode in color otherwise decode in gray. + cv_img = DecodeDatumToCVMat(datum, param_.force_color()); + } else { + cv_img = DecodeDatumToCVMatNative(datum); + } + // InferBlobShape using the cv::image. + return InferBlobShape(cv_img); + } + + const int crop_size = param_.crop_size(); + const int datum_channels = datum.channels(); + const int datum_height = datum.height(); + const int datum_width = datum.width(); + // Check dimensions. + CHECK_GT(datum_channels, 0); + CHECK_GE(datum_height, crop_size); + CHECK_GE(datum_width, crop_size); + // Build BlobShape. + vector shape(4); + shape[0] = 1; + shape[1] = datum_channels; + shape[2] = (crop_size)? crop_size: datum_height; + shape[3] = (crop_size)? crop_size: datum_width; + return shape; +} + +template +vector DataTransformer::InferBlobShape( + const vector & datum_vector) { + const int num = datum_vector.size(); + CHECK_GT(num, 0) << "There is no datum to in the vector"; + // Use first datum in the vector to InferBlobShape. + vector shape = InferBlobShape(datum_vector[0]); + // Adjust num to the size of the vector. + shape[0] = num; + return shape; +} + +template +vector DataTransformer::InferBlobShape(const cv::Mat& cv_img) { + const int crop_size = param_.crop_size(); + const int img_channels = cv_img.channels(); + const int img_height = cv_img.rows; + const int img_width = cv_img.cols; + // Check dimensions. + CHECK_GT(img_channels, 0); + CHECK_GE(img_height, crop_size); + CHECK_GE(img_width, crop_size); + // Build BlobShape. + vector shape(4); + shape[0] = 1; + shape[1] = img_channels; + shape[2] = (crop_size)? crop_size: img_height; + shape[3] = (crop_size)? crop_size: img_width; + return shape; +} + +template +vector DataTransformer::InferBlobShape( + const vector & mat_vector) { + const int num = mat_vector.size(); + CHECK_GT(num, 0) << "There is no cv_img to in the vector"; + // Use first cv_img in the vector to InferBlobShape. + vector shape = InferBlobShape(mat_vector[0]); + // Adjust num to the size of the vector. + shape[0] = num; + return shape; +} + template void DataTransformer::InitRand() { const bool needs_rand = param_.mirror() || From 3c46df772247e7bccfd23e340c75245f921ad0d1 Mon Sep 17 00:00:00 2001 From: Sergio Guadarrama Date: Wed, 8 Apr 2015 12:09:33 -0700 Subject: [PATCH 014/446] Simplify data_layer reshapes and encodings by letting data_transformer do the job. --- src/caffe/layers/base_data_layer.cpp | 10 ++-- src/caffe/layers/base_data_layer.cu | 6 +- src/caffe/layers/data_layer.cpp | 90 ++++++++-------------------- 3 files changed, 35 insertions(+), 71 deletions(-) diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp index 352200915d7..aa3f613d1a3 100644 --- a/src/caffe/layers/base_data_layer.cpp +++ b/src/caffe/layers/base_data_layer.cpp @@ -21,11 +21,11 @@ void BaseDataLayer::LayerSetUp(const vector*>& bottom, } else { output_labels_ = true; } - // The subclasses should setup the size of bottom and top - DataLayerSetUp(bottom, top); data_transformer_.reset( new DataTransformer(transform_param_, this->phase_)); data_transformer_->InitRand(); + // The subclasses should setup the size of bottom and top + DataLayerSetUp(bottom, top); } template @@ -63,13 +63,15 @@ void BasePrefetchingDataLayer::Forward_cpu( JoinPrefetchThread(); DLOG(INFO) << "Thread joined"; // Reshape to loaded data. - top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(), - this->prefetch_data_.height(), this->prefetch_data_.width()); + top[0]->ReshapeLike(prefetch_data_); // Copy the data caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(), top[0]->mutable_cpu_data()); DLOG(INFO) << "Prefetch copied"; if (this->output_labels_) { + // Reshape to loaded labels. + top[1]->ReshapeLike(prefetch_label_); + // Copy the labels. caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(), top[1]->mutable_cpu_data()); } diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu index 775f6c47f7e..9335a5bc9a9 100644 --- a/src/caffe/layers/base_data_layer.cu +++ b/src/caffe/layers/base_data_layer.cu @@ -10,12 +10,14 @@ void BasePrefetchingDataLayer::Forward_gpu( // First, join the thread JoinPrefetchThread(); // Reshape to loaded data. - top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(), - this->prefetch_data_.height(), this->prefetch_data_.width()); + top[0]->ReshapeLike(this->prefetch_data_); // Copy the data caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(), top[0]->mutable_gpu_data()); if (this->output_labels_) { + // Reshape to loaded labels. + top[1]->ReshapeLike(prefetch_label_); + // Copy the labels. caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(), top[1]->mutable_gpu_data()); } diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp index 0f2d66776a9..161a75e0c8c 100644 --- a/src/caffe/layers/data_layer.cpp +++ b/src/caffe/layers/data_layer.cpp @@ -38,32 +38,17 @@ void DataLayer::DataLayerSetUp(const vector*>& bottom, cursor_->Next(); } } - // Read a data point, and use it to initialize the top blob. + // Read a data point, to initialize the prefetch and top blobs. Datum datum; datum.ParseFromString(cursor_->value()); + // Use data_transformer to infer the expected blob shape from datum. + vector top_shape = this->data_transformer_->InferBlobShape(datum); + this->transformed_data_.Reshape(top_shape); + // Reshape top[0] and prefetch_data according to the batch_size. + top_shape[0] = this->layer_param_.data_param().batch_size(); + this->prefetch_data_.Reshape(top_shape); + top[0]->ReshapeLike(this->prefetch_data_); - bool force_color = this->layer_param_.data_param().force_encoded_color(); - if ((force_color && DecodeDatum(&datum, true)) || - DecodeDatumNative(&datum)) { - LOG(INFO) << "Decoding Datum"; - } - // image - int crop_size = this->layer_param_.transform_param().crop_size(); - if (crop_size > 0) { - top[0]->Reshape(this->layer_param_.data_param().batch_size(), - datum.channels(), crop_size, crop_size); - this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(), - datum.channels(), crop_size, crop_size); - this->transformed_data_.Reshape(1, datum.channels(), crop_size, crop_size); - } else { - top[0]->Reshape( - this->layer_param_.data_param().batch_size(), datum.channels(), - datum.height(), datum.width()); - this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(), - datum.channels(), datum.height(), datum.width()); - this->transformed_data_.Reshape(1, datum.channels(), - datum.height(), datum.width()); - } LOG(INFO) << "output data size: " << top[0]->num() << "," << top[0]->channels() << "," << top[0]->height() << "," << top[0]->width(); @@ -86,25 +71,17 @@ void DataLayer::InternalThreadEntry() { CHECK(this->prefetch_data_.count()); CHECK(this->transformed_data_.count()); - // Reshape on single input batches for inputs of varying dimension. + // Reshape according to the first datum of each batch + // on single input batches allows for inputs of varying dimension. const int batch_size = this->layer_param_.data_param().batch_size(); - const int crop_size = this->layer_param_.transform_param().crop_size(); - bool force_color = this->layer_param_.data_param().force_encoded_color(); - if (batch_size == 1 && crop_size == 0) { - Datum datum; - datum.ParseFromString(cursor_->value()); - if (datum.encoded()) { - if (force_color) { - DecodeDatum(&datum, true); - } else { - DecodeDatumNative(&datum); - } - } - this->prefetch_data_.Reshape(1, datum.channels(), - datum.height(), datum.width()); - this->transformed_data_.Reshape(1, datum.channels(), - datum.height(), datum.width()); - } + Datum datum; + datum.ParseFromString(cursor_->value()); + // Use data_transformer to infer the expected blob shape from datum. + vector top_shape = this->data_transformer_->InferBlobShape(datum); + this->transformed_data_.Reshape(top_shape); + // Reshape prefetch_data according to the batch_size. + top_shape[0] = batch_size; + this->prefetch_data_.Reshape(top_shape); Dtype* top_data = this->prefetch_data_.mutable_cpu_data(); Dtype* top_label = NULL; // suppress warnings about uninitialized variables @@ -112,48 +89,31 @@ void DataLayer::InternalThreadEntry() { if (this->output_labels_) { top_label = this->prefetch_label_.mutable_cpu_data(); } + timer.Start(); for (int item_id = 0; item_id < batch_size; ++item_id) { - timer.Start(); - // get a blob + // get a datum Datum datum; datum.ParseFromString(cursor_->value()); - - cv::Mat cv_img; - if (datum.encoded()) { - if (force_color) { - cv_img = DecodeDatumToCVMat(datum, true); - } else { - cv_img = DecodeDatumToCVMatNative(datum); - } - if (cv_img.channels() != this->transformed_data_.channels()) { - LOG(WARNING) << "Your dataset contains encoded images with mixed " - << "channel sizes. Consider adding a 'force_color' flag to the " - << "model definition, or rebuild your dataset using " - << "convert_imageset."; - } - } read_time += timer.MicroSeconds(); timer.Start(); - // Apply data transformations (mirror, scale, crop...) int offset = this->prefetch_data_.offset(item_id); this->transformed_data_.set_cpu_data(top_data + offset); - if (datum.encoded()) { - this->data_transformer_->Transform(cv_img, &(this->transformed_data_)); - } else { - this->data_transformer_->Transform(datum, &(this->transformed_data_)); - } + this->data_transformer_->Transform(datum, &(this->transformed_data_)); + // Copy label. if (this->output_labels_) { top_label[item_id] = datum.label(); } trans_time += timer.MicroSeconds(); - // go to the next iter + timer.Start(); + // go to the next item. cursor_->Next(); if (!cursor_->valid()) { DLOG(INFO) << "Restarting data prefetching from start."; cursor_->SeekToFirst(); } } + timer.Stop(); batch_timer.Stop(); DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms."; DLOG(INFO) << " Read time: " << read_time / 1000 << " ms."; From e048b1741e8a5dfafeecff96c5a69d3211224fd6 Mon Sep 17 00:00:00 2001 From: Sergio Guadarrama Date: Wed, 8 Apr 2015 12:44:48 -0700 Subject: [PATCH 015/446] Simplify image_data_layer reshapes by letting data_transformer do the job. --- src/caffe/layers/image_data_layer.cpp | 42 ++++++++++++--------------- 1 file changed, 18 insertions(+), 24 deletions(-) diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp index 38ebbd5ec14..18c035cba9d 100644 --- a/src/caffe/layers/image_data_layer.cpp +++ b/src/caffe/layers/image_data_layer.cpp @@ -62,21 +62,15 @@ void ImageDataLayer::DataLayerSetUp(const vector*>& bottom, // Read an image, and use it to initialize the top blob. cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first, new_height, new_width, is_color); - const int channels = cv_img.channels(); - const int height = cv_img.rows; - const int width = cv_img.cols; - // image - const int crop_size = this->layer_param_.transform_param().crop_size(); + // Use data_transformer to infer the expected blob shape from a cv_image. + vector top_shape = this->data_transformer_->InferBlobShape(cv_img); + this->transformed_data_.Reshape(top_shape); + // Reshape prefetch_data and top[0] according to the batch_size. const int batch_size = this->layer_param_.image_data_param().batch_size(); - if (crop_size > 0) { - top[0]->Reshape(batch_size, channels, crop_size, crop_size); - this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size); - this->transformed_data_.Reshape(1, channels, crop_size, crop_size); - } else { - top[0]->Reshape(batch_size, channels, height, width); - this->prefetch_data_.Reshape(batch_size, channels, height, width); - this->transformed_data_.Reshape(1, channels, height, width); - } + top_shape[0] = batch_size; + this->prefetch_data_.Reshape(top_shape); + top[0]->ReshapeLike(this->prefetch_data_); + LOG(INFO) << "output data size: " << top[0]->num() << "," << top[0]->channels() << "," << top[0]->height() << "," << top[0]->width(); @@ -107,19 +101,19 @@ void ImageDataLayer::InternalThreadEntry() { const int batch_size = image_data_param.batch_size(); const int new_height = image_data_param.new_height(); const int new_width = image_data_param.new_width(); - const int crop_size = this->layer_param_.transform_param().crop_size(); const bool is_color = image_data_param.is_color(); string root_folder = image_data_param.root_folder(); - // Reshape on single input batches for inputs of varying dimension. - if (batch_size == 1 && crop_size == 0 && new_height == 0 && new_width == 0) { - cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first, - 0, 0, is_color); - this->prefetch_data_.Reshape(1, cv_img.channels(), - cv_img.rows, cv_img.cols); - this->transformed_data_.Reshape(1, cv_img.channels(), - cv_img.rows, cv_img.cols); - } + // Reshape according to the first image of each batch + // on single input batches allows for inputs of varying dimension. + cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first, + new_height, new_width, is_color); + // Use data_transformer to infer the expected blob shape from a cv_img. + vector top_shape = this->data_transformer_->InferBlobShape(cv_img); + this->transformed_data_.Reshape(top_shape); + // Reshape prefetch_data according to the batch_size. + top_shape[0] = batch_size; + this->prefetch_data_.Reshape(top_shape); Dtype* prefetch_data = this->prefetch_data_.mutable_cpu_data(); Dtype* prefetch_label = this->prefetch_label_.mutable_cpu_data(); From ef27496b2abbd7adf8369eeb0a85b57f99955c1a Mon Sep 17 00:00:00 2001 From: Akiomi KAMAKURA Date: Fri, 10 Apr 2015 15:48:41 +0900 Subject: [PATCH 016/446] improved installation for osx --- docs/install_osx.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/install_osx.md b/docs/install_osx.md index c4ebd45f553..39cb02fe232 100644 --- a/docs/install_osx.md +++ b/docs/install_osx.md @@ -18,7 +18,7 @@ In other `ENV` settings, things may not work as expected. brew install --fresh -vd snappy leveldb gflags glog szip lmdb # need the homebrew science source for OpenCV and hdf5 brew tap homebrew/science - hdf5 opencv + brew install hdf5 opencv If using Anaconda Python, a modification to the OpenCV formula might be needed Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below. From 7e2fceb1e91cfe48eddb3569e29aaef4b9ca1a2a Mon Sep 17 00:00:00 2001 From: Nick Carlevaris-Bianco Date: Fri, 17 Apr 2015 14:16:26 +0930 Subject: [PATCH 017/446] Fixed contrastive loss layer to be the same as proposed in Hadsell et al 2006 --- src/caffe/layers/contrastive_loss_layer.cpp | 9 ++++++--- src/caffe/layers/contrastive_loss_layer.cu | 13 ++++++++----- src/caffe/test/test_contrastive_loss_layer.cpp | 3 ++- 3 files changed, 16 insertions(+), 9 deletions(-) diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp index 0692c11c257..d5e3c8a48bb 100644 --- a/src/caffe/layers/contrastive_loss_layer.cpp +++ b/src/caffe/layers/contrastive_loss_layer.cpp @@ -48,7 +48,8 @@ void ContrastiveLossLayer::Forward_cpu( if (static_cast(bottom[2]->cpu_data()[i])) { // similar pairs loss += dist_sq_.cpu_data()[i]; } else { // dissimilar pairs - loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0)); + Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), 0.0); + loss += dist*dist; } } loss = loss / static_cast(bottom[0]->num()) / Dtype(2); @@ -76,10 +77,12 @@ void ContrastiveLossLayer::Backward_cpu(const vector*>& top, Dtype(0.0), bout + (j*channels)); } else { // dissimilar pairs - if ((margin-dist_sq_.cpu_data()[j]) > Dtype(0.0)) { + Dtype dist = sqrt(dist_sq_.cpu_data()[j]); + Dtype mdist = (margin - dist); + if (mdist > Dtype(0.0)) { caffe_cpu_axpby( channels, - -alpha, + -alpha * mdist / dist, diff_.cpu_data() + (j*channels), Dtype(0.0), bout + (j*channels)); diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu index 78a55995a0a..255480ced05 100644 --- a/src/caffe/layers/contrastive_loss_layer.cu +++ b/src/caffe/layers/contrastive_loss_layer.cu @@ -37,7 +37,8 @@ void ContrastiveLossLayer::Forward_gpu( if (static_cast(bottom[2]->cpu_data()[i])) { // similar pairs loss += dist_sq_.cpu_data()[i]; } else { // dissimilar pairs - loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0)); + Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), Dtype(0.0)); + loss += dist*dist; } } loss = loss / static_cast(bottom[0]->num()) / Dtype(2); @@ -45,7 +46,7 @@ void ContrastiveLossLayer::Forward_gpu( } template -__global__ void CLLForward(const int count, const int channels, +__global__ void CLLBackward(const int count, const int channels, const Dtype margin, const Dtype alpha, const Dtype* y, const Dtype* diff, const Dtype* dist_sq, Dtype *bottom_diff) { @@ -54,8 +55,10 @@ __global__ void CLLForward(const int count, const int channels, if (static_cast(y[n])) { // similar pairs bottom_diff[i] = alpha * diff[i]; } else { // dissimilar pairs - if ((margin-dist_sq[n]) > 0.0) { - bottom_diff[i] = -alpha * diff[i]; + Dtype dist = sqrt(dist_sq[n]); + Dtype mdist = (margin - dist); + if (mdist > 0.0) { + bottom_diff[i] = -alpha * mdist / dist * diff[i]; } else { bottom_diff[i] = 0; } @@ -75,7 +78,7 @@ void ContrastiveLossLayer::Backward_gpu(const vector*>& top, const Dtype alpha = sign * top[0]->cpu_diff()[0] / static_cast(bottom[0]->num()); // NOLINT_NEXT_LINE(whitespace/operators) - CLLForward<<>>( + CLLBackward<<>>( count, channels, margin, alpha, bottom[2]->gpu_data(), // pair similarity 0 or 1 diff_.gpu_data(), // the cached eltwise difference between a and b diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp index d269fbc26f2..5fab25f8832 100644 --- a/src/caffe/test/test_contrastive_loss_layer.cpp +++ b/src/caffe/test/test_contrastive_loss_layer.cpp @@ -79,7 +79,8 @@ TYPED_TEST(ContrastiveLossLayerTest, TestForward) { if (this->blob_bottom_y_->cpu_data()[i]) { // similar pairs loss += dist_sq; } else { - loss += std::max(margin-dist_sq, Dtype(0)); + Dtype dist = std::max(margin - sqrt(dist_sq), 0.0); + loss += dist*dist; } } loss /= static_cast(num) * Dtype(2); From c6414ea7cab5917c904729c87f786e0a5909475c Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 17 Apr 2015 12:26:48 -0700 Subject: [PATCH 018/446] set default DISTRIBUTE_DIR -- fix #2328 --- Makefile | 1 + 1 file changed, 1 insertion(+) diff --git a/Makefile b/Makefile index 706959ad0ca..db0f531eaa0 100644 --- a/Makefile +++ b/Makefile @@ -171,6 +171,7 @@ WARNINGS := -Wall -Wno-sign-compare # Set build directories ############################## +DISTRIBUTE_DIR ?= distribute DISTRIBUTE_SUBDIRS := $(DISTRIBUTE_DIR)/bin $(DISTRIBUTE_DIR)/lib DIST_ALIASES := dist ifneq ($(strip $(DISTRIBUTE_DIR)),distribute) From 797587d73a8372db11b1f143a2868064f985fade Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Fri, 17 Apr 2015 15:53:12 -0700 Subject: [PATCH 019/446] Abort Makefile parsing if the configuration file cannot be found. --- Makefile | 3 +++ 1 file changed, 3 insertions(+) diff --git a/Makefile b/Makefile index db0f531eaa0..55ca5f3734e 100644 --- a/Makefile +++ b/Makefile @@ -1,6 +1,9 @@ PROJECT := caffe CONFIG_FILE := Makefile.config +ifeq ($(wildcard $(CONFIG_FILE)),) +$(error $(CONFIG_FILE): file not found.) +endif include $(CONFIG_FILE) BUILD_DIR_LINK := $(BUILD_DIR) From d91c353577baf2b27d303962bb039ba39211871b Mon Sep 17 00:00:00 2001 From: Nick Carlevaris-Bianco Date: Tue, 21 Apr 2015 17:14:47 +0930 Subject: [PATCH 020/446] added epsilon to prevent possible division by zero in gradient calculation --- src/caffe/layers/contrastive_loss_layer.cpp | 2 +- src/caffe/layers/contrastive_loss_layer.cu | 2 +- src/caffe/test/test_contrastive_loss_layer.cpp | 12 ++++++------ 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp index d5e3c8a48bb..4cf04d4dd15 100644 --- a/src/caffe/layers/contrastive_loss_layer.cpp +++ b/src/caffe/layers/contrastive_loss_layer.cpp @@ -82,7 +82,7 @@ void ContrastiveLossLayer::Backward_cpu(const vector*>& top, if (mdist > Dtype(0.0)) { caffe_cpu_axpby( channels, - -alpha * mdist / dist, + -alpha * mdist / (dist + Dtype(1e-4)), diff_.cpu_data() + (j*channels), Dtype(0.0), bout + (j*channels)); diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu index 255480ced05..83ff3c90f0f 100644 --- a/src/caffe/layers/contrastive_loss_layer.cu +++ b/src/caffe/layers/contrastive_loss_layer.cu @@ -58,7 +58,7 @@ __global__ void CLLBackward(const int count, const int channels, Dtype dist = sqrt(dist_sq[n]); Dtype mdist = (margin - dist); if (mdist > 0.0) { - bottom_diff[i] = -alpha * mdist / dist * diff[i]; + bottom_diff[i] = -alpha * mdist / (dist + Dtype(1e-4)) * diff[i]; } else { bottom_diff[i] = 0; } diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp index 5fab25f8832..460fc8f32fc 100644 --- a/src/caffe/test/test_contrastive_loss_layer.cpp +++ b/src/caffe/test/test_contrastive_loss_layer.cpp @@ -22,15 +22,15 @@ class ContrastiveLossLayerTest : public MultiDeviceTest { protected: ContrastiveLossLayerTest() - : blob_bottom_data_i_(new Blob(128, 10, 1, 1)), - blob_bottom_data_j_(new Blob(128, 10, 1, 1)), - blob_bottom_y_(new Blob(128, 1, 1, 1)), + : blob_bottom_data_i_(new Blob(512, 2, 1, 1)), + blob_bottom_data_j_(new Blob(512, 2, 1, 1)), + blob_bottom_y_(new Blob(512, 1, 1, 1)), blob_top_loss_(new Blob()) { // fill the values FillerParameter filler_param; - filler_param.set_mean(0.0); - filler_param.set_std(0.3); // distances~=1.0 to test both sides of margin - GaussianFiller filler(filler_param); + filler_param.set_min(-1.0); + filler_param.set_max(1.0); // distances~=1.0 to test both sides of margin + UniformFiller filler(filler_param); filler.Fill(this->blob_bottom_data_i_); blob_bottom_vec_.push_back(blob_bottom_data_i_); filler.Fill(this->blob_bottom_data_j_); From 23d28fdef8ba936b2a6a4a123de625084faec1dd Mon Sep 17 00:00:00 2001 From: Daniel Golden Date: Tue, 9 Dec 2014 09:20:38 -0800 Subject: [PATCH 021/446] Improvements to python log parser Over version introduced in https://github.com/BVLC/caffe/pull/1384 Highlights: * Interface change: column order is now determined by using a list of `OrderedDict` objects instead of `dict` objects, which obviates the need to pass around a tuple with the column orders. * The outputs are now named according to their names in the network protobuffer; e.g., if your top is named `loss`, then the corresponding column header will also be `loss`; we no longer rename it to, e.g., `TrainingLoss` or `TestLoss`. * Fixed the bug/feature of the first version where the initial learning rate was always NaN. * Add optional parameter to specify output table delimiter. It's still a comma by default. You can use Matlab code from [this gist](https://gist.github.com/drdan14/d8b45999c4a1cbf7ad85) to verify that your results are the same before and after the changes introduced in this pull request. That code assumes that your `top` names are `accuracy` and `loss`, but you can modify the code if that's not true. --- tools/extra/parse_log.py | 165 ++++++++++++++++++++++++--------------- 1 file changed, 102 insertions(+), 63 deletions(-) diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py index 16ba077aee6..09ea216ced3 100755 --- a/tools/extra/parse_log.py +++ b/tools/extra/parse_log.py @@ -3,7 +3,7 @@ """ Parse training log -Competitor to parse_log.sh +Evolved from parse_log.sh """ import os @@ -11,18 +11,7 @@ import extract_seconds import argparse import csv - - -def get_line_type(line): - """Return either 'test' or 'train' depending on line type - """ - - line_type = None - if line.find('Train') != -1: - line_type = 'train' - elif line.find('Test') != -1: - line_type = 'test' - return line_type +from collections import OrderedDict def parse_log(path_to_log): @@ -36,70 +25,112 @@ def parse_log(path_to_log): for the two dict_lists """ - re_iteration = re.compile('Iteration (\d+)') - re_accuracy = re.compile('output #\d+: accuracy = ([\.\d]+)') - re_train_loss = re.compile('Iteration \d+, loss = ([\.\d]+)') - re_output_loss = re.compile('output #\d+: loss = ([\.\d]+)') - re_lr = re.compile('lr = ([\.\d]+)') + regex_iteration = re.compile('Iteration (\d+)') + regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)') + regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)') + regex_learning_rate = re.compile('lr = ([\.\d]+)') # Pick out lines of interest iteration = -1 - test_accuracy = -1 learning_rate = float('NaN') train_dict_list = [] test_dict_list = [] - train_dict_names = ('NumIters', 'Seconds', 'TrainingLoss', 'LearningRate') - test_dict_names = ('NumIters', 'Seconds', 'TestAccuracy', 'TestLoss') + train_row = None + test_row = None logfile_year = extract_seconds.get_log_created_year(path_to_log) with open(path_to_log) as f: start_time = extract_seconds.get_start_time(f, logfile_year) for line in f: - iteration_match = re_iteration.search(line) + iteration_match = regex_iteration.search(line) if iteration_match: iteration = float(iteration_match.group(1)) if iteration == -1: - # Only look for other stuff if we've found the first iteration + # Only start parsing for other stuff if we've found the first + # iteration continue time = extract_seconds.extract_datetime_from_line(line, logfile_year) seconds = (time - start_time).total_seconds() - lr_match = re_lr.search(line) - if lr_match: - learning_rate = float(lr_match.group(1)) - - accuracy_match = re_accuracy.search(line) - if accuracy_match and get_line_type(line) == 'test': - test_accuracy = float(accuracy_match.group(1)) - - train_loss_match = re_train_loss.search(line) - if train_loss_match: - train_loss = float(train_loss_match.group(1)) - train_dict_list.append({'NumIters': iteration, - 'Seconds': seconds, - 'TrainingLoss': train_loss, - 'LearningRate': learning_rate}) - - output_loss_match = re_output_loss.search(line) - if output_loss_match and get_line_type(line) == 'test': - test_loss = float(output_loss_match.group(1)) - # NOTE: we assume that (1) accuracy always comes right before - # loss for test data so the test_accuracy variable is already - # correctly populated and (2) there's one and only one output - # named "accuracy" for the test net - test_dict_list.append({'NumIters': iteration, - 'Seconds': seconds, - 'TestAccuracy': test_accuracy, - 'TestLoss': test_loss}) - - return train_dict_list, train_dict_names, test_dict_list, test_dict_names - - -def save_csv_files(logfile_path, output_dir, train_dict_list, train_dict_names, - test_dict_list, test_dict_names, verbose=False): + learning_rate_match = regex_learning_rate.search(line) + if learning_rate_match: + learning_rate = float(learning_rate_match.group(1)) + + train_dict_list, train_row = parse_line_for_net_output( + regex_train_output, train_row, train_dict_list, + line, iteration, seconds, learning_rate + ) + test_dict_list, test_row = parse_line_for_net_output( + regex_test_output, test_row, test_dict_list, + line, iteration, seconds, learning_rate + ) + + fix_initial_nan_learning_rate(train_dict_list) + fix_initial_nan_learning_rate(test_dict_list) + + return train_dict_list, test_dict_list + + +def parse_line_for_net_output(regex_obj, row, row_dict_list, + line, iteration, seconds, learning_rate): + """Parse a single line for training or test output + + Returns a a tuple with (row_dict_list, row) + row: may be either a new row or an augmented version of the current row + row_dict_list: may be either the current row_dict_list or an augmented + version of the current row_dict_list + """ + + output_match = regex_obj.search(line) + if output_match: + if not row or row['NumIters'] != iteration: + # Push the last row and start a new one + if row: + # If we're on a new iteration, push the last row + # This will probably only happen for the first row; otherwise + # the full row checking logic below will push and clear full + # rows + row_dict_list.append(row) + + row = OrderedDict([ + ('NumIters', iteration), + ('Seconds', seconds), + ('LearningRate', learning_rate) + ]) + + # output_num is not used; may be used in the future + # output_num = output_match.group(1) + output_name = output_match.group(2) + output_val = output_match.group(3) + row[output_name] = float(output_val) + + if row and len(row_dict_list) >= 1 and len(row) == len(row_dict_list[0]): + # The row is full, based on the fact that it has the same number of + # columns as the first row; append it to the list + row_dict_list.append(row) + row = None + + return row_dict_list, row + + +def fix_initial_nan_learning_rate(dict_list): + """Correct initial value of learning rate + + Learning rate is normally not printed until after the initial test and + training step, which means the initial testing and training rows have + LearningRate = NaN. Fix this by copying over the LearningRate from the + second row, if it exists. + """ + + if len(dict_list) > 1: + dict_list[0]['LearningRate'] = dict_list[1]['LearningRate'] + + +def save_csv_files(logfile_path, output_dir, train_dict_list, test_dict_list, + delimiter=',', verbose=False): """Save CSV files to output_dir If the input log file is, e.g., caffe.INFO, the names will be @@ -108,18 +139,22 @@ def save_csv_files(logfile_path, output_dir, train_dict_list, train_dict_names, log_basename = os.path.basename(logfile_path) train_filename = os.path.join(output_dir, log_basename + '.train') - write_csv(train_filename, train_dict_list, train_dict_names, verbose) + write_csv(train_filename, train_dict_list, delimiter, verbose) test_filename = os.path.join(output_dir, log_basename + '.test') - write_csv(test_filename, test_dict_list, test_dict_names, verbose) + write_csv(test_filename, test_dict_list, delimiter, verbose) -def write_csv(output_filename, dict_list, header_names, verbose=False): +def write_csv(output_filename, dict_list, delimiter, verbose=False): """Write a CSV file """ + dialect = csv.excel + dialect.delimiter = delimiter + with open(output_filename, 'w') as f: - dict_writer = csv.DictWriter(f, header_names) + dict_writer = csv.DictWriter(f, fieldnames=dict_list[0].keys(), + dialect=dialect) dict_writer.writeheader() dict_writer.writerows(dict_list) if verbose: @@ -141,16 +176,20 @@ def parse_args(): action='store_true', help='Print some extra info (e.g., output filenames)') + parser.add_argument('--delimiter', + default=',', + help=('Column delimiter in output files ' + '(default: \'%(default)s\')')) + args = parser.parse_args() return args def main(): args = parse_args() - train_dict_list, train_dict_names, test_dict_list, test_dict_names = \ - parse_log(args.logfile_path) + train_dict_list, test_dict_list = parse_log(args.logfile_path) save_csv_files(args.logfile_path, args.output_dir, train_dict_list, - train_dict_names, test_dict_list, test_dict_names) + test_dict_list, delimiter=args.delimiter) if __name__ == '__main__': From a57d19d9de33463f71ac66b3e7f6790d514deb40 Mon Sep 17 00:00:00 2001 From: PETER_GAO Date: Wed, 22 Apr 2015 11:33:41 -0700 Subject: [PATCH 022/446] Fix RCNN model fetching script --- examples/detection.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/detection.ipynb b/examples/detection.ipynb index 2ccf21f09eb..e343feefd20 100644 --- a/examples/detection.ipynb +++ b/examples/detection.ipynb @@ -27,7 +27,7 @@ "\n", "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n", "\n", - "-Run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the Caffe R-CNN ImageNet model.\n", + "-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\n", "\n", "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`." ] From caaf321a1dc6d51adb1ee0c80f76d4d172145884 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Fri, 24 Apr 2015 19:52:33 -0700 Subject: [PATCH 023/446] clarify Makefile.config check --- Makefile | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 55ca5f3734e..15d30dfc0b7 100644 --- a/Makefile +++ b/Makefile @@ -1,8 +1,9 @@ PROJECT := caffe CONFIG_FILE := Makefile.config +# Explicitly check for the config file, otherwise make -k will proceed anyway. ifeq ($(wildcard $(CONFIG_FILE)),) -$(error $(CONFIG_FILE): file not found.) +$(error $(CONFIG_FILE) not found. See $(CONFIG_FILE).example.) endif include $(CONFIG_FILE) From afa2d591b6e72a3c29c9efdff4c97d1141cd1ae0 Mon Sep 17 00:00:00 2001 From: Takuma Wakamori Date: Sat, 25 Apr 2015 17:09:43 +0900 Subject: [PATCH 024/446] fix typo: swap the titles of xlabel and ylabel --- tools/extra/plot_log.gnuplot.example | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/extra/plot_log.gnuplot.example b/tools/extra/plot_log.gnuplot.example index 334ff1f2858..748b96e6925 100644 --- a/tools/extra/plot_log.gnuplot.example +++ b/tools/extra/plot_log.gnuplot.example @@ -39,8 +39,8 @@ set key right # Training loss vs. training iterations set title "Training loss vs. training iterations" -set xlabel "Training loss" -set ylabel "Training iterations" +set xlabel "Training iterations" +set ylabel "Training loss" plot "mnist.log.train" using 1:3 title "mnist" # Training loss vs. training time From ccfd28f0ce802aa11b1e0eac0e3dab239a0c9028 Mon Sep 17 00:00:00 2001 From: gdh1995 Date: Sun, 26 Apr 2015 22:51:21 +0800 Subject: [PATCH 025/446] fix a typo that GFLAGS_GFLAGS_H_ -> GFLAGS_GFAGS_H_ --- include/caffe/common.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp index 6cf80a37bc1..5f86bc2625b 100644 --- a/include/caffe/common.hpp +++ b/include/caffe/common.hpp @@ -19,7 +19,7 @@ #include "caffe/util/device_alternate.hpp" // gflags 2.1 issue: namespace google was changed to gflags without warning. -// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version +// Luckily we will be able to use GFLAGS_GFLAGS_H_ to detect if it is version // 2.1. If yes, we will add a temporary solution to redirect the namespace. // TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's // remove the following hack. From 23685324b94ef1248ad5b377121f4e71cda24293 Mon Sep 17 00:00:00 2001 From: gdh1995 Date: Sun, 26 Apr 2015 22:52:41 +0800 Subject: [PATCH 026/446] Net::Update: CPU_ONLY is in wrong place --- src/caffe/net.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index fd00b122630..888eec1d501 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -756,15 +756,15 @@ void Net::Update() { owner_diff = params_[param_owners_[i]]->mutable_cpu_diff(); caffe_add(count, this_diff, owner_diff, owner_diff); break; -#ifndef CPU_ONLY case Caffe::GPU: +#ifndef CPU_ONLY this_diff = params_[i]->gpu_diff(); owner_diff = params_[param_owners_[i]]->mutable_gpu_diff(); caffe_gpu_add(count, this_diff, owner_diff, owner_diff); - break; #else NO_GPU; #endif + break; default: LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); } From 4202c16d14ae445325803a1c44c4f123986df08f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sebasti=C3=A1n=20Ram=C3=ADrez?= Date: Thu, 2 Apr 2015 20:01:24 -0500 Subject: [PATCH 027/446] Import Pandas in HDF5 IPython notebook. Fix for issue BVLC/caffe#2247 --- examples/hdf5_classification.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb index 19d27372754..03c811b5120 100644 --- a/examples/hdf5_classification.ipynb +++ b/examples/hdf5_classification.ipynb @@ -25,6 +25,7 @@ "collapsed": false, "input": [ "import numpy as np\n", + "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", From c1c493900271867da92cea8789a670b1d08c2f1d Mon Sep 17 00:00:00 2001 From: Anatoly Baksheev Date: Wed, 29 Apr 2015 21:53:51 +0300 Subject: [PATCH 028/446] minor cmake fix - now Caffe complains when cmake is executed if glog/gflags are not found. --- cmake/Modules/FindGFlags.cmake | 2 +- cmake/Modules/FindGlog.cmake | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/cmake/Modules/FindGFlags.cmake b/cmake/Modules/FindGFlags.cmake index 146e8455a50..29b60f05037 100644 --- a/cmake/Modules/FindGFlags.cmake +++ b/cmake/Modules/FindGFlags.cmake @@ -38,7 +38,7 @@ else() find_library(GFLAGS_LIBRARY gflags) endif() -find_package_handle_standard_args(GFLAGS DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY) +find_package_handle_standard_args(GFlags DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY) if(GFLAGS_FOUND) diff --git a/cmake/Modules/FindGlog.cmake b/cmake/Modules/FindGlog.cmake index 56c76434897..99abbe478a0 100644 --- a/cmake/Modules/FindGlog.cmake +++ b/cmake/Modules/FindGlog.cmake @@ -37,7 +37,7 @@ else() PATH_SUFFIXES lib lib64) endif() -find_package_handle_standard_args(GLOG DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY) +find_package_handle_standard_args(Glog DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY) if(GLOG_FOUND) set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR}) From 63703edb0a4debd92c2b4e843612502c6015d203 Mon Sep 17 00:00:00 2001 From: Delbert Date: Thu, 30 Apr 2015 10:15:58 +0800 Subject: [PATCH 029/446] Correct the REPO_DIRNAME --- examples/web_demo/app.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py index c667ea94c11..09411f33f10 100644 --- a/examples/web_demo/app.py +++ b/examples/web_demo/app.py @@ -17,7 +17,7 @@ import caffe -REPO_DIRNAME = os.path.abspath(os.path.dirname(__file__) + '/../..') +REPO_DIRNAME = os.path.abspath(os.path.dirname(os.path.abspath(__file__)) + '/../..') UPLOAD_FOLDER = '/tmp/caffe_demos_uploads' ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif']) From e02e66ce0ed9381837f17065e3a70e53d673be22 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Sat, 25 Apr 2015 20:57:34 -0700 Subject: [PATCH 030/446] Makefile bugfix: OTHER_BUILD_DIR name set incorrectly when empty due to lazy variable expansion when using the `?=` operator -- change them to explicit empty string checks with simple assignment operator `:=`. --- Makefile | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index db0f531eaa0..42df20d27ae 100644 --- a/Makefile +++ b/Makefile @@ -4,8 +4,12 @@ CONFIG_FILE := Makefile.config include $(CONFIG_FILE) BUILD_DIR_LINK := $(BUILD_DIR) -RELEASE_BUILD_DIR ?= .$(BUILD_DIR)_release -DEBUG_BUILD_DIR ?= .$(BUILD_DIR)_debug +ifeq ($(RELEASE_BUILD_DIR),) + RELEASE_BUILD_DIR := .$(BUILD_DIR)_release +endif +ifeq ($(DEBUG_BUILD_DIR),) + DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug +endif DEBUG ?= 0 ifeq ($(DEBUG), 1) From ca673fdd05458980f62a909b64b51b22f0ddd21e Mon Sep 17 00:00:00 2001 From: Nick Carlevaris-Bianco Date: Mon, 4 May 2015 11:41:44 +0930 Subject: [PATCH 031/446] Added support for original implementation, using (margin - d^2), through the legacy_version parameter. --- src/caffe/layers/contrastive_loss_layer.cpp | 26 ++++++++--- src/caffe/layers/contrastive_loss_layer.cu | 31 ++++++++++--- src/caffe/proto/caffe.proto | 9 +++- .../test/test_contrastive_loss_layer.cpp | 43 +++++++++++++++++++ 4 files changed, 96 insertions(+), 13 deletions(-) diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp index 4cf04d4dd15..25e167819d3 100644 --- a/src/caffe/layers/contrastive_loss_layer.cpp +++ b/src/caffe/layers/contrastive_loss_layer.cpp @@ -41,6 +41,8 @@ void ContrastiveLossLayer::Forward_cpu( diff_.mutable_cpu_data()); // a_i-b_i const int channels = bottom[0]->channels(); Dtype margin = this->layer_param_.contrastive_loss_param().margin(); + bool legacy_version = + this->layer_param_.contrastive_loss_param().legacy_version(); Dtype loss(0.0); for (int i = 0; i < bottom[0]->num(); ++i) { dist_sq_.mutable_cpu_data()[i] = caffe_cpu_dot(channels, @@ -48,8 +50,12 @@ void ContrastiveLossLayer::Forward_cpu( if (static_cast(bottom[2]->cpu_data()[i])) { // similar pairs loss += dist_sq_.cpu_data()[i]; } else { // dissimilar pairs - Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), 0.0); - loss += dist*dist; + if (legacy_version) { + loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0)); + } else { + Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), 0.0); + loss += dist*dist; + } } } loss = loss / static_cast(bottom[0]->num()) / Dtype(2); @@ -60,6 +66,8 @@ template void ContrastiveLossLayer::Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { Dtype margin = this->layer_param_.contrastive_loss_param().margin(); + bool legacy_version = + this->layer_param_.contrastive_loss_param().legacy_version(); for (int i = 0; i < 2; ++i) { if (propagate_down[i]) { const Dtype sign = (i == 0) ? 1 : -1; @@ -77,12 +85,20 @@ void ContrastiveLossLayer::Backward_cpu(const vector*>& top, Dtype(0.0), bout + (j*channels)); } else { // dissimilar pairs - Dtype dist = sqrt(dist_sq_.cpu_data()[j]); - Dtype mdist = (margin - dist); + Dtype mdist(0.0); + Dtype beta(0.0); + if (legacy_version) { + mdist = margin - dist_sq_.cpu_data()[j]; + beta = -alpha; + } else { + Dtype dist = sqrt(dist_sq_.cpu_data()[j]); + mdist = margin - dist; + beta = -alpha * mdist / (dist + Dtype(1e-4)); + } if (mdist > Dtype(0.0)) { caffe_cpu_axpby( channels, - -alpha * mdist / (dist + Dtype(1e-4)), + beta, diff_.cpu_data() + (j*channels), Dtype(0.0), bout + (j*channels)); diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu index 83ff3c90f0f..931239316ac 100644 --- a/src/caffe/layers/contrastive_loss_layer.cu +++ b/src/caffe/layers/contrastive_loss_layer.cu @@ -32,13 +32,20 @@ void ContrastiveLossLayer::Forward_gpu( Dtype(0.0), dist_sq_.mutable_gpu_data()); // \Sum (a_i-b_i)^2 Dtype margin = this->layer_param_.contrastive_loss_param().margin(); + bool legacy_version = + this->layer_param_.contrastive_loss_param().legacy_version(); Dtype loss(0.0); for (int i = 0; i < bottom[0]->num(); ++i) { if (static_cast(bottom[2]->cpu_data()[i])) { // similar pairs loss += dist_sq_.cpu_data()[i]; } else { // dissimilar pairs - Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), Dtype(0.0)); - loss += dist*dist; + if (legacy_version) { + loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0)); + } else { + Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), + Dtype(0.0)); + loss += dist*dist; + } } } loss = loss / static_cast(bottom[0]->num()) / Dtype(2); @@ -47,7 +54,7 @@ void ContrastiveLossLayer::Forward_gpu( template __global__ void CLLBackward(const int count, const int channels, - const Dtype margin, const Dtype alpha, + const Dtype margin, const bool legacy_version, const Dtype alpha, const Dtype* y, const Dtype* diff, const Dtype* dist_sq, Dtype *bottom_diff) { CUDA_KERNEL_LOOP(i, count) { @@ -55,10 +62,18 @@ __global__ void CLLBackward(const int count, const int channels, if (static_cast(y[n])) { // similar pairs bottom_diff[i] = alpha * diff[i]; } else { // dissimilar pairs - Dtype dist = sqrt(dist_sq[n]); - Dtype mdist = (margin - dist); + Dtype mdist(0.0); + Dtype beta(0.0); + if (legacy_version) { + mdist = (margin - dist_sq[n]); + beta = -alpha; + } else { + Dtype dist = sqrt(dist_sq[n]); + mdist = (margin - dist); + beta = -alpha * mdist / (dist + Dtype(1e-4)) * diff[i]; + } if (mdist > 0.0) { - bottom_diff[i] = -alpha * mdist / (dist + Dtype(1e-4)) * diff[i]; + bottom_diff[i] = beta; } else { bottom_diff[i] = 0; } @@ -74,12 +89,14 @@ void ContrastiveLossLayer::Backward_gpu(const vector*>& top, const int count = bottom[0]->count(); const int channels = bottom[0]->channels(); Dtype margin = this->layer_param_.contrastive_loss_param().margin(); + const bool legacy_version = + this->layer_param_.contrastive_loss_param().legacy_version(); const Dtype sign = (i == 0) ? 1 : -1; const Dtype alpha = sign * top[0]->cpu_diff()[0] / static_cast(bottom[0]->num()); // NOLINT_NEXT_LINE(whitespace/operators) CLLBackward<<>>( - count, channels, margin, alpha, + count, channels, margin, legacy_version, alpha, bottom[2]->gpu_data(), // pair similarity 0 or 1 diff_.gpu_data(), // the cached eltwise difference between a and b dist_sq_.gpu_data(), // the cached square distance between a and b diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 5b21cf20028..fe4ce366972 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -401,8 +401,15 @@ message ConcatParameter { // Message that stores parameters used by ContrastiveLossLayer message ContrastiveLossParameter { - //margin for dissimilar pair + // margin for dissimilar pair optional float margin = 1 [default = 1.0]; + // The first implementation of this cost did not exactly match the cost of + // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2. + // legacy_version = false (the default) uses (margin - d)^2 as proposed in the + // Hadsell paper. New models should probably use this version. + // legacy_version = true uses (margin - d^2). This is kept to support / + // reproduce existing models and results + optional bool legacy_version = 2 [default = false]; } // Message that stores parameters used by ConvolutionLayer diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp index 460fc8f32fc..1e9447cbc51 100644 --- a/src/caffe/test/test_contrastive_loss_layer.cpp +++ b/src/caffe/test/test_contrastive_loss_layer.cpp @@ -100,4 +100,47 @@ TYPED_TEST(ContrastiveLossLayerTest, TestGradient) { this->blob_top_vec_, 1); } +TYPED_TEST(ContrastiveLossLayerTest, TestForwardLegacy) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_contrastive_loss_param()->set_legacy_version(true); + ContrastiveLossLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // manually compute to compare + const Dtype margin = layer_param.contrastive_loss_param().margin(); + const int num = this->blob_bottom_data_i_->num(); + const int channels = this->blob_bottom_data_i_->channels(); + Dtype loss(0); + for (int i = 0; i < num; ++i) { + Dtype dist_sq(0); + for (int j = 0; j < channels; ++j) { + Dtype diff = this->blob_bottom_data_i_->cpu_data()[i*channels+j] - + this->blob_bottom_data_j_->cpu_data()[i*channels+j]; + dist_sq += diff*diff; + } + if (this->blob_bottom_y_->cpu_data()[i]) { // similar pairs + loss += dist_sq; + } else { + loss += std::max(margin - dist_sq, Dtype(0.0)); + } + } + loss /= static_cast(num) * Dtype(2); + EXPECT_NEAR(this->blob_top_loss_->cpu_data()[0], loss, 1e-6); +} + +TYPED_TEST(ContrastiveLossLayerTest, TestGradientLegacy) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_contrastive_loss_param()->set_legacy_version(true); + ContrastiveLossLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + GradientChecker checker(1e-2, 1e-2, 1701); + // check the gradient for the first two bottom layers + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_, 0); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_, 1); +} + } // namespace caffe From 63ed23bd83d5c30e74a83a976524dadf22d6dd49 Mon Sep 17 00:00:00 2001 From: Takuya Narihira Date: Mon, 4 May 2015 11:44:44 -0700 Subject: [PATCH 032/446] Fix redundancy of parameter backward computation --- src/caffe/layers/prelu_layer.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu index fd0eda5d191..5fd69d6c4d4 100644 --- a/src/caffe/layers/prelu_layer.cu +++ b/src/caffe/layers/prelu_layer.cu @@ -89,7 +89,7 @@ void PReLULayer::Backward_gpu(const vector*>& top, Dtype* temp_buff = multiplier_.mutable_gpu_diff(); // compute element-wise diff // NOLINT_NEXT_LINE(whitespace/operators) - PReLUParamBackward<<<<>>( cdim, top_diff + top[0]->offset(n), bottom_data + bottom[0]->offset(n), multiplier_.mutable_gpu_diff()); From 4348c6f4c905e9eb2c4dee32614a1880e074b217 Mon Sep 17 00:00:00 2001 From: Takuya Narihira Date: Mon, 4 May 2015 11:45:33 -0700 Subject: [PATCH 033/446] Modify for better readability regarding temporary bufffer for backward computation --- include/caffe/neuron_layers.hpp | 3 ++- src/caffe/layers/prelu_layer.cpp | 3 ++- src/caffe/layers/prelu_layer.cu | 8 ++++---- 3 files changed, 8 insertions(+), 6 deletions(-) diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp index 323215134c7..aff58233e5c 100644 --- a/include/caffe/neuron_layers.hpp +++ b/include/caffe/neuron_layers.hpp @@ -734,7 +734,8 @@ class PReLULayer : public NeuronLayer { const vector& propagate_down, const vector*>& bottom); bool channel_shared_; - Blob multiplier_; // dot multipler for backward computation of params + Blob multiplier_; // dot multiplier for backward computation of params + Blob backward_buff_; // temporary buffer for backward computation Blob bottom_memory_; // memory for in-place computation }; diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp index 7119a274dd3..7a38f9fac80 100644 --- a/src/caffe/layers/prelu_layer.cpp +++ b/src/caffe/layers/prelu_layer.cpp @@ -45,7 +45,8 @@ void PReLULayer::LayerSetUp(const vector*>& bottom, // Propagate gradients to the parameters (as directed by backward pass). this->param_propagate_down_.resize(this->blobs_.size(), true); - multiplier_.Reshape(vector(1, bottom[0]->count() / bottom[0]->num())); + multiplier_.Reshape(vector(1, bottom[0]->count(1))); + backward_buff_.Reshape(vector(1, bottom[0]->count(1))); caffe_set(multiplier_.count(), Dtype(1), multiplier_.mutable_cpu_data()); } diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu index 5fd69d6c4d4..dfa238d85bd 100644 --- a/src/caffe/layers/prelu_layer.cu +++ b/src/caffe/layers/prelu_layer.cu @@ -86,22 +86,22 @@ void PReLULayer::Backward_gpu(const vector*>& top, int cdim = channels * dim; Dtype dsum = 0.; for (int n = 0; n < bottom[0]->num(); ++n) { - Dtype* temp_buff = multiplier_.mutable_gpu_diff(); // compute element-wise diff // NOLINT_NEXT_LINE(whitespace/operators) PReLUParamBackward<<>>( cdim, top_diff + top[0]->offset(n), - bottom_data + bottom[0]->offset(n), multiplier_.mutable_gpu_diff()); + bottom_data + bottom[0]->offset(n), + backward_buff_.mutable_gpu_diff()); CUDA_POST_KERNEL_CHECK; if (channel_shared_) { Dtype d; - caffe_gpu_dot(channels * dim, multiplier_.gpu_diff(), + caffe_gpu_dot(channels * dim, backward_buff_.gpu_diff(), multiplier_.gpu_data(), &d); dsum += d; } else { caffe_gpu_gemv(CblasNoTrans, channels, dim, 1., - multiplier_.gpu_diff(), multiplier_.gpu_data(), 1., + backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1., slope_diff); } } From a046c571c5d66a09cd6d2dd51ea1ee0957c574fd Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Wed, 6 May 2015 17:40:12 -0700 Subject: [PATCH 034/446] check that count_ does not overflow in Blob::Reshape --- src/caffe/blob.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp index 6d2b3f502d9..94fdcc35fb6 100644 --- a/src/caffe/blob.cpp +++ b/src/caffe/blob.cpp @@ -26,6 +26,7 @@ void Blob::Reshape(const vector& shape) { shape_.resize(shape.size()); for (int i = 0; i < shape.size(); ++i) { CHECK_GE(shape[i], 0); + CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX"; count_ *= shape[i]; shape_[i] = shape[i]; } From a0e9f95817ea593aeef6ad8b4bed0da417e5a66e Mon Sep 17 00:00:00 2001 From: Gustav Larsson Date: Mon, 11 May 2015 11:54:45 -0500 Subject: [PATCH 035/446] This imports the wrong io module in Python 3. The Python standard lib has a module called io, so instead of Python 3 throwing an error, it imports the wrong module without complaining. --- python/caffe/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py index 37e8956da4f..fbe7112e868 100644 --- a/python/caffe/__init__.py +++ b/python/caffe/__init__.py @@ -3,4 +3,4 @@ from .proto.caffe_pb2 import TRAIN, TEST from .classifier import Classifier from .detector import Detector -import io +from . import io From 7ed310f2835c6a7843fd3362f290dbfda1585936 Mon Sep 17 00:00:00 2001 From: Martin Thoma Date: Mon, 11 May 2015 22:35:48 +0200 Subject: [PATCH 036/446] python: PEP8; changed docstring documentation style to NumPyDoc style --- python/caffe/classifier.py | 5 +- python/caffe/detector.py | 35 +++++++------ python/caffe/draw.py | 45 ++++++++++------- python/caffe/io.py | 27 ++++------ python/caffe/pycaffe.py | 68 +++++++++++++++----------- python/caffe/test/test_net.py | 2 + python/caffe/test/test_python_layer.py | 3 ++ python/caffe/test/test_solver.py | 1 + 8 files changed, 103 insertions(+), 83 deletions(-) diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py index 49f8003ce9d..7fb2ccc8ff3 100644 --- a/python/caffe/classifier.py +++ b/python/caffe/classifier.py @@ -29,7 +29,7 @@ def __init__(self, model_file, pretrained_file, image_dims=None, in_ = self.inputs[0] self.transformer = caffe.io.Transformer( {in_: self.blobs[in_].data.shape}) - self.transformer.set_transpose(in_, (2,0,1)) + self.transformer.set_transpose(in_, (2, 0, 1)) if mean is not None: self.transformer.set_mean(in_, mean) if input_scale is not None: @@ -44,7 +44,6 @@ def __init__(self, model_file, pretrained_file, image_dims=None, image_dims = self.crop_dims self.image_dims = image_dims - def predict(self, inputs, oversample=True): """ Predict classification probabilities of inputs. @@ -78,7 +77,7 @@ def predict(self, inputs, oversample=True): input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :] # Classify - caffe_in = np.zeros(np.array(input_.shape)[[0,3,1,2]], + caffe_in = np.zeros(np.array(input_.shape)[[0, 3, 1, 2]], dtype=np.float32) for ix, in_ in enumerate(input_): caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_) diff --git a/python/caffe/detector.py b/python/caffe/detector.py index a67b818b93f..f72b548ac9a 100644 --- a/python/caffe/detector.py +++ b/python/caffe/detector.py @@ -41,7 +41,7 @@ def __init__(self, model_file, pretrained_file, mean=None, in_ = self.inputs[0] self.transformer = caffe.io.Transformer( {in_: self.blobs[in_].data.shape}) - self.transformer.set_transpose(in_, (2,0,1)) + self.transformer.set_transpose(in_, (2, 0, 1)) if mean is not None: self.transformer.set_mean(in_, mean) if input_scale is not None: @@ -53,17 +53,18 @@ def __init__(self, model_file, pretrained_file, mean=None, self.configure_crop(context_pad) - def detect_windows(self, images_windows): """ Do windowed detection over given images and windows. Windows are extracted then warped to the input dimensions of the net. - Take + Parameters + ---------- images_windows: (image filename, window list) iterable. context_crop: size of context border to crop in pixels. - Give + Returns + ------- detections: list of {filename: image filename, window: crop coordinates, predictions: prediction vector} dicts. """ @@ -82,7 +83,7 @@ def detect_windows(self, images_windows): for ix, window_in in enumerate(window_inputs): caffe_in[ix] = self.transformer.preprocess(in_, window_in) out = self.forward_all(**{in_: caffe_in}) - predictions = out[self.outputs[0]].squeeze(axis=(2,3)) + predictions = out[self.outputs[0]].squeeze(axis=(2, 3)) # Package predictions with images and windows. detections = [] @@ -97,16 +98,17 @@ def detect_windows(self, images_windows): ix += 1 return detections - def detect_selective_search(self, image_fnames): """ Do windowed detection over Selective Search proposals by extracting the crop and warping to the input dimensions of the net. - Take + Parameters + ---------- image_fnames: list - Give + Returns + ------- detections: list of {filename: image filename, window: crop coordinates, predictions: prediction vector} dicts. """ @@ -120,17 +122,18 @@ def detect_selective_search(self, image_fnames): # Run windowed detection on the selective search list. return self.detect_windows(zip(image_fnames, windows_list)) - def crop(self, im, window): """ Crop a window from the image for detection. Include surrounding context according to the `context_pad` configuration. - Take + Parameters + ---------- im: H x W x K image ndarray to crop. window: bounding box coordinates as ymin, xmin, ymax, xmax. - Give + Returns + ------- crop: cropped window. """ # Crop window from the image. @@ -175,14 +178,14 @@ def crop(self, im, window): return crop - def configure_crop(self, context_pad): """ Configure crop dimensions and amount of context for cropping. If context is included, make the special input mean for context padding. - Take - context_pad: amount of context for cropping. + Parameters + ---------- + context_pad : amount of context for cropping. """ # crop dimensions in_ = self.inputs[0] @@ -204,8 +207,8 @@ def configure_crop(self, context_pad): crop_mean = mean.copy().transpose(inv_transpose) if channel_order is not None: channel_order_inverse = [channel_order.index(i) - for i in range(crop_mean.shape[2])] - crop_mean = crop_mean[:,:, channel_order_inverse] + for i in range(crop_mean.shape[2])] + crop_mean = crop_mean[:, :, channel_order_inverse] if raw_scale is not None: crop_mean /= raw_scale self.crop_mean = crop_mean diff --git a/python/caffe/draw.py b/python/caffe/draw.py index 6a4dbd47351..08b7c1de14b 100644 --- a/python/caffe/draw.py +++ b/python/caffe/draw.py @@ -11,19 +11,23 @@ import pydot # Internal layer and blob styles. -LAYER_STYLE_DEFAULT = {'shape': 'record', 'fillcolor': '#6495ED', - 'style': 'filled'} -NEURON_LAYER_STYLE = {'shape': 'record', 'fillcolor': '#90EE90', - 'style': 'filled'} -BLOB_STYLE = {'shape': 'octagon', 'fillcolor': '#E0E0E0', - 'style': 'filled'} +LAYER_STYLE_DEFAULT = {'shape': 'record', + 'fillcolor': '#6495ED', + 'style': 'filled'} +NEURON_LAYER_STYLE = {'shape': 'record', + 'fillcolor': '#90EE90', + 'style': 'filled'} +BLOB_STYLE = {'shape': 'octagon', + 'fillcolor': '#E0E0E0', + 'style': 'filled'} + def get_pooling_types_dict(): """Get dictionary mapping pooling type number to type name """ desc = caffe_pb2.PoolingParameter.PoolMethod.DESCRIPTOR d = {} - for k,v in desc.values_by_name.items(): + for k, v in desc.values_by_name.items(): d[v.number] = k return d @@ -145,21 +149,24 @@ def get_pydot_graph(caffe_net, rankdir, label_edges=True): label=edge['label'])) return pydot_graph + def draw_net(caffe_net, rankdir, ext='png'): - """Draws a caffe net and returns the image string encoded using the given - extension. + """Draws a caffe net and returns the image string encoded using the given + extension. - Input: + Parameters + ---------- caffe_net: a caffe.proto.caffe_pb2.NetParameter protocol buffer. ext: the image extension. Default 'png'. - """ - return get_pydot_graph(caffe_net, rankdir).create(format=ext) + """ + return get_pydot_graph(caffe_net, rankdir).create(format=ext) + def draw_net_to_file(caffe_net, filename, rankdir='LR'): - """Draws a caffe net, and saves it to file using the format given as the - file extension. Use '.raw' to output raw text that you can manually feed - to graphviz to draw graphs. - """ - ext = filename[filename.rfind('.')+1:] - with open(filename, 'wb') as fid: - fid.write(draw_net(caffe_net, rankdir, ext)) + """Draws a caffe net, and saves it to file using the format given as the + file extension. Use '.raw' to output raw text that you can manually feed + to graphviz to draw graphs. + """ + ext = filename[filename.rfind('.')+1:] + with open(filename, 'wb') as fid: + fid.write(draw_net(caffe_net, rankdir, ext)) diff --git a/python/caffe/io.py b/python/caffe/io.py index 6ae2cf13cc0..e5feff38796 100644 --- a/python/caffe/io.py +++ b/python/caffe/io.py @@ -8,16 +8,16 @@ from caffe.proto import caffe_pb2 except: import sys - if sys.version_info >= (3,0): + if sys.version_info >= (3, 0): print("Failed to include caffe_pb2, things might go wrong!") else: raise -## proto / datum / ndarray conversion +## proto / datum / ndarray conversion def blobproto_to_array(blob, return_diff=False): - """Convert a blob proto to an array. In default, we will just return the data, - unless return_diff is True, in which case we will return the diff. + """Convert a blob proto to an array. In default, we will just return the + data, unless return_diff is True, in which case we will return the diff. """ if return_diff: return np.array(blob.diff).reshape( @@ -35,7 +35,7 @@ def array_to_blobproto(arr, diff=None): if arr.ndim != 4: raise ValueError('Incorrect array shape.') blob = caffe_pb2.BlobProto() - blob.num, blob.channels, blob.height, blob.width = arr.shape; + blob.num, blob.channels, blob.height, blob.width = arr.shape blob.data.extend(arr.astype(float).flat) if diff is not None: blob.diff.extend(diff.astype(float).flat) @@ -81,7 +81,7 @@ def datum_to_array(datum): as one can easily get it by calling datum.label. """ if len(datum.data): - return np.fromstring(datum.data, dtype = np.uint8).reshape( + return np.fromstring(datum.data, dtype=np.uint8).reshape( datum.channels, datum.height, datum.width) else: return np.array(datum.float_data).astype(float).reshape( @@ -97,8 +97,9 @@ class Transformer: Note: this is mostly for illustrative purposes and it is likely better to define your own input preprocessing routine for your needs. - Take - net: a Net for which the input should be prepared + Parameters + ---------- + net : a Net for which the input should be prepared """ def __init__(self, inputs): self.inputs = inputs @@ -108,13 +109,11 @@ def __init__(self, inputs): self.mean = {} self.input_scale = {} - def __check_input(self, in_): if in_ not in self.inputs: raise Exception('{} is not one of the net inputs: {}'.format( in_, self.inputs)) - def preprocess(self, in_, data): """ Format input for Caffe: @@ -155,7 +154,6 @@ def preprocess(self, in_, data): caffe_in *= input_scale return caffe_in - def deprocess(self, in_, data): """ Invert Caffe formatting; see preprocess(). @@ -179,7 +177,6 @@ def deprocess(self, in_, data): decaf_in = decaf_in.transpose([transpose[t] for t in transpose]) return decaf_in - def set_transpose(self, in_, order): """ Set the input channel order for e.g. RGB to BGR conversion @@ -195,7 +192,6 @@ def set_transpose(self, in_, order): 'dimensions as the input.') self.transpose[in_] = order - def set_channel_swap(self, in_, order): """ Set the input channel order for e.g. RGB to BGR conversion @@ -213,7 +209,6 @@ def set_channel_swap(self, in_, order): 'dimensions as the input channels.') self.channel_swap[in_] = order - def set_raw_scale(self, in_, scale): """ Set the scale of raw features s.t. the input blob = input * scale. @@ -228,7 +223,6 @@ def set_raw_scale(self, in_, scale): self.__check_input(in_) self.raw_scale[in_] = scale - def set_mean(self, in_, mean): """ Set the mean to subtract for centering the data. @@ -254,7 +248,6 @@ def set_mean(self, in_, mean): raise ValueError('Mean shape incompatible with input shape.') self.mean[in_] = mean - def set_input_scale(self, in_, scale): """ Set the scale of preprocessed inputs s.t. the blob = blob * scale. @@ -359,7 +352,7 @@ def oversample(images, crop_dims): # Extract crops crops = np.empty((10 * len(images), crop_dims[0], crop_dims[1], - im_shape[-1]), dtype=np.float32) + im_shape[-1]), dtype=np.float32) ix = 0 for im in images: for crop in crops_ix: diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py index 3c19261f690..e8a676a26d2 100644 --- a/python/caffe/pycaffe.py +++ b/python/caffe/pycaffe.py @@ -5,9 +5,9 @@ from collections import OrderedDict try: - from itertools import izip_longest + from itertools import izip_longest except: - from itertools import zip_longest as izip_longest + from itertools import zip_longest as izip_longest import numpy as np from ._caffe import Net, SGDSolver @@ -53,16 +53,19 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs): """ Forward pass: prepare inputs and run the net forward. - Take - blobs: list of blobs to return in addition to output blobs. - kwargs: Keys are input blob names and values are blob ndarrays. - For formatting inputs for Caffe, see Net.preprocess(). - If None, input is taken from data layers. - start: optional name of layer at which to begin the forward pass - end: optional name of layer at which to finish the forward pass (inclusive) - - Give - outs: {blob name: blob ndarray} dict. + Parameters + ---------- + blobs : list of blobs to return in addition to output blobs. + kwargs : Keys are input blob names and values are blob ndarrays. + For formatting inputs for Caffe, see Net.preprocess(). + If None, input is taken from data layers. + start : optional name of layer at which to begin the forward pass + end : optional name of layer at which to finish the forward pass + (inclusive) + + Returns + ------- + outs : {blob name: blob ndarray} dict. """ if blobs is None: blobs = [] @@ -99,14 +102,17 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs): """ Backward pass: prepare diffs and run the net backward. - Take - diffs: list of diffs to return in addition to bottom diffs. - kwargs: Keys are output blob names and values are diff ndarrays. + Parameters + ---------- + diffs : list of diffs to return in addition to bottom diffs. + kwargs : Keys are output blob names and values are diff ndarrays. If None, top diffs are taken from forward loss. - start: optional name of layer at which to begin the backward pass - end: optional name of layer at which to finish the backward pass (inclusive) + start : optional name of layer at which to begin the backward pass + end : optional name of layer at which to finish the backward pass + (inclusive) - Give + Returns + ------- outs: {blob name: diff ndarray} dict. """ if diffs is None: @@ -146,13 +152,15 @@ def _Net_forward_all(self, blobs=None, **kwargs): """ Run net forward in batches. - Take - blobs: list of blobs to extract as in forward() - kwargs: Keys are input blob names and values are blob ndarrays. - Refer to forward(). + Parameters + ---------- + blobs : list of blobs to extract as in forward() + kwargs : Keys are input blob names and values are blob ndarrays. + Refer to forward(). - Give - all_outs: {blob name: list of blobs} dict. + Returns + ------- + all_outs : {blob name: list of blobs} dict. """ # Collect outputs from batches all_outs = {out: [] for out in set(self.outputs + (blobs or []))} @@ -175,14 +183,16 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs): """ Run net forward + backward in batches. - Take + Parameters + ---------- blobs: list of blobs to extract as in forward() diffs: list of diffs to extract as in backward() kwargs: Keys are input (for forward) and output (for backward) blob names and values are ndarrays. Refer to forward() and backward(). Prefilled variants are called for lack of input or output blobs. - Give + Returns + ------- all_blobs: {blob name: blob ndarray} dict. all_diffs: {blob name: diff ndarray} dict. """ @@ -229,11 +239,13 @@ def _Net_batch(self, blobs): """ Batch blob lists according to net's batch size. - Take + Parameters + ---------- blobs: Keys blob names and values are lists of blobs (of any length). Naturally, all the lists should have the same length. - Give (yield) + Yields + ------ batch: {blob name: list of blobs} dict for a single batch. """ num = len(blobs.itervalues().next()) diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py index 62b407da8aa..cc367477752 100644 --- a/python/caffe/test/test_net.py +++ b/python/caffe/test/test_net.py @@ -5,6 +5,7 @@ import caffe + def simple_net_file(num_output): """Make a simple net prototxt, based on test_net.cpp, returning the name of the (temporary) file.""" @@ -31,6 +32,7 @@ def simple_net_file(num_output): f.close() return f.name + class TestNet(unittest.TestCase): def setUp(self): self.num_output = 13 diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py index dd99f6f15b9..6fba49143bb 100644 --- a/python/caffe/test/test_python_layer.py +++ b/python/caffe/test/test_python_layer.py @@ -4,6 +4,7 @@ import caffe + class SimpleLayer(caffe.Layer): """A layer that just multiplies by ten""" @@ -19,6 +20,7 @@ def forward(self, bottom, top): def backward(self, top, propagate_down, bottom): bottom[0].diff[...] = 10 * top[0].diff + def python_net_file(): with tempfile.NamedTemporaryFile(delete=False) as f: f.write("""name: 'pythonnet' force_backward: true @@ -31,6 +33,7 @@ def python_net_file(): python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""") return f.name + class TestPythonLayer(unittest.TestCase): def setUp(self): net_file = python_net_file() diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py index d59f23d973a..09b974dad66 100644 --- a/python/caffe/test/test_solver.py +++ b/python/caffe/test/test_solver.py @@ -6,6 +6,7 @@ import caffe from test_net import simple_net_file + class TestSolver(unittest.TestCase): def setUp(self): self.num_output = 13 From 7cf8b830cd24b73ebed89ef19e6bc8bd25d938cd Mon Sep 17 00:00:00 2001 From: Takuya Narihira Date: Wed, 13 May 2015 21:16:28 -0700 Subject: [PATCH 037/446] [pycaffe] use bp::object instead of PyObject* for self in Python layer This simply allows direct use of the nicer bp::object interface. --- include/caffe/python_layer.hpp | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp index 816ef453720..19cf18c9742 100644 --- a/include/caffe/python_layer.hpp +++ b/include/caffe/python_layer.hpp @@ -14,12 +14,12 @@ template class PythonLayer : public Layer { public: PythonLayer(PyObject* self, const LayerParameter& param) - : Layer(param), self_(self) { } + : Layer(param), self_(bp::handle<>(bp::borrowed(self))) { } virtual void LayerSetUp(const vector*>& bottom, const vector*>& top) { try { - bp::call_method(self_, "setup", bottom, top); + self_.attr("setup")(bottom, top); } catch (bp::error_already_set) { PyErr_Print(); throw; @@ -29,7 +29,7 @@ class PythonLayer : public Layer { virtual void Reshape(const vector*>& bottom, const vector*>& top) { try { - bp::call_method(self_, "reshape", bottom, top); + self_.attr("reshape")(bottom, top); } catch (bp::error_already_set) { PyErr_Print(); throw; @@ -42,7 +42,7 @@ class PythonLayer : public Layer { virtual void Forward_cpu(const vector*>& bottom, const vector*>& top) { try { - bp::call_method(self_, "forward", bottom, top); + self_.attr("forward")(bottom, top); } catch (bp::error_already_set) { PyErr_Print(); throw; @@ -51,8 +51,7 @@ class PythonLayer : public Layer { virtual void Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { try { - bp::call_method(self_, "backward", top, propagate_down, - bottom); + self_.attr("backward")(top, propagate_down, bottom); } catch (bp::error_already_set) { PyErr_Print(); throw; @@ -60,7 +59,7 @@ class PythonLayer : public Layer { } private: - PyObject* self_; + bp::object self_; }; } // namespace caffe From e266a4d368b22a8940468d68f41fe4392f5200da Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Wed, 13 May 2015 22:08:57 -0700 Subject: [PATCH 038/446] remove superfluous empty destructors The removed definitions do nothing; these classes already have virtual destructors inherited from their respective base classes. --- include/caffe/data_layers.hpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp index 2bb9d948169..4dcf5501522 100644 --- a/include/caffe/data_layers.hpp +++ b/include/caffe/data_layers.hpp @@ -29,7 +29,6 @@ template class BaseDataLayer : public Layer { public: explicit BaseDataLayer(const LayerParameter& param); - virtual ~BaseDataLayer() {} // LayerSetUp: implements common data layer setup functionality, and calls // DataLayerSetUp to do special data layer setup for individual layer types. // This method may not be overridden except by the BasePrefetchingDataLayer. @@ -58,7 +57,6 @@ class BasePrefetchingDataLayer : public: explicit BasePrefetchingDataLayer(const LayerParameter& param) : BaseDataLayer(param) {} - virtual ~BasePrefetchingDataLayer() {} // LayerSetUp: implements common data layer setup functionality, and calls // DataLayerSetUp to do special data layer setup for individual layer types. // This method may not be overridden. From 6153231594b98c0933be21685708282bc6160b6c Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 12 Mar 2015 17:59:28 -0700 Subject: [PATCH 039/446] remove bogus implementation of SigmoidCrossEntropyLossLayer::Forward_gpu It was a verbatim copy of Forward_cpu; there is no proper GPU implementation. --- include/caffe/loss_layers.hpp | 2 -- .../sigmoid_cross_entropy_loss_layer.cpp | 2 +- .../sigmoid_cross_entropy_loss_layer.cu | 22 +------------------ 3 files changed, 2 insertions(+), 24 deletions(-) diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp index d3eecd2e510..86c34241168 100644 --- a/include/caffe/loss_layers.hpp +++ b/include/caffe/loss_layers.hpp @@ -605,8 +605,6 @@ class SigmoidCrossEntropyLossLayer : public LossLayer { /// @copydoc SigmoidCrossEntropyLossLayer virtual void Forward_cpu(const vector*>& bottom, const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); /** * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp index 077d949981c..cc236fe1e8e 100644 --- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp +++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp @@ -71,7 +71,7 @@ void SigmoidCrossEntropyLossLayer::Backward_cpu( } #ifdef CPU_ONLY -STUB_GPU(SigmoidCrossEntropyLossLayer); +STUB_GPU_BACKWARD(SigmoidCrossEntropyLossLayer, Backward); #endif INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer); diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu index 08f7f492297..547fa80c72f 100644 --- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu +++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu @@ -8,26 +8,6 @@ namespace caffe { -template -void SigmoidCrossEntropyLossLayer::Forward_gpu( - const vector*>& bottom, const vector*>& top) { - // The forward pass computes the sigmoid outputs. - sigmoid_bottom_vec_[0] = bottom[0]; - sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_); - // Compute the loss (negative log likelihood) - const int count = bottom[0]->count(); - const int num = bottom[0]->num(); - // Stable version of loss computation from input data - const Dtype* input_data = bottom[0]->cpu_data(); - const Dtype* target = bottom[1]->cpu_data(); - Dtype loss = 0; - for (int i = 0; i < count; ++i) { - loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) - - log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0))); - } - top[0]->mutable_cpu_data()[0] = loss / num; -} - template void SigmoidCrossEntropyLossLayer::Backward_gpu( const vector*>& top, const vector& propagate_down, @@ -51,7 +31,7 @@ void SigmoidCrossEntropyLossLayer::Backward_gpu( } } -INSTANTIATE_LAYER_GPU_FUNCS(SigmoidCrossEntropyLossLayer); +INSTANTIATE_LAYER_GPU_BACKWARD(SigmoidCrossEntropyLossLayer); } // namespace caffe From 438cf0e9a660676b1526eb8178b020ad2d745f6f Mon Sep 17 00:00:00 2001 From: PETER_GAO Date: Sat, 21 Mar 2015 16:00:05 -0700 Subject: [PATCH 040/446] Spatial Pyramid Pooling Layer --- include/caffe/vision_layers.hpp | 66 ++++++++++ src/caffe/layers/spp_layer.cpp | 193 ++++++++++++++++++++++++++++++ src/caffe/proto/caffe.proto | 20 +++- src/caffe/test/test_spp_layer.cpp | 131 ++++++++++++++++++++ 4 files changed, 409 insertions(+), 1 deletion(-) create mode 100644 src/caffe/layers/spp_layer.cpp create mode 100644 src/caffe/test/test_spp_layer.cpp diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp index 6cb507a5780..e9023230439 100644 --- a/include/caffe/vision_layers.hpp +++ b/include/caffe/vision_layers.hpp @@ -451,6 +451,72 @@ class CuDNNPoolingLayer : public PoolingLayer { }; #endif +/** + * @brief Does spatial pyramid pooling on the input image + * by taking the max, average, etc. within regions + * so that the result vector of different sized + * images are of the same size. + */ +template +class SPPLayer : public Layer { + public: + explicit SPPLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "SPP"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int MinTopBlobs() const { return 1; } + // MAX POOL layers can output an extra top blob for the mask; + // others can only output the pooled inputs. + virtual inline int MaxTopBlobs() const { + return (this->layer_param_.pooling_param().pool() == + PoolingParameter_PoolMethod_MAX) ? 2 : 1; + } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + // calculates the kernel and stride dimensions for the pooling layer, + // returns a correctly configured LayerParameter for a PoolingLayer + virtual LayerParameter GetPoolingParam(const int pyramid_level, + const int bottom_h, const int bottom_w, const SPPParameter spp_param); + + int pyramid_height_; + int bottom_h_, bottom_w_; + int channels_; + int kernel_h_, kernel_w_; + int pad_h_, pad_w_; + + /// the internal Split layer that feeds the pooling layers + shared_ptr > split_layer_; + /// top vector holder used in call to the underlying SplitLayer::Forward + vector*> split_top_vec_; + /// bottom vector holder used in call to the underlying PoolingLayer::Forward + vector*>*> pooling_bottom_vecs_; + /// the internal Pooling layers of different kernel sizes + vector > > pooling_layers_; + /// top vector holders used in call to the underlying PoolingLayer::Forward + vector*>*> pooling_top_vecs_; + /// pooling_outputs stores the outputs of the PoolingLayers + vector*> pooling_outputs_; + /// the internal Flatten layers that the Pooling layers feed into + vector*> flatten_layers_; + /// top vector holders used in call to the underlying FlattenLayer::Forward + vector*>*> flatten_top_vecs_; + /// flatten_outputs stores the outputs of the FlattenLayers + vector*> flatten_outputs_; + /// bottom vector holder used in call to the underlying ConcatLayer::Forward + vector*> concat_bottom_vec_; + /// the internal Concat layers that the Flatten layers feed into + shared_ptr > concat_layer_; +}; + } // namespace caffe #endif // CAFFE_VISION_LAYERS_HPP_ diff --git a/src/caffe/layers/spp_layer.cpp b/src/caffe/layers/spp_layer.cpp new file mode 100644 index 00000000000..795dd71693e --- /dev/null +++ b/src/caffe/layers/spp_layer.cpp @@ -0,0 +1,193 @@ +#include +#include +#include + +#include "caffe/common.hpp" +#include "caffe/layer.hpp" +#include "caffe/syncedmem.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +using std::min; +using std::max; + +template +LayerParameter SPPLayer::GetPoolingParam(const int pyramid_level, + const int bottom_h, const int bottom_w, const SPPParameter spp_param) { + LayerParameter pooling_param; + int num_bins = pow(2, pyramid_level); + + // find padding and kernel size so that the pooling is + // performed across the entire image + int kernel_h = ceil(bottom_h / static_cast(num_bins)); + // remainder_h is the min number of pixels that need to be padded before + // entire image height is pooled over with the chosen kernel dimension + int remainder_h = kernel_h * num_bins - bottom_h; + // pooling layer pads (2 * pad_h) pixels on the top and bottom of the + // image. + int pad_h = (remainder_h + 1) / 2; + + // similar logic for width + int kernel_w = ceil(bottom_w / static_cast(num_bins)); + int remainder_w = kernel_w * num_bins - bottom_w; + int pad_w = (remainder_w + 1) / 2; + + pooling_param.mutable_pooling_param()->set_pad_h(pad_h); + pooling_param.mutable_pooling_param()->set_pad_w(pad_w); + pooling_param.mutable_pooling_param()->set_kernel_h(kernel_h); + pooling_param.mutable_pooling_param()->set_kernel_w(kernel_w); + pooling_param.mutable_pooling_param()->set_stride_h(kernel_h); + pooling_param.mutable_pooling_param()->set_stride_w(kernel_w); + + switch (spp_param.pool()) { + case SPPParameter_PoolMethod_MAX: + pooling_param.mutable_pooling_param()->set_pool( + PoolingParameter_PoolMethod_MAX); + break; + case SPPParameter_PoolMethod_AVE: + pooling_param.mutable_pooling_param()->set_pool( + PoolingParameter_PoolMethod_AVE); + break; + case SPPParameter_PoolMethod_STOCHASTIC: + pooling_param.mutable_pooling_param()->set_pool( + PoolingParameter_PoolMethod_STOCHASTIC); + break; + default: + LOG(FATAL) << "Unknown pooling method."; + } + + return pooling_param; +} + +template +void SPPLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + SPPParameter spp_param = this->layer_param_.spp_param(); + + bottom_h_ = bottom[0]->height(); + bottom_w_ = bottom[0]->width(); + CHECK_GT(bottom_h_, 0) << "Input dimensions cannot be zero."; + CHECK_GT(bottom_w_, 0) << "Input dimensions cannot be zero."; + + pyramid_height_ = spp_param.pyramid_height(); + split_top_vec_.clear(); + pooling_bottom_vecs_.clear(); + pooling_layers_.clear(); + pooling_top_vecs_.clear(); + pooling_outputs_.clear(); + flatten_layers_.clear(); + flatten_top_vecs_.clear(); + flatten_outputs_.clear(); + concat_bottom_vec_.clear(); + + // split layer output holders setup + for (int i = 0; i < pyramid_height_; i++) { + split_top_vec_.push_back(new Blob()); + } + + // split layer setup + LayerParameter split_param; + split_layer_.reset(new SplitLayer(split_param)); + split_layer_->SetUp(bottom, split_top_vec_); + + for (int i = 0; i < pyramid_height_; i++) { + // pooling layer input holders setup + pooling_bottom_vecs_.push_back(new vector*>); + pooling_bottom_vecs_[i]->push_back(split_top_vec_[i]); + + // pooling layer output holders setup + pooling_outputs_.push_back(new Blob()); + pooling_top_vecs_.push_back(new vector*>); + pooling_top_vecs_[i]->push_back(pooling_outputs_[i]); + + // pooling layer setup + LayerParameter pooling_param = GetPoolingParam( + i, bottom_h_, bottom_w_, spp_param); + + pooling_layers_.push_back(shared_ptr > ( + new PoolingLayer(pooling_param))); + pooling_layers_[i]->SetUp(*pooling_bottom_vecs_[i], *pooling_top_vecs_[i]); + + // flatten layer output holders setup + flatten_outputs_.push_back(new Blob()); + flatten_top_vecs_.push_back(new vector*>); + flatten_top_vecs_[i]->push_back(flatten_outputs_[i]); + + // flatten layer setup + LayerParameter flatten_param; + flatten_layers_.push_back(new FlattenLayer(flatten_param)); + flatten_layers_[i]->SetUp(*pooling_top_vecs_[i], *flatten_top_vecs_[i]); + + // concat layer input holders setup + concat_bottom_vec_.push_back(flatten_outputs_[i]); + } + + // concat layer setup + LayerParameter concat_param; + concat_layer_.reset(new ConcatLayer(concat_param)); + concat_layer_->SetUp(concat_bottom_vec_, top); +} + +template +void SPPLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, " + << "corresponding to (num, channels, height, width)"; + channels_ = bottom[0]->channels(); + bottom_h_ = bottom[0]->height(); + bottom_w_ = bottom[0]->width(); + SPPParameter spp_param = this->layer_param_.spp_param(); + split_layer_->Reshape(bottom, split_top_vec_); + for (int i = 0; i < pyramid_height_; i++) { + LayerParameter pooling_param = GetPoolingParam( + i, bottom_h_, bottom_w_, spp_param); + + pooling_layers_[i].reset( + new PoolingLayer(pooling_param)); + pooling_layers_[i]->SetUp( + *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]); + pooling_layers_[i]->Reshape( + *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]); + flatten_layers_[i]->Reshape( + *pooling_top_vecs_[i], *flatten_top_vecs_[i]); + } + concat_layer_->Reshape(concat_bottom_vec_, top); +} + +template +void SPPLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + split_layer_->Forward(bottom, split_top_vec_); + for (int i = 0; i < pyramid_height_; i++) { + pooling_layers_[i]->Forward( + *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]); + flatten_layers_[i]->Forward( + *pooling_top_vecs_[i], *flatten_top_vecs_[i]); + } + concat_layer_->Forward(concat_bottom_vec_, top); +} + +template +void SPPLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (!propagate_down[0]) { + return; + } + vector concat_propagate_down(pyramid_height_, true); + concat_layer_->Backward(top, concat_propagate_down, concat_bottom_vec_); + for (int i = 0; i < pyramid_height_; i++) { + flatten_layers_[i]->Backward( + *flatten_top_vecs_[i], propagate_down, *pooling_top_vecs_[i]); + pooling_layers_[i]->Backward( + *pooling_top_vecs_[i], propagate_down, *pooling_bottom_vecs_[i]); + } + split_layer_->Backward(split_top_vec_, propagate_down, bottom); +} + + +INSTANTIATE_CLASS(SPPLayer); +REGISTER_LAYER_CLASS(SPP); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 5b21cf20028..b11fdf62dec 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -259,7 +259,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 132 (last added: prelu_param) +// LayerParameter next available layer-specific ID: 133 (last added: spp_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -328,6 +328,7 @@ message LayerParameter { optional ReLUParameter relu_param = 123; optional SigmoidParameter sigmoid_param = 124; optional SoftmaxParameter softmax_param = 125; + optional SPPParameter spp_param = 132; optional SliceParameter slice_param = 126; optional TanHParameter tanh_param = 127; optional ThresholdParameter threshold_param = 128; @@ -768,6 +769,23 @@ message WindowDataParameter { optional string root_folder = 13 [default = ""]; } +// Message that stores parameters used by SPPLayer +message SPPParameter { + enum PoolMethod { + MAX = 0; + AVE = 1; + STOCHASTIC = 2; + } + optional uint32 pyramid_height = 1; + optional PoolMethod pool = 2 [default = MAX]; // The pooling method + enum Engine { + DEFAULT = 0; + CAFFE = 1; + CUDNN = 2; + } + optional Engine engine = 6 [default = DEFAULT]; +} + // DEPRECATED: use LayerParameter. message V1LayerParameter { repeated string bottom = 2; diff --git a/src/caffe/test/test_spp_layer.cpp b/src/caffe/test/test_spp_layer.cpp new file mode 100644 index 00000000000..b2585f1a5fa --- /dev/null +++ b/src/caffe/test/test_spp_layer.cpp @@ -0,0 +1,131 @@ +#include +#include +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/vision_layers.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class SPPLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + SPPLayerTest() + : blob_bottom_(new Blob()), + blob_bottom_2_(new Blob()), + blob_bottom_3_(new Blob()), + blob_top_(new Blob()) {} + virtual void SetUp() { + Caffe::set_random_seed(1701); + blob_bottom_->Reshape(2, 3, 9, 8); + blob_bottom_2_->Reshape(4, 3, 1024, 765); + blob_bottom_3_->Reshape(10, 3, 7, 7); + // fill the values + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + blob_bottom_vec_.push_back(blob_bottom_); + blob_bottom_vec_2_.push_back(blob_bottom_2_); + blob_bottom_vec_3_.push_back(blob_bottom_3_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~SPPLayerTest() { delete blob_bottom_; delete blob_top_; } + + Blob* const blob_bottom_; + Blob* const blob_bottom_2_; + Blob* const blob_bottom_3_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_bottom_vec_2_; + vector*> blob_bottom_vec_3_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(SPPLayerTest, TestDtypesAndDevices); + +TYPED_TEST(SPPLayerTest, TestSetup) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_spp_param()->set_pyramid_height(3); + SPPLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + // expected number of pool results is geometric sum + // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height + // (1 - 4 ** 3)/(1 - 4) = 21 + // multiply bottom num_channels * expected_pool_results + // to get expected num_channels (3 * 21 = 63) + EXPECT_EQ(this->blob_top_->num(), 2); + EXPECT_EQ(this->blob_top_->channels(), 63); + EXPECT_EQ(this->blob_top_->height(), 1); + EXPECT_EQ(this->blob_top_->width(), 1); +} + +TYPED_TEST(SPPLayerTest, TestEqualOutputDims) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_spp_param()->set_pyramid_height(5); + SPPLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_2_, this->blob_top_vec_); + // expected number of pool results is geometric sum + // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height + // (1 - 4 ** 5)/(1 - 4) = 341 + // multiply bottom num_channels * expected_pool_results + // to get expected num_channels (3 * 341 = 1023) + EXPECT_EQ(this->blob_top_->num(), 4); + EXPECT_EQ(this->blob_top_->channels(), 1023); + EXPECT_EQ(this->blob_top_->height(), 1); + EXPECT_EQ(this->blob_top_->width(), 1); +} + +TYPED_TEST(SPPLayerTest, TestEqualOutputDims2) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_spp_param()->set_pyramid_height(3); + SPPLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_3_, this->blob_top_vec_); + // expected number of pool results is geometric sum + // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height + // (1 - 4 ** 3)/(1 - 4) = 21 + // multiply bottom num_channels * expected_pool_results + // to get expected num_channels (3 * 21 = 63) + EXPECT_EQ(this->blob_top_->num(), 10); + EXPECT_EQ(this->blob_top_->channels(), 63); + EXPECT_EQ(this->blob_top_->height(), 1); + EXPECT_EQ(this->blob_top_->width(), 1); +} + +TYPED_TEST(SPPLayerTest, TestForwardBackward) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_spp_param()->set_pyramid_height(3); + SPPLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + vector propagate_down(this->blob_bottom_vec_.size(), true); + layer.Backward(this->blob_top_vec_, propagate_down, + this->blob_bottom_vec_); +} + +TYPED_TEST(SPPLayerTest, TestGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + SPPParameter* spp_param = layer_param.mutable_spp_param(); + spp_param->set_pyramid_height(3); + SPPLayer layer(layer_param); + GradientChecker checker(1e-4, 1e-2); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + + +} // namespace caffe From 4fb3c9e6a5ac80804c910639d14651c2ecdcb5f3 Mon Sep 17 00:00:00 2001 From: Simon Safar Date: Wed, 15 Oct 2014 20:15:14 -0700 Subject: [PATCH 041/446] Added a Reshape layer for copying-free modification of blob dimensions. --- docs/tutorial/layers.md | 42 +++++++++ include/caffe/common_layers.hpp | 35 ++++++++ src/caffe/layers/reshape_layer.cpp | 113 ++++++++++++++++++++++++ src/caffe/layers/reshape_layer.cu | 23 +++++ src/caffe/proto/caffe.proto | 16 +++- src/caffe/test/test_reshape_layer.cpp | 120 ++++++++++++++++++++++++++ 6 files changed, 348 insertions(+), 1 deletion(-) create mode 100644 src/caffe/layers/reshape_layer.cpp create mode 100644 src/caffe/layers/reshape_layer.cu create mode 100644 src/caffe/test/test_reshape_layer.cpp diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md index 839939f5ad6..422ee01f201 100644 --- a/docs/tutorial/layers.md +++ b/docs/tutorial/layers.md @@ -419,6 +419,48 @@ The `SPLIT` layer is a utility layer that splits an input blob to multiple outpu The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w) * 1 * 1`. +#### Reshape + +* LayerType: `RESHAPE` +* CPU implementation: `./src/caffe/layers/reshape_layer.cpp` +* CUDA GPU implementation: `./src/caffe/layers/reshape_layer.cu` +* Parameters (`ReshapeParameter reshape_param`) + - Optional: (also see detailed description below) + - `num` [default 0] + - `channels` [default 0] + - `width` [default 0] + - `height` [default 0] + +* Input + - a single blob with arbitrary dimensions +* Output + - the same blob, with modified dimensions, as specified by `reshape_param` + +* Sample + + layers { + name: "reshape" + type: RESHAPE + bottom: "input" + top: "output" + + reshape_param { + num: 0 # copy the dimension from below + channels: 2 + width: 3 + height: -1 # infer it from the other dimensions + } + } + +The `RESHAPE` layer can be used to change the dimensions of its input, without changing its data. Just like the `FLATTEN` layer, only the dimensions are changed, no data is copied in the process. + +Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values: + +* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom layer has 2 channels, the top one will have 2 channels too, given `channels: 0` as target dimension. Since the default value of all the target dimensions is 0, omitting any of the target dimensions will also cause it to be copied. +* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. If this is not possible, an error is raised. Also, at most one -1 can be used in a reshape operation. + +As another example, giving `num: 0, channels: -1, height: 1, width: 1` as parameters makes the layer behave in exactly the same way as the `FLATTEN` layer. + #### Concatenation * LayerType: `CONCAT` diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index cae1c3e4ee6..945c0cef1b6 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -297,6 +297,41 @@ class MVNLayer : public Layer { Blob sum_multiplier_; }; +/* + * @brief Reshapes the input Blob into an arbitrary-sized output Blob. + * + * Note: similarly to FlattenLayer, this layer does not change the input values + * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff). + */ +template +class ReshapeLayer : public Layer { + public: + explicit ReshapeLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& bottom, + const vector& propagate_down, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + void FillInSingleUnspecifiedDimension(int bottom_count); + + int num_out; + int channels_out; + int height_out; + int width_out; +}; + /** * @brief Ignores bottom blobs while producing no top blobs. (This is useful * to suppress outputs during testing.) diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp new file mode 100644 index 00000000000..7e8704e058a --- /dev/null +++ b/src/caffe/layers/reshape_layer.cpp @@ -0,0 +1,113 @@ +#include + +#include "caffe/common_layers.hpp" +#include "caffe/layer.hpp" + +namespace caffe { + +template +void ReshapeLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + CHECK_EQ(bottom.size(), 1) << "Reshape Layer takes a single blob as input."; + CHECK_EQ(top.size(), 1) << "Reshape Layer takes a single blob as output."; + + num_out = this->layer_param_.reshape_param().num(); + // Dimensions set to 0 (either by default or explicitly) will be copied from + // the bottom layer. + if (num_out == 0) { + num_out = bottom[0]->num(); + } + + channels_out = this->layer_param_.reshape_param().channels(); + if (channels_out == 0) { + channels_out = bottom[0]->channels(); + } + + width_out = this->layer_param_.reshape_param().width(); + if (width_out == 0) { + width_out = bottom[0]->width(); + } + + height_out = this->layer_param_.reshape_param().height(); + if (height_out == 0) { + height_out = bottom[0]->height(); + } + + FillInSingleUnspecifiedDimension(bottom[0]->count()); +} + +template +void ReshapeLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + top[0]->Reshape(num_out, channels_out, height_out, width_out); + + const size_t out_count = num_out * channels_out * height_out * width_out; + CHECK_EQ(out_count, bottom[0]->count()) << + "Bottom layer count isn't equal to predicted; output layer size is " << + num_out << "x" << channels_out << "x" << height_out << "x" << width_out; +} + +template +void ReshapeLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + top[0]->ShareData(*bottom[0]); +} + +template +void ReshapeLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + bottom[0]->ShareDiff(*top[0]); +} + +/** + * @brief Fill in a single dimension left unspecified. + * + * If a dimension is set to -1, it will be filled in with a value inferred from + * the count of the bottom layer (if the product of the nonzero dimensions is a + * divisor of the count). + * + * @param bottom_count Count of the bottom layer. + */ +template +void ReshapeLayer::FillInSingleUnspecifiedDimension(int bottom_count) { + int* const dimensions[] = {&num_out, &channels_out, &width_out, &height_out}; + const size_t N_DIMENSIONS = 4; + + // How many -1 dimensions do we have. + int n_unspecified = 0; + // Product of the remaining dimensions. + int product_without_unspecified_dim = 1; + + for (size_t i = 0; i < N_DIMENSIONS; i++) { + if (*(dimensions[i]) == -1) { + n_unspecified++; + } else { + product_without_unspecified_dim *= *(dimensions[i]); + } + } + + if (n_unspecified == 0) { + // Everything is filled out, nothing to do. + return; + } + + CHECK_EQ(n_unspecified, 1) << "Only one dimension can be set -1."; + CHECK_EQ(bottom_count % product_without_unspecified_dim, 0) << + "Bottom layer count " << bottom_count << " not divisible by product " << + product_without_unspecified_dim; + + // Fill up the one remaining dimension. + for (size_t i = 0; i < N_DIMENSIONS; i++) { + if (*(dimensions[i]) == -1) { + *(dimensions[i]) = bottom_count / product_without_unspecified_dim; + } + } +} + +#ifdef CPU_ONLY +STUB_GPU(ReshapeLayer); +#endif + +INSTANTIATE_CLASS(ReshapeLayer); +REGISTER_LAYER_CLASS(RESHAPE, ReshapeLayer); +} // namespace caffe diff --git a/src/caffe/layers/reshape_layer.cu b/src/caffe/layers/reshape_layer.cu new file mode 100644 index 00000000000..3023ce3ae88 --- /dev/null +++ b/src/caffe/layers/reshape_layer.cu @@ -0,0 +1,23 @@ +#include + +#include "caffe/common_layers.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void ReshapeLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + top[0]->ShareData(*bottom[0]); +} + +template +void ReshapeLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + bottom[0]->ShareDiff(*top[0]); +} + +INSTANTIATE_LAYER_GPU_FUNCS(ReshapeLayer); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 4516106428c..e8bf240c1b3 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -259,7 +259,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 133 (last added: spp_param) +// LayerParameter next available layer-specific ID: 134 (last added: reshape_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -326,6 +326,7 @@ message LayerParameter { optional PReLUParameter prelu_param = 131; optional PythonParameter python_param = 130; optional ReLUParameter relu_param = 123; + optional ReshapeParameter reshape_param = 133; optional SigmoidParameter sigmoid_param = 124; optional SoftmaxParameter softmax_param = 125; optional SPPParameter spp_param = 132; @@ -690,6 +691,19 @@ message ReLUParameter { optional Engine engine = 2 [default = DEFAULT]; } +// Message that stores parameters used by ReshapeLayer +message ReshapeParameter { + // Specify the output dimensions. If some of the following parameters are + // omitted or set to 0 explicitly, the corresponding dimension from the bottom + // layer is used (unchanged). Also, if exactly one of them is set to -1, its + // value is calculated from the count of the bottom layer and the remaining + // dimensions, if possible. + optional int32 num = 1 [default = 0]; + optional int32 channels = 2 [default = 0]; + optional int32 height = 3 [default = 0]; + optional int32 width = 4 [default = 0]; +} + // Message that stores parameters used by SigmoidLayer message SigmoidParameter { enum Engine { diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp new file mode 100644 index 00000000000..878d40bb4d5 --- /dev/null +++ b/src/caffe/test/test_reshape_layer.cpp @@ -0,0 +1,120 @@ +#include +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/common_layers.hpp" +#include "caffe/filler.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class ReshapeLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + protected: + ReshapeLayerTest() + : blob_bottom_(new Blob(2, 3, 6, 5)), + blob_top_(new Blob()) { + // fill the values + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + blob_bottom_vec_.push_back(blob_bottom_); + blob_top_vec_.push_back(blob_top_); + } + + virtual ~ReshapeLayerTest() { delete blob_bottom_; delete blob_top_; } + + Blob* const blob_bottom_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(ReshapeLayerTest, TestDtypesAndDevices); + +TYPED_TEST(ReshapeLayerTest, TestFlattenOutputSizes) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ReshapeParameter* reshape_param = + layer_param.mutable_reshape_param(); + reshape_param->set_channels(-1); + reshape_param->set_height(1); + reshape_param->set_width(1); + + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + EXPECT_EQ(this->blob_top_->num(), 2); + EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5); + EXPECT_EQ(this->blob_top_->height(), 1); + EXPECT_EQ(this->blob_top_->width(), 1); +} + +TYPED_TEST(ReshapeLayerTest, TestFlattenValues) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ReshapeParameter* reshape_param = + layer_param.mutable_reshape_param(); + reshape_param->set_channels(-1); + reshape_param->set_height(1); + reshape_param->set_width(1); + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int c = 0; c < 3 * 6 * 5; ++c) { + EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0), + this->blob_bottom_->data_at(0, c / (6 * 5), (c / 5) % 6, c % 5)); + EXPECT_EQ(this->blob_top_->data_at(1, c, 0, 0), + this->blob_bottom_->data_at(1, c / (6 * 5), (c / 5) % 6, c % 5)); + } +} + +// Test whether setting output dimensions to 0 either explicitly or implicitly +// copies the respective dimension of the input layer. +TYPED_TEST(ReshapeLayerTest, TestCopyDimensions) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ReshapeParameter* reshape_param = + layer_param.mutable_reshape_param(); + // Omitting num to test implicit zeroes. + reshape_param->set_channels(0); + reshape_param->set_height(0); + reshape_param->set_width(0); + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + + EXPECT_EQ(this->blob_top_->num(), 2); + EXPECT_EQ(this->blob_top_->channels(), 3); + EXPECT_EQ(this->blob_top_->height(), 6); + EXPECT_EQ(this->blob_top_->width(), 5); +} + +// When a dimension is set to -1, we should infer its value from the other +// dimensions (including those that get copied from below). +TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ReshapeParameter* reshape_param = + layer_param.mutable_reshape_param(); + // Since omitted, num is implicitly set to 0 (thus, copies 2). + reshape_param->set_channels(3); + reshape_param->set_height(10); + reshape_param->set_width(-1); + + // Count is 180, thus height should be 180 / (2*3*10) = 3. + + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + + EXPECT_EQ(this->blob_top_->num(), 2); + EXPECT_EQ(this->blob_top_->channels(), 3); + EXPECT_EQ(this->blob_top_->height(), 10); + EXPECT_EQ(this->blob_top_->width(), 3); +} + +} // namespace caffe From fa6169ee799f97f80d33d6b4525c7fd4b891774a Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 25 Mar 2015 17:44:37 -0700 Subject: [PATCH 042/446] ReshapeLayer fixups for ND blobs --- docs/tutorial/layers.md | 33 +++---- include/caffe/common_layers.hpp | 31 +++--- src/caffe/layers/reshape_layer.cpp | 135 ++++++++------------------ src/caffe/layers/reshape_layer.cu | 23 ----- src/caffe/proto/caffe.proto | 14 +-- src/caffe/test/test_reshape_layer.cpp | 42 ++++---- 6 files changed, 101 insertions(+), 177 deletions(-) delete mode 100644 src/caffe/layers/reshape_layer.cu diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md index 422ee01f201..c4529e6afc0 100644 --- a/docs/tutorial/layers.md +++ b/docs/tutorial/layers.md @@ -421,15 +421,11 @@ The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c * #### Reshape -* LayerType: `RESHAPE` -* CPU implementation: `./src/caffe/layers/reshape_layer.cpp` -* CUDA GPU implementation: `./src/caffe/layers/reshape_layer.cu` +* Layer type: `Reshape` +* Implementation: `./src/caffe/layers/reshape_layer.cpp` * Parameters (`ReshapeParameter reshape_param`) - Optional: (also see detailed description below) - - `num` [default 0] - - `channels` [default 0] - - `width` [default 0] - - `height` [default 0] + - `shape` * Input - a single blob with arbitrary dimensions @@ -438,28 +434,29 @@ The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c * * Sample - layers { + layer { name: "reshape" - type: RESHAPE + type: "Reshape" bottom: "input" top: "output" - reshape_param { - num: 0 # copy the dimension from below - channels: 2 - width: 3 - height: -1 # infer it from the other dimensions + shape { + dim: 0 # copy the dimension from below + dim: 2 + dim: 3 + dim: -1 # infer it from the other dimensions + } } } -The `RESHAPE` layer can be used to change the dimensions of its input, without changing its data. Just like the `FLATTEN` layer, only the dimensions are changed, no data is copied in the process. +The `Reshape` layer can be used to change the dimensions of its input, without changing its data. Just like the `Flatten` layer, only the dimensions are changed; no data is copied in the process. Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values: -* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom layer has 2 channels, the top one will have 2 channels too, given `channels: 0` as target dimension. Since the default value of all the target dimensions is 0, omitting any of the target dimensions will also cause it to be copied. -* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. If this is not possible, an error is raised. Also, at most one -1 can be used in a reshape operation. +* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given `dim: 0` as the 1st target dimension. +* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation. -As another example, giving `num: 0, channels: -1, height: 1, width: 1` as parameters makes the layer behave in exactly the same way as the `FLATTEN` layer. +As another example, specifying `reshape_param { shape { dim: 0 dim: -1 } }` makes the layer behave in exactly the same way as the `Flatten` layer. #### Concatenation diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 945c0cef1b6..ccdfd62d5be 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -313,23 +313,28 @@ class ReshapeLayer : public Layer { virtual void Reshape(const vector*>& bottom, const vector*>& top); + virtual inline const char* type() const { return "Reshape"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + protected: virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& bottom, - const vector& propagate_down, - const vector*>& top); + const vector*>& top) {} + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) {} virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); + const vector*>& top) {} virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - void FillInSingleUnspecifiedDimension(int bottom_count); - - int num_out; - int channels_out; - int height_out; - int width_out; + const vector& propagate_down, const vector*>& bottom) {} + + /// @brief the current output shape + vector top_shape_; + /// @brief vector of axes indices whose dimensions we'll copy from the bottom + vector copy_axes_; + /// @brief the index of the axis whose dimension we infer, or -1 if none + int inferred_axis_; + /// @brief the product of the "constant" output dimensions + int constant_count_; }; /** diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp index 7e8704e058a..618edf31824 100644 --- a/src/caffe/layers/reshape_layer.cpp +++ b/src/caffe/layers/reshape_layer.cpp @@ -7,107 +7,58 @@ namespace caffe { template void ReshapeLayer::LayerSetUp(const vector*>& bottom, - const vector*>& top) { - CHECK_EQ(bottom.size(), 1) << "Reshape Layer takes a single blob as input."; - CHECK_EQ(top.size(), 1) << "Reshape Layer takes a single blob as output."; - - num_out = this->layer_param_.reshape_param().num(); - // Dimensions set to 0 (either by default or explicitly) will be copied from - // the bottom layer. - if (num_out == 0) { - num_out = bottom[0]->num(); - } - - channels_out = this->layer_param_.reshape_param().channels(); - if (channels_out == 0) { - channels_out = bottom[0]->channels(); - } - - width_out = this->layer_param_.reshape_param().width(); - if (width_out == 0) { - width_out = bottom[0]->width(); - } - - height_out = this->layer_param_.reshape_param().height(); - if (height_out == 0) { - height_out = bottom[0]->height(); + const vector*>& top) { + inferred_axis_ = -1; + copy_axes_.clear(); + const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape(); + const int top_num_axes = top_blob_shape.dim_size(); + top_shape_.resize(top_num_axes); + constant_count_ = 1; + for (int i = 0; i < top_num_axes; ++i) { + top_shape_[i] = top_blob_shape.dim(i); + if (top_shape_[i] == 0) { + copy_axes_.push_back(i); + } else if (top_shape_[i] == -1) { + CHECK_EQ(inferred_axis_, -1) << "new shape contains multiple " + << "-1 dims; at most a single (1) value of -1 may be specified"; + inferred_axis_ = i; + } else { + constant_count_ *= top_shape_[i]; + } } - - FillInSingleUnspecifiedDimension(bottom[0]->count()); } template void ReshapeLayer::Reshape(const vector*>& bottom, - const vector*>& top) { - top[0]->Reshape(num_out, channels_out, height_out, width_out); - - const size_t out_count = num_out * channels_out * height_out * width_out; - CHECK_EQ(out_count, bottom[0]->count()) << - "Bottom layer count isn't equal to predicted; output layer size is " << - num_out << "x" << channels_out << "x" << height_out << "x" << width_out; -} - -template -void ReshapeLayer::Forward_cpu(const vector*>& bottom, - const vector*>& top) { - top[0]->ShareData(*bottom[0]); -} - -template -void ReshapeLayer::Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) { - bottom[0]->ShareDiff(*top[0]); -} - -/** - * @brief Fill in a single dimension left unspecified. - * - * If a dimension is set to -1, it will be filled in with a value inferred from - * the count of the bottom layer (if the product of the nonzero dimensions is a - * divisor of the count). - * - * @param bottom_count Count of the bottom layer. - */ -template -void ReshapeLayer::FillInSingleUnspecifiedDimension(int bottom_count) { - int* const dimensions[] = {&num_out, &channels_out, &width_out, &height_out}; - const size_t N_DIMENSIONS = 4; - - // How many -1 dimensions do we have. - int n_unspecified = 0; - // Product of the remaining dimensions. - int product_without_unspecified_dim = 1; - - for (size_t i = 0; i < N_DIMENSIONS; i++) { - if (*(dimensions[i]) == -1) { - n_unspecified++; - } else { - product_without_unspecified_dim *= *(dimensions[i]); - } - } - - if (n_unspecified == 0) { - // Everything is filled out, nothing to do. - return; + const vector*>& top) { + for (int i = 0; i < copy_axes_.size(); ++i) { + const int copy_axis_index = copy_axes_[i]; + CHECK_GT(bottom[0]->num_axes(), copy_axis_index) << "new shape contains " + << "a 0, but there is no corresponding bottom axis to copy"; + top_shape_[copy_axis_index] = bottom[0]->shape(copy_axis_index); } - - CHECK_EQ(n_unspecified, 1) << "Only one dimension can be set -1."; - CHECK_EQ(bottom_count % product_without_unspecified_dim, 0) << - "Bottom layer count " << bottom_count << " not divisible by product " << - product_without_unspecified_dim; - - // Fill up the one remaining dimension. - for (size_t i = 0; i < N_DIMENSIONS; i++) { - if (*(dimensions[i]) == -1) { - *(dimensions[i]) = bottom_count / product_without_unspecified_dim; + if (inferred_axis_ >= 0) { + // A -1 dim was specified; infer the correct dimension by computing the + // product of the other dimensions. + int explicit_count = constant_count_; + for (int i = 0; i < copy_axes_.size(); ++i) { + const int copy_axis_index = copy_axes_[i]; + explicit_count *= top_shape_[copy_axis_index]; } + CHECK_EQ(0, bottom[0]->count() % explicit_count) << "bottom count (" + << bottom[0]->count() << ") must be divisible by the product of " + << "the specified dimensions (" << explicit_count << ")"; + const int inferred_dim = bottom[0]->count() / explicit_count; + top_shape_[inferred_axis_] = inferred_dim; } + top[0]->Reshape(top_shape_); + CHECK_EQ(top[0]->count(), bottom[0]->count()) + << "output count must match input count"; + top[0]->ShareData(*bottom[0]); + top[0]->ShareDiff(*bottom[0]); } -#ifdef CPU_ONLY -STUB_GPU(ReshapeLayer); -#endif - INSTANTIATE_CLASS(ReshapeLayer); -REGISTER_LAYER_CLASS(RESHAPE, ReshapeLayer); +REGISTER_LAYER_CLASS(Reshape); + } // namespace caffe diff --git a/src/caffe/layers/reshape_layer.cu b/src/caffe/layers/reshape_layer.cu deleted file mode 100644 index 3023ce3ae88..00000000000 --- a/src/caffe/layers/reshape_layer.cu +++ /dev/null @@ -1,23 +0,0 @@ -#include - -#include "caffe/common_layers.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/math_functions.hpp" - -namespace caffe { - -template -void ReshapeLayer::Forward_gpu(const vector*>& bottom, - const vector*>& top) { - top[0]->ShareData(*bottom[0]); -} - -template -void ReshapeLayer::Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) { - bottom[0]->ShareDiff(*top[0]); -} - -INSTANTIATE_LAYER_GPU_FUNCS(ReshapeLayer); - -} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index e8bf240c1b3..d36f1d511df 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -693,15 +693,11 @@ message ReLUParameter { // Message that stores parameters used by ReshapeLayer message ReshapeParameter { - // Specify the output dimensions. If some of the following parameters are - // omitted or set to 0 explicitly, the corresponding dimension from the bottom - // layer is used (unchanged). Also, if exactly one of them is set to -1, its - // value is calculated from the count of the bottom layer and the remaining - // dimensions, if possible. - optional int32 num = 1 [default = 0]; - optional int32 channels = 2 [default = 0]; - optional int32 height = 3 [default = 0]; - optional int32 width = 4 [default = 0]; + // Specify the output dimensions. If some of the dimensions are set to 0, + // the corresponding dimension from the bottom layer is used (unchanged). + // Exactly one dimension may be set to -1, in which case its value is + // inferred from the count of the bottom layer and the remaining dimensions. + optional BlobShape shape = 1; } // Message that stores parameters used by SigmoidLayer diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp index 878d40bb4d5..0c8e2427aa7 100644 --- a/src/caffe/test/test_reshape_layer.cpp +++ b/src/caffe/test/test_reshape_layer.cpp @@ -41,11 +41,11 @@ TYPED_TEST_CASE(ReshapeLayerTest, TestDtypesAndDevices); TYPED_TEST(ReshapeLayerTest, TestFlattenOutputSizes) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; - ReshapeParameter* reshape_param = - layer_param.mutable_reshape_param(); - reshape_param->set_channels(-1); - reshape_param->set_height(1); - reshape_param->set_width(1); + BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape(); + blob_shape->add_dim(0); + blob_shape->add_dim(-1); + blob_shape->add_dim(1); + blob_shape->add_dim(1); ReshapeLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); @@ -58,11 +58,11 @@ TYPED_TEST(ReshapeLayerTest, TestFlattenOutputSizes) { TYPED_TEST(ReshapeLayerTest, TestFlattenValues) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; - ReshapeParameter* reshape_param = - layer_param.mutable_reshape_param(); - reshape_param->set_channels(-1); - reshape_param->set_height(1); - reshape_param->set_width(1); + BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape(); + blob_shape->add_dim(0); + blob_shape->add_dim(-1); + blob_shape->add_dim(1); + blob_shape->add_dim(1); ReshapeLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); @@ -79,12 +79,11 @@ TYPED_TEST(ReshapeLayerTest, TestFlattenValues) { TYPED_TEST(ReshapeLayerTest, TestCopyDimensions) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; - ReshapeParameter* reshape_param = - layer_param.mutable_reshape_param(); - // Omitting num to test implicit zeroes. - reshape_param->set_channels(0); - reshape_param->set_height(0); - reshape_param->set_width(0); + BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape(); + blob_shape->add_dim(0); + blob_shape->add_dim(0); + blob_shape->add_dim(0); + blob_shape->add_dim(0); ReshapeLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); @@ -99,12 +98,11 @@ TYPED_TEST(ReshapeLayerTest, TestCopyDimensions) { TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; - ReshapeParameter* reshape_param = - layer_param.mutable_reshape_param(); - // Since omitted, num is implicitly set to 0 (thus, copies 2). - reshape_param->set_channels(3); - reshape_param->set_height(10); - reshape_param->set_width(-1); + BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape(); + blob_shape->add_dim(0); + blob_shape->add_dim(3); + blob_shape->add_dim(10); + blob_shape->add_dim(-1); // Count is 180, thus height should be 180 / (2*3*10) = 3. From 6b64f121c272b3d1464004554b9e6a9c4033a8f5 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Thu, 26 Mar 2015 02:25:48 -0700 Subject: [PATCH 043/446] basic tests (Forward, Gradient) for ReshapeLayer --- src/caffe/test/test_reshape_layer.cpp | 57 +++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp index 0c8e2427aa7..8635792a66e 100644 --- a/src/caffe/test/test_reshape_layer.cpp +++ b/src/caffe/test/test_reshape_layer.cpp @@ -115,4 +115,61 @@ TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) { EXPECT_EQ(this->blob_top_->width(), 3); } +TYPED_TEST(ReshapeLayerTest, TestForward) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape(); + shape->add_dim(6); + shape->add_dim(2); + shape->add_dim(3); + shape->add_dim(5); + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_EQ(this->blob_top_->cpu_data()[i], + this->blob_bottom_->cpu_data()[i]); + } +} + +TYPED_TEST(ReshapeLayerTest, TestForwardAfterReshape) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape(); + shape->add_dim(6); + shape->add_dim(2); + shape->add_dim(3); + shape->add_dim(5); + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // We know the above produced the correct result from TestForward. + // Reshape the bottom and call layer.Reshape, then try again. + vector new_bottom_shape(1, 2 * 3 * 6 * 5); + this->blob_bottom_->Reshape(new_bottom_shape); + layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_); + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_EQ(this->blob_top_->cpu_data()[i], + this->blob_bottom_->cpu_data()[i]); + } +} + +TYPED_TEST(ReshapeLayerTest, TestGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape(); + shape->add_dim(6); + shape->add_dim(2); + shape->add_dim(3); + shape->add_dim(5); + ReshapeLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + } // namespace caffe From 21032b2b0911cd4d907df46c114b8e96e55c2313 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Thu, 26 Mar 2015 01:13:18 -0700 Subject: [PATCH 044/446] Add ReshapeParameter axis and num_axes to reshape only a particular span of the input shape --- include/caffe/common_layers.hpp | 2 - src/caffe/layers/reshape_layer.cpp | 53 ++++++++++--- src/caffe/proto/caffe.proto | 58 +++++++++++++- src/caffe/test/test_reshape_layer.cpp | 105 ++++++++++++++++++++++++++ 4 files changed, 204 insertions(+), 14 deletions(-) diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index ccdfd62d5be..8da6d68096b 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -327,8 +327,6 @@ class ReshapeLayer : public Layer { virtual void Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) {} - /// @brief the current output shape - vector top_shape_; /// @brief vector of axes indices whose dimensions we'll copy from the bottom vector copy_axes_; /// @brief the index of the axis whose dimension we infer, or -1 if none diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp index 618edf31824..ffe970f2689 100644 --- a/src/caffe/layers/reshape_layer.cpp +++ b/src/caffe/layers/reshape_layer.cpp @@ -12,18 +12,17 @@ void ReshapeLayer::LayerSetUp(const vector*>& bottom, copy_axes_.clear(); const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape(); const int top_num_axes = top_blob_shape.dim_size(); - top_shape_.resize(top_num_axes); constant_count_ = 1; for (int i = 0; i < top_num_axes; ++i) { - top_shape_[i] = top_blob_shape.dim(i); - if (top_shape_[i] == 0) { + const int top_dim = top_blob_shape.dim(i); + if (top_dim == 0) { copy_axes_.push_back(i); - } else if (top_shape_[i] == -1) { + } else if (top_dim == -1) { CHECK_EQ(inferred_axis_, -1) << "new shape contains multiple " << "-1 dims; at most a single (1) value of -1 may be specified"; inferred_axis_ = i; } else { - constant_count_ *= top_shape_[i]; + constant_count_ *= top_dim; } } } @@ -31,27 +30,59 @@ void ReshapeLayer::LayerSetUp(const vector*>& bottom, template void ReshapeLayer::Reshape(const vector*>& bottom, const vector*>& top) { + const int input_start_axis = this->layer_param_.reshape_param().axis(); + const int start_axis = (input_start_axis >= 0) ? input_start_axis : + bottom[0]->num_axes() + input_start_axis + 1; + CHECK_GE(start_axis, 0) << "axis " << input_start_axis << " out of range"; + CHECK_LE(start_axis, bottom[0]->num_axes()) << "axis " << input_start_axis + << " out of range for " << bottom[0]->num_axes() << "-D input blob"; + const int num_axes = this->layer_param_.reshape_param().num_axes(); + CHECK_GE(num_axes, -1) << "num_axes must be >= 0, or -1 for all"; + const int end_axis = + (num_axes == -1) ? bottom[0]->num_axes() : (start_axis + num_axes); + CHECK_LE(end_axis, bottom[0]->num_axes()) + << "end_axis = axis + num_axes is out of range"; + const int num_axes_replaced = end_axis - start_axis; + const int num_axes_retained = bottom[0]->num_axes() - num_axes_replaced; + const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape(); + const int num_new_axes = top_blob_shape.dim_size(); + vector top_shape(num_axes_retained + num_new_axes); + int top_shape_index = 0; + for (int i = 0; i < start_axis; ++i) { + top_shape[top_shape_index++] = bottom[0]->shape(i); + } + for (int i = 0; i < num_new_axes; ++i) { + top_shape[top_shape_index++] = top_blob_shape.dim(i); + } + for (int i = end_axis; i < bottom[0]->num_axes(); ++i) { + top_shape[top_shape_index++] = bottom[0]->shape(i); + } + CHECK_EQ(top_shape_index, top_shape.size()); for (int i = 0; i < copy_axes_.size(); ++i) { const int copy_axis_index = copy_axes_[i]; - CHECK_GT(bottom[0]->num_axes(), copy_axis_index) << "new shape contains " - << "a 0, but there is no corresponding bottom axis to copy"; - top_shape_[copy_axis_index] = bottom[0]->shape(copy_axis_index); + CHECK_GT(bottom[0]->num_axes(), start_axis + copy_axis_index) + << "new shape contains a 0, but there was no corresponding bottom axis " + << "to copy"; + top_shape[start_axis + copy_axis_index] = + bottom[0]->shape(start_axis + copy_axis_index); } if (inferred_axis_ >= 0) { // A -1 dim was specified; infer the correct dimension by computing the // product of the other dimensions. int explicit_count = constant_count_; + explicit_count *= bottom[0]->count(0, start_axis); + explicit_count *= bottom[0]->count(end_axis); for (int i = 0; i < copy_axes_.size(); ++i) { const int copy_axis_index = copy_axes_[i]; - explicit_count *= top_shape_[copy_axis_index]; + explicit_count *= top_shape[start_axis + copy_axis_index]; } CHECK_EQ(0, bottom[0]->count() % explicit_count) << "bottom count (" << bottom[0]->count() << ") must be divisible by the product of " << "the specified dimensions (" << explicit_count << ")"; const int inferred_dim = bottom[0]->count() / explicit_count; - top_shape_[inferred_axis_] = inferred_dim; + top_shape[start_axis + inferred_axis_] = inferred_dim; } - top[0]->Reshape(top_shape_); + top[0]->Reshape(top_shape); CHECK_EQ(top[0]->count(), bottom[0]->count()) << "output count must match input count"; top[0]->ShareData(*bottom[0]); diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index d36f1d511df..d43e560a1fa 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -696,8 +696,64 @@ message ReshapeParameter { // Specify the output dimensions. If some of the dimensions are set to 0, // the corresponding dimension from the bottom layer is used (unchanged). // Exactly one dimension may be set to -1, in which case its value is - // inferred from the count of the bottom layer and the remaining dimensions. + // inferred from the count of the bottom blob and the remaining dimensions. + // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8: + // + // layer { + // type: "Reshape" bottom: "input" top: "output" + // reshape_param { ... } + // } + // + // If "input" is 2D with shape 2 x 8, then the following reshape_param + // specifications are all equivalent, producing a 3D blob "output" with shape + // 2 x 2 x 4: + // + // reshape_param { shape { dim: 2 dim: 2 dim: 4 } } + // reshape_param { shape { dim: 0 dim: 2 dim: 4 } } + // reshape_param { shape { dim: 0 dim: 2 dim: -1 } } + // reshape_param { shape { dim: -1 dim: 0 dim: 2 } } + // optional BlobShape shape = 1; + + // axis and num_axes control the portion of the bottom blob's shape that are + // replaced by (included in) the reshape. By default (axis == 0 and + // num_axes == -1), the entire bottom blob shape is included in the reshape, + // and hence the shape field must specify the entire output shape. + // + // axis may be non-zero to retain some portion of the beginning of the input + // shape (and may be negative to index from the end; e.g., -1 to begin the + // reshape after the last axis, including nothing in the reshape, + // -2 to include only the last axis, etc.). + // + // For example, suppose "input" is a 2D blob with shape 2 x 8. + // Then the following ReshapeLayer specifications are all equivalent, + // producing a blob "output" with shape 2 x 2 x 4: + // + // reshape_param { shape { dim: 2 dim: 2 dim: 4 } } + // reshape_param { shape { dim: 2 dim: 4 } axis: 1 } + // reshape_param { shape { dim: 2 dim: 4 } axis: -3 } + // + // num_axes specifies the extent of the reshape. + // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on + // input axes in the range [axis, axis+num_axes]. + // num_axes may also be -1, the default, to include all remaining axes + // (starting from axis). + // + // For example, suppose "input" is a 2D blob with shape 2 x 8. + // Then the following ReshapeLayer specifications are equivalent, + // producing a blob "output" with shape 1 x 2 x 8. + // + // reshape_param { shape { dim: 1 dim: 2 dim: 8 } } + // reshape_param { shape { dim: 1 dim: 2 } num_axes: 1 } + // reshape_param { shape { dim: 1 } num_axes: 0 } + // + // On the other hand, these would produce output blob shape 2 x 1 x 8: + // + // reshape_param { shape { dim: 2 dim: 1 dim: 8 } } + // reshape_param { shape { dim: 1 } axis: 1 num_axes: 0 } + // + optional int32 axis = 2 [default = 0]; + optional int32 num_axes = 3 [default = -1]; } // Message that stores parameters used by SigmoidLayer diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp index 8635792a66e..9d08ec60d4e 100644 --- a/src/caffe/test/test_reshape_layer.cpp +++ b/src/caffe/test/test_reshape_layer.cpp @@ -115,6 +115,111 @@ TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) { EXPECT_EQ(this->blob_top_->width(), 3); } +TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecifiedWithStartAxis) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_reshape_param()->set_axis(1); + BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape(); + blob_shape->add_dim(3); + blob_shape->add_dim(10); + blob_shape->add_dim(-1); + + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + + ASSERT_EQ(this->blob_top_->num_axes(), 4); + EXPECT_EQ(this->blob_top_->num(), 2); + EXPECT_EQ(this->blob_top_->channels(), 3); + EXPECT_EQ(this->blob_top_->height(), 10); + EXPECT_EQ(this->blob_top_->width(), 3); +} + +TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesStart) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_reshape_param()->set_axis(0); + layer_param.mutable_reshape_param()->set_num_axes(0); + BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape(); + blob_shape->add_dim(1); + blob_shape->add_dim(1); + blob_shape->add_dim(1); + + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + + ASSERT_EQ(this->blob_top_->num_axes(), 7); + EXPECT_EQ(this->blob_top_->shape(0), 1); + EXPECT_EQ(this->blob_top_->shape(1), 1); + EXPECT_EQ(this->blob_top_->shape(2), 1); + EXPECT_EQ(this->blob_top_->shape(3), 2); + EXPECT_EQ(this->blob_top_->shape(4), 3); + EXPECT_EQ(this->blob_top_->shape(5), 6); + EXPECT_EQ(this->blob_top_->shape(6), 5); +} + +TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesMiddle) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_reshape_param()->set_axis(2); + layer_param.mutable_reshape_param()->set_num_axes(0); + BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape(); + blob_shape->add_dim(1); + blob_shape->add_dim(1); + blob_shape->add_dim(1); + + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + + ASSERT_EQ(this->blob_top_->num_axes(), 7); + EXPECT_EQ(this->blob_top_->shape(0), 2); + EXPECT_EQ(this->blob_top_->shape(1), 3); + EXPECT_EQ(this->blob_top_->shape(2), 1); + EXPECT_EQ(this->blob_top_->shape(3), 1); + EXPECT_EQ(this->blob_top_->shape(4), 1); + EXPECT_EQ(this->blob_top_->shape(5), 6); + EXPECT_EQ(this->blob_top_->shape(6), 5); +} + +TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesEnd) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_reshape_param()->set_axis(-1); + layer_param.mutable_reshape_param()->set_num_axes(0); + BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape(); + blob_shape->add_dim(1); + blob_shape->add_dim(1); + blob_shape->add_dim(1); + + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + + ASSERT_EQ(this->blob_top_->num_axes(), 7); + EXPECT_EQ(this->blob_top_->shape(0), 2); + EXPECT_EQ(this->blob_top_->shape(1), 3); + EXPECT_EQ(this->blob_top_->shape(2), 6); + EXPECT_EQ(this->blob_top_->shape(3), 5); + EXPECT_EQ(this->blob_top_->shape(4), 1); + EXPECT_EQ(this->blob_top_->shape(5), 1); + EXPECT_EQ(this->blob_top_->shape(6), 1); +} + +TYPED_TEST(ReshapeLayerTest, TestFlattenMiddle) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_reshape_param()->set_axis(1); + layer_param.mutable_reshape_param()->set_num_axes(2); + BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape(); + blob_shape->add_dim(-1); + + ReshapeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + + ASSERT_EQ(this->blob_top_->num_axes(), 3); + EXPECT_EQ(this->blob_top_->shape(0), 2); + EXPECT_EQ(this->blob_top_->shape(1), 3 * 6); + EXPECT_EQ(this->blob_top_->shape(2), 5); +} + TYPED_TEST(ReshapeLayerTest, TestForward) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; From cf0e6b7215b8c571b02bae84d7674d867156660f Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 25 Mar 2015 18:45:29 -0700 Subject: [PATCH 045/446] Update docs for ND blobs (#1970) and layer type is a string (#1694) --- docs/tutorial/data.md | 12 +-- docs/tutorial/layers.md | 160 +++++++++++++++---------------- docs/tutorial/loss.md | 20 ++-- docs/tutorial/net_layer_blob.md | 30 +++--- examples/mnist/readme.md | 50 +++++----- examples/siamese/readme.md | 66 +++++++------ matlab/caffe/hdf5creation/demo.m | 4 +- 7 files changed, 174 insertions(+), 168 deletions(-) diff --git a/docs/tutorial/data.md b/docs/tutorial/data.md index 40605f7cd73..3bf7d932eda 100644 --- a/docs/tutorial/data.md +++ b/docs/tutorial/data.md @@ -10,15 +10,15 @@ New input types are supported by developing a new data layer -- the rest of the This data layer definition - layers { + layer { name: "mnist" - # DATA layer loads leveldb or lmdb storage DBs for high-throughput. - type: DATA + # Data layer loads leveldb or lmdb storage DBs for high-throughput. + type: "Data" # the 1st top is the data itself: the name is only convention top: "data" # the 2nd top is the ground truth: the name is only convention top: "label" - # the DATA layer configuration + # the Data layer configuration data_param { # path to the DB source: "examples/mnist/mnist_train_lmdb" @@ -46,9 +46,9 @@ The (data, label) pairing is a convenience for classification models. **Transformations**: data preprocessing is parametrized by transformation messages within the data layer definition. - layers { + layer { name: "data" - type: DATA + type: "Data" [...] transform_param { scale: 0.1 diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md index 839939f5ad6..74d236c1194 100644 --- a/docs/tutorial/layers.md +++ b/docs/tutorial/layers.md @@ -23,7 +23,7 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of #### Convolution -* LayerType: `CONVOLUTION` +* Layer type: `Convolution` * CPU implementation: `./src/caffe/layers/convolution_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu` * Parameters (`ConvolutionParameter convolution_param`) @@ -43,15 +43,15 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise. * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`) - layers { + layer { name: "conv1" - type: CONVOLUTION + type: "Convolution" bottom: "data" top: "conv1" - blobs_lr: 1 # learning rate multiplier for the filters - blobs_lr: 2 # learning rate multiplier for the biases - weight_decay: 1 # weight decay multiplier for the filters - weight_decay: 0 # weight decay multiplier for the biases + # learning rate and decay multipliers for the filters + param { lr_mult: 1 decay_mult: 1 } + # learning rate and decay multipliers for the biases + param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 96 # learn 96 filters kernel_size: 11 # each filter is 11x11 @@ -67,11 +67,11 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of } } -The `CONVOLUTION` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image. +The `Convolution` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image. #### Pooling -* LayerType: `POOLING` +* Layer type: `Pooling` * CPU implementation: `./src/caffe/layers/pooling_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/pooling_layer.cu` * Parameters (`PoolingParameter pooling_param`) @@ -87,9 +87,9 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution. * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`) - layers { + layer { name: "pool1" - type: POOLING + type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { @@ -101,7 +101,7 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter #### Local Response Normalization (LRN) -* LayerType: `LRN` +* Layer type: `LRN` * CPU Implementation: `./src/caffe/layers/lrn_layer.cpp` * CUDA GPU Implementation: `./src/caffe/layers/lrn_layer.cu` * Parameters (`LRNParameter lrn_param`) @@ -115,7 +115,7 @@ The local response normalization layer performs a kind of "lateral inhibition" b #### im2col -`IM2COL` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix. +`Im2col` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix. ### Loss Layers @@ -123,19 +123,19 @@ Loss drives learning by comparing an output to a target and assigning cost to mi #### Softmax -* LayerType: `SOFTMAX_LOSS` +* Layer type: `SoftmaxWithLoss` The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient. #### Sum-of-Squares / Euclidean -* LayerType: `EUCLIDEAN_LOSS` +* Layer type: `EuclideanLoss` The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$. #### Hinge / Margin -* LayerType: `HINGE_LOSS` +* Layer type: `HingeLoss` * CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp` * CUDA GPU implementation: none yet * Parameters (`HingeLossParameter hinge_loss_param`) @@ -149,17 +149,17 @@ The Euclidean loss layer computes the sum of squares of differences of its two i * Samples # L1 Norm - layers { + layer { name: "loss" - type: HINGE_LOSS + type: "HingeLoss" bottom: "pred" bottom: "label" } # L2 Norm - layers { + layer { name: "loss" - type: HINGE_LOSS + type: "HingeLoss" bottom: "pred" bottom: "label" top: "loss" @@ -172,15 +172,15 @@ The hinge loss layer computes a one-vs-all hinge or squared hinge loss. #### Sigmoid Cross-Entropy -`SIGMOID_CROSS_ENTROPY_LOSS` +`SigmoidCrossEntropyLoss` #### Infogain -`INFOGAIN_LOSS` +`InfogainLoss` #### Accuracy and Top-k -`ACCURACY` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step. +`Accuracy` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step. ### Activation / Neuron Layers @@ -193,7 +193,7 @@ In general, activation / Neuron layers are element-wise operators, taking one bo #### ReLU / Rectified-Linear and Leaky-ReLU -* LayerType: `RELU` +* Layer type: `ReLU` * CPU implementation: `./src/caffe/layers/relu_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu` * Parameters (`ReLUParameter relu_param`) @@ -201,66 +201,66 @@ In general, activation / Neuron layers are element-wise operators, taking one bo - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0. * Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`) - layers { + layer { name: "relu1" - type: RELU + type: "ReLU" bottom: "conv1" top: "conv1" } -Given an input value x, The `RELU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption. +Given an input value x, The `ReLU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption. #### Sigmoid -* LayerType: `SIGMOID` +* Layer type: `Sigmoid` * CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu` * Sample (as seen in `./examples/imagenet/mnist_autoencoder.prototxt`) - layers { + layer { name: "encode1neuron" bottom: "encode1" top: "encode1neuron" - type: SIGMOID + type: "Sigmoid" } -The `SIGMOID` layer computes the output as sigmoid(x) for each input element x. +The `Sigmoid` layer computes the output as sigmoid(x) for each input element x. #### TanH / Hyperbolic Tangent -* LayerType: `TANH` +* Layer type: `TanH` * CPU implementation: `./src/caffe/layers/tanh_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/tanh_layer.cu` * Sample - layers { + layer { name: "layer" bottom: "in" top: "out" - type: TANH + type: "TanH" } -The `TANH` layer computes the output as tanh(x) for each input element x. +The `TanH` layer computes the output as tanh(x) for each input element x. #### Absolute Value -* LayerType: `ABSVAL` +* Layer type: `AbsVal` * CPU implementation: `./src/caffe/layers/absval_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/absval_layer.cu` * Sample - layers { + layer { name: "layer" bottom: "in" top: "out" - type: ABSVAL + type: "AbsVal" } -The `ABSVAL` layer computes the output as abs(x) for each input element x. +The `AbsVal` layer computes the output as abs(x) for each input element x. #### Power -* LayerType: `POWER` +* Layer type: `Power` * CPU implementation: `./src/caffe/layers/power_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/power_layer.cu` * Parameters (`PowerParameter power_param`) @@ -270,11 +270,11 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x. - `shift` [default 0] * Sample - layers { + layer { name: "layer" bottom: "in" top: "out" - type: POWER + type: "Power" power_param { power: 1 scale: 1 @@ -282,16 +282,16 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x. } } -The `POWER` layer computes the output as (shift + scale * x) ^ power for each input element x. +The `Power` layer computes the output as (shift + scale * x) ^ power for each input element x. #### BNLL -* LayerType: `BNLL` +* Layer type: `BNLL` * CPU implementation: `./src/caffe/layers/bnll_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/bnll_layer.cu` * Sample - layers { + layer { name: "layer" bottom: "in" top: "out" @@ -309,7 +309,7 @@ Common input preprocessing (mean subtraction, scaling, random cropping, and mirr #### Database -* LayerType: `DATA` +* Layer type: `Data` * Parameters - Required - `source`: the name of the directory containing the database @@ -322,7 +322,7 @@ Common input preprocessing (mean subtraction, scaling, random cropping, and mirr #### In-Memory -* LayerType: `MEMORY_DATA` +* Layer type: `MemoryData` * Parameters - Required - `batch_size`, `channels`, `height`, `width`: specify the size of input chunks to read from memory @@ -331,7 +331,7 @@ The memory data layer reads data directly from memory, without copying it. In or #### HDF5 Input -* LayerType: `HDF5_DATA` +* Layer type: `HDF5Data` * Parameters - Required - `source`: the name of the file to read from @@ -339,7 +339,7 @@ The memory data layer reads data directly from memory, without copying it. In or #### HDF5 Output -* LayerType: `HDF5_OUTPUT` +* Layer type: `HDF5Output` * Parameters - Required - `file_name`: name of file to write to @@ -348,7 +348,7 @@ The HDF5 output layer performs the opposite function of the other layers in this #### Images -* LayerType: `IMAGE_DATA` +* Layer type: `ImageData` * Parameters - Required - `source`: name of a text file, with each line giving an image filename and label @@ -360,17 +360,17 @@ The HDF5 output layer performs the opposite function of the other layers in this #### Windows -`WINDOW_DATA` +`WindowData` #### Dummy -`DUMMY_DATA` is for development and debugging. See `DummyDataParameter`. +`DummyData` is for development and debugging. See `DummyDataParameter`. ### Common Layers #### Inner Product -* LayerType: `INNER_PRODUCT` +* Layer type: `InnerProduct` * CPU implementation: `./src/caffe/layers/inner_product_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/inner_product_layer.cu` * Parameters (`InnerProductParameter inner_product_param`) @@ -387,13 +387,13 @@ The HDF5 output layer performs the opposite function of the other layers in this - `n * c_o * 1 * 1` * Sample - layers { + layer { name: "fc8" - type: INNER_PRODUCT - blobs_lr: 1 # learning rate multiplier for the filters - blobs_lr: 2 # learning rate multiplier for the biases - weight_decay: 1 # weight decay multiplier for the filters - weight_decay: 0 # weight decay multiplier for the biases + type: "InnerProduct" + # learning rate and decay multipliers for the weights + param { lr_mult: 1 decay_mult: 1 } + # learning rate and decay multipliers for the biases + param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1000 weight_filler { @@ -409,79 +409,79 @@ The HDF5 output layer performs the opposite function of the other layers in this top: "fc8" } -The `INNER_PRODUCT` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1). +The `InnerProduct` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1). #### Splitting -The `SPLIT` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers. +The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers. #### Flattening -The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w) * 1 * 1`. +The `Flatten` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w)` #### Concatenation -* LayerType: `CONCAT` +* Layer type: `Concat` * CPU implementation: `./src/caffe/layers/concat_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/concat_layer.cu` * Parameters (`ConcatParameter concat_param`) - Optional - - `concat_dim` [default 1]: 0 for concatenation along num and 1 for channels. + - `axis` [default 1]: 0 for concatenation along num and 1 for channels. * Input - `n_i * c_i * h * w` for each input blob i from 1 to K. * Output - - if `concat_dim = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same. - - if `concat_dim = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same. + - if `axis = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same. + - if `axis = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same. * Sample - layers { + layer { name: "concat" bottom: "in1" bottom: "in2" top: "out" - type: CONCAT + type: "Concat" concat_param { - concat_dim: 1 + axis: 1 } } -The `CONCAT` layer is a utility layer that concatenates its multiple input blobs to one single output blob. Currently, the layer supports concatenation along num or channels only. +The `Concat` layer is a utility layer that concatenates its multiple input blobs to one single output blob. #### Slicing -The `SLICE` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices. +The `Slice` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices. * Sample - layers { + layer { name: "slicer_label" - type: SLICE + type: "Slice" bottom: "label" ## Example of label with a shape N x 3 x 1 x 1 top: "label1" top: "label2" top: "label3" slice_param { - slice_dim: 1 - slice_point: 1 - slice_point: 2 + axis: 1 + slice_point: 1 + slice_point: 2 } } -`slice_dim` indicates the target dimension and can assume only two values: 0 for num or 1 for channel; `slice_point` indicates indexes in the selected dimension (the number of indexes must be equal to the number of top blobs minus one). +`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one). #### Elementwise Operations -`ELTWISE` +`Eltwise` #### Argmax -`ARGMAX` +`ArgMax` #### Softmax -`SOFTMAX` +`Softmax` #### Mean-Variance Normalization diff --git a/docs/tutorial/loss.md b/docs/tutorial/loss.md index aac561774bb..d2d0e77fbed 100644 --- a/docs/tutorial/loss.md +++ b/docs/tutorial/loss.md @@ -10,30 +10,30 @@ Hence, the goal of learning is to find a setting of the weights that *minimizes* The loss in Caffe is computed by the Forward pass of the network. Each layer takes a set of input (`bottom`) blobs and produces a set of output (`top`) blobs. Some of these layers' outputs may be used in the loss function. -A typical choice of loss function for one-versus-all classification tasks is the `SOFTMAX_LOSS` function, used in a network definition as follows, for example: +A typical choice of loss function for one-versus-all classification tasks is the `SoftmaxWithLoss` function, used in a network definition as follows, for example: - layers { + layer { name: "loss" - type: SOFTMAX_LOSS + type: "SoftmaxWithLoss" bottom: "pred" bottom: "label" top: "loss" } -In a `SOFTMAX_LOSS` function, the `top` blob is a scalar (dimensions $$1 \times 1 \times 1 \times 1$$) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch. +In a `SoftmaxWithLoss` function, the `top` blob is a scalar (empty shape) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch. ### Loss weights -For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SOFTMAX_LOSS` layer and reconstructs it using a `EUCLIDEAN_LOSS` layer), *loss weights* can be used to specify their relative importance. +For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SoftmaxWithLoss` layer and reconstructs it using a `EuclideanLoss` layer), *loss weights* can be used to specify their relative importance. -By convention, Caffe layer types with the suffix `_LOSS` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations. +By convention, Caffe layer types with the suffix `Loss` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations. However, any layer can be used as a loss by adding a field `loss_weight: ` to a layer definition for each `top` blob produced by the layer. -Layers with the suffix `_LOSS` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s. -So, the above `SOFTMAX_LOSS` layer could be equivalently written as: +Layers with the suffix `Loss` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s. +So, the above `SoftmaxWithLoss` layer could be equivalently written as: - layers { + layer { name: "loss" - type: SOFTMAX_LOSS + type: "SoftmaxWithLoss" bottom: "pred" bottom: "label" top: "loss" diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md index 1f0966f88a4..e8b7bd316a9 100644 --- a/docs/tutorial/net_layer_blob.md +++ b/docs/tutorial/net_layer_blob.md @@ -11,22 +11,20 @@ We will go over the details of these components in more detail. ## Blob storage and communication -A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is a 4-dimensional array that stores things in the order of (Num, Channels, Height and Width), from major to minor, and stored in a C-contiguous fashion. The main reason for putting Num (the name is due to legacy reasons, and is equivalent to the notation of "batch" as in minibatch SGD). +A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is an N-dimensional array stored in a C-contiguous fashion. -Caffe stores and communicates data in 4-dimensional arrays called blobs. Blobs provide a unified memory interface, holding data e.g. batches of images, model parameters, and derivatives for optimization. +Caffe stores and communicates data using blobs. Blobs provide a unified memory interface holding data; e.g., batches of images, model parameters, and derivatives for optimization. Blobs conceal the computational and mental overhead of mixed CPU/GPU operation by synchronizing from the CPU host to the GPU device as needed. Memory on the host and device is allocated on demand (lazily) for efficient memory usage. -The conventional blob dimensions for data are number N x channel K x height H x width W. Blob memory is row-major in layout so the last / rightmost dimension changes fastest. For example, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w. +The conventional blob dimensions for batches of image data are number N x channel K x height H x width W. Blob memory is row-major in layout, so the last / rightmost dimension changes fastest. For example, in a 4D blob, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w. - Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256. - Channel / K is the feature dimension e.g. for RGB images K = 3. -Note that although we have designed blobs with its dimensions corresponding to image applications, they are named purely for notational purpose and it is totally valid for you to do non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use blobs of dimensions (Num, Channels, 1, 1) and call the InnerProductLayer (which we will cover soon). +Note that although many blobs in Caffe examples are 4D with axes for image applications, it is totally valid to use blobs for non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use 2D blobs (shape (N, D)) and call the InnerProductLayer (which we will cover soon). -Caffe operations are general with respect to the channel dimension / K. Grayscale and hyperspectral imagery are fine. Caffe can likewise model and process arbitrary vectors in blobs with singleton. That is, the shape of blob holding 1000 vectors of 16 feature dimensions is 1000 x 16 x 1 x 1. - -Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1 x 1 x 1000 x 1024. +Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1000 x 1024. For custom data it may be necessary to hack your own input preparation tool or data layer. However once your data is in your job is done. The modularity of layers accomplishes the rest of the work for you. @@ -95,9 +93,9 @@ A simple logistic regression classifier is defined by name: "LogReg" - layers { + layer { name: "mnist" - type: DATA + type: "Data" top: "data" top: "label" data_param { @@ -105,18 +103,18 @@ is defined by batch_size: 64 } } - layers { + layer { name: "ip" - type: INNER_PRODUCT + type: "InnerProduct" bottom: "data" top: "ip" inner_product_param { num_output: 2 } } - layers { + layer { name: "loss" - type: SOFTMAX_LOSS + type: "SoftmaxWithLoss" bottom: "ip" bottom: "label" top: "loss" @@ -135,19 +133,19 @@ Model initialization is handled by `Net::Init()`. The initialization mainly does I0902 22:52:17.935807 2079114000 data_layer.cpp:135] Opening leveldb input_leveldb I0902 22:52:17.937155 2079114000 data_layer.cpp:195] output data size: 64,1,28,28 I0902 22:52:17.938570 2079114000 net.cpp:103] Top shape: 64 1 28 28 (50176) - I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 1 1 1 (64) + I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 (64) I0902 22:52:17.938611 2079114000 net.cpp:67] Creating Layer ip I0902 22:52:17.938617 2079114000 net.cpp:394] ip <- data I0902 22:52:17.939177 2079114000 net.cpp:356] ip -> ip I0902 22:52:17.939196 2079114000 net.cpp:96] Setting up ip - I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 1 1 (128) + I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 (128) I0902 22:52:17.941270 2079114000 net.cpp:67] Creating Layer loss I0902 22:52:17.941305 2079114000 net.cpp:394] loss <- ip I0902 22:52:17.941314 2079114000 net.cpp:394] loss <- label I0902 22:52:17.941323 2079114000 net.cpp:356] loss -> loss # set up the loss and configure the backward pass I0902 22:52:17.941328 2079114000 net.cpp:96] Setting up loss - I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: 1 1 1 1 (1) + I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: (1) I0902 22:52:17.941329 2079114000 net.cpp:109] with loss weight 1 I0902 22:52:17.941779 2079114000 net.cpp:170] loss needs backward computation. I0902 22:52:17.941787 2079114000 net.cpp:170] ip needs backward computation. diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md index ef7f5da67d5..269e53ab9b9 100644 --- a/examples/mnist/readme.md +++ b/examples/mnist/readme.md @@ -38,9 +38,9 @@ Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto. Currently, we will read the MNIST data from the lmdb we created earlier in the demo. This is defined by a data layer: - layers { + layer { name: "mnist" - type: DATA + type: "Data" data_param { source: "mnist_train_lmdb" backend: LMDB @@ -57,14 +57,14 @@ Specifically, this layer has name `mnist`, type `data`, and it reads the data fr Let's define the first convolution layer: - layers { + layer { name: "conv1" - type: CONVOLUTION - blobs_lr: 1. - blobs_lr: 2. + type: "Convolution" + param { lr_mult: 1 } + param { lr_mult: 2 } convolution_param { num_output: 20 - kernelsize: 5 + kernel_size: 5 stride: 1 weight_filler { type: "xavier" @@ -81,15 +81,15 @@ This layer takes the `data` blob (it is provided by the data layer), and produce The fillers allow us to randomly initialize the value of the weights and bias. For the weight filler, we will use the `xavier` algorithm that automatically determines the scale of initialization based on the number of input and output neurons. For the bias filler, we will simply initialize it as constant, with the default filling value 0. -`blobs_lr` are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates. +`lr_mult`s are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates. ### Writing the Pooling Layer Phew. Pooling layers are actually much easier to define: - layers { + layer { name: "pool1" - type: POOLING + type: "Pooling" pooling_param { kernel_size: 2 stride: 2 @@ -107,11 +107,11 @@ Similarly, you can write up the second convolution and pooling layers. Check `$C Writing a fully connected layer is also simple: - layers { + layer { name: "ip1" - type: INNER_PRODUCT - blobs_lr: 1. - blobs_lr: 2. + type: "InnerProduct" + param { lr_mult: 1 } + param { lr_mult: 2 } inner_product_param { num_output: 500 weight_filler { @@ -125,15 +125,15 @@ Writing a fully connected layer is also simple: top: "ip1" } -This defines a fully connected layer (for some legacy reason, Caffe calls it an `innerproduct` layer) with 500 outputs. All other lines look familiar, right? +This defines a fully connected layer (known in Caffe as an `InnerProduct` layer) with 500 outputs. All other lines look familiar, right? ### Writing the ReLU Layer A ReLU Layer is also simple: - layers { + layer { name: "relu1" - type: RELU + type: "ReLU" bottom: "ip1" top: "ip1" } @@ -142,11 +142,11 @@ Since ReLU is an element-wise operation, we can do *in-place* operations to save After the ReLU layer, we will write another innerproduct layer: - layers { + layer { name: "ip2" - type: INNER_PRODUCT - blobs_lr: 1. - blobs_lr: 2. + type: "InnerProduct" + param { lr_mult: 1 } + param { lr_mult: 2 } inner_product_param { num_output: 10 weight_filler { @@ -164,9 +164,9 @@ After the ReLU layer, we will write another innerproduct layer: Finally, we will write the loss! - layers { + layer { name: "loss" - type: SOFTMAX_LOSS + type: "SoftmaxWithLoss" bottom: "ip2" bottom: "label" } @@ -178,7 +178,7 @@ The `softmax_loss` layer implements both the softmax and the multinomial logisti Layer definitions can include rules for whether and when they are included in the network definition, like the one below: - layers { + layer { // ...layer definition... include: { phase: TRAIN } } @@ -190,7 +190,7 @@ In the above example, this layer will be included only in `TRAIN` phase. If we change `TRAIN` with `TEST`, then this layer will be used only in test phase. By default, that is without layer rules, a layer is always included in the network. Thus, `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for the training phase and one for the testing phase. -Also, there is an `ACCURACY` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`. +Also, there is an `Accuracy` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`. ## Define the MNIST Solver diff --git a/examples/siamese/readme.md b/examples/siamese/readme.md index ce98ec10819..83db8c94395 100644 --- a/examples/siamese/readme.md +++ b/examples/siamese/readme.md @@ -39,13 +39,19 @@ exactly the same as the [LeNet model](mnist.html), the only difference is that we have replaced the top layers that produced probabilities over the 10 digit classes with a linear "feature" layer that produces a 2 dimensional vector. - layers { + layer { name: "feat" - type: INNER_PRODUCT + type: "InnerProduct" bottom: "ip2" top: "feat" - blobs_lr: 1 - blobs_lr: 2 + param { + name: "feat_w" + lr_mult: 1 + } + param { + name: "feat_b" + lr_mult: 2 + } inner_product_param { num_output: 2 } @@ -64,17 +70,19 @@ earlier. Each entry in this database contains the image data for a pair of images (`pair_data`) and a binary label saying if they belong to the same class or different classes (`sim`). - layers { + layer { name: "pair_data" - type: DATA + type: "Data" top: "pair_data" top: "sim" - data_param { - source: "examples/siamese/mnist-siamese-train-leveldb" + include { phase: TRAIN } + transform_param { scale: 0.00390625 + } + data_param { + source: "examples/siamese/mnist_siamese_train_leveldb" batch_size: 64 } - include: { phase: TRAIN } } In order to pack a pair of images into the same blob in the database we pack one @@ -83,16 +91,16 @@ so we add a slice layer after the data layer. This takes the `pair_data` and slices it along the channel dimension so that we have a single image in `data` and its paired image in `data_p.` - layers { - name: "slice_pair" - type: SLICE - bottom: "pair_data" - top: "data" - top: "data_p" - slice_param { - slice_dim: 1 - slice_point: 1 - } + layer { + name: "slice_pair" + type: "Slice" + bottom: "pair_data" + top: "data" + top: "data_p" + slice_param { + slice_dim: 1 + slice_point: 1 + } } ### Building the First Side of the Siamese Net @@ -105,17 +113,17 @@ parameters allows Caffe to share the parameters between layers on both sides of the siamese net. In the definition this looks like: ... - param: "conv1_w" - param: "conv1_b" + param { name: "conv1_w" ... } + param { name: "conv1_b" ... } ... - param: "conv2_w" - param: "conv2_b" + param { name: "conv2_w" ... } + param { name: "conv2_b" ... } ... - param: "ip1_w" - param: "ip1_b" + param { name: "ip1_w" ... } + param { name: "ip1_b" ... } ... - param: "ip2_w" - param: "ip2_b" + param { name: "ip2_w" ... } + param { name: "ip2_b" ... } ... ### Building the Second Side of the Siamese Net @@ -133,9 +141,9 @@ an Invariant Mapping". This loss function encourages matching pairs to be close together in feature space while pushing non-matching pairs apart. This cost function is implemented with the `CONTRASTIVE_LOSS` layer: - layers { + layer { name: "loss" - type: CONTRASTIVE_LOSS + type: "ContrastiveLoss" contrastive_loss_param { margin: 1.0 } diff --git a/matlab/caffe/hdf5creation/demo.m b/matlab/caffe/hdf5creation/demo.m index f554b87e5f6..4f9f7b5a454 100644 --- a/matlab/caffe/hdf5creation/demo.m +++ b/matlab/caffe/hdf5creation/demo.m @@ -52,9 +52,9 @@ fprintf('HDF5 filename listed in %s \n', 'list.txt'); % NOTE: In net definition prototxt, use list.txt as input to HDF5_DATA as: -% layers { +% layer { % name: "data" -% type: HDF5_DATA +% type: "HDF5Data" % top: "data" % top: "labelvec" % hdf5_data_param { From adb955f8b38f1379d3213deba57e5ea129cb1289 Mon Sep 17 00:00:00 2001 From: Ashwani001 Date: Fri, 15 May 2015 10:33:36 +0800 Subject: [PATCH 046/446] Update generate_sample_data.py made code more clearer and more concise --- src/caffe/test/test_data/generate_sample_data.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py index 3b49773c3fe..ab5572685cb 100644 --- a/src/caffe/test/test_data/generate_sample_data.py +++ b/src/caffe/test/test_data/generate_sample_data.py @@ -5,6 +5,8 @@ import numpy as np import h5py +script_dir = os.path.dirname(os.path.abspath(__file__)) + num_cols = 8 num_rows = 10 height = 6 @@ -27,12 +29,12 @@ print data print label -with h5py.File(os.path.dirname(os.path.abspath(__file__)) + '/sample_data.h5', 'w') as f: +with h5py.File(script_dir + '/sample_data.h5', 'w') as f: f['data'] = data f['label'] = label f['label2'] = label2 -with h5py.File(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_2_gzip.h5', 'w') as f: +with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f: f.create_dataset( 'data', data=data + total_size, compression='gzip', compression_opts=1 @@ -46,6 +48,6 @@ compression='gzip', compression_opts=1 ) -with open(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_list.txt', 'w') as f: - f.write(os.path.dirname(os.path.abspath(__file__)) + '/sample_data.h5\n') - f.write(os.path.dirname(os.path.abspath(__file__)) + '/sample_data_2_gzip.h5\n') +with open(script_dir + '/sample_data_list.txt', 'w') as f: + f.write(script_dir + '/sample_data.h5\n') + f.write(script_dir + '/sample_data_2_gzip.h5\n') From 41d13c08e883588badf9d00b60c0dc24c7905fd1 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 14 May 2015 22:14:13 -0700 Subject: [PATCH 047/446] [pycaffe] correct exceptions from Python; remove PyErr_Print Previously, PyErr_Print was used to print Python exceptions. This has the side effect of clearing the exception, which results in (an additional) SystemError in the Python interpreter. Exception printing from the caffe binary tool is re-added in a future commit. --- include/caffe/python_layer.hpp | 29 ++++------------------------- 1 file changed, 4 insertions(+), 25 deletions(-) diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp index 19cf18c9742..9c30250c1b5 100644 --- a/include/caffe/python_layer.hpp +++ b/include/caffe/python_layer.hpp @@ -18,22 +18,11 @@ class PythonLayer : public Layer { virtual void LayerSetUp(const vector*>& bottom, const vector*>& top) { - try { - self_.attr("setup")(bottom, top); - } catch (bp::error_already_set) { - PyErr_Print(); - throw; - } + self_.attr("setup")(bottom, top); } - virtual void Reshape(const vector*>& bottom, const vector*>& top) { - try { - self_.attr("reshape")(bottom, top); - } catch (bp::error_already_set) { - PyErr_Print(); - throw; - } + self_.attr("reshape")(bottom, top); } virtual inline const char* type() const { return "Python"; } @@ -41,21 +30,11 @@ class PythonLayer : public Layer { protected: virtual void Forward_cpu(const vector*>& bottom, const vector*>& top) { - try { - self_.attr("forward")(bottom, top); - } catch (bp::error_already_set) { - PyErr_Print(); - throw; - } + self_.attr("forward")(bottom, top); } virtual void Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { - try { - self_.attr("backward")(top, propagate_down, bottom); - } catch (bp::error_already_set) { - PyErr_Print(); - throw; - } + self_.attr("backward")(top, propagate_down, bottom); } private: From cebce771309b1c07dd0fb2eb18b9b37667d955dc Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 14 May 2015 22:17:19 -0700 Subject: [PATCH 048/446] print Python exceptions when using Python layer with the caffe tool --- tools/caffe.cpp | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 70b15f890f7..6967a46f758 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -8,6 +8,11 @@ #include "boost/algorithm/string.hpp" #include "caffe/caffe.hpp" +#ifdef WITH_PYTHON_LAYER +#include "boost/python.hpp" +namespace bp = boost::python; +#endif + using caffe::Blob; using caffe::Caffe; using caffe::Net; @@ -304,7 +309,16 @@ int main(int argc, char** argv) { // Run tool or show usage. caffe::GlobalInit(&argc, &argv); if (argc == 2) { - return GetBrewFunction(caffe::string(argv[1]))(); +#ifdef WITH_PYTHON_LAYER + try { +#endif + return GetBrewFunction(caffe::string(argv[1]))(); +#ifdef WITH_PYTHON_LAYER + } catch (bp::error_already_set) { + PyErr_Print(); + return 1; + } +#endif } else { gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe"); } From 977023f171da5ff04253d413f1d2440f22dd510b Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 14 May 2015 22:20:58 -0700 Subject: [PATCH 049/446] [pytest] check that Python receives (correct) exceptions from Python layers --- python/caffe/test/test_python_layer.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py index 6fba49143bb..46c6e885d53 100644 --- a/python/caffe/test/test_python_layer.py +++ b/python/caffe/test/test_python_layer.py @@ -21,6 +21,13 @@ def backward(self, top, propagate_down, bottom): bottom[0].diff[...] = 10 * top[0].diff +class ExceptionLayer(caffe.Layer): + """A layer for checking exceptions from Python""" + + def setup(self, bottom, top): + raise RuntimeError + + def python_net_file(): with tempfile.NamedTemporaryFile(delete=False) as f: f.write("""name: 'pythonnet' force_backward: true @@ -34,6 +41,16 @@ def python_net_file(): return f.name +def exception_net_file(): + with tempfile.NamedTemporaryFile(delete=False) as f: + f.write("""name: 'pythonnet' force_backward: true + input: 'data' input_shape { dim: 10 dim: 9 dim: 8 } + layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top' + python_param { module: 'test_python_layer' layer: 'ExceptionLayer' } } + """) + return f.name + + class TestPythonLayer(unittest.TestCase): def setUp(self): net_file = python_net_file() @@ -61,3 +78,8 @@ def test_reshape(self): for blob in self.net.blobs.itervalues(): for d in blob.data.shape: self.assertEqual(s, d) + + def test_exception(self): + net_file = exception_net_file() + self.assertRaises(RuntimeError, caffe.Net, net_file, caffe.TEST) + os.remove(net_file) From c7c4c648c48177171c358f8c8c805ebe7e7cea9b Mon Sep 17 00:00:00 2001 From: manuele Date: Fri, 15 May 2015 11:17:00 +0200 Subject: [PATCH 050/446] Added "propagate_down" param to LayerParameter --- include/caffe/net.hpp | 3 + src/caffe/net.cpp | 43 ++++++++++- src/caffe/proto/caffe.proto | 4 + src/caffe/test/test_net.cpp | 145 ++++++++++++++++++++++++++++++++++++ 4 files changed, 191 insertions(+), 4 deletions(-) diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index 075afebc9b0..5665df1edf2 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -137,6 +137,9 @@ class Net { inline const vector& blob_loss_weights() const { return blob_loss_weights_; } + inline const vector& layer_need_backward() const { + return layer_need_backward_; + } /// @brief returns the parameters inline const vector > >& params() const { return params_; diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index fd00b122630..482b7c5ac32 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -79,10 +79,17 @@ void Net::Init(const NetParameter& in_param) { } // Setup layer. const LayerParameter& layer_param = param.layer(layer_id); + if (layer_param.propagate_down_size() > 0) { + CHECK_EQ(layer_param.propagate_down_size(), + layer_param.bottom_size()) + << "propagate_down param must be specified " + << "either 0 or bottom_size times "; + } layers_.push_back(LayerRegistry::CreateLayer(layer_param)); layer_names_.push_back(layer_param.name()); LOG(INFO) << "Creating Layer " << layer_param.name(); bool need_backward = false; + // Figure out this layer's input and output for (int bottom_id = 0; bottom_id < layer_param.bottom_size(); ++bottom_id) { @@ -151,15 +158,33 @@ void Net::Init(const NetParameter& in_param) { // Go through the net backwards to determine which blobs contribute to the // loss. We can skip backward computation for blobs that don't contribute // to the loss. + // Also checks if all bottom blobs don't need backward computation (possible + // because the skip_propagate_down param) and so we can skip bacward + // computation for the entire layer set blobs_under_loss; + set blobs_skip_backp; for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) { bool layer_contributes_loss = false; + bool layer_skip_propagate_down = true; for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) { const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]]; if (layers_[layer_id]->loss(top_id) || (blobs_under_loss.find(blob_name) != blobs_under_loss.end())) { layer_contributes_loss = true; + } + if (blobs_skip_backp.find(blob_name) == blobs_skip_backp.end()) { + layer_skip_propagate_down = false; + } + if (layer_contributes_loss && !layer_skip_propagate_down) break; + } + // If this layer can skip backward computation, also all his bottom blobs + // don't need backpropagation + if (layer_need_backward_[layer_id] && layer_skip_propagate_down) { + layer_need_backward_[layer_id] = false; + for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size(); + ++bottom_id) { + bottom_need_backward_[layer_id][bottom_id] = false; } } if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; } @@ -178,6 +203,11 @@ void Net::Init(const NetParameter& in_param) { } else { bottom_need_backward_[layer_id][bottom_id] = false; } + if (!bottom_need_backward_[layer_id][bottom_id]) { + const string& blob_name = + blob_names_[bottom_id_vecs_[layer_id][bottom_id]]; + blobs_skip_backp.insert(blob_name); + } } } // Handle force_backward if needed. @@ -367,9 +397,9 @@ void Net::AppendTop(const NetParameter& param, const int layer_id, // Helper for Net::Init: add a new bottom blob to the net. template -int Net::AppendBottom(const NetParameter& param, - const int layer_id, const int bottom_id, - set* available_blobs, map* blob_name_to_idx) { +int Net::AppendBottom(const NetParameter& param, const int layer_id, + const int bottom_id, set* available_blobs, + map* blob_name_to_idx) { const LayerParameter& layer_param = param.layer(layer_id); const string& blob_name = layer_param.bottom(bottom_id); if (available_blobs->find(blob_name) == available_blobs->end()) { @@ -381,7 +411,12 @@ int Net::AppendBottom(const NetParameter& param, bottom_vecs_[layer_id].push_back(blobs_[blob_id].get()); bottom_id_vecs_[layer_id].push_back(blob_id); available_blobs->erase(blob_name); - const bool need_backward = blob_need_backward_[blob_id]; + bool propagate_down = true; + // Check if the backpropagation on bottom_id should be skipped + if (layer_param.propagate_down_size() > 0) + propagate_down = layer_param.propagate_down(bottom_id); + const bool need_backward = blob_need_backward_[blob_id] && + propagate_down; bottom_need_backward_[layer_id].push_back(need_backward); return blob_id; } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index e523efa50f1..bc175753803 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -280,6 +280,10 @@ message LayerParameter { // The blobs containing the numeric parameters of the layer. repeated BlobProto blobs = 7; + + // Specifies on which bottoms the backpropagation should be skipped. + // The size must be either 0 or equal to the number of bottoms. + repeated bool propagate_down = 11; // Rules controlling whether and when a layer is included in the network, // based on the current NetState. You may specify a non-zero number of rules diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp index 08106e79274..782a96bc9b6 100644 --- a/src/caffe/test/test_net.cpp +++ b/src/caffe/test/test_net.cpp @@ -613,6 +613,103 @@ class NetTest : public MultiDeviceTest { InitNetFromProtoString(proto); } + virtual void InitSkipPropNet(bool test_skip_true) { + string proto = + "name: 'SkipPropTestNetwork' " + "layer { " + " name: 'data' " + " type: 'DummyData' " + " dummy_data_param { " + " shape { " + " dim: 5 " + " dim: 2 " + " dim: 3 " + " dim: 4 " + " } " + " data_filler { " + " type: 'gaussian' " + " std: 0.01 " + " } " + " shape { " + " dim: 5 " + " } " + " data_filler { " + " type: 'constant' " + " value: 0 " + " } " + " } " + " top: 'data' " + " top: 'label' " + "} " + "layer { " + " name: 'silence' " + " bottom: 'label' " + " type: 'Silence' " + "} " + "layer { " + " name: 'innerproduct' " + " type: 'InnerProduct' " + " inner_product_param { " + " num_output: 1 " + " weight_filler { " + " type: 'gaussian' " + " std: 0.01 " + " } " + " bias_filler { " + " type: 'constant' " + " value: 0 " + " } " + " } " + " param { " + " lr_mult: 1 " + " decay_mult: 1 " + " } " + " param { " + " lr_mult: 2 " + " decay_mult: 0 " + " } " + " bottom: 'data' " + " top: 'innerproduct' " + "} " + "layer { " + " name: 'ip_fake_labels' " + " type: 'InnerProduct' " + " inner_product_param { " + " num_output: 1 " + " weight_filler { " + " type: 'gaussian' " + " std: 0.01 " + " } " + " bias_filler { " + " type: 'constant' " + " value: 0 " + " } " + " } " + " bottom: 'data' " + " top: 'fake_labels' " + "} " + "layer { " + " name: 'argmax' " + " bottom: 'fake_labels' " + " top: 'label_argmax' " + " type: 'ArgMax' " + "} " + "layer { " + " name: 'loss' " + " bottom: 'innerproduct' " + " bottom: 'label_argmax' "; + if (test_skip_true) + proto += " propagate_down: [true, false] "; + else + proto += " propagate_down: [true, true] "; + proto += + " top: 'cross_entropy_loss' " + " type: 'SigmoidCrossEntropyLoss' " + " loss_weight: 0.1 " + "} "; + InitNetFromProtoString(proto); + } + int seed_; shared_ptr > net_; }; @@ -2224,4 +2321,52 @@ TYPED_TEST(NetTest, TestReshape) { } } +TYPED_TEST(NetTest, TestSkipPropagateDown) { + // check bottom_need_backward if propagate_down is true + this->InitSkipPropNet(false); + vector vec_layer_need_backward = this->net_->layer_need_backward(); + for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) { + string layer_name = this->net_->layer_names()[layer_id]; + if (layer_name == "loss") { + // access to bottom_need_backward coresponding to label's blob + bool need_back = this->net_->bottom_need_backward()[layer_id][1]; + // if propagate_down is true, the loss layer will try to + // backpropagate on labels + EXPECT_TRUE(need_back) << "bottom_need_backward should be True"; + } + // layer_need_backward should be True except for data and silence layers + if (layer_name.find("data") != std::string::npos || + layer_name == "silence") { + EXPECT_FALSE(vec_layer_need_backward[layer_id]) + << "layer_need_backward for " << layer_name << " should be False"; + } else { + EXPECT_TRUE(vec_layer_need_backward[layer_id]) + << "layer_need_backward for " << layer_name << " should be True"; + } + } + // check bottom_need_backward if propagat_down is false + this->InitSkipPropNet(true); + vec_layer_need_backward.clear(); + vec_layer_need_backward = this->net_->layer_need_backward(); + for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) { + string layer_name = this->net_->layer_names()[layer_id]; + if (layer_name == "loss") { + // access to bottom_need_backward coresponding to label's blob + bool need_back = this->net_->bottom_need_backward()[layer_id][1]; + // if propagate_down is false, the loss layer will not try to + // backpropagate on labels + EXPECT_FALSE(need_back) << "bottom_need_backward should be False"; + } + // layer_need_backward should be False except for innerproduct and + // loss layers + if (layer_name == "innerproduct" || layer_name == "loss") { + EXPECT_TRUE(vec_layer_need_backward[layer_id]) + << "layer_need_backward for " << layer_name << " should be True"; + } else { + EXPECT_FALSE(vec_layer_need_backward[layer_id]) + << "layer_need_backward for " << layer_name << " should be False"; + } + } +} + } // namespace caffe From b866d14ae86df3bb1548117f22818c2fefb5778b Mon Sep 17 00:00:00 2001 From: Dmytro Mishkin Date: Fri, 15 May 2015 16:14:02 +0300 Subject: [PATCH 051/446] Remove unnecessary variance computation from backward in MVN layer --- include/caffe/common_layers.hpp | 1 + src/caffe/layers/mvn_layer.cpp | 23 ++--------------------- src/caffe/layers/mvn_layer.cu | 23 +---------------------- src/caffe/proto/caffe.proto | 3 +++ 4 files changed, 7 insertions(+), 43 deletions(-) diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 8da6d68096b..e6b42c14587 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -295,6 +295,7 @@ class MVNLayer : public Layer { /// sum_multiplier is used to carry out sum using BLAS Blob sum_multiplier_; + Dtype eps_; }; /* diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp index b74d7b4f300..3e79bddcdde 100644 --- a/src/caffe/layers/mvn_layer.cpp +++ b/src/caffe/layers/mvn_layer.cpp @@ -22,6 +22,7 @@ void MVNLayer::Reshape(const vector*>& bottom, bottom[0]->height(), bottom[0]->width()); Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data(); caffe_set(sum_multiplier_.count(), Dtype(1), multiplier_data); + eps_ = this->layer_param_.mvn_param().eps(); } template @@ -36,7 +37,6 @@ void MVNLayer::Forward_cpu(const vector*>& bottom, num = bottom[0]->num() * bottom[0]->channels(); int dim = bottom[0]->count() / num; - Dtype eps = 1e-10; if (this->layer_param_.mvn_param().normalize_variance()) { // put the squares of bottom into temp_ @@ -66,7 +66,7 @@ void MVNLayer::Forward_cpu(const vector*>& bottom, caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5), variance_.mutable_cpu_data()); - caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data()); + caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data()); caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, 1., variance_.cpu_data(), sum_multiplier_.cpu_data(), 0., @@ -102,7 +102,6 @@ void MVNLayer::Backward_cpu(const vector*>& top, num = bottom[0]->num() * bottom[0]->channels(); int dim = bottom[0]->count() / num; - Dtype eps = 1e-10; if (this->layer_param_.mvn_param().normalize_variance()) { caffe_mul(temp_.count(), top_data, top_diff, bottom_diff); @@ -125,24 +124,6 @@ void MVNLayer::Backward_cpu(const vector*>& top, // put the squares of bottom into temp_ caffe_powx(temp_.count(), bottom_data, Dtype(2), temp_.mutable_cpu_data()); - - // computes variance using var(X) = E(X^2) - (EX)^2 - caffe_cpu_gemv(CblasNoTrans, num, dim, 1. / dim, bottom_data, - sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX - caffe_cpu_gemv(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(), - sum_multiplier_.cpu_data(), 0., - variance_.mutable_cpu_data()); // E(X^2) - caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2), - temp_.mutable_cpu_data()); // (EX)^2 - caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(), - variance_.mutable_cpu_data()); // variance - - // normalize variance - caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5), - variance_.mutable_cpu_data()); - - caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data()); - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, 1., variance_.cpu_data(), sum_multiplier_.cpu_data(), 0., temp_.mutable_cpu_data()); diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu index 0667f50380f..3888a0c7106 100644 --- a/src/caffe/layers/mvn_layer.cu +++ b/src/caffe/layers/mvn_layer.cu @@ -36,8 +36,6 @@ void MVNLayer::Forward_gpu(const vector*>& bottom, caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(), variance_.mutable_gpu_data()); // variance - Dtype eps = 1e-10; - // do mean and variance normalization // subtract mean caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, -1., @@ -50,7 +48,7 @@ void MVNLayer::Forward_gpu(const vector*>& bottom, caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5), variance_.mutable_gpu_data()); - caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data()); + caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data()); caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, 1., variance_.gpu_data(), sum_multiplier_.gpu_data(), 0., @@ -87,8 +85,6 @@ void MVNLayer::Backward_gpu(const vector*>& top, int dim = bottom[0]->count() / num; - Dtype eps = 1e-10; - if (this->layer_param_.mvn_param().normalize_variance()) { caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff); caffe_gpu_gemv(CblasNoTrans, num, dim, 1., bottom_diff, @@ -111,23 +107,6 @@ void MVNLayer::Backward_gpu(const vector*>& top, caffe_gpu_powx(temp_.count(), bottom_data, Dtype(2), temp_.mutable_gpu_data()); - // computes variance using var(X) = E(X^2) - (EX)^2 - caffe_gpu_gemv(CblasNoTrans, num, dim, 1. / dim, bottom_data, - sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX - caffe_gpu_gemv(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(), - sum_multiplier_.gpu_data(), 0., - variance_.mutable_gpu_data()); // E(X^2) - caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2), - temp_.mutable_gpu_data()); // (EX)^2 - caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(), - variance_.mutable_gpu_data()); // variance - - // normalize variance - caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5), - variance_.mutable_gpu_data()); - - caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data()); - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, 1., variance_.gpu_data(), sum_multiplier_.gpu_data(), 0., temp_.mutable_gpu_data()); diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index d43e560a1fa..1923627fd97 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -629,6 +629,9 @@ message MVNParameter { // This parameter can be set to true to perform DNN-like MVN optional bool across_channels = 2 [default = false]; + + // Epsilon for not dividing by zero while normalizing variance + optional float eps = 3 [default = 1e-9]; } // Message that stores parameters used by PoolingLayer From 04cb5401c96a5cd28748ab1b119f3bb648a83403 Mon Sep 17 00:00:00 2001 From: Martin Thoma Date: Fri, 15 May 2015 16:06:49 +0200 Subject: [PATCH 052/446] Python: Formatted docstrings to numpydoc (Take, Give -> Parameters, Returns) --- python/caffe/classifier.py | 37 ++++++++------- python/caffe/detector.py | 16 +++---- python/caffe/io.py | 97 ++++++++++++++++++++++---------------- 3 files changed, 86 insertions(+), 64 deletions(-) diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py index 7fb2ccc8ff3..537193db8f8 100644 --- a/python/caffe/classifier.py +++ b/python/caffe/classifier.py @@ -12,17 +12,17 @@ class Classifier(caffe.Net): """ Classifier extends Net for image class prediction by scaling, center cropping, or oversampling. + + Parameters + ---------- + image_dims : dimensions to scale input for cropping/sampling. + Default is to scale to net input size for whole-image crop. + mean, input_scale, raw_scale, channel_swap: params for + preprocessing options. """ def __init__(self, model_file, pretrained_file, image_dims=None, mean=None, input_scale=None, raw_scale=None, channel_swap=None): - """ - Take - image_dims: dimensions to scale input for cropping/sampling. - Default is to scale to net input size for whole-image crop. - mean, input_scale, raw_scale, channel_swap: params for - preprocessing options. - """ caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST) # configure pre-processing @@ -48,19 +48,24 @@ def predict(self, inputs, oversample=True): """ Predict classification probabilities of inputs. - Take - inputs: iterable of (H x W x K) input ndarrays. - oversample: average predictions across center, corners, and mirrors - when True (default). Center-only prediction when False. + Parameters + ---------- + inputs : iterable of (H x W x K) input ndarrays. + oversample : boolean + average predictions across center, corners, and mirrors + when True (default). Center-only prediction when False. - Give - predictions: (N x C) ndarray of class probabilities - for N images and C classes. + Returns + ------- + predictions: (N x C) ndarray of class probabilities for N images and C + classes. """ # Scale to standardize input dimensions. input_ = np.zeros((len(inputs), - self.image_dims[0], self.image_dims[1], inputs[0].shape[2]), - dtype=np.float32) + self.image_dims[0], + self.image_dims[1], + inputs[0].shape[2]), + dtype=np.float32) for ix, in_ in enumerate(inputs): input_[ix] = caffe.io.resize_image(in_, self.image_dims) diff --git a/python/caffe/detector.py b/python/caffe/detector.py index f72b548ac9a..75cd3b1202f 100644 --- a/python/caffe/detector.py +++ b/python/caffe/detector.py @@ -23,18 +23,18 @@ class Detector(caffe.Net): """ Detector extends Net for windowed detection by a list of crops or selective search proposals. + + Parameters + ---------- + mean, input_scale, raw_scale, channel_swap : params for preprocessing + options. + context_pad : amount of surrounding context to take s.t. a `context_pad` + sized border of pixels in the network input image is context, as in + R-CNN feature extraction. """ def __init__(self, model_file, pretrained_file, mean=None, input_scale=None, raw_scale=None, channel_swap=None, context_pad=None): - """ - Take - mean, input_scale, raw_scale, channel_swap: params for - preprocessing options. - context_pad: amount of surrounding context to take s.t. a `context_pad` - sized border of pixels in the network input image is context, as in - R-CNN feature extraction. - """ caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST) # configure pre-processing diff --git a/python/caffe/io.py b/python/caffe/io.py index e5feff38796..fc96266085f 100644 --- a/python/caffe/io.py +++ b/python/caffe/io.py @@ -16,8 +16,9 @@ ## proto / datum / ndarray conversion def blobproto_to_array(blob, return_diff=False): - """Convert a blob proto to an array. In default, we will just return the - data, unless return_diff is True, in which case we will return the diff. + """ + Convert a blob proto to an array. In default, we will just return the data, + unless return_diff is True, in which case we will return the diff. """ if return_diff: return np.array(blob.diff).reshape( @@ -125,12 +126,14 @@ def preprocess(self, in_, data): - subtract mean - scale feature - Take - in_: name of input blob to preprocess for - data: (H' x W' x K) ndarray + Parameters + ---------- + in_ : name of input blob to preprocess for + data : (H' x W' x K) ndarray - Give - caffe_in: (K x H x W) ndarray for input to a Net + Returns + ------- + caffe_in : (K x H x W) ndarray for input to a Net """ self.__check_input(in_) caffe_in = data.astype(np.float32, copy=False) @@ -182,9 +185,10 @@ def set_transpose(self, in_, order): Set the input channel order for e.g. RGB to BGR conversion as needed for the reference ImageNet model. - Take - in_: which input to assign this channel order - order: the order to transpose the dimensions + Parameters + ---------- + in_ : which input to assign this channel order + order : the order to transpose the dimensions """ self.__check_input(in_) if len(order) != len(self.inputs[in_]) - 1: @@ -198,9 +202,10 @@ def set_channel_swap(self, in_, order): as needed for the reference ImageNet model. N.B. this assumes the channels are the first dimension AFTER transpose. - Take - in_: which input to assign this channel order - order: the order to take the channels. + Parameters + ---------- + in_ : which input to assign this channel order + order : the order to take the channels. (2,1,0) maps RGB to BGR for example. """ self.__check_input(in_) @@ -216,9 +221,10 @@ def set_raw_scale(self, in_, scale): like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale of these models must be 255. - Take - in_: which input to assign this scale factor - scale: scale coefficient + Parameters + ---------- + in_ : which input to assign this scale factor + scale : scale coefficient """ self.__check_input(in_) self.raw_scale[in_] = scale @@ -227,9 +233,10 @@ def set_mean(self, in_, mean): """ Set the mean to subtract for centering the data. - Take - in_: which input to assign this mean. - mean: mean ndarray (input dimensional or broadcastable) + Parameters + ---------- + in_ : which input to assign this mean. + mean : mean ndarray (input dimensional or broadcastable) """ self.__check_input(in_) ms = mean.shape @@ -254,9 +261,10 @@ def set_input_scale(self, in_, scale): N.B. input_scale is done AFTER mean subtraction and other preprocessing while raw_scale is done BEFORE. - Take - in_: which input to assign this scale factor - scale: scale coefficient + Parameters + ---------- + in_ : which input to assign this scale factor + scale : scale coefficient """ self.__check_input(in_) self.input_scale[in_] = scale @@ -268,13 +276,16 @@ def load_image(filename, color=True): """ Load an image converting from grayscale or alpha as needed. - Take - filename: string - color: flag for color format. True (default) loads as RGB while False + Parameters + ---------- + filename : string + color : boolean + flag for color format. True (default) loads as RGB while False loads as intensity (if image is already grayscale). - Give - image: an image with type np.float32 in range [0, 1] + Returns + ------- + image : an image with type np.float32 in range [0, 1] of size (H x W x 3) in RGB or of size (H x W x 1) in grayscale. """ @@ -292,24 +303,28 @@ def resize_image(im, new_dims, interp_order=1): """ Resize an image array with interpolation. - Take - im: (H x W x K) ndarray - new_dims: (height, width) tuple of new dimensions. - interp_order: interpolation order, default is linear. + Parameters + ---------- + im : (H x W x K) ndarray + new_dims : (height, width) tuple of new dimensions. + interp_order : interpolation order, default is linear. - Give - im: resized ndarray with shape (new_dims[0], new_dims[1], K) + Returns + ------- + im : resized ndarray with shape (new_dims[0], new_dims[1], K) """ if im.shape[-1] == 1 or im.shape[-1] == 3: im_min, im_max = im.min(), im.max() if im_max > im_min: - # skimage is fast but only understands {1,3} channel images in [0, 1]. + # skimage is fast but only understands {1,3} channel images + # in [0, 1]. im_std = (im - im_min) / (im_max - im_min) resized_std = resize(im_std, new_dims, order=interp_order) resized_im = resized_std * (im_max - im_min) + im_min else: # the image is a constant -- avoid divide by 0 - ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]), dtype=np.float32) + ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]), + dtype=np.float32) ret.fill(im_min) return ret else: @@ -323,12 +338,14 @@ def oversample(images, crop_dims): """ Crop images into the four corners, center, and their mirrored versions. - Take - image: iterable of (H x W x K) ndarrays - crop_dims: (height, width) tuple for the crops. + Parameters + ---------- + image : iterable of (H x W x K) ndarrays + crop_dims : (height, width) tuple for the crops. - Give - crops: (10*N x H x W x K) ndarray of crops for number of inputs N. + Returns + ------- + crops : (10*N x H x W x K) ndarray of crops for number of inputs N. """ # Dimensions and center. im_shape = np.array(images[0].shape) From 749306eb6438b4a09089f785471d1d9cb00b6666 Mon Sep 17 00:00:00 2001 From: Anatoly Baksheev Date: Fri, 15 May 2015 17:39:11 +0300 Subject: [PATCH 053/446] minor fix in cmake.config generation - do not force client libs to include numpy include dirs --- cmake/ConfigGen.cmake | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake index c82047dcc5f..a9101e34350 100644 --- a/cmake/ConfigGen.cmake +++ b/cmake/ConfigGen.cmake @@ -11,6 +11,17 @@ function(caffe_get_current_includes includes_variable) list(FIND current_includes ${PROJECT_BINARY_DIR} __index) list(REMOVE_AT current_includes ${__index}) + # removing numpy includes (since not required for client libs) + set(__toremove "") + foreach(__i ${current_includes}) + if(${__i} MATCHES "python") + list(APPEND __toremove ${__i}) + endif() + endforeach() + if(__toremove) + list(REMOVE_ITEM current_includes ${__toremove}) + endif() + caffe_list_unique(current_includes) set(${includes_variable} ${current_includes} PARENT_SCOPE) endfunction() From dbd83195206f02bf3996cc5d97894571e7c26756 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Mon, 18 May 2015 00:45:26 -0700 Subject: [PATCH 054/446] clean up redundant message comments --- src/caffe/proto/caffe.proto | 33 +++------------------------------ 1 file changed, 3 insertions(+), 30 deletions(-) diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 580e36ed130..307015f42c9 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -368,7 +368,9 @@ message LossParameter { optional bool normalize = 2 [default = true]; } -// Message that stores parameters used by AccuracyLayer +// Messages that store parameters used by individual layer types follow, in +// alphabetical order. + message AccuracyParameter { // When computing accuracy, count as correct by comparing the true label to // the top k scoring classes. By default, only compare to the top scoring @@ -386,14 +388,12 @@ message AccuracyParameter { optional int32 ignore_label = 3; } -// Message that stores parameters used by ArgMaxLayer message ArgMaxParameter { // If true produce pairs (argmax, maxval) optional bool out_max_val = 1 [default = false]; optional uint32 top_k = 2 [default = 1]; } -// Message that stores parameters used by ConcatLayer message ConcatParameter { // The axis along which to concatenate -- may be negative to index from the // end (e.g., -1 for the last axis). Other axes must have the @@ -405,7 +405,6 @@ message ConcatParameter { optional uint32 concat_dim = 1 [default = 1]; } -// Message that stores parameters used by ContrastiveLossLayer message ContrastiveLossParameter { // margin for dissimilar pair optional float margin = 1 [default = 1.0]; @@ -418,7 +417,6 @@ message ContrastiveLossParameter { optional bool legacy_version = 2 [default = false]; } -// Message that stores parameters used by ConvolutionLayer message ConvolutionParameter { optional uint32 num_output = 1; // The number of outputs for the layer optional bool bias_term = 2 [default = true]; // whether to have bias terms @@ -444,7 +442,6 @@ message ConvolutionParameter { optional Engine engine = 15 [default = DEFAULT]; } -// Message that stores parameters used by DataLayer message DataParameter { enum DB { LEVELDB = 0; @@ -475,12 +472,10 @@ message DataParameter { optional bool force_encoded_color = 9 [default = false]; } -// Message that stores parameters used by DropoutLayer message DropoutParameter { optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio } -// Message that stores parameters used by DummyDataLayer. // DummyDataLayer fills any number of arbitrarily shaped blobs with random // (or constant) data generated by "Fillers" (see "message FillerParameter"). message DummyDataParameter { @@ -500,7 +495,6 @@ message DummyDataParameter { repeated uint32 width = 5; } -// Message that stores parameters used by EltwiseLayer message EltwiseParameter { enum EltwiseOp { PROD = 0; @@ -515,7 +509,6 @@ message EltwiseParameter { optional bool stable_prod_grad = 3 [default = true]; } -// Message that stores parameters used by ExpLayer message ExpParameter { // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0. // Or if base is set to the default (-1), base is set to e, @@ -525,7 +518,6 @@ message ExpParameter { optional float shift = 3 [default = 0.0]; } -// Message that stores parameters used by HDF5DataLayer message HDF5DataParameter { // Specify the data source. optional string source = 1; @@ -540,7 +532,6 @@ message HDF5DataParameter { optional bool shuffle = 3 [default = false]; } -// Message that stores parameters used by HDF5OutputLayer message HDF5OutputParameter { optional string file_name = 1; } @@ -554,7 +545,6 @@ message HingeLossParameter { optional Norm norm = 1 [default = L1]; } -// Message that stores parameters used by ImageDataLayer message ImageDataParameter { // Specify the data source. optional string source = 1; @@ -586,13 +576,11 @@ message ImageDataParameter { optional string root_folder = 12 [default = ""]; } -// Message that stores parameters InfogainLossLayer message InfogainLossParameter { // Specify the infogain matrix source. optional string source = 1; } -// Message that stores parameters used by InnerProductLayer message InnerProductParameter { optional uint32 num_output = 1; // The number of outputs for the layer optional bool bias_term = 2 [default = true]; // whether to have bias terms @@ -605,7 +593,6 @@ message InnerProductParameter { optional int32 axis = 5 [default = 1]; } -// Message that stores parameters used by LRNLayer message LRNParameter { optional uint32 local_size = 1 [default = 5]; optional float alpha = 2 [default = 1.]; @@ -618,7 +605,6 @@ message LRNParameter { optional float k = 5 [default = 1.]; } -// Message that stores parameters used by MemoryDataLayer message MemoryDataParameter { optional uint32 batch_size = 1; optional uint32 channels = 2; @@ -626,7 +612,6 @@ message MemoryDataParameter { optional uint32 width = 4; } -// Message that stores parameters used by MVNLayer message MVNParameter { // This parameter can be set to false to normalize mean only optional bool normalize_variance = 1 [default = true]; @@ -638,7 +623,6 @@ message MVNParameter { optional float eps = 3 [default = 1e-9]; } -// Message that stores parameters used by PoolingLayer message PoolingParameter { enum PoolMethod { MAX = 0; @@ -668,7 +652,6 @@ message PoolingParameter { optional bool global_pooling = 12 [default = false]; } -// Message that stores parameters used by PowerLayer message PowerParameter { // PowerLayer computes outputs y = (shift + scale * x) ^ power. optional float power = 1 [default = 1.0]; @@ -676,13 +659,11 @@ message PowerParameter { optional float shift = 3 [default = 0.0]; } -// Message that stores parameters used by PythonLayer message PythonParameter { optional string module = 1; optional string layer = 2; } -// Message that stores parameters used by ReLULayer message ReLUParameter { // Allow non-zero slope for negative inputs to speed up optimization // Described in: @@ -698,7 +679,6 @@ message ReLUParameter { optional Engine engine = 2 [default = DEFAULT]; } -// Message that stores parameters used by ReshapeLayer message ReshapeParameter { // Specify the output dimensions. If some of the dimensions are set to 0, // the corresponding dimension from the bottom layer is used (unchanged). @@ -763,7 +743,6 @@ message ReshapeParameter { optional int32 num_axes = 3 [default = -1]; } -// Message that stores parameters used by SigmoidLayer message SigmoidParameter { enum Engine { DEFAULT = 0; @@ -773,7 +752,6 @@ message SigmoidParameter { optional Engine engine = 1 [default = DEFAULT]; } -// Message that stores parameters used by SliceLayer message SliceParameter { // The axis along which to slice -- may be negative to index from the end // (e.g., -1 for the last axis). @@ -800,7 +778,6 @@ message SoftmaxParameter { optional int32 axis = 2 [default = 1]; } -// Message that stores parameters used by TanHLayer message TanHParameter { enum Engine { DEFAULT = 0; @@ -810,12 +787,10 @@ message TanHParameter { optional Engine engine = 1 [default = DEFAULT]; } -// Message that stores parameters used by ThresholdLayer message ThresholdParameter { optional float threshold = 1 [default = 0]; // Strictly positive values } -// Message that stores parameters used by WindowDataLayer message WindowDataParameter { // Specify the data source. optional string source = 1; @@ -849,7 +824,6 @@ message WindowDataParameter { optional string root_folder = 13 [default = ""]; } -// Message that stores parameters used by SPPLayer message SPPParameter { enum PoolMethod { MAX = 0; @@ -1053,7 +1027,6 @@ message V0LayerParameter { optional HDF5OutputParameter hdf5_output_param = 1001; } -// Message that stores parameters used by PReLULayer message PReLUParameter { // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers: // Surpassing Human-Level Performance on ImageNet Classification, 2015. From 4ceefaaf7159110b6c815b7d50f64465787abf32 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Mon, 18 May 2015 20:27:19 +0800 Subject: [PATCH 055/446] fix blob_loss_weights index in test() in caffe.cpp Correct the index for blob_loss_weights during output. Previously it was set to test_score index by mistake. --- tools/caffe.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 70b15f890f7..0b7523fccf9 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -187,8 +187,8 @@ int test() { for (int i = 0; i < test_score.size(); ++i) { const std::string& output_name = caffe_net.blob_names()[ caffe_net.output_blob_indices()[test_score_output_id[i]]]; - const float loss_weight = - caffe_net.blob_loss_weights()[caffe_net.output_blob_indices()[i]]; + const float loss_weight = caffe_net.blob_loss_weights()[ + caffe_net.output_blob_indices()[test_score_output_id[i]]]; std::ostringstream loss_msg_stream; const float mean_score = test_score[i] / FLAGS_iterations; if (loss_weight) { From 2fed2a998ae8c8894866c9adb4afccc046620c4d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sebasti=C3=A1n=20Ram=C3=ADrez?= Date: Mon, 18 May 2015 10:21:44 -0500 Subject: [PATCH 056/446] Update IPython Notebooks to version 4 --- examples/classification.ipynb | 3667 ++++++- examples/detection.ipynb | 9253 +++++++++++++++-- examples/filter_visualization.ipynb | 13754 +++++++++++++++++++++++-- examples/hdf5_classification.ipynb | 7329 +++++++++++-- examples/net_surgery.ipynb | 7231 ++++++++++++- examples/siamese/mnist_siamese.ipynb | 2049 +++- 6 files changed, 39807 insertions(+), 3476 deletions(-) diff --git a/examples/classification.ipynb b/examples/classification.ipynb index 0babf79f304..a76cfb10773 100644 --- a/examples/classification.ipynb +++ b/examples/classification.ipynb @@ -1,397 +1,3342 @@ { - "metadata": { - "description": "Use the pre-trained ImageNet model to classify images with the Python interface.", - "example_name": "ImageNet classification", - "include_in_docs": true, - "priority": 1, - "signature": "sha256:a2b12abaa1eb252f436d59833c08ab97948c8a7a0513197f31afad0a0690e318" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Classifying ImageNet: the instant Caffe way\n", - "===========================================\n", - "\n", - "Caffe has a Python interface, pycaffe, with a `caffe.Net` interface for models. There are both Python and MATLAB interfaces. While this example uses the off-the-shelf Python `caffe.Classifier` interface there is also a MATLAB example at `matlab/caffe/matcaffe_demo.m`.\n", - "\n", - "Before we begin, you must compile Caffe. You should add the Caffe module to your `PYTHONPATH` although this example includes it automatically. If you haven't yet done so, please refer to the [installation instructions](http://caffe.berkeleyvision.org/installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet` or let the first step of this example download it for you.\n", - "\n", - "Ready? Let's start." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe\n", - "\n", - "# Set the right path to your model definition file, pretrained model weights,\n", - "# and the image you would like to classify.\n", - "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n", - "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", - "IMAGE_FILE = 'images/cat.jpg'\n", - "\n", - "import os\n", - "if not os.path.isfile(PRETRAINED):\n", - " print(\"Downloading pre-trained CaffeNet model...\")\n", - " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n", - "\n", - "We will set the phase to test since we are doing testing, and will first use CPU for the computation." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "caffe.set_mode_cpu()\n", - "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n", - " mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n", - " channel_swap=(2,1,0),\n", - " raw_scale=255,\n", - " image_dims=(256, 256))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at our example image with Caffe's image loading helper." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "input_image = caffe.io.load_image(IMAGE_FILE)\n", - "plt.imshow(input_image)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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/+dP/NH/oX/73OL56j5/7E/8pt+v71GJ4AZGaz0b0iV+8dpUsa+F4WLBiBDui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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt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\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Time to classify. The default is to actually do 10 predictions, cropping the center and corners of the image as well as their mirrored versions, and average over the predictions:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prediction = net.predict([input_image]) # predict takes any number of images, and formats them for the Caffe net automatically\n", - "print 'prediction shape:', prediction[0].shape\n", - "plt.plot(prediction[0])\n", - "print 'predicted class:', prediction[0].argmax()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "prediction shape: (1000,)\n", - "predicted class: 281\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Classifying ImageNet: the instant Caffe way\n", + "===========================================\n", + "\n", + "Caffe has a Python interface, pycaffe, with a `caffe.Net` interface for models. There are both Python and MATLAB interfaces. While this example uses the off-the-shelf Python `caffe.Classifier` interface there is also a MATLAB example at `matlab/caffe/matcaffe_demo.m`.\n", + "\n", + "Before we begin, you must compile Caffe. You should add the Caffe module to your `PYTHONPATH` although this example includes it automatically. If you haven't yet done so, please refer to the [installation instructions](http://caffe.berkeleyvision.org/installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet` or let the first step of this example download it for you.\n", + "\n", + "Ready? Let's start." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# Make sure that caffe is on the python path:\n", + "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "\n", + "import caffe\n", + "\n", + "# Set the right path to your model definition file, pretrained model weights,\n", + "# and the image you would like to classify.\n", + "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n", + "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", + "IMAGE_FILE = 'images/cat.jpg'\n", + "\n", + "import os\n", + "if not os.path.isfile(PRETRAINED):\n", + " print(\"Downloading pre-trained CaffeNet model...\")\n", + " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n", + "\n", + "We will set the phase to test since we are doing testing, and will first use CPU for the computation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "caffe.set_mode_cpu()\n", + "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n", + " mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n", + " channel_swap=(2,1,0),\n", + " raw_scale=255,\n", + " image_dims=(256, 256))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at our example image with Caffe's image loading helper." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, "metadata": {}, - "source": [ - "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n", - "\n", - "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n", - "\n", - "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!" - ] + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "prediction = net.predict([input_image], oversample=False)\n", - "print 'prediction shape:', prediction[0].shape\n", - "plt.plot(prediction[0])\n", - "print 'predicted class:', prediction[0].argmax()" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAU0AAAEACAYAAAA3NiR2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvU2sbVt23/UbY8y51t77nHPvfa9euSpVZXAqrthWYgWS\n", + "2MIdPpJITkkE6EAiBAIpoLQQSPRoICEhpEjQwh1EI42AIiQ6ER8xKFG+MEEmUqQkhpjICqRcrirX\n", + "q/fuxzln77XmnGPQGHOfVyahiKWKXiGd0XjvnXfP3WvvteYc8z/+//8YWyIieI7neI7neI5/oNBP\n", + "+w08x3M8x3P8/ymek+ZzPMdzPMdvIp6T5nM8x3M8x28inpPmczzHczzHbyKek+ZzPMdzPMdvIp6T\n", + "5nM8x3M8x28i/qEkzZ//+Z/nx3/8x/nKV77CH//jf/wfxiWe4zme4zk+lZDvt09zjMGP/diP8Wf/\n", + 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, + "output_type": "display_data" + } + ], + "source": [ + "input_image = caffe.io.load_image(IMAGE_FILE)\n", + "plt.imshow(input_image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time to classify. The default is to actually do 10 predictions, cropping the center and corners of the image as well as their mirrored versions, and average over the predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences." + "name": "stdout", + "output_type": "stream", + "text": [ + "prediction shape: (1000,)\n", + "predicted class: 281\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "%timeit net.predict([input_image])" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAG2ZJREFUeJzt3X9w02WCx/FPnOaOG1xRKmJJulNoCgnQ1q4pDMsyU1dK\n", + "B06ytDo7XRn0dnvawUF393bXvX92FrxZseM4t2Jv5rqcv3VL//DGuh7magczQBVyCgyO9UfLtWcI\n", + "1mWBrvxQS+Nzf9TGJIX0BykBnvdrJtN8v9/n+ebJY/L95Hm+3y86jDFGAABrXZXtBgAAsosgAADL\n", + "EQQAYDmCAAAsRxAAgOUIAgCw3KhBEAwG5fV6VVRUpIaGhhHbW1tbVVpaqrKyMt18883asWNHfFtB\n", + "QYFKSkpUVlamRYsWZbblAICMcKS7jyAWi2nevHlqb2+Xy+VSeXm5mpub5fP54mVOnz6tqVOnSpLe\n", + "ffddVVdXq7u7W5I0e/ZsvfPOO5o+ffokvw0AwESlHRGEw2F5PB4VFBTI6XSqtrZWra2tSWWGQ0CS\n", + 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and formats them for the Caffe net automatically\n", + "print 'prediction shape:', prediction[0].shape\n", + "plt.plot(prediction[0])\n", + "print 'predicted class:', prediction[0].argmax()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n", + "\n", + "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n", + "\n", + "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It may look a little slow, but note that time is spent on cropping, python interfacing, and running 10 images. For performance, if you really want to make prediction fast, you can optionally code in C++ and pipeline operations better. For experimenting and prototyping the current speed is fine.\n", - "\n", - "Let's time classifying a single image with input preprocessed:" + "name": "stdout", + "output_type": "stream", + "text": [ + "prediction shape: (1000,)\n", + "predicted class: 281\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n", - "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n", - "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n", - "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n", - "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n", - "%timeit net.forward(data=caffe_input)" - ], - "language": "python", + "data": { + "image/png": [ + 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it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OK, so how about GPU? it is actually pretty easy:" + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loops, best of 3: 355 ms per loop\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "caffe.set_mode_gpu()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, + } + ], + "source": [ + "%timeit net.predict([input_image])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It may look a little slow, but note that time is spent on cropping, python interfacing, and running 10 images. For performance, if you really want to make prediction fast, you can optionally code in C++ and pipeline operations better. For experimenting and prototyping the current speed is fine.\n", + "\n", + "Let's time classifying a single image with input preprocessed:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Voila! Now we are in GPU mode. Let's see if the code gives the same result:" + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loops, best of 3: 210 ms per loop\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prediction = net.predict([input_image])\n", - "print 'prediction shape:', prediction[0].shape\n", - "plt.plot(prediction[0])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "prediction shape: (1000,)\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "[]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, + } + ], + "source": [ + "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n", + "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n", + "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n", + "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n", + "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n", + "%timeit net.forward(data=caffe_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OK, so how about GPU? it is actually pretty easy:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "caffe.set_mode_gpu()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voila! Now we are in GPU mode. Let's see if the code gives the same result:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:" + "name": "stdout", + "output_type": "stream", + "text": [ + "prediction shape: (1000,)\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Full pipeline timing.\n", - "%timeit net.predict([input_image])" - ], - "language": "python", + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "10 loops, best of 3: 174 ms per loop\n" - ] - } - ], - "prompt_number": 10 + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Forward pass timing.\n", - "%timeit net.forward(data=caffe_input)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAG2ZJREFUeJzt3X9w1OWBx/HPOtk7O1hRImLYTSeQDewCSUzdwFDKTFoJ\n", + 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"dDqN2+02Tz755ITe+1tvvWUWLFhgCgsLzX333Tem1+aGMgCwHP+rSgCwHEEAAJYjCADAcgQBAFiO\n", + "IAAAyxEEAGA5ggAALEcQAIDl/h+B3FfVQOwSzQAAAABJRU5ErkJggg==\n" + ], + "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "10 loops, best of 3: 34.2 ms per loop\n" - ] - } - ], - "prompt_number": 11 - }, + "output_type": "display_data" + } + ], + "source": [ + "prediction = net.predict([input_image])\n", + "print 'prediction shape:', prediction[0].shape\n", + "plt.plot(prediction[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n", - "\n", - "To fully utilize the power of GPUs, you really want to:\n", - "\n", - "* Use larger batches, and minimize python call and data transfer overheads.\n", - "* Pipeline data load operations, like using a subprocess.\n", - "* Code in C++. A little inconvenient, but maybe worth it if your dataset is really, really large." + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 174 ms per loop\n" ] - }, + } + ], + "source": [ + "# Full pipeline timing.\n", + "%timeit net.predict([input_image])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parting Words\n", - "-------------\n", - "\n", - "So this is python! We hope the interface is easy enough for one to use. The python wrapper is interfaced with boost::python, and source code can be found at `python/caffe` with the main interface in `pycaffe.py` and the classification wrapper in `classifier.py`. If you have customizations to make, start there! Do let us know if you make improvements by sending a pull request!" + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 34.2 ms per loop\n" ] } ], - "metadata": {} + "source": [ + "# Forward pass timing.\n", + "%timeit net.forward(data=caffe_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n", + "\n", + "To fully utilize the power of GPUs, you really want to:\n", + "\n", + "* Use larger batches, and minimize python call and data transfer overheads.\n", + "* Pipeline data load operations, like using a subprocess.\n", + "* Code in C++. A little inconvenient, but maybe worth it if your dataset is really, really large." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parting Words\n", + "-------------\n", + "\n", + "So this is python! We hope the interface is easy enough for one to use. The python wrapper is interfaced with boost::python, and source code can be found at `python/caffe` with the main interface in `pycaffe.py` and the classification wrapper in `classifier.py`. If you have customizations to make, start there! Do let us know if you make improvements by sending a pull request!" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "description": "Use the pre-trained ImageNet model to classify images with the Python interface.", + "example_name": "ImageNet classification", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 1 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/detection.ipynb b/examples/detection.ipynb index e343feefd20..e2a981a00d7 100644 --- a/examples/detection.ipynb +++ b/examples/detection.ipynb @@ -1,933 +1,8392 @@ { - "metadata": { - "description": "Run a pretrained model as a detector in Python.", - "example_name": "R-CNN detection", - "include_in_docs": true, - "priority": 3, - "signature": "sha256:5d53dc49c9b6b93c1a2714c99043a763029ec98aebfb44acfa8d9e61781c9499" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n", - "\n", - "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n", - "\n", - "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n", - "\n", - "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge\u2014no joke).\n", - "\n", - "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n", - "\n", - "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n", - "\n", - "-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\n", - "\n", - "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "!mkdir -p _temp\n", - "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n", - "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n", - "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \r\n", - "name: \"R-CNN-ilsvrc13\"\r\n", - "input: \"data\"\r\n", - "input_dim: 10\r\n", - "input_dim: 3\r\n", - "input_dim: 227\r\n", - "input_dim: 227\r\n", - "state {\r\n", - " phase: TEST\r\n", - "}\r\n", - "layer {\r\n", - " name: \"conv1\"\r\n", - " type: \"Convolution\"\r\n", - " bottom: \"data\"\r\n", - " top: \"conv1\"\r\n", - " convolution_param {\r\n", - " num_output: 96\r\n", - " kernel_size: 11\r\n", - " stride: 4\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"relu1\"\r\n", - " type: \"ReLU\"\r\n", - " bottom: \"conv1\"\r\n", - " top: \"conv1\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"pool1\"\r\n", - " type: \"Pooling\"\r\n", - " bottom: \"conv1\"\r\n", - " top: \"pool1\"\r\n", - " pooling_param {\r\n", - " pool: MAX\r\n", - " kernel_size: 3\r\n", - " stride: 2\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"norm1\"\r\n", - " type: \"LRN\"\r\n", - " bottom: \"pool1\"\r\n", - " top: \"norm1\"\r\n", - " lrn_param {\r\n", - " local_size: 5\r\n", - " alpha: 0.0001\r\n", - " beta: 0.75\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"conv2\"\r\n", - " type: \"Convolution\"\r\n", - " bottom: \"norm1\"\r\n", - " top: \"conv2\"\r\n", - " convolution_param {\r\n", - " num_output: 256\r\n", - " pad: 2\r\n", - " kernel_size: 5\r\n", - " group: 2\r\n", - " }\r\n", - "}\r\n", - "layer {\r", - "\r\n", - " name: \"relu2\"\r\n", - " type: \"ReLU\"\r\n", - " bottom: \"conv2\"\r\n", - " top: \"conv2\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"pool2\"\r\n", - " type: \"Pooling\"\r\n", - " bottom: \"conv2\"\r\n", - " top: \"pool2\"\r\n", - " pooling_param {\r\n", - " pool: MAX\r\n", - " kernel_size: 3\r\n", - " stride: 2\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"norm2\"\r\n", - " type: \"LRN\"\r\n", - " bottom: \"pool2\"\r\n", - " top: \"norm2\"\r\n", - " lrn_param {\r\n", - " local_size: 5\r\n", - " alpha: 0.0001\r\n", - " beta: 0.75\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"conv3\"\r\n", - " type: \"Convolution\"\r\n", - " bottom: \"norm2\"\r\n", - " top: \"conv3\"\r\n", - " convolution_param {\r\n", - " num_output: 384\r\n", - " pad: 1\r\n", - " kernel_size: 3\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"relu3\"\r\n", - " type: \"ReLU\"\r\n", - " bottom: \"conv3\"\r\n", - " top: \"conv3\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"conv4\"\r\n", - " type: \"Convolution\"\r\n", - " bottom: \"conv3\"\r\n", - " top: \"conv4\"\r\n", - " convolution_param {\r\n", - " num_output: 384\r\n", - " pad: 1\r\n", - " kernel_size: 3\r\n", - " group: 2\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"relu4\"\r\n", - " type: \"ReLU\"\r\n", - " bottom: \"conv4\"\r\n", - " top: \"conv4\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"conv5\"\r\n", - " type: \"Convolution\"\r\n", - " bottom: \"conv4\"\r\n", - " top: \"conv5\"\r\n", - " convolution_param {\r\n", - " num_output: 256\r\n", - " pad: 1\r\n", - " kernel_size: 3\r\n", - " group: 2\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"relu5\"\r\n", - " type: \"ReLU\"\r\n", - " bottom: \"conv5\"\r\n", - " top: \"conv5\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"pool5\"\r\n", - " type: \"Pooling\"\r\n", - " bottom: \"conv5\"\r\n", - " top: \"pool5\"\r\n", - " pooling_param {\r\n", - " pool: MAX\r\n", - " kernel_size: 3\r\n", - " stride: 2\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"fc6\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"pool5\"\r\n", - " top: \"fc6\"\r\n", - " inner_product_param {\r\n", - " num_output: 4096\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"relu6\"\r\n", - " type: \"ReLU\"\r\n", - " bottom: \"fc6\"\r\n", - " top: \"fc6\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"drop6\"\r\n", - " type: \"Dropout\"\r\n", - " bottom: \"fc6\"\r\n", - " top: \"fc6\"\r\n", - " dropout_param {\r\n", - " dropout_ratio: 0.5\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"fc7\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"fc6\"\r\n", - " top: \"fc7\"\r\n", - " inner_product_param {\r\n", - " num_output: 4096\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"relu7\"\r\n", - " type: \"ReLU\"\r\n", - " bottom: \"fc7\"\r\n", - " top: \"fc7\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"drop7\"\r\n", - " type: \"Dropout\"\r\n", - " bottom: \"fc7\"\r\n", - " top: \"fc7\"\r\n", - " dropout_param {\r\n", - " dropout_ratio: 0.5\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"fc-rcnn\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"fc7\"\r\n", - " top: \"fc-rcnn\"\r\n", - " inner_product_param {\r\n", - " num_output: 200\r\n", - " }\r\n", - "}\r\n", - "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\r\n", - "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\r\n", - "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\r\n", - "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\r\n", - "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\r\n", - "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\r\n", - "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n", - "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n", - "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\r\n", - "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\r\n", - "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\r\n", - "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\r\n", - "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n", - "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\r\n", - "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\r\n", - "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\r\n", - "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\r\n", - "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\r\n", - "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n", - "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\r\n", - "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\r\n", - "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\r\n", - "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\r\n", - "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\r\n", - "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n", - "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\r\n", - "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\r\n", - "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\r\n", - "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\r\n", - "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\r\n", - "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n", - "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\r\n", - "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\r\n", - "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\r\n", - "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\r\n", - "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\r\n", - "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n", - "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\r\n", - "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\r\n", - "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\r\n", - "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\r\n", - "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\r\n", - "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n", - "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\r\n", - "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\r\n", - "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\r\n", - "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\r\n", - "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\r\n", - "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n", - "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\r\n", - "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\r\n", - "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\r\n", - "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\r\n", - "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\r\n", - "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n", - "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\r\n", - "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\r\n", - "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\r\n", - "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\r\n", - "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\r\n", - "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n", - "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\r\n", - "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\r\n", - "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\r\n", - "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\r\n", - "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\r\n", - "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n", - "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\r\n", - "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\r\n", - "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\r\n", - "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\r\n", - "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\r\n", - "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n", - "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\r\n", - "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\r\n", - "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\r\n", - "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\r\n", - "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\r\n", - "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n", - "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\r\n", - "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\r\n", - "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\r\n", - "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\r\n", - "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\r\n", - "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n", - "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\r\n", - "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\r\n", - "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\r\n", - "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\r\n", - "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\r\n", - "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\r\n", - "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\r\n", - "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\r\n", - "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\r\n", - "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\r\n", - "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n", - "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\r\n", - "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\r\n", - "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\r\n", - "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\r\n", - "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\r\n", - "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n", - "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\r\n", - "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\r\n", - "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\r\n", - "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\r\n", - "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\r\n", - "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n", - "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\r\n", - "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\r\n", - "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\r\n", - "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\r\n", - "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n", - "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\r\n", - "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\r\n", - "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\r\n", - "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\r\n", - "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\r\n", - "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n", - "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\r\n", - "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\r\n", - "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\r\n", - "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\r\n", - "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\r\n", - "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n", - "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\r\n", - "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\r\n", - "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\r\n", - "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\r\n", - "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\r\n", - "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\r\n", - "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\r\n", - "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\r\n", - "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\r\n", - "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\r\n", - "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\r\n", - "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\r\n", - "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\r\n", - "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\r\n", - "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\r\n", - "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\r\n", - "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\r\n", - "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\r\n", - "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\r\n", - "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\r\n", - "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\r\n", - "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\r\n", - "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\r\n", - "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\r\n", - "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\r\n", - "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\r\n", - "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\r\n", - "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\r\n", - "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\r\n", - "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\r\n", - "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\r\n", - "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "GPU mode\r\n", - "Loading input...\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Processed 1570 windows in 102.895 s.\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \r\n", - "your performance may suffer as PyTables will pickle object types that it cannot\r\n", - "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n", - "\r\n", - " warnings.warn(ws, PerformanceWarning)\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Saved to _temp/det_output.h5 in 0.298 s.\r\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n", - "\n", - "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n", - "(We only ran on one image, so the filenames will all be the same.)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "df = pd.read_hdf('_temp/det_output.h5', 'df')\n", - "print(df.shape)\n", - "print(df.iloc[0])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "(1570, 5)\n", - "prediction [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n", - "ymin 79.846\n", - "xmin 9.62\n", - "ymax 246.31\n", - "xmax 339.624\n", - "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n" - ] - } - ], - "prompt_number": 2 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n", + "\n", + "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n", + "\n", + "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n", + "\n", + "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge—no joke).\n", + "\n", + "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n", + "\n", + "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n", + "\n", + "-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\n", + "\n", + "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n", - "\n", - "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n", - "Simply list an image per line in the `images_file`, and it will process all of them.\n", + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: Logging before InitGoogleLogging() is written to STDERR\n", + "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \n", + "name: \"R-CNN-ilsvrc13\"\n", + "input: \"data\"\n", + "input_dim: 10\n", + "input_dim: 3\n", + "input_dim: 227\n", + "input_dim: 227\n", + "state {\n", + " phase: TEST\n", + "}\n", + "layer {\n", + " name: \"conv1\"\n", + " type: \"Convolution\"\n", + " bottom: \"data\"\n", + " top: \"conv1\"\n", + " convolution_param {\n", + " num_output: 96\n", + " kernel_size: 11\n", + " stride: 4\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu1\"\n", + " type: \"ReLU\"\n", + " bottom: \"conv1\"\n", + " top: \"conv1\"\n", + "}\n", + "layer {\n", + " name: \"pool1\"\n", + " type: \"Pooling\"\n", + " bottom: \"conv1\"\n", + " top: \"pool1\"\n", + " pooling_param {\n", + " pool: MAX\n", + " kernel_size: 3\n", + " stride: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"norm1\"\n", + " type: \"LRN\"\n", + " bottom: \"pool1\"\n", + " top: \"norm1\"\n", + " lrn_param {\n", + " local_size: 5\n", + " alpha: 0.0001\n", + " beta: 0.75\n", + " }\n", + "}\n", + "layer {\n", + " name: \"conv2\"\n", + " type: \"Convolution\"\n", + " bottom: \"norm1\"\n", + " top: \"conv2\"\n", + " convolution_param {\n", + " num_output: 256\n", + " pad: 2\n", + " kernel_size: 5\n", + " group: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu2\"\n", + " type: \"ReLU\"\n", + " bottom: \"conv2\"\n", + " top: \"conv2\"\n", + "}\n", + "layer {\n", + " name: \"pool2\"\n", + " type: \"Pooling\"\n", + " bottom: \"conv2\"\n", + " top: \"pool2\"\n", + " pooling_param {\n", + " pool: MAX\n", + " kernel_size: 3\n", + " stride: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"norm2\"\n", + " type: \"LRN\"\n", + " bottom: \"pool2\"\n", + " top: \"norm2\"\n", + " lrn_param {\n", + " local_size: 5\n", + " alpha: 0.0001\n", + " beta: 0.75\n", + " }\n", + "}\n", + "layer {\n", + " name: \"conv3\"\n", + " type: \"Convolution\"\n", + " bottom: \"norm2\"\n", + " top: \"conv3\"\n", + " convolution_param {\n", + " num_output: 384\n", + " pad: 1\n", + " kernel_size: 3\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu3\"\n", + " type: \"ReLU\"\n", + " bottom: \"conv3\"\n", + " top: \"conv3\"\n", + "}\n", + "layer {\n", + " name: \"conv4\"\n", + " type: \"Convolution\"\n", + " bottom: \"conv3\"\n", + " top: \"conv4\"\n", + " convolution_param {\n", + " num_output: 384\n", + " pad: 1\n", + " kernel_size: 3\n", + " group: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu4\"\n", + " type: \"ReLU\"\n", + " bottom: \"conv4\"\n", + " top: \"conv4\"\n", + "}\n", + "layer {\n", + " name: \"conv5\"\n", + " type: \"Convolution\"\n", + " bottom: \"conv4\"\n", + " top: \"conv5\"\n", + " convolution_param {\n", + " num_output: 256\n", + " pad: 1\n", + " kernel_size: 3\n", + " group: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu5\"\n", + " type: \"ReLU\"\n", + " bottom: \"conv5\"\n", + " top: \"conv5\"\n", + "}\n", + "layer {\n", + " name: \"pool5\"\n", + " type: \"Pooling\"\n", + " bottom: \"conv5\"\n", + " top: \"pool5\"\n", + " pooling_param {\n", + " pool: MAX\n", + " kernel_size: 3\n", + " stride: 2\n", + " }\n", + "}\n", + "layer {\n", + " name: \"fc6\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"pool5\"\n", + " top: \"fc6\"\n", + " inner_product_param {\n", + " num_output: 4096\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu6\"\n", + " type: \"ReLU\"\n", + " bottom: \"fc6\"\n", + " top: \"fc6\"\n", + "}\n", + "layer {\n", + " name: \"drop6\"\n", + " type: \"Dropout\"\n", + " bottom: \"fc6\"\n", + " top: \"fc6\"\n", + " dropout_param {\n", + " dropout_ratio: 0.5\n", + " }\n", + "}\n", + "layer {\n", + " name: \"fc7\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"fc6\"\n", + " top: \"fc7\"\n", + " inner_product_param {\n", + " num_output: 4096\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu7\"\n", + " type: \"ReLU\"\n", + " bottom: \"fc7\"\n", + " top: \"fc7\"\n", + "}\n", + "layer {\n", + " name: \"drop7\"\n", + " type: \"Dropout\"\n", + " bottom: \"fc7\"\n", + " top: \"fc7\"\n", + " dropout_param {\n", + " dropout_ratio: 0.5\n", + " }\n", + "}\n", + "layer {\n", + " name: \"fc-rcnn\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"fc7\"\n", + " top: \"fc-rcnn\"\n", + " inner_product_param {\n", + " num_output: 200\n", + " }\n", + "}\n", + "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\n", + "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\n", + "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\n", + "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\n", + "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\n", + "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\n", + "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n", + "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\n", + "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\n", + "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\n", + "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\n", + "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\n", + "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n", + "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\n", + "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\n", + "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\n", + "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\n", + "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\n", + "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n", + "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\n", + "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\n", + "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\n", + "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\n", + "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\n", + "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n", + "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\n", + "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\n", + "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\n", + "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\n", + "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\n", + "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n", + "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\n", + "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\n", + "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\n", + "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\n", + "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\n", + "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n", + "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\n", + "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\n", + "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\n", + "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\n", + "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\n", + "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n", + "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\n", + "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\n", + "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\n", + "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\n", + "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\n", + "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n", + "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\n", + "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\n", + "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\n", + "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\n", + "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\n", + "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n", + "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\n", + "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\n", + "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\n", + "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\n", + "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\n", + "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n", + "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\n", + "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\n", + "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\n", + "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\n", + "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\n", + "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n", + "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\n", + "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\n", + "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\n", + "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\n", + "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\n", + "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n", + "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\n", + "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\n", + "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\n", + "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\n", + "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\n", + "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n", + "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\n", + "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\n", + "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\n", + "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\n", + "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\n", + "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n", + "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\n", + "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\n", + "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\n", + "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\n", + "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\n", + "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\n", + "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\n", + "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\n", + "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\n", + "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\n", + "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\n", + "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\n", + "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\n", + "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\n", + "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\n", + "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\n", + "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\n", + "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\n", + "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\n", + "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\n", + "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\n", + "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\n", + "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\n", + "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\n", + "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\n", + "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\n", + "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\n", + "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\n", + "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\n", + "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\n", + "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\n", + "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\n", + "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\n", + "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\n", + "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\n", + "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\n", + "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n", + "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\n", + "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\n", + "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\n", + "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\n", + "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\n", + "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\n", + "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\n", + "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\n", + "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\n", + "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\n", + "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\n", + "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\n", + "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\n", + "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\n", + "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\n", + "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\n", + "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\n", + "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\n", + "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\n", + "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\n", + "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\n", + "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\n", + "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\n", + "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\n", + "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\n", + "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\n", + "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\n", + "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\n", + "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\n", + "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\n", + "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\n", + "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\n", + "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\n", + "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\n", + "GPU mode\n", + "Loading input...\n", + "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\n", + "Processed 1570 windows in 102.895 s.\n", + "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \n", + "your performance may suffer as PyTables will pickle object types that it cannot\n", + "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\n", "\n", - "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models\u2019 input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n", - "\n", - "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`." + " warnings.warn(ws, PerformanceWarning)\n", + "Saved to _temp/det_output.h5 in 0.298 s.\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n", - " labels_df = pd.DataFrame([\n", - " {\n", - " 'synset_id': l.strip().split(' ')[0],\n", - " 'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n", - " }\n", - " for l in f.readlines()\n", - " ])\n", - "labels_df.sort('synset_id')\n", - "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n", - "print(predictions_df.iloc[0])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "name\n", - "accordion -2.622471\n", - "airplane -2.845788\n", - "ant -2.851219\n", - "antelope -3.208377\n", - "apple -1.949950\n", - "armadillo -2.472935\n", - "artichoke -2.201684\n", - "axe -2.327404\n", - "baby bed -2.737925\n", - "backpack -2.176763\n", - "bagel -2.681061\n", - "balance beam -2.722538\n", - "banana -2.390628\n", - "band aid -1.598909\n", - "banjo -2.298197\n", - "...\n", - "trombone -2.582361\n", - "trumpet -2.352853\n", - "turtle -2.360859\n", - "tv or monitor -2.761043\n", - "unicycle -2.218467\n", - "vacuum -1.907717\n", - "violin -2.757079\n", - "volleyball -2.723689\n", - "waffle iron -2.418540\n", - "washer -2.408994\n", - "water bottle -2.174899\n", - "watercraft -2.837425\n", - "whale -3.120338\n", - "wine bottle -2.772960\n", - "zebra -2.742913\n", - "Name: 0, Length: 200, dtype: float32\n" - ] - } - ], - "prompt_number": 3 - }, + } + ], + "source": [ + "!mkdir -p _temp\n", + "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n", + "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n", + "\n", + "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n", + "(We only ran on one image, so the filenames will all be the same.)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the activations." + "name": "stdout", + "output_type": "stream", + "text": [ + "(1570, 5)\n", + "prediction [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n", + "ymin 79.846\n", + "xmin 9.62\n", + "ymax 246.31\n", + "xmax 339.624\n", + "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plt.gray()\n", - "plt.matshow(predictions_df.values)\n", - "plt.xlabel('Classes')\n", - "plt.ylabel('Windows')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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4HA4kEgncv39fMGZN05DJZOD1euFyuUTUFAqF0G634ff7YTKZ4PF44PV65X0F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V1fX1cul9PNzY22trZ0dnZmhgGqG77++muzMaH7WFm5c5Dxc6RSKYMbh8OhhTs6c6pJ0/+t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\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zj9o3yJlgigksRhlDLSvJO+twOLRhIT8b2RcRx/A0JJmOiWK90+mYlonMC3Sm0+lYiAAa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\nbDYNl93d3b1FMAI9oRnFS5mOneAaXh8km0qlYr5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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "df = pd.read_hdf('_temp/det_output.h5', 'df')\n", + "print(df.shape)\n", + "print(df.iloc[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n", + "\n", + "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n", + "Simply list an image per line in the `images_file`, and it will process all of them.\n", + "\n", + "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models’ input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n", + "\n", + "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's take max across all windows and plot the top classes." + "name": "stdout", + "output_type": "stream", + "text": [ + "name\n", + "accordion -2.622471\n", + "airplane -2.845788\n", + "ant -2.851219\n", + "antelope -3.208377\n", + "apple -1.949950\n", + "armadillo -2.472935\n", + "artichoke -2.201684\n", + "axe -2.327404\n", + "baby bed -2.737925\n", + "backpack -2.176763\n", + "bagel -2.681061\n", + "balance beam -2.722538\n", + "banana -2.390628\n", + "band aid -1.598909\n", + "banjo -2.298197\n", + "...\n", + "trombone -2.582361\n", + "trumpet -2.352853\n", + "turtle -2.360859\n", + "tv or monitor -2.761043\n", + "unicycle -2.218467\n", + "vacuum -1.907717\n", + "violin -2.757079\n", + "volleyball -2.723689\n", + "waffle iron -2.418540\n", + "washer -2.408994\n", + "water bottle -2.174899\n", + "watercraft -2.837425\n", + "whale -3.120338\n", + "wine bottle -2.772960\n", + "zebra -2.742913\n", + "Name: 0, Length: 200, dtype: float32\n" ] - }, + } + ], + "source": [ + "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n", + " labels_df = pd.DataFrame([\n", + " {\n", + " 'synset_id': l.strip().split(' ')[0],\n", + " 'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n", + " }\n", + " for l in f.readlines()\n", + " ])\n", + "labels_df.sort('synset_id')\n", + "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n", + "print(predictions_df.iloc[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the activations." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "max_s = predictions_df.max(0)\n", - "max_s.sort(ascending=False)\n", - "print(max_s[:10])" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "name\n", - "person 1.835771\n", - "bicycle 0.866110\n", - "unicycle 0.057080\n", - "motorcycle -0.006122\n", - "banjo -0.028209\n", - "turtle -0.189831\n", - "electric fan -0.206788\n", - "cart -0.214235\n", - "lizard -0.393519\n", - "helmet -0.477942\n", - "dtype: float32\n" - ] - } - ], - "prompt_number": 5 + "output_type": "execute_result" }, { - "cell_type": "markdown", + "data": { + "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "The top detections are in fact a person and bicycle.\n", - "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections." - ] + "output_type": "display_data" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Find, print, and display the top detections: person and bicycle.\n", - "i = predictions_df['person'].argmax()\n", - "j = predictions_df['bicycle'].argmax()\n", - "\n", - "# Show top predictions for top detection.\n", - "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n", - "print('Top detection:')\n", - "print(f.order(ascending=False)[:5])\n", - "print('')\n", - "\n", - "# Show top predictions for second-best detection.\n", - "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n", - "print('Second-best detection:')\n", - "print(f.order(ascending=False)[:5])\n", - "\n", - "# Show top detection in red, second-best top detection in blue.\n", - "im = plt.imread('images/fish-bike.jpg')\n", - "plt.imshow(im)\n", - "currentAxis = plt.gca()\n", - "\n", - "det = df.iloc[i]\n", - "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n", - "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n", - "\n", - "det = df.iloc[j]\n", - "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n", - "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAALMAAAOoCAYAAACa7cU2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvUmMZel1Jvbd9+6b75vnMV5MGZlZmSlmJYulEkWCpIQ2\n", + "e+O2V+0GBBE25AXhCTQBEe2FbEAbw4AhayFIC9NA24s2BAluSAuBLUEDSIKqYjIzq3KKOd48z/fN\n", + "0/Ui8juMYIsSkZESuwJ5gUJlRUW+8b//f843HcUwDLy93l7X4TL9vF/A2+vt9aaut4v57XVtrreL\n", + "+e11ba63i/ntdW2ut4v57XVtrreL+e11ba5rsZgVRfmqoij7iqIcKYryrX+k58gpivKJoiiPFUX5\n", + "6NXPAoqi/LmiKIeKovx7RVF8V3j8/0tRlLqiKE8v/OynPr6iKP/61fvdVxTln73B5/xfFEUpvXqf\n", + "jxVF+edv6jkVRUkrivJXiqI8VxTlmaIo//0bfZ+GYXyq/wFgBnAMIAvAAuAJgFv/CM9zBiDwEz/7\n", + "3wD85qs/fwvA/3qFx/8CgPsAnv5Djw/g9qv3aXn1vo8BmN7Qc/7PAP7Hv+N3r/ycAGIAPvPqzxqA\n", + 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CKjRVWzPaXKeal4TgOZuNMVKh8oy93V3Qmv76CKUUa6M1nh8dMlpfp5cXjCdj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1y8chlzqcXWZgcjAykhSALe9oY38ofPPMdxWxDtdBmtbnH+sXv5zPZnqM+7nDtxD8P5Rf78S1/k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b9V2kEn/LVRlfCHm4HigqWxpN25ZTPfBB6aa2Shz9LGXRqAbLnFg+itBzKDyLY53z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fXVV/mVf/dLHB0d8a//1b9iZWWFH/6hH6Lb7dLr9Th//jz9bo9WENJtdxCVvm/nHnqExx57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\nYAMcO3qUM6dOc2xnh9lsJnn11Eoj1NYWR49sybXHk0vb1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- "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, + "output_type": "display_data" + } + ], + "source": [ + "plt.gray()\n", + "plt.matshow(predictions_df.values)\n", + "plt.xlabel('Classes')\n", + "plt.ylabel('Windows')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's take max across all windows and plot the top classes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows." + "name": "stdout", + "output_type": "stream", + "text": [ + "name\n", + "person 1.835771\n", + "bicycle 0.866110\n", + "unicycle 0.057080\n", + "motorcycle -0.006122\n", + "banjo -0.028209\n", + "turtle -0.189831\n", + "electric fan -0.206788\n", + "cart -0.214235\n", + "lizard -0.393519\n", + "helmet -0.477942\n", + "dtype: float32\n" ] - }, + } + ], + "source": [ + "max_s = predictions_df.max(0)\n", + "max_s.sort(ascending=False)\n", + "print(max_s[:10])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top detections are in fact a person and bicycle.\n", + "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "def nms_detections(dets, overlap=0.3):\n", - " \"\"\"\n", - " Non-maximum suppression: Greedily select high-scoring detections and\n", - " skip detections that are significantly covered by a previously\n", - " selected detection.\n", - "\n", - " This version is translated from Matlab code by Tomasz Malisiewicz,\n", - " who sped up Pedro Felzenszwalb's code.\n", - "\n", - " Parameters\n", - " ----------\n", - " dets: ndarray\n", - " each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n", - " overlap: float\n", - " minimum overlap ratio (0.3 default)\n", - "\n", - " Output\n", - " ------\n", - " dets: ndarray\n", - " remaining after suppression.\n", - " \"\"\"\n", - " x1 = dets[:, 0]\n", - " y1 = dets[:, 1]\n", - " x2 = dets[:, 2]\n", - " y2 = dets[:, 3]\n", - " ind = np.argsort(dets[:, 4])\n", - "\n", - " w = x2 - x1\n", - " h = y2 - y1\n", - " area = (w * h).astype(float)\n", - "\n", - " pick = []\n", - " while len(ind) > 0:\n", - " i = ind[-1]\n", - " pick.append(i)\n", - " ind = ind[:-1]\n", - "\n", - " xx1 = np.maximum(x1[i], x1[ind])\n", - " yy1 = np.maximum(y1[i], y1[ind])\n", - " xx2 = np.minimum(x2[i], x2[ind])\n", - " yy2 = np.minimum(y2[i], y2[ind])\n", - "\n", - " w = np.maximum(0., xx2 - xx1)\n", - " h = np.maximum(0., yy2 - yy1)\n", - "\n", - " wh = w * h\n", - " o = wh / (area[i] + area[ind] - wh)\n", - "\n", - " ind = ind[np.nonzero(o <= overlap)[0]]\n", + "name": "stdout", + "output_type": "stream", + "text": [ + "Top detection:\n", + "name\n", + "person 1.835771\n", + "swimming trunks -1.150371\n", + "rubber eraser -1.231106\n", + "turtle -1.266037\n", + "plastic bag -1.303265\n", + "dtype: float32\n", "\n", - " return dets[pick, :]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "scores = predictions_df['bicycle']\n", - "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n", - "dets = np.hstack((windows, scores[:, np.newaxis]))\n", - "nms_dets = nms_detections(dets)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes." + "Second-best detection:\n", + "name\n", + "bicycle 0.866110\n", + "unicycle -0.359139\n", + "scorpion -0.811621\n", + "lobster -0.982891\n", + "lamp -1.096808\n", + "dtype: float32\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "plt.imshow(im)\n", - "currentAxis = plt.gca()\n", - "colors = ['r', 'b', 'y']\n", - "for c, det in zip(colors, nms_dets[:3]):\n", - " currentAxis.add_patch(\n", - " plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n", - " fill=False, edgecolor=c, linewidth=5)\n", - " )\n", - "print 'scores:', nms_dets[:3, 4]" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "scores: [ 0.86610985 -0.70051557 -1.34796357]\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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w7PUhxMaYuqqRUnLj+nUCMByOGK6N2NjaYDQa8elnn6G1xnrPlhCMJ2eoPPKE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W6Ho01Mx7k+XnVkV+t3SCYqsljKn0N1IirrMAUn5rlqgjX9XHPiMIMFTBk9Smb176Zcuv4ryPPp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wenNsjbnpcteZYHjsvsSnvQ2N63I73aBJkm6Ge5gS0tZwG2//+gtcCoL8R7bNYSxS5SGPqx\n0hU8iMujTbAXUnB66zSvvv5GI+5VFAVh3GJ/f59ut8vNmzcpi8z0Ln23ISklSUqru8oju4/R6nTY\n37vP3Xt30Ag2T59hNJriuyFZqcm0aR3kaVq3NhSX336LZ595FgUkyZTPfPunKMuM3bO7FGVOlkzR\nZYEr4fULL5khpRfiCchmU6bHx6ysDLh9+zaDwQDHdYnbbV599VVzCLuG9OPXqAGtFU899WTDN1BK\nsb9/0Gjh+L7PaGSesZ0zZ5nN5kjpMHzA5fvzf+7PgKWmq4rjg0M86RD6AVmaMhgMyLMM1zHGtmC0\nuhECx/fo9QbNPRbCYK0NVtkzZCchkNq6oM+ZTqesrKyyu7vLlStXkFLS7fQYDkekeY7j+Lx04RWU\nKuufpcmyHN8xSUZZaFxXMksyynKOrBOKPCsa0pjveTy6s8Px0ZC7d+8xnUwI4xDhebz++ut89+/8\nnbzy8gUuvvIacRSRlhUrfaP/ffX6NZIk4fHz5/nIBz/EB597juHxkI0VY9239ZkzhhXbBEtBkiXM\nZnOS8dzMOeRCatURRsZBIDi6f4/ACSio1Qvlw9EYYatDv9vD88zerlQF2qmJM7oeTC7giVaIylTD\nFVVVGvcfYRjgllwE4NTuREBTTS8Lzy13ArIsPeEL8E5zF8sePtHGgqbKt8iV6XTaxJuH2Sva9Z4H\n8HfipbXWDTLEOF8HzYWwfV/7ywohGmlXm7na/rDFeVo8pVUdtAHZ84LmvZdvhg269hS0mE57Ii4P\nFWym/M6Bqj0kbO9rURqagG/75VKKOoib94/juDkYHrSWtRNM9eE3rSVzyCwGOQ9aYRBQVIYVubOz\ng+sarROrFyOEIC8Vh4eHfPnLX+aP/dd/lIP9Pa68fZNzj+3SbrWYpV2QLkprfuPFl+l2O4zHQx57\nbJfRZM69+4c8tvs4+wfHuG1JO26RTXO2d7a5ev0KW9unEI42eiZCks5TPvvpb0PlFXEr4itf/jd8\n5rOfYTIZG3u8UjEbH7O2sk5RlnjSmOSeO/cY16/f4PrNG7z2+usGxYCRVnWAsj60qyrn0d0dhsMj\ntLZ6HMaVxWZEvu/jSEkyTXClw2j04B74eDyiQtFqtanynFYcN/hm3/cb04/A85vnxnVcNIJkntXP\niGEbxu1WHcgdXM+lqApjVKLqeYkXGoOF2Yhnn32Wnd1HOTg4YD6f8+hjuwxWVjk4HPGVr3+TVhSg\n8oqqAoFDu9tnPp0ymyW0Yo8waOG3PZSuGA6H9R5xaiVIxyBP/IALFy6wsjLg9PYWmSo5c/o0WZJw\n5tRp3nz9DXpnWqy0e3jS4Xh6zDe+8RuEQcDZzdPEccyX/vUv8qlPfcrA73oDDu7uEQQhSVIQRiGj\n4YgwjvBwaUURaZqgVEW320JoqCqj2SKkZLXb5+DudYosrZEuD89Eu93VOlM1Tk+OY1uvugng1rbO\n7KOFOqBJ8hyEXnA0WAr2eVE2Q0abhS+CvzqReC0PI63QnM2+l3HkjuMwHA5PJGr2dfY9bAJqlFcf\nvt7zAO4HIVVZIaSgKHJUpXBccxO6XXOT89wILtkg6brGNdrzAlzXRwhJkqa4rjBtFKXQdpgo6qxM\naSqlcJRGC2kgZNatQxotYyEX+sHLmVK1pIho9BOMC7fjyBPoElljdW0PPApDyso8VHl9oORosrTG\nkEuB0tYDz7iZpGlyYqq9vJLESHkuH0RWoMc+WBYH+6Dluh6OK41ht+uysbHO1WvXcYKAsqpASAaD\nFfb2D3n/U0/xD/7BF/jkxz/G4+fOkyYzZlrjxDFFVnJ4eFQP5DSbm2vEccj9vQNOb21TVpq1tQ0q\nx9CTx6MRO4/s8NLNm2xvb3Pzxg3ObJ8xB5pjZh7Xb9zm7CNn2XnkUa5evcZgMCCO4lpbGm7cvEGr\n1abX6zGbzpjNUtbXN8iKkkuXr7C/f2jcxIWRS/Ucged46LTk6aeeZjyZEIURUkiCujdqrolDmqQU\nypTQeZHT6bSZPiAF7/d7VLWejuP5pEmKdBzTWwUmkwmddqdGTJmD2/ONzEMUGwKLUkajYz6fN+1B\nhcnutAJVlLjSRWnFeDxisNLn+HiIHwSsr22YmYXvY2RbzaDeVo+u6zY4dM/zWRmsIHBwpFNj7z3W\n1lZxHJfRaEhRFniOy+W33mbrzBmee+5DBGHIaDKi02kzOh4SBCHPf+xjxFHE5cuXOb21hR/4DAYD\n1lZXObW5SbfTJc8LHj9/notvvsn6+hpaKdpxizRNEUIym04YDHokaY3uEEasLgoChBRIqzyvoVIl\n0+mYvFKkRYl0XKTzcDRGnhW0WyFpagK0rZQt+AAWJuOu6xqCkU3aqHXKCyMx63ou1plHCONCZBAy\nqtZSWbRNFuxOC/FdmDDYXrtFx9mATH2vbbK2nDw+DF33bus9D+Cu46NVbvpPjo8fehSlYVS5jslE\njA5xTlnlhGFAXpa4jodKU/MrCEWea4LAZGeeY3WEDZjf9Xyk6+JKYaiywtDNtdKEYYCmbi04AofF\nRS3rVozrOA30qShKYNEqEYB2JLJ2rjaEo6q2GTE477yqe9y+azKf+rVaCDw/aMoyKcWSMP2Dlunb\nL2spm89ULAVvaSjFD1hZmqPQII2Yz/d+z3fx1/7aXzcloJAIx2Oe5Egn5LXX3+IjH/oga2sbhL6L\nK8BzHW7v3SMMQzqdAOlU3Ll9l1ObmxR5STpP6Hf6qLLEky6qqLh18yZbm5v4jkMUhMShIYCUxWLC\nLoWk1e0Txh1W1pwaG6uZaYOJbfdCvMhgdt++cYPTG+ugoSw1L770CmHcYzS9TavVNvdbmn59nqX8\n8A9+njCMjMtQ3X80sw23ZpQWeJ7TVF5h/DAZAsjSxZxBIJHSRQpJnpnhlOcFKA2ivv6OdKgqY0tn\nIaPSte7tC915RwiE4xrUQrBoi8Xt2GT3QVwHC4nvmedWaGhFYU3aqrNHx8H1zBCwzHOKMiXyAzxH\nICMPJQQ3b90weO4ootftozU88eRT9fPnMh4bopCDIApDw7Idj4m6bb7re7+bq1evkuU5WZryyPY2\n+/v7HE9HfOhDH0JKSX99wHQ65frtG/hLgcu0DDw81yeKI1qyZRAi0WKwKCTkaUan2+XK9esoN6CS\nPiUKqR8ewEPfJ00XUrZludDXfycfJM9zfM8wiW0AdhyHoizqfbDYW1VVMa1VRI0bUdxUq7Y3bVQk\nRU0WCk+wKW3wtlm3xf/bYL2scGgTReuhC7wrIs2u9zyATyfj5oE2SjOaMPRxXVOOFLk5SaM4qFmM\nBWVlyDxlVYIukNKts+8cz3PrrNxYI1VVSZnnlHntTuJ6+H5AlZdUWpHWkrBKKULPJS8KpGN6ydJx\nqNAUlcaRAscx5Z1WBmsdxbGB9umKSpvSuCgVWjgIabJ/PzAkpKqeQCV5PaiRDkot0CRWS9lCJx+0\n3knFFeKk1KV9YK2LzjvXYDBgNp+jhCYrC06tb/DE+fPsHY0YjSZ4Xk3j14K0KLh08RJUBZ//ns9R\nZDmzWdH061ZWVrh96zZrq4O6B6hZWenjehLpuziOoEwNNM1xHK5evcrGxgZFUfDoo48ainndSprP\n58Z0Ok8IQo/9g30eOfsI9+/fJwhNwPJcl26ny8bGBpfevMTaxgbXbt7kcDjk9Tcv0u8PiGOjeJgk\nGUJo0IoXXniBw8PDE60w+75As6Fs6WrbZg9admM7jgNa4jgLUtcyGsHeq+ZvYeQN3olUsFCyZmBW\nl9jL6nx2oy/rRlu5Cc8P6+em3eD/qwpk4NBqtwxNvjcgJ+doNKLd7bK9vd387DwvcJ2F5V+eG9Nv\nxxXkpcFGTyYT3nzzTT7xbd/GL37xiyRJwu7uLvP5nPe///188pOf5Bvf+AY3b94kiiLW19eRUnJ2\ne5uiKGi32w2kzigqzpqqsSxL7ty5Y+JAjcrIsgyJYDqb4bdilNJ1Jv3wQFYUBUqfHBQ27RB7v1jw\nKOz32UBcFEUjB51lGVmWNRBA6yQENOAD++wYdyndAAqUUoxGowbssPxM2GfPylLb+20/nx18wgIZ\n95sJWcFvgwDebrdPDAnzPDtRgkQtowWeFwUIiFoxus58zdBB1w+8IqofkmQ+BUEdrM0AZT5PKEuF\ntUEKghYISZaVSEciHY9ZktXCOibgSsDoCTtG0EkLpKrdq4VheGkEQnoIbTYQdQZcKUVZt37MUMVA\nlzw3QKONO7syLZMgCClLhefZTfjwh3WZ9WVLLPs1+2A+DPw/Ho+N07yoWwezGT/0g7+bv/l3fqo2\noSgQGC1whGSaJhwOh/zyl/8tO2e3OX/+MSpKZtMp9+7cpcjzJhju7e2xuXmKqipxg4BKmQxnY2OD\nOI6ZTCaUZdm4m1hyVBybLHM2mVHkGQLNqVPrHB7tc+r0Bnfv3iUMQwaDFUajEWmWsHF6i6/8+le5\nf3DAcDwxwlFhQJImGC0LqIqC7/v893Dnzp3mPW2P0h6ayz3L2WzWbJp305+wrSvTlkgbjQ4rCBZF\nkZEx1RgJiKpCi8Wsxd5Dy+RdJoKZDG3hj+g4Es/zm9cahxtNFJngErbazfcbZnJOp9OiKitaUcyb\nb7zJCx9/ARyHU5unKJYU7yxEzpGLgOR5LlVVIB23OfTeeustzp07x8HeHt/5nd/J7du32dnZYX/f\nUOZv377N1taWgZwqxXA4bFjNRV4wmUyYTCY8+eSTzfxngZ9eGJJsbW2RJElzsH/1a7+O1oqiyHE9\nQ7F/2ArCgCxbNoJYzLIskMEOIF3XNXILSxBjoD5cJs3nWj6ILcxPa93o+ttrZp8tq8e0PJOy19lW\nxvZZs1m2/X/L+G+LYPv3NTV+d5T4f4A1m6fkRYWQ0qio+RFhEOP7EQKHojAwMaNl4JFlOWmaUVYV\nqh7IOVLie25TrnmehyONk3yapIyGI2PTFgREYcTKYKVha7bahnIdhiGtOKqzI9VscMdx8Go5TV1j\nsMvCeDemqTElyPOibn8EtFpt4jgmjiOiOCaIQoLQ/NFakxfGh7MojaWZ6y40VEAY/HL+YDnZPM+b\nib0tt2ABV1zOyh+0NBVRGJKlKbpS6FIRByHPPvss+3v75n7UEqFxp42QLm++9Tb7x0OCuE1WKsIg\n4PTp0wgh2NjYIK01ZSaTCXmeAYrZbEJZFgyHQxzH4eDggNXVVSM+lSSMx+NmCm99TitVNggOtOLW\nrRukadIEhvFkjB/4tFox0yznYDTm1p27jRyxUY2UJPMpge/QikKe/+iHm+G1FUqzyyICliuaVqtF\nFEWN8cY7lxCikVbIixzHdWi1YqIoJIpC4lZkkDRZSl5kKFXhuEZrxoqzWZSVzdDs+7Xb7SYLjKKQ\nOI7qTWy18asGsTGdTmppYGqnJ8P01TVjMau5CZPZjHani9JQLmXySqkGtnY8PDSf03GagH10dMT7\n3/9+Xn31VT74wQ/yxBNPENb65hYW+xu/8Rs8/fTTTTD8tV/7tROgAc81rYOtrS2ef/55Xn/9dS5c\nuMC1a9eaZxYWGkOWhdhqtbhz5w5hFGElIUy2/PBQZcwpFqqB9t8W/WJ/bwsu6Pf7dDqdRm/GBnsL\nfFBKMZ/PGY/NMNse+HEcn4AKh2HYCIIdHx83v4v9GVYHyUIm7ed7J8NyuSrLsuyEJ+9v+xbKS6+8\nRq/Xbcov13PR9SDSkRKnkhRlTlGYUzgM2gihKcq8GS6CKWN0LSXpYpEaFQhBGIUIxIkNK6SFWmWN\nGL7nefSDjpH+XCqnVGWJAF49pa5x5ZUCXRmYkjb2b8lsSlGWOK4J/FVlgr0pnTzQmsA3KA40eJFx\nbbEnfbcbPFTPe1m03pbbNjOwmQfw0FM7q7OvTsuUtdKTFFrzPb/zu3n5xZdRqqKsCjzPbTDwYavF\nW29fY55kPPP0+/j4889y6dXX2NjYwBAnfO7evcv29hmMfZlpsezv77O2ttY8jJPJpJEl6Ha73L59\nuzlEtda02y0O9g9qrQmXZ555ppFGbbXbTKczRqMxk+mUX/36BfIiZzKdNwzSUpVk4zmuFBzs7/Hf\n/eiPkmVziqJqZF3DMGxYu7YUtoe+LWvtBnvYaiCAdTa1XPbaQLE8j5BSImp9DaB5T6DZoPYwq+qB\nt2n/LQwOLAw2TdOm1QOmovrsZz/Lv/t3v1r3WCtj+BC30BgETlEapqnhHEQnKjfb151MxzWBzufU\n6U3m8xkvvvgiL7zwAuvr6w0qzA4CL168yLd/+7cznU7Z3Nw0xhXtNkqpxuQBZVBV+/v7HB8f84EP\nfICDgwMQBmJ4cGCy3cD3KevWRhAEDFZWODg6Zp6mBKGxTPzNWgnm3uUnyHA2S7b3ehmq27Sgam0h\nmwlbDLlFhNlAbJ9R+3wsB1zbkrNuRBaNYiss+9/LAXs5y7dVgT38Gob1Es783dZ7HsC/+vUXUbqs\nyRA50nE4c+YMH/3oRzizdQZX+nheSKsVMx6PGU9ShNRIKfCkh+s6BnivJMLVOK5vxOQrjeO7uEI2\nWYFbZwVlUSAwveKitDR8E8zTpGxulOsanebF5sspC4NWMPAzg4ZRSuHWg6vA90hzky0r7ClaMpsk\nzUDDBoiiNDoP9sGygxD5ENKCzQAtEcRmkeazLYKH1W9451pZXaEqS1PeKw0KQs8jFYIf/x/+e/7i\nX/rLxGFMUZVmWIYmiCKyJOXNt96m0+tz7dolPv1tn6Ld6RtauuOSF0bm1DqoKKW4c+cO/f4qs1my\n1OaxWUZBv79CVZn7WBQVcRgwHk94dHeXeZLgeyFlNSNJc6PDMRhwcDTil3/l30LU5eq1G/QGXQLf\nYzI1+iZCQJok/P7f93tZXR2Q1LK5dkMopRpYp+3LWocYW24vK1a+c9nNZV87nU6ZTqcnsmo4mVHZ\n79fKbMbAd9HaIB3s9yrPQauKMFh4pkqx0MzwbHCvD+wmk0byue/6HL/yK7+C43gNdG4ymRLHLTwv\n4OhwyO5jO2YA/NZbzQyi0+k0JDWb+V6qpWCTBKajCVunTjM8PiYKI5KZcZ3au3efXq+H59SfyXG5\ne/cuzz33HG+//aMHrRYAACAASURBVDbnzp0zvfvab3ZlZYUkMYJQq2uraODu7Tu0223iuEVVVHiu\nxu8FZHnBr//613n94pukeYGiREjRtN4etkQNh7TZttYLUxOb6S9Ls2pVNq00G4TtIer7RpbWZtdJ\nmhLHcSNidnR0RL/fbzJxW9kVRcHx8XFdfccnDgb7Ho3NnTBOPfYzDwaDJjZY6LFlZf5mh9d7HsDP\nnDWT7LIscTyjWXDr9m329vfJ85zNVcMY29raotPuNNha6UikANf3CUNDby+qFI2m3WrXZgzguS5h\nEOAFZtqttMbxPMoiaXrfqlLMZxlFkSOtu0lZURX1cLVOyBzhINwFezPLzNAUBaWucKTTZJxBEBhD\nVCFx43ZzI6uyRFUVruMShS5eEDXMriAw8LCHsa9s/3VZLncZZ26/5kn/ga8fjcemFy8dcwghKKsS\nEYQEruRz3/WdfOEL/4jeyqB52JUyfb5Wu8Orr7/B9uk1vvbiBT7hR3Q6bYbjGX4QAw5B4DMeT8jz\ngqdqZEO/3+f69etsb28zHo9J05R+v49SiosXL/KBD3wAgKPhkFanQ5qWzJMcjcd0mhBEHYbjGRcv\nXuIb3/wmAPdv3abd6YIyBgG+65EmMzwpePzcLk8+8QSqqgxOW+ulg3GhHGdISQvGq82grATog9ay\nkNhy+8NuZMsgXuYN2IzX/tuW43bD2p9nbfVsC2A+nzf32fqdWqauXXme4foe7XaL8XiC44TkuQl2\neV7Q7vd5++oVxpMxp05t8uSTTzYVgM1Qh8NjQHB0dMTa2irz+ZR+v8f6+lpNHOo27Q17yJ05c6Z5\n3jzP44UXXuBLX/oSu7u7XLp0id3dXQPjqw+bOI45OjpiOBzSG/RpddpUZUWeZk0LMc9zHOmye/4c\nr775Bq12m2Q+PCGt8bBlD+HlNqIN2MtyrnZZ7L/lZiwPIpsKfaklYwX2gCazXrZWs8ne6dOnm6C8\n3AZZfl/7HLRarRPy0nmeN0NtK+Bnf493W+95AP/O7/isgfDUJ+Lx8TE3b95kb+8e89mE27dv4PsB\nw+ERCAcpXUMjroNcUZYG5icFji8p8hw/qE/HwmQkfo39REAcxayurFDmM6qqpNfv8eQTT7K5uYn0\nQqIwghrbqVTNUkyLuu/l1nhxgxsvi4IsNzRpx5bO0gMtqZRCSEmlocpKKlXhOMY9HSEpKg2VIitm\nTfukqhRai4fKydqHwaITyrJsJHVtdlhVVe0F+K2rgcAJq/hmWj9FmpLlOZ/4+POk6Zx/9a9/gTBu\nIR2XSiuiVkwyT3Fdj/uHQ0pcvvhLX+apJx+n3+uytblh8M55xurKJgcHe/S6A5IsXWK+LhhnFqO7\ntrbWlJhf+MI/5kd+5Ed4++p1Ht3dJS9KVjdO8/M///NMZnOjczKeURQlUatNVRbo2oVICoUuKwYb\nq/zhP/SHSJM5oW9aUVrIE3INy9dieVBkg++7tVAsUsC2guzBuYx2aJi3S0HAZv+e5zVDX0NSi5p7\nuiyiZstyi+23mbJl6dlyv6oqptMpu7uPcvXqdaOkKCyTMyPPS46Ph3z+85/n6OjwxHW4desWruvS\n7Rp5VNPLhvX1da5cucLq6mpjym0157/+9a+zu7vbBEwhBEEYMB6P2dnZQUrJJz/5SV599VV6nW7T\n90+ShH6/33jHSinxQ48yL5hOZ3S7XUpV0R/0+MrXv0ZRlvQ6baaTw6Zd9LA9Ye5L1Tjs2ArKXlsb\nAJfx2WVhDoNlmrvNkJelXe0A1O5P2+ZZblsCzfsukxLtH3s4Lwvd2efDHjL2fWzlUBRFIz38255K\nH/kOkWc0gduRx6n1Ps8+/QRxbEom6UgOD4946aVXuLd3wPB4QlYURrHJcZD2b8chVyXSj8nKCrRC\nSoMLrbTGrzOX4XjO8WhGVsyRUlDduMNXvnEBUffPtdKEvk+71SZuxXi116LVWQhD086J4oiovplG\nd0HgeUbbpKo1vrEIBSHrMswI5UdRROD7hIGHK0Rz4nq+h+NIkuTBrvQ201s+yZeFdhZlpuZo+q2v\nNxrihqVYlTm6qhAapOfjOxJV5Hzf934v9+/v8eKFV3BcD8+PGA6N9kaRFwhHcvPOHhtrK3z5336F\nM2e26Hz623Bdn053wN79+3huSJ6Za3Djxg1Onz7dZBlWj6SqKlZWVho96uksMSbJeYVwPPbv7vPq\n669zb98wKN+8dMXQ6oMIoSzbcU5VFAgJvW6HP/7H/hvKoqg9F42KnV7KhJeJWbBgttrNtNx/fNiy\nr4mioGmR2CDguhIhLCzPmjVAFIVU1eLAtQFjGb9vg7fxSJQ4jofrLhQurWZHEHhNFh5In6JUnHvs\nHNeu3TDtIm30s8vS/I6T2Zyvfe3rPLpzlv3JGKUM03ZnZ6cZ3NmKQ0rJnTt3OHt2m6paOMgEQcDB\nwQGf/vSnuXjxIrPZjHPnzpk+emp03WezGVEUcfHiRdbW1jg6OGxePxgMGA6HdDod9o8PiIKIKAgJ\nPJ9er8+tW7fp9vvcunObl15+mbDV4tr1m6x0feI4NtoynQcbTQP192SNqcryPMLi7ZeHgXaoaAOq\n/d1te9J+r6qhvzbhWNZJst9jM/bAVvhL72Wfs+V2y3Jfe3noa/e0rTjm83nz+d9tvecBXBQZrueg\nigKhFQKXqsyZpCZDJjCwtlNn1gnigDS7QjrKjMN8afrcCsjyjFIJfN9s3qosmw2VFSVZVisEuq4x\ngQ2kMcFVFW4U4dc3vypLhHSYZIpxOqlvdNlsVltyCalRhTE6NjdOE8cRUormMChyI0gfBL4RsVdG\n09y0eFykquh32qysDNjYWGfrzJb5+kMyQJttL0+0YcHYsrhU/ZDJdV5kCBxUVTVmzGiNrgqSLMfx\nPI6qgh/6oR8kbrf5hS/9Mp2eg1aCJEnp9fsU2hBHDo7HeI7gzp17/It/8a/otlus9LuEnsenP/Vt\ngMl49vb22NraarC1Vu8mz3Pa7TbD4ZCyLHlk5zGyvOT6zVt88+VX0EJy9949hqMRVQWDlTXS3EDc\ntCpJ8xRVlQitKfKCP/Enf4wwCJjPp4R+SFEYLYqiZs7ZzQc0GZTdQECDrrAl8IOW/X+2NAdOZODL\nWN5lSKc1z7WDUqDBHFtEw6JcPok+WC7x7XvawF8qw1HY2toyeuZxi1liMu9Oq839vT3WVld56cIr\nBL5Hv9/FdV0eeeSRGkbnMZuZwNfv9zk8PKyJZLrBZVtd6larxcWLF+l0Oty7d69BQdl+r9ZGoOn4\n+LgRmLMO7xa9kqYpg/6ALE2ZjCe0Wy3KsuLs2bOkWcGvfuUr9Ho90rLC8QzbuWlFqIdbqk0mE4we\n0mIQaO/hOwf6SilKvSBLNV97R2BdbrXZoD6fz5uAbRMRO4hcViVdbr/Yn7E82LYqpfbeW8LP8nC8\n2zVmG7/tM/CqSJHaqRUiDLOpKEujmNZqMS5mqEox6HfZOr3FmTPbTKYpb158i3t7e+SlQYF4vkM+\nyyly42Kvhbm4gR8QuKYf7sg6A1WKUgu8sEUoJdZjUigJwgMk0pUIjIawlC5IDRJ8P6zpvhWup80Q\nFINMmaYFvmvozUKYz1UUJbN53pTDWaGZzEwg9oTiDga2qHRlBowCNjc3+EsPuFZHwzFhEOK6AlUV\n6JpW77le0+t1PRcJTB6QRJZFBcIcfFJKpGtIJgZGJqlUhS4ULpIf+t0/QJbl/OqvfZVubwWNZDyZ\n4ngRvW6X2WyC1pK9/QP27is21lYZjyYks6lpg+zssHt+m/E04f7+EUoZIbGD/X3W19cpy5LZ/JD9\ng2OGoymT2Zy/+bf+Nq7n0e50+eaLLzJYWUFKl/6gz6R2Vk+zDFGlRnJAVaz0O/zFv/CTHB0eME9S\nWq02ydz02dM0pcqrBRqkzgiXA2QQBAtoZoMweXALypEOValwHRch7UBzubUlm9J9+T2aQZQ23AT7\nGosZNsHEfDalF1LJtj8spfGs1Frj1UgoR0ryuRmMn9k+w6OP7nDj5m1czzXO8FGIqqomGO8fHnLu\nsV2mMzN0VVXFZDpF1XOKNEnIs4xHdszA88yZLfLMPLdXr15la2uLnZ0dkmTOYNBHCMH6+jr7+3v0\nV1bMgDgImIzHdLtd+r1eU3FIxyEMA1rtFlmeE4UR03zCdDolimP29veYzRNu3brFPM3wo4hBr48n\nM2bTKZ1ulyiKHxpDzB4w3AtLgwersW+qIHsti3LhNwmLQ9ketk1LZan/vYwusfc2rVm5y9ol9gCw\nB7m9d7ZVYysDiyYqamKQkKJBI9kAbqW1f9v3wN3QI6/L19k8IYpiKiFQjsMsy3GFgysdQseBqmSl\nFbDajnjq0c8wm81IspTpNDGMvkozPD5mOJqwf3DA4cERWTnH80Nj0+T5FLbc9QVK55RFrWfgiHpj\ngXCF+UyOix+H6KqqBW4sS0oj3FqZ0BVIZ3Hiq7JAKEPXRwuE9I2kpRZoBApDFNJCoKVDYtEjVFTS\nZGq3Rw8e2Pzdn/vXUJNDpDDY98D36PX7BGFgpAAciUTxsRe+9fWT7PtOfuFBs7oKKGA6h+/4jPnz\nW1mPnIOkToKyFIIOJ9yCVk6Zvz/3PSdf933f/+//Hq9d/MLiP2pDnTsPN9b5/74qidQSnWuUo6m0\nQTJZrLfZuLr5t8ZWYy6qMlZf0rFsQaP9I4SmKPIa+aKIo5iyJnIpDRozaO/2+k2lZecInudQVjlV\nlfGf/54f5i//5F/BFxGuF5HkOd12h3v7Bzz15ONcfvsqH/nQRwiDmMDzubd/m26nQzxYBeDeaMrq\n6ibzWYHrhkzHhpm7t7/Hma0tsiwF7eM6jrHfcyTTyYg4iqiKElcaSOCgPzBDy+MjVldX8TyP+3t7\neNozWrhJRdAKKTyfrMxJ8oT1Mxv80j/4AvNkysrKOsPjEZHrU5UFURDSCiPOP7b70NviSs/Q46sK\n6YZNxmsgv1bATKGlwPVdZKnQ0khKWEJPlufGDEJryqKok5kcoRYH8XIWbVsl9qC2f9vvtUNJ6x+w\n3OrUWoOucKTVL4JkPiXNMoIgagK/vdfvtt7zAD4cDutyunNiym5OuTlB4OF5PrPZHM/3abVa5Hmx\nMPMVRqhdKVj3fbZPb6A1hEGE5wWMp1NefvkCV69cYzSZgIYwitEoykohVG1SrAxL0/d8qAoi10jY\nllmO40pUZYaSnnRJq8I8tJ6H53i1GFEtLIWlV1uHD4krXHS9qcuqMoeD5+EsZ284eJ5saPUPWrlS\nOBoc10egSLKcLC8YzUy7yfVcXM8DVT4wgP+n9VtbjmdQC7JmSFaVa4bpGlRVQzOBSljpY1kjk+qT\nUlid6AK55M1pYYlZZgwdpFww+Gzpvjw/WJTxLbSuyPKCXq/LJz7+Ai++dIHxeIyUDqNqTBgG3L51\nF991+Ps/+7P8yB/5I1y9dp31tRV836BETIvDIY5CHNdjPBqzurrGG2+8wZkzZ2i3DZ47mRvJ3Fa7\nS5YXuPVcRGqB74fkecXOzi6Hh4ekaU6SpNy7d5/Tp09z9+5d/HWflbVVRqMRQRShM8k8Tfi//6+f\n5uDgmLX1VQ4O9uh0epRVjitNRbOyssLu7sMDuIH9ipqZvVCZFEI04l5VVUEuyMuCVg2Rlc5C4tVZ\nGvDKOpC7oQ/17AIWrTfbbmnaqWIhKes4TsNEteiVxXxj4QEchf6J7NzzPBzXRSmagP9O9MyD1nse\nwAeDQX1iYqjcQjawIgt1Kopad7sZ+Bk5UHsB/Jqo4bsOQhgWnao0SuX0Wz6ffuEjfMenXkBVmoPD\nAw6Ojsjysh70aJQyDMTxeNz8KZUwusDSMdBCAUI4aFXiC40IDQlAUQIGleLHgekx16atWgCORmFa\nLlI6SNfFsf10pYzrNjaIa4Oq8R+CQ/YDqqI0CBetQTrmPYRG45JrQZ4bbZb/tP7/X7N0UtvPBWTZ\nSbNaxzV6MGDutaoUKJNFS2lMNkTNSTDLBOEoihqkh1Kqft4kShuWcRAEeK5Lu9UyGV7d462qCl0Z\nLetWHDOdp3zihU/wxhuXEMIlSzMIBK7rM5zMWOn30cLjn/z8/8Nnv/3TOK4xhNBaMZnP2dzcADSz\n2Zgg8Lh+7SabG6cJg4j79w/wvYDD4xGr6xusrW2QJPMat1yhtYvr+DjSoywUeVayvXWW4XDI6c0z\n5EnOoLvK6GhMGhht+LwoaHUGvHbxMrN5QbfXZzgao9AIoeh0O5SZaTVsb2+TZQ93pjEtD9B6YY7u\nOA55jZZZnmuEQUBeQxLFMl6/KJqePdQeuUvQRPu15X62Dej2M1i2qsV4W/Gr6h0/x/O8Botufy4Y\nyLMfRM08xLZs3m295wHclha+HxJFcaOtbMHzFitpTyhTkoSMRuN6Kh+i0ezv77Paaddlqah7jYaC\nG0cRfuChVIXQbdYGLcrKZEe272X1DuyFC8O4QQ5M0hmT2ZQrV67y5sWLTOYzcCStuI0WAqWgqr04\nlXDw/KAZdlV1RuAhlyBLdZ+sLCjKHKQdvJjb8TAmpRJG+bAsKiPIT80ekwKhF3oKQrznt/U/ylVW\nOd2O0R8pC1XDPnWtR6KbYVZTatfVr+sZ6KkZbLl1RmY0do6PjxukhBnALSjgtpdq5Qo8z6PMc+ZZ\nZnQ8HAObLUtjarK2ukq/1+PKtRv4fkhGgeuW9Ho9RpMpvXaH+/tHXL56g2effgrPlVSV4syZbcLQ\nGFu04pjJeEq/JvfkZclsOufMk9tcvnIFrTXdXo+ilkStqoo8LZohMGCctRSEXsjtG7c5deqUQXGF\nMfcP7hN3uiA8Lr99lS9+8ZeJ4pBut4OqoNWKcHzB/v5dNtc22djYZGfn0XfNRE1v2RymRWGqcysd\nGywhTuw+X0Z9vJPSvxwwXddlNps1Dl8m3gQNh+CdaBILUlhmWdr3XIb8BkFAkadNzFnW6jk8MqbZ\n/X6f9fX1h7Kym8/4bv9TmHT23wAB4AP/TGv9E0KIFeDngB3gGvB7tdbD+jU/AfxRTDf1x7TWX3zX\nT8ACXuO6btPIXyY/WMdoS7lVKmnKHdcx2cr6+jqeqmi1YtI0w+j2qlozO7O/j3lHKfC9qC5haw9K\nXZImGUJKpHBIk0k9/HDptUJ67YjTm2t87nd8hqKsuHHzFjdu3mI6T5jP5qRZTl4U5EVFmuWkmSnL\nfNfDWC2BZ8y3UY4RxvLiNqoyD19VlVCZIQzqwRm0Qhtj5BrVYkdtSiuk8Ex/XGvUQ9QI/9P6rS3f\ns0bSGUKGpirDlrkaoQwTWAjqNohRxMzKOY6QONJDKVP5mcN2gTMuywrX9agq1aAPLMXfZumz2ayB\n9ZVlCWox7AxDj0oL/ts/8Sf4q3/1f2MyT0jTlDAMSRKDJJmlKZ7r841vvkRVVZw/9yh5OgcGRotf\nKxxHkmYJ/c6ASlWMxyPW19eYzsYYCoNgOp1wcGCG0db13Q98XNepvTsFrZpebltANgD2V1a4cvU6\nd+/v8aUv/TKr6xtEccRkMiSKQvr9FrPZhLW1FTY2Nvnwhz9cE5re5b74PlVVnECHKGXqWts+WVYM\n7HQ6J0g8VljNfwfk0LZpsyxjPp9/C05/mUFt/9j3s2xuWDjVLzN2pWg3pDHL0KRu36yvr6O1Zm9v\n77cGI9Rap0KI79Baz4VJ635VCPEp4AeAX9Ba/xUhxP8E/CngTwkhngb+C+Bp4Azwi0KIJ7QRLHng\nskG1LMvG8sriLH3fBy05PLhrHhDPxXEKOp02WZYgpYPjuQ1xRWgH4Qi8ICCQEdZ5xXXN1L55BrQx\nSpVOTWipSTvSMeiRvMjrhyEiTc1ncV0PjSCdljiez9bmGttbp8mLAo1s+m9l3SsvckOtvXfvHnv7\n++zv7Rt7Kd837E3AARBQ6RwpjBu3kKZkftCSAtJ0jtcIABkrOQDpugb/LAUg+al/8mPour/neV4j\nmC8khoFZZwhKa5LciEhpVeIIA0N0ENY8hrJUTTZYsKgiBNogHcqKIjciY1WZAUZvvKyvicXYSilx\nagLROwdCbs1iNcw548odhwFCazzpIIAiS/Ech8qpFRPra+I4Ti2CZWQOojikrMoauucSBCGnT20S\n+AGDlQHnHjtnqPDjSb2h23WG2KYoM1R9kJaVESkTQJ6neI5PnmZUZYUmQwhj6qFqnXbpmlaayfhq\n6z0B3dBs1rIocB0fv86wk3lKnmZoX+O5ZkBYLlWDNpGxWZyFs9myvyhK/MA4uedVDkLiIviDf/AP\n8NM//XfJ89IM+pOUlZUV8lqjut/r8evf+A3u3rvL7/99v4csTdBVie863Lx5i8FggBYK4WhcTxK3\nAoqqpCwzozliZZ8jAzlUqiAMPdJsxupan/39A2Zzg6JotSP29/dR2sA6Azfm0uXLvPTyBfqrawRh\nyHB4TKsVEcUe0+mMViuk2+1ydvssaIkUrlH0fMiK45i8zE5AfauqVgD1PJz6GTPDQ01aB1RTJTmm\npZMZo4llvLcNsO1Wy7Q1yhLq+6Fsj7p+hrVSqJoMZMk6dpBqSUY2AxdCIFjo6NgeeFlWuLUtnO1/\n/5ZRKFpra8rm1zHnGBPALT7hp4FfwQTxHwR+VmtdANeEEJeBjwFfffjPp9kIyxhOu7nLQjWQMNf1\nGI/HRv+kbr0oNFluprVCS2QiUaoijCKzIUrDjDPys2aDgfGNFFKaDSqp8dc1bKeG9SRZWsO6aDak\nxqMqckNBr6FFDRRPg+8ZSGEgXVqn1tjZ2mg0WJRSHBwccPXqVe7du09ijX9FuxmO2L8ftPqdiDQx\nUqm6MFBLhTEQKFVpXEUcYzSgCpMZBK5EVQXCTrMryGrNc983JKWovuaqAIFokDmO9MwB52izkSRU\nJHWAq/N8F2Ro4VOGvGSzmExlTbDRdXvBEQv9iEbcvjLDWS/w63LSq1tbBnFTYKzUlAapBYWWTcYj\npUABRXPdNMk4xSrs5YeHOK7Lvfv75HmBqjeLFILBYMDpU0ZZcTqdEPsR3W6XOI5wHIdWK6Lb6xDH\nEa0opigUUvoGconFHYPvelRlSTrPFv6GlRWjEiSJme2EgXFMV6qiLAy+OI5bCCEXTFDR7Ltms9vW\nzLKYGRgcf1WqOgt0KauSPMtZW+nz5JOP8+KFC0b1UtRuV0oRRTGjyRjf87l89Tr/50/9NN/z3Z9j\nZdDn4PCAVqdXz1py5kmCFoqsrmBPnTqF40haUYvJZESSzHBdD1UqPM+hKIxo2draKvfv36fvr+B5\nLv2VgcHz5zn/4p/+Ey5deovVtXWElEynI0DR63WoipRuu0MQuqytrLC7e64Z+r0bHvro6BA/9Fk2\nTHAcY6ad1Th2+7zYoLrMA2hmaTXU1wq52a81768XAnL2kLCdgqIoyOvE08IUlweXloFrEURVaTwN\n7LLBvSgXPA/bc3+39ZsGcGGUnL4JnAP+D631a0KITa31/fpb7gOb9b+3OBmsb2Ey8Ycuz/XrMlQ0\nbhgW4G4HmfXnQDiS7e1tgxUvzCBnlswbDLRTX1jHc5nMplSlMRSgtK4cwkD4tCYrDD7VrQObkMJY\natWmEJbkIl0XUZlUVBhZOfOAiFqcSVVUldkcYRghpapPeAfHdcizBKoChwBVVvTbMR/78HPmQclL\ncExg93yPOIo5PjbEFv7a3/qWa3XukdMcHx+TzOYkiZlwF7kJkq7vEQYhlVYoVeA65mCywx3HEZTq\n/23vXGMly667/tt7n1fdqnu7+/a7Z8ZxkulhPMZ2t8cPktjETpzYMdhBiEAQQhaCzyAhhRBLCPgC\nASQeEiEoQBRhwDwEOA5YdhzZseIgP+dpjz3xiOmZzIy7e2a6+z6q6jz35sPa65xze3p6TBL3nfat\npW7dulV1q07tOmft9fiv/78jeAhWmB4T48F4EY3FYzInSt6ZwVpHVTUED3kmkmuCC3bYVBx78KFP\nF+U7csLKGJ12YlwfafflHo0gO98/FmKU3TSCbbdJgob/aZJJWSkInzoYXFbESCs6MitzBF3nhX/G\ngg+WroM0F4a5nWVNkeYUhThvgkzlbm09IU3yTKT5VAg5TRIZiDKe9fUNJpOcjY11Dh8+xB1nznDH\na84wWZuQxtpv5yHNCmkwG5ngCxgwUkpJXELXBqpWNzVPlhmqqtnjXNqmwYzqvYJl1lJggw4llWWJ\njXqavpKsha6laSqcs/yp97+X5XLBVx54kM2jxwXyN52xXIqU22IpTI9b8yX/7X/8Ovfdew+nTp7g\nj919N3VdYqzl6tWrHD9+TIIXH5hM1khcSlM3EAxrEymRmCj0DC1pmlOWNfNFxZHNhC6AcRlffeAR\nnn76abZ2djl+/DhVI0yJs9kaRZHT1oLrX5ts8H133cWb3vhG2g7yfNKLBb+crU0nEmmPHF/btn2m\npwNQGhzlkb9GAw3nYobXNEwmEyaTyZ7J3KG3NMgYKjWC+qbpdMrMDqyDikAZ0yAo26ExBmtCDxXU\n6yXPc5p2kFXTLOxm9p1E4B44Z4w5BHzKGPPu6x4PxpibtUpf5rG/B8Cv/OqjnHvjfZx70+v7MeHF\nYtGfrDul0JBevXqVjcOHegfbNIKzXF/f6IcfmqZjNhNSotlsim87rl1VEVeRv8IHrLE4l+ESF0Vn\nY9QYPGmaR0cShzR8R+hUDUc0NVUEV0+Y4D1lVVIt5thEJK2czXFpKp3lNO8n1FKXUjYLQoB8ktOG\nFms9vm65ttxlfbZOXd+4hv2eH/0RnHXkaUbbdjRtSz4pePLCU/zfJy9w5dpVdnZ3KBdLgm8B0bnM\n8pzFvKRrZBq1mIhDbpqSLHE4I8NMxliMbYSnseuYpAbrUkLwJC4wnRRU5RxE3kLKVUkCsUHcNp6m\n64Qq10CRyWi/MUZEOEY1RGFE7ESpKEBoO7LMyTRXnGYFhXgG8MI5472nbIWCM08HjUECWJdhrRH1\npLhmNmZ2eVIQAlR112cEzkaO7qRgd3dOWmS4LIcAZdfiIivk89d2cbtzLr64RdtewCVfw7clhsCZ\nO+7g/LnzxgMkvQAAGwdJREFUnDp5kuA9iXNkqSCXCAGDIXWO1raR/UFFp0MPO9Q18XEtJE6IWU3M\nQhI71FjbWjIZQV14yRgJwnlf5LRtRULgZ37mz+JDxwMPPUJRTNjZbpjOZlRVoCim7C4WJM6xsb7O\n5z7/fzhz8jTGJtxx5jQGWJQN83mNMRJFFnnOcikEW5N8yu7WIpZ2JnhvCD4heEs+KTh+/BTPfvsy\n8/mCT376UxBExnBz8xgvvHiZ6caUtSwlSQOGmjRL2ZjKANDZu++hrT3LWqd3b06r2nVd3DiHCcge\n8hcdsTrgLNLXEqP0fhI3Pl/Jr6RsmuyJpvX1hYM97QPNtm3Z2tqKSl7DEJf3vi9hqjSdbjDgyWNj\nup8CThKsgy9/5SG+/NWHvyMUinmlJ+x5sjF/B1gCfw14VwjhojHmNPDZEMK9xpi/HU/IX4zP/yTw\nd0MIX7zudYL69U9+7D/0O1znJQVWWkdhKMtjdzeVsff4YbNMopC12Yyd3S1CCBS5dJyzGLUYpJk0\nbvi1bRejfUlHtYwi6ZAnz7M+FZeTwFM3cew2DNSVJgTqppLNwI74m5OENsRdPShNZUoWR2WdTfoI\nsGqWYKMmHkjKV9U463jrOz/4kvX/+gO/RegCvgtRKzEb6q/OgTN0PtA2NcbXZKkgdMqq4elnnuHZ\n5y5RNw1lVTPfXRAMbB7a5MzRYzSdNJG9BTB4A9s721ENqWZrZ0s21lIiREnMjFD3GqmwW2vxxGGn\nTj5XFkntNRVsmgbicISPZbAkSYRi3dBzeejtAScf3y1Ah90TGTkrZSvfea6/zqu2wzIwwSkLY/Be\n6A1iLTNxCW2qG41w8xhjaKtaNikTaJu2RxgkxiPpjJSdRJG+E9GQPAoyxHR7tlZAEKqFQ4cOM5ut\nMZ1OOXHyRFTBGTjGx1N/erHrxqe/6zEEI2RSIfjYj2iHaN4Yyqqm9fAb/+sTPPzII5KOYdk4vIlL\nUoH9tS1pkgpd87UtnBG+lZMnj/KDP/AD3HvvvUJw9cQT3HHqFPP5HKJT6x1QRG9dfl7KVc9dvMiT\nF57iwtO/jw/g0qQXcA5tDaZjul7QtCVrk4y6Ljl1/BR3nrmT+8+/BXxC6DydDX0kDHD3638onnHX\nXxefJZ3IGmsGqOeM3tasz1oLWULwAavnIww17ei8syhOXivdtN3L/66Z0JikKo80sOMyjfaatDzc\nbxJmKJHpuSk+kD54VSz729/5AUK4sSjoK6FQjgFtCOGaMWYC/ATw94GPAx8C/lH8+bH4Jx8H/pMx\n5p8ipZOzwJdu9h5pmvX1SusKnBu6w5NJQZqsRQKggfNDKDwV5rMrC+MDiTOITFlD6KQu6JwjjWWK\nLBNFntQ6muBo4vvIju0jptX0m0c/Yu8S4eAIoW8Mdk1LlgtfuCJIkizFJIOST9cFJnYQNF3Liwhx\nEqcl9b0u1vyhKNZYn87kIrmBtXVFXTbkaUqRO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However, the person result is a true detection since this was not in the set for that class.\n", - "\n", - "You should try out detection on an image of your own next!" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "# Find, print, and display the top detections: person and bicycle.\n", + "i = predictions_df['person'].argmax()\n", + "j = predictions_df['bicycle'].argmax()\n", + "\n", + "# Show top predictions for top detection.\n", + "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n", + "print('Top detection:')\n", + "print(f.order(ascending=False)[:5])\n", + "print('')\n", + "\n", + "# Show top predictions for second-best detection.\n", + "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n", + "print('Second-best detection:')\n", + "print(f.order(ascending=False)[:5])\n", + "\n", + "# Show top detection in red, second-best top detection in blue.\n", + "im = plt.imread('images/fish-bike.jpg')\n", + "plt.imshow(im)\n", + "currentAxis = plt.gca()\n", + "\n", + "det = df.iloc[i]\n", + "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n", + "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n", + "\n", + "det = df.iloc[j]\n", + "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n", + "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def nms_detections(dets, overlap=0.3):\n", + " \"\"\"\n", + " Non-maximum suppression: Greedily select high-scoring detections and\n", + " skip detections that are significantly covered by a previously\n", + " selected detection.\n", + "\n", + " This version is translated from Matlab code by Tomasz Malisiewicz,\n", + " who sped up Pedro Felzenszwalb's code.\n", + "\n", + " Parameters\n", + " ----------\n", + " dets: ndarray\n", + " each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n", + " overlap: float\n", + " minimum overlap ratio (0.3 default)\n", + "\n", + " Output\n", + " ------\n", + " dets: ndarray\n", + " remaining after suppression.\n", + " \"\"\"\n", + " x1 = dets[:, 0]\n", + " y1 = dets[:, 1]\n", + " x2 = dets[:, 2]\n", + " y2 = dets[:, 3]\n", + " ind = np.argsort(dets[:, 4])\n", + "\n", + " w = x2 - x1\n", + " h = y2 - y1\n", + " area = (w * h).astype(float)\n", + "\n", + " pick = []\n", + " while len(ind) > 0:\n", + " i = ind[-1]\n", + " pick.append(i)\n", + " ind = ind[:-1]\n", + "\n", + " xx1 = np.maximum(x1[i], x1[ind])\n", + " yy1 = np.maximum(y1[i], y1[ind])\n", + " xx2 = np.minimum(x2[i], x2[ind])\n", + " yy2 = np.minimum(y2[i], y2[ind])\n", + "\n", + " w = np.maximum(0., xx2 - xx1)\n", + " h = np.maximum(0., yy2 - yy1)\n", + "\n", + " wh = w * h\n", + " o = wh / (area[i] + area[ind] - wh)\n", + "\n", + " ind = ind[np.nonzero(o <= overlap)[0]]\n", + "\n", + " return dets[pick, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "scores = predictions_df['bicycle']\n", + "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n", + "dets = np.hstack((windows, scores[:, np.newaxis]))\n", + "nms_dets = nms_detections(dets)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(Remove the temp directory to clean up, and we're done.)" + "name": "stdout", + "output_type": "stream", + "text": [ + "scores: [ 0.86610985 -0.70051557 -1.34796357]\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "!rm -rf _temp" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvUmMZll23/e7wxu+KeaInKuys+aq7ibdraZE0oIgU4Qt\n", + "mrAsGISgrTfaWAa88tYbwzagnQEbhGUv5I1XNiBKIE3SNCi2SDfZbLC72TVmVWVV5RQZ8ze+9+7k\n", + 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However, the person result is a true detection since this was not in the set for that class.\n", + "\n", + "You should try out detection on an image of your own next!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Remove the temp directory to clean up, and we're done.)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "!rm -rf _temp" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "description": "Run a pretrained model as a detector in Python.", + "example_name": "R-CNN detection", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 3 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb index 7125907f35e..6d629c5b635 100644 --- a/examples/filter_visualization.ipynb +++ b/examples/filter_visualization.ipynb @@ -1,620 +1,13214 @@ { - "metadata": { - "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.", - "example_name": "Filter visualization", - "include_in_docs": true, - "priority": 2, - "signature": "sha256:64c88129e2eeaa956e4c8a26467ff6119f24ea3d7ef15f8217326249973bea8f" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we visualize filters and outputs using the network architecture proposed by Krizhevsky et al. for ImageNet and implemented in `caffe`.\n", - "\n", - "(This page follows DeCAF visualizations originally by Yangqing Jia.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe\n", - "\n", - "plt.rcParams['figure.figsize'] = (10, 10)\n", - "plt.rcParams['image.interpolation'] = 'nearest'\n", - "plt.rcParams['image.cmap'] = 'gray'\n", - "\n", - "import os\n", - "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", - " print(\"Downloading pre-trained CaffeNet model...\")\n", - " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "caffe.set_mode_cpu()\n", - "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n", - " caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n", - " caffe.TEST)\n", - "\n", - "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n", - "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n", - "transformer.set_transpose('data', (2,0,1))\n", - "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n", - "transformer.set_raw_scale('data', 255) # the reference model operates on images in [0,255] range instead of [0,1]\n", - "transformer.set_channel_swap('data', (2,1,0)) # the reference model has channels in BGR order instead of RGB" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Classify the image by reshaping the net for the single input then doing the forward pass." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "net.blobs['data'].reshape(1,3,227,227)\n", - "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n", - "out = net.forward()\n", - "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Predicted class is #281.\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[(k, v.data.shape) for k, v in net.blobs.items()]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "[('data', (1, 3, 227, 227)),\n", - " ('conv1', (1, 96, 55, 55)),\n", - " ('pool1', (1, 96, 27, 27)),\n", - " ('norm1', (1, 96, 27, 27)),\n", - " ('conv2', (1, 256, 27, 27)),\n", - " ('pool2', (1, 256, 13, 13)),\n", - " ('norm2', (1, 256, 13, 13)),\n", - " ('conv3', (1, 384, 13, 13)),\n", - " ('conv4', (1, 384, 13, 13)),\n", - " ('conv5', (1, 256, 13, 13)),\n", - " ('pool5', (1, 256, 6, 6)),\n", - " ('fc6', (1, 4096)),\n", - " ('fc7', (1, 4096)),\n", - " ('fc8', (1, 1000)),\n", - " ('prob', (1, 1000))]" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[(k, v[0].data.shape) for k, v in net.params.items()]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "[('conv1', (96, 3, 11, 11)),\n", - " ('conv2', (256, 48, 5, 5)),\n", - " ('conv3', (384, 256, 3, 3)),\n", - " ('conv4', (384, 192, 3, 3)),\n", - " ('conv5', (256, 192, 3, 3)),\n", - " ('fc6', (4096, 9216)),\n", - " ('fc7', (4096, 4096)),\n", - " ('fc8', (1000, 4096))]" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Helper functions for visualization" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# take an array of shape (n, height, width) or (n, height, width, channels)\n", - "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n", - "def vis_square(data, padsize=1, padval=0):\n", - " data -= data.min()\n", - " data /= data.max()\n", - " \n", - " # force the number of filters to be square\n", - " n = int(np.ceil(np.sqrt(data.shape[0])))\n", - " padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n", - " data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n", - " \n", - " # tile the filters into an image\n", - " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n", - " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n", - " \n", - " plt.imshow(data)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The input image" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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jJc8ze0QrcgYroKgalNIAuaKERauINI7vdv78W6Hf1xKA0Jcn5nOsu97EEmGOMiYYk5Pc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zz12uoZORjd9bOyd5k6RErREhFTry5yfn93K41a5SKoDVfwWBxnO8/9yV1DbQsi0Wr+nhCR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\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7N24EL67Fm0KZgwKrrCQUce72vsMi6EOZi5ncQd+jpy5fKz47PAptc3ri44pzx2Dg/Y+U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\nWgyYl90QX6SchTyr4otjQTR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- "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first layer filters, `conv1`" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# the parameters are a list of [weights, biases]\n", - "filters = net.params['conv1'][0].data\n", - "vis_square(filters.transpose(0, 2, 3, 1))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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HsnuPT6BhpP/ARhMFuOtruPHh1MmwX3/7sWOQp1rDDRtjzpC3VCAbERyjo0ToTZ5v1Jly\nVqqsfrGNV9zznT6Lm1aWzq9D7OLSWpBuNrDuSkOYwWbEBDElMYO+TsTLxIjRC9fTDOfhAemg/W5Y\nBlLl1u/jvTztNs7DRTJux5ygf3SM9FfyzK1mODd3Gk3IkxJT0ILbGMQMf9kGGywRGaNsbnbff30y\nnxaK4bxYImbJbI6FjTN9NNZlfT8l4nZPi9xrwRlLnztFvueKOP6vvD7cbHLq1HHIs3sRN1Gtng83\nvLTXsY33HcCNa1uN7c1uL4d+iRJCCCGEyIEWUUIIIYQQOdAiSgghhBAiB1pECSGEEELkYEeE5bFz\nRPanmm8sozizT07XnnTux6PjKDDct4jupKMjoTBw+eIW5EmI0DpiR947zpxagtjFC6GwFE5ZN7Oj\n1x6A2P4D80F6bgFPuy+4FvQnqpvx09H98nlYF96aE5KXiTN3MapCDK4rk5PQifC64Cy12UnoTOiZ\nOHFmXMZyMryIMyOCWOYA7U8+7xM35DRD0Spo1Ekd0Gd2QlYmymf03Y6JuITXVUlfmJwO3br37EVR\n58qlNYitXQqFnmfO4fiYfhbdyBd2u9gQwut6HYXlERHc+9ZbXUdRaauJIudGI4ytr+Emks0NFLL6\nTRwjo3XI48cHg80bbDuIFwF7p24zswHZHNF3ouqUbPBh4ui++zxium/d/vabclrEsbw+gm06Ohpu\nMmLzRpOIxleXw/65uYHzfrWKc1epFM4dbLMCqU7MQ1prQBzL4e5kvkndhpcsY/M3EcC7+bREjiyI\nSBuzsns65Hum5L5r5/fOQZ71i7ghpRuFfWr2mnnIM7cXy/6tR8LTAFaXsE9NTOP429wc7sQOhn6J\nEkIIIYTIgRZRQgghhBA50CJKCCGEECIHO6KJ8u+UK5XQbIsZyG1u4jvudifUUk1MjUKeOjlpfaQS\nfn4yhnqERgvfkSbp9roTZog3Mho+39Q0ardmZlEXMjYevp9PEjQz29gItWLspPeImcN5HcGQy+k4\ndvcaoG6CGTiWnFDC6+LMuK4ndXoA9nzkYHnL/HXkNHhG151Ib+yUdXKvJA3b3WukzMwyci+oF6Ix\nY3izPa91uBwgwSLXMY1JzZVzYQE1Uc0jqD84Vw37fkba79KlVfy8Wjgma3XUqniOP4dGfutk3th0\nY4bpmFpubjFDrRHTxlUqOJfM7ArHdpXUQbO5vdlfm2hOBqQvekNVqm0iEpfEa6nI8xnRhSZOq8Xq\nZUD0OZ7MsJxjY2iyXB8J+4LX+Znx74uWM4hlKp9ijO1XcHMV05MNZVVM5reMXenqLyH6ymLJzxNM\nx0QLEaRiuI9ZuYx10Cls3z+rZE7vNsP5tFzGfjAxh7rlC2dWgvTpZ3BsH776CMTm94f3Ov70Bcgz\ntxdNuTsV1DcOi36JEkIIIYTIgRZRQgghhBA50CJKCCGEECIHWkQJIYQQQuRgR4Tl3r8wcsaBJSLO\nZAdgJ06gubmJ4jd2+nTViXcrpBYGZfzAdpucmO6IKyicK9dCIXmdnFpfruLnNVru1PEuil29VrlI\nxH3MjNKLnIezajQrOXPGiIhBM1LnaT8s+4CYIDKjSW9GlxFRJ/Nh9ALmxAvGL0O/5wTi5FT3iEg2\nvUlnQswF2XVeWF6uYB7WppF7aLZ5gNHvhs8TkfosFYgBpxskY2Mo9F5cRCM9r2DeWEODw14XjS1b\nTmhdHsIslZlfdplpbhTea3pmGvLMUOPXMB0TU1Jm6lpxdVcg9cvawdPp4bgqkJHrNzWwjRB+zjUz\n6/V9PiJWJtf5okfM5JE5cDqqxHB0pIYbg7xbaruJRszMbNPr+StVvDcTWg9jpDnM/FkkmziyDOcl\nPzezDSne4NQbpZpxAXyx4Od9LDkzUPbzFIX0M//M7DvMyEakXbvDMbl8AU16z51Asfmeg7vcdRuQ\nZ2MV56Bh5pfLoV+ihBBCCCFyoEWUEEIIIUQOtIgSQgghhMiBFlFCCCGEEDnYEWF5mnqx8PZu0gWy\n3vNOsszZtdlA0WpW9SfZYzXUiLNqkbhzewZwRrxZxQlQmRC63cZyps55mLtLhzfzJ8abcfdsv35m\nLtWMSiUU4BWJg3EUM2FyeF05Jp9H1JklJwLOSKaI9J++y8Zc8BnebZm5L7OCRk4szFx/M9J9okLY\nZ0vEMZkYMkMZmPiU4QWpRCdMHaBr1TBjuYQXzsyMQ6znBP0x2VCAgmazbi+8jgn1PQcO7oZYXEHB\nqBcPV2pY50UipPVDhImJu0w46wS+Pb+zxsz6xI3c0+6S0wGISL3jNsD0UyY6xut832B/Y0fsNAI3\nL2Vsz0hxe2FyHOOGm7FxdLOOS2F79cjpEhEru+vsbMMGcxWH3TtDbIBhMLf+LGGbVML2Yt8pmatk\nVndlIpL333Vs/LPvELY5yTMgfdGi8PmKZDNGh5wOUHb7ViZ34eaPfge/M9eXw9MPxifxFJN+B/sL\nG5PDol+ihBBCCCFyoEWUEEIIIUQOtIgSQgghhMhBNGCOXH+dH8jeOQshhBBC/IByuaWSfokSQggh\nhMiBFlFCCCGEEDnQIkoIIYQQIgdaRAkhhBBC5GBHzDbfe9etLhIKttjJ5BSn82K6rwJzE3O3p4Ix\nEis4o7l7P3gv5Lnt9tshFpfDD6zXRiDP8adOQ2xuX3gidX28CnkunL0U5qkyczpirNcPjcpKxAH0\n7ns/CrHbfu7fBOkucZBMiXFg5gzjiOeiVYjBqa87ZoxaJEZzqTMTTAfYDz7yIXy+O3/+/UF6QPpi\no9HGcjqTTHYifZah6aE32xsQQz5mCug3aCTkuvvuuwdiH77rg0F6ixjPdUj7dXrO+JWY79XIM5fj\nMB9pYqsRkz7n0UdnhNs//KEg/d7bfxbyFItYpkIxNCGMi+wEd2aoGNZxkhKDPtIOA2d2WSKGlcUC\nXnfX3eH88v7b74A8UYRl7yXh2B4dxzrYXMHT7Su1sFyHjxyEPE89cQFi586EBoeL+2YhT30C566f\n/8CdQfruD/085MmYyaPrL8xIt0sMY1MfI98NpQLO+xHMjWSMEiPkj9x1V5D+wK13Qp61VWyH2fnQ\nIHL3gV2Q59nj54J0q4V9cWpqDGK9btg3ChnWrzeHNkPD5o/ei3PL+277AMRs4Ps+juQ0RbPNipvz\n4hj7TykmhthReN36ZhPybJL5u1YLv0M+/nH8br8c+iVKCCGEECIHWkQJIYQQQuRAiyghhBBCiBxo\nESWEEEIIkYMdEZZHoOx2SSLqZk7nPsLN0Ilo3J24PayHuheWMhJyYvpEPRT4NTdRSMdOf981Nx6k\n1zYakKfXCss0NYliu0G/C7HI10u0/bOZmRW9SL6Iou7qJArnq/VQ3Fofq2GeCopkMycIbzexfjtd\nEnMng7fJSe+Mdju8bnwMTwGfn69DbG1lK0hvkbYaGcXrypVwCCZ9csI4EXqXy2F9VojIkuGzjdZx\nCmAC/4ITY/Z62F8arRbEatWwf6RkkA5i/Fuu6sStw/y1FxGBelxiG0vC5ysW8XlTIhC3gYuRsc42\npPj5LGP7WIgQGiDPlw7Y6fPhvdgmgK0mttXEbChgro1gf126sIafloYPNDs/BXmanS2Iefp9rM9S\nhQifXd8vjaLo2JfJzCzth3XVJ22csTZ1AvSI7YoZ4tyPao1sNulj/7xwdj1I7zu8G/JMzobz0sXv\nnIU8Y6T96vVw3m2sYz8okjFaYOMIwL6YuY1HfdL5C0TMH7t5o1LBNu738PNarVA432x2IM/A71ox\ns0ol/1JIv0QJIYQQQuRAiyghhBBCiBxoESWEEEIIkYMd0UT5F8j4Cpbon4YQLjHPTHad1yiARosX\nwQbDlIHopur1UCN05ukzkKdIDDEnnenayefOkw8Mm7BWw3fHaw3UI4B/YzRcVxgbD/VdcZVpFlDb\nVCyEMTSwM1vfQF3Yxnqo59pYQ6O0HtF8eXPPlAlRCLHTW6wsXYI8YxNoYjczE8YaTSxnmxjiWSVs\n9xJ5N58RwUXPmaUOCqi3YGSuHipl/Lxqhfxt5bQ3zRjLlLWx/VqtsG06Bbx3kmEZvMlpdQjNF5Ms\nZmTQgrSIlIndjKid2Afi57n7s/nmMgfEh9exPOTzvDYsTVBTlxKj0LmF6fDWfSznmRMrENu7by5I\nT82gjnDl2YsQ8zQbqF8p97Gc/U44tmojzIgR56CC033GxODUm8qamSWDsP5KGfYX+h3iqIzgGK0T\nndQzT4XzfLuB4+rgofkgferkMuS5tLwOscW9YRvXyOf3e9hfCgNmSBuSZsRk2WnMmJFnrYr62Imx\ncRfB+u200Eiz3QzrqkXmpArRgcbkO2tY9EuUEEIIIUQOtIgSQgghhMiBFlFCCCGEEDnQIkoIIYQQ\nIgc7Iyz3WkinGWPGmpk3ujOzwhBq8wG5jt0fL8TQMOJBerK0OyV+6QKKlY/ciIZq9XpolnbuNAoF\n5+dDUWdMToinBnLl0MxsONm1WZI44WUHn7e9yQwxQ4Hf1iaKwdfWUAC/teXEpkRdW69jGcYnQrHi\nCBF1MrwZ3WgdRY8Xz6O4dmsjFDlOzKD4PCZF6DpT0GIBRbL1OsY6nbCd+z0UUDISZ2gYE2PNGjE9\nLRXCzREFYqwZkemkkYXt1+5h32CGkVHkN59sL/yMSN9nBpx++Bcicl0RnyV145+NqwExecTNJkzo\nvb0ZrBfbmxndOVN2Ze91iWCbbB6Yng1Fx6e+i/PN0ulViN30qhuCdH0cDXg7Xdxo4WFmm2xmajnD\n3XYL+/7oKI5bb6hYLGHfr5KNFl1XrgER8w+o6WlIVsbvov2H5yH28Je+FaSPEyPNI9e9PEgv7puB\nPE89sQSxDTefTpB6KhIx/zA7HzKygcEb2bKNT2NkY0DFTZbrW9h/VtfQ0LjVDL9XemReHJ0kn0fm\nvGHRL1FCCCGEEDnQIkoIIYQQIgdaRAkhhBBC5ECLKCGEEEKIHOyQY3kohgTNGhEPM1Gljw2YPJo4\nFnthOdfMESfgIfToTDjX2gzFbetbKNg8cPQlENtcD8V0Gysorjt6VegOnA5Q3Dcgp2tHzkU5TYd4\nODOLBuHztZsolmRCz42NsOxr5PTwfh+vi8thuSYm8WTy6Vl0SPZOwMUhNgWYmV04H4r+p6fxRPpr\nbrgSYpurG0F6bR0FuCnpU20n0G400IV31y4UjZadEJI55TO8g2+JOPVWithfqs5RvziG7VAsEHG7\nE9xmRoTlCZa952ODIYTzRETONqTE7kR6alg+wGDRnTbfJ1WeJFjOQRpmJGb9lg0hTKYnMpA+lWXh\nvbp9nBPGJ3HMlEvhmDnx9DlWCojsPxxubumnKGRvEwd/D+vB/YS49ffCnH4MmZltbuLnedF4nQia\nq1UUxReLzgGeCsu3H38bW7hx5soXvwhiU7OhW/effvFxyPPaN98YpPfumYA8p0+hi3ni6rPXxX5X\nKGAHZeMIriMbNGq1cH6pVYm7PByfYdZz5dwgwvJLy2ukFGFb1Ufw3mNETF8oDLu1CtEvUUIIIYQQ\nOdAiSgghhBAiB1pECSGEEELkYEc0UahJylwarxlutbe9sR7LxvJQndQQoqix+gjEzp4KdTZxBe9z\n8Iq9EHvkoWeCdLeDepKZ+fBdeLOJRp7kFb4V3Hv+HjEJZFy8FL7Xb7dR/7C5hRqFTivUZRSK+HmT\nM0TvNBPW5+g45qnXUNfT7Yaf1yOnszN2zYX6oz9/7AnIc+y7JyH2qle8OEgfOTwNefoDot1ohPV5\n+hS239mz5yE2N7crSFeqw5mJdl09tFqolykRY8txd/p6nZh0FonWx3vPVsrYDlstNF7NXKdlOhRP\nqUjqgOqWwjIUSV+khopJOP4iojUsED1n6ua3lHTFNBuif5J7Mxlo3+njiFTFJidRQ9NwJrlnT6Op\n7PQCagR37Q6NZS9dwv7K/H49SYqNVSoRLax7nm5CzH3b2KfWXTtUN7G/MM1OzWmnKsRQeRhN26Ul\nrM/yKDbOD73lZUH6P97zWcjznUdOBOkXv+Eg5Bkfw3GcpmHZEzJACmTMVMkze5hxb6Xi6q6CdV4k\nGqymm79XVjYgT6OJ3z2TE+H3w8QEav+YJop+SQ6JfokSQgghhMiBFlFCCCGEEDnQIkoIIYQQIgda\nRAkhhBAHr2CGAAAgAElEQVRC5GCHzDa9iMulqX6bmW3mxd9ryDsNYTjGnPQuXQzFwvsOzUGeWg0F\n08e+dTpITxKR3K75UHh97s+ehjxFwzJFkTvRfBjlp5mtr2+6G2HdlYhZ4675UGDIDDJnSGx8IhSt\nZqSYHSJubzvxMBP8MubmQkH4j/6jH4Y8v/PbfwSx//vX/1OQvvbqqyDP0WsWIXbF0fAU98X9C5Dn\n6SePQ+zcmVCkykxBGR1XL9kW1h317RyEYsyxMRyPcRFjdWdsGVXJ4CZ/yvX7iUtv334DoqDOyMMU\n3Hj3QnMzs4yInDNXJqbYLsYo5vUi+YyYi/aJ6SFAjIMtIuJ2J3IuEFHwKJlvLp0NzQtbxODw4BU4\ndxWdoHhtBU0lC4PthcnjU+MQKxaxjv3GpKSPddBu4CaObjcUm7MexUyd+24nQKlEnoU7NodlIps4\nnnkWN6m87ObrgvQf/r97IM8jXwkNOK952X7IMzmKbby8Fo738ghuMOhsYPtlpB08JSIQj1yfjUj9\nNskmgGVnLL2yisaaxKPTpp1R6dgImqeWyRhtt7Y3g70c+iVKCCGEECIHWkQJIYQQQuRAiyghhBBC\niBxoESWEEEIIkYOdcSwHHbkTmxGNXkaPMN/eQXxAbuY/j51QzY3Ot/+8fg9PcW90QpHjdX/vOsiz\nsYbXnTl+IUjfeNNRLJMTcW+to0BuenYXxPpe3DqEZt7MbHraOYhPovvryDi6/k6OhQK/CnEZZ463\nfefSvr65CXkaDXzmxD1feQhhpJnZFx94OEj//R99LeS5+5P/CmIPfO5LQfoLv/co5Hnov16E2Le+\nEYo/r3/pYchz8KoDEKuPhO1w4Sw70RzxIuB+F5X6W6Q+s8Q7iON19TqbTsJ6LxF38EoB28Y76nsx\nMYWMYyZnLrp7eaG5mVlGHMQLfppiUxLbAOOHGhGtD3NgwIB8YIHUS8GVoVwm7UKuWzoXblYoV7H2\nFvahE3+7HfaXjQ0co1XiVO2JSZ4SE5a7WBmnGyuX8V6pE4iz7xS2EcHPJRHZQBGl2/fPehnnymPf\nwU0jN1wfbkr5e7e8CPJ89fcfC9LPPHEO8kzOT0JseflUkC6Qib9E+ks/xe8nT5F9cbsv+24P5411\nsrnl0upqkO718fMnJvF0kJHRsDPURshGD/LM7S6K/odFv0QJIYQQQuQg1yLq9OnT9oY3vMGuu+46\nu/766+2Xf/mXzcxsdXXVbrnlFjt69Ki95S1vsfX19Re0sEIIIYQQPyjkWkTFcWy/9Eu/ZI8//rh9\n/etft1/91V+1J5980u677z675ZZb7NixY/amN73J7rvvvhe6vEIIIYQQPxDk0kQtLCzYwsLzpoCj\no6N2zTXX2NmzZ+3zn/+8PfTQQ2Zm9lM/9VN2880304VU5N5Few85pitgqz3/Spvqn8irangVzkzs\n6Ptdks3R6qCepOrey+6an4E8zzx+CmL+ZPcrrtkLeVYuhVqYXgffOVfq+C5+c7MRpMvF7TULZmbj\nThNVrWHLVGJiWNcN32m3iEFmkxjkrS+H5ey08N14qcz0VeHzJEP+uTA3H5oJfvyOX4M8tzzyKoj9\n2E+/JUjf8GLUrz3+GBrrPfFnzwXpP33kCchz5swFiF11/cEgPTGFRqWMWi3UpiVF7C9pHzUDTafr\n62fYDuMJmvvVKqGupkCMCr3WyMwscqa1RDaFEF1flqDWwUuSvCGgmVmB6KsGbjwmRP+YEL2Tn3CY\npsaS7SeXQgHnJKaT8o9TjXFsNzaxXjY2mkF6Zg8auPrxb2a2sRZqoLpkjMYVnIM81KyRTs1O00p0\nTF43ZWZWjsOvO1Z37F6xiw1Inox1YsfUOI7Rs+cvQezZZ0KT5UPXo9nms0+Ec8mZM8uQ58oJvG60\nGrZD0kGjy2IR59NkiC+/ATGaTtzE20+G02D6fOPj2O9mptGcdWIinINi0g9abeyf7c72mq/L8X1r\nok6cOGGPPfaYvfKVr7SlpSWbn3/egXl+ft6Wlpa+39sLIYQQQvxA8n0tohqNhr3rXe+yT37ykzY2\nFh7PEUXRcDtqhBBCCCH+FpLb4qDf79u73vUu+8mf/El75zvfaWbP//p04cIFW1hYsPPnz9vcHJ6z\nZGb2lS/98ff+v//gftt/CM/9EUIIIYT4QSbXImowGNi73/1uu/baa+1nfuZnvhd/xzveYffff7/d\neuutdv/9939vceV57c2vCe+XpxBCCCGEEDtIrkXUV7/6VfvN3/xNu/HGG+0lL3mJmZnde++9dttt\nt9mP//iP26c+9Sk7ePCgffazn6XXw0s+d8o5ewtITey2ST8fZNd5RToRZ7JbDXFSNxNMz+4KTc8K\nAxTunT72HMTmdocnbE/OoHD3xLOh7qxGTmf35ntmZt5LsDCcrtzMma61NtGUsLmBl3nDunYHha3s\nNPbI9Y1SjM9Xr6Pb3iByBnlElMt49VtDY7sDV6CY/zP/8Xch9tjDT7r73Ah59pJ7/dCbbgjSW6to\ntvnUEyhIP/702SA9txtFwAxvpFckwss0xrrquY0BzByy2UVxZpqF969VsO/7jSZmKOwuDLZXHhSY\nMJm1u7s3ExMzkbovZlTE6bMYESNNC/t1gQhwIyLmB9izECF7FIV1NSB1xzZoRKUw38QoinkHxGR1\ncz3c/FEgXyvU8NORUZdlDJUKYTD27s1mlpB7DVy7U2F5is8HXxcZ+YIifc9TJwbDFSL6P30iNOVd\nWMCxvefIQpDubqFAfGsTBdtlJxrveNNlM8sGZB4eQvjDhpEfDkxYnvSZca8zzaxi3Y2P4maFcjFs\nh3YL66XVImbCpF8PS65F1Gte8xo+8ZjZgw8+mLswQgghhBB/W5BjuRBCCCFEDrSIEkIIIYTIwY4c\nQOxN1fy76gJx1iuSd87+hSJ9x00+H/Nt/z77L67cNkdKTPpmZkNN1PqlBuS5tIJCogNXhmZp/V4T\n8jTdgbz1EdQHdTuo0/LL52gIszgzs8iJYaKUtQvWU+q0TcUCvuOuj6A+oOIOJU3JQZ8R0aH4thpQ\nQ1XkCw8+FKSvv/FqyPNzd/+vEHvymyeC9NJp9EhbvnAMYnE1HILTs2ggt+8g7nLd2gzNL9tEj8Tw\nY496Q5JDgovOJDPLME8/RS2FOa0Bka9YHBNtkeuPTDcFH0V0dnRke01USvR5rGJcmdhBwn4uMzNL\n+uH9CwV83mQIyR77i5f1/JIrZ9rHm7fbqAGJ3Lxbq+F4pM/XDWP+882G+2vd15MZb7/ECXQi0qmY\n2sTnKrBysu8ZMEsl9x7mAHci9q1WsY67zVDH022jrqc+Ec7zGWnjJtEDxUV3MHuM83CX6JaoQawj\nJdq71H1f9InudUC+70dcvdQrmGdyFL/r/CHI3S7WAWUY0ddl0C9RQgghhBA50CJKCCGEECIHWkQJ\nIYQQQuRAiyghhBBCiBxEg2EcJF/ID9R5ekIIIYT4W8Tllkr6JUoIIYQQIgdaRAkhhBBC5ECLKCGE\nEEKIHGgRJYQQQgiRgx1xLL/1fbcGaX9CfH28DteMkhPFU3fyeYe4NvcS4oLrXZt7eF02QNfWcjV0\nSL3n7nsgzwfuuANi3U54/5mZUchzYRldzL376oGD85Dn0T9/JkgfOrgH8sQltNg9dWo5SC/Mo1P2\nXXd9GGK33ha2HTv1nJhZW8Wdrl0fwTauEPfepBfen7Vns8naL0yzjn7fL3wMYrfffnuQ3lzHE7/n\nd09DbGYhbNOnnjoBeToNrKvFPaEbOTvYOzG8zjv/RsTb+b57sH/edtudQXpATmxn5vV+Q0iBOPyy\nvoAbSch1VLC5vQX0vR+7N0j/m/fcSnIR93x3cn1KnNZ7PeKs7O/DPo08S6kUuTTWQbWCPfQTn/iF\nIP2v/+W/wg8kgw3ahpSpQOaEQjksQ22kBnn8HGhmFhXDz+v2sB+UyhWI/fx73xuk3//+2yFPRk8a\nSF0efL5yFT+vUgndub1Du5nZ0rmLEFtdDk+T2L1nAfJMT+H8+f73h98Fv/QLOB47XayrrUbost1s\n4/zWaIbzEjuxoFpFN/Kp8fB7dGYav1fLFWLJ7pzHf+59d0KWD9z5QYhVamF/KZWxTOx0gF7Pu7bj\nqRvNFp7gkbmxnCQ4SitlckKC68Of/MS/gzyXQ79ECSGEEELkQIsoIYQQQogcaBElhBBCCJGDHdFE\nlWvh++r9h/YG6V4b32OeeOYUxLYaW0G6OoLvwWt1fIefWfie3b+3NTObGJ+CWKOF72U9EdEa9Nx7\n2TJ5J+t1GmZmBacnmZhAjUJzI9RS1etYB1Tz5fRkTKfBKLq2y/qoWeiTE+KXL6yFZepcgjyTk2MQ\nW9gzE6SnprFdJiZQE9HphO/LW43hTvMeHws1Akkb2+XPvvwExN7yjlcG6Vff/FLI8we/+8cQO/Hc\nuSC9f/9eyFMkp9THtVBbsNXYvm8+T3ivArk388MduGPqmWnuUD665POKpOt5LcyACe0c/T72c/Z3\nYs9pdpiOiX1a7MZthYx1puXyWr84xjKViuxe7s5EO2JEQ1cohKWPiMiNtXs5Dp+vVEL9SqGIc9fA\n1xbRyzHdGWYifYN0qkEU1lW/hzrJVg+1jPFkeN2uhQnIs7i4C2KPf/vpIH38uTOQp9GYhZgnJX24\nSNrdSXatXMY81X7Yp6iGL8W6gz5E6rdAyjTM0M6IXjVLwnt53Z2ZWbWC/azqxky/gnrZmOir2u2w\n3ZM+lqlcwjIUmBB0SPRLlBBCCCFEDrSIEkIIIYTIgRZRQgghhBA50CJKCCGEECIHOyIst0G4dnv0\n698J0ufPXoBLZhfQ4PDK6w4F6fExNLEsErO2yIkjuz0UHZ87eR5ifSJ89AwyImR1RmgjYyj+bjZQ\nFFurhqK8sSkUXl+4sB6k63UU4G118fn6XSdE3P7Rni9TJRThj4yjKL9IjAMn5sKynzuJpnbPPHsa\nYsecaeXsLArL9+9Hg9Hx6dDMs0o2DzDa/VCYePTGQ5Dn6SdQWPr53/yvQfpn7v5fIM9r3vgyiD30\nh98I0qvrm5CHiWunZ0IBPNtQwMB7EUFzhGJlLyRPmRnmgFznVbLk3l60bmYW+b/vhtB9lkrY95kA\n3pv7MUF8HGMfrjjzwgqpcy/qfr5c4b2oHJ2YAnp6REDtDXmfD4afUCKfSLTfUA9FIsCtlLGOU9d+\nKZlzsyE2BpSIgLrfI/3FzcMZmZe7bZzzttbCTTgdkueGF18Jsde98aYgPTb5JOR54lvPQMzDtMsx\nMRiO3Xzd62MddPvh5oitLTSeLJEdG7VKWIiIDKwyMUYdJLhZCCDP55+Z7Z+IS9t/R5fJxqcB23Tg\nNol0ye9ExGPVCkNurGLolyghhBBCiBxoESWEEEIIkQMtooQQQgghcqBFlBBCCCFEDnZEWH5paSlI\nT+4KBeE/9OYfhmvm5mcgtrK0GqTPn1qBPKtL6xA7fyZ0y95soJh3bt8cxPYcwNO7PSWiHvROyhOT\nKIBvbqLjdL0WiofHx9CxfGsrvK5SQ1Hg6sYaxPwJ5lFxOMfW3nr4eeURFEbWiAPt4tXzQfqGV14F\neZbOLkPsiUdDwebZZ3DTwXe+cwxis3OhAH1mFt2JGetroQv+0etRCfnGd74KYv/u9l8P0l/4na9D\nnrf8g1dD7MChsE+1tlDs2muhqHP1UiiSnduDgntG0Qm9swzvzRyuvWCTuXwXiJAVBOnkOoorQsSU\n0P6SmAhUidq14sS1zK24WsM+XHYbJkplMmaYk7tLJ30UkVOBOFyHbeVd1HkRmCs9aSs/J5BnyUj7\nec04bSqyecBTIALjkQo5ccLVX4EohVNSx0vn3ffFGZxvNpYbEHvl624M0jfddD3kYS70n/mtMM3G\nB3Ujd+7c3S7Wnd8gkpK+keI+BHDr921uZlYhTuBGxhZ+HtkcUQ5jGRGoD/zmEzMruQ0abHSw75k0\nCfsLG/+MiGwIGRb9EiWEEEIIkQMtooQQQgghcqBFlBBCCCFEDnZEE3XNdQeC9MxcqHdavbAB13zu\nD78JsUtO75SSU6uTAWpMDl61GKRfTTQuk5NobHn6mbMQ82TkHWyahu+BqyN1yNNs4Avseech6TUZ\nZmYdp5cpM40EeaOMdTXcu+OGM4MsbeLnrZ1HDdaa06Htu3ov5Dl0BGOHrwxj552uwczsuW+fgNjK\nxbAPbbbxVHdGsxH2l2efOgl5fvidN0PsRa+/Nkg/+ABqoq5/BerAJkZDfVyBtFVhfARiF8+GfX99\nA832GAMnYEkyos/JqYliYhivoQETTeNjxuD+22sWyhVmrIex0mg4tutEl1IkGpc4DvP1iL4jSYYw\nIc2IxoU5AMJtmAkqeWYfI9omVi9xHGpxwCjVzDJSBt/GKTEOzdLtzUQ7XZwDR0dQ9zK1K9Q3sp7R\nbuK8nyTh/Z/41nOQ52tf+TbELi2Fc94tf/8myHPNVUdIKTxYd6zZS87otUj0qgN3L2ZmmhEdmq9j\nNo59Pzfj49aTJth+Sd99P7RwHo5I2Quu7EVifkvNRKsuH6kDPndhaFj0S5QQQgghRA60iBJCCCGE\nyIEWUUIIIYQQOdAiSgghhBAiBzsiLL94LhT9PvLl8FTsJSJMHiNC78WjoUB84dA05Ln+5Xgq957d\nocHh49/4LuT50uceglin2YMYQJalqTM0LBChYK+P945LzhiRiOS86RoTzRWZmHcQCj3ZSfYML4Rs\nd9EktE/MIc+fuBikjxEx+MIV8xA7dE0oLN97EPNc+6IrIHbpUii8Xl7CzQqMyYnQtPLRP8YT2735\nnpnZP/ynPxKk7/wn/x7yPPYwmoIeviHsw/026/tosjo5E4rN223irEdIvAiYiDoHTBwNp7ET0THR\na/r+yAwcC/Q0dmcmSATwnjFi/MqMQ0uu7DEZjxn5PF/OmJz8nqVE/F0Ip9msSETAEBkONm69aDwm\n5olxjLGSm2/oBgNSBv/MzFCx39u+fzLt+YULuJGk1w5F44sH0QT5iiOLELvmhnCemN+7C/J8+Qu4\ngembj4bfDwlxsbz5LS+HmIcZlTJFcxSF+ZgZrDeRpMJy0qkS11Z9sgkgIQ1RGmJjR6+LYn4/tOKU\nGHkyYbn/PPLdl5FNFX5BU4rYWCNz3vehLNcvUUIIIYQQOdAiSgghhBAiB1pECSGEEELkQIsoIYQQ\nQogc7Iiw/JIT+c7tDcXCr3jzy+Ca+UUUjdfHQpFaTBx2n/n2aYj9nx/4jSB98tg5yHPDy45C7LpX\nXA0xDxPlVsoVl4coKAcoViw5J+VOFwWblWp4anWbOMIyN2QvSGVuyIzZhckg7U8FNzNLOnivei0U\nR69cRAH18cfOQOzUk2HbzC3OQJ7dB1EgWndi7Ani+s2Y2x3e/+nHj0Oez3/6CxD76Z/9iSD9RuJq\n/MzjJyC2eGQuSGcptlWngYLNuOTEypXhTiH3ouosI0Jv4gQ88MJOosNkbtYoGmf9jAmYvbiWXObw\nwmgz7ugdWTj+0pQ5+hNhuRP4luIq5MmIYNuLqr1rvBkX8wKkiZko34v3i6Q9SyVysoGrq4hWOts9\nELYp9BUzS5PtN+WUyrgxwFp43fHjS0H6/LlLkOfo1ZsQe8Vrrg/S/+M/eAPkOXAYT034wu8/HKRP\nPIcnV3ztK9+CmCclmw4GbGOAa68yc9QvhReSbk6dwL2I22/geD7GbpbPcd47+FdizJP2MZY59/Nq\nDcdaXCH9xfV95pTPxzbW8bDolyghhBBCiBxoESWEEEIIkQMtooQQQgghcrAjmqgj1x4I0qPOOLDb\nQV3P49/4DsTOHV8O0s9+GzU1p589D7GrXxyeuP2zH/1pyLNrcRbvdRz1VR4mLSo5nUS3S8zMSsT8\nrhg2T7PRgjyj4+G74m4H3y8XSDOX3EndRMZA6biMZWJwODqJz7JrT6il2t/bA3k2lrcgdvHCSpBu\nbKBG4vRTSxAbdZqo8elRyMPo9sI6ftHLr4E8X/vyNyD2p18J++er3/gSyLN0Fo0DV5dD7UZETCz7\nREtRcLGY6F4YVX8aekpMM6n4xuUjxoHEE5DInZgGA++FUp/tn8/rKMzQ6PL5OznDUXbSe4rl7Hj9\nCNFy9IhusePMIZkxYjTEAKTGmqS/eC0Ty8NuBoampD6Z+MZrTFKi+RxOsYe5pqbHIVavhRrT0ydx\njv/yF/8MYsefPRmkb37zKyDPNdftwzK4ueRrD+F30XPPoq7W4/VBZmb9PqkrVw1Fol8rOwNVr5Ey\nM4vImPH3Yvqgfg/n2GJ5+/FHHgVMpHtEN9XpoOazWgmfr97FMjED7igK+3pKTKyZEC2SJkoIIYQQ\n4m8WLaKEEEIIIXKgRZQQQgghRA60iBJCCCGEyMGOCMvXN0Mx7cnjp4J0Yx0F1L0mCtKKpVDw99JX\nvxjy/JP3/yOIHb5hf5B+6vFjkOeL/+XLEGtuYLk8WYblrJTDam43UOzGDNX8CfTNTRTcj0/WgzQT\nrUfkRPqiO0meaB4pTSeSbbU6+Hnk5OySEz7XiXna5EINYiNTofldu4F1sEkE916L3VxtQB5Gq9kM\n0rtmcYPB4SsPQOxrXwrFpq+5BYXlh6/aDbFOO6y/sSk0Be10tu8vaYqCZsaIM6gbEBPEHrmXb1Hm\nx2fGzC4jH8B7M1E1iM23P2WdmQSmpGMXnYg0GhAxPylSpx2O7U4PBbGtFsa8iaU/2d7MjPiEAkUm\nECd401FvEmrGTR4zN2iiPvYDNk30nXg3IYJ0sncASMl1xRgLOjUdCorro3XIc/I4is2PPRVuDLq4\n9EeQ56ZXXQexw0fD74urr0XxuZE5D7KQ2IAKu8N6YBsRKlUnvB7B+bTbxvbz4zEhanAmdh+mf8bE\nLDXpbT9m2h2yGaMTfmf2EjK/RVgoL7jPyNxCv+rorpjh0C9RQgghhBA50CJKCCGEECIHWkQJIYQQ\nQuRAiyghhBBCiBzsiLC82wjFtCMjoVBwftc8XDMxiY7T9elQhFsZxcc5t4Qu47//0VBQuL60AXl2\n70UR8J696LLtGRCBWuyE5ZuNJuShjtruXq0tFHF7kWWXuL9WCihMjJ0wcQhd5PPXOeEeu6xIBMY9\nJyhc20Ix+FYbRc4VJ0iv1NANfbyMYuw0ca7UxEWZ4kTGa2trkGX/Fdg3TjrH4me/i+7545NYzpWe\nO20+IwJHUskDfxr7kH8PVZ34s9PB65IUP9BrWzOSh/WFge97RCQ7IJ0vcjJcKj53dPuk3Ewg3g3H\nUZ+oyHvEjdw7TidMgE/c5at+rBF3+WF0rSXiXF2MyUkHsf+8IQT/hJRUXkKez48tL1A3G05YXiyQ\nvkgExc1WuLmkWsMNKVdctR9iI2OhAH15CU8Q+PafH4fYRff9cOAgjv+ZGXRW9/gxa2aWkHmp54Td\n5DKL3QkXY2P4/VGIyCYH1xd65PPbZB4epNvPLyNjOL91ndN41MG+2G6SjWRufok6bHMUbjKq18Lr\nCiXm2o5lYBtQhkW/RAkhhBBC5ECLKCGEEEKIHGgRJYQQQgiRgx3RRPl3p94zKxnge/CzFy9ArH82\n1DY0iYnloIcvlBd2hdqma6+9GgsZ4TvSRhPfwQLEPK1UDE8dbxNtU51ofRJX9gZ5vmo1bMKkh3VX\niisQ8wqWlL14J/jT5jOmmyB1V4rDcrJTs4nMxjquPvtEXMHUHWAwWBzunXfs3pd7LZeZmWWoaZtb\n3OWuQ01N1MFY2bVfPx3OiNVrKQpDGjHG7l4lohkoG+mLvt6JgeuAqKJ8O2dDXgcimmj7v/cuLqO2\nkchzrO0MYpnOhxkcFl2fGh1Fg8OI6C18HVcrOO0OY/WXkrpjVeeLXmJ1x9w2XYjpmApEt1Rw+iqm\nbRoMIYqKiQspaz+vLWps4XisjaJO6sDBcN6fmBiDPKtrWxjbDOeA9MQS5Nk9O4EFdVCzTTbvurpK\ne9junW44lzBtFevX3sC5T+7dbuP3U5HMCZ6RcazP2Gmi4iq2C/suaDndW5sYv/Y3UUvVcbrIEjFr\nrVaYjjD/Uki/RAkhhBBC5ECLKCGEEEKIHGgRJYQQQgiRAy2ihBBCCCFyEA2GUfy9kB84hMmbEEII\nIcQPCpdbKumXKCGEEEKIHGgRJYQQQgiRAy2ihBBCCCFyoEWUEEIIIUQOdsSx/J+/52eCdMW5hc5O\no/vr9AzGppxDKjP03VhHB9pWK3SgXV1Bp+NWC11bvbDsl375FyHPv/gX/xpi8wszQXpqEk/8brfQ\nqfq5584G6fV1LOced++ZGTzNe4a46S5dWg/SK+v4vJ/8xL+F2G3vuy1IM/flYhFj5Wo5SNfIdeUY\nnWsHbpmfEkf4hMRSdxJ6p41u4Xf8/Icgdvtt4fOVSsypFzdHRC4WEddmxiDafl9HMkDn4YF3jid5\nPnb3PRD7P/73fx7em5xe3m4zZ/7w+SbZGJ2dhNj4RHg6QZk4Ayc97PtpEjoUJ31svw/ceVeYvv02\nyFMs4ecVimHblMlpAXQDjOtm3v3ZjDv4ewvxbp+4tpM+/KGP3B2k7/uF+yDPxmoDYufPLAfpSox9\ncXFxFmJF1xfrI2XIs9ZAB/+2N5MmfT8iotyP3f2RIH3r++6APIUS1nFcDtur28G5K+ljn6rXwucZ\nHalDnk4X22HDuZjHpL9UqngqxIfvCueX226/HfJU61jHfoyMjuC941roll8ibu+dDo7jdtePK3QC\n7/WwD/s+e+9dH4E8d95+K8RSOIaCOd5jzJehR07i6HawjVvN0MWcicGrpK3K9bAv/Mp/+AXIczn0\nS5QQQgghRA60iBJCCCGEyIEWUUIIIYQQOdgRTVTF6Uwmx0PdxNx8qPMxM5sYHYFY6vQcyxdXIc/S\nEsaWnR6o0SSnVhO9w/gklsHTaqNmoNMLYzHRAxFJBLzz7RDdVLkcvt8tkVPkjehuWp2wTO0Oak4Y\nXacZ6BNNTZZhOaMofD9fq6KuIC5j2cuVUDNQLmPdxTHqCoqxO807G66r91xbsXfqXv/0POHfI0xT\nw5XGl+gAACAASURBVNRPJafZYRqQiJz07rU3WYaaAcbGZqihabZQN9HrYV8YqYenrxdJPxsdwxPa\nR2ph/yyT61oJfh7VFm3DFhnHbFylTjxZLBIt3oC0n9N3pESHlpH+EjutH+s9rD7h3kT0WSX6HN/R\nekRPxgrhtWKsBTpdvFcvCT+wXEPNyWCY/kn6fqWC92o0Qt1Ls4Fa0SmiA53bFerAmJ7s0tlLEBvE\nYbnGZ1HTmpAx42k4vY6Z2amTFyC2vBQ+z9oKlrO1Gc6xlRjraXoONYoTU6H2pz6Gc+f4BNEMkfna\nMyhinszCdi+QSZBpC32/rpDPr9WxnP66PmkXMmwt6eH39rDolyghhBBCiBxoESWEEEIIkQMtooQQ\nQgghcqBFlBBCCCFEDnZEWD4+ERpCzjgjzZkpItwjgsaVi2tB+szJJchz+hQK9zadMLFSQXHdNBEm\nMoNBT6+LomqvZGPGoVslFMVubYXlZMahcSlcB9eIeVuVCPC8+NSL9C9HxwnSmdFlkxjygSCViKX7\nCROfhqLDag2fjwqaR3xse1NLM7PElYGJyJkQueREuUy4y8XmYbmiwhD3NrOsH7bXgMqVkY1GM0g3\nXdrMrFrH+qy6Oh4hdV5jJnZOVF0hz5IQk9V+3wn1mULcQwSqWxsoyt104vok2d6s1QybtBSzzREY\nGxkN66pGzBOr5F5YACLAJXNX0fWhDjGjTFMc73VnPjk6WoU8F1exvyROcF9hZrRMUezwhrxmZm0i\nxt7a3AzSk1PYFw8f3Y/3Wg3r4bnvnoE8fSKAP3j13iBdq2O9bJBNDZ6rrzkCsdk5/K6bmAljRTJm\nWo2wDy9fWIM8K8v4fdFx5SRDhpr7DrMvgLWw36xgZMMG5Hk+6u6N15WIqfOI26DRJ5sqmLB8q4H9\nbFj0S5QQQgghRA60iBJCCCGEyIEWUUIIIYQQOdAiSgghhBAiBzsiLJ+eDIXl486NPOmgiu38GXSS\nPfbdE0H6uefOQZ7lNXSzrTvR8cg4nuY9OTM6VMyTEcG0FyIzwWaXPHPTiQAbW+guHbsTv4vE0btE\nYt4lNh1GuGtmo85dvttBEXlGRKveyLlDHNJ7Xbyu3Qrvv7mJAs6NDRQFjo6FdRyXhuvqiRMUsxPG\nmUA8i8LrmOixQATpkRNVFsl1RSK89s70yWA4x3IvZK+AAN9semYKYrvmwlMExsbQvZ/VcakQPl9K\nhJ5UxJ1tn8dz9XWHINYlG1L8aQTMKb9YwGdhjuGeuEQE4q4P+ZPmzcwS5iruP58oYqsVnEuK7nky\n1IJbu4XjaHw87AtV4hJdJALxnqvjkREUiBeG2PiQpNiHV9fwxIm6mz+vvv4KyFMh7fe1rz8RpE+d\nwO+L6191DcT2HtoTpBsbWKH9IebPp/78GYgdIz9jxNWwrnbvw/G474qFMH1kGvIsHsaTPzbXw763\nfHET8mysYv9sk81CHubW7xkMyCYOdiJDGvapAZmH2Rj1luiFmFQw+bxCcbiNOQz9EiWEEEIIkYPv\naxGVpqm95CUvsbe//e1mZra6umq33HKLHT161N7ylrfY+vr6NncQQgghhPjbyfe1iPrkJz9p1157\n7fdeb9x33312yy232LFjx+xNb3qT3XfffS9IIYUQQgghftDIrYk6c+aMPfDAA3bHHXfYJz7xCTMz\n+/znP28PPfSQmZn91E/9lN188810IVVxuoGkF74LP0eMw777+LMQO/ZMaJa2soK/fBUr+F5/bHIs\nSO/Zvwvy7N4/B7GJCTRG8/R7aLaZuve7JfKelpmJNRqhBqrdRh3DwOlzmF7Ha0DMzCKnjfFaoMsx\n604GZ6eX93qobeh0wnppt/Ede7NBDEc3wjro9/He7F18yemPWL0wvHloKUW9TC/DNk6isFy1KmpV\nIvLevexMFmNiOMhOsveatm4y3CnkC7vDk+zLFdREjRHN3pgzjKyQPtzroJZi0Cu6PFh3XWIG6bWF\n3e72z1chhpWjxBhxbNKZShLjUCbvaDhDvnYLn6VPzHY7rq8zA0lmpOnx88jz4PN5TSIz1mRzidc2\nMVPJQgnbvdMOx2iaYn0WSziOPCvLOO9HpJ95DdTsNOqBvvT5r0Hska9+K0gffslhyPOy196ABXNN\n89yxU5ClQUxdPUx3c/7MCsROP3c+SJ85hXrgtY3QSNNrJM3MZmfx+2r/4VBLtXf/LOSZJEbTIzVi\n2Ozw3ylmZqnXEXqxo1H/TS8jtAHRA6YZxgpRWAYiMbVCRHSnRDc8LLl/iXrPe95jH//4x63wV4Sj\nS0tLNj8/b2Zm8/PztrSEDuJCCCGEEP9/INci6vd+7/dsbm7OXvKSl9jgMor8KIqG/utfCCGEEOJv\nG7l+w/qTP/kT+/znP28PPPCAdTod29zctJ/8yZ+0+fl5u3Dhgi0sLNj58+dtbg5fiZmZ/e7vPvC9\n/x89eqXdcP21+UovhBBCCLFD5Pol6p577rHTp0/b8ePH7TOf+Yy98Y1vtN/4jd+wd7zjHXb//feb\nmdn9999v73znO+n1b3/7j3zv31VXXZm/9EIIIYQQO8QLYrb5317b3XbbbfbjP/7j9qlPfcoOHjxo\nn/3sZ2l+73W1vh6K8s6evgDXnDqD+qotJ9CsjKKgcXYBDceuui4UFB4+shfyTEyiAWefCKY9EXm7\n6U9VZwK8ARHJDdzp6Mz4seRE+uz1Knvj6o0f2ecz6iOh2DQiIuRKZXujwiYR13aJAWfDic1bTRTu\ntohxIHz+kGaivm/2EmxzVldF9+q6QNq4wI5Md41TraKAs1pDY0svLC9n2ws/zcyOHg0NKUdqOGaS\nhBlihuLoEnlVzwwxG+1wbA8SrJcuEWP7XL1k+/757FMo+DUihK7UQvG+N5k0M4sMPy9xAu0+2VSR\ndLG/+HmjS/JEhe3/nh0Qk8BSzIx0vYEr21iCn+c3bdRrOLZZzD9flmI549r2XzXMD/fI4YMQW1wI\nNwI9+fBTkOehP/xTiE0shKaVb/2xN0CeK69Fw9av/MHDQfqM29BkZja/CzcneW54Of5g8MM/+jqI\n7dkbir2rdfwuajdCw8+L59A089IF3Gi1cjEUsjebaBzaJps4BkMYzTKjYI//vnr+OhTF++8s9r1W\nIH04G+J7rEDKyTZRDMv3vYh6/etfb69//evNzGx6etoefPDB7/eWQgghhBA/8MixXAghhBAiB1pE\nCSGEEELkQIsoIYQQQogcvCDC8v9ems1QCLy2EoriLl5C59oWOY19ZCwU3M3Mo3Pt4asPQOzI0YNB\nemIcRWVd4qi9vrz9WYBMIFp04s8+EcmmRPhcKociPOZ46wXTXEBNXMy9wy1TxBPiUnivSgUdtiem\nRyE2Ph6Ko+MYxYRJQk74dmJe7+JuZtZiTudboXC9uTWco3fmnHF7RDxsRLyYOqE305B7Mb+ZWdkJ\nySPSfjFrd/f3T4G4dTPm50JxbYU4HXeIg3i7GZaLOY9vbuBmgZXVcMx4cbYZ3/jgHZhLQzzfysoW\nxLrE4d63KRNes7FWLodl8n3azGxkBGNxOZxfiLbWOkRc72Gi2WKRbDYhMc+A2ET7jR3e+dyMbxrx\n04vfRPJ8lu3nl6mJMYyN4Vxy/IlQ2P2H/xndyftkrL39XaGI+1VveBHk+e6jxyD2x05YXi3hnDe3\newpini8+8AjE2qTd/f6h8Wmsl8nJsE+Nkr5Yq2DMt3u3g23V75PNScPsyyHfIQO3QYPaSpJNKv4U\nCnYqhb+3mVlmfjMW2VRB5uZ4iDFzOfRLlBBCCCFEDrSIEkIIIYTIgRZRQgghhBA52BFNlDfXXHan\nd7eJDsXrn8zMJmfCU6oXD+6BPIsHFiA2Nha+T2428ATutZUNiK1eWoWYp1xBwUPZ6TnaTXKCOtGY\njDhjy3aTGCq6d8XksGtq0ld0op1SYftT1s3MLp5fDu9DtE2bW6hN2bVrwqVRQzBK2tjrycbG0Bix\n1US90+hWmK/THs5ss+AEK1mP6bQw5rUGvT62Z4HpwFJnVEh0L0znljo9QGkIozszs7rTsBHpjxUq\n2M+yXvg8G2vYxpeWcXysrod6xwH5uy0mhpF1ZzBYrm+viUr7RKtG2m/Daae2iM6usYX6Lq9zqxET\nxBFi+DvmNJeTE6hVGeYUeaZRYjGvNxzGyNPMLEu21x+CYMfMYqcV6xPTVVZOzygx8ly5hCaSj3/z\nmSDdJlrK1/zwKyD22rfeFKRXL6HG9fc//QWILZ2+GKTf/D+8BvLE9e3nz6PXHIZYcwvreOl8+H24\nfBLr4PRT4TzMNHxVMmbGpsK+NzmFfbFex/FfLOTrn56U6fpSpvUN86XkOywj/Tp2jq0DUqaUmAkz\nneuw6JcoIYQQQogcaBElhBBCCJEDLaKEEEIIIXKgRZQQQgghRA52RFjecUaWXjLGjMOYkebUVCgs\nn5xCY7ZqiZjKtULR6MYqisjXiOiwRQThHiaSjZy719YmnpzdIuaeXkzf6WC9eHOxhJyg3iOGgx4m\nTGRcOB+KLLvEBLVPhJ7+FG4mwJ2ZnYDYhDPgq5PTtpmJ5cC5w8XEII9RrYV13E6x7rIEY4mLsVPH\nS33sG6m7rt/H+ux2iFGoM4eLhjSLS1xf8P3HjAvnvRFjq4VjodXGWMd9XlzGdmBGod6ENCZid0+N\nCGLHJnBOmJ4K+xkTQndIv06cSL3VwnbpE9HqYBC2TbdHxLXEoBJvhGOUmcFGbjywzR8lImT31/XI\nOC6QDSixMwX2hrV/cSWJhaTEhHhteQViPTdmrnnZVZDnptehkWbm6uqPfudLkOe7j6HZ5stf/dIg\nvfvQPORZWl6GmKc6hnWw+xBufHrlG64M0pUqznlxHPZ1No631nFzhN8c1STfO12yySkl48HDNjBE\nmc/DTDMRb7ZbJv2HzRuJm7sGbFyRcRSVZLYphBBCCPE3ihZRQgghhBA50CJKCCGEECIHWkQJIYQQ\nQuRgR4TlmXMfrdVCkVy9QIR0xAm8Vg9FquUY14R9IrxsboUO5RcvoNMyE41mQ6w5yxUUznpt29oq\nOqRT1+3RsB6SBE/z7g/hMlytYjN7R9ghTY2t5ESq/vPNzJI2ChMbjVB0fGkJxfzPPn0GYnEc1ufk\nJIrrp8gp52POFXpsFN2lGSXneFsignTmzNvzMaKW9P3eDIXeHSLOjqioMiwnc/RlbG6Ebt2s2Qek\n8Jtb4WaINhG7E72mVevhBgImLGdjpuo2EBRL2ztCT0+PQ6xSxw0MExNhXxgnG1lKZZxvvPN/SjYB\nNMjmk46bg/xYMDNrEedqgGhke0Tw60+pr5A6LxKBeGThhaxMXbKhoOJE6kWy0SNlncPRJvOGFwqb\nmY26DUT7r9wPeWLSX/70v/5ZkP7mn3wb8lz3oqshdu1LjwTpzQa69bPvC8/SeRTJHz9xFmL9Tljv\nCXHTTty8OyAbirz43MysPhL2hSoRrRdL+H1RHsKxnM1BaRr2zyLZc5CS0xZKTujt+7SZWdobor+Q\nDT6RP3rAzAZyLBdCCCGE+JtFiyghhBBCiBxoESWEEEIIkYMd0USVnHbJ6ySKxPixGGNspBbqFmJy\nwnirQQzHVkI9ToPkMfIOOCbaDU+NaCmKTmuQdNCssddmZmbhdf50djM0a4yIiV4hwusqzqjM6xou\nx7jTGo2OYbt4E1QzM/+6nJl0tprYDm1nBpcSA8C1ddQodLvhdf32EJoTMys7DVZWRU1NRgzcwICT\nvcQnuhCvLSqXUVvhdVpmZgX3rn9A+j7DG+l5k1AzMyIZAMNPIjWw+gjWVcXVQ7mMOo1SCceMN+5L\nhzghvlzGOkj62KcuLof95RLxSYyILmzg5qUqeZYC0eKUnOaDXGZFogOFPKRPJT1sv4EzuyxViCaS\n6FdAF0L0jnVicFhzRqhdMkaTIdovYUJCoq9a2D0XpEdHsO6efeJZEnsuSC/u3wt5bnzpNRBrbIbf\nF8vLa5AnrqGuzlOv4fiojqIZbOTmiSKZvz0llifCduj7eWOA83BG9EFeD8goFLFveHFoZMRolsxv\nA/fd1yemx2zG82MkInorOjcTjeCw6JcoIYQQQogcaBElhBBCCJEDLaKEEEIIIXKgRZQQQgghRA6i\nATtu/q/zA5moSwghhBDiB5TLLZX0S5QQQgghRA60iBJCCCGEyIEWUUIIIYQQOdAiSgghhBAiBzvi\nWH7HHR8I0t6dmHnbbq03IJZ2QxfTvXumIc/EBLrZbmyGJ9JvkhO4iyXiTu408ffcfTdkufOuu/Ay\nJ0jb3NiAPAMiuN+1ayZIV2r4LEsXwpPB280m5Bkfq0PMn97d7qCj9333fgxi7/m5nw3SzE27HGM5\nC4XYpfF52ZaDknOOj4kzb5E4gZecA22tjm7BP/2efwKx973vfUG6sYX1yZ754ME9QXqM1Pn62ibE\nNrfCvre8gnnaxN19YjSs47FRtMG++557IPbu/+2fBulmE9t9cxOf2TvMFwr491ccY73U6q6cpF7q\ndSx7zcUqxHX7no+Gz/fhO2+HPBnpVQXnYpwQZ25mnu377MDQEToqYr1kfXeqADNMJo7JH/zwR8P0\nnXdBHuY4H8G92Kn1xHXfzVOs7qISPl+3H/bhra11yNMiJwb81q/fH6RvvRXbr1zGMnjz6siw7grE\nvX51eTVIH7lyEfKcefYixCJ3wsbEDM4lW1vYh/7tfeH3w/tuvwPyFCPsaOjqTdrBzXkpuU9G2soq\nYaxExl65Rk4QGIT95YP/9P2Q544P3gqxgrtugrRnkQi2250w31aC3ykpGUelKKy7QYTtkpLvC+/g\n/wsfuxdvfhn0S5QQQgghRA60iBJCCCGEyIEWUUIIIYQQOdgRTVSWhS+1i8VQfxSTk9DjMha1sR6e\n0H5pGfUk9RF851t1sfUmnvTe76JOqlwmOilHlqLWYGMlPPV7dBJP/N69ex7L0A/f3T75zacgT6sd\nasWue/GVkGeE6IEuXgj1AexUd4Y/IZ6JR9IMdRpJGmpq6MnkRCySRV4AgdelpOx9V0x2ejgjroRt\n3FvFPkXkJFZwfbZMNDxML+PrLyE3Zyeo12phHx4dxTZmECkT4DWKZmZetsD6C+tCSRrWe7+P+q5+\ngp9XycL6y7Ltp6o+0TZFBbwudaKaJMWCF8kcNHCn22cwFswGKetn4f2LEavfYcYfKSfRFvoxSp+v\nSOZY1z8zptchfTgdhPcqxZinOkCdjadENDxMu1Vw9Zem2KfG61MQe27tbJCuEO1PIcZ66fXC+ozJ\n8w0yLAPcm2mbmPjO94+M9DO8EYFc5/RAdF4k83e/12Ef4K7DtvJ6pwL53SYh43+jE8baZN4vFdl4\n8DGiiSTzW4Fop4ZFv0QJIYQQQuRAiyghhBBCiBxoESWEEEIIkQMtooQQQgghcrAjwnKvm/OaMW/Q\nZ4YGa2Zml9LQtHJjDU0CW3MTEJudHw3S4x0UBa6v472GWXI2GmgKOjkTft7CIpq8XTy7BrEn/vxY\nkC6VUfz22lteHqTrNRTSP/HoMxDruGee2YX1xPD612KRiDOJyDFx4shkwATU+HyZEwaXytg3mODW\ni7G7wwgjzSxyYtp+HzveoITlrNVCQfrIKJpKbm5i3zAn1O20cUNDp9ODWFwJjVirxDSP4Q0jC0Um\nvMTrfL0wYTITFBMNNbk3EUe7tDeCZKQkD53g3Mexz2c6XfDoGwwnhPbZiuzz2EYLR0rGBxMd+w0a\nzNyTuol6ETDb6MFEzk5MXyljX4zj7YXlGSlTgWziGDjj3mYX585DM/shtnJpK0hXa9g7YmIGuXIp\nHJNX1GchT5Li5iQP68ER+1LxGUkW2L9A+n6RfmGFz5eR7740ISJ5tpvGUQJRt1nVzTdV8n2x0sY6\nbzix+YB8qVSYHzZshiDjES8j1w2PfokSQgghhMiBFlFCCCGEEDnQIkoIIYQQIgdaRAkhhBBC5GBn\nhOXe2tiJ1gpFFC9O7RqD2PkzobJs6dQlyDM2MQqxKSf0npxAEXBzi4iAk+3Fdd4N3cxsanZXkH7u\nydOQ5+TxsxDbtTgZpF/6iqP4eU6Y/O0/exryNDZRHD27MB2kE+K0zvDu8sUCOfGbiPRK/tTxAXHF\nJbsHvJg2JsLEEvlbIHJCxIS4SzPSvhOkd5kTMQ6bgmsHnzYzy0g5e/2w3tfXsd8xYbkXWjNRN8M7\ncY8QQfqAOWMXvdMxCj3LROkZl8P2qlax/WLiEl0qhnXsBfEc4qJOBLHgyE5F60RU7ccIU2wTVX7m\n+jUxELcBdbN3H0f6MBObFwpFlyafR+7lN38UDduFib+923qJjFH2eZCHOeUTkXO9GvazixdxzEzM\n4PdFY81t2iBC6PFJdP7/zqPng/TY5FWQJ0kuQMzDujDveWE/Y3XnT3wYkB0cbMRETpGeEpf/FKcb\ni4Zw9C5HONaq7vthkGE515p473W3D4js07EC2XXgxwgb2myoZbBrZHj0S5QQQgghRA60iBJCCCGE\nyIEWUUIIIYQQOdgRTVQvCTUCJaehScip3GPT4xCb2x9qjU4+ew7ynD5+EWJ7F0M90NQ0vgevEX1H\ngxiTeeojqME6dTx8X762ugF5Dl+7ALEDh8Pni8k74NNPhzqwbgNfAldqaFDZbIXmcKXy9mZ4ZmZF\np7fgPn4k6g0ASZ4SM+Tz8jnycSkz6XO3Z+aewzAgWrEBPZE+/ECvBbpczJeTmW0mRItXqYT3KpW2\nN2s0M6s6U1BvEmpmViM6qX43HCODIU9H9w8Yk3IWiHEnyI2GkCwwzVBUwHuD5ovqPbBPeR0Y1VuQ\nZ/EGnJnXhBrX0MHnM+dSokMZuFGSEhFWgZh7+vsznSTxFwWzUq/Jer4MeB2WCRs5zXDOrbh5otnA\nPONT2K/7bmhtbuJ1i4dmIHbp7CNBul4bgTyD0vZmjdTAlWrawkpmZrB+jmU6RjbHRk4DVSCf7/Wk\nz5dhe01UlWjMyqWwrTp9XHIsNVCE5a2RZyvEbJNop735bDqs1mlIzSxDv0QJIYQQQuRAiyghhBBC\niBxoESWEEEIIkQMtooQQQgghcrBDZpvh2i3thYK0NEHTxUoZxYqHjuwJ0qefPQ95Tjx1BmIXzq8H\n6alpFINXKiiu7Q1xkvXWRhti7XYok9t3CE8B3zWL4u8RJ0ROGlgH546HIvUzZ1fwPnN47yNHw7qb\n2TUFeRheqOuN9i4X8zBDvigjBodgnkbM4Yhkc+DKmQzRds/f35WdiKWZgNqLo0skDxNV+1Pcez0U\nWTJhqTe2LBHDSsbISOhax0wsC8QsNXXliogwmZul+nszE1IifGYi9W2ghoPsPt5UklzHyunboc82\nK5C28k/HpK7s8zAP1jnTo3thuR9DZrx+I3eztI/C6wIx4PR9vVAc0lUS8rA+hXVccHNHt4XlLKGu\n3Kqj4Zx+9sQq5HnRS2+AWMsZLyddIuIeor8yQ8cic+B0oZRtRPC/f7B+x8TSfk8FKROfv7d/vjrp\njKnbhHNhC69bbeHnTU2HS5PRGuaJyTzV92VnVUAE8GxTw7DolyghhBBCiBxoESWEEEIIkQMtooQQ\nQgghcqBFlBBCCCFEDnZEWO71bu12KMaOq+S0+7b3MDUbmw3F0Fdcsx/ynD6xBLFTJ0MH8cV9/x97\n7x5s2VXf+f3W2nufc+65t59SP/QACzBtIYE12IZ4SCjMMC3PeMYeykMpg11lFZ6Kk8IeG5sBCYEw\nIECNMUQxtiszHuLSjCsGqpLBpGqKOEplGKeCwY4cYwJYGCShV7cere6+r3P2a+WPdlFZ39+3OZtt\n7Cvw91PVVdpLa7/WXnuffc/5rO/yondR+WMol6vl3eXSi+XHr8hnFL/ssJ+Sen3uy5rNXMr7/f/5\nj12dP/q//zxbfulJL0aefPVLXdnhQ7lM/+n/9KeuDgOVvJ5IiJGl52JiOZ3SnEiO0Fc68/IyC9Pt\nUfodIraaWQKJc1L5W4QlMhus15LZ0ZcLL43XyzxGOaGJbWZGUrAxOb5j6xEmkHTOZHdMQ7+4g7zd\nCyLS4kwEZmZ1k18HNmv8ovZicMK+MODPPdanejKgAGXasvDXuCBlTcqPM5L47p6kg2NCOV47M5+G\nzmA1epI4jTBpncnfKCIz13aIfsv6cEmkeHdM7CZlgn+R14udr7O7veXKjj87/7x4+MGnXZ21uR+E\nM9vI+8L2pr+PK3KPIoGcH0v+R7GbXT+3JSKRsxTzAi4qG9RBH5UDHi+BvE5sLvL9ndn0z4g49ed3\nKP/ItFnh1+trX4bNwAblUN+eyPtD0TdRQgghhBAj0EuUEEIIIcQI9BIlhBBCCDGCPXGiKgjOPH8u\n94jOP+09hgPn9rmy+f58Nu2jVx5wdY5/11FXdv5M/lv4E09e8Ps74L2QOCBEcv/cN+mRw/mxp9a/\nu37l80+6ss/+wRey5fu/5sNE/9nP/YNs+af+xT90dR5/9LQr++i/+d+gjm8Dzuo2wLC/i4VwzuQ3\naDajOboi9Ddu4kn537iHhakV4IpMJv56zma+b6Bi0rXe82nAf2Lrra97JyOW/hjQZaI+CQF9p7WZ\nD5VdX19zZQVsn4V0NsSFWSxzf2Sx49uATbTegls0zFkYFirpgvWYJEGC/AK4KWxme+YIoifFglhj\nJB4awB4/GNZ6cWP5YkHajt0N3YBA2kDapQUXjrlUJUsFdZATJP5Y0+f31sb6uqtz9snzruyyK3IP\n9PRfnHN1tje9e3v5lfuhzo4/zCHhsKxPkVNGl4mod64vDPXX0MHqWNAt2SEL4EWa3q93bpHfIy1p\np/0b/j7aN4O+2JMgX/K86SAMdnCEJnN0h646ek0hhBBCiL/F6CVKCCGEEGIEeokSQgghhBiBXqKE\nEEIIIUawJ2L5bJ6LgPP1XObb2fJBaWcf9TNur0Mw2qFjl7k6J6692pV9fjeXW88RUXBtvt+VFeVq\n+fPI4UOu7OyZ7Wz5kYe90Hj6tC/bfzw/hrf+0j9ydV75j2/Ilj/7qT9xdf6HD/4vrqypcwHvHsR5\ndgAAIABJREFUFX//Ja7O//TvXZETZ6nvS0RdDJobOms2zo5ekOC5hojleAwoBQ/dH5udnQUVtiA5\nNiQIjomQKHpOiLQeK1+2hIDK2dQL4ozpBMTyNS+yr8+9WD6tQGQnYZQ1OeeigPDZngjpLQnEhPPr\nBoitbedDEGPh2w692brxgwAiOaYE4mxL6vREZS3LSbY8KXybswBOt21i4MforwNW6xIJKiTHziRj\nfwzk3gYpl93ZccC4gNT5Y6rIM3cJwctzIpZvnt12ZfsP5tt6fOLv7adO+8+CwxCWvLXrt10OCNtk\nwZrBiMwPzxw24IYN7HD7Y+HFKKST71FYP0urT8/qRJ4JsL+y8ueyMff7m4W8XViAa6ICPJSxoFI2\nkmW4gu73OXpNIYQQQoi/xeglSgghhBBiBHqJEkIIIYQYgV6ihBBCCCFGsCdieQsC49pGLrLWC58a\ne+GcF/6eOpPL2DOStHzZ4Q1XduhIXlZ3XrzcXnjZdD5b3VyPn/FJuU89laeBl2Sm8O996fNc2TUn\nrsyWLz/iz+/f/vonsuX//d9/xtWZz7wk/4//6cuy5WI6LPF6WPYxSxXHAiJZMvkbylgidNf5sgLE\nS5wZ/VKgwMhmZ8cUdTOzrc1cNo1EwF3sePG5b3KBclL6/ZUTL9c2y3y9neDvGUYP59cTmbegidp5\n32diuQUiiIMQjonwF7ft94fJ9EPGISzqXVfWBX8fuzpEso5x4sp6lOJ7f62qau7KJkUu/Vdk2yWL\npQaYBNwR6RjTl9PAQRzYyOye6dhIkoT3DEvYXz0oh81GwIR7fF6X635QxfaWf6aX0Pc2Nvx6Zx/3\n0viBA/nzs63JAIYh14+cHxuI4J+NLLLcbX3l/i9uGqR1OlCApZiv3nbN+icsz6dEIo/+WgXoe0yu\nNyPPZmjjnq5HPnuobD4MfRMlhBBCCDECvUQJIYQQQoxAL1FCCCGEECPYEycKf2ovJ/lv0/MN7xXs\nkEDMc0/krtH+fX69tQ3vRF1xPA/EPH/Bzyy/bPzvtDGy31dhvdavd/TqI/kxzf1v8R1Z7/QjZ7Ll\nz/2xDxx97KG87Hu/70WuznXf+xx/nE0eaPrAVx5zdRguiI14TCxIE30nlhXXslnkYX8x+N/Bi4Jd\nl/QNFy8JOB8sjHJt5q/f+iT3XKhLlcj5wezk04m/JcvKlzXL3MtoSbswOnCwcNmMh2YaNHFJmpzO\nGp/QUSC+BVkTQ/N6PADCDjnuQHzHHq5DQ+71MnqHpyrz6z6b+JDHggSjTmC99em4sE3mqtGw27D6\nnjESXtphkC7ZOPOk8KhYfy3K1R81HfHzYkk8IvQIiW7V1SScEdzJ+bpf8fzTPuh5PstdVHbP9MT1\nQ5rWtx1/Tqzu6xjcyVYh3cXwQUgfi+QaM3cK2SWOUgOeZAx+O1VBTFt0mWhYMjl6WI8Gv1JHcOgH\nBNne6DWFEEIIIf4Wo5coIYQQQogR6CVKCCGEEGIEeokSQgghhBhBSENTCL9VOxwa/CaEEEII8Qzg\nUq9K+iZKCCGEEGIEo1+izp07Z695zWvsBS94gV133XX2mc98xs6ePWsnT560EydO2I033mjnzp1b\nvSEhhBBCiG9DRr9E/cIv/IL9yI/8iH3xi1+0z33uc3bttdfaqVOn7OTJk3bffffZq171Kjt16tS3\n8liFEEIIIZ4xjHKizp8/by9+8Yvtq1/9alZ+7bXX2qc+9Sk7duyYnT592n7oh37IvvSlL+U7lBMl\nhBBCiG8jLvWqNCqx/P7777cjR47Y6173OvvTP/1T+/7v/36766677MyZM3bs2DEzMzt27JidOXOG\nrv+2t9yaLVeQVN2ThN269ynRNUSysuTqKvkZt0tIXw0kmbc1n2a72+cJxR94/22uzpve8RZXhm0f\nyPmxQFg8rEBSjRNUSiSJOJLo2s4gpZn0j7ve+R5Xdttb3pqv1pF08tIn+mLgLGuDuvXrVVW+/Yak\nUretb7yDhw5kyzvnL7g6p37l/a7so2/88Xx/M98wX5r4BPgvVt+dLT/VH3V1ji2fdmUvaP8iW15P\n/jjPFwdd2QLuh4PB/3T+tvfe5cpuu/WXs+UykBnpSRx53eVlDZn1PJFr6v9o8n0R+7CZWYFZwyTi\n/n3vuSNbPvW6d5P9uyJ/j7D7kc1OAM8XTD6/uCmSKj4gJZq13dt//fZs+U1v98+b2JO09z7fVmhY\nHfJMgHs5kATqjhx9mkA7lCQlvvJt9avvfG+2/Nbbf9nVSck/h7sW0+x9HZYOjs/BRFLwE0nULgs8\n9sbVCdGf8/vfnffHd97yRlenJ/2azmyApPyjO/X+WrHPMCzrov9cbZN/LahTnrL/m+//WVfnp3/V\nXz+DJPeO/PbVknsGLrF1pC8aSTrHD9KCvPiU7LpD0vn/+LN3uDqXYtTPeW3b2r333muvf/3r7d57\n77X19XX3010IQd86CSGEEOI7llEvUVdffbVdffXV9pKXvMTMzF7zmtfYvffea8ePH7fTp0+bmdlj\njz1mR4/6v8bNzD71B3/w9X8PPPjgyEMXQgghhNg7Rv2cd/z4cXvWs55l9913n504ccLuueceu/76\n6+3666+3u+++22655Ra7++677dWvfjVd/xUvf3lewCaeFUIIIYR4BjPqJcrM7EMf+pD95E/+pNV1\nbc973vPst3/7t63rOrvpppvswx/+sF1zzTX2sY99jK6LOkVf5IdR9/6wdoP/7XZRTLLlGP1v1Wuu\nxGyj38mWJ4HMHk48jTqubi4mn4WIM26T33LJL5/4c3mf/PkVVf5bMc7EbmbWB/97fUK3YfUk3ZQQ\niXtAZibv2/wYevNtnsi2AngMVeF/549EKEswI3zXDTvBBH1hp/A96MHyClf21fK52XKz9P31RPOY\nK7umfSjfP/GRHu6vdmVPlYez5SrsujqMBmSDydRfq0g6A2oLrDmJJuW+6qZqJvvZf0R/jOQACnZQ\nQEdcSqL6WQcORiJORkf6NTYenUOePRMAcnsYU7esyysWrX9uVaS7FC3cW8SNackO8W/gbsoc09Xn\n1/b+3u7JhejgcdZhgXkP1cwswLHT/sqeQVAvsSuI0ichkH7G+n4AX5U93wLcIG2cuDo9a8+AnxfE\nOQv+uqe0+vw2Ou9Xtj06Uf550/T++lVwz7TsXuvYtcrLAul35BFrRtzCoYx+ibrhhhvsj/7oj1z5\nPffcM/pghBBCCCG+XVBiuRBCCCHECPQSJYQQQggxAr1ECSGEEEKMYLQT9VcCJLwGxMQmeNltGWau\nbMvmsF1vjC2JHIkuX+y3XJ2CyOYVEbQdRAKMIOrRgDWaCgiSHAnkQ9u0I/vvmbzo0y/9tgmhQMuS\nBZf59ToIMysqIogTeXBtLRe0z2/5a9UTixTD56oBgwLMzDZjHtJ5xg64OmfDZX7FJm/P5yxOuyo/\nsPM5V/aSNi97aHaVq/NHEy+3b8a8rBsgtpqZRfi7qSDhrJMJCRwEYTrVvg4Vg+ERg93HjAxyuHig\n3zSBBEiS0zN0ZDtyP7YYIGlmzaxfWacjz6AC7q1AEgcDkb+RRExodn4RBg8US7+/2Q6RzXdyOTl0\nJMCxJANu1vPjqslxsmeQ2zZpl0RCMwN0jkj6PtsdSses3xUkmBiDGOkgoAEfpZEEciZyoAUERBfs\ngQrP/YYMxmKN0EHn78iArbYnkvqA61d5r9ywOVv2EUYaFIdQ9aROTyT1APcfE8sTEdLpAI2B6Jso\nIYQQQogR6CVKCCGEEGIEeokSQgghhBjBnjhRCX4aRmegMv/jKisrYPLEmrhULXGp6naZLfckHHJi\nC38MvS9DmLfU4e/65AdYNrkw/vjOJhJu0d0i59KQMLoC358HOlHJ/T5PfBKyPzy9UBEXgPkI0HZn\nHz/v6hw+ut+VVdO8L9Q7w370frrcyJafjIddnY44A1fWT2TLL13+mavzssW9ruy7Qj5J91ea57o6\nO8XclWG7B+LwMVrwhnrir0yIm1Y4F8bvb5tMdNujp0T8IxaS6cMnV4c1Uq2QhCdicF839X1jd8Of\n32KePzfamQ+/ZQGcBQT5FbV/7DJvCWFhm0Y8MJyUuCIiSkWcqPULeb2yJq7KxJdtQWBj7ybsNesG\nfdIM8JHMLMKFTiT5FQOOzXwPos1Jt4VHudpf5bDnsL82BUxAjJ6PmVmfYD3mhbL7KuXPrt6I/0Qm\nLkanlVE1JNgS2671bdAQ9y5V8JwiYbRxYHipW496hOOlKH0TJYQQQggxAr1ECSGEEEKMQC9RQggh\nhBAj0EuUEEIIIcQI9kQsj6D0VTAN+EHbduscKi+4slnaly1vwbKZWU3EuQ4COJfmA8cKMrN0HCQP\nEpkPwz1ZHbLtEiVxklSIvl9LtkODPLFsYFijCyojaX/JvKRXzXLBv+28fDohgZhb53ey5Z1tP/38\nC5/1PFdW1/n264UP6WTsFnlfWEYvdZetP/Yr26ez5efVD7g6+8KmK3ukOJ4tf9n8uTwdfbhniPm2\nJgPF8t0FyMPk76j1qS+bFPn9UEW/v0iuO4rPLDSP3Q9Yj0rVQEvuD3zWmJl107xsscbEci+N7+7P\ny9o13xf7CbmPYVPFlh/sMiXPqWH4/fUJpVx/PUsSbFnC2J0JGSjArkMNAnrb+m3XLAwSYMeZku9n\neAiRDsphKasQXrzyiC6xKfY4HdA/A7s/2LMSQjlT79ugRyGdHFMXfJvjZ19L5XPWp/z2kYo9gkDY\nLsk9WrDwSxhQVHjX3UgXdmG3gXRYMvbDAjmGoeibKCGEEEKIEeglSgghhBBiBHqJEkIIIYQYgV6i\nhBBCCCFGsCdieQ/vblXKBc3L7Cm3zmWtL9sBCfiBdLWr80j0ZUvLxc7dtO4PknhmxQB5t2Cvpc7K\nI7Oxs+RvtMaJEdejmMgE9ZLJg3nZEHHwIiik+xWnE28BRkgQrreWrs5kw3fHp5/KBxQcPnrA1bn6\nmitc2af/j/8nW079YI00h0jkbODD8S5PLN+IXmR/cHbUlX2+vC5bvrd8oatzofLtchVsf70dJs63\nbS4w7zb+/HZrf03XZ3nZ+tTXaUmKuYHo6fq0EUnW/P1AB0cgJCmbXfYaumc988e0IGnki/V8xoJm\nw89gkEqSSl3nD4VZIlI36WcIe0YwcR7t756lthMBHssiuVYYlH0RPHZyTEMeMPQSs+RxkI7J+fVk\n4EqEh3PHjolJx3gIbPCOX83vn2akk+sOU3qEAQcVWNo7KcPP3kQtefL5NOTrFnIqOGiEhK/blJxf\nBRU7fztaSwYUYDi/+3w0PjNGGP4B6NA3UUIIIYQQI9BLlBBCCCHECPQSJYQQQggxgj1xonZC7mWs\nxfwHz0nyvsxV6WFXVjbgW5TEqUmHXNkyrWXLdfJBd0X0bsPM/HE52MTSUNiR39QL8qNzD75RR2aa\nxm2R7DQ6AzYGYg7+SRhn5Sbhaewn/OUyT/JDP+FS6+FxPv/673J1zj9BQiwfeDJbPnGtX48CM4qX\nxA/Y1+24sv1d7m5txjVX58tTfwz/Z/kD2fKD0de5rHjclR3tz+TH1PvgR0YLF3Br198zU+Jgzaq8\nXdZn3keomH8AZYl4fakbMIP6ACcqJbId4vCgktQV/l5vJ17waKv8OdVNvKiRMFnTzCKEHnYLv7+e\n+Fwevx7zXgLMbt9Xfr2F754W4To0JHSVOmZr+BwmLhUJZ0UCeQYyZydGDBNlzhAL7oR6JMiTukXQ\nr2hPHBDETJ020q/R+RqyHt099XPzcw6R3DMj9aDYkSBN3BbpQAW5fiWEXXekDTryGd2C/4vLZj6Q\n08wsxZHOrOmbKCGEEEKIUeglSgghhBBiBHqJEkIIIYQYgV6ihBBCCCFGsCdi+bnp5dnyTpOHXU7N\ni8JH0mOu7DKQeVk4XEHEvQj2NcqvZpeYkT6QxC/cHw2Hw2A0ZlCzYwCBkoq7eC7DJMveHdOw92nf\nnP58ezbrOITfVZUP5Gxqv976Rt431mdTV+fL/++Drqwq8+1PN8g04ISF5dtPJBhxH+mf85AHcD49\n9QMavlg915U9WOVhsD0xRI+EM67sWH82Ww7esaSgpLpo/flt7Xobez7JB1+w+6MnciZmsbJeVrBQ\nQLhHUbZlsGvFZqR3ZSSskRT5LE82jTxpA1wv0GNafQFp4CEL4MVQYH/LWE2uX1fkHwdF7/sBc/cT\niOT9jAjw5WqxnA24YR2mTzgohonXfj3ceiz8x18i0jFuiw6mGeAl00E/7HHtPpaJHD0gNDOQgQhF\nAYMjUu3q+P2bFSRU2a3HPjKhrOr8dmZLf5zTOl+RdHMLJPy6hgFLy6k/l52J79c1GUwzFH0TJYQQ\nQggxAr1ECSGEEEKMQC9RQgghhBAj0EuUEEIIIcQI9kQs34bE8vPlRrYcib3IZjk/GvJU6qfCYVen\nD14oLts8ebyqSGJq8OnkBUt3dTtcLSZSQ5SkNmM1JqQGcOQSSydnM8QPmpabrAZmectmSycCJYrk\nzCFlM6+vz3MrdvuCFyHPPnnelV1+9GC+P2YKEzpo0EnvpcdD6YIr24h5ivlTcd3VqfFimdn+kB/7\nIXvS1Xle+poru6zJ67XdhqvDmE8hlbolYjKJ+V7AmIpJ6fsPmVTd9WvsP2aX6LNg4Q4ILDcWgs/u\nWDzM0PkDLxu/w8kSkseJLJ0av8eqy9eLC7+/yYBHC6b3m5lZQeRvSAfHmQ/MzOqSPEtmsB3y3IhU\nYHb2sKvTDfikieyRxARxfMYyY5v1Mzjlng7wIbNJQF+PRq77gMTyzvzMGIEMKCgGDKpAsZwOjmAD\nkdp8f6X5wVI9O7+4+gIWZGAHppEXRCwnIf9OLF8jg45K0nYt3A47rT/u2M1c2Q5JWx+KvokSQggh\nhBiBXqKEEEIIIUaglyghhBBCiBHsiRM1KfIZ5xcx/43y8faIW4f9JPt0m4d2st+OifJh82qRLQfz\nns209E5UYElzbofEI8Cfr8nv9YGIRE2d/+Y7JDg0EbGgJ79Dd/B7eUWFBE/Xo+Pi6wSyreRm7yYO\nGNlfBHfrwoUdX4fIMPsO505SS1w1RofGDPO7eha6mrdxS/yHisw6fqzPgzS/Kz3k6jy7ecSVrXf5\nth7p9pNj8syrvK8v15iLt9q9WZLZ2EvqoUBgLPFXWNArDTRcCTkXes/ky2Xt74/pggQOxtzPa1h+\nJOmLBYRylsSJqpYDzpfdouyZBPdfX5A65MEYqryev2ep8umeu4H4Od2Q60mfr8T1KTBokpwLlejS\nyjoYRnsReObRQOPVTlRPP27JRcV7hGw7goCYyDOehm2CA9UnJjKywOjVYanssYFuIwsX7QNpF3DM\nYufPZUL6cAmpnB1JAK3Jc7gd4HxdCn0TJYQQQggxAr1ECSGEEEKMQC9RQgghhBAj0EuUEEIIIcQI\n9kQsX7c8YDCB4LcovJS72fmpyFPMZdoJEekmJEwswMzVBREvexZsOSTwjzqHubzHQiVj6S8Fhmuy\nTLkEIjSbyZ6F7bltkSBIhs/oZHImEfxBuJ+Q822IPIjt0iz9IIDJ3PeN6Voe7rm1s+XqMNqU972W\nzGR/IezzK8Ipn0te9GZBc4chuHPDFq5O1665stNtHiz7qF3pj4kwm+b3w5zI2C3ze/G6k2TNklz3\nuoe+TvonE9kDhm2m1WJrT8V2UtbmZWXtnze2Q2T3Ni8rJuTxSWaWx2dC7Py2y3r1o7gf0E5mZuiD\nhyH3v7Fnia+DA1kurocH5ddj18ZtmwyuSYVvlwTPU+pGM897QB9iA3xQXGd9qh8wcIX4zC7c18ys\nA8G+IMI9PudxAI4Zd91dERkEwAZ60M6Am2JiOXZGcrHY4C+4Rd1AKDOzjsr8EO5Jnt8VCdItSfjs\nUPRNlBBCCCHECPQSJYQQQggxAr1ECSGEEEKMQC9RQgghhBAjCGnI9NPfyh0OsbOFEEIIIZ4hXOpV\nSd9ECSGEEEKMQC9RQgghhBAj0EuUEEIIIcQI9iRs8+d+5dZseQL5iWXt3+2qXe9Sre3mh7+25YO1\nptv+FCdLKOj9/urKFdlykv8m+saPvsXVefPtt/kVgYKEtZUkVK7rcSZrkma2A8GhLCeNXOUOQuVI\nE9id732fK3vbG96Zb5v8ThxJt+r7vCyQ4LnQkWA9CE+LbDZx+lM1nBBJgnvXv7rVld3+9rysICF2\nkZxzwmtFAvLYtiooakloX0dCAhs4v5YkHL7nPb/iym59x5uzZQyCNeNhe33fQh0GC17FWdxZiK0/\nhgTheokE673vXaey5X/2wV92ddZJsOXabr489/mtNsNnhJmV3epzaUnw4wKeG/XEt9Oi9Mf5r992\ne7b8plvf7ursBB80u2v5w2s3+bDWJpEHHFyrKUkOLpNvmAnckyW5uVl/+c335n3xTXfeQg6JrYnX\nwd8fidx/+HBkWY0lCWdcXsjPZ385d3Xi0h/nO97/jmz5jlvf6Opg8LOZWQXtWXU7vk4Pz336EPR9\nqot5WR99P+iCL4tl3p5veM9vuTo/9zO/TY4hP66W3euBXCsIrY2F71NFQdquXMLyrqsTSeppFfJt\n3fHeU67OpdA3UUIIIYQQI9BLlBBCCCHECPQSJYQQQggxAr1ECSGEEEKMYE/EcpyNvK1wxnYvyVU4\nrbOZhQXOdu3rzBpXZGu7KBgS0RNnnzaznsx4jRTEg0RZmDmPfU+2DSJkRd55dxcgIe6fuTo1Ez2r\n/NL37KAIAWVTNlN48vuLRX6cfeHlxb71giiKyYHtsCONDn0oMCGd0IFh35G+wSaNj06YXD3zuplZ\nhHoVGRkQOnLsODBg2OmZwfnQy06M2xjz80OR3swssD4c8fqRv9uITA+rWT8gpHfK7n8ygGFW5/Xm\nO37/811/LtMuL8NBD2Zmu0QaD9B2rAm6uPoCrkcv0kZ2raCtOtI5OvI8TXBgrG+wJ2CPgwBI3x8C\nDqQxMyqW4/OlJ8+bggzUQSE9dWSASOOPfQ3k66r3217sLsj+cto4cWVkvIsVKf/Q6sw/K1EkDz15\nxkff0RJ8rnXkipKxGGZkAIqvQz5s8fqRe5R93scib5hAPsPK4PdXxVwsL4K/Z5iQXpJ6Q9E3UUII\nIYQQI9BLlBBCCCHECPQSJYQQQggxAr1ECSGEEEKMYE/EcgzLTZBO2hLRrCHeJQrpPdqoZhaJrFg1\nUEYkcpbnWw1pLSbAgkzHhL+eZPqur+WS+PKxC353y1y4i0RMZr6mwTGgwH0pAqYDkzbvybt5KvP1\nqNBI5MWI9Yj4GYnoGerVgxUY2M3c/s0skbbqQG5P7NYifbiEc46Vr1SRNi7ASB129czw76bA+isR\nvVNYPRhj0KZIHRShzUga+IDLVxEjtlz6FTGN/MCO3/++LS+tzmoQmsm9dt4HiFsHA1Jqcv9PBjxb\npsHLy33w/aXGPmVeaGYp8Tiogj2HA0ndL1BIZ/e/K/GwOrxfw+wA5P6oSt+gDXyIJDJgo4r+AsaQ\nt1+9TYRmNuUD7p+koZekPfHjKZI62Fb0OdX548R7jd/H5P5vV1/BnvQpHEiSSDslMmALn0tsAEUo\n/PmVJSS5l/6eKaJfb0K2NRR9EyWEEEIIMQK9RAkhhBBCjEAvUUIIIYQQI9ibsE0I0nJhbcSRaEhg\n3GKa15vNiB+w5t8Ta8jVKskM3ESzsVCsDpFjoZXoJKFfYmZWESmihHyxC09tuTqTNfgNv2ShaMTw\ngkNgwXMM59CQtLhEfl9Os3z7HblWLTl03HxckPf+Hb9igbPbN8OsIe9qkb7BJDNwC7qWzKDe+utQ\nN3m9SALrphO/v97yzlGwmdAJPUhKkQSAskC8iEGFLGyT/E2GJR2bsJ0k1GI/Y/tDSuLLVeS6r0FI\n78bm0tW5/IIvW4cHx4I4UemAD7tdNnn/XJI2wEBexoy4HKxVGss9kJl5z2dB/JyAfYG4oixIEx9n\nzKkZ8vc6zXhkQawQXso80IY8z+pl3n77J75dysa3y85mvt6s83VmYfVH6TJ4N4191uFlSMRNS5Yf\nEwujZE889Jb6gX7uIKhnB6GgdNPMico/HyLp+1Xp79Gy3IFl4kQxl4q031D0TZQQQgghxAj0EiWE\nEEIIMQK9RAkhhBBCjEAvUUIIIYQQI9gjsTwXyVoM0iq8dNwzcQ+kw20SnhbZbOUp39Zs179LtiQA\nrGYJnCuOycysAOEuEfl8QsLh+idykbzf8ZLc5PjBfNtzv5126aW5CLIiEzgZHUiHPZGQU0nabr6b\nr3fYn0uabfoyMPybzTVXpzi7zx8oSNyhHdbV0bNkYa2RiNA1JOTVJPjx/DYJg8QwuuTl0wPsQOEY\nwsBZyPG6B3J+iVxTHDARyaODC7A5zCtNJEjP/Xk3ICyV5JTajDwTZhC6uFH7+2N9a9eVre3mZXHm\nxeTtNf+QKEB4j2TQAfG1/XaSv8ZrpEExzLfu/LnsRjKKwwUhsmBN354lSMAFu57kmYfQ4EdW0T27\n/P7aJRmgAc/Y+cQPAtg85/tCs5WXHZ77a4yDgBh19Pc2C6gsoR0qUgdt/sSCZjEY2chYgcIfU8sG\niJDPQ7dtNvgD9scHJpDPJxDJy4JI5IXv11WVf66U0dcJ5PPJyICeoeibKCGEEEKIEeglSgghhBBi\nBHqJEkIIIYQYgV6ihBBCCCFGsCdiOc7a7J04ltDsJbk0g5meSUptaIlsDuIec46Z0NiR2cL9ikRS\nh5jm+czL0WtEHtx8GsRysvvqsv3Z8pZ5Qa4jMdGY8ouze1+Krssbix1TVxDRcy2/DuWBJ12d2cHT\nrqxvQCy3Y37bO749ezi/nqW2E3A29Ipc88gkWRC9G/LnCc7Obmb2FEirPUkQZ0rnbJrXm9Ckeka+\nXsEEY9L7UwdluGxmgfR9NKaJO8ydcZBUMTGdUZE6JXkmlPBIKBr/jChakgCNZUQQD0RkDyjlk2Yq\nWCEwJXJvn/wAjRISw0kIt7VOIjfbhgvRk4TtSGTlCp7NkcwuwZ6LbtukDibsm5HeSfp9re9GAAAg\nAElEQVRPRQZ/zMr8fJZbZNaELb+/Nbh+R8nggW0/JsbR4SwKZmZkFgx8VnXJ98U+5nVSQdLCO983\nMNmczZ7Bvlthz3kEP9cv7m/1gIJY+PMryxqWvSA+mfgyl1heeSHdyMAHNhZiKPomSgghhBBiBHqJ\nEkIIIYQYgV6ihBBCCCFGsDdOFP4ODL+b9ux3WvLbcYLfd2vy4/g2ma0cnagZcXgK4jsQDcTBMisr\nCHlbI7OH12f9j+qLrfz33dkRH7sYNvJtNed3XJ1Ijqks85PpBoThmXlvIZGNp0iCGCN4E8UFf0zF\nWb8eeBldedDVIXmqRDwZ9vcC9oSOCDtMjSuhHdYr76YdnJMQS3BTOtKe5Fd9K8EZwoC+S5Iw+JVJ\nSr4owHEyV4WFbeLWexK2V1Tk2sCKPTXDYP/kmFiv7sD/a4hPtpyQ0FoQ3eqpX29REp8E7n+ivVka\n4o703h1hTTcrchcGA1bNzBJxRScQcLhMPoyyI/2lhIvFwjZZyCLCshqJXYUfF1YRn2xSeW9pcSE/\nv3lY98ew4z2bA7CpIxs+3PeJ01uuDOlo2KY/aQw0Lsnnk3NTiQ84oWGp+dOEqbBUjx0SlkpCsrEv\nJBbWWvoQ2aLKP8cmU/+5Nplsu7IZ1AvRP4cTOcGOOIJD0TdRQgghhBAj0EuUEEIIIcQI9BIlhBBC\nCDECvUQJIYQQQoxgT8RyFLQDiHMtm+mZyOapyustmRxGzMQCAvhQejYzq2oiRw4wywMR/IoCJMCF\nF+mW57xoHWf5etXhDVcHg0qbhdeQJywQE86PSbmMAqxOJgoWHQmHA1E/LHxAZtrx0njXgQi962XX\ngkzAjTLtkNA3My/OssnLG9JWGHBYERn0wMyXVRB2yQZVzCq/vwgC+hDx2owMfGBieUsEcViR9fMh\nMGm1Z2mbUDbEm09EMGahp0tozwtTMhBi3ffPapJfq2bNi8Lb637QyAKCUWtyTO2AtD8miJfB339T\nuCcLUidg4qiZTeG5uBv8fbzsSGgtPnfZ82ZAf0mkDg4CMjOLcK8FYurXCxJMCmm3BRu/tPQC8/Un\nnpctd1t+QMGZJ9jwj5yGfNwGUtbj5wV5CDHBH2ECdQUDS/A5YubDYc3MIukLbr3o60Tse6ROWflQ\n0OkExHIikU9KX1ZB2Cb7bO+Sb/P4V3gV0jdRQgghhBAjGP0Sdeedd9r1119vL3rRi+wnfuInbLlc\n2tmzZ+3kyZN24sQJu/HGG+3cuXPfymMVQgghhHjGMOol6oEHHrDf+q3fsnvvvdf+7M/+zLqus498\n5CN26tQpO3nypN133332qle9yk6dOvWtPl4hhBBCiGcEo16i9u/fb1VV2c7OjrVtazs7O3bllVfa\nJz7xCbv55pvNzOzmm2+2j3/849/SgxVCCCGEeKYwyqY6fPiwvfGNb7RnP/vZtra2Zj/8wz9sJ0+e\ntDNnztixY8fMzOzYsWN25swZuj5qeSjABSLEscDUACnmbUmEXyLlNlOUh4m0zoQ7Nh061iF+aAES\nYLNLZl4viXS4v4JlL7suQFLvFkQAJAIsHmgshnWFBKnJTHYNDRETt3PhtiuPuDo7u/78Isj8YdOn\ntselP78Cj6EbJpZjHHlgieVU6gSRncy8PiX9M4S8T7Xu7jAriViKci0TRBkFiOvYNy9FB3HSLOk8\nsNnRXbXVErmZF9eHzCLfEDG5It16CaL35oav1BDruOryPlyTOos5ScqG501N+gFLTUdIyLhNS3Id\noI1nRgZ6kGPv4W9qNqiCiewdzFDAhOZmQCJ0JCdYFOz8YFs4usbMmi0/eGejAul/28vgzzrkB+9c\nvp4PePkP/+l+V6eOPv0cSeQ7CyaI9yh/k3smxXyATU2eN2yqCrzVCjIwKJD14oDvWyKRxgtIDI+F\nb/NpRdLIq3wGj9nE1ylL/zkai/wYEmm7lpR1w8blUEZ9E/WVr3zF7rrrLnvggQfs0Ucfta2tLfud\n3/mdrE4Igb4MCSGEEEJ8JzDqm6g//uM/tpe97GV22WWXmZnZj//4j9unP/1pO378uJ0+fdqOHz9u\njz32mB09epSuf+/vffrr/33F91xtx1941ZjDEEIIIYTYM0a9RF177bV2xx132O7urs1mM7vnnnvs\npS99qa2vr9vdd99tt9xyi91999326le/mq7/ff/k72bLQyanFEIIIYR4JjHqJeqGG26wn/qpn7If\n+IEfsBijfd/3fZ/9zM/8jG1ubtpNN91kH/7wh+2aa66xj33sY3R99EwChKCRzDwaxIYhb+y3YzaP\nO+Q3WkN+dzeiERXELUDYT5ihh9+dO/87dJx4J6ItIIiReAy72/lvxcwhiMwrADemH+jGGGyrIMGT\nofXHGbbz3/D7jsxoXnrfKUFTla13TuI2CU/DcM+Bp4deT2Ihr3RG87wea07mUuEM9In8OB+YLoM3\nCQusJBQJvRfiHzJHKWEdv23WLAlCR1loLrtv0cEaku1J1Bh6by9KuMYz4tQQR6mAY2rJ/ViT58YC\n2qAj63Vx9Qkuzff9gjxL8GIxL4SVlfB8mxA/r6UGSH7sLfGfygHmSMBrbmZl9MewaHLPhuVAzsl6\n6zFvv9T5lN7DG4dc2Z/fl0f13H96y9W56oWX+4MAeuItJXbd4dBZUGkK+TMvkL4RjKQQw0c+y4+O\n5EYu4+pXhaLwHloJZUWx6+pUpS+blPl6MfhtByLt4XOpIw/PloRtdoncuAMZHdP55je/2d785jdn\nZYcPH7Z77rln9MEIIYQQQny7oMRyIYQQQogR6CVKCCGEEGIEeokSQgghhBjB+KmL/woEJ7OC7MoE\nVRbACdthaiYTNosy3183IWGGxMlLzFwFmByZQCxnIXYNsenbSX55CmLzdm3eWEXlRbpQ+LIewyiH\nho2B2E2lRyJHxw5mJvc5aRaYAI/iOkr6ZhYbIn/D/qjpTeihIVo0282MuPTWgcxbkL9PqEANAx9o\ne5L9YcZiN1AsbyCMNRIJuCVhqSibJ9LP2Q0YIeSUSfIFWbGAgRYdE6iBJZGJY8VGqeTH3pMbsib3\nPwYOpuDbqS18WQ33ZOMdYD64Bbfd+uNcsoEsUK0kz8BAQhZRrWWBqokEqmJ4YU3E3SEDV0oy6KBZ\n+gsxgU40IUHFWMfMLO3m58xCgc+e8wLzQw/lwY/Ty3wg59ErfZnbPzk/I8I23losCDLCR3csZq5O\ny54JsPGChKeW5LWgZSnSuF7pr1UJ4ZqTwj/4ZySAs7L8OkzIADH2eejeLVjIa09ee4ak+V4CfRMl\nhBBCCDECvUQJIYQQQoxAL1FCCCGEECPQS5QQQgghxAhCYvHEf5071KTEQgghhPg24lKvSvomSggh\nhBBiBHqJEkIIIYQYgV6ihBBCCCFGsCdhm7/3z38iW64wvZAEey1Ln1C3OZlmyxeqqauzQ9ZbQvgd\nmx29Dr5p6pDXu+vd73B1bn7fL7oyzH1jIX2JJRXCFNusTnAb95shOWVWQAjZhISN/au3nXJlb/3A\nh/LdNT48rV/u+B02eaBau/Azd/ckSDPgjPSdnwkd65j53Me29evd9aF/48pOnfpgvj8SVMhyJkvo\nUwW5s8qKhErG/JxL0jcCmf29x/ZsfJ2f/8V3urJffMMbsuUFCTPser+tqszPb33uw/021ueubH2+\nli3PZr5O2/jrvljmZXXr2+X2t78lW77tX/68q0PvBwg9pLdM5/fXgxMRmCPBQoEh7JYFTwbyvPnV\nD7w/W/6N229zdRqSxNgnCBOmjxYyu32Rr3eBBJw+emHblT345Nls+fyuv7dnM4zyNPvsR/5dtvxf\nvedOV6cg4aUBAnAjOcGCtEsFqbVT0u8mLNwT7oeKhV+2fltv/NB/my3/0h0fcHVYIGYPYaV9XPN1\n4HMN+6aZWYgk1RWenyUJ24ws0Bg2/9/d/t+4Orfe5vtnNcmfE9tbPliz3n3SlV37PSey5Qfv9/1u\n0VxwZZcdzvtZ15IwYZZBCk+B9935Hl/pEuibKCGEEEKIEeglSgghhBBiBHqJEkIIIYQYgV6ihBBC\nCCFGsCdi+QTEtTWQ3RKbtp7Iw9t9Ls6xicIbMkv2Muby2dKIZEnWa4n8icTCb6sHeTiQOpGJ5VAU\nk3/n7bESaYSCzBAfwJ8skz8mBsrfbe1nPY84UMD823pB2qAkZSiNh5K995P9wSnXA0Net3fz8+nI\n3xmBtGeJs6MT2bWi1yZfLxoR55MXPXuQYjsiljMubOcDARYLf/3a1su1KI2vzZgk7++PqsrvtWnl\n60QiOXd9fgxs0AHCgnwTu+4gljOznPWzCKJ1JNsOcfU9GkmfSnhMhLbxB9oRgRqfn2xwBBv4gO1S\nkfXmMy85b8w3suUmeKF5vuYH/TjQXjYzdnHwbiePRfY0tR4H6pDPmTKtFstL8vEUSZk7psWWKytI\nf7GYtxU7zmgwsIP1/d6fS4A+HAJ5bnRkPdqiOT0ZkNK2+fOlIo139NnHXdlTj+eDkx596DFX5++8\nxK/XwQCUp7b9uZRkZEAsx4eA65soIYQQQogR6CVKCCGEEGIEeokSQgghhBiBXqKEEEIIIUawJ2I5\nJv0WII0GIgoviT2YQDpkEvmi8Em5WyEX92qyXiLCH5OM3XrMiYdt9US8Jt68FXDOdBJpjM9mkixZ\nsYTEciaDMxJKxyTVOJBI7wTbL4nZymbJTpDWTeVhkraODRFpLrVnewGDHMhgglD6MvDDrSDHVLJr\nDDItcXktkk7VNVA27PLZDojkW9skXZ71lwkkJBPRNJZeEMek6unUS8cscR6FaRRGGYn0u0ANalhv\nZY2L4Pmxe5auFyGxnLUdS5cGWD9HWdrMrIN7rScH2jPpGKuRfjcjAvzlG/uy5f3r+10dTPRndGTb\nTBpP8LEVBg5EijCAiQ0MqMiNNIO2KlnkdbtaTJ6Qm7slx4CnzGYswOdiH9lgJTIoJuT3f0H6QST7\nG3KT1I1PI3/uVVdny1/43JdcnfnsgCvb2c53ePU1B12d2dyf831fOJctr+3zfdEKP5iGCv4D0TdR\nQgghhBAj0EuUEEIIIcQI9BIlhBBCCDGCPXGiavj9Fn8rZoF1DfEtljCT9U7pvYLN0jtRO0UeVNaS\n/SXiLTCXAYnEWwjwwz4LzQzsfbZDr4fUwZBHsu1IZqSfQD026zkjgQOVSAhiR8IaI3g2HfMY6A7z\nei1xsJhH1CUMBR0WRrm7RJ/EH2ciwZaxyftnJLPPlxXxpKDLFkQ+KFmoI4SjMg+NEWD7LPSUdfMK\nQjKZ48K8AvRVenL9mCPofJwBYZtsJvuClOH+2POG+TLO2SPbZs6e3x/zn1b/PRuIu2kkiDVAeDAN\nBaXhs3nZlAUOMzlmmj9jE/HQIvEI3baHBKNeLMyXEvEridtUOE+KuDGkf07gfp8QP69n8hbSkv2R\noFkUHFmdHp7XReU/59g9E/vcW4rkmLg/utppu/yYD7988CuPZ8tbF552da7/4f/Mlf2vn/iTbPnI\nlf6e2d7216Ht874w3yDvEiy0doBzeSn0TZQQQgghxAj0EiWEEEIIMQK9RAkhhBBCjEAvUUIIIYQQ\nI9gTsRyFcAzbS2Q2763KzwJ+YZIL4hdAGDcz2y78rOO7EMBJ8uqMpYvFATOtV4nMSF/n22K7Y/Kw\n4bZYkCYc/JRthzjVJYjI1RAx0vyM4igqm5lFYno7mZ7I7ixMFCVZI6I3mz3cBdYNGBRgZlY3q4MK\nWxZQB0maJPvOYuPbuALZPJLp4CfsLoX2KwdGRs7m+f1QTLyQymTzfbBeNTAsdVnnbdWRoMLFwl+/\nps6l2OXSC7AeFtbKghhX9wV2LijFswENRiRg3F3HQkHpxvCYyAAYcn7Y1+m2mWyO9whZbZ30jRIO\nK5E6LLzY1yGFTKqGY0+JifNssBAOViADEcgghwmUVaTOkGErNNCYtHER837Wkc+UHgMx612/P/Ls\nCiCbM4mchZf2AwZ2JPJ8e/D+L2fL//S//C9cna/++eOu7C/+/MFs+e/9w3/g6nzmD+93ZbO1/HlW\nVf64t7d8GQ6c+WbQN1FCCCGEECPQS5QQQgghxAj0EiWEEEIIMQK9RAkhhBBCjGBPxPJdkLha8OY6\nIiGiRG5mdqGaZ8s7sGzmJXIzs2UBM9IT8bKwgcnKQGzZtOOrxXKWNI5eYE9k7BKTx8nWi57IoLCp\ncpiXbAlniGdtwsRZlBXJ/tj5IZG1Hl0NBfhh9JBc2wU2g7rfWgdqKU2gJ/0MA5ID2XZion7I7yEm\n5TMO7N/It0MkYCZZTqGsKvz59eS6b23lCcl952d6Xy68qI/J9NjvGCyxnEmyOBtBYrPdMyEd+z45\nhtSR6w5tl0hieaDJ1QgTy1nPztuhJzI/3R1si4negVz3EiTuntzc7YB7m10/FpTdYjuwZwkbZOCa\nis3uQGYMgL5YDuiLDDbjBWlOlzQeycCZCY45IuI3m8UAB0wEOmPB0IT7nEceetSV/d2XX58tnz/n\nB4j8/n/4rCv7sde8LFve2fHPja98+RFX9sobb8iWnzjtjyn2bDDN+O+T9E2UEEIIIcQI9BIlhBBC\nCDECvUQJIYQQQoxgT5yoRZn/DlvDYdQkqfACCdvEAM4dEgDYYBKcmTXgXPVUK/CFZbH6d+EJCRxD\nl4LtMJIsswKOISTvUkTYFrugJfE78ChZMBujAJ/MovdZWDO5jEwWdEeuuwsTJDoCC0ENzkMb5jGg\nC1OQvzNK4hG4QEPaBmxW9XxbVD1gjhnUiyzdk7D/YO5EzWc+jHZC0j2x/ZqFdxt2ibewtZmHAO7u\n+jr1kvUh6NcD7j3mZAUSzup7P/GmyLXCwMGe3Mfl1PsW5WxfvveJb3P0tBgd8YrY2aGPR4NDqdCV\nHwPRg6whbdzCs4M+Twd80gTiwtKnEhwD7fvk4DHwl2Q6W0H2iAGc1YDgSQbrw6yfRcNQV1+naPMy\n9nyjbYdtRSq1LNQ1+M8e5MjRg66s3s3P+fN/cp+r87JX3ODKDhzKHeh7Pum9qRte/N2uLBZ5u5x9\nasfVufrZh1zZ9u62KxuKvokSQgghhBiBXqKEEEIIIUaglyghhBBCiBHoJUoIIYQQYgR7JJbnkloL\nyW9MLN8iYZs7EFq3JGJ5ywIOXVabr4MzhZtxcRWZekfWJtDMsfbbmXREUoUyDNa8eFD5Ip1Ynp4f\nSLLlsDhKFKgjkUEDs1bhlCO5LnTmdZjBvGvYtsl1caL8sPPr21yYZs5qQQRRlHlZm7PjRO+ZieWR\nBXDCtpyAfwnma/l9tG/dS87zub/XMAh1y7yIicGaZmab27lY/vTZC67Ogsjmkyq/t/et+4El7hhJ\nN2CDDlzSJO0/A8JSo5fIq43LXNm+y67MlouZDwWua98Gfv/sb15/7J0LVBwmbLv7j7Yd64t5GXt2\nBpaaidshvnYgR1piWOqA+8rMLPa4TKRuduywYsCEXOMhua5O6wdjsCuBInlh/kMl9ljGhxi4vaX8\ns6iP/jOzCL6s68gHG65HnosPPHA6Wz5x/XPJUfr2/PT/9fls+bnf8xxX57KjG67s85/7UrZ85VVX\nuDodaU8eWjsMfRMlhBBCCDECvUQJIYQQQoxAL1FCCCGEECPQS5QQQgghxAj2RCzfKXJxtQGBcUlm\nOd+KXiyti1zs7Ngs5wPeEwNL605+5uwBE1nbhMiRM0jPnRFHb6Pz4uWkydebsHPBQG+WMkykwwYk\n2XqgV4epyYOcbmNtxwROcn6wg7IiXZZJpDgjPTtQRgfyJ3WOSaoxypgktpm6yigBs8Mk54cyZiJy\nJqOC9pvOiBw98fdfW8P9QE6GDbzYhWTzzW2fILxc+BtiNsn3N5sOEJNRGDezRIXm1c8Edi44yGEy\n3+fqzA8edWWHjj9r5XoXNs+tPKaOicLsOkCHYbI0e5gFTOJndejgDxw1QtKtByTqozBuxgXx1ENf\nJANupmRwS9XnfbEkYjnOWGBm1kN/aYmM3Q0Q58swUGhGadxJ5GYRHwpEdmfXD/sGE/fZR8GACQNs\nd3vhyq64Mh9okcjn6oNffdSVXf3sfDDGdO6fU1/98oOu7PLL8/2Vle8bF877Z1BZrk5kvxT6JkoI\nIYQQYgR6iRJCCCGEGIFeooQQQgghRrAnTtSyzHe7hN+Tl4X//bMuiKcB61G3iQgleNLMI2KzebPf\nj916DQlPQ7cJ0z7NbJ3MOj6Hn49L8nt9AW3AZllfkt+zF/DLd10PC2vEn9lpRtkAeYy1JPOWEroN\nLFhvwLaGKlEYYsd8Ejb7OwaM0t/Y2Z8s0GcjOZuCrOj0sYEn2EM/q5fet2C3Udt033DZzKylaZd5\n/4ykXWLhPQkM7hviIwbm3RBPKsDGetbviOMS4blF3R+yP2yDQEKBA1vPH4ArapMP6US3sGXKJw2D\nhUXiP7HnCz6HmecTWVAwULJ7u/PPJeyfLPxySo6zavN6LLy4J9d9EfLPo5p+zqw+v8p8P09kW/g5\nE0kdPErWvIkEGkdYsyd1eubeDfnsw/vjL7f2/+fpJzddjUOHD7qyySw/oUcefszVWd+3Tspy3/r8\n035/VeX96kgCaYeib6KEEEIIIUaglyghhBBCiBHoJUoIIYQQYgR6iRJCCCGEGMGeiOULECSXMBt6\nQyTLlpR1IMUlYp8yWbEHCZhJq0zwHTLT85T4d2sgaLL56Jk0XsF6BROaIWSRhaIxSb4A+ZS6koSy\nzK9DRyR5Nh17D2UsRK/DED3zYnkgAwVSR4JRoT2bevUs5GZm1ueBcSzEMrUs2A5uJVInlCQM0rU7\nC99jbYyhp74NGMvdPHCQXYem9G2FIavbOz5Yr176WeoncM77981dnR3SaWeTvD0ns9VheC1rJxbg\nCtIxC5B0ErmZRRjcEomUWy93Xdn5s09myyUJHF0sfXsixZRI+aTvR5T5SZ/q2YAJOGcmpJekrTp4\nLrbsOUkGBiGBXL+CyeY9CuLk+UbuvwSDKlpiYy9t9YAQ9qFZ0OEtsF7rrzsbi+EG2LDnIj5j2fOb\nBbEGHOTgB3GxAVpDPh7ajnxmwglO5/7TryXnd+5C3lbr+3xAbTX1V2Lz/Fa2PKn8+ZWlP87l0M8H\ngr6JEkIIIYQYgV6ihBBCCCFGoJcoIYQQQogR6CVKCCGEEGIEIQ2e3v5btMMh0cNCCCGEEM8QLvWq\npG+ihBBCCCFGoJcoIYQQQogR6CVKCCGEEGIEexK2efub35Yt13X+LteTsLZAEir3X5EHoxUTH9q1\ns7vtyuo6r9e3JIyOhEgWEKj4K+845eq85Y7b/IFCyFtJAiPZDN8Rf4IlyWw4y3kkoaR1z8ryELLd\n1jfwBz9wuyt7w2/8i2yZze7NZiZ3l5Rd454EAMI50/nTaQhqXtaR4Llf+/m7XNntt7wrX2/iAyTb\nqS9LM6gTSYolBnKamUHfL5f+OCdLf/3KRX5+ofFt8I5f+2VX9u5TP58tY1CimbkgTzOfn5rItWLn\nh1vCoFszsxh9wGGLfZ0kzd55+wey5Tf98hv8IdEQxLCyDvMfMLczRH9dAgn37KDtahIEWZNj+I07\n3p8tv/2Wtw46zuD6ng8SjOR500HyalP4Z0I/O+jK2ph3/knr74/p9pOu7J3v+2C2/KY3v93VKUka\nbIFhiSRolq0XMPCXZYK6h64vo+sVfn//8vb8+XLbW8hnAwkmjnBN+44cU8qvadg95+rMGh/8in22\nm+53dVpS1pX5Nb7jzne5Or/0xjv9MWzlz4TZ+Zmvs+kDMSOcczP1fbje78uajWW23M7Is9rvzhq4\nqB885fvipdA3UUIIIYQQI9BLlBBCCCHECPQSJYQQQggxAr1ECSGEEEKMYE/Ecpy0PYBI1+x6ka5v\nfNl0M9/Q7ACRCYno6bxZItcyL3jIK2fvPUHrYXbrOHCWbJS2mSKbQIijjjWZrbw3EAzNS7KMHo+J\nnIsXW70TH9j+mKzs1iPXkzQeeqVDM167KhcR6/nC1VnOl66sA6ERRXMzLmMXi/wW7Hf8imnL36YJ\nZryvEpl9noAzrXfkmPre7y+CmM8GFGA/N/PhuoEMfGD3DHbkSGZeR4pI7mN20+JNQvoP9nMzs7LI\njz2Rmw1nrTfzonBV+GMKZOCD2zbpxOTWdg8KFKrNzHojZbD9pvAGblPMyQ7zdpl0Xua1hb+P3LbZ\nvU2eE4s6v7nLwvf9VKweqIOfOxdXJA9+fJbgB9jFFUkZrFf69kytl6NTCc9m2vfzg2JCfL9NpPwe\n2qX3z7LUeSE9kXsLqUh7roEgvm/b97v953wbVDW8E6z7c7lQ+bKtCaxHnsMtuR/cqJFvAn0TJYQQ\nQggxAr1ECSGEEEKMQC9RQgghhBAj0EuUEEIIIcQI9kQsDyCpFSBotksvjC2IkLa2kb8Drq37d8Ky\nIJJszOW6zjuIlmiy8mp5MBLBEAVGJgFWgYiQTtom4i6uRsTdJSlLfS5j9mmYWJ5cEjiTiZkpDNeG\nSuRELG3zskjXI22ObTdA3DUza8p8vXrNX5f6IEkxP7CTH9KcyLXEWY27uWzanmdiq7cjiya/fmU3\n7FYOKGyTlHE+GAOT8YkcPUByZnI9Xc31fTbSI4f5t0z0xv6CfdrMrCRtgHdIi5KumUWyHh55ZPfx\ngPML5LnRkfUKJ86SBHoyIKQOeV+s5z6dvJ9f7sribi4nh/asq1M2q8XynrTdkg2KgT7bEVE4EEF8\nAs8l1u9YHyqxIksZH/DZYOQzJRApvofPrFStuTo1PNNj5dPlE0blm9lkcT5fjyTXlyThnqXeIwXp\nZ25QBXlWT0giewn3bcc+j9nAAHi3YANg2CAAdj8MRd9ECSGEEEKMQC9RQgghhBAj0EuUEEIIIcQI\n9iZsE2Zkx1m5S6LntMSJWmzm74D7Dvp3wmpOyvA3ZxYOSX7jTiyIDQjsN1j4vbUg/hObdTxCWU98\nkhaCEfvof2PfbUhZm6+3PdCJcqoR/cmZbQuOvSPv741fr1jknkbB1iPBgQmuX+7RqhoAAB3KSURB\nVBrY07Gp6sq7AN0+X9Ye3M6X1zZdncB8oMlGtjzpfSBfveMPvprm9TqfmcdxQbO+SiKuAbpFzCGg\nigJ4Lqy/sP1h2CXzO5BJ5ftG0xAPxYVtEmeIRdvCeiw0k2X2tRgmTFyqYlDYJnH/yHGiB8JCQbvo\n+9myyvtiPTvi91ftc2XFVh7OWJJgzaJZ3UFb0jc64nO2cD7s1q5YMClsvyDPDeb1lLAiC+6NAz4b\neuZuUT8nfwh1LPR0ml+HsH7A74+5jRcgpLf1wZoshDT0q50oL+ia9RBMWs/8MW1uuCKr+vy6L9b8\nMe3MfNlyCvca+ShibmEq5EQJIYQQQvyN8g1fon76p3/ajh07Zi960Yu+Xnb27Fk7efKknThxwm68\n8UY7d+7c1//fnXfeac9//vPt2muvtd///d//6ztqIYQQQog95hu+RL3uda+zT37yk1nZqVOn7OTJ\nk3bffffZq171Kjt16pSZmX3hC1+wj370o/aFL3zBPvnJT9rrX/9668mwRCGEEEKI7wS+4UvUy1/+\ncjt06FBW9olPfMJuvvlmMzO7+eab7eMf/7iZmf3e7/2evfa1r7Wqquyaa66x7/7u77bPfvazf02H\nLYQQQgixt3zTYvmZM2fs2LFjZmZ27NgxO3PmjJmZPfroo/aDP/iDX6939dVX2yOPPEK3EUH2Kie5\nfDabehuMSZwYylnv+m++ZhMSwImzlbODJPLgkJnWWWgeip6RmYlMjoaDSD0RZ8GYXjY+dG2z9mFt\niy4PcEQB8FIkkMYjsUHZzPKhAxm09rJkseWPfbKV1ws12Tg59n6Sh10282HfiqYC6pFZwJvSB2nW\nVS7T9pWXawP5m6Voc4m0JrPPE6/UoDmtp33K00M3oxI5EZF9YiS7QcgOQbhlwnbPjHR3vw/QN5O/\nkyNZL0Bb0cNmpxfxPl4d5Gnmw3WZwtoPCPtjYjJrOrz/UvSP+WVJAhxnl+X7m/mwzViTEMudPMBx\nsnve1akGjHxgIa8dG0wDYZus/3QuGtUMW74kocBTIh3jlnDQgxkPUHV18OYzM0sk6RkDYoMfGGQg\nlnezQ65KIoMHnP+++6SrU5BrxZ4TSFuRYOK1vF22D/p2WvrHvgX4FauZkG2v+7IGwpETWc/IQDIc\niPTN8FcSy0MI7oGE/18IIYQQ4juRb/qbqGPHjtnp06ft+PHj9thjj9nRo0fNzOyqq66yhx566Ov1\nHn74YbvqqqvoNu75j5/6+n8/95rvsmdf8bxv9jCEEEIIIfaUb/qbqB/7sR+zu+++28zM7r77bnv1\nq1/99fKPfOQjVte13X///fblL3/ZXvrSl9Jt/P0fesXX/z33mmvGH70QQgghxB7xDb+Jeu1rX2uf\n+tSn7Mknn7RnPetZ9q53vctuvfVWu+mmm+zDH/6wXXPNNfaxj33MzMyuu+46u+mmm+y6666zsizt\nN3/zN/VznhBCCCG+Y/mGL1G/+7u/S8vvueceWn7bbbfZbbfdtnKnOCNzAdNkT9b8F2Qb+7xc14Lt\nutglSeBr/hRjBXI0kTp7Inr7meU9JUnY7QxnDycpyuSFE9OIezqjed4uO62XCc8RibuBSz8ZKtZh\nYjgTaZnLB1Hg1bY/ptnT3jCcP5WXFUvfD1jabLORt93WwQGJu2ZWgLxP3FOLxDoOXX7d05JIskwC\nXubrFS1JNUfR1PxXyEPETzOzZpFL8T2LciciMm4/EJk/lOSPJjgsmvrPBmzgvTYgUZi57h2515xK\nztKt2Yz0KKSzHbJrDBee/W3ZdQP6J2tz2ix5n+pwJI+ZteW6K+uneep1IHL2dPG0K5ttPpEvL8+5\nOuWA50tPxOsused+3l8imSEBU80vkpcVRGRnydxYjQ68oPuDbZO+H1iKOfSXfveCq9PCcz+Q0SeJ\nzF7RwWCBtvUSORP8bUBieUcGwDQxX6+vyP24j8wYgs888pjqichuFbRnJIMxyOwgQwaNXQollgsh\nhBBCjEAvUUIIIYQQI9BLlBBCCCHECL7piINvBR38Nhwh4HBCflud+Gw4CxDEhu6BmVmz9GXupMnP\n9Wx2dFaG4LldXG8IxHeC39k7UqcFH2BJHJeWeFoYUDck7M/Mh2Yy1QG9IjOzAO5PueV/r18jTtSB\n0/O8ziZpp6lv883L8+VlNewqFA34Ftvk74wLvj3LCjooma0cg0rNzIpl7qbEXZLuufDXtK8h+BFd\ntUtQL3IHomfOUMkCI8EnIeG3ccDjhKkHGEb7l4X5eiu3zB2lgnlEA/52ZN4bHgQNCSX3P/pkg7JF\nCSxQlfk5EW7KnjhRkTg0BXgocceHZs7OP+bK1iCwsex2XR2bkX4NlKxPER8wQRnRFq0mhW0B3mLv\nnZpIPDC3P+ZEDsjyZa5RZJ0B/LhJ648zbEMYZecDgDsI5DTz/i/zpiz55zC9R5GS+EfQF2viRKHb\nbEYCcYmciu8Nf7nHFctmgZTRe3kg+iZKCCGEEGIEeokSQgghhBiBXqKEEEIIIUaglyghhBBCiBHs\niVjeQtgmhpAxxatgYjCET/YkkK9rvJDmtsV2SGeyXy2fdURQQ3kQhfGL65HZ7VGcZ3qtK/IS4ozM\nZN2CiFxRSc+DQZqRzLIeWyJQNhAqWXuBc7Ljy+abedn+cyTEkvTiZgqBqoeGdfViN19vWhEh9oK/\nxjUY0+Xa3NXpiHAf63z7020vdU52vPw5qSG8tGWz1ntQEI0sMJKURQjEZYmRfU8E0YiC6LCASuzY\nLIgRKckzom9Xh22yY2IiratG71kmsuaUJJS0I0Kzq0MGJkRi6vvBAn69QNql6rez5SkJAF3bfsKV\nVe1OfpzRS+s9DrwgsDBKFkxcgGScSH/tybMSH80dabuG9DMcVMGCPCMJzXT7J4J/R1bD84udf35P\n+q28DtlfzwRqCNKNrJ3Yx8yQMFEiluN16II/FxpsDYMMCtK+bFAMCuKRfqyxExw/u4q+iRJCCCGE\nGIFeooQQQgghRqCXKCGEEEKIEeyJE9WDG9LC75FM0wgkADC18Psn2Vcizk7fYRlLACQ/Vg/62ZT9\nxgx+Bwt0I1vC9RKZgDjA78lTnIDR+O/Qqcx/LC7JRI2MEn4bD8QBYXPFYjAqm6uW9cYEQZqhIs4Z\n8bkwVI799s+YLsClKr2jxEJXC3CSOhIql5i/AhMVT8kEy2sL4o+5iYtdFcpknrtagfgrPPkR/EMS\nzhqJ8+GCJsmWWdAdBhP2A5QvOoEt6Z89TmBL5z8eMtkv6+js/s/7Qs8COYd0TzYxdOu9Jbx8PXFq\nrFu4oil0orXltqtTLTf9McADu6nI5MazQ/4YgIK0Z0m8lwqeeSwnkURIuqTXxn0OmFWkjdG9weey\nmVk7IKyRTSCPwahmZgkDk4nYg65Y7PxEwqnecmUFOp7kuOmpDMiiZBP7uhncyYOfTXSNE4Azb4oF\nPWML03mFaUguqTcQfRMlhBBCCDECvUQJIYQQQoxAL1FCCCGEECPQS5QQQgghxAj2RCyPKI1CSF8i\nwl8ghlgJPlrH5GEmzjn/bdjs6KzMwUIIcZlsp2bSOArpLBAMiopIgtmIlFuC8FcMDNtEd5D4xRyQ\nI1sSzLa77iXZCwfzg28L0gZk0MH2oXz7y7WBYaIgeidyi4TO76+CSdRbYj2Gwuuu2NdZCGm5YCGd\nUIbLlyDAIRAXnI7sQEGcz4S+2m5nAz0wkNPMi6VssInbP7mPye5cPRa2iYHAF1eEwQo0OJQ8b6Aa\nu4+7AWGNLHiS3YAYWskk+Yo8J2KzhGUvkQciOffTXFauJ14s7yY+fBYpSNuVRCjGy8weyxWRnPE6\ndyhwm1lNAkYLuEnIx9OwbyPI+fVsVEMBgZisDojkBbv3iGzeF/m5MEmefh7SBwXAJHm35dWBvGbm\ng60HDsbAQT/sM7sg9wyV4geib6KEEEIIIUaglyghhBBCiBHoJUoIIYQQYgR6iRJCCCGEGMEeJZbn\nywWKc9TpJGKZMzb9eh1ZL4AZGHCGejMrSOx2P0D+LEhieA9SfCIpykaE6Q4E5r4nMigIfxUmxJr5\n1Fgzi6D8lXy6awfKn6n3bccSqFGA7XwQuLX7/TEsQYAtD5EsYiJHNuv5tnY2hhnwxRJuCdIsZe+P\noW1ys7wk8mKXvJyJgnZsfHtWnV/PBcy3w/4eKqDdWaIvkyxRCGcJ8EyODjBggg1EYLcDVuyHiJ8s\nMZ2k9UcQWZlcyweRQN+nbefLeliP3R8sAd7vncm1ZMYAt0zWMy9Qxz4vi0Q+t4lPuO8g5TtVa35/\nA0YGsMEK9HkG7ccGFLD0c+xobL2G3RAsGhur0JR/rOSLaDg49OOeDGSJeJVXjzm6uG334Uuep2QW\nAzawy22bDfpxR0HnLPBrFfjc8NvGz7CLW8LBH35vqSXPkmKAOH8J9E2UEEIIIcQI9BIlhBBCCDEC\nvUQJIYQQQowgJEzR++veIQ33EkIIIYR4ZnKpVyV9EyWEEEIIMQK9RAkhhBBCjEAvUUIIIYQQI9BL\nlBBCCCHECPYkbPPUz/xkttwV+QzfSzIL+GJ+wJV183z2cBZGV5CwTcxOY7OjJzKbd4AQtDvf8U5X\n593v/llXZpaH1hUl2Z9LTzQLEDQX2ezTEEJG1TcSVNZ1eaAamyn8tlv+e1d2yy23ZctF5deL5FyW\nTd6eBzaOuzpf+erXXNn11x3Jljef8AGAW5sLVza/PL9WDUl0PHXHna7sttveli2zGb8jmVkex0vg\nbOJmZiUNYszbryXBmhi6evHAYH/J99c77jzlym75lf86W8bgyYs7ZAcKs7+zcE8SJmoYTEq2TbqL\nu0c7EvZ353vz6/fWW273h0T6fg9Bfj05gJ6F5kJZs0b6+Zrvn2mSn0ygYbT+mfCv//mv+3pCiGcU\n+iZKCCGEEGIEeokSQgghhBiBXqKEEEIIIUaglyghhBBCiBHsiVhuO5v5cgXi5dSL5XEyc2U9zjad\nyKzjnZeOrc+FUDZ7eOj8tno63XxO05IZsEHa7lsiJpPX2RKuTiQzr6OQ2vd+Q03yl7npYJbsftgs\n1rHM22C+5q/Lw1/zgvhzr31Otnz2oU1XpyqXruz4scPZ8hf/8EuuztHn7HNls438fBZnSdsREhjN\nHc56bmY9EYNxFvdIBjS0RLzuU75eTSRr5pUH2H7JhxQ4cObzggjwfSTnjH9vEeGeFBkOdQhoxF9i\nPfSsC7IeUrb+XIrGl7nLV/h7pp35NtgBCb9nsy8wtx7E9UieIx0R4IUQz3z0TZQQQgghxAj0EiWE\nEEIIMQK9RAkhhBBCjGBPnKi03M2XqzxsMxH/yaqpK+rg8EPvhZJI/JUCPI1E/BUWljjkjbMnbhGG\nTzJ1pCBbL/rc42EhnXiYiYVDEgejAE+r64c5GdU0D088+8TTrs6+wxu+bH4wW/7Mlz7t6rzyn1zn\nyh5/aDtbPn3mnKvzgz/2PFd23xcfyJZTmrs6jCFmUSACW8L2i8R/6vztttvl7bls2TVmfRh2R0JP\nGXg79MxtIsGPbgbzgiaHOgpw9FJHwllJAGdwt/JqJyoufPvON/3+pgsIqK18G2wf8Me0nMB6/iCt\nJYfZgnNVsWDdYUqbEOIZhr6JEkIIIYQYgV6ihBBCCCFGoJcoIYQQQogR6CVKCCGEEGIEexO2OQNJ\nfAZieeUDK3ui/MaAQX7EbE1kNnYQrWPBEvKIcOtrkW1Xrix0KLL79UjGohVweXqSuogieUrkvbgn\nlxkE+NT542bsbO5ky5OZb7sjVx5xZZ+/9yvZ8mVX+kDVq6866sr+7cf+Y7b8ov/8ua5Oij5I8/TX\n8jDP57zgsKvDSCBV02tOChOEJXatvw5btW+rczuwHhmYMF/zvWMNQkFLEpDJgeMi++vJPWNptSCe\nkr9vE4TPVq3vZ6n22wrQnti+jHLX73/jrK936Hzedv2UBYf6e2ZrDvfaAd92LRmkgrB7nVr5Qohn\nPPomSgghhBBiBHqJEkIIIYQYgV6ihBBCCCFGoJcoIYQQQogR7IlY3m3sy5fnuWTcBn9YLjHZzEIC\noTi1vk7vhU2XWE6EbfZ2STbl6xCxO3S5gBp6lnjtyxoQ3vvOi6yhWC2WdyQpu21BWh/4Pj2b5OtN\n52uuzqMPPkWOM19+wYue7er8yWf/wpVV83wQwrV/5ypX5w8/5dPPjx87ni0XxbBI6BBAYCY+M1Yx\nM+vg+jVE5t9a+rLz27lmPCm9drxOwtax3pyl2RNcP2N9kfSFhI8KNoCiJTMNgFieFn69uGSmfl4W\n42qxPBJBfW3h19vYyp8TaeHbbnvu16vqvA2Kxj9vqoYMZIFk80hHJqxOZBdCPPPQN1FCCCGEECPQ\nS5QQQgghxAj0EiWEEEIIMYK9caLWDmTL/RTCNpmH0vtAxYgVOxJjR0IzE4YJRhI42JNtxdXvnO6Y\nzJwQRA7JutZfCtxWQ5wvgxnhqTnCdBkIWQxh2Pt0KPP1LpzbIbV8e1559cFs+fGHHnd1lsSXef4N\nz8mWH37ga/6YuqkrO3JFHq555onT5Dg9Pbo4JOS1I34e9paatHlPLsQcgh73+QxSO7Dhr/usyPc4\nrQaGNYLLxPwn3odgvda3uS1IH17mTlSs/Xrlrj/2COIZuT0ciclqgQWHgkdItsXaADdV1r7OZEmc\nRLzXSDCqojaF+PZE30QJIYQQQoxAL1FCCCGEECPQS5QQQgghxAj0EiWEEEIIMYI9Ecv7KpdL+yJf\nDkTqLjoiY4IMzYI1A5GAUfQONFHRy9GJK7dwUGxG+vwYWvMCdUvCGXuUVAPRT+GQiuAl5DKSEFJQ\nWcOAMEMzs7bNzwVFbDOzQ4e9Hf30E0/mx1T5Njhy7Igre+ThR7Plo3OfPHno8oOu7Ny5zXx/cVhX\nT3g+RFZmAwN6uBAFyU6cT/312z/L+/rG3NdZm/jrV0BfSGwgBCF1eZ9i4ayW/LWxPg/SZMGasZu4\nsrDMt1Us/P6mjd9fSnh+q8NS+4lvg8W6L4OsTatLv+0dnyFrBkI4uSzWLX1ZAX2IBdtGcgxCiGc+\n+iZKCCGEEGIEeokSQgghhBiBXqKEEEIIIUaglyghhBBCiBHsiViOCeGo0rpEcTOLRCyNEVKUiXgd\nAhM2QUgnad19IhL3APezI5JsH3Lhtu69ddx0TDYH4Z7573DOMfq2q6K3XcsyT4APLA2dkh9ENfXH\nvdz1KeYo728c3O/qnDnzhCvbdyg3fCO5Vjubfn8bB3PxOQ1Imzcz6zq4yGS1QPoGitCRJGXPKt+B\nZiD9zwu/7Qnp1zjOYtkNGxgQcFBDN1Qshz7bEXO+8cdQwHHFJTnOmtzb0Nljsfr61Wu+nbb2+223\n0/zaNJXf9oJsqwNxHe89M7OSDGTp4dZiz5uOPW+EEM949E2UEEIIIcQI9BIlhBBCCDECvUQJIYQQ\nQoxgb8I2McgSfRLiP/FATAjkYz4CCb+MBQRGkjnU+czuq72TvvOz1PdwPok4J6klLhX6Kz1xTty5\neLepJUGaCVyVshz2Pu0VM+/+sEzQqsq9sN3dha8z8Z5NNcuPa+vCrqsznftkRNDurN4ZFkZZQltF\n4tSxXNIS9hdIDyrJtkroe6QJqAzXQUhmx0JlCRH7EPHzAvnbqgN3KjbkHu1IX0iwfRSEzGhArWF7\nklBXd4xTv//dA35/C+j7dUH6RrXaiWIWU2Bhu7D5yNxNZW0K8W2JvokSQgghhBiBXqKEEEIIIUag\nlyghhBBCiBHoJUoIIYQQYgR7IpajQ4k5cz0xNmP05qWb2Z3ImZFZwDDjfUIL2cyMirqr3zlZaKY7\nQRZmiBK5mQUINGRhmwlSF2NBpHUi6mMjd+3QsMZvvGxmlkiYYNPl4Z7rlZfBm6UXfBe7+XqzmRf3\nmyUJVKzrbLksB3Z1kH4D0YeZbI5mMAsFDUTCx07bkOvQEam6xWvK+jkDD531DXLOPkTSn0tB7tEQ\n8qDXNHFVrCfn5wY6kG07SNBsM2tcWQL5uy/8/d8z2bzEvsHCRclABCjqC79eTwaNCCGe+eibKCGE\nEEKIEeglSgghhBBiBHqJEkIIIYQYgV6ihBBCCCFGsCdiOcqtOEF7oBKpFy8DmNY9qZOIpR4DCtu+\nGRKTZAekJi9rLz4XICu3nd9f25NjgLTnRNsgP052jExy7uD9mZ0vI8H2WTJ31/mU6Mlafn5168Xd\nZesl4H378/bsGr9eS0Kwp1OQhwcmeqN/XjGxnJwzNnFBkuMjOQYU1xP5u6bH1G8zCyCSJxYTT8BB\nBmzgBSZsX1wPBmOQ3fVMuK9g+6QvpoIMDEh5+7FQc4SJ+2y9BNJ4x27rgs1iAG3Xk/2xQTErls3M\nuoH3nxDimYW+iRJCCCGEGIFeooQQQgghRqCXKCGEEEKIEYSUBsoi36odssRIIYQQQohnKJd6VdI3\nUUIIIYQQI9BLlBBCCCHECPQSJYQQQggxAr1ECSGEEEKM4G/8JeoVr3jF3/QuhRBCCCFG8Y3eW/7G\nR+cJIYQQQnwnoJ/zhBBCCCFGoJcoIYQQQogR7MlL1Cc/+Um79tpr7fnPf769733v24tD+I7noYce\nsle+8pV2/fXX2wtf+EL7tV/7N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- "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "feat = net.blobs['conv1'].data[0, :36]\n", - "vis_square(feat, padval=1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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ms2YoOpgSpqZhCsFvW62WPg9sBU7MjEwmo30ERsrKK5fP5z2z0cjIiMlIueZ5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wGNgv//Iv22uvvWb/83/+T/u1X/s1+6M/+iP77Gc/az/6oz9qX/va1+xHfuRH7LOf/ayZmb3+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dxu119/vW6kvD73MssD3mkyjxIf9gQk3w+IS9I8DIrForYvK3Czmw/PQNtw0l+0KasFY8OD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\nvIt12YdecMMjOfeE5Vhs0dqw5NbW1mK5U0Q6LIAbDs7h6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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second layer filters, `conv2`\n", - "\n", - "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "filters = net.params['conv2'][0].data\n", - "vis_square(filters[:48].reshape(48**2, 5, 5))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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0vzuvmqNWAfPO3EFL6ws5BltgaNfyy/LKGnOdQ8BnrH9o3PTtiEPGRv98b9eu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\nZsxo8/gdQWq+jUUreVymxRalLOrMsuefbf+Ots2sAAB+YKnms61ChvM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NAAAg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\n1k/7BsAeRj8v6JfrgWVZePb8OqPQWYG7du0ayyKM6HsB22sjRnPK9dhaT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dZbb+3kdrwB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\nxcV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c3/Gb9+s973lPRIy/53ekP5xi5p9yhg+A9wtOIb9/t9fMvG9TU1O92CV+gyy07dgO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- "text": [ - "" - ] - } - ], - "prompt_number": 10 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we visualize filters and outputs using the network architecture proposed by Krizhevsky et al. for ImageNet and implemented in `caffe`.\n", + "\n", + "(This page follows DeCAF visualizations originally by Yangqing Jia.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# Make sure that caffe is on the python path:\n", + "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "\n", + "import caffe\n", + "\n", + "plt.rcParams['figure.figsize'] = (10, 10)\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "import os\n", + "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", + " print(\"Downloading pre-trained CaffeNet model...\")\n", + " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "caffe.set_mode_cpu()\n", + "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n", + " caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n", + " caffe.TEST)\n", + "\n", + "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n", + "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n", + "transformer.set_transpose('data', (2,0,1))\n", + "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n", + "transformer.set_raw_scale('data', 255) # the reference model operates on images in [0,255] range instead of [0,1]\n", + "transformer.set_channel_swap('data', (2,1,0)) # the reference model has channels in BGR order instead of RGB" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Classify the image by reshaping the net for the single input then doing the forward pass." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)" + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted class is #281.\n" ] - }, + } + ], + "source": [ + "net.blobs['data'].reshape(1,3,227,227)\n", + "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n", + "out = net.forward()\n", + "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "feat = net.blobs['conv2'].data[0, :36]\n", - "vis_square(feat, padval=1)" - ], - "language": "python", + "data": { + "text/plain": [ + "[('data', (1, 3, 227, 227)),\n", + " ('conv1', (1, 96, 55, 55)),\n", + " ('pool1', (1, 96, 27, 27)),\n", + " ('norm1', (1, 96, 27, 27)),\n", + " ('conv2', (1, 256, 27, 27)),\n", + " ('pool2', (1, 256, 13, 13)),\n", + " ('norm2', (1, 256, 13, 13)),\n", + " ('conv3', (1, 384, 13, 13)),\n", + " ('conv4', (1, 384, 13, 13)),\n", + " ('conv5', (1, 256, 13, 13)),\n", + " ('pool5', (1, 256, 6, 6)),\n", + " ('fc6', (1, 4096)),\n", + " ('fc7', (1, 4096)),\n", + " ('fc8', (1, 1000)),\n", + " ('prob', (1, 1000))]" + ] + }, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Fpjm1cviUKVMi6+lvYN++fbHbW758uYv1HIWm35TOJhE6C4C+b+fOnTUfE6qJligAAIAA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\nTjSMcvnXuFZobhQLFy508YsvvljikSBv+/bti31Ny3cklS4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- "text": [ - "" - ] - } - ], - "prompt_number": 11 - }, + "output_type": "execute_result" + } + ], + "source": [ + "[(k, v.data.shape) for k, v in net.blobs.items()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "[('conv1', (96, 3, 11, 11)),\n", + " ('conv2', (256, 48, 5, 5)),\n", + " ('conv3', (384, 256, 3, 3)),\n", + " ('conv4', (384, 192, 3, 3)),\n", + " ('conv5', (256, 192, 3, 3)),\n", + " ('fc6', (4096, 9216)),\n", + " ('fc7', (4096, 4096)),\n", + " ('fc8', (1000, 4096))]" + ] + }, + "execution_count": 5, "metadata": {}, - "source": [ - "The third layer output, `conv3` (rectified, all 384 channels)" - ] - }, + "output_type": "execute_result" + } + ], + "source": [ + "[(k, v[0].data.shape) for k, v in net.params.items()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper functions for visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# take an array of shape (n, height, width) or (n, height, width, channels)\n", + "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n", + "def vis_square(data, padsize=1, padval=0):\n", + " data -= data.min()\n", + " data /= data.max()\n", + " \n", + " # force the number of filters to be square\n", + " n = int(np.ceil(np.sqrt(data.shape[0])))\n", + " padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n", + " data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n", + " \n", + " # tile the filters into an image\n", + " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n", + " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n", + " \n", + " plt.imshow(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input image" + ] + }, + { + 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Okec+WKymeN9jDGirKHE81xm3v7CvTg1LbQRziVSkkiRJkiRJOrJDKFLOM57x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\nbZbwMQj4m+t2jYZrq6B4Xi+i19yv0P0ugbY/LCiZbdsu9UM7od5ro+1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tmZrYJHWYfAy0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3h8fOeeE1eY0XPP+LLrpop+0BXo57YShweMucH/93qLvk/U8WFooX3rJnfXn2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\nGWN92fOEoBxw/pgiFUI5M6alcpjNnDlTUtpvNNzrsFnI3ffyy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- "text": [ - "" - ] - } - ], - "prompt_number": 13 - }, + "output_type": "display_data" + } + ], + "source": [ + "feat = net.blobs['conv1'].data[0, :36]\n", + "vis_square(feat, padval=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second layer filters, `conv2`\n", + "\n", + "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXuMr1V199fMnDmcO1c5wAEFFfBuVaiXat7GiMb+YWrS\n", + "WhtvpcoteI2gomKoaETF6FFMuWgoaY2paVrbNKmttrWttRFbQ1VEBZH7RZCLcO4zc94/Tj7Ps3+f\n", + "51nnR0d8533frO8/M7+Z/dvP2muvvZ+91l6Xmb179+6NQqFQKBQKhcL/GLMrTUChUCgUCoXC/6uo\n", + "g1ShUCgUCoXCMlEHqUKhUCgUCoVlog5ShUKhUCgUCstEHaQKhUKhUCgUlok6SBUKhUKhUCgsE7+S\n", + "g9RXvvKVeNKTnhTHH398fPSjH/1VPKJQKBQKhUJhxTHzaOeRWlxcjBNPPDG+9rWvxZYtW+Lkk0+O\n", + "L37xi/HkJz/50XxMoVAoFAqFworjUbdIXX311fHEJz4xjj322Jifn49Xv/rV8dd//deP9mMKhUKh\n", + "UCgUVhyP+kHq9ttvj2OOOab7fPTRR8ftt9/+aD+mUCgUCoVCYcWx6tHucGZmZmqbLVu2xB133PFo\n", + 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second layer output, `conv2` (rectified, only the first 36 of 256 channels)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "feat = net.blobs['conv5'].data[0]\n", - "vis_square(feat, padval=0.5)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlEAAAJNCAYAAAARaCA+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3VmQHWd5//FnbO37OqPRaKSRtXgsy0tsAQJMGeMFXAES\n", + "EuLEkISCcBOqcoFTgRQ3mFQlOBdJqkKKqn8BqSJcEKiQ4FAE4TLBAhvLWizZ1jraRtKMZkbLaN8X\n", + "/y+cefXrV9Otnvf0ds58P1dP6/Tp7tOnT0/rfd73eZvefvvttw0AAAAjclvZBwAAAFCPeIgCAAAI\n", + "wEMUAABAAB6iAAAAAvAQBQAAEICHKAAAgACZP0StWbPGOjs7bdmyZfb3f//3WW8eAACgEpqyrBN1\n", + "7do1u/POO+3FF1+0trY2e9e73mXf//737a677spqFwAAAJWQaUvU+vXrbenSpdbR0WFjx461P/qj\n", + "P7Lnn38+y10AAABUwpgsN9bb22vt7e1uecGCBfbaa69F1mlqaspylwAAALl5+OGH7aWXXhr2tUwf\n", + 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QkQIAAIjEhRQAAEAkUntoq8MtKb3izJYeyDJE3m1/c+sivW7dujC2d+/eoqaT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\nFWYWFf73BZ9W6On7wNhmyWVK7eVxPrL3ji9Ap3C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net.blobs['conv3'].data[0]\n", + "vis_square(feat, padval=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fourth layer output, `conv4` (rectified, all 384 channels)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "feat = net.blobs['pool5'].data[0]\n", - "vis_square(feat, padval=1)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmwZVV1/78vgBE1JkGQoZumu2nophtsVJxBoQwmpFJE\n", + "qhRRUziVQzRxLMVgJbwCkcYBE0sxZcpYRKscq+KQVFAxCAiFLcjYzdDN0EwNOMUkmkRRfn/4+7zT\n", + "9/ve7r3PPufce1+zPv/ceu/ee/Y5e6+971nfs9baMw8//PDDCoIgCIIgCFrzW5M+gSAIgiAIgsVK\n", + "3EgFQRAEQRBUEjdSQRAEQRAElcSNVBAEQRAEQSVxIxUEQRAEQVBJ3EgFQRAEQRBUMsiN1IUXXqg1\n", + 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GTMu3v7/fiGn7PS8vz2q72udN3M9bTUFBgRGzPW9tc9Gm9Gu0c1nT3t5uxOrq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\nAAA4oIgCAABwQBEFAADggCIKAADAQSS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- "text": [ - "" - ] - } - ], - "prompt_number": 15 - }, + "output_type": "display_data" + } + ], + "source": [ + "feat = net.blobs['conv4'].data[0]\n", + "vis_square(feat, padval=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fifth layer output, `conv5` (rectified, all 256 channels)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XuM3Gd1//HPEN/vXtu7vqzjTeI4xiShoSFQCUpTCBVS\n", + "GwUh0oYKIqBV1UoBmnJJ3RYGihRTFaFfC0H8gUQqJEjUKoR/QCFVSJXQYG5pEjuO7ZD1Zb273ptv\n", + "sZM4iX9/ROeZM/HXOzPPfG8z835JKF+eXe88Ozvzne/3nPOcp3Lu3LlzAgAAQMveUPQEAAAAOhUX\n", + "UgAAAJG4kAIAAIjEhRQAAEAkLqQAAAAicSEFAAAQKZMLqR//+MfaunWrLr/8cn3lK1/J4iEAAAAK\n", + 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net.blobs['conv5'].data[0]\n", + "vis_square(feat, padval=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fifth layer after pooling, `pool5`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "feat = net.blobs['fc6'].data[0]\n", - "plt.subplot(2, 1, 1)\n", - "plt.plot(feat.flat)\n", - "plt.subplot(2, 1, 2)\n", - "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlEAAAJMCAYAAADaNPObAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XuMHeV5+PHneO/3+8X22izxbVkbsI0hlLTBYK0JCBwS\n", + "KLkpsQClUmmrgNICUlUJWiU2StuEJM0/FaVWknKp2oKLEos4yTqhDhBDiAO2sU28eL3eXdt7v1/P\n", + "7482/sV+nzXvvvOemTlnvx8pUvZhzsyzM3POeTz7zDOJZDKZFAAAAMzLoqgTAAAASEcUUQAAAA4o\n", + 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SpqSP+cc+lmx+eVKtbu+Xeh+HP9Nzm7kk7cAZ5jhz+tx/v7RsWdqlSMfWrft+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\nvb29mRkQWbndL7/8cv/P119/vXfGGWd4npev7e7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- "text": [ - "" - ] - } - ], - "prompt_number": 16 - }, + "output_type": "display_data" + } + ], + "source": [ + "feat = net.blobs['pool5'].data[0]\n", + "vis_square(feat, padval=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first fully connected layer, `fc6` (rectified)\n", + "\n", + "We show the output values and the histogram of the positive values" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlgAAAJPCAYAAACgtar/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XucFNWd9/FvK+zmyQoGjAysmMUgBAWEiSZmEzVDcMhV\n", + "g4svozE4j5dsHvPkoibe4maDuegYV101vjbGNT6sPjFhn2yQZBUjwfaCAYxCYryEKKCAMxN0RG4i\n", + "MFPPH2MPPT3V3XU5p+pU9ef9evFipqfqnFNVp6p+fc6pUwXP8zwBAADAmP3SLgAAAEDeEGABAAAY\n", + "RoAFAABgGAEWAACAYQRYAAAAhhFgAQAAGBYowNqyZYtOPfVUHXHEETryyCO1YsUKdXd3q7W1VRMn\n", + 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"The second fully connected layer, `fc7` (rectified)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "feat = net.blobs['fc7'].data[0]\n", - "plt.subplot(2, 1, 1)\n", - "plt.plot(feat.flat)\n", - "plt.subplot(2, 1, 2)\n", - "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlcAAAJPCAYAAABRvvFyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XucFOWd7/FvJ5BNToAEVAYW3AOiKHKbWQ14XHGHKESO\n", + "eGExboiSeQmePWtiEqNH1M0mDm5UWDdrEF3DcSNhQw5KLiAxMl6i7S1RohlcE+8KK+LMKMLIDCiX\n", + "mTp/tD309FR11+WpW/fn/XrxYqan6qlf1VOXXz/11FMZy7IsAQAAwIiPxR0AAABAJSG5AgAAMIjk\n", + "CgAAwCCSKwAAAINIrgAAAAwiuQIAADCoZHK1bds2TZ8+XePHj9eECRN0yy23SJIaGxs1cuRI1dXV\n", + "qa6uTk1NTZEECwAAkHSZUuNctba2qrW1VbW1ters7NQJJ5yg9evXa+3atRo4cKAuv/zyKGMFAABI\n", + 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oo7xIJvWCXOztt6XLL+/92bBhuT4cTjIZ6c//XFq/PtzYomC6nm64IdzOy6tX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\n2+Ott97SiBEjog/aEC/rOXLkSI0YMUJ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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, + "output_type": "display_data" + } + ], + "source": [ + "feat = net.blobs['fc7'].data[0]\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(feat.flat)\n", + "plt.subplot(2, 1, 2)\n", + "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final probability output, `prob`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 18, "metadata": {}, - "source": [ - "The final probability output, `prob`" - ] + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "feat = net.blobs['prob'].data[0]\n", - "plt.plot(feat.flat)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAmEAAAJPCAYAAAA0UwMNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + 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QCAOgDEQYk95IF+MfyDVhAKQgwpj0BiPspMN8tRsJAyAFEUY2BkfDTEcCUAYi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\n", - "text": [ - "" - ] - } - ], - "prompt_number": 18 - }, + "output_type": "display_data" + } + ], + "source": [ + "feat = net.blobs['prob'].data[0]\n", + "plt.plot(feat.flat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see the top 5 predicted labels." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see the top 5 predicted labels." + "name": "stdout", + "output_type": "stream", + "text": [ + "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n", + " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n", + " 'n02127052 lynx, catamount']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# load labels\n", - "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", - "try:\n", - " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", - "except:\n", - " !../data/ilsvrc12/get_ilsvrc_aux.sh\n", - " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", - "\n", - "# sort top k predictions from softmax output\n", - "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n", - "print labels[top_k]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n", - " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n", - " 'n02127052 lynx, catamount']\n" - ] - } - ], - "prompt_number": 19 } ], - "metadata": {} + "source": [ + "# load labels\n", + "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", + "try:\n", + " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", + "except:\n", + " !../data/ilsvrc12/get_ilsvrc_aux.sh\n", + " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", + "\n", + "# sort top k predictions from softmax output\n", + "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n", + "print labels[top_k]" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.", + "example_name": "Filter visualization", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 2 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb index 03c811b5120..e98d13dd501 100644 --- a/examples/hdf5_classification.ipynb +++ b/examples/hdf5_classification.ipynb @@ -1,1075 +1,6290 @@ { - "metadata": { - "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.", - "example_name": "Off-the-shelf SGD for classification", - "include_in_docs": true, - "priority": 4, - "signature": "sha256:741422697d76b1667287180dc7c6360cf105ee774b1e2def800dc8fe80f78f67" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Caffeinated Logistic Regression of HDF5 Data\n", - "\n", - "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe\n", - "\n", - "import os\n", - "import h5py\n", - "import shutil\n", - "import tempfile\n", - "\n", - "# You may need to 'pip install scikit-learn'\n", - "import sklearn\n", - "import sklearn.datasets\n", - "import sklearn.linear_model" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "X, y = sklearn.datasets.make_classification(\n", - " n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n", - " n_clusters_per_class=2, hypercube=False, random_state=0\n", - ")\n", - "\n", - "# Split into train and test\n", - "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n", - "\n", - "# Visualize sample of the data\n", - "ind = np.random.permutation(X.shape[0])[:1000]\n", - "df = pd.DataFrame(X[ind])\n", - "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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ikQzBoItAYBQYobj4OhIRLz7VxtP7R7lm7Uz+n3vvPZ0qvJ2dO1vRNCeQRdNe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dJjiJgJ+tTFAgoOoKGjIvuDGOqs00PHJUVAGxWLFQ7SYAmbgJCYNjl0OY5wWzQHGcM+EygI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\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YTGe2SZKELx7HaDQyNjaGMhpFrVQiSVKi/ocoYk5JwWuxEAqFWLV4AT1HjjA82E+KR06f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\nIAIgiiLDqRQXTpx4ywUjvu9z7OmnOTg6elXKv6VpJGMxyqur6Lt2XdXKMG0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16fV9Hnvo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JUuMre6yvC11774GuX1dQpISFK8m4+EoNWp0OM5ZGWDjAiIgoj1loMLXDs8zOT4OIePHmXJ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xf38zLpcas7kCtVpDc3M7IyN7MBj0ZGYWnCKzTpdCT8/AhDECsGTJYtLSUvnyy4M4nTYW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\nb765DbXaTXf3XtLSLCxdOge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- "text": [ - "" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy." - ] - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Caffeinated Logistic Regression of HDF5 Data\n", + "\n", + "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# Make sure that caffe is on the python path:\n", + "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "\n", + "import caffe\n", + "\n", + "import os\n", + "import h5py\n", + "import shutil\n", + "import tempfile\n", + "\n", + "# You may need to 'pip install scikit-learn'\n", + "import sklearn\n", + "import sklearn.datasets\n", + "import sklearn.linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features." + ] + }, + { 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+ " n_clusters_per_class=2, hypercube=False, random_state=0\n", + ")\n", + "\n", + "# Split into train and test\n", + "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n", + "\n", + "# Visualize sample of the data\n", + "ind = np.random.permutation(X.shape[0])[:1000]\n", + "df = pd.DataFrame(X[ind])\n", + "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save the dataset to HDF5 for loading in Caffe." + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loops, best of 3: 499 ms per loop\n", + "Accuracy: 0.756\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Write out the data to HDF5 files in a temp directory.\n", - "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n", - "dirname = os.path.abspath('./hdf5_classification/data')\n", - "if not os.path.exists(dirname):\n", - " os.makedirs(dirname)\n", - "\n", - "train_filename = os.path.join(dirname, 'train.h5')\n", - "test_filename = os.path.join(dirname, 'test.h5')\n", - "\n", - "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n", - "# To show this off, we'll list the same data file twice.\n", - "with h5py.File(train_filename, 'w') as f:\n", - " f['data'] = X\n", - " f['label'] = y.astype(np.float32)\n", - "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n", - " f.write(train_filename + '\\n')\n", - " f.write(train_filename + '\\n')\n", - " \n", - "# HDF5 is pretty efficient, but can be further compressed.\n", - "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n", - "with h5py.File(test_filename, 'w') as f:\n", - " f.create_dataset('data', data=Xt, **comp_kwargs)\n", - " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n", - "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n", - " f.write(test_filename + '\\n')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, + } + ], + "source": [ + "# Train and test the scikit-learn SGD logistic regression.\n", + "clf = sklearn.linear_model.SGDClassifier(\n", + " loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n", + "\n", + "%timeit clf.fit(X, y)\n", + "yt_pred = clf.predict(Xt)\n", + "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save the dataset to HDF5 for loading in Caffe." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Write out the data to HDF5 files in a temp directory.\n", + "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n", + "dirname = os.path.abspath('./hdf5_classification/data')\n", + "if not os.path.exists(dirname):\n", + " os.makedirs(dirname)\n", + "\n", + "train_filename = os.path.join(dirname, 'train.h5')\n", + "test_filename = os.path.join(dirname, 'test.h5')\n", + "\n", + "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n", + "# To show this off, we'll list the same data file twice.\n", + "with h5py.File(train_filename, 'w') as f:\n", + " f['data'] = X\n", + " f['label'] = y.astype(np.float32)\n", + "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n", + " f.write(train_filename + '\\n')\n", + " f.write(train_filename + '\\n')\n", + " \n", + "# HDF5 is pretty efficient, but can be further compressed.\n", + "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n", + "with h5py.File(test_filename, 'w') as f:\n", + " f.create_dataset('data', data=Xt, **comp_kwargs)\n", + " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n", + "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n", + " f.write(test_filename + '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn and evaluate logistic regression in Caffe." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Learn and evaluate logistic regression in Caffe." + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loops, best of 3: 240 ms per loop\n", + "Accuracy: 0.752\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def learn_and_test(solver_file):\n", - " caffe.set_mode_cpu()\n", - " solver = caffe.get_solver(solver_file)\n", - " solver.solve()\n", - "\n", - " accuracy = 0\n", - " test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n", - " for i in range(test_iters):\n", - " solver.test_nets[0].forward()\n", - " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", - " accuracy /= test_iters\n", - " return accuracy\n", - "\n", - "%timeit learn_and_test('hdf5_classification/solver.prototxt')\n", - "acc = learn_and_test('hdf5_classification/solver.prototxt')\n", - "print(\"Accuracy: {:.3f}\".format(acc))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1 loops, best of 3: 240 ms per loop\n", - "Accuracy: 0.752" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 5 - }, + } + ], + "source": [ + "def learn_and_test(solver_file):\n", + " caffe.set_mode_cpu()\n", + " solver = caffe.get_solver(solver_file)\n", + " solver.solve()\n", + "\n", + " accuracy = 0\n", + " test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n", + " for i in range(test_iters):\n", + " solver.test_nets[0].forward()\n", + " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", + " accuracy /= test_iters\n", + " return accuracy\n", + "\n", + "%timeit learn_and_test('hdf5_classification/solver.prototxt')\n", + "acc = learn_and_test('hdf5_classification/solver.prototxt')\n", + "print(\"Accuracy: {:.3f}\".format(acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do the same through the command line interface for detailed output on the model and solving." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do the same through the command line interface for detailed output on the model and solving." + "name": "stdout", + "output_type": "stream", + "text": [ + "I0307 01:34:29.141863 2099749632 caffe.cpp:103] Use CPU.\n", + "I0307 01:34:29.418283 2099749632 caffe.cpp:107] Starting Optimization\n", + "I0307 01:34:29.418323 2099749632 solver.cpp:32] Initializing solver from parameters: \n", + "test_iter: 250\n", + "test_interval: 1000\n", + "base_lr: 0.01\n", + "display: 1000\n", + "max_iter: 10000\n", + "lr_policy: \"step\"\n", + "gamma: 0.1\n", + "momentum: 0.9\n", + "weight_decay: 0.0005\n", + "stepsize: 5000\n", + "snapshot: 10000\n", + "snapshot_prefix: \"hdf5_classification/data/train\"\n", + "solver_mode: CPU\n", + "net: \"hdf5_classification/train_val.prototxt\"\n", + "I0307 01:34:29.418416 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val.prototxt\n", + "I0307 01:34:29.418583 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\n", + "I0307 01:34:29.418598 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\n", + "I0307 01:34:29.418608 2099749632 net.cpp:42] Initializing net from parameters: \n", + "name: \"LogisticRegressionNet\"\n", + "state {\n", + " phase: TRAIN\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " include {\n", + " phase: TRAIN\n", + " }\n", + " hdf5_data_param {\n", + " source: \"hdf5_classification/data/train.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"fc1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"fc1\"\n", + " param {\n", + " lr_mult: 1\n", + " decay_mult: 1\n", + " }\n", + " param {\n", + " lr_mult: 2\n", + " decay_mult: 0\n", + " }\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"gaussian\"\n", + " std: 0.01\n", + " }\n", + " bias_filler {\n", + " type: \"constant\"\n", + " value: 0\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"fc1\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0307 01:34:29.418692 2099749632 layer_factory.hpp:74] Creating layer data\n", + "I0307 01:34:29.418853 2099749632 net.cpp:84] Creating Layer data\n", + "I0307 01:34:29.418879 2099749632 net.cpp:338] data -> data\n", + "I0307 01:34:29.418905 2099749632 net.cpp:338] data -> label\n", + "I0307 01:34:29.418918 2099749632 net.cpp:113] Setting up data\n", + "I0307 01:34:29.418926 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\n", + "I0307 01:34:29.418992 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n", + "I0307 01:34:29.420812 2099749632 net.cpp:120] Top shape: 10 4 (40)\n", + "I0307 01:34:29.420841 2099749632 net.cpp:120] Top shape: 10 (10)\n", + "I0307 01:34:29.420852 2099749632 layer_factory.hpp:74] Creating layer fc1\n", + "I0307 01:34:29.420866 2099749632 net.cpp:84] Creating Layer fc1\n", + "I0307 01:34:29.420872 2099749632 net.cpp:380] fc1 <- data\n", + "I0307 01:34:29.420882 2099749632 net.cpp:338] fc1 -> fc1\n", + "I0307 01:34:29.420894 2099749632 net.cpp:113] Setting up fc1\n", + "I0307 01:34:29.425689 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", + "I0307 01:34:29.425709 2099749632 layer_factory.hpp:74] Creating layer loss\n", + "I0307 01:34:29.425724 2099749632 net.cpp:84] Creating Layer loss\n", + "I0307 01:34:29.425731 2099749632 net.cpp:380] loss <- fc1\n", + "I0307 01:34:29.425739 2099749632 net.cpp:380] loss <- label\n", + "I0307 01:34:29.425747 2099749632 net.cpp:338] loss -> loss\n", + "I0307 01:34:29.425756 2099749632 net.cpp:113] Setting up loss\n", + "I0307 01:34:29.425767 2099749632 layer_factory.hpp:74] Creating layer loss\n", + "I0307 01:34:29.425781 2099749632 net.cpp:120] Top shape: (1)\n", + "I0307 01:34:29.425789 2099749632 net.cpp:122] with loss weight 1\n", + "I0307 01:34:29.425801 2099749632 net.cpp:167] loss needs backward computation.\n", + "I0307 01:34:29.425808 2099749632 net.cpp:167] fc1 needs backward computation.\n", + "I0307 01:34:29.425815 2099749632 net.cpp:169] data does not need backward computation.\n", + "I0307 01:34:29.425822 2099749632 net.cpp:205] This network produces output loss\n", + "I0307 01:34:29.425829 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", + "I0307 01:34:29.425837 2099749632 net.cpp:217] Network initialization done.\n", + "I0307 01:34:29.425843 2099749632 net.cpp:218] Memory required for data: 284\n", + "I0307 01:34:29.425961 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val.prototxt\n", + "I0307 01:34:29.425984 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\n", + "I0307 01:34:29.425997 2099749632 net.cpp:42] Initializing net from parameters: \n", + "name: \"LogisticRegressionNet\"\n", + "state {\n", + " phase: TEST\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " include {\n", + " phase: TEST\n", + " }\n", + " hdf5_data_param {\n", + " source: \"hdf5_classification/data/test.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"fc1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"fc1\"\n", + " param {\n", + " lr_mult: 1\n", + " decay_mult: 1\n", + " }\n", + " param {\n", + " lr_mult: 2\n", + " decay_mult: 0\n", + " }\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"gaussian\"\n", + " std: 0.01\n", + " }\n", + " bias_filler {\n", + " type: \"constant\"\n", + " value: 0\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"fc1\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"fc1\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + " include {\n", + " phase: TEST\n", + " }\n", + "}\n", + "I0307 01:34:29.426126 2099749632 layer_factory.hpp:74] Creating layer data\n", + "I0307 01:34:29.426311 2099749632 net.cpp:84] Creating Layer data\n", + "I0307 01:34:29.426331 2099749632 net.cpp:338] data -> data\n", + "I0307 01:34:29.426343 2099749632 net.cpp:338] data -> label\n", + "I0307 01:34:29.426354 2099749632 net.cpp:113] Setting up data\n", + "I0307 01:34:29.426362 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\n", + "I0307 01:34:29.426484 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n", + "I0307 01:34:29.427692 2099749632 net.cpp:120] Top shape: 10 4 (40)\n", + "I0307 01:34:29.427711 2099749632 net.cpp:120] Top shape: 10 (10)\n", + "I0307 01:34:29.427721 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\n", + "I0307 01:34:29.427731 2099749632 net.cpp:84] Creating Layer label_data_1_split\n", + "I0307 01:34:29.427738 2099749632 net.cpp:380] label_data_1_split <- label\n", + "I0307 01:34:29.427747 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", + "I0307 01:34:29.427759 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", + "I0307 01:34:29.427768 2099749632 net.cpp:113] Setting up label_data_1_split\n", + "I0307 01:34:29.427777 2099749632 net.cpp:120] Top shape: 10 (10)\n", + "I0307 01:34:29.427784 2099749632 net.cpp:120] Top shape: 10 (10)\n", + "I0307 01:34:29.427791 2099749632 layer_factory.hpp:74] Creating layer fc1\n", + "I0307 01:34:29.427804 2099749632 net.cpp:84] Creating Layer fc1\n", + "I0307 01:34:29.427813 2099749632 net.cpp:380] fc1 <- data\n", + "I0307 01:34:29.427821 2099749632 net.cpp:338] fc1 -> fc1\n", + "I0307 01:34:29.427831 2099749632 net.cpp:113] Setting up fc1\n", + "I0307 01:34:29.427845 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", + "I0307 01:34:29.427857 2099749632 layer_factory.hpp:74] Creating layer fc1_fc1_0_split\n", + "I0307 01:34:29.427866 2099749632 net.cpp:84] Creating Layer fc1_fc1_0_split\n", + "I0307 01:34:29.427872 2099749632 net.cpp:380] fc1_fc1_0_split <- fc1\n", + "I0307 01:34:29.427881 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_0\n", + "I0307 01:34:29.427891 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_1\n", + "I0307 01:34:29.427942 2099749632 net.cpp:113] Setting up fc1_fc1_0_split\n", + "I0307 01:34:29.427955 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", + "I0307 01:34:29.427965 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", + "I0307 01:34:29.427976 2099749632 layer_factory.hpp:74] Creating layer loss\n", + "I0307 01:34:29.427991 2099749632 net.cpp:84] Creating Layer loss\n", + "I0307 01:34:29.428001 2099749632 net.cpp:380] loss <- fc1_fc1_0_split_0\n", + "I0307 01:34:29.428009 2099749632 net.cpp:380] loss <- label_data_1_split_0\n", + "I0307 01:34:29.428017 2099749632 net.cpp:338] loss -> loss\n", + "I0307 01:34:29.428026 2099749632 net.cpp:113] Setting up loss\n", + "I0307 01:34:29.428035 2099749632 layer_factory.hpp:74] Creating layer loss\n", + "I0307 01:34:29.428048 2099749632 net.cpp:120] Top shape: (1)\n", + "I0307 01:34:29.428056 2099749632 net.cpp:122] with loss weight 1\n", + "I0307 01:34:29.428064 2099749632 layer_factory.hpp:74] Creating layer accuracy\n", + "I0307 01:34:29.428076 2099749632 net.cpp:84] Creating Layer accuracy\n", + "I0307 01:34:29.428084 2099749632 net.cpp:380] accuracy <- fc1_fc1_0_split_1\n", + "I0307 01:34:29.428092 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\n", + "I0307 01:34:29.428102 2099749632 net.cpp:338] accuracy -> accuracy\n", + "I0307 01:34:29.428131 2099749632 net.cpp:113] Setting up accuracy\n", + "I0307 01:34:29.428140 2099749632 net.cpp:120] Top shape: (1)\n", + "I0307 01:34:29.428148 2099749632 net.cpp:169] accuracy does not need backward computation.\n", + "I0307 01:34:29.428154 2099749632 net.cpp:167] loss needs backward computation.\n", + "I0307 01:34:29.428161 2099749632 net.cpp:167] fc1_fc1_0_split needs backward computation.\n", + "I0307 01:34:29.428167 2099749632 net.cpp:167] fc1 needs backward computation.\n", + "I0307 01:34:29.428174 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\n", + "I0307 01:34:29.428181 2099749632 net.cpp:169] data does not need backward computation.\n", + "I0307 01:34:29.428189 2099749632 net.cpp:205] This network produces output accuracy\n", + "I0307 01:34:29.428324 2099749632 net.cpp:205] This network produces output loss\n", + "I0307 01:34:29.428342 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", + "I0307 01:34:29.428350 2099749632 net.cpp:217] Network initialization done.\n", + "I0307 01:34:29.428357 2099749632 net.cpp:218] Memory required for data: 528\n", + "I0307 01:34:29.428388 2099749632 solver.cpp:42] Solver scaffolding done.\n", + "I0307 01:34:29.428412 2099749632 solver.cpp:222] Solving LogisticRegressionNet\n", + "I0307 01:34:29.428421 2099749632 solver.cpp:223] Learning Rate Policy: step\n", + "I0307 01:34:29.428431 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\n", + "I0307 01:34:29.471674 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.4532\n", + "I0307 01:34:29.471724 2099749632 solver.cpp:315] Test net output #1: loss = 0.694067 (* 1 = 0.694067 loss)\n", + "I0307 01:34:29.471853 2099749632 solver.cpp:189] Iteration 0, loss = 0.692695\n", + "I0307 01:34:29.471878 2099749632 solver.cpp:204] Train net output #0: loss = 0.692695 (* 1 = 0.692695 loss)\n", + "I0307 01:34:29.471890 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\n", + "I0307 01:34:29.483834 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\n", + "I0307 01:34:29.486868 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7424\n", + "I0307 01:34:29.486896 2099749632 solver.cpp:315] Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\n", + "I0307 01:34:29.486922 2099749632 solver.cpp:189] Iteration 1000, loss = 0.472665\n", + "I0307 01:34:29.486934 2099749632 solver.cpp:204] Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\n", + "I0307 01:34:29.486944 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\n", + "I0307 01:34:29.498821 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\n", + "I0307 01:34:29.501900 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7364\n", + "I0307 01:34:29.501941 2099749632 solver.cpp:315] Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\n", + "I0307 01:34:29.501988 2099749632 solver.cpp:189] Iteration 2000, loss = 0.6863\n", + "I0307 01:34:29.502003 2099749632 solver.cpp:204] Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\n", + "I0307 01:34:29.502013 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\n", + "I0307 01:34:29.513921 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\n", + "I0307 01:34:29.517227 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.6964\n", + "I0307 01:34:29.517300 2099749632 solver.cpp:315] Test net output #1: loss = 0.604707 (* 1 = 0.604707 loss)\n", + "I0307 01:34:29.518105 2099749632 solver.cpp:189] Iteration 3000, loss = 0.617542\n", + "I0307 01:34:29.518154 2099749632 solver.cpp:204] Train net output #0: loss = 0.617542 (* 1 = 0.617542 loss)\n", + "I0307 01:34:29.518170 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\n", + "I0307 01:34:29.531672 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\n", + "I0307 01:34:29.534873 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7424\n", + "I0307 01:34:29.534920 2099749632 solver.cpp:315] Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\n", + "I0307 01:34:29.534950 2099749632 solver.cpp:189] Iteration 4000, loss = 0.472666\n", + "I0307 01:34:29.534962 2099749632 solver.cpp:204] Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\n", + "I0307 01:34:29.534973 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\n", + "I0307 01:34:29.546567 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\n", + "I0307 01:34:29.549762 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7364\n", + "I0307 01:34:29.549789 2099749632 solver.cpp:315] Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\n", + "I0307 01:34:29.549815 2099749632 solver.cpp:189] Iteration 5000, loss = 0.686301\n", + "I0307 01:34:29.549828 2099749632 solver.cpp:204] Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\n", + "I0307 01:34:29.549837 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\n", + "I0307 01:34:29.562142 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\n", + "I0307 01:34:29.565335 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7476\n", + "I0307 01:34:29.565373 2099749632 solver.cpp:315] Test net output #1: loss = 0.59775 (* 1 = 0.59775 loss)\n", + "I0307 01:34:29.566051 2099749632 solver.cpp:189] Iteration 6000, loss = 0.664614\n", + "I0307 01:34:29.566086 2099749632 solver.cpp:204] Train net output #0: loss = 0.664614 (* 1 = 0.664614 loss)\n", + "I0307 01:34:29.566097 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\n", + "I0307 01:34:29.577900 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\n", + "I0307 01:34:29.580993 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7524\n", + "I0307 01:34:29.581015 2099749632 solver.cpp:315] Test net output #1: loss = 0.597349 (* 1 = 0.597349 loss)\n", + "I0307 01:34:29.581038 2099749632 solver.cpp:189] Iteration 7000, loss = 0.456775\n", + "I0307 01:34:29.581050 2099749632 solver.cpp:204] Train net output #0: loss = 0.456774 (* 1 = 0.456774 loss)\n", + "I0307 01:34:29.581059 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\n", + "I0307 01:34:29.592854 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\n", + "I0307 01:34:29.595973 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7568\n", + "I0307 01:34:29.596002 2099749632 solver.cpp:315] Test net output #1: loss = 0.597265 (* 1 = 0.597265 loss)\n", + "I0307 01:34:29.596027 2099749632 solver.cpp:189] Iteration 8000, loss = 0.673885\n", + "I0307 01:34:29.596040 2099749632 solver.cpp:204] Train net output #0: loss = 0.673885 (* 1 = 0.673885 loss)\n", + "I0307 01:34:29.596048 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\n", + "I0307 01:34:29.607822 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\n", + "I0307 01:34:29.610930 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7432\n", + "I0307 01:34:29.610960 2099749632 solver.cpp:315] Test net output #1: loss = 0.597777 (* 1 = 0.597777 loss)\n", + "I0307 01:34:29.611558 2099749632 solver.cpp:189] Iteration 9000, loss = 0.66526\n", + "I0307 01:34:29.611583 2099749632 solver.cpp:204] Train net output #0: loss = 0.66526 (* 1 = 0.66526 loss)\n", + "I0307 01:34:29.611593 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\n", + "I0307 01:34:29.623009 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\n", + "I0307 01:34:29.623209 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\n", + "I0307 01:34:29.623319 2099749632 solver.cpp:248] Iteration 10000, loss = 0.457922\n", + "I0307 01:34:29.623333 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\n", + "I0307 01:34:29.626454 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.752\n", + "I0307 01:34:29.626484 2099749632 solver.cpp:315] Test net output #1: loss = 0.597362 (* 1 = 0.597362 loss)\n", + "I0307 01:34:29.626493 2099749632 solver.cpp:253] Optimization Done.\n", + "I0307 01:34:29.626502 2099749632 caffe.cpp:121] Optimization Done.\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "!../build/tools/caffe train -solver hdf5_classification/solver.prototxt" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:29.141863 2099749632 caffe.cpp:103] Use CPU.\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:29.418283 2099749632 caffe.cpp:107] Starting Optimization\r\n", - "I0307 01:34:29.418323 2099749632 solver.cpp:32] Initializing solver from parameters: \r\n", - "test_iter: 250\r\n", - "test_interval: 1000\r\n", - "base_lr: 0.01\r\n", - "display: 1000\r\n", - "max_iter: 10000\r\n", - "lr_policy: \"step\"\r\n", - "gamma: 0.1\r\n", - "momentum: 0.9\r\n", - "weight_decay: 0.0005\r\n", - "stepsize: 5000\r\n", - "snapshot: 10000\r\n", - "snapshot_prefix: \"hdf5_classification/data/train\"\r\n", - "solver_mode: CPU\r\n", - "net: \"hdf5_classification/train_val.prototxt\"\r\n", - "I0307 01:34:29.418416 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val.prototxt\r\n", - "I0307 01:34:29.418583 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n", - "I0307 01:34:29.418598 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n", - "I0307 01:34:29.418608 2099749632 net.cpp:42] Initializing net from parameters: \r\n", - "name: \"LogisticRegressionNet\"\r\n", - "state {\r\n", - " phase: TRAIN\r\n", - "}\r\n", - "layer {\r\n", - " name: \"data\"\r\n", - " type: \"HDF5Data\"\r\n", - " top: \"data\"\r\n", - " top: \"label\"\r\n", - " include {\r\n", - " phase: TRAIN\r\n", - " }\r\n", - " hdf5_data_param {\r\n", - " source: \"hdf5_classification/data/train.txt\"\r\n", - " batch_size: 10\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"fc1\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"data\"\r\n", - " top: \"fc1\"\r\n", - " param {\r\n", - " lr_mult: 1\r\n", - " decay_mult: 1\r\n", - " }\r\n", - " param {\r\n", - " lr_mult: 2\r\n", - " decay_mult: 0\r\n", - " }\r\n", - " inner_product_param {\r\n", - " num_output: 2\r\n", - " weight_filler {\r\n", - " type: \"gaussian\"\r\n", - " std: 0.01\r\n", - " }\r\n", - " bias_filler {\r\n", - " type: \"constant\"\r\n", - " value: 0\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"loss\"\r\n", - " type: \"SoftmaxWithLoss\"\r\n", - " bottom: \"fc1\"\r\n", - " bottom: \"label\"\r\n", - " top: \"loss\"\r\n", - "}\r\n", - "I0307 01:34:29.418692 2099749632 layer_factory.hpp:74] Creating layer data\r\n", - "I0307 01:34:29.418853 2099749632 net.cpp:84] Creating Layer data\r\n", - "I0307 01:34:29.418879 2099749632 net.cpp:338] data -> data\r\n", - "I0307 01:34:29.418905 2099749632 net.cpp:338] data -> label\r\n", - "I0307 01:34:29.418918 2099749632 net.cpp:113] Setting up data\r\n", - "I0307 01:34:29.418926 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\r\n", - "I0307 01:34:29.418992 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\r\n", - "I0307 01:34:29.420812 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n", - "I0307 01:34:29.420841 2099749632 net.cpp:120] Top shape: 10 (10)\r\n", - "I0307 01:34:29.420852 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n", - "I0307 01:34:29.420866 2099749632 net.cpp:84] Creating Layer fc1\r\n", - "I0307 01:34:29.420872 2099749632 net.cpp:380] fc1 <- data\r\n", - "I0307 01:34:29.420882 2099749632 net.cpp:338] fc1 -> fc1\r\n", - "I0307 01:34:29.420894 2099749632 net.cpp:113] Setting up fc1\r\n", - "I0307 01:34:29.425689 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n", - "I0307 01:34:29.425709 2099749632 layer_factory.hpp:74] Creating layer loss\r\n", - "I0307 01:34:29.425724 2099749632 net.cpp:84] Creating Layer loss\r\n", - "I0307 01:34:29.425731 2099749632 net.cpp:380] loss <- fc1\r\n", - "I0307 01:34:29.425739 2099749632 net.cpp:380] loss <- label\r\n", - "I0307 01:34:29.425747 2099749632 net.cpp:338] loss -> loss\r\n", - "I0307 01:34:29.425756 2099749632 net.cpp:113] Setting up loss\r\n", - "I0307 01:34:29.425767 2099749632 layer_factory.hpp:74] Creating layer loss\r\n", - "I0307 01:34:29.425781 2099749632 net.cpp:120] Top shape: (1)\r\n", - "I0307 01:34:29.425789 2099749632 net.cpp:122] with loss weight 1\r\n", - "I0307 01:34:29.425801 2099749632 net.cpp:167] loss needs backward computation.\r\n", - "I0307 01:34:29.425808 2099749632 net.cpp:167] fc1 needs backward computation.\r\n", - "I0307 01:34:29.425815 2099749632 net.cpp:169] data does not need backward computation.\r\n", - "I0307 01:34:29.425822 2099749632 net.cpp:205] This network produces output loss\r\n", - "I0307 01:34:29.425829 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n", - "I0307 01:34:29.425837 2099749632 net.cpp:217] Network initialization done.\r\n", - "I0307 01:34:29.425843 2099749632 net.cpp:218] Memory required for data: 284\r\n", - "I0307 01:34:29.425961 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val.prototxt\r\n", - "I0307 01:34:29.425984 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n", - "I0307 01:34:29.425997 2099749632 net.cpp:42] Initializing net from parameters: \r\n", - "name: \"LogisticRegressionNet\"\r\n", - "state {\r\n", - " phase: TEST\r\n", - "}\r\n", - "layer {\r\n", - " name: \"data\"\r\n", - " type: \"HDF5Data\"\r\n", - " top: \"data\"\r\n", - " top: \"label\"\r\n", - " include {\r\n", - " phase: TEST\r\n", - " }\r\n", - " hdf5_data_param {\r\n", - " source: \"hdf5_classification/data/test.txt\"\r\n", - " batch_size: 10\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"fc1\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"data\"\r\n", - " top: \"fc1\"\r\n", - " param {\r\n", - " lr_mult: 1\r\n", - " decay_mult: 1\r\n", - " }\r\n", - " param {\r\n", - " lr_mult: 2\r\n", - " decay_mult: 0\r\n", - " }\r\n", - " inner_product_param {\r\n", - " num_output: 2\r\n", - " weight_filler {\r\n", - " type: \"gaussian\"\r\n", - " std: 0.01\r\n", - " }\r\n", - " bias_filler {\r\n", - " type: \"constant\"\r\n", - " value: 0\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"loss\"\r\n", - " type: \"SoftmaxWithLoss\"\r\n", - " bottom: \"fc1\"\r\n", - " bottom: \"label\"\r\n", - " top: \"loss\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"accuracy\"\r\n", - " type: \"Accuracy\"\r\n", - " bottom: \"fc1\"\r\n", - " bottom: \"label\"\r\n", - " top: \"accuracy\"\r\n", - " include {\r\n", - " phase: TEST\r\n", - " }\r\n", - "}\r\n", - "I0307 01:34:29.426126 2099749632 layer_factory.hpp:74] Creating layer data\r\n", - "I0307 01:34:29.426311 2099749632 net.cpp:84] Creating Layer data\r\n", - "I0307 01:34:29.426331 2099749632 net.cpp:338] data -> data\r\n", - "I0307 01:34:29.426343 2099749632 net.cpp:338] data -> label\r\n", - "I0307 01:34:29.426354 2099749632 net.cpp:113] Setting up data\r\n", - "I0307 01:34:29.426362 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\r\n", - "I0307 01:34:29.426484 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\r\n", - "I0307 01:34:29.427692 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n", - "I0307 01:34:29.427711 2099749632 net.cpp:120] Top shape: 10 (10)\r\n", - "I0307 01:34:29.427721 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\r\n", - "I0307 01:34:29.427731 2099749632 net.cpp:84] Creating Layer label_data_1_split\r\n", - "I0307 01:34:29.427738 2099749632 net.cpp:380] label_data_1_split <- label\r\n", - "I0307 01:34:29.427747 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\r\n", - "I0307 01:34:29.427759 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\r\n", - "I0307 01:34:29.427768 2099749632 net.cpp:113] Setting up label_data_1_split\r\n", - "I0307 01:34:29.427777 2099749632 net.cpp:120] Top shape: 10 (10)\r\n", - "I0307 01:34:29.427784 2099749632 net.cpp:120] Top shape: 10 (10)\r\n", - "I0307 01:34:29.427791 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n", - "I0307 01:34:29.427804 2099749632 net.cpp:84] Creating Layer fc1\r\n", - "I0307 01:34:29.427813 2099749632 net.cpp:380] fc1 <- data\r\n", - "I0307 01:34:29.427821 2099749632 net.cpp:338] fc1 -> fc1\r\n", - "I0307 01:34:29.427831 2099749632 net.cpp:113] Setting up fc1\r\n", - "I0307 01:34:29.427845 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n", - "I0307 01:34:29.427857 2099749632 layer_factory.hpp:74] Creating layer fc1_fc1_0_split\r\n", - "I0307 01:34:29.427866 2099749632 net.cpp:84] Creating Layer fc1_fc1_0_split\r\n", - "I0307 01:34:29.427872 2099749632 net.cpp:380] fc1_fc1_0_split <- fc1\r\n", - "I0307 01:34:29.427881 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_0\r\n", - "I0307 01:34:29.427891 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_1\r\n", - "I0307 01:34:29.427942 2099749632 net.cpp:113] Setting up fc1_fc1_0_split\r\n", - "I0307 01:34:29.427955 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n", - "I0307 01:34:29.427965 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n", - "I0307 01:34:29.427976 2099749632 layer_factory.hpp:74] Creating layer loss\r\n", - "I0307 01:34:29.427991 2099749632 net.cpp:84] Creating Layer loss\r\n", - "I0307 01:34:29.428001 2099749632 net.cpp:380] loss <- fc1_fc1_0_split_0\r\n", - "I0307 01:34:29.428009 2099749632 net.cpp:380] loss <- label_data_1_split_0\r\n", - "I0307 01:34:29.428017 2099749632 net.cpp:338] loss -> loss\r\n", - "I0307 01:34:29.428026 2099749632 net.cpp:113] Setting up loss\r\n", - "I0307 01:34:29.428035 2099749632 layer_factory.hpp:74] Creating layer loss\r\n", - "I0307 01:34:29.428048 2099749632 net.cpp:120] Top shape: (1)\r\n", - "I0307 01:34:29.428056 2099749632 net.cpp:122] with loss weight 1\r\n", - "I0307 01:34:29.428064 2099749632 layer_factory.hpp:74] Creating layer accuracy\r\n", - "I0307 01:34:29.428076 2099749632 net.cpp:84] Creating Layer accuracy\r\n", - "I0307 01:34:29.428084 2099749632 net.cpp:380] accuracy <- fc1_fc1_0_split_1\r\n", - "I0307 01:34:29.428092 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\r\n", - "I0307 01:34:29.428102 2099749632 net.cpp:338] accuracy -> accuracy\r\n", - "I0307 01:34:29.428131 2099749632 net.cpp:113] Setting up accuracy\r\n", - "I0307 01:34:29.428140 2099749632 net.cpp:120] Top shape: (1)\r\n", - "I0307 01:34:29.428148 2099749632 net.cpp:169] accuracy does not need backward computation.\r\n", - "I0307 01:34:29.428154 2099749632 net.cpp:167] loss needs backward computation.\r\n", - "I0307 01:34:29.428161 2099749632 net.cpp:167] fc1_fc1_0_split needs backward computation.\r\n", - "I0307 01:34:29.428167 2099749632 net.cpp:167] fc1 needs backward computation.\r\n", - "I0307 01:34:29.428174 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\r\n", - "I0307 01:34:29.428181 2099749632 net.cpp:169] data does not need backward computation.\r\n", - "I0307 01:34:29.428189 2099749632 net.cpp:205] This network produces output accuracy\r\n", - "I0307 01:34:29.428324 2099749632 net.cpp:205] This network produces output loss\r\n", - "I0307 01:34:29.428342 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n", - "I0307 01:34:29.428350 2099749632 net.cpp:217] Network initialization done.\r\n", - "I0307 01:34:29.428357 2099749632 net.cpp:218] Memory required for data: 528\r\n", - "I0307 01:34:29.428388 2099749632 solver.cpp:42] Solver scaffolding done.\r\n", - "I0307 01:34:29.428412 2099749632 solver.cpp:222] Solving LogisticRegressionNet\r\n", - "I0307 01:34:29.428421 2099749632 solver.cpp:223] Learning Rate Policy: step\r\n", - "I0307 01:34:29.428431 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:29.471674 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.4532\r\n", - "I0307 01:34:29.471724 2099749632 solver.cpp:315] Test net output #1: loss = 0.694067 (* 1 = 0.694067 loss)\r\n", - "I0307 01:34:29.471853 2099749632 solver.cpp:189] Iteration 0, loss = 0.692695\r\n", - "I0307 01:34:29.471878 2099749632 solver.cpp:204] Train net output #0: loss = 0.692695 (* 1 = 0.692695 loss)\r\n", - "I0307 01:34:29.471890 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\r\n", - "I0307 01:34:29.483834 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\r\n", - "I0307 01:34:29.486868 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7424\r\n", - "I0307 01:34:29.486896 2099749632 solver.cpp:315] Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\r\n", - "I0307 01:34:29.486922 2099749632 solver.cpp:189] Iteration 1000, loss = 0.472665\r\n", - "I0307 01:34:29.486934 2099749632 solver.cpp:204] Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\r\n", - "I0307 01:34:29.486944 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\r\n", - "I0307 01:34:29.498821 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\r\n", - "I0307 01:34:29.501900 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7364\r\n", - "I0307 01:34:29.501941 2099749632 solver.cpp:315] Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\r\n", - "I0307 01:34:29.501988 2099749632 solver.cpp:189] Iteration 2000, loss = 0.6863\r\n", - "I0307 01:34:29.502003 2099749632 solver.cpp:204] Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\r\n", - "I0307 01:34:29.502013 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\r\n", - "I0307 01:34:29.513921 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\r\n", - "I0307 01:34:29.517227 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.6964\r\n", - "I0307 01:34:29.517300 2099749632 solver.cpp:315] Test net output #1: loss = 0.604707 (* 1 = 0.604707 loss)\r\n", - "I0307 01:34:29.518105 2099749632 solver.cpp:189] Iteration 3000, loss = 0.617542\r\n", - "I0307 01:34:29.518154 2099749632 solver.cpp:204] Train net output #0: loss = 0.617542 (* 1 = 0.617542 loss)\r\n", - "I0307 01:34:29.518170 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:29.531672 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\r\n", - "I0307 01:34:29.534873 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7424\r\n", - "I0307 01:34:29.534920 2099749632 solver.cpp:315] Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\r\n", - "I0307 01:34:29.534950 2099749632 solver.cpp:189] Iteration 4000, loss = 0.472666\r\n", - "I0307 01:34:29.534962 2099749632 solver.cpp:204] Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\r\n", - "I0307 01:34:29.534973 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\r\n", - "I0307 01:34:29.546567 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\r\n", - "I0307 01:34:29.549762 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7364\r\n", - "I0307 01:34:29.549789 2099749632 solver.cpp:315] Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\r\n", - "I0307 01:34:29.549815 2099749632 solver.cpp:189] Iteration 5000, loss = 0.686301\r\n", - "I0307 01:34:29.549828 2099749632 solver.cpp:204] Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\r\n", - "I0307 01:34:29.549837 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\r\n", - "I0307 01:34:29.562142 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\r\n", - "I0307 01:34:29.565335 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7476\r\n", - "I0307 01:34:29.565373 2099749632 solver.cpp:315] Test net output #1: loss = 0.59775 (* 1 = 0.59775 loss)\r\n", - "I0307 01:34:29.566051 2099749632 solver.cpp:189] Iteration 6000, loss = 0.664614\r\n", - "I0307 01:34:29.566086 2099749632 solver.cpp:204] Train net output #0: loss = 0.664614 (* 1 = 0.664614 loss)\r\n", - "I0307 01:34:29.566097 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:29.577900 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\r\n", - "I0307 01:34:29.580993 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7524\r\n", - "I0307 01:34:29.581015 2099749632 solver.cpp:315] Test net output #1: loss = 0.597349 (* 1 = 0.597349 loss)\r\n", - "I0307 01:34:29.581038 2099749632 solver.cpp:189] Iteration 7000, loss = 0.456775\r\n", - "I0307 01:34:29.581050 2099749632 solver.cpp:204] Train net output #0: loss = 0.456774 (* 1 = 0.456774 loss)\r\n", - "I0307 01:34:29.581059 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\r\n", - "I0307 01:34:29.592854 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\r\n", - "I0307 01:34:29.595973 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7568\r\n", - "I0307 01:34:29.596002 2099749632 solver.cpp:315] Test net output #1: loss = 0.597265 (* 1 = 0.597265 loss)\r\n", - "I0307 01:34:29.596027 2099749632 solver.cpp:189] Iteration 8000, loss = 0.673885\r\n", - "I0307 01:34:29.596040 2099749632 solver.cpp:204] Train net output #0: loss = 0.673885 (* 1 = 0.673885 loss)\r\n", - "I0307 01:34:29.596048 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\r\n", - "I0307 01:34:29.607822 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\r\n", - "I0307 01:34:29.610930 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7432\r\n", - "I0307 01:34:29.610960 2099749632 solver.cpp:315] Test net output #1: loss = 0.597777 (* 1 = 0.597777 loss)\r\n", - "I0307 01:34:29.611558 2099749632 solver.cpp:189] Iteration 9000, loss = 0.66526\r\n", - "I0307 01:34:29.611583 2099749632 solver.cpp:204] Train net output #0: loss = 0.66526 (* 1 = 0.66526 loss)\r\n", - "I0307 01:34:29.611593 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\r\n", - "I0307 01:34:29.623009 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\r\n", - "I0307 01:34:29.623209 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\r\n", - "I0307 01:34:29.623319 2099749632 solver.cpp:248] Iteration 10000, loss = 0.457922\r\n", - "I0307 01:34:29.623333 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:29.626454 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.752\r\n", - "I0307 01:34:29.626484 2099749632 solver.cpp:315] Test net output #1: loss = 0.597362 (* 1 = 0.597362 loss)\r\n", - "I0307 01:34:29.626493 2099749632 solver.cpp:253] Optimization Done.\r\n", - "I0307 01:34:29.626502 2099749632 caffe.cpp:121] Optimization Done.\r\n" - ] - } - ], - "prompt_number": 6 - }, + } + ], + "source": [ + "!../build/tools/caffe train -solver hdf5_classification/solver.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you look at output or the `train_val.prototxt`, you'll see that the model is simple logistic regression.\n", + "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n", + "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n", + "\n", + "The final accuracy of the new network be higher than logistic regression!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you look at output or the `train_val.prototxt`, you'll see that the model is simple logistic regression.\n", - "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n", - "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n", - "\n", - "The final accuracy of the new network be higher than logistic regression!" + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loops, best of 3: 333 ms per loop\n", + "Accuracy: 0.818\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def learn_and_test(solver_file):\n", - " caffe.set_mode_cpu()\n", - " solver = caffe.get_solver(solver_file)\n", - " solver.solve()\n", - "\n", - " accuracy = 0\n", - " test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n", - " for i in range(test_iters):\n", - " solver.test_nets[0].forward()\n", - " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", - " accuracy /= test_iters\n", - " return accuracy\n", - "\n", - "%timeit learn_and_test('hdf5_classification/solver2.prototxt')\n", - "acc = learn_and_test('hdf5_classification/solver2.prototxt')\n", - "print(\"Accuracy: {:.3f}\".format(acc))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1 loops, best of 3: 333 ms per loop\n", - "Accuracy: 0.818" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 7 - }, + } + ], + "source": [ + "def learn_and_test(solver_file):\n", + " caffe.set_mode_cpu()\n", + " solver = caffe.get_solver(solver_file)\n", + " solver.solve()\n", + "\n", + " accuracy = 0\n", + " test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n", + " for i in range(test_iters):\n", + " solver.test_nets[0].forward()\n", + " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", + " accuracy /= test_iters\n", + " return accuracy\n", + "\n", + "%timeit learn_and_test('hdf5_classification/solver2.prototxt')\n", + "acc = learn_and_test('hdf5_classification/solver2.prototxt')\n", + "print(\"Accuracy: {:.3f}\".format(acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do the same through the command line interface for detailed output on the model and solving." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do the same through the command line interface for detailed output on the model and solving." + "name": "stdout", + "output_type": "stream", + "text": [ + "I0307 01:34:31.589234 2099749632 caffe.cpp:103] Use CPU.\n", + "I0307 01:34:31.872560 2099749632 caffe.cpp:107] Starting Optimization\n", + "I0307 01:34:31.872596 2099749632 solver.cpp:32] Initializing solver from parameters: \n", + "test_iter: 250\n", + "test_interval: 1000\n", + "base_lr: 0.01\n", + "display: 1000\n", + "max_iter: 10000\n", + "lr_policy: \"step\"\n", + "gamma: 0.1\n", + "momentum: 0.9\n", + "weight_decay: 0.0005\n", + "stepsize: 5000\n", + "snapshot: 10000\n", + "snapshot_prefix: \"hdf5_classification/data/train\"\n", + "solver_mode: CPU\n", + "net: \"hdf5_classification/train_val2.prototxt\"\n", + "I0307 01:34:31.872687 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val2.prototxt\n", + "I0307 01:34:31.872865 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\n", + "I0307 01:34:31.872882 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\n", + "I0307 01:34:31.872891 2099749632 net.cpp:42] Initializing net from parameters: \n", + "name: \"LogisticRegressionNet\"\n", + "state {\n", + " phase: TRAIN\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " include {\n", + " phase: TRAIN\n", + " }\n", + " hdf5_data_param {\n", + " source: \"hdf5_classification/data/train.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"fc1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"fc1\"\n", + " param {\n", + " lr_mult: 1\n", + " decay_mult: 1\n", + " }\n", + " param {\n", + " lr_mult: 2\n", + " decay_mult: 0\n", + " }\n", + " inner_product_param {\n", + " num_output: 40\n", + " weight_filler {\n", + " type: \"gaussian\"\n", + " std: 0.01\n", + " }\n", + " bias_filler {\n", + " type: \"constant\"\n", + " value: 0\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu1\"\n", + " type: \"ReLU\"\n", + " bottom: \"fc1\"\n", + " top: \"fc1\"\n", + "}\n", + "layer {\n", + " name: \"fc2\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"fc1\"\n", + " top: \"fc2\"\n", + " param {\n", + " lr_mult: 1\n", + " decay_mult: 1\n", + " }\n", + " param {\n", + " lr_mult: 2\n", + " decay_mult: 0\n", + " }\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"gaussian\"\n", + " std: 0.01\n", + " }\n", + " bias_filler {\n", + " type: \"constant\"\n", + " value: 0\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"fc2\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0307 01:34:31.873246 2099749632 layer_factory.hpp:74] Creating layer data\n", + "I0307 01:34:31.873276 2099749632 net.cpp:84] Creating Layer data\n", + "I0307 01:34:31.873292 2099749632 net.cpp:338] data -> data\n", + "I0307 01:34:31.873332 2099749632 net.cpp:338] data -> label\n", + "I0307 01:34:31.873352 2099749632 net.cpp:113] Setting up data\n", + "I0307 01:34:31.873361 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\n", + "I0307 01:34:31.873443 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n", + "I0307 01:34:31.875783 2099749632 net.cpp:120] Top shape: 10 4 (40)\n", + "I0307 01:34:31.875816 2099749632 net.cpp:120] Top shape: 10 (10)\n", + "I0307 01:34:31.875829 2099749632 layer_factory.hpp:74] Creating layer fc1\n", + "I0307 01:34:31.875846 2099749632 net.cpp:84] Creating Layer fc1\n", + "I0307 01:34:31.875857 2099749632 net.cpp:380] fc1 <- data\n", + "I0307 01:34:31.875875 2099749632 net.cpp:338] fc1 -> fc1\n", + "I0307 01:34:31.875892 2099749632 net.cpp:113] Setting up fc1\n", + "I0307 01:34:31.882478 2099749632 net.cpp:120] Top shape: 10 40 (400)\n", + "I0307 01:34:31.882505 2099749632 layer_factory.hpp:74] Creating layer relu1\n", + "I0307 01:34:31.882524 2099749632 net.cpp:84] Creating Layer relu1\n", + "I0307 01:34:31.882532 2099749632 net.cpp:380] relu1 <- fc1\n", + "I0307 01:34:31.882544 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\n", + "I0307 01:34:31.882555 2099749632 net.cpp:113] Setting up relu1\n", + "I0307 01:34:31.882565 2099749632 net.cpp:120] Top shape: 10 40 (400)\n", + "I0307 01:34:31.882583 2099749632 layer_factory.hpp:74] Creating layer fc2\n", + "I0307 01:34:31.882609 2099749632 net.cpp:84] Creating Layer fc2\n", + "I0307 01:34:31.882619 2099749632 net.cpp:380] fc2 <- fc1\n", + "I0307 01:34:31.882632 2099749632 net.cpp:338] fc2 -> fc2\n", + "I0307 01:34:31.882644 2099749632 net.cpp:113] Setting up fc2\n", + "I0307 01:34:31.882663 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", + "I0307 01:34:31.882678 2099749632 layer_factory.hpp:74] Creating layer loss\n", + "I0307 01:34:31.882694 2099749632 net.cpp:84] Creating Layer loss\n", + "I0307 01:34:31.882704 2099749632 net.cpp:380] loss <- fc2\n", + "I0307 01:34:31.882712 2099749632 net.cpp:380] loss <- label\n", + "I0307 01:34:31.882779 2099749632 net.cpp:338] loss -> loss\n", + "I0307 01:34:31.882796 2099749632 net.cpp:113] Setting up loss\n", + "I0307 01:34:31.882810 2099749632 layer_factory.hpp:74] Creating layer loss\n", + "I0307 01:34:31.882833 2099749632 net.cpp:120] Top shape: (1)\n", + "I0307 01:34:31.882844 2099749632 net.cpp:122] with loss weight 1\n", + "I0307 01:34:31.882860 2099749632 net.cpp:167] loss needs backward computation.\n", + "I0307 01:34:31.882869 2099749632 net.cpp:167] fc2 needs backward computation.\n", + "I0307 01:34:31.882877 2099749632 net.cpp:167] relu1 needs backward computation.\n", + "I0307 01:34:31.882886 2099749632 net.cpp:167] fc1 needs backward computation.\n", + "I0307 01:34:31.882894 2099749632 net.cpp:169] data does not need backward computation.\n", + "I0307 01:34:31.882904 2099749632 net.cpp:205] This network produces output loss\n", + "I0307 01:34:31.882931 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", + "I0307 01:34:31.882942 2099749632 net.cpp:217] Network initialization done.\n", + "I0307 01:34:31.882951 2099749632 net.cpp:218] Memory required for data: 3484\n", + "I0307 01:34:31.883157 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val2.prototxt\n", + "I0307 01:34:31.883189 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\n", + "I0307 01:34:31.883203 2099749632 net.cpp:42] Initializing net from parameters: \n", + "name: \"LogisticRegressionNet\"\n", + "state {\n", + " phase: TEST\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " include {\n", + " phase: TEST\n", + " }\n", + " hdf5_data_param {\n", + " source: \"hdf5_classification/data/test.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"fc1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"fc1\"\n", + " param {\n", + " lr_mult: 1\n", + " decay_mult: 1\n", + " }\n", + " param {\n", + " lr_mult: 2\n", + " decay_mult: 0\n", + " }\n", + " inner_product_param {\n", + " num_output: 40\n", + " weight_filler {\n", + " type: \"gaussian\"\n", + " std: 0.01\n", + " }\n", + " bias_filler {\n", + " type: \"constant\"\n", + " value: 0\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu1\"\n", + " type: \"ReLU\"\n", + " bottom: \"fc1\"\n", + " top: \"fc1\"\n", + "}\n", + "layer {\n", + " name: \"fc2\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"fc1\"\n", + " top: \"fc2\"\n", + " param {\n", + " lr_mult: 1\n", + " decay_mult: 1\n", + " }\n", + " param {\n", + " lr_mult: 2\n", + " decay_mult: 0\n", + " }\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"gaussian\"\n", + " std: 0.01\n", + " }\n", + " bias_filler {\n", + " type: \"constant\"\n", + " value: 0\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"fc2\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"fc2\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + " include {\n", + " phase: TEST\n", + " }\n", + "}\n", + "I0307 01:34:31.883535 2099749632 layer_factory.hpp:74] Creating layer data\n", + "I0307 01:34:31.883548 2099749632 net.cpp:84] Creating Layer data\n", + "I0307 01:34:31.883556 2099749632 net.cpp:338] data -> data\n", + "I0307 01:34:31.883569 2099749632 net.cpp:338] data -> label\n", + "I0307 01:34:31.883579 2099749632 net.cpp:113] Setting up data\n", + "I0307 01:34:31.883585 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\n", + "I0307 01:34:31.883664 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n", + "I0307 01:34:31.884842 2099749632 net.cpp:120] Top shape: 10 4 (40)\n", + "I0307 01:34:31.884860 2099749632 net.cpp:120] Top shape: 10 (10)\n", + "I0307 01:34:31.884870 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\n", + "I0307 01:34:31.884879 2099749632 net.cpp:84] Creating Layer label_data_1_split\n", + "I0307 01:34:31.884886 2099749632 net.cpp:380] label_data_1_split <- label\n", + "I0307 01:34:31.884896 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", + "I0307 01:34:31.884909 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", + "I0307 01:34:31.884919 2099749632 net.cpp:113] Setting up label_data_1_split\n", + "I0307 01:34:31.884927 2099749632 net.cpp:120] Top shape: 10 (10)\n", + "I0307 01:34:31.884934 2099749632 net.cpp:120] Top shape: 10 (10)\n", + "I0307 01:34:31.884941 2099749632 layer_factory.hpp:74] Creating layer fc1\n", + "I0307 01:34:31.884951 2099749632 net.cpp:84] Creating Layer fc1\n", + "I0307 01:34:31.884958 2099749632 net.cpp:380] fc1 <- data\n", + "I0307 01:34:31.884989 2099749632 net.cpp:338] fc1 -> fc1\n", + "I0307 01:34:31.885000 2099749632 net.cpp:113] Setting up fc1\n", + "I0307 01:34:31.885017 2099749632 net.cpp:120] Top shape: 10 40 (400)\n", + "I0307 01:34:31.885030 2099749632 layer_factory.hpp:74] Creating layer relu1\n", + "I0307 01:34:31.885041 2099749632 net.cpp:84] Creating Layer relu1\n", + "I0307 01:34:31.885048 2099749632 net.cpp:380] relu1 <- fc1\n", + "I0307 01:34:31.885056 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\n", + "I0307 01:34:31.885064 2099749632 net.cpp:113] Setting up relu1\n", + "I0307 01:34:31.885071 2099749632 net.cpp:120] Top shape: 10 40 (400)\n", + "I0307 01:34:31.885079 2099749632 layer_factory.hpp:74] Creating layer fc2\n", + "I0307 01:34:31.885088 2099749632 net.cpp:84] Creating Layer fc2\n", + "I0307 01:34:31.885094 2099749632 net.cpp:380] fc2 <- fc1\n", + "I0307 01:34:31.885103 2099749632 net.cpp:338] fc2 -> fc2\n", + "I0307 01:34:31.885113 2099749632 net.cpp:113] Setting up fc2\n", + "I0307 01:34:31.885126 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", + "I0307 01:34:31.885138 2099749632 layer_factory.hpp:74] Creating layer fc2_fc2_0_split\n", + "I0307 01:34:31.885149 2099749632 net.cpp:84] Creating Layer fc2_fc2_0_split\n", + "I0307 01:34:31.885155 2099749632 net.cpp:380] fc2_fc2_0_split <- fc2\n", + "I0307 01:34:31.885164 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_0\n", + "I0307 01:34:31.885174 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_1\n", + "I0307 01:34:31.885182 2099749632 net.cpp:113] Setting up fc2_fc2_0_split\n", + "I0307 01:34:31.885190 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", + "I0307 01:34:31.885242 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", + "I0307 01:34:31.885256 2099749632 layer_factory.hpp:74] Creating layer loss\n", + "I0307 01:34:31.885267 2099749632 net.cpp:84] Creating Layer loss\n", + "I0307 01:34:31.885275 2099749632 net.cpp:380] loss <- fc2_fc2_0_split_0\n", + "I0307 01:34:31.885285 2099749632 net.cpp:380] loss <- label_data_1_split_0\n", + "I0307 01:34:31.885296 2099749632 net.cpp:338] loss -> loss\n", + "I0307 01:34:31.885308 2099749632 net.cpp:113] Setting up loss\n", + "I0307 01:34:31.885316 2099749632 layer_factory.hpp:74] Creating layer loss\n", + "I0307 01:34:31.885330 2099749632 net.cpp:120] Top shape: (1)\n", + "I0307 01:34:31.885337 2099749632 net.cpp:122] with loss weight 1\n", + "I0307 01:34:31.885346 2099749632 layer_factory.hpp:74] Creating layer accuracy\n", + "I0307 01:34:31.885360 2099749632 net.cpp:84] Creating Layer accuracy\n", + "I0307 01:34:31.885368 2099749632 net.cpp:380] accuracy <- fc2_fc2_0_split_1\n", + "I0307 01:34:31.885375 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\n", + "I0307 01:34:31.885383 2099749632 net.cpp:338] accuracy -> accuracy\n", + "I0307 01:34:31.885392 2099749632 net.cpp:113] Setting up accuracy\n", + "I0307 01:34:31.885401 2099749632 net.cpp:120] Top shape: (1)\n", + "I0307 01:34:31.885407 2099749632 net.cpp:169] accuracy does not need backward computation.\n", + "I0307 01:34:31.885413 2099749632 net.cpp:167] loss needs backward computation.\n", + "I0307 01:34:31.885419 2099749632 net.cpp:167] fc2_fc2_0_split needs backward computation.\n", + "I0307 01:34:31.885426 2099749632 net.cpp:167] fc2 needs backward computation.\n", + "I0307 01:34:31.885432 2099749632 net.cpp:167] relu1 needs backward computation.\n", + "I0307 01:34:31.885438 2099749632 net.cpp:167] fc1 needs backward computation.\n", + "I0307 01:34:31.885444 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\n", + "I0307 01:34:31.885452 2099749632 net.cpp:169] data does not need backward computation.\n", + "I0307 01:34:31.885457 2099749632 net.cpp:205] This network produces output accuracy\n", + "I0307 01:34:31.885613 2099749632 net.cpp:205] This network produces output loss\n", + "I0307 01:34:31.885632 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", + "I0307 01:34:31.885639 2099749632 net.cpp:217] Network initialization done.\n", + "I0307 01:34:31.885645 2099749632 net.cpp:218] Memory required for data: 3728\n", + "I0307 01:34:31.885685 2099749632 solver.cpp:42] Solver scaffolding done.\n", + "I0307 01:34:31.885711 2099749632 solver.cpp:222] Solving LogisticRegressionNet\n", + "I0307 01:34:31.885721 2099749632 solver.cpp:223] Learning Rate Policy: step\n", + "I0307 01:34:31.885730 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\n", + "I0307 01:34:31.901005 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.5944\n", + "I0307 01:34:31.901049 2099749632 solver.cpp:315] Test net output #1: loss = 0.693021 (* 1 = 0.693021 loss)\n", + "I0307 01:34:31.901177 2099749632 solver.cpp:189] Iteration 0, loss = 0.693163\n", + "I0307 01:34:31.901192 2099749632 solver.cpp:204] Train net output #0: loss = 0.693163 (* 1 = 0.693163 loss)\n", + "I0307 01:34:31.901203 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\n", + "I0307 01:34:31.920586 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\n", + "I0307 01:34:31.924612 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7556\n", + "I0307 01:34:31.924646 2099749632 solver.cpp:315] Test net output #1: loss = 0.511002 (* 1 = 0.511002 loss)\n", + "I0307 01:34:31.924684 2099749632 solver.cpp:189] Iteration 1000, loss = 0.38536\n", + "I0307 01:34:31.924696 2099749632 solver.cpp:204] Train net output #0: loss = 0.38536 (* 1 = 0.38536 loss)\n", + "I0307 01:34:31.924706 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\n", + "I0307 01:34:31.944727 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\n", + "I0307 01:34:31.948729 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7824\n", + "I0307 01:34:31.948763 2099749632 solver.cpp:315] Test net output #1: loss = 0.489214 (* 1 = 0.489214 loss)\n", + "I0307 01:34:31.948799 2099749632 solver.cpp:189] Iteration 2000, loss = 0.532582\n", + "I0307 01:34:31.948812 2099749632 solver.cpp:204] Train net output #0: loss = 0.532582 (* 1 = 0.532582 loss)\n", + "I0307 01:34:31.948823 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\n", + "I0307 01:34:31.968670 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\n", + "I0307 01:34:31.972393 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7956\n", + "I0307 01:34:31.972411 2099749632 solver.cpp:315] Test net output #1: loss = 0.454184 (* 1 = 0.454184 loss)\n", + "I0307 01:34:31.973024 2099749632 solver.cpp:189] Iteration 3000, loss = 0.541374\n", + "I0307 01:34:31.973057 2099749632 solver.cpp:204] Train net output #0: loss = 0.541374 (* 1 = 0.541374 loss)\n", + "I0307 01:34:31.973067 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\n", + "I0307 01:34:31.994829 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\n", + "I0307 01:34:31.998638 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.798\n", + "I0307 01:34:31.998663 2099749632 solver.cpp:315] Test net output #1: loss = 0.456348 (* 1 = 0.456348 loss)\n", + "I0307 01:34:31.998705 2099749632 solver.cpp:189] Iteration 4000, loss = 0.490437\n", + "I0307 01:34:31.998718 2099749632 solver.cpp:204] Train net output #0: loss = 0.490437 (* 1 = 0.490437 loss)\n", + "I0307 01:34:31.998725 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\n", + "I0307 01:34:32.021085 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\n", + "I0307 01:34:32.024950 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.804\n", + "I0307 01:34:32.024981 2099749632 solver.cpp:315] Test net output #1: loss = 0.46184 (* 1 = 0.46184 loss)\n", + "I0307 01:34:32.025017 2099749632 solver.cpp:189] Iteration 5000, loss = 0.467703\n", + "I0307 01:34:32.025028 2099749632 solver.cpp:204] Train net output #0: loss = 0.467704 (* 1 = 0.467704 loss)\n", + "I0307 01:34:32.025038 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\n", + "I0307 01:34:32.044390 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\n", + "I0307 01:34:32.048216 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8208\n", + "I0307 01:34:32.048239 2099749632 solver.cpp:315] Test net output #1: loss = 0.423084 (* 1 = 0.423084 loss)\n", + "I0307 01:34:32.048790 2099749632 solver.cpp:189] Iteration 6000, loss = 0.480104\n", + "I0307 01:34:32.048809 2099749632 solver.cpp:204] Train net output #0: loss = 0.480105 (* 1 = 0.480105 loss)\n", + "I0307 01:34:32.048827 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\n", + "I0307 01:34:32.067795 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\n", + "I0307 01:34:32.071524 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8124\n", + "I0307 01:34:32.071542 2099749632 solver.cpp:315] Test net output #1: loss = 0.423947 (* 1 = 0.423947 loss)\n", + "I0307 01:34:32.071570 2099749632 solver.cpp:189] Iteration 7000, loss = 0.447471\n", + "I0307 01:34:32.071617 2099749632 solver.cpp:204] Train net output #0: loss = 0.447472 (* 1 = 0.447472 loss)\n", + "I0307 01:34:32.071626 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\n", + "I0307 01:34:32.091625 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\n", + "I0307 01:34:32.095410 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.814\n", + "I0307 01:34:32.095432 2099749632 solver.cpp:315] Test net output #1: loss = 0.423586 (* 1 = 0.423586 loss)\n", + "I0307 01:34:32.095461 2099749632 solver.cpp:189] Iteration 8000, loss = 0.386258\n", + "I0307 01:34:32.095474 2099749632 solver.cpp:204] Train net output #0: loss = 0.386259 (* 1 = 0.386259 loss)\n", + "I0307 01:34:32.095481 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\n", + "I0307 01:34:32.117184 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\n", + "I0307 01:34:32.121587 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8208\n", + "I0307 01:34:32.121608 2099749632 solver.cpp:315] Test net output #1: loss = 0.419969 (* 1 = 0.419969 loss)\n", + "I0307 01:34:32.122161 2099749632 solver.cpp:189] Iteration 9000, loss = 0.468262\n", + "I0307 01:34:32.122181 2099749632 solver.cpp:204] Train net output #0: loss = 0.468262 (* 1 = 0.468262 loss)\n", + "I0307 01:34:32.122191 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\n", + "I0307 01:34:32.141635 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\n", + "I0307 01:34:32.141860 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\n", + "I0307 01:34:32.141978 2099749632 solver.cpp:248] Iteration 10000, loss = 0.441529\n", + "I0307 01:34:32.141995 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\n", + "I0307 01:34:32.145747 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8148\n", + "I0307 01:34:32.145771 2099749632 solver.cpp:315] Test net output #1: loss = 0.4216 (* 1 = 0.4216 loss)\n", + "I0307 01:34:32.145779 2099749632 solver.cpp:253] Optimization Done.\n", + "I0307 01:34:32.145786 2099749632 caffe.cpp:121] Optimization Done.\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "!../build/tools/caffe train -solver hdf5_classification/solver2.prototxt" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:31.589234 2099749632 caffe.cpp:103] Use CPU.\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:31.872560 2099749632 caffe.cpp:107] Starting Optimization\r\n", - "I0307 01:34:31.872596 2099749632 solver.cpp:32] Initializing solver from parameters: \r\n", - "test_iter: 250\r\n", - "test_interval: 1000\r\n", - "base_lr: 0.01\r\n", - "display: 1000\r\n", - "max_iter: 10000\r\n", - "lr_policy: \"step\"\r\n", - "gamma: 0.1\r\n", - "momentum: 0.9\r\n", - "weight_decay: 0.0005\r\n", - "stepsize: 5000\r\n", - "snapshot: 10000\r\n", - "snapshot_prefix: \"hdf5_classification/data/train\"\r\n", - "solver_mode: CPU\r\n", - "net: \"hdf5_classification/train_val2.prototxt\"\r\n", - "I0307 01:34:31.872687 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val2.prototxt\r\n", - "I0307 01:34:31.872865 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n", - "I0307 01:34:31.872882 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n", - "I0307 01:34:31.872891 2099749632 net.cpp:42] Initializing net from parameters: \r\n", - "name: \"LogisticRegressionNet\"\r\n", - "state {\r\n", - " phase: TRAIN\r\n", - "}\r\n", - "layer {\r\n", - " name: \"data\"\r\n", - " type: \"HDF5Data\"\r\n", - " top: \"data\"\r\n", - " top: \"label\"\r\n", - " include {\r\n", - " phase: TRAIN\r\n", - " }\r\n", - " hdf5_data_param {\r\n", - " source: \"hdf5_classification/data/train.txt\"\r\n", - " batch_size: 10\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"fc1\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"data\"\r\n", - " top: \"fc1\"\r\n", - " param {\r\n", - " lr_mult: 1\r\n", - " decay_mult: 1\r\n", - " }\r\n", - " param {\r\n", - " lr_mult: 2\r\n", - " decay_mult: 0\r\n", - " }\r\n", - " inner_product_param {\r\n", - " num_output: 40\r\n", - " weight_filler {\r\n", - " type: \"gaussian\"\r\n", - " std: 0.01\r\n", - " }\r\n", - " bias_filler {\r\n", - " type: \"constant\"\r\n", - " value: 0\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"relu1\"\r\n", - " type: \"ReLU\"\r\n", - " bottom: \"fc1\"\r\n", - " top: \"fc1\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"fc2\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"fc1\"\r\n", - " top: \"fc2\"\r\n", - " param {\r\n", - " lr_mult: 1\r\n", - " decay_mult: 1\r\n", - " }\r\n", - " param {\r\n", - " lr_mult: 2\r\n", - " decay_mult: 0\r\n", - " }\r\n", - " inner_product_param {\r\n", - " num_output: 2\r\n", - " weight_filler {\r\n", - " type: \"gaussian\"\r\n", - " std: 0.01\r\n", - " }\r\n", - " bias_filler {\r\n", - " type: \"constant\"\r\n", - " value: 0\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"loss\"\r\n", - " type: \"SoftmaxWithLoss\"\r\n", - " bottom: \"fc2\"\r\n", - " bottom: \"label\"\r\n", - " top: \"loss\"\r\n", - "}\r\n", - "I0307 01:34:31.873246 2099749632 layer_factory.hpp:74] Creating layer data\r\n", - "I0307 01:34:31.873276 2099749632 net.cpp:84] Creating Layer data\r\n", - "I0307 01:34:31.873292 2099749632 net.cpp:338] data -> data\r\n", - "I0307 01:34:31.873332 2099749632 net.cpp:338] data -> label\r\n", - "I0307 01:34:31.873352 2099749632 net.cpp:113] Setting up data\r\n", - "I0307 01:34:31.873361 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\r\n", - "I0307 01:34:31.873443 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\r\n", - "I0307 01:34:31.875783 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n", - "I0307 01:34:31.875816 2099749632 net.cpp:120] Top shape: 10 (10)\r\n", - "I0307 01:34:31.875829 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n", - "I0307 01:34:31.875846 2099749632 net.cpp:84] Creating Layer fc1\r\n", - "I0307 01:34:31.875857 2099749632 net.cpp:380] fc1 <- data\r\n", - "I0307 01:34:31.875875 2099749632 net.cpp:338] fc1 -> fc1\r\n", - "I0307 01:34:31.875892 2099749632 net.cpp:113] Setting up fc1\r\n", - "I0307 01:34:31.882478 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n", - "I0307 01:34:31.882505 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n", - "I0307 01:34:31.882524 2099749632 net.cpp:84] Creating Layer relu1\r\n", - "I0307 01:34:31.882532 2099749632 net.cpp:380] relu1 <- fc1\r\n", - "I0307 01:34:31.882544 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\r\n", - "I0307 01:34:31.882555 2099749632 net.cpp:113] Setting up relu1\r\n", - "I0307 01:34:31.882565 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n", - "I0307 01:34:31.882583 2099749632 layer_factory.hpp:74] Creating layer fc2\r\n", - "I0307 01:34:31.882609 2099749632 net.cpp:84] Creating Layer fc2\r\n", - "I0307 01:34:31.882619 2099749632 net.cpp:380] fc2 <- fc1\r\n", - "I0307 01:34:31.882632 2099749632 net.cpp:338] fc2 -> fc2\r\n", - "I0307 01:34:31.882644 2099749632 net.cpp:113] Setting up fc2\r\n", - "I0307 01:34:31.882663 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n", - "I0307 01:34:31.882678 2099749632 layer_factory.hpp:74] Creating layer loss\r\n", - "I0307 01:34:31.882694 2099749632 net.cpp:84] Creating Layer loss\r\n", - "I0307 01:34:31.882704 2099749632 net.cpp:380] loss <- fc2\r\n", - "I0307 01:34:31.882712 2099749632 net.cpp:380] loss <- label\r\n", - "I0307 01:34:31.882779 2099749632 net.cpp:338] loss -> loss\r\n", - "I0307 01:34:31.882796 2099749632 net.cpp:113] Setting up loss\r\n", - "I0307 01:34:31.882810 2099749632 layer_factory.hpp:74] Creating layer loss\r\n", - "I0307 01:34:31.882833 2099749632 net.cpp:120] Top shape: (1)\r\n", - "I0307 01:34:31.882844 2099749632 net.cpp:122] with loss weight 1\r\n", - "I0307 01:34:31.882860 2099749632 net.cpp:167] loss needs backward computation.\r\n", - "I0307 01:34:31.882869 2099749632 net.cpp:167] fc2 needs backward computation.\r\n", - "I0307 01:34:31.882877 2099749632 net.cpp:167] relu1 needs backward computation.\r\n", - "I0307 01:34:31.882886 2099749632 net.cpp:167] fc1 needs backward computation.\r\n", - "I0307 01:34:31.882894 2099749632 net.cpp:169] data does not need backward computation.\r\n", - "I0307 01:34:31.882904 2099749632 net.cpp:205] This network produces output loss\r\n", - "I0307 01:34:31.882931 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n", - "I0307 01:34:31.882942 2099749632 net.cpp:217] Network initialization done.\r\n", - "I0307 01:34:31.882951 2099749632 net.cpp:218] Memory required for data: 3484\r\n", - "I0307 01:34:31.883157 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val2.prototxt\r\n", - "I0307 01:34:31.883189 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n", - "I0307 01:34:31.883203 2099749632 net.cpp:42] Initializing net from parameters: \r\n", - "name: \"LogisticRegressionNet\"\r\n", - "state {\r\n", - " phase: TEST\r\n", - "}\r\n", - "layer {\r\n", - " name: \"data\"\r\n", - " type: \"HDF5Data\"\r\n", - " top: \"data\"\r\n", - " top: \"label\"\r\n", - " include {\r\n", - " phase: TEST\r\n", - " }\r\n", - " hdf5_data_param {\r\n", - " source: \"hdf5_classification/data/test.txt\"\r\n", - " batch_size: 10\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"fc1\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"data\"\r\n", - " top: \"fc1\"\r\n", - " param {\r\n", - " lr_mult: 1\r\n", - " decay_mult: 1\r\n", - " }\r\n", - " param {\r\n", - " lr_mult: 2\r\n", - " decay_mult: 0\r\n", - " }\r\n", - " inner_product_param {\r\n", - " num_output: 40\r\n", - " weight_filler {\r\n", - " type: \"gaussian\"\r\n", - " std: 0.01\r\n", - " }\r\n", - " bias_filler {\r\n", - " type: \"constant\"\r\n", - " value: 0\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"relu1\"\r\n", - " type: \"ReLU\"\r\n", - " bottom: \"fc1\"\r\n", - " top: \"fc1\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"fc2\"\r\n", - " type: \"InnerProduct\"\r\n", - " bottom: \"fc1\"\r\n", - " top: \"fc2\"\r\n", - " param {\r\n", - " lr_mult: 1\r\n", - " decay_mult: 1\r\n", - " }\r\n", - " param {\r\n", - " lr_mult: 2\r\n", - " decay_mult: 0\r\n", - " }\r\n", - " inner_product_param {\r\n", - " num_output: 2\r\n", - " weight_filler {\r\n", - " type: \"gaussian\"\r\n", - " std: 0.01\r\n", - " }\r\n", - " bias_filler {\r\n", - " type: \"constant\"\r\n", - " value: 0\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"loss\"\r\n", - " type: \"SoftmaxWithLoss\"\r\n", - " bottom: \"fc2\"\r\n", - " bottom: \"label\"\r\n", - " top: \"loss\"\r\n", - "}\r\n", - "layer {\r\n", - " name: \"accuracy\"\r\n", - " type: \"Accuracy\"\r\n", - " bottom: \"fc2\"\r\n", - " bottom: \"label\"\r\n", - " top: \"accuracy\"\r\n", - " include {\r\n", - " phase: TEST\r\n", - " }\r\n", - "}\r\n", - "I0307 01:34:31.883535 2099749632 layer_factory.hpp:74] Creating layer data\r\n", - "I0307 01:34:31.883548 2099749632 net.cpp:84] Creating Layer data\r\n", - "I0307 01:34:31.883556 2099749632 net.cpp:338] data -> data\r\n", - "I0307 01:34:31.883569 2099749632 net.cpp:338] data -> label\r\n", - "I0307 01:34:31.883579 2099749632 net.cpp:113] Setting up data\r\n", - "I0307 01:34:31.883585 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\r\n", - "I0307 01:34:31.883664 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\r\n", - "I0307 01:34:31.884842 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n", - "I0307 01:34:31.884860 2099749632 net.cpp:120] Top shape: 10 (10)\r\n", - "I0307 01:34:31.884870 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\r\n", - "I0307 01:34:31.884879 2099749632 net.cpp:84] Creating Layer label_data_1_split\r\n", - "I0307 01:34:31.884886 2099749632 net.cpp:380] label_data_1_split <- label\r\n", - "I0307 01:34:31.884896 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\r\n", - "I0307 01:34:31.884909 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\r\n", - "I0307 01:34:31.884919 2099749632 net.cpp:113] Setting up label_data_1_split\r\n", - "I0307 01:34:31.884927 2099749632 net.cpp:120] Top shape: 10 (10)\r\n", - "I0307 01:34:31.884934 2099749632 net.cpp:120] Top shape: 10 (10)\r\n", - "I0307 01:34:31.884941 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n", - "I0307 01:34:31.884951 2099749632 net.cpp:84] Creating Layer fc1\r\n", - "I0307 01:34:31.884958 2099749632 net.cpp:380] fc1 <- data\r\n", - "I0307 01:34:31.884989 2099749632 net.cpp:338] fc1 -> fc1\r\n", - "I0307 01:34:31.885000 2099749632 net.cpp:113] Setting up fc1\r\n", - "I0307 01:34:31.885017 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n", - "I0307 01:34:31.885030 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n", - "I0307 01:34:31.885041 2099749632 net.cpp:84] Creating Layer relu1\r\n", - "I0307 01:34:31.885048 2099749632 net.cpp:380] relu1 <- fc1\r\n", - "I0307 01:34:31.885056 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\r\n", - "I0307 01:34:31.885064 2099749632 net.cpp:113] Setting up relu1\r\n", - "I0307 01:34:31.885071 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n", - "I0307 01:34:31.885079 2099749632 layer_factory.hpp:74] Creating layer fc2\r\n", - "I0307 01:34:31.885088 2099749632 net.cpp:84] Creating Layer fc2\r\n", - "I0307 01:34:31.885094 2099749632 net.cpp:380] fc2 <- fc1\r\n", - "I0307 01:34:31.885103 2099749632 net.cpp:338] fc2 -> fc2\r\n", - "I0307 01:34:31.885113 2099749632 net.cpp:113] Setting up fc2\r\n", - "I0307 01:34:31.885126 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n", - "I0307 01:34:31.885138 2099749632 layer_factory.hpp:74] Creating layer fc2_fc2_0_split\r\n", - "I0307 01:34:31.885149 2099749632 net.cpp:84] Creating Layer fc2_fc2_0_split\r\n", - "I0307 01:34:31.885155 2099749632 net.cpp:380] fc2_fc2_0_split <- fc2\r\n", - "I0307 01:34:31.885164 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_0\r\n", - "I0307 01:34:31.885174 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_1\r\n", - "I0307 01:34:31.885182 2099749632 net.cpp:113] Setting up fc2_fc2_0_split\r\n", - "I0307 01:34:31.885190 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n", - "I0307 01:34:31.885242 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n", - "I0307 01:34:31.885256 2099749632 layer_factory.hpp:74] Creating layer loss\r\n", - "I0307 01:34:31.885267 2099749632 net.cpp:84] Creating Layer loss\r\n", - "I0307 01:34:31.885275 2099749632 net.cpp:380] loss <- fc2_fc2_0_split_0\r\n", - "I0307 01:34:31.885285 2099749632 net.cpp:380] loss <- label_data_1_split_0\r\n", - "I0307 01:34:31.885296 2099749632 net.cpp:338] loss -> loss\r\n", - "I0307 01:34:31.885308 2099749632 net.cpp:113] Setting up loss\r\n", - "I0307 01:34:31.885316 2099749632 layer_factory.hpp:74] Creating layer loss\r\n", - "I0307 01:34:31.885330 2099749632 net.cpp:120] Top shape: (1)\r\n", - "I0307 01:34:31.885337 2099749632 net.cpp:122] with loss weight 1\r\n", - "I0307 01:34:31.885346 2099749632 layer_factory.hpp:74] Creating layer accuracy\r\n", - "I0307 01:34:31.885360 2099749632 net.cpp:84] Creating Layer accuracy\r\n", - "I0307 01:34:31.885368 2099749632 net.cpp:380] accuracy <- fc2_fc2_0_split_1\r\n", - "I0307 01:34:31.885375 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\r\n", - "I0307 01:34:31.885383 2099749632 net.cpp:338] accuracy -> accuracy\r\n", - "I0307 01:34:31.885392 2099749632 net.cpp:113] Setting up accuracy\r\n", - "I0307 01:34:31.885401 2099749632 net.cpp:120] Top shape: (1)\r\n", - "I0307 01:34:31.885407 2099749632 net.cpp:169] accuracy does not need backward computation.\r\n", - "I0307 01:34:31.885413 2099749632 net.cpp:167] loss needs backward computation.\r\n", - "I0307 01:34:31.885419 2099749632 net.cpp:167] fc2_fc2_0_split needs backward computation.\r\n", - "I0307 01:34:31.885426 2099749632 net.cpp:167] fc2 needs backward computation.\r\n", - "I0307 01:34:31.885432 2099749632 net.cpp:167] relu1 needs backward computation.\r\n", - "I0307 01:34:31.885438 2099749632 net.cpp:167] fc1 needs backward computation.\r\n", - "I0307 01:34:31.885444 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\r\n", - "I0307 01:34:31.885452 2099749632 net.cpp:169] data does not need backward computation.\r\n", - "I0307 01:34:31.885457 2099749632 net.cpp:205] This network produces output accuracy\r\n", - "I0307 01:34:31.885613 2099749632 net.cpp:205] This network produces output loss\r\n", - "I0307 01:34:31.885632 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n", - "I0307 01:34:31.885639 2099749632 net.cpp:217] Network initialization done.\r\n", - "I0307 01:34:31.885645 2099749632 net.cpp:218] Memory required for data: 3728\r\n", - "I0307 01:34:31.885685 2099749632 solver.cpp:42] Solver scaffolding done.\r\n", - "I0307 01:34:31.885711 2099749632 solver.cpp:222] Solving LogisticRegressionNet\r\n", - "I0307 01:34:31.885721 2099749632 solver.cpp:223] Learning Rate Policy: step\r\n", - "I0307 01:34:31.885730 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\r\n", - "I0307 01:34:31.901005 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.5944\r\n", - "I0307 01:34:31.901049 2099749632 solver.cpp:315] Test net output #1: loss = 0.693021 (* 1 = 0.693021 loss)\r\n", - "I0307 01:34:31.901177 2099749632 solver.cpp:189] Iteration 0, loss = 0.693163\r\n", - "I0307 01:34:31.901192 2099749632 solver.cpp:204] Train net output #0: loss = 0.693163 (* 1 = 0.693163 loss)\r\n", - "I0307 01:34:31.901203 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\r\n", - "I0307 01:34:31.920586 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:31.924612 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7556\r\n", - "I0307 01:34:31.924646 2099749632 solver.cpp:315] Test net output #1: loss = 0.511002 (* 1 = 0.511002 loss)\r\n", - "I0307 01:34:31.924684 2099749632 solver.cpp:189] Iteration 1000, loss = 0.38536\r\n", - "I0307 01:34:31.924696 2099749632 solver.cpp:204] Train net output #0: loss = 0.38536 (* 1 = 0.38536 loss)\r\n", - "I0307 01:34:31.924706 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\r\n", - "I0307 01:34:31.944727 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\r\n", - "I0307 01:34:31.948729 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7824\r\n", - "I0307 01:34:31.948763 2099749632 solver.cpp:315] Test net output #1: loss = 0.489214 (* 1 = 0.489214 loss)\r\n", - "I0307 01:34:31.948799 2099749632 solver.cpp:189] Iteration 2000, loss = 0.532582\r\n", - "I0307 01:34:31.948812 2099749632 solver.cpp:204] Train net output #0: loss = 0.532582 (* 1 = 0.532582 loss)\r\n", - "I0307 01:34:31.948823 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\r\n", - "I0307 01:34:31.968670 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:31.972393 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7956\r\n", - "I0307 01:34:31.972411 2099749632 solver.cpp:315] Test net output #1: loss = 0.454184 (* 1 = 0.454184 loss)\r\n", - "I0307 01:34:31.973024 2099749632 solver.cpp:189] Iteration 3000, loss = 0.541374\r\n", - "I0307 01:34:31.973057 2099749632 solver.cpp:204] Train net output #0: loss = 0.541374 (* 1 = 0.541374 loss)\r\n", - "I0307 01:34:31.973067 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\r\n", - "I0307 01:34:31.994829 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\r\n", - "I0307 01:34:31.998638 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.798\r\n", - "I0307 01:34:31.998663 2099749632 solver.cpp:315] Test net output #1: loss = 0.456348 (* 1 = 0.456348 loss)\r\n", - "I0307 01:34:31.998705 2099749632 solver.cpp:189] Iteration 4000, loss = 0.490437\r\n", - "I0307 01:34:31.998718 2099749632 solver.cpp:204] Train net output #0: loss = 0.490437 (* 1 = 0.490437 loss)\r\n", - "I0307 01:34:31.998725 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\r\n", - "I0307 01:34:32.021085 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:32.024950 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.804\r\n", - "I0307 01:34:32.024981 2099749632 solver.cpp:315] Test net output #1: loss = 0.46184 (* 1 = 0.46184 loss)\r\n", - "I0307 01:34:32.025017 2099749632 solver.cpp:189] Iteration 5000, loss = 0.467703\r\n", - "I0307 01:34:32.025028 2099749632 solver.cpp:204] Train net output #0: loss = 0.467704 (* 1 = 0.467704 loss)\r\n", - "I0307 01:34:32.025038 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\r\n", - "I0307 01:34:32.044390 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\r\n", - "I0307 01:34:32.048216 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8208\r\n", - "I0307 01:34:32.048239 2099749632 solver.cpp:315] Test net output #1: loss = 0.423084 (* 1 = 0.423084 loss)\r\n", - "I0307 01:34:32.048790 2099749632 solver.cpp:189] Iteration 6000, loss = 0.480104\r\n", - "I0307 01:34:32.048809 2099749632 solver.cpp:204] Train net output #0: loss = 0.480105 (* 1 = 0.480105 loss)\r\n", - "I0307 01:34:32.048827 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\r\n", - "I0307 01:34:32.067795 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\r\n", - "I0307 01:34:32.071524 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8124\r\n", - "I0307 01:34:32.071542 2099749632 solver.cpp:315] Test net output #1: loss = 0.423947 (* 1 = 0.423947 loss)\r\n", - "I0307 01:34:32.071570 2099749632 solver.cpp:189] Iteration 7000, loss = 0.447471\r\n", - "I0307 01:34:32.071617 2099749632 solver.cpp:204] Train net output #0: loss = 0.447472 (* 1 = 0.447472 loss)\r\n", - "I0307 01:34:32.071626 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:32.091625 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\r\n", - "I0307 01:34:32.095410 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.814\r\n", - "I0307 01:34:32.095432 2099749632 solver.cpp:315] Test net output #1: loss = 0.423586 (* 1 = 0.423586 loss)\r\n", - "I0307 01:34:32.095461 2099749632 solver.cpp:189] Iteration 8000, loss = 0.386258\r\n", - "I0307 01:34:32.095474 2099749632 solver.cpp:204] Train net output #0: loss = 0.386259 (* 1 = 0.386259 loss)\r\n", - "I0307 01:34:32.095481 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\r\n", - "I0307 01:34:32.117184 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\r\n", - "I0307 01:34:32.121587 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8208\r\n", - "I0307 01:34:32.121608 2099749632 solver.cpp:315] Test net output #1: loss = 0.419969 (* 1 = 0.419969 loss)\r\n", - "I0307 01:34:32.122161 2099749632 solver.cpp:189] Iteration 9000, loss = 0.468262\r\n", - "I0307 01:34:32.122181 2099749632 solver.cpp:204] Train net output #0: loss = 0.468262 (* 1 = 0.468262 loss)\r\n", - "I0307 01:34:32.122191 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "I0307 01:34:32.141635 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\r\n", - "I0307 01:34:32.141860 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\r\n", - "I0307 01:34:32.141978 2099749632 solver.cpp:248] Iteration 10000, loss = 0.441529\r\n", - "I0307 01:34:32.141995 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\r\n", - "I0307 01:34:32.145747 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8148\r\n", - "I0307 01:34:32.145771 2099749632 solver.cpp:315] Test net output #1: loss = 0.4216 (* 1 = 0.4216 loss)\r\n", - "I0307 01:34:32.145779 2099749632 solver.cpp:253] Optimization Done.\r\n", - "I0307 01:34:32.145786 2099749632 caffe.cpp:121] Optimization Done.\r\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n", - "shutil.rmtree(dirname)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 } ], - "metadata": {} + "source": [ + "!../build/tools/caffe train -solver hdf5_classification/solver2.prototxt" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n", + "shutil.rmtree(dirname)" + ] } - ] + ], + "metadata": { + "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.", + "example_name": "Off-the-shelf SGD for classification", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 4 + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb index 75c9889fb5a..1fb99bd3726 100644 --- a/examples/net_surgery.ipynb +++ b/examples/net_surgery.ipynb @@ -1,464 +1,6827 @@ { - "metadata": { - "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.", - "example_name": "Editing model parameters", - "include_in_docs": true, - "priority": 5, - "signature": "sha256:f21c804f76329e70847ccb87e28a91e5d8a375f5da0ba6dd85d3b87a05bebd72" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Net Surgery\n", - "\n", - "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n", - "\n", - "Roll up your sleeves for net surgery with pycaffe!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import Image\n", - "\n", - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe\n", - "\n", - "# configure plotting\n", - "plt.rcParams['figure.figsize'] = (10, 10)\n", - "plt.rcParams['image.interpolation'] = 'nearest'\n", - "plt.rcParams['image.cmap'] = 'gray'" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Designer Filters\n", - "\n", - "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Load the net, list its data and params, and filter an example image.\n", - "caffe.set_mode_cpu()\n", - "net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\n", - "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n", - "\n", - "# load image and prepare as a single input batch for Caffe\n", - "im = np.array(Image.open('images/cat_gray.jpg'))\n", - "plt.title(\"original image\")\n", - "plt.imshow(im)\n", - "plt.axis('off')\n", - "\n", - "im_input = im[np.newaxis, np.newaxis, :, :]\n", - "net.blobs['data'].reshape(*im_input.shape)\n", - "net.blobs['data'].data[...] = im_input" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "blobs ['data', 'conv']\n", - "params ['conv']\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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wOD+7rZYez/FQzmvBLddIBpl0ZLwxxp9nDRd1dKIXWRtjJ2NlZaU4fDbAGQCw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lUint2qvVaolWPu/c52sIw9DlCoYViATzjiKAfhyYLJ0bNr1eLxnQrVYr0ebu3buSzsOR8CRz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\nfKhKpaJarZb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OPk8YQq7YY0jd++IoDzLQHWE3XjC24BXQQ2rJeVjbK5s7qkdfHWFx9Gw2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\nJ30z36Xk8TmIYK3hpNqRMuWiTHmxFng2mzV4XrPZjHfv3iWV4uLiI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eYRUa6eoFaRyd8m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\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CizFwuhBPlujJi4SBtEIz5MjGQeAsrL1erxRH3CeEjNFRgo2yo7WIeg2WvLkRCMZ5f38f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\nl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j4jBfpSej2/+81P/g8v8pauFrLQIAAm7bnizYtvZ8ZRe8QuakOoysi7JJv6+Y7WbUB4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\no5hg26exMZZ+TpELSPFu2uPx8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- "text": [ - "" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# helper show filter outputs\n", - "def show_filters(net):\n", - " net.forward()\n", - " plt.figure()\n", - " filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n", - " for i in range(3):\n", - " plt.subplot(1,4,i+2)\n", - " plt.title(\"filter #{} output\".format(i))\n", - " plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n", - " plt.tight_layout()\n", - " plt.axis('off')\n", - "\n", - "# filter the image with initial \n", - "show_filters(net)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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4URgkf3g6BJoVFgCwUMY2oJwMJk7RC2ERFMyLr/xYR7A+Ed9++2392I/9WFJo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\nyy3K1DsYDocFyXNjKP9OmsQwM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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Raising the bias of a filter will correspondingly raise its output:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# pick first filter output\n", - "conv0 = net.blobs['conv'].data[0, 0]\n", - "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n", - "# set first filter bias to 10\n", - "net.params['conv'][1].data[0] = 1.\n", - "net.forward()\n", - "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "pre-surgery output mean -12.93\n", - "post-surgery output mean -11.93\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n", - "\n", - "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ksize = net.params['conv'][0].data.shape[2:]\n", - "# make Gaussian blur\n", - "sigma = 1.\n", - "y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\n", - "g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n", - "gaussian = (g / g.sum()).astype(np.float32)\n", - "net.params['conv'][0].data[0] = gaussian\n", - "# make Sobel operator for edge detection\n", - "net.params['conv'][0].data[1:] = 0.\n", - "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n", - "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel # horizontal\n", - "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T # vertical\n", - "show_filters(net)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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6rjiupf4Cj5aQOy8AIySOjNHfLJs/HKpUOi3GzfPTY+lRaO6ds8MGg0PE5N69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\nXi/AHOsJhfHw8BCsD5/n83m1Wi1Jq+A8gMTh4WFcy8fRgwJhcxgzZ46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lxGYlBsKBKwod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\neJp22rYCTlKa3xVsVpDoJqXjI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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Net Surgery\n", + "\n", + "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n", + "\n", + "Roll up your sleeves for net surgery with pycaffe!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import Image\n", + "\n", + "# Make sure that caffe is on the python path:\n", + "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "\n", + "import caffe\n", + "\n", + "# configure plotting\n", + "plt.rcParams['figure.figsize'] = (10, 10)\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Designer Filters\n", + "\n", + "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes." + "name": "stdout", + "output_type": "stream", + "text": [ + "blobs ['data', 'conv']\n", + "params ['conv']\n" ] }, { - "cell_type": "markdown", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlIAAAHNCAYAAADVB5V4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWuMZdl13/c/tx733np393T3PPkYDUccPsQZiaRkCYpE\n", + "CYklOwYhfwjCIAEiJDLswAkQf3AQIEoC64OcIEDiIHESBAiCCAkkJ4GtJHCM+KHQjmGZtmxKJBVC\n", + "wxkOZyac4Uz3dHe97q1bt+7Jh+r/rt/517499ER008xZQKGq7j1nn73XXns9/mvtfZq2bdVTTz31\n", 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This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n", - "\n", - "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding." - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "# Load the net, list its data and params, and filter an example image.\n", + "caffe.set_mode_cpu()\n", + "net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\n", + "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n", + "\n", + "# load image and prepare as a single input batch for Caffe\n", + "im = np.array(Image.open('images/cat_gray.jpg'))\n", + "plt.title(\"original image\")\n", + "plt.imshow(im)\n", + "plt.axis('off')\n", + "\n", + "im_input = im[np.newaxis, np.newaxis, :, :]\n", + "net.blobs['data'].reshape(*im_input.shape)\n", + "net.blobs['data'].data[...] = im_input" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "!diff imagenet/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAicAAACbCAYAAAC5xzv6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVuMbVl2pvWvfb/FjkueW568VN5dXSUbl4sHbBBYbYRK\n", + "jRqEJW7qfkD90MItN4gGgQC3QHYJiwdejJFfcNvgRtBuaBAPyA9gt5FBcrnc1bbLVemqPFmZlZdz\n", + "TuaJc+KybxH7sniI8839rxFrx4lMU7mjKveQQhGx97rMNeeYY/zjH2POleV5ro1sZCMb2chGNrKR\n", + "qyKVdTdgIxvZyEY2spGNbMRlA042spGNbGQjG9nIlZINONnIRjaykY1sZCNXSjbgZCMb2chGNrKR\n", + "jVwp2YCTjWxkIxvZyEY2cqVkA042spGNbGQjG9nIlZJPDTjJsuyHsiz7x1mWHWVZ9jezLPuVLMt+\n", + "7vF3P5ll2TvrbuNGNvJxZKPbG/lBlY1uf3rlUwNOJP2Hkv6vPM/7eZ7/13me/0ye518uOzDLsrey\n", + 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"output_type": "stream", - "stream": "stdout", - "text": [ - "diff: imagenet/bvlc_caffenet_full_conv.prototxt: No such file or directory\r\n" - ] - } - ], - "prompt_number": 6 - }, + "output_type": "display_data" + } + ], + "source": [ + "# helper show filter outputs\n", + "def show_filters(net):\n", + " net.forward()\n", + " plt.figure()\n", + " filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n", + " for i in range(3):\n", + " plt.subplot(1,4,i+2)\n", + " plt.title(\"filter #{} output\".format(i))\n", + " plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n", + " plt.tight_layout()\n", + " plt.axis('off')\n", + "\n", + "# filter the image with initial \n", + "show_filters(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Raising the bias of a filter will correspondingly raise its output:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model." + "name": "stdout", + "output_type": "stream", + "text": [ + "pre-surgery output mean -12.93\n", + "post-surgery output mean -11.93\n" ] - }, + } + ], + "source": [ + "# pick first filter output\n", + "conv0 = net.blobs['conv'].data[0, 0]\n", + "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n", + "# set first filter bias to 10\n", + "net.params['conv'][1].data[0] = 1.\n", + "net.forward()\n", + "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n", + "\n", + "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe\n", - "\n", - "# Load the original network and extract the fully connected layers' parameters.\n", - "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n", - " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n", - " caffe.TEST)\n", - "params = ['fc6', 'fc7', 'fc8']\n", - "# fc_params = {name: (weights, biases)}\n", - "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n", - "\n", - "for fc in params:\n", - " print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAicAAACbCAYAAAC5xzv6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWuMbNl13/c/9eh6V7/uvT1zHzNDzgw5HNIWNInpMCEi\n", + "2wkCwYElBFASBTLg2DCM2LATSAkSJ5GlWDJi5EMAA0ngL/EjkQPFcuIQgREEcCIbAkJD9JhDgdJ4\n", + "yOFjHnfuq2/fflV1VXc9Tj7U/e3+1+pTfe+MqOkmWQtodHfVOfvsvfbaa/3XY++T5XmuJS1pSUta\n", + "0pKWtKTLQqWL7sCSlrSkJS1pSUtaktMSnCxpSUta0pKWtKRLRUtwsqQlLWlJS1rSki4VLcHJkpa0\n", + "pCUtaUlLulS0BCdLWtKSlrSkJS3pUtESnCxpSUta0pKWtKRLRT804CTLsk9nWfa1LMsOsiz7C1mW\n", + "/fUsy37+8Xd/KMuy9y+6j0ta0kehpWwv6QeVlrL9w0s/NOBE0n8q6f/N87yb5/l/l+f5n83z/K8U\n", + 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Gaussian blur\n", + "sigma = 1.\n", + "y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\n", + "g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n", + "gaussian = (g / g.sum()).astype(np.float32)\n", + "net.params['conv'][0].data[0] = gaussian\n", + "# make Sobel operator for edge detection\n", + "net.params['conv'][0].data[1:] = 0.\n", + "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n", + "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel # horizontal\n", + "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T # vertical\n", + "show_filters(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Casting a Classifier into a Fully Convolutional Network\n", + "\n", + "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n", + "\n", + "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size." + "name": "stdout", + "output_type": "stream", + "text": [ + "diff: imagenet/bvlc_caffenet_full_conv.prototxt: No such file or directory\r\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Load the fully convolutional network to transplant the parameters.\n", - "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n", - " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n", - " caffe.TEST)\n", - "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n", - "# conv_params = {name: (weights, biases)}\n", - "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n", - "\n", - "for conv in params_full_conv:\n", - " print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n", - "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n", - "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n" - ] - } - ], - "prompt_number": 8 - }, + } + ], + "source": [ + "!diff imagenet/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n", - "\n", - "The biases are identical to those of the inner product.\n", - "\n", - "Let's transplant!" + "name": "stdout", + "output_type": "stream", + "text": [ + "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n", + "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n", + "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for pr, pr_conv in zip(params, params_full_conv):\n", - " conv_params[pr_conv][0].flat = fc_params[pr][0].flat # flat unrolls the arrays\n", - " conv_params[pr_conv][1][...] = fc_params[pr][1]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, + } + ], + "source": [ + "# Make sure that caffe is on the python path:\n", + "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "\n", + "import caffe\n", + "\n", + "# Load the original network and extract the fully connected layers' parameters.\n", + "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n", + " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n", + " caffe.TEST)\n", + "params = ['fc6', 'fc7', 'fc8']\n", + "# fc_params = {name: (weights, biases)}\n", + "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n", + "\n", + "for fc in params:\n", + " print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, save the new model weights." + "name": "stdout", + "output_type": "stream", + "text": [ + "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n", + "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n", + "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, + } + ], + "source": [ + "# Load the fully convolutional network to transplant the parameters.\n", + "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n", + " '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n", + " caffe.TEST)\n", + "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n", + "# conv_params = {name: (weights, biases)}\n", + "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n", + "\n", + "for conv in params_full_conv:\n", + " print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n", + "\n", + "The biases are identical to those of the inner product.\n", + "\n", + "Let's transplant!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for pr, pr_conv in zip(params, params_full_conv):\n", + " conv_params[pr_conv][0].flat = fc_params[pr][0].flat # flat unrolls the arrays\n", + " conv_params[pr_conv][1][...] = fc_params[pr][1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, save the new model weights." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input." + "name": "stdout", + "output_type": "stream", + "text": [ + "[[282 282 281 281 281 281 277 282]\n", + " [281 283 283 281 281 281 281 282]\n", + " [283 283 283 283 283 283 287 282]\n", + " [283 283 283 281 283 283 283 259]\n", + " [283 283 283 283 283 283 283 259]\n", + " [283 283 283 283 283 283 259 259]\n", + " [283 283 283 283 259 259 259 277]\n", + " [335 335 283 259 263 263 263 277]]\n" ] }, { - "cell_type": "code", - "collapsed": true, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# load input and configure preprocessing\n", - "im = caffe.io.load_image('images/cat.jpg')\n", - "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n", - "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n", - "transformer.set_transpose('data', (2,0,1))\n", - "transformer.set_channel_swap('data', (2,1,0))\n", - "transformer.set_raw_scale('data', 255.0)\n", - "# make classification map by forward and print prediction indices at each location\n", - "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n", - "print out['prob'][0].argmax(axis=0)\n", - "# show net input and confidence map (probability of the top prediction at each location)\n", - "plt.subplot(1, 2, 1)\n", - "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n", - "plt.subplot(1, 2, 2)\n", - "plt.imshow(out['prob'][0,281])" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[[282 282 281 281 281 281 277 282]\n", - " [281 283 283 281 281 281 281 282]\n", - " [283 283 283 283 283 283 287 282]\n", - " [283 283 283 281 283 283 283 259]\n", - " [283 283 283 283 283 283 283 259]\n", - " [283 283 283 283 283 283 259 259]\n", - " [283 283 283 283 259 259 259 277]\n", - " [335 335 283 259 263 263 263 277]]\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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m4NvfEI5zItkeCZ5geiiRuN9wtdtQUsGXQtKCLRZNlegjxRowrkn05kqYWYy2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\nc6pqy/2Hj9ibL+m6Yafy8Q6ld3cDhN87A3BY70hGy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classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*A thank you to Rowland Depp for first suggesting this trick.*" - ] + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# load input and configure preprocessing\n", + "im = caffe.io.load_image('images/cat.jpg')\n", + "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n", + "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n", + "transformer.set_transpose('data', (2,0,1))\n", + "transformer.set_channel_swap('data', (2,1,0))\n", + "transformer.set_raw_scale('data', 255.0)\n", + "# make classification map by forward and print prediction indices at each location\n", + "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n", + "print out['prob'][0].argmax(axis=0)\n", + "# show net input and confidence map (probability of the top prediction at each location)\n", + "plt.subplot(1, 2, 1)\n", + "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n", + "plt.subplot(1, 2, 2)\n", + "plt.imshow(out['prob'][0,281])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n", + "\n", + "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n", + "\n", + "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*A thank you to Rowland Depp for first suggesting this trick.*" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.", + "example_name": "Editing model parameters", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 5 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/siamese/mnist_siamese.ipynb b/examples/siamese/mnist_siamese.ipynb index 8e076663ca6..11cae120db2 100644 --- a/examples/siamese/mnist_siamese.ipynb +++ b/examples/siamese/mnist_siamese.ipynb @@ -1,154 +1,1909 @@ { - "metadata": { - "description": "Extracting features and plotting the Siamese network embedding.", - "example_name": "Siamese network embedding", - "include_in_docs": true, - "priority": 6, - "signature": "sha256:845bb18929f96543ba2611eb5eca744fd98939cbef876df6bc319c29f616fc64" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup\n", - "\n", - "Import Caffe and the usual modules." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../../' # this file is expected to be in {caffe_root}/examples/siamese\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the trained net\n", - "\n", - "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "MODEL_FILE = 'mnist_siamese.prototxt'\n", - "# decrease if you want to preview during training\n", - "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n", - "caffe.set_mode_cpu()\n", - "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load some MNIST test data" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n", - "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n", - "n = 10000\n", - "\n", - "with open(TEST_DATA_FILE, 'rb') as f:\n", - " f.read(16) # skip the header\n", - " raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n", - "\n", - "with open(TEST_LABEL_FILE, 'rb') as f:\n", - " f.read(8) # skip the header\n", - " labels = np.fromstring(f.read(n), dtype=np.uint8)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate the Siamese features" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# reshape and preprocess\n", - "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n", - "out = net.forward_all(data=caffe_in)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize the learned Siamese embedding" - ] - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup\n", + "\n", + "Import Caffe and the usual modules." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# Make sure that caffe is on the python path:\n", + "caffe_root = '../../' # this file is expected to be in {caffe_root}/examples/siamese\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "\n", + "import caffe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the trained net\n", + "\n", + "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "MODEL_FILE = 'mnist_siamese.prototxt'\n", + "# decrease if you want to preview during training\n", + "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n", + "caffe.set_mode_cpu()\n", + "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load some MNIST test data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n", + "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n", + "n = 10000\n", + "\n", + "with open(TEST_DATA_FILE, 'rb') as f:\n", + " f.read(16) # skip the header\n", + " raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n", + "\n", + "with open(TEST_LABEL_FILE, 'rb') as f:\n", + " f.read(8) # skip the header\n", + " labels = np.fromstring(f.read(n), dtype=np.uint8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate the Siamese features" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# reshape and preprocess\n", + "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n", + "out = net.forward_all(data=caffe_in)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize the learned Siamese embedding" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "feat = out['feat']\n", - "f = plt.figure(figsize=(16,9))\n", - "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n", - " '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n", - "for i in range(10):\n", - " plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n", - "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n", - "plt.grid()\n", - "plt.show()" - ], - "language": "python", + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAA54AAAIXCAYAAAD0R4FDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXtwXOWZr/usvurWUktqGdmxaawEHEMuthGXITiIyMaJ\n", + "wbEMFmCTDMkkoyqSyTnZMwdqpmYyzEyS2ruKue2ZqSTHO/vYGQbhCxdjwI637ViWMEEEMJhgB4MB\n", + "gSRLsizJkiypuyX1+WP1Wlp971YvSd3y+1S5rF69Lt/6+lOrf/2+v/dVgsEggiAIgiAIgiAIgjBT\n", + "WOZ6AIIgCIIgCIIgCML8RoSnIAiCIAiCIAiCMKOI8BQEQRAEQRAEQRBmFBGegiAIgiAIgiAIwowi\n", + "wlMQBEEQBEEQBEGYUUR4CoIgCIIgCIIgCDNKRsJTUZQ8RVFaFUV5U1GUU4qi/HezBiYIgiAIgiAI\n", + "giDMD5RM+3gqilIQDAZHFEWxAS8B/08wGHzJlNEJgiAIgiAIgiAIOU/GqbbBYHAk9KMDsAJ9mZ5T\n", + "EARBEARBEARBmD9kLDwVRbEoivIm0A0cDQaDpzIfliAIgiAIgiAIgjBfMCPiORkMBlcAi4EvK4pS\n", + "k/GoBEEQBEEQBEEQhHmDzawTBYPBi4qivAhUA03adkVRMjORCoIgCIIgCIIgCFlNMBhUEj2fkfBU\n", + "FMUDjAeDwQFFUfKBtcDfxxhEJpcRhDC+9a1vsWPHjrkehjCPkDUlmImsJ8FsZE0JZiNrSjAbRUmo\n", + 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V//ELwP+gpqYC2J4j95vbj2+//fZ54/E8evQob775JgMDamXzjz76iF/96ldJ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\nmQpmVsCdLcyKeM1mXGC6H+dTJdHH1XTny+FQP2S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DVVVKG7x6aYNEwD6xZw/6T5/Gjbt3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w6+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\ndu/Ji42Nat2zJ+HS4UhE0+rr9cCmTe61vMe/vHSpJKl01iyd2rv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- "text": [ - "" - ] - } - ], - "prompt_number": 5 + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "feat = out['feat']\n", + "f = plt.figure(figsize=(16,9))\n", + "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n", + " '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n", + "for i in range(10):\n", + " plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n", + "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n", + "plt.grid()\n", + "plt.show()" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "description": "Extracting features and plotting the Siamese network embedding.", + "example_name": "Siamese network embedding", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 6 + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 83b5dcd40bd61fe61b88fb90c3615e2096316724 Mon Sep 17 00:00:00 2001 From: Kibum Bae Date: Tue, 19 May 2015 22:43:49 +0900 Subject: [PATCH 057/446] fix typos in docs fix typos in install_osx.md and performance_hardware.md --- docs/install_osx.md | 2 +- docs/performance_hardware.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/install_osx.md b/docs/install_osx.md index 39cb02fe232..ad98a85d47f 100644 --- a/docs/install_osx.md +++ b/docs/install_osx.md @@ -10,7 +10,7 @@ In the following, we assume that you're using Anaconda Python and Homebrew. **CUDA**: Install via the NVIDIA package that includes both CUDA and the bundled driver. **CUDA 7 is strongly suggested.** Older CUDA require `libstdc++` while clang++ is the default compiler and `libc++` the default standard library on OS X 10.9+. This disagreement makes it necessary to change the compilation settings for each of the dependencies. This is prone to error. -**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`). +**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`). In other `ENV` settings, things may not work as expected. **General dependencies** diff --git a/docs/performance_hardware.md b/docs/performance_hardware.md index b35246feabd..cdd4b361dea 100644 --- a/docs/performance_hardware.md +++ b/docs/performance_hardware.md @@ -48,7 +48,7 @@ and then set the clock speed with sudo nvidia-smi -i 0 -ac 3004,875 # repeat with -i x for each GPU ID -but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to intialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu). +but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to initialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu). ## NVIDIA Titan From 7a8fcc763dc89e717a58319a77da9d9813a829c0 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Tue, 19 May 2015 22:41:50 -0700 Subject: [PATCH 058/446] avoid dangerous state in pooling layer CUDA kernels Previously, pointers were modified with the assumption that they would only be modified once. While this is true so far in practice, the introduction of CUDA_KERNEL_LOOP makes this a dangerous assumption. --- src/caffe/layers/pooling_layer.cu | 57 +++++++++++++++++-------------- 1 file changed, 32 insertions(+), 25 deletions(-) diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu index d1d48501af3..a1080ebf33a 100644 --- a/src/caffe/layers/pooling_layer.cu +++ b/src/caffe/layers/pooling_layer.cu @@ -28,12 +28,13 @@ __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data, wstart = max(wstart, 0); Dtype maxval = -FLT_MAX; int maxidx = -1; - bottom_data += (n * channels + c) * height * width; + const Dtype* const bottom_slice = + bottom_data + (n * channels + c) * height * width; for (int h = hstart; h < hend; ++h) { for (int w = wstart; w < wend; ++w) { - if (bottom_data[h * width + w] > maxval) { + if (bottom_slice[h * width + w] > maxval) { maxidx = h * width + w; - maxval = bottom_data[maxidx]; + maxval = bottom_slice[maxidx]; } } } @@ -67,10 +68,11 @@ __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data, hend = min(hend, height); wend = min(wend, width); Dtype aveval = 0; - bottom_data += (n * channels + c) * height * width; + const Dtype* const bottom_slice = + bottom_data + (n * channels + c) * height * width; for (int h = hstart; h < hend; ++h) { for (int w = wstart; w < wend; ++w) { - aveval += bottom_data[h * width + w]; + aveval += bottom_slice[h * width + w]; } } top_data[index] = aveval / pool_size; @@ -94,11 +96,12 @@ __global__ void StoPoolForwardTrain(const int nthreads, int wstart = pw * stride_w; int wend = min(wstart + kernel_w, width); Dtype cumsum = 0.; - bottom_data += (n * channels + c) * height * width; + const Dtype* const bottom_slice = + bottom_data + (n * channels + c) * height * width; // First pass: get sum for (int h = hstart; h < hend; ++h) { for (int w = wstart; w < wend; ++w) { - cumsum += bottom_data[h * width + w]; + cumsum += bottom_slice[h * width + w]; } } float thres = rand_idx[index] * cumsum; @@ -106,10 +109,10 @@ __global__ void StoPoolForwardTrain(const int nthreads, cumsum = 0; for (int h = hstart; h < hend; ++h) { for (int w = wstart; w < wend; ++w) { - cumsum += bottom_data[h * width + w]; + cumsum += bottom_slice[h * width + w]; if (cumsum >= thres) { rand_idx[index] = ((n * channels + c) * height + h) * width + w; - top_data[index] = bottom_data[h * width + w]; + top_data[index] = bottom_slice[h * width + w]; return; } } @@ -137,12 +140,13 @@ __global__ void StoPoolForwardTest(const int nthreads, // We set cumsum to be 0 to avoid divide-by-zero problems Dtype cumsum = FLT_MIN; Dtype cumvalues = 0.; - bottom_data += (n * channels + c) * height * width; + const Dtype* const bottom_slice = + bottom_data + (n * channels + c) * height * width; // First pass: get sum for (int h = hstart; h < hend; ++h) { for (int w = wstart; w < wend; ++w) { - cumsum += bottom_data[h * width + w]; - cumvalues += bottom_data[h * width + w] * bottom_data[h * width + w]; + cumsum += bottom_slice[h * width + w]; + cumvalues += bottom_slice[h * width + w] * bottom_slice[h * width + w]; } } top_data[index] = cumvalues / cumsum; @@ -231,22 +235,22 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff, int pwend = min((w + pad_w) / stride_w + 1, pooled_width); Dtype gradient = 0; int offset = (n * channels + c) * pooled_height * pooled_width; - top_diff += offset; + const Dtype* const top_diff_slice = top_diff + offset; if (mask) { - mask += offset; + const int* const mask_slice = mask + offset; for (int ph = phstart; ph < phend; ++ph) { for (int pw = pwstart; pw < pwend; ++pw) { - if (mask[ph * pooled_width + pw] == h * width + w) { - gradient += top_diff[ph * pooled_width + pw]; + if (mask_slice[ph * pooled_width + pw] == h * width + w) { + gradient += top_diff_slice[ph * pooled_width + pw]; } } } } else { - top_mask += offset; + const Dtype* const top_mask_slice = top_mask + offset; for (int ph = phstart; ph < phend; ++ph) { for (int pw = pwstart; pw < pwend; ++pw) { - if (top_mask[ph * pooled_width + pw] == h * width + w) { - gradient += top_diff[ph * pooled_width + pw]; + if (top_mask_slice[ph * pooled_width + pw] == h * width + w) { + gradient += top_diff_slice[ph * pooled_width + pw]; } } } @@ -274,7 +278,8 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff, int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1; int pwend = min(w / stride_w + 1, pooled_width); Dtype gradient = 0; - top_diff += (n * channels + c) * pooled_height * pooled_width; + const Dtype* const top_diff_slice = + top_diff + (n * channels + c) * pooled_height * pooled_width; for (int ph = phstart; ph < phend; ++ph) { for (int pw = pwstart; pw < pwend; ++pw) { // figure out the pooling size @@ -283,7 +288,7 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff, int hend = min(hstart + kernel_h, height + pad_h); int wend = min(wstart + kernel_w, width + pad_w); int pool_size = (hend - hstart) * (wend - wstart); - gradient += top_diff[ph * pooled_width + pw] / pool_size; + gradient += top_diff_slice[ph * pooled_width + pw] / pool_size; } } bottom_diff[index] = gradient; @@ -310,12 +315,14 @@ __global__ void StoPoolBackward(const int nthreads, int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1; int pwend = min(w / stride_w + 1, pooled_width); Dtype gradient = 0; - rand_idx += (n * channels + c) * pooled_height * pooled_width; - top_diff += (n * channels + c) * pooled_height * pooled_width; + const Dtype* const rand_idx_slice = + rand_idx + (n * channels + c) * pooled_height * pooled_width; + const Dtype* const top_diff_slice = + top_diff + (n * channels + c) * pooled_height * pooled_width; for (int ph = phstart; ph < phend; ++ph) { for (int pw = pwstart; pw < pwend; ++pw) { - gradient += top_diff[ph * pooled_width + pw] * - (index == static_cast(rand_idx[ph * pooled_width + pw])); + gradient += top_diff_slice[ph * pooled_width + pw] * + (index == static_cast(rand_idx_slice[ph * pooled_width + pw])); } } bottom_diff[index] = gradient; From 44d35bd071517a6bab8f2d2d7fc3ddf2d5df9762 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Tue, 19 May 2015 22:52:12 -0700 Subject: [PATCH 059/446] avoid dangerous state in LRN layer CUDA kernels --- src/caffe/layers/lrn_layer.cu | 58 +++++++++++++++++++---------------- 1 file changed, 31 insertions(+), 27 deletions(-) diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu index 24aa6a30130..e50ae8df83b 100644 --- a/src/caffe/layers/lrn_layer.cu +++ b/src/caffe/layers/lrn_layer.cu @@ -18,8 +18,8 @@ __global__ void LRNFillScale(const int nthreads, const Dtype* in, int n = index / width / height; int offset = (n * channels * height + h) * width + w; int step = height * width; - in += offset; - scale += offset; + const Dtype* const in_off = in + offset; + Dtype* const scale_off = scale + offset; int head = 0; int pre_pad = (size - 1) / 2; int post_pad = size - pre_pad - 1; @@ -27,24 +27,26 @@ __global__ void LRNFillScale(const int nthreads, const Dtype* in, // fill the scale at [n, :, h, w] // accumulate values while (head < post_pad && head < channels) { - accum_scale += in[head * step] * in[head * step]; + accum_scale += in_off[head * step] * in_off[head * step]; ++head; } // both add and subtract while (head < channels) { - accum_scale += in[head * step] * in[head * step]; + accum_scale += in_off[head * step] * in_off[head * step]; if (head - size >= 0) { - accum_scale -= in[(head - size) * step] * in[(head - size) * step]; + accum_scale -= in_off[(head - size) * step] + * in_off[(head - size) * step]; } - scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size; + scale_off[(head - post_pad) * step] = k + accum_scale * alpha_over_size; ++head; } // subtract only while (head < channels + post_pad) { if (head - size >= 0) { - accum_scale -= in[(head - size) * step] * in[(head - size) * step]; + accum_scale -= in_off[(head - size) * step] + * in_off[(head - size) * step]; } - scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size; + scale_off[(head - post_pad) * step] = k + accum_scale * alpha_over_size; ++head; } } @@ -131,43 +133,45 @@ __global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data, int n = index / width / height; int offset = (n * channels * height + h) * width + w; int step = height * width; - bottom_data += offset; - top_data += offset; - scale += offset; - top_diff += offset; - bottom_diff += offset; + const Dtype* const bottom_off = bottom_data + offset; + const Dtype* const top_off = top_data + offset; + const Dtype* const scale_off = scale + offset; + const Dtype* const top_diff_off = top_diff + offset; + Dtype* const bottom_diff_off = bottom_diff + offset; int head = 0; int pre_pad = size - (size + 1) / 2; int post_pad = size - pre_pad - 1; Dtype accum_ratio = 0; // accumulate values while (head < post_pad && head < channels) { - accum_ratio += top_diff[head * step] * top_data[head * step] / - scale[head * step]; + accum_ratio += top_diff_off[head * step] * top_off[head * step] / + scale_off[head * step]; ++head; } // both add and subtract while (head < channels) { - accum_ratio += top_diff[head * step] * top_data[head * step] / - scale[head * step]; + accum_ratio += top_diff_off[head * step] * top_off[head * step] / + scale_off[head * step]; if (head - size >= 0) { - accum_ratio -= top_diff[(head - size) * step] * - top_data[(head - size) * step] / scale[(head - size) * step]; + accum_ratio -= top_diff_off[(head - size) * step] * + top_off[(head - size) * step] / scale_off[(head - size) * step]; } - bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step] - * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio * - bottom_data[(head - post_pad) * step] * accum_ratio; + bottom_diff_off[(head - post_pad) * step] = + top_diff_off[(head - post_pad) * step] + * pow(scale_off[(head - post_pad) * step], negative_beta) + - cache_ratio * bottom_off[(head - post_pad) * step] * accum_ratio; ++head; } // subtract only while (head < channels + post_pad) { if (head - size >= 0) { - accum_ratio -= top_diff[(head - size) * step] * - top_data[(head - size) * step] / scale[(head - size) * step]; + accum_ratio -= top_diff_off[(head - size) * step] * + top_off[(head - size) * step] / scale_off[(head - size) * step]; } - bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step] - * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio * - bottom_data[(head - post_pad) * step] * accum_ratio; + bottom_diff_off[(head - post_pad) * step] = + top_diff_off[(head - post_pad) * step] + * pow(scale_off[(head - post_pad) * step], negative_beta) + - cache_ratio * bottom_off[(head - post_pad) * step] * accum_ratio; ++head; } } From d73f02b5aa81ce6808cb204f2540f2f585450890 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Tue, 19 May 2015 22:59:23 -0700 Subject: [PATCH 060/446] more const in pooling layer CUDA kernels This treats pointer arguments in the same way as non-pointer arguments, and should help to avoid issues like the previous dangerous state issue. --- src/caffe/layers/pooling_layer.cu | 161 +++++++++++++++--------------- 1 file changed, 81 insertions(+), 80 deletions(-) diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu index a1080ebf33a..ca4b13f7c41 100644 --- a/src/caffe/layers/pooling_layer.cu +++ b/src/caffe/layers/pooling_layer.cu @@ -9,21 +9,21 @@ namespace caffe { template -__global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data, - const int num, const int channels, const int height, - const int width, const int pooled_height, const int pooled_width, - const int kernel_h, const int kernel_w, const int stride_h, - const int stride_w, const int pad_h, const int pad_w, Dtype* top_data, - int* mask, Dtype* top_mask) { +__global__ void MaxPoolForward(const int nthreads, + const Dtype* const bottom_data, const int num, const int channels, + const int height, const int width, const int pooled_height, + const int pooled_width, const int kernel_h, const int kernel_w, + const int stride_h, const int stride_w, const int pad_h, const int pad_w, + Dtype* const top_data, int* mask, Dtype* top_mask) { CUDA_KERNEL_LOOP(index, nthreads) { - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; + const int pw = index % pooled_width; + const int ph = (index / pooled_width) % pooled_height; + const int c = (index / pooled_width / pooled_height) % channels; + const int n = index / pooled_width / pooled_height / channels; int hstart = ph * stride_h - pad_h; int wstart = pw * stride_w - pad_w; - int hend = min(hstart + kernel_h, height); - int wend = min(wstart + kernel_w, width); + const int hend = min(hstart + kernel_h, height); + const int wend = min(wstart + kernel_w, width); hstart = max(hstart, 0); wstart = max(wstart, 0); Dtype maxval = -FLT_MAX; @@ -48,21 +48,22 @@ __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data, } template -__global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data, - const int num, const int channels, const int height, - const int width, const int pooled_height, const int pooled_width, - const int kernel_h, const int kernel_w, const int stride_h, - const int stride_w, const int pad_h, const int pad_w, Dtype* top_data) { +__global__ void AvePoolForward(const int nthreads, + const Dtype* const bottom_data, const int num, const int channels, + const int height, const int width, const int pooled_height, + const int pooled_width, const int kernel_h, const int kernel_w, + const int stride_h, const int stride_w, const int pad_h, const int pad_w, + Dtype* const top_data) { CUDA_KERNEL_LOOP(index, nthreads) { - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; + const int pw = index % pooled_width; + const int ph = (index / pooled_width) % pooled_height; + const int c = (index / pooled_width / pooled_height) % channels; + const int n = index / pooled_width / pooled_height / channels; int hstart = ph * stride_h - pad_h; int wstart = pw * stride_w - pad_w; int hend = min(hstart + kernel_h, height + pad_h); int wend = min(wstart + kernel_w, width + pad_w); - int pool_size = (hend - hstart) * (wend - wstart); + const int pool_size = (hend - hstart) * (wend - wstart); hstart = max(hstart, 0); wstart = max(wstart, 0); hend = min(hend, height); @@ -81,20 +82,20 @@ __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data, template __global__ void StoPoolForwardTrain(const int nthreads, - const Dtype* bottom_data, + const Dtype* const bottom_data, const int num, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const int kernel_h, const int kernel_w, const int stride_h, - const int stride_w, Dtype* rand_idx, Dtype* top_data) { + const int stride_w, Dtype* const rand_idx, Dtype* const top_data) { CUDA_KERNEL_LOOP(index, nthreads) { - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; - int hstart = ph * stride_h; - int hend = min(hstart + kernel_h, height); - int wstart = pw * stride_w; - int wend = min(wstart + kernel_w, width); + const int pw = index % pooled_width; + const int ph = (index / pooled_width) % pooled_height; + const int c = (index / pooled_width / pooled_height) % channels; + const int n = index / pooled_width / pooled_height / channels; + const int hstart = ph * stride_h; + const int hend = min(hstart + kernel_h, height); + const int wstart = pw * stride_w; + const int wend = min(wstart + kernel_w, width); Dtype cumsum = 0.; const Dtype* const bottom_slice = bottom_data + (n * channels + c) * height * width; @@ -104,7 +105,7 @@ __global__ void StoPoolForwardTrain(const int nthreads, cumsum += bottom_slice[h * width + w]; } } - float thres = rand_idx[index] * cumsum; + const float thres = rand_idx[index] * cumsum; // Second pass: get value, and set index. cumsum = 0; for (int h = hstart; h < hend; ++h) { @@ -123,20 +124,20 @@ __global__ void StoPoolForwardTrain(const int nthreads, template __global__ void StoPoolForwardTest(const int nthreads, - const Dtype* bottom_data, + const Dtype* const bottom_data, const int num, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const int kernel_h, const int kernel_w, const int stride_h, - const int stride_w, Dtype* top_data) { + const int stride_w, Dtype* const top_data) { CUDA_KERNEL_LOOP(index, nthreads) { - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; - int hstart = ph * stride_h; - int hend = min(hstart + kernel_h, height); - int wstart = pw * stride_w; - int wend = min(wstart + kernel_w, width); + const int pw = index % pooled_width; + const int ph = (index / pooled_width) % pooled_height; + const int c = (index / pooled_width / pooled_height) % channels; + const int n = index / pooled_width / pooled_height / channels; + const int hstart = ph * stride_h; + const int hend = min(hstart + kernel_h, height); + const int wstart = pw * stride_w; + const int wend = min(wstart + kernel_w, width); // We set cumsum to be 0 to avoid divide-by-zero problems Dtype cumsum = FLT_MIN; Dtype cumvalues = 0.; @@ -214,27 +215,27 @@ void PoolingLayer::Forward_gpu(const vector*>& bottom, template -__global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff, - const int* mask, const Dtype* top_mask, const int num, const int channels, - const int height, const int width, const int pooled_height, - const int pooled_width, const int kernel_h, const int kernel_w, - const int stride_h, const int stride_w, const int pad_h, const int pad_w, - Dtype* bottom_diff) { +__global__ void MaxPoolBackward(const int nthreads, const Dtype* const top_diff, + const int* const mask, const Dtype* const top_mask, const int num, + const int channels, const int height, const int width, + const int pooled_height, const int pooled_width, const int kernel_h, + const int kernel_w, const int stride_h, const int stride_w, const int pad_h, + const int pad_w, Dtype* const bottom_diff) { CUDA_KERNEL_LOOP(index, nthreads) { // find out the local index // find out the local offset - int w = index % width; - int h = (index / width) % height; - int c = (index / width / height) % channels; - int n = index / width / height / channels; - int phstart = - (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1; - int phend = min((h + pad_h) / stride_h + 1, pooled_height); - int pwstart = - (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1; - int pwend = min((w + pad_w) / stride_w + 1, pooled_width); + const int w = index % width; + const int h = (index / width) % height; + const int c = (index / width / height) % channels; + const int n = index / width / height / channels; + const int phstart = + (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1; + const int phend = min((h + pad_h) / stride_h + 1, pooled_height); + const int pwstart = + (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1; + const int pwend = min((w + pad_w) / stride_w + 1, pooled_width); Dtype gradient = 0; - int offset = (n * channels + c) * pooled_height * pooled_width; + const int offset = (n * channels + c) * pooled_height * pooled_width; const Dtype* const top_diff_slice = top_diff + offset; if (mask) { const int* const mask_slice = mask + offset; @@ -260,23 +261,23 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff, } template -__global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff, +__global__ void AvePoolBackward(const int nthreads, const Dtype* const top_diff, const int num, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const int kernel_h, const int kernel_w, const int stride_h, const int stride_w, const int pad_h, const int pad_w, - Dtype* bottom_diff) { + Dtype* const bottom_diff) { CUDA_KERNEL_LOOP(index, nthreads) { // find out the local index // find out the local offset - int w = index % width + pad_w; - int h = (index / width) % height + pad_h; - int c = (index / width / height) % channels; - int n = index / width / height / channels; - int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1; - int phend = min(h / stride_h + 1, pooled_height); - int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1; - int pwend = min(w / stride_w + 1, pooled_width); + const int w = index % width + pad_w; + const int h = (index / width) % height + pad_h; + const int c = (index / width / height) % channels; + const int n = index / width / height / channels; + const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1; + const int phend = min(h / stride_h + 1, pooled_height); + const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1; + const int pwend = min(w / stride_w + 1, pooled_width); Dtype gradient = 0; const Dtype* const top_diff_slice = top_diff + (n * channels + c) * pooled_height * pooled_width; @@ -298,22 +299,22 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff, template __global__ void StoPoolBackward(const int nthreads, - const Dtype* rand_idx, const Dtype* top_diff, + const Dtype* const rand_idx, const Dtype* const top_diff, const int num, const int channels, const int height, const int width, const int pooled_height, const int pooled_width, const int kernel_h, const int kernel_w, const int stride_h, - const int stride_w, Dtype* bottom_diff) { + const int stride_w, Dtype* const bottom_diff) { CUDA_KERNEL_LOOP(index, nthreads) { // find out the local index // find out the local offset - int w = index % width; - int h = (index / width) % height; - int c = (index / width / height) % channels; - int n = index / width / height / channels; - int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1; - int phend = min(h / stride_h + 1, pooled_height); - int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1; - int pwend = min(w / stride_w + 1, pooled_width); + const int w = index % width; + const int h = (index / width) % height; + const int c = (index / width / height) % channels; + const int n = index / width / height / channels; + const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1; + const int phend = min(h / stride_h + 1, pooled_height); + const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1; + const int pwend = min(w / stride_w + 1, pooled_width); Dtype gradient = 0; const Dtype* const rand_idx_slice = rand_idx + (n * channels + c) * pooled_height * pooled_width; From 5a6b0d6883df97a8f329f5ba7615b0a74b6ea651 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Tue, 19 May 2015 22:59:27 -0700 Subject: [PATCH 061/446] more const in LRN layer CUDA kernels --- src/caffe/layers/lrn_layer.cu | 44 +++++++++++++++++------------------ 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu index e50ae8df83b..001b3c34ac1 100644 --- a/src/caffe/layers/lrn_layer.cu +++ b/src/caffe/layers/lrn_layer.cu @@ -7,22 +7,22 @@ namespace caffe { template -__global__ void LRNFillScale(const int nthreads, const Dtype* in, +__global__ void LRNFillScale(const int nthreads, const Dtype* const in, const int num, const int channels, const int height, const int width, const int size, const Dtype alpha_over_size, - const Dtype k, Dtype* scale) { + const Dtype k, Dtype* const scale) { CUDA_KERNEL_LOOP(index, nthreads) { // find out the local offset - int w = index % width; - int h = (index / width) % height; - int n = index / width / height; - int offset = (n * channels * height + h) * width + w; - int step = height * width; + const int w = index % width; + const int h = (index / width) % height; + const int n = index / width / height; + const int offset = (n * channels * height + h) * width + w; + const int step = height * width; const Dtype* const in_off = in + offset; Dtype* const scale_off = scale + offset; int head = 0; - int pre_pad = (size - 1) / 2; - int post_pad = size - pre_pad - 1; + const int pre_pad = (size - 1) / 2; + const int post_pad = size - pre_pad - 1; Dtype accum_scale = 0; // fill the scale at [n, :, h, w] // accumulate values @@ -70,8 +70,8 @@ void LRNLayer::Forward_gpu(const vector*>& bottom, // TODO: check if it would be faster to just put it into the previous kernel. template -__global__ void LRNComputeOutput(const int nthreads, const Dtype* in, - const Dtype* scale, const Dtype negative_beta, Dtype* out) { +__global__ void LRNComputeOutput(const int nthreads, const Dtype* const in, + const Dtype* const scale, const Dtype negative_beta, Dtype* const out) { CUDA_KERNEL_LOOP(index, nthreads) { out[index] = in[index] * pow(scale[index], negative_beta); } @@ -120,27 +120,27 @@ void LRNLayer::Backward_gpu(const vector*>& top, } template -__global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data, - const Dtype* top_data, const Dtype* scale, const Dtype* top_diff, +__global__ void LRNComputeDiff(const int nthreads, + const Dtype* const bottom_data, const Dtype* const top_data, + const Dtype* const scale, const Dtype* const top_diff, const int num, const int channels, const int height, const int width, const int size, const Dtype negative_beta, - const Dtype cache_ratio, - Dtype* bottom_diff) { + const Dtype cache_ratio, Dtype* const bottom_diff) { CUDA_KERNEL_LOOP(index, nthreads) { // find out the local offset - int w = index % width; - int h = (index / width) % height; - int n = index / width / height; - int offset = (n * channels * height + h) * width + w; - int step = height * width; + const int w = index % width; + const int h = (index / width) % height; + const int n = index / width / height; + const int offset = (n * channels * height + h) * width + w; + const int step = height * width; const Dtype* const bottom_off = bottom_data + offset; const Dtype* const top_off = top_data + offset; const Dtype* const scale_off = scale + offset; const Dtype* const top_diff_off = top_diff + offset; Dtype* const bottom_diff_off = bottom_diff + offset; int head = 0; - int pre_pad = size - (size + 1) / 2; - int post_pad = size - pre_pad - 1; + const int pre_pad = size - (size + 1) / 2; + const int post_pad = size - pre_pad - 1; Dtype accum_ratio = 0; // accumulate values while (head < post_pad && head < channels) { From 74a8aefaf7082601ca611f12380386633b5ba643 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sebasti=C3=A1n=20Ram=C3=ADrez?= Date: Thu, 21 May 2015 20:19:59 -0500 Subject: [PATCH 062/446] Update python/requirements.txt to have ipython>=3.0.0 --- python/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/requirements.txt b/python/requirements.txt index 7bc164a42b5..e0c86c7e51e 100644 --- a/python/requirements.txt +++ b/python/requirements.txt @@ -3,7 +3,7 @@ numpy>=1.7.1 scipy>=0.13.2 scikit-image>=0.9.3 matplotlib>=1.3.1 -ipython>=1.1.0 +ipython>=3.0.0 h5py>=2.2.0 leveldb>=0.191 networkx>=1.8.1 From 25538ce07d0e71e2ed1a523f50b8f9a40daeb424 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:15 -0700 Subject: [PATCH 063/446] Refactor types FloatCPU and DoubleCPU into a new type CPUDevice Similarly, FloatGPU and DoubleGPU are replaced by a new type GPUDevice. --- include/caffe/test/test_caffe_main.hpp | 28 ++++++++++---------------- 1 file changed, 11 insertions(+), 17 deletions(-) diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp index bd5f31e063f..89b44052c3c 100644 --- a/include/caffe/test/test_caffe_main.hpp +++ b/include/caffe/test/test_caffe_main.hpp @@ -40,34 +40,28 @@ class MultiDeviceTest : public ::testing::Test { typedef ::testing::Types TestDtypes; -struct FloatCPU { - typedef float Dtype; - static const Caffe::Brew device = Caffe::CPU; -}; - -struct DoubleCPU { - typedef double Dtype; +template +struct CPUDevice { + typedef TypeParam Dtype; static const Caffe::Brew device = Caffe::CPU; }; #ifdef CPU_ONLY -typedef ::testing::Types TestDtypesAndDevices; +typedef ::testing::Types, + CPUDevice > TestDtypesAndDevices; #else -struct FloatGPU { - typedef float Dtype; - static const Caffe::Brew device = Caffe::GPU; -}; - -struct DoubleGPU { - typedef double Dtype; +template +struct GPUDevice { + typedef TypeParam Dtype; static const Caffe::Brew device = Caffe::GPU; }; -typedef ::testing::Types - TestDtypesAndDevices; +typedef ::testing::Types, CPUDevice, + GPUDevice, GPUDevice > + TestDtypesAndDevices; #endif From 2cd27fd1d53c0dfded35ea34baed59f1898a038d Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:28 -0700 Subject: [PATCH 064/446] Split class MathFunctionsTest into CPUMathFunctionsTest and GPUMathFunctionsTest --- src/caffe/test/test_math_functions.cpp | 51 ++++++++++++++++---------- 1 file changed, 31 insertions(+), 20 deletions(-) diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp index 667f744bdd7..a095b544e17 100644 --- a/src/caffe/test/test_math_functions.cpp +++ b/src/caffe/test/test_math_functions.cpp @@ -15,8 +15,10 @@ namespace caffe { -template -class MathFunctionsTest : public ::testing::Test { +template +class MathFunctionsTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + protected: MathFunctionsTest() : blob_bottom_(new Blob()), @@ -64,14 +66,19 @@ class MathFunctionsTest : public ::testing::Test { Blob* const blob_top_; }; -TYPED_TEST_CASE(MathFunctionsTest, TestDtypes); +template +class CPUMathFunctionsTest + : public MathFunctionsTest > { +}; + +TYPED_TEST_CASE(CPUMathFunctionsTest, TestDtypes); -TYPED_TEST(MathFunctionsTest, TestNothing) { +TYPED_TEST(CPUMathFunctionsTest, TestNothing) { // The first test case of a test suite takes the longest time // due to the set up overhead. } -TYPED_TEST(MathFunctionsTest, TestHammingDistanceCPU) { +TYPED_TEST(CPUMathFunctionsTest, TestHammingDistance) { int n = this->blob_bottom_->count(); const TypeParam* x = this->blob_bottom_->cpu_data(); const TypeParam* y = this->blob_top_->cpu_data(); @@ -79,7 +86,7 @@ TYPED_TEST(MathFunctionsTest, TestHammingDistanceCPU) { caffe_cpu_hamming_distance(n, x, y)); } -TYPED_TEST(MathFunctionsTest, TestAsumCPU) { +TYPED_TEST(CPUMathFunctionsTest, TestAsum) { int n = this->blob_bottom_->count(); const TypeParam* x = this->blob_bottom_->cpu_data(); TypeParam std_asum = 0; @@ -90,7 +97,7 @@ TYPED_TEST(MathFunctionsTest, TestAsumCPU) { EXPECT_LT((cpu_asum - std_asum) / std_asum, 1e-2); } -TYPED_TEST(MathFunctionsTest, TestSignCPU) { +TYPED_TEST(CPUMathFunctionsTest, TestSign) { int n = this->blob_bottom_->count(); const TypeParam* x = this->blob_bottom_->cpu_data(); caffe_cpu_sign(n, x, this->blob_bottom_->mutable_cpu_diff()); @@ -100,7 +107,7 @@ TYPED_TEST(MathFunctionsTest, TestSignCPU) { } } -TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) { +TYPED_TEST(CPUMathFunctionsTest, TestSgnbit) { int n = this->blob_bottom_->count(); const TypeParam* x = this->blob_bottom_->cpu_data(); caffe_cpu_sgnbit(n, x, this->blob_bottom_->mutable_cpu_diff()); @@ -110,7 +117,7 @@ TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) { } } -TYPED_TEST(MathFunctionsTest, TestFabsCPU) { +TYPED_TEST(CPUMathFunctionsTest, TestFabs) { int n = this->blob_bottom_->count(); const TypeParam* x = this->blob_bottom_->cpu_data(); caffe_abs(n, x, this->blob_bottom_->mutable_cpu_diff()); @@ -120,7 +127,7 @@ TYPED_TEST(MathFunctionsTest, TestFabsCPU) { } } -TYPED_TEST(MathFunctionsTest, TestScaleCPU) { +TYPED_TEST(CPUMathFunctionsTest, TestScale) { int n = this->blob_bottom_->count(); TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() % this->blob_bottom_->count()]; @@ -133,11 +140,10 @@ TYPED_TEST(MathFunctionsTest, TestScaleCPU) { } } -TYPED_TEST(MathFunctionsTest, TestCopyCPU) { +TYPED_TEST(CPUMathFunctionsTest, TestCopy) { const int n = this->blob_bottom_->count(); const TypeParam* bottom_data = this->blob_bottom_->cpu_data(); TypeParam* top_data = this->blob_top_->mutable_cpu_data(); - Caffe::set_mode(Caffe::CPU); caffe_copy(n, bottom_data, top_data); for (int i = 0; i < n; ++i) { EXPECT_EQ(bottom_data[i], top_data[i]); @@ -146,8 +152,14 @@ TYPED_TEST(MathFunctionsTest, TestCopyCPU) { #ifndef CPU_ONLY +template +class GPUMathFunctionsTest : public MathFunctionsTest > { +}; + +TYPED_TEST_CASE(GPUMathFunctionsTest, TestDtypes); + // TODO: Fix caffe_gpu_hamming_distance and re-enable this test. -TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) { +TYPED_TEST(GPUMathFunctionsTest, DISABLED_TestHammingDistance) { int n = this->blob_bottom_->count(); const TypeParam* x = this->blob_bottom_->cpu_data(); const TypeParam* y = this->blob_top_->cpu_data(); @@ -158,7 +170,7 @@ TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) { EXPECT_EQ(reference_distance, computed_distance); } -TYPED_TEST(MathFunctionsTest, TestAsumGPU) { +TYPED_TEST(GPUMathFunctionsTest, TestAsum) { int n = this->blob_bottom_->count(); const TypeParam* x = this->blob_bottom_->cpu_data(); TypeParam std_asum = 0; @@ -170,7 +182,7 @@ TYPED_TEST(MathFunctionsTest, TestAsumGPU) { EXPECT_LT((gpu_asum - std_asum) / std_asum, 1e-2); } -TYPED_TEST(MathFunctionsTest, TestSignGPU) { +TYPED_TEST(GPUMathFunctionsTest, TestSign) { int n = this->blob_bottom_->count(); caffe_gpu_sign(n, this->blob_bottom_->gpu_data(), this->blob_bottom_->mutable_gpu_diff()); @@ -181,7 +193,7 @@ TYPED_TEST(MathFunctionsTest, TestSignGPU) { } } -TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) { +TYPED_TEST(GPUMathFunctionsTest, TestSgnbit) { int n = this->blob_bottom_->count(); caffe_gpu_sgnbit(n, this->blob_bottom_->gpu_data(), this->blob_bottom_->mutable_gpu_diff()); @@ -192,7 +204,7 @@ TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) { } } -TYPED_TEST(MathFunctionsTest, TestFabsGPU) { +TYPED_TEST(GPUMathFunctionsTest, TestFabs) { int n = this->blob_bottom_->count(); caffe_gpu_abs(n, this->blob_bottom_->gpu_data(), this->blob_bottom_->mutable_gpu_diff()); @@ -203,7 +215,7 @@ TYPED_TEST(MathFunctionsTest, TestFabsGPU) { } } -TYPED_TEST(MathFunctionsTest, TestScaleGPU) { +TYPED_TEST(GPUMathFunctionsTest, TestScale) { int n = this->blob_bottom_->count(); TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() % this->blob_bottom_->count()]; @@ -216,11 +228,10 @@ TYPED_TEST(MathFunctionsTest, TestScaleGPU) { } } -TYPED_TEST(MathFunctionsTest, TestCopyGPU) { +TYPED_TEST(GPUMathFunctionsTest, TestCopy) { const int n = this->blob_bottom_->count(); const TypeParam* bottom_data = this->blob_bottom_->gpu_data(); TypeParam* top_data = this->blob_top_->mutable_gpu_data(); - Caffe::set_mode(Caffe::GPU); caffe_copy(n, bottom_data, top_data); bottom_data = this->blob_bottom_->cpu_data(); top_data = this->blob_top_->mutable_cpu_data(); From 65af68d8914b6f509f776a9ffae785127c477fa7 Mon Sep 17 00:00:00 2001 From: Nick Carlevaris-Bianco Date: Mon, 16 Feb 2015 15:49:43 +1030 Subject: [PATCH 065/446] Added MSRAFiller, an Xavier-like filler designed for use with ReLUs ...instead of tanh. Based on paper: He et al, "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification," 2015. - add VarianceNorm option to FillerParameters which allows one to normalize by fan_in, fan_out or their average. - update XavierFiller to use the VarianceNorm option (default behavior unchanged). - add tests for MSRAFiller and XavierFiller. --- include/caffe/filler.hpp | 68 +++++++++++++++++++---- src/caffe/proto/caffe.proto | 8 +++ src/caffe/test/test_filler.cpp | 98 ++++++++++++++++++++++++++++++++++ 3 files changed, 165 insertions(+), 9 deletions(-) diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp index eebf565b1d5..0125b3093f2 100644 --- a/include/caffe/filler.hpp +++ b/include/caffe/filler.hpp @@ -127,17 +127,18 @@ class PositiveUnitballFiller : public Filler { }; /** - * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$ - * is set inversely proportional to the number of incoming nodes. + * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$ is + * set inversely proportional to number of incoming nodes, outgoing + * nodes, or their average. * * A Filler based on the paper [Bengio and Glorot 2010]: Understanding - * the difficulty of training deep feedforward neuralnetworks, but does not - * use the fan_out value. + * the difficulty of training deep feedforward neuralnetworks. * - * It fills the incoming matrix by randomly sampling uniform data from - * [-scale, scale] where scale = sqrt(3 / fan_in) where fan_in is the number - * of input nodes. You should make sure the input blob has shape (num, a, b, c) - * where a * b * c = fan_in. + * It fills the incoming matrix by randomly sampling uniform data from [-scale, + * scale] where scale = sqrt(3 / n) where n is the fan_in, fan_out, or their + * average, depending on the variance_norm option. You should make sure the + * input blob has shape (num, a, b, c) where a * b * c = fan_in and num * b * c + * = fan_out. Note that this is currently not the case for inner product layers. * * TODO(dox): make notation in above comment consistent with rest & use LaTeX. */ @@ -149,7 +150,16 @@ class XavierFiller : public Filler { virtual void Fill(Blob* blob) { CHECK(blob->count()); int fan_in = blob->count() / blob->num(); - Dtype scale = sqrt(Dtype(3) / fan_in); + int fan_out = blob->count() / blob->channels(); + Dtype n = fan_in; // default to fan_in + if (this->filler_param_.variance_norm() == + FillerParameter_VarianceNorm_AVERAGE) { + n = (fan_in + fan_out) / Dtype(2); + } else if (this->filler_param_.variance_norm() == + FillerParameter_VarianceNorm_FAN_OUT) { + n = fan_out; + } + Dtype scale = sqrt(Dtype(3) / n); caffe_rng_uniform(blob->count(), -scale, scale, blob->mutable_cpu_data()); CHECK_EQ(this->filler_param_.sparse(), -1) @@ -157,6 +167,44 @@ class XavierFiller : public Filler { } }; +/** + * @brief Fills a Blob with values @f$ x \sim N(0, \sigma^2) @f$ where + * @f$ \sigma^2 @f$ is set inversely proportional to number of incoming + * nodes, outgoing nodes, or their average. + * + * A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically + * accounts for ReLU nonlinearities. + * + * It fills the incoming matrix by randomly sampling Gaussian data with std = + * sqrt(2 / n) where n is the fan_in, fan_out, or their average, depending on + * the variance_norm option. You should make sure the input blob has shape (num, + * a, b, c) where a * b * c = fan_in and num * b * c = fan_out. Note that this + * is currently not the case for inner product layers. + */ +template +class MSRAFiller : public Filler { + public: + explicit MSRAFiller(const FillerParameter& param) + : Filler(param) {} + virtual void Fill(Blob* blob) { + CHECK(blob->count()); + int fan_in = blob->count() / blob->num(); + int fan_out = blob->count() / blob->channels(); + Dtype n = fan_in; // default to fan_in + if (this->filler_param_.variance_norm() == + FillerParameter_VarianceNorm_AVERAGE) { + n = (fan_in + fan_out) / Dtype(2); + } else if (this->filler_param_.variance_norm() == + FillerParameter_VarianceNorm_FAN_OUT) { + n = fan_out; + } + Dtype std = sqrt(Dtype(2) / n); + caffe_rng_gaussian(blob->count(), Dtype(0), std, + blob->mutable_cpu_data()); + CHECK_EQ(this->filler_param_.sparse(), -1) + << "Sparsity not supported by this Filler."; + } +}; /** * @brief Get a specific filler from the specification given in FillerParameter. @@ -177,6 +225,8 @@ Filler* GetFiller(const FillerParameter& param) { return new UniformFiller(param); } else if (type == "xavier") { return new XavierFiller(param); + } else if (type == "msra") { + return new MSRAFiller(param); } else { CHECK(false) << "Unknown filler name: " << param.type(); } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 84b475ce3cd..f9d4d614d10 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -41,6 +41,14 @@ message FillerParameter { // The expected number of non-zero output weights for a given input in // Gaussian filler -- the default -1 means don't perform sparsification. optional int32 sparse = 7 [default = -1]; + // Normalize the filler variance by fan_in, fan_out, or their average. + // Applies to 'xavier' and 'msra' fillers. + enum VarianceNorm { + FAN_IN = 0; + FAN_OUT = 1; + AVERAGE = 2; + } + optional VarianceNorm variance_norm = 8 [default = FAN_IN]; } message NetParameter { diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp index e04b0fd22af..728b8dc5f0d 100644 --- a/src/caffe/test/test_filler.cpp +++ b/src/caffe/test/test_filler.cpp @@ -142,4 +142,102 @@ TYPED_TEST(GaussianFillerTest, TestFill) { EXPECT_LE(var, target_var * 5.); } +template +class XavierFillerTest : public ::testing::Test { + protected: + XavierFillerTest() + : blob_(new Blob(1000, 2, 4, 5)), + filler_param_() { + } + virtual void test_params(FillerParameter_VarianceNorm variance_norm, + Dtype n) { + this->filler_param_.set_variance_norm(variance_norm); + this->filler_.reset(new XavierFiller(this->filler_param_)); + this->filler_->Fill(blob_); + EXPECT_TRUE(this->blob_); + const int count = this->blob_->count(); + const Dtype* data = this->blob_->cpu_data(); + Dtype mean = 0.; + Dtype ex2 = 0.; + for (int i = 0; i < count; ++i) { + mean += data[i]; + ex2 += data[i] * data[i]; + } + mean /= count; + ex2 /= count; + Dtype std = sqrt(ex2 - mean*mean); + Dtype target_std = sqrt(2.0 / n); + EXPECT_NEAR(mean, 0.0, 0.1); + EXPECT_NEAR(std, target_std, 0.1); + } + virtual ~XavierFillerTest() { delete blob_; } + Blob* const blob_; + FillerParameter filler_param_; + shared_ptr > filler_; +}; + +TYPED_TEST_CASE(XavierFillerTest, TestDtypes); + +TYPED_TEST(XavierFillerTest, TestFillFanIn) { + TypeParam n = 2*4*5; + this->test_params(FillerParameter_VarianceNorm_FAN_IN, n); +} +TYPED_TEST(XavierFillerTest, TestFillFanOut) { + TypeParam n = 1000*4*5; + this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n); +} +TYPED_TEST(XavierFillerTest, TestFillAverage) { + TypeParam n = (2*4*5 + 1000*4*5) / 2.0; + this->test_params(FillerParameter_VarianceNorm_AVERAGE, n); +} + +template +class MSRAFillerTest : public ::testing::Test { + protected: + MSRAFillerTest() + : blob_(new Blob(1000, 2, 4, 5)), + filler_param_() { + } + virtual void test_params(FillerParameter_VarianceNorm variance_norm, + Dtype n) { + this->filler_param_.set_variance_norm(variance_norm); + this->filler_.reset(new MSRAFiller(this->filler_param_)); + this->filler_->Fill(blob_); + EXPECT_TRUE(this->blob_); + const int count = this->blob_->count(); + const Dtype* data = this->blob_->cpu_data(); + Dtype mean = 0.; + Dtype ex2 = 0.; + for (int i = 0; i < count; ++i) { + mean += data[i]; + ex2 += data[i] * data[i]; + } + mean /= count; + ex2 /= count; + Dtype std = sqrt(ex2 - mean*mean); + Dtype target_std = sqrt(2.0 / n); + EXPECT_NEAR(mean, 0.0, 0.1); + EXPECT_NEAR(std, target_std, 0.1); + } + virtual ~MSRAFillerTest() { delete blob_; } + Blob* const blob_; + FillerParameter filler_param_; + shared_ptr > filler_; +}; + +TYPED_TEST_CASE(MSRAFillerTest, TestDtypes); + +TYPED_TEST(MSRAFillerTest, TestFillFanIn) { + TypeParam n = 2*4*5; + this->test_params(FillerParameter_VarianceNorm_FAN_IN, n); +} +TYPED_TEST(MSRAFillerTest, TestFillFanOut) { + TypeParam n = 1000*4*5; + this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n); +} +TYPED_TEST(MSRAFillerTest, TestFillAverage) { + TypeParam n = (2*4*5 + 1000*4*5) / 2.0; + this->test_params(FillerParameter_VarianceNorm_AVERAGE, n); +} + } // namespace caffe From 59de6c7190eb57bfa7511ede30c965c1d5963a06 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Tue, 26 May 2015 12:39:14 -0700 Subject: [PATCH 066/446] include comment on Saxe and sqrt(2) scaling factor although different and independent, the derivation of Saxe et al. with regards to the scaling factor might be of interest. --- include/caffe/filler.hpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp index 0125b3093f2..446f5b5c4eb 100644 --- a/include/caffe/filler.hpp +++ b/include/caffe/filler.hpp @@ -175,6 +175,9 @@ class XavierFiller : public Filler { * A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically * accounts for ReLU nonlinearities. * + * Aside: for another perspective on the scaling factor, see the derivation of + * [Saxe, McClelland, and Ganguli 2013 (v3)]. + * * It fills the incoming matrix by randomly sampling Gaussian data with std = * sqrt(2 / n) where n is the fan_in, fan_out, or their average, depending on * the variance_norm option. You should make sure the input blob has shape (num, From 43d538f0969601a6cb96b51d64741482b45f9e0b Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:32 -0700 Subject: [PATCH 067/446] Split class StochasticPoolingLayerTest into CPUStochasticPoolingLayerTest and GPUStochasticPoolingLayerTest --- src/caffe/test/test_stochastic_pooling.cpp | 35 +++++++++++++++------- 1 file changed, 24 insertions(+), 11 deletions(-) diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp index 12962c65d85..f84464c322c 100644 --- a/src/caffe/test/test_stochastic_pooling.cpp +++ b/src/caffe/test/test_stochastic_pooling.cpp @@ -16,8 +16,10 @@ using std::min; namespace caffe { -template -class StochasticPoolingLayerTest : public ::testing::Test { +template +class StochasticPoolingLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + protected: StochasticPoolingLayerTest() : blob_bottom_(new Blob()), @@ -45,9 +47,14 @@ class StochasticPoolingLayerTest : public ::testing::Test { vector*> blob_top_vec_; }; -TYPED_TEST_CASE(StochasticPoolingLayerTest, TestDtypes); +template +class CPUStochasticPoolingLayerTest + : public StochasticPoolingLayerTest > { +}; + +TYPED_TEST_CASE(CPUStochasticPoolingLayerTest, TestDtypes); -TYPED_TEST(StochasticPoolingLayerTest, TestSetup) { +TYPED_TEST(CPUStochasticPoolingLayerTest, TestSetup) { LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_size(3); @@ -60,8 +67,16 @@ TYPED_TEST(StochasticPoolingLayerTest, TestSetup) { EXPECT_EQ(this->blob_top_->width(), 2); } -TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) { - Caffe::set_mode(Caffe::GPU); +#ifndef CPU_ONLY + +template +class GPUStochasticPoolingLayerTest + : public StochasticPoolingLayerTest > { +}; + +TYPED_TEST_CASE(GPUStochasticPoolingLayerTest, TestDtypes); + +TYPED_TEST(GPUStochasticPoolingLayerTest, TestStochastic) { LayerParameter layer_param; layer_param.set_phase(TRAIN); PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); @@ -104,8 +119,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) { EXPECT_GE(total / this->blob_top_->count(), 0.55); } -TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) { - Caffe::set_mode(Caffe::GPU); +TYPED_TEST(GPUStochasticPoolingLayerTest, TestStochasticTestPhase) { LayerParameter layer_param; layer_param.set_phase(TEST); PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); @@ -142,8 +156,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) { } } -TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) { - Caffe::set_mode(Caffe::GPU); +TYPED_TEST(GPUStochasticPoolingLayerTest, TestGradient) { LayerParameter layer_param; layer_param.set_phase(TRAIN); PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); @@ -158,6 +171,6 @@ TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) { this->blob_top_vec_); } - +#endif } // namespace caffe From 8d2010489a2e291b4747d3b21b5cadc97d84cb00 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:36 -0700 Subject: [PATCH 068/446] Add classes GPUDeviceTest and CPUDeviceTest. These new classes can be used to implement test cases that are only running on the GPU or the CPU. The goal is to move all calls to Caffe::set_mode() inside the test framework, to discourage any test to change the mode halfway through the execution, which is documented to be illegal. --- include/caffe/test/test_caffe_main.hpp | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp index 89b44052c3c..fc156091476 100644 --- a/include/caffe/test/test_caffe_main.hpp +++ b/include/caffe/test/test_caffe_main.hpp @@ -46,6 +46,10 @@ struct CPUDevice { static const Caffe::Brew device = Caffe::CPU; }; +template +class CPUDeviceTest : public MultiDeviceTest > { +}; + #ifdef CPU_ONLY typedef ::testing::Types, @@ -59,6 +63,10 @@ struct GPUDevice { static const Caffe::Brew device = Caffe::GPU; }; +template +class GPUDeviceTest : public MultiDeviceTest > { +}; + typedef ::testing::Types, CPUDevice, GPUDevice, GPUDevice > TestDtypesAndDevices; From 8a5abbfcd4a51e92c3731ad03ce95786a7e395b9 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:39 -0700 Subject: [PATCH 069/446] Make class Im2colKernelTest derive from GPUDeviceTest --- src/caffe/test/test_im2col_kernel.cu | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu index ee684c00255..0017ac23e69 100644 --- a/src/caffe/test/test_im2col_kernel.cu +++ b/src/caffe/test/test_im2col_kernel.cu @@ -25,7 +25,7 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im, extern cudaDeviceProp CAFFE_TEST_CUDA_PROP; template -class Im2colKernelTest : public ::testing::Test { +class Im2colKernelTest : public GPUDeviceTest { protected: Im2colKernelTest() // big so launches > 1024 threads @@ -68,8 +68,6 @@ class Im2colKernelTest : public ::testing::Test { TYPED_TEST_CASE(Im2colKernelTest, TestDtypes); TYPED_TEST(Im2colKernelTest, TestGPU) { - Caffe::set_mode(Caffe::GPU); - // Reshape the blobs to correct size for im2col output this->blob_top_->Reshape(this->blob_bottom_->num(), this->channels_ * this->kernel_size_ * this->kernel_size_, From 8437c649a2701e34321dfa54860ecdefe3ea8815 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:41 -0700 Subject: [PATCH 070/446] Make class AccuracyLayerTest derive from CPUDeviceTest --- src/caffe/test/test_accuracy_layer.cpp | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp index 6cbf51df45e..c14b67cc0e9 100644 --- a/src/caffe/test/test_accuracy_layer.cpp +++ b/src/caffe/test/test_accuracy_layer.cpp @@ -16,7 +16,7 @@ namespace caffe { template -class AccuracyLayerTest : public ::testing::Test { +class AccuracyLayerTest : public CPUDeviceTest { protected: AccuracyLayerTest() : blob_bottom_data_(new Blob()), @@ -92,7 +92,6 @@ TYPED_TEST(AccuracyLayerTest, TestSetupTopK) { TYPED_TEST(AccuracyLayerTest, TestForwardCPU) { LayerParameter layer_param; - Caffe::set_mode(Caffe::CPU); AccuracyLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); @@ -118,7 +117,6 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPU) { } TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) { - Caffe::set_mode(Caffe::CPU); this->blob_bottom_data_->Reshape(2, 10, 4, 5); vector label_shape(3); label_shape[0] = 2; label_shape[1] = 4; label_shape[2] = 5; @@ -162,7 +160,6 @@ TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) { } TYPED_TEST(AccuracyLayerTest, TestForwardIgnoreLabel) { - Caffe::set_mode(Caffe::CPU); LayerParameter layer_param; const TypeParam kIgnoreLabelValue = -1; layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue); From f48ceadc1f7e4ff60f7fdc0ada1d7298870e7084 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:44 -0700 Subject: [PATCH 071/446] Make class CuDNNNeuronLayerTest derive from GPUDeviceTest --- src/caffe/test/test_neuron_layer.cpp | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp index 030f4bbae7f..37b54713b46 100644 --- a/src/caffe/test/test_neuron_layer.cpp +++ b/src/caffe/test/test_neuron_layer.cpp @@ -586,7 +586,7 @@ TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) { #ifdef USE_CUDNN template -class CuDNNNeuronLayerTest : public ::testing::Test { +class CuDNNNeuronLayerTest : public GPUDeviceTest { protected: CuDNNNeuronLayerTest() : blob_bottom_(new Blob(2, 3, 4, 5)), @@ -609,7 +609,6 @@ class CuDNNNeuronLayerTest : public ::testing::Test { TYPED_TEST_CASE(CuDNNNeuronLayerTest, TestDtypes); TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; CuDNNReLULayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); @@ -624,7 +623,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) { } TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; CuDNNReLULayer layer(layer_param); GradientChecker checker(1e-2, 1e-3, 1701, 0., 0.01); @@ -633,7 +631,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) { } TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; CHECK(google::protobuf::TextFormat::ParseFromString( "relu_param { negative_slope: 0.01 }", &layer_param)); @@ -653,7 +650,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) { } TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; CHECK(google::protobuf::TextFormat::ParseFromString( "relu_param { negative_slope: 0.01 }", &layer_param)); @@ -664,7 +660,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) { } TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; CuDNNSigmoidLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); @@ -681,7 +676,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) { } TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; CuDNNSigmoidLayer layer(layer_param); GradientChecker checker(1e-2, 1e-3, 1701, 0., 0.01); @@ -690,7 +684,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) { } TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; CuDNNTanHLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); @@ -713,7 +706,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) { } TYPED_TEST(CuDNNNeuronLayerTest, TestTanHGradientCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; CuDNNTanHLayer layer(layer_param); GradientChecker checker(1e-2, 1e-3); From 5ca280a3df52fd8693dce00711b39b1cd9391ae3 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:46 -0700 Subject: [PATCH 072/446] Make class ArgMaxLayerTest derive from CPUDeviceTest --- src/caffe/test/test_argmax_layer.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp index 3487d42f21e..895c3d372ff 100644 --- a/src/caffe/test/test_argmax_layer.cpp +++ b/src/caffe/test/test_argmax_layer.cpp @@ -13,13 +13,12 @@ namespace caffe { template -class ArgMaxLayerTest : public ::testing::Test { +class ArgMaxLayerTest : public CPUDeviceTest { protected: ArgMaxLayerTest() : blob_bottom_(new Blob(10, 20, 1, 1)), blob_top_(new Blob()), top_k_(5) { - Caffe::set_mode(Caffe::CPU); Caffe::set_random_seed(1701); // fill the values FillerParameter filler_param; From 4feaa0e2ecae38f7a68540507d9fef53c36a2dee Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:48 -0700 Subject: [PATCH 073/446] Make class CuDNNConvolutionLayerTest derive from GPUDeviceTest --- src/caffe/test/test_convolution_layer.cpp | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp index c1fe3b58c58..67d41fff844 100644 --- a/src/caffe/test/test_convolution_layer.cpp +++ b/src/caffe/test/test_convolution_layer.cpp @@ -424,7 +424,7 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) { #ifdef USE_CUDNN template -class CuDNNConvolutionLayerTest : public ::testing::Test { +class CuDNNConvolutionLayerTest : public GPUDeviceTest { protected: CuDNNConvolutionLayerTest() : blob_bottom_(new Blob(2, 3, 6, 4)), @@ -467,7 +467,6 @@ class CuDNNConvolutionLayerTest : public ::testing::Test { TYPED_TEST_CASE(CuDNNConvolutionLayerTest, TestDtypes); TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) { - Caffe::set_mode(Caffe::GPU); this->blob_bottom_vec_.push_back(this->blob_bottom_2_); this->blob_top_vec_.push_back(this->blob_top_2_); LayerParameter layer_param; @@ -505,7 +504,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) { } TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) { - Caffe::set_mode(Caffe::GPU); this->blob_bottom_vec_.push_back(this->blob_bottom_2_); this->blob_top_vec_.push_back(this->blob_top_2_); LayerParameter layer_param; @@ -541,7 +539,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) { } TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); @@ -572,7 +569,7 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) { // Test separable convolution by computing the Sobel operator // as a single filter then comparing the result // as the convolution of two rectangular filters. - Caffe::set_mode(Caffe::GPU); + // Fill bottoms with identical Gaussian noise. shared_ptr > filler; FillerParameter filler_param; @@ -665,7 +662,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) { } TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); @@ -683,7 +679,6 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) { } TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); From 307c4b63a172f1c7445f486b2443ff7f83ead224 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:50 -0700 Subject: [PATCH 074/446] Make class CuDNNPoolingLayerTest derive from GPUDeviceTest --- src/caffe/test/test_pooling_layer.cpp | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp index e9964e7f0b7..69f2d5c1135 100644 --- a/src/caffe/test/test_pooling_layer.cpp +++ b/src/caffe/test/test_pooling_layer.cpp @@ -608,7 +608,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) { #ifdef USE_CUDNN template -class CuDNNPoolingLayerTest : public ::testing::Test { +class CuDNNPoolingLayerTest : public GPUDeviceTest { protected: CuDNNPoolingLayerTest() : blob_bottom_(new Blob()), @@ -963,7 +963,6 @@ class CuDNNPoolingLayerTest : public ::testing::Test { TYPED_TEST_CASE(CuDNNPoolingLayerTest, TestDtypes); TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_size(3); @@ -977,7 +976,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) { } TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_size(3); @@ -994,7 +992,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) { /* TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; layer_param.set_kernelsize(3); layer_param.set_stride(2); @@ -1020,7 +1017,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) { */ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxCuDNN) { - Caffe::set_mode(Caffe::GPU); this->TestForwardSquare(); this->TestForwardRectHigh(); this->TestForwardRectWide(); @@ -1030,7 +1026,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxCuDNN) { // the corresponding backward test. /* TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) { - Caffe::set_mode(Caffe::GPU); this->blob_top_vec_.push_back(this->blob_top_mask_); this->TestForwardSquare(); this->TestForwardRectHigh(); @@ -1039,7 +1034,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) { */ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) { - Caffe::set_mode(Caffe::GPU); for (int kernel_h = 3; kernel_h <= 4; kernel_h++) { for (int kernel_w = 3; kernel_w <= 4; kernel_w++) { LayerParameter layer_param; @@ -1059,7 +1053,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) { } TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_size(3); @@ -1105,7 +1098,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) { /* TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) { - Caffe::set_mode(Caffe::GPU); for (int kernel_h = 3; kernel_h <= 4; kernel_h++) { for (int kernel_w = 3; kernel_w <= 4; kernel_w++) { LayerParameter layer_param; @@ -1126,7 +1118,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) { */ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_size(3); @@ -1152,7 +1143,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) { } TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) { - Caffe::set_mode(Caffe::GPU); for (int kernel_h = 3; kernel_h <= 4; kernel_h++) { for (int kernel_w = 3; kernel_w <= 4; kernel_w++) { LayerParameter layer_param; @@ -1170,7 +1160,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) { } TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) { - Caffe::set_mode(Caffe::GPU); for (int kernel_h = 3; kernel_h <= 4; kernel_h++) { for (int kernel_w = 3; kernel_w <= 4; kernel_w++) { LayerParameter layer_param; From 58f9ea37a9c5c6a0253be5235504b8399aae54ec Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:53 -0700 Subject: [PATCH 075/446] Make class DummyDataLayerTest derive from CPUDeviceTest --- src/caffe/test/test_dummy_data_layer.cpp | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp index 99548352746..c9ed38db3a5 100644 --- a/src/caffe/test/test_dummy_data_layer.cpp +++ b/src/caffe/test/test_dummy_data_layer.cpp @@ -13,7 +13,7 @@ namespace caffe { template -class DummyDataLayerTest : public ::testing::Test { +class DummyDataLayerTest : public CPUDeviceTest { protected: DummyDataLayerTest() : blob_top_a_(new Blob()), @@ -44,7 +44,6 @@ class DummyDataLayerTest : public ::testing::Test { TYPED_TEST_CASE(DummyDataLayerTest, TestDtypes); TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) { - Caffe::set_mode(Caffe::CPU); LayerParameter param; DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param(); dummy_data_param->add_num(5); @@ -74,7 +73,6 @@ TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) { } TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) { - Caffe::set_mode(Caffe::CPU); LayerParameter param; DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param(); dummy_data_param->add_num(5); @@ -113,7 +111,6 @@ TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) { } TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) { - Caffe::set_mode(Caffe::CPU); LayerParameter param; DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param(); dummy_data_param->add_num(5); From 89bf3c3d99aee902c49de56c25a32d5837f8962f Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:56 -0700 Subject: [PATCH 076/446] Make class CuDNNSoftmaxLayerTest derive from GPUDeviceTest --- src/caffe/test/test_softmax_layer.cpp | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp index f6674422e56..996da4b8f7c 100644 --- a/src/caffe/test/test_softmax_layer.cpp +++ b/src/caffe/test/test_softmax_layer.cpp @@ -82,7 +82,7 @@ TYPED_TEST(SoftmaxLayerTest, TestGradient) { #ifdef USE_CUDNN template -class CuDNNSoftmaxLayerTest : public ::testing::Test { +class CuDNNSoftmaxLayerTest : public GPUDeviceTest { protected: CuDNNSoftmaxLayerTest() : blob_bottom_(new Blob(2, 10, 2, 3)), @@ -104,7 +104,6 @@ class CuDNNSoftmaxLayerTest : public ::testing::Test { TYPED_TEST_CASE(CuDNNSoftmaxLayerTest, TestDtypes); TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; CuDNNSoftmaxLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); @@ -138,7 +137,6 @@ TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) { } TYPED_TEST(CuDNNSoftmaxLayerTest, TestGradientCuDNN) { - Caffe::set_mode(Caffe::GPU); LayerParameter layer_param; CuDNNSoftmaxLayer layer(layer_param); GradientChecker checker(1e-2, 1e-3); From 68133e7848517c0bf4b25ae60e11985600942130 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Tue, 26 May 2015 11:21:58 -0700 Subject: [PATCH 077/446] Make class MultinomialLogisticLossLayerTest derive from CPUDeviceTest --- src/caffe/test/test_multinomial_logistic_loss_layer.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp index 9038017e3e2..b2db984feb1 100644 --- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp +++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp @@ -16,7 +16,7 @@ namespace caffe { template -class MultinomialLogisticLossLayerTest : public ::testing::Test { +class MultinomialLogisticLossLayerTest : public CPUDeviceTest { protected: MultinomialLogisticLossLayerTest() : blob_bottom_data_(new Blob(10, 5, 1, 1)), @@ -51,7 +51,6 @@ TYPED_TEST_CASE(MultinomialLogisticLossLayerTest, TestDtypes); TYPED_TEST(MultinomialLogisticLossLayerTest, TestGradientCPU) { LayerParameter layer_param; - Caffe::set_mode(Caffe::CPU); MultinomialLogisticLossLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); GradientChecker checker(1e-2, 2*1e-2, 1701, 0, 0.05); From 9ea3da42fec3d1ec54de7ad064ae5070cdede524 Mon Sep 17 00:00:00 2001 From: Mohammad Norouzi Date: Tue, 26 May 2015 17:25:56 -0400 Subject: [PATCH 078/446] add leading zeros to keys in feature DB files --- tools/extract_features.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp index 364c436dfd8..1ffbfbed08d 100644 --- a/tools/extract_features.cpp +++ b/tools/extract_features.cpp @@ -157,7 +157,7 @@ int feature_extraction_pipeline(int argc, char** argv) { for (int d = 0; d < dim_features; ++d) { datum.add_float_data(feature_blob_data[d]); } - int length = snprintf(key_str, kMaxKeyStrLength, "%d", + int length = snprintf(key_str, kMaxKeyStrLength, "%010d", image_indices[i]); string out; CHECK(datum.SerializeToString(&out)); From ca816676ac9caf28c8627980549a2e12aa529294 Mon Sep 17 00:00:00 2001 From: Cyprien Noel Date: Mon, 18 May 2015 18:30:00 -0700 Subject: [PATCH 079/446] Refactor solvers regularization and logging code --- include/caffe/solver.hpp | 12 +- src/caffe/solver.cpp | 500 ++++++++++++++++----------------------- 2 files changed, 214 insertions(+), 298 deletions(-) diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 4dcdc3dc20b..c92067917c8 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -39,8 +39,8 @@ class Solver { int iter() { return iter_; } protected: - // Get the update value for the current iteration. - virtual void ComputeUpdateValue() = 0; + // Get and apply the update value for the current iteration. + virtual void MakeUpdate() = 0; // The Solver::Snapshot function implements the basic snapshotting utility // that stores the learned net. You should implement the SnapshotSolverState() // function that produces a SolverState protocol buffer that needs to be @@ -80,7 +80,9 @@ class SGDSolver : public Solver { protected: void PreSolve(); Dtype GetLearningRate(); - virtual void ComputeUpdateValue(); + virtual void MakeUpdate(); + virtual void Regularize(int param_id); + virtual void ComputeUpdateValue(int param_id, Dtype rate); virtual void ClipGradients(); virtual void SnapshotSolverState(SolverState * state); virtual void RestoreSolverState(const SolverState& state); @@ -102,7 +104,7 @@ class NesterovSolver : public SGDSolver { : SGDSolver(param_file) {} protected: - virtual void ComputeUpdateValue(); + virtual void ComputeUpdateValue(int param_id, Dtype rate); DISABLE_COPY_AND_ASSIGN(NesterovSolver); }; @@ -116,7 +118,7 @@ class AdaGradSolver : public SGDSolver { : SGDSolver(param_file) { constructor_sanity_check(); } protected: - virtual void ComputeUpdateValue(); + virtual void ComputeUpdateValue(int param_id, Dtype rate); void constructor_sanity_check() { CHECK_EQ(0, this->param_.momentum()) << "Momentum cannot be used with AdaGrad."; diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 877b19b86f8..88f6d314fc7 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -207,8 +207,7 @@ void Solver::Step(int iters) { } } } - ComputeUpdateValue(); - net_->Update(); + MakeUpdate(); // Increment the internal iter_ counter -- its value should always indicate // the number of times the weights have been updated. @@ -456,95 +455,118 @@ void SGDSolver::ClipGradients() { } template -void SGDSolver::ComputeUpdateValue() { - const vector > >& net_params = this->net_->params(); - const vector& net_params_lr = this->net_->params_lr(); - const vector& net_params_weight_decay = - this->net_->params_weight_decay(); - // get the learning rate +void SGDSolver::MakeUpdate() { Dtype rate = GetLearningRate(); if (this->param_.display() && this->iter_ % this->param_.display() == 0) { LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate; } ClipGradients(); - Dtype momentum = this->param_.momentum(); + for (int param_id = 0; param_id < this->net_->params().size(); ++param_id) { + Regularize(param_id); + ComputeUpdateValue(param_id, rate); + } + this->net_->Update(); +} + +template +void SGDSolver::Regularize(int param_id) { + const vector > >& net_params = this->net_->params(); + const vector& net_params_weight_decay = + this->net_->params_weight_decay(); Dtype weight_decay = this->param_.weight_decay(); string regularization_type = this->param_.regularization_type(); switch (Caffe::mode()) { - case Caffe::CPU: - for (int param_id = 0; param_id < net_params.size(); ++param_id) { - // Compute the value to history, and then copy them to the blob's diff. - Dtype local_rate = rate * net_params_lr[param_id]; - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; - - if (local_decay) { - if (regularization_type == "L2") { - // add weight decay - caffe_axpy(net_params[param_id]->count(), - local_decay, - net_params[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } else if (regularization_type == "L1") { - caffe_cpu_sign(net_params[param_id]->count(), - net_params[param_id]->cpu_data(), - temp_[param_id]->mutable_cpu_data()); - caffe_axpy(net_params[param_id]->count(), - local_decay, - temp_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } else { - LOG(FATAL) << "Unknown regularization type: " << regularization_type; - } + case Caffe::CPU: { + Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; + if (local_decay) { + if (regularization_type == "L2") { + // add weight decay + caffe_axpy(net_params[param_id]->count(), + local_decay, + net_params[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + } else if (regularization_type == "L1") { + caffe_cpu_sign(net_params[param_id]->count(), + net_params[param_id]->cpu_data(), + temp_[param_id]->mutable_cpu_data()); + caffe_axpy(net_params[param_id]->count(), + local_decay, + temp_[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + } else { + LOG(FATAL) << "Unknown regularization type: " << regularization_type; } - - caffe_cpu_axpby(net_params[param_id]->count(), local_rate, - net_params[param_id]->cpu_diff(), momentum, - history_[param_id]->mutable_cpu_data()); - // copy - caffe_copy(net_params[param_id]->count(), - history_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); } break; - case Caffe::GPU: + } + case Caffe::GPU: { #ifndef CPU_ONLY - for (int param_id = 0; param_id < net_params.size(); ++param_id) { - // Compute the value to history, and then copy them to the blob's diff. - Dtype local_rate = rate * net_params_lr[param_id]; - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; - - if (local_decay) { - if (regularization_type == "L2") { - // add weight decay - caffe_gpu_axpy(net_params[param_id]->count(), - local_decay, - net_params[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } else if (regularization_type == "L1") { - caffe_gpu_sign(net_params[param_id]->count(), - net_params[param_id]->gpu_data(), - temp_[param_id]->mutable_gpu_data()); - caffe_gpu_axpy(net_params[param_id]->count(), - local_decay, - temp_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } else { - LOG(FATAL) << "Unknown regularization type: " << regularization_type; - } + Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; + if (local_decay) { + if (regularization_type == "L2") { + // add weight decay + caffe_gpu_axpy(net_params[param_id]->count(), + local_decay, + net_params[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); + } else if (regularization_type == "L1") { + caffe_gpu_sign(net_params[param_id]->count(), + net_params[param_id]->gpu_data(), + temp_[param_id]->mutable_gpu_data()); + caffe_gpu_axpy(net_params[param_id]->count(), + local_decay, + temp_[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); + } else { + LOG(FATAL) << "Unknown regularization type: " << regularization_type; } - - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - net_params[param_id]->gpu_diff(), momentum, - history_[param_id]->mutable_gpu_data()); - // copy - caffe_copy(net_params[param_id]->count(), - history_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); } #else NO_GPU; #endif break; + } + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + +template +void SGDSolver::ComputeUpdateValue(int param_id, Dtype rate) { + const vector > >& net_params = this->net_->params(); + const vector& net_params_lr = this->net_->params_lr(); + Dtype momentum = this->param_.momentum(); + switch (Caffe::mode()) { + case Caffe::CPU: { + // Compute the value to history, and then copy them to the blob's diff. + Dtype local_rate = rate * net_params_lr[param_id]; + + caffe_cpu_axpby(net_params[param_id]->count(), local_rate, + net_params[param_id]->cpu_diff(), momentum, + history_[param_id]->mutable_cpu_data()); + // copy + caffe_copy(net_params[param_id]->count(), + history_[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + break; + } + case Caffe::GPU: { +#ifndef CPU_ONLY + // Compute the value to history, and then copy them to the blob's diff. + Dtype local_rate = rate * net_params_lr[param_id]; + + caffe_gpu_axpby(net_params[param_id]->count(), local_rate, + net_params[param_id]->gpu_diff(), momentum, + history_[param_id]->mutable_gpu_data()); + // copy + caffe_copy(net_params[param_id]->count(), + history_[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + } default: LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); } @@ -571,252 +593,144 @@ void SGDSolver::RestoreSolverState(const SolverState& state) { } template -void NesterovSolver::ComputeUpdateValue() { +void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { const vector > >& net_params = this->net_->params(); const vector& net_params_lr = this->net_->params_lr(); - const vector& net_params_weight_decay = - this->net_->params_weight_decay(); - // get the learning rate - Dtype rate = this->GetLearningRate(); - if (this->param_.display() && this->iter_ % this->param_.display() == 0) { - LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate; - } - SGDSolver::ClipGradients(); Dtype momentum = this->param_.momentum(); - Dtype weight_decay = this->param_.weight_decay(); - string regularization_type = this->param_.regularization_type(); switch (Caffe::mode()) { - case Caffe::CPU: - for (int param_id = 0; param_id < net_params.size(); ++param_id) { - // save history momentum for stepping back - caffe_copy(net_params[param_id]->count(), - this->history_[param_id]->cpu_data(), - this->update_[param_id]->mutable_cpu_data()); - - Dtype local_rate = rate * net_params_lr[param_id]; - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; - - if (local_decay) { - if (regularization_type == "L2") { - // add weight decay - caffe_axpy(net_params[param_id]->count(), - local_decay, - net_params[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } else if (regularization_type == "L1") { - caffe_cpu_sign(net_params[param_id]->count(), - net_params[param_id]->cpu_data(), - this->temp_[param_id]->mutable_cpu_data()); - caffe_axpy(net_params[param_id]->count(), - local_decay, - this->temp_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } else { - LOG(FATAL) << "Unknown regularization type: " << regularization_type; - } - } - - // update history - caffe_cpu_axpby(net_params[param_id]->count(), local_rate, - net_params[param_id]->cpu_diff(), momentum, - this->history_[param_id]->mutable_cpu_data()); - - // compute udpate: step back then over step - caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum, - this->history_[param_id]->cpu_data(), -momentum, - this->update_[param_id]->mutable_cpu_data()); - - // copy - caffe_copy(net_params[param_id]->count(), - this->update_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } + case Caffe::CPU: { + // save history momentum for stepping back + caffe_copy(net_params[param_id]->count(), + this->history_[param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + Dtype local_rate = rate * net_params_lr[param_id]; + + // update history + caffe_cpu_axpby(net_params[param_id]->count(), local_rate, + net_params[param_id]->cpu_diff(), momentum, + this->history_[param_id]->mutable_cpu_data()); + + // compute update: step back then over step + caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum, + this->history_[param_id]->cpu_data(), -momentum, + this->update_[param_id]->mutable_cpu_data()); + + // copy + caffe_copy(net_params[param_id]->count(), + this->update_[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); break; - case Caffe::GPU: + } + case Caffe::GPU: { #ifndef CPU_ONLY - for (int param_id = 0; param_id < net_params.size(); ++param_id) { - // save history momentum for stepping back - caffe_copy(net_params[param_id]->count(), - this->history_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - Dtype local_rate = rate * net_params_lr[param_id]; - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; - - if (local_decay) { - if (regularization_type == "L2") { - // add weight decay - caffe_gpu_axpy(net_params[param_id]->count(), - local_decay, - net_params[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } else if (regularization_type == "L1") { - caffe_gpu_sign(net_params[param_id]->count(), - net_params[param_id]->gpu_data(), - this->temp_[param_id]->mutable_gpu_data()); - caffe_gpu_axpy(net_params[param_id]->count(), - local_decay, - this->temp_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } else { - LOG(FATAL) << "Unknown regularization type: " << regularization_type; - } - } - - // update history - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - net_params[param_id]->gpu_diff(), momentum, - this->history_[param_id]->mutable_gpu_data()); - - // compute udpate: step back then over step - caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum, - this->history_[param_id]->gpu_data(), -momentum, - this->update_[param_id]->mutable_gpu_data()); - - // copy - caffe_copy(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } + // save history momentum for stepping back + caffe_copy(net_params[param_id]->count(), + this->history_[param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + Dtype local_rate = rate * net_params_lr[param_id]; + + // update history + caffe_gpu_axpby(net_params[param_id]->count(), local_rate, + net_params[param_id]->gpu_diff(), momentum, + this->history_[param_id]->mutable_gpu_data()); + + // compute update: step back then over step + caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum, + this->history_[param_id]->gpu_data(), -momentum, + this->update_[param_id]->mutable_gpu_data()); + + // copy + caffe_copy(net_params[param_id]->count(), + this->update_[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); #else NO_GPU; #endif break; + } default: LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); } } template -void AdaGradSolver::ComputeUpdateValue() { +void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { const vector > >& net_params = this->net_->params(); const vector& net_params_lr = this->net_->params_lr(); - const vector& net_params_weight_decay = - this->net_->params_weight_decay(); - // get the learning rate - Dtype rate = this->GetLearningRate(); Dtype delta = this->param_.delta(); - if (this->param_.display() && this->iter_ % this->param_.display() == 0) { - LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate; - } - SGDSolver::ClipGradients(); - Dtype weight_decay = this->param_.weight_decay(); - string regularization_type = this->param_.regularization_type(); switch (Caffe::mode()) { - case Caffe::CPU: - for (int param_id = 0; param_id < net_params.size(); ++param_id) { - Dtype local_rate = rate * net_params_lr[param_id]; - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; - - if (local_decay) { - if (regularization_type == "L2") { - // add weight decay - caffe_axpy(net_params[param_id]->count(), - local_decay, - net_params[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } else if (regularization_type == "L1") { - caffe_cpu_sign(net_params[param_id]->count(), - net_params[param_id]->cpu_data(), - this->temp_[param_id]->mutable_cpu_data()); - caffe_axpy(net_params[param_id]->count(), - local_decay, - this->temp_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } else { - LOG(FATAL) << "Unknown regularization type: " << regularization_type; - } - } - - // compute square of gradient in update - caffe_powx(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), Dtype(2), - this->update_[param_id]->mutable_cpu_data()); - - // update history - caffe_add(net_params[param_id]->count(), - this->update_[param_id]->cpu_data(), - this->history_[param_id]->cpu_data(), - this->history_[param_id]->mutable_cpu_data()); - - // prepare update - caffe_powx(net_params[param_id]->count(), - this->history_[param_id]->cpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_cpu_data()); - - caffe_add_scalar(net_params[param_id]->count(), - delta, this->update_[param_id]->mutable_cpu_data()); - - caffe_div(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), - this->update_[param_id]->cpu_data(), - this->update_[param_id]->mutable_cpu_data()); - - // scale and copy - caffe_cpu_axpby(net_params[param_id]->count(), local_rate, - this->update_[param_id]->cpu_data(), Dtype(0), - net_params[param_id]->mutable_cpu_diff()); - } + case Caffe::CPU: { + Dtype local_rate = rate * net_params_lr[param_id]; + + // compute square of gradient in update + caffe_powx(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), Dtype(2), + this->update_[param_id]->mutable_cpu_data()); + + // update history + caffe_add(net_params[param_id]->count(), + this->update_[param_id]->cpu_data(), + this->history_[param_id]->cpu_data(), + this->history_[param_id]->mutable_cpu_data()); + + // prepare update + caffe_powx(net_params[param_id]->count(), + this->history_[param_id]->cpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_cpu_data()); + + caffe_add_scalar(net_params[param_id]->count(), + delta, this->update_[param_id]->mutable_cpu_data()); + + caffe_div(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), + this->update_[param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + // scale and copy + caffe_cpu_axpby(net_params[param_id]->count(), local_rate, + this->update_[param_id]->cpu_data(), Dtype(0), + net_params[param_id]->mutable_cpu_diff()); break; - case Caffe::GPU: + } + case Caffe::GPU: { #ifndef CPU_ONLY - for (int param_id = 0; param_id < net_params.size(); ++param_id) { - Dtype local_rate = rate * net_params_lr[param_id]; - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; - - if (local_decay) { - if (regularization_type == "L2") { - // add weight decay - caffe_gpu_axpy(net_params[param_id]->count(), - local_decay, - net_params[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } else if (regularization_type == "L1") { - caffe_gpu_sign(net_params[param_id]->count(), - net_params[param_id]->gpu_data(), - this->temp_[param_id]->mutable_gpu_data()); - caffe_gpu_axpy(net_params[param_id]->count(), - local_decay, - this->temp_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } else { - LOG(FATAL) << "Unknown regularization type: " << regularization_type; - } - } - - // compute square of gradient in update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history - caffe_gpu_add(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), - this->history_[param_id]->gpu_data(), - this->history_[param_id]->mutable_gpu_data()); - - // prepare update - caffe_gpu_powx(net_params[param_id]->count(), - this->history_[param_id]->gpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_add_scalar(net_params[param_id]->count(), - delta, this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_div(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), - this->update_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - // scale and copy - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - this->update_[param_id]->gpu_data(), Dtype(0), - net_params[param_id]->mutable_gpu_diff()); - } + Dtype local_rate = rate * net_params_lr[param_id]; + + // compute square of gradient in update + caffe_gpu_powx(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), Dtype(2), + this->update_[param_id]->mutable_gpu_data()); + + // update history + caffe_gpu_add(net_params[param_id]->count(), + this->update_[param_id]->gpu_data(), + this->history_[param_id]->gpu_data(), + this->history_[param_id]->mutable_gpu_data()); + + // prepare update + caffe_gpu_powx(net_params[param_id]->count(), + this->history_[param_id]->gpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_add_scalar(net_params[param_id]->count(), + delta, this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_div(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), + this->update_[param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + // scale and copy + caffe_gpu_axpby(net_params[param_id]->count(), local_rate, + this->update_[param_id]->gpu_data(), Dtype(0), + net_params[param_id]->mutable_gpu_diff()); #else NO_GPU; #endif break; + } default: LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); } From a85f7f1955c434e46a39cbbc91df82601d5e9646 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 21 May 2015 16:34:43 -0700 Subject: [PATCH 080/446] deduplicate decay and local rate in solver updates --- src/caffe/solver.cpp | 23 +++++------------------ 1 file changed, 5 insertions(+), 18 deletions(-) diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 88f6d314fc7..6a0151837bb 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -475,9 +475,9 @@ void SGDSolver::Regularize(int param_id) { this->net_->params_weight_decay(); Dtype weight_decay = this->param_.weight_decay(); string regularization_type = this->param_.regularization_type(); + Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; switch (Caffe::mode()) { case Caffe::CPU: { - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; if (local_decay) { if (regularization_type == "L2") { // add weight decay @@ -501,7 +501,6 @@ void SGDSolver::Regularize(int param_id) { } case Caffe::GPU: { #ifndef CPU_ONLY - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; if (local_decay) { if (regularization_type == "L2") { // add weight decay @@ -536,15 +535,13 @@ void SGDSolver::ComputeUpdateValue(int param_id, Dtype rate) { const vector > >& net_params = this->net_->params(); const vector& net_params_lr = this->net_->params_lr(); Dtype momentum = this->param_.momentum(); + Dtype local_rate = rate * net_params_lr[param_id]; + // Compute the update to history, then copy it to the parameter diff. switch (Caffe::mode()) { case Caffe::CPU: { - // Compute the value to history, and then copy them to the blob's diff. - Dtype local_rate = rate * net_params_lr[param_id]; - caffe_cpu_axpby(net_params[param_id]->count(), local_rate, net_params[param_id]->cpu_diff(), momentum, history_[param_id]->mutable_cpu_data()); - // copy caffe_copy(net_params[param_id]->count(), history_[param_id]->cpu_data(), net_params[param_id]->mutable_cpu_diff()); @@ -552,13 +549,9 @@ void SGDSolver::ComputeUpdateValue(int param_id, Dtype rate) { } case Caffe::GPU: { #ifndef CPU_ONLY - // Compute the value to history, and then copy them to the blob's diff. - Dtype local_rate = rate * net_params_lr[param_id]; - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, net_params[param_id]->gpu_diff(), momentum, history_[param_id]->mutable_gpu_data()); - // copy caffe_copy(net_params[param_id]->count(), history_[param_id]->gpu_data(), net_params[param_id]->mutable_gpu_diff()); @@ -597,6 +590,7 @@ void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { const vector > >& net_params = this->net_->params(); const vector& net_params_lr = this->net_->params_lr(); Dtype momentum = this->param_.momentum(); + Dtype local_rate = rate * net_params_lr[param_id]; switch (Caffe::mode()) { case Caffe::CPU: { // save history momentum for stepping back @@ -604,8 +598,6 @@ void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { this->history_[param_id]->cpu_data(), this->update_[param_id]->mutable_cpu_data()); - Dtype local_rate = rate * net_params_lr[param_id]; - // update history caffe_cpu_axpby(net_params[param_id]->count(), local_rate, net_params[param_id]->cpu_diff(), momentum, @@ -629,8 +621,6 @@ void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { this->history_[param_id]->gpu_data(), this->update_[param_id]->mutable_gpu_data()); - Dtype local_rate = rate * net_params_lr[param_id]; - // update history caffe_gpu_axpby(net_params[param_id]->count(), local_rate, net_params[param_id]->gpu_diff(), momentum, @@ -660,10 +650,9 @@ void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { const vector > >& net_params = this->net_->params(); const vector& net_params_lr = this->net_->params_lr(); Dtype delta = this->param_.delta(); + Dtype local_rate = rate * net_params_lr[param_id]; switch (Caffe::mode()) { case Caffe::CPU: { - Dtype local_rate = rate * net_params_lr[param_id]; - // compute square of gradient in update caffe_powx(net_params[param_id]->count(), net_params[param_id]->cpu_diff(), Dtype(2), @@ -696,8 +685,6 @@ void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { } case Caffe::GPU: { #ifndef CPU_ONLY - Dtype local_rate = rate * net_params_lr[param_id]; - // compute square of gradient in update caffe_gpu_powx(net_params[param_id]->count(), net_params[param_id]->gpu_diff(), Dtype(2), From e1cc9d3c78fb3a7cf779abf36c15dd0d38496d46 Mon Sep 17 00:00:00 2001 From: Mohammad Norouzi Date: Wed, 27 May 2015 10:33:19 -0400 Subject: [PATCH 081/446] fix the bug with db_type when the number of features to be extracted is larger than 1 --- tools/extract_features.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp index 1ffbfbed08d..365dd495bbf 100644 --- a/tools/extract_features.cpp +++ b/tools/extract_features.cpp @@ -122,9 +122,10 @@ int feature_extraction_pipeline(int argc, char** argv) { std::vector > feature_dbs; std::vector > txns; + const char* db_type = argv[++arg_pos]; for (size_t i = 0; i < num_features; ++i) { LOG(INFO)<< "Opening dataset " << dataset_names[i]; - shared_ptr db(db::GetDB(argv[++arg_pos])); + shared_ptr db(db::GetDB(db_type)); db->Open(dataset_names.at(i), db::NEW); feature_dbs.push_back(db); shared_ptr txn(db->NewTransaction()); From 76db47e1de6159daa8e38b33d3930308e4078f66 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Wed, 27 May 2015 12:24:06 -0700 Subject: [PATCH 082/446] Solver::MakeUpdate() -> Solver::ApplyUpdate Designate `Solver::ApplyUpdate()` as the core method to compute and apply parameter updates given the current state of the Net. Make `Solver::ComputeUpdateValue()` a subordinate call overloaded by the `SGDSolver`s to take care of optimization algorithm details. --- include/caffe/solver.hpp | 8 ++++---- src/caffe/solver.cpp | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index c92067917c8..da1bab13663 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -11,7 +11,7 @@ namespace caffe { /** * @brief An interface for classes that perform optimization on Net%s. * - * Requires implementation of ComputeUpdateValue to compute a parameter update + * Requires implementation of ApplyUpdate to compute a parameter update * given the current state of the Net parameters. */ template @@ -39,8 +39,8 @@ class Solver { int iter() { return iter_; } protected: - // Get and apply the update value for the current iteration. - virtual void MakeUpdate() = 0; + // Make and apply the update value for the current iteration. + virtual void ApplyUpdate() = 0; // The Solver::Snapshot function implements the basic snapshotting utility // that stores the learned net. You should implement the SnapshotSolverState() // function that produces a SolverState protocol buffer that needs to be @@ -80,7 +80,7 @@ class SGDSolver : public Solver { protected: void PreSolve(); Dtype GetLearningRate(); - virtual void MakeUpdate(); + virtual void ApplyUpdate(); virtual void Regularize(int param_id); virtual void ComputeUpdateValue(int param_id, Dtype rate); virtual void ClipGradients(); diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 6a0151837bb..fa334edaa60 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -207,7 +207,7 @@ void Solver::Step(int iters) { } } } - MakeUpdate(); + ApplyUpdate(); // Increment the internal iter_ counter -- its value should always indicate // the number of times the weights have been updated. @@ -455,7 +455,7 @@ void SGDSolver::ClipGradients() { } template -void SGDSolver::MakeUpdate() { +void SGDSolver::ApplyUpdate() { Dtype rate = GetLearningRate(); if (this->param_.display() && this->iter_ % this->param_.display() == 0) { LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate; From 41cf06cc6e40e1b41d04b5b26e19395611bdcf5d Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Mon, 11 Aug 2014 21:38:59 -0700 Subject: [PATCH 083/446] zero-init param diffs and accumulate gradients (With layers whose backward accumulates gradients), this effectively decouples the computational batch from the SGD minibatch. Each iteration accumulates gradients over iter_size batches, then parameters are updated. --- src/caffe/proto/caffe.proto | 4 +++- src/caffe/solver.cpp | 27 ++++++++++++++++++++++++++- 2 files changed, 29 insertions(+), 2 deletions(-) diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index c471fa0a93e..94836421a42 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -96,7 +96,7 @@ message NetParameter { // NOTE // Update the next available ID when you add a new SolverParameter field. // -// SolverParameter next available ID: 36 (last added: clip_gradients) +// SolverParameter next available ID: 37 (last added: iter_size) message SolverParameter { ////////////////////////////////////////////////////////////////////////////// // Specifying the train and test networks @@ -149,6 +149,8 @@ message SolverParameter { // Display the loss averaged over the last average_loss iterations optional int32 average_loss = 33 [default = 1]; optional int32 max_iter = 7; // the maximum number of iterations + // accumulate gradients over `iter_size` x `batch_size` instances + optional int32 iter_size = 36 [default = 1]; optional string lr_policy = 8; // The learning rate decay policy. optional float gamma = 9; // The parameter to compute the learning rate. optional float power = 10; // The parameter to compute the learning rate. diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index fa334edaa60..ad041b8f268 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -168,6 +168,25 @@ void Solver::Step(int iters) { Dtype smoothed_loss = 0; while (iter_ < stop_iter) { + // zero-init the params + for (int i = 0; i < net_->params().size(); ++i) { + shared_ptr > blob = net_->params()[i]; + switch(Caffe::mode()) { + case Caffe::CPU: + caffe_set(blob->count(), static_cast(0), + blob->mutable_cpu_diff()); + break; + case Caffe::GPU: +#ifndef CPU_ONLY + caffe_gpu_set(blob->count(), static_cast(0), + blob->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + } + } + if (param_.test_interval() && iter_ % param_.test_interval() == 0 && (iter_ > 0 || param_.test_initialization())) { TestAll(); @@ -175,7 +194,13 @@ void Solver::Step(int iters) { const bool display = param_.display() && iter_ % param_.display() == 0; net_->set_debug_info(display && param_.debug_info()); - Dtype loss = net_->ForwardBackward(bottom_vec); + // accumulate the loss and gradient + Dtype loss = 0; + for (int i = 0; i < param_.iter_size(); ++i) { + loss += net_->ForwardBackward(bottom_vec); + } + loss /= param_.iter_size(); + // average the loss across iterations for smoothed reporting if (losses.size() < average_loss) { losses.push_back(loss); int size = losses.size(); From 539f8798233e25ac8110f545995f5b8f7340718f Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Tue, 30 Dec 2014 22:52:07 -0800 Subject: [PATCH 084/446] zero-init param diffs in gradient checker --- include/caffe/test/test_gradient_check_util.hpp | 7 +++++-- src/caffe/solver.cpp | 2 +- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp index 22937711b58..cc5dcbad0ee 100644 --- a/include/caffe/test/test_gradient_check_util.hpp +++ b/include/caffe/test/test_gradient_check_util.hpp @@ -80,11 +80,14 @@ void GradientChecker::CheckGradientSingle(Layer* layer, CHECK_EQ(top_count, bottom[blob_id]->count()); } } - // First, figure out what blobs we need to check against. + // First, figure out what blobs we need to check against, and zero init + // parameter blobs. vector*> blobs_to_check; vector propagate_down(bottom.size(), check_bottom < 0); for (int i = 0; i < layer->blobs().size(); ++i) { - blobs_to_check.push_back(layer->blobs()[i].get()); + Blob* blob = layer->blobs()[i].get(); + caffe_set(blob->count(), static_cast(0), blob->mutable_cpu_diff()); + blobs_to_check.push_back(blob); } if (check_bottom < 0) { for (int i = 0; i < bottom.size(); ++i) { diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index ad041b8f268..d104522002b 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -171,7 +171,7 @@ void Solver::Step(int iters) { // zero-init the params for (int i = 0; i < net_->params().size(); ++i) { shared_ptr > blob = net_->params()[i]; - switch(Caffe::mode()) { + switch (Caffe::mode()) { case Caffe::CPU: caffe_set(blob->count(), static_cast(0), blob->mutable_cpu_diff()); From 3262e464b06f1ecd8a04db1487cc5878c0cfd852 Mon Sep 17 00:00:00 2001 From: Sergio Date: Fri, 26 Sep 2014 23:03:26 -0700 Subject: [PATCH 085/446] accumulate gradients in inner product layer --- src/caffe/layers/inner_product_layer.cpp | 4 ++-- src/caffe/layers/inner_product_layer.cu | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp index 89e0c8fbad7..83c3235eb71 100644 --- a/src/caffe/layers/inner_product_layer.cpp +++ b/src/caffe/layers/inner_product_layer.cpp @@ -101,13 +101,13 @@ void InnerProductLayer::Backward_cpu(const vector*>& top, const Dtype* bottom_data = bottom[0]->cpu_data(); // Gradient with respect to weight caffe_cpu_gemm(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1., - top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff()); + top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_cpu_diff()); } if (bias_term_ && this->param_propagate_down_[1]) { const Dtype* top_diff = top[0]->cpu_diff(); // Gradient with respect to bias caffe_cpu_gemv(CblasTrans, M_, N_, (Dtype)1., top_diff, - bias_multiplier_.cpu_data(), (Dtype)0., + bias_multiplier_.cpu_data(), (Dtype)1., this->blobs_[1]->mutable_cpu_diff()); } if (propagate_down[0]) { diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu index a9e1784a205..dd90cac12a8 100644 --- a/src/caffe/layers/inner_product_layer.cu +++ b/src/caffe/layers/inner_product_layer.cu @@ -33,13 +33,13 @@ void InnerProductLayer::Backward_gpu(const vector*>& top, const Dtype* bottom_data = bottom[0]->gpu_data(); // Gradient with respect to weight caffe_gpu_gemm(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1., - top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff()); + top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_gpu_diff()); } if (bias_term_ && this->param_propagate_down_[1]) { const Dtype* top_diff = top[0]->gpu_diff(); // Gradient with respect to bias caffe_gpu_gemv(CblasTrans, M_, N_, (Dtype)1., top_diff, - bias_multiplier_.gpu_data(), (Dtype)0., + bias_multiplier_.gpu_data(), (Dtype)1., this->blobs_[1]->mutable_gpu_diff()); } if (propagate_down[0]) { From 8cc9af01941cd8c7c32c664672e5f31106d2cc40 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Tue, 30 Dec 2014 22:29:35 -0800 Subject: [PATCH 086/446] accumulate gradients in (de)conv layers --- src/caffe/layers/conv_layer.cpp | 7 ------- src/caffe/layers/conv_layer.cu | 7 ------- src/caffe/layers/deconv_layer.cpp | 7 ------- src/caffe/layers/deconv_layer.cu | 7 ------- 4 files changed, 28 deletions(-) diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp index c0c9f6f3371..928ef5ee468 100644 --- a/src/caffe/layers/conv_layer.cpp +++ b/src/caffe/layers/conv_layer.cpp @@ -39,13 +39,6 @@ void ConvolutionLayer::Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { const Dtype* weight = this->blobs_[0]->cpu_data(); Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff(); - if (this->param_propagate_down_[0]) { - caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff); - } - if (this->bias_term_ && this->param_propagate_down_[1]) { - caffe_set(this->blobs_[1]->count(), Dtype(0), - this->blobs_[1]->mutable_cpu_diff()); - } for (int i = 0; i < top.size(); ++i) { const Dtype* top_diff = top[i]->cpu_diff(); const Dtype* bottom_data = bottom[i]->cpu_data(); diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu index 3902fdf3930..b8a98ff7cc9 100644 --- a/src/caffe/layers/conv_layer.cu +++ b/src/caffe/layers/conv_layer.cu @@ -31,13 +31,6 @@ void ConvolutionLayer::Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { const Dtype* weight = this->blobs_[0]->gpu_data(); Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff(); - if (this->param_propagate_down_[0]) { - caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff); - } - if (this->bias_term_ && this->param_propagate_down_[1]) { - caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), - this->blobs_[1]->mutable_gpu_diff()); - } for (int i = 0; i < top.size(); ++i) { const Dtype* top_diff = top[i]->gpu_diff(); // Bias gradient, if necessary. diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp index e6d65ab526b..a4612963b6b 100644 --- a/src/caffe/layers/deconv_layer.cpp +++ b/src/caffe/layers/deconv_layer.cpp @@ -39,13 +39,6 @@ void DeconvolutionLayer::Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { const Dtype* weight = this->blobs_[0]->cpu_data(); Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff(); - if (this->param_propagate_down_[0]) { - caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff); - } - if (this->bias_term_ && this->param_propagate_down_[1]) { - caffe_set(this->blobs_[1]->count(), Dtype(0), - this->blobs_[1]->mutable_cpu_diff()); - } for (int i = 0; i < top.size(); ++i) { const Dtype* top_diff = top[i]->cpu_diff(); const Dtype* bottom_data = bottom[i]->cpu_data(); diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu index 9198dd64c72..39bc4de8c66 100644 --- a/src/caffe/layers/deconv_layer.cu +++ b/src/caffe/layers/deconv_layer.cu @@ -31,13 +31,6 @@ void DeconvolutionLayer::Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { const Dtype* weight = this->blobs_[0]->gpu_data(); Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff(); - if (this->param_propagate_down_[0]) { - caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff); - } - if (this->bias_term_ && this->param_propagate_down_[1]) { - caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), - this->blobs_[1]->mutable_gpu_diff()); - } for (int i = 0; i < top.size(); ++i) { const Dtype* top_diff = top[i]->gpu_diff(); const Dtype* bottom_data = bottom[i]->gpu_data(); From 67b1ff3114320188b5046ff899d4fb0f87fd7b63 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Sat, 13 Sep 2014 17:41:59 -0700 Subject: [PATCH 087/446] accumulate gradients in cudnn conv layer --- src/caffe/layers/cudnn_conv_layer.cu | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu index 4a1a4c4f4f2..b4e802e13d1 100644 --- a/src/caffe/layers/cudnn_conv_layer.cu +++ b/src/caffe/layers/cudnn_conv_layer.cu @@ -101,12 +101,10 @@ void CuDNNConvolutionLayer::Backward_gpu(const vector*>& top, if (this->param_propagate_down_[0]) { weight = this->blobs_[0]->gpu_data(); weight_diff = this->blobs_[0]->mutable_gpu_diff(); - caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff); } Dtype* bias_diff = NULL; if (this->bias_term_ && this->param_propagate_down_[1]) { bias_diff = this->blobs_[1]->mutable_gpu_diff(); - caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff); } for (int i = 0; i < top.size(); ++i) { const Dtype* top_diff = top[i]->gpu_diff(); From 55585f5bfab61328a61125b3d49627a69022d817 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 21 May 2015 17:06:42 -0700 Subject: [PATCH 088/446] adjust local learning rate and decay according to gradient accumulation Divide local rate by `iter_size` to normalize the gradient according to the full minibatch size and not only the computational batch size. Multiply the local decay by `iter_size` to counter the division of the local learning rate since the decay is multiplied by the rate in the update equation. --- src/caffe/solver.cpp | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index d104522002b..4c8fa25c955 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -488,7 +488,7 @@ void SGDSolver::ApplyUpdate() { ClipGradients(); for (int param_id = 0; param_id < this->net_->params().size(); ++param_id) { Regularize(param_id); - ComputeUpdateValue(param_id, rate); + ComputeUpdateValue(param_id, rate / this->param_.iter_size()); } this->net_->Update(); } @@ -500,7 +500,8 @@ void SGDSolver::Regularize(int param_id) { this->net_->params_weight_decay(); Dtype weight_decay = this->param_.weight_decay(); string regularization_type = this->param_.regularization_type(); - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; + Dtype local_decay = weight_decay * net_params_weight_decay[param_id] + * this->param_.iter_size(); switch (Caffe::mode()) { case Caffe::CPU: { if (local_decay) { From adf31899fde826b98d64e4c344be1924bfa52b7e Mon Sep 17 00:00:00 2001 From: Martin Thoma Date: Wed, 27 May 2015 23:20:16 +0200 Subject: [PATCH 089/446] python/draw_net.py and python/caffe/draw.py: Simplified code; added more docstrings; adjusted code according to PEP8 --- python/caffe/draw.py | 163 +++++++++++++++++++++++++++---------------- python/draw_net.py | 13 ++-- 2 files changed, 109 insertions(+), 67 deletions(-) diff --git a/python/caffe/draw.py b/python/caffe/draw.py index 08b7c1de14b..834ea15ac9a 100644 --- a/python/caffe/draw.py +++ b/python/caffe/draw.py @@ -1,13 +1,15 @@ """ Caffe network visualization: draw the NetParameter protobuffer. -NOTE: this requires pydot>=1.0.2, which is not included in requirements.txt -since it requires graphviz and other prerequisites outside the scope of the -Caffe. + +.. note:: + + This requires pydot>=1.0.2, which is not included in requirements.txt since + it requires graphviz and other prerequisites outside the scope of the + Caffe. """ from caffe.proto import caffe_pb2 -from google.protobuf import text_format import pydot # Internal layer and blob styles. @@ -32,15 +34,15 @@ def get_pooling_types_dict(): return d -def determine_edge_label_by_layertype(layer, layertype): - """Define edge label based on layer type +def get_edge_label(layer): + """Define edge label based on layer type. """ - if layertype == 'Data': + if layer.type == 'Data': edge_label = 'Batch ' + str(layer.data_param.batch_size) - elif layertype == 'Convolution': + elif layer.type == 'Convolution': edge_label = str(layer.convolution_param.num_output) - elif layertype == 'InnerProduct': + elif layer.type == 'InnerProduct': edge_label = str(layer.inner_product_param.num_output) else: edge_label = '""' @@ -48,8 +50,19 @@ def determine_edge_label_by_layertype(layer, layertype): return edge_label -def determine_node_label_by_layertype(layer, layertype, rankdir): - """Define node label based on layer type +def get_layer_label(layer, rankdir): + """Define node label based on layer type. + + Parameters + ---------- + layer : ? + rankdir : {'LR', 'TB', 'BT'} + Direction of graph layout. + + Returns + ------- + string : + A label for the current layer """ if rankdir in ('TB', 'BT'): @@ -61,26 +74,26 @@ def determine_node_label_by_layertype(layer, layertype, rankdir): # horizontal space is not; separate words with newlines separator = '\n' - if layertype == 'Convolution': + if layer.type == 'Convolution': # Outer double quotes needed or else colon characters don't parse # properly node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\ (layer.name, separator, - layertype, + layer.type, separator, layer.convolution_param.kernel_size, separator, layer.convolution_param.stride, separator, layer.convolution_param.pad) - elif layertype == 'Pooling': + elif layer.type == 'Pooling': pooling_types_dict = get_pooling_types_dict() node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\ (layer.name, separator, pooling_types_dict[layer.pooling_param.pool], - layertype, + layer.type, separator, layer.pooling_param.kernel_size, separator, @@ -88,12 +101,12 @@ def determine_node_label_by_layertype(layer, layertype, rankdir): separator, layer.pooling_param.pad) else: - node_label = '"%s%s(%s)"' % (layer.name, separator, layertype) + node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type) return node_label def choose_color_by_layertype(layertype): - """Define colors for nodes based on the layer type + """Define colors for nodes based on the layer type. """ color = '#6495ED' # Default if layertype == 'Convolution': @@ -106,48 +119,62 @@ def choose_color_by_layertype(layertype): def get_pydot_graph(caffe_net, rankdir, label_edges=True): - pydot_graph = pydot.Dot(caffe_net.name, graph_type='digraph', rankdir=rankdir) - pydot_nodes = {} - pydot_edges = [] - for layer in caffe_net.layer: - name = layer.name - layertype = layer.type - node_label = determine_node_label_by_layertype(layer, layertype, rankdir) - if (len(layer.bottom) == 1 and len(layer.top) == 1 and - layer.bottom[0] == layer.top[0]): - # We have an in-place neuron layer. - pydot_nodes[name + '_' + layertype] = pydot.Node( - node_label, **NEURON_LAYER_STYLE) - else: - layer_style = LAYER_STYLE_DEFAULT - layer_style['fillcolor'] = choose_color_by_layertype(layertype) - pydot_nodes[name + '_' + layertype] = pydot.Node( - node_label, **layer_style) - for bottom_blob in layer.bottom: - pydot_nodes[bottom_blob + '_blob'] = pydot.Node( - '%s' % (bottom_blob), **BLOB_STYLE) - edge_label = '""' - pydot_edges.append({'src': bottom_blob + '_blob', - 'dst': name + '_' + layertype, - 'label': edge_label}) - for top_blob in layer.top: - pydot_nodes[top_blob + '_blob'] = pydot.Node( - '%s' % (top_blob)) - if label_edges: - edge_label = determine_edge_label_by_layertype(layer, layertype) - else: - edge_label = '""' - pydot_edges.append({'src': name + '_' + layertype, - 'dst': top_blob + '_blob', - 'label': edge_label}) - # Now, add the nodes and edges to the graph. - for node in pydot_nodes.values(): - pydot_graph.add_node(node) - for edge in pydot_edges: - pydot_graph.add_edge( - pydot.Edge(pydot_nodes[edge['src']], pydot_nodes[edge['dst']], - label=edge['label'])) - return pydot_graph + """Create a data structure which represents the `caffe_net`. + + Parameters + ---------- + caffe_net : object + rankdir : {'LR', 'TB', 'BT'} + Direction of graph layout. + label_edges : boolean, optional + Label the edges (default is True). + + Returns + ------- + pydot graph object + """ + pydot_graph = pydot.Dot(caffe_net.name, + graph_type='digraph', + rankdir=rankdir) + pydot_nodes = {} + pydot_edges = [] + for layer in caffe_net.layer: + node_label = get_layer_label(layer, rankdir) + node_name = "%s_%s" % (layer.name, layer.type) + if (len(layer.bottom) == 1 and len(layer.top) == 1 and + layer.bottom[0] == layer.top[0]): + # We have an in-place neuron layer. + pydot_nodes[node_name] = pydot.Node(node_label, + **NEURON_LAYER_STYLE) + else: + layer_style = LAYER_STYLE_DEFAULT + layer_style['fillcolor'] = choose_color_by_layertype(layer.type) + pydot_nodes[node_name] = pydot.Node(node_label, **layer_style) + for bottom_blob in layer.bottom: + pydot_nodes[bottom_blob + '_blob'] = pydot.Node('%s' % bottom_blob, + **BLOB_STYLE) + edge_label = '""' + pydot_edges.append({'src': bottom_blob + '_blob', + 'dst': node_name, + 'label': edge_label}) + for top_blob in layer.top: + pydot_nodes[top_blob + '_blob'] = pydot.Node('%s' % (top_blob)) + if label_edges: + edge_label = get_edge_label(layer) + else: + edge_label = '""' + pydot_edges.append({'src': node_name, + 'dst': top_blob + '_blob', + 'label': edge_label}) + # Now, add the nodes and edges to the graph. + for node in pydot_nodes.values(): + pydot_graph.add_node(node) + for edge in pydot_edges: + pydot_graph.add_edge( + pydot.Edge(pydot_nodes[edge['src']], + pydot_nodes[edge['dst']], + label=edge['label'])) + return pydot_graph def draw_net(caffe_net, rankdir, ext='png'): @@ -156,8 +183,14 @@ def draw_net(caffe_net, rankdir, ext='png'): Parameters ---------- - caffe_net: a caffe.proto.caffe_pb2.NetParameter protocol buffer. - ext: the image extension. Default 'png'. + caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer. + ext : string, optional + The image extension (the default is 'png'). + + Returns + ------- + string : + Postscript representation of the graph. """ return get_pydot_graph(caffe_net, rankdir).create(format=ext) @@ -166,6 +199,14 @@ def draw_net_to_file(caffe_net, filename, rankdir='LR'): """Draws a caffe net, and saves it to file using the format given as the file extension. Use '.raw' to output raw text that you can manually feed to graphviz to draw graphs. + + Parameters + ---------- + caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer. + filename : string + The path to a file where the networks visualization will be stored. + rankdir : {'LR', 'TB', 'BT'} + Direction of graph layout. """ ext = filename[filename.rfind('.')+1:] with open(filename, 'wb') as fid: diff --git a/python/draw_net.py b/python/draw_net.py index 6320f775ef7..ec76a744da3 100755 --- a/python/draw_net.py +++ b/python/draw_net.py @@ -2,7 +2,7 @@ """ Draw a graph of the net architecture. """ -import argparse +from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter from google.protobuf import text_format import caffe @@ -14,7 +14,8 @@ def parse_args(): """Parse input arguments """ - parser = argparse.ArgumentParser(description='Draw a network graph') + parser = ArgumentParser(description=__doc__, + formatter_class=ArgumentDefaultsHelpFormatter) parser.add_argument('input_net_proto_file', help='Input network prototxt file') @@ -22,10 +23,10 @@ def parse_args(): help='Output image file') parser.add_argument('--rankdir', help=('One of TB (top-bottom, i.e., vertical), ' - 'RL (right-left, i.e., horizontal), or another' - 'valid dot option; see' - 'http://www.graphviz.org/doc/info/attrs.html#k:rankdir' - '(default: LR)'), + 'RL (right-left, i.e., horizontal), or another ' + 'valid dot option; see ' + 'http://www.graphviz.org/doc/info/' + 'attrs.html#k:rankdir'), default='LR') args = parser.parse_args() From 92ab737adad6d686ac75cdf934472f6a97b52fe7 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 21 May 2015 18:14:16 -0700 Subject: [PATCH 090/446] test equivalence of solving with accumulating gradients Compare the parameters after solving with a given batch size and the halved batch size + two iter accumulation of gradients equivalent. Note: the test net dummy data layer now makes constant data and random gaussian targets. This assures the standard and gradient accumulation cases check the same data. Otherwise the difference in batch sizes causes different orders of random number draws. --- src/caffe/test/test_gradient_based_solver.cpp | 82 ++++++++++++++++++- 1 file changed, 79 insertions(+), 3 deletions(-) diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index eb2569c04f2..c9135d64e70 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -23,7 +23,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { protected: GradientBasedSolverTest() : - seed_(1701), num_(5), channels_(3), height_(10), width_(10) {} + seed_(1701), num_(4), channels_(3), height_(10), width_(10) {} shared_ptr > solver_; int seed_; @@ -56,19 +56,21 @@ class GradientBasedSolverTest : public MultiDeviceTest { } void RunLeastSquaresSolver(const Dtype learning_rate, - const Dtype weight_decay, const Dtype momentum, const int num_iters) { + const Dtype weight_decay, const Dtype momentum, const int num_iters, + const int iter_size = 1) { ostringstream proto; proto << "max_iter: " << num_iters << " " "base_lr: " << learning_rate << " " "lr_policy: 'fixed' " + "iter_size: " << iter_size << " " "net_param { " " name: 'TestNetwork' " " layer { " " name: 'data' " " type: 'DummyData' " " dummy_data_param { " - " num: " << num_ << " " + " num: " << num_ / iter_size << " " " channels: " << channels_ << " " " height: " << height_ << " " " width: " << width_ << " " @@ -76,6 +78,10 @@ class GradientBasedSolverTest : public MultiDeviceTest { " height: 1 " " width: 1 " " data_filler { " + " type: 'constant' " + " value: 1.0 " + " } " + " data_filler { " " type: 'gaussian' " " std: 1.0 " " } " @@ -270,6 +276,45 @@ class GradientBasedSolverTest : public MultiDeviceTest { } } + void CheckAccumulation(const Dtype kLearningRate, const Dtype kWeightDecay, + const Dtype kMomentum, const int kNumIters, const int kIterSize) { + const double kPrecision = 1e-2; + const double kMinPrecision = 1e-7; + // Solve without accumulation and save parameters. + this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum, + kNumIters); + // Save parameters for comparison. + Net& net = *this->solver_->net(); + const vector > >& param_blobs = + net.layer_by_name("innerprod")->blobs(); + vector > > noaccum_params(param_blobs.size()); + for (int i = 0; i < param_blobs.size(); ++i) { + noaccum_params[i].reset(new Blob()); + noaccum_params[i]->CopyFrom(*param_blobs[i], false, true); + } + // Solve by equivalent accumulation of gradients over divided batches. + this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum, + kNumIters, kIterSize); + Net& net_accum = *this->solver_->net(); + const vector > >& accum_params = + net_accum.layer_by_name("innerprod")->blobs(); + // Compare accumulated parameters against no accumulation standard. + const int D = this->channels_ * this->height_ * this->width_; + for (int i = 0; i < D; ++i) { + const Dtype expected_param = noaccum_params[0]->cpu_data()[i]; + const Dtype accum_param = accum_params[0]->cpu_data()[i]; + const Dtype error_margin = std::max(kMinPrecision, kPrecision * + std::min(fabs(expected_param), fabs(accum_param))); + EXPECT_NEAR(expected_param, accum_param, error_margin); + } + ASSERT_EQ(1, accum_params[1]->count()); + const Dtype expected_bias = noaccum_params[1]->cpu_data()[0]; + const Dtype accum_bias = accum_params[1]->cpu_data()[0]; + const Dtype error_margin = std::max(kMinPrecision, kPrecision * + std::min(fabs(expected_bias), fabs(accum_bias))); + EXPECT_NEAR(expected_bias, accum_bias, error_margin); + } + // Test that the correct update is computed for a regularized least squares // problem: // @@ -372,6 +417,16 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) { } } +TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + const int kIterSize = 2; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); +} template class AdaGradSolverTest : public GradientBasedSolverTest { @@ -416,6 +471,16 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) { } } +TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.0; + const int kNumIters = 4; + const int kIterSize = 2; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); +} template class NesterovSolverTest : public GradientBasedSolverTest { @@ -482,4 +547,15 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) { } } +TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + const int kIterSize = 2; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); +} + } // namespace caffe From 0e7a0785db224aa7cf2bd925d8b7910bdc3f7a98 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 28 May 2015 12:43:29 -0700 Subject: [PATCH 091/446] directly normalize accumulated gradients `SGDSolver::Normalize()` normalizes accumulated gradients by scaling inversely to the accumulation as `1 / iter_size`. This fixes accumulation for AdaGrad and is more obvious than fooling with rates and decays in 55585f5. --- include/caffe/solver.hpp | 1 + src/caffe/solver.cpp | 32 +++++++++++++++++++++++++++++--- 2 files changed, 30 insertions(+), 3 deletions(-) diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index da1bab13663..c2ced487d6f 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -81,6 +81,7 @@ class SGDSolver : public Solver { void PreSolve(); Dtype GetLearningRate(); virtual void ApplyUpdate(); + virtual void Normalize(int param_id); virtual void Regularize(int param_id); virtual void ComputeUpdateValue(int param_id, Dtype rate); virtual void ClipGradients(); diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 4c8fa25c955..aabe0edec80 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -487,12 +487,39 @@ void SGDSolver::ApplyUpdate() { } ClipGradients(); for (int param_id = 0; param_id < this->net_->params().size(); ++param_id) { + Normalize(param_id); Regularize(param_id); - ComputeUpdateValue(param_id, rate / this->param_.iter_size()); + ComputeUpdateValue(param_id, rate); } this->net_->Update(); } +template +void SGDSolver::Normalize(int param_id) { + if (this->param_.iter_size() == 1) { return; } + // Scale gradient to counterbalance accumulation. + const vector > >& net_params = this->net_->params(); + const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size(); + switch (Caffe::mode()) { + case Caffe::CPU: { + caffe_scal(net_params[param_id]->count(), accum_normalization, + net_params[param_id]->mutable_cpu_diff()); + break; + } + case Caffe::GPU: { +#ifndef CPU_ONLY + caffe_gpu_scal(net_params[param_id]->count(), accum_normalization, + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + } + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + template void SGDSolver::Regularize(int param_id) { const vector > >& net_params = this->net_->params(); @@ -500,8 +527,7 @@ void SGDSolver::Regularize(int param_id) { this->net_->params_weight_decay(); Dtype weight_decay = this->param_.weight_decay(); string regularization_type = this->param_.regularization_type(); - Dtype local_decay = weight_decay * net_params_weight_decay[param_id] - * this->param_.iter_size(); + Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; switch (Caffe::mode()) { case Caffe::CPU: { if (local_decay) { From ee0c93137472981554ad30a766a1542cf155bf7b Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Fri, 22 May 2015 01:45:14 +0800 Subject: [PATCH 092/446] MatCaffe3 : a powerful matlab interface for caffe Added matcaffe3, a powerful matlab interface. To test it, run 'make mattest' --- Makefile | 22 +- matlab/+caffe/+test/test_net.m | 74 +++ matlab/+caffe/+test/test_solver.m | 43 ++ matlab/+caffe/Blob.m | 71 +++ matlab/+caffe/Layer.m | 32 ++ matlab/+caffe/Net.m | 133 ++++++ matlab/+caffe/Solver.m | 56 +++ matlab/+caffe/get_net.m | 37 ++ matlab/+caffe/get_solver.m | 11 + matlab/+caffe/io.m | 22 + matlab/+caffe/private/CHECK.m | 7 + matlab/+caffe/private/CHECK_FILE_EXIST.m | 7 + matlab/+caffe/private/caffe_.cpp | 546 +++++++++++++++++++++++ matlab/+caffe/private/is_valid_handle.m | 28 ++ matlab/+caffe/reset.m | 8 + matlab/+caffe/run_tests.m | 12 + matlab/+caffe/set_device.m | 11 + matlab/+caffe/set_mode_cpu.m | 7 + matlab/+caffe/set_mode_gpu.m | 7 + matlab/CMakeLists.txt | 8 + 20 files changed, 1141 insertions(+), 1 deletion(-) create mode 100644 matlab/+caffe/+test/test_net.m create mode 100644 matlab/+caffe/+test/test_solver.m create mode 100644 matlab/+caffe/Blob.m create mode 100644 matlab/+caffe/Layer.m create mode 100644 matlab/+caffe/Net.m create mode 100644 matlab/+caffe/Solver.m create mode 100644 matlab/+caffe/get_net.m create mode 100644 matlab/+caffe/get_solver.m create mode 100644 matlab/+caffe/io.m create mode 100644 matlab/+caffe/private/CHECK.m create mode 100644 matlab/+caffe/private/CHECK_FILE_EXIST.m create mode 100644 matlab/+caffe/private/caffe_.cpp create mode 100644 matlab/+caffe/private/is_valid_handle.m create mode 100644 matlab/+caffe/reset.m create mode 100644 matlab/+caffe/run_tests.m create mode 100644 matlab/+caffe/set_device.m create mode 100644 matlab/+caffe/set_mode_cpu.m create mode 100644 matlab/+caffe/set_mode_gpu.m diff --git a/Makefile b/Makefile index d2e5e5720ed..74b167763c3 100644 --- a/Makefile +++ b/Makefile @@ -66,6 +66,7 @@ NONGEN_CXX_SRCS := $(shell find \ include/$(PROJECT) \ python/$(PROJECT) \ matlab/$(PROJECT) \ + matlab/+$(PROJECT)/private \ examples \ tools \ -name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh") @@ -81,10 +82,13 @@ PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp # MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT) MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp +# MAT$(PROJECT)_PKG_SRC is the mex entrance point of matlab package for $(PROJECT) +MAT$(PROJECT)_PKG_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp ifneq ($(MATLAB_DIR),) MAT_SO_EXT := $(shell $(MATLAB_DIR)/bin/mexext) endif MAT$(PROJECT)_SO := matlab/$(PROJECT)/$(PROJECT).$(MAT_SO_EXT) +MAT$(PROJECT)_PKG_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT) ############################## # Derive generated files @@ -447,7 +451,7 @@ $(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME) mat$(PROJECT): mat -mat: $(MAT$(PROJECT)_SO) +mat: $(MAT$(PROJECT)_SO) $(MAT$(PROJECT)_PKG_SO) $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME) @ if [ -z "$(MATLAB_DIR)" ]; then \ @@ -460,6 +464,18 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME) CXX="$(CXX)" \ CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \ CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@ + +$(MAT$(PROJECT)_PKG_SO): $(MAT$(PROJECT)_PKG_SRC) $(STATIC_NAME) + @ if [ -z "$(MATLAB_DIR)" ]; then \ + echo "MATLAB_DIR must be specified in $(CONFIG_FILE)" \ + "to build mat$(PROJECT)."; \ + exit 1; \ + fi + @ echo MEX $< + $(Q)$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_PKG_SRC) \ + CXX="$(CXX)" \ + CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \ + CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@ runtest: $(TEST_ALL_BIN) $(TOOL_BUILD_DIR)/caffe @@ -467,6 +483,9 @@ runtest: $(TEST_ALL_BIN) pytest: py cd python; python -m unittest discover -s caffe/test + +mattest: mat + cd matlab; $(MATLAB_DIR)/bin/matlab -nodisplay -r 'caffe.run_tests(), exit()' warn: $(EMPTY_WARN_REPORT) @@ -582,6 +601,7 @@ clean: @- $(RM) -rf $(DISTRIBUTE_DIR) @- $(RM) $(PY$(PROJECT)_SO) @- $(RM) $(MAT$(PROJECT)_SO) + @- $(RM) $(MAT$(PROJECT)_PKG_SO) supercleanfiles: $(eval SUPERCLEAN_FILES := $(strip \ diff --git a/matlab/+caffe/+test/test_net.m b/matlab/+caffe/+test/test_net.m new file mode 100644 index 00000000000..4958d50f9e3 --- /dev/null +++ b/matlab/+caffe/+test/test_net.m @@ -0,0 +1,74 @@ +classdef test_net < matlab.unittest.TestCase + + properties + num_output + model_file + net + end + + methods (Static) + function model_file = simple_net_file(num_output) + model_file = tempname(); + fid = fopen(model_file, 'w'); + fprintf(fid, [ ... + 'name: "testnet" force_backward: true\n' ... + 'layer { type: "DummyData" name: "data" top: "data" top: "label"\n' ... + 'dummy_data_param { num: 5 channels: 2 height: 3 width: 4\n' ... + ' num: 5 channels: 1 height: 1 width: 1\n' ... + ' data_filler { type: "gaussian" std: 1 }\n' ... + ' data_filler { type: "constant" } } }\n' ... + 'layer { type: "Convolution" name: "conv" bottom: "data" top: "conv"\n' ... + ' convolution_param { num_output: 11 kernel_size: 2 pad: 3\n' ... + ' weight_filler { type: "gaussian" std: 1 }\n' ... + ' bias_filler { type: "constant" value: 2 } }\n' ... + ' param { decay_mult: 1 } param { decay_mult: 0 }\n' ... + ' }\n' ... + 'layer { type: "InnerProduct" name: "ip" bottom: "conv" top: "ip"\n' ... + ' inner_product_param { num_output: ' num2str(num_output) ... + ' weight_filler { type: "gaussian" std: 2.5 }\n' ... + ' bias_filler { type: "constant" value: -3 } } }\n' ... + 'layer { type: "SoftmaxWithLoss" name: "loss" bottom: "ip" bottom: "label"\n' ... + ' top: "loss" }' ]); + fclose(fid); + end + end + methods + function self = test_net() + self.num_output = 13; + self.model_file = caffe.test.test_net.simple_net_file(self.num_output); + self.net = caffe.Net(self.model_file, 'train'); + % also make sure get_solver runs + caffe.get_net(self.model_file, 'train'); + + % fill in valid labels + self.net.blobs('label').set_data(randi( ... + self.num_output - 1, self.net.blobs('label').shape)); + + delete(self.model_file); + end + end + methods (Test) + function test_forward_backward(self) + self.net.forward_prefilled(); + self.net.backward_prefilled(); + end + function test_inputs_outputs(self) + self.verifyEqual(self.net.inputs, cell(0, 1)) + self.verifyEqual(self.net.outputs, {'loss'}); + end + function test_save_and_read(self) + weights_file = tempname(); + self.net.save(weights_file); + model_file2 = caffe.test.test_net.simple_net_file(self.num_output); + net2 = caffe.Net(model_file2, weights_file, 'train'); + delete(model_file2); + delete(weights_file); + for l = 1:length(self.net.layer_vec) + for i = 1:length(self.net.layer_vec(l).params) + self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ... + net2.layer_vec(l).params(i).get_data()); + end + end + end + end +end \ No newline at end of file diff --git a/matlab/+caffe/+test/test_solver.m b/matlab/+caffe/+test/test_solver.m new file mode 100644 index 00000000000..9774ec1d2fd --- /dev/null +++ b/matlab/+caffe/+test/test_solver.m @@ -0,0 +1,43 @@ +classdef test_solver < matlab.unittest.TestCase + + properties + num_output + solver + end + + methods + function self = test_solver() + self.num_output = 13; + model_file = caffe.test.test_net.simple_net_file(self.num_output); + solver_file = tempname(); + + fid = fopen(solver_file, 'w'); + fprintf(fid, [ ... + 'net: "' model_file '"\n' ... + 'test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9\n' ... + 'weight_decay: 0.0005 lr_policy: "inv" gamma: 0.0001 power: 0.75\n' ... + 'display: 100 max_iter: 100 snapshot_after_train: false\n' ]); + fclose(fid); + + self.solver = caffe.Solver(solver_file); + % also make sure get_solver runs + caffe.get_solver(solver_file); + caffe.set_mode_cpu(); + % fill in valid labels + self.solver.net.blobs('label').set_data(randi( ... + self.num_output - 1, self.solver.net.blobs('label').shape)); + self.solver.test_nets(1).blobs('label').set_data(randi( ... + self.num_output - 1, self.solver.test_nets(1).blobs('label').shape)); + + delete(solver_file); + delete(model_file); + end + end + methods (Test) + function test_solve(self) + self.verifyEqual(self.solver.iter(), 0) + self.solver.solve() + self.verifyEqual(self.solver.iter(), 100) + end + end +end \ No newline at end of file diff --git a/matlab/+caffe/Blob.m b/matlab/+caffe/Blob.m new file mode 100644 index 00000000000..a6326d1a36c --- /dev/null +++ b/matlab/+caffe/Blob.m @@ -0,0 +1,71 @@ +classdef Blob < handle + % Wrapper class of caffe::Blob in matlab + + properties (Access = private) + hBlob_self + end + + methods + function self = Blob(hBlob_blob) + CHECK(is_valid_handle(hBlob_blob), 'invalid input handle'); + + % setup self handle and attributes + self.hBlob_self = hBlob_blob; + end + function shape = shape(self) + shape = caffe_('blob_get_shape', self.hBlob_self); + end + function reshape(self, shape) + shape = self.check_and_preprocess_shape(shape); + caffe_('blob_reshape', self.hBlob_self, shape); + end + function data = get_data(self) + data = caffe_('blob_get_data', self.hBlob_self); + end + function set_data(self, data) + data = self.check_and_preprocess_data(data); + caffe_('blob_set_data', self.hBlob_self, data); + end + function diff = get_diff(self) + diff = caffe_('blob_get_diff', self.hBlob_self); + end + function set_diff(self, diff) + diff = self.check_and_preprocess_data(diff); + caffe_('blob_set_diff', self.hBlob_self, diff); + end + end + + methods (Access = private) + function shape = check_and_preprocess_shape(~, shape) + CHECK(isempty(shape) || isnumeric(shape) && isrow(shape), ... + 'shape must be a integer row vector'); + shape = double(shape); + end + function data = check_and_preprocess_data(self, data) + CHECK(isnumeric(data), 'data or diff must be numeric types'); + self.check_data_size_matches(data) + data = single(data); + end + function check_data_size_matches(self, data) + % check whether size of data matches shape of this blob + % note: matlab arrays always have at least 2 dimensions. To compare + % shape between size of data and shape of this blob, extend shape of + % this blob to have at least 2 dimensions + data_size = size(data); + self_shape_extended = self.shape; + if isempty(self_shape_extended) + % target blob is a scalar (0 dim) + self_shape_extended = [1, 1]; + elseif isscalar(self_shape_extended) + % target blob is a vector (1 dim) + self_shape_extended = [self_shape_extended, 1]; + end + is_matched = (length(self_shape_extended) == length(data_size)) ... + && all(self_shape_extended == data_size); + CHECK(is_matched, ... + sprintf('%s, data size: [ %s], blob shape: [ %s]', ... + 'data size does not match blob shape', ... + sprintf('%d ', data_size), sprintf('%d ', self_shape_extended))); + end + end +end \ No newline at end of file diff --git a/matlab/+caffe/Layer.m b/matlab/+caffe/Layer.m new file mode 100644 index 00000000000..7587ed7ddf7 --- /dev/null +++ b/matlab/+caffe/Layer.m @@ -0,0 +1,32 @@ +classdef Layer < handle + % Wrapper class of caffe::Layer in matlab + + properties (Access = private) + hLayer_self + attributes + % attributes fields: + % hBlob_blobs + end + properties (SetAccess = private) + params + end + + methods + function self = Layer(hLayer_layer) + CHECK(is_valid_handle(hLayer_layer), 'invalid input handle'); + + % setup self handle and attributes + self.hLayer_self = hLayer_layer; + self.attributes = caffe_('layer_get_attr', self.hLayer_self); + + % setup weights + self.params = caffe.Blob.empty(); + for n = 1:length(self.attributes.hBlob_blobs) + self.params(n) = caffe.Blob(self.attributes.hBlob_blobs(n)); + end + end + function layer_type = type(self) + layer_type = caffe_('layer_get_type', self.hLayer_self); + end + end +end diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m new file mode 100644 index 00000000000..5319634dc8b --- /dev/null +++ b/matlab/+caffe/Net.m @@ -0,0 +1,133 @@ +classdef Net < handle + % Wrapper class of caffe::Net in matlab + + properties (Access = private) + hNet_self + attributes + % attribute fields + % hLayer_layers + % hBlob_blobs + % input_blob_indices + % output_blob_indices + % layer_names + % blob_names + end + properties (SetAccess = private) + layer_vec + blob_vec + inputs + outputs + name2layer_index + name2blob_index + layer_names + blob_names + end + + methods + function self = Net(varargin) + % decide whether to construct a net from model_file or handle + if ~(nargin == 1 && isstruct(varargin{1})) + % construct a net from model_file + self = caffe.get_net(varargin{:}); + return + end + % construct a net from handle + hNet_net = varargin{1}; + CHECK(is_valid_handle(hNet_net), 'invalid input handle'); + + % setup self handle and attributes + self.hNet_self = hNet_net; + self.attributes = caffe_('net_get_attr', self.hNet_self); + + % setup layer_vec + self.layer_vec = caffe.Layer.empty(); + for n = 1:length(self.attributes.hLayer_layers) + self.layer_vec(n) = caffe.Layer(self.attributes.hLayer_layers(n)); + end + + % setup blob_vec + self.blob_vec = caffe.Blob.empty(); + for n = 1:length(self.attributes.hBlob_blobs); + self.blob_vec(n) = caffe.Blob(self.attributes.hBlob_blobs(n)); + end + + % setup input and output blob and their names + % note: add 1 to indices as matlab is 1-indexed while C++ is 0-indexed + self.inputs = ... + self.attributes.blob_names(self.attributes.input_blob_indices + 1); + self.outputs = ... + self.attributes.blob_names(self.attributes.output_blob_indices + 1); + + % create map objects to map from name to layers and blobs + self.name2layer_index = containers.Map(self.attributes.layer_names, ... + 1:length(self.attributes.layer_names)); + self.name2blob_index = containers.Map(self.attributes.blob_names, ... + 1:length(self.attributes.blob_names)); + + % expose layer_names and blob_names for public access + self.layer_names = self.attributes.layer_names; + self.blob_names = self.attributes.blob_names; + end + function layer = layers(self, layer_name) + CHECK(ischar(layer_name), 'layer_name must be a string'); + layer = self.layer_vec(self.name2layer_index(layer_name)); + end + function blob = blobs(self, blob_name) + CHECK(ischar(blob_name), 'blob_name must be a string'); + blob = self.blob_vec(self.name2blob_index(blob_name)); + end + function blob = params(self, layer_name, blob_index) + CHECK(ischar(layer_name), 'layer_name must be a string'); + CHECK(isscalar(blob_index), 'blob_index must be a scalar'); + blob = self.layer_vec(self.name2layer_index(layer_name)).params(blob_index); + end + function forward_prefilled(self) + caffe_('net_forward', self.hNet_self); + end + function backward_prefilled(self) + caffe_('net_backward', self.hNet_self); + end + function res = forward(self, input_data) + CHECK(iscell(input_data), 'input_data must be a cell array'); + CHECK(length(input_data) == length(self.inputs), ... + 'input data cell length must match input blob number'); + % copy data to input_blobs + for n = 1:length(self.inputs) + self.blobs(self.inputs{n}).set_data(input_data{n}); + end + self.forward_prefilled(); + % retrieve data from output_blobs + res = cell(length(self.outputs), 1); + for n = 1:length(self.outputs) + res{n} = self.blobs(self.outputs{n}).get_data(); + end + end + function res = backward(self, output_diff) + CHECK(iscell(output_diff), 'output_diff must be a cell array'); + CHECK(length(output_diff) == length(self.outputs), ... + 'output diff cell length must match output blob number'); + % copy diff to output_blobs + for n = 1:length(self.outputs) + self.blobs(self.outputs{n}).set_diff(output_diff{n}); + end + self.backward_prefilled(); + % retrieve diff from input_blobs + res = cell(length(self.inputs), 1); + for n = 1:length(self.inputs) + res{n} = self.blobs(self.inputs{n}).get_diff(); + end + end + function copy_from(self, weights_file) + CHECK(ischar(weights_file), 'weights_file must be a string'); + CHECK_FILE_EXIST(weights_file); + caffe_('net_copy_from', self.hNet_self, weights_file); + end + function reshape(self) + caffe_('net_reshape', self.hNet_self); + end + function save(self, weights_file) + CHECK(ischar(weights_file), 'weights_file must be a string'); + caffe_('net_save', self.hNet_self, weights_file); + end + end +end diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m new file mode 100644 index 00000000000..80fa5394739 --- /dev/null +++ b/matlab/+caffe/Solver.m @@ -0,0 +1,56 @@ +classdef Solver < handle + % Wrapper class of caffe::SGDSolver in matlab + + properties (Access = private) + hSolver_self + attributes + % attribute fields + % hNet_net + % hNet_test_nets + end + properties (SetAccess = private) + net + test_nets + end + + methods + function self = Solver(varargin) + % decide whether to construct a solver from solver_file or handle + if ~(nargin == 1 && isstruct(varargin{1})) + % construct a solver from solver_file + self = caffe.get_solver(varargin{:}); + return + end + % construct a solver from handle + hSolver_solver = varargin{1}; + CHECK(is_valid_handle(hSolver_solver), 'invalid input handle'); + + % setup self handle and attributes + self.hSolver_self = hSolver_solver; + self.attributes = caffe_('solver_get_attr', self.hSolver_self); + + % setup net and test_nets + self.net = caffe.Net(self.attributes.hNet_net); + self.test_nets = caffe.Net.empty(); + for n = 1:length(self.attributes.hNet_test_nets) + self.test_nets(n) = caffe.Net(self.attributes.hNet_test_nets(n)); + end + end + function iter = iter(self) + iter = caffe_('solver_get_iter', self.hSolver_self); + end + function restore(self, snapshot_filename) + CHECK(ischar(snapshot_filename), 'snapshot_filename must be a string'); + CHECK_FILE_EXIST(snapshot_filename) + caffe_('solver_restore', self.hSolver_self, snapshot_filename); + end + function solve(self) + caffe_('solver_solve', self.hSolver_self); + end + function step(self, iters) + CHECK(isscalar(iters) && iters > 0, 'iters must be positive integer'); + iters = double(iters); + caffe_('solver_step', self.hSolver_self, iters); + end + end +end diff --git a/matlab/+caffe/get_net.m b/matlab/+caffe/get_net.m new file mode 100644 index 00000000000..04872b263c8 --- /dev/null +++ b/matlab/+caffe/get_net.m @@ -0,0 +1,37 @@ +function net = get_net(varargin) +% net = get_net(model_file, phase_name) or +% net = get_net(model_file, weights_file, phase_name) +% Construct a net from model_file, and load weights from weights_file +% phase_name can only be 'train' or 'test' + +CHECK(nargin == 2 || nargin == 3, ['usage: ' ... + 'net = get_net(model_file, phase_name) or ', ... + 'net = get_net(model_file, weights_file, phase_name)']); +if nargin == 3 + model_file = varargin{1}; + weights_file = varargin{2}; + phase_name = varargin{3}; +elseif nargin == 2 + model_file = varargin{1}; + phase_name = varargin{2}; +end + +CHECK(ischar(model_file), 'model_file must be a string'); +CHECK(ischar(phase_name), 'phase_name must be a string'); +CHECK_FILE_EXIST(model_file); +CHECK(strcmp(phase_name, 'train') || strcmp(phase_name, 'test'), ... + sprintf('phase_name can only be %strain%s or %stest%s', ... + char(39), char(39), char(39), char(39))); + +hNet = caffe_('get_net', model_file, phase_name); +net = caffe.Net(hNet); + +% load weights from weights_file +if nargin == 3 + CHECK(ischar(weights_file), 'weights_file must be a string'); + CHECK_FILE_EXIST(weights_file); + net.copy_from(weights_file); +end + +end + diff --git a/matlab/+caffe/get_solver.m b/matlab/+caffe/get_solver.m new file mode 100644 index 00000000000..855709c725e --- /dev/null +++ b/matlab/+caffe/get_solver.m @@ -0,0 +1,11 @@ +function solver = get_solver(solver_file) +% solver = get_solver(solver_file) +% Construct a Solver object from solver_file + +CHECK(ischar(solver_file), 'solver_file must be a string'); +CHECK_FILE_EXIST(solver_file); + +pSolver = caffe_('get_solver', solver_file); +solver = caffe.Solver(pSolver); + +end \ No newline at end of file diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m new file mode 100644 index 00000000000..95d58909abd --- /dev/null +++ b/matlab/+caffe/io.m @@ -0,0 +1,22 @@ +classdef io + % a class for input and output functions + + methods (Static) + function im_data = load_image(im_file) + CHECK(ischar(im_file), 'im_file must be a string'); + CHECK_FILE_EXIST(im_file); + % load an image from disk into Caffe-supported data format + % switch channels from RGB to BGR, make width the fastest dimension, and + % convert to single + im = imread(im_file); + im_data = im(:, :, [3, 2, 1]); + im_data = permute(im_data, [2 1 3]); + im_data = single(im_data); + end + function mean_data = read_mean(mean_proto_file) + CHECK(ischar(mean_proto_file), 'im_file must be a string'); + CHECK_FILE_EXIST(mean_proto_file); + mean_data = caffe_('read_mean', mean_proto_file); + end + end +end \ No newline at end of file diff --git a/matlab/+caffe/private/CHECK.m b/matlab/+caffe/private/CHECK.m new file mode 100644 index 00000000000..1cf21032b36 --- /dev/null +++ b/matlab/+caffe/private/CHECK.m @@ -0,0 +1,7 @@ +function CHECK(expr, error_msg) + +if ~expr + error(error_msg); +end + +end \ No newline at end of file diff --git a/matlab/+caffe/private/CHECK_FILE_EXIST.m b/matlab/+caffe/private/CHECK_FILE_EXIST.m new file mode 100644 index 00000000000..1db464c8258 --- /dev/null +++ b/matlab/+caffe/private/CHECK_FILE_EXIST.m @@ -0,0 +1,7 @@ +function CHECK_FILE_EXIST(filename) + +if exist(filename, 'file') == 0 + error('%s does not exist', filename); +end + +end \ No newline at end of file diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp new file mode 100644 index 00000000000..96a1920ac39 --- /dev/null +++ b/matlab/+caffe/private/caffe_.cpp @@ -0,0 +1,546 @@ +// +// caffe_.cpp provides wrappers of the caffe::Solver class, caffe::Net class, +// caffe::Layer class and caffe::Blob class and some caffe::Caffe functions, +// so that one could easily use Caffe from matlab. +// Note that for matlab, we will simply use float as the data type. + +// Internally, data is stored with dimensions reversed from Caffe's: +// e.g., if the Caffe blob axes are (num, channels, height, width), +// the matcaffe data is stored as (width, height, channels, num) +// where width is the fastest dimension. + +#include +#include +#include + +#include "mex.h" + +#include "caffe/caffe.hpp" + +#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs + +using namespace caffe; // NOLINT(build/namespaces) + +// Do CHECK and throw a Mex error if check failsf +inline void mxCHECK(bool expr, const std::string &msg) { + if (!expr) { + LOG(ERROR) << msg; + mexErrMsgTxt(msg.c_str()); + } +} +inline void mxERROR(const std::string &msg) { mxCHECK(false, msg); } + +// Check if a file exists and can be opened +void mxCHECK_FILE_EXIST(const char* file) { + std::ifstream f(file); + if (!f.good()) { + f.close(); + mxERROR("Could not open file " + string(file)); + } + f.close(); +} + +// The pointers to caffe::Solver and caffe::Net instances +static vector > > solvers_; +static vector > > nets_; +static double init_key = static_cast(caffe_rng_rand()); + +/** ----------------------------------------------------------------- + ** data conversion functions + **/ +// Enum indicates which blob memory to use +enum WhichMemory { DATA, DIFF }; + +// Copy matlab array to Blob data or diff +static void mx_mat_to_blob(const mxArray* mx_mat, Blob* blob, + WhichMemory data_or_diff) { + mxCHECK(blob->count() == mxGetNumberOfElements(mx_mat), + "number of elements in target blob doesn't match that in input mxArray"); + const float* mat_mem_ptr = reinterpret_cast(mxGetData(mx_mat)); + float* blob_mem_ptr = NULL; + switch (Caffe::mode()) { + case Caffe::CPU: + blob_mem_ptr = (data_or_diff == DATA ? + blob->mutable_cpu_data() : blob->mutable_cpu_diff()); + break; + case Caffe::GPU: + blob_mem_ptr = (data_or_diff == DATA ? + blob->mutable_gpu_data() : blob->mutable_gpu_diff()); + break; + default: + mxERROR("Unknown Caffe mode"); + } + caffe_copy(blob->count(), mat_mem_ptr, blob_mem_ptr); +} + +// Copy Blob data or diff to matlab array +static mxArray* blob_to_mx_mat(const Blob* blob, + WhichMemory data_or_diff) { + const int num_axes = blob->num_axes(); + vector dims(num_axes); + for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes; + ++blob_axis, --mat_axis) { + dims[mat_axis] = static_cast(blob->shape(blob_axis)); + } + // matlab array needs to have at least one dimension, convert scalar to 1-dim + if (num_axes == 0) { + dims.push_back(1); + } + mxArray* mx_mat = + mxCreateNumericArray(dims.size(), dims.data(), mxSINGLE_CLASS, mxREAL); + float* mat_mem_ptr = reinterpret_cast(mxGetData(mx_mat)); + const float* blob_mem_ptr = NULL; + switch (Caffe::mode()) { + case Caffe::CPU: + blob_mem_ptr = (data_or_diff == DATA ? blob->cpu_data() : blob->cpu_diff()); + break; + case Caffe::GPU: + blob_mem_ptr = (data_or_diff == DATA ? blob->gpu_data() : blob->gpu_diff()); + break; + default: + mxERROR("Unknown Caffe mode"); + } + caffe_copy(blob->count(), blob_mem_ptr, mat_mem_ptr); + return mx_mat; +} + +// convert vector to matlab vector +static mxArray* int_vec_to_mx_vec(const vector& int_vec) { + mxArray* mx_vec = mxCreateDoubleMatrix(int_vec.size(), 1, mxREAL); + double* vec_mem_ptr = mxGetPr(mx_vec); + for (int i = 0; i < int_vec.size(); i++) { + vec_mem_ptr[i] = int_vec[i]; + } + return mx_vec; +} + +// convert vector to matlab string cell vector +static mxArray* str_vec_to_mx_strcell(const vector& str_vec) { + mxArray* mx_strcell = mxCreateCellMatrix(str_vec.size(), 1); + for (int i = 0; i < str_vec.size(); i++) { + mxSetCell(mx_strcell, i, mxCreateString(str_vec[i].c_str())); + } + return mx_strcell; +} + +/** ----------------------------------------------------------------- + ** handle and pointer conversion functions + ** a handle is a struct array with the following fields + ** (uint64) ptr : the pointer to the C++ object + ** (double) init_key : caffe initialization key + **/ +// Convert a handle in matlab to a pointer in C++. Check if init_key matches +template +static T* handle_to_ptr(const mxArray* mx_handle) { + mxArray* mx_ptr = mxGetField(mx_handle, 0, "ptr"); + mxArray* mx_init_key = mxGetField(mx_handle, 0, "init_key"); + mxCHECK(mxIsUint64(mx_ptr), "pointer type must be uint64"); + mxCHECK(mxGetScalar(mx_init_key) == init_key, "incorrect handle init_key"); + return reinterpret_cast(*reinterpret_cast(mxGetData(mx_ptr))); +} + +// Create an empty handle struct array +template +static mxArray* create_handles(int ptr_num) { + const int handle_field_num = 2; + const char* handle_fields[handle_field_num] = { "ptr", "init_key" }; + return mxCreateStructMatrix(ptr_num, 1, handle_field_num, handle_fields); +} + +// Set up each handle in a handle struct array +template +static void setup_handle(const T* ptr, int index, mxArray* mx_handles) { + mxArray* mx_ptr = mxCreateNumericMatrix(1, 1, mxUINT64_CLASS, mxREAL); + *reinterpret_cast(mxGetData(mx_ptr)) = + reinterpret_cast(ptr); + mxSetField(mx_handles, index, "ptr", mx_ptr); + mxSetField(mx_handles, index, "init_key", mxCreateDoubleScalar(init_key)); +} + +// Convert a pointer in C++ to a handle in matlab +template +static mxArray* ptr_to_handle(const T* ptr) { + mxArray* mx_handle = create_handles(1); + setup_handle(ptr, 0, mx_handle); + return mx_handle; +} + +// Convert a vector of shared_ptr in C++ to handle struct array in matlab +template +static mxArray* ptr_vec_to_handles(const vector >& ptr_vec) { + mxArray* mx_handle = create_handles(ptr_vec.size()); + for (int i = 0; i < ptr_vec.size(); i++) { + setup_handle(ptr_vec[i].get(), i, mx_handle); + } + return mx_handle; +} + +/** ----------------------------------------------------------------- + ** matlab command functions: caffe_(api_command, arg1, arg2, ...) + **/ +// Usage: caffe_('get_solver', solver_file); +static void get_solver(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsChar(prhs[0]), + "Usage: caffe_('get_solver', solver_file)"); + const char* solver_file = mxArrayToString(prhs[0]); + mxCHECK_FILE_EXIST(solver_file); + shared_ptr > solver; + solver.reset(new caffe::SGDSolver(solver_file)); + solvers_.push_back(solver); + plhs[0] = ptr_to_handle >(solver.get()); +} + +// Usage: caffe_('solver_get_attr', hSolver) +static void solver_get_attr(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('solver_get_attr', hSolver)"); + Solver* solver = handle_to_ptr >(prhs[0]); + const int solver_attr_num = 2; + const char* solver_attrs[solver_attr_num] = { "hNet_net", "hNet_test_nets" }; + mxArray* mx_solver_attr = mxCreateStructMatrix(1, 1, solver_attr_num, + solver_attrs); + mxSetField(mx_solver_attr, 0, "hNet_net", + ptr_to_handle >(solver->net().get())); + mxSetField(mx_solver_attr, 0, "hNet_test_nets", + ptr_vec_to_handles >(solver->test_nets())); + plhs[0] = mx_solver_attr; +} + +// Usage: caffe_('solver_get_iter', hSolver) +static void solver_get_iter(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('solver_get_iter', hSolver)"); + Solver* solver = handle_to_ptr >(prhs[0]); + plhs[0] = mxCreateDoubleScalar(solver->iter()); +} + +// Usage: caffe_('solver_restore', hSolver, snapshot_file) +static void solver_restore(MEX_ARGS) { + mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]), + "Usage: caffe_('solver_restore', hSolver, snapshot_file)"); + Solver* solver = handle_to_ptr >(prhs[0]); + const char* snapshot_file = mxArrayToString(prhs[1]); + mxCHECK_FILE_EXIST(snapshot_file); + solver->Restore(snapshot_file); +} + +// Usage: caffe_('solver_solve', hSolver) +static void solver_solve(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('solver_solve', hSolver)"); + Solver* solver = handle_to_ptr >(prhs[0]); + solver->Solve(); +} + +// Usage: caffe_('solver_solve', hSolver, iters) +static void solver_step(MEX_ARGS) { + mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]), + "Usage: caffe_('solver_solve', hSolver, iters)"); + Solver* solver = handle_to_ptr >(prhs[0]); + int iters = mxGetScalar(prhs[1]); + solver->Step(iters); +} + +// Usage: caffe_('get_net', model_file, phase_name) +static void get_net(MEX_ARGS) { + mxCHECK(nrhs == 2 && mxIsChar(prhs[0]) && mxIsChar(prhs[1]), + "Usage: caffe_('get_net', model_file, phase_name)"); + const char* model_file = mxArrayToString(prhs[0]); + mxCHECK_FILE_EXIST(model_file); + const char* phase_name = mxArrayToString(prhs[1]); + Phase phase; + if (strcmp(phase_name, "train") == 0) { + phase = TRAIN; + } else if (strcmp(phase_name, "test") == 0) { + phase = TEST; + } else { + mxERROR("Unknown phase."); + } + shared_ptr > net; + net.reset(new caffe::Net(model_file, phase)); + nets_.push_back(net); + plhs[0] = ptr_to_handle >(net.get()); +} + +// Usage: caffe_('net_get_attr', hNet) +static void net_get_attr(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('net_get_attr', hNet)"); + Net* net = handle_to_ptr >(prhs[0]); + const int net_attr_num = 6; + const char* net_attrs[net_attr_num] = { "hLayer_layers", "hBlob_blobs", + "input_blob_indices", "output_blob_indices", "layer_names", "blob_names"}; + mxArray* mx_net_attr = mxCreateStructMatrix(1, 1, net_attr_num, + net_attrs); + mxSetField(mx_net_attr, 0, "hLayer_layers", + ptr_vec_to_handles >(net->layers())); + mxSetField(mx_net_attr, 0, "hBlob_blobs", + ptr_vec_to_handles >(net->blobs())); + mxSetField(mx_net_attr, 0, "input_blob_indices", + int_vec_to_mx_vec(net->input_blob_indices())); + mxSetField(mx_net_attr, 0, "output_blob_indices", + int_vec_to_mx_vec(net->output_blob_indices())); + mxSetField(mx_net_attr, 0, "layer_names", + str_vec_to_mx_strcell(net->layer_names())); + mxSetField(mx_net_attr, 0, "blob_names", + str_vec_to_mx_strcell(net->blob_names())); + plhs[0] = mx_net_attr; +} + +// Usage: caffe_('net_forward', hNet) +static void net_forward(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('net_forward', hNet)"); + Net* net = handle_to_ptr >(prhs[0]); + net->ForwardPrefilled(); +} + +// Usage: caffe_('net_backward', hNet) +static void net_backward(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('net_backward', hNet)"); + Net* net = handle_to_ptr >(prhs[0]); + net->Backward(); +} + +// Usage: caffe_('net_copy_from', hNet, weights_file) +static void net_copy_from(MEX_ARGS) { + mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]), + "Usage: caffe_('net_copy_from', hNet, weights_file)"); + Net* net = handle_to_ptr >(prhs[0]); + const char* weights_file = mxArrayToString(prhs[1]); + mxCHECK_FILE_EXIST(weights_file); + net->CopyTrainedLayersFrom(weights_file); +} + +// Usage: caffe_('net_reshape', hNet) +static void net_reshape(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('net_reshape', hNet)"); + Net* net = handle_to_ptr >(prhs[0]); + net->Reshape(); +} + +// Usage: caffe_('net_save', hNet, save_file) +static void net_save(MEX_ARGS) { + mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]), + "Usage: caffe_('net_save', hNet, save_file)"); + Net* net = handle_to_ptr >(prhs[0]); + const char* weights_file = mxArrayToString(prhs[1]); + NetParameter net_param; + net->ToProto(&net_param, false); + WriteProtoToBinaryFile(net_param, weights_file); +} + +// Usage: caffe_('layer_get_attr', hLayer) +static void layer_get_attr(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('layer_get_attr', hLayer)"); + Layer* layer = handle_to_ptr >(prhs[0]); + const int layer_attr_num = 1; + const char* layer_attrs[layer_attr_num] = { "hBlob_blobs" }; + mxArray* mx_layer_attr = mxCreateStructMatrix(1, 1, layer_attr_num, + layer_attrs); + mxSetField(mx_layer_attr, 0, "hBlob_blobs", + ptr_vec_to_handles >(layer->blobs())); + plhs[0] = mx_layer_attr; +} + +// Usage: caffe_('layer_get_attr', hLayer) +static void layer_get_type(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('layer_get_attr', hLayer)"); + Layer* layer = handle_to_ptr >(prhs[0]); + plhs[0] = mxCreateString(layer->type()); +} + +// Usage: caffe_('blob_get_shape', hBlob) +static void blob_get_shape(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('blob_get_shape', hBlob)"); + Blob* blob = handle_to_ptr >(prhs[0]); + const int num_axes = blob->num_axes(); + mxArray* mx_shape = mxCreateDoubleMatrix(1, num_axes, mxREAL); + double* shape_mem_mtr = mxGetPr(mx_shape); + for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes; + ++blob_axis, --mat_axis) { + shape_mem_mtr[mat_axis] = static_cast(blob->shape(blob_axis)); + } + plhs[0] = mx_shape; +} + +// Usage: caffe_('blob_reshape', hBlob, new_shape) +static void blob_reshape(MEX_ARGS) { + mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]), + "Usage: caffe_('blob_reshape', hBlob, new_shape)"); + Blob* blob = handle_to_ptr >(prhs[0]); + const mxArray* mx_shape = prhs[1]; + double* shape_mem_mtr = mxGetPr(mx_shape); + const int num_axes = mxGetNumberOfElements(mx_shape); + vector blob_shape(num_axes); + for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes; + ++blob_axis, --mat_axis) { + blob_shape[blob_axis] = static_cast(shape_mem_mtr[mat_axis]); + } + blob->Reshape(blob_shape); +} + +// Usage: caffe_('blob_get_data', hBlob) +static void blob_get_data(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('blob_get_data', hBlob)"); + Blob* blob = handle_to_ptr >(prhs[0]); + plhs[0] = blob_to_mx_mat(blob, DATA); +} + +// Usage: caffe_('blob_set_data', hBlob, new_data) +static void blob_set_data(MEX_ARGS) { + mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsSingle(prhs[1]), + "Usage: caffe_('blob_set_data', hBlob, new_data)"); + Blob* blob = handle_to_ptr >(prhs[0]); + mx_mat_to_blob(prhs[1], blob, DATA); +} + +// Usage: caffe_('blob_get_diff', hBlob) +static void blob_get_diff(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), + "Usage: caffe_('blob_get_diff', hBlob)"); + Blob* blob = handle_to_ptr >(prhs[0]); + plhs[0] = blob_to_mx_mat(blob, DIFF); +} + +// Usage: caffe_('blob_set_diff', hBlob, new_diff) +static void blob_set_diff(MEX_ARGS) { + mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsSingle(prhs[1]), + "Usage: caffe_('blob_set_diff', hBlob, new_diff)"); + Blob* blob = handle_to_ptr >(prhs[0]); + mx_mat_to_blob(prhs[1], blob, DIFF); +} + +// Usage: caffe_('set_mode_cpu') +static void set_mode_cpu(MEX_ARGS) { + mxCHECK(nrhs == 0, "Usage: caffe_('set_mode_cpu')"); + Caffe::set_mode(Caffe::CPU); +} + +// Usage: caffe_('set_mode_gpu') +static void set_mode_gpu(MEX_ARGS) { + mxCHECK(nrhs == 0, "Usage: caffe_('set_mode_gpu')"); + Caffe::set_mode(Caffe::GPU); +} + +// Usage: caffe_('set_device', device_id) +static void set_device(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsDouble(prhs[0]), + "Usage: caffe_('set_device', device_id)"); + int device_id = static_cast(mxGetScalar(prhs[0])); + Caffe::SetDevice(device_id); +} + +// Usage: caffe_('get_init_key') +static void get_init_key(MEX_ARGS) { + mxCHECK(nrhs == 0, "Usage: caffe_('get_init_key')"); + plhs[0] = mxCreateDoubleScalar(init_key); +} + +// Usage: caffe_('reset') +static void reset(MEX_ARGS) { + mxCHECK(nrhs == 0, "Usage: caffe_('reset')"); + mexPrintf("cleared %d solvers and %d stand-alone nets\n", solvers_.size(), + nets_.size()); + solvers_.clear(); + nets_.clear(); + init_key = static_cast(caffe_rng_rand()); +} + +// Usage: caffe_('read_mean', mean_proto_file) +static void read_mean(MEX_ARGS) { + mxCHECK(nrhs == 1 && mxIsChar(prhs[0]), + "Usage: caffe_('read_mean', mean_proto_file)"); + const char* mean_proto_file = mxArrayToString(prhs[0]); + Blob data_mean; + mexPrintf("Loading mean file from: %s\n", mean_proto_file); + BlobProto blob_proto; + bool result = ReadProtoFromBinaryFile(mean_proto_file, &blob_proto); + mxCHECK(result, "Couldn't read the file"); + data_mean.FromProto(blob_proto); + mwSize dims[4] = {data_mean.width(), data_mean.height(), + data_mean.channels(), data_mean.num() }; + mxArray* mx_blob = mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL); + float* data_ptr = reinterpret_cast(mxGetData(mx_blob)); + caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr); + mexPrintf("Remember that Caffe saves in [width, height, channels]" + " format and channels are also BGR!\n"); + plhs[0] = mx_blob; +} + +/** ----------------------------------------------------------------- + ** Available commands. + **/ +struct handler_registry { + string cmd; + void (*func)(MEX_ARGS); +}; + +static handler_registry handlers[] = { + // Public API functions + { "get_solver", get_solver }, + { "solver_get_attr", solver_get_attr }, + { "solver_get_iter", solver_get_iter }, + { "solver_restore", solver_restore }, + { "solver_solve", solver_solve }, + { "solver_step", solver_step }, + { "get_net", get_net }, + { "net_get_attr", net_get_attr }, + { "net_forward", net_forward }, + { "net_backward", net_backward }, + { "net_copy_from", net_copy_from }, + { "net_reshape", net_reshape }, + { "net_save", net_save }, + { "layer_get_attr", layer_get_attr }, + { "layer_get_type", layer_get_type }, + { "blob_get_shape", blob_get_shape }, + { "blob_reshape", blob_reshape }, + { "blob_get_data", blob_get_data }, + { "blob_set_data", blob_set_data }, + { "blob_get_diff", blob_get_diff }, + { "blob_set_diff", blob_set_diff }, + { "set_mode_cpu", set_mode_cpu }, + { "set_mode_gpu", set_mode_gpu }, + { "set_device", set_device }, + { "get_init_key", get_init_key }, + { "reset", reset }, + { "read_mean", read_mean }, + // The end. + { "END", NULL }, +}; + +/** ----------------------------------------------------------------- + ** matlab entry point: caffe_(api_command, arg1, arg2, ...) + **/ +void mexFunction(MEX_ARGS) { + mexLock(); // Avoid clearing the mex file. + if (nrhs == 0) { + mxERROR("No API command given"); + return; + } + + { // Handle input command + char* cmd = mxArrayToString(prhs[0]); + bool dispatched = false; + // Dispatch to cmd handler + for (int i = 0; handlers[i].func != NULL; i++) { + if (handlers[i].cmd.compare(cmd) == 0) { + handlers[i].func(nlhs, plhs, nrhs-1, prhs+1); + dispatched = true; + break; + } + } + if (!dispatched) { + ostringstream error_msg; + error_msg << "Unknown command '" << cmd << "'"; + mxERROR(error_msg.str().c_str()); + } + mxFree(cmd); + } +} diff --git a/matlab/+caffe/private/is_valid_handle.m b/matlab/+caffe/private/is_valid_handle.m new file mode 100644 index 00000000000..f03469c4f86 --- /dev/null +++ b/matlab/+caffe/private/is_valid_handle.m @@ -0,0 +1,28 @@ +function valid = is_valid_handle(hObj) +% valid = is_valid_handle(hObj) or is_valid_handle('get_new_init_key') +% Check if a handle is valid (has the right data type and init_key matches) +% Use is_valid_handle('get_new_init_key') to get new init_key from C++; + +% a handle is a struct array with the following fields +% (uint64) ptr : the pointer to the C++ object +% (double) init_key : caffe initialization key + +persistent init_key; +if isempty(init_key) + init_key = caffe_('get_init_key'); +end + +% is_valid_handle('get_new_init_key') to get new init_key from C++; +if ischar(hObj) && strcmp(hObj, 'get_new_init_key') + init_key = caffe_('get_init_key'); + valid = true; + return +else + % check whether data types are correct and init_key matches + valid = isstruct(hObj) ... + && isscalar(hObj.ptr) && isa(hObj.ptr, 'uint64') ... + && isscalar(hObj.init_key) && isa(hObj.init_key, 'double') ... + && hObj.init_key == init_key; +end + +end \ No newline at end of file diff --git a/matlab/+caffe/reset.m b/matlab/+caffe/reset.m new file mode 100644 index 00000000000..2df812b3f1d --- /dev/null +++ b/matlab/+caffe/reset.m @@ -0,0 +1,8 @@ +function reset() +% reset() +% reset Caffe to initial status + +caffe_('reset'); +is_valid_handle('get_new_init_key'); + +end \ No newline at end of file diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m new file mode 100644 index 00000000000..20834d497c2 --- /dev/null +++ b/matlab/+caffe/run_tests.m @@ -0,0 +1,12 @@ +function results = run_tests() +% results = run_tests() +% run all tests in this caffe matlab wrapper package + +caffe.reset(); +results = [... + run(caffe.test.test_net) ... + run(caffe.test.test_solver) + ]; +caffe.reset(); + +end \ No newline at end of file diff --git a/matlab/+caffe/set_device.m b/matlab/+caffe/set_device.m new file mode 100644 index 00000000000..43f00bddee6 --- /dev/null +++ b/matlab/+caffe/set_device.m @@ -0,0 +1,11 @@ +function set_device(device_id) +% set_device(device_id) +% set Caffe's GPU device ID + +CHECK(isscalar(device_id) && device_id >= 0, ... + 'device_id must be non-negative integer'); +device_id = double(device_id); + +caffe_('set_device', device_id); + +end \ No newline at end of file diff --git a/matlab/+caffe/set_mode_cpu.m b/matlab/+caffe/set_mode_cpu.m new file mode 100644 index 00000000000..8c0576ccbe4 --- /dev/null +++ b/matlab/+caffe/set_mode_cpu.m @@ -0,0 +1,7 @@ +function set_mode_cpu() +% set_mode_cpu() +% set Caffe to CPU mode + +caffe_('set_mode_cpu'); + +end \ No newline at end of file diff --git a/matlab/+caffe/set_mode_gpu.m b/matlab/+caffe/set_mode_gpu.m new file mode 100644 index 00000000000..ecd13b59bba --- /dev/null +++ b/matlab/+caffe/set_mode_gpu.m @@ -0,0 +1,7 @@ +function set_mode_gpu() +% set_mode_gpu() +% set Caffe to GPU mode + +caffe_('set_mode_gpu'); + +end \ No newline at end of file diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt index 791a4e70f43..63182abcc44 100644 --- a/matlab/CMakeLists.txt +++ b/matlab/CMakeLists.txt @@ -32,7 +32,9 @@ endfunction() # global settings file(GLOB Matlab_srcs caffe/matcaffe.cpp) +file(GLOB Matlab_pkg_srcs +caffe/private/caffe_.cpp) set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/caffe/caffe.mex) +set(Matlab_caffe_pkg_mex ${PROJECT_SOURCE_DIR}/matlab/+caffe/private/caffe_.mex) caffe_get_current_cflags(cflags) caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_frameworks) @@ -50,9 +52,15 @@ if(build_using MATCHES "Matlab") ARGS -output ${Matlab_caffe_mex} ${Matlab_srcs} ${cflags} ${link_folders} ${libflags} DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_mex}" VERBATIM) add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_mex} SOURCES ${Matlab_srcs}) + caffe_fetch_and_set_proper_mexext(Matlab_caffe_pkg_mex) + add_custom_command(OUTPUT ${Matlab_caffe_pkg_mex} COMMAND ${Matlab_mex} + ARGS -output ${Matlab_caffe_pkg_mex} ${Matlab_pkg_srcs} ${cflags} ${link_folders} ${libflags} + DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_pkg_mex}" VERBATIM) + add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_pkg_mex} SOURCES ${Matlab_pkg_srcs}) elseif(build_using MATCHES "Octave") + # Note: Matlab Caffe package cannot be used in Octave, so we don't build it if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") set(libflags -Wl,-force_load,$ ${libflags}) elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU") From a53df9892c49758bfee1de3455054dec9fc6dbdb Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Wed, 27 May 2015 12:37:20 +0800 Subject: [PATCH 093/446] Fix matlab tailing dimension 1 issue for shape match Matlab cannot have tailing dimension 1 for ndim > 2, so you cannot create 20 x 10 x 1 x 1 array in matlab as it becomes 20 x 10. Extend matlab arrays to have tailing dimension 1 during shape match. --- matlab/+caffe/+test/test_net.m | 2 +- matlab/+caffe/+test/test_solver.m | 2 +- matlab/+caffe/Blob.m | 15 ++++++++++----- matlab/+caffe/get_net.m | 1 - matlab/+caffe/get_solver.m | 2 +- matlab/+caffe/io.m | 2 +- matlab/+caffe/private/CHECK.m | 2 +- matlab/+caffe/private/CHECK_FILE_EXIST.m | 2 +- matlab/+caffe/private/is_valid_handle.m | 2 +- matlab/+caffe/reset.m | 2 +- matlab/+caffe/run_tests.m | 2 +- matlab/+caffe/set_device.m | 2 +- matlab/+caffe/set_mode_cpu.m | 2 +- matlab/+caffe/set_mode_gpu.m | 2 +- 14 files changed, 22 insertions(+), 18 deletions(-) diff --git a/matlab/+caffe/+test/test_net.m b/matlab/+caffe/+test/test_net.m index 4958d50f9e3..5d9ba000209 100644 --- a/matlab/+caffe/+test/test_net.m +++ b/matlab/+caffe/+test/test_net.m @@ -71,4 +71,4 @@ function test_save_and_read(self) end end end -end \ No newline at end of file +end diff --git a/matlab/+caffe/+test/test_solver.m b/matlab/+caffe/+test/test_solver.m index 9774ec1d2fd..682dad48a3b 100644 --- a/matlab/+caffe/+test/test_solver.m +++ b/matlab/+caffe/+test/test_solver.m @@ -40,4 +40,4 @@ function test_solve(self) self.verifyEqual(self.solver.iter(), 100) end end -end \ No newline at end of file +end diff --git a/matlab/+caffe/Blob.m b/matlab/+caffe/Blob.m index a6326d1a36c..f9b64096adc 100644 --- a/matlab/+caffe/Blob.m +++ b/matlab/+caffe/Blob.m @@ -51,7 +51,6 @@ function check_data_size_matches(self, data) % note: matlab arrays always have at least 2 dimensions. To compare % shape between size of data and shape of this blob, extend shape of % this blob to have at least 2 dimensions - data_size = size(data); self_shape_extended = self.shape; if isempty(self_shape_extended) % target blob is a scalar (0 dim) @@ -60,12 +59,18 @@ function check_data_size_matches(self, data) % target blob is a vector (1 dim) self_shape_extended = [self_shape_extended, 1]; end - is_matched = (length(self_shape_extended) == length(data_size)) ... - && all(self_shape_extended == data_size); + % also, matlab cannot have tailing dimension 1 for ndim > 2, so you + % cannot create 20 x 10 x 1 x 1 array in matlab as it becomes 20 x 10 + % extend matlab arrays to have tailing dimension 1 during shape match + data_size_extended = ... + [size(data), ones(1, length(self_shape_extended) - ndims(data))]; + is_matched = ... + (length(self_shape_extended) == length(data_size_extended)) ... + && all(self_shape_extended == data_size_extended); CHECK(is_matched, ... sprintf('%s, data size: [ %s], blob shape: [ %s]', ... 'data size does not match blob shape', ... - sprintf('%d ', data_size), sprintf('%d ', self_shape_extended))); + sprintf('%d ', data_size_extended), sprintf('%d ', self_shape_extended))); end end -end \ No newline at end of file +end diff --git a/matlab/+caffe/get_net.m b/matlab/+caffe/get_net.m index 04872b263c8..d60979d04b5 100644 --- a/matlab/+caffe/get_net.m +++ b/matlab/+caffe/get_net.m @@ -34,4 +34,3 @@ end end - diff --git a/matlab/+caffe/get_solver.m b/matlab/+caffe/get_solver.m index 855709c725e..30366d851e4 100644 --- a/matlab/+caffe/get_solver.m +++ b/matlab/+caffe/get_solver.m @@ -8,4 +8,4 @@ pSolver = caffe_('get_solver', solver_file); solver = caffe.Solver(pSolver); -end \ No newline at end of file +end diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m index 95d58909abd..7fad9686ac1 100644 --- a/matlab/+caffe/io.m +++ b/matlab/+caffe/io.m @@ -19,4 +19,4 @@ mean_data = caffe_('read_mean', mean_proto_file); end end -end \ No newline at end of file +end diff --git a/matlab/+caffe/private/CHECK.m b/matlab/+caffe/private/CHECK.m index 1cf21032b36..21706549cfa 100644 --- a/matlab/+caffe/private/CHECK.m +++ b/matlab/+caffe/private/CHECK.m @@ -4,4 +4,4 @@ function CHECK(expr, error_msg) error(error_msg); end -end \ No newline at end of file +end diff --git a/matlab/+caffe/private/CHECK_FILE_EXIST.m b/matlab/+caffe/private/CHECK_FILE_EXIST.m index 1db464c8258..8c80fb8094f 100644 --- a/matlab/+caffe/private/CHECK_FILE_EXIST.m +++ b/matlab/+caffe/private/CHECK_FILE_EXIST.m @@ -4,4 +4,4 @@ function CHECK_FILE_EXIST(filename) error('%s does not exist', filename); end -end \ No newline at end of file +end diff --git a/matlab/+caffe/private/is_valid_handle.m b/matlab/+caffe/private/is_valid_handle.m index f03469c4f86..77abf21dd11 100644 --- a/matlab/+caffe/private/is_valid_handle.m +++ b/matlab/+caffe/private/is_valid_handle.m @@ -25,4 +25,4 @@ && hObj.init_key == init_key; end -end \ No newline at end of file +end diff --git a/matlab/+caffe/reset.m b/matlab/+caffe/reset.m index 2df812b3f1d..c1cfea41a76 100644 --- a/matlab/+caffe/reset.m +++ b/matlab/+caffe/reset.m @@ -5,4 +5,4 @@ function reset() caffe_('reset'); is_valid_handle('get_new_init_key'); -end \ No newline at end of file +end diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m index 20834d497c2..fb1089c2d96 100644 --- a/matlab/+caffe/run_tests.m +++ b/matlab/+caffe/run_tests.m @@ -9,4 +9,4 @@ ]; caffe.reset(); -end \ No newline at end of file +end diff --git a/matlab/+caffe/set_device.m b/matlab/+caffe/set_device.m index 43f00bddee6..f94068cbe98 100644 --- a/matlab/+caffe/set_device.m +++ b/matlab/+caffe/set_device.m @@ -8,4 +8,4 @@ function set_device(device_id) caffe_('set_device', device_id); -end \ No newline at end of file +end diff --git a/matlab/+caffe/set_mode_cpu.m b/matlab/+caffe/set_mode_cpu.m index 8c0576ccbe4..a87e0e2852b 100644 --- a/matlab/+caffe/set_mode_cpu.m +++ b/matlab/+caffe/set_mode_cpu.m @@ -4,4 +4,4 @@ function set_mode_cpu() caffe_('set_mode_cpu'); -end \ No newline at end of file +end diff --git a/matlab/+caffe/set_mode_gpu.m b/matlab/+caffe/set_mode_gpu.m index ecd13b59bba..78e5f6773a1 100644 --- a/matlab/+caffe/set_mode_gpu.m +++ b/matlab/+caffe/set_mode_gpu.m @@ -4,4 +4,4 @@ function set_mode_gpu() caffe_('set_mode_gpu'); -end \ No newline at end of file +end From 9735f4b3b257379ac4e6c9d310aab32bfb198661 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Thu, 28 May 2015 13:40:26 +0800 Subject: [PATCH 094/446] Aesthetic changes on code style and some minor fix --- matlab/+caffe/Blob.m | 22 ++-- matlab/+caffe/Layer.m | 2 +- matlab/+caffe/Net.m | 10 +- matlab/+caffe/Solver.m | 2 +- matlab/+caffe/get_net.m | 3 +- matlab/+caffe/get_solver.m | 1 - matlab/+caffe/io.m | 15 ++- matlab/+caffe/private/caffe_.cpp | 154 ++++++++++++------------ matlab/+caffe/private/is_valid_handle.m | 1 - matlab/+caffe/run_tests.m | 8 +- 10 files changed, 113 insertions(+), 105 deletions(-) diff --git a/matlab/+caffe/Blob.m b/matlab/+caffe/Blob.m index f9b64096adc..e39f7ee3f20 100644 --- a/matlab/+caffe/Blob.m +++ b/matlab/+caffe/Blob.m @@ -7,9 +7,9 @@ methods function self = Blob(hBlob_blob) - CHECK(is_valid_handle(hBlob_blob), 'invalid input handle'); + CHECK(is_valid_handle(hBlob_blob), 'invalid Blob handle'); - % setup self handle and attributes + % setup self handle self.hBlob_self = hBlob_blob; end function shape = shape(self) @@ -37,14 +37,16 @@ function set_diff(self, diff) methods (Access = private) function shape = check_and_preprocess_shape(~, shape) - CHECK(isempty(shape) || isnumeric(shape) && isrow(shape), ... + CHECK(isempty(shape) || (isnumeric(shape) && isrow(shape)), ... 'shape must be a integer row vector'); shape = double(shape); end function data = check_and_preprocess_data(self, data) CHECK(isnumeric(data), 'data or diff must be numeric types'); - self.check_data_size_matches(data) - data = single(data); + self.check_data_size_matches(data); + if ~isa(data, 'single') + data = single(data); + end end function check_data_size_matches(self, data) % check whether size of data matches shape of this blob @@ -59,17 +61,17 @@ function check_data_size_matches(self, data) % target blob is a vector (1 dim) self_shape_extended = [self_shape_extended, 1]; end - % also, matlab cannot have tailing dimension 1 for ndim > 2, so you + % Also, matlab cannot have tailing dimension 1 for ndim > 2, so you % cannot create 20 x 10 x 1 x 1 array in matlab as it becomes 20 x 10 - % extend matlab arrays to have tailing dimension 1 during shape match + % Extend matlab arrays to have tailing dimension 1 during shape match data_size_extended = ... [size(data), ones(1, length(self_shape_extended) - ndims(data))]; is_matched = ... - (length(self_shape_extended) == length(data_size_extended)) ... + (length(self_shape_extended) == length(data_size_extended)) ... && all(self_shape_extended == data_size_extended); CHECK(is_matched, ... - sprintf('%s, data size: [ %s], blob shape: [ %s]', ... - 'data size does not match blob shape', ... + sprintf('%s, input data/diff size: [ %s] vs target blob shape: [ %s]', ... + 'input data/diff size does not match target blob shape', ... sprintf('%d ', data_size_extended), sprintf('%d ', self_shape_extended))); end end diff --git a/matlab/+caffe/Layer.m b/matlab/+caffe/Layer.m index 7587ed7ddf7..4c2023101a5 100644 --- a/matlab/+caffe/Layer.m +++ b/matlab/+caffe/Layer.m @@ -13,7 +13,7 @@ methods function self = Layer(hLayer_layer) - CHECK(is_valid_handle(hLayer_layer), 'invalid input handle'); + CHECK(is_valid_handle(hLayer_layer), 'invalid Layer handle'); % setup self handle and attributes self.hLayer_self = hLayer_layer; diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m index 5319634dc8b..a6761060dfb 100644 --- a/matlab/+caffe/Net.m +++ b/matlab/+caffe/Net.m @@ -33,7 +33,7 @@ end % construct a net from handle hNet_net = varargin{1}; - CHECK(is_valid_handle(hNet_net), 'invalid input handle'); + CHECK(is_valid_handle(hNet_net), 'invalid Net handle'); % setup self handle and attributes self.hNet_self = hNet_net; @@ -64,7 +64,7 @@ self.name2blob_index = containers.Map(self.attributes.blob_names, ... 1:length(self.attributes.blob_names)); - % expose layer_names and blob_names for public access + % expose layer_names and blob_names for public read access self.layer_names = self.attributes.layer_names; self.blob_names = self.attributes.blob_names; end @@ -91,12 +91,12 @@ function backward_prefilled(self) CHECK(iscell(input_data), 'input_data must be a cell array'); CHECK(length(input_data) == length(self.inputs), ... 'input data cell length must match input blob number'); - % copy data to input_blobs + % copy data to input blobs for n = 1:length(self.inputs) self.blobs(self.inputs{n}).set_data(input_data{n}); end self.forward_prefilled(); - % retrieve data from output_blobs + % retrieve data from output blobs res = cell(length(self.outputs), 1); for n = 1:length(self.outputs) res{n} = self.blobs(self.outputs{n}).get_data(); @@ -106,7 +106,7 @@ function backward_prefilled(self) CHECK(iscell(output_diff), 'output_diff must be a cell array'); CHECK(length(output_diff) == length(self.outputs), ... 'output diff cell length must match output blob number'); - % copy diff to output_blobs + % copy diff to output blobs for n = 1:length(self.outputs) self.blobs(self.outputs{n}).set_diff(output_diff{n}); end diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m index 80fa5394739..daaa8022b91 100644 --- a/matlab/+caffe/Solver.m +++ b/matlab/+caffe/Solver.m @@ -23,7 +23,7 @@ end % construct a solver from handle hSolver_solver = varargin{1}; - CHECK(is_valid_handle(hSolver_solver), 'invalid input handle'); + CHECK(is_valid_handle(hSolver_solver), 'invalid Solver handle'); % setup self handle and attributes self.hSolver_self = hSolver_solver; diff --git a/matlab/+caffe/get_net.m b/matlab/+caffe/get_net.m index d60979d04b5..4b5683eb82e 100644 --- a/matlab/+caffe/get_net.m +++ b/matlab/+caffe/get_net.m @@ -5,7 +5,7 @@ % phase_name can only be 'train' or 'test' CHECK(nargin == 2 || nargin == 3, ['usage: ' ... - 'net = get_net(model_file, phase_name) or ', ... + 'net = get_net(model_file, phase_name) or ' ... 'net = get_net(model_file, weights_file, phase_name)']); if nargin == 3 model_file = varargin{1}; @@ -23,6 +23,7 @@ sprintf('phase_name can only be %strain%s or %stest%s', ... char(39), char(39), char(39), char(39))); +% construct caffe net from model_file hNet = caffe_('get_net', model_file, phase_name); net = caffe.Net(hNet); diff --git a/matlab/+caffe/get_solver.m b/matlab/+caffe/get_solver.m index 30366d851e4..74d576eb31b 100644 --- a/matlab/+caffe/get_solver.m +++ b/matlab/+caffe/get_solver.m @@ -4,7 +4,6 @@ CHECK(ischar(solver_file), 'solver_file must be a string'); CHECK_FILE_EXIST(solver_file); - pSolver = caffe_('get_solver', solver_file); solver = caffe.Solver(pSolver); diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m index 7fad9686ac1..7a30bfb5772 100644 --- a/matlab/+caffe/io.m +++ b/matlab/+caffe/io.m @@ -3,17 +3,20 @@ methods (Static) function im_data = load_image(im_file) + % im_data = load_image(im_file) + % load an image from disk into Caffe-supported data format + % switch channels from RGB to BGR, make width the fastest dimension + % and convert to single CHECK(ischar(im_file), 'im_file must be a string'); CHECK_FILE_EXIST(im_file); - % load an image from disk into Caffe-supported data format - % switch channels from RGB to BGR, make width the fastest dimension, and - % convert to single - im = imread(im_file); - im_data = im(:, :, [3, 2, 1]); - im_data = permute(im_data, [2 1 3]); + im_data = imread(im_file); + im_data = im_data(:, :, [3, 2, 1]); + im_data = permute(im_data, [2, 1, 3]); im_data = single(im_data); end function mean_data = read_mean(mean_proto_file) + % mean_data = read_mean(mean_proto_file) + % read image mean data from binaryproto file CHECK(ischar(mean_proto_file), 'im_file must be a string'); CHECK_FILE_EXIST(mean_proto_file); mean_data = caffe_('read_mean', mean_proto_file); diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp index 96a1920ac39..4e0ebc1c00a 100644 --- a/matlab/+caffe/private/caffe_.cpp +++ b/matlab/+caffe/private/caffe_.cpp @@ -21,21 +21,22 @@ using namespace caffe; // NOLINT(build/namespaces) -// Do CHECK and throw a Mex error if check failsf -inline void mxCHECK(bool expr, const std::string &msg) { +// Do CHECK and throw a Mex error if check fails +inline void mxCHECK(bool expr, const char* msg) { if (!expr) { - LOG(ERROR) << msg; - mexErrMsgTxt(msg.c_str()); + mexErrMsgTxt(msg); } } -inline void mxERROR(const std::string &msg) { mxCHECK(false, msg); } +inline void mxERROR(const char* msg) { mexErrMsgTxt(msg); } // Check if a file exists and can be opened void mxCHECK_FILE_EXIST(const char* file) { std::ifstream f(file); if (!f.good()) { f.close(); - mxERROR("Could not open file " + string(file)); + std::string msg("Could not open file "); + msg += file; + mxERROR(msg.c_str()); } f.close(); } @@ -43,6 +44,7 @@ void mxCHECK_FILE_EXIST(const char* file) { // The pointers to caffe::Solver and caffe::Net instances static vector > > solvers_; static vector > > nets_; +// init_key is generated at the beginning and everytime you call reset static double init_key = static_cast(caffe_rng_rand()); /** ----------------------------------------------------------------- @@ -104,17 +106,17 @@ static mxArray* blob_to_mx_mat(const Blob* blob, return mx_mat; } -// convert vector to matlab vector +// Convert vector to matlab row vector static mxArray* int_vec_to_mx_vec(const vector& int_vec) { mxArray* mx_vec = mxCreateDoubleMatrix(int_vec.size(), 1, mxREAL); double* vec_mem_ptr = mxGetPr(mx_vec); for (int i = 0; i < int_vec.size(); i++) { - vec_mem_ptr[i] = int_vec[i]; + vec_mem_ptr[i] = static_cast(int_vec[i]); } return mx_vec; } -// convert vector to matlab string cell vector +// Convert vector to matlab cell vector of strings static mxArray* str_vec_to_mx_strcell(const vector& str_vec) { mxArray* mx_strcell = mxCreateCellMatrix(str_vec.size(), 1); for (int i = 0; i < str_vec.size(); i++) { @@ -135,44 +137,46 @@ static T* handle_to_ptr(const mxArray* mx_handle) { mxArray* mx_ptr = mxGetField(mx_handle, 0, "ptr"); mxArray* mx_init_key = mxGetField(mx_handle, 0, "init_key"); mxCHECK(mxIsUint64(mx_ptr), "pointer type must be uint64"); - mxCHECK(mxGetScalar(mx_init_key) == init_key, "incorrect handle init_key"); + mxCHECK(mxGetScalar(mx_init_key) == init_key, + "Could not convert handle to pointer due to invalid init_key. " + "The object might have been cleared."); return reinterpret_cast(*reinterpret_cast(mxGetData(mx_ptr))); } -// Create an empty handle struct array +// Create a handle struct vector, without setting up each handle in it template -static mxArray* create_handles(int ptr_num) { +static mxArray* create_handle_vec(int ptr_num) { const int handle_field_num = 2; const char* handle_fields[handle_field_num] = { "ptr", "init_key" }; return mxCreateStructMatrix(ptr_num, 1, handle_field_num, handle_fields); } -// Set up each handle in a handle struct array +// Set up a handle in a handle struct vector by its index template -static void setup_handle(const T* ptr, int index, mxArray* mx_handles) { +static void setup_handle(const T* ptr, int index, mxArray* mx_handle_vec) { mxArray* mx_ptr = mxCreateNumericMatrix(1, 1, mxUINT64_CLASS, mxREAL); *reinterpret_cast(mxGetData(mx_ptr)) = reinterpret_cast(ptr); - mxSetField(mx_handles, index, "ptr", mx_ptr); - mxSetField(mx_handles, index, "init_key", mxCreateDoubleScalar(init_key)); + mxSetField(mx_handle_vec, index, "ptr", mx_ptr); + mxSetField(mx_handle_vec, index, "init_key", mxCreateDoubleScalar(init_key)); } // Convert a pointer in C++ to a handle in matlab template static mxArray* ptr_to_handle(const T* ptr) { - mxArray* mx_handle = create_handles(1); + mxArray* mx_handle = create_handle_vec(1); setup_handle(ptr, 0, mx_handle); return mx_handle; } -// Convert a vector of shared_ptr in C++ to handle struct array in matlab +// Convert a vector of shared_ptr in C++ to handle struct vector template -static mxArray* ptr_vec_to_handles(const vector >& ptr_vec) { - mxArray* mx_handle = create_handles(ptr_vec.size()); +static mxArray* ptr_vec_to_handle_vec(const vector >& ptr_vec) { + mxArray* mx_handle_vec = create_handle_vec(ptr_vec.size()); for (int i = 0; i < ptr_vec.size(); i++) { - setup_handle(ptr_vec[i].get(), i, mx_handle); + setup_handle(ptr_vec[i].get(), i, mx_handle_vec); } - return mx_handle; + return mx_handle_vec; } /** ----------------------------------------------------------------- @@ -182,12 +186,12 @@ static mxArray* ptr_vec_to_handles(const vector >& ptr_vec) { static void get_solver(MEX_ARGS) { mxCHECK(nrhs == 1 && mxIsChar(prhs[0]), "Usage: caffe_('get_solver', solver_file)"); - const char* solver_file = mxArrayToString(prhs[0]); + char* solver_file = mxArrayToString(prhs[0]); mxCHECK_FILE_EXIST(solver_file); - shared_ptr > solver; - solver.reset(new caffe::SGDSolver(solver_file)); + shared_ptr > solver(new caffe::SGDSolver(solver_file)); solvers_.push_back(solver); plhs[0] = ptr_to_handle >(solver.get()); + mxFree(solver_file); } // Usage: caffe_('solver_get_attr', hSolver) @@ -202,7 +206,7 @@ static void solver_get_attr(MEX_ARGS) { mxSetField(mx_solver_attr, 0, "hNet_net", ptr_to_handle >(solver->net().get())); mxSetField(mx_solver_attr, 0, "hNet_test_nets", - ptr_vec_to_handles >(solver->test_nets())); + ptr_vec_to_handle_vec >(solver->test_nets())); plhs[0] = mx_solver_attr; } @@ -219,9 +223,10 @@ static void solver_restore(MEX_ARGS) { mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]), "Usage: caffe_('solver_restore', hSolver, snapshot_file)"); Solver* solver = handle_to_ptr >(prhs[0]); - const char* snapshot_file = mxArrayToString(prhs[1]); + char* snapshot_file = mxArrayToString(prhs[1]); mxCHECK_FILE_EXIST(snapshot_file); solver->Restore(snapshot_file); + mxFree(snapshot_file); } // Usage: caffe_('solver_solve', hSolver) @@ -232,10 +237,10 @@ static void solver_solve(MEX_ARGS) { solver->Solve(); } -// Usage: caffe_('solver_solve', hSolver, iters) +// Usage: caffe_('solver_step', hSolver, iters) static void solver_step(MEX_ARGS) { mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]), - "Usage: caffe_('solver_solve', hSolver, iters)"); + "Usage: caffe_('solver_step', hSolver, iters)"); Solver* solver = handle_to_ptr >(prhs[0]); int iters = mxGetScalar(prhs[1]); solver->Step(iters); @@ -245,21 +250,22 @@ static void solver_step(MEX_ARGS) { static void get_net(MEX_ARGS) { mxCHECK(nrhs == 2 && mxIsChar(prhs[0]) && mxIsChar(prhs[1]), "Usage: caffe_('get_net', model_file, phase_name)"); - const char* model_file = mxArrayToString(prhs[0]); + char* model_file = mxArrayToString(prhs[0]); + char* phase_name = mxArrayToString(prhs[1]); mxCHECK_FILE_EXIST(model_file); - const char* phase_name = mxArrayToString(prhs[1]); Phase phase; if (strcmp(phase_name, "train") == 0) { phase = TRAIN; } else if (strcmp(phase_name, "test") == 0) { phase = TEST; } else { - mxERROR("Unknown phase."); + mxERROR("Unknown phase"); } - shared_ptr > net; - net.reset(new caffe::Net(model_file, phase)); + shared_ptr > net(new caffe::Net(model_file, phase)); nets_.push_back(net); plhs[0] = ptr_to_handle >(net.get()); + mxFree(model_file); + mxFree(phase_name); } // Usage: caffe_('net_get_attr', hNet) @@ -273,15 +279,15 @@ static void net_get_attr(MEX_ARGS) { mxArray* mx_net_attr = mxCreateStructMatrix(1, 1, net_attr_num, net_attrs); mxSetField(mx_net_attr, 0, "hLayer_layers", - ptr_vec_to_handles >(net->layers())); + ptr_vec_to_handle_vec >(net->layers())); mxSetField(mx_net_attr, 0, "hBlob_blobs", - ptr_vec_to_handles >(net->blobs())); + ptr_vec_to_handle_vec >(net->blobs())); mxSetField(mx_net_attr, 0, "input_blob_indices", int_vec_to_mx_vec(net->input_blob_indices())); mxSetField(mx_net_attr, 0, "output_blob_indices", int_vec_to_mx_vec(net->output_blob_indices())); mxSetField(mx_net_attr, 0, "layer_names", - str_vec_to_mx_strcell(net->layer_names())); + str_vec_to_mx_strcell(net->layer_names())); mxSetField(mx_net_attr, 0, "blob_names", str_vec_to_mx_strcell(net->blob_names())); plhs[0] = mx_net_attr; @@ -308,9 +314,10 @@ static void net_copy_from(MEX_ARGS) { mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]), "Usage: caffe_('net_copy_from', hNet, weights_file)"); Net* net = handle_to_ptr >(prhs[0]); - const char* weights_file = mxArrayToString(prhs[1]); + char* weights_file = mxArrayToString(prhs[1]); mxCHECK_FILE_EXIST(weights_file); net->CopyTrainedLayersFrom(weights_file); + mxFree(weights_file); } // Usage: caffe_('net_reshape', hNet) @@ -326,10 +333,11 @@ static void net_save(MEX_ARGS) { mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]), "Usage: caffe_('net_save', hNet, save_file)"); Net* net = handle_to_ptr >(prhs[0]); - const char* weights_file = mxArrayToString(prhs[1]); + char* weights_file = mxArrayToString(prhs[1]); NetParameter net_param; net->ToProto(&net_param, false); WriteProtoToBinaryFile(net_param, weights_file); + mxFree(weights_file); } // Usage: caffe_('layer_get_attr', hLayer) @@ -342,14 +350,14 @@ static void layer_get_attr(MEX_ARGS) { mxArray* mx_layer_attr = mxCreateStructMatrix(1, 1, layer_attr_num, layer_attrs); mxSetField(mx_layer_attr, 0, "hBlob_blobs", - ptr_vec_to_handles >(layer->blobs())); + ptr_vec_to_handle_vec >(layer->blobs())); plhs[0] = mx_layer_attr; } -// Usage: caffe_('layer_get_attr', hLayer) +// Usage: caffe_('layer_get_type', hLayer) static void layer_get_type(MEX_ARGS) { mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]), - "Usage: caffe_('layer_get_attr', hLayer)"); + "Usage: caffe_('layer_get_type', hLayer)"); Layer* layer = handle_to_ptr >(prhs[0]); plhs[0] = mxCreateString(layer->type()); } @@ -446,10 +454,12 @@ static void get_init_key(MEX_ARGS) { // Usage: caffe_('reset') static void reset(MEX_ARGS) { mxCHECK(nrhs == 0, "Usage: caffe_('reset')"); - mexPrintf("cleared %d solvers and %d stand-alone nets\n", solvers_.size(), - nets_.size()); + // Clear solvers and stand-alone nets + mexPrintf("Cleared %d solvers and %d stand-alone nets\n", + solvers_.size(), nets_.size()); solvers_.clear(); nets_.clear(); + // Generate new init_key, so that handles created before becomes invalid init_key = static_cast(caffe_rng_rand()); } @@ -457,21 +467,15 @@ static void reset(MEX_ARGS) { static void read_mean(MEX_ARGS) { mxCHECK(nrhs == 1 && mxIsChar(prhs[0]), "Usage: caffe_('read_mean', mean_proto_file)"); - const char* mean_proto_file = mxArrayToString(prhs[0]); + char* mean_proto_file = mxArrayToString(prhs[0]); + mxCHECK_FILE_EXIST(mean_proto_file); Blob data_mean; - mexPrintf("Loading mean file from: %s\n", mean_proto_file); BlobProto blob_proto; bool result = ReadProtoFromBinaryFile(mean_proto_file, &blob_proto); - mxCHECK(result, "Couldn't read the file"); + mxCHECK(result, "Could not read your mean file"); data_mean.FromProto(blob_proto); - mwSize dims[4] = {data_mean.width(), data_mean.height(), - data_mean.channels(), data_mean.num() }; - mxArray* mx_blob = mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL); - float* data_ptr = reinterpret_cast(mxGetData(mx_blob)); - caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr); - mexPrintf("Remember that Caffe saves in [width, height, channels]" - " format and channels are also BGR!\n"); - plhs[0] = mx_blob; + plhs[0] = blob_to_mx_mat(&data_mean, DATA); + mxFree(mean_proto_file); } /** ----------------------------------------------------------------- @@ -516,31 +520,27 @@ static handler_registry handlers[] = { }; /** ----------------------------------------------------------------- - ** matlab entry point: caffe_(api_command, arg1, arg2, ...) + ** matlab entry point. **/ +// Usage: caffe_(api_command, arg1, arg2, ...) void mexFunction(MEX_ARGS) { mexLock(); // Avoid clearing the mex file. - if (nrhs == 0) { - mxERROR("No API command given"); - return; - } - - { // Handle input command - char* cmd = mxArrayToString(prhs[0]); - bool dispatched = false; - // Dispatch to cmd handler - for (int i = 0; handlers[i].func != NULL; i++) { - if (handlers[i].cmd.compare(cmd) == 0) { - handlers[i].func(nlhs, plhs, nrhs-1, prhs+1); - dispatched = true; - break; - } - } - if (!dispatched) { - ostringstream error_msg; - error_msg << "Unknown command '" << cmd << "'"; - mxERROR(error_msg.str().c_str()); + mxCHECK(nrhs > 0, "Usage: caffe_(api_command, arg1, arg2, ...)"); + // Handle input command + char* cmd = mxArrayToString(prhs[0]); + bool dispatched = false; + // Dispatch to cmd handler + for (int i = 0; handlers[i].func != NULL; i++) { + if (handlers[i].cmd.compare(cmd) == 0) { + handlers[i].func(nlhs, plhs, nrhs-1, prhs+1); + dispatched = true; + break; } - mxFree(cmd); } + if (!dispatched) { + ostringstream error_msg; + error_msg << "Unknown command '" << cmd << "'"; + mxERROR(error_msg.str().c_str()); + } + mxFree(cmd); } diff --git a/matlab/+caffe/private/is_valid_handle.m b/matlab/+caffe/private/is_valid_handle.m index 77abf21dd11..a0648ecdf61 100644 --- a/matlab/+caffe/private/is_valid_handle.m +++ b/matlab/+caffe/private/is_valid_handle.m @@ -15,7 +15,6 @@ % is_valid_handle('get_new_init_key') to get new init_key from C++; if ischar(hObj) && strcmp(hObj, 'get_new_init_key') init_key = caffe_('get_init_key'); - valid = true; return else % check whether data types are correct and init_key matches diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m index fb1089c2d96..afdd8f3309d 100644 --- a/matlab/+caffe/run_tests.m +++ b/matlab/+caffe/run_tests.m @@ -2,11 +2,15 @@ % results = run_tests() % run all tests in this caffe matlab wrapper package +% reset caffe before testing caffe.reset(); + +% put all test cases here results = [... run(caffe.test.test_net) ... - run(caffe.test.test_solver) - ]; + run(caffe.test.test_solver) ]; + +% reset caffe after testing caffe.reset(); end From 6af8efc0bf51d475077be146e5ab7b7899ad705d Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Thu, 28 May 2015 14:52:47 +0800 Subject: [PATCH 095/446] Add MatCaffe docs to docs/tutorial/interfaces.md --- docs/tutorial/interfaces.md | 209 +++++++++++++++++++++++++++++++++++- 1 file changed, 205 insertions(+), 4 deletions(-) diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md index 17430b35c57..f824c7cc54b 100644 --- a/docs/tutorial/interfaces.md +++ b/docs/tutorial/interfaces.md @@ -67,10 +67,211 @@ Compile pycaffe by `make pycaffe`. The module dir caffe/python/caffe should be i ## MATLAB -The MATLAB interface -- matcaffe -- is the `caffe` mex and its helper m-files in caffe/matlab. Load models, do forward and backward, extract output and read-only model weights, and load the binaryproto format mean as a matrix. +The MATLAB interface -- matcaffe -- is a `caffe` package in caffe/matlab in which you can integrate Caffe in your Matlab code. -A MATLAB demo is in caffe/matlab/caffe/matcaffe_demo.m +In MatCaffe, you can -Note that MATLAB matrices and memory are in column-major layout counter to Caffe's row-major layout! Double-check your work accordingly. +* Creating multiple Nets in Matlab +* Do forward and backward computation +* Access any layer within a network, and any parameter blob in a layer +* Get and set data or diff to any blob within a network, not restricting to input blobs or output blobs +* Save a network's parameters to file, and load parameters from file +* Reshape a blob and reshape a network +* Edit network parameter and do network surgery +* Create multiple Solvers in Matlab for training +* Resume training from solver snapshots +* Access train net and test nets in a solver +* Run for a certain number of iterations and give back control to Matlab +* Intermingle arbitrary Matlab code to with gradient steps -Compile matcaffe by `make matcaffe`. +A MATLAB demo is in caffe/matlab/matcaffe_demo.m + +### Build MatCaffe + +Build MatCaffe with `make all matcaffe`. After that, you may test it using `make mattest`. + +Common issue: if you run into error messages `libstdc++.so.6:version 'GLIBCXX_3.4.15' not found` during `make mattest`, then it means that your Matlab's runtime libraries does not match your compile-time libraries. You may need to do the following before you start matlab: + + export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64 + export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6 + +Or the equivalent based on where things are installed on your system, and do `make mattest` again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your `LD_LIBRARY_PATH` environment variable. You can run `!ldd ./matlab/+caffe/private/caffe_.mexa64` in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your `LD_PRELOAD` environment variable. + +After successful building and testing, add this package to Matlab search PATH by starting `matlab` from caffe root folder and running the following commands in Matlab command window. + + addpath ./matlab + +You can save your Matlab search PATH by running `savepath` so that you don't have to run the command above again every time you use MatCaffe. + +### Use MatCaffe + +MatCaffe is very similar to PyCaffe in usage. + +Examples below shows detailed usages and assumes you have downloaded BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) and started `matlab` from caffe root folder. + + model = './models/bvlc_reference_caffenet/deploy.prototxt'; + weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'; + +#### Set mode and device + +**Mode and device should always be set BEFORE you create a net or a solver.** + +Use CPU: + + caffe.set_mode_cpu(); + +Use GPU and specify its gpu_id: + + caffe.set_mode_gpu(); + caffe.set_device(gpu_id); + +#### Create a network and access its layers and blobs + +Create a network: + + net = caffe.Net(model, weights, 'test'); % create net and load weights + +Or + + net = caffe.Net(model, 'test'); % create net but not load weights + net.copy_from(weights); % load weights + +which creates `net` object as + + Net with properties: + + layer_vec: [1x23 caffe.Layer] + blob_vec: [1x15 caffe.Blob] + inputs: {'data'} + outputs: {'prob'} + name2layer_index: [23x1 containers.Map] + name2blob_index: [15x1 containers.Map] + layer_names: {23x1 cell} + blob_names: {15x1 cell} + +The two `containers.Map` objects are useful to find the index of a layer or a blob by its name. + +You have access to every blob in this network. To fill blob 'data' with all ones: + + net.blobs('data').set_data(ones(net.blobs('data').shape)); + +To multiply all values in blob 'data' by 10: + + net.blobs('data').set_data(net.blobs('data').get_data() * 10); + +**Be aware that since Matlab is 1-indexed and column-major, the usual 4 blob dimensions in Matlab are `[width, height, channels, num]`, and `width` is the fastest dimension. Also be aware that images are in BGR channels.** Also, Caffe uses single-precision float data. If your data is not single, `set_data` will automatically convert it to single. + +You also have access to every layer, so you can do network surgery. For example, to multiply conv1 parameters by 10: + + net.params('conv1', 1).set_data(net.params('conv1', 1).get_data() * 10); % set weights + net.params('conv1', 2).set_data(net.params('conv1', 2).get_data() * 10); % set bias + +Alternatively, you can use + + net.layers('conv1').params(1).set_data(net.layers('conv1').params(1).get_data() * 10); + net.layers('conv1').params(2).set_data(net.layers('conv1').params(2).get_data() * 10); + +To save the network you just modified: + + net.save('my_net.caffemodel'); + +To get a layer's type (string): + + layer_type = net.layers('conv1').type; + +#### Forward and backward + +Forward pass can be done using `net.forward` or `net.forward_prefilled`. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run + + res = net.forward({data}); + prob = res{1}; + +Or + + net.blobs('data').set_data(data); + net.forward_prefilled(); + prob = net.blobs('prob').get_data(); + +Backward is similar using `net.backward` or `net.backward_prefilled` and replacing `get_data` and `set_data` with `get_diff` and `set_diff`. After creating some gradients for output blobs like `prob_diff = rand(net.blobs('prob').shape);` you can run + + res = net.backward({prob_diff}); + data_diff = res{1}; + +Or + + net.blobs('prob').set_diff(prob_diff); + net.backward_prefilled(); + data_diff = net.blobs('data').get_diff(); + +**However, the backward computation above doesn't get correct results, because Caffe decides that the network does not need backward computation. To get correct backward results, you need to set `'force_backward: true'` in your network prototxt.** + +After performing forward or backward pass, you can also get the data or diff in internal blobs. For example, to extract pool5 features after forward pass: + + pool5_feat = net.blobs('pool5').get_data(); + +#### Reshape + +Assume you want to run 1 image at a time instead of 10: + + net.blobs('data').reshape([227 227 3 1]); % reshape blob 'data' + net.reshape(); + +Then the whole network is reshaped, and now `net.blobs('prob').shape` should be `[1000 1]`; + +#### Training + +Assume you have created training and validation lmdbs following our [ImageNET Tutorial](http://caffe.berkeleyvision.org/gathered/examples/imagenet.html), to create a solver and train on ILSVRC 2012 classification dataset: + + solver = caffe.Solver('./models/bvlc_reference_caffenet/solver.prototxt'); + +which creates `solver` object as + + Solver with properties: + + net: [1x1 caffe.Net] + test_nets: [1x1 caffe.Net] + +To train: + + solver.solve(); + +Or train for only 1000 iterations (so that you can do something to its net before training more iterations) + + solver.step(1000); + +To get iteration number: + + iter = solver.iter(); + +To get its network: + + train_net = solver.net; + test_net = solver.test_nets(1); + +To resume from a snapshot "your_snapshot.solverstate": + + solver.restore('your_snapshot.solverstate'); + +#### Input and output + +`caffe.io` class provides basic input functions `load_image` and `read_mean`. For example, to read ILSVRC 2012 mean file (assume you have downloaded imagenet example auxiliary files by running `./data/ilsvrc12/get_ilsvrc_aux.sh`): + + mean_data = caffe.io.read_mean('./data/ilsvrc12/imagenet_mean.binaryproto'); + +To read Caffe's example image and resize to `[width, height]` and suppose we want `width = 256; height = 256;` + + im_data = caffe.io.load_image('./examples/images/cat.jpg'); + im_data = imresize(im_data, [width, height]); % resize using Matlab's imresize + +**Keep in mind that `width` is the fastest dimension and channels are BGR, which is different from the usual way that Matlab stores an image.** If you don't want to use `caffe.io.load_image` and prefer to load an image by yourself, you can do + + im_data = imread('./examples/images/cat.jpg'); % read image + im_data = im_data(:, :, [3, 2, 1]); % convert from RGB to BGR + im_data = permute(im_data, [2, 1, 3]); % permute width and height + im_data = single(im_data); % convert to single precision + +We do not provide extra functions for data output as Matlab itself is already quite powerful in output. + +#### Clear nets and solvers + +Call `caffe.reset()` to clear all solvers and stand-alone nets you have created. From d3d7d07be981ab046da99e1ad5d84217bf0ec4c3 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Thu, 28 May 2015 16:24:30 +0800 Subject: [PATCH 096/446] Clean up old matcaffe wrapper and rename caffe.reset to caffe.reset_all Remove old matlab wrapper but keep the classification demo and hdf5 demo Change 'caffe.reset()' to 'caffe.reset_all()' to avoid potential name conflict. Otherwise, Matlab R2015a complains: Warning: Function reset has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict. --- Makefile | 25 +- docs/tutorial/interfaces.md | 8 +- .../imagenet}/ilsvrc_2012_mean.mat | Bin matlab/+caffe/reset.m | 8 - matlab/+caffe/reset_all.m | 8 + matlab/+caffe/run_tests.m | 4 +- matlab/CMakeLists.txt | 12 +- matlab/caffe/matcaffe.cpp | 421 ------------------ matlab/caffe/matcaffe_batch.m | 75 ---- matlab/caffe/matcaffe_demo_vgg.m | 96 ---- matlab/caffe/matcaffe_demo_vgg_mean_pix.m | 102 ----- matlab/caffe/matcaffe_init.m | 41 -- matlab/caffe/prepare_batch.m | 41 -- matlab/caffe/print_cell.m | 42 -- matlab/caffe/read_cell.m | 21 - .../matcaffe_demo.m => classification_demo.m} | 64 ++- matlab/{caffe => }/hdf5creation/.gitignore | 0 matlab/{caffe => }/hdf5creation/demo.m | 0 matlab/{caffe => }/hdf5creation/store2hdf5.m | 0 19 files changed, 63 insertions(+), 905 deletions(-) rename matlab/{caffe => +caffe/imagenet}/ilsvrc_2012_mean.mat (100%) delete mode 100644 matlab/+caffe/reset.m create mode 100644 matlab/+caffe/reset_all.m delete mode 100644 matlab/caffe/matcaffe.cpp delete mode 100644 matlab/caffe/matcaffe_batch.m delete mode 100644 matlab/caffe/matcaffe_demo_vgg.m delete mode 100644 matlab/caffe/matcaffe_demo_vgg_mean_pix.m delete mode 100644 matlab/caffe/matcaffe_init.m delete mode 100644 matlab/caffe/prepare_batch.m delete mode 100644 matlab/caffe/print_cell.m delete mode 100644 matlab/caffe/read_cell.m rename matlab/{caffe/matcaffe_demo.m => classification_demo.m} (56%) rename matlab/{caffe => }/hdf5creation/.gitignore (100%) rename matlab/{caffe => }/hdf5creation/demo.m (100%) rename matlab/{caffe => }/hdf5creation/store2hdf5.m (100%) diff --git a/Makefile b/Makefile index 74b167763c3..3748b52ca47 100644 --- a/Makefile +++ b/Makefile @@ -65,7 +65,6 @@ NONGEN_CXX_SRCS := $(shell find \ src/$(PROJECT) \ include/$(PROJECT) \ python/$(PROJECT) \ - matlab/$(PROJECT) \ matlab/+$(PROJECT)/private \ examples \ tools \ @@ -80,15 +79,12 @@ NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT) PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp -# MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT) -MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp -# MAT$(PROJECT)_PKG_SRC is the mex entrance point of matlab package for $(PROJECT) -MAT$(PROJECT)_PKG_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp +# MAT$(PROJECT)_SRC is the mex entrance point of matlab package for $(PROJECT) +MAT$(PROJECT)_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp ifneq ($(MATLAB_DIR),) MAT_SO_EXT := $(shell $(MATLAB_DIR)/bin/mexext) endif -MAT$(PROJECT)_SO := matlab/$(PROJECT)/$(PROJECT).$(MAT_SO_EXT) -MAT$(PROJECT)_PKG_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT) +MAT$(PROJECT)_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT) ############################## # Derive generated files @@ -451,7 +447,7 @@ $(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME) mat$(PROJECT): mat -mat: $(MAT$(PROJECT)_SO) $(MAT$(PROJECT)_PKG_SO) +mat: $(MAT$(PROJECT)_SO) $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME) @ if [ -z "$(MATLAB_DIR)" ]; then \ @@ -464,18 +460,6 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME) CXX="$(CXX)" \ CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \ CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@ - -$(MAT$(PROJECT)_PKG_SO): $(MAT$(PROJECT)_PKG_SRC) $(STATIC_NAME) - @ if [ -z "$(MATLAB_DIR)" ]; then \ - echo "MATLAB_DIR must be specified in $(CONFIG_FILE)" \ - "to build mat$(PROJECT)."; \ - exit 1; \ - fi - @ echo MEX $< - $(Q)$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_PKG_SRC) \ - CXX="$(CXX)" \ - CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \ - CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@ runtest: $(TEST_ALL_BIN) $(TOOL_BUILD_DIR)/caffe @@ -601,7 +585,6 @@ clean: @- $(RM) -rf $(DISTRIBUTE_DIR) @- $(RM) $(PY$(PROJECT)_SO) @- $(RM) $(MAT$(PROJECT)_SO) - @- $(RM) $(MAT$(PROJECT)_PKG_SO) supercleanfiles: $(eval SUPERCLEAN_FILES := $(strip \ diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md index f824c7cc54b..a57e17339b0 100644 --- a/docs/tutorial/interfaces.md +++ b/docs/tutorial/interfaces.md @@ -67,7 +67,7 @@ Compile pycaffe by `make pycaffe`. The module dir caffe/python/caffe should be i ## MATLAB -The MATLAB interface -- matcaffe -- is a `caffe` package in caffe/matlab in which you can integrate Caffe in your Matlab code. +The MATLAB interface -- matcaffe -- is the `caffe` package in caffe/matlab in which you can integrate Caffe in your Matlab code. In MatCaffe, you can @@ -84,7 +84,7 @@ In MatCaffe, you can * Run for a certain number of iterations and give back control to Matlab * Intermingle arbitrary Matlab code to with gradient steps -A MATLAB demo is in caffe/matlab/matcaffe_demo.m +An ILSVRC image classification demo is in caffe/matlab/classification_demo.m ### Build MatCaffe @@ -181,7 +181,7 @@ To get a layer's type (string): #### Forward and backward -Forward pass can be done using `net.forward` or `net.forward_prefilled`. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run +Forward pass can be done using `net.forward` or `net.forward_prefilled`. Function `net.forward` takes in a cell array of N-D arrays containing data of input blob(s) and outputs a cell array containing data from output blob(s). Function `net.forward_prefilled` uses existing data in input blob(s) during forward pass, takes no input and produces no output. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run res = net.forward({data}); prob = res{1}; @@ -274,4 +274,4 @@ We do not provide extra functions for data output as Matlab itself is already qu #### Clear nets and solvers -Call `caffe.reset()` to clear all solvers and stand-alone nets you have created. +Call `caffe.reset_all()` to clear all solvers and stand-alone nets you have created. diff --git a/matlab/caffe/ilsvrc_2012_mean.mat b/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat similarity index 100% rename from matlab/caffe/ilsvrc_2012_mean.mat rename to matlab/+caffe/imagenet/ilsvrc_2012_mean.mat diff --git a/matlab/+caffe/reset.m b/matlab/+caffe/reset.m deleted file mode 100644 index c1cfea41a76..00000000000 --- a/matlab/+caffe/reset.m +++ /dev/null @@ -1,8 +0,0 @@ -function reset() -% reset() -% reset Caffe to initial status - -caffe_('reset'); -is_valid_handle('get_new_init_key'); - -end diff --git a/matlab/+caffe/reset_all.m b/matlab/+caffe/reset_all.m new file mode 100644 index 00000000000..a8b33dee8d5 --- /dev/null +++ b/matlab/+caffe/reset_all.m @@ -0,0 +1,8 @@ +function reset_all() +% reset_all() +% clear all solvers and stand-alone nets and reset Caffe to initial status + +caffe_('reset'); +is_valid_handle('get_new_init_key'); + +end diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m index afdd8f3309d..8773c9f638b 100644 --- a/matlab/+caffe/run_tests.m +++ b/matlab/+caffe/run_tests.m @@ -3,7 +3,7 @@ % run all tests in this caffe matlab wrapper package % reset caffe before testing -caffe.reset(); +caffe.reset_all(); % put all test cases here results = [... @@ -11,6 +11,6 @@ run(caffe.test.test_solver) ]; % reset caffe after testing -caffe.reset(); +caffe.reset_all(); end diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt index 63182abcc44..4b0d549f07f 100644 --- a/matlab/CMakeLists.txt +++ b/matlab/CMakeLists.txt @@ -31,10 +31,8 @@ function(caffe_fetch_and_set_proper_mexext mexfile_variable) endfunction() # global settings -file(GLOB Matlab_srcs caffe/matcaffe.cpp) -file(GLOB Matlab_pkg_srcs +caffe/private/caffe_.cpp) -set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/caffe/caffe.mex) -set(Matlab_caffe_pkg_mex ${PROJECT_SOURCE_DIR}/matlab/+caffe/private/caffe_.mex) +file(GLOB Matlab_srcs +caffe/private/caffe_.cpp) +set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/+caffe/private/caffe_.mex) caffe_get_current_cflags(cflags) caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_frameworks) @@ -52,15 +50,9 @@ if(build_using MATCHES "Matlab") ARGS -output ${Matlab_caffe_mex} ${Matlab_srcs} ${cflags} ${link_folders} ${libflags} DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_mex}" VERBATIM) add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_mex} SOURCES ${Matlab_srcs}) - caffe_fetch_and_set_proper_mexext(Matlab_caffe_pkg_mex) - add_custom_command(OUTPUT ${Matlab_caffe_pkg_mex} COMMAND ${Matlab_mex} - ARGS -output ${Matlab_caffe_pkg_mex} ${Matlab_pkg_srcs} ${cflags} ${link_folders} ${libflags} - DEPENDS caffe COMMENT "Building Matlab interface: ${Matlab_caffe_pkg_mex}" VERBATIM) - add_custom_target(matlab ALL DEPENDS ${Matlab_caffe_pkg_mex} SOURCES ${Matlab_pkg_srcs}) elseif(build_using MATCHES "Octave") - # Note: Matlab Caffe package cannot be used in Octave, so we don't build it if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") set(libflags -Wl,-force_load,$ ${libflags}) elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU") diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp deleted file mode 100644 index da37d920b20..00000000000 --- a/matlab/caffe/matcaffe.cpp +++ /dev/null @@ -1,421 +0,0 @@ -// -// matcaffe.cpp provides a wrapper of the caffe::Net class as well as some -// caffe::Caffe functions so that one could easily call it from matlab. -// Note that for matlab, we will simply use float as the data type. - -#include -#include -#include - -#include "mex.h" - -#include "caffe/caffe.hpp" - -#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs - -// Log and throw a Mex error -inline void mex_error(const std::string &msg) { - LOG(ERROR) << msg; - mexErrMsgTxt(msg.c_str()); -} - -using namespace caffe; // NOLINT(build/namespaces) - -// The pointer to the internal caffe::Net instance -static shared_ptr > net_; -static int init_key = -2; - -// Five things to be aware of: -// caffe uses row-major order -// matlab uses column-major order -// caffe uses BGR color channel order -// matlab uses RGB color channel order -// images need to have the data mean subtracted -// -// Data coming in from matlab needs to be in the order -// [width, height, channels, images] -// where width is the fastest dimension. -// Here is the rough matlab for putting image data into the correct -// format: -// % convert from uint8 to single -// im = single(im); -// % reshape to a fixed size (e.g., 227x227) -// im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear'); -// % permute from RGB to BGR and subtract the data mean (already in BGR) -// im = im(:,:,[3 2 1]) - data_mean; -// % flip width and height to make width the fastest dimension -// im = permute(im, [2 1 3]); -// -// If you have multiple images, cat them with cat(4, ...) -// -// The actual forward function. It takes in a cell array of 4-D arrays as -// input and outputs a cell array. - -static mxArray* do_forward(const mxArray* const bottom) { - const vector*>& input_blobs = net_->input_blobs(); - if (static_cast(mxGetDimensions(bottom)[0]) != - input_blobs.size()) { - mex_error("Invalid input size"); - } - for (unsigned int i = 0; i < input_blobs.size(); ++i) { - const mxArray* const elem = mxGetCell(bottom, i); - if (!mxIsSingle(elem)) { - mex_error("MatCaffe require single-precision float point data"); - } - if (mxGetNumberOfElements(elem) != input_blobs[i]->count()) { - std::string error_msg; - error_msg += "MatCaffe input size does not match the input size "; - error_msg += "of the network"; - mex_error(error_msg); - } - - const float* const data_ptr = - reinterpret_cast(mxGetPr(elem)); - switch (Caffe::mode()) { - case Caffe::CPU: - caffe_copy(input_blobs[i]->count(), data_ptr, - input_blobs[i]->mutable_cpu_data()); - break; - case Caffe::GPU: - caffe_copy(input_blobs[i]->count(), data_ptr, - input_blobs[i]->mutable_gpu_data()); - break; - default: - mex_error("Unknown Caffe mode"); - } // switch (Caffe::mode()) - } - const vector*>& output_blobs = net_->ForwardPrefilled(); - mxArray* mx_out = mxCreateCellMatrix(output_blobs.size(), 1); - for (unsigned int i = 0; i < output_blobs.size(); ++i) { - // internally data is stored as (width, height, channels, num) - // where width is the fastest dimension - mwSize dims[4] = {output_blobs[i]->width(), output_blobs[i]->height(), - output_blobs[i]->channels(), output_blobs[i]->num()}; - mxArray* mx_blob = mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL); - mxSetCell(mx_out, i, mx_blob); - float* data_ptr = reinterpret_cast(mxGetPr(mx_blob)); - switch (Caffe::mode()) { - case Caffe::CPU: - caffe_copy(output_blobs[i]->count(), output_blobs[i]->cpu_data(), - data_ptr); - break; - case Caffe::GPU: - caffe_copy(output_blobs[i]->count(), output_blobs[i]->gpu_data(), - data_ptr); - break; - default: - mex_error("Unknown Caffe mode"); - } // switch (Caffe::mode()) - } - - return mx_out; -} - -static mxArray* do_backward(const mxArray* const top_diff) { - const vector*>& output_blobs = net_->output_blobs(); - const vector*>& input_blobs = net_->input_blobs(); - if (static_cast(mxGetDimensions(top_diff)[0]) != - output_blobs.size()) { - mex_error("Invalid input size"); - } - // First, copy the output diff - for (unsigned int i = 0; i < output_blobs.size(); ++i) { - const mxArray* const elem = mxGetCell(top_diff, i); - const float* const data_ptr = - reinterpret_cast(mxGetPr(elem)); - switch (Caffe::mode()) { - case Caffe::CPU: - caffe_copy(output_blobs[i]->count(), data_ptr, - output_blobs[i]->mutable_cpu_diff()); - break; - case Caffe::GPU: - caffe_copy(output_blobs[i]->count(), data_ptr, - output_blobs[i]->mutable_gpu_diff()); - break; - default: - mex_error("Unknown Caffe mode"); - } // switch (Caffe::mode()) - } - // LOG(INFO) << "Start"; - net_->Backward(); - // LOG(INFO) << "End"; - mxArray* mx_out = mxCreateCellMatrix(input_blobs.size(), 1); - for (unsigned int i = 0; i < input_blobs.size(); ++i) { - // internally data is stored as (width, height, channels, num) - // where width is the fastest dimension - mwSize dims[4] = {input_blobs[i]->width(), input_blobs[i]->height(), - input_blobs[i]->channels(), input_blobs[i]->num()}; - mxArray* mx_blob = mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL); - mxSetCell(mx_out, i, mx_blob); - float* data_ptr = reinterpret_cast(mxGetPr(mx_blob)); - switch (Caffe::mode()) { - case Caffe::CPU: - caffe_copy(input_blobs[i]->count(), input_blobs[i]->cpu_diff(), data_ptr); - break; - case Caffe::GPU: - caffe_copy(input_blobs[i]->count(), input_blobs[i]->gpu_diff(), data_ptr); - break; - default: - mex_error("Unknown Caffe mode"); - } // switch (Caffe::mode()) - } - - return mx_out; -} - -static mxArray* do_get_weights() { - const vector > >& layers = net_->layers(); - const vector& layer_names = net_->layer_names(); - - // Step 1: count the number of layers with weights - int num_layers = 0; - { - string prev_layer_name = ""; - for (unsigned int i = 0; i < layers.size(); ++i) { - vector > >& layer_blobs = layers[i]->blobs(); - if (layer_blobs.size() == 0) { - continue; - } - if (layer_names[i] != prev_layer_name) { - prev_layer_name = layer_names[i]; - num_layers++; - } - } - } - - // Step 2: prepare output array of structures - mxArray* mx_layers; - { - const mwSize dims[2] = {num_layers, 1}; - const char* fnames[2] = {"weights", "layer_names"}; - mx_layers = mxCreateStructArray(2, dims, 2, fnames); - } - - // Step 3: copy weights into output - { - string prev_layer_name = ""; - int mx_layer_index = 0; - for (unsigned int i = 0; i < layers.size(); ++i) { - vector > >& layer_blobs = layers[i]->blobs(); - if (layer_blobs.size() == 0) { - continue; - } - - mxArray* mx_layer_cells = NULL; - if (layer_names[i] != prev_layer_name) { - prev_layer_name = layer_names[i]; - const mwSize dims[2] = {static_cast(layer_blobs.size()), 1}; - mx_layer_cells = mxCreateCellArray(2, dims); - mxSetField(mx_layers, mx_layer_index, "weights", mx_layer_cells); - mxSetField(mx_layers, mx_layer_index, "layer_names", - mxCreateString(layer_names[i].c_str())); - mx_layer_index++; - } - - for (unsigned int j = 0; j < layer_blobs.size(); ++j) { - // internally data is stored as (width, height, channels, num) - // where width is the fastest dimension - mwSize dims[4] = {layer_blobs[j]->width(), layer_blobs[j]->height(), - layer_blobs[j]->channels(), layer_blobs[j]->num()}; - - mxArray* mx_weights = - mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL); - mxSetCell(mx_layer_cells, j, mx_weights); - float* weights_ptr = reinterpret_cast(mxGetPr(mx_weights)); - - switch (Caffe::mode()) { - case Caffe::CPU: - caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->cpu_data(), - weights_ptr); - break; - case Caffe::GPU: - caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->gpu_data(), - weights_ptr); - break; - default: - mex_error("Unknown Caffe mode"); - } - } - } - } - - return mx_layers; -} - -static void get_weights(MEX_ARGS) { - plhs[0] = do_get_weights(); -} - -static void set_mode_cpu(MEX_ARGS) { - Caffe::set_mode(Caffe::CPU); -} - -static void set_mode_gpu(MEX_ARGS) { - Caffe::set_mode(Caffe::GPU); -} - -static void set_device(MEX_ARGS) { - if (nrhs != 1) { - ostringstream error_msg; - error_msg << "Expected 1 argument, got " << nrhs; - mex_error(error_msg.str()); - } - - int device_id = static_cast(mxGetScalar(prhs[0])); - Caffe::SetDevice(device_id); -} - -static void get_init_key(MEX_ARGS) { - plhs[0] = mxCreateDoubleScalar(init_key); -} - -static void init(MEX_ARGS) { - if (nrhs != 3) { - ostringstream error_msg; - error_msg << "Expected 3 arguments, got " << nrhs; - mex_error(error_msg.str()); - } - - char* param_file = mxArrayToString(prhs[0]); - char* model_file = mxArrayToString(prhs[1]); - char* phase_name = mxArrayToString(prhs[2]); - - Phase phase; - if (strcmp(phase_name, "train") == 0) { - phase = TRAIN; - } else if (strcmp(phase_name, "test") == 0) { - phase = TEST; - } else { - mex_error("Unknown phase."); - } - - net_.reset(new Net(string(param_file), phase)); - net_->CopyTrainedLayersFrom(string(model_file)); - - mxFree(param_file); - mxFree(model_file); - mxFree(phase_name); - - init_key = random(); // NOLINT(caffe/random_fn) - - if (nlhs == 1) { - plhs[0] = mxCreateDoubleScalar(init_key); - } -} - -static void reset(MEX_ARGS) { - if (net_) { - net_.reset(); - init_key = -2; - LOG(INFO) << "Network reset, call init before use it again"; - } -} - -static void forward(MEX_ARGS) { - if (nrhs != 1) { - ostringstream error_msg; - error_msg << "Expected 1 argument, got " << nrhs; - mex_error(error_msg.str()); - } - - plhs[0] = do_forward(prhs[0]); -} - -static void backward(MEX_ARGS) { - if (nrhs != 1) { - ostringstream error_msg; - error_msg << "Expected 1 argument, got " << nrhs; - mex_error(error_msg.str()); - } - - plhs[0] = do_backward(prhs[0]); -} - -static void is_initialized(MEX_ARGS) { - if (!net_) { - plhs[0] = mxCreateDoubleScalar(0); - } else { - plhs[0] = mxCreateDoubleScalar(1); - } -} - -static void read_mean(MEX_ARGS) { - if (nrhs != 1) { - mexErrMsgTxt("Usage: caffe('read_mean', 'path_to_binary_mean_file'"); - return; - } - const string& mean_file = mxArrayToString(prhs[0]); - Blob data_mean; - LOG(INFO) << "Loading mean file from: " << mean_file; - BlobProto blob_proto; - bool result = ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto); - if (!result) { - mexErrMsgTxt("Couldn't read the file"); - return; - } - data_mean.FromProto(blob_proto); - mwSize dims[4] = {data_mean.width(), data_mean.height(), - data_mean.channels(), data_mean.num() }; - mxArray* mx_blob = mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL); - float* data_ptr = reinterpret_cast(mxGetPr(mx_blob)); - caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr); - mexWarnMsgTxt("Remember that Caffe saves in [width, height, channels]" - " format and channels are also BGR!"); - plhs[0] = mx_blob; -} - -/** ----------------------------------------------------------------- - ** Available commands. - **/ -struct handler_registry { - string cmd; - void (*func)(MEX_ARGS); -}; - -static handler_registry handlers[] = { - // Public API functions - { "forward", forward }, - { "backward", backward }, - { "init", init }, - { "is_initialized", is_initialized }, - { "set_mode_cpu", set_mode_cpu }, - { "set_mode_gpu", set_mode_gpu }, - { "set_device", set_device }, - { "get_weights", get_weights }, - { "get_init_key", get_init_key }, - { "reset", reset }, - { "read_mean", read_mean }, - // The end. - { "END", NULL }, -}; - - -/** ----------------------------------------------------------------- - ** matlab entry point: caffe(api_command, arg1, arg2, ...) - **/ -void mexFunction(MEX_ARGS) { - mexLock(); // Avoid clearing the mex file. - if (nrhs == 0) { - mex_error("No API command given"); - return; - } - - { // Handle input command - char *cmd = mxArrayToString(prhs[0]); - bool dispatched = false; - // Dispatch to cmd handler - for (int i = 0; handlers[i].func != NULL; i++) { - if (handlers[i].cmd.compare(cmd) == 0) { - handlers[i].func(nlhs, plhs, nrhs-1, prhs+1); - dispatched = true; - break; - } - } - if (!dispatched) { - ostringstream error_msg; - error_msg << "Unknown command '" << cmd << "'"; - mex_error(error_msg.str()); - } - mxFree(cmd); - } -} diff --git a/matlab/caffe/matcaffe_batch.m b/matlab/caffe/matcaffe_batch.m deleted file mode 100644 index f6d1aa83b84..00000000000 --- a/matlab/caffe/matcaffe_batch.m +++ /dev/null @@ -1,75 +0,0 @@ -function [scores,list_im] = matcaffe_batch(list_im, use_gpu) -% scores = matcaffe_batch(list_im, use_gpu) -% -% Demo of the matlab wrapper using the ILSVRC network. -% -% input -% list_im list of images files -% use_gpu 1 to use the GPU, 0 to use the CPU -% -% output -% scores 1000 x num_images ILSVRC output vector -% -% You may need to do the following before you start matlab: -% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64 -% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6 -% Or the equivalent based on where things are installed on your system -% -% Usage: -% scores = matcaffe_batch({'peppers.png','onion.png'}); -% scores = matcaffe_batch('list_images.txt', 1); -if nargin < 1 - % For test purposes - list_im = {'peppers.png','onions.png'}; -end -if ischar(list_im) - %Assume it is a file contaning the list of images - filename = list_im; - list_im = read_cell(filename); -end -% Adjust the batch size and dim to match with models/bvlc_reference_caffenet/deploy.prototxt -batch_size = 10; -dim = 1000; -disp(list_im) -if mod(length(list_im),batch_size) - warning(['Assuming batches of ' num2str(batch_size) ' images rest will be filled with zeros']) -end - -% init caffe network (spews logging info) -if exist('use_gpu', 'var') - matcaffe_init(use_gpu); -else - matcaffe_init(); -end - -d = load('ilsvrc_2012_mean'); -IMAGE_MEAN = d.image_mean; - -% prepare input - -num_images = length(list_im); -scores = zeros(dim,num_images,'single'); -num_batches = ceil(length(list_im)/batch_size) -initic=tic; -for bb = 1 : num_batches - batchtic = tic; - range = 1+batch_size*(bb-1):min(num_images,batch_size * bb); - tic - input_data = prepare_batch(list_im(range),IMAGE_MEAN,batch_size); - toc, tic - fprintf('Batch %d out of %d %.2f%% Complete ETA %.2f seconds\n',... - bb,num_batches,bb/num_batches*100,toc(initic)/bb*(num_batches-bb)); - output_data = caffe('forward', {input_data}); - toc - output_data = squeeze(output_data{1}); - scores(:,range) = output_data(:,mod(range-1,batch_size)+1); - toc(batchtic) -end -toc(initic); - -if exist('filename', 'var') - save([filename '.probs.mat'],'list_im','scores','-v7.3'); -end - - - diff --git a/matlab/caffe/matcaffe_demo_vgg.m b/matlab/caffe/matcaffe_demo_vgg.m deleted file mode 100644 index 4e5a98eb5f4..00000000000 --- a/matlab/caffe/matcaffe_demo_vgg.m +++ /dev/null @@ -1,96 +0,0 @@ -function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file) -% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file) -% -% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets" -% -% INPUT -% im - color image as uint8 HxWx3 -% use_gpu - 1 to use the GPU, 0 to use the CPU -% model_def_file - network configuration (.prototxt file) -% model_file - network weights (.caffemodel file) -% mean_file - mean BGR image as uint8 HxWx3 (.mat file) -% -% OUTPUT -% scores 1000-dimensional ILSVRC score vector -% -% EXAMPLE USAGE -% model_def_file = 'zoo/VGG_CNN_F_deploy.prototxt'; -% model_file = 'zoo/VGG_CNN_F.caffemodel'; -% mean_file = 'zoo/VGG_mean.mat'; -% use_gpu = true; -% im = imread('../../examples/images/cat.jpg'); -% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file); -% -% NOTES -% the image crops are prepared as described in the paper (the aspect ratio is preserved) -% -% PREREQUISITES -% You may need to do the following before you start matlab: -% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64 -% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6 -% Or the equivalent based on where things are installed on your system - -% init caffe network (spews logging info) -matcaffe_init(use_gpu, model_def_file, model_file); - -% prepare oversampled input -% input_data is Height x Width x Channel x Num -tic; -input_data = {prepare_image(im, mean_file)}; -toc; - -% do forward pass to get scores -% scores are now Width x Height x Channels x Num -tic; -scores = caffe('forward', input_data); -toc; - -scores = scores{1}; -% size(scores) -scores = squeeze(scores); -% scores = mean(scores,2); - -% [~,maxlabel] = max(scores); - -% ------------------------------------------------------------------------ -function images = prepare_image(im, mean_file) -% ------------------------------------------------------------------------ -IMAGE_DIM = 256; -CROPPED_DIM = 224; - -d = load(mean_file); -IMAGE_MEAN = d.image_mean; - -% resize to fixed input size -im = single(im); - -if size(im, 1) < size(im, 2) - im = imresize(im, [IMAGE_DIM NaN]); -else - im = imresize(im, [NaN IMAGE_DIM]); -end - -% RGB -> BGR -im = im(:, :, [3 2 1]); - -% oversample (4 corners, center, and their x-axis flips) -images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single'); - -indices_y = [0 size(im,1)-CROPPED_DIM] + 1; -indices_x = [0 size(im,2)-CROPPED_DIM] + 1; -center_y = floor(indices_y(2) / 2)+1; -center_x = floor(indices_x(2) / 2)+1; - -curr = 1; -for i = indices_y - for j = indices_x - images(:, :, :, curr) = ... - permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :)-IMAGE_MEAN, [2 1 3]); - images(:, :, :, curr+5) = images(end:-1:1, :, :, curr); - curr = curr + 1; - end -end -images(:,:,:,5) = ... - permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:)-IMAGE_MEAN, ... - [2 1 3]); -images(:,:,:,10) = images(end:-1:1, :, :, curr); diff --git a/matlab/caffe/matcaffe_demo_vgg_mean_pix.m b/matlab/caffe/matcaffe_demo_vgg_mean_pix.m deleted file mode 100644 index 5f7898a7029..00000000000 --- a/matlab/caffe/matcaffe_demo_vgg_mean_pix.m +++ /dev/null @@ -1,102 +0,0 @@ -function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file) -% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file) -% -% Demo of the matlab wrapper based on the networks used for the "VGG" entry -% in the ILSVRC-2014 competition and described in the tech. report -% "Very Deep Convolutional Networks for Large-Scale Image Recognition" -% http://arxiv.org/abs/1409.1556/ -% -% INPUT -% im - color image as uint8 HxWx3 -% use_gpu - 1 to use the GPU, 0 to use the CPU -% model_def_file - network configuration (.prototxt file) -% model_file - network weights (.caffemodel file) -% -% OUTPUT -% scores 1000-dimensional ILSVRC score vector -% -% EXAMPLE USAGE -% model_def_file = 'zoo/deploy.prototxt'; -% model_file = 'zoo/model.caffemodel'; -% use_gpu = true; -% im = imread('../../examples/images/cat.jpg'); -% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file); -% -% NOTES -% mean pixel subtraction is used instead of the mean image subtraction -% -% PREREQUISITES -% You may need to do the following before you start matlab: -% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64 -% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6 -% Or the equivalent based on where things are installed on your system - -% init caffe network (spews logging info) -matcaffe_init(use_gpu, model_def_file, model_file); - -% mean BGR pixel -mean_pix = [103.939, 116.779, 123.68]; - -% prepare oversampled input -% input_data is Height x Width x Channel x Num -tic; -input_data = {prepare_image(im, mean_pix)}; -toc; - -% do forward pass to get scores -% scores are now Width x Height x Channels x Num -tic; -scores = caffe('forward', input_data); -toc; - -scores = scores{1}; -% size(scores) -scores = squeeze(scores); -% scores = mean(scores,2); - -% [~,maxlabel] = max(scores); - -% ------------------------------------------------------------------------ -function images = prepare_image(im, mean_pix) -% ------------------------------------------------------------------------ -IMAGE_DIM = 256; -CROPPED_DIM = 224; - -% resize to fixed input size -im = single(im); - -if size(im, 1) < size(im, 2) - im = imresize(im, [IMAGE_DIM NaN]); -else - im = imresize(im, [NaN IMAGE_DIM]); -end - -% RGB -> BGR -im = im(:, :, [3 2 1]); - -% oversample (4 corners, center, and their x-axis flips) -images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single'); - -indices_y = [0 size(im,1)-CROPPED_DIM] + 1; -indices_x = [0 size(im,2)-CROPPED_DIM] + 1; -center_y = floor(indices_y(2) / 2)+1; -center_x = floor(indices_x(2) / 2)+1; - -curr = 1; -for i = indices_y - for j = indices_x - images(:, :, :, curr) = ... - permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]); - images(:, :, :, curr+5) = images(end:-1:1, :, :, curr); - curr = curr + 1; - end -end -images(:,:,:,5) = ... - permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:), ... - [2 1 3]); -images(:,:,:,10) = images(end:-1:1, :, :, curr); - -% mean BGR pixel subtraction -for c = 1:3 - images(:, :, c, :) = images(:, :, c, :) - mean_pix(c); -end diff --git a/matlab/caffe/matcaffe_init.m b/matlab/caffe/matcaffe_init.m deleted file mode 100644 index 5d0a0a70bde..00000000000 --- a/matlab/caffe/matcaffe_init.m +++ /dev/null @@ -1,41 +0,0 @@ -function matcaffe_init(use_gpu, model_def_file, model_file) -% matcaffe_init(model_def_file, model_file, use_gpu) -% Initilize matcaffe wrapper - -if nargin < 1 - % By default use CPU - use_gpu = 0; -end -if nargin < 2 || isempty(model_def_file) - % By default use imagenet_deploy - model_def_file = '../../models/bvlc_reference_caffenet/deploy.prototxt'; -end -if nargin < 3 || isempty(model_file) - % By default use caffe reference model - model_file = '../../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'; -end - - -if caffe('is_initialized') == 0 - if exist(model_file, 'file') == 0 - % NOTE: you'll have to get the pre-trained ILSVRC network - error('You need a network model file'); - end - if ~exist(model_def_file,'file') - % NOTE: you'll have to get network definition - error('You need the network prototxt definition'); - end - % load network in TEST phase - caffe('init', model_def_file, model_file, 'test') -end -fprintf('Done with init\n'); - -% set to use GPU or CPU -if use_gpu - fprintf('Using GPU Mode\n'); - caffe('set_mode_gpu'); -else - fprintf('Using CPU Mode\n'); - caffe('set_mode_cpu'); -end -fprintf('Done with set_mode\n'); diff --git a/matlab/caffe/prepare_batch.m b/matlab/caffe/prepare_batch.m deleted file mode 100644 index 345c8eb5f0b..00000000000 --- a/matlab/caffe/prepare_batch.m +++ /dev/null @@ -1,41 +0,0 @@ -% ------------------------------------------------------------------------ -function images = prepare_batch(image_files,IMAGE_MEAN,batch_size) -% ------------------------------------------------------------------------ -if nargin < 2 - d = load('ilsvrc_2012_mean'); - IMAGE_MEAN = d.image_mean; -end -num_images = length(image_files); -if nargin < 3 - batch_size = num_images; -end - -IMAGE_DIM = 256; -CROPPED_DIM = 227; -indices = [0 IMAGE_DIM-CROPPED_DIM] + 1; -center = floor(indices(2) / 2)+1; - -num_images = length(image_files); -images = zeros(CROPPED_DIM,CROPPED_DIM,3,batch_size,'single'); - -parfor i=1:num_images - % read file - fprintf('%c Preparing %s\n',13,image_files{i}); - try - im = imread(image_files{i}); - % resize to fixed input size - im = single(im); - im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear'); - % Transform GRAY to RGB - if size(im,3) == 1 - im = cat(3,im,im,im); - end - % permute from RGB to BGR (IMAGE_MEAN is already BGR) - im = im(:,:,[3 2 1]) - IMAGE_MEAN; - % Crop the center of the image - images(:,:,:,i) = permute(im(center:center+CROPPED_DIM-1,... - center:center+CROPPED_DIM-1,:),[2 1 3]); - catch - warning('Problems with file',image_files{i}); - end -end \ No newline at end of file diff --git a/matlab/caffe/print_cell.m b/matlab/caffe/print_cell.m deleted file mode 100644 index 864340d4be9..00000000000 --- a/matlab/caffe/print_cell.m +++ /dev/null @@ -1,42 +0,0 @@ -function res=print_cell(input,file,linesep,cellsep) -assert(iscell(input),'The input should be a cell') -if nargin < 4 - cellsep = '\t'; -end -if nargin < 3 - linesep = '\n'; -end -if exist('file','var') && ~isempty(file) - %% - fid = fopen(file,'w'); - for l=1:length(input) - if iscell(input{l}) - for i=1:length(input{l}) - fprintf(fid,['%s' cellsep],input{l}{i}); - end - fprintf(fid,linesep); - else - if size(input,2) > 1 - for i=1:size(input,2) - fprintf(fid,'%s ',input{l,i}); - end - fprintf(fid,linesep); - else - fprintf(fid,['%s' linesep],input{l}); - end - end - end - fclose(fid); -else - res = ''; - for l=1:length(input) - if iscell(input{l}) - for i=1:length(input{l}) - res = [res sprintf([cellsep{1} '%s' cellsep{2}],input{l}{i})]; - end - res = [res sprintf(linesep)]; - else - res = [res sprintf(['%s' linesep],input{l}(:))]; - end - end -end \ No newline at end of file diff --git a/matlab/caffe/read_cell.m b/matlab/caffe/read_cell.m deleted file mode 100644 index 19831167106..00000000000 --- a/matlab/caffe/read_cell.m +++ /dev/null @@ -1,21 +0,0 @@ -function res=read_cell(filename,linesep,cellsep) -if nargin < 2, linesep='\n'; end -if nargin < 3, cellsep = '\t'; end -if exist(filename,'file') - fid = fopen(filename); -else - % Assume that filename is either a file ide or a string - fid = filename; -end - -fileLines = textscan(fid,'%s','delimiter',linesep,'BufSize',100000); - -fileLines = fileLines{1}; - -if regexp(fileLines{1},cellsep,'once') - fileLines = regexprep(fileLines,['^' cellsep '|' cellsep '$'],''); - res = regexp(fileLines,cellsep,'split'); - res = cell2matcell(res); -else - res = fileLines; -end diff --git a/matlab/caffe/matcaffe_demo.m b/matlab/classification_demo.m similarity index 56% rename from matlab/caffe/matcaffe_demo.m rename to matlab/classification_demo.m index a931f910cbf..43a7bf62fdf 100644 --- a/matlab/caffe/matcaffe_demo.m +++ b/matlab/classification_demo.m @@ -1,7 +1,16 @@ -function [scores, maxlabel] = matcaffe_demo(im, use_gpu) -% scores = matcaffe_demo(im, use_gpu) +function [scores, maxlabel] = classification_demo(im, use_gpu) +% scores = classification_demo(im, use_gpu) % -% Demo of the matlab wrapper using the ILSVRC network. +% Image classification demo using BVLC CaffeNet. +% +% IMPORTANT: before you run this demo, you should download BVLC CaffeNet +% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html) +% +% **************************************************************************** +% For detailed documentation and usage on Caffe's Matlab interface, please +% refer to Caffe Interface Tutorial at +% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab +% **************************************************************************** % % input % im color image as uint8 HxWx3 @@ -16,8 +25,8 @@ % Or the equivalent based on where things are installed on your system % % Usage: -% im = imread('../../examples/images/cat.jpg'); -% scores = matcaffe_demo(im, 1); +% im = imread('../examples/images/cat.jpg'); +% scores = classification_demo(im, 1); % [score, class] = max(scores); % Five things to be aware of: % caffe uses row-major order @@ -26,7 +35,7 @@ % matlab uses RGB color channel order % images need to have the data mean subtracted -% Data coming in from matlab needs to be in the order +% Data coming in from matlab needs to be in the order % [width, height, channels, images] % where width is the fastest dimension. % Here is the rough matlab for putting image data into the correct @@ -42,20 +51,27 @@ % If you have multiple images, cat them with cat(4, ...) -% The actual forward function. It takes in a cell array of 4-D arrays as -% input and outputs a cell array. - - -% init caffe network (spews logging info) -if exist('use_gpu', 'var') - matcaffe_init(use_gpu); +% Set caffe mode +if exist('use_gpu', 'var') && use_gpu + caffe.set_mode_gpu(); + gpu_id = 0; % we will use the first gpu in this demo + caffe.set_device(gpu_id); else - matcaffe_init(); + caffe.set_mode_cpu(); end +% Initialize the network using BVLC CaffeNet for image classification +% Weights (parameter) file needs to be downloaded from Model Zoo. +model_dir = '../models/bvlc_reference_caffenet/'; +net_model = [model_dir 'deploy.prototxt']; +net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel']; +phase = 'test'; +net = caffe.Net(net_model, net_weights, phase); + if nargin < 1 - % For demo purposes we will use the peppers image - im = imread('peppers.png'); + % For demo purposes we will use the cat image + fprintf('using ../examples/images/cat.jpg as input image\n'); + im = imread('../examples/images/cat.jpg'); end % prepare oversampled input @@ -67,7 +83,10 @@ % do forward pass to get scores % scores are now Width x Height x Channels x Num tic; -scores = caffe('forward', input_data); +% The net forward function. It takes in a cell array of N-D arrays +% (where N == 4 here) containing data of input blob(s) and outputs a cell +% array containing data from output blob(s) +scores = net.forward(input_data); toc; scores = scores{1}; @@ -77,10 +96,13 @@ [~,maxlabel] = max(scores); +% call caffe.reset_all() to reset caffe +caffe.reset_all(); + % ------------------------------------------------------------------------ function images = prepare_image(im) % ------------------------------------------------------------------------ -d = load('ilsvrc_2012_mean'); +d = load('+caffe/imagenet/ilsvrc_2012_mean.mat'); IMAGE_MEAN = d.image_mean; IMAGE_DIM = 256; CROPPED_DIM = 227; @@ -98,13 +120,13 @@ for i = indices for j = indices images(:, :, :, curr) = ... - permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]); + permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]); images(:, :, :, curr+5) = images(end:-1:1, :, :, curr); curr = curr + 1; end end center = floor(indices(2) / 2)+1; images(:,:,:,5) = ... - permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ... - [2 1 3]); + permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ... + [2 1 3]); images(:,:,:,10) = images(end:-1:1, :, :, curr); diff --git a/matlab/caffe/hdf5creation/.gitignore b/matlab/hdf5creation/.gitignore similarity index 100% rename from matlab/caffe/hdf5creation/.gitignore rename to matlab/hdf5creation/.gitignore diff --git a/matlab/caffe/hdf5creation/demo.m b/matlab/hdf5creation/demo.m similarity index 100% rename from matlab/caffe/hdf5creation/demo.m rename to matlab/hdf5creation/demo.m diff --git a/matlab/caffe/hdf5creation/store2hdf5.m b/matlab/hdf5creation/store2hdf5.m similarity index 100% rename from matlab/caffe/hdf5creation/store2hdf5.m rename to matlab/hdf5creation/store2hdf5.m From 0f13feef342fc59aa113bafb45041b35ff97b649 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Thu, 28 May 2015 18:33:54 +0800 Subject: [PATCH 097/446] Move demo to demo/ and check weights file existence Move all Matlab demo to caffe/matlab/demo. Since we want the user to add caffe/matlab to Matlab search PATH, we don't want to mess it up with too many files Check if CaffeNet is already downloaded in classification demo. --- docs/tutorial/interfaces.md | 10 +++++---- matlab/{ => demo}/classification_demo.m | 27 ++++++++++++++++++------- 2 files changed, 26 insertions(+), 11 deletions(-) rename matlab/{ => demo}/classification_demo.m (84%) diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md index a57e17339b0..a59a410d964 100644 --- a/docs/tutorial/interfaces.md +++ b/docs/tutorial/interfaces.md @@ -84,18 +84,18 @@ In MatCaffe, you can * Run for a certain number of iterations and give back control to Matlab * Intermingle arbitrary Matlab code to with gradient steps -An ILSVRC image classification demo is in caffe/matlab/classification_demo.m +An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m ### Build MatCaffe Build MatCaffe with `make all matcaffe`. After that, you may test it using `make mattest`. -Common issue: if you run into error messages `libstdc++.so.6:version 'GLIBCXX_3.4.15' not found` during `make mattest`, then it means that your Matlab's runtime libraries does not match your compile-time libraries. You may need to do the following before you start matlab: +Common issue: if you run into error messages like `libstdc++.so.6:version 'GLIBCXX_3.4.15' not found` during `make mattest`, then it usually means that your Matlab's runtime libraries do not match your compile-time libraries. You may need to do the following before you start Matlab: export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64 export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6 -Or the equivalent based on where things are installed on your system, and do `make mattest` again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your `LD_LIBRARY_PATH` environment variable. You can run `!ldd ./matlab/+caffe/private/caffe_.mexa64` in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your `LD_PRELOAD` environment variable. +Or the equivalent based on where things are installed on your system, and do `make mattest` again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your `LD_LIBRARY_PATH` environment variable. You can run `!ldd ./matlab/+caffe/private/caffe_.mexa64` (the mex extension may differ on your system) in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your `LD_PRELOAD` environment variable. After successful building and testing, add this package to Matlab search PATH by starting `matlab` from caffe root folder and running the following commands in Matlab command window. @@ -270,7 +270,9 @@ To read Caffe's example image and resize to `[width, height]` and suppose we wan im_data = permute(im_data, [2, 1, 3]); % permute width and height im_data = single(im_data); % convert to single precision -We do not provide extra functions for data output as Matlab itself is already quite powerful in output. +Also, you may take a look at caffe/matlab/demo/classification_demo.m to see how to prepare input by taking crops from an image. + +We show in caffe/matlab/hdf5creation how to read and write HDF5 data with Matlab. We do not provide extra functions for data output as Matlab itself is already quite powerful in output. #### Clear nets and solvers diff --git a/matlab/classification_demo.m b/matlab/demo/classification_demo.m similarity index 84% rename from matlab/classification_demo.m rename to matlab/demo/classification_demo.m index 43a7bf62fdf..453582476be 100644 --- a/matlab/classification_demo.m +++ b/matlab/demo/classification_demo.m @@ -1,5 +1,5 @@ function [scores, maxlabel] = classification_demo(im, use_gpu) -% scores = classification_demo(im, use_gpu) +% [scores, maxlabel] = classification_demo(im, use_gpu) % % Image classification demo using BVLC CaffeNet. % @@ -18,6 +18,7 @@ % % output % scores 1000-dimensional ILSVRC score vector +% maxlabel the label of the highest score % % You may need to do the following before you start matlab: % $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64 @@ -25,7 +26,7 @@ % Or the equivalent based on where things are installed on your system % % Usage: -% im = imread('../examples/images/cat.jpg'); +% im = imread('../../examples/images/cat.jpg'); % scores = classification_demo(im, 1); % [score, class] = max(scores); % Five things to be aware of: @@ -51,6 +52,13 @@ % If you have multiple images, cat them with cat(4, ...) +% Add caffe/matlab to you Matlab search PATH to use matcaffe +if exist('../+caffe', 'dir') + addpath('..'); +else + error('Please run this demo from caffe/matlab/demo'); +end + % Set caffe mode if exist('use_gpu', 'var') && use_gpu caffe.set_mode_gpu(); @@ -62,16 +70,21 @@ % Initialize the network using BVLC CaffeNet for image classification % Weights (parameter) file needs to be downloaded from Model Zoo. -model_dir = '../models/bvlc_reference_caffenet/'; +model_dir = '../../models/bvlc_reference_caffenet/'; net_model = [model_dir 'deploy.prototxt']; net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel']; -phase = 'test'; +phase = 'test'; % run with phase test (so that dropout isn't applied) +if ~exist(net_weights, 'file') + error('Please download CaffeNet from Model Zoo before you run this demo'); +end + +% Initialize a network net = caffe.Net(net_model, net_weights, phase); if nargin < 1 % For demo purposes we will use the cat image - fprintf('using ../examples/images/cat.jpg as input image\n'); - im = imread('../examples/images/cat.jpg'); + fprintf('using caffe/examples/images/cat.jpg as input image\n'); + im = imread('../../examples/images/cat.jpg'); end % prepare oversampled input @@ -102,7 +115,7 @@ % ------------------------------------------------------------------------ function images = prepare_image(im) % ------------------------------------------------------------------------ -d = load('+caffe/imagenet/ilsvrc_2012_mean.mat'); +d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat'); IMAGE_MEAN = d.image_mean; IMAGE_DIM = 256; CROPPED_DIM = 227; From 18adbb8d1a1be91598aa23bad6550eed954e32a9 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Fri, 29 May 2015 00:23:06 +0800 Subject: [PATCH 098/446] Fix automatic header file dependency for MatCaffe Automatic header file dependency was introduced in #1472, but not correctly applied to matcaffe. Fix it by moving ./caffe_.d to build/matlab/+caffe/private/caffe_.d and add it to DEPS --- Makefile | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 3748b52ca47..e4e66dfd138 100644 --- a/Makefile +++ b/Makefile @@ -118,7 +118,7 @@ GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o}) EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o}) # Output files for automatic dependency generation DEPS := ${CXX_OBJS:.o=.d} ${CU_OBJS:.o=.d} ${TEST_CXX_OBJS:.o=.d} \ - ${TEST_CU_OBJS:.o=.d} + ${TEST_CU_OBJS:.o=.d} $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d} # tool, example, and test bins TOOL_BINS := ${TOOL_OBJS:.o=.bin} EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin} @@ -460,6 +460,9 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME) CXX="$(CXX)" \ CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \ CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@ + @ if [ -f "$(PROJECT)_.d" ]; then \ + mv -f $(PROJECT)_.d $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}; \ + fi runtest: $(TEST_ALL_BIN) $(TOOL_BUILD_DIR)/caffe From d07e5f796907a2bc048bdab3cdb4ace05fa60d7a Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Fri, 29 May 2015 07:50:23 +0800 Subject: [PATCH 099/446] More tests for Blob, Layer, copy_from and step, fix some typos More testes are added into test_net.m and test_solver.m --- docs/tutorial/interfaces.md | 4 ++-- matlab/+caffe/+test/test_net.m | 24 +++++++++++++++++++++++- matlab/+caffe/+test/test_solver.m | 2 ++ matlab/+caffe/Net.m | 2 +- matlab/+caffe/Solver.m | 2 +- matlab/+caffe/io.m | 2 +- matlab/+caffe/run_tests.m | 3 +++ 7 files changed, 33 insertions(+), 6 deletions(-) diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md index a59a410d964..12963318485 100644 --- a/docs/tutorial/interfaces.md +++ b/docs/tutorial/interfaces.md @@ -82,9 +82,9 @@ In MatCaffe, you can * Resume training from solver snapshots * Access train net and test nets in a solver * Run for a certain number of iterations and give back control to Matlab -* Intermingle arbitrary Matlab code to with gradient steps +* Intermingle arbitrary Matlab code with gradient steps -An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m +An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it). ### Build MatCaffe diff --git a/matlab/+caffe/+test/test_net.m b/matlab/+caffe/+test/test_net.m index 5d9ba000209..3dabe84d111 100644 --- a/matlab/+caffe/+test/test_net.m +++ b/matlab/+caffe/+test/test_net.m @@ -48,6 +48,24 @@ end end methods (Test) + function self = test_blob(self) + self.net.blobs('data').set_data(10 * ones(self.net.blobs('data').shape)); + self.verifyEqual(self.net.blobs('data').get_data(), ... + 10 * ones(self.net.blobs('data').shape, 'single')); + self.net.blobs('data').set_diff(-2 * ones(self.net.blobs('data').shape)); + self.verifyEqual(self.net.blobs('data').get_diff(), ... + -2 * ones(self.net.blobs('data').shape, 'single')); + original_shape = self.net.blobs('data').shape; + self.net.blobs('data').reshape([6 5 4 3 2 1]); + self.verifyEqual(self.net.blobs('data').shape, [6 5 4 3 2 1]); + self.net.blobs('data').reshape(original_shape); + self.net.reshape(); + end + function self = test_layer(self) + self.verifyEqual(self.net.params('conv', 1).shape, [2 2 2 11]); + self.verifyEqual(self.net.layers('conv').params(2).shape, 11); + self.verifyEqual(self.net.layers('conv').type(), 'Convolution'); + end function test_forward_backward(self) self.net.forward_prefilled(); self.net.backward_prefilled(); @@ -60,13 +78,17 @@ function test_save_and_read(self) weights_file = tempname(); self.net.save(weights_file); model_file2 = caffe.test.test_net.simple_net_file(self.num_output); - net2 = caffe.Net(model_file2, weights_file, 'train'); + net2 = caffe.Net(model_file2, 'train'); + net2.copy_from(weights_file); + net3 = caffe.Net(model_file2, weights_file, 'train'); delete(model_file2); delete(weights_file); for l = 1:length(self.net.layer_vec) for i = 1:length(self.net.layer_vec(l).params) self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ... net2.layer_vec(l).params(i).get_data()); + self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ... + net3.layer_vec(l).params(i).get_data()); end end end diff --git a/matlab/+caffe/+test/test_solver.m b/matlab/+caffe/+test/test_solver.m index 682dad48a3b..739258b0e85 100644 --- a/matlab/+caffe/+test/test_solver.m +++ b/matlab/+caffe/+test/test_solver.m @@ -36,6 +36,8 @@ methods (Test) function test_solve(self) self.verifyEqual(self.solver.iter(), 0) + self.solver.step(30); + self.verifyEqual(self.solver.iter(), 30) self.solver.solve() self.verifyEqual(self.solver.iter(), 100) end diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m index a6761060dfb..e6295bba1a4 100644 --- a/matlab/+caffe/Net.m +++ b/matlab/+caffe/Net.m @@ -111,7 +111,7 @@ function backward_prefilled(self) self.blobs(self.outputs{n}).set_diff(output_diff{n}); end self.backward_prefilled(); - % retrieve diff from input_blobs + % retrieve diff from input blobs res = cell(length(self.inputs), 1); for n = 1:length(self.inputs) res{n} = self.blobs(self.inputs{n}).get_diff(); diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m index daaa8022b91..f8bdc4e22b2 100644 --- a/matlab/+caffe/Solver.m +++ b/matlab/+caffe/Solver.m @@ -41,7 +41,7 @@ end function restore(self, snapshot_filename) CHECK(ischar(snapshot_filename), 'snapshot_filename must be a string'); - CHECK_FILE_EXIST(snapshot_filename) + CHECK_FILE_EXIST(snapshot_filename); caffe_('solver_restore', self.hSolver_self, snapshot_filename); end function solve(self) diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m index 7a30bfb5772..c9e07aee9e8 100644 --- a/matlab/+caffe/io.m +++ b/matlab/+caffe/io.m @@ -17,7 +17,7 @@ function mean_data = read_mean(mean_proto_file) % mean_data = read_mean(mean_proto_file) % read image mean data from binaryproto file - CHECK(ischar(mean_proto_file), 'im_file must be a string'); + CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string'); CHECK_FILE_EXIST(mean_proto_file); mean_data = caffe_('read_mean', mean_proto_file); end diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m index 8773c9f638b..93896855ac2 100644 --- a/matlab/+caffe/run_tests.m +++ b/matlab/+caffe/run_tests.m @@ -2,6 +2,9 @@ % results = run_tests() % run all tests in this caffe matlab wrapper package +% use CPU for testing +caffe.set_mode_cpu(); + % reset caffe before testing caffe.reset_all(); From 2e03d89fd4578849d7793e166a1993ae521b6561 Mon Sep 17 00:00:00 2001 From: TorosFanny Date: Fri, 29 May 2015 21:19:56 -0700 Subject: [PATCH 100/446] [example] fix path for diff in net surgery --- examples/net_surgery.ipynb | 88 +++++++++++++++++++++++++++++++++++++- 1 file changed, 86 insertions(+), 2 deletions(-) diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb index 1fb99bd3726..303c22ba3fd 100644 --- a/examples/net_surgery.ipynb +++ b/examples/net_surgery.ipynb @@ -5494,12 +5494,96 @@ "name": "stdout", "output_type": "stream", "text": [ - "diff: imagenet/bvlc_caffenet_full_conv.prototxt: No such file or directory\r\n" + "1,2c1\r\n", + "< # Fully convolutional network version of CaffeNet.\r\n", + "< name: \"CaffeNetConv\"\r\n", + "---\r\n", + "> name: \"CaffeNet\"\r\n", + "4c3\r\n", + "< input_dim: 1\r\n", + "---\r\n", + "> input_dim: 10\r\n", + "6,7c5,6\r\n", + "< input_dim: 451\r\n", + "< input_dim: 451\r\n", + "---\r\n", + "> input_dim: 227\r\n", + "> input_dim: 227\r\n", + "152,153c151,152\r\n", + "< name: \"fc6-conv\"\r\n", + "< type: \"Convolution\"\r\n", + "---\r\n", + "> name: \"fc6\"\r\n", + "> type: \"InnerProduct\"\r\n", + "155,156c154,155\r\n", + "< top: \"fc6-conv\"\r\n", + "< convolution_param {\r\n", + "---\r\n", + "> top: \"fc6\"\r\n", + "> inner_product_param {\r\n", + "158d156\r\n", + "< kernel_size: 6\r\n", + "164,165c162,163\r\n", + "< bottom: \"fc6-conv\"\r\n", + "< top: \"fc6-conv\"\r\n", + "---\r\n", + "> bottom: \"fc6\"\r\n", + "> top: \"fc6\"\r\n", + "170,171c168,169\r\n", + "< bottom: \"fc6-conv\"\r\n", + "< top: \"fc6-conv\"\r\n", + "---\r\n", + "> bottom: \"fc6\"\r\n", + "> top: \"fc6\"\r\n", + "177,181c175,179\r\n", + "< name: \"fc7-conv\"\r\n", + "< type: \"Convolution\"\r\n", + "< bottom: \"fc6-conv\"\r\n", + "< top: \"fc7-conv\"\r\n", + "< convolution_param {\r\n", + "---\r\n", + "> name: \"fc7\"\r\n", + "> type: \"InnerProduct\"\r\n", + "> bottom: \"fc6\"\r\n", + "> top: \"fc7\"\r\n", + "> inner_product_param {\r\n", + "183d180\r\n", + "< kernel_size: 1\r\n", + "189,190c186,187\r\n", + "< bottom: \"fc7-conv\"\r\n", + "< top: \"fc7-conv\"\r\n", + "---\r\n", + "> bottom: \"fc7\"\r\n", + "> top: \"fc7\"\r\n", + "195,196c192,193\r\n", + "< bottom: \"fc7-conv\"\r\n", + "< top: \"fc7-conv\"\r\n", + "---\r\n", + "> bottom: \"fc7\"\r\n", + "> top: \"fc7\"\r\n", + "202,206c199,203\r\n", + "< name: \"fc8-conv\"\r\n", + "< type: \"Convolution\"\r\n", + "< bottom: \"fc7-conv\"\r\n", + "< top: \"fc8-conv\"\r\n", + "< convolution_param {\r\n", + "---\r\n", + "> name: \"fc8\"\r\n", + "> type: \"InnerProduct\"\r\n", + "> bottom: \"fc7\"\r\n", + "> top: \"fc8\"\r\n", + "> inner_product_param {\r\n", + "208d204\r\n", + "< kernel_size: 1\r\n", + "214c210\r\n", + "< bottom: \"fc8-conv\"\r\n", + "---\r\n", + "> bottom: \"fc8\"\r\n" ] } ], "source": [ - "!diff imagenet/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt" + "!diff net_surgery/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt" ] }, { From 4874c01487f552dd4b16313d0a6723634f1912e5 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Fri, 29 May 2015 22:32:25 -0700 Subject: [PATCH 101/446] Add a simple C++ classification example Closes #2487 Example usage: ./build/examples/cpp_classification/classification.bin \ models/bvlc_reference_caffenet/deploy.prototxt \ models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \ data/ilsvrc12/imagenet_mean.binaryproto \ data/ilsvrc12/synset_words.txt \ examples/images/cat.jpg --- .../cpp_classification/classification.cpp | 255 ++++++++++++++++++ examples/cpp_classification/readme.md | 77 ++++++ 2 files changed, 332 insertions(+) create mode 100644 examples/cpp_classification/classification.cpp create mode 100644 examples/cpp_classification/readme.md diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp new file mode 100644 index 00000000000..1c6371e382b --- /dev/null +++ b/examples/cpp_classification/classification.cpp @@ -0,0 +1,255 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include + +using namespace caffe; // NOLINT(build/namespaces) +using std::string; + +/* Pair (label, confidence) representing a prediction. */ +typedef std::pair Prediction; + +class Classifier { + public: + Classifier(const string& model_file, + const string& trained_file, + const string& mean_file, + const string& label_file); + + std::vector Classify(const cv::Mat& img, int N = 5); + + private: + void SetMean(const string& mean_file); + + std::vector Predict(const cv::Mat& img); + + void WrapInputLayer(std::vector* input_channels); + + void Preprocess(const cv::Mat& img, + std::vector* input_channels); + + private: + shared_ptr > net_; + cv::Size input_geometry_; + int num_channels_; + cv::Mat mean_; + std::vector labels_; +}; + +Classifier::Classifier(const string& model_file, + const string& trained_file, + const string& mean_file, + const string& label_file) { +#ifdef CPU_ONLY + Caffe::set_mode(Caffe::CPU); +#else + Caffe::set_mode(Caffe::GPU); +#endif + + /* Load the network. */ + net_.reset(new Net(model_file, TEST)); + net_->CopyTrainedLayersFrom(trained_file); + + CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input."; + CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output."; + + Blob* input_layer = net_->input_blobs()[0]; + num_channels_ = input_layer->channels(); + CHECK(num_channels_ == 3 || num_channels_ == 1) + << "Input layer should have 1 or 3 channels."; + input_geometry_ = cv::Size(input_layer->width(), input_layer->height()); + + /* Load the binaryproto mean file. */ + SetMean(mean_file); + + /* Load labels. */ + std::ifstream labels(label_file.c_str()); + CHECK(labels) << "Unable to open labels file " << label_file; + string line; + while (std::getline(labels, line)) + labels_.push_back(string(line)); + + Blob* output_layer = net_->output_blobs()[0]; + CHECK_EQ(labels_.size(), output_layer->channels()) + << "Number of labels is different from the output layer dimension."; +} + +static bool PairCompare(const std::pair& lhs, + const std::pair& rhs) { + return lhs.first > rhs.first; +} + +/* Return the indices of the top N values of vector v. */ +static std::vector Argmax(const std::vector& v, int N) { + std::vector > pairs; + for (size_t i = 0; i < v.size(); ++i) + pairs.push_back(std::make_pair(v[i], i)); + std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare); + + std::vector result; + for (int i = 0; i < N; ++i) + result.push_back(pairs[i].second); + return result; +} + +/* Return the top N predictions. */ +std::vector Classifier::Classify(const cv::Mat& img, int N) { + std::vector output = Predict(img); + + std::vector maxN = Argmax(output, N); + std::vector predictions; + for (int i = 0; i < N; ++i) { + int idx = maxN[i]; + predictions.push_back(std::make_pair(labels_[idx], output[idx])); + } + + return predictions; +} + +/* Load the mean file in binaryproto format. */ +void Classifier::SetMean(const string& mean_file) { + BlobProto blob_proto; + ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto); + + /* Convert from BlobProto to Blob */ + Blob mean_blob; + mean_blob.FromProto(blob_proto); + CHECK_EQ(mean_blob.channels(), num_channels_) + << "Number of channels of mean file doesn't match input layer."; + + /* The format of the mean file is planar 32-bit float BGR or grayscale. */ + std::vector channels; + float* data = mean_blob.mutable_cpu_data(); + for (int i = 0; i < num_channels_; ++i) { + /* Extract an individual channel. */ + cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data); + channels.push_back(channel); + data += mean_blob.height() * mean_blob.width(); + } + + /* Merge the separate channels into a single image. */ + cv::Mat mean; + cv::merge(channels, mean); + + /* Compute the global mean pixel value and create a mean image + * filled with this value. */ + cv::Scalar channel_mean = cv::mean(mean); + mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean); +} + +std::vector Classifier::Predict(const cv::Mat& img) { + Blob* input_layer = net_->input_blobs()[0]; + input_layer->Reshape(1, num_channels_, + input_geometry_.height, input_geometry_.width); + /* Forward dimension change to all layers. */ + net_->Reshape(); + + std::vector input_channels; + WrapInputLayer(&input_channels); + + Preprocess(img, &input_channels); + + net_->ForwardPrefilled(); + + /* Copy the output layer to a std::vector */ + Blob* output_layer = net_->output_blobs()[0]; + const float* begin = output_layer->cpu_data(); + const float* end = begin + output_layer->channels(); + return std::vector(begin, end); +} + +/* Wrap the input layer of the network in separate cv::Mat objects + * (one per channel). This way we save one memcpy operation and we + * don't need to rely on cudaMemcpy2D. The last preprocessing + * operation will write the separate channels directly to the input + * layer. */ +void Classifier::WrapInputLayer(std::vector* input_channels) { + Blob* input_layer = net_->input_blobs()[0]; + + int width = input_layer->width(); + int height = input_layer->height(); + float* input_data = input_layer->mutable_cpu_data(); + for (int i = 0; i < input_layer->channels(); ++i) { + cv::Mat channel(height, width, CV_32FC1, input_data); + input_channels->push_back(channel); + input_data += width * height; + } +} + +void Classifier::Preprocess(const cv::Mat& img, + std::vector* input_channels) { + /* Convert the input image to the input image format of the network. */ + cv::Mat sample; + if (img.channels() == 3 && num_channels_ == 1) + cv::cvtColor(img, sample, CV_BGR2GRAY); + else if (img.channels() == 4 && num_channels_ == 1) + cv::cvtColor(img, sample, CV_BGRA2GRAY); + else if (img.channels() == 4 && num_channels_ == 3) + cv::cvtColor(img, sample, CV_BGRA2BGR); + else if (img.channels() == 1 && num_channels_ == 3) + cv::cvtColor(img, sample, CV_GRAY2BGR); + else + sample = img; + + cv::Mat sample_resized; + if (sample.size() != input_geometry_) + cv::resize(sample, sample_resized, input_geometry_); + else + sample_resized = sample; + + cv::Mat sample_float; + if (num_channels_ == 3) + sample_resized.convertTo(sample_float, CV_32FC3); + else + sample_resized.convertTo(sample_float, CV_32FC1); + + cv::Mat sample_normalized; + cv::subtract(sample_float, mean_, sample_normalized); + + /* This operation will write the separate BGR planes directly to the + * input layer of the network because it is wrapped by the cv::Mat + * objects in input_channels. */ + cv::split(sample_normalized, *input_channels); + + CHECK(reinterpret_cast(input_channels->at(0).data) + == net_->input_blobs()[0]->cpu_data()) + << "Input channels are not wrapping the input layer of the network."; +} + +int main(int argc, char** argv) { + if (argc != 6) { + std::cerr << "Usage: " << argv[0] + << " deploy.prototxt network.caffemodel" + << " mean.binaryproto labels.txt img.jpg" << std::endl; + return 1; + } + + ::google::InitGoogleLogging(argv[0]); + + string model_file = argv[1]; + string trained_file = argv[2]; + string mean_file = argv[3]; + string label_file = argv[4]; + Classifier classifier(model_file, trained_file, mean_file, label_file); + + string file = argv[5]; + + std::cout << "---------- Prediction for " + << file << " ----------" << std::endl; + + cv::Mat img = cv::imread(file, -1); + CHECK(!img.empty()) << "Unable to decode image " << file; + std::vector predictions = classifier.Classify(img); + + /* Print the top N predictions. */ + for (size_t i = 0; i < predictions.size(); ++i) { + Prediction p = predictions[i]; + std::cout << std::fixed << std::setprecision(4) << p.second << " - \"" + << p.first << "\"" << std::endl; + } +} diff --git a/examples/cpp_classification/readme.md b/examples/cpp_classification/readme.md new file mode 100644 index 00000000000..a086db1a035 --- /dev/null +++ b/examples/cpp_classification/readme.md @@ -0,0 +1,77 @@ +--- +title: CaffeNet C++ Classification example +description: A simple example performing image classification using the low-level C++ API. +category: example +include_in_docs: true +priority: 10 +--- + +# Classifying ImageNet: using the C++ API + +Caffe, at its core, is written in C++. It is possible to use the C++ +API of Caffe to implement an image classification application similar +to the Python code presented in one of the Notebook example. To look +at a more general-purpose example of the Caffe C++ API, you should +study the source code of the command line tool `caffe` in `tools/caffe.cpp`. + +## Presentation + +A simple C++ code is proposed in +`examples/cpp_classification/classification.cpp`. For the sake of +simplicity, this example does not support oversampling of a single +sample nor batching of multiple independant samples. This example is +not trying to reach the maximum possible classification throughput on +a system, but special care was given to avoid unnecessary +pessimization while keeping the code readable. + +## Compiling + +The C++ example is built automatically when compiling Caffe. To +compile Caffe you should follow the documented instructions. The +classification example will be built as `examples/classification.bin` +in your build directory. + +## Usage + +To use the pre-trained CaffeNet model with the classification example, +you need to download it from the "Model Zoo" using the following +script: +``` +./scripts/download_model_binary.py models/bvlc_reference_caffenet +``` +The ImageNet labels file (also called the *synset file*) is also +required in order to map a prediction to the name of the class: +``` +./data/ilsvrc12/get_ilsvrc_aux.sh. +``` +Using the files that were downloaded, we can classify the provided cat +image (`examples/images/cat.jpg`) using this command: +``` +./build/examples/cpp_classification/classification.bin \ + models/bvlc_reference_caffenet/deploy.prototxt \ + models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \ + data/ilsvrc12/imagenet_mean.binaryproto \ + data/ilsvrc12/synset_words.txt \ + examples/images/cat.jpg +``` +The output should look like this: +``` +---------- Prediction for examples/images/cat.jpg ---------- +0.3134 - "n02123045 tabby, tabby cat" +0.2380 - "n02123159 tiger cat" +0.1235 - "n02124075 Egyptian cat" +0.1003 - "n02119022 red fox, Vulpes vulpes" +0.0715 - "n02127052 lynx, catamount" +``` + +## Improving Performance + +To further improve performance, you will need to leverage the GPU +more, here are some guidelines: + +* Move the data on the GPU early and perform all preprocessing +operations there. +* If you have many images to classify simultaneously, you should use +batching (independent images are classified in a single forward pass). +* Use multiple classification threads to ensure the GPU is always fully +utilized and not waiting for an I/O blocked CPU thread. From 97b4c1453377e6257f6b5efc32e2d5bc472e9323 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Sat, 30 May 2015 09:37:32 +0800 Subject: [PATCH 102/446] Update ilsvrc_2012_mean.mat to W x H x C, update demo and add comments Update previously ilsvrc_2012_mean.mat stores 'image_mean' variable in H x W x C with BGR channels, which is inconsistent with Caffe's data format and inconsistent with caffe.io.read_mean(..). Replace 'image_mean' with 'mean_data' variable in W x H x C and update classification_demo.m. 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zR`5CeeA9x^@4)d(J*e%(cAiZ(=e@C)`yyUGe0+}U+1D|%V1DmlGj@66;s0qyvxCPQ zdRHfR@b51oz6;OL$ztN$43BS9g`J!hMH;?PWCI+28+`f~7AX=#P5&Z_1k=mUN4MAD z$#;)s*9Lyv2BWlM>WFWzmR711rN+P2NPRGUkt29>!!+!((?~IQ4Srm;xYN&%ja5s( zP_^_CwXCY9mPKIt)d|$~b@cOtRr08$N|wIOloHhUkBc*UuH1lmLYcs jWJqs^44MDp8~pG$nbYT+bSnntPf3>%tJ1}`O1k_HUTyRW diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m index c9e07aee9e8..af8369ddfab 100644 --- a/matlab/+caffe/io.m +++ b/matlab/+caffe/io.m @@ -7,16 +7,24 @@ % load an image from disk into Caffe-supported data format % switch channels from RGB to BGR, make width the fastest dimension % and convert to single + % returns im_data in W x H x C. For colored images, C = 3 in BGR + % channels, and for grayscale images, C = 1 CHECK(ischar(im_file), 'im_file must be a string'); CHECK_FILE_EXIST(im_file); im_data = imread(im_file); - im_data = im_data(:, :, [3, 2, 1]); + % permute channels from RGB to BGR for colored images + if size(im_data, 3) == 3 + im_data = im_data(:, :, [3, 2, 1]); + end + % flip width and height to make width the fastest dimension im_data = permute(im_data, [2, 1, 3]); + % convert from uint8 to single im_data = single(im_data); end function mean_data = read_mean(mean_proto_file) % mean_data = read_mean(mean_proto_file) % read image mean data from binaryproto file + % returns mean_data in W x H x C with BGR channels CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string'); CHECK_FILE_EXIST(mean_proto_file); mean_data = caffe_('read_mean', mean_proto_file); diff --git a/matlab/demo/classification_demo.m b/matlab/demo/classification_demo.m index 453582476be..2b60332970b 100644 --- a/matlab/demo/classification_demo.m +++ b/matlab/demo/classification_demo.m @@ -40,15 +40,17 @@ % [width, height, channels, images] % where width is the fastest dimension. % Here is the rough matlab for putting image data into the correct -% format: -% % convert from uint8 to single -% im = single(im); -% % reshape to a fixed size (e.g., 227x227) -% im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear'); -% % permute from RGB to BGR and subtract the data mean (already in BGR) -% im = im(:,:,[3 2 1]) - data_mean; +% format in W x H x C with BGR channels: +% % permute channels from RGB to BGR +% im_data = im(:, :, [3, 2, 1]); % % flip width and height to make width the fastest dimension -% im = permute(im, [2 1 3]); +% im_data = permute(im_data, [2, 1, 3]); +% % convert from uint8 to single +% im_data = single(im_data); +% % reshape to a fixed size (e.g., 227x227). +% im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); +% % subtract mean_data (already in W x H x C with BGR channels) +% im_data = im_data - mean_data; % If you have multiple images, cat them with cat(4, ...) @@ -94,7 +96,7 @@ toc; % do forward pass to get scores -% scores are now Width x Height x Channels x Num +% scores are now Channels x Num, where Channels == 1000 tic; % The net forward function. It takes in a cell array of N-D arrays % (where N == 4 here) containing data of input blob(s) and outputs a cell @@ -103,43 +105,43 @@ toc; scores = scores{1}; -size(scores) -scores = squeeze(scores); -scores = mean(scores,2); +scores = mean(scores, 2); % take average scores over 10 crops -[~,maxlabel] = max(scores); +[~, maxlabel] = max(scores); % call caffe.reset_all() to reset caffe caffe.reset_all(); % ------------------------------------------------------------------------ -function images = prepare_image(im) +function crops_data = prepare_image(im) % ------------------------------------------------------------------------ +% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that +% is already in W x H x C with BGR channels d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat'); -IMAGE_MEAN = d.image_mean; +mean_data = d.mean_data; IMAGE_DIM = 256; CROPPED_DIM = 227; -% resize to fixed input size -im = single(im); -im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear'); -% permute from RGB to BGR (IMAGE_MEAN is already BGR) -im = im(:,:,[3 2 1]) - IMAGE_MEAN; +% Convert an image returned by Matlab's imread to im_data in caffe's data +% format: W x H x C with BGR channels +im_data = im(:, :, [3, 2, 1]); % permute channels from RGB to BGR +im_data = permute(im_data, [2, 1, 3]); % flip width and height +im_data = single(im_data); % convert from uint8 to single +im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); % resize im_data +im_data = im_data - mean_data; % subtract mean_data (already in W x H x C, BGR) % oversample (4 corners, center, and their x-axis flips) -images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single'); +crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single'); indices = [0 IMAGE_DIM-CROPPED_DIM] + 1; -curr = 1; +n = 1; for i = indices for j = indices - images(:, :, :, curr) = ... - permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]); - images(:, :, :, curr+5) = images(end:-1:1, :, :, curr); - curr = curr + 1; + crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :); + crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n); + n = n + 1; end end -center = floor(indices(2) / 2)+1; -images(:,:,:,5) = ... - permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ... - [2 1 3]); -images(:,:,:,10) = images(end:-1:1, :, :, curr); +center = floor(indices(2) / 2) + 1; +crops_data(:,:,:,5) = ... + im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:); +crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5); From 66583ec8a2c613fccd5037d0fd873ddd74c44864 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 29 May 2015 23:33:26 -0700 Subject: [PATCH 103/446] [travis] install lmdb through git mirror switch to github mirror of latest tag as tarball to fix stale url. --- scripts/travis/travis_install.sh | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh index 0e8c37861b0..b6e6f6ce821 100755 --- a/scripts/travis/travis_install.sh +++ b/scripts/travis/travis_install.sh @@ -47,12 +47,12 @@ if $WITH_CUDA; then fi # Install LMDB -LMDB_URL=ftp://ftp.openldap.org/pub/OpenLDAP/openldap-release/openldap-2.4.39.tgz -LMDB_FILE=/tmp/openldap.tgz +LMDB_URL=https://github.com/LMDB/lmdb/archive/LMDB_0.9.14.tar.gz +LMDB_FILE=/tmp/lmdb.tar.gz pushd . -curl $LMDB_URL -o $LMDB_FILE +wget $LMDB_URL -O $LMDB_FILE tar -C /tmp -xzvf $LMDB_FILE -cd /tmp/openldap*/libraries/liblmdb/ +cd /tmp/lmdb*/libraries/liblmdb/ $MAKE $MAKE install popd From a02a8ba0c252dfa41e80f8dcab071c3166ced7e3 Mon Sep 17 00:00:00 2001 From: Takuya Narihira Date: Thu, 26 Feb 2015 18:59:27 -0800 Subject: [PATCH 104/446] PReLU accumulates grad --- src/caffe/layers/prelu_layer.cpp | 1 - src/caffe/layers/prelu_layer.cu | 6 ++---- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp index 7a38f9fac80..81831755512 100644 --- a/src/caffe/layers/prelu_layer.cpp +++ b/src/caffe/layers/prelu_layer.cpp @@ -113,7 +113,6 @@ void PReLULayer::Backward_cpu(const vector*>& top, // keep top_diff unchanged. if (this->param_propagate_down_[0]) { Dtype* slope_diff = this->blobs_[0]->mutable_cpu_diff(); - caffe_set(this->blobs_[0]->count(), Dtype(0), slope_diff); for (int i = 0; i < count; ++i) { int c = (i / dim) % channels / div_factor; slope_diff[c] += top_diff[i] * bottom_data[i] * (bottom_data[i] <= 0); diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu index dfa238d85bd..e1f20048f60 100644 --- a/src/caffe/layers/prelu_layer.cu +++ b/src/caffe/layers/prelu_layer.cu @@ -75,14 +75,12 @@ void PReLULayer::Backward_gpu(const vector*>& top, bottom_data = bottom_memory_.gpu_data(); } - // Propagte to param + // Propagate to param // Since to write bottom diff will affect top diff if top and bottom blobs // are identical (in-place computaion), we first compute param backward to // keep top_diff unchanged. if (this->param_propagate_down_[0]) { Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff(); - // slope_diff is set as 0, then accumulated over batches - caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), slope_diff); int cdim = channels * dim; Dtype dsum = 0.; for (int n = 0; n < bottom[0]->num(); ++n) { @@ -106,7 +104,7 @@ void PReLULayer::Backward_gpu(const vector*>& top, } } if (channel_shared_) { - caffe_gpu_set(this->blobs_[0]->count(), Dtype(dsum), slope_diff); + caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(dsum), slope_diff); } } // Propagate to bottom From 86758be9323a2bbefa751132dd58376e7667294d Mon Sep 17 00:00:00 2001 From: Kibum Bae Date: Tue, 2 Jun 2015 22:20:27 +0900 Subject: [PATCH 105/446] Remove unnecessary filler parameter in the sample model In bvlc_googlenet model, unnecessary filler parameters, 'std', were used for Xavier weight fillers. --- models/bvlc_googlenet/train_val.prototxt | 63 ------------------------ 1 file changed, 63 deletions(-) diff --git a/models/bvlc_googlenet/train_val.prototxt b/models/bvlc_googlenet/train_val.prototxt index 79ede2b9d9c..5dee3abe28f 100644 --- a/models/bvlc_googlenet/train_val.prototxt +++ b/models/bvlc_googlenet/train_val.prototxt @@ -61,7 +61,6 @@ layer { stride: 2 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" @@ -115,7 +114,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" @@ -148,7 +146,6 @@ layer { kernel_size: 3 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -202,7 +199,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -234,7 +230,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.09 } bias_filler { type: "constant" @@ -267,7 +262,6 @@ layer { kernel_size: 3 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -299,7 +293,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.2 } bias_filler { type: "constant" @@ -332,7 +325,6 @@ layer { kernel_size: 5 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -376,7 +368,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" @@ -417,7 +408,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -449,7 +439,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.09 } bias_filler { type: "constant" @@ -482,7 +471,6 @@ layer { kernel_size: 3 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -514,7 +502,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.2 } bias_filler { type: "constant" @@ -547,7 +534,6 @@ layer { kernel_size: 5 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -591,7 +577,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" @@ -643,7 +628,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -675,7 +659,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.09 } bias_filler { type: "constant" @@ -708,7 +691,6 @@ layer { kernel_size: 3 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -740,7 +722,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.2 } bias_filler { type: "constant" @@ -773,7 +754,6 @@ layer { kernel_size: 5 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -817,7 +797,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" @@ -869,7 +848,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.08 } bias_filler { type: "constant" @@ -900,7 +878,6 @@ layer { num_output: 1024 weight_filler { type: "xavier" - std: 0.02 } bias_filler { type: "constant" @@ -940,7 +917,6 @@ layer { num_output: 1000 weight_filler { type: "xavier" - std: 0.0009765625 } bias_filler { type: "constant" @@ -997,7 +973,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1029,7 +1004,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.09 } bias_filler { type: "constant" @@ -1062,7 +1036,6 @@ layer { kernel_size: 3 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1094,7 +1067,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.2 } bias_filler { type: "constant" @@ -1127,7 +1099,6 @@ layer { kernel_size: 5 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1171,7 +1142,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" @@ -1212,7 +1182,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1244,7 +1213,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.09 } bias_filler { type: "constant" @@ -1277,7 +1245,6 @@ layer { kernel_size: 3 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1309,7 +1276,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.2 } bias_filler { type: "constant" @@ -1342,7 +1308,6 @@ layer { kernel_size: 5 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1386,7 +1351,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" @@ -1427,7 +1391,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1459,7 +1422,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.09 } bias_filler { type: "constant" @@ -1492,7 +1454,6 @@ layer { kernel_size: 3 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1524,7 +1485,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.2 } bias_filler { type: "constant" @@ -1557,7 +1517,6 @@ layer { kernel_size: 5 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1601,7 +1560,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" @@ -1653,7 +1611,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.08 } bias_filler { type: "constant" @@ -1684,7 +1641,6 @@ layer { num_output: 1024 weight_filler { type: "xavier" - std: 0.02 } bias_filler { type: "constant" @@ -1724,7 +1680,6 @@ layer { num_output: 1000 weight_filler { type: "xavier" - std: 0.0009765625 } bias_filler { type: "constant" @@ -1781,7 +1736,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1813,7 +1767,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.09 } bias_filler { type: "constant" @@ -1846,7 +1799,6 @@ layer { kernel_size: 3 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1878,7 +1830,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.2 } bias_filler { type: "constant" @@ -1911,7 +1862,6 @@ layer { kernel_size: 5 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -1955,7 +1905,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" @@ -2007,7 +1956,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -2039,7 +1987,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.09 } bias_filler { type: "constant" @@ -2072,7 +2019,6 @@ layer { kernel_size: 3 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -2104,7 +2050,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.2 } bias_filler { type: "constant" @@ -2137,7 +2082,6 @@ layer { kernel_size: 5 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -2181,7 +2125,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" @@ -2222,7 +2165,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -2254,7 +2196,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.09 } bias_filler { type: "constant" @@ -2287,7 +2228,6 @@ layer { kernel_size: 3 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -2319,7 +2259,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.2 } bias_filler { type: "constant" @@ -2352,7 +2291,6 @@ layer { kernel_size: 5 weight_filler { type: "xavier" - std: 0.03 } bias_filler { type: "constant" @@ -2396,7 +2334,6 @@ layer { kernel_size: 1 weight_filler { type: "xavier" - std: 0.1 } bias_filler { type: "constant" From 5bde20b1e9274b11b71e1c0970d4a22a5256b205 Mon Sep 17 00:00:00 2001 From: manuele Date: Fri, 29 May 2015 15:54:38 +0200 Subject: [PATCH 106/446] Filter Layer implemented --- include/caffe/common_layers.hpp | 63 +++++++++++++ src/caffe/layers/filter_layer.cpp | 128 +++++++++++++++++++++++++++ src/caffe/layers/filter_layer.cu | 70 +++++++++++++++ src/caffe/test/test_filter_layer.cpp | 128 +++++++++++++++++++++++++++ 4 files changed, 389 insertions(+) create mode 100644 src/caffe/layers/filter_layer.cpp create mode 100644 src/caffe/layers/filter_layer.cu create mode 100644 src/caffe/test/test_filter_layer.cpp diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index e6b42c14587..5d018e7389d 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -180,6 +180,69 @@ class EltwiseLayer : public Layer { bool stable_prod_grad_; }; +/** + * @brief Takes two+ Blobs, interprets last Blob as a selector and + * filter remaining Blobs accordingly with selector data (0 means that + * the corresponding item has to be filtered, non-zero means that corresponding + * item needs to stay). + */ +template +class FilterLayer : public Layer { + public: + explicit FilterLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Filter"; } + virtual inline int MinBottomBlobs() const { return 2; } + virtual inline int MinTopBlobs() const { return 1; } + + protected: + /** + * @param bottom input Blob vector (length 2+) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs to be filtered @f$ x_1 @f$ + * -# ... + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs to be filtered @f$ x_K @f$ + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the selector blob + * @param top output Blob vector (length 1+) + * -# @f$ (S \times C \times H \times W) @f$ () + * the filtered output @f$ x_1 @f$ + * where S is the number of items + * that haven't been filtered + * @f$ (S \times C \times H \times W) @f$ + * the filtered output @f$ x_K @f$ + * where S is the number of items + * that haven't been filtered + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the forwarded inputs. + * + * @param top output Blob vector (length 1+), providing the error gradient with + * respect to the outputs + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 2+), into which the top error + * gradient is copied + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool first_reshape_; + vector indices_to_forward_; +}; + /** * @brief Reshapes the input Blob into flat vectors. * diff --git a/src/caffe/layers/filter_layer.cpp b/src/caffe/layers/filter_layer.cpp new file mode 100644 index 00000000000..d7fc59e0ebe --- /dev/null +++ b/src/caffe/layers/filter_layer.cpp @@ -0,0 +1,128 @@ +#include +#include + +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +template +void FilterLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + CHECK_EQ(top.size(), bottom.size()-1) << + "Top.size() should be equal to bottom.size() - 1"; + first_reshape_ = true; +} + +template +void FilterLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + // bottom[0...k-1] are the blobs to filter + // bottom[last] is the "selector_blob" + int selector_index = bottom.size() - 1; + for (int i = 1; i < bottom[selector_index]->num_axes(); ++i) { + CHECK_EQ(bottom[selector_index]->shape(i), 1) + << "Selector blob must have all shapes == 1 (except the first one)"; + } + for (int i = 0; i < bottom.size()-1; i++) { + CHECK_EQ(bottom[selector_index]->shape(0), bottom[i]->shape(0)) << + "Each bottom should have the same dimension as the selector blob"; + } + + const Dtype* bottom_data_selector = bottom[selector_index]->cpu_data(); + indices_to_forward_.clear(); + + // look for non-zero elements in bottom[0]. Items of each bottom that + // have the same index as the items in bottom[0] with value == non-zero + // will be forwarded + for (int item_id = 0; item_id < bottom[selector_index]->shape(0); ++item_id) { + // we don't need an offset because item size == 1 + const Dtype* tmp_data_selector = bottom_data_selector + item_id; + if (*tmp_data_selector) { + indices_to_forward_.push_back(item_id); + } + } + // only filtered items will be forwarded + int new_tops_num = indices_to_forward_.size(); + // init + if (first_reshape_) { + new_tops_num = bottom[0]->shape(0); + first_reshape_ = false; + } + for (int t = 0; t < top.size(); t++) { + int num_axes = bottom[t]->num_axes(); + vector shape_top(num_axes); + shape_top[0] = new_tops_num; + for (int ts = 1; ts < num_axes; ts++) + shape_top[ts] = bottom[t]->shape(ts); + top[t]->Reshape(shape_top); + } +} + +template +void FilterLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + int new_tops_num = indices_to_forward_.size(); + // forward all filtered items for all bottoms but the Selector (bottom[last]) + for (int t = 0; t < top.size(); t++) { + const Dtype* bottom_data = bottom[t]->cpu_data(); + Dtype* top_data = top[t]->mutable_cpu_data(); + int dim = bottom[t]->count() / bottom[t]->shape(0); + for (int n = 0; n < new_tops_num; n++) { + int data_offset_top = top[t]->offset(n); + int data_offset_bottom = bottom[t]->offset(indices_to_forward_[n]); + caffe_copy(dim, bottom_data + data_offset_bottom, + top_data + data_offset_top); + } + } +} + +template +void FilterLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (propagate_down[bottom.size() - 1]) { + LOG(FATAL) << this->type() + << "Layer cannot backpropagate to filter index inputs"; + } + for (int i = 0; i < top.size(); i++) { + // bottom[last] is the selector and never needs backpropagation + // so we can iterate over top vector because top.size() == bottom.size() -1 + if (propagate_down[i]) { + const int dim = top[i]->count() / top[i]->shape(0); + int next_to_backward_offset = 0; + int batch_offset = 0; + int data_offset_bottom = 0; + int data_offset_top = 0; + for (int n = 0; n < bottom[i]->shape(0); n++) { + data_offset_bottom = bottom[i]->offset(n); + if (next_to_backward_offset >= indices_to_forward_.size()) { + // we already visited all items that were been forwarded, so + // just set to zero remaining ones + caffe_set(dim, Dtype(0), + bottom[i]->mutable_cpu_diff() + data_offset_bottom); + } else { + batch_offset = indices_to_forward_[next_to_backward_offset]; + if (n != batch_offset) { // this data was not been forwarded + caffe_set(dim, Dtype(0), + bottom[i]->mutable_cpu_diff() + data_offset_bottom); + } else { // this data was been forwarded + data_offset_top = top[i]->offset(next_to_backward_offset); + next_to_backward_offset++; // point to next forwarded item index + caffe_copy(dim, top[i]->mutable_cpu_diff() + data_offset_top, + bottom[i]->mutable_cpu_diff() + data_offset_bottom); + } + } + } + } + } +} + +#ifdef CPU_ONLY +STUB_GPU(FilterLayer); +#endif + +INSTANTIATE_CLASS(FilterLayer); +REGISTER_LAYER_CLASS(Filter); + +} // namespace caffe diff --git a/src/caffe/layers/filter_layer.cu b/src/caffe/layers/filter_layer.cu new file mode 100644 index 00000000000..4a9e674de1a --- /dev/null +++ b/src/caffe/layers/filter_layer.cu @@ -0,0 +1,70 @@ +#include + +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +template +void FilterLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + int new_tops_num = indices_to_forward_.size(); + // forward all filtered items for all bottoms but the Selector (bottom[last]) + for (int t = 0; t < top.size(); t++) { + const Dtype* bottom_data = bottom[t]->gpu_data(); + Dtype* top_data = top[t]->mutable_gpu_data(); + int dim = bottom[t]->count() / bottom[t]->shape(0); + for (int n = 0; n < new_tops_num; n++) { + int data_offset_top = top[t]->offset(n); + int data_offset_bottom = bottom[t]->offset(indices_to_forward_[n]); + caffe_copy(dim, bottom_data + data_offset_bottom, + top_data + data_offset_top); + } + } +} + +template +void FilterLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (propagate_down[bottom.size() - 1]) { + LOG(FATAL) << this->type() + << "Layer cannot backpropagate to filter index inputs"; + } + for (int i = 0; i < top.size(); i++) { + // bottom[last] is the selector and never needs backpropagation + // so we can iterate over top vector because top.size() == bottom.size() -1 + if (propagate_down[i]) { + const int dim = top[i]->count() / top[i]->shape(0); + int next_to_backward_offset = 0; + int batch_offset = 0; + int data_offset_bottom = 0; + int data_offset_top = 0; + for (int n = 0; n < bottom[i]->shape(0); n++) { + if (next_to_backward_offset >= indices_to_forward_.size()) { + // we already visited all items that were been forwarded, so + // just set to zero remaining ones + data_offset_bottom = top[i]->offset(n); + caffe_set(dim, Dtype(0), + bottom[i]->mutable_gpu_diff() + data_offset_bottom); + } else { + batch_offset = indices_to_forward_[next_to_backward_offset]; + data_offset_bottom = top[i]->offset(n); + if (n != batch_offset) { // this data was not been forwarded + caffe_set(dim, Dtype(0), + bottom[i]->mutable_gpu_diff() + data_offset_bottom); + } else { // this data was been forwarded + data_offset_top = top[i]->offset(next_to_backward_offset); + next_to_backward_offset++; // point to next forwarded item index + caffe_copy(dim, top[i]->mutable_gpu_diff() + data_offset_top, + bottom[i]->mutable_gpu_diff() + data_offset_bottom); + } + } + } + } + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(FilterLayer); + +} // namespace caffe diff --git a/src/caffe/test/test_filter_layer.cpp b/src/caffe/test/test_filter_layer.cpp new file mode 100644 index 00000000000..c641b6ef6e8 --- /dev/null +++ b/src/caffe/test/test_filter_layer.cpp @@ -0,0 +1,128 @@ +#include +#include +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/vision_layers.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class FilterLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + FilterLayerTest() + : blob_bottom_data_(new Blob(4, 3, 6, 4)), + blob_bottom_labels_(new Blob(4, 1, 1, 1)), + blob_bottom_selector_(new Blob(4, 1, 1, 1)), + blob_top_data_(new Blob()), + blob_top_labels_(new Blob()) {} + virtual void SetUp() { + // fill the values + Caffe::set_random_seed(1890); + FillerParameter filler_param; + GaussianFiller filler(filler_param); + // fill the selector blob + Dtype* bottom_data_selector_ = blob_bottom_selector_->mutable_cpu_data(); + bottom_data_selector_[0] = 0; + bottom_data_selector_[1] = 1; + bottom_data_selector_[2] = 1; + bottom_data_selector_[3] = 0; + // fill the other bottom blobs + filler.Fill(blob_bottom_data_); + for (int i = 0; i < blob_bottom_labels_->count(); ++i) { + blob_bottom_labels_->mutable_cpu_data()[i] = caffe_rng_rand() % 5; + } + blob_bottom_vec_.push_back(blob_bottom_data_); + blob_bottom_vec_.push_back(blob_bottom_labels_); + blob_bottom_vec_.push_back(blob_bottom_selector_); + blob_top_vec_.push_back(blob_top_data_); + blob_top_vec_.push_back(blob_top_labels_); + } + virtual ~FilterLayerTest() { + delete blob_bottom_data_; + delete blob_bottom_labels_; + delete blob_bottom_selector_; + delete blob_top_data_; + delete blob_top_labels_; + } + Blob* const blob_bottom_data_; + Blob* const blob_bottom_labels_; + Blob* const blob_bottom_selector_; + // blobs for the top of FilterLayer + Blob* const blob_top_data_; + Blob* const blob_top_labels_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(FilterLayerTest, TestDtypesAndDevices); + +TYPED_TEST(FilterLayerTest, TestReshape) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + FilterLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_); + // In the test first and last items should have been filtered + // so we just expect 2 remaining items + EXPECT_EQ(this->blob_top_data_->shape(0), 2); + EXPECT_EQ(this->blob_top_labels_->shape(0), 2); + EXPECT_GT(this->blob_bottom_data_->shape(0), + this->blob_top_data_->shape(0)); + EXPECT_GT(this->blob_bottom_labels_->shape(0), + this->blob_top_labels_->shape(0)); + for (int i = 1; i < this->blob_bottom_labels_->num_axes(); i++) { + EXPECT_EQ(this->blob_bottom_labels_->shape(i), + this->blob_top_labels_->shape(i)); + } +} + +TYPED_TEST(FilterLayerTest, TestForward) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + FilterLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + EXPECT_EQ(this->blob_top_labels_->data_at(0, 0, 0, 0), + this->blob_bottom_labels_->data_at(1, 0, 0, 0)); + EXPECT_EQ(this->blob_top_labels_->data_at(1, 0, 0, 0), + this->blob_bottom_labels_->data_at(2, 0, 0, 0)); + + int dim = this->blob_top_data_->count() / + this->blob_top_data_->shape(0); + const Dtype* top_data = this->blob_top_data_->cpu_data(); + const Dtype* bottom_data = this->blob_bottom_data_->cpu_data(); + // selector is 0 1 1 0, so we need to compare bottom(1,c,h,w) + // with top(0,c,h,w) and bottom(2,c,h,w) with top(1,c,h,w) + bottom_data += dim; // bottom(1,c,h,w) + for (size_t n = 0; n < dim; n++) + EXPECT_EQ(top_data[n], bottom_data[n]); + + bottom_data += dim; // bottom(2,c,h,w) + top_data += dim; // top(1,c,h,w) + for (size_t n = 0; n < dim; n++) + EXPECT_EQ(top_data[n], bottom_data[n]); +} + +TYPED_TEST(FilterLayerTest, TestGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + FilterLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + // check only input 0 (data) because labels and selector + // don't need backpropagation + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_, 0); +} + +} // namespace caffe From 0ccf336bfb19a82144d63399034cd886f02d8745 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Tue, 2 Jun 2015 18:21:15 -0700 Subject: [PATCH 107/446] FilterLayer cleanup and bugfix for GPU backward -caffe_set -> caffe_gpu_set (backward was segfaulting before) -remove uses of 'offset' (to support >4D blobs) -change var++ -> ++var (per Google style guide) -cleanup comments/whitespace --- include/caffe/common_layers.hpp | 6 +++--- src/caffe/layers/filter_layer.cpp | 25 ++++++++++++------------- src/caffe/layers/filter_layer.cu | 26 +++++++++++++------------- 3 files changed, 28 insertions(+), 29 deletions(-) diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 5d018e7389d..3155b45bbbc 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -211,12 +211,12 @@ class FilterLayer : public Layer { * -# @f$ (N \times 1 \times 1 \times 1) @f$ * the selector blob * @param top output Blob vector (length 1+) - * -# @f$ (S \times C \times H \times W) @f$ () - * the filtered output @f$ x_1 @f$ + * -# @f$ (S \times C \times H \times W) @f$ () + * the filtered output @f$ x_1 @f$ * where S is the number of items * that haven't been filtered * @f$ (S \times C \times H \times W) @f$ - * the filtered output @f$ x_K @f$ + * the filtered output @f$ x_K @f$ * where S is the number of items * that haven't been filtered */ diff --git a/src/caffe/layers/filter_layer.cpp b/src/caffe/layers/filter_layer.cpp index d7fc59e0ebe..be1db32dbaa 100644 --- a/src/caffe/layers/filter_layer.cpp +++ b/src/caffe/layers/filter_layer.cpp @@ -10,8 +10,7 @@ namespace caffe { template void FilterLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { - CHECK_EQ(top.size(), bottom.size()-1) << - "Top.size() should be equal to bottom.size() - 1"; + CHECK_EQ(top.size(), bottom.size() - 1); first_reshape_ = true; } @@ -23,11 +22,11 @@ void FilterLayer::Reshape(const vector*>& bottom, int selector_index = bottom.size() - 1; for (int i = 1; i < bottom[selector_index]->num_axes(); ++i) { CHECK_EQ(bottom[selector_index]->shape(i), 1) - << "Selector blob must have all shapes == 1 (except the first one)"; + << "Selector blob dimensions must be singletons (1), except the first"; } - for (int i = 0; i < bottom.size()-1; i++) { + for (int i = 0; i < bottom.size() - 1; ++i) { CHECK_EQ(bottom[selector_index]->shape(0), bottom[i]->shape(0)) << - "Each bottom should have the same dimension as the selector blob"; + "Each bottom should have the same 0th dimension as the selector blob"; } const Dtype* bottom_data_selector = bottom[selector_index]->cpu_data(); @@ -50,11 +49,11 @@ void FilterLayer::Reshape(const vector*>& bottom, new_tops_num = bottom[0]->shape(0); first_reshape_ = false; } - for (int t = 0; t < top.size(); t++) { + for (int t = 0; t < top.size(); ++t) { int num_axes = bottom[t]->num_axes(); vector shape_top(num_axes); shape_top[0] = new_tops_num; - for (int ts = 1; ts < num_axes; ts++) + for (int ts = 1; ts < num_axes; ++ts) shape_top[ts] = bottom[t]->shape(ts); top[t]->Reshape(shape_top); } @@ -65,13 +64,13 @@ void FilterLayer::Forward_cpu(const vector*>& bottom, const vector*>& top) { int new_tops_num = indices_to_forward_.size(); // forward all filtered items for all bottoms but the Selector (bottom[last]) - for (int t = 0; t < top.size(); t++) { + for (int t = 0; t < top.size(); ++t) { const Dtype* bottom_data = bottom[t]->cpu_data(); Dtype* top_data = top[t]->mutable_cpu_data(); int dim = bottom[t]->count() / bottom[t]->shape(0); - for (int n = 0; n < new_tops_num; n++) { - int data_offset_top = top[t]->offset(n); - int data_offset_bottom = bottom[t]->offset(indices_to_forward_[n]); + for (int n = 0; n < new_tops_num; ++n) { + int data_offset_top = n * dim; + int data_offset_bottom = indices_to_forward_[n] * bottom[t]->count(1); caffe_copy(dim, bottom_data + data_offset_bottom, top_data + data_offset_top); } @@ -95,7 +94,7 @@ void FilterLayer::Backward_cpu(const vector*>& top, int data_offset_bottom = 0; int data_offset_top = 0; for (int n = 0; n < bottom[i]->shape(0); n++) { - data_offset_bottom = bottom[i]->offset(n); + data_offset_bottom = n * dim; if (next_to_backward_offset >= indices_to_forward_.size()) { // we already visited all items that were been forwarded, so // just set to zero remaining ones @@ -107,7 +106,7 @@ void FilterLayer::Backward_cpu(const vector*>& top, caffe_set(dim, Dtype(0), bottom[i]->mutable_cpu_diff() + data_offset_bottom); } else { // this data was been forwarded - data_offset_top = top[i]->offset(next_to_backward_offset); + data_offset_top = next_to_backward_offset * dim; next_to_backward_offset++; // point to next forwarded item index caffe_copy(dim, top[i]->mutable_cpu_diff() + data_offset_top, bottom[i]->mutable_cpu_diff() + data_offset_bottom); diff --git a/src/caffe/layers/filter_layer.cu b/src/caffe/layers/filter_layer.cu index 4a9e674de1a..cf929eeeadf 100644 --- a/src/caffe/layers/filter_layer.cu +++ b/src/caffe/layers/filter_layer.cu @@ -11,13 +11,13 @@ void FilterLayer::Forward_gpu(const vector*>& bottom, const vector*>& top) { int new_tops_num = indices_to_forward_.size(); // forward all filtered items for all bottoms but the Selector (bottom[last]) - for (int t = 0; t < top.size(); t++) { + for (int t = 0; t < top.size(); ++t) { const Dtype* bottom_data = bottom[t]->gpu_data(); Dtype* top_data = top[t]->mutable_gpu_data(); int dim = bottom[t]->count() / bottom[t]->shape(0); - for (int n = 0; n < new_tops_num; n++) { - int data_offset_top = top[t]->offset(n); - int data_offset_bottom = bottom[t]->offset(indices_to_forward_[n]); + for (int n = 0; n < new_tops_num; ++n) { + int data_offset_top = n * dim; + int data_offset_bottom = indices_to_forward_[n] * dim; caffe_copy(dim, bottom_data + data_offset_bottom, top_data + data_offset_top); } @@ -29,9 +29,9 @@ void FilterLayer::Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { if (propagate_down[bottom.size() - 1]) { LOG(FATAL) << this->type() - << "Layer cannot backpropagate to filter index inputs"; + << "Layer cannot backpropagate to filter index inputs"; } - for (int i = 0; i < top.size(); i++) { + for (int i = 0; i < top.size(); ++i) { // bottom[last] is the selector and never needs backpropagation // so we can iterate over top vector because top.size() == bottom.size() -1 if (propagate_down[i]) { @@ -40,22 +40,22 @@ void FilterLayer::Backward_gpu(const vector*>& top, int batch_offset = 0; int data_offset_bottom = 0; int data_offset_top = 0; - for (int n = 0; n < bottom[i]->shape(0); n++) { + for (int n = 0; n < bottom[i]->shape(0); ++n) { if (next_to_backward_offset >= indices_to_forward_.size()) { // we already visited all items that were been forwarded, so // just set to zero remaining ones - data_offset_bottom = top[i]->offset(n); - caffe_set(dim, Dtype(0), + data_offset_bottom = n * dim; + caffe_gpu_set(dim, Dtype(0), bottom[i]->mutable_gpu_diff() + data_offset_bottom); } else { batch_offset = indices_to_forward_[next_to_backward_offset]; - data_offset_bottom = top[i]->offset(n); + data_offset_bottom = n * dim; if (n != batch_offset) { // this data was not been forwarded - caffe_set(dim, Dtype(0), + caffe_gpu_set(dim, Dtype(0), bottom[i]->mutable_gpu_diff() + data_offset_bottom); } else { // this data was been forwarded - data_offset_top = top[i]->offset(next_to_backward_offset); - next_to_backward_offset++; // point to next forwarded item index + data_offset_top = next_to_backward_offset * dim; + ++next_to_backward_offset; // point to next forwarded item index caffe_copy(dim, top[i]->mutable_gpu_diff() + data_offset_top, bottom[i]->mutable_gpu_diff() + data_offset_bottom); } From 8c72fe310977b217c47d8a505aafc5d57666a087 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Thu, 1 Jan 2015 23:07:44 -0800 Subject: [PATCH 108/446] Add LogLayer --- include/caffe/neuron_layers.hpp | 66 +++++++++++++ include/caffe/util/math_functions.hpp | 6 ++ include/caffe/util/mkl_alternate.hpp | 1 + src/caffe/layers/log_layer.cpp | 136 ++++++++++++++++++++++++++ src/caffe/proto/caffe.proto | 14 ++- src/caffe/test/test_neuron_layer.cpp | 125 +++++++++++++++++++++++ src/caffe/util/math_functions.cpp | 10 ++ src/caffe/util/math_functions.cu | 21 ++++ 8 files changed, 378 insertions(+), 1 deletion(-) create mode 100644 src/caffe/layers/log_layer.cpp diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp index 9cf233f0eb3..c2e0774aaa2 100644 --- a/include/caffe/neuron_layers.hpp +++ b/include/caffe/neuron_layers.hpp @@ -267,6 +267,72 @@ class ExpLayer : public NeuronLayer { Dtype inner_scale_, outer_scale_; }; +/** + * @brief Computes @f$ y = log_{\gamma}(\alpha x + \beta) @f$, + * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$, + * and base @f$ \gamma @f$. + */ +template +class LogLayer : public NeuronLayer { + public: + /** + * @param param provides LogParameter log_param, + * with LogLayer options: + * - scale (\b optional, default 1) the scale @f$ \alpha @f$ + * - shift (\b optional, default 0) the shift @f$ \beta @f$ + * - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$) + * the base @f$ \gamma @f$ + */ + explicit LogLayer(const LayerParameter& param) + : NeuronLayer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Log"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = log_{\gamma}(\alpha x + \beta) + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the exp inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} = + * \frac{\partial E}{\partial y} y \alpha \log_e(gamma) + * @f$ if propagate_down[0] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + Dtype base_scale_; + Dtype input_scale_, input_shift_; + Dtype backward_num_scale_; +}; + /** * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$, * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$, diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp index f43036fcebc..2cacd8e72cd 100644 --- a/include/caffe/util/math_functions.hpp +++ b/include/caffe/util/math_functions.hpp @@ -88,6 +88,9 @@ void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r); template void caffe_exp(const int n, const Dtype* a, Dtype* y); +template +void caffe_log(const int n, const Dtype* a, Dtype* y); + template void caffe_abs(const int n, const Dtype* a, Dtype* y); @@ -203,6 +206,9 @@ void caffe_gpu_abs(const int n, const Dtype* a, Dtype* y); template void caffe_gpu_exp(const int n, const Dtype* a, Dtype* y); +template +void caffe_gpu_log(const int n, const Dtype* a, Dtype* y); + template void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y); diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp index 32fdbf79932..3355b6658a3 100644 --- a/include/caffe/util/mkl_alternate.hpp +++ b/include/caffe/util/mkl_alternate.hpp @@ -33,6 +33,7 @@ extern "C" { DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]); DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i])); +DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i])); DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i])); // A simple way to define the vsl unary functions with singular parameter b. diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp new file mode 100644 index 00000000000..45f73950d7e --- /dev/null +++ b/src/caffe/layers/log_layer.cpp @@ -0,0 +1,136 @@ +#include +#include + +#include "caffe/layer.hpp" +#include "caffe/neuron_layers.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void LogLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + NeuronLayer::LayerSetUp(bottom, top); + const Dtype base = this->layer_param_.log_param().base(); + if (base != Dtype(-1)) { + CHECK_GT(base, 0) << "base must be strictly positive."; + } + // If base == -1, interpret the base as e and set log_base = 1 exactly. + // Otherwise, calculate its log explicitly. + const Dtype log_base = (base == Dtype(-1)) ? Dtype(1) : log(base); + CHECK(!isnan(log_base)) + << "NaN result: log(base) = log(" << base << ") = " << log_base; + CHECK(!isinf(log_base)) + << "Inf result: log(base) = log(" << base << ") = " << log_base; + base_scale_ = Dtype(1) / log_base; + CHECK(!isnan(base_scale_)) + << "NaN result: 1/log(base) = 1/log(" << base << ") = " << base_scale_; + CHECK(!isinf(base_scale_)) + << "Inf result: 1/log(base) = 1/log(" << base << ") = " << base_scale_; + input_scale_ = this->layer_param_.log_param().scale(); + input_shift_ = this->layer_param_.log_param().shift(); + backward_num_scale_ = input_scale_ / log_base; +} + +template +void LogLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const int count = bottom[0]->count(); + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) { + caffe_log(count, bottom_data, top_data); + } else { + caffe_copy(count, bottom_data, top_data); + if (input_scale_ != Dtype(1)) { + caffe_scal(count, input_scale_, top_data); + } + if (input_shift_ != Dtype(0)) { + caffe_add_scalar(count, input_shift_, top_data); + } + caffe_log(count, top_data, top_data); + } + if (base_scale_ != Dtype(1)) { + caffe_scal(count, base_scale_, top_data); + } +} + +template +void LogLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (!propagate_down[0]) { return; } + const int count = bottom[0]->count(); + const Dtype* bottom_data = bottom[0]->cpu_data(); + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + caffe_copy(count, bottom_data, bottom_diff); + if (input_scale_ != Dtype(1)) { + caffe_scal(count, input_scale_, bottom_diff); + } + if (input_shift_ != Dtype(0)) { + caffe_add_scalar(count, input_shift_, bottom_diff); + } + caffe_powx(count, bottom_diff, Dtype(-1), bottom_diff); + if (backward_num_scale_ != Dtype(1)) { + caffe_scal(count, backward_num_scale_, bottom_diff); + } + caffe_mul(count, top_diff, bottom_diff, bottom_diff); +} + +#ifdef CPU_ONLY +STUB_GPU(LogLayer); +#else + +template +void LogLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const int count = bottom[0]->count(); + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) { + caffe_gpu_log(count, bottom_data, top_data); + } else { + caffe_copy(count, bottom_data, top_data); + if (input_scale_ != Dtype(1)) { + caffe_gpu_scal(count, input_scale_, top_data); + } + if (input_shift_ != Dtype(0)) { + caffe_gpu_add_scalar(count, input_shift_, top_data); + } + caffe_gpu_log(count, top_data, top_data); + } + if (base_scale_ != Dtype(1)) { + caffe_gpu_scal(count, base_scale_, top_data); + } +} + +template +void LogLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (!propagate_down[0]) { return; } + const int count = bottom[0]->count(); + const Dtype* bottom_data = bottom[0]->gpu_data(); + const Dtype* top_diff = top[0]->gpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + caffe_copy(count, bottom_data, bottom_diff); + if (input_scale_ != Dtype(1)) { + caffe_gpu_scal(count, input_scale_, bottom_diff); + } + if (input_shift_ != Dtype(0)) { + caffe_gpu_add_scalar(count, input_shift_, bottom_diff); + } + caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff); + if (backward_num_scale_ != Dtype(1)) { + caffe_gpu_scal(count, backward_num_scale_, bottom_diff); + } + caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff); +} + +INSTANTIATE_LAYER_GPU_FUNCS(LogLayer); + +#endif + +INSTANTIATE_CLASS(LogLayer); +REGISTER_LAYER_CLASS(Log); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 94f713e3cb7..619642f2d32 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -269,7 +269,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 134 (last added: reshape_param) +// LayerParameter next available layer-specific ID: 135 (last added: log_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -332,6 +332,7 @@ message LayerParameter { optional ImageDataParameter image_data_param = 115; optional InfogainLossParameter infogain_loss_param = 116; optional InnerProductParameter inner_product_param = 117; + optional LogParameter log_param = 134; optional LRNParameter lrn_param = 118; optional MemoryDataParameter memory_data_param = 119; optional MVNParameter mvn_param = 120; @@ -607,6 +608,17 @@ message InnerProductParameter { optional int32 axis = 5 [default = 1]; } +// Message that stores parameters used by LogLayer +message LogParameter { + // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0. + // Or if base is set to the default (-1), base is set to e, + // so y = ln(shift + scale * x) = log_e(shift + scale * x) + optional float base = 1 [default = -1.0]; + optional float scale = 2 [default = 1.0]; + optional float shift = 3 [default = 0.0]; +} + +// Message that stores parameters used by LRNLayer message LRNParameter { optional uint32 local_size = 1 [default = 5]; optional float alpha = 2 [default = 1.]; diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp index 37b54713b46..c6e4d27b903 100644 --- a/src/caffe/test/test_neuron_layer.cpp +++ b/src/caffe/test/test_neuron_layer.cpp @@ -117,6 +117,49 @@ class NeuronLayerTest : public MultiDeviceTest { + slope_data[c] * std::min(bottom_data[i], (Dtype)(0))); } } + + void LogBottomInit() { + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + Dtype* bottom_data = this->blob_bottom_->mutable_cpu_data(); + caffe_exp(this->blob_bottom_->count(), bottom_data, bottom_data); + } + + void TestLogForward(const float base, const float scale, const float shift) { + LogBottomInit(); + LayerParameter layer_param; + layer_param.mutable_log_param()->set_base(base); + layer_param.mutable_log_param()->set_scale(scale); + layer_param.mutable_log_param()->set_shift(shift); + LogLayer layer(layer_param); + layer.SetUp(blob_bottom_vec_, blob_top_vec_); + layer.Forward(blob_bottom_vec_, blob_top_vec_); + const Dtype kDelta = 2e-4; + const Dtype* bottom_data = blob_bottom_->cpu_data(); + const Dtype* top_data = blob_top_->cpu_data(); + for (int i = 0; i < blob_bottom_->count(); ++i) { + const Dtype bottom_val = bottom_data[i]; + const Dtype top_val = top_data[i]; + if (base == -1) { + EXPECT_NEAR(top_val, log(shift + scale * bottom_val), kDelta); + } else { + EXPECT_NEAR(top_val, log(shift + scale * bottom_val) / log(base), + kDelta); + } + } + } + + void TestLogGradient(const float base, const float scale, const float shift) { + LogBottomInit(); + LayerParameter layer_param; + layer_param.mutable_log_param()->set_base(base); + layer_param.mutable_log_param()->set_scale(scale); + layer_param.mutable_log_param()->set_shift(shift); + LogLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + checker.CheckGradientEltwise(&layer, blob_bottom_vec_, blob_top_vec_); + } }; TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices); @@ -339,6 +382,88 @@ TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Shift1Scale3) { this->TestExpGradient(kBase, kScale, kShift); } +TYPED_TEST(NeuronLayerTest, TestLogLayer) { + typedef typename TypeParam::Dtype Dtype; + // Test default base of "-1" -- should actually set base := e. + const Dtype kBase = -1; + const Dtype kScale = 1; + const Dtype kShift = 0; + this->TestLogForward(kBase, kScale, kShift); +} + +TYPED_TEST(NeuronLayerTest, TestLogGradient) { + typedef typename TypeParam::Dtype Dtype; + // Test default base of "-1" -- should actually set base := e. + const Dtype kBase = -1; + const Dtype kScale = 1; + const Dtype kShift = 0; + this->TestLogGradient(kBase, kScale, kShift); +} + +TYPED_TEST(NeuronLayerTest, TestLogLayerBase2) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kBase = 2; + const Dtype kScale = 1; + const Dtype kShift = 0; + this->TestLogForward(kBase, kScale, kShift); +} + +TYPED_TEST(NeuronLayerTest, TestLogGradientBase2) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kBase = 2; + const Dtype kScale = 1; + const Dtype kShift = 0; + this->TestLogGradient(kBase, kScale, kShift); +} + +TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kBase = 2; + const Dtype kScale = 1; + const Dtype kShift = 1; + this->TestLogForward(kBase, kScale, kShift); +} + +TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kBase = 2; + const Dtype kScale = 1; + const Dtype kShift = 1; + this->TestLogGradient(kBase, kScale, kShift); +} + +TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Scale3) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kBase = 2; + const Dtype kScale = 3; + const Dtype kShift = 0; + this->TestLogForward(kBase, kScale, kShift); +} + +TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Scale3) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kBase = 2; + const Dtype kScale = 3; + const Dtype kShift = 0; + this->TestLogGradient(kBase, kScale, kShift); +} + +TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1Scale3) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kBase = 2; + const Dtype kScale = 3; + const Dtype kShift = 1; + this->TestLogForward(kBase, kScale, kShift); +} + +TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1Scale3) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kBase = 2; + const Dtype kScale = 3; + const Dtype kShift = 1; + this->TestLogGradient(kBase, kScale, kShift); +} + TYPED_TEST(NeuronLayerTest, TestDropoutHalf) { const float kDropoutRatio = 0.5; this->TestDropoutForward(kDropoutRatio); diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp index 13e17be582b..0aab6b17b85 100644 --- a/src/caffe/util/math_functions.cpp +++ b/src/caffe/util/math_functions.cpp @@ -206,6 +206,16 @@ void caffe_exp(const int n, const double* a, double* y) { vdExp(n, a, y); } +template <> +void caffe_log(const int n, const float* a, float* y) { + vsLn(n, a, y); +} + +template <> +void caffe_log(const int n, const double* a, double* y) { + vdLn(n, a, y); +} + template <> void caffe_abs(const int n, const float* a, float* y) { vsAbs(n, a, y); diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu index 43e65eb9a69..2631a0740d6 100644 --- a/src/caffe/util/math_functions.cu +++ b/src/caffe/util/math_functions.cu @@ -324,6 +324,27 @@ void caffe_gpu_exp(const int N, const double* a, double* y) { N, a, y); } +template +__global__ void log_kernel(const int n, const Dtype* a, Dtype* y) { + CUDA_KERNEL_LOOP(index, n) { + y[index] = log(a[index]); + } +} + +template <> +void caffe_gpu_log(const int N, const float* a, float* y) { + // NOLINT_NEXT_LINE(whitespace/operators) + log_kernel<<>>( + N, a, y); +} + +template <> +void caffe_gpu_log(const int N, const double* a, double* y) { + // NOLINT_NEXT_LINE(whitespace/operators) + log_kernel<<>>( + N, a, y); +} + template __global__ void powx_kernel(const int n, const Dtype* a, const Dtype alpha, Dtype* y) { From eb442b9bc9ca206bd0606f259115f01b53144e7a Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 31 Dec 2014 18:02:12 -0800 Subject: [PATCH 109/446] FlattenLayer gets a FlattenParameter with an axis, end_axis --- src/caffe/layers/flatten_layer.cpp | 16 ++++++++-- src/caffe/proto/caffe.proto | 16 +++++++++- src/caffe/test/test_flatten_layer.cpp | 46 +++++++++++++++++++++++---- 3 files changed, 68 insertions(+), 10 deletions(-) diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp index 745f271ea45..f7e5c9c2172 100644 --- a/src/caffe/layers/flatten_layer.cpp +++ b/src/caffe/layers/flatten_layer.cpp @@ -9,9 +9,19 @@ namespace caffe { template void FlattenLayer::Reshape(const vector*>& bottom, const vector*>& top) { - vector top_shape(2); - top_shape[0] = bottom[0]->num(); - top_shape[1] = bottom[0]->count() / bottom[0]->num(); + const int start_axis = bottom[0]->CanonicalAxisIndex( + this->layer_param_.flatten_param().axis()); + const int end_axis = bottom[0]->CanonicalAxisIndex( + this->layer_param_.flatten_param().end_axis()); + vector top_shape; + for (int i = 0; i < start_axis; ++i) { + top_shape.push_back(bottom[0]->shape(i)); + } + const int flattened_dim = bottom[0]->count(start_axis, end_axis + 1); + top_shape.push_back(flattened_dim); + for (int i = end_axis + 1; i < bottom[0]->num_axes(); ++i) { + top_shape.push_back(bottom[0]->shape(i)); + } top[0]->Reshape(top_shape); CHECK_EQ(top[0]->count(), bottom[0]->count()); } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 619642f2d32..f79cf80cc05 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -269,7 +269,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 135 (last added: log_param) +// LayerParameter next available layer-specific ID: 136 (last added: flatten_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -326,6 +326,7 @@ message LayerParameter { optional DummyDataParameter dummy_data_param = 109; optional EltwiseParameter eltwise_param = 110; optional ExpParameter exp_param = 111; + optional FlattenParameter flatten_param = 135; optional HDF5DataParameter hdf5_data_param = 112; optional HDF5OutputParameter hdf5_output_param = 113; optional HingeLossParameter hinge_loss_param = 114; @@ -533,6 +534,19 @@ message ExpParameter { optional float shift = 3 [default = 0.0]; } +/// Message that stores parameters used by FlattenLayer +message FlattenParameter { + // The first axis to flatten: all preceding axes are retained in the output. + // May be negative to index from the end (e.g., -1 for the last axis). + optional int32 axis = 1 [default = 1]; + + // The last axis to flatten: all following axes are retained in the output. + // May be negative to index from the end (e.g., the default -1 for the last + // axis). + optional int32 end_axis = 2 [default = -1]; +} + +// Message that stores parameters used by HDF5DataLayer message HDF5DataParameter { // Specify the data source. optional string source = 1; diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp index 3042d293cf7..7b6757cba32 100644 --- a/src/caffe/test/test_flatten_layer.cpp +++ b/src/caffe/test/test_flatten_layer.cpp @@ -42,13 +42,48 @@ TYPED_TEST(FlattenLayerTest, TestSetup) { LayerParameter layer_param; FlattenLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); - EXPECT_EQ(this->blob_top_->num(), 2); - EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5); - EXPECT_EQ(this->blob_top_->height(), 1); - EXPECT_EQ(this->blob_top_->width(), 1); + ASSERT_EQ(this->blob_top_->num_axes(), 2); + EXPECT_EQ(this->blob_top_->shape(0), 2); + EXPECT_EQ(this->blob_top_->shape(1), 3 * 6 * 5); } -TYPED_TEST(FlattenLayerTest, Test) { +TYPED_TEST(FlattenLayerTest, TestSetupWithAxis) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_flatten_param()->set_axis(2); + FlattenLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_top_->num_axes(), 3); + EXPECT_EQ(this->blob_top_->shape(0), 2); + EXPECT_EQ(this->blob_top_->shape(1), 3); + EXPECT_EQ(this->blob_top_->shape(2), 6 * 5); +} + +TYPED_TEST(FlattenLayerTest, TestSetupWithEndAxis) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_flatten_param()->set_end_axis(-2); + FlattenLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_top_->num_axes(), 3); + EXPECT_EQ(this->blob_top_->shape(0), 2); + EXPECT_EQ(this->blob_top_->shape(1), 3 * 6); + EXPECT_EQ(this->blob_top_->shape(2), 5); +} + +TYPED_TEST(FlattenLayerTest, TestSetupWithStartAndEndAxis) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_flatten_param()->set_axis(0); + layer_param.mutable_flatten_param()->set_end_axis(-2); + FlattenLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_top_->num_axes(), 2); + EXPECT_EQ(this->blob_top_->shape(0), 2 * 3 * 6); + EXPECT_EQ(this->blob_top_->shape(1), 5); +} + +TYPED_TEST(FlattenLayerTest, TestForward) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; FlattenLayer layer(layer_param); @@ -71,5 +106,4 @@ TYPED_TEST(FlattenLayerTest, TestGradient) { this->blob_top_vec_); } - } // namespace caffe From 823d055bc927912528b0f53aa15fab6d988d4a4c Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Sun, 2 Nov 2014 17:21:37 -0800 Subject: [PATCH 110/446] Add ReductionLayer to reduce any number of "tail" axes to a scalar value Currently implements operations SUM, MEAN, ASUM (sum of absolute values), and SUMSQ (sum of squares) --- include/caffe/common_layers.hpp | 45 ++++ src/caffe/layers/reduction_layer.cpp | 132 +++++++++++ src/caffe/layers/reduction_layer.cu | 93 ++++++++ src/caffe/proto/caffe.proto | 33 ++- src/caffe/test/test_reduction_layer.cpp | 297 ++++++++++++++++++++++++ 5 files changed, 599 insertions(+), 1 deletion(-) create mode 100644 src/caffe/layers/reduction_layer.cpp create mode 100644 src/caffe/layers/reduction_layer.cu create mode 100644 src/caffe/test/test_reduction_layer.cpp diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 3155b45bbbc..d2c0ce6d0c6 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -399,6 +399,51 @@ class ReshapeLayer : public Layer { int constant_count_; }; +/** + * @brief Compute "reductions" -- operations that return a scalar output Blob + * for an input Blob of arbitrary size, such as the sum, absolute sum, + * and sum of squares. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class ReductionLayer : public Layer { + public: + explicit ReductionLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Reduction"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + /// @brief the reduction operation performed by the layer + ReductionParameter_ReductionOp op_; + /// @brief a scalar coefficient applied to all outputs + Dtype coeff_; + /// @brief the index of the first input axis to reduce + int axis_; + /// @brief the number of reductions performed + int num_; + /// @brief the input size of each reduction + int dim_; + /// @brief a helper Blob used for summation (op_ == SUM) + Blob sum_multiplier_; +}; + /** * @brief Ignores bottom blobs while producing no top blobs. (This is useful * to suppress outputs during testing.) diff --git a/src/caffe/layers/reduction_layer.cpp b/src/caffe/layers/reduction_layer.cpp new file mode 100644 index 00000000000..8ae6329ebe4 --- /dev/null +++ b/src/caffe/layers/reduction_layer.cpp @@ -0,0 +1,132 @@ +#include +#include +#include + +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +template +void ReductionLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + op_ = this->layer_param_.reduction_param().operation(); +} + +template +void ReductionLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + axis_ = bottom[0]->CanonicalAxisIndex( + this->layer_param_.reduction_param().axis()); + // In the output, we'll keep all axes up to the reduction axis, but + // throw away any after that. + // Note: currently reducing along non-tail axes is not supported; otherwise, + // we'd need to also copy any axes following an "end_axis". + vector top_shape(bottom[0]->shape().begin(), + bottom[0]->shape().begin() + axis_); + top[0]->Reshape(top_shape); + num_ = bottom[0]->count(0, axis_); + dim_ = bottom[0]->count(axis_); + CHECK_EQ(num_, top[0]->count()); + if (op_ == ReductionParameter_ReductionOp_SUM || + op_ == ReductionParameter_ReductionOp_MEAN) { + vector sum_mult_shape(1, dim_); + sum_multiplier_.Reshape(sum_mult_shape); + caffe_set(dim_, Dtype(1), sum_multiplier_.mutable_cpu_data()); + } + coeff_ = this->layer_param().reduction_param().coeff(); + if (op_ == ReductionParameter_ReductionOp_MEAN) { + coeff_ /= dim_; + } +} + +template +void ReductionLayer::Forward_cpu( + const vector*>& bottom, const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + const Dtype* mult_data = NULL; + if (sum_multiplier_.count() > 0) { + mult_data = sum_multiplier_.cpu_data(); + } + Dtype* top_data = top[0]->mutable_cpu_data(); + for (int i = 0; i < num_; ++i) { + switch (op_) { + case ReductionParameter_ReductionOp_SUM: + case ReductionParameter_ReductionOp_MEAN: + *top_data = caffe_cpu_dot(dim_, mult_data, bottom_data); + break; + case ReductionParameter_ReductionOp_ASUM: + *top_data = caffe_cpu_asum(dim_, bottom_data); + break; + case ReductionParameter_ReductionOp_SUMSQ: + *top_data = caffe_cpu_dot(dim_, bottom_data, bottom_data); + break; + default: + LOG(FATAL) << "Unknown reduction op: " + << ReductionParameter_ReductionOp_Name(op_); + } + bottom_data += dim_; + ++top_data; + } + if (coeff_ != Dtype(1)) { + // Reset the top_data pointer. + top_data = top[0]->mutable_cpu_data(); + caffe_scal(num_, coeff_, top_data); + } +} + +template +void ReductionLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (!propagate_down[0]) { return; } + // Get bottom_data, if needed. + const Dtype* bottom_data = NULL; + switch (op_) { + // Operations that don't need bottom_data + case ReductionParameter_ReductionOp_SUM: + case ReductionParameter_ReductionOp_MEAN: + break; + // Operations that need bottom_data + case ReductionParameter_ReductionOp_ASUM: + case ReductionParameter_ReductionOp_SUMSQ: + bottom_data = bottom[0]->cpu_data(); + break; + default: + LOG(FATAL) << "Unknown reduction op: " + << ReductionParameter_ReductionOp_Name(op_); + } + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + for (int i = 0; i < num_; ++i) { + const Dtype bottom_coeff = (*top_diff) * coeff_; + switch (op_) { + case ReductionParameter_ReductionOp_SUM: + case ReductionParameter_ReductionOp_MEAN: + caffe_set(dim_, bottom_coeff, bottom_diff); + break; + case ReductionParameter_ReductionOp_ASUM: + caffe_cpu_sign(dim_, bottom_data, bottom_diff); + caffe_scal(dim_, bottom_coeff, bottom_diff); + break; + case ReductionParameter_ReductionOp_SUMSQ: + caffe_cpu_scale(dim_, 2 * bottom_coeff, bottom_data, bottom_diff); + break; + default: + LOG(FATAL) << "Unknown reduction op: " + << ReductionParameter_ReductionOp_Name(op_); + } + bottom_data += dim_; + bottom_diff += dim_; + ++top_diff; + } +} + +#ifdef CPU_ONLY +STUB_GPU(ReductionLayer); +#endif + +INSTANTIATE_CLASS(ReductionLayer); +REGISTER_LAYER_CLASS(Reduction); + +} // namespace caffe diff --git a/src/caffe/layers/reduction_layer.cu b/src/caffe/layers/reduction_layer.cu new file mode 100644 index 00000000000..2dbd3bc9f94 --- /dev/null +++ b/src/caffe/layers/reduction_layer.cu @@ -0,0 +1,93 @@ +#include +#include + +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +template +void ReductionLayer::Forward_gpu( + const vector*>& bottom, const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + const Dtype* mult_data = NULL; + if (sum_multiplier_.count() > 0) { + mult_data = sum_multiplier_.gpu_data(); + } + Dtype* top_data = top[0]->mutable_cpu_data(); + for (int i = 0; i < num_; ++i) { + switch (op_) { + case ReductionParameter_ReductionOp_SUM: + case ReductionParameter_ReductionOp_MEAN: + caffe_gpu_dot(dim_, mult_data, bottom_data, top_data); + break; + case ReductionParameter_ReductionOp_ASUM: + caffe_gpu_asum(dim_, bottom_data, top_data); + break; + case ReductionParameter_ReductionOp_SUMSQ: + caffe_gpu_dot(dim_, bottom_data, bottom_data, top_data); + break; + default: + LOG(FATAL) << "Unknown reduction op: " + << ReductionParameter_ReductionOp_Name(op_); + } + bottom_data += dim_; + ++top_data; + } + if (coeff_ != Dtype(1)) { + // Reset the top_data pointer. + top_data = top[0]->mutable_gpu_data(); + caffe_gpu_scal(num_, coeff_, top_data); + } +} + +template +void ReductionLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (!propagate_down[0]) { return; } + // Get bottom_data, if needed. + const Dtype* bottom_data = NULL; + switch (op_) { + // Operations that don't need bottom_data + case ReductionParameter_ReductionOp_SUM: + case ReductionParameter_ReductionOp_MEAN: + break; + // Operations that need bottom_data + case ReductionParameter_ReductionOp_ASUM: + case ReductionParameter_ReductionOp_SUMSQ: + bottom_data = bottom[0]->gpu_data(); + break; + default: + LOG(FATAL) << "Unknown reduction op: " + << ReductionParameter_ReductionOp_Name(op_); + } + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + for (int i = 0; i < num_; ++i) { + const Dtype bottom_coeff = (*top_diff) * coeff_; + switch (op_) { + case ReductionParameter_ReductionOp_SUM: + case ReductionParameter_ReductionOp_MEAN: + caffe_gpu_set(dim_, bottom_coeff, bottom_diff); + break; + case ReductionParameter_ReductionOp_ASUM: + caffe_gpu_sign(dim_, bottom_data, bottom_diff); + caffe_gpu_scal(dim_, bottom_coeff, bottom_diff); + break; + case ReductionParameter_ReductionOp_SUMSQ: + caffe_gpu_scale(dim_, 2 * bottom_coeff, bottom_data, bottom_diff); + break; + default: + LOG(FATAL) << "Unknown reduction op: " + << ReductionParameter_ReductionOp_Name(op_); + } + bottom_data += dim_; + bottom_diff += dim_; + ++top_diff; + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(ReductionLayer); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index f79cf80cc05..81a8c69d88e 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -269,7 +269,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 136 (last added: flatten_param) +// LayerParameter next available layer-specific ID: 137 (last added: reduction_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -341,6 +341,7 @@ message LayerParameter { optional PowerParameter power_param = 122; optional PReLUParameter prelu_param = 131; optional PythonParameter python_param = 130; + optional ReductionParameter reduction_param = 136; optional ReLUParameter relu_param = 123; optional ReshapeParameter reshape_param = 133; optional SigmoidParameter sigmoid_param = 124; @@ -704,6 +705,36 @@ message PythonParameter { optional string layer = 2; } +// Message that stores parameters used by ReductionLayer +message ReductionParameter { + enum ReductionOp { + SUM = 1; + ASUM = 2; + SUMSQ = 3; + MEAN = 4; + } + + optional ReductionOp operation = 1 [default = SUM]; // reduction operation + + // The first axis to reduce to a scalar -- may be negative to index from the + // end (e.g., -1 for the last axis). + // (Currently, only reduction along ALL "tail" axes is supported; reduction + // of axis M through N, where N < num_axes - 1, is unsupported.) + // Suppose we have an n-axis bottom Blob with shape: + // (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)). + // If axis == m, the output Blob will have shape + // (d0, d1, d2, ..., d(m-1)), + // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1)) + // times, each including (dm * d(m+1) * ... * d(n-1)) individual data. + // If axis == 0 (the default), the output Blob always has the empty shape + // (count 1), performing reduction across the entire input -- + // often useful for creating new loss functions. + optional int32 axis = 2 [default = 0]; + + optional float coeff = 3 [default = 1.0]; // coefficient for output +} + +// Message that stores parameters used by ReLULayer message ReLUParameter { // Allow non-zero slope for negative inputs to speed up optimization // Described in: diff --git a/src/caffe/test/test_reduction_layer.cpp b/src/caffe/test/test_reduction_layer.cpp new file mode 100644 index 00000000000..f568a18089a --- /dev/null +++ b/src/caffe/test/test_reduction_layer.cpp @@ -0,0 +1,297 @@ +#include +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/vision_layers.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class ReductionLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + ReductionLayerTest() + : blob_bottom_(new Blob(2, 3, 4, 5)), + blob_top_(new Blob()) { + // fill the values + Caffe::set_random_seed(1701); + FillerParameter filler_param; + UniformFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + blob_bottom_vec_.push_back(blob_bottom_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~ReductionLayerTest() { + delete blob_bottom_; + delete blob_top_; + } + + void TestForward(ReductionParameter_ReductionOp op, + float coeff = 1, int axis = 0) { + LayerParameter layer_param; + ReductionParameter* reduction_param = layer_param.mutable_reduction_param(); + reduction_param->set_operation(op); + if (coeff != 1.0) { reduction_param->set_coeff(coeff); } + if (axis != 0) { reduction_param->set_axis(axis); } + shared_ptr > layer( + new ReductionLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* in_data = this->blob_bottom_->cpu_data(); + const int num = this->blob_bottom_->count(0, axis); + const int dim = this->blob_bottom_->count(axis); + for (int n = 0; n < num; ++n) { + Dtype expected_result = 0; + for (int d = 0; d < dim; ++d) { + switch (op) { + case ReductionParameter_ReductionOp_SUM: + expected_result += *in_data; + break; + case ReductionParameter_ReductionOp_MEAN: + expected_result += *in_data / dim; + break; + case ReductionParameter_ReductionOp_ASUM: + expected_result += fabs(*in_data); + break; + case ReductionParameter_ReductionOp_SUMSQ: + expected_result += (*in_data) * (*in_data); + break; + default: + LOG(FATAL) << "Unknown reduction op: " + << ReductionParameter_ReductionOp_Name(op); + } + ++in_data; + } + expected_result *= coeff; + const Dtype computed_result = this->blob_top_->cpu_data()[n]; + EXPECT_FLOAT_EQ(expected_result, computed_result) + << "Incorrect result computed with op " + << ReductionParameter_ReductionOp_Name(op) << ", coeff " << coeff; + } + } + + void TestGradient(ReductionParameter_ReductionOp op, + float coeff = 1, int axis = 0) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ReductionParameter* reduction_param = layer_param.mutable_reduction_param(); + reduction_param->set_operation(op); + reduction_param->set_coeff(coeff); + reduction_param->set_axis(axis); + ReductionLayer layer(layer_param); + GradientChecker checker(1e-2, 2e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); + } + + Blob* const blob_bottom_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(ReductionLayerTest, TestDtypesAndDevices); + +TYPED_TEST(ReductionLayerTest, TestSetUp) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + shared_ptr > layer( + new ReductionLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_top_->num_axes(), 0); +} + +TYPED_TEST(ReductionLayerTest, TestSetUpWithAxis1) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_reduction_param()->set_axis(1); + shared_ptr > layer( + new ReductionLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_top_->num_axes(), 1); + EXPECT_EQ(this->blob_top_->shape(0), 2); +} + +TYPED_TEST(ReductionLayerTest, TestSetUpWithAxis2) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_reduction_param()->set_axis(2); + shared_ptr > layer( + new ReductionLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_top_->num_axes(), 2); + EXPECT_EQ(this->blob_top_->shape(0), 2); + EXPECT_EQ(this->blob_top_->shape(1), 3); +} + +TYPED_TEST(ReductionLayerTest, TestSum) { + const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM; + this->TestForward(kOp); +} + +TYPED_TEST(ReductionLayerTest, TestSumCoeff) { + const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM; + const float kCoeff = 2.3; + this->TestForward(kOp, kCoeff); +} + +TYPED_TEST(ReductionLayerTest, TestSumCoeffAxis1) { + const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM; + const float kCoeff = 2.3; + const int kAxis = 1; + this->TestForward(kOp, kCoeff, kAxis); +} + +TYPED_TEST(ReductionLayerTest, TestSumGradient) { + const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM; + this->TestGradient(kOp); +} + +TYPED_TEST(ReductionLayerTest, TestSumCoeffGradient) { + const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM; + const float kCoeff = 2.3; + this->TestGradient(kOp, kCoeff); +} + +TYPED_TEST(ReductionLayerTest, TestSumCoeffAxis1Gradient) { + const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM; + const float kCoeff = 2.3; + const int kAxis = 1; + this->TestGradient(kOp, kCoeff, kAxis); +} + +TYPED_TEST(ReductionLayerTest, TestMean) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_MEAN; + this->TestForward(kOp); +} + +TYPED_TEST(ReductionLayerTest, TestMeanCoeff) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_MEAN; + const float kCoeff = 2.3; + this->TestForward(kOp, kCoeff); +} + +TYPED_TEST(ReductionLayerTest, TestMeanCoeffAxis1) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_MEAN; + const float kCoeff = 2.3; + const int kAxis = 1; + this->TestForward(kOp, kCoeff, kAxis); +} + +TYPED_TEST(ReductionLayerTest, TestMeanGradient) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_MEAN; + this->TestGradient(kOp); +} + +TYPED_TEST(ReductionLayerTest, TestMeanCoeffGradient) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_MEAN; + const float kCoeff = 2.3; + this->TestGradient(kOp, kCoeff); +} + +TYPED_TEST(ReductionLayerTest, TestMeanCoeffGradientAxis1) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_MEAN; + const float kCoeff = 2.3; + const int kAxis = 1; + this->TestGradient(kOp, kCoeff, kAxis); +} + +TYPED_TEST(ReductionLayerTest, TestAbsSum) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_ASUM; + this->TestForward(kOp); +} + +TYPED_TEST(ReductionLayerTest, TestAbsSumCoeff) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_ASUM; + const float kCoeff = 2.3; + this->TestForward(kOp, kCoeff); +} + +TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffAxis1) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_ASUM; + const float kCoeff = 2.3; + const int kAxis = 1; + this->TestForward(kOp, kCoeff, kAxis); +} + +TYPED_TEST(ReductionLayerTest, TestAbsSumGradient) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_ASUM; + this->TestGradient(kOp); +} + +TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffGradient) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_ASUM; + const float kCoeff = 2.3; + this->TestGradient(kOp, kCoeff); +} + +TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffAxis1Gradient) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_ASUM; + const float kCoeff = 2.3; + const int kAxis = 1; + this->TestGradient(kOp, kCoeff, kAxis); +} + +TYPED_TEST(ReductionLayerTest, TestSumOfSquares) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_SUMSQ; + this->TestForward(kOp); +} + +TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeff) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_SUMSQ; + const float kCoeff = 2.3; + this->TestForward(kOp, kCoeff); +} + +TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffAxis1) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_SUMSQ; + const float kCoeff = 2.3; + const int kAxis = 1; + this->TestForward(kOp, kCoeff, kAxis); +} + +TYPED_TEST(ReductionLayerTest, TestSumOfSquaresGradient) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_SUMSQ; + this->TestGradient(kOp); +} + +TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffGradient) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_SUMSQ; + const float kCoeff = 2.3; + this->TestGradient(kOp, kCoeff); +} + +TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffAxis1Gradient) { + const ReductionParameter_ReductionOp kOp = + ReductionParameter_ReductionOp_SUMSQ; + const float kCoeff = 2.3; + const int kAxis = 1; + this->TestGradient(kOp, kCoeff, kAxis); +} + +} // namespace caffe From 72d70892ad489815589b8e680813c350610b3f2a Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Wed, 3 Jun 2015 10:46:53 -0700 Subject: [PATCH 111/446] [bug] fix double instantiation of GPU methods in LogLayer --- src/caffe/layers/log_layer.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp index 45f73950d7e..1e86a751e9e 100644 --- a/src/caffe/layers/log_layer.cpp +++ b/src/caffe/layers/log_layer.cpp @@ -126,8 +126,6 @@ void LogLayer::Backward_gpu(const vector*>& top, caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff); } -INSTANTIATE_LAYER_GPU_FUNCS(LogLayer); - #endif INSTANTIATE_CLASS(LogLayer); From f8efc628dfb6cd979f3aeb798099dcbb6f119694 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Malte=20St=C3=A6r=20Nissen?= Date: Thu, 4 Jun 2015 11:13:45 +0200 Subject: [PATCH 112/446] LogLayer gpu functionality moved to .cu file --- src/caffe/layers/log_layer.cpp | 47 ---------------------------- src/caffe/layers/log_layer.cu | 57 ++++++++++++++++++++++++++++++++++ 2 files changed, 57 insertions(+), 47 deletions(-) create mode 100644 src/caffe/layers/log_layer.cu diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp index 1e86a751e9e..55a227f6226 100644 --- a/src/caffe/layers/log_layer.cpp +++ b/src/caffe/layers/log_layer.cpp @@ -79,53 +79,6 @@ void LogLayer::Backward_cpu(const vector*>& top, #ifdef CPU_ONLY STUB_GPU(LogLayer); -#else - -template -void LogLayer::Forward_gpu(const vector*>& bottom, - const vector*>& top) { - const int count = bottom[0]->count(); - const Dtype* bottom_data = bottom[0]->gpu_data(); - Dtype* top_data = top[0]->mutable_gpu_data(); - if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) { - caffe_gpu_log(count, bottom_data, top_data); - } else { - caffe_copy(count, bottom_data, top_data); - if (input_scale_ != Dtype(1)) { - caffe_gpu_scal(count, input_scale_, top_data); - } - if (input_shift_ != Dtype(0)) { - caffe_gpu_add_scalar(count, input_shift_, top_data); - } - caffe_gpu_log(count, top_data, top_data); - } - if (base_scale_ != Dtype(1)) { - caffe_gpu_scal(count, base_scale_, top_data); - } -} - -template -void LogLayer::Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) { - if (!propagate_down[0]) { return; } - const int count = bottom[0]->count(); - const Dtype* bottom_data = bottom[0]->gpu_data(); - const Dtype* top_diff = top[0]->gpu_diff(); - Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); - caffe_copy(count, bottom_data, bottom_diff); - if (input_scale_ != Dtype(1)) { - caffe_gpu_scal(count, input_scale_, bottom_diff); - } - if (input_shift_ != Dtype(0)) { - caffe_gpu_add_scalar(count, input_shift_, bottom_diff); - } - caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff); - if (backward_num_scale_ != Dtype(1)) { - caffe_gpu_scal(count, backward_num_scale_, bottom_diff); - } - caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff); -} - #endif INSTANTIATE_CLASS(LogLayer); diff --git a/src/caffe/layers/log_layer.cu b/src/caffe/layers/log_layer.cu new file mode 100644 index 00000000000..847c86cd10c --- /dev/null +++ b/src/caffe/layers/log_layer.cu @@ -0,0 +1,57 @@ +#include +#include + +#include "caffe/layer.hpp" +#include "caffe/neuron_layers.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void LogLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const int count = bottom[0]->count(); + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) { + caffe_gpu_log(count, bottom_data, top_data); + } else { + caffe_copy(count, bottom_data, top_data); + if (input_scale_ != Dtype(1)) { + caffe_gpu_scal(count, input_scale_, top_data); + } + if (input_shift_ != Dtype(0)) { + caffe_gpu_add_scalar(count, input_shift_, top_data); + } + caffe_gpu_log(count, top_data, top_data); + } + if (base_scale_ != Dtype(1)) { + caffe_gpu_scal(count, base_scale_, top_data); + } +} + +template +void LogLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (!propagate_down[0]) { return; } + const int count = bottom[0]->count(); + const Dtype* bottom_data = bottom[0]->gpu_data(); + const Dtype* top_diff = top[0]->gpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + caffe_copy(count, bottom_data, bottom_diff); + if (input_scale_ != Dtype(1)) { + caffe_gpu_scal(count, input_scale_, bottom_diff); + } + if (input_shift_ != Dtype(0)) { + caffe_gpu_add_scalar(count, input_shift_, bottom_diff); + } + caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff); + if (backward_num_scale_ != Dtype(1)) { + caffe_gpu_scal(count, backward_num_scale_, bottom_diff); + } + caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff); +} + +INSTANTIATE_LAYER_GPU_FUNCS(LogLayer); + +} // namespace caffe From eeb428e38029003572ca52182e11e62ab33e4e50 Mon Sep 17 00:00:00 2001 From: Sergio Guadarrama Date: Mon, 30 Mar 2015 17:29:50 -0700 Subject: [PATCH 113/446] Split db.hpp into leveldb_db.hpp and lmdb_db.hpp --- include/caffe/util/db.hpp | 136 ------------------------------ include/caffe/util/db_leveldb.hpp | 73 ++++++++++++++++ include/caffe/util/db_lmdb.hpp | 91 ++++++++++++++++++++ src/caffe/util/db.cpp | 57 +------------ src/caffe/util/db_leveldb.cpp | 21 +++++ src/caffe/util/db_lmdb.cpp | 51 +++++++++++ 6 files changed, 238 insertions(+), 191 deletions(-) create mode 100644 include/caffe/util/db_leveldb.hpp create mode 100644 include/caffe/util/db_lmdb.hpp create mode 100644 src/caffe/util/db_leveldb.cpp create mode 100644 src/caffe/util/db_lmdb.cpp diff --git a/include/caffe/util/db.hpp b/include/caffe/util/db.hpp index afdb8d2c4f8..59ec3d390ba 100644 --- a/include/caffe/util/db.hpp +++ b/include/caffe/util/db.hpp @@ -3,10 +3,6 @@ #include -#include "leveldb/db.h" -#include "leveldb/write_batch.h" -#include "lmdb.h" - #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" @@ -49,138 +45,6 @@ class DB { DISABLE_COPY_AND_ASSIGN(DB); }; -class LevelDBCursor : public Cursor { - public: - explicit LevelDBCursor(leveldb::Iterator* iter) - : iter_(iter) { SeekToFirst(); } - ~LevelDBCursor() { delete iter_; } - virtual void SeekToFirst() { iter_->SeekToFirst(); } - virtual void Next() { iter_->Next(); } - virtual string key() { return iter_->key().ToString(); } - virtual string value() { return iter_->value().ToString(); } - virtual bool valid() { return iter_->Valid(); } - - private: - leveldb::Iterator* iter_; -}; - -class LevelDBTransaction : public Transaction { - public: - explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); } - virtual void Put(const string& key, const string& value) { - batch_.Put(key, value); - } - virtual void Commit() { - leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_); - CHECK(status.ok()) << "Failed to write batch to leveldb " - << std::endl << status.ToString(); - } - - private: - leveldb::DB* db_; - leveldb::WriteBatch batch_; - - DISABLE_COPY_AND_ASSIGN(LevelDBTransaction); -}; - -class LevelDB : public DB { - public: - LevelDB() : db_(NULL) { } - virtual ~LevelDB() { Close(); } - virtual void Open(const string& source, Mode mode); - virtual void Close() { - if (db_ != NULL) { - delete db_; - db_ = NULL; - } - } - virtual LevelDBCursor* NewCursor() { - return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions())); - } - virtual LevelDBTransaction* NewTransaction() { - return new LevelDBTransaction(db_); - } - - private: - leveldb::DB* db_; -}; - -inline void MDB_CHECK(int mdb_status) { - CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status); -} - -class LMDBCursor : public Cursor { - public: - explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor) - : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) { - SeekToFirst(); - } - virtual ~LMDBCursor() { - mdb_cursor_close(mdb_cursor_); - mdb_txn_abort(mdb_txn_); - } - virtual void SeekToFirst() { Seek(MDB_FIRST); } - virtual void Next() { Seek(MDB_NEXT); } - virtual string key() { - return string(static_cast(mdb_key_.mv_data), mdb_key_.mv_size); - } - virtual string value() { - return string(static_cast(mdb_value_.mv_data), - mdb_value_.mv_size); - } - virtual bool valid() { return valid_; } - - private: - void Seek(MDB_cursor_op op) { - int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op); - if (mdb_status == MDB_NOTFOUND) { - valid_ = false; - } else { - MDB_CHECK(mdb_status); - valid_ = true; - } - } - - MDB_txn* mdb_txn_; - MDB_cursor* mdb_cursor_; - MDB_val mdb_key_, mdb_value_; - bool valid_; -}; - -class LMDBTransaction : public Transaction { - public: - explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn) - : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { } - virtual void Put(const string& key, const string& value); - virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); } - - private: - MDB_dbi* mdb_dbi_; - MDB_txn* mdb_txn_; - - DISABLE_COPY_AND_ASSIGN(LMDBTransaction); -}; - -class LMDB : public DB { - public: - LMDB() : mdb_env_(NULL) { } - virtual ~LMDB() { Close(); } - virtual void Open(const string& source, Mode mode); - virtual void Close() { - if (mdb_env_ != NULL) { - mdb_dbi_close(mdb_env_, mdb_dbi_); - mdb_env_close(mdb_env_); - mdb_env_ = NULL; - } - } - virtual LMDBCursor* NewCursor(); - virtual LMDBTransaction* NewTransaction(); - - private: - MDB_env* mdb_env_; - MDB_dbi mdb_dbi_; -}; - DB* GetDB(DataParameter::DB backend); DB* GetDB(const string& backend); diff --git a/include/caffe/util/db_leveldb.hpp b/include/caffe/util/db_leveldb.hpp new file mode 100644 index 00000000000..10623554b67 --- /dev/null +++ b/include/caffe/util/db_leveldb.hpp @@ -0,0 +1,73 @@ +#ifndef CAFFE_UTIL_DB_LEVELDB_HPP +#define CAFFE_UTIL_DB_LEVELDB_HPP + +#include + +#include "leveldb/db.h" +#include "leveldb/write_batch.h" + +#include "caffe/util/db.hpp" + +namespace caffe { namespace db { + +class LevelDBCursor : public Cursor { + public: + explicit LevelDBCursor(leveldb::Iterator* iter) + : iter_(iter) { SeekToFirst(); } + ~LevelDBCursor() { delete iter_; } + virtual void SeekToFirst() { iter_->SeekToFirst(); } + virtual void Next() { iter_->Next(); } + virtual string key() { return iter_->key().ToString(); } + virtual string value() { return iter_->value().ToString(); } + virtual bool valid() { return iter_->Valid(); } + + private: + leveldb::Iterator* iter_; +}; + +class LevelDBTransaction : public Transaction { + public: + explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); } + virtual void Put(const string& key, const string& value) { + batch_.Put(key, value); + } + virtual void Commit() { + leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_); + CHECK(status.ok()) << "Failed to write batch to leveldb " + << std::endl << status.ToString(); + } + + private: + leveldb::DB* db_; + leveldb::WriteBatch batch_; + + DISABLE_COPY_AND_ASSIGN(LevelDBTransaction); +}; + +class LevelDB : public DB { + public: + LevelDB() : db_(NULL) { } + virtual ~LevelDB() { Close(); } + virtual void Open(const string& source, Mode mode); + virtual void Close() { + if (db_ != NULL) { + delete db_; + db_ = NULL; + } + } + virtual LevelDBCursor* NewCursor() { + return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions())); + } + virtual LevelDBTransaction* NewTransaction() { + return new LevelDBTransaction(db_); + } + + private: + leveldb::DB* db_; +}; + + +} // namespace db +} // namespace caffe + +#endif // CAFFE_UTIL_DB_LEVELDB_HPP diff --git a/include/caffe/util/db_lmdb.hpp b/include/caffe/util/db_lmdb.hpp new file mode 100644 index 00000000000..cc7c90afc4c --- /dev/null +++ b/include/caffe/util/db_lmdb.hpp @@ -0,0 +1,91 @@ +#ifndef CAFFE_UTIL_DB_LMDB_HPP +#define CAFFE_UTIL_DB_LMDB_HPP + +#include + +#include "lmdb.h" + +#include "caffe/util/db.hpp" + +namespace caffe { namespace db { + +inline void MDB_CHECK(int mdb_status) { + CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status); +} + +class LMDBCursor : public Cursor { + public: + explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor) + : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) { + SeekToFirst(); + } + virtual ~LMDBCursor() { + mdb_cursor_close(mdb_cursor_); + mdb_txn_abort(mdb_txn_); + } + virtual void SeekToFirst() { Seek(MDB_FIRST); } + virtual void Next() { Seek(MDB_NEXT); } + virtual string key() { + return string(static_cast(mdb_key_.mv_data), mdb_key_.mv_size); + } + virtual string value() { + return string(static_cast(mdb_value_.mv_data), + mdb_value_.mv_size); + } + virtual bool valid() { return valid_; } + + private: + void Seek(MDB_cursor_op op) { + int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op); + if (mdb_status == MDB_NOTFOUND) { + valid_ = false; + } else { + MDB_CHECK(mdb_status); + valid_ = true; + } + } + + MDB_txn* mdb_txn_; + MDB_cursor* mdb_cursor_; + MDB_val mdb_key_, mdb_value_; + bool valid_; +}; + +class LMDBTransaction : public Transaction { + public: + explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn) + : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { } + virtual void Put(const string& key, const string& value); + virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); } + + private: + MDB_dbi* mdb_dbi_; + MDB_txn* mdb_txn_; + + DISABLE_COPY_AND_ASSIGN(LMDBTransaction); +}; + +class LMDB : public DB { + public: + LMDB() : mdb_env_(NULL) { } + virtual ~LMDB() { Close(); } + virtual void Open(const string& source, Mode mode); + virtual void Close() { + if (mdb_env_ != NULL) { + mdb_dbi_close(mdb_env_, mdb_dbi_); + mdb_env_close(mdb_env_); + mdb_env_ = NULL; + } + } + virtual LMDBCursor* NewCursor(); + virtual LMDBTransaction* NewTransaction(); + + private: + MDB_env* mdb_env_; + MDB_dbi mdb_dbi_; +}; + +} // namespace db +} // namespace caffe + +#endif // CAFFE_UTIL_DB_LMDB_HPP diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp index 7f7018107ec..f55420e9840 100644 --- a/src/caffe/util/db.cpp +++ b/src/caffe/util/db.cpp @@ -1,64 +1,11 @@ #include "caffe/util/db.hpp" +#include "caffe/util/db_leveldb.hpp" +#include "caffe/util/db_lmdb.hpp" -#include #include namespace caffe { namespace db { -const size_t LMDB_MAP_SIZE = 1099511627776; // 1 TB - -void LevelDB::Open(const string& source, Mode mode) { - leveldb::Options options; - options.block_size = 65536; - options.write_buffer_size = 268435456; - options.max_open_files = 100; - options.error_if_exists = mode == NEW; - options.create_if_missing = mode != READ; - leveldb::Status status = leveldb::DB::Open(options, source, &db_); - CHECK(status.ok()) << "Failed to open leveldb " << source - << std::endl << status.ToString(); - LOG(INFO) << "Opened leveldb " << source; -} - -void LMDB::Open(const string& source, Mode mode) { - MDB_CHECK(mdb_env_create(&mdb_env_)); - MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE)); - if (mode == NEW) { - CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed"; - } - int flags = 0; - if (mode == READ) { - flags = MDB_RDONLY | MDB_NOTLS; - } - MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664)); - LOG(INFO) << "Opened lmdb " << source; -} - -LMDBCursor* LMDB::NewCursor() { - MDB_txn* mdb_txn; - MDB_cursor* mdb_cursor; - MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn)); - MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_)); - MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor)); - return new LMDBCursor(mdb_txn, mdb_cursor); -} - -LMDBTransaction* LMDB::NewTransaction() { - MDB_txn* mdb_txn; - MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn)); - MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_)); - return new LMDBTransaction(&mdb_dbi_, mdb_txn); -} - -void LMDBTransaction::Put(const string& key, const string& value) { - MDB_val mdb_key, mdb_value; - mdb_key.mv_data = const_cast(key.data()); - mdb_key.mv_size = key.size(); - mdb_value.mv_data = const_cast(value.data()); - mdb_value.mv_size = value.size(); - MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0)); -} - DB* GetDB(DataParameter::DB backend) { switch (backend) { case DataParameter_DB_LEVELDB: diff --git a/src/caffe/util/db_leveldb.cpp b/src/caffe/util/db_leveldb.cpp new file mode 100644 index 00000000000..06c46627d31 --- /dev/null +++ b/src/caffe/util/db_leveldb.cpp @@ -0,0 +1,21 @@ +#include "caffe/util/db_leveldb.hpp" + +#include + +namespace caffe { namespace db { + +void LevelDB::Open(const string& source, Mode mode) { + leveldb::Options options; + options.block_size = 65536; + options.write_buffer_size = 268435456; + options.max_open_files = 100; + options.error_if_exists = mode == NEW; + options.create_if_missing = mode != READ; + leveldb::Status status = leveldb::DB::Open(options, source, &db_); + CHECK(status.ok()) << "Failed to open leveldb " << source + << std::endl << status.ToString(); + LOG(INFO) << "Opened leveldb " << source; +} + +} // namespace db +} // namespace caffe diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp new file mode 100644 index 00000000000..a054b796806 --- /dev/null +++ b/src/caffe/util/db_lmdb.cpp @@ -0,0 +1,51 @@ +#include "caffe/util/db_lmdb.hpp" + +#include + +#include + +namespace caffe { namespace db { + +const size_t LMDB_MAP_SIZE = 1099511627776; // 1 TB + +void LMDB::Open(const string& source, Mode mode) { + MDB_CHECK(mdb_env_create(&mdb_env_)); + MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE)); + if (mode == NEW) { + CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed"; + } + int flags = 0; + if (mode == READ) { + flags = MDB_RDONLY | MDB_NOTLS; + } + MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664)); + LOG(INFO) << "Opened lmdb " << source; +} + +LMDBCursor* LMDB::NewCursor() { + MDB_txn* mdb_txn; + MDB_cursor* mdb_cursor; + MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn)); + MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_)); + MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor)); + return new LMDBCursor(mdb_txn, mdb_cursor); +} + +LMDBTransaction* LMDB::NewTransaction() { + MDB_txn* mdb_txn; + MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn)); + MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_)); + return new LMDBTransaction(&mdb_dbi_, mdb_txn); +} + +void LMDBTransaction::Put(const string& key, const string& value) { + MDB_val mdb_key, mdb_value; + mdb_key.mv_data = const_cast(key.data()); + mdb_key.mv_size = key.size(); + mdb_value.mv_data = const_cast(value.data()); + mdb_value.mv_size = value.size(); + MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0)); +} + +} // namespace db +} // namespace caffe From cdab89a2109956c3514d881c5456522e4181066f Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Tue, 9 Jun 2015 10:45:16 -0700 Subject: [PATCH 114/446] Small platform-specific bugfix for draw.py Close #2376 On Gentoo and CentOS (and others?), you get this error: Warning: /tmp/tmpjqPQBC:6: string ran past end of line --- python/caffe/draw.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/caffe/draw.py b/python/caffe/draw.py index 834ea15ac9a..324929deca4 100644 --- a/python/caffe/draw.py +++ b/python/caffe/draw.py @@ -72,7 +72,7 @@ def get_layer_label(layer, rankdir): else: # If graph orientation is horizontal, vertical space is free and # horizontal space is not; separate words with newlines - separator = '\n' + separator = '\\n' if layer.type == 'Convolution': # Outer double quotes needed or else colon characters don't parse From 543afd36b769675b6de113b3903aaa9866d517d0 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Wed, 10 Jun 2015 17:43:30 -0400 Subject: [PATCH 115/446] [docs] drop out-of-date reference to dev branch --- docs/tutorial/layers.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md index ff2ee491244..806374e3f93 100644 --- a/docs/tutorial/layers.md +++ b/docs/tutorial/layers.md @@ -5,9 +5,7 @@ title: Layer Catalogue To create a Caffe model you need to define the model architecture in a protocol buffer definition file (prototxt). -Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto). The latest definitions are in the [dev caffe.proto](https://github.com/BVLC/caffe/blob/dev/src/caffe/proto/caffe.proto). - -TODO complete list of layers linking to headings +Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto). ### Vision Layers @@ -507,7 +505,7 @@ The `Slice` layer is a utility layer that slices an input layer to multiple outp } } -`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one). +`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one). #### Elementwise Operations From d512d0c5c5efc276db7c864b6b5b6da41824cd88 Mon Sep 17 00:00:00 2001 From: Aaron Schumacher Date: Mon, 15 Jun 2015 19:53:05 -0400 Subject: [PATCH 116/446] typo: "a fixed steps" to "at fixed steps" fixing in the correct place as per @shelhamer's advice from #2602 --- examples/mnist/readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md index 269e53ab9b9..413d4a1f40b 100644 --- a/examples/mnist/readme.md +++ b/examples/mnist/readme.md @@ -283,5 +283,5 @@ and you will be using CPU for training. Isn't that easy? MNIST is a small dataset, so training with GPU does not really introduce too much benefit due to communication overheads. On larger datasets with more complex models, such as ImageNet, the computation speed difference will be more significant. -### How to reduce the learning rate a fixed steps? +### How to reduce the learning rate at fixed steps? Look at lenet_multistep_solver.prototxt From 94a5be14cb7775ba0567348631baa6d595ddf568 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Tue, 16 Jun 2015 09:12:32 -0700 Subject: [PATCH 117/446] Fix HTML display for docs Doesn't change display in Markdown --- docs/development.md | 33 +++++++++++++++------------------ 1 file changed, 15 insertions(+), 18 deletions(-) diff --git a/docs/development.md b/docs/development.md index ccb6a29701d..107c2c3b281 100644 --- a/docs/development.md +++ b/docs/development.md @@ -62,28 +62,25 @@ The following is a poetic presentation of the protocol in code form. #### [Shelhamer's](https://github.com/shelhamer) “life of a branch in four acts” Make the `feature` branch off of the latest `bvlc/master` -``` -git checkout master -git pull upstream master -git checkout -b feature -# do your work, make commits -``` + + git checkout master + git pull upstream master + git checkout -b feature + # do your work, make commits Prepare to merge by rebasing your branch on the latest `bvlc/master` -``` -# make sure master is fresh -git checkout master -git pull upstream master -# rebase your branch on the tip of master -git checkout feature -git rebase master -``` + + # make sure master is fresh + git checkout master + git pull upstream master + # rebase your branch on the tip of master + git checkout feature + git rebase master Push your branch to pull request it into `BVLC/caffe:master` -``` -git push origin feature -# ...make pull request to master... -``` + + git push origin feature + # ...make pull request to master... Now make a pull request! You can do this from the command line (`git pull-request -b master`) if you install [hub](https://github.com/github/hub). Hub has many other magical uses. From 98fb438c0d14f279d434719abd3e79b7f9d231ae Mon Sep 17 00:00:00 2001 From: Dylan Paiton Date: Tue, 16 Jun 2015 17:57:07 -0700 Subject: [PATCH 118/446] fixed two bugs with prototext format The first bug was in InitUnsharedWeightsNet. Proto var was of type ostringstream, which converted the bool bias_term into an int. I wrote an inline conditional to convert the term into a string. This allows backwards compatibility with earlier prototext versions (e.g. version 2.3.0 on Redhat was failing without this). The second bug was in the syntax for repeated bool parameters, assigned to the propagate_down parameter. The style used for e.g. propagate_down: [true,true] does not work with earlier prototext versions (failed with version 2.3.0 on Redhat). New syntax works for all versions. --- src/caffe/test/test_net.cpp | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp index 782a96bc9b6..56959f4793b 100644 --- a/src/caffe/test/test_net.cpp +++ b/src/caffe/test/test_net.cpp @@ -288,6 +288,7 @@ class NetTest : public MultiDeviceTest { const bool force_backward = false, const bool bias_term = false, const Dtype blobs_lr_w1 = 1, const Dtype blobs_lr_b1 = 2, const Dtype blobs_lr_w2 = 1, const Dtype blobs_lr_b2 = 2) { + string bias_str = bias_term ? "true ":"false "; ostringstream proto; proto << "name: 'UnsharedWeightsNetwork' "; if (force_backward) { @@ -314,7 +315,7 @@ class NetTest : public MultiDeviceTest { " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " - " bias_term: " << bias_term << + " bias_term: " << bias_str << " weight_filler { " " type: 'gaussian' " " std: 10 " @@ -340,7 +341,7 @@ class NetTest : public MultiDeviceTest { " type: 'InnerProduct' " " inner_product_param { " " num_output: 10 " - " bias_term: " << bias_term << + " bias_term: " << bias_str << " weight_filler { " " type: 'gaussian' " " std: 10 " @@ -699,9 +700,11 @@ class NetTest : public MultiDeviceTest { " bottom: 'innerproduct' " " bottom: 'label_argmax' "; if (test_skip_true) - proto += " propagate_down: [true, false] "; + proto += " propagate_down: true " + " propagate_down: false "; else - proto += " propagate_down: [true, true] "; + proto += " propagate_down: true " + " propagate_down: true "; proto += " top: 'cross_entropy_loss' " " type: 'SigmoidCrossEntropyLoss' " From e342e155c41887379f3088e3e5b42b2a776d0b87 Mon Sep 17 00:00:00 2001 From: Manuel Date: Mon, 22 Jun 2015 11:49:45 +0200 Subject: [PATCH 119/446] Update parse_log.py Correct parsing (exponential notation learning rates were not being interpreted correctly) --- tools/extra/parse_log.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py index 09ea216ced3..48f9bee0b49 100755 --- a/tools/extra/parse_log.py +++ b/tools/extra/parse_log.py @@ -28,7 +28,7 @@ def parse_log(path_to_log): regex_iteration = re.compile('Iteration (\d+)') regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)') regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)') - regex_learning_rate = re.compile('lr = ([\.\d]+)') + regex_learning_rate = re.compile('lr = ([-+]?[0-9]*\.?[0-9]+([eE]?[-+]?[0-9]+)?)') # Pick out lines of interest iteration = -1 From d2acfedb9256bb5b12614348e932ec448cdf60c9 Mon Sep 17 00:00:00 2001 From: berleon Date: Mon, 22 Jun 2015 14:41:59 +0200 Subject: [PATCH 120/446] fixed _force_color check, fixes #2635 --- src/caffe/data_transformer.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp index 6f75bdb3852..22633922a01 100644 --- a/src/caffe/data_transformer.cpp +++ b/src/caffe/data_transformer.cpp @@ -127,7 +127,7 @@ void DataTransformer::Transform(const Datum& datum, Blob* transformed_blob) { // If datum is encoded, decoded and transform the cv::image. if (datum.encoded()) { - CHECK(!param_.force_color() && !param_.force_gray()) + CHECK(!(param_.force_color() && param_.force_gray())) << "cannot set both force_color and force_gray"; cv::Mat cv_img; if (param_.force_color() || param_.force_gray()) { @@ -430,7 +430,7 @@ void DataTransformer::Transform(Blob* input_blob, template vector DataTransformer::InferBlobShape(const Datum& datum) { if (datum.encoded()) { - CHECK(!param_.force_color() && !param_.force_gray()) + CHECK(!(param_.force_color() && param_.force_gray())) << "cannot set both force_color and force_gray"; cv::Mat cv_img; if (param_.force_color() || param_.force_gray()) { From ba8899301ee5033a30eecd7726fb898279db3032 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Tue, 23 Jun 2015 09:59:22 -0700 Subject: [PATCH 121/446] copyright 2015 --- LICENSE | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/LICENSE b/LICENSE index efcc5c5b6b0..d69d16f5bc7 100644 --- a/LICENSE +++ b/LICENSE @@ -1,11 +1,11 @@ COPYRIGHT All contributions by the University of California: -Copyright (c) 2014, The Regents of the University of California (Regents) +Copyright (c) 2014, 2015, The Regents of the University of California (Regents) All rights reserved. All other contributions: -Copyright (c) 2014, the respective contributors +Copyright (c) 2014, 2015, the respective contributors All rights reserved. Caffe uses a shared copyright model: each contributor holds copyright over From c23722302e1306ace7bce9e9469304ce040aabf2 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 15 Jan 2015 01:27:42 -0800 Subject: [PATCH 122/446] [pycaffe] basic net specification --- python/caffe/__init__.py | 1 + python/caffe/net_spec.py | 203 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 204 insertions(+) create mode 100644 python/caffe/net_spec.py diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py index fbe7112e868..1b2da510a90 100644 --- a/python/caffe/__init__.py +++ b/python/caffe/__init__.py @@ -4,3 +4,4 @@ from .classifier import Classifier from .detector import Detector from . import io +from .net_spec import layers, params, NetSpec, to_proto diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py new file mode 100644 index 00000000000..f54328d56f1 --- /dev/null +++ b/python/caffe/net_spec.py @@ -0,0 +1,203 @@ +"""Python net specification. + +This module provides a way to write nets directly in Python, using a natural, +functional style. See examples/python_nets/caffenet.py for an example. + +Currently this works as a thin wrapper around the Python protobuf interface, +with layers and parameters automatically generated for the "layers" and +"params" pseudo-modules, which are actually objects using __getattr__ magic +to generate protobuf messages. + +Note that when using to_proto or Top.to_proto, names of intermediate blobs will +be automatically generated. To explicitly specify blob names, use the NetSpec +class -- assign to its attributes directly to name layers, and call +NetSpec.to_proto to serialize all assigned layers. + +This interface is expected to continue to evolve as Caffe gains new capabilities +for specifying nets. In particular, the automatically generated layer names +are not guaranteed to be forward-compatible. +""" + +from collections import OrderedDict + +from .proto import caffe_pb2 +from google import protobuf + + +def param_name_dict(): + """Find out the correspondence between layer names and parameter names.""" + + layer = caffe_pb2.LayerParameter() + # get all parameter names (typically underscore case) and corresponding + # type names (typically camel case), which contain the layer names + # (note that not all parameters correspond to layers, but we'll ignore that) + param_names = [s for s in dir(layer) if s.endswith('_param')] + param_type_names = [type(getattr(layer, s)).__name__ for s in param_names] + # strip the final '_param' or 'Parameter' + param_names = [s[:-len('_param')] for s in param_names] + param_type_names = [s[:-len('Parameter')] for s in param_type_names] + return dict(zip(param_type_names, param_names)) + + +def to_proto(*tops): + """Generate a NetParameter that contains all layers needed to compute + all arguments.""" + + if not isinstance(tops, tuple): + tops = (tops,) + layers = OrderedDict() + autonames = {} + for top in tops: + top.fn._to_proto(layers, {}, autonames) + net = caffe_pb2.NetParameter() + net.layer.extend(layers.values()) + return net + + +def assign_proto(proto, name, val): + """Assign a Python object to a protobuf message, based on the Python + type (in recursive fashion). Lists become repeated fields/messages, dicts + become messages, and other types are assigned directly.""" + + if isinstance(val, list): + if isinstance(val[0], dict): + for item in val: + proto_item = getattr(proto, name).add() + for k, v in item.iteritems(): + assign_proto(proto_item, k, v) + else: + getattr(proto, name).extend(val) + elif isinstance(val, dict): + for k, v in val.iteritems(): + assign_proto(getattr(proto, name), k, v) + else: + setattr(proto, name, val) + + +class Top(object): + """A Top specifies a single output blob (which could be one of several + produced by a layer.)""" + + def __init__(self, fn, n): + self.fn = fn + self.n = n + + def to_proto(self): + """Generate a NetParameter that contains all layers needed to compute + this top.""" + + return to_proto(self) + + +class Function(object): + """A Function specifies a layer, its parameters, and its inputs (which + are Tops from other layers).""" + + def __init__(self, type_name, inputs, params): + self.type_name = type_name + self.inputs = inputs + self.params = params + self.ntop = self.params.get('ntop', 1) + # use del to make sure kwargs are not double-processed as layer params + if 'ntop' in self.params: + del self.params['ntop'] + self.in_place = self.params.get('in_place', False) + if 'in_place' in self.params: + del self.params['in_place'] + self.tops = tuple(Top(self, n) for n in range(self.ntop)) + + def _get_name(self, top, names, autonames): + if top not in names: + n = autonames.setdefault(top.fn.type_name, 1) + autonames[top.fn.type_name] += 1 + names[top] = top.fn.type_name + str(n) + return names[top] + + def _to_proto(self, layers, names, autonames): + if self in layers: + return + bottom_names = [] + for inp in self.inputs: + inp.fn._to_proto(layers, names, autonames) + bottom_names.append(layers[inp.fn].top[inp.n]) + layer = caffe_pb2.LayerParameter() + layer.type = self.type_name + layer.bottom.extend(bottom_names) + + if self.in_place: + layer.top.extend(layer.bottom) + else: + for top in self.tops: + layer.top.append(self._get_name(top, names, autonames)) + layer.name = self._get_name(self.tops[0], names, autonames) + + for k, v in self.params.iteritems(): + # special case to handle generic *params + if k.endswith('param'): + assign_proto(layer, k, v) + else: + try: + assign_proto(getattr(layer, + _param_names[self.type_name] + '_param'), k, v) + except (AttributeError, KeyError): + assign_proto(layer, k, v) + + layers[self] = layer + + +class NetSpec(object): + """A NetSpec contains a set of Tops (assigned directly as attributes). + Calling NetSpec.to_proto generates a NetParameter containing all of the + layers needed to produce all of the assigned Tops, using the assigned + names.""" + + def __init__(self): + super(NetSpec, self).__setattr__('tops', OrderedDict()) + + def __setattr__(self, name, value): + self.tops[name] = value + + def __getattr__(self, name): + return self.tops[name] + + def to_proto(self): + names = {v: k for k, v in self.tops.iteritems()} + autonames = {} + layers = OrderedDict() + for name, top in self.tops.iteritems(): + top.fn._to_proto(layers, names, autonames) + net = caffe_pb2.NetParameter() + net.layer.extend(layers.values()) + return net + + +class Layers(object): + """A Layers object is a pseudo-module which generates functions that specify + layers; e.g., Layers().Convolution(bottom, kernel_size=3) will produce a Top + specifying a 3x3 convolution applied to bottom.""" + + def __getattr__(self, name): + def layer_fn(*args, **kwargs): + fn = Function(name, args, kwargs) + if fn.ntop == 1: + return fn.tops[0] + else: + return fn.tops + return layer_fn + + +class Parameters(object): + """A Parameters object is a pseudo-module which generates constants used + in layer parameters; e.g., Parameters().Pooling.MAX is the value used + to specify max pooling.""" + + def __getattr__(self, name): + class Param: + def __getattr__(self, param_name): + return getattr(getattr(caffe_pb2, name + 'Parameter'), param_name) + return Param() + + +_param_names = param_name_dict() +layers = Layers() +params = Parameters() From ad2e7f467d610d21c22d40f37a5a137ef9939a2b Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Mon, 9 Mar 2015 17:43:53 -0700 Subject: [PATCH 123/446] [examples] caffenet python spec --- examples/pycaffe/caffenet.py | 54 ++++++++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 examples/pycaffe/caffenet.py diff --git a/examples/pycaffe/caffenet.py b/examples/pycaffe/caffenet.py new file mode 100644 index 00000000000..f9801d7cbb8 --- /dev/null +++ b/examples/pycaffe/caffenet.py @@ -0,0 +1,54 @@ +from caffe import layers as L, params as P, to_proto +from caffe.proto import caffe_pb2 + +# helper function for common structures + +def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1): + conv = L.Convolution(bottom, kernel_size=ks, stride=stride, + num_output=nout, pad=pad, group=group) + return conv, L.ReLU(conv, in_place=True) + +def fc_relu(bottom, nout): + fc = L.InnerProduct(bottom, num_output=nout) + return fc, L.ReLU(fc, in_place=True) + +def max_pool(bottom, ks, stride=1): + return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride) + +def caffenet(lmdb, batch_size=256, include_acc=False): + data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2, + transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True)) + + # the net itself + conv1, relu1 = conv_relu(data, 11, 96, stride=4) + pool1 = max_pool(relu1, 3, stride=2) + norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75) + conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2) + pool2 = max_pool(relu2, 3, stride=2) + norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75) + conv3, relu3 = conv_relu(norm2, 3, 384, pad=1) + conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2) + conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2) + pool5 = max_pool(relu5, 3, stride=2) + fc6, relu6 = fc_relu(pool5, 4096) + drop6 = L.Dropout(relu6, in_place=True) + fc7, relu7 = fc_relu(drop6, 4096) + drop7 = L.Dropout(relu7, in_place=True) + fc8 = L.InnerProduct(drop7, num_output=1000) + loss = L.SoftmaxWithLoss(fc8, label) + + if include_acc: + acc = L.Accuracy(fc8, label) + return to_proto(loss, acc) + else: + return to_proto(loss) + +def make_net(): + with open('train.prototxt', 'w') as f: + print >>f, caffenet('/path/to/caffe-train-lmdb') + + with open('test.prototxt', 'w') as f: + print >>f, caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True) + +if __name__ == '__main__': + make_net() From 1cdad89d8c0e1b31b5509f91b932ee3a2d376fd3 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 18 Jun 2015 13:25:08 -0700 Subject: [PATCH 124/446] [pytest] minimal testing of net specification --- python/caffe/test/test_net_spec.py | 67 ++++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 python/caffe/test/test_net_spec.py diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py new file mode 100644 index 00000000000..65b73b96f73 --- /dev/null +++ b/python/caffe/test/test_net_spec.py @@ -0,0 +1,67 @@ +import unittest +import tempfile +import caffe +from caffe import layers as L +from caffe import params as P + +def lenet(batch_size): + n = caffe.NetSpec() + n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]), + dict(dim=[batch_size, 1, 1, 1])], + transform_param=dict(scale=1./255), ntop=2) + n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, + weight_filler=dict(type='xavier')) + n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX) + n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, + weight_filler=dict(type='xavier')) + n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX) + n.ip1 = L.InnerProduct(n.pool2, num_output=500, + weight_filler=dict(type='xavier')) + n.relu1 = L.ReLU(n.ip1, in_place=True) + n.ip2 = L.InnerProduct(n.relu1, num_output=10, + weight_filler=dict(type='xavier')) + n.loss = L.SoftmaxWithLoss(n.ip2, n.label) + return n.to_proto() + +def anon_lenet(batch_size): + data, label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]), + dict(dim=[batch_size, 1, 1, 1])], + transform_param=dict(scale=1./255), ntop=2) + conv1 = L.Convolution(data, kernel_size=5, num_output=20, + weight_filler=dict(type='xavier')) + pool1 = L.Pooling(conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX) + conv2 = L.Convolution(pool1, kernel_size=5, num_output=50, + weight_filler=dict(type='xavier')) + pool2 = L.Pooling(conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX) + ip1 = L.InnerProduct(pool2, num_output=500, + weight_filler=dict(type='xavier')) + relu1 = L.ReLU(ip1, in_place=True) + ip2 = L.InnerProduct(relu1, num_output=10, + weight_filler=dict(type='xavier')) + loss = L.SoftmaxWithLoss(ip2, label) + return loss.to_proto() + +class TestNetSpec(unittest.TestCase): + def load_net(self, net_proto): + f = tempfile.NamedTemporaryFile(delete=False) + f.write(str(net_proto)) + f.close() + return caffe.Net(f.name, caffe.TEST) + + def test_lenet(self): + """Construct and build the Caffe version of LeNet.""" + + net_proto = lenet(50) + # check that relu is in-place + self.assertEqual(net_proto.layer[6].bottom, + net_proto.layer[6].top) + net = self.load_net(net_proto) + # check that all layers are present + self.assertEqual(len(net.layers), 9) + + # now the check the version with automatically-generated layer names + net_proto = anon_lenet(50) + self.assertEqual(net_proto.layer[6].bottom, + net_proto.layer[6].top) + net = self.load_net(net_proto) + self.assertEqual(len(net.layers), 9) From ccda10250d8adeee82cdc577204069b79a9d7d6f Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 12 Mar 2015 01:03:51 -0700 Subject: [PATCH 125/446] [examples] draft Python solving example --- examples/python_solving.ipynb | 5148 +++++++++++++++++++++++++++++++++ 1 file changed, 5148 insertions(+) create mode 100644 examples/python_solving.ipynb diff --git a/examples/python_solving.ipynb b/examples/python_solving.ipynb new file mode 100644 index 00000000000..de6c40196ad --- /dev/null +++ b/examples/python_solving.ipynb @@ -0,0 +1,5148 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python solving with LeNet\n", + "\n", + "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('..')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, './python')\n", + "import caffe\n", + "\n", + "from pylab import *\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll be running the provided LeNet example (make sure you've downloaded the data and created the databases, as below)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading...\n", + "--2015-03-12 11:54:02-- http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", + "Resolving yann.lecun.com... 128.122.47.89\n", + "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 9912422 (9.5M) [application/x-gzip]\n", + "Saving to: 'train-images-idx3-ubyte.gz'\n", + "\n", + "train-images-idx3-u 100%[=====================>] 9.45M 194KB/s in 51s \n", + "\n", + "2015-03-12 11:54:54 (188 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n", + "\n", + "--2015-03-12 11:54:54-- http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", + "Resolving yann.lecun.com... 128.122.47.89\n", + "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 28881 (28K) [application/x-gzip]\n", + "Saving to: 'train-labels-idx1-ubyte.gz'\n", + "\n", + "train-labels-idx1-u 100%[=====================>] 28.20K 104KB/s in 0.3s \n", + "\n", + "2015-03-12 11:54:55 (104 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n", + "\n", + "--2015-03-12 11:54:55-- http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", + "Resolving yann.lecun.com... 128.122.47.89\n", + "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1648877 (1.6M) [application/x-gzip]\n", + "Saving to: 't10k-images-idx3-ubyte.gz'\n", + "\n", + "t10k-images-idx3-ub 100%[=====================>] 1.57M 224KB/s in 9.2s \n", + "\n", + "2015-03-12 11:55:04 (176 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n", + "\n", + "--2015-03-12 11:55:04-- http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", + "Resolving yann.lecun.com... 128.122.47.89\n", + "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 4542 (4.4K) [application/x-gzip]\n", + "Saving to: 't10k-labels-idx1-ubyte.gz'\n", + "\n", + "t10k-labels-idx1-ub 100%[=====================>] 4.44K --.-KB/s in 0.09s \n", + "\n", + "2015-03-12 11:55:04 (50.0 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n", + "\n", + "Unzipping...\n", + "train-images-idx3-ubyte already exists -- do you wish to overwrite (y or n)? ^Ctrain-labels-idx1-ubyte already exists -- do you wish to overwrite (y or n)? \n", + "\tnot overwriting\n", + "t10k-images-idx3-ubyte already exists -- do you wish to overwrite (y or n)? Creating lmdb...\n", + "Done.\n" + ] + } + ], + "source": [ + "!data/mnist/get_mnist.sh\n", + "!examples/mnist/create_mnist.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need two external files to help out:\n", + "* the solver prototxt, defining the learning parameters\n", + "* the net prototxt, defining the architecture and train/test data\n", + "\n", + "Here are the learning parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# The train/test net protocol buffer definition\r\n", + "net: \"examples/mnist/lenet_train_test.prototxt\"\r\n", + "# test_iter specifies how many forward passes the test should carry out.\r\n", + "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n", + "# covering the full 10,000 testing images.\r\n", + "test_iter: 100\r\n", + "# Carry out testing every 500 training iterations.\r\n", + "test_interval: 500\r\n", + "# The base learning rate, momentum and the weight decay of the network.\r\n", + "base_lr: 0.01\r\n", + "momentum: 0.9\r\n", + "weight_decay: 0.0005\r\n", + "# The learning rate policy\r\n", + "lr_policy: \"inv\"\r\n", + "gamma: 0.0001\r\n", + "power: 0.75\r\n", + "# Display every 100 iterations\r\n", + "display: 100\r\n", + "# The maximum number of iterations\r\n", + "max_iter: 10000\r\n", + "# snapshot intermediate results\r\n", + "snapshot: 5000\r\n", + "snapshot_prefix: \"examples/mnist/lenet\"\r\n", + "# solver mode: CPU or GPU\r\n", + "solver_mode: GPU\r\n" + ] + } + ], + "source": [ + "!cat examples/mnist/lenet_solver.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here is the version of LeNet we'll be using. In future version of Caffe, networks can be defined directly in Python (see [PR #2086](https://github.com/BVLC/caffe/pull/2086)).\n", + "\n", + "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: \"LeNet\"\r\n", + "layer {\r\n", + " name: \"mnist\"\r\n", + " type: \"Data\"\r\n", + " top: \"data\"\r\n", + " top: \"label\"\r\n", + " include {\r\n", + " phase: TRAIN\r\n", + " }\r\n", + " transform_param {\r\n", + " scale: 0.00390625\r\n", + " }\r\n", + " data_param {\r\n", + " source: \"examples/mnist/mnist_train_lmdb\"\r\n", + " batch_size: 64\r\n", + " backend: LMDB\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"mnist\"\r\n", + " type: \"Data\"\r\n", + " top: \"data\"\r\n", + " top: \"label\"\r\n", + " include {\r\n", + " phase: TEST\r\n", + " }\r\n", + " transform_param {\r\n", + " scale: 0.00390625\r\n", + " }\r\n", + " data_param {\r\n", + " source: \"examples/mnist/mnist_test_lmdb\"\r\n", + " batch_size: 100\r\n", + " backend: LMDB\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"conv1\"\r\n", + " type: \"Convolution\"\r\n", + " bottom: \"data\"\r\n", + " top: \"conv1\"\r\n", + " param {\r\n", + " lr_mult: 1\r\n", + " }\r\n", + " param {\r\n", + " lr_mult: 2\r\n", + " }\r\n", + " convolution_param {\r\n", + " num_output: 20\r\n", + " kernel_size: 5\r\n", + " stride: 1\r\n", + " weight_filler {\r\n", + " type: \"xavier\"\r\n", + " }\r\n", + " bias_filler {\r\n", + " type: \"constant\"\r\n", + " }\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"pool1\"\r\n", + " type: \"Pooling\"\r\n", + " bottom: \"conv1\"\r\n", + " top: \"pool1\"\r\n", + " pooling_param {\r\n", + " pool: MAX\r\n", + " kernel_size: 2\r\n", + " stride: 2\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"conv2\"\r\n", + " type: \"Convolution\"\r\n", + " bottom: \"pool1\"\r\n", + " top: \"conv2\"\r\n", + " param {\r\n", + " lr_mult: 1\r\n", + " }\r\n", + " param {\r\n", + " lr_mult: 2\r\n", + " }\r\n", + " convolution_param {\r\n", + " num_output: 50\r\n", + " kernel_size: 5\r\n", + " stride: 1\r\n", + " weight_filler {\r\n", + " type: \"xavier\"\r\n", + " }\r\n", + " bias_filler {\r\n", + " type: \"constant\"\r\n", + " }\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"pool2\"\r\n", + " type: \"Pooling\"\r\n", + " bottom: \"conv2\"\r\n", + " top: \"pool2\"\r\n", + " pooling_param {\r\n", + " pool: MAX\r\n", + " kernel_size: 2\r\n", + " stride: 2\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"ip1\"\r\n", + " type: \"InnerProduct\"\r\n", + " bottom: \"pool2\"\r\n", + " top: \"ip1\"\r\n", + " param {\r\n", + " lr_mult: 1\r\n", + " }\r\n", + " param {\r\n", + " lr_mult: 2\r\n", + " }\r\n", + " inner_product_param {\r\n", + " num_output: 500\r\n", + " weight_filler {\r\n", + " type: \"xavier\"\r\n", + " }\r\n", + " bias_filler {\r\n", + " type: \"constant\"\r\n", + " }\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"relu1\"\r\n", + " type: \"ReLU\"\r\n", + " bottom: \"ip1\"\r\n", + " top: \"ip1\"\r\n", + "}\r\n", + "layer {\r\n", + " name: \"ip2\"\r\n", + " type: \"InnerProduct\"\r\n", + " bottom: \"ip1\"\r\n", + " top: \"ip2\"\r\n", + " param {\r\n", + " lr_mult: 1\r\n", + " }\r\n", + " param {\r\n", + " lr_mult: 2\r\n", + " }\r", + "\r\n", + " inner_product_param {\r\n", + " num_output: 10\r\n", + " weight_filler {\r\n", + " type: \"xavier\"\r\n", + " }\r\n", + " bias_filler {\r\n", + " type: \"constant\"\r\n", + " }\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"accuracy\"\r\n", + " type: \"Accuracy\"\r\n", + " bottom: \"ip2\"\r\n", + " bottom: \"label\"\r\n", + " top: \"accuracy\"\r\n", + " include {\r\n", + " phase: TEST\r\n", + " }\r\n", + "}\r\n", + "layer {\r\n", + " name: \"loss\"\r\n", + " type: \"SoftmaxWithLoss\"\r\n", + " bottom: \"ip2\"\r\n", + " bottom: \"label\"\r\n", + " top: \"loss\"\r\n", + "}\r\n" + ] + } + ], + "source": [ + "!cat examples/mnist/lenet_train_test.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's pick a device and load the solver. We'll use SGD (with momentum), but Adagrad and Nesterov's accelerated gradient are also available." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "caffe.set_device(0)\n", + "caffe.set_mode_gpu()\n", + "solver = caffe.SGDSolver('examples/mnist/lenet_solver.prototxt')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('data', (64, 1, 28, 28)),\n", + " ('label', (64,)),\n", + " ('conv1', (64, 20, 24, 24)),\n", + " ('pool1', (64, 20, 12, 12)),\n", + " ('conv2', (64, 50, 8, 8)),\n", + " ('pool2', (64, 50, 4, 4)),\n", + " ('ip1', (64, 500)),\n", + " ('ip2', (64, 10)),\n", + " ('loss', ())]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# each output is (batch size, feature dim, spatial dim)\n", + "[(k, v.data.shape) for k, v in solver.net.blobs.items()]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('conv1', (20, 1, 5, 5)),\n", + " ('conv2', (50, 20, 5, 5)),\n", + " ('ip1', (500, 800)),\n", + " ('ip2', (10, 500))]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# just print the weight sizes (not biases)\n", + "[(k, v[0].data.shape) for k, v in solver.net.params.items()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'accuracy': array(0.07000000029802322, dtype=float32),\n", + " 'loss': array(2.375746726989746, dtype=float32)}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solver.net.forward() # train net\n", + "solver.test_nets[0].forward() # test net (there can be more than one)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 5. 0. 4. 1. 9. 2. 1. 3.]\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAWwAAABKCAYAAACfHW4mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + 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"print solver.test_nets[0].blobs['label'].data[:8]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both train and test nets seem to be loading data, and to have correct labels.\n", + "\n", + "Let's take one step of (minibatch) SGD and see what happens." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "solver.step(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do we have gradients propagating through our filters? Let's see the updates to the first layer, shown here as a $4 \\times 5$ grid of $5 \\times 5$ filters." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAATQAAAD7CAYAAADkSGhKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJztvV2sb0tW3TfX2fvce9tOAxcRXUOLNA04tIksgfhQJCeC\n", + "liILy5IdHrDFS3hwIgslThTlwfjFdpKHxJGMkGwJReFDGFk4liI+8mDHuOUo8EAAicSdxIZY3S3R\n", + "DnQjpSF033vO2XuflYd75r5jjz3GnLP+++yPe/hPaWnVqrVWrapZVb8aVWvt/972fY+jHe1oR3sV\n", + "7NF9Z+BoRzva0V6WHYF2tKMd7ZWxI9COdrSjvTJ2BNrRjna0V8aOQDva0Y72ytgRaEc72tFeGTu9\n", + "rYS3bTt+D3K0ox3tVmzf903FHwy0bdu+KyJ+KCJOIuJH9n3/G3zNxz72sWv3fepTn4qv//qvj23b\n", + "Ytu2ePTokQ1fXFxcbufn59fCGHdxcRH7vsfz589j3/crYbXP7eLiogxHRHzJl3xJfOmXfumVvQp/\n", + 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Let's run the net for a while, keeping track of a few things as it goes.\n", + "Note that this process will be the same as if training through the `caffe` binary. In particular:\n", + "* logging will continue to happen as normal\n", + "* snapshots will be taken at the interval specified in the solver prototxt (here, every 5000 iterations)\n", + "* testing will happen at the interval specified (here, every 500 iterations)\n", + "\n", + "Since we have control of the loop in Python, we're free to compute additional things as we go, as we show below. We can do many other things as well, for example:\n", + "* write a custom stopping criterion\n", + "* change the solving process by updating the net in the loop" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 13 s, sys: 134 ms, total: 13.1 s\n", + "Wall time: 13 s\n" + ] + } + ], + "source": [ + "%%time\n", + "niter = 200\n", + "# losses will also be stored in the log\n", + "train_loss = zeros(niter)\n", + "output = zeros((niter, 8, 10))\n", + "\n", + "# the main solver loop\n", + "for it in range(niter):\n", + " solver.step(1) # SGD by Caffe\n", + " \n", + " # store the train loss\n", + " train_loss[it] = solver.net.blobs['loss'].data\n", + " \n", + " # store the output on the first test batch\n", + " # (start at conv1 to avoid loading new data)\n", + " solver.test_nets[0].forward(start='conv1')\n", + " output[it] = solver.test_nets[0].blobs['ip2'].data[:8]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the train loss. (Normally we should plot the test loss as well, but we're only testing every 500 iterations, and loss drops very quickly for this example.)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXncXGV5978XOyEJkIVsBMIuYZFoi6ioqS0tYMUVkde9\n", + "1q1ara+teyttfVvf2ipq0aJVXrAuRSyKFRRUYnFjDYFA2IRASCAhCQQCRAJe7x/XfZ45c+bM8jzP\n", + "nGfmmfl9P5/nM8/MnDnnmjPn3L/7Wu77NndHCCGEyLNDrw0QQgjRf0gchBBCNCBxEEII0YDEQQgh\n", + "RAMSByGEEA1IHIQQQjRQmTiY2UIzu9zMbjKzlWb27pJtlprZFjNbnv4+WpU9QgghOmenCve9HXiv\n", + "u19vZlOBa83sMndfVdjup+5+SoV2CCGEGCWVeQ7ufr+7X5/+3wqsAuaXbGpV2SCEEGJsTEjOwcwW\n", + 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We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAI0AAACPCAYAAADHlliuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAFZtJREFUeJztnVtsY8d5x//f4Z2H94skaiXvemUbsAsD9otbwA2ahyCw\n", + "USBpXxoYKFD0EvShN7QPddyHJo9pgAZF+1CgiB30hqRFCxfpQ1vbRQukD724sGOnaydZY8XVihJF\n", + "iXfykDwipw/kNzuHklbiRRRJzQ8Y8OgsdXYk/vXNN9988w0JIaDRjIJx1R3QLB5aNJqR0aLRjIwW\n", + "jWZktGg0I6NFoxmZsUVDRC8R0cdE9CMienWandLMNzROnIaIXAB+AOAzAHYB/A+AV4QQH023e5p5\n", + "ZFxL8wKAu0KIbSGEDeDbAD4/vW5p5hn3mN93A8CO8vUDAD+uvoGIdKh5wRFC0Gn3x7U0WhDXmHFF\n", + "swtgU/l6E31ro7kGjCuadwE8SUS3iMgL4AsAvjO9bmnmmbF8GiHEMRH9OoB/AeAC8LqeOV0fxppy\n", + "X+jB2hFeeKbtCGuuMVo0mpHRotGMjBaNZmS0aDQjo0WjGRktGs3IaNFoRkaLRjMyWjSakdGi0YzM\n", + 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ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n", + "\n", + "Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAI0AAACPCAYAAADHlliuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAFZtJREFUeJztnVtsY8d5x//f4Z2H94skaiXvemUbsAsD9otbwA2ahyCw\n", + "USBpXxoYKFD0EvShN7QPddyHJo9pgAZF+1CgiB30hqRFCxfpQ1vbRQukD724sGOnaydZY8XVihJF\n", + "iXfykDwipw/kNzuHklbiRRRJzQ8Y8OgsdXYk/vXNN9988w0JIaDRjIJx1R3QLB5aNJqR0aLRjIwW\n", + "jWZktGg0I6NFoxmZsUVDRC8R0cdE9CMienWandLMNzROnIaIXAB+AOAzAHYB/A+AV4QQH023e5p5\n", + "ZFxL8wKAu0KIbSGEDeDbAD4/vW5p5hn3mN93A8CO8vUDAD+uvoGIdKh5wRFC0Gn3x7U0WhDXmHFF\n", + "swtgU/l6E31ro7kGjCuadwE8SUS3iMgL4AsAvjO9bmnmmbF8GiHEMRH9OoB/AeAC8LqeOV0fxppy\n", + "X+jB2hFeeKbtCGuuMVo0mpHRotGMjBaNZmS0aDQjo0WjGRktGs3IaNFoRkaLRjMyWjSakdGi0YzM\n", + 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}, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From b8cc2974fc557fa163187e9ce0a879d0e13833b4 Mon Sep 17 00:00:00 2001 From: Yangqing Jia Date: Tue, 17 Mar 2015 10:50:42 -0700 Subject: [PATCH 126/446] [examples] flickr fine-tuning notebook --- .../Finetune with Flickr Style Data.ipynb | 951 ++++++++++++++++++ .../finetune_flickr_style/assemble_data.py | 8 + .../finetune_flickr_style/train_val.prototxt | 1 + 3 files changed, 960 insertions(+) create mode 100644 examples/Finetune with Flickr Style Data.ipynb diff --git a/examples/Finetune with Flickr Style Data.ipynb b/examples/Finetune with Flickr Style Data.ipynb new file mode 100644 index 00000000000..d8cfc7a3925 --- /dev/null +++ b/examples/Finetune with Flickr Style Data.ipynb @@ -0,0 +1,951 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finetune a Pretrained Network with Flickr Style Data\n", + "\n", + "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network, and finetune the last few layers using your custom data.\n", + "\n", + "The upside of such approach is that, since pre-trained networks are trained on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we will need to prepare the data. This involves the following parts:\n", + "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n", + "(2) Download a subset of the overall Flickr style dataset for this demo.\n", + "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jiayq/Research/caffe\n" + ] + } + ], + "source": [ + "import os\n", + "os.chdir('..')\n", + "import sys\n", + "sys.path.insert(0, './python')\n", + "print os.getcwd()\n", + "\n", + "import caffe\n", + "import numpy as np\n", + "from pylab import *\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading...\n", + "--2015-03-17 10:51:07-- http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n", + "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n", + "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 17858008 (17M) [application/octet-stream]\n", + "Saving to: ‘caffe_ilsvrc12.tar.gz’\n", + "\n", + "100%[======================================>] 17,858,008 287KB/s in 55s \n", + "\n", + "2015-03-17 10:52:02 (318 KB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n", + "\n", + "Unzipping...\n", + "Done.\n", + "Model already exists.\n", + "Downloading 2000 images with 3 workers...\n", + "Writing train/val for 1903 successfully downloaded images.\n" + ] + } + ], + "source": [ + "# This downloads the ilsvrc auxiliary data (mean file, etc),\n", + "# and a subset of 2000 images for the style recognition task.\n", + "\n", + "# You won't need to run this - we should have already created it for you.\n", + "!data/ilsvrc12/get_ilsvrc_aux.sh\n", + "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n", + "!python examples/finetune_flickr_style/assemble_data.py \\\n", + " --workers=-1 --images=2000 --seed=1701 --label=5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's show what is the difference between the finetune network and the original caffe model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1c1\r\n", + "< name: \"CaffeNet\"\r\n", + "---\r\n", + "> name: \"FlickrStyleCaffeNet\"\r\n", + "4c4\r\n", + "< type: \"Data\"\r\n", + "---\r\n", + "> type: \"ImageData\"\r\n", + "15,26c15,19\r\n", + "< # mean pixel / channel-wise mean instead of mean image\r\n", + "< # transform_param {\r\n", + "< # crop_size: 227\r\n", + "< # mean_value: 104\r\n", + "< # mean_value: 117\r\n", + "< # mean_value: 123\r\n", + "< # mirror: true\r\n", + "< # }\r\n", + "< data_param {\r\n", + "< source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n", + "< batch_size: 256\r\n", + "< backend: LMDB\r\n", + "---\r\n", + "> image_data_param {\r\n", + "> source: \"data/flickr_style/train.txt\"\r\n", + "> batch_size: 50\r\n", + "> new_height: 256\r\n", + "> new_width: 256\r\n", + "31c24\r\n", + "< type: \"Data\"\r\n", + "---\r\n", + "> type: \"ImageData\"\r\n", + "42,51c35,36\r\n", + "< # mean pixel / channel-wise mean instead of mean image\r\n", + "< # transform_param {\r\n", + "< # crop_size: 227\r\n", + "< # mean_value: 104\r\n", + "< # mean_value: 117\r\n", + "< # mean_value: 123\r\n", + "< # mirror: true\r\n", + "< # }\r\n", + "< data_param {\r\n", + "< source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n", + "---\r\n", + "> image_data_param {\r\n", + "> source: \"data/flickr_style/test.txt\"\r\n", + "53c38,39\r\n", + "< backend: LMDB\r\n", + "---\r\n", + "> new_height: 256\r\n", + "> new_width: 256\r\n", + "323a310\r\n", + "> # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n", + "360c347\r\n", + "< name: \"fc8\"\r\n", + "---\r\n", + "> name: \"fc8_flickr\"\r\n", + "363c350,351\r\n", + "< top: \"fc8\"\r\n", + "---\r\n", + "> top: \"fc8_flickr\"\r\n", + "> # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n", + "365c353\r\n", + "< lr_mult: 1\r\n", + "---\r\n", + "> lr_mult: 10\r\n", + "369c357\r\n", + "< lr_mult: 2\r\n", + "---\r\n", + "> lr_mult: 20\r\n", + "373c361\r\n", + "< num_output: 1000\r\n", + "---\r\n", + "> num_output: 20\r\n", + "384a373,379\r\n", + "> name: \"loss\"\r\n", + "> type: \"SoftmaxWithLoss\"\r\n", + "> bottom: \"fc8_flickr\"\r\n", + "> bottom: \"label\"\r\n", + "> top: \"loss\"\r\n", + "> }\r\n", + "> layer {\r\n", + "387c382\r\n", + "< bottom: \"fc8\"\r\n", + "---\r\n", + "> bottom: \"fc8_flickr\"\r\n", + "393,399d387\r\n", + "< }\r\n", + "< layer {\r\n", + "< name: \"loss\"\r\n", + "< type: \"SoftmaxWithLoss\"\r\n", + "< bottom: \"fc8\"\r\n", + "< bottom: \"label\"\r\n", + "< top: \"loss\"\r\n" + ] + } + ], + "source": [ + "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For your record, if you want to train the network in pure C++ tools, here is the command:\n", + "\n", + "\n", + "build/tools/caffe train \\\n", + " -solver models/finetune_flickr_style/solver.prototxt \\\n", + " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n", + " -gpu 0\n", + "\n", + "\n", + "However, we will train using Python in this example." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iter 0, loss=3.786610, scratch_loss=3.163587\n", + "iter 10, loss=2.556661, scratch_loss=8.774073\n", + "iter 20, loss=2.035326, scratch_loss=2.266603\n", + "iter 30, loss=1.943101, scratch_loss=1.703273\n", + "iter 40, loss=1.982698, scratch_loss=1.831079\n", + "iter 50, loss=1.559268, scratch_loss=2.041238\n", + "iter 60, loss=1.464433, scratch_loss=1.836157\n", + "iter 70, loss=1.481868, scratch_loss=1.705826\n", + "iter 80, loss=1.394870, scratch_loss=1.695532\n", + "iter 90, loss=1.055422, scratch_loss=1.867379\n", + "iter 100, loss=1.407976, scratch_loss=1.881758\n", + "iter 110, loss=1.569579, scratch_loss=1.701803\n", + "iter 120, loss=0.951682, scratch_loss=1.764299\n", + "iter 130, loss=0.905122, scratch_loss=1.879305\n", + "iter 140, loss=1.020678, scratch_loss=1.746009\n", + "iter 150, loss=0.784985, scratch_loss=1.739624\n", + "iter 160, loss=0.911735, scratch_loss=1.673230\n", + "iter 170, loss=0.965255, scratch_loss=1.725484\n", + "iter 180, loss=1.028102, scratch_loss=1.676103\n", + "iter 190, loss=0.905020, scratch_loss=1.885763\n", + "done\n" + ] + } + ], + "source": [ + "niter = 200\n", + "# losses will also be stored in the log\n", + "train_loss = np.zeros(niter)\n", + "scratch_train_loss = np.zeros(niter)\n", + "\n", + "caffe.set_device(0)\n", + "caffe.set_mode_gpu()\n", + "# We create a solver that finetunes from a previously trained network.\n", + "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n", + "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", + "# For reference, we also create a solver that does no finetuning.\n", + "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n", + "\n", + "# We run the solver for niter times, and record the training loss.\n", + "for it in range(niter):\n", + " solver.step(1) # SGD by Caffe\n", + " scratch_solver.step(1)\n", + " # store the train loss\n", + " train_loss[it] = solver.net.blobs['loss'].data\n", + " scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n", + " if it % 10 == 0:\n", + " print 'iter %d, loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n", + "print 'done'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the training loss produced by the two training procedures respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmYFNXVx/FvD8O+DYsCIgpiRAxE1LiiEY0LGvdEo3Eh\n", + "aoxZXkSTGJdouHE3CaAxica4IVETlATEJCohjkrcArIpohFBRFllUUAWmfv+ce4wPUP3dPV09XTT\n", + 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A closer look at small values, clipping to avoid showing too large loss during training:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXe4JGWV/z81Odw7OScmz5DzEEQYdJUgAioqqGsWTAs/\n", + "d1dXV11rjWvOAQMKiJhFRERQHBRRchgYZpicmByZfGemfn+c9731VnWlvre6+/blfJ7nPrdDdVV1\n", + "dfe3Tn3Pec8LiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoz2t6Ao8Cv0t5/mvAYuBx4MR6\n", + "7ZSiKIqSTo+Cy10DLACChOcuBKYDM4ArgW+Xs2uKoihKZygi8BMQEf8+4CU8fzFwvbl9PzAEGF3K\n", + "3imKoigdpojAfxl4P3A45fnxwGrn/hrkpKAoiqI0kDyBvwjYiPjvSdG7Jf5ckpWjKIqi1JFeOc+f\n", + "iVgwFwL9gEHADcAbnWXWAhOd+xPMY3GWANM6vKeKoijPT5Yiec6acg7JVTQXAreb26cD/0x5fYDP\n", + 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"text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(np.vstack([train_loss, scratch_train_loss]).clip(0, 4).T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the testing accuracy after running 200 iterations. Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for fine-tuning: 0.570000001788\n", + "Accuracy for training from scratch: 0.224000000954\n" + ] + } + ], + "source": [ + "test_iters = 10\n", + "accuracy = 0\n", + "scratch_accuracy = 0\n", + "for it in arange(test_iters):\n", + " solver.test_nets[0].forward()\n", + " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", + " scratch_solver.test_nets[0].forward()\n", + " scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n", + "accuracy /= test_iters\n", + "scratch_accuracy /= test_iters\n", + "print 'Accuracy for fine-tuning:', accuracy\n", + "print 'Accuracy for training from scratch:', scratch_accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n", + "\n", + "http://demo.vislab.berkeleyvision.org/" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/finetune_flickr_style/assemble_data.py b/examples/finetune_flickr_style/assemble_data.py index b4c995e8eae..09bfa2618a4 100755 --- a/examples/finetune_flickr_style/assemble_data.py +++ b/examples/finetune_flickr_style/assemble_data.py @@ -9,6 +9,7 @@ import argparse import numpy as np import pandas as pd +from skimage import io import multiprocessing # Flickr returns a special image if the request is unavailable. @@ -27,6 +28,7 @@ def download_image(args_tuple): urllib.urlretrieve(url, filename) with open(filename) as f: assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1 + test_read_image = io.imread(filename) return True except KeyboardInterrupt: raise Exception() # multiprocessing doesn't catch keyboard exceptions @@ -48,6 +50,10 @@ def download_image(args_tuple): '-w', '--workers', type=int, default=-1, help="num workers used to download images. -x uses (all - x) cores [-1 default]." ) + parser.add_argument( + '-l', '--labels', type=int, default=0, + help="if set to a positive value, only sample images from the first number of labels." + ) args = parser.parse_args() np.random.seed(args.seed) @@ -56,6 +62,8 @@ def download_image(args_tuple): csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz') df = pd.read_csv(csv_filename, index_col=0, compression='gzip') df = df.iloc[np.random.permutation(df.shape[0])] + if args.labels > 0: + df = df.loc[df['label'] < args.labels] if args.images > 0 and args.images < df.shape[0]: df = df.iloc[:args.images] diff --git a/models/finetune_flickr_style/train_val.prototxt b/models/finetune_flickr_style/train_val.prototxt index aa9c73e17ce..848a426c914 100644 --- a/models/finetune_flickr_style/train_val.prototxt +++ b/models/finetune_flickr_style/train_val.prototxt @@ -374,6 +374,7 @@ layer { type: "SoftmaxWithLoss" bottom: "fc8_flickr" bottom: "label" + top: "loss" } layer { name: "accuracy" From a6cb8ec62cb6f92f48e8a19da3c71902d3282bfc Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Tue, 17 Mar 2015 19:01:02 -0700 Subject: [PATCH 127/446] [examples] sequence and revise notebooks - combine classification + filter visualization - order by classification, learning LeNet, brewing logreg, and fine-tuning to flickr style - improve flow of content in classification + filter visualization - include solver needed for learning LeNet - edit notebook descriptions for site catalogue --- examples/00-classification.ipynb | 13187 +++++++++++++++ ..._solving.ipynb => 01-learning-lenet.ipynb} | 4414 +++--- examples/02-brewing-logreg.ipynb | 5771 +++++++ examples/03-fine-tuning.ipynb | 947 ++ .../Finetune with Flickr Style Data.ipynb | 951 -- examples/classification.ipynb | 3342 ---- examples/detection.ipynb | 2 +- examples/filter_visualization.ipynb | 13214 ---------------- examples/hdf5_classification.ipynb | 6290 -------- .../nonlinear_auto_test.prototxt | 54 + .../nonlinear_auto_train.prototxt | 54 + .../nonlinear_solver.prototxt | 15 + ....prototxt => nonlinear_train_val.prototxt} | 10 +- examples/hdf5_classification/solver.prototxt | 5 +- examples/hdf5_classification/solver2.prototxt | 14 - .../hdf5_classification/train_val.prototxt | 7 +- examples/mnist/lenet_auto_solver.prototxt | 24 + examples/net_surgery.ipynb | 2 +- examples/siamese/mnist_siamese.ipynb | 2 +- 19 files changed, 22296 insertions(+), 26009 deletions(-) create mode 100644 examples/00-classification.ipynb rename examples/{python_solving.ipynb => 01-learning-lenet.ipynb} (53%) create mode 100644 examples/02-brewing-logreg.ipynb create mode 100644 examples/03-fine-tuning.ipynb delete mode 100644 examples/Finetune with Flickr Style Data.ipynb delete mode 100644 examples/classification.ipynb delete mode 100644 examples/filter_visualization.ipynb delete mode 100644 examples/hdf5_classification.ipynb create mode 100644 examples/hdf5_classification/nonlinear_auto_test.prototxt create mode 100644 examples/hdf5_classification/nonlinear_auto_train.prototxt create mode 100644 examples/hdf5_classification/nonlinear_solver.prototxt rename examples/hdf5_classification/{train_val2.prototxt => nonlinear_train_val.prototxt} (87%) delete mode 100644 examples/hdf5_classification/solver2.prototxt create mode 100644 examples/mnist/lenet_auto_solver.prototxt diff --git a/examples/00-classification.ipynb b/examples/00-classification.ipynb new file mode 100644 index 00000000000..46bbb193fe7 --- /dev/null +++ b/examples/00-classification.ipynb @@ -0,0 +1,13187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Instant Recognition with Caffe\n", + "\n", + "In this example we'll classify an image with the bundled CaffeNet model based on the network architecture of Krizhevsky et al. for ImageNet. We'll compare CPU and GPU operation then reach into the model to inspect features and the output.\n", + "\n", + "(These feature visualizations follow the DeCAF visualizations originally by Yangqing Jia.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# Make sure that caffe is on the python path:\n", + "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "\n", + "import caffe\n", + "\n", + "plt.rcParams['figure.figsize'] = (10, 10)\n", + "plt.rcParams['image.interpolation'] = 'nearest'\n", + "plt.rcParams['image.cmap'] = 'gray'\n", + "\n", + "import os\n", + "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", + " print(\"Downloading pre-trained CaffeNet model...\")\n", + " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "caffe.set_mode_cpu()\n", + "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n", + " caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n", + " caffe.TEST)\n", + "\n", + "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n", + "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n", + "transformer.set_transpose('data', (2,0,1))\n", + "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n", + "transformer.set_raw_scale('data', 255) # the reference model operates on images in [0,255] range instead of [0,1]\n", + "transformer.set_channel_swap('data', (2,1,0)) # the reference model has channels in BGR order instead of RGB" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with a simple classification. We'll set a batch of 50 to demonstrate batch processing, even though we'll only be classifying one image. (Note that the batch size can also be changed on-the-fly.)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set net to batch size of 50\n", + "net.blobs['data'].reshape(50,3,227,227)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Feed in the image (with some preprocessing) and classify with a forward pass." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted class is #281.\n" + ] + } + ], + "source": [ + "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n", + "out = net.forward()\n", + "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What did the input look like?" + ] 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classification correct?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n", + " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n", + " 'n02127052 lynx, catamount']\n" + ] + } + ], + "source": [ + "# load labels\n", + "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", + "try:\n", + " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", + "except:\n", + " !../data/ilsvrc12/get_ilsvrc_aux.sh\n", + " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", + "\n", + "# sort top k predictions from softmax output\n", + "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n", + "print labels[top_k]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indeed! But how long did it take?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 loops, best of 3: 7.14 s per loop\n" + ] + } + ], + "source": [ + "# CPU mode\n", + "net.forward() # call once for allocation\n", + "%timeit net.forward()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's a while, even for a batch size of 50 images. Let's switch to GPU mode." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 90.9 ms per loop\n" + ] + } + ], + "source": [ + "# GPU mode\n", + "caffe.set_device(0)\n", + "caffe.set_mode_gpu()\n", + "net.forward() # call once for allocation\n", + "%timeit net.forward()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Much better. Now let's look at the net in more detail.\n", + "\n", + "First, the layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('data', (50, 3, 227, 227)),\n", + " ('conv1', (50, 96, 55, 55)),\n", + " ('pool1', (50, 96, 27, 27)),\n", + " ('norm1', (50, 96, 27, 27)),\n", + " ('conv2', (50, 256, 27, 27)),\n", + " ('pool2', (50, 256, 13, 13)),\n", + " ('norm2', (50, 256, 13, 13)),\n", + " ('conv3', (50, 384, 13, 13)),\n", + " ('conv4', (50, 384, 13, 13)),\n", + " ('conv5', (50, 256, 13, 13)),\n", + " ('pool5', (50, 256, 6, 6)),\n", + " ('fc6', (50, 4096)),\n", + " ('fc7', (50, 4096)),\n", + " ('fc8', (50, 1000)),\n", + " ('prob', (50, 1000))]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[(k, v.data.shape) for k, v in net.blobs.items()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('conv1', (96, 3, 11, 11)),\n", + " ('conv2', (256, 48, 5, 5)),\n", + " ('conv3', (384, 256, 3, 3)),\n", + " ('conv4', (384, 192, 3, 3)),\n", + " ('conv5', (256, 192, 3, 3)),\n", + " ('fc6', (4096, 9216)),\n", + " ('fc7', (4096, 4096)),\n", + " ('fc8', (1000, 4096))]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[(k, v[0].data.shape) for k, v in net.params.items()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper functions for visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# take an array of shape (n, height, width) or (n, height, width, channels)\n", + "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n", + "def vis_square(data, padsize=1, padval=0):\n", + " data -= data.min()\n", + " data /= data.max()\n", + " \n", + " # force the number of filters to be square\n", + " n = int(np.ceil(np.sqrt(data.shape[0])))\n", + " padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n", + " data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n", + " \n", + " # tile the filters into an image\n", + " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n", + " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n", + " \n", + " plt.imshow(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first layer filters, `conv1`" + ] + 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We show only the first 48 filters, with each channel shown separately, so that each filter is a row." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmsbldxv1nvOb7zgCc8GwMGMxkIMyKgRCgk/+RDuiMl\n", + "6UQBOg4eZQYj7GADRg42wthGcMEoHkCOlaB0R1GCWpGSNJmDiIAEOYAx4BHPxsb25frOZ+gPl2fv\n", + "9T57132dg92n8+/6fTnnPWe9a9eqVWvtVbVqmCwvL0ehUCgUCoVC4b+OudUmoFAoFAqFQuG/K+og\n", + "VSgUCoVCobBC1EGqUCgUCoVCYYWog1ShUCgUCoXCClEHqUKhUCgUCoUVog5ShUKhUCgUCivE03KQ\n", + "mkwm/2MymXx3MpncOplM3v90PKNQKBQKhUJhtTF5qvNITSaT+Yj4XkT8QkTcFxFfj4jfXl5evuUp\n", + "fVChUCgUCoXCKuPpsEi9NiJuW15evmt5eXl/RPwfEfG/PA3PKRQKhUKhUFhVPB0HqeMj4p7m870/\n", + "+VuhUCgUCoXC/1Q45Gnoc+Zd4WQyqbo0hUKhUCgU/ttgeXl5Mvb3p+MgdV9EnNh8PjEOWKWmcNxx\n", + 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{ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fifth layer after pooling, `pool5`" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlEAAAJMCAYAAADaNPObAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmMXfd14PlzWPvG2lhciquojSIVWZsj27GgcqA4GseQ\n", + "nX9sB4ghpNMBgo67Y4+nZSuDNKQ/0tM20OMMMsgf44kNJZioo0k7XgYtWEuz5FYUWZJlmSEliqTF\n", + "EllkVZFVxdr3qt/8wZJC1u+U9OPv3nfvfa++H8CweHiXU/fe997hrXPPU+ecAAAA4NpsyjsBAACA\n", + "ckQRBQAAEIEiCgAAIAJFFAAAQASKKAAAgAgUUQAAABFSL6JU9QFVPa6qJ1X1a2lvHwAAoAg0zTlR\n", + "qlolIm+JyP0ick5EXhGR33HOvZnaTgAAAAqgOuXt/aqInHLO9YmIqOp/EZHPiMh7RZSqMt0TAACU\n", + "DeecWvG0i6idInL2ij/3i8g9axdS1XeTElWVhoYGb0MzMzNe7N31rhR6J81aztpeFpLk0tHREbSc\n", + "dfzm5ubeN5dHH31UHn300bI8LmlLkstdd90VtNzPfvazkueStqLkklce1dX+W+bi4mKquVj7WFpa\n", + 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"execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAmEAAAJPCAYAAAA0UwMNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X2Q7md93/fPV0fGMuLZphYWcnBAtoEB29iVabCdg00Y\n", + "hXEsppkxCD+kDkNoU9m0zXQI6YyR23/atJ0kDgmRXcVJXGJNkgKRW4jASc+YOg4gm4BjJCoFa6oH\n", + "TDDgBzyxfRR9+8d9L9xa7e6955zdvX6r6/WaObN7P+7vnN+59/fe6/rd11Z3BwCAk3XZ6A0AAJiR\n", + "CAMAGECEAQAMIMIAAAYQYQAAA4gwAIABtkZYVV1fVXdX1T1V9eY9br+hqj5aVR+pql+pqu/euO2+\n", + "qvrY+rYPHfXGAwCcVnXQOmFVdSbJJ5K8IsmDST6c5MbuvmvjPld29++vP39Rknd19/PWl38jybd2\n", + "9+eO768AAHD6bBsJuy7Jvd19X3efT3Jbkhs277ATYGtPSvJbu56jLnkrAQAeZ7ZF2NVJ7t+4/MD6\n", + "ukepqldX1V1J3pvkxzZu6iS/UFV3VtUbLnVjAQAeLy7fcvuhfqdRd787ybur6juT/GySb1jf9LLu\n", + "/lRVPTPJ+6vq7u7+wMVvLgDA48O2CHswyTUbl6/JajRsT939gaq6vKq+srs/292fWl//map6V1bT\n", 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top 5 predicted labels." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n", + " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n", + " 'n02127052 lynx, catamount']\n" + ] + } + ], + "source": [ + "# load labels\n", + "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", + "try:\n", + " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", + "except:\n", + " !../data/ilsvrc12/get_ilsvrc_aux.sh\n", + " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", + "\n", + "# sort top k predictions from softmax output\n", + "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n", + "print labels[top_k]" + ] + } + ], + "metadata": { + "description": "Instant recognition with a pre-trained model and a tour of the net interface for visualizing features and parameters layer-by-layer.", + "example_name": "Image Classification and Filter Visualization", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 1 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/python_solving.ipynb b/examples/01-learning-lenet.ipynb similarity index 53% rename from examples/python_solving.ipynb rename to examples/01-learning-lenet.ipynb index de6c40196ad..3562c7adaf2 100644 --- a/examples/python_solving.ipynb +++ b/examples/01-learning-lenet.ipynb @@ -56,59 +56,59 @@ "output_type": "stream", "text": [ "Downloading...\n", - "--2015-03-12 11:54:02-- 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128.122.47.89\n", "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 28881 (28K) [application/x-gzip]\n", "Saving to: 'train-labels-idx1-ubyte.gz'\n", "\n", - "train-labels-idx1-u 100%[=====================>] 28.20K 104KB/s in 0.3s \n", + "train-labels-idx1-u 100%[=====================>] 28.20K 107KB/s in 0.3s \n", "\n", - "2015-03-12 11:54:55 (104 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n", + "2015-06-30 14:42:53 (107 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n", "\n", - "--2015-03-12 11:54:55-- http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", + "--2015-06-30 14:42:53-- http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", "Resolving yann.lecun.com... 128.122.47.89\n", "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1648877 (1.6M) [application/x-gzip]\n", "Saving to: 't10k-images-idx3-ubyte.gz'\n", "\n", - "t10k-images-idx3-ub 100%[=====================>] 1.57M 224KB/s in 9.2s \n", + "t10k-images-idx3-ub 100%[=====================>] 1.57M 205KB/s in 8.2s \n", "\n", - "2015-03-12 11:55:04 (176 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n", + "2015-06-30 14:43:02 (197 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n", "\n", - "--2015-03-12 11:55:04-- http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", + "--2015-06-30 14:43:02-- http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", "Resolving yann.lecun.com... 128.122.47.89\n", "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 4542 (4.4K) [application/x-gzip]\n", "Saving to: 't10k-labels-idx1-ubyte.gz'\n", "\n", - "t10k-labels-idx1-ub 100%[=====================>] 4.44K --.-KB/s in 0.09s \n", + "t10k-labels-idx1-ub 100%[=====================>] 4.44K 26.9KB/s in 0.2s \n", "\n", - "2015-03-12 11:55:04 (50.0 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n", + "2015-06-30 14:43:02 (26.9 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n", "\n", "Unzipping...\n", - "train-images-idx3-ubyte already exists -- do you wish to overwrite (y or n)? ^Ctrain-labels-idx1-ubyte already exists -- do you wish to overwrite (y or n)? \n", - "\tnot overwriting\n", - "t10k-images-idx3-ubyte already exists -- do you wish to overwrite (y or n)? Creating lmdb...\n", + "Done.\n", + "Creating lmdb...\n", "Done.\n" ] } ], "source": [ + "# Download and prepare data\n", "!data/mnist/get_mnist.sh\n", "!examples/mnist/create_mnist.sh" ] @@ -118,67 +118,57 @@ "metadata": {}, "source": [ "We need two external files to help out:\n", + "* the net prototxt, defining the architecture and pointing to the train/test data\n", "* the solver prototxt, defining the learning parameters\n", - "* the net prototxt, defining the architecture and train/test data\n", "\n", - "Here are the learning parameters." + "We start with the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n", + "\n", + "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# The train/test net protocol buffer definition\r\n", - "net: \"examples/mnist/lenet_train_test.prototxt\"\r\n", - "# test_iter specifies how many forward passes the test should carry out.\r\n", - "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n", - "# covering the full 10,000 testing images.\r\n", - "test_iter: 100\r\n", - "# Carry out testing every 500 training iterations.\r\n", - "test_interval: 500\r\n", - "# The base learning rate, momentum and the weight decay of the network.\r\n", - "base_lr: 0.01\r\n", - "momentum: 0.9\r\n", - "weight_decay: 0.0005\r\n", - "# The learning rate policy\r\n", - "lr_policy: \"inv\"\r\n", - "gamma: 0.0001\r\n", - "power: 0.75\r\n", - "# Display every 100 iterations\r\n", - "display: 100\r\n", - "# The maximum number of iterations\r\n", - "max_iter: 10000\r\n", - "# snapshot intermediate results\r\n", - "snapshot: 5000\r\n", - "snapshot_prefix: \"examples/mnist/lenet\"\r\n", - "# solver mode: CPU or GPU\r\n", - "solver_mode: GPU\r\n" - ] - } - ], + "outputs": [], "source": [ - "!cat examples/mnist/lenet_solver.prototxt" + "from caffe import layers as L\n", + "from caffe import params as P\n", + "\n", + "def lenet(lmdb, batch_size):\n", + " # our version of LeNet: a series of linear and simple nonlinear transformations\n", + " n = caffe.NetSpec()\n", + " n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n", + " transform_param=dict(scale=1./255), ntop=2)\n", + " n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n", + " n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", + " n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n", + " n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", + " n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n", + " n.relu1 = L.ReLU(n.ip1, in_place=True)\n", + " n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n", + " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n", + " return n.to_proto()\n", + " \n", + "with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:\n", + " f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))\n", + " \n", + "with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:\n", + " f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "And here is the version of LeNet we'll be using. In future version of Caffe, networks can be defined directly in Python (see [PR #2086](https://github.com/BVLC/caffe/pull/2086)).\n", - "\n", - "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`." + "The net has been written to disk in more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -187,17 +177,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "name: \"LeNet\"\r\n", "layer {\r\n", - " name: \"mnist\"\r\n", + " name: \"data\"\r\n", " type: \"Data\"\r\n", " top: \"data\"\r\n", " top: \"label\"\r\n", - " include {\r\n", - " phase: TRAIN\r\n", - " }\r\n", " transform_param {\r\n", - " scale: 0.00390625\r\n", + " scale: 0.00392156862745\r\n", " }\r\n", " data_param {\r\n", " source: \"examples/mnist/mnist_train_lmdb\"\r\n", @@ -206,43 +192,16 @@ " }\r\n", "}\r\n", "layer {\r\n", - " name: \"mnist\"\r\n", - " type: \"Data\"\r\n", - " top: \"data\"\r\n", - " top: \"label\"\r\n", - " include {\r\n", - " phase: TEST\r\n", - " }\r\n", - " transform_param {\r\n", - " scale: 0.00390625\r\n", - " }\r\n", - " data_param {\r\n", - " source: \"examples/mnist/mnist_test_lmdb\"\r\n", - " batch_size: 100\r\n", - " backend: LMDB\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", " name: \"conv1\"\r\n", " type: \"Convolution\"\r\n", " bottom: \"data\"\r\n", " top: \"conv1\"\r\n", - " param {\r\n", - " lr_mult: 1\r\n", - " }\r\n", - " param {\r\n", - " lr_mult: 2\r\n", - " }\r\n", " convolution_param {\r\n", " num_output: 20\r\n", " kernel_size: 5\r\n", - " stride: 1\r\n", " weight_filler {\r\n", " type: \"xavier\"\r\n", " }\r\n", - " bias_filler {\r\n", - " type: \"constant\"\r\n", - " }\r\n", " }\r\n", "}\r\n", "layer {\r\n", @@ -261,22 +220,12 @@ " type: \"Convolution\"\r\n", " bottom: \"pool1\"\r\n", " top: \"conv2\"\r\n", - " param {\r\n", - " lr_mult: 1\r\n", - " }\r\n", - " param {\r\n", - " lr_mult: 2\r\n", - " }\r\n", " convolution_param {\r\n", " num_output: 50\r\n", " kernel_size: 5\r\n", - " stride: 1\r\n", " weight_filler {\r\n", " type: \"xavier\"\r\n", " }\r\n", - " bias_filler {\r\n", - " type: \"constant\"\r\n", - " }\r\n", " }\r\n", "}\r\n", "layer {\r\n", @@ -295,20 +244,11 @@ " type: \"InnerProduct\"\r\n", " bottom: \"pool2\"\r\n", " top: \"ip1\"\r\n", - " param {\r\n", - " lr_mult: 1\r\n", - " }\r\n", - " param {\r\n", - " lr_mult: 2\r\n", - " }\r\n", " inner_product_param {\r\n", " num_output: 500\r\n", " weight_filler {\r\n", " type: \"xavier\"\r\n", " }\r\n", - " bias_filler {\r\n", - " type: \"constant\"\r\n", - " }\r\n", " }\r\n", "}\r\n", "layer {\r\n", @@ -322,31 +262,11 @@ " type: \"InnerProduct\"\r\n", " bottom: \"ip1\"\r\n", " top: \"ip2\"\r\n", - " param {\r\n", - " lr_mult: 1\r\n", - " }\r\n", - " param {\r\n", - " lr_mult: 2\r\n", - " }\r", - "\r\n", " inner_product_param {\r\n", " num_output: 10\r\n", " weight_filler {\r\n", " type: \"xavier\"\r\n", " }\r\n", - " bias_filler {\r\n", - " type: \"constant\"\r\n", - " }\r\n", - " }\r\n", - "}\r\n", - "layer {\r\n", - " name: \"accuracy\"\r\n", - " type: \"Accuracy\"\r\n", - " bottom: \"ip2\"\r\n", - " bottom: \"label\"\r\n", - " top: \"accuracy\"\r\n", - " include {\r\n", - " phase: TEST\r\n", " }\r\n", "}\r\n", "layer {\r\n", @@ -360,7 +280,56 @@ } ], "source": [ - "!cat examples/mnist/lenet_train_test.prototxt" + "!cat examples/mnist/lenet_auto_train.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's see the learning parameters, which are also written as a `prototxt` file. We're using SGD with momentum, weight decay, and a specific learning rate schedule." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# The train/test net protocol buffer definition\r\n", + "train_net: \"examples/mnist/lenet_auto_train.prototxt\"\r\n", + "test_net: \"examples/mnist/lenet_auto_test.prototxt\"\r\n", + "# test_iter specifies how many forward passes the test should carry out.\r\n", + "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n", + "# covering the full 10,000 testing images.\r\n", + "test_iter: 100\r\n", + "# Carry out testing every 500 training iterations.\r\n", + "test_interval: 500\r\n", + "# The base learning rate, momentum and the weight decay of the network.\r\n", + "base_lr: 0.01\r\n", + "momentum: 0.9\r\n", + "weight_decay: 0.0005\r\n", + "# The learning rate policy\r\n", + "lr_policy: \"inv\"\r\n", + "gamma: 0.0001\r\n", + "power: 0.75\r\n", + "# Display every 100 iterations\r\n", + "display: 100\r\n", + "# The maximum number of iterations\r\n", + "max_iter: 10000\r\n", + "# snapshot intermediate results\r\n", + "snapshot: 5000\r\n", + "snapshot_prefix: \"examples/mnist/lenet\"\r\n" + ] + } + ], + "source": [ + "!cat examples/mnist/lenet_auto_solver.prototxt" ] }, { @@ -372,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -380,7 +349,7 @@ "source": [ "caffe.set_device(0)\n", "caffe.set_mode_gpu()\n", - "solver = caffe.SGDSolver('examples/mnist/lenet_solver.prototxt')" + "solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')" ] }, { @@ -392,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false @@ -412,7 +381,7 @@ " ('loss', ())]" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -424,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -438,7 +407,7 @@ " ('ip2', (10, 500))]" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -457,7 +426,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -465,11 +434,10 @@ { "data": { "text/plain": [ - "{'accuracy': array(0.07000000029802322, dtype=float32),\n", - " 'loss': array(2.375746726989746, dtype=float32)}" + "{'loss': array(2.301163673400879, dtype=float32)}" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -481,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -699,7 +667,7 @@ "eU5QDbaKiorKc8L/DzAr6bE92WeRAAAAAElFTkSuQmCC\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -714,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -920,7 +888,7 @@ "AAAASUVORK5CYII=\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -943,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": true }, @@ -961,7 +929,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -969,10 +937,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -980,470 +948,437 @@ "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAATQAAAD7CAYAAADkSGhKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJztvV2sb0tW3TfX2fvce9tOAxcRXUOLNA04tIksgfhQJCeC\n", - "liILy5IdHrDFS3hwIgslThTlwfjFdpKHxJGMkGwJReFDGFk4liI+8mDHuOUo8EAAicSdxIZY3S3R\n", - 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"Iteration 125 testing...\n", + "Iteration 150 testing...\n", + "Iteration 175 testing...\n", + "CPU times: user 12.3 s, sys: 3.96 s, total: 16.2 s\n", + "Wall time: 15.7 s\n" ] } ], "source": [ "%%time\n", "niter = 200\n", + "test_interval = 25\n", "# losses will also be stored in the log\n", "train_loss = zeros(niter)\n", + "test_acc = zeros(int(np.ceil(niter / test_interval)))\n", "output = zeros((niter, 8, 10))\n", "\n", "# the main solver loop\n", @@ -1501,21 +1446,33 @@ " train_loss[it] = solver.net.blobs['loss'].data\n", " \n", " # store the output on the first test batch\n", - " # (start at conv1 to avoid loading new data)\n", + " # (start the forward pass at conv1 to avoid loading new data)\n", " solver.test_nets[0].forward(start='conv1')\n", - " output[it] = solver.test_nets[0].blobs['ip2'].data[:8]" + " output[it] = solver.test_nets[0].blobs['ip2'].data[:8]\n", + " \n", + " # run a full test every so often\n", + " # (Caffe can also do this for us and write to a log, but we show here\n", + " # how to do it directly in Python, where more complicated things are easier.)\n", + " if it % test_interval == 0:\n", + " print 'Iteration', it, 'testing...'\n", + " correct = 0\n", + " for test_it in range(100):\n", + " solver.test_nets[0].forward()\n", + " correct += sum(solver.test_nets[0].blobs['ip2'].data.argmax(1)\n", + " == solver.test_nets[0].blobs['label'].data)\n", + " test_acc[it // test_interval] = correct / 1e4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's plot the train loss. (Normally we should plot the test loss as well, but we're only testing every 500 iterations, and loss drops very quickly for this example.)" + "Let's plot the train loss and test accuracy." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -1523,258 +1480,340 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXncXGV5978XOyEJkIVsBMIuYZFoi6ioqS0tYMUVkde9\n", - "1q1ara+teyttfVvf2ipq0aJVXrAuRSyKFRRUYnFjDYFA2IRASCAhCQQCRAJe7x/XfZ45c+bM8jzP\n", - "nGfmmfl9P5/nM8/MnDnnmjPn3L/7Wu77NndHCCGEyLNDrw0QQgjRf0gchBBCNCBxEEII0YDEQQgh\n", - "RAMSByGEEA1IHIQQQjRQmTiY2UIzu9zMbjKzlWb27pJtlprZFjNbnv4+WpU9QgghOmenCve9HXiv\n", - 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Hooray!\n", "\n", "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": { "collapsed": false, "scrolled": false @@ -1908,7 +1951,7 @@ "WjSakdGi0YyMFo1mZLRoNCPz/yU19i71FpCwAAAAAElFTkSuQmCC\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1918,92 +1961,92 @@ "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAlQAAACbCAYAAACkuQVhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAEj9JREFUeJzt3X2QXXV9x/HPJ5vdZBNCd0g2S4HY0BZakglPFQbwkWo7\n", - "1FG0rRUpVWo7djpqpVQdkZn2r3a0Mh2p47QzFuoDPrVFQZkWhFaKgJXwkCcSINCREhQICQ8SYzab\n", - "5Ns/7t2wbrLZ892T395z4P2ayXDPud/9nd/e3zlnv5xz7u/riBAAAABmbk6vOwAAANB2JFQAAAA1\n", - "kVABAADUREIFAABQEwkVAABATSRUAAAANc3t5cZtM2cDAABojYjwwdYXTahsnyfpSkl9kq6KiL+d\n", - "HHPRRRcd8HPr16/XySefPLmtQr3MacK8XZnPIvu5HY7Pee3atTr11FNrt1NCZvxKxe7Zs6dy7N69\n", - "eyvHzp2bO5z7+/sPWLdu3TqdcsopB6wfGBgo0o/M/jY6Olo5dteuXZVjs/GZ8cvE7tu3r3LsVJ/x\n", 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diff --git a/examples/02-brewing-logreg.ipynb b/examples/02-brewing-logreg.ipynb new file mode 100644 index 00000000000..d36871fcdfd --- /dev/null +++ b/examples/02-brewing-logreg.ipynb @@ -0,0 +1,5771 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Brewing Logistic Regression then Going Deeper\n", + "\n", + "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import os\n", + "os.chdir('..')\n", + "\n", + "import sys\n", + "sys.path.insert(0, './python')\n", + "import caffe\n", + "\n", + "\n", + "import os\n", + "import h5py\n", + "import shutil\n", + "import tempfile\n", + "\n", + "import sklearn\n", + "import sklearn.datasets\n", + "import sklearn.linear_model\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + 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sample of the data\n", + "ind = np.random.permutation(X.shape[0])[:1000]\n", + "df = pd.DataFrame(X[ind])\n", + "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.783\n", + "Accuracy: 0.783\n", + "Accuracy: 0.783\n", + "Accuracy: 0.783\n", + "1 loops, best of 3: 508 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "# Train and test the scikit-learn SGD logistic regression.\n", + "clf = sklearn.linear_model.SGDClassifier(\n", + " loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n", + "\n", + "clf.fit(X, y)\n", + "yt_pred = clf.predict(Xt)\n", + "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save the dataset to HDF5 for loading in Caffe." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Write out the data to HDF5 files in a temp directory.\n", + "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n", + "dirname = os.path.abspath('./examples/hdf5_classification/data')\n", + "if not os.path.exists(dirname):\n", + " os.makedirs(dirname)\n", + "\n", + "train_filename = os.path.join(dirname, 'train.h5')\n", + "test_filename = os.path.join(dirname, 'test.h5')\n", + "\n", + "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n", + "# To show this off, we'll list the same data file twice.\n", + "with h5py.File(train_filename, 'w') as f:\n", + " f['data'] = X\n", + " f['label'] = y.astype(np.float32)\n", + "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n", + " f.write(train_filename + '\\n')\n", + " f.write(train_filename + '\\n')\n", + " \n", + "# HDF5 is pretty efficient, but can be further compressed.\n", + "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n", + "with h5py.File(test_filename, 'w') as f:\n", + " f.create_dataset('data', data=Xt, **comp_kwargs)\n", + " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n", + "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n", + " f.write(test_filename + '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from caffe import layers as L\n", + "from caffe import params as P\n", + "\n", + "def logreg(hdf5, batch_size):\n", + " # logistic regression: data, matrix multiplication, and 2-class softmax loss\n", + " n = caffe.NetSpec()\n", + " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n", + " n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n", + " n.accuracy = L.Accuracy(n.ip1, n.label)\n", + " n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n", + " return n.to_proto()\n", + " \n", + "with open('examples/hdf5_classification/logreg_auto_train.prototxt', 'w') as f:\n", + " f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n", + " \n", + "with open('examples/hdf5_classification/logreg_auto_test.prototxt', 'w') as f:\n", + " f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time to learn and evaluate our Caffeinated logistic regression in Python." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.782\n", + "Accuracy: 0.782\n", + "Accuracy: 0.782\n", + "Accuracy: 0.782\n", + "1 loops, best of 3: 287 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "caffe.set_mode_cpu()\n", + "solver = caffe.get_solver('examples/hdf5_classification/solver.prototxt')\n", + "solver.solve()\n", + "\n", + "accuracy = 0\n", + "batch_size = solver.test_nets[0].blobs['data'].num\n", + "test_iters = int(len(Xt) / batch_size)\n", + "for i in range(test_iters):\n", + " solver.test_nets[0].forward()\n", + " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", + "accuracy /= test_iters\n", + "\n", + "print(\"Accuracy: {:.3f}\".format(accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do the same through the command line interface for detailed output on the model and solving." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I0318 00:58:32.322571 2013098752 caffe.cpp:117] Use CPU.\n", + "I0318 00:58:32.643163 2013098752 caffe.cpp:121] Starting Optimization\n", + "I0318 00:58:32.643229 2013098752 solver.cpp:32] Initializing solver from parameters: \n", + "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n", + "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n", + "test_iter: 250\n", + "test_interval: 1000\n", + "base_lr: 0.01\n", + "display: 1000\n", + "max_iter: 10000\n", + "lr_policy: \"step\"\n", + "gamma: 0.1\n", + "momentum: 0.9\n", + "weight_decay: 0.0005\n", + "stepsize: 5000\n", + "snapshot: 10000\n", + "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n", + "solver_mode: CPU\n", + "I0318 00:58:32.643333 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n", + "I0318 00:58:32.643465 2013098752 net.cpp:42] Initializing net from parameters: \n", + "state {\n", + " phase: TRAIN\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/train.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0318 00:58:32.644197 2013098752 layer_factory.hpp:74] Creating layer data\n", + "I0318 00:58:32.644219 2013098752 net.cpp:84] Creating Layer data\n", + "I0318 00:58:32.644230 2013098752 net.cpp:338] data -> data\n", + "I0318 00:58:32.644256 2013098752 net.cpp:338] data -> label\n", + "I0318 00:58:32.644269 2013098752 net.cpp:113] Setting up data\n", + "I0318 00:58:32.644278 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n", + "I0318 00:58:32.644327 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n", + "I0318 00:58:32.646458 2013098752 net.cpp:120] Top shape: 10 4 (40)\n", + "I0318 00:58:32.646502 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:32.646518 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n", + "I0318 00:58:32.646538 2013098752 net.cpp:84] Creating Layer label_data_1_split\n", + "I0318 00:58:32.646546 2013098752 net.cpp:380] label_data_1_split <- label\n", + "I0318 00:58:32.646556 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", + "I0318 00:58:32.646569 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", + "I0318 00:58:32.646579 2013098752 net.cpp:113] Setting up label_data_1_split\n", + "I0318 00:58:32.646586 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:32.646595 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:32.646601 2013098752 layer_factory.hpp:74] Creating layer ip1\n", + "I0318 00:58:32.646615 2013098752 net.cpp:84] Creating Layer ip1\n", + "I0318 00:58:32.646622 2013098752 net.cpp:380] ip1 <- data\n", + "I0318 00:58:32.646664 2013098752 net.cpp:338] ip1 -> ip1\n", + "I0318 00:58:32.646689 2013098752 net.cpp:113] Setting up ip1\n", + "I0318 00:58:32.652330 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:32.652371 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n", + "I0318 00:58:32.652393 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n", + "I0318 00:58:32.652407 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n", + "I0318 00:58:32.652421 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n", + "I0318 00:58:32.652467 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n", + "I0318 00:58:32.652480 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n", + "I0318 00:58:32.652489 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:32.652498 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:32.652505 2013098752 layer_factory.hpp:74] Creating layer accuracy\n", + "I0318 00:58:32.652521 2013098752 net.cpp:84] Creating Layer accuracy\n", + "I0318 00:58:32.652534 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n", + "I0318 00:58:32.652545 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n", + "I0318 00:58:32.652562 2013098752 net.cpp:338] accuracy -> accuracy\n", + "I0318 00:58:32.652577 2013098752 net.cpp:113] Setting up accuracy\n", + "I0318 00:58:32.652590 2013098752 net.cpp:120] Top shape: (1)\n", + "I0318 00:58:32.652642 2013098752 layer_factory.hpp:74] Creating layer loss\n", + "I0318 00:58:32.652655 2013098752 net.cpp:84] Creating Layer loss\n", + "I0318 00:58:32.652663 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n", + "I0318 00:58:32.652672 2013098752 net.cpp:380] loss <- label_data_1_split_1\n", + "I0318 00:58:32.652679 2013098752 net.cpp:338] loss -> loss\n", + "I0318 00:58:32.652689 2013098752 net.cpp:113] Setting up loss\n", + "I0318 00:58:32.652701 2013098752 layer_factory.hpp:74] Creating layer loss\n", + "I0318 00:58:32.652716 2013098752 net.cpp:120] Top shape: (1)\n", + "I0318 00:58:32.652724 2013098752 net.cpp:122] with loss weight 1\n", + "I0318 00:58:32.652740 2013098752 net.cpp:167] loss needs backward computation.\n", + "I0318 00:58:32.652746 2013098752 net.cpp:169] accuracy does not need backward computation.\n", + "I0318 00:58:32.652753 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n", + "I0318 00:58:32.652760 2013098752 net.cpp:167] ip1 needs backward computation.\n", + "I0318 00:58:32.652786 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n", + "I0318 00:58:32.652801 2013098752 net.cpp:169] data does not need backward computation.\n", + "I0318 00:58:32.652808 2013098752 net.cpp:205] This network produces output accuracy\n", + "I0318 00:58:32.652815 2013098752 net.cpp:205] This network produces output loss\n", + "I0318 00:58:32.652825 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", + "I0318 00:58:32.652833 2013098752 net.cpp:217] Network initialization done.\n", + "I0318 00:58:32.652839 2013098752 net.cpp:218] Memory required for data: 528\n", + "I0318 00:58:32.652964 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n", + "I0318 00:58:32.652986 2013098752 net.cpp:42] Initializing net from parameters: \n", + "state {\n", + " phase: TEST\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/test.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0318 00:58:32.653069 2013098752 layer_factory.hpp:74] Creating layer data\n", + "I0318 00:58:32.653080 2013098752 net.cpp:84] Creating Layer data\n", + "I0318 00:58:32.653090 2013098752 net.cpp:338] data -> data\n", + "I0318 00:58:32.653128 2013098752 net.cpp:338] data -> label\n", + "I0318 00:58:32.653146 2013098752 net.cpp:113] Setting up data\n", + "I0318 00:58:32.653154 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n", + "I0318 00:58:32.653192 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n", + "I0318 00:58:32.654850 2013098752 net.cpp:120] Top shape: 10 4 (40)\n", + "I0318 00:58:32.654897 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:32.654914 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n", + "I0318 00:58:32.654933 2013098752 net.cpp:84] Creating Layer label_data_1_split\n", + "I0318 00:58:32.654943 2013098752 net.cpp:380] label_data_1_split <- label\n", + "I0318 00:58:32.654953 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", + "I0318 00:58:32.654966 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", + "I0318 00:58:32.654976 2013098752 net.cpp:113] Setting up label_data_1_split\n", + "I0318 00:58:32.654985 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:32.654992 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:32.655000 2013098752 layer_factory.hpp:74] Creating layer ip1\n", + "I0318 00:58:32.655010 2013098752 net.cpp:84] Creating Layer ip1\n", + "I0318 00:58:32.655017 2013098752 net.cpp:380] ip1 <- data\n", + "I0318 00:58:32.655030 2013098752 net.cpp:338] ip1 -> ip1\n", + "I0318 00:58:32.655041 2013098752 net.cpp:113] Setting up ip1\n", + "I0318 00:58:32.655061 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:32.655072 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n", + "I0318 00:58:32.655148 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n", + "I0318 00:58:32.655159 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n", + "I0318 00:58:32.655170 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n", + "I0318 00:58:32.655180 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n", + "I0318 00:58:32.655190 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n", + "I0318 00:58:32.655199 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:32.655206 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:32.655213 2013098752 layer_factory.hpp:74] Creating layer accuracy\n", + "I0318 00:58:32.655223 2013098752 net.cpp:84] Creating Layer accuracy\n", + "I0318 00:58:32.655230 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n", + "I0318 00:58:32.655237 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n", + "I0318 00:58:32.655251 2013098752 net.cpp:338] accuracy -> accuracy\n", + "I0318 00:58:32.655259 2013098752 net.cpp:113] Setting up accuracy\n", + "I0318 00:58:32.655267 2013098752 net.cpp:120] Top shape: (1)\n", + "I0318 00:58:32.655340 2013098752 layer_factory.hpp:74] Creating layer loss\n", + "I0318 00:58:32.655354 2013098752 net.cpp:84] Creating Layer loss\n", + "I0318 00:58:32.655361 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n", + "I0318 00:58:32.655369 2013098752 net.cpp:380] loss <- label_data_1_split_1\n", + "I0318 00:58:32.655378 2013098752 net.cpp:338] loss -> loss\n", + "I0318 00:58:32.655388 2013098752 net.cpp:113] Setting up loss\n", + "I0318 00:58:32.655397 2013098752 layer_factory.hpp:74] Creating layer loss\n", + "I0318 00:58:32.655414 2013098752 net.cpp:120] Top shape: (1)\n", + "I0318 00:58:32.655422 2013098752 net.cpp:122] with loss weight 1\n", + "I0318 00:58:32.655438 2013098752 net.cpp:167] loss needs backward computation.\n", + "I0318 00:58:32.655446 2013098752 net.cpp:169] accuracy does not need backward computation.\n", + "I0318 00:58:32.655455 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n", + "I0318 00:58:32.655462 2013098752 net.cpp:167] ip1 needs backward computation.\n", + "I0318 00:58:32.655469 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n", + "I0318 00:58:32.655477 2013098752 net.cpp:169] data does not need backward computation.\n", + "I0318 00:58:32.655483 2013098752 net.cpp:205] This network produces output accuracy\n", + "I0318 00:58:32.655489 2013098752 net.cpp:205] This network produces output loss\n", + "I0318 00:58:32.655503 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", + "I0318 00:58:32.655511 2013098752 net.cpp:217] Network initialization done.\n", + "I0318 00:58:32.655517 2013098752 net.cpp:218] Memory required for data: 528\n", + "I0318 00:58:32.655547 2013098752 solver.cpp:42] Solver scaffolding done.\n", + "I0318 00:58:32.655567 2013098752 solver.cpp:222] Solving \n", + "I0318 00:58:32.655575 2013098752 solver.cpp:223] Learning Rate Policy: step\n", + "I0318 00:58:32.655583 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n", + "I0318 00:58:32.683643 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.3736\n", + "I0318 00:58:32.683686 2013098752 solver.cpp:315] Test net output #1: loss = 1.00555 (* 1 = 1.00555 loss)\n", + "I0318 00:58:32.683846 2013098752 solver.cpp:189] Iteration 0, loss = 0.869394\n", + "I0318 00:58:32.683861 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.3\n", + "I0318 00:58:32.683871 2013098752 solver.cpp:204] Train net output #1: loss = 0.869394 (* 1 = 0.869394 loss)\n", + "I0318 00:58:32.683883 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n", + "I0318 00:58:32.698721 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n", + "I0318 00:58:32.701917 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n", + "I0318 00:58:32.701961 2013098752 solver.cpp:315] Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n", + "I0318 00:58:32.702014 2013098752 solver.cpp:189] Iteration 1000, loss = 0.54742\n", + "I0318 00:58:32.702029 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", + "I0318 00:58:32.702041 2013098752 solver.cpp:204] Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n", + "I0318 00:58:32.702051 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n", + "I0318 00:58:32.718360 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n", + "I0318 00:58:32.721529 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7696\n", + "I0318 00:58:32.721562 2013098752 solver.cpp:315] Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n", + "I0318 00:58:32.721593 2013098752 solver.cpp:189] Iteration 2000, loss = 0.729569\n", + "I0318 00:58:32.721603 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n", + "I0318 00:58:32.721613 2013098752 solver.cpp:204] Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n", + "I0318 00:58:32.721622 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n", + "I0318 00:58:32.740182 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n", + "I0318 00:58:32.743494 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.77\n", + "I0318 00:58:32.743544 2013098752 solver.cpp:315] Test net output #1: loss = 0.591229 (* 1 = 0.591229 loss)\n", + "I0318 00:58:32.744209 2013098752 solver.cpp:189] Iteration 3000, loss = 0.406097\n", + "I0318 00:58:32.744231 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.8\n", + "I0318 00:58:32.744249 2013098752 solver.cpp:204] Train net output #1: loss = 0.406096 (* 1 = 0.406096 loss)\n", + "I0318 00:58:32.744266 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n", + "I0318 00:58:32.764135 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n", + "I0318 00:58:32.769110 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n", + "I0318 00:58:32.769170 2013098752 solver.cpp:315] Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n", + "I0318 00:58:32.769223 2013098752 solver.cpp:189] Iteration 4000, loss = 0.54742\n", + "I0318 00:58:32.769242 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", + "I0318 00:58:32.769255 2013098752 solver.cpp:204] Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n", + "I0318 00:58:32.769265 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n", + "I0318 00:58:32.785846 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n", + "I0318 00:58:32.788722 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7696\n", + "I0318 00:58:32.788751 2013098752 solver.cpp:315] Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n", + "I0318 00:58:32.788811 2013098752 solver.cpp:189] Iteration 5000, loss = 0.72957\n", + "I0318 00:58:32.788833 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n", + "I0318 00:58:32.788846 2013098752 solver.cpp:204] Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n", + "I0318 00:58:32.788856 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n", + "I0318 00:58:32.804762 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n", + "I0318 00:58:32.808061 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7856\n", + "I0318 00:58:32.808112 2013098752 solver.cpp:315] Test net output #1: loss = 0.59028 (* 1 = 0.59028 loss)\n", + "I0318 00:58:32.808732 2013098752 solver.cpp:189] Iteration 6000, loss = 0.415444\n", + "I0318 00:58:32.808753 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", + "I0318 00:58:32.808773 2013098752 solver.cpp:204] Train net output #1: loss = 0.415444 (* 1 = 0.415444 loss)\n", + "I0318 00:58:32.808786 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n", + "I0318 00:58:32.827118 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n", + "I0318 00:58:32.831614 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n", + "I0318 00:58:32.831657 2013098752 solver.cpp:315] Test net output #1: loss = 0.589454 (* 1 = 0.589454 loss)\n", + "I0318 00:58:32.831707 2013098752 solver.cpp:189] Iteration 7000, loss = 0.538038\n", + "I0318 00:58:32.831728 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.8\n", + "I0318 00:58:32.831745 2013098752 solver.cpp:204] Train net output #1: loss = 0.538037 (* 1 = 0.538037 loss)\n", + "I0318 00:58:32.831759 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n", + "I0318 00:58:32.849634 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n", + "I0318 00:58:32.852712 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7796\n", + "I0318 00:58:32.852748 2013098752 solver.cpp:315] Test net output #1: loss = 0.589365 (* 1 = 0.589365 loss)\n", + "I0318 00:58:32.852792 2013098752 solver.cpp:189] Iteration 8000, loss = 0.684219\n", + "I0318 00:58:32.852840 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n", + "I0318 00:58:32.852852 2013098752 solver.cpp:204] Train net output #1: loss = 0.684219 (* 1 = 0.684219 loss)\n", + "I0318 00:58:32.852861 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n", + "I0318 00:58:32.868440 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n", + "I0318 00:58:32.871438 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7816\n", + "I0318 00:58:32.871461 2013098752 solver.cpp:315] Test net output #1: loss = 0.589656 (* 1 = 0.589656 loss)\n", + "I0318 00:58:32.872109 2013098752 solver.cpp:189] Iteration 9000, loss = 0.421879\n", + "I0318 00:58:32.872131 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", + "I0318 00:58:32.872143 2013098752 solver.cpp:204] Train net output #1: loss = 0.421879 (* 1 = 0.421879 loss)\n", + "I0318 00:58:32.872153 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n", + "I0318 00:58:32.889981 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n", + "I0318 00:58:32.890224 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n", + "I0318 00:58:32.890362 2013098752 solver.cpp:248] Iteration 10000, loss = 0.538933\n", + "I0318 00:58:32.890380 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n", + "I0318 00:58:32.893728 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.782\n", + "I0318 00:58:32.893757 2013098752 solver.cpp:315] Test net output #1: loss = 0.589366 (* 1 = 0.589366 loss)\n", + "I0318 00:58:32.893775 2013098752 solver.cpp:253] Optimization Done.\n", + "I0318 00:58:32.893786 2013098752 caffe.cpp:134] Optimization Done.\n" + ] + } + ], + "source": [ + "!./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n", + "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n", + "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_solver.prototxt` which we will now use.\n", + "\n", + "The final accuracy of the new network should be higher than logistic regression!" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from caffe import layers as L\n", + "from caffe import params as P\n", + "\n", + "def nonlinear_net(hdf5, batch_size):\n", + " # one small nonlinearity, one leap for model kind\n", + " n = caffe.NetSpec()\n", + " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n", + " # define a hidden layer of dimension 40\n", + " n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n", + " # transform the output through the ReLU (rectified linear) non-linearity\n", + " n.relu1 = L.ReLU(n.ip1, in_place=True)\n", + " # score the (now non-linear) features\n", + " n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n", + " # same accuracy and loss as before\n", + " n.accuracy = L.Accuracy(n.ip2, n.label)\n", + " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n", + " return n.to_proto()\n", + " \n", + "with open('examples/hdf5_classification/nonlinear_auto_train.prototxt', 'w') as f:\n", + " f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n", + " \n", + "with open('examples/hdf5_classification/nonlinear_auto_test.prototxt', 'w') as f:\n", + " f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.832\n", + "Accuracy: 0.832\n", + "Accuracy: 0.832\n", + "Accuracy: 0.831\n", + "1 loops, best of 3: 386 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "caffe.set_mode_cpu()\n", + "solver = caffe.get_solver('examples/hdf5_classification/nonlinear_solver.prototxt')\n", + "solver.solve()\n", + "\n", + "accuracy = 0\n", + "batch_size = solver.test_nets[0].blobs['data'].num\n", + "test_iters = int(len(Xt) / batch_size)\n", + "for i in range(test_iters):\n", + " solver.test_nets[0].forward()\n", + " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", + "accuracy /= test_iters\n", + "\n", + "print(\"Accuracy: {:.3f}\".format(accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do the same through the command line interface for detailed output on the model and solving." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I0318 00:58:43.336922 2013098752 caffe.cpp:117] Use CPU.\n", + "I0318 00:58:43.654698 2013098752 caffe.cpp:121] Starting Optimization\n", + "I0318 00:58:43.654747 2013098752 solver.cpp:32] Initializing solver from parameters: \n", + "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n", + "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n", + "test_iter: 250\n", + "test_interval: 1000\n", + "base_lr: 0.01\n", + "display: 1000\n", + "max_iter: 10000\n", + "lr_policy: \"step\"\n", + "gamma: 0.1\n", + "momentum: 0.9\n", + "weight_decay: 0.0005\n", + "stepsize: 5000\n", + "snapshot: 10000\n", + "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n", + "solver_mode: CPU\n", + "I0318 00:58:43.654855 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n", + "I0318 00:58:43.655004 2013098752 net.cpp:42] Initializing net from parameters: \n", + "state {\n", + " phase: TRAIN\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/train.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 40\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu1\"\n", + " type: \"ReLU\"\n", + " bottom: \"ip1\"\n", + " top: \"ip1\"\n", + "}\n", + "layer {\n", + " name: \"ip2\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"ip1\"\n", + " top: \"ip2\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0318 00:58:43.655120 2013098752 layer_factory.hpp:74] Creating layer data\n", + "I0318 00:58:43.655139 2013098752 net.cpp:84] Creating Layer data\n", + "I0318 00:58:43.655264 2013098752 net.cpp:338] data -> data\n", + "I0318 00:58:43.655297 2013098752 net.cpp:338] data -> label\n", + "I0318 00:58:43.655310 2013098752 net.cpp:113] Setting up data\n", + "I0318 00:58:43.655318 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n", + "I0318 00:58:43.655365 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n", + "I0318 00:58:43.657317 2013098752 net.cpp:120] Top shape: 10 4 (40)\n", + "I0318 00:58:43.657342 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:43.657356 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n", + "I0318 00:58:43.657373 2013098752 net.cpp:84] Creating Layer label_data_1_split\n", + "I0318 00:58:43.657384 2013098752 net.cpp:380] label_data_1_split <- label\n", + "I0318 00:58:43.657395 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", + "I0318 00:58:43.657407 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", + "I0318 00:58:43.657418 2013098752 net.cpp:113] Setting up label_data_1_split\n", + "I0318 00:58:43.657426 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:43.657433 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:43.657441 2013098752 layer_factory.hpp:74] Creating layer ip1\n", + "I0318 00:58:43.657451 2013098752 net.cpp:84] Creating Layer ip1\n", + "I0318 00:58:43.657459 2013098752 net.cpp:380] ip1 <- data\n", + "I0318 00:58:43.657467 2013098752 net.cpp:338] ip1 -> ip1\n", + "I0318 00:58:43.657479 2013098752 net.cpp:113] Setting up ip1\n", + "I0318 00:58:43.662454 2013098752 net.cpp:120] Top shape: 10 40 (400)\n", + "I0318 00:58:43.662477 2013098752 layer_factory.hpp:74] Creating layer relu1\n", + "I0318 00:58:43.662497 2013098752 net.cpp:84] Creating Layer relu1\n", + "I0318 00:58:43.662508 2013098752 net.cpp:380] relu1 <- ip1\n", + "I0318 00:58:43.662520 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n", + "I0318 00:58:43.662530 2013098752 net.cpp:113] Setting up relu1\n", + "I0318 00:58:43.662539 2013098752 net.cpp:120] Top shape: 10 40 (400)\n", + "I0318 00:58:43.662546 2013098752 layer_factory.hpp:74] Creating layer ip2\n", + "I0318 00:58:43.662555 2013098752 net.cpp:84] Creating Layer ip2\n", + "I0318 00:58:43.662562 2013098752 net.cpp:380] ip2 <- ip1\n", + "I0318 00:58:43.662571 2013098752 net.cpp:338] ip2 -> ip2\n", + "I0318 00:58:43.662580 2013098752 net.cpp:113] Setting up ip2\n", + "I0318 00:58:43.662595 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:43.662606 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n", + "I0318 00:58:43.662654 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n", + "I0318 00:58:43.662665 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n", + "I0318 00:58:43.662678 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n", + "I0318 00:58:43.662689 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n", + "I0318 00:58:43.662698 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n", + "I0318 00:58:43.662706 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:43.662714 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:43.662722 2013098752 layer_factory.hpp:74] Creating layer accuracy\n", + "I0318 00:58:43.662734 2013098752 net.cpp:84] Creating Layer accuracy\n", + "I0318 00:58:43.662740 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n", + "I0318 00:58:43.662749 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n", + "I0318 00:58:43.662756 2013098752 net.cpp:338] accuracy -> accuracy\n", + "I0318 00:58:43.662766 2013098752 net.cpp:113] Setting up accuracy\n", + "I0318 00:58:43.662818 2013098752 net.cpp:120] Top shape: (1)\n", + "I0318 00:58:43.662827 2013098752 layer_factory.hpp:74] Creating layer loss\n", + "I0318 00:58:43.662839 2013098752 net.cpp:84] Creating Layer loss\n", + "I0318 00:58:43.662847 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n", + "I0318 00:58:43.662854 2013098752 net.cpp:380] loss <- label_data_1_split_1\n", + "I0318 00:58:43.662863 2013098752 net.cpp:338] loss -> loss\n", + "I0318 00:58:43.662873 2013098752 net.cpp:113] Setting up loss\n", + "I0318 00:58:43.662883 2013098752 layer_factory.hpp:74] Creating layer loss\n", + "I0318 00:58:43.662901 2013098752 net.cpp:120] Top shape: (1)\n", + "I0318 00:58:43.662909 2013098752 net.cpp:122] with loss weight 1\n", + "I0318 00:58:43.662922 2013098752 net.cpp:167] loss needs backward computation.\n", + "I0318 00:58:43.662930 2013098752 net.cpp:169] accuracy does not need backward computation.\n", + "I0318 00:58:43.662936 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n", + "I0318 00:58:43.662942 2013098752 net.cpp:167] ip2 needs backward computation.\n", + "I0318 00:58:43.662976 2013098752 net.cpp:167] relu1 needs backward computation.\n", + "I0318 00:58:43.662988 2013098752 net.cpp:167] ip1 needs backward computation.\n", + "I0318 00:58:43.662997 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n", + "I0318 00:58:43.663003 2013098752 net.cpp:169] data does not need backward computation.\n", + "I0318 00:58:43.663009 2013098752 net.cpp:205] This network produces output accuracy\n", + "I0318 00:58:43.663017 2013098752 net.cpp:205] This network produces output loss\n", + "I0318 00:58:43.663028 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", + "I0318 00:58:43.663035 2013098752 net.cpp:217] Network initialization done.\n", + "I0318 00:58:43.663041 2013098752 net.cpp:218] Memory required for data: 3728\n", + "I0318 00:58:43.663158 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n", + "I0318 00:58:43.663179 2013098752 net.cpp:42] Initializing net from parameters: \n", + "state {\n", + " phase: TEST\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/test.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 40\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu1\"\n", + " type: \"ReLU\"\n", + " bottom: \"ip1\"\n", + " top: \"ip1\"\n", + "}\n", + "layer {\n", + " name: \"ip2\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"ip1\"\n", + " top: \"ip2\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0318 00:58:43.663349 2013098752 layer_factory.hpp:74] Creating layer data\n", + "I0318 00:58:43.663365 2013098752 net.cpp:84] Creating Layer data\n", + "I0318 00:58:43.663373 2013098752 net.cpp:338] data -> data\n", + "I0318 00:58:43.663385 2013098752 net.cpp:338] data -> label\n", + "I0318 00:58:43.663396 2013098752 net.cpp:113] Setting up data\n", + "I0318 00:58:43.663422 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n", + "I0318 00:58:43.663457 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n", + "I0318 00:58:43.664719 2013098752 net.cpp:120] Top shape: 10 4 (40)\n", + "I0318 00:58:43.664739 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:43.664754 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n", + "I0318 00:58:43.664772 2013098752 net.cpp:84] Creating Layer label_data_1_split\n", + "I0318 00:58:43.664783 2013098752 net.cpp:380] label_data_1_split <- label\n", + "I0318 00:58:43.664791 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", + "I0318 00:58:43.664803 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", + "I0318 00:58:43.664813 2013098752 net.cpp:113] Setting up label_data_1_split\n", + "I0318 00:58:43.664822 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:43.664829 2013098752 net.cpp:120] Top shape: 10 (10)\n", + "I0318 00:58:43.664837 2013098752 layer_factory.hpp:74] Creating layer ip1\n", + "I0318 00:58:43.664846 2013098752 net.cpp:84] Creating Layer ip1\n", + "I0318 00:58:43.664854 2013098752 net.cpp:380] ip1 <- data\n", + "I0318 00:58:43.664862 2013098752 net.cpp:338] ip1 -> ip1\n", + "I0318 00:58:43.664875 2013098752 net.cpp:113] Setting up ip1\n", + "I0318 00:58:43.664901 2013098752 net.cpp:120] Top shape: 10 40 (400)\n", + "I0318 00:58:43.664924 2013098752 layer_factory.hpp:74] Creating layer relu1\n", + "I0318 00:58:43.664945 2013098752 net.cpp:84] Creating Layer relu1\n", + "I0318 00:58:43.664958 2013098752 net.cpp:380] relu1 <- ip1\n", + "I0318 00:58:43.664966 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n", + "I0318 00:58:43.664975 2013098752 net.cpp:113] Setting up relu1\n", + "I0318 00:58:43.664983 2013098752 net.cpp:120] Top shape: 10 40 (400)\n", + "I0318 00:58:43.664990 2013098752 layer_factory.hpp:74] Creating layer ip2\n", + "I0318 00:58:43.665000 2013098752 net.cpp:84] Creating Layer ip2\n", + "I0318 00:58:43.665006 2013098752 net.cpp:380] ip2 <- ip1\n", + "I0318 00:58:43.665015 2013098752 net.cpp:338] ip2 -> ip2\n", + "I0318 00:58:43.665030 2013098752 net.cpp:113] Setting up ip2\n", + "I0318 00:58:43.665052 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:43.665066 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n", + "I0318 00:58:43.665077 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n", + "I0318 00:58:43.665086 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n", + "I0318 00:58:43.665093 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n", + "I0318 00:58:43.665103 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n", + "I0318 00:58:43.665113 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n", + "I0318 00:58:43.665122 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:43.665128 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", + "I0318 00:58:43.665137 2013098752 layer_factory.hpp:74] Creating layer accuracy\n", + "I0318 00:58:43.665144 2013098752 net.cpp:84] Creating Layer accuracy\n", + "I0318 00:58:43.665153 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n", + "I0318 00:58:43.665168 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n", + "I0318 00:58:43.665180 2013098752 net.cpp:338] accuracy -> accuracy\n", + "I0318 00:58:43.665192 2013098752 net.cpp:113] Setting up accuracy\n", + "I0318 00:58:43.665200 2013098752 net.cpp:120] Top shape: (1)\n", + "I0318 00:58:43.665207 2013098752 layer_factory.hpp:74] Creating layer loss\n", + "I0318 00:58:43.665216 2013098752 net.cpp:84] Creating Layer loss\n", + "I0318 00:58:43.665223 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n", + "I0318 00:58:43.665230 2013098752 net.cpp:380] loss <- label_data_1_split_1\n", + "I0318 00:58:43.665241 2013098752 net.cpp:338] loss -> loss\n", + "I0318 00:58:43.665251 2013098752 net.cpp:113] Setting up loss\n", + "I0318 00:58:43.665259 2013098752 layer_factory.hpp:74] Creating layer loss\n", + "I0318 00:58:43.665273 2013098752 net.cpp:120] Top shape: (1)\n", + "I0318 00:58:43.665282 2013098752 net.cpp:122] with loss weight 1\n", + "I0318 00:58:43.665290 2013098752 net.cpp:167] loss needs backward computation.\n", + "I0318 00:58:43.665338 2013098752 net.cpp:169] accuracy does not need backward computation.\n", + "I0318 00:58:43.665351 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n", + "I0318 00:58:43.665380 2013098752 net.cpp:167] ip2 needs backward computation.\n", + "I0318 00:58:43.665387 2013098752 net.cpp:167] relu1 needs backward computation.\n", + "I0318 00:58:43.665393 2013098752 net.cpp:167] ip1 needs backward computation.\n", + "I0318 00:58:43.665400 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n", + "I0318 00:58:43.665407 2013098752 net.cpp:169] data does not need backward computation.\n", + "I0318 00:58:43.665415 2013098752 net.cpp:205] This network produces output accuracy\n", + "I0318 00:58:43.665421 2013098752 net.cpp:205] This network produces output loss\n", + "I0318 00:58:43.665431 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", + "I0318 00:58:43.665441 2013098752 net.cpp:217] Network initialization done.\n", + "I0318 00:58:43.665446 2013098752 net.cpp:218] Memory required for data: 3728\n", + "I0318 00:58:43.665534 2013098752 solver.cpp:42] Solver scaffolding done.\n", + "I0318 00:58:43.665568 2013098752 solver.cpp:222] Solving \n", + "I0318 00:58:43.665577 2013098752 solver.cpp:223] Learning Rate Policy: step\n", + "I0318 00:58:43.665586 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n", + "I0318 00:58:43.683938 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.5184\n", + "I0318 00:58:43.683981 2013098752 solver.cpp:315] Test net output #1: loss = 0.716141 (* 1 = 0.716141 loss)\n", + "I0318 00:58:43.684236 2013098752 solver.cpp:189] Iteration 0, loss = 0.764954\n", + "I0318 00:58:43.684267 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n", + "I0318 00:58:43.684285 2013098752 solver.cpp:204] Train net output #1: loss = 0.764954 (* 1 = 0.764954 loss)\n", + "I0318 00:58:43.684305 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n", + "I0318 00:58:43.714700 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n", + "I0318 00:58:43.721762 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8168\n", + "I0318 00:58:43.721818 2013098752 solver.cpp:315] Test net output #1: loss = 0.434918 (* 1 = 0.434918 loss)\n", + "I0318 00:58:43.721899 2013098752 solver.cpp:189] Iteration 1000, loss = 0.282425\n", + "I0318 00:58:43.721917 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", + "I0318 00:58:43.721932 2013098752 solver.cpp:204] Train net output #1: loss = 0.282426 (* 1 = 0.282426 loss)\n", + "I0318 00:58:43.721942 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n", + "I0318 00:58:43.750509 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n", + "I0318 00:58:43.754590 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8224\n", + "I0318 00:58:43.754621 2013098752 solver.cpp:315] Test net output #1: loss = 0.416874 (* 1 = 0.416874 loss)\n", + "I0318 00:58:43.754660 2013098752 solver.cpp:189] Iteration 2000, loss = 0.51988\n", + "I0318 00:58:43.754672 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", + "I0318 00:58:43.754683 2013098752 solver.cpp:204] Train net output #1: loss = 0.51988 (* 1 = 0.51988 loss)\n", + "I0318 00:58:43.754690 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n", + "I0318 00:58:43.782609 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n", + "I0318 00:58:43.789728 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8176\n", + "I0318 00:58:43.789777 2013098752 solver.cpp:315] Test net output #1: loss = 0.415907 (* 1 = 0.415907 loss)\n", + "I0318 00:58:43.790487 2013098752 solver.cpp:189] Iteration 3000, loss = 0.5093\n", + "I0318 00:58:43.790510 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", + "I0318 00:58:43.790530 2013098752 solver.cpp:204] Train net output #1: loss = 0.509301 (* 1 = 0.509301 loss)\n", + "I0318 00:58:43.790544 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n", + "I0318 00:58:43.817451 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n", + "I0318 00:58:43.821740 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8252\n", + "I0318 00:58:43.821770 2013098752 solver.cpp:315] Test net output #1: loss = 0.409124 (* 1 = 0.409124 loss)\n", + "I0318 00:58:43.821822 2013098752 solver.cpp:189] Iteration 4000, loss = 0.284815\n", + "I0318 00:58:43.821835 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", + "I0318 00:58:43.821846 2013098752 solver.cpp:204] Train net output #1: loss = 0.284815 (* 1 = 0.284815 loss)\n", + "I0318 00:58:43.821890 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n", + "I0318 00:58:43.847015 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n", + "I0318 00:58:43.852102 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8256\n", + "I0318 00:58:43.852145 2013098752 solver.cpp:315] Test net output #1: loss = 0.404445 (* 1 = 0.404445 loss)\n", + "I0318 00:58:43.852188 2013098752 solver.cpp:189] Iteration 5000, loss = 0.511566\n", + "I0318 00:58:43.852200 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", + "I0318 00:58:43.852210 2013098752 solver.cpp:204] Train net output #1: loss = 0.511566 (* 1 = 0.511566 loss)\n", + "I0318 00:58:43.852219 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n", + "I0318 00:58:43.876060 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n", + "I0318 00:58:43.880080 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8328\n", + "I0318 00:58:43.880105 2013098752 solver.cpp:315] Test net output #1: loss = 0.396847 (* 1 = 0.396847 loss)\n", + "I0318 00:58:43.880700 2013098752 solver.cpp:189] Iteration 6000, loss = 0.397858\n", + "I0318 00:58:43.880718 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", + "I0318 00:58:43.880729 2013098752 solver.cpp:204] Train net output #1: loss = 0.397858 (* 1 = 0.397858 loss)\n", + "I0318 00:58:43.880738 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n", + "I0318 00:58:43.913795 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n", + "I0318 00:58:43.917851 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8316\n", + "I0318 00:58:43.917876 2013098752 solver.cpp:315] Test net output #1: loss = 0.398135 (* 1 = 0.398135 loss)\n", + "I0318 00:58:43.917956 2013098752 solver.cpp:189] Iteration 7000, loss = 0.243849\n", + "I0318 00:58:43.917971 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", + "I0318 00:58:43.917989 2013098752 solver.cpp:204] Train net output #1: loss = 0.243849 (* 1 = 0.243849 loss)\n", + "I0318 00:58:43.918002 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n", + "I0318 00:58:43.943681 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n", + "I0318 00:58:43.947589 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8312\n", + "I0318 00:58:43.947615 2013098752 solver.cpp:315] Test net output #1: loss = 0.394763 (* 1 = 0.394763 loss)\n", + "I0318 00:58:43.947651 2013098752 solver.cpp:189] Iteration 8000, loss = 0.513399\n", + "I0318 00:58:43.947664 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", + "I0318 00:58:43.947674 2013098752 solver.cpp:204] Train net output #1: loss = 0.513399 (* 1 = 0.513399 loss)\n", + "I0318 00:58:43.947682 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n", + "I0318 00:58:43.973080 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n", + "I0318 00:58:43.977033 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.834\n", + "I0318 00:58:43.977056 2013098752 solver.cpp:315] Test net output #1: loss = 0.395663 (* 1 = 0.395663 loss)\n", + "I0318 00:58:43.977710 2013098752 solver.cpp:189] Iteration 9000, loss = 0.399341\n", + "I0318 00:58:43.977735 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", + "I0318 00:58:43.977746 2013098752 solver.cpp:204] Train net output #1: loss = 0.399342 (* 1 = 0.399342 loss)\n", + "I0318 00:58:43.977756 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n", + "I0318 00:58:44.003437 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n", + "I0318 00:58:44.003702 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n", + "I0318 00:58:44.003850 2013098752 solver.cpp:248] Iteration 10000, loss = 0.244639\n", + "I0318 00:58:44.003871 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n", + "I0318 00:58:44.008216 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8308\n", + "I0318 00:58:44.008252 2013098752 solver.cpp:315] Test net output #1: loss = 0.397291 (* 1 = 0.397291 loss)\n", + "I0318 00:58:44.008262 2013098752 solver.cpp:253] Optimization Done.\n", + "I0318 00:58:44.008270 2013098752 caffe.cpp:134] Optimization Done.\n" + ] + } + ], + "source": [ + "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_solver.prototxt" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n", + "shutil.rmtree(dirname)" + ] + } + ], + "metadata": { + "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.", + "example_name": "Off-the-shelf SGD for classification", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 3 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/03-fine-tuning.ipynb b/examples/03-fine-tuning.ipynb new file mode 100644 index 00000000000..cc90b16bbfa --- /dev/null +++ b/examples/03-fine-tuning.ipynb @@ -0,0 +1,947 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fine-tuning a Pretrained Network for Style Recognition\n", + "\n", + "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n", + "\n", + "The upside of such approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we will need to prepare the data. This involves the following parts:\n", + "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n", + "(2) Download a subset of the overall Flickr style dataset for this demo.\n", + "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "os.chdir('..')\n", + "import sys\n", + "sys.path.insert(0, './python')\n", + "\n", + "import caffe\n", + "import numpy as np\n", + "from pylab import *\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# This downloads the ilsvrc auxiliary data (mean file, etc),\n", + "# and a subset of 2000 images for the style recognition task.\n", + "!data/ilsvrc12/get_ilsvrc_aux.sh\n", + "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n", + "!python examples/finetune_flickr_style/assemble_data.py \\\n", + " --workers=-1 --images=2000 --seed=1701 --label=5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's show what is the difference between the fine-tuning network and the original caffe model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1c1\r\n", + "< name: \"CaffeNet\"\r\n", + "---\r\n", + "> name: \"FlickrStyleCaffeNet\"\r\n", + "4c4\r\n", + "< type: \"Data\"\r\n", + "---\r\n", + "> type: \"ImageData\"\r\n", + "15,26c15,19\r\n", + "< # mean pixel / channel-wise mean instead of mean image\r\n", + "< # transform_param {\r\n", + "< # crop_size: 227\r\n", + "< # mean_value: 104\r\n", + "< # mean_value: 117\r\n", + "< # mean_value: 123\r\n", + "< # mirror: true\r\n", + "< # }\r\n", + "< data_param {\r\n", + "< source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n", + "< batch_size: 256\r\n", + "< backend: LMDB\r\n", + "---\r\n", + "> image_data_param {\r\n", + "> source: \"data/flickr_style/train.txt\"\r\n", + "> batch_size: 50\r\n", + "> new_height: 256\r\n", + "> new_width: 256\r\n", + "31c24\r\n", + "< type: \"Data\"\r\n", + "---\r\n", + "> type: \"ImageData\"\r\n", + "42,51c35,36\r\n", + "< # mean pixel / channel-wise mean instead of mean image\r\n", + "< # transform_param {\r\n", + "< # crop_size: 227\r\n", + "< # mean_value: 104\r\n", + "< # mean_value: 117\r\n", + "< # mean_value: 123\r\n", + "< # mirror: true\r\n", + "< # }\r\n", + "< data_param {\r\n", + "< source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n", + "---\r\n", + "> image_data_param {\r\n", + "> source: \"data/flickr_style/test.txt\"\r\n", + "53c38,39\r\n", + "< backend: LMDB\r\n", + "---\r\n", + "> new_height: 256\r\n", + "> new_width: 256\r\n", + "323a310\r\n", + "> # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n", + "360c347\r\n", + "< name: \"fc8\"\r\n", + "---\r\n", + "> name: \"fc8_flickr\"\r\n", + "363c350,351\r\n", + "< top: \"fc8\"\r\n", + "---\r\n", + "> top: \"fc8_flickr\"\r\n", + "> # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n", + "365c353\r\n", + "< lr_mult: 1\r\n", + "---\r\n", + "> lr_mult: 10\r\n", + "369c357\r\n", + "< lr_mult: 2\r\n", + "---\r\n", + "> lr_mult: 20\r\n", + "373c361\r\n", + "< num_output: 1000\r\n", + "---\r\n", + "> num_output: 20\r\n", + "384a373,379\r\n", + "> name: \"loss\"\r\n", + "> type: \"SoftmaxWithLoss\"\r\n", + "> bottom: \"fc8_flickr\"\r\n", + "> bottom: \"label\"\r\n", + "> top: \"loss\"\r\n", + "> }\r\n", + "> layer {\r\n", + "387c382\r\n", + "< bottom: \"fc8\"\r\n", + "---\r\n", + "> bottom: \"fc8_flickr\"\r\n", + "393,399d387\r\n", + "< }\r\n", + "< layer {\r\n", + "< name: \"loss\"\r\n", + "< type: \"SoftmaxWithLoss\"\r\n", + "< bottom: \"fc8\"\r\n", + "< bottom: \"label\"\r\n", + "< top: \"loss\"\r\n" + ] + } + ], + "source": [ + "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For your record, if you want to train the network in pure C++ tools, here is the command:\n", + "\n", + "\n", + "build/tools/caffe train \\\n", + " -solver models/finetune_flickr_style/solver.prototxt \\\n", + " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n", + " -gpu 0\n", + "\n", + "\n", + "However, we will train using Python in this example." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iter 0, finetune_loss=3.360094, scratch_loss=3.136188\n", + "iter 10, finetune_loss=2.672608, scratch_loss=9.736364\n", + "iter 20, finetune_loss=2.071996, scratch_loss=2.250404\n", + "iter 30, finetune_loss=1.758295, scratch_loss=2.049553\n", + "iter 40, finetune_loss=1.533391, scratch_loss=1.941318\n", + "iter 50, finetune_loss=1.561658, scratch_loss=1.839706\n", + "iter 60, finetune_loss=1.461696, scratch_loss=1.880035\n", + "iter 70, finetune_loss=1.267941, scratch_loss=1.719161\n", + "iter 80, finetune_loss=1.192778, scratch_loss=1.627453\n", + "iter 90, finetune_loss=1.541176, scratch_loss=1.822061\n", + "iter 100, finetune_loss=1.029039, scratch_loss=1.654087\n", + "iter 110, finetune_loss=1.138547, scratch_loss=1.735837\n", + "iter 120, finetune_loss=0.917412, scratch_loss=1.851918\n", + "iter 130, finetune_loss=0.971519, scratch_loss=1.801927\n", + "iter 140, finetune_loss=0.868252, scratch_loss=1.745545\n", + "iter 150, finetune_loss=0.790020, scratch_loss=1.844925\n", + "iter 160, finetune_loss=1.092668, scratch_loss=1.695591\n", + "iter 170, finetune_loss=1.055344, scratch_loss=1.661715\n", + "iter 180, finetune_loss=0.969769, scratch_loss=1.823639\n", + "iter 190, finetune_loss=0.780566, scratch_loss=1.820862\n", + "done\n" + ] + } + ], + "source": [ + "niter = 200\n", + "# losses will also be stored in the log\n", + "train_loss = np.zeros(niter)\n", + "scratch_train_loss = np.zeros(niter)\n", + "\n", + "caffe.set_device(0)\n", + "caffe.set_mode_gpu()\n", + "# We create a solver that fine-tunes from a previously trained network.\n", + "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n", + "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", + "# For reference, we also create a solver that does no finetuning.\n", + "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n", + "\n", + "# We run the solver for niter times, and record the training loss.\n", + "for it in range(niter):\n", + " solver.step(1) # SGD by Caffe\n", + " scratch_solver.step(1)\n", + " # store the train loss\n", + " train_loss[it] = solver.net.blobs['loss'].data\n", + " scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n", + " if it % 10 == 0:\n", + " print 'iter %d, finetune_loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n", + "print 'done'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the training loss produced by the two training procedures respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcXFWd9/HPtzt7AlkkJCGAgbCIqCSyuIDaRECEYZvB\n", + 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A closer look at small values, clipping to avoid showing too large loss during training:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXeYHNWVt98jgXIY5ZyQMNlIJJMMwhhssI0Dxsbr8Dms\n", + "zTpne9e73qa9tnFYrzMYe53WOeyuFzA4YBAYTEYiCQQCCSRAaZQTEtL5/jj3TlXXVHdX9/SMZsR5\n", + "n2ee6a6uqq5Ov3vu7557rqgqjuM4zv5Hv319AY7jOE734ALvOI6zn+IC7ziOs5/iAu84jrOf4gLv\n", + "OI6zn+IC7ziOs59SSOBFpL+ILBSRK6s8/g0ReURE7hGRea29RMdxHKcZikbwHwQWA52S5kXkXGCO\n", + "qh4MvAu4rHWX5ziO4zRLXYEXkanAucB/ApKzy3nAjwFU9TagTUQmtPIiHcdxnMYpEsF/Ffg4sLfK\n", + "41OAFan7K4GpXbwux3Ecp4vUFHgReTmwRlUXkh+9d+yaue/1DxzHcfYxB9R5/GTgvOCzDwJGiMh/\n", + 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Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for fine-tuning: 0.570000001788\n", + "Accuracy for training from scratch: 0.224000000954\n" + ] + } + ], + "source": [ + "test_iters = 10\n", + "accuracy = 0\n", + "scratch_accuracy = 0\n", + "for it in arange(test_iters):\n", + " solver.test_nets[0].forward()\n", + " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", + " scratch_solver.test_nets[0].forward()\n", + " scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n", + "accuracy /= test_iters\n", + "scratch_accuracy /= test_iters\n", + "print 'Accuracy for fine-tuning:', accuracy\n", + "print 'Accuracy for training from scratch:', scratch_accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n", + "\n", + "http://demo.vislab.berkeleyvision.org/" + ] + } + ], + "metadata": { + "description": "Fine-tune the ImageNet-trained CaffeNet on new data.", + "example_name": "Fine-tuning for Style Recognition", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + }, + "priority": 4 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/Finetune with Flickr Style Data.ipynb b/examples/Finetune with Flickr Style Data.ipynb deleted file mode 100644 index d8cfc7a3925..00000000000 --- a/examples/Finetune with Flickr Style Data.ipynb +++ /dev/null @@ -1,951 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Finetune a Pretrained Network with Flickr Style Data\n", - "\n", - "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network, and finetune the last few layers using your custom data.\n", - "\n", - "The upside of such approach is that, since pre-trained networks are trained on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we will need to prepare the data. This involves the following parts:\n", - "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n", - "(2) Download a subset of the overall Flickr style dataset for this demo.\n", - "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jiayq/Research/caffe\n" - ] - } - ], - "source": [ - "import os\n", - "os.chdir('..')\n", - "import sys\n", - "sys.path.insert(0, './python')\n", - "print os.getcwd()\n", - "\n", - "import caffe\n", - "import numpy as np\n", - "from pylab import *\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading...\n", - "--2015-03-17 10:51:07-- http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n", - "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n", - "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 17858008 (17M) [application/octet-stream]\n", - "Saving to: ‘caffe_ilsvrc12.tar.gz’\n", - "\n", - "100%[======================================>] 17,858,008 287KB/s in 55s \n", - "\n", - "2015-03-17 10:52:02 (318 KB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n", - "\n", - "Unzipping...\n", - "Done.\n", - "Model already exists.\n", - "Downloading 2000 images with 3 workers...\n", - "Writing train/val for 1903 successfully downloaded images.\n" - ] - } - ], - "source": [ - "# This downloads the ilsvrc auxiliary data (mean file, etc),\n", - "# and a subset of 2000 images for the style recognition task.\n", - "\n", - "# You won't need to run this - we should have already created it for you.\n", - "!data/ilsvrc12/get_ilsvrc_aux.sh\n", - "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n", - "!python examples/finetune_flickr_style/assemble_data.py \\\n", - " --workers=-1 --images=2000 --seed=1701 --label=5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's show what is the difference between the finetune network and the original caffe model." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1c1\r\n", - "< name: \"CaffeNet\"\r\n", - "---\r\n", - "> name: \"FlickrStyleCaffeNet\"\r\n", - "4c4\r\n", - "< type: \"Data\"\r\n", - "---\r\n", - "> type: \"ImageData\"\r\n", - "15,26c15,19\r\n", - "< # mean pixel / channel-wise mean instead of mean image\r\n", - "< # transform_param {\r\n", - "< # crop_size: 227\r\n", - "< # mean_value: 104\r\n", - "< # mean_value: 117\r\n", - "< # mean_value: 123\r\n", - "< # mirror: true\r\n", - "< # }\r\n", - "< data_param {\r\n", - "< source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n", - "< batch_size: 256\r\n", - "< backend: LMDB\r\n", - "---\r\n", - "> image_data_param {\r\n", - "> source: \"data/flickr_style/train.txt\"\r\n", - "> batch_size: 50\r\n", - "> new_height: 256\r\n", - "> new_width: 256\r\n", - "31c24\r\n", - "< type: \"Data\"\r\n", - "---\r\n", - "> type: \"ImageData\"\r\n", - "42,51c35,36\r\n", - "< # mean pixel / channel-wise mean instead of mean image\r\n", - "< # transform_param {\r\n", - "< # crop_size: 227\r\n", - "< # mean_value: 104\r\n", - "< # mean_value: 117\r\n", - "< # mean_value: 123\r\n", - "< # mirror: true\r\n", - "< # }\r\n", - "< data_param {\r\n", - "< source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n", - "---\r\n", - "> image_data_param {\r\n", - "> source: \"data/flickr_style/test.txt\"\r\n", - "53c38,39\r\n", - "< backend: LMDB\r\n", - "---\r\n", - "> new_height: 256\r\n", - "> new_width: 256\r\n", - "323a310\r\n", - "> # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n", - "360c347\r\n", - "< name: \"fc8\"\r\n", - "---\r\n", - "> name: \"fc8_flickr\"\r\n", - "363c350,351\r\n", - "< top: \"fc8\"\r\n", - "---\r\n", - "> top: \"fc8_flickr\"\r\n", - "> # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n", - "365c353\r\n", - "< lr_mult: 1\r\n", - "---\r\n", - "> lr_mult: 10\r\n", - "369c357\r\n", - "< lr_mult: 2\r\n", - "---\r\n", - "> lr_mult: 20\r\n", - "373c361\r\n", - "< num_output: 1000\r\n", - "---\r\n", - "> num_output: 20\r\n", - "384a373,379\r\n", - "> name: \"loss\"\r\n", - "> type: \"SoftmaxWithLoss\"\r\n", - "> bottom: \"fc8_flickr\"\r\n", - "> bottom: \"label\"\r\n", - "> top: \"loss\"\r\n", - "> }\r\n", - "> layer {\r\n", - "387c382\r\n", - "< bottom: \"fc8\"\r\n", - "---\r\n", - "> bottom: \"fc8_flickr\"\r\n", - "393,399d387\r\n", - "< }\r\n", - "< layer {\r\n", - "< name: \"loss\"\r\n", - "< type: \"SoftmaxWithLoss\"\r\n", - "< bottom: \"fc8\"\r\n", - "< bottom: \"label\"\r\n", - "< top: \"loss\"\r\n" - ] - } - ], - "source": [ - "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For your record, if you want to train the network in pure C++ tools, here is the command:\n", - "\n", - "\n", - "build/tools/caffe train \\\n", - " -solver models/finetune_flickr_style/solver.prototxt \\\n", - " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n", - " -gpu 0\n", - "\n", - "\n", - "However, we will train using Python in this example." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "iter 0, loss=3.786610, scratch_loss=3.163587\n", - "iter 10, loss=2.556661, scratch_loss=8.774073\n", - "iter 20, loss=2.035326, scratch_loss=2.266603\n", - "iter 30, loss=1.943101, scratch_loss=1.703273\n", - "iter 40, loss=1.982698, scratch_loss=1.831079\n", - "iter 50, loss=1.559268, scratch_loss=2.041238\n", - "iter 60, loss=1.464433, scratch_loss=1.836157\n", - "iter 70, loss=1.481868, scratch_loss=1.705826\n", - "iter 80, loss=1.394870, scratch_loss=1.695532\n", - "iter 90, loss=1.055422, scratch_loss=1.867379\n", - "iter 100, loss=1.407976, scratch_loss=1.881758\n", - "iter 110, loss=1.569579, scratch_loss=1.701803\n", - "iter 120, loss=0.951682, scratch_loss=1.764299\n", - "iter 130, loss=0.905122, scratch_loss=1.879305\n", - "iter 140, loss=1.020678, scratch_loss=1.746009\n", - "iter 150, loss=0.784985, scratch_loss=1.739624\n", - "iter 160, loss=0.911735, scratch_loss=1.673230\n", - "iter 170, loss=0.965255, scratch_loss=1.725484\n", - "iter 180, loss=1.028102, scratch_loss=1.676103\n", - "iter 190, loss=0.905020, scratch_loss=1.885763\n", - "done\n" - ] - } - ], - "source": [ - "niter = 200\n", - "# losses will also be stored in the log\n", - "train_loss = np.zeros(niter)\n", - "scratch_train_loss = np.zeros(niter)\n", - "\n", - "caffe.set_device(0)\n", - "caffe.set_mode_gpu()\n", - "# We create a solver that finetunes from a previously trained network.\n", - "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n", - "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", - "# For reference, we also create a solver that does no finetuning.\n", - "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n", - "\n", - "# We run the solver for niter times, and record the training loss.\n", - "for it in range(niter):\n", - " solver.step(1) # SGD by Caffe\n", - " scratch_solver.step(1)\n", - " # store the train loss\n", - " train_loss[it] = solver.net.blobs['loss'].data\n", - " scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n", - " if it % 10 == 0:\n", - " print 'iter %d, loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n", - "print 'done'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the training loss produced by the two training procedures respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmYFNXVx/FvD8O+DYsCIgpiRAxE1LiiEY0LGvdEo3Eh\n", - "aoxZXkSTGJdouHE3CaAxica4IVETlATEJCohjkrcArIpohFBRFllUUAWmfv+ce4wPUP3dPV09XTT\n", - "/D7P089MV1dX3aquPnX6VNUtEBERERERERERERERERERERERERHZbjQBpgMTw3MHLArDpgODC9Ms\n", - "ERFJVh5xvGHAHKBteO6BkeEhIiJFoizCOLsCJwL3AYkwLJH0v4iIFIkoQX0UcCVQlTTMA0OBmcD9\n", - "QEX8TRMRkWxlCuonAcuwunlyZn430AsYACwGRuSldSIikpVMJZRbgPOBz4EWQDtgHHBB0jg9sQOo\n", - "/VO8/12gd86tFBHZscwD9sz3TI6k5uyXbknDrwAeTfMen9cW7VhcoRtQYlyhG1BiXKEbUGIaHDuj\n", - 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] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXe4JGWV/z81Odw7OScmz5DzEEQYdJUgAioqqGsWTAs/\n", - "d1dXV11rjWvOAQMKiJhFRERQHBRRchgYZpicmByZfGemfn+c9731VnWlvre6+/blfJ7nPrdDdVV1\n", - "dfe3Tn3Pec8LiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoz2t6Ao8Cv0t5/mvAYuBx4MR6\n", - "7ZSiKIqSTo+Cy10DLACChOcuBKYDM4ArgW+Xs2uKoihKZygi8BMQEf8+4CU8fzFwvbl9PzAEGF3K\n", - "3imKoigdpojAfxl4P3A45fnxwGrn/hrkpKAoiqI0kDyBvwjYiPjvSdG7Jf5ckpWjKIqi1JFeOc+f\n", - "iVgwFwL9gEHADcAbnWXWAhOd+xPMY3GWANM6vKeKoijPT5Yiec6acg7JVTQXAreb26cD/0x5fYDP\n", - "Snwmd3gPfAbgs6fDr+9e+I3egW6E3+gd6Gb4jd6BbkaHHZG8CD5tQ1eZ/9ci4n4hEqHvBt6S8fp+\n", - 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Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for fine-tuning: 0.570000001788\n", - "Accuracy for training from scratch: 0.224000000954\n" - ] - } - ], - "source": [ - "test_iters = 10\n", - "accuracy = 0\n", - "scratch_accuracy = 0\n", - "for it in arange(test_iters):\n", - " solver.test_nets[0].forward()\n", - " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", - " scratch_solver.test_nets[0].forward()\n", - " scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n", - "accuracy /= test_iters\n", - "scratch_accuracy /= test_iters\n", - "print 'Accuracy for fine-tuning:', accuracy\n", - "print 'Accuracy for training from scratch:', scratch_accuracy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n", - "\n", - "http://demo.vislab.berkeleyvision.org/" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/classification.ipynb b/examples/classification.ipynb deleted file mode 100644 index a76cfb10773..00000000000 --- a/examples/classification.ipynb +++ /dev/null @@ -1,3342 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Classifying ImageNet: the instant Caffe way\n", - "===========================================\n", - "\n", - "Caffe has a Python interface, pycaffe, with a `caffe.Net` interface for models. There are both Python and MATLAB interfaces. While this example uses the off-the-shelf Python `caffe.Classifier` interface there is also a MATLAB example at `matlab/caffe/matcaffe_demo.m`.\n", - "\n", - "Before we begin, you must compile Caffe. You should add the Caffe module to your `PYTHONPATH` although this example includes it automatically. If you haven't yet done so, please refer to the [installation instructions](http://caffe.berkeleyvision.org/installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet` or let the first step of this example download it for you.\n", - "\n", - "Ready? Let's start." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe\n", - "\n", - "# Set the right path to your model definition file, pretrained model weights,\n", - "# and the image you would like to classify.\n", - "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n", - "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", - "IMAGE_FILE = 'images/cat.jpg'\n", - "\n", - "import os\n", - "if not os.path.isfile(PRETRAINED):\n", - " print(\"Downloading pre-trained CaffeNet model...\")\n", - " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n", - "\n", - "We will set the phase to test since we are doing testing, and will first use CPU for the computation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "caffe.set_mode_cpu()\n", - "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n", - " mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n", - " channel_swap=(2,1,0),\n", - " raw_scale=255,\n", - " image_dims=(256, 256))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at our example image with Caffe's image loading helper." - ] - }, - { - "cell_type": "code", - 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"metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "input_image = caffe.io.load_image(IMAGE_FILE)\n", - "plt.imshow(input_image)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Time to classify. The default is to actually do 10 predictions, cropping the center and corners of the image as well as their mirrored versions, and average over the predictions:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "prediction shape: (1000,)\n", - "predicted class: 281\n" - ] - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAG2ZJREFUeJzt3X9w02WCx/FPnOaOG1xRKmJJulNoCgnQ1q4pDMsyU1dK\n", - "B06ytDo7XRn0dnvawUF393bXvX92FrxZseM4t2Jv5rqcv3VL//DGuh7magczQBVyCgyO9UfLtWcI\n", - "1mWBrvxQS+Nzf9TGJIX0BykBnvdrJtN8v9/n+ebJY/L95Hm+3y86jDFGAABrXZXtBgAAsosgAADL\n", - "EQQAYDmCAAAsRxAAgOUIAgCw3KhBEAwG5fV6VVRUpIaGhhHbW1tbVVpaqrKyMt18883asWNHfFtB\n", - "QYFKSkpUVlamRYsWZbblAICMcKS7jyAWi2nevHlqb2+Xy+VSeXm5mpub5fP54mVOnz6tqVOnSpLe\n", - "ffddVVdXq7u7W5I0e/ZsvfPOO5o+ffokvw0AwESlHRGEw2F5PB4VFBTI6XSqtrZWra2tSWWGQ0CS\n", - 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"print 'prediction shape:', prediction[0].shape\n", - "plt.plot(prediction[0])\n", - "print 'predicted class:', prediction[0].argmax()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n", - "\n", - "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n", - "\n", - "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "prediction shape: (1000,)\n", - "predicted class: 281\n" - ] - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAG6ZJREFUeJzt3X9sk+eBB/Dvy9l3vYUNSppCsDMZYoMNJGlWhxztRXJb\n", - "kghWvCStqrSI9raIWkxpt2ldK92p16S3AtGENGikXcpBV0ovRLpKBFWpL4uoB0sJVht66S3QOlyi\n", - "GtNQDsiRhLYm5rk/vJjXb+CNE+yY8Hw/kmW/7/s8r5/3cfx+/bw/QBFCCBARkbTmpLsBRESUXgwC\n", - "IiLJMQiIiCTHICAikhyDgIhIcgwCIiLJTRoEXq8XdrsdNpsNDQ0NE5a3traioKAAhYWFuP/++3H4\n", - "8OHYMovFgvz8fBQWFmL16tXJbTkRESWFoncfQSQSwfLly9HR0QGTyYSioiI0NzfD4XDEyoyOjiIj\n", - "IwMA8Omnn6KyshJ9fX0AgCVLluDjjz/GggULUrwZREQ0XbojAr/fD6vVCovFAqPRiOrqarS2tsaV\n", - "GQ8BABgZGcE999wTt5z3qxER3d50gyAUCiEnJyc2bTabEQqFJpQ7ePAgHA4H1q1bh127dsXmK4qC\n", - 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"print 'prediction shape:', prediction[0].shape\n", - "plt.plot(prediction[0])\n", - "print 'predicted class:', prediction[0].argmax()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loops, best of 3: 355 ms per loop\n" - ] - } - ], - "source": [ - "%timeit net.predict([input_image])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It may look a little slow, but note that time is spent on cropping, python interfacing, and running 10 images. For performance, if you really want to make prediction fast, you can optionally code in C++ and pipeline operations better. For experimenting and prototyping the current speed is fine.\n", - "\n", - "Let's time classifying a single image with input preprocessed:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loops, best of 3: 210 ms per loop\n" - ] - } - ], - "source": [ - "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n", - "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n", - "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n", - "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n", - "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n", - "%timeit net.forward(data=caffe_input)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "OK, so how about GPU? it is actually pretty easy:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "caffe.set_mode_gpu()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Voila! Now we are in GPU mode. Let's see if the code gives the same result:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "prediction shape: (1000,)\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAG2ZJREFUeJzt3X9w1OWBx/HPOtk7O1hRImLYTSeQDewCSUzdwFDKTFoJ\n", - "GTjYkuh0Uhn02pxmcNC219beP52CNxUzjnMV05lLOX9rQ/7wxlgP92IGd4Ao7CkwOMYfCZecy2Is\n", - "BVL5oYasz/0Rs+5uYPODDQs879fMTvb7/T7Pd5993P1+9nm+3y86jDFGAABrXZXtBgAAsosgAADL\n", - "EQQAYDmCAAAsRxAAgOUIAgCw3KhBEAwG5fV6VVRUpIaGhhHbW1tbVVpaqrKyMt1yyy3asWNHfFtB\n", - "QYFKSkpUVlamhQsXZrblAICMcKS7jyAWi2nu3Llqb2+Xy+VSeXm5mpub5fP54mVOnz6tKVOmSJLe\n", - "eecdVVdXq7u7W5I0a9Ysvf3225o2bdokvw0AwESlHRGEw2F5PB4VFBTI6XSqtrZWra2tSWWGQ0CS\n", - 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"plt.plot(prediction[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 loops, best of 3: 174 ms per loop\n" - ] - } - ], - "source": [ - "# Full pipeline timing.\n", - "%timeit net.predict([input_image])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 loops, best of 3: 34.2 ms per loop\n" - ] - } - ], - "source": [ - "# Forward pass timing.\n", - "%timeit net.forward(data=caffe_input)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n", - "\n", - "To fully utilize the power of GPUs, you really want to:\n", - "\n", - "* Use larger batches, and minimize python call and data transfer overheads.\n", - "* Pipeline data load operations, like using a subprocess.\n", - "* Code in C++. A little inconvenient, but maybe worth it if your dataset is really, really large." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parting Words\n", - "-------------\n", - "\n", - "So this is python! We hope the interface is easy enough for one to use. The python wrapper is interfaced with boost::python, and source code can be found at `python/caffe` with the main interface in `pycaffe.py` and the classification wrapper in `classifier.py`. If you have customizations to make, start there! Do let us know if you make improvements by sending a pull request!" - ] - } - ], - "metadata": { - "description": "Use the pre-trained ImageNet model to classify images with the Python interface.", - "example_name": "ImageNet classification", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" - }, - "priority": 1 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/detection.ipynb b/examples/detection.ipynb index e2a981a00d7..6a03c996245 100644 --- a/examples/detection.ipynb +++ b/examples/detection.ipynb @@ -8385,7 +8385,7 @@ "pygments_lexer": "ipython2", "version": "2.7.9" }, - "priority": 3 + "priority": 6 }, "nbformat": 4, "nbformat_minor": 0 diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb deleted file mode 100644 index 6d629c5b635..00000000000 --- a/examples/filter_visualization.ipynb +++ /dev/null @@ -1,13214 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we visualize filters and outputs using the network architecture proposed by Krizhevsky et al. for ImageNet and implemented in `caffe`.\n", - "\n", - "(This page follows DeCAF visualizations originally by Yangqing Jia.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe\n", - "\n", - "plt.rcParams['figure.figsize'] = (10, 10)\n", - "plt.rcParams['image.interpolation'] = 'nearest'\n", - "plt.rcParams['image.cmap'] = 'gray'\n", - "\n", - "import os\n", - "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", - " print(\"Downloading pre-trained CaffeNet model...\")\n", - " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "caffe.set_mode_cpu()\n", - "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n", - " caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n", - " caffe.TEST)\n", - "\n", - "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n", - "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n", - "transformer.set_transpose('data', (2,0,1))\n", - "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n", - "transformer.set_raw_scale('data', 255) # the reference model operates on images in [0,255] range instead of [0,1]\n", - "transformer.set_channel_swap('data', (2,1,0)) # the reference model has channels in BGR order instead of RGB" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Classify the image by reshaping the net for the single input then doing the forward pass." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Predicted class is #281.\n" - ] - } - ], - "source": [ - "net.blobs['data'].reshape(1,3,227,227)\n", - "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n", - "out = net.forward()\n", - "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('data', (1, 3, 227, 227)),\n", - " ('conv1', (1, 96, 55, 55)),\n", - " ('pool1', (1, 96, 27, 27)),\n", - " ('norm1', (1, 96, 27, 27)),\n", - " ('conv2', (1, 256, 27, 27)),\n", - " ('pool2', (1, 256, 13, 13)),\n", - " ('norm2', (1, 256, 13, 13)),\n", - " ('conv3', (1, 384, 13, 13)),\n", - " ('conv4', (1, 384, 13, 13)),\n", - " ('conv5', (1, 256, 13, 13)),\n", - " ('pool5', (1, 256, 6, 6)),\n", - " ('fc6', (1, 4096)),\n", - " ('fc7', (1, 4096)),\n", - " ('fc8', (1, 1000)),\n", - " ('prob', (1, 1000))]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(k, v.data.shape) for k, v in net.blobs.items()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('conv1', (96, 3, 11, 11)),\n", - " ('conv2', (256, 48, 5, 5)),\n", - " ('conv3', (384, 256, 3, 3)),\n", - " ('conv4', (384, 192, 3, 3)),\n", - " ('conv5', (256, 192, 3, 3)),\n", - " ('fc6', (4096, 9216)),\n", - " ('fc7', (4096, 4096)),\n", - " ('fc8', (1000, 4096))]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(k, v[0].data.shape) for k, v in net.params.items()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Helper functions for visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# take an array of shape (n, height, width) or (n, height, width, channels)\n", - "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n", - "def vis_square(data, padsize=1, padval=0):\n", - " data -= data.min()\n", - " data /= data.max()\n", - " \n", - " # force the number of filters to be square\n", - " n = int(np.ceil(np.sqrt(data.shape[0])))\n", - " padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n", - " data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n", - " \n", - " # tile the filters into an image\n", - " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n", - " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n", - " \n", - " plt.imshow(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The input image" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - 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"filters = net.params['conv1'][0].data\n", - "vis_square(filters.transpose(0, 2, 3, 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmMpFd1Pn5qr+6q6m1mumfp2ccz3m083mIb8BgMRkQY\n", - "J8FsIUAQIkokpEhByodIAUWKiBIJKUo+ZRMghLFQiBEIh0VgYztj43Uw9tgeL+NZerae3qura/19\n", - "qDynnvfeU29V95iM/3/d50vPVL31vnd/733OOc9JtFqtlgQEBAQEBAQEBKwayYtdgICAgICAgICA\n", - "/68ibKQCAgICAgICAtaIsJEKCAgICAgICFgjwkYqICAgICAgIGCNCBupgICAgICAgIA1ImykAgIC\n", - "AgICAgLWiN/KRurBBx+USy+9VC655BL5u7/7u9/GIwICAgICAgICLjoSb7WOVKPRkH379slPf/pT\n", - "2bJli9xwww3y7W9/Wy677LK38jEBAQEBAQEBARcdbzkj9cQTT8iePXtkx44dkslk5GMf+5g88MAD\n", - "b/VjAgICAgICAgIuOt7yjdSJEydk69at+v/JyUk5ceLEW/2YgICAgICAgICLjvRbfcNEIvGWXBMQ\n", - 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We show only the first 48 filters, with each channel shown separately, so that each filter is a row." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXuMr1V199fMnDmcO1c5wAEFFfBuVaiXat7GiMb+YWrS\n", - "WhtvpcoteI2gomKoaETF6FFMuWgoaY2paVrbNKmttrWttRFbQ1VEBZH7RZCLcO4zc94/Tj7Ps3+f\n", - "51nnR0d8533frO8/M7+Z/dvP2muvvZ+91l6Xmb179+6NQqFQKBQKhcL/GLMrTUChUCgUCoXC/6uo\n", - "g1ShUCgUCoXCMlEHqUKhUCgUCoVlog5ShUKhUCgUCstEHaQKhUKhUCgUlok6SBUKhUKhUCgsE7+S\n", - "g9RXvvKVeNKTnhTHH398fPSjH/1VPKJQKBQKhUJhxTHzaOeRWlxcjBNPPDG+9rWvxZYtW+Lkk0+O\n", - "L37xi/HkJz/50XxMoVAoFAqFworjUbdIXX311fHEJz4xjj322Jifn49Xv/rV8dd//deP9mMKhUKh\n", - "UCgUVhyP+kHq9ttvj2OOOab7fPTRR8ftt9/+aD+mUCgUCoVCYcWx6tHucGZmZmqbLVu2xB133PFo\n", - "P7pQKBQKhULhV4LME+pRP0ht2bIlbr311u7zrbfeGkcfffREmzvuuCOOOuqoOPbYYyNin9XqmGOO\n", - 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] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second fully connected layer, `fc7` (rectified)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlcAAAJPCAYAAABRvvFyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XucFOWd7/FvJ5BNToAEVAYW3AOiKHKbWQ14XHGHKESO\n", - "eGExboiSeQmePWtiEqNH1M0mDm5UWDdrEF3DcSNhQw5KLiAxMl6i7S1RohlcE+8KK+LMKMLIDCiX\n", - "mTp/tD309FR11+WpW/fn/XrxYqan6qlf1VOXXz/11FMZy7IsAQAAwIiPxR0AAABAJSG5AgAAMIjk\n", - "CgAAwCCSKwAAAINIrgAAAAwiuQIAADCoZHK1bds2TZ8+XePHj9eECRN0yy23SJIaGxs1cuRI1dXV\n", - "qa6uTk1NTZEECwAAkHSZUuNctba2qrW1VbW1ters7NQJJ5yg9evXa+3atRo4cKAuv/zyKGMFAABI\n", - "vH6l/jhs2DANGzZMkjRgwACNGzdO27dvlyQx9igAAEBfrvtcbd26Vc3NzTrppJMkScuXL9fkyZO1\n", - "cOFCtbe3hxYgAABAmrhKrjo7O3Xeeedp2bJlGjBggC655BJt2bJFmzdv1vDhw3XFFVeEHScAAEAq\n", - "lOxzJUkHDhzQ7NmzNWvWLF122WV9/r5161adddZZev7553t9fvTRR+v11183Gy0AAEAIxowZo9de\n", - 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"[]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAmEAAAJPCAYAAAA0UwMNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X9w1/Wd4PHX1ya37flbpAoJO1ESSayCbAPW7TGTdkVO\n", - "d+XE/lhaV52WWsYd225nu+3dzXWqnWsrs9u5teX+YL3a7a9B5q4zYndozqM11dpCzmIPZ9EKLLQh\n", - "iroq/gAV8/Vzf3ybCCF8CZDw/iTvx2Mmk3zz/Xy++YRPQp55vz/fdypFURQBAMAJdVLqAwAAyJEI\n", - "AwBIQIQBACQgwgAAEhBhAAAJiDAAgASOGGHd3d3R3t4ebW1tsWLFikPuX7t2bcyZMyfmzp0b7373\n", - "u+OnP/3p0H0tLS0xe/bsmDt3bsyfP39sjxwAYAKr1FsnrFqtxqxZs2L9+vXR1NQU8+bNi9WrV0dH\n", - "R8fQNnv37o2TTz45IiIeffTRWLJkSWzbti0iIs4777z41a9+FWedddY4fxoAABNL3ZGw3t7eaG1t\n", - "jZaWlmhsbIylS5fG2rVrD9pmMMAiIl555ZU4++yzD7rfWrAAAIeqG2H9/f0xY8aModvNzc3R399/\n", - "yHb33HNPdHR0xJVXXhnf+MY3ht5fqVTi8ssvj87OzrjzzjvH8LABACa2hnp3ViqVUT3INddcE9dc\n", - "c008+OCDcf3118dvfvObiIh46KGHYtq0afHss8/GwoULo729PRYsWHD8Rw0AMMHVjbCmpqbo6+sb\n", - "ut3X1xfNzc2H3X7BggUxMDAQzz33XEyZMiWmTZsWERFTp06NJUuWRG9v7yER1traGtu3bz+ezwEA\n", - 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" 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n", - " 'n02127052 lynx, catamount']\n" - ] - } - ], - "source": [ - "# load labels\n", - "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", - "try:\n", - " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", - "except:\n", - " !../data/ilsvrc12/get_ilsvrc_aux.sh\n", - " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", - "\n", - "# sort top k predictions from softmax output\n", - "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n", - "print labels[top_k]" - ] - } - ], - "metadata": { - "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.", - "example_name": "Filter visualization", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" - }, - "priority": 2 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb deleted file mode 100644 index e98d13dd501..00000000000 --- a/examples/hdf5_classification.ipynb +++ /dev/null @@ -1,6290 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Caffeinated Logistic Regression of HDF5 Data\n", - "\n", - "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", - "import sys\n", - "sys.path.insert(0, caffe_root + 'python')\n", - "\n", - "import caffe\n", - "\n", - "import os\n", - "import h5py\n", - "import shutil\n", - "import tempfile\n", - "\n", - "# You may need to 'pip install scikit-learn'\n", - "import sklearn\n", - "import sklearn.datasets\n", - "import sklearn.linear_model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features." - ] - }, - { - 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Time and check the classifier's accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loops, best of 3: 499 ms per loop\n", - "Accuracy: 0.756\n" - ] - } - ], - "source": [ - "# Train and test the scikit-learn SGD logistic regression.\n", - "clf = sklearn.linear_model.SGDClassifier(\n", - " loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n", - "\n", - "%timeit clf.fit(X, y)\n", - "yt_pred = clf.predict(Xt)\n", - "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save the dataset to HDF5 for loading in Caffe." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Write out the data to HDF5 files in a temp directory.\n", - "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n", - "dirname = os.path.abspath('./hdf5_classification/data')\n", - "if not os.path.exists(dirname):\n", - " os.makedirs(dirname)\n", - "\n", - "train_filename = os.path.join(dirname, 'train.h5')\n", - "test_filename = os.path.join(dirname, 'test.h5')\n", - "\n", - "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n", - "# To show this off, we'll list the same data file twice.\n", - "with h5py.File(train_filename, 'w') as f:\n", - " f['data'] = X\n", - " f['label'] = y.astype(np.float32)\n", - "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n", - " f.write(train_filename + '\\n')\n", - " f.write(train_filename + '\\n')\n", - " \n", - "# HDF5 is pretty efficient, but can be further compressed.\n", - "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n", - "with h5py.File(test_filename, 'w') as f:\n", - " f.create_dataset('data', data=Xt, **comp_kwargs)\n", - " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n", - "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n", - " f.write(test_filename + '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Learn and evaluate logistic regression in Caffe." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loops, best of 3: 240 ms per loop\n", - "Accuracy: 0.752\n" - ] - } - ], - "source": [ - "def learn_and_test(solver_file):\n", - " caffe.set_mode_cpu()\n", - " solver = caffe.get_solver(solver_file)\n", - " solver.solve()\n", - "\n", - " accuracy = 0\n", - " test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n", - " for i in range(test_iters):\n", - " solver.test_nets[0].forward()\n", - " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", - " accuracy /= test_iters\n", - " return accuracy\n", - "\n", - "%timeit learn_and_test('hdf5_classification/solver.prototxt')\n", - "acc = learn_and_test('hdf5_classification/solver.prototxt')\n", - "print(\"Accuracy: {:.3f}\".format(acc))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do the same through the command line interface for detailed output on the model and solving." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I0307 01:34:29.141863 2099749632 caffe.cpp:103] Use CPU.\n", - "I0307 01:34:29.418283 2099749632 caffe.cpp:107] Starting Optimization\n", - "I0307 01:34:29.418323 2099749632 solver.cpp:32] Initializing solver from parameters: \n", - "test_iter: 250\n", - "test_interval: 1000\n", - "base_lr: 0.01\n", - "display: 1000\n", - "max_iter: 10000\n", - "lr_policy: \"step\"\n", - "gamma: 0.1\n", - "momentum: 0.9\n", - "weight_decay: 0.0005\n", - "stepsize: 5000\n", - "snapshot: 10000\n", - "snapshot_prefix: \"hdf5_classification/data/train\"\n", - "solver_mode: CPU\n", - "net: \"hdf5_classification/train_val.prototxt\"\n", - "I0307 01:34:29.418416 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val.prototxt\n", - "I0307 01:34:29.418583 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\n", - "I0307 01:34:29.418598 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\n", - "I0307 01:34:29.418608 2099749632 net.cpp:42] Initializing net from parameters: \n", - "name: \"LogisticRegressionNet\"\n", - "state {\n", - " phase: TRAIN\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " include {\n", - " phase: TRAIN\n", - " }\n", - " hdf5_data_param {\n", - " source: \"hdf5_classification/data/train.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"fc1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"fc1\"\n", - " param {\n", - " lr_mult: 1\n", - " decay_mult: 1\n", - " }\n", - " param {\n", - " lr_mult: 2\n", - " decay_mult: 0\n", - " }\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"gaussian\"\n", - " std: 0.01\n", - " }\n", - " bias_filler {\n", - " type: \"constant\"\n", - " value: 0\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"fc1\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "I0307 01:34:29.418692 2099749632 layer_factory.hpp:74] Creating layer data\n", - "I0307 01:34:29.418853 2099749632 net.cpp:84] Creating Layer data\n", - "I0307 01:34:29.418879 2099749632 net.cpp:338] data -> data\n", - "I0307 01:34:29.418905 2099749632 net.cpp:338] data -> label\n", - "I0307 01:34:29.418918 2099749632 net.cpp:113] Setting up data\n", - "I0307 01:34:29.418926 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\n", - "I0307 01:34:29.418992 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n", - "I0307 01:34:29.420812 2099749632 net.cpp:120] Top shape: 10 4 (40)\n", - "I0307 01:34:29.420841 2099749632 net.cpp:120] Top shape: 10 (10)\n", - "I0307 01:34:29.420852 2099749632 layer_factory.hpp:74] Creating layer fc1\n", - "I0307 01:34:29.420866 2099749632 net.cpp:84] Creating Layer fc1\n", - "I0307 01:34:29.420872 2099749632 net.cpp:380] fc1 <- data\n", - "I0307 01:34:29.420882 2099749632 net.cpp:338] fc1 -> fc1\n", - "I0307 01:34:29.420894 2099749632 net.cpp:113] Setting up fc1\n", - "I0307 01:34:29.425689 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", - "I0307 01:34:29.425709 2099749632 layer_factory.hpp:74] Creating layer loss\n", - "I0307 01:34:29.425724 2099749632 net.cpp:84] Creating Layer loss\n", - "I0307 01:34:29.425731 2099749632 net.cpp:380] loss <- fc1\n", - "I0307 01:34:29.425739 2099749632 net.cpp:380] loss <- label\n", - "I0307 01:34:29.425747 2099749632 net.cpp:338] loss -> loss\n", - "I0307 01:34:29.425756 2099749632 net.cpp:113] Setting up loss\n", - "I0307 01:34:29.425767 2099749632 layer_factory.hpp:74] Creating layer loss\n", - "I0307 01:34:29.425781 2099749632 net.cpp:120] Top shape: (1)\n", - "I0307 01:34:29.425789 2099749632 net.cpp:122] with loss weight 1\n", - "I0307 01:34:29.425801 2099749632 net.cpp:167] loss needs backward computation.\n", - "I0307 01:34:29.425808 2099749632 net.cpp:167] fc1 needs backward computation.\n", - "I0307 01:34:29.425815 2099749632 net.cpp:169] data does not need backward computation.\n", - "I0307 01:34:29.425822 2099749632 net.cpp:205] This network produces output loss\n", - "I0307 01:34:29.425829 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", - "I0307 01:34:29.425837 2099749632 net.cpp:217] Network initialization done.\n", - "I0307 01:34:29.425843 2099749632 net.cpp:218] Memory required for data: 284\n", - "I0307 01:34:29.425961 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val.prototxt\n", - "I0307 01:34:29.425984 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\n", - "I0307 01:34:29.425997 2099749632 net.cpp:42] Initializing net from parameters: \n", - "name: \"LogisticRegressionNet\"\n", - "state {\n", - " phase: TEST\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " include {\n", - " phase: TEST\n", - " }\n", - " hdf5_data_param {\n", - " source: \"hdf5_classification/data/test.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"fc1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"fc1\"\n", - " param {\n", - " lr_mult: 1\n", - " decay_mult: 1\n", - " }\n", - " param {\n", - " lr_mult: 2\n", - " decay_mult: 0\n", - " }\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"gaussian\"\n", - " std: 0.01\n", - " }\n", - " bias_filler {\n", - " type: \"constant\"\n", - " value: 0\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"fc1\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "layer {\n", - " name: \"accuracy\"\n", - " type: \"Accuracy\"\n", - " bottom: \"fc1\"\n", - " bottom: \"label\"\n", - " top: \"accuracy\"\n", - " include {\n", - " phase: TEST\n", - " }\n", - "}\n", - "I0307 01:34:29.426126 2099749632 layer_factory.hpp:74] Creating layer data\n", - "I0307 01:34:29.426311 2099749632 net.cpp:84] Creating Layer data\n", - "I0307 01:34:29.426331 2099749632 net.cpp:338] data -> data\n", - "I0307 01:34:29.426343 2099749632 net.cpp:338] data -> label\n", - "I0307 01:34:29.426354 2099749632 net.cpp:113] Setting up data\n", - "I0307 01:34:29.426362 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\n", - "I0307 01:34:29.426484 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n", - "I0307 01:34:29.427692 2099749632 net.cpp:120] Top shape: 10 4 (40)\n", - "I0307 01:34:29.427711 2099749632 net.cpp:120] Top shape: 10 (10)\n", - "I0307 01:34:29.427721 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\n", - "I0307 01:34:29.427731 2099749632 net.cpp:84] Creating Layer label_data_1_split\n", - "I0307 01:34:29.427738 2099749632 net.cpp:380] label_data_1_split <- label\n", - "I0307 01:34:29.427747 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", - "I0307 01:34:29.427759 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", - "I0307 01:34:29.427768 2099749632 net.cpp:113] Setting up label_data_1_split\n", - "I0307 01:34:29.427777 2099749632 net.cpp:120] Top shape: 10 (10)\n", - "I0307 01:34:29.427784 2099749632 net.cpp:120] Top shape: 10 (10)\n", - "I0307 01:34:29.427791 2099749632 layer_factory.hpp:74] Creating layer fc1\n", - "I0307 01:34:29.427804 2099749632 net.cpp:84] Creating Layer fc1\n", - "I0307 01:34:29.427813 2099749632 net.cpp:380] fc1 <- data\n", - "I0307 01:34:29.427821 2099749632 net.cpp:338] fc1 -> fc1\n", - "I0307 01:34:29.427831 2099749632 net.cpp:113] Setting up fc1\n", - "I0307 01:34:29.427845 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", - "I0307 01:34:29.427857 2099749632 layer_factory.hpp:74] Creating layer fc1_fc1_0_split\n", - "I0307 01:34:29.427866 2099749632 net.cpp:84] Creating Layer fc1_fc1_0_split\n", - "I0307 01:34:29.427872 2099749632 net.cpp:380] fc1_fc1_0_split <- fc1\n", - "I0307 01:34:29.427881 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_0\n", - "I0307 01:34:29.427891 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_1\n", - "I0307 01:34:29.427942 2099749632 net.cpp:113] Setting up fc1_fc1_0_split\n", - "I0307 01:34:29.427955 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", - "I0307 01:34:29.427965 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", - "I0307 01:34:29.427976 2099749632 layer_factory.hpp:74] Creating layer loss\n", - "I0307 01:34:29.427991 2099749632 net.cpp:84] Creating Layer loss\n", - "I0307 01:34:29.428001 2099749632 net.cpp:380] loss <- fc1_fc1_0_split_0\n", - "I0307 01:34:29.428009 2099749632 net.cpp:380] loss <- label_data_1_split_0\n", - "I0307 01:34:29.428017 2099749632 net.cpp:338] loss -> loss\n", - "I0307 01:34:29.428026 2099749632 net.cpp:113] Setting up loss\n", - "I0307 01:34:29.428035 2099749632 layer_factory.hpp:74] Creating layer loss\n", - "I0307 01:34:29.428048 2099749632 net.cpp:120] Top shape: (1)\n", - "I0307 01:34:29.428056 2099749632 net.cpp:122] with loss weight 1\n", - "I0307 01:34:29.428064 2099749632 layer_factory.hpp:74] Creating layer accuracy\n", - "I0307 01:34:29.428076 2099749632 net.cpp:84] Creating Layer accuracy\n", - "I0307 01:34:29.428084 2099749632 net.cpp:380] accuracy <- fc1_fc1_0_split_1\n", - "I0307 01:34:29.428092 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\n", - "I0307 01:34:29.428102 2099749632 net.cpp:338] accuracy -> accuracy\n", - "I0307 01:34:29.428131 2099749632 net.cpp:113] Setting up accuracy\n", - "I0307 01:34:29.428140 2099749632 net.cpp:120] Top shape: (1)\n", - "I0307 01:34:29.428148 2099749632 net.cpp:169] accuracy does not need backward computation.\n", - "I0307 01:34:29.428154 2099749632 net.cpp:167] loss needs backward computation.\n", - "I0307 01:34:29.428161 2099749632 net.cpp:167] fc1_fc1_0_split needs backward computation.\n", - "I0307 01:34:29.428167 2099749632 net.cpp:167] fc1 needs backward computation.\n", - "I0307 01:34:29.428174 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\n", - "I0307 01:34:29.428181 2099749632 net.cpp:169] data does not need backward computation.\n", - "I0307 01:34:29.428189 2099749632 net.cpp:205] This network produces output accuracy\n", - "I0307 01:34:29.428324 2099749632 net.cpp:205] This network produces output loss\n", - "I0307 01:34:29.428342 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", - "I0307 01:34:29.428350 2099749632 net.cpp:217] Network initialization done.\n", - "I0307 01:34:29.428357 2099749632 net.cpp:218] Memory required for data: 528\n", - "I0307 01:34:29.428388 2099749632 solver.cpp:42] Solver scaffolding done.\n", - "I0307 01:34:29.428412 2099749632 solver.cpp:222] Solving LogisticRegressionNet\n", - "I0307 01:34:29.428421 2099749632 solver.cpp:223] Learning Rate Policy: step\n", - "I0307 01:34:29.428431 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\n", - "I0307 01:34:29.471674 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.4532\n", - "I0307 01:34:29.471724 2099749632 solver.cpp:315] Test net output #1: loss = 0.694067 (* 1 = 0.694067 loss)\n", - "I0307 01:34:29.471853 2099749632 solver.cpp:189] Iteration 0, loss = 0.692695\n", - "I0307 01:34:29.471878 2099749632 solver.cpp:204] Train net output #0: loss = 0.692695 (* 1 = 0.692695 loss)\n", - "I0307 01:34:29.471890 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\n", - "I0307 01:34:29.483834 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\n", - "I0307 01:34:29.486868 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7424\n", - "I0307 01:34:29.486896 2099749632 solver.cpp:315] Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\n", - "I0307 01:34:29.486922 2099749632 solver.cpp:189] Iteration 1000, loss = 0.472665\n", - "I0307 01:34:29.486934 2099749632 solver.cpp:204] Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\n", - "I0307 01:34:29.486944 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\n", - "I0307 01:34:29.498821 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\n", - "I0307 01:34:29.501900 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7364\n", - "I0307 01:34:29.501941 2099749632 solver.cpp:315] Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\n", - "I0307 01:34:29.501988 2099749632 solver.cpp:189] Iteration 2000, loss = 0.6863\n", - "I0307 01:34:29.502003 2099749632 solver.cpp:204] Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\n", - "I0307 01:34:29.502013 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\n", - "I0307 01:34:29.513921 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\n", - "I0307 01:34:29.517227 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.6964\n", - "I0307 01:34:29.517300 2099749632 solver.cpp:315] Test net output #1: loss = 0.604707 (* 1 = 0.604707 loss)\n", - "I0307 01:34:29.518105 2099749632 solver.cpp:189] Iteration 3000, loss = 0.617542\n", - "I0307 01:34:29.518154 2099749632 solver.cpp:204] Train net output #0: loss = 0.617542 (* 1 = 0.617542 loss)\n", - "I0307 01:34:29.518170 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\n", - "I0307 01:34:29.531672 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\n", - "I0307 01:34:29.534873 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7424\n", - "I0307 01:34:29.534920 2099749632 solver.cpp:315] Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\n", - "I0307 01:34:29.534950 2099749632 solver.cpp:189] Iteration 4000, loss = 0.472666\n", - "I0307 01:34:29.534962 2099749632 solver.cpp:204] Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\n", - "I0307 01:34:29.534973 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\n", - "I0307 01:34:29.546567 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\n", - "I0307 01:34:29.549762 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7364\n", - "I0307 01:34:29.549789 2099749632 solver.cpp:315] Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\n", - "I0307 01:34:29.549815 2099749632 solver.cpp:189] Iteration 5000, loss = 0.686301\n", - "I0307 01:34:29.549828 2099749632 solver.cpp:204] Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\n", - "I0307 01:34:29.549837 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\n", - "I0307 01:34:29.562142 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\n", - "I0307 01:34:29.565335 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7476\n", - "I0307 01:34:29.565373 2099749632 solver.cpp:315] Test net output #1: loss = 0.59775 (* 1 = 0.59775 loss)\n", - "I0307 01:34:29.566051 2099749632 solver.cpp:189] Iteration 6000, loss = 0.664614\n", - "I0307 01:34:29.566086 2099749632 solver.cpp:204] Train net output #0: loss = 0.664614 (* 1 = 0.664614 loss)\n", - "I0307 01:34:29.566097 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\n", - "I0307 01:34:29.577900 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\n", - "I0307 01:34:29.580993 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7524\n", - "I0307 01:34:29.581015 2099749632 solver.cpp:315] Test net output #1: loss = 0.597349 (* 1 = 0.597349 loss)\n", - "I0307 01:34:29.581038 2099749632 solver.cpp:189] Iteration 7000, loss = 0.456775\n", - "I0307 01:34:29.581050 2099749632 solver.cpp:204] Train net output #0: loss = 0.456774 (* 1 = 0.456774 loss)\n", - "I0307 01:34:29.581059 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\n", - "I0307 01:34:29.592854 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\n", - "I0307 01:34:29.595973 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7568\n", - "I0307 01:34:29.596002 2099749632 solver.cpp:315] Test net output #1: loss = 0.597265 (* 1 = 0.597265 loss)\n", - "I0307 01:34:29.596027 2099749632 solver.cpp:189] Iteration 8000, loss = 0.673885\n", - "I0307 01:34:29.596040 2099749632 solver.cpp:204] Train net output #0: loss = 0.673885 (* 1 = 0.673885 loss)\n", - "I0307 01:34:29.596048 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\n", - "I0307 01:34:29.607822 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\n", - "I0307 01:34:29.610930 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7432\n", - "I0307 01:34:29.610960 2099749632 solver.cpp:315] Test net output #1: loss = 0.597777 (* 1 = 0.597777 loss)\n", - "I0307 01:34:29.611558 2099749632 solver.cpp:189] Iteration 9000, loss = 0.66526\n", - "I0307 01:34:29.611583 2099749632 solver.cpp:204] Train net output #0: loss = 0.66526 (* 1 = 0.66526 loss)\n", - "I0307 01:34:29.611593 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\n", - "I0307 01:34:29.623009 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\n", - "I0307 01:34:29.623209 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\n", - "I0307 01:34:29.623319 2099749632 solver.cpp:248] Iteration 10000, loss = 0.457922\n", - "I0307 01:34:29.623333 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\n", - "I0307 01:34:29.626454 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.752\n", - "I0307 01:34:29.626484 2099749632 solver.cpp:315] Test net output #1: loss = 0.597362 (* 1 = 0.597362 loss)\n", - "I0307 01:34:29.626493 2099749632 solver.cpp:253] Optimization Done.\n", - "I0307 01:34:29.626502 2099749632 caffe.cpp:121] Optimization Done.\n" - ] - } - ], - "source": [ - "!../build/tools/caffe train -solver hdf5_classification/solver.prototxt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you look at output or the `train_val.prototxt`, you'll see that the model is simple logistic regression.\n", - "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n", - "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n", - "\n", - "The final accuracy of the new network be higher than logistic regression!" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loops, best of 3: 333 ms per loop\n", - "Accuracy: 0.818\n" - ] - } - ], - "source": [ - "def learn_and_test(solver_file):\n", - " caffe.set_mode_cpu()\n", - " solver = caffe.get_solver(solver_file)\n", - " solver.solve()\n", - "\n", - " accuracy = 0\n", - " test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n", - " for i in range(test_iters):\n", - " solver.test_nets[0].forward()\n", - " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", - " accuracy /= test_iters\n", - " return accuracy\n", - "\n", - "%timeit learn_and_test('hdf5_classification/solver2.prototxt')\n", - "acc = learn_and_test('hdf5_classification/solver2.prototxt')\n", - "print(\"Accuracy: {:.3f}\".format(acc))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do the same through the command line interface for detailed output on the model and solving." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I0307 01:34:31.589234 2099749632 caffe.cpp:103] Use CPU.\n", - "I0307 01:34:31.872560 2099749632 caffe.cpp:107] Starting Optimization\n", - "I0307 01:34:31.872596 2099749632 solver.cpp:32] Initializing solver from parameters: \n", - "test_iter: 250\n", - "test_interval: 1000\n", - "base_lr: 0.01\n", - "display: 1000\n", - "max_iter: 10000\n", - "lr_policy: \"step\"\n", - "gamma: 0.1\n", - "momentum: 0.9\n", - "weight_decay: 0.0005\n", - "stepsize: 5000\n", - "snapshot: 10000\n", - "snapshot_prefix: \"hdf5_classification/data/train\"\n", - "solver_mode: CPU\n", - "net: \"hdf5_classification/train_val2.prototxt\"\n", - "I0307 01:34:31.872687 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val2.prototxt\n", - "I0307 01:34:31.872865 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\n", - "I0307 01:34:31.872882 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\n", - "I0307 01:34:31.872891 2099749632 net.cpp:42] Initializing net from parameters: \n", - "name: \"LogisticRegressionNet\"\n", - "state {\n", - " phase: TRAIN\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " include {\n", - " phase: TRAIN\n", - " }\n", - " hdf5_data_param {\n", - " source: \"hdf5_classification/data/train.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"fc1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"fc1\"\n", - " param {\n", - " lr_mult: 1\n", - " decay_mult: 1\n", - " }\n", - " param {\n", - " lr_mult: 2\n", - " decay_mult: 0\n", - " }\n", - " inner_product_param {\n", - " num_output: 40\n", - " weight_filler {\n", - " type: \"gaussian\"\n", - " std: 0.01\n", - " }\n", - " bias_filler {\n", - " type: \"constant\"\n", - " value: 0\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"relu1\"\n", - " type: \"ReLU\"\n", - " bottom: \"fc1\"\n", - " top: \"fc1\"\n", - "}\n", - "layer {\n", - " name: \"fc2\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"fc1\"\n", - " top: \"fc2\"\n", - " param {\n", - " lr_mult: 1\n", - " decay_mult: 1\n", - " }\n", - " param {\n", - " lr_mult: 2\n", - " decay_mult: 0\n", - " }\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"gaussian\"\n", - " std: 0.01\n", - " }\n", - " bias_filler {\n", - " type: \"constant\"\n", - " value: 0\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"fc2\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "I0307 01:34:31.873246 2099749632 layer_factory.hpp:74] Creating layer data\n", - "I0307 01:34:31.873276 2099749632 net.cpp:84] Creating Layer data\n", - "I0307 01:34:31.873292 2099749632 net.cpp:338] data -> data\n", - "I0307 01:34:31.873332 2099749632 net.cpp:338] data -> label\n", - "I0307 01:34:31.873352 2099749632 net.cpp:113] Setting up data\n", - "I0307 01:34:31.873361 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\n", - "I0307 01:34:31.873443 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n", - "I0307 01:34:31.875783 2099749632 net.cpp:120] Top shape: 10 4 (40)\n", - "I0307 01:34:31.875816 2099749632 net.cpp:120] Top shape: 10 (10)\n", - "I0307 01:34:31.875829 2099749632 layer_factory.hpp:74] Creating layer fc1\n", - "I0307 01:34:31.875846 2099749632 net.cpp:84] Creating Layer fc1\n", - "I0307 01:34:31.875857 2099749632 net.cpp:380] fc1 <- data\n", - "I0307 01:34:31.875875 2099749632 net.cpp:338] fc1 -> fc1\n", - "I0307 01:34:31.875892 2099749632 net.cpp:113] Setting up fc1\n", - "I0307 01:34:31.882478 2099749632 net.cpp:120] Top shape: 10 40 (400)\n", - "I0307 01:34:31.882505 2099749632 layer_factory.hpp:74] Creating layer relu1\n", - "I0307 01:34:31.882524 2099749632 net.cpp:84] Creating Layer relu1\n", - "I0307 01:34:31.882532 2099749632 net.cpp:380] relu1 <- fc1\n", - "I0307 01:34:31.882544 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\n", - "I0307 01:34:31.882555 2099749632 net.cpp:113] Setting up relu1\n", - "I0307 01:34:31.882565 2099749632 net.cpp:120] Top shape: 10 40 (400)\n", - "I0307 01:34:31.882583 2099749632 layer_factory.hpp:74] Creating layer fc2\n", - "I0307 01:34:31.882609 2099749632 net.cpp:84] Creating Layer fc2\n", - "I0307 01:34:31.882619 2099749632 net.cpp:380] fc2 <- fc1\n", - "I0307 01:34:31.882632 2099749632 net.cpp:338] fc2 -> fc2\n", - "I0307 01:34:31.882644 2099749632 net.cpp:113] Setting up fc2\n", - "I0307 01:34:31.882663 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", - "I0307 01:34:31.882678 2099749632 layer_factory.hpp:74] Creating layer loss\n", - "I0307 01:34:31.882694 2099749632 net.cpp:84] Creating Layer loss\n", - "I0307 01:34:31.882704 2099749632 net.cpp:380] loss <- fc2\n", - "I0307 01:34:31.882712 2099749632 net.cpp:380] loss <- label\n", - "I0307 01:34:31.882779 2099749632 net.cpp:338] loss -> loss\n", - "I0307 01:34:31.882796 2099749632 net.cpp:113] Setting up loss\n", - "I0307 01:34:31.882810 2099749632 layer_factory.hpp:74] Creating layer loss\n", - "I0307 01:34:31.882833 2099749632 net.cpp:120] Top shape: (1)\n", - "I0307 01:34:31.882844 2099749632 net.cpp:122] with loss weight 1\n", - "I0307 01:34:31.882860 2099749632 net.cpp:167] loss needs backward computation.\n", - "I0307 01:34:31.882869 2099749632 net.cpp:167] fc2 needs backward computation.\n", - "I0307 01:34:31.882877 2099749632 net.cpp:167] relu1 needs backward computation.\n", - "I0307 01:34:31.882886 2099749632 net.cpp:167] fc1 needs backward computation.\n", - "I0307 01:34:31.882894 2099749632 net.cpp:169] data does not need backward computation.\n", - "I0307 01:34:31.882904 2099749632 net.cpp:205] This network produces output loss\n", - "I0307 01:34:31.882931 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", - "I0307 01:34:31.882942 2099749632 net.cpp:217] Network initialization done.\n", - "I0307 01:34:31.882951 2099749632 net.cpp:218] Memory required for data: 3484\n", - "I0307 01:34:31.883157 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val2.prototxt\n", - "I0307 01:34:31.883189 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\n", - "I0307 01:34:31.883203 2099749632 net.cpp:42] Initializing net from parameters: \n", - "name: \"LogisticRegressionNet\"\n", - "state {\n", - " phase: TEST\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " include {\n", - " phase: TEST\n", - " }\n", - " hdf5_data_param {\n", - " source: \"hdf5_classification/data/test.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"fc1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"fc1\"\n", - " param {\n", - " lr_mult: 1\n", - " decay_mult: 1\n", - " }\n", - " param {\n", - " lr_mult: 2\n", - " decay_mult: 0\n", - " }\n", - " inner_product_param {\n", - " num_output: 40\n", - " weight_filler {\n", - " type: \"gaussian\"\n", - " std: 0.01\n", - " }\n", - " bias_filler {\n", - " type: \"constant\"\n", - " value: 0\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"relu1\"\n", - " type: \"ReLU\"\n", - " bottom: \"fc1\"\n", - " top: \"fc1\"\n", - "}\n", - "layer {\n", - " name: \"fc2\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"fc1\"\n", - " top: \"fc2\"\n", - " param {\n", - " lr_mult: 1\n", - " decay_mult: 1\n", - " }\n", - " param {\n", - " lr_mult: 2\n", - " decay_mult: 0\n", - " }\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"gaussian\"\n", - " std: 0.01\n", - " }\n", - " bias_filler {\n", - " type: \"constant\"\n", - " value: 0\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"fc2\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "layer {\n", - " name: \"accuracy\"\n", - " type: \"Accuracy\"\n", - " bottom: \"fc2\"\n", - " bottom: \"label\"\n", - " top: \"accuracy\"\n", - " include {\n", - " phase: TEST\n", - " }\n", - "}\n", - "I0307 01:34:31.883535 2099749632 layer_factory.hpp:74] Creating layer data\n", - "I0307 01:34:31.883548 2099749632 net.cpp:84] Creating Layer data\n", - "I0307 01:34:31.883556 2099749632 net.cpp:338] data -> data\n", - "I0307 01:34:31.883569 2099749632 net.cpp:338] data -> label\n", - "I0307 01:34:31.883579 2099749632 net.cpp:113] Setting up data\n", - "I0307 01:34:31.883585 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\n", - "I0307 01:34:31.883664 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n", - "I0307 01:34:31.884842 2099749632 net.cpp:120] Top shape: 10 4 (40)\n", - "I0307 01:34:31.884860 2099749632 net.cpp:120] Top shape: 10 (10)\n", - "I0307 01:34:31.884870 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\n", - "I0307 01:34:31.884879 2099749632 net.cpp:84] Creating Layer label_data_1_split\n", - "I0307 01:34:31.884886 2099749632 net.cpp:380] label_data_1_split <- label\n", - "I0307 01:34:31.884896 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", - "I0307 01:34:31.884909 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", - "I0307 01:34:31.884919 2099749632 net.cpp:113] Setting up label_data_1_split\n", - "I0307 01:34:31.884927 2099749632 net.cpp:120] Top shape: 10 (10)\n", - "I0307 01:34:31.884934 2099749632 net.cpp:120] Top shape: 10 (10)\n", - "I0307 01:34:31.884941 2099749632 layer_factory.hpp:74] Creating layer fc1\n", - "I0307 01:34:31.884951 2099749632 net.cpp:84] Creating Layer fc1\n", - "I0307 01:34:31.884958 2099749632 net.cpp:380] fc1 <- data\n", - "I0307 01:34:31.884989 2099749632 net.cpp:338] fc1 -> fc1\n", - "I0307 01:34:31.885000 2099749632 net.cpp:113] Setting up fc1\n", - "I0307 01:34:31.885017 2099749632 net.cpp:120] Top shape: 10 40 (400)\n", - "I0307 01:34:31.885030 2099749632 layer_factory.hpp:74] Creating layer relu1\n", - "I0307 01:34:31.885041 2099749632 net.cpp:84] Creating Layer relu1\n", - "I0307 01:34:31.885048 2099749632 net.cpp:380] relu1 <- fc1\n", - "I0307 01:34:31.885056 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\n", - "I0307 01:34:31.885064 2099749632 net.cpp:113] Setting up relu1\n", - "I0307 01:34:31.885071 2099749632 net.cpp:120] Top shape: 10 40 (400)\n", - "I0307 01:34:31.885079 2099749632 layer_factory.hpp:74] Creating layer fc2\n", - "I0307 01:34:31.885088 2099749632 net.cpp:84] Creating Layer fc2\n", - "I0307 01:34:31.885094 2099749632 net.cpp:380] fc2 <- fc1\n", - "I0307 01:34:31.885103 2099749632 net.cpp:338] fc2 -> fc2\n", - "I0307 01:34:31.885113 2099749632 net.cpp:113] Setting up fc2\n", - "I0307 01:34:31.885126 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", - "I0307 01:34:31.885138 2099749632 layer_factory.hpp:74] Creating layer fc2_fc2_0_split\n", - "I0307 01:34:31.885149 2099749632 net.cpp:84] Creating Layer fc2_fc2_0_split\n", - "I0307 01:34:31.885155 2099749632 net.cpp:380] fc2_fc2_0_split <- fc2\n", - "I0307 01:34:31.885164 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_0\n", - "I0307 01:34:31.885174 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_1\n", - "I0307 01:34:31.885182 2099749632 net.cpp:113] Setting up fc2_fc2_0_split\n", - "I0307 01:34:31.885190 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", - "I0307 01:34:31.885242 2099749632 net.cpp:120] Top shape: 10 2 (20)\n", - "I0307 01:34:31.885256 2099749632 layer_factory.hpp:74] Creating layer loss\n", - "I0307 01:34:31.885267 2099749632 net.cpp:84] Creating Layer loss\n", - "I0307 01:34:31.885275 2099749632 net.cpp:380] loss <- fc2_fc2_0_split_0\n", - "I0307 01:34:31.885285 2099749632 net.cpp:380] loss <- label_data_1_split_0\n", - "I0307 01:34:31.885296 2099749632 net.cpp:338] loss -> loss\n", - "I0307 01:34:31.885308 2099749632 net.cpp:113] Setting up loss\n", - "I0307 01:34:31.885316 2099749632 layer_factory.hpp:74] Creating layer loss\n", - "I0307 01:34:31.885330 2099749632 net.cpp:120] Top shape: (1)\n", - "I0307 01:34:31.885337 2099749632 net.cpp:122] with loss weight 1\n", - "I0307 01:34:31.885346 2099749632 layer_factory.hpp:74] Creating layer accuracy\n", - "I0307 01:34:31.885360 2099749632 net.cpp:84] Creating Layer accuracy\n", - "I0307 01:34:31.885368 2099749632 net.cpp:380] accuracy <- fc2_fc2_0_split_1\n", - "I0307 01:34:31.885375 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\n", - "I0307 01:34:31.885383 2099749632 net.cpp:338] accuracy -> accuracy\n", - "I0307 01:34:31.885392 2099749632 net.cpp:113] Setting up accuracy\n", - "I0307 01:34:31.885401 2099749632 net.cpp:120] Top shape: (1)\n", - "I0307 01:34:31.885407 2099749632 net.cpp:169] accuracy does not need backward computation.\n", - "I0307 01:34:31.885413 2099749632 net.cpp:167] loss needs backward computation.\n", - "I0307 01:34:31.885419 2099749632 net.cpp:167] fc2_fc2_0_split needs backward computation.\n", - "I0307 01:34:31.885426 2099749632 net.cpp:167] fc2 needs backward computation.\n", - "I0307 01:34:31.885432 2099749632 net.cpp:167] relu1 needs backward computation.\n", - "I0307 01:34:31.885438 2099749632 net.cpp:167] fc1 needs backward computation.\n", - "I0307 01:34:31.885444 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\n", - "I0307 01:34:31.885452 2099749632 net.cpp:169] data does not need backward computation.\n", - "I0307 01:34:31.885457 2099749632 net.cpp:205] This network produces output accuracy\n", - "I0307 01:34:31.885613 2099749632 net.cpp:205] This network produces output loss\n", - "I0307 01:34:31.885632 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", - "I0307 01:34:31.885639 2099749632 net.cpp:217] Network initialization done.\n", - "I0307 01:34:31.885645 2099749632 net.cpp:218] Memory required for data: 3728\n", - "I0307 01:34:31.885685 2099749632 solver.cpp:42] Solver scaffolding done.\n", - "I0307 01:34:31.885711 2099749632 solver.cpp:222] Solving LogisticRegressionNet\n", - "I0307 01:34:31.885721 2099749632 solver.cpp:223] Learning Rate Policy: step\n", - "I0307 01:34:31.885730 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\n", - "I0307 01:34:31.901005 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.5944\n", - "I0307 01:34:31.901049 2099749632 solver.cpp:315] Test net output #1: loss = 0.693021 (* 1 = 0.693021 loss)\n", - "I0307 01:34:31.901177 2099749632 solver.cpp:189] Iteration 0, loss = 0.693163\n", - "I0307 01:34:31.901192 2099749632 solver.cpp:204] Train net output #0: loss = 0.693163 (* 1 = 0.693163 loss)\n", - "I0307 01:34:31.901203 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\n", - "I0307 01:34:31.920586 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\n", - "I0307 01:34:31.924612 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7556\n", - "I0307 01:34:31.924646 2099749632 solver.cpp:315] Test net output #1: loss = 0.511002 (* 1 = 0.511002 loss)\n", - "I0307 01:34:31.924684 2099749632 solver.cpp:189] Iteration 1000, loss = 0.38536\n", - "I0307 01:34:31.924696 2099749632 solver.cpp:204] Train net output #0: loss = 0.38536 (* 1 = 0.38536 loss)\n", - "I0307 01:34:31.924706 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\n", - "I0307 01:34:31.944727 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\n", - "I0307 01:34:31.948729 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7824\n", - "I0307 01:34:31.948763 2099749632 solver.cpp:315] Test net output #1: loss = 0.489214 (* 1 = 0.489214 loss)\n", - "I0307 01:34:31.948799 2099749632 solver.cpp:189] Iteration 2000, loss = 0.532582\n", - "I0307 01:34:31.948812 2099749632 solver.cpp:204] Train net output #0: loss = 0.532582 (* 1 = 0.532582 loss)\n", - "I0307 01:34:31.948823 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\n", - "I0307 01:34:31.968670 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\n", - "I0307 01:34:31.972393 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.7956\n", - "I0307 01:34:31.972411 2099749632 solver.cpp:315] Test net output #1: loss = 0.454184 (* 1 = 0.454184 loss)\n", - "I0307 01:34:31.973024 2099749632 solver.cpp:189] Iteration 3000, loss = 0.541374\n", - "I0307 01:34:31.973057 2099749632 solver.cpp:204] Train net output #0: loss = 0.541374 (* 1 = 0.541374 loss)\n", - "I0307 01:34:31.973067 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\n", - "I0307 01:34:31.994829 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\n", - "I0307 01:34:31.998638 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.798\n", - "I0307 01:34:31.998663 2099749632 solver.cpp:315] Test net output #1: loss = 0.456348 (* 1 = 0.456348 loss)\n", - "I0307 01:34:31.998705 2099749632 solver.cpp:189] Iteration 4000, loss = 0.490437\n", - "I0307 01:34:31.998718 2099749632 solver.cpp:204] Train net output #0: loss = 0.490437 (* 1 = 0.490437 loss)\n", - "I0307 01:34:31.998725 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\n", - "I0307 01:34:32.021085 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\n", - "I0307 01:34:32.024950 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.804\n", - "I0307 01:34:32.024981 2099749632 solver.cpp:315] Test net output #1: loss = 0.46184 (* 1 = 0.46184 loss)\n", - "I0307 01:34:32.025017 2099749632 solver.cpp:189] Iteration 5000, loss = 0.467703\n", - "I0307 01:34:32.025028 2099749632 solver.cpp:204] Train net output #0: loss = 0.467704 (* 1 = 0.467704 loss)\n", - "I0307 01:34:32.025038 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\n", - "I0307 01:34:32.044390 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\n", - "I0307 01:34:32.048216 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8208\n", - "I0307 01:34:32.048239 2099749632 solver.cpp:315] Test net output #1: loss = 0.423084 (* 1 = 0.423084 loss)\n", - "I0307 01:34:32.048790 2099749632 solver.cpp:189] Iteration 6000, loss = 0.480104\n", - "I0307 01:34:32.048809 2099749632 solver.cpp:204] Train net output #0: loss = 0.480105 (* 1 = 0.480105 loss)\n", - "I0307 01:34:32.048827 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\n", - "I0307 01:34:32.067795 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\n", - "I0307 01:34:32.071524 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8124\n", - "I0307 01:34:32.071542 2099749632 solver.cpp:315] Test net output #1: loss = 0.423947 (* 1 = 0.423947 loss)\n", - "I0307 01:34:32.071570 2099749632 solver.cpp:189] Iteration 7000, loss = 0.447471\n", - "I0307 01:34:32.071617 2099749632 solver.cpp:204] Train net output #0: loss = 0.447472 (* 1 = 0.447472 loss)\n", - "I0307 01:34:32.071626 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\n", - "I0307 01:34:32.091625 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\n", - "I0307 01:34:32.095410 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.814\n", - "I0307 01:34:32.095432 2099749632 solver.cpp:315] Test net output #1: loss = 0.423586 (* 1 = 0.423586 loss)\n", - "I0307 01:34:32.095461 2099749632 solver.cpp:189] Iteration 8000, loss = 0.386258\n", - "I0307 01:34:32.095474 2099749632 solver.cpp:204] Train net output #0: loss = 0.386259 (* 1 = 0.386259 loss)\n", - "I0307 01:34:32.095481 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\n", - "I0307 01:34:32.117184 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\n", - "I0307 01:34:32.121587 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8208\n", - "I0307 01:34:32.121608 2099749632 solver.cpp:315] Test net output #1: loss = 0.419969 (* 1 = 0.419969 loss)\n", - "I0307 01:34:32.122161 2099749632 solver.cpp:189] Iteration 9000, loss = 0.468262\n", - "I0307 01:34:32.122181 2099749632 solver.cpp:204] Train net output #0: loss = 0.468262 (* 1 = 0.468262 loss)\n", - "I0307 01:34:32.122191 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\n", - "I0307 01:34:32.141635 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\n", - "I0307 01:34:32.141860 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\n", - "I0307 01:34:32.141978 2099749632 solver.cpp:248] Iteration 10000, loss = 0.441529\n", - "I0307 01:34:32.141995 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\n", - "I0307 01:34:32.145747 2099749632 solver.cpp:315] Test net output #0: accuracy = 0.8148\n", - "I0307 01:34:32.145771 2099749632 solver.cpp:315] Test net output #1: loss = 0.4216 (* 1 = 0.4216 loss)\n", - "I0307 01:34:32.145779 2099749632 solver.cpp:253] Optimization Done.\n", - "I0307 01:34:32.145786 2099749632 caffe.cpp:121] Optimization Done.\n" - ] - } - ], - "source": [ - "!../build/tools/caffe train -solver hdf5_classification/solver2.prototxt" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n", - "shutil.rmtree(dirname)" - ] - } - ], - "metadata": { - "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.", - "example_name": "Off-the-shelf SGD for classification", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" - }, - "priority": 4 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/hdf5_classification/nonlinear_auto_test.prototxt b/examples/hdf5_classification/nonlinear_auto_test.prototxt new file mode 100644 index 00000000000..53eda6ee8a0 --- /dev/null +++ b/examples/hdf5_classification/nonlinear_auto_test.prototxt @@ -0,0 +1,54 @@ +layer { + name: "data" + type: "HDF5Data" + top: "data" + top: "label" + hdf5_data_param { + source: "examples/hdf5_classification/data/test.txt" + batch_size: 10 + } +} +layer { + name: "ip1" + type: "InnerProduct" + bottom: "data" + top: "ip1" + inner_product_param { + num_output: 40 + weight_filler { + type: "xavier" + } + } +} +layer { + name: "relu1" + type: "ReLU" + bottom: "ip1" + top: "ip1" +} +layer { + name: "ip2" + type: "InnerProduct" + bottom: "ip1" + top: "ip2" + inner_product_param { + num_output: 2 + weight_filler { + type: "xavier" + } + } +} +layer { + name: "accuracy" + type: "Accuracy" + bottom: "ip2" + bottom: "label" + top: "accuracy" +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "ip2" + bottom: "label" + top: "loss" +} diff --git a/examples/hdf5_classification/nonlinear_auto_train.prototxt b/examples/hdf5_classification/nonlinear_auto_train.prototxt new file mode 100644 index 00000000000..fc0688fa652 --- /dev/null +++ b/examples/hdf5_classification/nonlinear_auto_train.prototxt @@ -0,0 +1,54 @@ +layer { + name: "data" + type: "HDF5Data" + top: "data" + top: "label" + hdf5_data_param { + source: "examples/hdf5_classification/data/train.txt" + batch_size: 10 + } +} +layer { + name: "ip1" + type: "InnerProduct" + bottom: "data" + top: "ip1" + inner_product_param { + num_output: 40 + weight_filler { + type: "xavier" + } + } +} +layer { + name: "relu1" + type: "ReLU" + bottom: "ip1" + top: "ip1" +} +layer { + name: "ip2" + type: "InnerProduct" + bottom: "ip1" + top: "ip2" + inner_product_param { + num_output: 2 + weight_filler { + type: "xavier" + } + } +} +layer { + name: "accuracy" + type: "Accuracy" + bottom: "ip2" + bottom: "label" + top: "accuracy" +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "ip2" + bottom: "label" + top: "loss" +} diff --git a/examples/hdf5_classification/nonlinear_solver.prototxt b/examples/hdf5_classification/nonlinear_solver.prototxt new file mode 100644 index 00000000000..b4aacf6e423 --- /dev/null +++ b/examples/hdf5_classification/nonlinear_solver.prototxt @@ -0,0 +1,15 @@ +train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt" +test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt" +test_iter: 250 +test_interval: 1000 +base_lr: 0.01 +lr_policy: "step" +gamma: 0.1 +stepsize: 5000 +display: 1000 +max_iter: 10000 +momentum: 0.9 +weight_decay: 0.0005 +snapshot: 10000 +snapshot_prefix: "examples/hdf5_classification/data/train" +solver_mode: CPU diff --git a/examples/hdf5_classification/train_val2.prototxt b/examples/hdf5_classification/nonlinear_train_val.prototxt similarity index 87% rename from examples/hdf5_classification/train_val2.prototxt rename to examples/hdf5_classification/nonlinear_train_val.prototxt index 8795e8facb6..8f7ef04f58a 100644 --- a/examples/hdf5_classification/train_val2.prototxt +++ b/examples/hdf5_classification/nonlinear_train_val.prototxt @@ -8,7 +8,7 @@ layer { phase: TRAIN } hdf5_data_param { - source: "hdf5_classification/data/train.txt" + source: "examples/hdf5_classification/data/train.txt" batch_size: 10 } } @@ -21,7 +21,7 @@ layer { phase: TEST } hdf5_data_param { - source: "hdf5_classification/data/test.txt" + source: "examples/hdf5_classification/data/test.txt" batch_size: 10 } } @@ -41,8 +41,7 @@ layer { inner_product_param { num_output: 40 weight_filler { - type: "gaussian" - std: 0.01 + type: "xavier" } bias_filler { type: "constant" @@ -72,8 +71,7 @@ layer { inner_product_param { num_output: 2 weight_filler { - type: "gaussian" - std: 0.01 + type: "xavier" } bias_filler { type: "constant" diff --git a/examples/hdf5_classification/solver.prototxt b/examples/hdf5_classification/solver.prototxt index 65a6eb9e9fb..8587b5a1e5a 100644 --- a/examples/hdf5_classification/solver.prototxt +++ b/examples/hdf5_classification/solver.prototxt @@ -1,4 +1,5 @@ -net: "hdf5_classification/train_val.prototxt" +train_net: "examples/hdf5_classification/logreg_auto_train.prototxt" +test_net: "examples/hdf5_classification/logreg_auto_test.prototxt" test_iter: 250 test_interval: 1000 base_lr: 0.01 @@ -10,5 +11,5 @@ max_iter: 10000 momentum: 0.9 weight_decay: 0.0005 snapshot: 10000 -snapshot_prefix: "hdf5_classification/data/train" +snapshot_prefix: "examples/hdf5_classification/data/train" solver_mode: CPU diff --git a/examples/hdf5_classification/solver2.prototxt b/examples/hdf5_classification/solver2.prototxt deleted file mode 100644 index 32b9feba346..00000000000 --- a/examples/hdf5_classification/solver2.prototxt +++ /dev/null @@ -1,14 +0,0 @@ -net: "hdf5_classification/train_val2.prototxt" -test_iter: 250 -test_interval: 1000 -base_lr: 0.01 -lr_policy: "step" -gamma: 0.1 -stepsize: 5000 -display: 1000 -max_iter: 10000 -momentum: 0.9 -weight_decay: 0.0005 -snapshot: 10000 -snapshot_prefix: "hdf5_classification/data/train" -solver_mode: CPU diff --git a/examples/hdf5_classification/train_val.prototxt b/examples/hdf5_classification/train_val.prototxt index d5e8dbfa169..13ddf47524a 100644 --- a/examples/hdf5_classification/train_val.prototxt +++ b/examples/hdf5_classification/train_val.prototxt @@ -8,7 +8,7 @@ layer { phase: TRAIN } hdf5_data_param { - source: "hdf5_classification/data/train.txt" + source: "examples/hdf5_classification/data/train.txt" batch_size: 10 } } @@ -21,7 +21,7 @@ layer { phase: TEST } hdf5_data_param { - source: "hdf5_classification/data/test.txt" + source: "examples/hdf5_classification/data/test.txt" batch_size: 10 } } @@ -41,8 +41,7 @@ layer { inner_product_param { num_output: 2 weight_filler { - type: "gaussian" - std: 0.01 + type: "xavier" } bias_filler { type: "constant" diff --git a/examples/mnist/lenet_auto_solver.prototxt b/examples/mnist/lenet_auto_solver.prototxt new file mode 100644 index 00000000000..fa4bbf02710 --- /dev/null +++ b/examples/mnist/lenet_auto_solver.prototxt @@ -0,0 +1,24 @@ +# The train/test net protocol buffer definition +train_net: "examples/mnist/lenet_auto_train.prototxt" +test_net: "examples/mnist/lenet_auto_test.prototxt" +# test_iter specifies how many forward passes the test should carry out. +# In the case of MNIST, we have test batch size 100 and 100 test iterations, +# covering the full 10,000 testing images. +test_iter: 100 +# Carry out testing every 500 training iterations. +test_interval: 500 +# The base learning rate, momentum and the weight decay of the network. +base_lr: 0.01 +momentum: 0.9 +weight_decay: 0.0005 +# The learning rate policy +lr_policy: "inv" +gamma: 0.0001 +power: 0.75 +# Display every 100 iterations +display: 100 +# The maximum number of iterations +max_iter: 10000 +# snapshot intermediate results +snapshot: 5000 +snapshot_prefix: "examples/mnist/lenet" diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb index 303c22ba3fd..ff780fbb9f7 100644 --- a/examples/net_surgery.ipynb +++ b/examples/net_surgery.ipynb @@ -6884,7 +6884,7 @@ } ], "metadata": { - "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.", + "description": "How to do net surgery and manually change model parameters for custom use.", "example_name": "Editing model parameters", "include_in_docs": true, "kernelspec": { diff --git a/examples/siamese/mnist_siamese.ipynb b/examples/siamese/mnist_siamese.ipynb index 11cae120db2..1a4e30eda43 100644 --- a/examples/siamese/mnist_siamese.ipynb +++ b/examples/siamese/mnist_siamese.ipynb @@ -1902,7 +1902,7 @@ "pygments_lexer": "ipython2", "version": "2.7.9" }, - "priority": 6 + "priority": 7 }, "nbformat": 4, "nbformat_minor": 0 From 7003d1b8e24416cb5bdb5537a7805cb5a9de2ca1 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Tue, 30 Jun 2015 15:35:21 -0700 Subject: [PATCH 128/446] [examples] add Euclidean loss PythonLayer --- examples/pycaffe/layers/pyloss.py | 37 +++++++++++++++++++ examples/pycaffe/linreg.prototxt | 60 +++++++++++++++++++++++++++++++ 2 files changed, 97 insertions(+) create mode 100644 examples/pycaffe/layers/pyloss.py create mode 100644 examples/pycaffe/linreg.prototxt diff --git a/examples/pycaffe/layers/pyloss.py b/examples/pycaffe/layers/pyloss.py new file mode 100644 index 00000000000..6200e6bbc55 --- /dev/null +++ b/examples/pycaffe/layers/pyloss.py @@ -0,0 +1,37 @@ +import caffe +import numpy as np + + +class EuclideanLossLayer(caffe.Layer): + """ + Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer + to demonstrate the class interface for developing layers in Python. + """ + + def setup(self, bottom, top): + # check input pair + if len(bottom) != 2: + raise Exception("Need two inputs to compute distance.") + + def reshape(self, bottom, top): + # check input dimensions match + if bottom[0].count != bottom[1].count: + raise Exception("Inputs must have the same dimension.") + # difference is shape of inputs + self.diff = np.zeros_like(bottom[0].data, dtype=np.float32) + # loss output is scalar + top[0].reshape(1) + + def forward(self, bottom, top): + self.diff[...] = bottom[0].data - bottom[1].data + top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2. + + def backward(self, top, propagate_down, bottom): + for i in range(2): + if not propagate_down[i]: + continue + if i == 0: + sign = 1 + else: + sign = -1 + bottom[i].diff[...] = sign * self.diff / bottom[i].num diff --git a/examples/pycaffe/linreg.prototxt b/examples/pycaffe/linreg.prototxt new file mode 100644 index 00000000000..c0fb0776d0a --- /dev/null +++ b/examples/pycaffe/linreg.prototxt @@ -0,0 +1,60 @@ +name: 'LinearRegressionExample' +# define a simple network for linear regression on dummy data +# that computes the loss by a PythonLayer. +layer { + type: 'DummyData' + name: 'x' + top: 'x' + dummy_data_param { + shape: { dim: 10 dim: 3 dim: 2 } + data_filler: { type: 'gaussian' } + } +} +layer { + type: 'DummyData' + name: 'y' + top: 'y' + dummy_data_param { + shape: { dim: 10 dim: 3 dim: 2 } + data_filler: { type: 'gaussian' } + } +} +# include InnerProduct layers for parameters +# so the net will need backward +layer { + type: 'InnerProduct' + name: 'ipx' + top: 'ipx' + bottom: 'x' + inner_product_param { + num_output: 10 + weight_filler { type: 'xavier' } + } +} +layer { + type: 'InnerProduct' + name: 'ipy' + top: 'ipy' + bottom: 'y' + inner_product_param { + num_output: 10 + weight_filler { type: 'xavier' } + } +} +layer { + type: 'Python' + name: 'loss' + top: 'loss' + bottom: 'ipx' + bottom: 'ipy' + python_param { + # the module name -- usually the filename -- that needs to be in $PYTHONPATH + module: 'pyloss' + # the layer name -- the class name in the module + layer: 'EuclideanLossLayer' + } + # set loss weight so Caffe knows this is a loss layer. + # since PythonLayer inherits directly from Layer, this isn't automatically + # known to Caffe + loss_weight: 1 +} From 41df4cdce942c22f0d89ad8b0990d3ee7bd9cf31 Mon Sep 17 00:00:00 2001 From: Takuya Narihira Date: Thu, 26 Mar 2015 19:04:15 -0700 Subject: [PATCH 129/446] bilinear filler -- useful for interpolation with DeconvolutionLayer This filler is a convenience for interpolating with DeconvolutionLayer or smoothing + downsampling with ConvolutionLayer for stride > 1. --- include/caffe/filler.hpp | 56 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp index ff3542e1f99..888f4a4ba3b 100644 --- a/include/caffe/filler.hpp +++ b/include/caffe/filler.hpp @@ -208,6 +208,60 @@ class MSRAFiller : public Filler { } }; +/*! +@brief Fills a Blob with coefficients for bilinear interpolation. + +A common use case is with the DeconvolutionLayer acting as upsampling. +You can upsample a feature map with shape of (B, C, H, W) by any integer factor +using the following proto. +\code +layer { + name: "upsample", type: "Deconvolution" + bottom: "{{bottom_name}}" top: "{{top_name}}" + convolution_param { + kernel_size: {{2 * factor - factor % 2}} stride: {{factor}} + num_output: {{C}} group: {{C}} + pad: {{ceil((factor - 1) / 2.)}} + weight_filler: { type: "bilinear" } bias_term: false + } + param { lr_mult: 0 decay_mult: 0 } +} +\endcode +Please use this by replacing `{{}}` with your values. By specifying +`num_output: {{C}} group: {{C}}`, it behaves as +channel-wise convolution. The filter shape of this deconvolution layer will be +(C, 1, K, K) where K is `kernel_size`, and this filler will set a (K, K) +interpolation kernel for every channel of the filter identically. The resulting +shape of the top feature map will be (B, C, factor * H, factor * W). +Note that the learning rate and the +weight decay are set to 0 in order to keep coefficient values of bilinear +interpolation unchanged during training. If you apply this to an image, this +operation is equivalent to the following call in Python with Scikit.Image. +\code{.py} +out = skimage.transform.rescale(img, factor, mode='constant', cval=0) +\endcode + */ +template +class BilinearFiller : public Filler { + public: + explicit BilinearFiller(const FillerParameter& param) + : Filler(param) {} + virtual void Fill(Blob* blob) { + CHECK_EQ(blob->num_axes(), 4) << "Blob must be 4 dim."; + CHECK_EQ(blob->width(), blob->height()) << "Filter must be square"; + Dtype* data = blob->mutable_cpu_data(); + int f = ceil(blob->width() / 2.); + float c = (2 * f - 1 - f % 2) / (2. * f); + for (int i = 0; i < blob->count(); ++i) { + float x = i % blob->width(); + float y = (i / blob->width()) % blob->height(); + data[i] = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c)); + } + CHECK_EQ(this->filler_param_.sparse(), -1) + << "Sparsity not supported by this Filler."; + } +}; + /** * @brief Get a specific filler from the specification given in FillerParameter. * @@ -229,6 +283,8 @@ Filler* GetFiller(const FillerParameter& param) { return new XavierFiller(param); } else if (type == "msra") { return new MSRAFiller(param); + } else if (type == "bilinear") { + return new BilinearFiller(param); } else { CHECK(false) << "Unknown filler name: " << param.type(); } From 7d3a8e9f34bb8ac44ec9ab91b7229f6c57fd7ee1 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Wed, 1 Jul 2015 16:44:05 -0700 Subject: [PATCH 130/446] Update installation docs for boost - close #2454 --- docs/install_apt.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/install_apt.md b/docs/install_apt.md index 75f8bec0e95..0fa205ab2cf 100644 --- a/docs/install_apt.md +++ b/docs/install_apt.md @@ -6,7 +6,8 @@ title: Installation: Ubuntu **General dependencies** - sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev + sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev + sudo apt-get install --no-install-recommends libboost-all-dev **CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions. Install the library and latest driver separately; the driver bundled with the library is usually out-of-date. From 90750550d5f6ee7dd1937777b522189b9266634f Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Thu, 2 Jul 2015 11:42:37 -0700 Subject: [PATCH 131/446] Optimize inner product layer for special case M == 1 Using gemm with one of the operand being a vector or a scalar is suboptimal. Functions gemv and axpy should be prefered in these cases. --- src/caffe/layers/inner_product_layer.cu | 19 ++++++--- src/caffe/test/test_inner_product_layer.cpp | 43 ++++++++++++++++++++- 2 files changed, 54 insertions(+), 8 deletions(-) diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu index dd90cac12a8..c0ebd2c47da 100644 --- a/src/caffe/layers/inner_product_layer.cu +++ b/src/caffe/layers/inner_product_layer.cu @@ -15,12 +15,19 @@ void InnerProductLayer::Forward_gpu(const vector*>& bottom, const Dtype* bottom_data = bottom[0]->gpu_data(); Dtype* top_data = top[0]->mutable_gpu_data(); const Dtype* weight = this->blobs_[0]->gpu_data(); - caffe_gpu_gemm(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1., - bottom_data, weight, (Dtype)0., top_data); - if (bias_term_) { - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1., - bias_multiplier_.gpu_data(), - this->blobs_[1]->gpu_data(), (Dtype)1., top_data); + if (M_ == 1) { + caffe_gpu_gemv(CblasNoTrans, N_, K_, (Dtype)1., + weight, bottom_data, (Dtype)0., top_data); + if (bias_term_) + caffe_gpu_axpy(N_, bias_multiplier_.cpu_data()[0], + this->blobs_[1]->gpu_data(), top_data); + } else { + caffe_gpu_gemm(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1., + bottom_data, weight, (Dtype)0., top_data); + if (bias_term_) + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1., + bias_multiplier_.gpu_data(), + this->blobs_[1]->gpu_data(), (Dtype)1., top_data); } } diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp index c03df17383a..fbf0c851220 100644 --- a/src/caffe/test/test_inner_product_layer.cpp +++ b/src/caffe/test/test_inner_product_layer.cpp @@ -23,16 +23,21 @@ class InnerProductLayerTest : public MultiDeviceTest { protected: InnerProductLayerTest() : blob_bottom_(new Blob(2, 3, 4, 5)), + blob_bottom_nobatch_(new Blob(1, 2, 3, 4)), blob_top_(new Blob()) { // fill the values FillerParameter filler_param; UniformFiller filler(filler_param); filler.Fill(this->blob_bottom_); - blob_bottom_vec_.push_back(blob_bottom_); blob_top_vec_.push_back(blob_top_); } - virtual ~InnerProductLayerTest() { delete blob_bottom_; delete blob_top_; } + virtual ~InnerProductLayerTest() { + delete blob_bottom_; + delete blob_bottom_nobatch_; + delete blob_top_; + } Blob* const blob_bottom_; + Blob* const blob_bottom_nobatch_; Blob* const blob_top_; vector*> blob_bottom_vec_; vector*> blob_top_vec_; @@ -42,6 +47,7 @@ TYPED_TEST_CASE(InnerProductLayerTest, TestDtypesAndDevices); TYPED_TEST(InnerProductLayerTest, TestSetUp) { typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_); LayerParameter layer_param; InnerProductParameter* inner_product_param = layer_param.mutable_inner_product_param(); @@ -57,6 +63,38 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) { TYPED_TEST(InnerProductLayerTest, TestForward) { typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_); + bool IS_VALID_CUDA = false; +#ifndef CPU_ONLY + IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2; +#endif + if (Caffe::mode() == Caffe::CPU || + sizeof(Dtype) == 4 || IS_VALID_CUDA) { + LayerParameter layer_param; + InnerProductParameter* inner_product_param = + layer_param.mutable_inner_product_param(); + inner_product_param->set_num_output(10); + inner_product_param->mutable_weight_filler()->set_type("uniform"); + inner_product_param->mutable_bias_filler()->set_type("uniform"); + inner_product_param->mutable_bias_filler()->set_min(1); + inner_product_param->mutable_bias_filler()->set_max(2); + shared_ptr > layer( + new InnerProductLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_top_->cpu_data(); + const int count = this->blob_top_->count(); + for (int i = 0; i < count; ++i) { + EXPECT_GE(data[i], 1.); + } + } else { + LOG(ERROR) << "Skipping test due to old architecture."; + } +} + +TYPED_TEST(InnerProductLayerTest, TestForwardNoBatch) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_nobatch_); bool IS_VALID_CUDA = false; #ifndef CPU_ONLY IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2; @@ -87,6 +125,7 @@ TYPED_TEST(InnerProductLayerTest, TestForward) { TYPED_TEST(InnerProductLayerTest, TestGradient) { typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_); bool IS_VALID_CUDA = false; #ifndef CPU_ONLY IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2; From 3fa0de93b059753ed378b474e1568980ed131a10 Mon Sep 17 00:00:00 2001 From: AdamStelmaszczyk Date: Sat, 4 Jul 2015 20:57:17 +0100 Subject: [PATCH 132/446] Deprecated OpenCV consts --- examples/cpp_classification/classification.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp index 1c6371e382b..0683a4975ac 100644 --- a/examples/cpp_classification/classification.cpp +++ b/examples/cpp_classification/classification.cpp @@ -186,13 +186,13 @@ void Classifier::Preprocess(const cv::Mat& img, /* Convert the input image to the input image format of the network. */ cv::Mat sample; if (img.channels() == 3 && num_channels_ == 1) - cv::cvtColor(img, sample, CV_BGR2GRAY); + cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY); else if (img.channels() == 4 && num_channels_ == 1) - cv::cvtColor(img, sample, CV_BGRA2GRAY); + cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY); else if (img.channels() == 4 && num_channels_ == 3) - cv::cvtColor(img, sample, CV_BGRA2BGR); + cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR); else if (img.channels() == 1 && num_channels_ == 3) - cv::cvtColor(img, sample, CV_GRAY2BGR); + cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR); else sample = img; From 58b114ff68416c24fe7b7a344c51e686edc0c2fb Mon Sep 17 00:00:00 2001 From: semitrivial Date: Tue, 7 Jul 2015 08:29:16 +0100 Subject: [PATCH 133/446] List protobuf-compiler dependency in the correct place (it is in the package managers for both 14.04 and 12.04) --- docs/install_apt.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/install_apt.md b/docs/install_apt.md index 0fa205ab2cf..f588b74dcdb 100644 --- a/docs/install_apt.md +++ b/docs/install_apt.md @@ -6,7 +6,7 @@ title: Installation: Ubuntu **General dependencies** - sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev + sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler sudo apt-get install --no-install-recommends libboost-all-dev **CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions. @@ -21,7 +21,7 @@ This can be skipped for CPU-only installation. Everything is packaged in 14.04. - sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler + sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev **Remaining dependencies, 12.04** From a08bfb5c9200533c845ac05ae2a6d419fcae69f2 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Tue, 7 Jul 2015 21:30:33 -0700 Subject: [PATCH 134/446] Fix CMake typos No functional changes, just fixing whitespace errors and typos in comments --- CMakeLists.txt | 4 ++-- cmake/ConfigGen.cmake | 2 +- cmake/External/gflags.cmake | 4 ++-- cmake/External/glog.cmake | 2 +- cmake/Misc.cmake | 2 +- cmake/ProtoBuf.cmake | 4 ++-- cmake/Summary.cmake | 4 ++-- cmake/Targets.cmake | 6 +++--- cmake/Templates/CaffeConfig.cmake.in | 6 +++--- cmake/Templates/CaffeConfigVersion.cmake.in | 2 +- cmake/Utils.cmake | 10 +++++----- cmake/lint.cmake | 4 ++-- 12 files changed, 25 insertions(+), 25 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index f22aa5763a3..e202350224f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -15,14 +15,14 @@ include(cmake/Summary.cmake) include(cmake/ConfigGen.cmake) # ---[ Options -caffe_option(CPU_ONLY "Build Caffe wihtout CUDA support" OFF) # TODO: rename to USE_CUDA +caffe_option(CPU_ONLY "Build Caffe without CUDA support" OFF) # TODO: rename to USE_CUDA caffe_option(USE_CUDNN "Build Caffe with cuDNN libary support" ON IF NOT CPU_ONLY) caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON) caffe_option(BUILD_python "Build Python wrapper" ON) set(python_version "2" CACHE STRING "Specify which python version to use") caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE) caffe_option(BUILD_docs "Build documentation" ON IF UNIX OR APPLE) -caffe_option(BUILD_python_layer "Build the caffe python layer" ON) +caffe_option(BUILD_python_layer "Build the Caffe python layer" ON) # ---[ Dependencies include(cmake/Dependencies.cmake) diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake index a9101e34350..566d6ca0aa7 100644 --- a/cmake/ConfigGen.cmake +++ b/cmake/ConfigGen.cmake @@ -88,7 +88,7 @@ function(caffe_generate_export_configs) configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY) - # Install the CaffeConfig.cmake and export set to use wuth install-tree + # Install the CaffeConfig.cmake and export set to use with install-tree install(FILES "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" DESTINATION ${install_cmake_suffix}) install(EXPORT CaffeTargets DESTINATION ${install_cmake_suffix}) diff --git a/cmake/External/gflags.cmake b/cmake/External/gflags.cmake index a50a0925086..e3dba04f33f 100644 --- a/cmake/External/gflags.cmake +++ b/cmake/External/gflags.cmake @@ -43,10 +43,10 @@ if (NOT __GFLAGS_INCLUDED) # guard against multiple includes LOG_DOWNLOAD 1 LOG_INSTALL 1 ) - + set(GFLAGS_FOUND TRUE) set(GFLAGS_INCLUDE_DIRS ${gflags_INSTALL}/include) - set(GFLAGS_LIBRARIES ${gflags_INSTALL}/lib/libgflags.a ${CMAKE_THREAD_LIBS_INIT}) + set(GFLAGS_LIBRARIES ${gflags_INSTALL}/lib/libgflags.a ${CMAKE_THREAD_LIBS_INIT}) set(GFLAGS_LIBRARY_DIRS ${gflags_INSTALL}/lib) set(GFLAGS_EXTERNAL TRUE) diff --git a/cmake/External/glog.cmake b/cmake/External/glog.cmake index 02b39dde676..a44672f2753 100644 --- a/cmake/External/glog.cmake +++ b/cmake/External/glog.cmake @@ -29,7 +29,7 @@ if (NOT __GLOG_INCLUDED) if (GFLAGS_EXTERNAL) set(GLOG_DEPENDS gflags) endif() - + ExternalProject_Add(glog DEPENDS ${GLOG_DEPENDS} PREFIX ${glog_PREFIX} diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake index 39569eaf996..7676754fe04 100644 --- a/cmake/Misc.cmake +++ b/cmake/Misc.cmake @@ -1,4 +1,4 @@ -# ---[ Configurations types +# ---[ Configuration types set(CMAKE_CONFIGURATION_TYPES "Debug;Release" CACHE STRING "Possible configurations" FORCE) mark_as_advanced(CMAKE_CONFIGURATION_TYPES) diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake index 8946d66c57b..fc799bd3906 100644 --- a/cmake/ProtoBuf.cmake +++ b/cmake/ProtoBuf.cmake @@ -1,12 +1,12 @@ # Finds Google Protocol Buffers library and compilers and extends -# the standart cmake script with version and python generation support +# the standard cmake script with version and python generation support find_package( Protobuf REQUIRED ) include_directories(SYSTEM ${PROTOBUF_INCLUDE_DIR}) list(APPEND Caffe_LINKER_LIBS ${PROTOBUF_LIBRARIES}) # As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package -# and should be installed separately as in: sudo apt-get install protobuf-compiler +# and should be installed separately as in: sudo apt-get install protobuf-compiler if(EXISTS ${PROTOBUF_PROTOC_EXECUTABLE}) message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}") else() diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake index 32931942846..e094ac0040e 100644 --- a/cmake/Summary.cmake +++ b/cmake/Summary.cmake @@ -87,7 +87,7 @@ endfunction() ################################################################################################ -# Prints accumulatd caffe configuration summary +# Prints accumulated caffe configuration summary # Usage: # caffe_print_configuration_summary() @@ -119,7 +119,7 @@ function(caffe_print_configuration_summary) caffe_status(" BLAS : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})") caffe_status(" Boost : Yes (ver. ${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION})") caffe_status(" glog : Yes") - caffe_status(" gflags : Yes") + caffe_status(" gflags : Yes") caffe_status(" protobuf : " PROTOBUF_FOUND THEN "Yes (ver. ${PROTOBUF_VERSION})" ELSE "No" ) caffe_status(" lmdb : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No") caffe_status(" Snappy : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" ) diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake index ed0ff9660fd..2401f252e93 100644 --- a/cmake/Targets.cmake +++ b/cmake/Targets.cmake @@ -31,7 +31,7 @@ endfunction() ################################################################################################ # Collecting sources from globbing and appending to output list variable # Usage: -# caffe_source_group( GLOB[_RECURSE] ) +# caffe_collect_sources( GLOB[_RECURSE] ) function(caffe_collect_sources variable) cmake_parse_arguments(CAFFE_COLLECT_SOURCES "" "" "GLOB;GLOB_RECURSE" ${ARGN}) if(CAFFE_COLLECT_SOURCES_GLOB) @@ -144,12 +144,12 @@ function(caffe_configure_testdatafile file) set(result "") foreach(line ${__lines}) set(result "${result}${PROJECT_SOURCE_DIR}/${line}\n") - endforeach() + endforeach() file(WRITE ${file}.gen.cmake ${result}) endfunction() ################################################################################################ -# Filter outs all files that are not inlcuded in selected list +# Filter out all files that are not included in selected list # Usage: # caffe_leave_only_selected_tests( ) function(caffe_leave_only_selected_tests file_list) diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in index a4b03d961e0..8f23742e52e 100644 --- a/cmake/Templates/CaffeConfig.cmake.in +++ b/cmake/Templates/CaffeConfig.cmake.in @@ -4,7 +4,7 @@ # Caffe and this config file depends on opencv, # so put `find_package(OpenCV)` before searching Caffe # via `find_package(Caffe)`. All other lib/includes -# dependencies are hard coded int the file +# dependencies are hard coded in the file # # After successful configuration the following variables # will be defined: @@ -15,7 +15,7 @@ # # Caffe_HAVE_CUDA - signals about CUDA support # Caffe_HAVE_CUDNN - signals about cuDNN support - + # OpenCV dependency @@ -40,7 +40,7 @@ get_filename_component(Caffe_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) set(Caffe_INCLUDE_DIRS "@Caffe_INCLUDE_DIRS@") @Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND@ - + # Our library dependencies if(NOT TARGET caffe AND NOT caffe_BINARY_DIR) include("${Caffe_CMAKE_DIR}/CaffeTargets.cmake") diff --git a/cmake/Templates/CaffeConfigVersion.cmake.in b/cmake/Templates/CaffeConfigVersion.cmake.in index cbfa514f1a6..19f85309a5f 100644 --- a/cmake/Templates/CaffeConfigVersion.cmake.in +++ b/cmake/Templates/CaffeConfigVersion.cmake.in @@ -1,5 +1,5 @@ set(PACKAGE_VERSION "@Caffe_VERSION@") - + # Check whether the requested PACKAGE_FIND_VERSION is compatible if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}") set(PACKAGE_VERSION_COMPATIBLE FALSE) diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake index a56c7c300c0..a1bde1ae95b 100644 --- a/cmake/Utils.cmake +++ b/cmake/Utils.cmake @@ -1,7 +1,7 @@ ################################################################################################ # Command alias for debugging messages # Usage: -# dmgs() +# dmsg() function(dmsg) message(STATUS ${ARGN}) endfunction() @@ -19,9 +19,9 @@ macro(caffe_list_unique) endmacro() ################################################################################################ -# Clears variables from lsit +# Clears variables from list # Usage: -# caffe_list_unique() +# caffe_clear_vars() macro(caffe_clear_vars) foreach(_var ${ARGN}) unset(${_var}) @@ -118,7 +118,7 @@ macro(caffe_parse_header FILENAME FILE_VAR) if(__add_cache) set(${name} ${${name}} CACHE INTERNAL "${name} parsed from ${FILENAME}" FORCE) elseif(__parnet_scope) - set(${name} "${${name}}" PARENT_SCOPE) + set(${name} "${${name}}" PARENT_SCOPE) endif() else() unset(${name} CACHE) @@ -303,7 +303,7 @@ function(caffe_get_current_cflags cflags_var) endfunction() ################################################################################################ -# Helper function to parse current linker libs into link directoris, libflags and osx frameworks +# Helper function to parse current linker libs into link directories, libflags and osx frameworks # Usage: # caffe_parse_linker_libs( ) function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_var frameworks_var) diff --git a/cmake/lint.cmake b/cmake/lint.cmake index 585babb3587..70a006572bb 100644 --- a/cmake/lint.cmake +++ b/cmake/lint.cmake @@ -5,7 +5,7 @@ set(SRC_FILE_EXTENSIONS h hpp hu c cpp cu cc) set(EXCLUDE_FILE_EXTENSTIONS pb.h pb.cc) set(LINT_DIRS include src/caffe examples tools python matlab) -cmake_policy(SET CMP0009 NEW) # supress cmake warning +cmake_policy(SET CMP0009 NEW) # suppress cmake warning # find all files of interest foreach(ext ${SRC_FILE_EXTENSIONS}) @@ -26,7 +26,7 @@ list(REMOVE_ITEM LINT_SOURCES ${EXCLUDED_FILES}) execute_process( COMMAND ${LINT_COMMAND} ${LINT_SOURCES} - ERROR_VARIABLE LINT_OUTPUT + ERROR_VARIABLE LINT_OUTPUT ERROR_STRIP_TRAILING_WHITESPACE ) From 7093b0b6c9216e116046ab1d4d2b9bf2e487a641 Mon Sep 17 00:00:00 2001 From: philkr Date: Tue, 7 Jul 2015 16:32:46 -0700 Subject: [PATCH 135/446] Making the net_spec python3 compatible --- examples/pycaffe/caffenet.py | 5 +++-- python/caffe/net_spec.py | 11 ++++++----- python/requirements.txt | 1 + 3 files changed, 10 insertions(+), 7 deletions(-) diff --git a/examples/pycaffe/caffenet.py b/examples/pycaffe/caffenet.py index f9801d7cbb8..06c5a02d4ee 100644 --- a/examples/pycaffe/caffenet.py +++ b/examples/pycaffe/caffenet.py @@ -1,5 +1,6 @@ from caffe import layers as L, params as P, to_proto from caffe.proto import caffe_pb2 +from __future__ import print_function # helper function for common structures @@ -45,10 +46,10 @@ def caffenet(lmdb, batch_size=256, include_acc=False): def make_net(): with open('train.prototxt', 'w') as f: - print >>f, caffenet('/path/to/caffe-train-lmdb') + print(caffenet('/path/to/caffe-train-lmdb'), file=f) with open('test.prototxt', 'w') as f: - print >>f, caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True) + print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f) if __name__ == '__main__': make_net() diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py index f54328d56f1..1b4814a45c6 100644 --- a/python/caffe/net_spec.py +++ b/python/caffe/net_spec.py @@ -22,6 +22,7 @@ class -- assign to its attributes directly to name layers, and call from .proto import caffe_pb2 from google import protobuf +import six def param_name_dict(): @@ -63,12 +64,12 @@ def assign_proto(proto, name, val): if isinstance(val[0], dict): for item in val: proto_item = getattr(proto, name).add() - for k, v in item.iteritems(): + for k, v in six.iteritems(item): assign_proto(proto_item, k, v) else: getattr(proto, name).extend(val) elif isinstance(val, dict): - for k, v in val.iteritems(): + for k, v in six.iteritems(val): assign_proto(getattr(proto, name), k, v) else: setattr(proto, name, val) @@ -131,7 +132,7 @@ def _to_proto(self, layers, names, autonames): layer.top.append(self._get_name(top, names, autonames)) layer.name = self._get_name(self.tops[0], names, autonames) - for k, v in self.params.iteritems(): + for k, v in six.iteritems(self.params): # special case to handle generic *params if k.endswith('param'): assign_proto(layer, k, v) @@ -161,10 +162,10 @@ def __getattr__(self, name): return self.tops[name] def to_proto(self): - names = {v: k for k, v in self.tops.iteritems()} + names = {v: k for k, v in six.iteritems(self.tops)} autonames = {} layers = OrderedDict() - for name, top in self.tops.iteritems(): + for name, top in six.iteritems(self.tops): top.fn._to_proto(layers, names, autonames) net = caffe_pb2.NetParameter() net.layer.extend(layers.values()) diff --git a/python/requirements.txt b/python/requirements.txt index e0c86c7e51e..e7d89e67f48 100644 --- a/python/requirements.txt +++ b/python/requirements.txt @@ -14,3 +14,4 @@ protobuf>=2.5.0 python-gflags>=2.0 pyyaml>=3.10 Pillow>=2.3.0 +six>=1.1.0 \ No newline at end of file From c51e8d623260bb921daf799ba846e13d8b47e843 Mon Sep 17 00:00:00 2001 From: AdamStelmaszczyk Date: Fri, 10 Jul 2015 21:41:46 +0100 Subject: [PATCH 136/446] One command less Also more readable and compatible with format of instructions for MNIST https://github.com/BVLC/caffe/tree/master/examples/mnist --- examples/cifar10/readme.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md index 4a95cee9e8f..5d8d81e3efb 100644 --- a/examples/cifar10/readme.md +++ b/examples/cifar10/readme.md @@ -22,9 +22,8 @@ Prepare the Dataset You will first need to download and convert the data format from the [CIFAR-10 website](http://www.cs.toronto.edu/~kriz/cifar.html). To do this, simply run the following commands: - cd $CAFFE_ROOT/data/cifar10 - ./get_cifar10.sh cd $CAFFE_ROOT + ./data/cifar10/get_cifar10.sh ./examples/cifar10/create_cifar10.sh If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be the dataset, `./cifar10-leveldb`, and the data set image mean `./mean.binaryproto`. From d0423a8682b92a101f26b3093c88f08c235fb3d1 Mon Sep 17 00:00:00 2001 From: philkr Date: Fri, 10 Jul 2015 17:03:03 -0700 Subject: [PATCH 137/446] Removes a unused variable warning --- src/caffe/layers/absval_layer.cu | 1 - 1 file changed, 1 deletion(-) diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu index 91f3c77fe9a..bb310e1afbb 100644 --- a/src/caffe/layers/absval_layer.cu +++ b/src/caffe/layers/absval_layer.cu @@ -18,7 +18,6 @@ template void AbsValLayer::Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { const int count = top[0]->count(); - const Dtype* top_data = top[0]->gpu_data(); const Dtype* top_diff = top[0]->gpu_diff(); if (propagate_down[0]) { const Dtype* bottom_data = bottom[0]->gpu_data(); From 4c8515a05ae63400166e9311feac8037df3d59c6 Mon Sep 17 00:00:00 2001 From: Gleb Mazovetskiy Date: Sun, 12 Jul 2015 04:16:14 +0100 Subject: [PATCH 138/446] examples/imagenet: fix broken link --- examples/imagenet/readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md index b1ebfafbf46..65174d601f2 100644 --- a/examples/imagenet/readme.md +++ b/examples/imagenet/readme.md @@ -102,4 +102,4 @@ Hope you liked this recipe! Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or fine-tuning the various parameters in the network to address new data and tasks. **Caffe lets you explore different network choices more easily by simply writing different prototxt files** - isn't that exciting? -And since now you have a trained network, check out how to use it with the Python interface for [classifying ImageNet](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/classification.ipynb). +And since now you have a trained network, check out how to use it with the Python interface for [classifying ImageNet](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb). From 4ccc052e6e2bd4293dfb530febc9bf441786363d Mon Sep 17 00:00:00 2001 From: Keiji Yoshida Date: Sun, 12 Jul 2015 20:06:31 +0900 Subject: [PATCH 139/446] Update net_layer_blob.md --- docs/tutorial/net_layer_blob.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md index e8b7bd316a9..d6df737439a 100644 --- a/docs/tutorial/net_layer_blob.md +++ b/docs/tutorial/net_layer_blob.md @@ -19,7 +19,7 @@ Blobs conceal the computational and mental overhead of mixed CPU/GPU operation b The conventional blob dimensions for batches of image data are number N x channel K x height H x width W. Blob memory is row-major in layout, so the last / rightmost dimension changes fastest. For example, in a 4D blob, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w. -- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256. +- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images N = 256. - Channel / K is the feature dimension e.g. for RGB images K = 3. Note that although many blobs in Caffe examples are 4D with axes for image applications, it is totally valid to use blobs for non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use 2D blobs (shape (N, D)) and call the InnerProductLayer (which we will cover soon). From 4702f85a1f31d5541cba0bf5500b2daea222da91 Mon Sep 17 00:00:00 2001 From: Youssef Kashef Date: Tue, 14 Jul 2015 11:26:03 +0200 Subject: [PATCH 140/446] tiny fix in Layer::Backward documentation --- include/caffe/layer.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp index 8f924a75755..e2eba196346 100644 --- a/include/caffe/layer.hpp +++ b/include/caffe/layer.hpp @@ -139,7 +139,7 @@ class Layer { * (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the * top blob diffs. * - * Your layer should implement Forward_cpu and (optionally) Forward_gpu. + * Your layer should implement Backward_cpu and (optionally) Backward_gpu. */ inline void Backward(const vector*>& top, const vector& propagate_down, From 6d92d8fcfe0eea9495ffbc326256ec5b70c3eed1 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 16 Jul 2015 21:30:37 -0700 Subject: [PATCH 141/446] [examples] fix link to point to new tutorial notebook --- examples/feature_extraction/readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md index 6c8917e27e1..a980b8b3203 100644 --- a/examples/feature_extraction/readme.md +++ b/examples/feature_extraction/readme.md @@ -10,7 +10,7 @@ Extracting Features =================== In this tutorial, we will extract features using a pre-trained model with the included C++ utility. -Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb). +Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb). Follow instructions for [installing Caffe](../../installation.html) and run `scripts/download_model_binary.py models/bvlc_reference_caffenet` from caffe root directory. If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided. From 4d3b1844fc70ab52fe9e22343f9a1154617be6a4 Mon Sep 17 00:00:00 2001 From: philkr Date: Wed, 8 Jul 2015 14:30:29 -0700 Subject: [PATCH 142/446] Travis scripts for python3 and pytest for cmake. Also fixes CUDA CMake build issue #2722. --- .travis.yml | 13 +++-- CMakeLists.txt | 4 ++ Makefile | 4 +- cmake/Dependencies.cmake | 9 ++-- python/caffe/test/test_net.py | 7 +-- python/caffe/test/test_net_spec.py | 2 +- python/caffe/test/test_python_layer.py | 5 +- python/caffe/test/test_solver.py | 7 +-- scripts/travis/travis_build_and_test.sh | 11 +++- scripts/travis/travis_install.sh | 52 ++++++++++++++----- .../travis/travis_setup_makefile_config.sh | 4 +- src/caffe/layer_factory.cpp | 5 ++ 12 files changed, 90 insertions(+), 33 deletions(-) diff --git a/.travis.yml b/.travis.yml index 955aa8c3ba2..b920a935d0d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,24 +6,31 @@ env: - WITH_CUDA=false WITH_CMAKE=true - WITH_CUDA=true WITH_CMAKE=false - WITH_CUDA=true WITH_CMAKE=true + - WITH_CUDA=false WITH_CMAKE=true PYTHON_VERSION=3 language: cpp # Cache Ubuntu apt packages. -cache: apt +cache: + apt: true + directories: + - /home/travis/miniconda + - /home/travis/miniconda2 + - /home/travis/miniconda3 compiler: gcc before_install: - export NUM_THREADS=4 - export SCRIPTS=./scripts/travis + - export CONDA_DIR="/home/travis/miniconda$PYTHON_VERSION" install: - sudo -E $SCRIPTS/travis_install.sh before_script: - - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64 - - export PATH=/home/travis/miniconda/bin:$PATH + - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64:$CONDA_DIR/lib + - export PATH=$CONDA_DIR/bin:$PATH - if ! $WITH_CMAKE; then $SCRIPTS/travis_setup_makefile_config.sh; fi script: $SCRIPTS/travis_build_and_test.sh diff --git a/CMakeLists.txt b/CMakeLists.txt index e202350224f..ef599b68922 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -62,6 +62,10 @@ add_subdirectory(docs) # ---[ Linter target add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${PROJECT_SOURCE_DIR}/cmake/lint.cmake) +# ---[ pytest target +add_custom_target(pytest COMMAND python${python_version} -m unittest discover -s caffe/test WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python ) +add_dependencies(pytest pycaffe) + # ---[ Configuration summary caffe_print_configuration_summary() diff --git a/Makefile b/Makefile index e4e66dfd138..05b783af35c 100644 --- a/Makefile +++ b/Makefile @@ -228,7 +228,7 @@ ifeq ($(LINUX), 1) CXX ?= /usr/bin/g++ GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.) # older versions of gcc are too dumb to build boost with -Wuninitalized - ifeq ($(shell echo $(GCCVERSION) \< 4.6 | bc), 1) + ifeq ($(shell echo | awk '{exit $(GCCVERSION) < 4.6;}'), 1) WARNINGS += -Wno-uninitialized endif # boost::thread is reasonably called boost_thread (compare OS X) @@ -243,7 +243,7 @@ ifeq ($(OSX), 1) CXX := /usr/bin/clang++ ifneq ($(CPU_ONLY), 1) CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d') - ifeq ($(shell echo $(CUDA_VERSION) \< 7.0 | bc), 1) + ifeq ($(shell echo | awk '{exit $(CUDA_VERSION) < 7.0;}'), 1) CXXFLAGS += -stdlib=libstdc++ LINKFLAGS += -stdlib=libstdc++ endif diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake index 7cae5c9da25..7c86dd55a30 100644 --- a/cmake/Dependencies.cmake +++ b/cmake/Dependencies.cmake @@ -106,14 +106,15 @@ if(BUILD_python) while(NOT "${version}" STREQUAL "" AND NOT Boost_PYTHON_FOUND) STRING( REGEX REPLACE "([0-9.]+).[0-9]+" "\\1" version ${version} ) - STRING( REGEX MATCHALL "([0-9.]+).[0-9]+" has_more_version ${version} ) - if("${has_more_version}" STREQUAL "") - break() - endif() STRING( REPLACE "." "" boost_py_version ${version} ) find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}") set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND}) + + STRING( REGEX MATCHALL "([0-9.]+).[0-9]+" has_more_version ${version} ) + if("${has_more_version}" STREQUAL "") + break() + endif() endwhile() if(NOT Boost_PYTHON_FOUND) find_package(Boost 1.46 COMPONENTS python) diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py index cc367477752..aad828aa8aa 100644 --- a/python/caffe/test/test_net.py +++ b/python/caffe/test/test_net.py @@ -2,6 +2,7 @@ import tempfile import os import numpy as np +import six import caffe @@ -10,7 +11,7 @@ def simple_net_file(num_output): """Make a simple net prototxt, based on test_net.cpp, returning the name of the (temporary) file.""" - f = tempfile.NamedTemporaryFile(delete=False) + f = tempfile.NamedTemporaryFile(mode='w+', delete=False) f.write("""name: 'testnet' force_backward: true layer { type: 'DummyData' name: 'data' top: 'data' top: 'label' dummy_data_param { num: 5 channels: 2 height: 3 width: 4 @@ -47,7 +48,7 @@ def setUp(self): def test_memory(self): """Check that holding onto blob data beyond the life of a Net is OK""" - params = sum(map(list, self.net.params.itervalues()), []) + params = sum(map(list, six.itervalues(self.net.params)), []) blobs = self.net.blobs.values() del self.net @@ -67,7 +68,7 @@ def test_inputs_outputs(self): self.assertEqual(self.net.outputs, ['loss']) def test_save_and_read(self): - f = tempfile.NamedTemporaryFile(delete=False) + f = tempfile.NamedTemporaryFile(mode='w+', delete=False) f.close() self.net.save(f.name) net_file = simple_net_file(self.num_output) diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py index 65b73b96f73..909a101bbfb 100644 --- a/python/caffe/test/test_net_spec.py +++ b/python/caffe/test/test_net_spec.py @@ -43,7 +43,7 @@ def anon_lenet(batch_size): class TestNetSpec(unittest.TestCase): def load_net(self, net_proto): - f = tempfile.NamedTemporaryFile(delete=False) + f = tempfile.NamedTemporaryFile(mode='w+', delete=False) f.write(str(net_proto)) f.close() return caffe.Net(f.name, caffe.TEST) diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py index 6fba49143bb..f41e283fa4d 100644 --- a/python/caffe/test/test_python_layer.py +++ b/python/caffe/test/test_python_layer.py @@ -1,6 +1,7 @@ import unittest import tempfile import os +import six import caffe @@ -22,7 +23,7 @@ def backward(self, top, propagate_down, bottom): def python_net_file(): - with tempfile.NamedTemporaryFile(delete=False) as f: + with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f: f.write("""name: 'pythonnet' force_backward: true input: 'data' input_shape { dim: 10 dim: 9 dim: 8 } layer { type: 'Python' name: 'one' bottom: 'data' top: 'one' @@ -58,6 +59,6 @@ def test_reshape(self): s = 4 self.net.blobs['data'].reshape(s, s, s, s) self.net.forward() - for blob in self.net.blobs.itervalues(): + for blob in six.itervalues(self.net.blobs): for d in blob.data.shape: self.assertEqual(s, d) diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py index 09b974dad66..9cfc10d29a9 100644 --- a/python/caffe/test/test_solver.py +++ b/python/caffe/test/test_solver.py @@ -2,6 +2,7 @@ import tempfile import os import numpy as np +import six import caffe from test_net import simple_net_file @@ -11,7 +12,7 @@ class TestSolver(unittest.TestCase): def setUp(self): self.num_output = 13 net_f = simple_net_file(self.num_output) - f = tempfile.NamedTemporaryFile(delete=False) + f = tempfile.NamedTemporaryFile(mode='w+', delete=False) f.write("""net: '""" + net_f + """' test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9 weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75 @@ -45,8 +46,8 @@ def test_net_memory(self): total = 0 for net in nets: - for ps in net.params.itervalues(): + for ps in six.itervalues(net.params): for p in ps: total += p.data.sum() + p.diff.sum() - for bl in net.blobs.itervalues(): + for bl in six.itervalues(net.blobs): total += bl.data.sum() + bl.diff.sum() diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh index 8ff63f31fdd..9ba737e28a9 100755 --- a/scripts/travis/travis_build_and_test.sh +++ b/scripts/travis/travis_build_and_test.sh @@ -7,8 +7,17 @@ MAKE="make --jobs=$NUM_THREADS --keep-going" if $WITH_CMAKE; then mkdir build cd build - cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON .. + CPU_ONLY=" -DCPU_ONLY=ON" + if ! $WITH_CUDA; then + CPU_ONLY=" -DCPU_ONLY=OFF" + fi + PYTHON_ARGS="" + if [ "$PYTHON_VERSION" = "3" ]; then + PYTHON_ARGS="$PYTHON_ARGS -Dpython_version=3 -DBOOST_LIBRARYDIR=$CONDA_DIR/lib/" + fi + cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" .. $MAKE + $MAKE pytest if ! $WITH_CUDA; then $MAKE runtest $MAKE lint diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh index b6e6f6ce821..d6c6e228b58 100755 --- a/scripts/travis/travis_install.sh +++ b/scripts/travis/travis_install.sh @@ -4,7 +4,6 @@ set -e MAKE="make --jobs=$NUM_THREADS" - # Install apt packages where the Ubuntu 12.04 default and ppa works for Caffe # This ppa is for gflags and glog @@ -12,9 +11,8 @@ add-apt-repository -y ppa:tuleu/precise-backports apt-get -y update apt-get install \ wget git curl \ - python-dev python-numpy \ + python-dev python-numpy python3-dev\ libleveldb-dev libsnappy-dev libopencv-dev \ - libboost-dev libboost-system-dev libboost-python-dev libboost-thread-dev \ libprotobuf-dev protobuf-compiler \ libatlas-dev libatlas-base-dev \ libhdf5-serial-dev libgflags-dev libgoogle-glog-dev \ @@ -24,9 +22,10 @@ apt-get install \ # if needed. By default, Aptitude in Ubuntu 12.04 installs CMake 2.8.7, but # Caffe requires a minimum CMake version of 2.8.8. if $WITH_CMAKE; then - add-apt-repository -y ppa:ubuntu-sdk-team/ppa - apt-get -y update - apt-get -y install cmake + # cmake 3 will make sure that the python interpreter and libraries match + wget http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh + chmod +x cmake3.sh + ./cmake3.sh --prefix=/usr/ --skip-license --exclude-subdir fi # Install CUDA, if needed @@ -60,10 +59,37 @@ rm -f $LMDB_FILE # Install the Python runtime dependencies via miniconda (this is much faster # than using pip for everything). -wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh -chmod +x miniconda.sh -./miniconda.sh -b -export PATH=/home/travis/miniconda/bin:$PATH -conda update --yes conda -conda install --yes numpy scipy matplotlib scikit-image pip -pip install protobuf +export PATH=$CONDA_DIR/bin:$PATH +if [ ! -d $CONDA_DIR ]; then + if [ "$PYTHON_VERSION" -eq "3" ]; then + wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh + else + wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh + fi + chmod +x miniconda.sh + ./miniconda.sh -b -p $CONDA_DIR + + conda update --yes conda + conda install --yes numpy scipy matplotlib scikit-image pip + # Let conda install boost (so that boost_python matches) + conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0 +fi + +# install protobuf 3 (just use the miniconda3 directory to avoid having to setup the path again) +if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then + pushd . + wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz + tar -C /tmp -xzvf protobuf-3.tar.gz + cd /tmp/protobuf-3*/ + ./autogen.sh + ./configure --prefix=$CONDA_DIR + $MAKE + $MAKE install + popd +fi + +if [ "$PYTHON_VERSION" -eq "3" ]; then + pip install --pre protobuf +else + pip install protobuf +fi diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh index ba326262bf8..1440be2af8b 100755 --- a/scripts/travis/travis_setup_makefile_config.sh +++ b/scripts/travis/travis_setup_makefile_config.sh @@ -12,7 +12,9 @@ if $WITH_CUDA; then fi cat << 'EOF' >> Makefile.config -ANACONDA_HOME := $(HOME)/miniconda +# Travis' nvcc doesn't like newer boost versions +NVCCFLAGS := -Xcudafe --diag_suppress=cc_clobber_ignored -Xcudafe --diag_suppress=useless_using_declaration -Xcudafe --diag_suppress=set_but_not_used +ANACONDA_HOME := $(CONDA_DIR) PYTHON_INCLUDE := $(ANACONDA_HOME)/include \ $(ANACONDA_HOME)/include/python2.7 \ $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp index d6a1cac5090..926c7d8ff78 100644 --- a/src/caffe/layer_factory.cpp +++ b/src/caffe/layer_factory.cpp @@ -1,3 +1,8 @@ +// Make sure we include Python.h before any system header +// to avoid _POSIX_C_SOURCE redefinition +#ifdef WITH_PYTHON_LAYER +#include +#endif #include #include "caffe/layer.hpp" From b2d7b9a31613d69dc4ea1fe6f3d0fe10ac7ee95d Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Tue, 21 Jul 2015 10:33:31 -0700 Subject: [PATCH 143/446] [docs] matlab 2015a compatible --- docs/installation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/installation.md b/docs/installation.md index 144e6a34f67..d535c6d093d 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -75,7 +75,7 @@ To import the `caffe` Python module after completing the installation, add the m Install MATLAB, and make sure that its `mex` is in your `$PATH`. -*Caffe's MATLAB interface works with versions 2014a/b, 2013a/b, and 2012b.* +*Caffe's MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.* #### Windows From 2d328815afa90c2b9bf10f1e5795bbcc2bc877ed Mon Sep 17 00:00:00 2001 From: Eric Zeiberg Date: Tue, 21 Jul 2015 22:12:48 -0700 Subject: [PATCH 144/446] [docs] set lmdb url to github mirror --- docs/install_apt.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/install_apt.md b/docs/install_apt.md index f588b74dcdb..d5deee1c093 100644 --- a/docs/install_apt.md +++ b/docs/install_apt.md @@ -41,7 +41,7 @@ These dependencies need manual installation in 12.04. export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 make && make install # lmdb - git clone https://gitorious.org/mdb/mdb.git + git clone https://github.com/LMDB/lmdb cd mdb/libraries/liblmdb make && make install From 075321931671330a8591554182dfa7e9f94697f4 Mon Sep 17 00:00:00 2001 From: Franck Dernoncourt Date: Wed, 22 Jul 2015 17:32:12 -0700 Subject: [PATCH 145/446] Fix path to mnist_autoencoder.prototxt --- docs/tutorial/layers.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md index 806374e3f93..eabc792b704 100644 --- a/docs/tutorial/layers.md +++ b/docs/tutorial/layers.md @@ -213,7 +213,7 @@ Given an input value x, The `ReLU` layer computes the output as x if x > 0 and n * Layer type: `Sigmoid` * CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp` * CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu` -* Sample (as seen in `./examples/imagenet/mnist_autoencoder.prototxt`) +* Sample (as seen in `./examples/mnist/mnist_autoencoder.prototxt`) layer { name: "encode1neuron" From 129d222fbd885c1a9e727e6134d2387676fc3dac Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 23 Jul 2015 20:23:42 -0700 Subject: [PATCH 146/446] [pycaffe] remove dead code --- python/caffe/net_spec.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py index 1b4814a45c6..95c03bcb9c5 100644 --- a/python/caffe/net_spec.py +++ b/python/caffe/net_spec.py @@ -44,8 +44,6 @@ def to_proto(*tops): """Generate a NetParameter that contains all layers needed to compute all arguments.""" - if not isinstance(tops, tuple): - tops = (tops,) layers = OrderedDict() autonames = {} for top in tops: From 7488163bf4b3d44be5fbc931ba58ccde07098818 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 23 Jul 2015 20:26:46 -0700 Subject: [PATCH 147/446] [pycaffe] use a Counter instead of a dict for counting net spec names --- python/caffe/net_spec.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py index 95c03bcb9c5..37e333d7cc5 100644 --- a/python/caffe/net_spec.py +++ b/python/caffe/net_spec.py @@ -18,7 +18,7 @@ class -- assign to its attributes directly to name layers, and call are not guaranteed to be forward-compatible. """ -from collections import OrderedDict +from collections import OrderedDict, Counter from .proto import caffe_pb2 from google import protobuf @@ -45,7 +45,7 @@ def to_proto(*tops): all arguments.""" layers = OrderedDict() - autonames = {} + autonames = Counter() for top in tops: top.fn._to_proto(layers, {}, autonames) net = caffe_pb2.NetParameter() @@ -107,9 +107,8 @@ def __init__(self, type_name, inputs, params): def _get_name(self, top, names, autonames): if top not in names: - n = autonames.setdefault(top.fn.type_name, 1) autonames[top.fn.type_name] += 1 - names[top] = top.fn.type_name + str(n) + names[top] = top.fn.type_name + str(autonames[top.fn.type_name]) return names[top] def _to_proto(self, layers, names, autonames): @@ -161,7 +160,7 @@ def __getattr__(self, name): def to_proto(self): names = {v: k for k, v in six.iteritems(self.tops)} - autonames = {} + autonames = Counter() layers = OrderedDict() for name, top in six.iteritems(self.tops): top.fn._to_proto(layers, names, autonames) From f16195aa8b1569e0260c48d4159b7d2ce0ea2fab Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 23 Jul 2015 20:32:04 -0700 Subject: [PATCH 148/446] [pycaffe] add Top._to_proto convenience function This makes it possible to serialize Functions or Tops with a uniform interface. --- python/caffe/net_spec.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py index 37e333d7cc5..5fb26ed45ba 100644 --- a/python/caffe/net_spec.py +++ b/python/caffe/net_spec.py @@ -87,6 +87,9 @@ def to_proto(self): return to_proto(self) + def _to_proto(self, layers, names, autonames): + return self.fn._to_proto(layers, names, autonames) + class Function(object): """A Function specifies a layer, its parameters, and its inputs (which @@ -116,7 +119,7 @@ def _to_proto(self, layers, names, autonames): return bottom_names = [] for inp in self.inputs: - inp.fn._to_proto(layers, names, autonames) + inp._to_proto(layers, names, autonames) bottom_names.append(layers[inp.fn].top[inp.n]) layer = caffe_pb2.LayerParameter() layer.type = self.type_name @@ -163,7 +166,7 @@ def to_proto(self): autonames = Counter() layers = OrderedDict() for name, top in six.iteritems(self.tops): - top.fn._to_proto(layers, names, autonames) + top._to_proto(layers, names, autonames) net = caffe_pb2.NetParameter() net.layer.extend(layers.values()) return net From 96c2fe1de80c9752b992c4578a3ce46028d21fc5 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 23 Jul 2015 20:35:42 -0700 Subject: [PATCH 149/446] [pycaffe] allow layers to have names different from their first tops Previously, net spec only allowed names to be assigned to Tops, giving layers the names of their first tops. Now, names can be assigned to Functions, which become layer names in serialization. Unnamed Functions still get named after their first top, if present, or autogenerated, if not. (This will allow top-less layers in a natural way.) --- python/caffe/net_spec.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py index 5fb26ed45ba..16f30008579 100644 --- a/python/caffe/net_spec.py +++ b/python/caffe/net_spec.py @@ -108,7 +108,15 @@ def __init__(self, type_name, inputs, params): del self.params['in_place'] self.tops = tuple(Top(self, n) for n in range(self.ntop)) - def _get_name(self, top, names, autonames): + def _get_name(self, names, autonames): + if self not in names and self.ntop > 0: + names[self] = self._get_top_name(self.tops[0], names, autonames) + elif self not in names: + autonames[self.type_name] += 1 + names[self] = self.type_name + str(autonames[self.type_name]) + return names[self] + + def _get_top_name(self, top, names, autonames): if top not in names: autonames[top.fn.type_name] += 1 names[top] = top.fn.type_name + str(autonames[top.fn.type_name]) @@ -129,8 +137,8 @@ def _to_proto(self, layers, names, autonames): layer.top.extend(layer.bottom) else: for top in self.tops: - layer.top.append(self._get_name(top, names, autonames)) - layer.name = self._get_name(self.tops[0], names, autonames) + layer.top.append(self._get_top_name(top, names, autonames)) + layer.name = self._get_name(names, autonames) for k, v in six.iteritems(self.params): # special case to handle generic *params From b464a6e5d2e381185bd8aa0b9cbcba53c9819585 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 23 Jul 2015 20:40:02 -0700 Subject: [PATCH 150/446] [pycaffe] net spec layers can have ntop=0 In this case, the Function is returned instead of a Top, which can be assigned a name if desired. Thanks @philkr for an earlier implementation of this. --- python/caffe/net_spec.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py index 16f30008579..31cde7ad946 100644 --- a/python/caffe/net_spec.py +++ b/python/caffe/net_spec.py @@ -188,7 +188,9 @@ class Layers(object): def __getattr__(self, name): def layer_fn(*args, **kwargs): fn = Function(name, args, kwargs) - if fn.ntop == 1: + if fn.ntop == 0: + return fn + elif fn.ntop == 1: return fn.tops[0] else: return fn.tops From c80f6f585549334022996395015cd490cc2644c8 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 23 Jul 2015 20:41:40 -0700 Subject: [PATCH 151/446] [pytest] simple test of top-less layers --- python/caffe/test/test_net_spec.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py index 65b73b96f73..b344946932a 100644 --- a/python/caffe/test/test_net_spec.py +++ b/python/caffe/test/test_net_spec.py @@ -41,6 +41,14 @@ def anon_lenet(batch_size): loss = L.SoftmaxWithLoss(ip2, label) return loss.to_proto() +def silent_net(): + n = caffe.NetSpec() + n.data, n.data2 = L.DummyData(shape=[dict(dim=[3]), dict(dim=[4, 2])], + ntop=2) + n.silence_data = L.Silence(n.data, ntop=0) + n.silence_data2 = L.Silence(n.data2, ntop=0) + return n.to_proto() + class TestNetSpec(unittest.TestCase): def load_net(self, net_proto): f = tempfile.NamedTemporaryFile(delete=False) @@ -65,3 +73,10 @@ def test_lenet(self): net_proto.layer[6].top) net = self.load_net(net_proto) self.assertEqual(len(net.layers), 9) + + def test_zero_tops(self): + """Test net construction for top-less layers.""" + + net_proto = silent_net() + net = self.load_net(net_proto) + self.assertEqual(len(net.forward()), 0) From de6d444445261fc9859143bfd969d538ae7a2108 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Mon, 27 Jul 2015 10:58:11 -0700 Subject: [PATCH 152/446] [docs] clear up PYTHONPATH confusion Use the same language as the installation page to explain the Python module path. reported by @sdemyanov --- docs/tutorial/interfaces.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md index 12963318485..40602948cc3 100644 --- a/docs/tutorial/interfaces.md +++ b/docs/tutorial/interfaces.md @@ -11,8 +11,8 @@ The command line interface -- cmdcaffe -- is the `caffe` tool for model training **Training**: `caffe train` learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks: -* All training requires a solver configuration through the `-solver solver.prototxt` argument. -* Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot. +* All training requires a solver configuration through the `-solver solver.prototxt` argument. +* Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot. * Fine-tuning requires the `-weights model.caffemodel` argument for the model initialization. For example, you can run: @@ -31,8 +31,7 @@ For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but **Testing**: `caffe test` scores models by running them in the test phase and reports the net output as its score. The net architecture must be properly defined to output an accuracy measure or loss as its output. The per-batch score is reported and then the grand average is reported last. - # - # score the learned LeNet model on the validation set as defined in the + # score the learned LeNet model on the validation set as defined in the # model architeture lenet_train_test.prototxt caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 100 @@ -63,7 +62,8 @@ The Python interface -- pycaffe -- is the `caffe` module and its scripts in caff Tutorial IPython notebooks are found in caffe/examples: do `ipython notebook caffe/examples` to try them. For developer reference docstrings can be found throughout the code. -Compile pycaffe by `make pycaffe`. The module dir caffe/python/caffe should be installed in your PYTHONPATH for `import caffe`. +Compile pycaffe by `make pycaffe`. +Add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like for `import caffe`. ## MATLAB @@ -182,7 +182,7 @@ To get a layer's type (string): #### Forward and backward Forward pass can be done using `net.forward` or `net.forward_prefilled`. Function `net.forward` takes in a cell array of N-D arrays containing data of input blob(s) and outputs a cell array containing data from output blob(s). Function `net.forward_prefilled` uses existing data in input blob(s) during forward pass, takes no input and produces no output. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run - + res = net.forward({data}); prob = res{1}; @@ -202,7 +202,7 @@ Or net.blobs('prob').set_diff(prob_diff); net.backward_prefilled(); data_diff = net.blobs('data').get_diff(); - + **However, the backward computation above doesn't get correct results, because Caffe decides that the network does not need backward computation. To get correct backward results, you need to set `'force_backward: true'` in your network prototxt.** After performing forward or backward pass, you can also get the data or diff in internal blobs. For example, to extract pool5 features after forward pass: From e4aed047a2203bbc1f5bd36ed90bb7d9882b2e11 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Tue, 28 Jul 2015 10:27:39 -0700 Subject: [PATCH 153/446] [docs] fix lmdb fetch url and path --- docs/install_apt.md | 2 +- docs/install_yum.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/install_apt.md b/docs/install_apt.md index d5deee1c093..2976e3cd07c 100644 --- a/docs/install_apt.md +++ b/docs/install_apt.md @@ -42,7 +42,7 @@ These dependencies need manual installation in 12.04. make && make install # lmdb git clone https://github.com/LMDB/lmdb - cd mdb/libraries/liblmdb + cd lmdb/libraries/liblmdb make && make install Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first. diff --git a/docs/install_yum.md b/docs/install_yum.md index 478e7d952cc..2104912e482 100644 --- a/docs/install_yum.md +++ b/docs/install_yum.md @@ -28,8 +28,8 @@ title: Installation: RHEL / Fedora / CentOS export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 make && make install # lmdb - git clone git://gitorious.org/mdb/mdb.git - cd mdb/libraries/liblmdb + git clone https://github.com/LMDB/lmdb + cd lmdb/libraries/liblmdb make && make install Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first. From 7f7085439cbe4eb9d5fff95b41d6345168398142 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Wed, 29 Jul 2015 17:20:31 -0700 Subject: [PATCH 154/446] [docs] fix contrastive loss eq make documented equation match the correct implementation of the `max(margin - d, 0)^2` term in the loss. see #2321 --- include/caffe/loss_layers.hpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp index 86c34241168..528266394f1 100644 --- a/include/caffe/loss_layers.hpp +++ b/include/caffe/loss_layers.hpp @@ -128,9 +128,9 @@ class LossLayer : public Layer { /** * @brief Computes the contrastive loss @f$ * E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d + - * \left(1-y\right) \max \left(margin-d, 0\right) + * \left(1-y\right) \max \left(margin-d, 0\right)^2 * @f$ where @f$ - * d = \left| \left| a_n - b_n \right| \right|_2^2 @f$. This can be + * d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be * used to train siamese networks. * * @param bottom input Blob vector (length 3) @@ -144,9 +144,9 @@ class LossLayer : public Layer { * -# @f$ (1 \times 1 \times 1 \times 1) @f$ * the computed contrastive loss: @f$ E = * \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d + - * \left(1-y\right) \max \left(margin-d, 0\right) + * \left(1-y\right) \max \left(margin-d, 0\right)^2 * @f$ where @f$ - * d = \left| \left| a_n - b_n \right| \right|_2^2 @f$. + * d = \left| \left| a_n - b_n \right| \right|_2 @f$. * This can be used to train siamese networks. */ template From ccc35d361d6c90b7691b3ed8de7ea0678b488dff Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Wed, 29 Jul 2015 20:16:31 -0700 Subject: [PATCH 155/446] [docs] add CONTRIBUTING.md which will appear on GitHub new Issue/PR pages --- CONTRIBUTING.md | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000000..8cd5e56ca49 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,30 @@ +# Contributing + +## Issues + +Specific Caffe design and development issues, bugs, and feature requests are maintained by GitHub Issues. + +_Please do not post usage, installation, or modeling questions, or other requests for help to Issues._ +Use the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users) instead. This helps developers maintain a clear, uncluttered, and efficient view of the state of Caffe. + +When reporting a bug, it's most helpful to provide the following information, where applicable: + +* What steps reproduce the bug? +* Can you reproduce the bug using the latest [master](https://github.com/BVLC/caffe/tree/master), compiled with the `DEBUG` make option? +* What hardware and operating system/distribution are you running? +* If the bug is a crash, provide the backtrace (usually printed by Caffe; always obtainable with `gdb`). + +Try to give your issue a title that is succinct and specific. The devs will rename issues as needed to keep track of them. + +## Pull Requests + +Caffe welcomes all contributions. + +See the [contributing guide](http://caffe.berkeleyvision.org/development.html) for details. + +Briefly: read commit by commit, a PR should tell a clean, compelling story of _one_ improvement to Caffe. In particular: + +* A PR should do one clear thing that obviously improves Caffe, and nothing more. Making many smaller PRs is better than making one large PR; review effort is superlinear in the amount of code involved. +* Similarly, each commit should be a small, atomic change representing one step in development. PRs should be made of many commits where appropriate. +* Please do rewrite PR history to be clean rather than chronological. Within-PR bugfixes, style cleanups, reversions, etc. should be squashed and should not appear in merged PR history. +* Anything nonobvious from the code should be explained in comments, commit messages, or the PR description, as appropriate. From 63e45b79932f3f990db32d75f157693809679230 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Tue, 9 Jun 2015 17:50:40 -0700 Subject: [PATCH 156/446] Change log levels in upgrade_proto It's not an error if the upgrade succeeds --- src/caffe/util/upgrade_proto.cpp | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp index 38a06026adf..92e5cf55fa9 100644 --- a/src/caffe/util/upgrade_proto.cpp +++ b/src/caffe/util/upgrade_proto.cpp @@ -588,8 +588,8 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) { if (NetNeedsV0ToV1Upgrade(*param)) { // NetParameter was specified using the old style (V0LayerParameter); try to // upgrade it. - LOG(ERROR) << "Attempting to upgrade input file specified using deprecated " - << "V0LayerParameter: " << param_file; + LOG(INFO) << "Attempting to upgrade input file specified using deprecated " + << "V0LayerParameter: " << param_file; NetParameter original_param(*param); if (!UpgradeV0Net(original_param, param)) { success = false; @@ -599,29 +599,29 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) { LOG(INFO) << "Successfully upgraded file specified using deprecated " << "V0LayerParameter"; } - LOG(ERROR) << "Note that future Caffe releases will not support " + LOG(WARNING) << "Note that future Caffe releases will not support " << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for " << "prototxt and ./build/tools/upgrade_net_proto_binary for model " << "weights upgrade this and any other net protos to the new format."; } // NetParameter uses old style data transformation fields; try to upgrade it. if (NetNeedsDataUpgrade(*param)) { - LOG(ERROR) << "Attempting to upgrade input file specified using deprecated " - << "transformation parameters: " << param_file; + LOG(INFO) << "Attempting to upgrade input file specified using deprecated " + << "transformation parameters: " << param_file; UpgradeNetDataTransformation(param); LOG(INFO) << "Successfully upgraded file specified using deprecated " << "data transformation parameters."; - LOG(ERROR) << "Note that future Caffe releases will only support " - << "transform_param messages for transformation fields."; + LOG(WARNING) << "Note that future Caffe releases will only support " + << "transform_param messages for transformation fields."; } if (NetNeedsV1ToV2Upgrade(*param)) { - LOG(ERROR) << "Attempting to upgrade input file specified using deprecated " - << "V1LayerParameter: " << param_file; + LOG(INFO) << "Attempting to upgrade input file specified using deprecated " + << "V1LayerParameter: " << param_file; NetParameter original_param(*param); if (!UpgradeV1Net(original_param, param)) { success = false; LOG(ERROR) << "Warning: had one or more problems upgrading " - << "V1LayerParameter (see above); continuing anyway."; + << "V1LayerParameter (see above); continuing anyway."; } else { LOG(INFO) << "Successfully upgraded file specified using deprecated " << "V1LayerParameter"; From e7b2b4eab67390885b8cb360750cfc1553fbb70e Mon Sep 17 00:00:00 2001 From: philkr Date: Tue, 4 Aug 2015 10:27:34 -0700 Subject: [PATCH 157/446] ImageData layer default batch size of 1, and check for zero batch size --- src/caffe/layers/image_data_layer.cpp | 3 +++ src/caffe/proto/caffe.proto | 6 +++--- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp index 18c035cba9d..dcc53348304 100644 --- a/src/caffe/layers/image_data_layer.cpp +++ b/src/caffe/layers/image_data_layer.cpp @@ -62,11 +62,13 @@ void ImageDataLayer::DataLayerSetUp(const vector*>& bottom, // Read an image, and use it to initialize the top blob. cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first, new_height, new_width, is_color); + CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first; // Use data_transformer to infer the expected blob shape from a cv_image. vector top_shape = this->data_transformer_->InferBlobShape(cv_img); this->transformed_data_.Reshape(top_shape); // Reshape prefetch_data and top[0] according to the batch_size. const int batch_size = this->layer_param_.image_data_param().batch_size(); + CHECK_GT(batch_size, 0) << "Positive batch size required"; top_shape[0] = batch_size; this->prefetch_data_.Reshape(top_shape); top[0]->ReshapeLike(this->prefetch_data_); @@ -108,6 +110,7 @@ void ImageDataLayer::InternalThreadEntry() { // on single input batches allows for inputs of varying dimension. cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first, new_height, new_width, is_color); + CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first; // Use data_transformer to infer the expected blob shape from a cv_img. vector top_shape = this->data_transformer_->InferBlobShape(cv_img); this->transformed_data_.Reshape(top_shape); diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 81a8c69d88e..8c3f0723600 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -290,7 +290,7 @@ message LayerParameter { // The blobs containing the numeric parameters of the layer. repeated BlobProto blobs = 7; - + // Specifies on which bottoms the backpropagation should be skipped. // The size must be either 0 or equal to the number of bottoms. repeated bool propagate_down = 11; @@ -431,7 +431,7 @@ message ContrastiveLossParameter { // Hadsell paper. New models should probably use this version. // legacy_version = true uses (margin - d^2). This is kept to support / // reproduce existing models and results - optional bool legacy_version = 2 [default = false]; + optional bool legacy_version = 2 [default = false]; } message ConvolutionParameter { @@ -579,7 +579,7 @@ message ImageDataParameter { // Specify the data source. optional string source = 1; // Specify the batch size. - optional uint32 batch_size = 4; + optional uint32 batch_size = 4 [default = 1]; // The rand_skip variable is for the data layer to skip a few data points // to avoid all asynchronous sgd clients to start at the same point. The skip // point would be set as rand_skip * rand(0,1). Note that rand_skip should not From 4c23e93a95db0c6c6c5d4239070966e3bdc24fdc Mon Sep 17 00:00:00 2001 From: Gustavo Serra Scalet Date: Thu, 6 Aug 2015 14:20:55 -0300 Subject: [PATCH 158/446] Fix download model script to use zip archive Currently GitHub is not using tarballs as archive for downloading gists therefore the script was broken as actually a zip archive was being downloaded. --- scripts/download_model_from_gist.sh | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/scripts/download_model_from_gist.sh b/scripts/download_model_from_gist.sh index a1dccf78b5b..89527b7516f 100755 --- a/scripts/download_model_from_gist.sh +++ b/scripts/download_model_from_gist.sh @@ -18,7 +18,7 @@ fi echo "Downloading Caffe model info to $MODEL_DIR ..." mkdir -p $MODEL_DIR -wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.tar.gz -tar xzf $MODEL_DIR/gist.tar.gz --directory=$MODEL_DIR --strip-components=1 -rm $MODEL_DIR/gist.tar.gz +wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.zip +unzip -j $MODEL_DIR/gist.zip -d $MODEL_DIR +rm $MODEL_DIR/gist.zip echo "Done" From fda9229a426d9c0e4496c700099a7126da72ba64 Mon Sep 17 00:00:00 2001 From: Gustavo Serra Scalet Date: Thu, 6 Aug 2015 14:37:20 -0300 Subject: [PATCH 159/446] Fix download model binary script to get correct lines on parsing table The base reference of "bottom" variable was relative to the "top+1" and not to the whole readlines output. It ended up without all the lines. That could work for some gists however for the model I was looking for (see below) the sha1 key was not being parsed, as it was missing the last line. tested with the following gist: longjon/1bf3aa1e0b8e788d7e1d --- scripts/download_model_binary.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/download_model_binary.py b/scripts/download_model_binary.py index 48e9015fd26..03a50f6776a 100755 --- a/scripts/download_model_binary.py +++ b/scripts/download_model_binary.py @@ -32,7 +32,7 @@ def parse_readme_frontmatter(dirname): with open(readme_filename) as f: lines = [line.strip() for line in f.readlines()] top = lines.index('---') - bottom = lines[top + 1:].index('---') + bottom = lines.index('---', top + 1) frontmatter = yaml.load('\n'.join(lines[top + 1:bottom])) assert all(key in frontmatter for key in required_keys) return dirname, frontmatter From d958b5a45c95db4f4e90abda29b69d5bfed2ef07 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 6 Aug 2015 13:03:50 -0700 Subject: [PATCH 160/446] [pycaffe,build] include Python first in caffe tool --- tools/caffe.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 9de3abdc722..46f99594800 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -1,3 +1,8 @@ +#ifdef WITH_PYTHON_LAYER +#include "boost/python.hpp" +namespace bp = boost::python; +#endif + #include #include @@ -8,11 +13,6 @@ #include "boost/algorithm/string.hpp" #include "caffe/caffe.hpp" -#ifdef WITH_PYTHON_LAYER -#include "boost/python.hpp" -namespace bp = boost::python; -#endif - using caffe::Blob; using caffe::Caffe; using caffe::Net; From 1934feec285c901066ef28567ccc6a7cb5b4ce92 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 6 Aug 2015 13:04:15 -0700 Subject: [PATCH 161/446] [pytest] open exception file with mode for python3 --- python/caffe/test/test_python_layer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py index ff070a35aee..a1e11bc248d 100644 --- a/python/caffe/test/test_python_layer.py +++ b/python/caffe/test/test_python_layer.py @@ -43,7 +43,7 @@ def python_net_file(): def exception_net_file(): - with tempfile.NamedTemporaryFile(delete=False) as f: + with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f: f.write("""name: 'pythonnet' force_backward: true input: 'data' input_shape { dim: 10 dim: 9 dim: 8 } layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top' From a756cfec0056fc59bf8719997df7f3b233babf38 Mon Sep 17 00:00:00 2001 From: Takuya Narihira Date: Fri, 27 Feb 2015 19:34:51 -0800 Subject: [PATCH 162/446] PythonLayer takes parameters by string --- include/caffe/python_layer.hpp | 2 + .../test/test_python_layer_with_param_str.py | 59 +++++++++++++++++++ src/caffe/proto/caffe.proto | 5 ++ 3 files changed, 66 insertions(+) create mode 100644 python/caffe/test/test_python_layer_with_param_str.py diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp index 9c30250c1b5..2957e7426be 100644 --- a/include/caffe/python_layer.hpp +++ b/include/caffe/python_layer.hpp @@ -18,6 +18,8 @@ class PythonLayer : public Layer { virtual void LayerSetUp(const vector*>& bottom, const vector*>& top) { + self_.attr("param_str") = bp::str( + this->layer_param_.python_param().param_str()); self_.attr("setup")(bottom, top); } virtual void Reshape(const vector*>& bottom, diff --git a/python/caffe/test/test_python_layer_with_param_str.py b/python/caffe/test/test_python_layer_with_param_str.py new file mode 100644 index 00000000000..3d0f107b3bb --- /dev/null +++ b/python/caffe/test/test_python_layer_with_param_str.py @@ -0,0 +1,59 @@ +import unittest +import tempfile +import os +import six + +import caffe + + +class SimpleParamLayer(caffe.Layer): + """A layer that just multiplies by the numeric value of its param string""" + + def setup(self, bottom, top): + try: + self.value = float(self.param_str) + except ValueError: + raise ValueError("Parameter string must be a legible float") + + def reshape(self, bottom, top): + top[0].reshape(*bottom[0].data.shape) + + def forward(self, bottom, top): + top[0].data[...] = self.value * bottom[0].data + + def backward(self, top, propagate_down, bottom): + bottom[0].diff[...] = self.value * top[0].diff + + +def python_param_net_file(): + with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f: + f.write("""name: 'pythonnet' force_backward: true + input: 'data' input_shape { dim: 10 dim: 9 dim: 8 } + layer { type: 'Python' name: 'mul10' bottom: 'data' top: 'mul10' + python_param { module: 'test_python_layer_with_param_str' + layer: 'SimpleParamLayer' param_str: '10' } } + layer { type: 'Python' name: 'mul2' bottom: 'mul10' top: 'mul2' + python_param { module: 'test_python_layer_with_param_str' + layer: 'SimpleParamLayer' param_str: '2' } }""") + return f.name + + +class TestLayerWithParam(unittest.TestCase): + def setUp(self): + net_file = python_param_net_file() + self.net = caffe.Net(net_file, caffe.TRAIN) + os.remove(net_file) + + def test_forward(self): + x = 8 + self.net.blobs['data'].data[...] = x + self.net.forward() + for y in self.net.blobs['mul2'].data.flat: + self.assertEqual(y, 2 * 10 * x) + + def test_backward(self): + x = 7 + self.net.blobs['mul2'].diff[...] = x + self.net.backward() + for y in self.net.blobs['data'].diff.flat: + self.assertEqual(y, 2 * 10 * x) diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 8c3f0723600..adcf4e2fd5a 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -703,6 +703,11 @@ message PowerParameter { message PythonParameter { optional string module = 1; optional string layer = 2; + // This value is set to the attribute `param_str` of the `PythonLayer` object + // in Python before calling the `setup()` method. This could be a number, + // string, dictionary in Python dict format, JSON, etc. You may parse this + // string in `setup` method and use it in `forward` and `backward`. + optional string param_str = 3 [default = '']; } // Message that stores parameters used by ReductionLayer From f5116a1d2827a5e1490ce047df7835b66e92f99f Mon Sep 17 00:00:00 2001 From: "koki1.saitoh" Date: Fri, 7 Aug 2015 18:22:28 +0900 Subject: [PATCH 163/446] Fix typo --- include/caffe/layer.hpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp index e2eba196346..0771b6a8fb4 100644 --- a/include/caffe/layer.hpp +++ b/include/caffe/layer.hpp @@ -86,7 +86,7 @@ class Layer { const vector*>& top) {} /** - * @brief Adjust the shapes of top blobs and internal buffers to accomodate + * @brief Adjust the shapes of top blobs and internal buffers to accommodate * the shapes of the bottom blobs. * * @param bottom the input blobs, with the requested input shapes @@ -95,7 +95,7 @@ class Layer { * This method should reshape top blobs as needed according to the shapes * of the bottom (input) blobs, as well as reshaping any internal buffers * and making any other necessary adjustments so that the layer can - * accomodate the bottom blobs. + * accommodate the bottom blobs. */ virtual void Reshape(const vector*>& bottom, const vector*>& top) = 0; From a3c7425b80fd83ef550323836304a7255d35e033 Mon Sep 17 00:00:00 2001 From: Tian Zhi Date: Sat, 8 Aug 2015 02:31:19 +0800 Subject: [PATCH 164/446] add [] to "delete pixels". see https://isocpp.org/wiki/faq/freestore-mgmt#delete-array-built-ins. --- examples/mnist/convert_mnist_data.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp index 2749e4521b6..54443f11dd3 100644 --- a/examples/mnist/convert_mnist_data.cpp +++ b/examples/mnist/convert_mnist_data.cpp @@ -166,7 +166,7 @@ void convert_dataset(const char* image_filename, const char* label_filename, } LOG(ERROR) << "Processed " << count << " files."; } - delete pixels; + delete[] pixels; } int main(int argc, char** argv) { From f973819240768df207ed8e4d307564b105950333 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 29 Jul 2015 17:27:04 -0700 Subject: [PATCH 165/446] add double_data, double_diff to BlobProto for weights/snapshots saved when using Dtype == double --- src/caffe/blob.cpp | 49 +++++++++++++++++++++++++++++++------ src/caffe/proto/caffe.proto | 2 ++ 2 files changed, 44 insertions(+), 7 deletions(-) diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp index 94fdcc35fb6..8450aa140be 100644 --- a/src/caffe/blob.cpp +++ b/src/caffe/blob.cpp @@ -456,10 +456,25 @@ void Blob::FromProto(const BlobProto& proto, bool reshape) { } // copy data Dtype* data_vec = mutable_cpu_data(); - for (int i = 0; i < count_; ++i) { - data_vec[i] = proto.data(i); + if (proto.double_data_size() > 0) { + CHECK_EQ(count_, proto.double_data_size()); + for (int i = 0; i < count_; ++i) { + data_vec[i] = proto.double_data(i); + } + } else { + CHECK_EQ(count_, proto.data_size()); + for (int i = 0; i < count_; ++i) { + data_vec[i] = proto.data(i); + } } - if (proto.diff_size() > 0) { + if (proto.double_diff_size() > 0) { + CHECK_EQ(count_, proto.double_diff_size()); + Dtype* diff_vec = mutable_cpu_diff(); + for (int i = 0; i < count_; ++i) { + diff_vec[i] = proto.double_diff(i); + } + } else if (proto.diff_size() > 0) { + CHECK_EQ(count_, proto.diff_size()); Dtype* diff_vec = mutable_cpu_diff(); for (int i = 0; i < count_; ++i) { diff_vec[i] = proto.diff(i); @@ -467,20 +482,40 @@ void Blob::FromProto(const BlobProto& proto, bool reshape) { } } -template -void Blob::ToProto(BlobProto* proto, bool write_diff) const { +template <> +void Blob::ToProto(BlobProto* proto, bool write_diff) const { + proto->clear_shape(); + for (int i = 0; i < shape_.size(); ++i) { + proto->mutable_shape()->add_dim(shape_[i]); + } + proto->clear_double_data(); + proto->clear_double_diff(); + const double* data_vec = cpu_data(); + for (int i = 0; i < count_; ++i) { + proto->add_double_data(data_vec[i]); + } + if (write_diff) { + const double* diff_vec = cpu_diff(); + for (int i = 0; i < count_; ++i) { + proto->add_double_diff(diff_vec[i]); + } + } +} + +template <> +void Blob::ToProto(BlobProto* proto, bool write_diff) const { proto->clear_shape(); for (int i = 0; i < shape_.size(); ++i) { proto->mutable_shape()->add_dim(shape_[i]); } proto->clear_data(); proto->clear_diff(); - const Dtype* data_vec = cpu_data(); + const float* data_vec = cpu_data(); for (int i = 0; i < count_; ++i) { proto->add_data(data_vec[i]); } if (write_diff) { - const Dtype* diff_vec = cpu_diff(); + const float* diff_vec = cpu_diff(); for (int i = 0; i < count_; ++i) { proto->add_diff(diff_vec[i]); } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index adcf4e2fd5a..03daa808bf8 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -11,6 +11,8 @@ message BlobProto { optional BlobShape shape = 7; repeated float data = 5 [packed = true]; repeated float diff = 6 [packed = true]; + repeated double double_data = 8 [packed = true]; + repeated double double_diff = 9 [packed = true]; // 4D dimensions -- deprecated. Use "shape" instead. optional int32 num = 1 [default = 0]; From 1e740e1e8568063ffe69162a30a97961c5c62af0 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 29 Jul 2015 17:27:58 -0700 Subject: [PATCH 166/446] TestGradientBasedSolver: add TestSnapshot to verify behavior when restoring net/solver from snapshot --- src/caffe/test/test_gradient_based_solver.cpp | 112 +++++++++++++++++- 1 file changed, 107 insertions(+), 5 deletions(-) diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index c9135d64e70..94b500124aa 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -10,6 +10,7 @@ #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/solver.hpp" +#include "caffe/util/io.hpp" #include "caffe/test/test_caffe_main.hpp" @@ -25,6 +26,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { GradientBasedSolverTest() : seed_(1701), num_(4), channels_(3), height_(10), width_(10) {} + string snapshot_prefix_; shared_ptr > solver_; int seed_; int num_, channels_, height_, width_; @@ -36,9 +38,6 @@ class GradientBasedSolverTest : public MultiDeviceTest { virtual void InitSolverFromProtoString(const string& proto) { SolverParameter param; CHECK(google::protobuf::TextFormat::ParseFromString(proto, ¶m)); - // Disable saving a final snapshot so the tests don't pollute the user's - // working directory with useless snapshots. - param.set_snapshot_after_train(false); // Set the solver_mode according to current Caffe::mode. switch (Caffe::mode()) { case Caffe::CPU: @@ -55,11 +54,13 @@ class GradientBasedSolverTest : public MultiDeviceTest { param.delta() : 0; } - void RunLeastSquaresSolver(const Dtype learning_rate, + string RunLeastSquaresSolver(const Dtype learning_rate, const Dtype weight_decay, const Dtype momentum, const int num_iters, - const int iter_size = 1) { + const int iter_size = 1, const bool snapshot = false, + const char* from_snapshot = NULL) { ostringstream proto; proto << + "snapshot_after_train: " << snapshot << " " "max_iter: " << num_iters << " " "base_lr: " << learning_rate << " " "lr_policy: 'fixed' " @@ -119,9 +120,30 @@ class GradientBasedSolverTest : public MultiDeviceTest { if (momentum != 0) { proto << "momentum: " << momentum << " "; } + MakeTempDir(&snapshot_prefix_); + proto << "snapshot_prefix: '" << snapshot_prefix_ << "/' "; + if (snapshot) { + proto << "snapshot: " << num_iters << " "; + } Caffe::set_random_seed(this->seed_); this->InitSolverFromProtoString(proto.str()); + Caffe::set_random_seed(this->seed_); + if (from_snapshot != NULL) { + this->solver_->Restore(from_snapshot); + vector*> empty_bottom_vec; + for (int i = 0; i < this->solver_->iter(); ++i) { + this->solver_->net()->Forward(empty_bottom_vec); + } + } this->solver_->Solve(); + if (snapshot) { + ostringstream resume_file; + resume_file << snapshot_prefix_ << "/_iter_" << num_iters + << ".solverstate"; + string resume_filename = resume_file.str(); + return resume_filename; + } + return string(); } // Compute an update value given the current state of the train net, @@ -348,6 +370,51 @@ class GradientBasedSolverTest : public MultiDeviceTest { // Check that the solver's solution matches ours. CheckLeastSquaresUpdate(updated_params); } + + void TestSnapshot(const Dtype learning_rate = 1.0, + const Dtype weight_decay = 0.0, const Dtype momentum = 0.0, + const int num_iters = 1) { + // Run the solver for num_iters * 2 iterations. + const int total_num_iters = num_iters * 2; + bool snapshot = false; + const int kIterSize = 1; + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, + total_num_iters, kIterSize, snapshot); + + // Save the resulting param values. + vector > > param_copies; + const vector > >& orig_params = + solver_->net()->params(); + param_copies.resize(orig_params.size()); + for (int i = 0; i < orig_params.size(); ++i) { + param_copies[i].reset(new Blob()); + const bool kReshape = true; + for (int copy_diff = false; copy_diff <= true; ++copy_diff) { + param_copies[i]->CopyFrom(*orig_params[i], copy_diff, kReshape); + } + } + + // Run the solver for num_iters iterations and snapshot. + snapshot = true; + string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay, + momentum, num_iters, kIterSize, snapshot); + + // Reinitialize the solver and run for num_iters more iterations. + snapshot = false; + RunLeastSquaresSolver(learning_rate, weight_decay, + momentum, total_num_iters, kIterSize, snapshot, snapshot_name.c_str()); + + // Check that params now match. + const vector > >& params = solver_->net()->params(); + for (int i = 0; i < params.size(); ++i) { + for (int j = 0; j < params[i]->count(); ++j) { + EXPECT_EQ(param_copies[i]->cpu_data()[j], params[i]->cpu_data()[j]) + << "param " << i << " data differed at dim " << j; + EXPECT_EQ(param_copies[i]->cpu_diff()[j], params[i]->cpu_diff()[j]) + << "param " << i << " diff differed at dim " << j; + } + } + } }; @@ -428,6 +495,18 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { kIterSize); } +TYPED_TEST(SGDSolverTest, TestSnapshot) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + } +} + + template class AdaGradSolverTest : public GradientBasedSolverTest { typedef typename TypeParam::Dtype Dtype; @@ -482,6 +561,18 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { kIterSize); } +TYPED_TEST(AdaGradSolverTest, TestSnapshot) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.0; + const int kNumIters = 4; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + } +} + + template class NesterovSolverTest : public GradientBasedSolverTest { typedef typename TypeParam::Dtype Dtype; @@ -558,4 +649,15 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { kIterSize); } +TYPED_TEST(NesterovSolverTest, TestSnapshot) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.0; + const int kNumIters = 4; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + } +} + } // namespace caffe From 50195e353293a7f41e7c95e158844eef53a70a2b Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Thu, 6 Aug 2015 13:46:11 -0700 Subject: [PATCH 167/446] pycaffe: add shape accessor --- python/caffe/_caffe.cpp | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index dff7f627016..bb5130fddd0 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -230,6 +230,11 @@ BOOST_PYTHON_MODULE(_caffe) { bp::class_, shared_ptr >, boost::noncopyable>( "Blob", bp::no_init) + .add_property("shape", + bp::make_function( + static_cast& (Blob::*)() const>( + &Blob::shape), + bp::return_value_policy())) .add_property("num", &Blob::num) .add_property("channels", &Blob::channels) .add_property("height", &Blob::height) From ada055bbf155882534907a7fb98a35e4f7bba392 Mon Sep 17 00:00:00 2001 From: Eric Tzeng Date: Wed, 22 Jul 2015 16:17:01 -0700 Subject: [PATCH 168/446] Snapshot model weights/solver state to HDF5 files. Summary of changes: - HDF5 helper functions were moved into a separate file util/hdf5.cpp - hdf5_save_nd_dataset now saves n-d blobs, can save diffs instead of data - Minor fix for memory leak in HDF5 functions (delete instead of delete[]) - Extra methods have been added to both Net/Solver enabling snapshotting and restoring from HDF5 files - snapshot_format was added to SolverParameters, with possible values HDF5 or BINARYPROTO (default HDF5) - kMaxBlobAxes was reduced to 32 to match the limitations of HDF5 --- include/caffe/blob.hpp | 2 +- include/caffe/net.hpp | 4 + include/caffe/solver.hpp | 21 ++- include/caffe/util/hdf5.hpp | 39 +++++ include/caffe/util/io.hpp | 18 --- src/caffe/layers/hdf5_data_layer.cpp | 2 +- src/caffe/layers/hdf5_output_layer.cpp | 2 +- src/caffe/layers/hdf5_output_layer.cu | 1 - src/caffe/net.cpp | 122 +++++++++++++++- src/caffe/proto/caffe.proto | 7 +- src/caffe/solver.cpp | 164 ++++++++++++++++++---- src/caffe/test/test_hdf5_output_layer.cpp | 1 + src/caffe/util/hdf5.cpp | 160 +++++++++++++++++++++ src/caffe/util/io.cpp | 74 ---------- 14 files changed, 482 insertions(+), 135 deletions(-) create mode 100644 include/caffe/util/hdf5.hpp create mode 100644 src/caffe/util/hdf5.cpp diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp index 472cc1841f7..9b813e739e9 100644 --- a/include/caffe/blob.hpp +++ b/include/caffe/blob.hpp @@ -10,7 +10,7 @@ #include "caffe/syncedmem.hpp" #include "caffe/util/math_functions.hpp" -const int kMaxBlobAxes = INT_MAX; +const int kMaxBlobAxes = 32; namespace caffe { diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index 5665df1edf2..dfd2e556a2b 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -98,8 +98,12 @@ class Net { */ void CopyTrainedLayersFrom(const NetParameter& param); void CopyTrainedLayersFrom(const string trained_filename); + void CopyTrainedLayersFromBinaryProto(const string trained_filename); + void CopyTrainedLayersFromHDF5(const string trained_filename); /// @brief Writes the net to a proto. void ToProto(NetParameter* param, bool write_diff = false) const; + /// @brief Writes the net to an HDF5 file. + void ToHDF5(const string& filename, bool write_diff = false) const; /// @brief returns the network name. inline const string& name() const { return name_; } diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index c2ced487d6f..703434b5fcf 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -27,9 +27,9 @@ class Solver { virtual void Solve(const char* resume_file = NULL); inline void Solve(const string resume_file) { Solve(resume_file.c_str()); } void Step(int iters); - // The Restore function implements how one should restore the solver to a - // previously snapshotted state. You should implement the RestoreSolverState() - // function that restores the state from a SolverState protocol buffer. + // The Restore method simply dispatches to one of the + // RestoreSolverStateFrom___ protected methods. You should implement these + // methods to restore the state from the appropriate snapshot type. void Restore(const char* resume_file); virtual ~Solver() {} inline shared_ptr > net() { return net_; } @@ -46,11 +46,15 @@ class Solver { // function that produces a SolverState protocol buffer that needs to be // written to disk together with the learned net. void Snapshot(); + string SnapshotFilename(const string extension); + string SnapshotToBinaryProto(); + string SnapshotToHDF5(); // The test routine void TestAll(); void Test(const int test_net_id = 0); - virtual void SnapshotSolverState(SolverState* state) = 0; - virtual void RestoreSolverState(const SolverState& state) = 0; + virtual void SnapshotSolverState(const string& model_filename) = 0; + virtual void RestoreSolverStateFromHDF5(const string& state_file) = 0; + virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0; void DisplayOutputBlobs(const int net_id); SolverParameter param_; @@ -85,8 +89,11 @@ class SGDSolver : public Solver { virtual void Regularize(int param_id); virtual void ComputeUpdateValue(int param_id, Dtype rate); virtual void ClipGradients(); - virtual void SnapshotSolverState(SolverState * state); - virtual void RestoreSolverState(const SolverState& state); + virtual void SnapshotSolverState(const string& model_filename); + virtual void SnapshotSolverStateToBinaryProto(const string& model_filename); + virtual void SnapshotSolverStateToHDF5(const string& model_filename); + virtual void RestoreSolverStateFromHDF5(const string& state_file); + virtual void RestoreSolverStateFromBinaryProto(const string& state_file); // history maintains the historical momentum data. // update maintains update related data and is not needed in snapshots. // temp maintains other information that might be needed in computation diff --git a/include/caffe/util/hdf5.hpp b/include/caffe/util/hdf5.hpp new file mode 100644 index 00000000000..ce568c5eb0d --- /dev/null +++ b/include/caffe/util/hdf5.hpp @@ -0,0 +1,39 @@ +#ifndef CAFFE_UTIL_HDF5_H_ +#define CAFFE_UTIL_HDF5_H_ + +#include + +#include "hdf5.h" +#include "hdf5_hl.h" + +#include "caffe/blob.hpp" + +namespace caffe { + +template +void hdf5_load_nd_dataset_helper( + hid_t file_id, const char* dataset_name_, int min_dim, int max_dim, + Blob* blob); + +template +void hdf5_load_nd_dataset( + hid_t file_id, const char* dataset_name_, int min_dim, int max_dim, + Blob* blob); + +template +void hdf5_save_nd_dataset( + const hid_t file_id, const string& dataset_name, const Blob& blob, + bool write_diff = false); + +int hdf5_load_int(hid_t loc_id, const string& dataset_name); +void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i); +string hdf5_load_string(hid_t loc_id, const string& dataset_name); +void hdf5_save_string(hid_t loc_id, const string& dataset_name, + const string& s); + +int hdf5_get_num_links(hid_t loc_id); +string hdf5_get_name_by_idx(hid_t loc_id, int idx); + +} // namespace caffe + +#endif // CAFFE_UTIL_HDF5_H_ diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp index 3a62c3c9fa9..c0938ad0625 100644 --- a/include/caffe/util/io.hpp +++ b/include/caffe/util/io.hpp @@ -5,15 +5,11 @@ #include #include "google/protobuf/message.h" -#include "hdf5.h" -#include "hdf5_hl.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" -#define HDF5_NUM_DIMS 4 - namespace caffe { using ::google::protobuf::Message; @@ -140,20 +136,6 @@ cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color); void CVMatToDatum(const cv::Mat& cv_img, Datum* datum); -template -void hdf5_load_nd_dataset_helper( - hid_t file_id, const char* dataset_name_, int min_dim, int max_dim, - Blob* blob); - -template -void hdf5_load_nd_dataset( - hid_t file_id, const char* dataset_name_, int min_dim, int max_dim, - Blob* blob); - -template -void hdf5_save_nd_dataset( - const hid_t file_id, const string& dataset_name, const Blob& blob); - } // namespace caffe #endif // CAFFE_UTIL_IO_H_ diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp index 8a782f7e524..8ced51039cf 100644 --- a/src/caffe/layers/hdf5_data_layer.cpp +++ b/src/caffe/layers/hdf5_data_layer.cpp @@ -16,7 +16,7 @@ #include "caffe/data_layers.hpp" #include "caffe/layer.hpp" -#include "caffe/util/io.hpp" +#include "caffe/util/hdf5.hpp" namespace caffe { diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp index f63375c3dc6..56788c21e5e 100644 --- a/src/caffe/layers/hdf5_output_layer.cpp +++ b/src/caffe/layers/hdf5_output_layer.cpp @@ -6,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/layer.hpp" -#include "caffe/util/io.hpp" +#include "caffe/util/hdf5.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu index ae497c34fc2..eb6d0e470b0 100644 --- a/src/caffe/layers/hdf5_output_layer.cu +++ b/src/caffe/layers/hdf5_output_layer.cu @@ -6,7 +6,6 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/layer.hpp" -#include "caffe/util/io.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index a18ee63818e..0812b367ac3 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -5,12 +5,14 @@ #include #include +#include "hdf5.h" + #include "caffe/common.hpp" #include "caffe/layer.hpp" #include "caffe/net.hpp" #include "caffe/proto/caffe.pb.h" +#include "caffe/util/hdf5.hpp" #include "caffe/util/insert_splits.hpp" -#include "caffe/util/io.hpp" #include "caffe/util/math_functions.hpp" #include "caffe/util/upgrade_proto.hpp" @@ -747,11 +749,72 @@ void Net::CopyTrainedLayersFrom(const NetParameter& param) { template void Net::CopyTrainedLayersFrom(const string trained_filename) { + if (trained_filename.size() >= 3 && + trained_filename.compare(trained_filename.size() - 3, 3, ".h5") == 0) { + CopyTrainedLayersFromHDF5(trained_filename); + } else { + CopyTrainedLayersFromBinaryProto(trained_filename); + } +} + +template +void Net::CopyTrainedLayersFromBinaryProto( + const string trained_filename) { NetParameter param; ReadNetParamsFromBinaryFileOrDie(trained_filename, ¶m); CopyTrainedLayersFrom(param); } +template +void Net::CopyTrainedLayersFromHDF5(const string trained_filename) { + hid_t file_hid = H5Fopen(trained_filename.c_str(), H5F_ACC_RDONLY, + H5P_DEFAULT); + CHECK_GE(file_hid, 0) << "Couldn't open " << trained_filename; + hid_t data_hid = H5Gopen2(file_hid, "data", H5P_DEFAULT); + CHECK_GE(data_hid, 0) << "Error reading weights from " << trained_filename; + int num_layers = hdf5_get_num_links(data_hid); + for (int i = 0; i < num_layers; ++i) { + string source_layer_name = hdf5_get_name_by_idx(data_hid, i); + if (!layer_names_index_.count(source_layer_name)) { + DLOG(INFO) << "Ignoring source layer " << source_layer_name; + continue; + } + int target_layer_id = layer_names_index_[source_layer_name]; + DLOG(INFO) << "Copying source layer " << source_layer_name; + vector > >& target_blobs = + layers_[target_layer_id]->blobs(); + hid_t layer_hid = H5Gopen2(data_hid, source_layer_name.c_str(), + H5P_DEFAULT); + CHECK_GE(layer_hid, 0) + << "Error reading weights from " << trained_filename; + // Check that source layer doesn't have more params than target layer + int num_source_params = hdf5_get_num_links(layer_hid); + CHECK_LE(num_source_params, target_blobs.size()) + << "Incompatible number of blobs for layer " << source_layer_name; + for (int j = 0; j < target_blobs.size(); ++j) { + ostringstream oss; + oss << j; + string dataset_name = oss.str(); + int target_net_param_id = param_id_vecs_[target_layer_id][j]; + if (!H5Lexists(layer_hid, dataset_name.c_str(), H5P_DEFAULT)) { + // Target param doesn't exist in source weights... + if (param_owners_[target_net_param_id] != -1) { + // ...but it's weight-shared in target, so that's fine. + continue; + } else { + LOG(FATAL) << "Incompatible number of blobs for layer " + << source_layer_name; + } + } + hdf5_load_nd_dataset(layer_hid, dataset_name.c_str(), 0, kMaxBlobAxes, + target_blobs[j].get()); + } + H5Gclose(layer_hid); + } + H5Gclose(data_hid); + H5Fclose(file_hid); +} + template void Net::ToProto(NetParameter* param, bool write_diff) const { param->Clear(); @@ -773,6 +836,63 @@ void Net::ToProto(NetParameter* param, bool write_diff) const { } } +template +void Net::ToHDF5(const string& filename, bool write_diff) const { + hid_t file_hid = H5Fcreate(filename.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT, + H5P_DEFAULT); + CHECK_GE(file_hid, 0) + << "Couldn't open " << filename << " to save weights."; + hid_t data_hid = H5Gcreate2(file_hid, "data", H5P_DEFAULT, H5P_DEFAULT, + H5P_DEFAULT); + CHECK_GE(data_hid, 0) << "Error saving weights to " << filename << "."; + hid_t diff_hid = -1; + if (write_diff) { + diff_hid = H5Gcreate2(file_hid, "diff", H5P_DEFAULT, H5P_DEFAULT, + H5P_DEFAULT); + CHECK_GE(diff_hid, 0) << "Error saving weights to " << filename << "."; + } + for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) { + const LayerParameter& layer_param = layers_[layer_id]->layer_param(); + string layer_name = layer_param.name(); + hid_t layer_data_hid = H5Gcreate2(data_hid, layer_name.c_str(), + H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT); + CHECK_GE(layer_data_hid, 0) + << "Error saving weights to " << filename << "."; + hid_t layer_diff_hid = -1; + if (write_diff) { + layer_diff_hid = H5Gcreate2(diff_hid, layer_name.c_str(), + H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT); + CHECK_GE(layer_diff_hid, 0) + << "Error saving weights to " << filename << "."; + } + int num_params = layers_[layer_id]->blobs().size(); + for (int param_id = 0; param_id < num_params; ++param_id) { + ostringstream dataset_name; + dataset_name << param_id; + const int net_param_id = param_id_vecs_[layer_id][param_id]; + if (param_owners_[net_param_id] == -1) { + // Only save params that own themselves + hdf5_save_nd_dataset(layer_data_hid, dataset_name.str(), + *params_[net_param_id]); + } + if (write_diff) { + // Write diffs regardless of weight-sharing + hdf5_save_nd_dataset(layer_diff_hid, dataset_name.str(), + *params_[net_param_id], true); + } + } + H5Gclose(layer_data_hid); + if (write_diff) { + H5Gclose(layer_diff_hid); + } + } + H5Gclose(data_hid); + if (write_diff) { + H5Gclose(diff_hid); + } + H5Fclose(file_hid); +} + template void Net::Update() { // First, accumulate the diffs of any shared parameters into their owner's diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 03daa808bf8..96e975be566 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -98,7 +98,7 @@ message NetParameter { // NOTE // Update the next available ID when you add a new SolverParameter field. // -// SolverParameter next available ID: 37 (last added: iter_size) +// SolverParameter next available ID: 38 (last added: snapshot_format) message SolverParameter { ////////////////////////////////////////////////////////////////////////////// // Specifying the train and test networks @@ -175,6 +175,11 @@ message SolverParameter { // whether to snapshot diff in the results or not. Snapshotting diff will help // debugging but the final protocol buffer size will be much larger. optional bool snapshot_diff = 16 [default = false]; + enum SnapshotFormat { + HDF5 = 0; + BINARYPROTO = 1; + } + optional SnapshotFormat snapshot_format = 37 [default = HDF5]; // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default. enum SolverMode { CPU = 0; diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index aabe0edec80..75271138bdd 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -4,9 +4,13 @@ #include #include +#include "hdf5.h" +#include "hdf5_hl.h" + #include "caffe/net.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/solver.hpp" +#include "caffe/util/hdf5.hpp" #include "caffe/util/io.hpp" #include "caffe/util/math_functions.hpp" #include "caffe/util/upgrade_proto.hpp" @@ -348,42 +352,58 @@ void Solver::Test(const int test_net_id) { template void Solver::Snapshot() { - NetParameter net_param; - // For intermediate results, we will also dump the gradient values. - net_->ToProto(&net_param, param_.snapshot_diff()); + string model_filename; + switch (param_.snapshot_format()) { + case caffe::SolverParameter_SnapshotFormat_BINARYPROTO: + model_filename = SnapshotToBinaryProto(); + break; + case caffe::SolverParameter_SnapshotFormat_HDF5: + model_filename = SnapshotToHDF5(); + break; + default: + LOG(FATAL) << "Unsupported snapshot format."; + } + + SnapshotSolverState(model_filename); +} + +template +string Solver::SnapshotFilename(const string extension) { string filename(param_.snapshot_prefix()); - string model_filename, snapshot_filename; const int kBufferSize = 20; char iter_str_buffer[kBufferSize]; snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_); - filename += iter_str_buffer; - model_filename = filename + ".caffemodel"; - LOG(INFO) << "Snapshotting to " << model_filename; - WriteProtoToBinaryFile(net_param, model_filename.c_str()); - SolverState state; - SnapshotSolverState(&state); - state.set_iter(iter_); - state.set_learned_net(model_filename); - state.set_current_step(current_step_); - snapshot_filename = filename + ".solverstate"; - LOG(INFO) << "Snapshotting solver state to " << snapshot_filename; - WriteProtoToBinaryFile(state, snapshot_filename.c_str()); + return filename + iter_str_buffer + extension; } template -void Solver::Restore(const char* state_file) { - SolverState state; +string Solver::SnapshotToBinaryProto() { + string model_filename = SnapshotFilename(".caffemodel"); + LOG(INFO) << "Snapshotting to binary proto file " << model_filename; NetParameter net_param; - ReadProtoFromBinaryFile(state_file, &state); - if (state.has_learned_net()) { - ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param); - net_->CopyTrainedLayersFrom(net_param); - } - iter_ = state.iter(); - current_step_ = state.current_step(); - RestoreSolverState(state); + net_->ToProto(&net_param, param_.snapshot_diff()); + WriteProtoToBinaryFile(net_param, model_filename); + return model_filename; +} + +template +string Solver::SnapshotToHDF5() { + string model_filename = SnapshotFilename(".caffemodel.h5"); + LOG(INFO) << "Snapshotting to HDF5 file " << model_filename; + net_->ToHDF5(model_filename, param_.snapshot_diff()); + return model_filename; } +template +void Solver::Restore(const char* state_file) { + string state_filename(state_file); + if (state_filename.size() >= 3 && + state_filename.compare(state_filename.size() - 3, 3, ".h5") == 0) { + RestoreSolverStateFromHDF5(state_filename); + } else { + RestoreSolverStateFromBinaryProto(state_filename); + } +} // Return the current learning rate. The currently implemented learning rate // policies are as follows: @@ -618,17 +638,76 @@ void SGDSolver::ComputeUpdateValue(int param_id, Dtype rate) { } template -void SGDSolver::SnapshotSolverState(SolverState* state) { - state->clear_history(); +void SGDSolver::SnapshotSolverState(const string& model_filename) { + switch (this->param_.snapshot_format()) { + case caffe::SolverParameter_SnapshotFormat_BINARYPROTO: + SnapshotSolverStateToBinaryProto(model_filename); + break; + case caffe::SolverParameter_SnapshotFormat_HDF5: + SnapshotSolverStateToHDF5(model_filename); + break; + default: + LOG(FATAL) << "Unsupported snapshot format."; + } +} + +template +void SGDSolver::SnapshotSolverStateToBinaryProto( + const string& model_filename) { + SolverState state; + state.set_iter(this->iter_); + state.set_learned_net(model_filename); + state.set_current_step(this->current_step_); + state.clear_history(); for (int i = 0; i < history_.size(); ++i) { // Add history - BlobProto* history_blob = state->add_history(); + BlobProto* history_blob = state.add_history(); history_[i]->ToProto(history_blob); } + string snapshot_filename = Solver::SnapshotFilename(".solverstate"); + LOG(INFO) + << "Snapshotting solver state to binary proto file" << snapshot_filename; + WriteProtoToBinaryFile(state, snapshot_filename.c_str()); +} + +template +void SGDSolver::SnapshotSolverStateToHDF5( + const string& model_filename) { + string snapshot_filename = + Solver::SnapshotFilename(".solverstate.h5"); + LOG(INFO) << "Snapshotting solver state to HDF5 file " << snapshot_filename; + hid_t file_hid = H5Fcreate(snapshot_filename.c_str(), H5F_ACC_TRUNC, + H5P_DEFAULT, H5P_DEFAULT); + CHECK_GE(file_hid, 0) + << "Couldn't open " << snapshot_filename << " to save solver state."; + hdf5_save_int(file_hid, "iter", this->iter_); + hdf5_save_string(file_hid, "learned_net", model_filename); + hdf5_save_int(file_hid, "current_step", this->current_step_); + hid_t history_hid = H5Gcreate2(file_hid, "history", H5P_DEFAULT, H5P_DEFAULT, + H5P_DEFAULT); + CHECK_GE(history_hid, 0) + << "Error saving solver state to " << snapshot_filename << "."; + for (int i = 0; i < history_.size(); ++i) { + ostringstream oss; + oss << i; + hdf5_save_nd_dataset(history_hid, oss.str(), *history_[i]); + } + H5Gclose(history_hid); + H5Fclose(file_hid); } template -void SGDSolver::RestoreSolverState(const SolverState& state) { +void SGDSolver::RestoreSolverStateFromBinaryProto( + const string& state_file) { + SolverState state; + ReadProtoFromBinaryFile(state_file, &state); + this->iter_ = state.iter(); + if (state.has_learned_net()) { + NetParameter net_param; + ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param); + this->net_->CopyTrainedLayersFrom(net_param); + } + this->current_step_ = state.current_step(); CHECK_EQ(state.history_size(), history_.size()) << "Incorrect length of history blobs."; LOG(INFO) << "SGDSolver: restoring history"; @@ -637,6 +716,31 @@ void SGDSolver::RestoreSolverState(const SolverState& state) { } } +template +void SGDSolver::RestoreSolverStateFromHDF5(const string& state_file) { + hid_t file_hid = H5Fopen(state_file.c_str(), H5F_ACC_RDONLY, H5P_DEFAULT); + CHECK_GE(file_hid, 0) << "Couldn't open solver state file " << state_file; + this->iter_ = hdf5_load_int(file_hid, "iter"); + if (H5LTfind_dataset(file_hid, "learned_net")) { + string learned_net = hdf5_load_string(file_hid, "learned_net"); + this->net_->CopyTrainedLayersFrom(learned_net); + } + this->current_step_ = hdf5_load_int(file_hid, "current_step"); + hid_t history_hid = H5Gopen2(file_hid, "history", H5P_DEFAULT); + CHECK_GE(history_hid, 0) << "Error reading history from " << state_file; + int state_history_size = hdf5_get_num_links(history_hid); + CHECK_EQ(state_history_size, history_.size()) + << "Incorrect length of history blobs."; + for (int i = 0; i < history_.size(); ++i) { + ostringstream oss; + oss << i; + hdf5_load_nd_dataset(history_hid, oss.str().c_str(), 0, + kMaxBlobAxes, history_[i].get()); + } + H5Gclose(history_hid); + H5Fclose(file_hid); +} + template void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { const vector > >& net_params = this->net_->params(); diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp index a23034f284a..b56277b53ae 100644 --- a/src/caffe/test/test_hdf5_output_layer.cpp +++ b/src/caffe/test/test_hdf5_output_layer.cpp @@ -6,6 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" +#include "caffe/util/hdf5.hpp" #include "caffe/util/io.hpp" #include "caffe/vision_layers.hpp" diff --git a/src/caffe/util/hdf5.cpp b/src/caffe/util/hdf5.cpp new file mode 100644 index 00000000000..d0d05f70f8f --- /dev/null +++ b/src/caffe/util/hdf5.cpp @@ -0,0 +1,160 @@ +#include "caffe/util/hdf5.hpp" + +#include +#include + +namespace caffe { + +// Verifies format of data stored in HDF5 file and reshapes blob accordingly. +template +void hdf5_load_nd_dataset_helper( + hid_t file_id, const char* dataset_name_, int min_dim, int max_dim, + Blob* blob) { + // Verify that the dataset exists. + CHECK(H5LTfind_dataset(file_id, dataset_name_)) + << "Failed to find HDF5 dataset " << dataset_name_; + // Verify that the number of dimensions is in the accepted range. + herr_t status; + int ndims; + status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims); + CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_; + CHECK_GE(ndims, min_dim); + CHECK_LE(ndims, max_dim); + + // Verify that the data format is what we expect: float or double. + std::vector dims(ndims); + H5T_class_t class_; + status = H5LTget_dataset_info( + file_id, dataset_name_, dims.data(), &class_, NULL); + CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_; + CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data"; + + vector blob_dims(dims.size()); + for (int i = 0; i < dims.size(); ++i) { + blob_dims[i] = dims[i]; + } + blob->Reshape(blob_dims); +} + +template <> +void hdf5_load_nd_dataset(hid_t file_id, const char* dataset_name_, + int min_dim, int max_dim, Blob* blob) { + hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob); + herr_t status = H5LTread_dataset_float( + file_id, dataset_name_, blob->mutable_cpu_data()); + CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_; +} + +template <> +void hdf5_load_nd_dataset(hid_t file_id, const char* dataset_name_, + int min_dim, int max_dim, Blob* blob) { + hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob); + herr_t status = H5LTread_dataset_double( + file_id, dataset_name_, blob->mutable_cpu_data()); + CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_; +} + +template <> +void hdf5_save_nd_dataset( + const hid_t file_id, const string& dataset_name, const Blob& blob, + bool write_diff) { + int num_axes = blob.num_axes(); + hsize_t *dims = new hsize_t[num_axes]; + for (int i = 0; i < num_axes; ++i) { + dims[i] = blob.shape(i); + } + const float* data; + if (write_diff) { + data = blob.cpu_diff(); + } else { + data = blob.cpu_data(); + } + herr_t status = H5LTmake_dataset_float( + file_id, dataset_name.c_str(), num_axes, dims, data); + CHECK_GE(status, 0) << "Failed to make float dataset " << dataset_name; + delete[] dims; +} + +template <> +void hdf5_save_nd_dataset( + hid_t file_id, const string& dataset_name, const Blob& blob, + bool write_diff) { + int num_axes = blob.num_axes(); + hsize_t *dims = new hsize_t[num_axes]; + for (int i = 0; i < num_axes; ++i) { + dims[i] = blob.shape(i); + } + const double* data; + if (write_diff) { + data = blob.cpu_diff(); + } else { + data = blob.cpu_data(); + } + herr_t status = H5LTmake_dataset_double( + file_id, dataset_name.c_str(), num_axes, dims, data); + CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name; + delete[] dims; +} + +string hdf5_load_string(hid_t loc_id, const string& dataset_name) { + // Get size of dataset + size_t size; + H5T_class_t class_; + herr_t status = \ + H5LTget_dataset_info(loc_id, dataset_name.c_str(), NULL, &class_, &size); + CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name; + char *buf = new char[size]; + status = H5LTread_dataset_string(loc_id, dataset_name.c_str(), buf); + CHECK_GE(status, 0) + << "Failed to load int dataset with name " << dataset_name; + string val(buf); + delete[] buf; + return val; +} + +void hdf5_save_string(hid_t loc_id, const string& dataset_name, + const string& s) { + herr_t status = \ + H5LTmake_dataset_string(loc_id, dataset_name.c_str(), s.c_str()); + CHECK_GE(status, 0) + << "Failed to save string dataset with name " << dataset_name; +} + +int hdf5_load_int(hid_t loc_id, const string& dataset_name) { + int val; + herr_t status = H5LTread_dataset_int(loc_id, dataset_name.c_str(), &val); + CHECK_GE(status, 0) + << "Failed to load int dataset with name " << dataset_name; + return val; +} + +void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i) { + hsize_t one = 1; + herr_t status = \ + H5LTmake_dataset_int(loc_id, dataset_name.c_str(), 1, &one, &i); + CHECK_GE(status, 0) + << "Failed to save int dataset with name " << dataset_name; +} + +int hdf5_get_num_links(hid_t loc_id) { + H5G_info_t info; + herr_t status = H5Gget_info(loc_id, &info); + CHECK_GE(status, 0) << "Error while counting HDF5 links."; + return info.nlinks; +} + +string hdf5_get_name_by_idx(hid_t loc_id, int idx) { + ssize_t str_size = H5Lget_name_by_idx( + loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, NULL, 0, H5P_DEFAULT); + CHECK_GE(str_size, 0) << "Error retrieving HDF5 dataset at index " << idx; + char *c_str = new char[str_size+1]; + ssize_t status = H5Lget_name_by_idx( + loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, c_str, str_size+1, + H5P_DEFAULT); + CHECK_GE(status, 0) << "Error retrieving HDF5 dataset at index " << idx; + string result(c_str); + delete[] c_str; + return result; +} + +} // namespace caffe diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp index 77ef7f257f4..6f03314202c 100644 --- a/src/caffe/util/io.cpp +++ b/src/caffe/util/io.cpp @@ -228,79 +228,5 @@ void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) { datum->set_data(buffer); } -// Verifies format of data stored in HDF5 file and reshapes blob accordingly. -template -void hdf5_load_nd_dataset_helper( - hid_t file_id, const char* dataset_name_, int min_dim, int max_dim, - Blob* blob) { - // Verify that the dataset exists. - CHECK(H5LTfind_dataset(file_id, dataset_name_)) - << "Failed to find HDF5 dataset " << dataset_name_; - // Verify that the number of dimensions is in the accepted range. - herr_t status; - int ndims; - status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims); - CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_; - CHECK_GE(ndims, min_dim); - CHECK_LE(ndims, max_dim); - - // Verify that the data format is what we expect: float or double. - std::vector dims(ndims); - H5T_class_t class_; - status = H5LTget_dataset_info( - file_id, dataset_name_, dims.data(), &class_, NULL); - CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_; - CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data"; - - vector blob_dims(dims.size()); - for (int i = 0; i < dims.size(); ++i) { - blob_dims[i] = dims[i]; - } - blob->Reshape(blob_dims); -} - -template <> -void hdf5_load_nd_dataset(hid_t file_id, const char* dataset_name_, - int min_dim, int max_dim, Blob* blob) { - hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob); - herr_t status = H5LTread_dataset_float( - file_id, dataset_name_, blob->mutable_cpu_data()); - CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_; -} - -template <> -void hdf5_load_nd_dataset(hid_t file_id, const char* dataset_name_, - int min_dim, int max_dim, Blob* blob) { - hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob); - herr_t status = H5LTread_dataset_double( - file_id, dataset_name_, blob->mutable_cpu_data()); - CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_; -} - -template <> -void hdf5_save_nd_dataset( - const hid_t file_id, const string& dataset_name, const Blob& blob) { - hsize_t dims[HDF5_NUM_DIMS]; - dims[0] = blob.num(); - dims[1] = blob.channels(); - dims[2] = blob.height(); - dims[3] = blob.width(); - herr_t status = H5LTmake_dataset_float( - file_id, dataset_name.c_str(), HDF5_NUM_DIMS, dims, blob.cpu_data()); - CHECK_GE(status, 0) << "Failed to make float dataset " << dataset_name; -} - -template <> -void hdf5_save_nd_dataset( - const hid_t file_id, const string& dataset_name, const Blob& blob) { - hsize_t dims[HDF5_NUM_DIMS]; - dims[0] = blob.num(); - dims[1] = blob.channels(); - dims[2] = blob.height(); - dims[3] = blob.width(); - herr_t status = H5LTmake_dataset_double( - file_id, dataset_name.c_str(), HDF5_NUM_DIMS, dims, blob.cpu_data()); - CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name; -} } // namespace caffe From 5c89c64ff1cf97a3578b11803f83d73e3e933592 Mon Sep 17 00:00:00 2001 From: Eric Tzeng Date: Wed, 29 Jul 2015 18:40:38 -0700 Subject: [PATCH 169/446] TestSnapshot expects .h5 snapshots, explicitly checks history. --- src/caffe/test/test_gradient_based_solver.cpp | 25 ++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 94b500124aa..78bf4b3121e 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -139,7 +139,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { if (snapshot) { ostringstream resume_file; resume_file << snapshot_prefix_ << "/_iter_" << num_iters - << ".solverstate"; + << ".solverstate.h5"; string resume_filename = resume_file.str(); return resume_filename; } @@ -394,6 +394,18 @@ class GradientBasedSolverTest : public MultiDeviceTest { } } + // Save the solver history + vector > > history_copies; + const vector > >& orig_history = solver_->history(); + history_copies.resize(orig_history.size()); + for (int i = 0; i < orig_history.size(); ++i) { + history_copies[i].reset(new Blob()); + const bool kReshape = true; + for (int copy_diff = false; copy_diff <= true; ++copy_diff) { + history_copies[i]->CopyFrom(*orig_history[i], copy_diff, kReshape); + } + } + // Run the solver for num_iters iterations and snapshot. snapshot = true; string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay, @@ -414,6 +426,17 @@ class GradientBasedSolverTest : public MultiDeviceTest { << "param " << i << " diff differed at dim " << j; } } + + // Check that history now matches. + const vector > >& history = solver_->history(); + for (int i = 0; i < history.size(); ++i) { + for (int j = 0; j < history[i]->count(); ++j) { + EXPECT_EQ(history_copies[i]->cpu_data()[j], history[i]->cpu_data()[j]) + << "history blob " << i << " data differed at dim " << j; + EXPECT_EQ(history_copies[i]->cpu_diff()[j], history[i]->cpu_diff()[j]) + << "history blob " << i << " diff differed at dim " << j; + } + } } }; From c9b333e06212365e3ea166bacbd4b560158316f0 Mon Sep 17 00:00:00 2001 From: Eric Tzeng Date: Fri, 7 Aug 2015 13:17:26 -0700 Subject: [PATCH 170/446] Update example bash scripts to expect .h5, new extensions in .gitignore --- .gitignore | 2 ++ examples/cifar10/train_full.sh | 4 ++-- examples/cifar10/train_quick.sh | 2 +- examples/imagenet/resume_training.sh | 2 +- 4 files changed, 6 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index 28f2aca854b..53c1fb056bb 100644 --- a/.gitignore +++ b/.gitignore @@ -61,7 +61,9 @@ Makefile.config data/* models/* *.caffemodel +*.caffemodel.h5 *.solverstate +*.solverstate.h5 *.binaryproto *leveldb *lmdb diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh index 4285a5d6468..ef112e1f6db 100755 --- a/examples/cifar10/train_full.sh +++ b/examples/cifar10/train_full.sh @@ -8,9 +8,9 @@ $TOOLS/caffe train \ # reduce learning rate by factor of 10 $TOOLS/caffe train \ --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \ - --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate + --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5 # reduce learning rate by factor of 10 $TOOLS/caffe train \ --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \ - --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate + --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5 diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh index 2830c40945c..6b7d228879b 100755 --- a/examples/cifar10/train_quick.sh +++ b/examples/cifar10/train_quick.sh @@ -8,4 +8,4 @@ $TOOLS/caffe train \ # reduce learning rate by factor of 10 after 8 epochs $TOOLS/caffe train \ --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \ - --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate + --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5 diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh index d1febff8d39..bf7945c0fd0 100755 --- a/examples/imagenet/resume_training.sh +++ b/examples/imagenet/resume_training.sh @@ -2,4 +2,4 @@ ./build/tools/caffe train \ --solver=models/bvlc_reference_caffenet/solver.prototxt \ - --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate + --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5 From f81ed077cbfd96b3a9ec98898347eb5d2966ccea Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Fri, 7 Aug 2015 15:27:02 -0700 Subject: [PATCH 171/446] TestGradientBasedSolver: restore Gaussian filler to all tests except accumulation one --- src/caffe/test/test_gradient_based_solver.cpp | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 78bf4b3121e..235a7d85ad0 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -24,12 +24,14 @@ class GradientBasedSolverTest : public MultiDeviceTest { protected: GradientBasedSolverTest() : - seed_(1701), num_(4), channels_(3), height_(10), width_(10) {} + seed_(1701), num_(4), channels_(3), height_(10), width_(10), + constant_data_(false) {} string snapshot_prefix_; shared_ptr > solver_; int seed_; int num_, channels_, height_, width_; + bool constant_data_; Dtype delta_; // Stability constant for AdaGrad. virtual SolverParameter_SolverType solver_type() = 0; @@ -79,7 +81,9 @@ class GradientBasedSolverTest : public MultiDeviceTest { " height: 1 " " width: 1 " " data_filler { " - " type: 'constant' " + " type: '" << string(constant_data_ ? "constant" : "gaussian") + << "' " + " std: 1.0 " " value: 1.0 " " } " " data_filler { " @@ -302,6 +306,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { const Dtype kMomentum, const int kNumIters, const int kIterSize) { const double kPrecision = 1e-2; const double kMinPrecision = 1e-7; + constant_data_ = true; // Solve without accumulation and save parameters. this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum, kNumIters); From c251b2581b02904ea6b5501786d4514734aef3d6 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Fri, 7 Aug 2015 15:34:00 -0700 Subject: [PATCH 172/446] TestGradientBasedSolver: make tests across solver types more consistent --- src/caffe/test/test_gradient_based_solver.cpp | 91 ++++++++++++------- 1 file changed, 59 insertions(+), 32 deletions(-) diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 235a7d85ad0..54606dbd7ed 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -466,23 +466,38 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdate) { this->TestLeastSquaresUpdate(); } -TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateLROneTenth) { +TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateLROneHundredth) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 0.1; + const Dtype kLearningRate = 0.01; this->TestLeastSquaresUpdate(kLearningRate); } TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithWeightDecay) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay); + const Dtype kMomentum = 0; + const int kNumIters = 1; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + +TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithWeightDecayMultiIter) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0; + const int kNumIters = 4; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } } TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; - const Dtype kWeightDecay = 0.0; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; const int kNumIters = 1; for (int i = 0; i <= kNumIters; ++i) { @@ -492,8 +507,8 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) { TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; - const Dtype kWeightDecay = 0.0; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { @@ -504,8 +519,8 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; - const Dtype kWeightDecay = 0.1; - const Dtype kMomentum = 0.9; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.5; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); @@ -515,7 +530,7 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) { TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; - const Dtype kWeightDecay = 0.1; + const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; const int kNumIters = 4; const int kIterSize = 2; @@ -526,7 +541,7 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { TYPED_TEST(SGDSolverTest, TestSnapshot) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; - const Dtype kWeightDecay = 0.1; + const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; const int kNumIters = 4; for (int i = 1; i <= kNumIters; ++i) { @@ -554,15 +569,15 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdate) { this->TestLeastSquaresUpdate(); } -TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneTenth) { +TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneHundredth) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 0.1; + const Dtype kLearningRate = 0.01; this->TestLeastSquaresUpdate(kLearningRate); } TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithWeightDecay) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay); } @@ -570,8 +585,8 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithWeightDecay) { TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; - const Dtype kWeightDecay = 0.1; - const Dtype kMomentum = 0.0; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); @@ -581,8 +596,8 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) { TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; - const Dtype kWeightDecay = 0.1; - const Dtype kMomentum = 0.0; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0; const int kNumIters = 4; const int kIterSize = 2; this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, @@ -592,8 +607,8 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { TYPED_TEST(AdaGradSolverTest, TestSnapshot) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; - const Dtype kWeightDecay = 0.1; - const Dtype kMomentum = 0.0; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0; const int kNumIters = 4; for (int i = 1; i <= kNumIters; ++i) { this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); @@ -620,23 +635,35 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdate) { this->TestLeastSquaresUpdate(); } -TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneTenth) { +TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneHundredth) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 0.1; + const Dtype kLearningRate = 0.01; this->TestLeastSquaresUpdate(kLearningRate); } TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithWeightDecay) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay); } +TYPED_TEST(NesterovSolverTest, + TestNesterovLeastSquaresUpdateWithWeightDecayMultiIter) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0; + const int kNumIters = 4; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; - const Dtype kWeightDecay = 0.0; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; const int kNumIters = 1; for (int i = 0; i <= kNumIters; ++i) { @@ -646,8 +673,8 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) { TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; - const Dtype kWeightDecay = 0.0; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { @@ -658,7 +685,7 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; - const Dtype kWeightDecay = 0.1; + const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { @@ -669,7 +696,7 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) { TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; - const Dtype kWeightDecay = 0.1; + const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; const int kNumIters = 4; const int kIterSize = 2; @@ -680,8 +707,8 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { TYPED_TEST(NesterovSolverTest, TestSnapshot) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; - const Dtype kWeightDecay = 0.1; - const Dtype kMomentum = 0.0; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; const int kNumIters = 4; for (int i = 1; i <= kNumIters; ++i) { this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); From d5b42bf7d6fa0ed85069745c56104503b7888b63 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Mon, 20 Jul 2015 20:50:50 -0700 Subject: [PATCH 173/446] Net: add learnable_params_ used by solvers to correctly handle shared params -Params now share diffs as well as data (works due to layers accumulating gradients into param diffs, rather than overwriting) -It's now required that any shared params with specified lr_mult's, decay_mult's match -TestGradientBasedSolver checks that behavior remains correct with shared weights --- include/caffe/net.hpp | 41 +++- src/caffe/net.cpp | 89 +++++---- src/caffe/solver.cpp | 44 ++--- src/caffe/test/test_gradient_based_solver.cpp | 177 +++++++++++++++++- src/caffe/test/test_net.cpp | 29 ++- 5 files changed, 276 insertions(+), 104 deletions(-) diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index dfd2e556a2b..bf997553ee2 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -57,6 +57,12 @@ class Net { */ string Forward(const string& input_blob_protos, Dtype* loss = NULL); + /** + * @brief Zeroes out the diffs of all net parameters. + * Should be run before Backward. + */ + void ClearParamDiffs(); + /** * The network backward should take no input and output, since it solely * computes the gradient w.r.t the parameters, and the data has already been @@ -84,6 +90,13 @@ class Net { /// @brief Updates the network weights based on the diff values computed. void Update(); + /** + * @brief Shares weight data of owner blobs with shared blobs. + * + * Note: this is called by Net::Init, and thus should normally not be + * called manually. + */ + void ShareWeights(); /** * @brief For an already initialized net, implicitly copies (i.e., using no @@ -148,11 +161,19 @@ class Net { inline const vector > >& params() const { return params_; } - /// @brief returns the parameter learning rate multipliers + inline const vector*>& learnable_params() const { + return learnable_params_; + } + /// @brief returns the learnable parameter learning rate multipliers inline const vector& params_lr() const { return params_lr_; } + inline const vector& has_params_lr() const { return has_params_lr_; } + /// @brief returns the learnable parameter decay multipliers inline const vector& params_weight_decay() const { return params_weight_decay_; } + inline const vector& has_params_decay() const { + return has_params_decay_; + } const map& param_names_index() const { return param_names_index_; } @@ -213,9 +234,6 @@ class Net { /// @brief Helper for displaying debug info in Update. void UpdateDebugInfo(const int param_id); - /// @brief Get misc parameters, e.g. the LR multiplier and weight decay. - void GetLearningRateAndWeightDecay(); - /// @brief The network name string name_; /// @brief The phase: TRAIN or TEST @@ -254,10 +272,21 @@ class Net { vector*> net_output_blobs_; /// The parameters in the network. vector > > params_; - /// the learning rate multipliers + vector*> learnable_params_; + /** + * The mapping from params_ -> learnable_params_: we have + * learnable_param_ids_.size() == params_.size(), + * and learnable_params_[learnable_param_ids_[i]] == params_[i].get() + * if and only if params_[i] is an "owner"; otherwise, params_[i] is a sharer + * and learnable_params_[learnable_param_ids_[i]] gives its owner. + */ + vector learnable_param_ids_; + /// the learning rate multipliers for learnable_params_ vector params_lr_; - /// the weight decay multipliers + vector has_params_lr_; + /// the weight decay multipliers for learnable_params_ vector params_weight_decay_; + vector has_params_decay_; /// The bytes of memory used by this net size_t memory_used_; /// Whether to compute and display debug info for the net. diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 0812b367ac3..0e5ed804b73 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -244,7 +244,7 @@ void Net::Init(const NetParameter& in_param) { for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) { layer_names_index_[layer_names_[layer_id]] = layer_id; } - GetLearningRateAndWeightDecay(); + ShareWeights(); debug_info_ = param.debug_info(); LOG(INFO) << "Network initialization done."; LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype); @@ -441,6 +441,9 @@ void Net::AppendParam(const NetParameter& param, const int layer_id, params_.push_back(layers_[layer_id]->blobs()[param_id]); param_id_vecs_[layer_id].push_back(net_param_id); param_layer_indices_.push_back(make_pair(layer_id, param_id)); + ParamSpec default_param_spec; + const ParamSpec* param_spec = (layer_param.param_size() > param_id) ? + &layer_param.param(param_id) : &default_param_spec; if (!param_size || !param_name.size() || (param_name.size() && param_names_index_.find(param_name) == param_names_index_.end())) { // This layer "owns" this parameter blob -- it is either anonymous @@ -450,6 +453,13 @@ void Net::AppendParam(const NetParameter& param, const int layer_id, if (param_name.size()) { param_names_index_[param_name] = net_param_id; } + const int learnable_param_id = learnable_params_.size(); + learnable_params_.push_back(params_[net_param_id].get()); + learnable_param_ids_.push_back(learnable_param_id); + has_params_lr_.push_back(param_spec->has_lr_mult()); + has_params_decay_.push_back(param_spec->has_decay_mult()); + params_lr_.push_back(param_spec->lr_mult()); + params_weight_decay_.push_back(param_spec->decay_mult()); } else { // Named param blob with name we've seen before: share params const int owner_net_param_id = param_names_index_[param_name]; @@ -474,23 +484,25 @@ void Net::AppendParam(const NetParameter& param, const int layer_id, // Strict dimension checking -- all dims must be the same. CHECK(this_blob->shape() == owner_blob->shape()); } - layers_[layer_id]->blobs()[param_id]->ShareData( - *layers_[owner_layer_id]->blobs()[owner_param_id]); - } -} - -template -void Net::GetLearningRateAndWeightDecay() { - LOG(INFO) << "Collecting Learning Rate and Weight Decay."; - ParamSpec default_param_spec; - for (int i = 0; i < layers_.size(); ++i) { - vector > >& layer_blobs = layers_[i]->blobs(); - for (int j = 0; j < layer_blobs.size(); ++j) { - const ParamSpec* param_spec = - (layers_[i]->layer_param().param_size() > j) ? - &layers_[i]->layer_param().param(j) : &default_param_spec; - params_lr_.push_back(param_spec->lr_mult()); - params_weight_decay_.push_back(param_spec->decay_mult()); + const int learnable_param_id = learnable_param_ids_[owner_net_param_id]; + if (param_spec->has_lr_mult()) { + if (has_params_lr_[learnable_param_id]) { + CHECK_EQ(param_spec->lr_mult(), params_lr_[learnable_param_id]) + << "Shared param '" << param_name << "' has mismatched lr_mult."; + } else { + has_params_lr_[learnable_param_id] = true; + params_lr_[learnable_param_id] = param_spec->lr_mult(); + } + } + if (param_spec->has_decay_mult()) { + if (has_params_decay_[learnable_param_id]) { + CHECK_EQ(param_spec->decay_mult(), + params_weight_decay_[learnable_param_id]) + << "Shared param '" << param_name << "' has mismatched decay_mult."; + } else { + has_params_decay_[learnable_param_id] = true; + params_weight_decay_[learnable_param_id] = param_spec->decay_mult(); + } } } } @@ -895,39 +907,38 @@ void Net::ToHDF5(const string& filename, bool write_diff) const { template void Net::Update() { - // First, accumulate the diffs of any shared parameters into their owner's - // diff. (Assumes that the learning rate, weight decay, etc. have already been - // accounted for in the current diff.) - for (int i = 0; i < params_.size(); ++i) { - if (param_owners_[i] < 0) { continue; } - if (debug_info_) { UpdateDebugInfo(i); } - const int count = params_[i]->count(); - const Dtype* this_diff; - Dtype* owner_diff; + for (int i = 0; i < learnable_params_.size(); ++i) { + learnable_params_[i]->Update(); + } +} + +template +void Net::ClearParamDiffs() { + for (int i = 0; i < learnable_params_.size(); ++i) { + Blob* blob = learnable_params_[i]; switch (Caffe::mode()) { case Caffe::CPU: - this_diff = params_[i]->cpu_diff(); - owner_diff = params_[param_owners_[i]]->mutable_cpu_diff(); - caffe_add(count, this_diff, owner_diff, owner_diff); + caffe_set(blob->count(), static_cast(0), + blob->mutable_cpu_diff()); break; case Caffe::GPU: #ifndef CPU_ONLY - this_diff = params_[i]->gpu_diff(); - owner_diff = params_[param_owners_[i]]->mutable_gpu_diff(); - caffe_gpu_add(count, this_diff, owner_diff, owner_diff); + caffe_gpu_set(blob->count(), static_cast(0), + blob->mutable_gpu_diff()); #else NO_GPU; #endif break; - default: - LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); } } - // Now, update the owned parameters. +} + +template +void Net::ShareWeights() { for (int i = 0; i < params_.size(); ++i) { - if (param_owners_[i] >= 0) { continue; } - if (debug_info_) { UpdateDebugInfo(i); } - params_[i]->Update(); + if (param_owners_[i] < 0) { continue; } + params_[i]->ShareData(*params_[param_owners_[i]]); + params_[i]->ShareDiff(*params_[param_owners_[i]]); } } diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 75271138bdd..32276ac148a 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -173,24 +173,7 @@ void Solver::Step(int iters) { while (iter_ < stop_iter) { // zero-init the params - for (int i = 0; i < net_->params().size(); ++i) { - shared_ptr > blob = net_->params()[i]; - switch (Caffe::mode()) { - case Caffe::CPU: - caffe_set(blob->count(), static_cast(0), - blob->mutable_cpu_diff()); - break; - case Caffe::GPU: -#ifndef CPU_ONLY - caffe_gpu_set(blob->count(), static_cast(0), - blob->mutable_gpu_diff()); -#else - NO_GPU; -#endif - break; - } - } - + net_->ClearParamDiffs(); if (param_.test_interval() && iter_ % param_.test_interval() == 0 && (iter_ > 0 || param_.test_initialization())) { TestAll(); @@ -462,7 +445,7 @@ Dtype SGDSolver::GetLearningRate() { template void SGDSolver::PreSolve() { // Initialize the history - const vector > >& net_params = this->net_->params(); + const vector*>& net_params = this->net_->learnable_params(); history_.clear(); update_.clear(); temp_.clear(); @@ -478,12 +461,10 @@ template void SGDSolver::ClipGradients() { const Dtype clip_gradients = this->param_.clip_gradients(); if (clip_gradients < 0) { return; } - const vector > >& net_params = this->net_->params(); + const vector*>& net_params = this->net_->learnable_params(); Dtype sumsq_diff = 0; for (int i = 0; i < net_params.size(); ++i) { - if (this->net_->param_owners()[i] < 0) { - sumsq_diff += net_params[i]->sumsq_diff(); - } + sumsq_diff += net_params[i]->sumsq_diff(); } const Dtype l2norm_diff = std::sqrt(sumsq_diff); if (l2norm_diff > clip_gradients) { @@ -492,9 +473,7 @@ void SGDSolver::ClipGradients() { << l2norm_diff << " > " << clip_gradients << ") " << "by scale factor " << scale_factor; for (int i = 0; i < net_params.size(); ++i) { - if (this->net_->param_owners()[i] < 0) { - net_params[i]->scale_diff(scale_factor); - } + net_params[i]->scale_diff(scale_factor); } } } @@ -506,7 +485,8 @@ void SGDSolver::ApplyUpdate() { LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate; } ClipGradients(); - for (int param_id = 0; param_id < this->net_->params().size(); ++param_id) { + for (int param_id = 0; param_id < this->net_->learnable_params().size(); + ++param_id) { Normalize(param_id); Regularize(param_id); ComputeUpdateValue(param_id, rate); @@ -518,7 +498,7 @@ template void SGDSolver::Normalize(int param_id) { if (this->param_.iter_size() == 1) { return; } // Scale gradient to counterbalance accumulation. - const vector > >& net_params = this->net_->params(); + const vector*>& net_params = this->net_->learnable_params(); const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size(); switch (Caffe::mode()) { case Caffe::CPU: { @@ -542,7 +522,7 @@ void SGDSolver::Normalize(int param_id) { template void SGDSolver::Regularize(int param_id) { - const vector > >& net_params = this->net_->params(); + const vector*>& net_params = this->net_->learnable_params(); const vector& net_params_weight_decay = this->net_->params_weight_decay(); Dtype weight_decay = this->param_.weight_decay(); @@ -604,7 +584,7 @@ void SGDSolver::Regularize(int param_id) { template void SGDSolver::ComputeUpdateValue(int param_id, Dtype rate) { - const vector > >& net_params = this->net_->params(); + const vector*>& net_params = this->net_->learnable_params(); const vector& net_params_lr = this->net_->params_lr(); Dtype momentum = this->param_.momentum(); Dtype local_rate = rate * net_params_lr[param_id]; @@ -743,7 +723,7 @@ void SGDSolver::RestoreSolverStateFromHDF5(const string& state_file) { template void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { - const vector > >& net_params = this->net_->params(); + const vector*>& net_params = this->net_->learnable_params(); const vector& net_params_lr = this->net_->params_lr(); Dtype momentum = this->param_.momentum(); Dtype local_rate = rate * net_params_lr[param_id]; @@ -803,7 +783,7 @@ void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { template void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { - const vector > >& net_params = this->net_->params(); + const vector*>& net_params = this->net_->learnable_params(); const vector& net_params_lr = this->net_->params_lr(); Dtype delta = this->param_.delta(); Dtype local_rate = rate * net_params_lr[param_id]; diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 54606dbd7ed..e1bbbf17726 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -25,13 +25,13 @@ class GradientBasedSolverTest : public MultiDeviceTest { protected: GradientBasedSolverTest() : seed_(1701), num_(4), channels_(3), height_(10), width_(10), - constant_data_(false) {} + constant_data_(false), share_(false) {} string snapshot_prefix_; shared_ptr > solver_; int seed_; int num_, channels_, height_, width_; - bool constant_data_; + bool constant_data_, share_; Dtype delta_; // Stability constant for AdaGrad. virtual SolverParameter_SolverType solver_type() = 0; @@ -93,10 +93,26 @@ class GradientBasedSolverTest : public MultiDeviceTest { " } " " top: 'data' " " top: 'targets' " - " } " + " } "; + if (share_) { + proto << + " layer { " + " name: 'slice' " + " type: 'Slice' " + " bottom: 'data' " + " top: 'data1' " + " top: 'data2' " + " slice_param { " + " axis: 0 " + " } " + " } "; + } + proto << " layer { " " name: 'innerprod' " " type: 'InnerProduct' " + " param { name: 'weights' } " + " param { name: 'bias' } " " inner_product_param { " " num_output: 1 " " weight_filler { " @@ -108,9 +124,42 @@ class GradientBasedSolverTest : public MultiDeviceTest { " std: 1.0 " " } " " } " - " bottom: 'data' " - " top: 'innerprod' " - " } " + " bottom: '" << string(share_ ? "data1": "data") << "' " + " top: '" << string(share_ ? "innerprod1": "innerprod") << "' " + " } "; + if (share_) { + proto << + " layer { " + " name: 'innerprod2' " + " type: 'InnerProduct' " + " param { name: 'weights' } " + " param { name: 'bias' } " + " inner_product_param { " + " num_output: 1 " + " weight_filler { " + " type: 'gaussian' " + " std: 1.0 " + " } " + " bias_filler { " + " type: 'gaussian' " + " std: 1.0 " + " } " + " } " + " bottom: 'data2' " + " top: 'innerprod2' " + " } " + " layer { " + " name: 'concat' " + " type: 'Concat' " + " bottom: 'innerprod1' " + " bottom: 'innerprod2' " + " top: 'innerprod' " + " concat_param { " + " axis: 0 " + " } " + " } "; + } + proto << " layer { " " name: 'loss' " " type: 'EuclideanLoss' " @@ -388,8 +437,8 @@ class GradientBasedSolverTest : public MultiDeviceTest { // Save the resulting param values. vector > > param_copies; - const vector > >& orig_params = - solver_->net()->params(); + const vector*>& orig_params = + solver_->net()->learnable_params(); param_copies.resize(orig_params.size()); for (int i = 0; i < orig_params.size(); ++i) { param_copies[i].reset(new Blob()); @@ -422,7 +471,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { momentum, total_num_iters, kIterSize, snapshot, snapshot_name.c_str()); // Check that params now match. - const vector > >& params = solver_->net()->params(); + const vector*>& params = solver_->net()->learnable_params(); for (int i = 0; i < params.size(); ++i) { for (int j = 0; j < params[i]->count(); ++j) { EXPECT_EQ(param_copies[i]->cpu_data()[j], params[i]->cpu_data()[j]) @@ -527,6 +576,18 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) { } } +TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.5; + const int kNumIters = 4; + this->share_ = true; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; @@ -538,6 +599,18 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { kIterSize); } +TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + const int kIterSize = 2; + this->share_ = true; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); +} + TYPED_TEST(SGDSolverTest, TestSnapshot) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; @@ -549,6 +622,18 @@ TYPED_TEST(SGDSolverTest, TestSnapshot) { } } +TYPED_TEST(SGDSolverTest, TestSnapshotShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + this->share_ = true; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + } +} + template class AdaGradSolverTest : public GradientBasedSolverTest { @@ -593,6 +678,19 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) { } } +TYPED_TEST(AdaGradSolverTest, + TestAdaGradLeastSquaresUpdateWithEverythingShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0; + const int kNumIters = 4; + this->share_ = true; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; @@ -604,6 +702,18 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { kIterSize); } +TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0; + const int kNumIters = 4; + const int kIterSize = 2; + this->share_ = true; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); +} + TYPED_TEST(AdaGradSolverTest, TestSnapshot) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; @@ -615,6 +725,18 @@ TYPED_TEST(AdaGradSolverTest, TestSnapshot) { } } +TYPED_TEST(AdaGradSolverTest, TestSnapshotShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0; + const int kNumIters = 4; + this->share_ = true; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + } +} + template class NesterovSolverTest : public GradientBasedSolverTest { @@ -693,6 +815,19 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) { } } +TYPED_TEST(NesterovSolverTest, + TestNesterovLeastSquaresUpdateWithEverythingShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + this->share_ = true; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; @@ -704,6 +839,18 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { kIterSize); } +TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + const int kIterSize = 2; + this->share_ = true; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); +} + TYPED_TEST(NesterovSolverTest, TestSnapshot) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; @@ -715,4 +862,16 @@ TYPED_TEST(NesterovSolverTest, TestSnapshot) { } } +TYPED_TEST(NesterovSolverTest, TestSnapshotShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + this->share_ = true; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + } +} + } // namespace caffe diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp index 56959f4793b..12998d8912f 100644 --- a/src/caffe/test/test_net.cpp +++ b/src/caffe/test/test_net.cpp @@ -1107,11 +1107,10 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) { EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2"); Blob* ip1_weights = this->net_->layers()[1]->blobs()[0].get(); Blob* ip2_weights = this->net_->layers()[2]->blobs()[0].get(); - // Check that data blobs of shared weights share the same location in memory. + // Check that data and diff blobs of shared weights share the same memory + // locations. EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data()); - // Check that diff blobs of shared weights are at different locations in - // memory. (The diffs should be accumulated at update time.) - EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff()); + EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff()); this->net_->Forward(bottom); this->net_->Backward(); // Compute the expected update as the data minus the two diffs. @@ -1124,11 +1123,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) { // Make sure the diffs are non-trivial. for (int i = 0; i < count; ++i) { EXPECT_NE(0, ip1_weights->cpu_diff()[i]); - EXPECT_NE(0, ip2_weights->cpu_diff()[i]); - EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]); } - caffe_axpy(count, Dtype(1), ip2_weights->cpu_diff(), - shared_params.mutable_cpu_diff()); caffe_axpy(count, Dtype(-1), shared_params.cpu_diff(), shared_params.mutable_cpu_data()); const Dtype* expected_updated_params = shared_params.cpu_data(); @@ -1165,8 +1160,8 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) { EXPECT_NE(0, ip1_weights->cpu_diff()[i]); EXPECT_NE(0, ip2_weights->cpu_diff()[i]); EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]); - EXPECT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i], - shared_params.cpu_diff()[i]); + EXPECT_FLOAT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i], + shared_params.cpu_diff()[i]); } caffe_axpy(count, Dtype(-1), ip1_weights->cpu_diff(), unshared_params1.mutable_cpu_data()); @@ -1196,11 +1191,10 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) { EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2"); Blob* ip1_weights = this->net_->layers()[1]->blobs()[0].get(); Blob* ip2_weights = this->net_->layers()[2]->blobs()[0].get(); - // Check that data blobs of shared weights share the same location in memory. + // Check that data and diff blobs of shared weights share the same memory + // locations. EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data()); - // Check that diff blobs of shared weights are at different locations in - // memory. (The diffs should be accumulated at update time.) - EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff()); + EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff()); this->net_->ForwardBackward(bottom); this->net_->Update(); Blob shared_params; @@ -1223,14 +1217,13 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) { ASSERT_FALSE(NULL == ip1_weights); ASSERT_FALSE(NULL == ip2_weights); EXPECT_NE(ip1_weights, ip2_weights); - // Check that data blobs of shared weights share the same location in memory. + // Check that data and diff blobs of shared weights share the same memory + // locations. EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data()); + EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff()); for (int i = 0; i < count; ++i) { EXPECT_FLOAT_EQ(shared_params.cpu_data()[i], ip1_weights->cpu_data()[i]); } - // Check that diff blobs of shared weights are at different locations in - // memory. (The diffs should be accumulated at update time.) - EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff()); } TYPED_TEST(NetTest, TestParamPropagateDown) { From 4227828ad43b78ad0c2ab4ab276dd0991ecb2ccb Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Fri, 7 Aug 2015 20:03:04 -0700 Subject: [PATCH 174/446] temporarily switch the snapshot_format default back to BINARYPROTO out of anticipation for user issues due to issue #2885, which causes Caffe to crash when it attempts to snapshot nets with duplicate layer names --- src/caffe/proto/caffe.proto | 2 +- src/caffe/test/test_gradient_based_solver.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 96e975be566..a13c0e79d80 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -179,7 +179,7 @@ message SolverParameter { HDF5 = 0; BINARYPROTO = 1; } - optional SnapshotFormat snapshot_format = 37 [default = HDF5]; + optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO]; // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default. enum SolverMode { CPU = 0; diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index e1bbbf17726..7bb0ec18a09 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -192,7 +192,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { if (snapshot) { ostringstream resume_file; resume_file << snapshot_prefix_ << "/_iter_" << num_iters - << ".solverstate.h5"; + << ".solverstate"; string resume_filename = resume_file.str(); return resume_filename; } From 443b16f84fb8948dbddecaf775c7da44dac6c6b1 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 21 Jan 2015 22:21:13 -0800 Subject: [PATCH 175/446] Add gpu_util.cuh, with caffe_gpu_atomic_add (double impl from NVIDIA dev docs; float impl included in CUDA as "atomicAdd") --- include/caffe/util/gpu_util.cuh | 35 +++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 include/caffe/util/gpu_util.cuh diff --git a/include/caffe/util/gpu_util.cuh b/include/caffe/util/gpu_util.cuh new file mode 100644 index 00000000000..994202f2a1a --- /dev/null +++ b/include/caffe/util/gpu_util.cuh @@ -0,0 +1,35 @@ +#ifndef CAFFE_UTIL_GPU_UTIL_H_ +#define CAFFE_UTIL_GPU_UTIL_H_ + +namespace caffe { + +template +inline __device__ Dtype caffe_gpu_atomic_add(const Dtype val, Dtype* address); + +template <> +inline __device__ +float caffe_gpu_atomic_add(const float val, float* address) { + return atomicAdd(address, val); +} + +// double atomicAdd implementation taken from: +// http://docs.nvidia.com/cuda/cuda-c-programming-guide/#axzz3PVCpVsEG +template <> +inline __device__ +double caffe_gpu_atomic_add(const double val, double* address) { + unsigned long long int* address_as_ull = // NOLINT(runtime/int) + // NOLINT_NEXT_LINE(runtime/int) + reinterpret_cast(address); + unsigned long long int old = *address_as_ull; // NOLINT(runtime/int) + unsigned long long int assumed; // NOLINT(runtime/int) + do { + assumed = old; + old = atomicCAS(address_as_ull, assumed, + __double_as_longlong(val + __longlong_as_double(assumed))); + } while (assumed != old); + return __longlong_as_double(old); +} + +} // namespace caffe + +#endif // CAFFE_UTIL_GPU_UTIL_H_ From 6067869f8339344ecd68c486a6e47d07e8997b6f Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 21 Jan 2015 14:23:34 -0800 Subject: [PATCH 176/446] test_gradient_check_util: check_bottom < -1 only checks params --- include/caffe/test/test_gradient_check_util.hpp | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp index cc5dcbad0ee..25f35d1589e 100644 --- a/include/caffe/test/test_gradient_check_util.hpp +++ b/include/caffe/test/test_gradient_check_util.hpp @@ -45,6 +45,10 @@ class GradientChecker { void CheckGradientEltwise(Layer* layer, const vector*>& bottom, const vector*>& top); + // Checks the gradient of a single output with respect to particular input + // blob(s). If check_bottom = i >= 0, check only the ith bottom Blob. + // If check_bottom == -1, check everything -- all bottom Blobs and all + // param Blobs. Otherwise (if check_bottom < -1), check only param Blobs. void CheckGradientSingle(Layer* layer, const vector*>& bottom, const vector*>& top, int check_bottom, int top_id, int top_data_id, bool element_wise = false); @@ -83,21 +87,22 @@ void GradientChecker::CheckGradientSingle(Layer* layer, // First, figure out what blobs we need to check against, and zero init // parameter blobs. vector*> blobs_to_check; - vector propagate_down(bottom.size(), check_bottom < 0); + vector propagate_down(bottom.size(), check_bottom == -1); for (int i = 0; i < layer->blobs().size(); ++i) { Blob* blob = layer->blobs()[i].get(); caffe_set(blob->count(), static_cast(0), blob->mutable_cpu_diff()); blobs_to_check.push_back(blob); } - if (check_bottom < 0) { + if (check_bottom == -1) { for (int i = 0; i < bottom.size(); ++i) { blobs_to_check.push_back(bottom[i]); } - } else { + } else if (check_bottom >= 0) { CHECK_LT(check_bottom, bottom.size()); blobs_to_check.push_back(bottom[check_bottom]); propagate_down[check_bottom] = true; } + CHECK_GT(blobs_to_check.size(), 0) << "No blobs to check."; // Compute the gradient analytically using Backward Caffe::set_random_seed(seed_); // Ignore the loss from the layer (it's just the weighted sum of the losses From 4d299c3071039e7c49c01b2435e11549f764df88 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Sun, 15 Feb 2015 16:00:04 -0800 Subject: [PATCH 177/446] Add EmbedLayer for inner products with sparse input (one-hot vectors), with unit tests --- include/caffe/common_layers.hpp | 38 ++++++ src/caffe/layers/embed_layer.cpp | 122 +++++++++++++++++++ src/caffe/layers/embed_layer.cu | 80 ++++++++++++ src/caffe/proto/caffe.proto | 18 ++- src/caffe/test/test_embed_layer.cpp | 183 ++++++++++++++++++++++++++++ 5 files changed, 440 insertions(+), 1 deletion(-) create mode 100644 src/caffe/layers/embed_layer.cpp create mode 100644 src/caffe/layers/embed_layer.cu create mode 100644 src/caffe/test/test_embed_layer.cpp diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index d2c0ce6d0c6..691e755f88f 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -180,6 +180,44 @@ class EltwiseLayer : public Layer { bool stable_prod_grad_; }; +/** + * @brief A layer for learning "embeddings" of one-hot vector input. + * Equivalent to an InnerProductLayer with one-hot vectors as input, but + * for efficiency the input is the "hot" index of each column itself. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class EmbedLayer : public Layer { + public: + explicit EmbedLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Embed"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + int M_; + int K_; + int N_; + bool bias_term_; + Blob bias_multiplier_; +}; + /** * @brief Takes two+ Blobs, interprets last Blob as a selector and * filter remaining Blobs accordingly with selector data (0 means that diff --git a/src/caffe/layers/embed_layer.cpp b/src/caffe/layers/embed_layer.cpp new file mode 100644 index 00000000000..be6b2cd2727 --- /dev/null +++ b/src/caffe/layers/embed_layer.cpp @@ -0,0 +1,122 @@ +#include + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/common_layers.hpp" +#include "caffe/filler.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void EmbedLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + N_ = this->layer_param_.embed_param().num_output(); + CHECK_GT(N_, 0) << "EmbedLayer num_output must be positive."; + K_ = this->layer_param_.embed_param().input_dim(); + CHECK_GT(K_, 0) << "EmbedLayer input_dim must be positive."; + bias_term_ = this->layer_param_.embed_param().bias_term(); + // Check if we need to set up the weights + if (this->blobs_.size() > 0) { + LOG(INFO) << "Skipping parameter initialization"; + } else { + if (bias_term_) { + this->blobs_.resize(2); + } else { + this->blobs_.resize(1); + } + // Initialize the weights -- + // transposed from InnerProductLayer for spatial locality. + vector weight_shape(2); + weight_shape[0] = K_; + weight_shape[1] = N_; + this->blobs_[0].reset(new Blob(weight_shape)); + // fill the weights + shared_ptr > weight_filler(GetFiller( + this->layer_param_.embed_param().weight_filler())); + weight_filler->Fill(this->blobs_[0].get()); + // If necessary, initialize and fill the bias term + if (bias_term_) { + vector bias_shape(1, N_); + this->blobs_[1].reset(new Blob(bias_shape)); + shared_ptr > bias_filler(GetFiller( + this->layer_param_.embed_param().bias_filler())); + bias_filler->Fill(this->blobs_[1].get()); + } + } // parameter initialization + this->param_propagate_down_.resize(this->blobs_.size(), true); +} + +template +void EmbedLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + // Figure out the dimensions + M_ = bottom[0]->count(); + vector top_shape = bottom[0]->shape(); + top_shape.push_back(N_); + top[0]->Reshape(top_shape); + // Set up the bias multiplier + if (bias_term_) { + vector bias_shape(1, M_); + bias_multiplier_.Reshape(bias_shape); + caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data()); + } +} + +template +void EmbedLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + const Dtype* weight = this->blobs_[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + int index; + for (int n = 0; n < M_; ++n) { + index = static_cast(bottom_data[n]); + DCHECK_GE(index, 0); + DCHECK_LT(index, K_); + DCHECK_EQ(static_cast(index), bottom_data[n]) << "non-integer input"; + caffe_copy(N_, weight + index * N_, top_data + n * N_); + } + if (bias_term_) { + const Dtype* bias = this->blobs_[1]->cpu_data(); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, M_, N_, 1, Dtype(1), + bias_multiplier_.cpu_data(), bias, Dtype(1), top_data); + } +} + +template +void EmbedLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input."; + if (this->param_propagate_down_[0]) { + const Dtype* top_diff = top[0]->cpu_diff(); + const Dtype* bottom_data = bottom[0]->cpu_data(); + // Gradient with respect to weight + Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff(); + int index; + for (int n = 0; n < M_; ++n) { + index = static_cast(bottom_data[n]); + DCHECK_GE(index, 0); + DCHECK_LT(index, K_); + DCHECK_EQ(static_cast(index), bottom_data[n]) + << "non-integer input"; + caffe_axpy(N_, Dtype(1), top_diff + n * N_, weight_diff + index * N_); + } + } + if (bias_term_ && this->param_propagate_down_[1]) { + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff(); + caffe_cpu_gemv(CblasTrans, M_, N_, Dtype(1), top_diff, + bias_multiplier_.cpu_data(), Dtype(1), bias_diff); + } +} + +#ifdef CPU_ONLY +STUB_GPU(EmbedLayer); +#endif + +INSTANTIATE_CLASS(EmbedLayer); +REGISTER_LAYER_CLASS(Embed); + +} // namespace caffe diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu new file mode 100644 index 00000000000..37a4f7e35cd --- /dev/null +++ b/src/caffe/layers/embed_layer.cu @@ -0,0 +1,80 @@ +#include + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/common_layers.hpp" +#include "caffe/filler.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +__global__ void EmbedForward(const int nthreads, const Dtype* bottom_data, + const Dtype* weight, const int M, const int N, const int K, + Dtype* top_data) { + CUDA_KERNEL_LOOP(top_index, nthreads) { + const int n = top_index / N; + const int d = top_index % N; + const int index = static_cast(bottom_data[n]); + const int weight_index = index * N + d; + top_data[top_index] = weight[weight_index]; + } +} + +template +__global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data, + const Dtype* top_diff, const int M, const int N, const int K, + Dtype* weight_diff) { + CUDA_KERNEL_LOOP(weight_index, nthreads) { + const int index = weight_index / N; + const int output_index = weight_index % N; + for (int n = 0; n < M; ++n) { + if (static_cast(bottom_data[n]) == index) { + weight_diff[weight_index] += top_diff[n * N + output_index]; + } + } + } +} + +template +void EmbedLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + const Dtype* weight = this->blobs_[0]->gpu_data(); + const int count = top[0]->count(); + EmbedForward // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, bottom_data, weight, M_, N_, K_, top_data); + if (bias_term_) { + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, M_, N_, 1, Dtype(1), + bias_multiplier_.gpu_data(), + this->blobs_[1]->gpu_data(), Dtype(1), top_data); + } +} + +template +void EmbedLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input."; + if (this->param_propagate_down_[0]) { + const int count = this->blobs_[0]->count(); + const Dtype* top_diff = top[0]->gpu_diff(); + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff(); + EmbedBackward // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, bottom_data, top_diff, M_, N_, K_, weight_diff); + } + if (bias_term_ && this->param_propagate_down_[1]) { + const Dtype* top_diff = top[0]->gpu_diff(); + Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff(); + caffe_gpu_gemv(CblasTrans, M_, N_, Dtype(1), top_diff, + bias_multiplier_.gpu_data(), Dtype(1), bias_diff); + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(EmbedLayer); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index a13c0e79d80..705cceb098b 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -276,7 +276,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 137 (last added: reduction_param) +// LayerParameter next available layer-specific ID: 138 (last added: embed_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -332,6 +332,7 @@ message LayerParameter { optional DropoutParameter dropout_param = 108; optional DummyDataParameter dummy_data_param = 109; optional EltwiseParameter eltwise_param = 110; + optional EmbedParameter embed_param = 137; optional ExpParameter exp_param = 111; optional FlattenParameter flatten_param = 135; optional HDF5DataParameter hdf5_data_param = 112; @@ -533,6 +534,21 @@ message EltwiseParameter { optional bool stable_prod_grad = 3 [default = true]; } +// Message that stores parameters used by EmbedLayer +message EmbedParameter { + optional uint32 num_output = 1; // The number of outputs for the layer + // The input is given as integers to be interpreted as one-hot + // vector indices with dimension num_input. Hence num_input should be + // 1 greater than the maximum possible input value. + optional uint32 input_dim = 2; + + optional bool bias_term = 3 [default = true]; // Whether to use a bias term + optional FillerParameter weight_filler = 4; // The filler for the weight + optional FillerParameter bias_filler = 5; // The filler for the bias + +} + +// Message that stores parameters used by ExpLayer message ExpParameter { // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0. // Or if base is set to the default (-1), base is set to e, diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp new file mode 100644 index 00000000000..7a4fb9800f2 --- /dev/null +++ b/src/caffe/test/test_embed_layer.cpp @@ -0,0 +1,183 @@ +#include +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/vision_layers.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +#ifndef CPU_ONLY +extern cudaDeviceProp CAFFE_TEST_CUDA_PROP; +#endif + +template +class EmbedLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + protected: + EmbedLayerTest() + : blob_bottom_(new Blob(4, 1, 1, 1)), + blob_top_(new Blob()) { + // fill the values + FillerParameter filler_param; + UniformFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + blob_bottom_vec_.push_back(blob_bottom_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~EmbedLayerTest() { delete blob_bottom_; delete blob_top_; } + Blob* const blob_bottom_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(EmbedLayerTest, TestDtypesAndDevices); + +TYPED_TEST(EmbedLayerTest, TestSetUp) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + EmbedParameter* embed_param = layer_param.mutable_embed_param(); + embed_param->set_num_output(10); + embed_param->set_input_dim(5); + shared_ptr > layer(new EmbedLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_top_->num_axes(), 5); + EXPECT_EQ(this->blob_top_->shape(0), 4); + EXPECT_EQ(this->blob_top_->shape(1), 1); + EXPECT_EQ(this->blob_top_->shape(2), 1); + EXPECT_EQ(this->blob_top_->shape(3), 1); + EXPECT_EQ(this->blob_top_->shape(4), 10); +} + +TYPED_TEST(EmbedLayerTest, TestForward) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + EmbedParameter* embed_param = layer_param.mutable_embed_param(); + const int kNumOutput = 10; + const int kInputDim = 5; + embed_param->set_num_output(kNumOutput); + embed_param->set_input_dim(kInputDim); + embed_param->mutable_weight_filler()->set_type("uniform"); + embed_param->mutable_weight_filler()->set_min(-10); + embed_param->mutable_weight_filler()->set_max(10); + embed_param->set_bias_term(false); + shared_ptr > layer(new EmbedLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(1, layer->blobs().size()); + vector weight_shape(2); + weight_shape[0] = kInputDim; + weight_shape[1] = kNumOutput; + ASSERT_TRUE(weight_shape == layer->blobs()[0]->shape()); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + this->blob_bottom_->mutable_cpu_data()[i] = caffe_rng_rand() % kInputDim; + } + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + vector weight_offset(2, 0); + vector top_offset(5, 0); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + weight_offset[0] = static_cast(this->blob_bottom_->cpu_data()[i]); + weight_offset[1] = 0; + top_offset[0] = i; + top_offset[4] = 0; + for (int j = 0; j < kNumOutput; ++j) { + EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset), + this->blob_top_->data_at(top_offset)); + ++top_offset[4]; + ++weight_offset[1]; + } + } +} + +TYPED_TEST(EmbedLayerTest, TestForwardWithBias) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + EmbedParameter* embed_param = layer_param.mutable_embed_param(); + const int kNumOutput = 10; + const int kInputDim = 5; + embed_param->set_num_output(kNumOutput); + embed_param->set_input_dim(kInputDim); + embed_param->mutable_weight_filler()->set_type("uniform"); + embed_param->mutable_weight_filler()->set_min(-10); + embed_param->mutable_weight_filler()->set_max(10); + embed_param->mutable_bias_filler()->CopyFrom(embed_param->weight_filler()); + embed_param->set_bias_term(true); + shared_ptr > layer(new EmbedLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(2, layer->blobs().size()); + vector weight_shape(2); + weight_shape[0] = kInputDim; + weight_shape[1] = kNumOutput; + ASSERT_TRUE(weight_shape == layer->blobs()[0]->shape()); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + this->blob_bottom_->mutable_cpu_data()[i] = caffe_rng_rand() % kInputDim; + } + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + vector bias_offset(1, 0); + vector weight_offset(2, 0); + vector top_offset(5, 0); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + weight_offset[0] = static_cast(this->blob_bottom_->cpu_data()[i]); + weight_offset[1] = 0; + top_offset[0] = i; + top_offset[4] = 0; + bias_offset[0] = 0; + for (int j = 0; j < kNumOutput; ++j) { + EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset) + + layer->blobs()[1]->data_at(bias_offset), + this->blob_top_->data_at(top_offset)); + ++top_offset[4]; + ++weight_offset[1]; + ++bias_offset[0]; + } + } +} + +TYPED_TEST(EmbedLayerTest, TestGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + EmbedParameter* embed_param = layer_param.mutable_embed_param(); + embed_param->set_num_output(10); + embed_param->set_input_dim(5); + embed_param->set_bias_term(false); + embed_param->mutable_weight_filler()->set_type("uniform"); + embed_param->mutable_weight_filler()->set_min(-10); + embed_param->mutable_weight_filler()->set_max(10); + EmbedLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + this->blob_bottom_->mutable_cpu_data()[0] = 4; + this->blob_bottom_->mutable_cpu_data()[1] = 2; + this->blob_bottom_->mutable_cpu_data()[2] = 2; + this->blob_bottom_->mutable_cpu_data()[3] = 3; + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_, -2); +} + +TYPED_TEST(EmbedLayerTest, TestGradientWithBias) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + EmbedParameter* embed_param = layer_param.mutable_embed_param(); + embed_param->set_num_output(10); + embed_param->set_input_dim(5); + embed_param->set_bias_term(true); + embed_param->mutable_weight_filler()->set_type("uniform"); + embed_param->mutable_weight_filler()->set_min(-10); + embed_param->mutable_weight_filler()->set_max(10); + embed_param->mutable_bias_filler()->CopyFrom(embed_param->weight_filler()); + EmbedLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + this->blob_bottom_->mutable_cpu_data()[0] = 4; + this->blob_bottom_->mutable_cpu_data()[1] = 2; + this->blob_bottom_->mutable_cpu_data()[2] = 2; + this->blob_bottom_->mutable_cpu_data()[3] = 3; + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_, -2); +} + +} // namespace caffe From ac9e29fd7b90a665a956f460715669bf05445a13 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 21 Jan 2015 16:12:12 -0800 Subject: [PATCH 178/446] EmbedBackward with no loops -- use caffe_gpu_atomic_add instead --- src/caffe/layers/embed_layer.cu | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu index 37a4f7e35cd..672fb9c608c 100644 --- a/src/caffe/layers/embed_layer.cu +++ b/src/caffe/layers/embed_layer.cu @@ -5,6 +5,7 @@ #include "caffe/common_layers.hpp" #include "caffe/filler.hpp" #include "caffe/layer.hpp" +#include "caffe/util/gpu_util.cuh" #include "caffe/util/math_functions.hpp" namespace caffe { @@ -22,18 +23,21 @@ __global__ void EmbedForward(const int nthreads, const Dtype* bottom_data, } } +template +__global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data, + const Dtype* top_diff, const int M, const int N, const int K, + Dtype* weight_diff); + template __global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data, const Dtype* top_diff, const int M, const int N, const int K, Dtype* weight_diff) { - CUDA_KERNEL_LOOP(weight_index, nthreads) { - const int index = weight_index / N; - const int output_index = weight_index % N; - for (int n = 0; n < M; ++n) { - if (static_cast(bottom_data[n]) == index) { - weight_diff[weight_index] += top_diff[n * N + output_index]; - } - } + CUDA_KERNEL_LOOP(top_index, nthreads) { + const int n = top_index / N; + const int d = top_index % N; + const int index = static_cast(bottom_data[n]); + const int weight_index = index * N + d; + caffe_gpu_atomic_add(top_diff[top_index], weight_diff + weight_index); } } @@ -59,13 +63,14 @@ void EmbedLayer::Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input."; if (this->param_propagate_down_[0]) { + const int top_count = top[0]->count(); const int count = this->blobs_[0]->count(); const Dtype* top_diff = top[0]->gpu_diff(); const Dtype* bottom_data = bottom[0]->gpu_data(); Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff(); EmbedBackward // NOLINT_NEXT_LINE(whitespace/operators) - <<>>( - count, bottom_data, top_diff, M_, N_, K_, weight_diff); + <<>>( + top_count, bottom_data, top_diff, M_, N_, K_, weight_diff); } if (bias_term_ && this->param_propagate_down_[1]) { const Dtype* top_diff = top[0]->gpu_diff(); From 2c356a4ed4c33f662288166dfa74dd5d71d6c194 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Sat, 8 Aug 2015 12:57:45 -0700 Subject: [PATCH 179/446] TestGradientBasedSolver: drop doubled seed inititialization --- src/caffe/test/test_gradient_based_solver.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 7bb0ec18a09..30b041fa6b5 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -180,7 +180,6 @@ class GradientBasedSolverTest : public MultiDeviceTest { } Caffe::set_random_seed(this->seed_); this->InitSolverFromProtoString(proto.str()); - Caffe::set_random_seed(this->seed_); if (from_snapshot != NULL) { this->solver_->Restore(from_snapshot); vector*> empty_bottom_vec; From 6019246930f54b108decc100f9cb801e4aea81a4 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Sat, 8 Aug 2015 13:07:13 -0700 Subject: [PATCH 180/446] TestGradientBasedSolver: replace dummy data with hdf5 Rely on fixed hdf5 data for determinism of solver tests. - draw random Gaussian data and targets for test and save to hdf5 - use the same data for all tests without constant / gaussian condition previously needed for accumulation - avoid test artifacts due to order of random draws in dummy data --- .../test/test_data/generate_sample_data.py | 28 ++++++++++++- src/caffe/test/test_data/solver_data.h5 | Bin 0 -> 11776 bytes src/caffe/test/test_data/solver_data_list.txt | 1 + src/caffe/test/test_gradient_based_solver.cpp | 39 ++++++++---------- 4 files changed, 45 insertions(+), 23 deletions(-) create mode 100644 src/caffe/test/test_data/solver_data.h5 create mode 100644 src/caffe/test/test_data/solver_data_list.txt diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py index ab5572685cb..3703b41823b 100644 --- a/src/caffe/test/test_data/generate_sample_data.py +++ b/src/caffe/test/test_data/generate_sample_data.py @@ -1,5 +1,5 @@ """ -Generate data used in the HDF5DataLayer test. +Generate data used in the HDF5DataLayer and GradientBasedSolver tests. """ import os import numpy as np @@ -7,6 +7,8 @@ script_dir = os.path.dirname(os.path.abspath(__file__)) +# Generate HDF5DataLayer sample_data.h5 + num_cols = 8 num_rows = 10 height = 6 @@ -51,3 +53,27 @@ with open(script_dir + '/sample_data_list.txt', 'w') as f: f.write(script_dir + '/sample_data.h5\n') f.write(script_dir + '/sample_data_2_gzip.h5\n') + +# Generate GradientBasedSolver solver_data.h5 + +num_cols = 3 +num_rows = 8 +height = 10 +width = 10 + +data = np.random.randn(num_rows, num_cols, height, width) +data = data.reshape(num_rows, num_cols, height, width) +data = data.astype('float32') + +targets = np.random.randn(num_rows, 1) +targets = targets.astype('float32') + +print data +print targets + +with h5py.File(script_dir + '/solver_data.h5', 'w') as f: + f['data'] = data + f['targets'] = targets + +with open(script_dir + '/solver_data_list.txt', 'w') as f: + f.write(script_dir + '/solver_data.h5\n') diff --git a/src/caffe/test/test_data/solver_data.h5 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zr_3U{Ql^ME`aKs(H1bVZ(3p3dPybI}!*y;(2&wb~gLU zhKGB|=ryx9;sD_}x*A<#Cvo4(DRXUIkHXc;9@7484M|bffh376!hbG_9JM}xRvsqA z(6o)1s4gLEww)!L(!ZftL?D)G_7gMdYBKC+1dBJSlg&$vLENFvY?<>lG*_DruR`lE z`dSVue@@58OH$~^+;q~TJN946okY@6ma*YaqN~J-yifm<=&Wf#!HsEU;@Kkwd(kwM#C* wmt0A1SGqmY2a`AkdQad!N^-6$dxNjH3+}iP15b7OnFcK$%vdiE`fvsQ2apE6>i_@% literal 0 HcmV?d00001 diff --git a/src/caffe/test/test_data/solver_data_list.txt b/src/caffe/test/test_data/solver_data_list.txt new file mode 100644 index 00000000000..a6552f50073 --- /dev/null +++ b/src/caffe/test/test_data/solver_data_list.txt @@ -0,0 +1 @@ +src/caffe/test/test_data/solver_data.h5 diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 30b041fa6b5..a8b211b7d43 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -25,15 +25,26 @@ class GradientBasedSolverTest : public MultiDeviceTest { protected: GradientBasedSolverTest() : seed_(1701), num_(4), channels_(3), height_(10), width_(10), - constant_data_(false), share_(false) {} + share_(false) { + input_file_ = new string( + CMAKE_SOURCE_DIR "caffe/test/test_data/solver_data_list.txt" CMAKE_EXT); + } + ~GradientBasedSolverTest() { + delete input_file_; + } string snapshot_prefix_; shared_ptr > solver_; int seed_; + // Dimensions are determined by generate_sample_data.py + // TODO this is brittle and the hdf5 file should be checked instead. int num_, channels_, height_, width_; - bool constant_data_, share_; + bool share_; Dtype delta_; // Stability constant for AdaGrad. + // Test data: check out generate_sample_data.py in the same directory. + string* input_file_; + virtual SolverParameter_SolverType solver_type() = 0; virtual void InitSolver(const SolverParameter& param) = 0; @@ -71,25 +82,10 @@ class GradientBasedSolverTest : public MultiDeviceTest { " name: 'TestNetwork' " " layer { " " name: 'data' " - " type: 'DummyData' " - " dummy_data_param { " - " num: " << num_ / iter_size << " " - " channels: " << channels_ << " " - " height: " << height_ << " " - " width: " << width_ << " " - " channels: 1 " - " height: 1 " - " width: 1 " - " data_filler { " - " type: '" << string(constant_data_ ? "constant" : "gaussian") - << "' " - " std: 1.0 " - " value: 1.0 " - " } " - " data_filler { " - " type: 'gaussian' " - " std: 1.0 " - " } " + " type: 'HDF5Data' " + " hdf5_data_param { " + " source: '" << *(this->input_file_) << "' " + " batch_size: " << num_ / iter_size << " " " } " " top: 'data' " " top: 'targets' " @@ -354,7 +350,6 @@ class GradientBasedSolverTest : public MultiDeviceTest { const Dtype kMomentum, const int kNumIters, const int kIterSize) { const double kPrecision = 1e-2; const double kMinPrecision = 1e-7; - constant_data_ = true; // Solve without accumulation and save parameters. this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum, kNumIters); From abe99e8748ad7f583c87d1a6132ff2d79e70dd9c Mon Sep 17 00:00:00 2001 From: Eren Golge Date: Sat, 8 Aug 2015 23:45:08 -0700 Subject: [PATCH 181/446] Implement RMSProp Solver Implement RMSProp solver and cleaned up to adjust to new solver interface that uses accumulated gradients and refactored regularization. --- examples/mnist/lenet_solver_rmsprop.prototxt | 27 ++ examples/mnist/train_lenet_rmsprop.sh | 3 + include/caffe/solver.hpp | 25 ++ src/caffe/proto/caffe.proto | 25 +- src/caffe/solver.cpp | 76 ++++++ src/caffe/test/test_gradient_based_solver.cpp | 245 ++++++++++++++---- 6 files changed, 353 insertions(+), 48 deletions(-) create mode 100644 examples/mnist/lenet_solver_rmsprop.prototxt create mode 100755 examples/mnist/train_lenet_rmsprop.sh diff --git a/examples/mnist/lenet_solver_rmsprop.prototxt b/examples/mnist/lenet_solver_rmsprop.prototxt new file mode 100644 index 00000000000..74dadc51069 --- /dev/null +++ b/examples/mnist/lenet_solver_rmsprop.prototxt @@ -0,0 +1,27 @@ +# The train/test net protocol buffer definition +net: "examples/mnist/lenet_train_test.prototxt" +# test_iter specifies how many forward passes the test should carry out. +# In the case of MNIST, we have test batch size 100 and 100 test iterations, +# covering the full 10,000 testing images. +test_iter: 100 +# Carry out testing every 500 training iterations. +test_interval: 500 +# The base learning rate, momentum and the weight decay of the network. +base_lr: 0.01 +momentum: 0.0 +weight_decay: 0.0005 +# The learning rate policy +lr_policy: "inv" +gamma: 0.0001 +power: 0.75 +# Display every 100 iterations +display: 100 +# The maximum number of iterations +max_iter: 10000 +# snapshot intermediate results +snapshot: 5000 +snapshot_prefix: "examples/mnist/lenet_rmsprop" +# solver mode: CPU or GPU +solver_mode: GPU +solver_type: RMSPROP +rms_decay: 0.98 diff --git a/examples/mnist/train_lenet_rmsprop.sh b/examples/mnist/train_lenet_rmsprop.sh new file mode 100755 index 00000000000..621cab238bf --- /dev/null +++ b/examples/mnist/train_lenet_rmsprop.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env sh + +./build/tools/caffe train --solver=examples/mnist/lenet_solver_rmsprop.prototxt diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 703434b5fcf..fbade9389ff 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -135,6 +135,29 @@ class AdaGradSolver : public SGDSolver { DISABLE_COPY_AND_ASSIGN(AdaGradSolver); }; + +template +class RMSPropSolver : public SGDSolver { + public: + explicit RMSPropSolver(const SolverParameter& param) + : SGDSolver(param) { constructor_sanity_check(); } + explicit RMSPropSolver(const string& param_file) + : SGDSolver(param_file) { constructor_sanity_check(); } + + protected: + virtual void ComputeUpdateValue(int param_id, Dtype rate); + void constructor_sanity_check() { + CHECK_EQ(0, this->param_.momentum()) + << "Momentum cannot be used with RMSProp."; + CHECK_GE(this->param_.rms_decay(), 0) + << "rms_decay should lie between 0 and 1."; + CHECK_LT(this->param_.rms_decay(), 1) + << "rms_decay should lie between 0 and 1."; + } + + DISABLE_COPY_AND_ASSIGN(RMSPropSolver); +}; + template Solver* GetSolver(const SolverParameter& param) { SolverParameter_SolverType type = param.solver_type(); @@ -146,6 +169,8 @@ Solver* GetSolver(const SolverParameter& param) { return new NesterovSolver(param); case SolverParameter_SolverType_ADAGRAD: return new AdaGradSolver(param); + case SolverParameter_SolverType_RMSPROP: + return new RMSPropSolver(param); default: LOG(FATAL) << "Unknown SolverType: " << type; } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index a13c0e79d80..89f14595ba6 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -98,7 +98,7 @@ message NetParameter { // NOTE // Update the next available ID when you add a new SolverParameter field. // -// SolverParameter next available ID: 38 (last added: snapshot_format) +// SolverParameter next available ID: 39 (last added: rms_decay) message SolverParameter { ////////////////////////////////////////////////////////////////////////////// // Specifying the train and test networks @@ -153,7 +153,23 @@ message SolverParameter { optional int32 max_iter = 7; // the maximum number of iterations // accumulate gradients over `iter_size` x `batch_size` instances optional int32 iter_size = 36 [default = 1]; - optional string lr_policy = 8; // The learning rate decay policy. + + // The learning rate decay policy. The currently implemented learning rate + // policies are as follows: + // - fixed: always return base_lr. + // - step: return base_lr * gamma ^ (floor(iter / step)) + // - exp: return base_lr * gamma ^ iter + // - inv: return base_lr * (1 + gamma * iter) ^ (- power) + // - multistep: similar to step but it allows non uniform steps defined by + // stepvalue + // - poly: the effective learning rate follows a polynomial decay, to be + // zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power) + // - sigmoid: the effective learning rate follows a sigmod decay + // return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize)))) + // + // where base_lr, max_iter, gamma, step, stepvalue and power are defined + // in the solver parameter protocol buffer, and iter is the current iteration. + optional string lr_policy = 8; optional float gamma = 9; // The parameter to compute the learning rate. optional float power = 10; // The parameter to compute the learning rate. optional float momentum = 11; // The momentum value. @@ -198,11 +214,16 @@ message SolverParameter { SGD = 0; NESTEROV = 1; ADAGRAD = 2; + RMSPROP = 3; } optional SolverType solver_type = 30 [default = SGD]; // numerical stability for AdaGrad optional float delta = 31 [default = 1e-8]; + // RMSProp decay value + // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t) + optional float rms_decay = 38; + // If true, print information about the state of the net that may help with // debugging learning problems. optional bool debug_info = 23 [default = false]; diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 32276ac148a..43834c0c569 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -859,9 +859,85 @@ void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { } } +template +void RMSPropSolver::ComputeUpdateValue(int param_id, Dtype rate) { + const vector > >& net_params = this->net_->params(); + const vector& net_params_lr = this->net_->params_lr(); + + // get the learning rate + Dtype delta = this->param_.delta(); + Dtype rms_decay = this->param_.rms_decay(); + Dtype local_rate = rate * net_params_lr[param_id]; + + switch (Caffe::mode()) { + case Caffe::CPU: + // compute square of gradient in update + caffe_powx(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), Dtype(2), + this->update_[param_id]->mutable_cpu_data()); + + // update history + caffe_cpu_axpby(net_params[param_id] -> count(), + Dtype(1-rms_decay), this->update_[param_id]->cpu_data(), + rms_decay, this->history_[param_id]-> mutable_cpu_data()); + + // prepare update + caffe_powx(net_params[param_id]->count(), + this->history_[param_id]->cpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_cpu_data()); + + caffe_add_scalar(net_params[param_id]->count(), + delta, this->update_[param_id]->mutable_cpu_data()); + + caffe_div(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), this->update_[param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + // scale and copy + caffe_cpu_axpby(net_params[param_id]->count(), local_rate, + this->update_[param_id]->cpu_data(), Dtype(0), + net_params[param_id]->mutable_cpu_diff()); + break; + case Caffe::GPU: +#ifndef CPU_ONLY + // compute square of gradient in update + caffe_gpu_powx(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), Dtype(2), + this->update_[param_id]->mutable_gpu_data()); + + // update history + caffe_gpu_axpby(net_params[param_id] -> count(), + Dtype(1-rms_decay), this->update_[param_id]->gpu_data(), + rms_decay, this->history_[param_id]-> mutable_gpu_data()); + + // prepare update + caffe_gpu_powx(net_params[param_id]->count(), + this->history_[param_id]->gpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_add_scalar(net_params[param_id]->count(), + delta, this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_div(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), this->update_[param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_axpby(net_params[param_id]->count(), local_rate, + this->update_[param_id]->gpu_data(), Dtype(0), + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + INSTANTIATE_CLASS(Solver); INSTANTIATE_CLASS(SGDSolver); INSTANTIATE_CLASS(NesterovSolver); INSTANTIATE_CLASS(AdaGradSolver); +INSTANTIATE_CLASS(RMSPropSolver); } // namespace caffe diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 7bb0ec18a09..b09189228ba 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -52,13 +52,14 @@ class GradientBasedSolverTest : public MultiDeviceTest { LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode(); } InitSolver(param); - delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD) ? - param.delta() : 0; + delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD || + solver_type() == SolverParameter_SolverType_RMSPROP) ? + param.delta() : 0; } string RunLeastSquaresSolver(const Dtype learning_rate, - const Dtype weight_decay, const Dtype momentum, const int num_iters, - const int iter_size = 1, const bool snapshot = false, + const Dtype weight_decay, const Dtype momentum, const Dtype rms_decay, + const int num_iters, const int iter_size = 1, const bool snapshot = false, const char* from_snapshot = NULL) { ostringstream proto; proto << @@ -173,6 +174,9 @@ class GradientBasedSolverTest : public MultiDeviceTest { if (momentum != 0) { proto << "momentum: " << momentum << " "; } + if (rms_decay != 0) { + proto << "rms_decay: " << rms_decay << " "; + } MakeTempDir(&snapshot_prefix_); proto << "snapshot_prefix: '" << snapshot_prefix_ << "/' "; if (snapshot) { @@ -204,7 +208,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { // updated_params will store the updated weight and bias results, // using the blobs' diffs to hold the update values themselves. void ComputeLeastSquaresUpdate(const Dtype learning_rate, - const Dtype weight_decay, const Dtype momentum, + const Dtype weight_decay, const Dtype momentum, const Dtype rms_decay, vector > >* updated_params) { const int N = num_; const int D = channels_ * height_ * width_; @@ -287,6 +291,10 @@ class GradientBasedSolverTest : public MultiDeviceTest { case SolverParameter_SolverType_ADAGRAD: update_value /= std::sqrt(history_value + grad * grad) + delta_; break; + case SolverParameter_SolverType_RMSPROP: + update_value /= std::sqrt(rms_decay*history_value + + grad * grad * (1 - rms_decay)) + delta_; + break; default: LOG(FATAL) << "Unknown solver type: " << solver_type(); } @@ -352,13 +360,14 @@ class GradientBasedSolverTest : public MultiDeviceTest { } void CheckAccumulation(const Dtype kLearningRate, const Dtype kWeightDecay, - const Dtype kMomentum, const int kNumIters, const int kIterSize) { + const Dtype kMomentum, const Dtype rms_decay, const int kNumIters, + const int kIterSize) { const double kPrecision = 1e-2; const double kMinPrecision = 1e-7; constant_data_ = true; // Solve without accumulation and save parameters. this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum, - kNumIters); + rms_decay, kNumIters); // Save parameters for comparison. Net& net = *this->solver_->net(); const vector > >& param_blobs = @@ -370,7 +379,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { } // Solve by equivalent accumulation of gradients over divided batches. this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum, - kNumIters, kIterSize); + rms_decay, kNumIters, kIterSize); Net& net_accum = *this->solver_->net(); const vector > >& accum_params = net_accum.layer_by_name("innerprod")->blobs(); @@ -408,18 +417,19 @@ class GradientBasedSolverTest : public MultiDeviceTest { // matches the solver's (K+1)th update. void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0, const Dtype weight_decay = 0.0, const Dtype momentum = 0.0, - const int iter_to_check = 0) { + const Dtype rms_decay = 0.0, const int iter_to_check = 0) { // Initialize the solver and run K (= iter_to_check) solver iterations. - RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check); + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay, + iter_to_check); // Compute the (K+1)th update using the analytic least squares gradient. vector > > updated_params; ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum, - &updated_params); + rms_decay, &updated_params); // Reinitialize the solver and run K+1 solver iterations. - RunLeastSquaresSolver(learning_rate, weight_decay, momentum, - iter_to_check + 1); + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay, + iter_to_check + 1); // Check that the solver's solution matches ours. CheckLeastSquaresUpdate(updated_params); @@ -427,12 +437,12 @@ class GradientBasedSolverTest : public MultiDeviceTest { void TestSnapshot(const Dtype learning_rate = 1.0, const Dtype weight_decay = 0.0, const Dtype momentum = 0.0, - const int num_iters = 1) { + const Dtype rms_decay = 0.0, const int num_iters = 1) { // Run the solver for num_iters * 2 iterations. const int total_num_iters = num_iters * 2; bool snapshot = false; const int kIterSize = 1; - RunLeastSquaresSolver(learning_rate, weight_decay, momentum, + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay, total_num_iters, kIterSize, snapshot); // Save the resulting param values. @@ -463,12 +473,12 @@ class GradientBasedSolverTest : public MultiDeviceTest { // Run the solver for num_iters iterations and snapshot. snapshot = true; string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay, - momentum, num_iters, kIterSize, snapshot); + momentum, rms_decay, num_iters, kIterSize, snapshot); // Reinitialize the solver and run for num_iters more iterations. snapshot = false; - RunLeastSquaresSolver(learning_rate, weight_decay, - momentum, total_num_iters, kIterSize, snapshot, snapshot_name.c_str()); + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay, + total_num_iters, kIterSize, snapshot, snapshot_name.c_str()); // Check that params now match. const vector*>& params = solver_->net()->learnable_params(); @@ -548,9 +558,11 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; + const Dtype kRMSDecay = 0; const int kNumIters = 1; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); } } @@ -559,9 +571,11 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; + const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); } } @@ -570,9 +584,11 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.5; + const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); } } @@ -581,10 +597,12 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.5; + const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); } } @@ -593,10 +611,11 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; + const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, - kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, + kNumIters, kIterSize); } TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { @@ -604,11 +623,12 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; + const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; this->share_ = true; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, - kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, + kNumIters, kIterSize); } TYPED_TEST(SGDSolverTest, TestSnapshot) { @@ -616,9 +636,10 @@ TYPED_TEST(SGDSolverTest, TestSnapshot) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; + const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); } } @@ -627,10 +648,11 @@ TYPED_TEST(SGDSolverTest, TestSnapshotShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; + const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); } } @@ -672,22 +694,26 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; + const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); } } TYPED_TEST(AdaGradSolverTest, - TestAdaGradLeastSquaresUpdateWithEverythingShare) { + TestAdaGradLeastSquaresUpdateWithEverythingShare) { typedef typename TypeParam::Dtype Dtype; const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; + const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); } } @@ -696,10 +722,11 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; + const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, - kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, + kNumIters, kIterSize); } TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { @@ -707,11 +734,12 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; + const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; this->share_ = true; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, - kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, + kNumIters, kIterSize); } TYPED_TEST(AdaGradSolverTest, TestSnapshot) { @@ -719,9 +747,10 @@ TYPED_TEST(AdaGradSolverTest, TestSnapshot) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; + const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); } } @@ -730,10 +759,11 @@ TYPED_TEST(AdaGradSolverTest, TestSnapshotShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; + const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); } } @@ -787,9 +817,11 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; + const Dtype kRMSDecay = 0; const int kNumIters = 1; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); } } @@ -798,9 +830,11 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; + const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); } } @@ -821,10 +855,12 @@ TYPED_TEST(NesterovSolverTest, const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; + const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); } } @@ -833,10 +869,11 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; + const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, - kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, + kNumIters, kIterSize); } TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { @@ -844,11 +881,12 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; + const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; this->share_ = true; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, - kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, + kNumIters, kIterSize); } TYPED_TEST(NesterovSolverTest, TestSnapshot) { @@ -856,9 +894,10 @@ TYPED_TEST(NesterovSolverTest, TestSnapshot) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; + const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); } } @@ -867,10 +906,124 @@ TYPED_TEST(NesterovSolverTest, TestSnapshotShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; + const Dtype kRMSDecay = 0; + const int kNumIters = 4; + this->share_ = true; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); + } +} + +template +class RMSPropSolverTest : public GradientBasedSolverTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + virtual void InitSolver(const SolverParameter& param) { + this->solver_.reset(new RMSPropSolver(param)); + } + virtual SolverParameter_SolverType solver_type() { + return SolverParameter_SolverType_RMSPROP; + } +}; + +TYPED_TEST_CASE(RMSPropSolverTest, TestDtypesAndDevices); + +TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithWeightDecay) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.5; + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay); +} + +TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithRmsDecay) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.0; + const Dtype kMomentum = 0.0; + const Dtype kRMSDecay = 0.95; + const int kNumIters = 4; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); + } +} + +TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithEverything) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.0; + const Dtype kRMSDecay = 0.95; + const int kNumIters = 4; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); + } +} + +TYPED_TEST(RMSPropSolverTest, + TestRMSPropLeastSquaresUpdateWithEverythingShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.0; + const Dtype kRMSDecay = 0.95; + const int kNumIters = 4; + this->share_ = true; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, + kRMSDecay, i); + } +} + +TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.0; + const Dtype kRMSDecay = 0.95; + const int kNumIters = 4; + const int kIterSize = 2; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, + kNumIters, kIterSize); +} + +TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.0; + const Dtype kRMSDecay = 0.95; + const int kNumIters = 4; + const int kIterSize = 2; + this->share_ = true; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, + kNumIters, kIterSize); +} + +TYPED_TEST(RMSPropSolverTest, TestSnapshot) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0; + const Dtype kRMSDecay = 0.95; + const int kNumIters = 4; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); + } +} + +TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0; + const Dtype kRMSDecay = 0.95; const int kNumIters = 4; this->share_ = true; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); } } From 4dcbe9287d334a06049bc95af37143fc73f15536 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Sun, 9 Aug 2015 01:32:25 -0700 Subject: [PATCH 182/446] Encapsulate kRMSDecay in solver tests Instead of introducing another argument kRMSDecay and setting it for every test, this param could be set by the RMSProp test class for encapsulation. --- src/caffe/test/test_gradient_based_solver.cpp | 156 +++++++----------- 1 file changed, 58 insertions(+), 98 deletions(-) diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index b09189228ba..e3c6b6d7964 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -58,8 +58,8 @@ class GradientBasedSolverTest : public MultiDeviceTest { } string RunLeastSquaresSolver(const Dtype learning_rate, - const Dtype weight_decay, const Dtype momentum, const Dtype rms_decay, - const int num_iters, const int iter_size = 1, const bool snapshot = false, + const Dtype weight_decay, const Dtype momentum, const int num_iters, + const int iter_size = 1, const bool snapshot = false, const char* from_snapshot = NULL) { ostringstream proto; proto << @@ -174,9 +174,6 @@ class GradientBasedSolverTest : public MultiDeviceTest { if (momentum != 0) { proto << "momentum: " << momentum << " "; } - if (rms_decay != 0) { - proto << "rms_decay: " << rms_decay << " "; - } MakeTempDir(&snapshot_prefix_); proto << "snapshot_prefix: '" << snapshot_prefix_ << "/' "; if (snapshot) { @@ -208,7 +205,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { // updated_params will store the updated weight and bias results, // using the blobs' diffs to hold the update values themselves. void ComputeLeastSquaresUpdate(const Dtype learning_rate, - const Dtype weight_decay, const Dtype momentum, const Dtype rms_decay, + const Dtype weight_decay, const Dtype momentum, vector > >* updated_params) { const int N = num_; const int D = channels_ * height_ * width_; @@ -291,9 +288,11 @@ class GradientBasedSolverTest : public MultiDeviceTest { case SolverParameter_SolverType_ADAGRAD: update_value /= std::sqrt(history_value + grad * grad) + delta_; break; - case SolverParameter_SolverType_RMSPROP: + case SolverParameter_SolverType_RMSPROP: { + const Dtype rms_decay = 0.95; update_value /= std::sqrt(rms_decay*history_value + grad * grad * (1 - rms_decay)) + delta_; + } break; default: LOG(FATAL) << "Unknown solver type: " << solver_type(); @@ -360,14 +359,13 @@ class GradientBasedSolverTest : public MultiDeviceTest { } void CheckAccumulation(const Dtype kLearningRate, const Dtype kWeightDecay, - const Dtype kMomentum, const Dtype rms_decay, const int kNumIters, - const int kIterSize) { + const Dtype kMomentum, const int kNumIters, const int kIterSize) { const double kPrecision = 1e-2; const double kMinPrecision = 1e-7; constant_data_ = true; // Solve without accumulation and save parameters. this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum, - rms_decay, kNumIters); + kNumIters); // Save parameters for comparison. Net& net = *this->solver_->net(); const vector > >& param_blobs = @@ -379,7 +377,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { } // Solve by equivalent accumulation of gradients over divided batches. this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum, - rms_decay, kNumIters, kIterSize); + kNumIters, kIterSize); Net& net_accum = *this->solver_->net(); const vector > >& accum_params = net_accum.layer_by_name("innerprod")->blobs(); @@ -417,18 +415,17 @@ class GradientBasedSolverTest : public MultiDeviceTest { // matches the solver's (K+1)th update. void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0, const Dtype weight_decay = 0.0, const Dtype momentum = 0.0, - const Dtype rms_decay = 0.0, const int iter_to_check = 0) { + const int iter_to_check = 0) { // Initialize the solver and run K (= iter_to_check) solver iterations. - RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay, - iter_to_check); + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check); // Compute the (K+1)th update using the analytic least squares gradient. vector > > updated_params; ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum, - rms_decay, &updated_params); + &updated_params); // Reinitialize the solver and run K+1 solver iterations. - RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay, + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check + 1); // Check that the solver's solution matches ours. @@ -437,13 +434,13 @@ class GradientBasedSolverTest : public MultiDeviceTest { void TestSnapshot(const Dtype learning_rate = 1.0, const Dtype weight_decay = 0.0, const Dtype momentum = 0.0, - const Dtype rms_decay = 0.0, const int num_iters = 1) { + const int num_iters = 1) { // Run the solver for num_iters * 2 iterations. const int total_num_iters = num_iters * 2; bool snapshot = false; const int kIterSize = 1; - RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay, - total_num_iters, kIterSize, snapshot); + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, + total_num_iters, kIterSize, snapshot); // Save the resulting param values. vector > > param_copies; @@ -473,11 +470,11 @@ class GradientBasedSolverTest : public MultiDeviceTest { // Run the solver for num_iters iterations and snapshot. snapshot = true; string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay, - momentum, rms_decay, num_iters, kIterSize, snapshot); + momentum, num_iters, kIterSize, snapshot); // Reinitialize the solver and run for num_iters more iterations. snapshot = false; - RunLeastSquaresSolver(learning_rate, weight_decay, momentum, rms_decay, + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, total_num_iters, kIterSize, snapshot, snapshot_name.c_str()); // Check that params now match. @@ -558,11 +555,9 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; - const Dtype kRMSDecay = 0; const int kNumIters = 1; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -571,11 +566,9 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; - const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -584,11 +577,9 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.5; - const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -597,12 +588,10 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.5; - const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -611,11 +600,10 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; - const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, - kNumIters, kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); } TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { @@ -623,12 +611,11 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; - const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; this->share_ = true; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, - kNumIters, kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); } TYPED_TEST(SGDSolverTest, TestSnapshot) { @@ -636,10 +623,9 @@ TYPED_TEST(SGDSolverTest, TestSnapshot) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; - const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -648,11 +634,10 @@ TYPED_TEST(SGDSolverTest, TestSnapshotShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; - const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -694,11 +679,9 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; - const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -708,12 +691,10 @@ TYPED_TEST(AdaGradSolverTest, const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; - const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -722,11 +703,10 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; - const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, - kNumIters, kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); } TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { @@ -734,12 +714,11 @@ TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; - const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; this->share_ = true; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, - kNumIters, kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); } TYPED_TEST(AdaGradSolverTest, TestSnapshot) { @@ -747,10 +726,9 @@ TYPED_TEST(AdaGradSolverTest, TestSnapshot) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; - const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -759,11 +737,10 @@ TYPED_TEST(AdaGradSolverTest, TestSnapshotShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; - const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -817,11 +794,9 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; - const Dtype kRMSDecay = 0; const int kNumIters = 1; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -830,11 +805,9 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0; const Dtype kMomentum = 0.5; - const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -855,12 +828,10 @@ TYPED_TEST(NesterovSolverTest, const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; - const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -869,11 +840,10 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; - const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, - kNumIters, kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); } TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { @@ -881,12 +851,11 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; - const Dtype kRMSDecay = 0; const int kNumIters = 4; const int kIterSize = 2; this->share_ = true; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, - kNumIters, kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); } TYPED_TEST(NesterovSolverTest, TestSnapshot) { @@ -894,10 +863,9 @@ TYPED_TEST(NesterovSolverTest, TestSnapshot) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; - const Dtype kRMSDecay = 0; const int kNumIters = 4; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -906,11 +874,10 @@ TYPED_TEST(NesterovSolverTest, TestSnapshotShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.9; - const Dtype kRMSDecay = 0; const int kNumIters = 4; this->share_ = true; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -920,7 +887,10 @@ class RMSPropSolverTest : public GradientBasedSolverTest { protected: virtual void InitSolver(const SolverParameter& param) { - this->solver_.reset(new RMSPropSolver(param)); + const Dtype rms_decay = 0.95; + SolverParameter new_param = param; + new_param.set_rms_decay(rms_decay); + this->solver_.reset(new RMSPropSolver(new_param)); } virtual SolverParameter_SolverType solver_type() { return SolverParameter_SolverType_RMSPROP; @@ -941,11 +911,9 @@ TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithRmsDecay) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.0; const Dtype kMomentum = 0.0; - const Dtype kRMSDecay = 0.95; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -954,11 +922,9 @@ TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithEverything) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.0; - const Dtype kRMSDecay = 0.95; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -968,12 +934,10 @@ TYPED_TEST(RMSPropSolverTest, const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.0; - const Dtype kRMSDecay = 0.95; const int kNumIters = 4; this->share_ = true; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, - kRMSDecay, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -982,11 +946,10 @@ TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.0; - const Dtype kRMSDecay = 0.95; const int kNumIters = 4; const int kIterSize = 2; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, - kNumIters, kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); } TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { @@ -994,12 +957,11 @@ TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.0; - const Dtype kRMSDecay = 0.95; const int kNumIters = 4; const int kIterSize = 2; this->share_ = true; - this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, - kNumIters, kIterSize); + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); } TYPED_TEST(RMSPropSolverTest, TestSnapshot) { @@ -1007,10 +969,9 @@ TYPED_TEST(RMSPropSolverTest, TestSnapshot) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; - const Dtype kRMSDecay = 0.95; const int kNumIters = 4; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); } } @@ -1019,11 +980,10 @@ TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) { const Dtype kLearningRate = 0.01; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0; - const Dtype kRMSDecay = 0.95; const int kNumIters = 4; this->share_ = true; for (int i = 1; i <= kNumIters; ++i) { - this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, kRMSDecay, i); + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); } } From b97ca6d4d17b4a661d553c233c2dfe03b88033c5 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Sun, 9 Aug 2015 11:03:23 -0700 Subject: [PATCH 183/446] Use net_->learnable_params() instead of net_->params() in RMSprop In RMSProp solver, use const vector*>& net_params = this->net_->learnable_params(); instead of const vector > >& net_params = this->net_->params(); --- src/caffe/solver.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 43834c0c569..54e085a63e5 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -861,7 +861,7 @@ void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { template void RMSPropSolver::ComputeUpdateValue(int param_id, Dtype rate) { - const vector > >& net_params = this->net_->params(); + const vector*>& net_params = this->net_->learnable_params(); const vector& net_params_lr = this->net_->params_lr(); // get the learning rate From 9b71fd038018d7c910ed0b75003b76edce1c5af9 Mon Sep 17 00:00:00 2001 From: Fisher Yu Date: Sun, 9 Aug 2015 14:22:04 -0700 Subject: [PATCH 184/446] from __future__ imports must occur at the beginning of the file --- examples/pycaffe/caffenet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/pycaffe/caffenet.py b/examples/pycaffe/caffenet.py index 06c5a02d4ee..82af2294435 100644 --- a/examples/pycaffe/caffenet.py +++ b/examples/pycaffe/caffenet.py @@ -1,6 +1,6 @@ +from __future__ import print_function from caffe import layers as L, params as P, to_proto from caffe.proto import caffe_pb2 -from __future__ import print_function # helper function for common structures From d94ca3f4b15d1e02bdf520733b04999f1134d078 Mon Sep 17 00:00:00 2001 From: Cyprien Noel Date: Tue, 28 Apr 2015 14:28:04 -0700 Subject: [PATCH 185/446] Add BlockingQueue for inter-thread communication --- include/caffe/util/blocking_queue.hpp | 47 +++++++++++++++ src/caffe/util/blocking_queue.cpp | 86 +++++++++++++++++++++++++++ 2 files changed, 133 insertions(+) create mode 100644 include/caffe/util/blocking_queue.hpp create mode 100644 src/caffe/util/blocking_queue.cpp diff --git a/include/caffe/util/blocking_queue.hpp b/include/caffe/util/blocking_queue.hpp new file mode 100644 index 00000000000..955e12cc567 --- /dev/null +++ b/include/caffe/util/blocking_queue.hpp @@ -0,0 +1,47 @@ +#ifndef CAFFE_UTIL_BLOCKING_QUEUE_HPP_ +#define CAFFE_UTIL_BLOCKING_QUEUE_HPP_ + +#include +#include + +#include "caffe/common.hpp" + +namespace caffe { + +template +class BlockingQueue { + public: + explicit BlockingQueue(); + + void push(const T& t); + + bool try_pop(T* t); + + // This logs a message if the threads needs to be blocked + // useful for detecting e.g. when data feeding is too slow + T pop(const string& log_on_wait = ""); + + bool try_peek(T* t); + + // Return element without removing it + T peek(); + + size_t size() const; + + protected: + /** + Move synchronization fields out instead of including boost/thread.hpp + to avoid a boost/NVCC issues (#1009, #1010) on OSX. Also fails on + Linux CUDA 7.0.18. + */ + class sync; + + std::queue queue_; + shared_ptr sync_; + +DISABLE_COPY_AND_ASSIGN(BlockingQueue); +}; + +} // namespace caffe + +#endif diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp new file mode 100644 index 00000000000..73c9564c0dd --- /dev/null +++ b/src/caffe/util/blocking_queue.cpp @@ -0,0 +1,86 @@ +#include +#include + +#include "caffe/util/blocking_queue.hpp" + +namespace caffe { + +template +class BlockingQueue::sync { + public: + mutable boost::mutex mutex_; + boost::condition_variable condition_; +}; + +template +BlockingQueue::BlockingQueue() + : sync_(new sync()) { +} + +template +void BlockingQueue::push(const T& t) { + boost::mutex::scoped_lock lock(sync_->mutex_); + queue_.push(t); + lock.unlock(); + sync_->condition_.notify_one(); +} + +template +bool BlockingQueue::try_pop(T* t) { + boost::mutex::scoped_lock lock(sync_->mutex_); + + if (queue_.empty()) { + return false; + } + + *t = queue_.front(); + queue_.pop(); + return true; +} + +template +T BlockingQueue::pop(const string& log_on_wait) { + boost::mutex::scoped_lock lock(sync_->mutex_); + + while (queue_.empty()) { + if (!log_on_wait.empty()) { + LOG_EVERY_N(INFO, 1000)<< log_on_wait; + } + sync_->condition_.wait(lock); + } + + T t = queue_.front(); + queue_.pop(); + return t; +} + +template +bool BlockingQueue::try_peek(T* t) { + boost::mutex::scoped_lock lock(sync_->mutex_); + + if (queue_.empty()) { + return false; + } + + *t = queue_.front(); + return true; +} + +template +T BlockingQueue::peek() { + boost::mutex::scoped_lock lock(sync_->mutex_); + + while (queue_.empty()) { + sync_->condition_.wait(lock); + } + + return queue_.front(); +} + +template +size_t BlockingQueue::size() const { + boost::mutex::scoped_lock lock(sync_->mutex_); + return queue_.size(); +} + +} // namespace caffe From 45d792e8b1e44acb467ab9be3debdd9e819c11d1 Mon Sep 17 00:00:00 2001 From: Cyprien Noel Date: Mon, 27 Apr 2015 19:48:10 -0700 Subject: [PATCH 186/446] Thread-local Caffe --- include/caffe/common.hpp | 13 +++++------ include/caffe/internal_thread.hpp | 12 ++++++++-- src/caffe/common.cpp | 11 ++++++++- src/caffe/internal_thread.cpp | 29 +++++++++++++++++++++--- src/caffe/test/test_internal_thread.cpp | 30 +++++++++++++++++++++++++ 5 files changed, 82 insertions(+), 13 deletions(-) diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp index 5f86bc2625b..3fa81431314 100644 --- a/include/caffe/common.hpp +++ b/include/caffe/common.hpp @@ -98,12 +98,12 @@ void GlobalInit(int* pargc, char*** pargv); class Caffe { public: ~Caffe(); - inline static Caffe& Get() { - if (!singleton_.get()) { - singleton_.reset(new Caffe()); - } - return *singleton_; - } + + // Thread local context for Caffe. Moved to common.cpp instead of + // including boost/thread.hpp to avoid a boost/NVCC issues (#1009, #1010) + // on OSX. Also fails on Linux with CUDA 7.0.18. + static Caffe& Get(); + enum Brew { CPU, GPU }; // This random number generator facade hides boost and CUDA rng @@ -158,7 +158,6 @@ class Caffe { shared_ptr random_generator_; Brew mode_; - static shared_ptr singleton_; private: // The private constructor to avoid duplicate instantiation. diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp index 815ca54605e..bcff318e866 100644 --- a/include/caffe/internal_thread.hpp +++ b/include/caffe/internal_thread.hpp @@ -14,14 +14,19 @@ namespace caffe { /** * Virtual class encapsulate boost::thread for use in base class * The child class will acquire the ability to run a single thread, - * by reimplementing the virutal function InternalThreadEntry. + * by reimplementing the virtual function InternalThreadEntry. */ class InternalThread { public: InternalThread() : thread_() {} virtual ~InternalThread(); - /** Returns true if the thread was successfully started. **/ + /** + * Caffe's thread local state will be initialized using the current + * thread values, e.g. device id, solver index etc. The random seed + * is initialized using caffe_rng_rand. + * Will not return until the internal thread has exited. + */ bool StartInternalThread(); /** Will not return until the internal thread has exited. */ @@ -34,6 +39,9 @@ class InternalThread { with the code you want your thread to run. */ virtual void InternalThreadEntry() {} + private: + void entry(int device, Caffe::Brew mode, int rand_seed); + shared_ptr thread_; }; diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp index af96cac40aa..0215c76ef76 100644 --- a/src/caffe/common.cpp +++ b/src/caffe/common.cpp @@ -1,3 +1,4 @@ +#include #include #include #include @@ -7,7 +8,15 @@ namespace caffe { -shared_ptr Caffe::singleton_; +// Make sure each thread can have different values. +static boost::thread_specific_ptr thread_instance_; + +Caffe& Caffe::Get() { + if (!thread_instance_.get()) { + thread_instance_.reset(new Caffe()); + } + return *(thread_instance_.get()); +} // random seeding int64_t cluster_seedgen(void) { diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp index c2d19d433b4..2be88b31fa0 100644 --- a/src/caffe/internal_thread.cpp +++ b/src/caffe/internal_thread.cpp @@ -1,8 +1,14 @@ #include + #include "caffe/internal_thread.hpp" +#include "caffe/util/math_functions.hpp" namespace caffe { +InternalThread::~InternalThread() { + StopInternalThread(); +} + InternalThread::~InternalThread() { WaitForInternalThreadToExit(); } @@ -11,20 +17,37 @@ bool InternalThread::is_started() const { return thread_.get() != NULL && thread_->joinable(); } - bool InternalThread::StartInternalThread() { if (!WaitForInternalThreadToExit()) { return false; } + + int device = 0; +#ifndef CPU_ONLY + CUDA_CHECK(cudaGetDevice(&device)); +#endif + Caffe::Brew mode = Caffe::mode(); + int rand_seed = caffe_rng_rand(); + try { - thread_.reset( - new boost::thread(&InternalThread::InternalThreadEntry, this)); + thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode, + rand_seed)); } catch (...) { return false; } return true; } +void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed) { +#ifndef CPU_ONLY + CUDA_CHECK(cudaSetDevice(device)); +#endif + Caffe::set_mode(mode); + Caffe::set_random_seed(rand_seed); + + InternalThreadEntry(); +} + /** Will not return until the internal thread has exited. */ bool InternalThread::WaitForInternalThreadToExit() { if (is_started()) { diff --git a/src/caffe/test/test_internal_thread.cpp b/src/caffe/test/test_internal_thread.cpp index 31882b6db1d..390c8eda19b 100644 --- a/src/caffe/test/test_internal_thread.cpp +++ b/src/caffe/test/test_internal_thread.cpp @@ -2,6 +2,7 @@ #include "gtest/gtest.h" #include "caffe/internal_thread.hpp" +#include "caffe/util/math_functions.hpp" #include "caffe/test/test_caffe_main.hpp" @@ -19,5 +20,34 @@ TEST_F(InternalThreadTest, TestStartAndExit) { EXPECT_FALSE(thread.is_started()); } +class TestThreadA : public InternalThread { + void InternalThreadEntry() { + EXPECT_EQ(4244559767, caffe_rng_rand()); + } +}; + +class TestThreadB : public InternalThread { + void InternalThreadEntry() { + EXPECT_EQ(1726478280, caffe_rng_rand()); + } +}; + +TEST_F(InternalThreadTest, TestRandomSeed) { + TestThreadA t1; + Caffe::set_random_seed(9658361); + EXPECT_TRUE(t1.StartInternalThread()); + EXPECT_TRUE(t1.WaitForInternalThreadToExit()); + + TestThreadA t2; + Caffe::set_random_seed(9658361); + EXPECT_TRUE(t2.StartInternalThread()); + EXPECT_TRUE(t2.WaitForInternalThreadToExit()); + + TestThreadB t3; + Caffe::set_random_seed(3435563); + EXPECT_TRUE(t3.StartInternalThread()); + EXPECT_TRUE(t3.WaitForInternalThreadToExit()); +} + } // namespace caffe From 73b3d13b68bedad9d19f70755b0ee4ef376e2a30 Mon Sep 17 00:00:00 2001 From: Cyprien Noel Date: Tue, 28 Apr 2015 14:46:20 -0700 Subject: [PATCH 187/446] Change the way threads are started and stopped - Interrupt the thread before waiting on join - Provide a method for looping threads to exit on demand - CHECK if start and stop succeed instead of returning an error --- include/caffe/internal_thread.hpp | 8 +++--- src/caffe/internal_thread.cpp | 33 +++++++++++++------------ src/caffe/layers/base_data_layer.cpp | 4 +-- src/caffe/test/test_internal_thread.cpp | 16 ++++++------ 4 files changed, 32 insertions(+), 29 deletions(-) diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp index bcff318e866..be6ff7fbac7 100644 --- a/include/caffe/internal_thread.hpp +++ b/include/caffe/internal_thread.hpp @@ -25,12 +25,11 @@ class InternalThread { * Caffe's thread local state will be initialized using the current * thread values, e.g. device id, solver index etc. The random seed * is initialized using caffe_rng_rand. - * Will not return until the internal thread has exited. */ - bool StartInternalThread(); + void StartInternalThread(); /** Will not return until the internal thread has exited. */ - bool WaitForInternalThreadToExit(); + void StopInternalThread(); bool is_started() const; @@ -39,6 +38,9 @@ class InternalThread { with the code you want your thread to run. */ virtual void InternalThreadEntry() {} + /* Should be tested when running loops to exit when requested. */ + bool must_stop(); + private: void entry(int device, Caffe::Brew mode, int rand_seed); diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp index 2be88b31fa0..d6c26559925 100644 --- a/src/caffe/internal_thread.cpp +++ b/src/caffe/internal_thread.cpp @@ -1,4 +1,5 @@ #include +#include #include "caffe/internal_thread.hpp" #include "caffe/util/math_functions.hpp" @@ -9,18 +10,19 @@ InternalThread::~InternalThread() { StopInternalThread(); } -InternalThread::~InternalThread() { - WaitForInternalThreadToExit(); +bool InternalThread::is_started() const { + return thread_ && thread_->joinable(); } -bool InternalThread::is_started() const { - return thread_.get() != NULL && thread_->joinable(); +bool InternalThread::must_stop() { + return thread_ && thread_->interruption_requested(); } -bool InternalThread::StartInternalThread() { - if (!WaitForInternalThreadToExit()) { - return false; - } +void InternalThread::StartInternalThread() { + // TODO switch to failing once Caffe prefetch thread is persistent. + // Threads should not be started and stopped repeatedly. + // CHECK(!is_started()); + StopInternalThread(); int device = 0; #ifndef CPU_ONLY @@ -32,10 +34,9 @@ bool InternalThread::StartInternalThread() { try { thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode, rand_seed)); - } catch (...) { - return false; + } catch (std::exception& e) { + LOG(FATAL) << "Thread exception: " << e.what(); } - return true; } void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed) { @@ -48,16 +49,16 @@ void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed) { InternalThreadEntry(); } -/** Will not return until the internal thread has exited. */ -bool InternalThread::WaitForInternalThreadToExit() { +void InternalThread::StopInternalThread() { if (is_started()) { + thread_->interrupt(); try { thread_->join(); - } catch (...) { - return false; + } catch (boost::thread_interrupted&) { + } catch (std::exception& e) { + LOG(FATAL) << "Thread exception: " << e.what(); } } - return true; } } // namespace caffe diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp index 26a1118282f..facaed7f11c 100644 --- a/src/caffe/layers/base_data_layer.cpp +++ b/src/caffe/layers/base_data_layer.cpp @@ -47,12 +47,12 @@ void BasePrefetchingDataLayer::LayerSetUp( template void BasePrefetchingDataLayer::CreatePrefetchThread() { this->data_transformer_->InitRand(); - CHECK(StartInternalThread()) << "Thread execution failed"; + StartInternalThread(); } template void BasePrefetchingDataLayer::JoinPrefetchThread() { - CHECK(WaitForInternalThreadToExit()) << "Thread joining failed"; + StopInternalThread(); } template diff --git a/src/caffe/test/test_internal_thread.cpp b/src/caffe/test/test_internal_thread.cpp index 390c8eda19b..93f1cc541cd 100644 --- a/src/caffe/test/test_internal_thread.cpp +++ b/src/caffe/test/test_internal_thread.cpp @@ -14,9 +14,9 @@ class InternalThreadTest : public ::testing::Test {}; TEST_F(InternalThreadTest, TestStartAndExit) { InternalThread thread; EXPECT_FALSE(thread.is_started()); - EXPECT_TRUE(thread.StartInternalThread()); + thread.StartInternalThread(); EXPECT_TRUE(thread.is_started()); - EXPECT_TRUE(thread.WaitForInternalThreadToExit()); + thread.StopInternalThread(); EXPECT_FALSE(thread.is_started()); } @@ -35,18 +35,18 @@ class TestThreadB : public InternalThread { TEST_F(InternalThreadTest, TestRandomSeed) { TestThreadA t1; Caffe::set_random_seed(9658361); - EXPECT_TRUE(t1.StartInternalThread()); - EXPECT_TRUE(t1.WaitForInternalThreadToExit()); + t1.StartInternalThread(); + t1.StopInternalThread(); TestThreadA t2; Caffe::set_random_seed(9658361); - EXPECT_TRUE(t2.StartInternalThread()); - EXPECT_TRUE(t2.WaitForInternalThreadToExit()); + t2.StartInternalThread(); + t2.StopInternalThread(); TestThreadB t3; Caffe::set_random_seed(3435563); - EXPECT_TRUE(t3.StartInternalThread()); - EXPECT_TRUE(t3.WaitForInternalThreadToExit()); + t3.StartInternalThread(); + t3.StopInternalThread(); } } // namespace caffe From ddcdc9d711e81312caf127e8aa512c3298101297 Mon Sep 17 00:00:00 2001 From: Cyprien Noel Date: Mon, 18 May 2015 17:45:20 -0700 Subject: [PATCH 188/446] Persistent prefetch thread --- include/caffe/data_layers.hpp | 31 +++++---- include/caffe/syncedmem.hpp | 4 ++ src/caffe/internal_thread.cpp | 5 +- src/caffe/layers/base_data_layer.cpp | 88 ++++++++++++++++++-------- src/caffe/layers/base_data_layer.cu | 15 ++--- src/caffe/layers/data_layer.cpp | 26 ++++---- src/caffe/layers/image_data_layer.cpp | 28 ++++---- src/caffe/layers/window_data_layer.cpp | 20 +++--- src/caffe/syncedmem.cpp | 12 ++++ src/caffe/util/blocking_queue.cpp | 4 ++ 10 files changed, 153 insertions(+), 80 deletions(-) diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp index 3958cb7ecb0..f57ab6b0dba 100644 --- a/include/caffe/data_layers.hpp +++ b/include/caffe/data_layers.hpp @@ -15,6 +15,7 @@ #include "caffe/internal_thread.hpp" #include "caffe/layer.hpp" #include "caffe/proto/caffe.pb.h" +#include "caffe/util/blocking_queue.hpp" #include "caffe/util/db.hpp" namespace caffe { @@ -50,12 +51,17 @@ class BaseDataLayer : public Layer { bool output_labels_; }; +template +class Batch { + public: + Blob data_, label_; +}; + template class BasePrefetchingDataLayer : public BaseDataLayer, public InternalThread { public: - explicit BasePrefetchingDataLayer(const LayerParameter& param) - : BaseDataLayer(param) {} + explicit BasePrefetchingDataLayer(const LayerParameter& param); // LayerSetUp: implements common data layer setup functionality, and calls // DataLayerSetUp to do special data layer setup for individual layer types. // This method may not be overridden. @@ -67,14 +73,17 @@ class BasePrefetchingDataLayer : virtual void Forward_gpu(const vector*>& bottom, const vector*>& top); - virtual void CreatePrefetchThread(); - virtual void JoinPrefetchThread(); - // The thread's function - virtual void InternalThreadEntry() {} + // Prefetches batches (asynchronously if to GPU memory) + static const int PREFETCH_COUNT = 3; protected: - Blob prefetch_data_; - Blob prefetch_label_; + virtual void InternalThreadEntry(); + virtual void load_batch(Batch* batch) = 0; + + Batch prefetch_[PREFETCH_COUNT]; + BlockingQueue*> prefetch_free_; + BlockingQueue*> prefetch_full_; + Blob transformed_data_; }; @@ -93,7 +102,7 @@ class DataLayer : public BasePrefetchingDataLayer { virtual inline int MaxTopBlobs() const { return 2; } protected: - virtual void InternalThreadEntry(); + virtual void load_batch(Batch* batch); shared_ptr db_; shared_ptr cursor_; @@ -235,7 +244,7 @@ class ImageDataLayer : public BasePrefetchingDataLayer { protected: shared_ptr prefetch_rng_; virtual void ShuffleImages(); - virtual void InternalThreadEntry(); + virtual void load_batch(Batch* batch); vector > lines_; int lines_id_; @@ -307,7 +316,7 @@ class WindowDataLayer : public BasePrefetchingDataLayer { protected: virtual unsigned int PrefetchRand(); - virtual void InternalThreadEntry(); + virtual void load_batch(Batch* batch); shared_ptr prefetch_rng_; vector > > image_database_; diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp index 1b726de9564..4d339bf4e57 100644 --- a/include/caffe/syncedmem.hpp +++ b/include/caffe/syncedmem.hpp @@ -56,6 +56,10 @@ class SyncedMemory { SyncedHead head() { return head_; } size_t size() { return size_; } +#ifndef CPU_ONLY + void async_gpu_push(const cudaStream_t& stream); +#endif + private: void to_cpu(); void to_gpu(); diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp index d6c26559925..b193826c1a6 100644 --- a/src/caffe/internal_thread.cpp +++ b/src/caffe/internal_thread.cpp @@ -19,10 +19,7 @@ bool InternalThread::must_stop() { } void InternalThread::StartInternalThread() { - // TODO switch to failing once Caffe prefetch thread is persistent. - // Threads should not be started and stopped repeatedly. - // CHECK(!is_started()); - StopInternalThread(); + CHECK(!is_started()) << "Threads should persist and not be restarted."; int device = 0; #ifndef CPU_ONLY diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp index facaed7f11c..9288d91339a 100644 --- a/src/caffe/layers/base_data_layer.cpp +++ b/src/caffe/layers/base_data_layer.cpp @@ -1,7 +1,9 @@ +#include #include #include #include "caffe/data_layers.hpp" +#include "caffe/net.hpp" #include "caffe/util/io.hpp" namespace caffe { @@ -27,56 +29,92 @@ void BaseDataLayer::LayerSetUp(const vector*>& bottom, DataLayerSetUp(bottom, top); } +template +BasePrefetchingDataLayer::BasePrefetchingDataLayer( + const LayerParameter& param) + : BaseDataLayer(param), + prefetch_free_(), prefetch_full_() { + for (int i = 0; i < PREFETCH_COUNT; ++i) { + prefetch_free_.push(&prefetch_[i]); + } +} + template void BasePrefetchingDataLayer::LayerSetUp( const vector*>& bottom, const vector*>& top) { BaseDataLayer::LayerSetUp(bottom, top); - // Now, start the prefetch thread. Before calling prefetch, we make two - // cpu_data calls so that the prefetch thread does not accidentally make - // simultaneous cudaMalloc calls when the main thread is running. In some - // GPUs this seems to cause failures if we do not so. - this->prefetch_data_.mutable_cpu_data(); - if (this->output_labels_) { - this->prefetch_label_.mutable_cpu_data(); + // Before starting the prefetch thread, we make cpu_data and gpu_data + // calls so that the prefetch thread does not accidentally make simultaneous + // cudaMalloc calls when the main thread is running. In some GPUs this + // seems to cause failures if we do not so. + for (int i = 0; i < PREFETCH_COUNT; ++i) { + prefetch_[i].data_.mutable_cpu_data(); + if (this->output_labels_) { + prefetch_[i].label_.mutable_cpu_data(); + } } +#ifndef CPU_ONLY + if (Caffe::mode() == Caffe::GPU) { + for (int i = 0; i < PREFETCH_COUNT; ++i) { + prefetch_[i].data_.mutable_gpu_data(); + if (this->output_labels_) { + prefetch_[i].label_.mutable_gpu_data(); + } + } + } +#endif DLOG(INFO) << "Initializing prefetch"; - this->CreatePrefetchThread(); - DLOG(INFO) << "Prefetch initialized."; -} - -template -void BasePrefetchingDataLayer::CreatePrefetchThread() { this->data_transformer_->InitRand(); StartInternalThread(); + DLOG(INFO) << "Prefetch initialized."; } template -void BasePrefetchingDataLayer::JoinPrefetchThread() { - StopInternalThread(); +void BasePrefetchingDataLayer::InternalThreadEntry() { +#ifndef CPU_ONLY + cudaStream_t stream; + if (Caffe::mode() == Caffe::GPU) { + cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking); + } +#endif + + try { + while (!must_stop()) { + Batch* batch = prefetch_free_.pop(); + load_batch(batch); +#ifndef CPU_ONLY + if (Caffe::mode() == Caffe::GPU) { + batch->data_.data().get()->async_gpu_push(stream); + cudaStreamSynchronize(stream); + } +#endif + prefetch_full_.push(batch); + } + } catch (boost::thread_interrupted&) { + // Interrupted exception is expected on shutdown + } } template void BasePrefetchingDataLayer::Forward_cpu( const vector*>& bottom, const vector*>& top) { - // First, join the thread - JoinPrefetchThread(); - DLOG(INFO) << "Thread joined"; + Batch* batch = prefetch_full_.pop("Data layer prefetch queue empty"); // Reshape to loaded data. - top[0]->ReshapeLike(prefetch_data_); + top[0]->Reshape(batch->data_.num(), batch->data_.channels(), + batch->data_.height(), batch->data_.width()); // Copy the data - caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(), + caffe_copy(batch->data_.count(), batch->data_.cpu_data(), top[0]->mutable_cpu_data()); DLOG(INFO) << "Prefetch copied"; if (this->output_labels_) { // Reshape to loaded labels. top[1]->ReshapeLike(prefetch_label_); // Copy the labels. - caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(), - top[1]->mutable_cpu_data()); + caffe_copy(batch->label_.count(), batch->label_.cpu_data(), + top[1]->mutable_cpu_data()); } - // Start a new prefetch thread - DLOG(INFO) << "CreatePrefetchThread"; - CreatePrefetchThread(); + + prefetch_free_.push(batch); } #ifdef CPU_ONLY diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu index 9335a5bc9a9..56439bc506a 100644 --- a/src/caffe/layers/base_data_layer.cu +++ b/src/caffe/layers/base_data_layer.cu @@ -7,22 +7,21 @@ namespace caffe { template void BasePrefetchingDataLayer::Forward_gpu( const vector*>& bottom, const vector*>& top) { - // First, join the thread - JoinPrefetchThread(); + Batch* batch = prefetch_full_.pop("Data layer prefetch queue empty"); // Reshape to loaded data. - top[0]->ReshapeLike(this->prefetch_data_); + top[0]->ReshapeLike(batch->data_); // Copy the data - caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(), + caffe_copy(batch->data_.count(), batch->data_.gpu_data(), top[0]->mutable_gpu_data()); if (this->output_labels_) { // Reshape to loaded labels. - top[1]->ReshapeLike(prefetch_label_); + top[1]->ReshapeLike(batch->label_); // Copy the labels. - caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(), + caffe_copy(batch->label_.count(), batch->label_.gpu_data(), top[1]->mutable_gpu_data()); } - // Start a new prefetch thread - CreatePrefetchThread(); + + prefetch_free_.push(batch); } INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingDataLayer); diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp index 161a75e0c8c..22d9f436185 100644 --- a/src/caffe/layers/data_layer.cpp +++ b/src/caffe/layers/data_layer.cpp @@ -17,8 +17,8 @@ namespace caffe { template -DataLayer::~DataLayer() { - this->JoinPrefetchThread(); +DataLayer::~DataLayer() { + this->StopInternalThread(); } template @@ -54,21 +54,23 @@ void DataLayer::DataLayerSetUp(const vector*>& bottom, << top[0]->width(); // label if (this->output_labels_) { - vector label_shape(1, this->layer_param_.data_param().batch_size()); + vector label_shape(1, batch_size); top[1]->Reshape(label_shape); - this->prefetch_label_.Reshape(label_shape); + for (int i = 0; i < this->PREFETCH_COUNT; ++i) { + this->prefetch_[i].label_.Reshape(label_shape); + } } } -// This function is used to create a thread that prefetches the data. -template -void DataLayer::InternalThreadEntry() { +// This function is called on prefetch thread +template +void DataLayer::load_batch(Batch* batch) { CPUTimer batch_timer; batch_timer.Start(); double read_time = 0; double trans_time = 0; CPUTimer timer; - CHECK(this->prefetch_data_.count()); + CHECK(batch->data_.count()); CHECK(this->transformed_data_.count()); // Reshape according to the first datum of each batch @@ -81,13 +83,13 @@ void DataLayer::InternalThreadEntry() { this->transformed_data_.Reshape(top_shape); // Reshape prefetch_data according to the batch_size. top_shape[0] = batch_size; - this->prefetch_data_.Reshape(top_shape); + batch->data_.Reshape(top_shape); - Dtype* top_data = this->prefetch_data_.mutable_cpu_data(); + Dtype* top_data = batch->data_.mutable_cpu_data(); Dtype* top_label = NULL; // suppress warnings about uninitialized variables if (this->output_labels_) { - top_label = this->prefetch_label_.mutable_cpu_data(); + top_label = batch->label_.mutable_cpu_data(); } timer.Start(); for (int item_id = 0; item_id < batch_size; ++item_id) { @@ -97,7 +99,7 @@ void DataLayer::InternalThreadEntry() { read_time += timer.MicroSeconds(); timer.Start(); // Apply data transformations (mirror, scale, crop...) - int offset = this->prefetch_data_.offset(item_id); + int offset = batch->data_.offset(item_id); this->transformed_data_.set_cpu_data(top_data + offset); this->data_transformer_->Transform(datum, &(this->transformed_data_)); // Copy label. diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp index dcc53348304..223ba3a75ca 100644 --- a/src/caffe/layers/image_data_layer.cpp +++ b/src/caffe/layers/image_data_layer.cpp @@ -17,7 +17,7 @@ namespace caffe { template ImageDataLayer::~ImageDataLayer() { - this->JoinPrefetchThread(); + this->StopInternalThread(); } template @@ -70,8 +70,10 @@ void ImageDataLayer::DataLayerSetUp(const vector*>& bottom, const int batch_size = this->layer_param_.image_data_param().batch_size(); CHECK_GT(batch_size, 0) << "Positive batch size required"; top_shape[0] = batch_size; - this->prefetch_data_.Reshape(top_shape); - top[0]->ReshapeLike(this->prefetch_data_); + for (int i = 0; i < this->PREFETCH_COUNT; ++i) { + this->prefetch_[i].data_.Reshape(top_shape); + } + top[0]->Reshape(top_shape); LOG(INFO) << "output data size: " << top[0]->num() << "," << top[0]->channels() << "," << top[0]->height() << "," @@ -79,7 +81,9 @@ void ImageDataLayer::DataLayerSetUp(const vector*>& bottom, // label vector label_shape(1, batch_size); top[1]->Reshape(label_shape); - this->prefetch_label_.Reshape(label_shape); + for (int i = 0; i < this->PREFETCH_COUNT; ++i) { + this->prefetch_[i].label_.Reshape(label_shape); + } } template @@ -89,15 +93,15 @@ void ImageDataLayer::ShuffleImages() { shuffle(lines_.begin(), lines_.end(), prefetch_rng); } -// This function is used to create a thread that prefetches the data. +// This function is called on prefetch thread template -void ImageDataLayer::InternalThreadEntry() { +void ImageDataLayer::load_batch(Batch* batch) { CPUTimer batch_timer; batch_timer.Start(); double read_time = 0; double trans_time = 0; CPUTimer timer; - CHECK(this->prefetch_data_.count()); + CHECK(batch->data_.count()); CHECK(this->transformed_data_.count()); ImageDataParameter image_data_param = this->layer_param_.image_data_param(); const int batch_size = image_data_param.batch_size(); @@ -114,12 +118,12 @@ void ImageDataLayer::InternalThreadEntry() { // Use data_transformer to infer the expected blob shape from a cv_img. vector top_shape = this->data_transformer_->InferBlobShape(cv_img); this->transformed_data_.Reshape(top_shape); - // Reshape prefetch_data according to the batch_size. + // Reshape batch according to the batch_size. top_shape[0] = batch_size; - this->prefetch_data_.Reshape(top_shape); + batch->data_.Reshape(top_shape); - Dtype* prefetch_data = this->prefetch_data_.mutable_cpu_data(); - Dtype* prefetch_label = this->prefetch_label_.mutable_cpu_data(); + Dtype* prefetch_data = batch->data_.mutable_cpu_data(); + Dtype* prefetch_label = batch->label_.mutable_cpu_data(); // datum scales const int lines_size = lines_.size(); @@ -133,7 +137,7 @@ void ImageDataLayer::InternalThreadEntry() { read_time += timer.MicroSeconds(); timer.Start(); // Apply transformations (mirror, crop...) to the image - int offset = this->prefetch_data_.offset(item_id); + int offset = batch->data_.offset(item_id); this->transformed_data_.set_cpu_data(prefetch_data + offset); this->data_transformer_->Transform(cv_img, &(this->transformed_data_)); trans_time += timer.MicroSeconds(); diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp index c127d56bc46..f637f2ec6d4 100644 --- a/src/caffe/layers/window_data_layer.cpp +++ b/src/caffe/layers/window_data_layer.cpp @@ -27,7 +27,7 @@ namespace caffe { template WindowDataLayer::~WindowDataLayer() { - this->JoinPrefetchThread(); + this->StopInternalThread(); } template @@ -171,7 +171,9 @@ void WindowDataLayer::DataLayerSetUp(const vector*>& bottom, CHECK_GT(crop_size, 0); const int batch_size = this->layer_param_.window_data_param().batch_size(); top[0]->Reshape(batch_size, channels, crop_size, crop_size); - this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size); + for (int i = 0; i < this->PREFETCH_COUNT; ++i) + this->prefetch_[i].data_.Reshape( + batch_size, channels, crop_size, crop_size); LOG(INFO) << "output data size: " << top[0]->num() << "," << top[0]->channels() << "," << top[0]->height() << "," @@ -179,7 +181,9 @@ void WindowDataLayer::DataLayerSetUp(const vector*>& bottom, // label vector label_shape(1, batch_size); top[1]->Reshape(label_shape); - this->prefetch_label_.Reshape(label_shape); + for (int i = 0; i < this->PREFETCH_COUNT; ++i) { + this->prefetch_[i].label_.Reshape(label_shape); + } // data mean has_mean_file_ = this->transform_param_.has_mean_file(); @@ -217,9 +221,9 @@ unsigned int WindowDataLayer::PrefetchRand() { return (*prefetch_rng)(); } -// Thread fetching the data +// This function is called on prefetch thread template -void WindowDataLayer::InternalThreadEntry() { +void WindowDataLayer::load_batch(Batch* batch) { // At each iteration, sample N windows where N*p are foreground (object) // windows and N*(1-p) are background (non-object) windows CPUTimer batch_timer; @@ -227,8 +231,8 @@ void WindowDataLayer::InternalThreadEntry() { double read_time = 0; double trans_time = 0; CPUTimer timer; - Dtype* top_data = this->prefetch_data_.mutable_cpu_data(); - Dtype* top_label = this->prefetch_label_.mutable_cpu_data(); + Dtype* top_data = batch->data_.mutable_cpu_data(); + Dtype* top_label = batch->label_.mutable_cpu_data(); const Dtype scale = this->layer_param_.window_data_param().scale(); const int batch_size = this->layer_param_.window_data_param().batch_size(); const int context_pad = this->layer_param_.window_data_param().context_pad(); @@ -252,7 +256,7 @@ void WindowDataLayer::InternalThreadEntry() { bool use_square = (crop_mode == "square") ? true : false; // zero out batch - caffe_set(this->prefetch_data_.count(), Dtype(0), top_data); + caffe_set(batch->data_.count(), Dtype(0), top_data); const int num_fg = static_cast(static_cast(batch_size) * fg_fraction); diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp index 7617ccfb27f..0da7a3bac79 100644 --- a/src/caffe/syncedmem.cpp +++ b/src/caffe/syncedmem.cpp @@ -108,6 +108,18 @@ void* SyncedMemory::mutable_gpu_data() { #endif } +#ifndef CPU_ONLY +void SyncedMemory::async_gpu_push(const cudaStream_t& stream) { + CHECK(head_ == HEAD_AT_CPU); + if (gpu_ptr_ == NULL) { + CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_)); + } + const cudaMemcpyKind put = cudaMemcpyHostToDevice; + CUDA_CHECK(cudaMemcpyAsync(gpu_ptr_, cpu_ptr_, size_, put, stream)); + // Assume caller will synchronize on the stream before use + head_ = SYNCED; +} +#endif } // namespace caffe diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp index 73c9564c0dd..6ab6ba06780 100644 --- a/src/caffe/util/blocking_queue.cpp +++ b/src/caffe/util/blocking_queue.cpp @@ -1,6 +1,7 @@ #include #include +#include "caffe/data_layers.hpp" #include "caffe/util/blocking_queue.hpp" namespace caffe { @@ -83,4 +84,7 @@ size_t BlockingQueue::size() const { return queue_.size(); } +template class BlockingQueue*>; +template class BlockingQueue*>; + } // namespace caffe From bcc8f50a95ecad954d1887f3fb273eaa298e2274 Mon Sep 17 00:00:00 2001 From: Cyprien Noel Date: Mon, 18 May 2015 18:06:09 -0700 Subject: [PATCH 189/446] Add DataReader for parallel training with one DB session - Make sure each solver accesses a different subset of the data - Sequential reading of DB for performance - Prefetch a configurable amount of data to host memory - Distribute data to solvers in round-robin way for determinism --- include/caffe/data_layers.hpp | 8 +- include/caffe/data_reader.hpp | 82 +++++++++++++++++ src/caffe/data_reader.cpp | 121 ++++++++++++++++++++++++++ src/caffe/layers/base_data_layer.cpp | 2 +- src/caffe/layers/data_layer.cpp | 55 +++++------- src/caffe/proto/caffe.proto | 4 + src/caffe/test/test_layer_factory.cpp | 14 ++- src/caffe/test/test_upgrade_proto.cpp | 12 +++ src/caffe/util/blocking_queue.cpp | 3 + 9 files changed, 259 insertions(+), 42 deletions(-) create mode 100644 include/caffe/data_reader.hpp create mode 100644 src/caffe/data_reader.cpp diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp index f57ab6b0dba..12e6c366620 100644 --- a/include/caffe/data_layers.hpp +++ b/include/caffe/data_layers.hpp @@ -5,11 +5,11 @@ #include #include -#include "boost/scoped_ptr.hpp" #include "hdf5.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/data_reader.hpp" #include "caffe/data_transformer.hpp" #include "caffe/filler.hpp" #include "caffe/internal_thread.hpp" @@ -90,8 +90,7 @@ class BasePrefetchingDataLayer : template class DataLayer : public BasePrefetchingDataLayer { public: - explicit DataLayer(const LayerParameter& param) - : BasePrefetchingDataLayer(param) {} + explicit DataLayer(const LayerParameter& param); virtual ~DataLayer(); virtual void DataLayerSetUp(const vector*>& bottom, const vector*>& top); @@ -104,8 +103,7 @@ class DataLayer : public BasePrefetchingDataLayer { protected: virtual void load_batch(Batch* batch); - shared_ptr db_; - shared_ptr cursor_; + DataReader reader_; }; /** diff --git a/include/caffe/data_reader.hpp b/include/caffe/data_reader.hpp new file mode 100644 index 00000000000..8ed5542cb8d --- /dev/null +++ b/include/caffe/data_reader.hpp @@ -0,0 +1,82 @@ +#ifndef CAFFE_DATA_READER_HPP_ +#define CAFFE_DATA_READER_HPP_ + +#include +#include +#include + +#include "caffe/common.hpp" +#include "caffe/internal_thread.hpp" +#include "caffe/util/blocking_queue.hpp" +#include "caffe/util/db.hpp" + +namespace caffe { + +/** + * @brief Reads data from a source to queues available to data layers. + * A single reading thread is created per source, even if multiple solvers + * are running in parallel, e.g. for multi-GPU training. This makes sure + * databases are read sequentially, and that each solver accesses a different + * subset of the database. Data is distributed to solvers in a round-robin + * way to keep parallel training deterministic. + */ +class DataReader { + public: + explicit DataReader(const LayerParameter& param); + ~DataReader(); + + inline BlockingQueue& free() const { + return queue_pair_->free_; + } + inline BlockingQueue& full() const { + return queue_pair_->full_; + } + + protected: + // Queue pairs are shared between a body and its readers + class QueuePair { + public: + explicit QueuePair(int size); + ~QueuePair(); + + BlockingQueue free_; + BlockingQueue full_; + + DISABLE_COPY_AND_ASSIGN(QueuePair); + }; + + // A single body is created per source + class Body : public InternalThread { + public: + explicit Body(const LayerParameter& param); + virtual ~Body(); + + protected: + void InternalThreadEntry(); + void read_one(db::Cursor* cursor, QueuePair* qp); + + const LayerParameter param_; + BlockingQueue > new_queue_pairs_; + + friend class DataReader; + + DISABLE_COPY_AND_ASSIGN(Body); + }; + + // A source is uniquely identified by its layer name + path, in case + // the same database is read from two different locations in the net. + static inline string source_key(const LayerParameter& param) { + return param.name() + ":" + param.data_param().source(); + } + + const shared_ptr queue_pair_; + shared_ptr body_; + + static map > bodies_; + +DISABLE_COPY_AND_ASSIGN(DataReader); +}; + +} // namespace caffe + +#endif // CAFFE_DATA_READER_HPP_ diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp new file mode 100644 index 00000000000..60606f0d6c8 --- /dev/null +++ b/src/caffe/data_reader.cpp @@ -0,0 +1,121 @@ +#include +#include +#include +#include + +#include "caffe/common.hpp" +#include "caffe/data_layers.hpp" +#include "caffe/data_reader.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +using boost::weak_ptr; + +map > DataReader::bodies_; +static boost::mutex bodies_mutex_; + +DataReader::DataReader(const LayerParameter& param) + : queue_pair_(new QueuePair( // + param.data_param().prefetch() * param.data_param().batch_size())) { + // Get or create a body + boost::mutex::scoped_lock lock(bodies_mutex_); + string key = source_key(param); + weak_ptr& weak = bodies_[key]; + body_ = weak.lock(); + if (!body_) { + body_.reset(new Body(param)); + bodies_[key] = weak_ptr(body_); + } + body_->new_queue_pairs_.push(queue_pair_); +} + +DataReader::~DataReader() { + string key = source_key(body_->param_); + body_.reset(); + boost::mutex::scoped_lock lock(bodies_mutex_); + if (bodies_[key].expired()) { + bodies_.erase(key); + } +} + +// + +DataReader::QueuePair::QueuePair(int size) { + // Initialize the free queue with requested number of datums + for (int i = 0; i < size; ++i) { + free_.push(new Datum()); + } +} + +DataReader::QueuePair::~QueuePair() { + Datum* datum; + while (free_.try_pop(&datum)) { + delete datum; + } + while (full_.try_pop(&datum)) { + delete datum; + } +} + +// + +DataReader::Body::Body(const LayerParameter& param) + : param_(param), + new_queue_pairs_() { + StartInternalThread(); +} + +DataReader::Body::~Body() { + StopInternalThread(); +} + +void DataReader::Body::InternalThreadEntry() { + shared_ptr db(db::GetDB(param_.data_param().backend())); + db->Open(param_.data_param().source(), db::READ); + shared_ptr cursor(db->NewCursor()); + vector > qps; + try { + // int solver_count = param_.phase() == TRAIN ? Caffe::solver_count() : 1; + // TODO single solver until multi-gpu merge + int solver_count = 1; + + // To ensure deterministic runs, only start running once all solvers + // are ready. But solvers need to peek on one item during initialization, + // so read one item, then wait for the next solver. + for (int i = 0; i < solver_count; ++i) { + shared_ptr qp(new_queue_pairs_.pop()); + read_one(cursor.get(), qp.get()); + qps.push_back(qp); + } + // Main loop + while (!must_stop()) { + for (int i = 0; i < solver_count; ++i) { + read_one(cursor.get(), qps[i].get()); + } + // Check no additional readers have been created. This can happen if + // more than one net is trained at a time per process, whether single + // or multi solver. It might also happen if two data layers have same + // name and same source. + CHECK_EQ(new_queue_pairs_.size(), 0); + } + } catch (boost::thread_interrupted&) { + // Interrupted exception is expected on shutdown + } +} + +void DataReader::Body::read_one(db::Cursor* cursor, QueuePair* qp) { + Datum* datum = qp->free_.pop(); + // TODO deserialize in-place instead of copy? + datum->ParseFromString(cursor->value()); + qp->full_.push(datum); + + // go to the next iter + cursor->Next(); + if (!cursor->valid()) { + DLOG(INFO) << "Restarting data prefetching from start."; + cursor->SeekToFirst(); + } +} + +} // namespace caffe diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp index 9288d91339a..20f76f62994 100644 --- a/src/caffe/layers/base_data_layer.cpp +++ b/src/caffe/layers/base_data_layer.cpp @@ -108,7 +108,7 @@ void BasePrefetchingDataLayer::Forward_cpu( DLOG(INFO) << "Prefetch copied"; if (this->output_labels_) { // Reshape to loaded labels. - top[1]->ReshapeLike(prefetch_label_); + top[1]->ReshapeLike(batch->label_); // Copy the labels. caffe_copy(batch->label_.count(), batch->label_.cpu_data(), top[1]->mutable_cpu_data()); diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp index 22d9f436185..0932d9feff3 100644 --- a/src/caffe/layers/data_layer.cpp +++ b/src/caffe/layers/data_layer.cpp @@ -11,11 +11,15 @@ #include "caffe/proto/caffe.pb.h" #include "caffe/util/benchmark.hpp" #include "caffe/util/io.hpp" -#include "caffe/util/math_functions.hpp" -#include "caffe/util/rng.hpp" namespace caffe { +template +DataLayer::DataLayer(const LayerParameter& param) + : BasePrefetchingDataLayer(param), + reader_(param) { +} + template DataLayer::~DataLayer() { this->StopInternalThread(); @@ -24,31 +28,19 @@ DataLayer::~DataLayer() { template void DataLayer::DataLayerSetUp(const vector*>& bottom, const vector*>& top) { - // Initialize DB - db_.reset(db::GetDB(this->layer_param_.data_param().backend())); - db_->Open(this->layer_param_.data_param().source(), db::READ); - cursor_.reset(db_->NewCursor()); + const int batch_size = this->layer_param_.data_param().batch_size(); + // Read a data point, and use it to initialize the top blob. + Datum& datum = *(reader_.full().peek()); - // Check if we should randomly skip a few data points - if (this->layer_param_.data_param().rand_skip()) { - unsigned int skip = caffe_rng_rand() % - this->layer_param_.data_param().rand_skip(); - LOG(INFO) << "Skipping first " << skip << " data points."; - while (skip-- > 0) { - cursor_->Next(); - } - } - // Read a data point, to initialize the prefetch and top blobs. - Datum datum; - datum.ParseFromString(cursor_->value()); // Use data_transformer to infer the expected blob shape from datum. vector top_shape = this->data_transformer_->InferBlobShape(datum); this->transformed_data_.Reshape(top_shape); // Reshape top[0] and prefetch_data according to the batch_size. - top_shape[0] = this->layer_param_.data_param().batch_size(); - this->prefetch_data_.Reshape(top_shape); - top[0]->ReshapeLike(this->prefetch_data_); - + top_shape[0] = batch_size; + top[0]->Reshape(top_shape); + for (int i = 0; i < this->PREFETCH_COUNT; ++i) { + this->prefetch_[i].data_.Reshape(top_shape); + } LOG(INFO) << "output data size: " << top[0]->num() << "," << top[0]->channels() << "," << top[0]->height() << "," << top[0]->width(); @@ -76,12 +68,11 @@ void DataLayer::load_batch(Batch* batch) { // Reshape according to the first datum of each batch // on single input batches allows for inputs of varying dimension. const int batch_size = this->layer_param_.data_param().batch_size(); - Datum datum; - datum.ParseFromString(cursor_->value()); + Datum& datum = *(reader_.full().peek()); // Use data_transformer to infer the expected blob shape from datum. vector top_shape = this->data_transformer_->InferBlobShape(datum); this->transformed_data_.Reshape(top_shape); - // Reshape prefetch_data according to the batch_size. + // Reshape batch according to the batch_size. top_shape[0] = batch_size; batch->data_.Reshape(top_shape); @@ -91,11 +82,10 @@ void DataLayer::load_batch(Batch* batch) { if (this->output_labels_) { top_label = batch->label_.mutable_cpu_data(); } - timer.Start(); for (int item_id = 0; item_id < batch_size; ++item_id) { + timer.Start(); // get a datum - Datum datum; - datum.ParseFromString(cursor_->value()); + Datum& datum = *(reader_.full().pop("Waiting for data")); read_time += timer.MicroSeconds(); timer.Start(); // Apply data transformations (mirror, scale, crop...) @@ -107,13 +97,8 @@ void DataLayer::load_batch(Batch* batch) { top_label[item_id] = datum.label(); } trans_time += timer.MicroSeconds(); - timer.Start(); - // go to the next item. - cursor_->Next(); - if (!cursor_->valid()) { - DLOG(INFO) << "Restarting data prefetching from start."; - cursor_->SeekToFirst(); - } + + reader_.free().push(const_cast(&datum)); } timer.Stop(); batch_timer.Stop(); diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 89f14595ba6..41165410f33 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -500,6 +500,7 @@ message DataParameter { // to avoid all asynchronous sgd clients to start at the same point. The skip // point would be set as rand_skip * rand(0,1). Note that rand_skip should not // be larger than the number of keys in the database. + // DEPRECATED. Each solver accesses a different subset of the database. optional uint32 rand_skip = 7 [default = 0]; optional DB backend = 8 [default = LEVELDB]; // DEPRECATED. See TransformationParameter. For data pre-processing, we can do @@ -515,6 +516,9 @@ message DataParameter { optional bool mirror = 6 [default = false]; // Force the encoded image to have 3 color channels optional bool force_encoded_color = 9 [default = false]; + // Prefetch queue (Number of batches to prefetch to host memory, increase if + // data access bandwidth varies). + optional uint32 prefetch = 10 [default = 4]; } message DropoutParameter { diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp index efb1b37ac42..c86fafd000c 100644 --- a/src/caffe/test/test_layer_factory.cpp +++ b/src/caffe/test/test_layer_factory.cpp @@ -1,11 +1,14 @@ #include #include +#include "boost/scoped_ptr.hpp" #include "gtest/gtest.h" #include "caffe/common.hpp" #include "caffe/layer.hpp" #include "caffe/layer_factory.hpp" +#include "caffe/util/db.hpp" +#include "caffe/util/io.hpp" #include "caffe/test/test_caffe_main.hpp" @@ -21,11 +24,20 @@ TYPED_TEST(LayerFactoryTest, TestCreateLayer) { typename LayerRegistry::CreatorRegistry& registry = LayerRegistry::Registry(); shared_ptr > layer; - LayerParameter layer_param; for (typename LayerRegistry::CreatorRegistry::iterator iter = registry.begin(); iter != registry.end(); ++iter) { // Special case: PythonLayer is checked by pytest if (iter->first == "Python") { continue; } + LayerParameter layer_param; + // Data layers expect a DB + if (iter->first == "Data") { + string tmp; + MakeTempDir(&tmp); + boost::scoped_ptr db(db::GetDB(DataParameter_DB_LEVELDB)); + db->Open(tmp, db::NEW); + db->Close(); + layer_param.mutable_data_param()->set_source(tmp); + } layer_param.set_type(iter->first); layer = LayerRegistry::CreateLayer(layer_param); EXPECT_EQ(iter->first, layer->type()); diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp index eec627656ef..006720231a5 100644 --- a/src/caffe/test/test_upgrade_proto.cpp +++ b/src/caffe/test/test_upgrade_proto.cpp @@ -2,12 +2,15 @@ #include #include +#include "boost/scoped_ptr.hpp" #include "google/protobuf/text_format.h" #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/layer.hpp" +#include "caffe/util/db.hpp" +#include "caffe/util/io.hpp" #include "caffe/util/upgrade_proto.hpp" #include "caffe/test/test_caffe_main.hpp" @@ -2901,6 +2904,15 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) { continue; // Empty string isn't actually a valid layer type. } layer_param.set_type(v2_layer_type); + // Data layers expect a DB + if (v2_layer_type == "Data") { + string tmp; + MakeTempDir(&tmp); + boost::scoped_ptr db(db::GetDB(DataParameter_DB_LEVELDB)); + db->Open(tmp, db::NEW); + db->Close(); + layer_param.mutable_data_param()->set_source(tmp); + } layer = LayerRegistry::CreateLayer(layer_param); EXPECT_EQ(v2_layer_type, layer->type()); } diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp index 6ab6ba06780..f7c53f22225 100644 --- a/src/caffe/util/blocking_queue.cpp +++ b/src/caffe/util/blocking_queue.cpp @@ -2,6 +2,7 @@ #include #include "caffe/data_layers.hpp" +#include "caffe/data_reader.hpp" #include "caffe/util/blocking_queue.hpp" namespace caffe { @@ -86,5 +87,7 @@ size_t BlockingQueue::size() const { template class BlockingQueue*>; template class BlockingQueue*>; +template class BlockingQueue; +template class BlockingQueue >; } // namespace caffe From d2f045768cba7d494abb4d168fc366d6fce80b85 Mon Sep 17 00:00:00 2001 From: Cyprien Noel Date: Mon, 18 May 2015 20:07:36 -0700 Subject: [PATCH 190/446] Allocate host memory through cudaMallocHost thanks to discussion by @thatguymike and @flx42 --- include/caffe/syncedmem.hpp | 31 +++++++++++++++++-------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp index 4d339bf4e57..4a1a2f3fcc0 100644 --- a/include/caffe/syncedmem.hpp +++ b/include/caffe/syncedmem.hpp @@ -8,26 +8,29 @@ namespace caffe { -// Theoretically, CaffeMallocHost and CaffeFreeHost should simply call the -// cudaMallocHost and cudaFree functions in order to create pinned memory. -// However, those codes rely on the existence of a cuda GPU (I don't know -// why that is a must since allocating memory should not be accessing the -// GPU resource, but it just creates an error as of Cuda 5.0) and will cause -// problem when running on a machine without GPU. Thus, we simply define -// these two functions for safety and possible future change if the problem -// of calling cuda functions disappears in a future version. -// -// In practice, although we are creating unpinned memory here, as long as we -// are constantly accessing them the memory pages almost always stays in -// the physical memory (assuming we have large enough memory installed), and -// does not seem to create a memory bottleneck here. - +// If CUDA is available and in GPU mode, host memory will be allocated pinned, +// using cudaMallocHost. It avoids dynamic pinning for transfers (DMA). +// The improvement in performance seems negligible in the single GPU case, +// but might be more significant for parallel training. Most importantly, +// it improved stability for large models on many GPUs. inline void CaffeMallocHost(void** ptr, size_t size) { +#ifndef CPU_ONLY + if (Caffe::mode() == Caffe::GPU) { + CUDA_CHECK(cudaMallocHost(ptr, size)); + return; + } +#endif *ptr = malloc(size); CHECK(*ptr) << "host allocation of size " << size << " failed"; } inline void CaffeFreeHost(void* ptr) { +#ifndef CPU_ONLY + if (Caffe::mode() == Caffe::GPU) { + CUDA_CHECK(cudaFreeHost(ptr)); + return; + } +#endif free(ptr); } From e5575cf17a43a56e4ba9bc5465548ac0512197d8 Mon Sep 17 00:00:00 2001 From: Cyprien Noel Date: Tue, 19 May 2015 11:11:05 -0700 Subject: [PATCH 191/446] Multi-GPU - Parallelize batches among GPUs and tree-reduce the gradients - The effective batch size scales with the number of devices - Batch size is multiplied by the number of devices - Split batches between GPUs, and tree-reduce the gradients - Detect machine topology (twin-GPU boards, P2P connectivity) - Track device in syncedmem (thanks @thatguymike) - Insert a callback in the solver for minimal code change - Accept list for gpu flag of caffe tool, e.g. '-gpu 0,1' or '-gpu all'. Run on default GPU if no ID given. - Add multi-GPU solver test - Deterministic architecture for reproducible runs --- include/caffe/caffe.hpp | 1 + include/caffe/common.hpp | 7 + include/caffe/internal_thread.hpp | 3 +- include/caffe/layer_factory.hpp | 4 +- include/caffe/parallel.hpp | 118 +++++ include/caffe/solver.hpp | 38 ++ include/caffe/syncedmem.hpp | 7 +- src/caffe/common.cpp | 5 +- src/caffe/data_reader.cpp | 4 +- src/caffe/data_transformer.cpp | 4 +- src/caffe/internal_thread.cpp | 9 +- src/caffe/net.cpp | 180 +++++--- src/caffe/parallel.cpp | 430 ++++++++++++++++++ src/caffe/solver.cpp | 57 ++- src/caffe/syncedmem.cpp | 34 +- src/caffe/test/test_gradient_based_solver.cpp | 75 ++- src/caffe/util/blocking_queue.cpp | 3 + tools/caffe.cpp | 111 +++-- 18 files changed, 949 insertions(+), 141 deletions(-) create mode 100644 include/caffe/parallel.hpp create mode 100644 src/caffe/parallel.cpp diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp index 3c829f2f9b0..68a5e1d1d1a 100644 --- a/include/caffe/caffe.hpp +++ b/include/caffe/caffe.hpp @@ -10,6 +10,7 @@ #include "caffe/layer.hpp" #include "caffe/layer_factory.hpp" #include "caffe/net.hpp" +#include "caffe/parallel.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/solver.hpp" #include "caffe/util/benchmark.hpp" diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp index 3fa81431314..1df6b9a14fb 100644 --- a/include/caffe/common.hpp +++ b/include/caffe/common.hpp @@ -149,6 +149,11 @@ class Caffe { static void SetDevice(const int device_id); // Prints the current GPU status. static void DeviceQuery(); + // Parallel training info + inline static int solver_count() { return Get().solver_count_; } + inline static void set_solver_count(int val) { Get().solver_count_ = val; } + inline static bool root_solver() { return Get().root_solver_; } + inline static void set_root_solver(bool val) { Get().root_solver_ = val; } protected: #ifndef CPU_ONLY @@ -158,6 +163,8 @@ class Caffe { shared_ptr random_generator_; Brew mode_; + int solver_count_; + bool root_solver_; private: // The private constructor to avoid duplicate instantiation. diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp index be6ff7fbac7..6a8c5a02892 100644 --- a/include/caffe/internal_thread.hpp +++ b/include/caffe/internal_thread.hpp @@ -42,7 +42,8 @@ class InternalThread { bool must_stop(); private: - void entry(int device, Caffe::Brew mode, int rand_seed); + void entry(int device, Caffe::Brew mode, int rand_seed, int solver_count, + bool root_solver); shared_ptr thread_; }; diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp index 2fcd93869a0..32e849de0d2 100644 --- a/include/caffe/layer_factory.hpp +++ b/include/caffe/layer_factory.hpp @@ -71,7 +71,9 @@ class LayerRegistry { // Get a layer using a LayerParameter. static shared_ptr > CreateLayer(const LayerParameter& param) { - LOG(INFO) << "Creating layer " << param.name(); + if (Caffe::root_solver()) { + LOG(INFO) << "Creating layer " << param.name(); + } const string& type = param.type(); CreatorRegistry& registry = Registry(); CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type diff --git a/include/caffe/parallel.hpp b/include/caffe/parallel.hpp new file mode 100644 index 00000000000..85fc2b55984 --- /dev/null +++ b/include/caffe/parallel.hpp @@ -0,0 +1,118 @@ +#ifndef CAFFE_PARALLEL_HPP_ +#define CAFFE_PARALLEL_HPP_ + +#include + +#include + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/internal_thread.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" +#include "caffe/solver.hpp" +#include "caffe/syncedmem.hpp" +#include "caffe/util/blocking_queue.hpp" + +namespace caffe { + +// Represents a net parameters. Once a net is created, its parameter buffers can +// be replaced by ones from Params, to allow parallelization. Params ensures +// parameters are allocated in one consecutive array. +template +class Params { + public: + explicit Params(shared_ptr > root_solver); + virtual ~Params() { + } + + inline size_t size() const { + return size_; + } + inline Dtype* data() const { + return data_; + } + inline Dtype* diff() const { + return diff_; + } + + protected: + const size_t size_; // Size of buffers + Dtype* data_; // Network parameters + Dtype* diff_; // Gradient + +DISABLE_COPY_AND_ASSIGN(Params); +}; + +// Params stored in GPU memory. +template +class GPUParams : public Params { + public: + GPUParams(shared_ptr > root_solver, int device); + virtual ~GPUParams(); + + void configure(Solver* solver) const; + + protected: + using Params::size_; + using Params::data_; + using Params::diff_; +}; + +class DevicePair { + public: + DevicePair(int parent, int device) + : parent_(parent), + device_(device) { + } + inline int parent() { + return parent_; + } + inline int device() { + return device_; + } + + // Group GPUs in pairs, by proximity depending on machine's topology + static void compute(const vector devices, vector* pairs); + + protected: + int parent_; + int device_; +}; + +// Synchronous data parallelism using map-reduce between local GPUs. +template +class P2PSync : public GPUParams, public Solver::Callback, + public InternalThread { + public: + explicit P2PSync(shared_ptr > root_solver, + P2PSync* parent, const SolverParameter& param); + virtual ~P2PSync(); + + inline const shared_ptr >& solver() const { + return solver_; + } + + void run(const vector& gpus); + + protected: + void on_start(); + void on_gradients_ready(); + + void InternalThreadEntry(); + + P2PSync* parent_; + vector*> children_; + BlockingQueue*> queue_; + const int initial_iter_; + Dtype* parent_grads_; + shared_ptr > solver_; + + using Params::size_; + using Params::data_; + using Params::diff_; +}; + +} // namespace caffe + +#endif diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index fbade9389ff..89a6c76d5f7 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -32,12 +32,27 @@ class Solver { // methods to restore the state from the appropriate snapshot type. void Restore(const char* resume_file); virtual ~Solver() {} + inline const SolverParameter& param() const { return param_; } inline shared_ptr > net() { return net_; } inline const vector > >& test_nets() { return test_nets_; } int iter() { return iter_; } + // Invoked at specific points during an iteration + class Callback { + protected: + virtual void on_start() = 0; + virtual void on_gradients_ready() = 0; + + template + friend class Solver; + }; + const vector& callbacks() const { return callbacks_; } + void add_callback(Callback* value) { + callbacks_.push_back(value); + } + protected: // Make and apply the update value for the current iteration. virtual void ApplyUpdate() = 0; @@ -62,10 +77,33 @@ class Solver { int current_step_; shared_ptr > net_; vector > > test_nets_; + vector callbacks_; DISABLE_COPY_AND_ASSIGN(Solver); }; +/** + * @brief Solver that only computes gradients, used as worker + * for multi-GPU training. + */ +template +class WorkerSolver : public Solver { + public: + explicit WorkerSolver(const SolverParameter& param) + : Solver(param) {} + + protected: + void ApplyUpdate() {} + void SnapshotSolverState(const string& model_filename) { + LOG(FATAL) << "Should not be called on worker solver."; + } + void RestoreSolverStateFromBinaryProto(const string& state_file) { + LOG(FATAL) << "Should not be called on worker solver."; + } + void RestoreSolverStateFromHDF5(const string& state_file) { + LOG(FATAL) << "Should not be called on worker solver."; + } +}; /** * @brief Optimizes the parameters of a Net using diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp index 4a1a2f3fcc0..62aadef498d 100644 --- a/include/caffe/syncedmem.hpp +++ b/include/caffe/syncedmem.hpp @@ -45,14 +45,15 @@ class SyncedMemory { public: SyncedMemory() : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED), - own_cpu_data_(false) {} + own_cpu_data_(false), own_gpu_data_(false), gpu_device_(-1) {} explicit SyncedMemory(size_t size) : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED), - own_cpu_data_(false) {} + own_cpu_data_(false), own_gpu_data_(false), gpu_device_(-1) {} ~SyncedMemory(); const void* cpu_data(); void set_cpu_data(void* data); const void* gpu_data(); + void set_gpu_data(void* data); void* mutable_cpu_data(); void* mutable_gpu_data(); enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED }; @@ -71,6 +72,8 @@ class SyncedMemory { size_t size_; SyncedHead head_; bool own_cpu_data_; + bool own_gpu_data_; + int gpu_device_; DISABLE_COPY_AND_ASSIGN(SyncedMemory); }; // class SyncedMemory diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp index 0215c76ef76..7077f3789d7 100644 --- a/src/caffe/common.cpp +++ b/src/caffe/common.cpp @@ -51,7 +51,8 @@ void GlobalInit(int* pargc, char*** pargv) { #ifdef CPU_ONLY // CPU-only Caffe. Caffe::Caffe() - : random_generator_(), mode_(Caffe::CPU) { } + : random_generator_(), mode_(Caffe::CPU), + solver_count_(1), root_solver_(true) { } Caffe::~Caffe() { } @@ -95,7 +96,7 @@ void* Caffe::RNG::generator() { Caffe::Caffe() : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(), - mode_(Caffe::CPU) { + mode_(Caffe::CPU), solver_count_(1), root_solver_(true) { // Try to create a cublas handler, and report an error if failed (but we will // keep the program running as one might just want to run CPU code). if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) { diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp index 60606f0d6c8..16378203a88 100644 --- a/src/caffe/data_reader.cpp +++ b/src/caffe/data_reader.cpp @@ -76,9 +76,7 @@ void DataReader::Body::InternalThreadEntry() { shared_ptr cursor(db->NewCursor()); vector > qps; try { - // int solver_count = param_.phase() == TRAIN ? Caffe::solver_count() : 1; - // TODO single solver until multi-gpu merge - int solver_count = 1; + int solver_count = param_.phase() == TRAIN ? Caffe::solver_count() : 1; // To ensure deterministic runs, only start running once all solvers // are ready. But solvers need to peek on one item during initialization, diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp index 22633922a01..4666d9bd881 100644 --- a/src/caffe/data_transformer.cpp +++ b/src/caffe/data_transformer.cpp @@ -19,7 +19,9 @@ DataTransformer::DataTransformer(const TransformationParameter& param, CHECK_EQ(param_.mean_value_size(), 0) << "Cannot specify mean_file and mean_value at the same time"; const string& mean_file = param.mean_file(); - LOG(INFO) << "Loading mean file from: " << mean_file; + if (Caffe::root_solver()) { + LOG(INFO) << "Loading mean file from: " << mean_file; + } BlobProto blob_proto; ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto); data_mean_.FromProto(blob_proto); diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp index b193826c1a6..104884e0295 100644 --- a/src/caffe/internal_thread.cpp +++ b/src/caffe/internal_thread.cpp @@ -27,21 +27,26 @@ void InternalThread::StartInternalThread() { #endif Caffe::Brew mode = Caffe::mode(); int rand_seed = caffe_rng_rand(); + int solver_count = Caffe::solver_count(); + bool root_solver = Caffe::root_solver(); try { thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode, - rand_seed)); + rand_seed, solver_count, root_solver)); } catch (std::exception& e) { LOG(FATAL) << "Thread exception: " << e.what(); } } -void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed) { +void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed, + int solver_count, bool root_solver) { #ifndef CPU_ONLY CUDA_CHECK(cudaSetDevice(device)); #endif Caffe::set_mode(mode); Caffe::set_random_seed(rand_seed); + Caffe::set_solver_count(solver_count); + Caffe::set_root_solver(root_solver); InternalThreadEntry(); } diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 0e5ed804b73..5d0f4322d19 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -10,6 +10,7 @@ #include "caffe/common.hpp" #include "caffe/layer.hpp" #include "caffe/net.hpp" +#include "caffe/parallel.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/hdf5.hpp" #include "caffe/util/insert_splits.hpp" @@ -41,8 +42,10 @@ void Net::Init(const NetParameter& in_param) { // the current NetState. NetParameter filtered_param; FilterNet(in_param, &filtered_param); - LOG(INFO) << "Initializing net from parameters: " << std::endl - << filtered_param.DebugString(); + if (Caffe::root_solver()) { + LOG(INFO) << "Initializing net from parameters: " << std::endl + << filtered_param.DebugString(); + } // Create a copy of filtered_param with splits added where necessary. NetParameter param; InsertSplits(filtered_param, ¶m); @@ -66,7 +69,8 @@ void Net::Init(const NetParameter& in_param) { const int layer_id = -1; // inputs have fake layer ID -1 AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx); } - DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype); + DLOG_IF(INFO, Caffe::root_solver()) + << "Memory required for data: " << memory_used_ * sizeof(Dtype); // For each layer, set up its input and output bottom_vecs_.resize(param.layer_size()); top_vecs_.resize(param.layer_size()); @@ -89,7 +93,9 @@ void Net::Init(const NetParameter& in_param) { } layers_.push_back(LayerRegistry::CreateLayer(layer_param)); layer_names_.push_back(layer_param.name()); - LOG(INFO) << "Creating Layer " << layer_param.name(); + if (Caffe::root_solver()) { + LOG(INFO) << "Creating Layer " << layer_param.name(); + } bool need_backward = false; // Figure out this layer's input and output @@ -119,20 +125,30 @@ void Net::Init(const NetParameter& in_param) { } } // After this layer is connected, set it up. - LOG(INFO) << "Setting up " << layer_names_[layer_id]; + if (Caffe::root_solver()) { + LOG(INFO) << "Setting up " << layer_names_[layer_id]; + } layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]); for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) { if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) { blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0)); } blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id); - LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string(); + if (Caffe::root_solver()) { + LOG(INFO) << "Top shape: " + << top_vecs_[layer_id][top_id]->shape_string(); + } if (layer->loss(top_id)) { - LOG(INFO) << " with loss weight " << layer->loss(top_id); + if (Caffe::root_solver()) { + LOG(INFO) << " with loss weight " << layer->loss(top_id); + } } memory_used_ += top_vecs_[layer_id][top_id]->count(); } - DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype); + if (Caffe::root_solver()) { + DLOG(INFO) << "Memory required for data: " + << memory_used_ * sizeof(Dtype); + } const int param_size = layer_param.param_size(); const int num_param_blobs = layers_[layer_id]->blobs().size(); CHECK_LE(param_size, num_param_blobs) @@ -191,10 +207,14 @@ void Net::Init(const NetParameter& in_param) { } if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; } if (layer_need_backward_[layer_id]) { - LOG(INFO) << layer_names_[layer_id] << " needs backward computation."; + if (Caffe::root_solver()) { + LOG(INFO) << layer_names_[layer_id] << " needs backward computation."; + } } else { - LOG(INFO) << layer_names_[layer_id] - << " does not need backward computation."; + if (Caffe::root_solver()) { + LOG(INFO) << layer_names_[layer_id] + << " does not need backward computation."; + } } for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size(); ++bottom_id) { @@ -234,7 +254,9 @@ void Net::Init(const NetParameter& in_param) { // In the end, all remaining blobs are considered output blobs. for (set::iterator it = available_blobs.begin(); it != available_blobs.end(); ++it) { - LOG(INFO) << "This network produces output " << *it; + if (Caffe::root_solver()) { + LOG(INFO) << "This network produces output " << *it; + } net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get()); net_output_blob_indices_.push_back(blob_name_to_idx[*it]); } @@ -246,8 +268,10 @@ void Net::Init(const NetParameter& in_param) { } ShareWeights(); debug_info_ = param.debug_info(); - LOG(INFO) << "Network initialization done."; - LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype); + if (Caffe::root_solver()) { + LOG(INFO) << "Network initialization done."; + LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype); + } } template @@ -286,27 +310,33 @@ bool Net::StateMeetsRule(const NetState& state, // Check whether the rule is broken due to phase. if (rule.has_phase()) { if (rule.phase() != state.phase()) { - LOG(INFO) << "The NetState phase (" << state.phase() - << ") differed from the phase (" << rule.phase() - << ") specified by a rule in layer " << layer_name; + if (Caffe::root_solver()) { + LOG(INFO) << "The NetState phase (" << state.phase() + << ") differed from the phase (" << rule.phase() + << ") specified by a rule in layer " << layer_name; + } return false; } } // Check whether the rule is broken due to min level. if (rule.has_min_level()) { if (state.level() < rule.min_level()) { - LOG(INFO) << "The NetState level (" << state.level() - << ") is above the min_level (" << rule.min_level() - << ") specified by a rule in layer " << layer_name; + if (Caffe::root_solver()) { + LOG(INFO) << "The NetState level (" << state.level() + << ") is above the min_level (" << rule.min_level() + << ") specified by a rule in layer " << layer_name; + } return false; } } // Check whether the rule is broken due to max level. if (rule.has_max_level()) { if (state.level() > rule.max_level()) { - LOG(INFO) << "The NetState level (" << state.level() - << ") is above the max_level (" << rule.max_level() - << ") specified by a rule in layer " << layer_name; + if (Caffe::root_solver()) { + LOG(INFO) << "The NetState level (" << state.level() + << ") is above the max_level (" << rule.max_level() + << ") specified by a rule in layer " << layer_name; + } return false; } } @@ -319,8 +349,10 @@ bool Net::StateMeetsRule(const NetState& state, if (rule.stage(i) == state.stage(j)) { has_stage = true; } } if (!has_stage) { - LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i) - << "' specified by a rule in layer " << layer_name; + if (Caffe::root_solver()) { + LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i) + << "' specified by a rule in layer " << layer_name; + } return false; } } @@ -333,8 +365,10 @@ bool Net::StateMeetsRule(const NetState& state, if (rule.not_stage(i) == state.stage(j)) { has_stage = true; } } if (has_stage) { - LOG(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i) - << "' specified by a rule in layer " << layer_name; + if (Caffe::root_solver()) { + LOG(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i) + << "' specified by a rule in layer " << layer_name; + } return false; } } @@ -356,7 +390,9 @@ void Net::AppendTop(const NetParameter& param, const int layer_id, if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id && blob_name == layer_param->bottom(top_id)) { // In-place computation - LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)"; + if (Caffe::root_solver()) { + LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)"; + } top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get()); top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]); } else if (blob_name_to_idx && @@ -366,10 +402,12 @@ void Net::AppendTop(const NetParameter& param, const int layer_id, LOG(FATAL) << "Duplicate blobs produced by multiple sources."; } else { // Normal output. - if (layer_param) { - LOG(INFO) << layer_param->name() << " -> " << blob_name; - } else { - LOG(INFO) << "Input " << top_id << " -> " << blob_name; + if (Caffe::root_solver()) { + if (layer_param) { + LOG(INFO) << layer_param->name() << " -> " << blob_name; + } else { + LOG(INFO) << "Input " << top_id << " -> " << blob_name; + } } shared_ptr > blob_pointer(new Blob()); const int blob_id = blobs_.size(); @@ -409,7 +447,9 @@ int Net::AppendBottom(const NetParameter& param, const int layer_id, << " (at index " << bottom_id << ") to layer " << layer_id; } const int blob_id = (*blob_name_to_idx)[blob_name]; - LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name; + if (Caffe::root_solver()) { + LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name; + } bottom_vecs_[layer_id].push_back(blobs_[blob_id].get()); bottom_id_vecs_[layer_id].push_back(blob_id); available_blobs->erase(blob_name); @@ -468,9 +508,10 @@ void Net::AppendParam(const NetParameter& param, const int layer_id, param_layer_indices_[owner_net_param_id]; const int owner_layer_id = owner_index.first; const int owner_param_id = owner_index.second; - LOG(INFO) << "Sharing parameters '" << param_name << "' owned by " - << "layer '" << layer_names_[owner_layer_id] << "', param " - << "index " << owner_param_id; + LOG_IF(INFO, Caffe::root_solver()) << "Sharing parameters '" << param_name + << "' owned by " + << "layer '" << layer_names_[owner_layer_id] << "', param " + << "index " << owner_param_id; Blob* this_blob = layers_[layer_id]->blobs()[param_id].get(); Blob* owner_blob = layers_[owner_layer_id]->blobs()[owner_param_id].get(); @@ -595,8 +636,10 @@ void Net::InputDebugInfo(const int input_id) { const Blob& blob = *net_input_blobs_[input_id]; const string& blob_name = blob_names_[net_input_blob_indices_[input_id]]; const Dtype data_abs_val_mean = blob.asum_data() / blob.count(); - LOG(INFO) << " [Forward] " - << "Input " << blob_name << " data: " << data_abs_val_mean; + if (Caffe::root_solver()) { + LOG(INFO) << " [Forward] " + << "Input " << blob_name << " data: " << data_abs_val_mean; + } } template @@ -605,9 +648,12 @@ void Net::ForwardDebugInfo(const int layer_id) { const Blob& blob = *top_vecs_[layer_id][top_id]; const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]]; const Dtype data_abs_val_mean = blob.asum_data() / blob.count(); - LOG(INFO) << " [Forward] " - << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name - << " data: " << data_abs_val_mean; + if (Caffe::root_solver()) { + LOG(INFO) << " [Forward] " + << "Layer " << layer_names_[layer_id] + << ", top blob " << blob_name + << " data: " << data_abs_val_mean; + } } for (int param_id = 0; param_id < layers_[layer_id]->blobs().size(); ++param_id) { @@ -615,9 +661,12 @@ void Net::ForwardDebugInfo(const int layer_id) { const int net_param_id = param_id_vecs_[layer_id][param_id]; const string& blob_name = param_display_names_[net_param_id]; const Dtype data_abs_val_mean = blob.asum_data() / blob.count(); - LOG(INFO) << " [Forward] " - << "Layer " << layer_names_[layer_id] << ", param blob " << blob_name - << " data: " << data_abs_val_mean; + if (Caffe::root_solver()) { + LOG(INFO) << " [Forward] " + << "Layer " << layer_names_[layer_id] + << ", param blob " << blob_name + << " data: " << data_abs_val_mean; + } } } @@ -629,18 +678,24 @@ void Net::BackwardDebugInfo(const int layer_id) { const Blob& blob = *bottom_vec[bottom_id]; const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]]; const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count(); - LOG(INFO) << " [Backward] " - << "Layer " << layer_names_[layer_id] << ", bottom blob " << blob_name - << " diff: " << diff_abs_val_mean; + if (Caffe::root_solver()) { + LOG(INFO) << " [Backward] " + << "Layer " << layer_names_[layer_id] + << ", bottom blob " << blob_name + << " diff: " << diff_abs_val_mean; + } } for (int param_id = 0; param_id < layers_[layer_id]->blobs().size(); ++param_id) { if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; } const Blob& blob = *layers_[layer_id]->blobs()[param_id]; const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count(); - LOG(INFO) << " [Backward] " - << "Layer " << layer_names_[layer_id] << ", param blob " << param_id - << " diff: " << diff_abs_val_mean; + if (Caffe::root_solver()) { + LOG(INFO) << " [Backward] " + << "Layer " << layer_names_[layer_id] + << ", param blob " << param_id + << " diff: " << diff_abs_val_mean; + } } } @@ -653,17 +708,22 @@ void Net::UpdateDebugInfo(const int param_id) { const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count(); if (param_owner < 0) { const Dtype data_abs_val_mean = blob.asum_data() / blob.count(); - LOG(INFO) << " [Update] Layer " << layer_name - << ", param " << param_display_name - << " data: " << data_abs_val_mean << "; diff: " << diff_abs_val_mean; + if (Caffe::root_solver()) { + LOG(INFO) << " [Update] Layer " << layer_name + << ", param " << param_display_name + << " data: " << data_abs_val_mean + << "; diff: " << diff_abs_val_mean; + } } else { const string& owner_layer_name = layer_names_[param_layer_indices_[param_owner].first]; - LOG(INFO) << " [Update] Layer " << layer_name - << ", param blob " << param_display_name - << " (owned by layer " << owner_layer_name << ", " - << "param " << param_display_names_[param_owners_[param_id]] << ")" - << " diff: " << diff_abs_val_mean; + if (Caffe::root_solver()) { + LOG(INFO) << " [Update] Layer " << layer_name + << ", param blob " << param_display_name + << " (owned by layer " << owner_layer_name << ", " << "param " + << param_display_names_[param_owners_[param_id]] << ")" + << " diff: " << diff_abs_val_mean; + } } } @@ -720,8 +780,8 @@ void Net::Backward() { const Dtype l2norm_data = std::sqrt(sumsq_data); const Dtype l2norm_diff = std::sqrt(sumsq_diff); LOG(ERROR) << " [Backward] All net params (data, diff): " - << "L1 norm = (" << asum_data << ", " << asum_diff << "); " - << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")"; + << "L1 norm = (" << asum_data << ", " << asum_diff << "); " + << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")"; } } diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp new file mode 100644 index 00000000000..3fef8cfdb33 --- /dev/null +++ b/src/caffe/parallel.cpp @@ -0,0 +1,430 @@ +#ifndef CPU_ONLY +#include +#endif +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +#include "boost/thread.hpp" +#include "caffe/caffe.hpp" +#include "caffe/parallel.hpp" + +namespace caffe { + +enum Op { + copy, + replace_cpu, + replace_gpu, + replace_cpu_diff, + replace_gpu_diff +}; + +template +static void apply_buffers(const vector*>& blobs, + Dtype* buffer, size_t total_size, Op op) { + Dtype* ptr = buffer; + for (int i = 0; i < blobs.size(); ++i) { + int size = blobs[i]->count(); + switch (op) { + case copy: { + // Init buffer to current values of blobs + caffe_copy(size, + reinterpret_cast(blobs[i]->data()->cpu_data()), + ptr); + break; + } + case replace_cpu: + blobs[i]->data()->set_cpu_data(ptr); + break; + case replace_gpu: + blobs[i]->data()->set_gpu_data(ptr); + break; + case replace_cpu_diff: + blobs[i]->diff()->set_cpu_data(ptr); + break; + case replace_gpu_diff: + blobs[i]->diff()->set_gpu_data(ptr); + break; + } + ptr += size; + } + CHECK_EQ(total_size, ptr - buffer); +} + +// Buffer size necessary to store given blobs +template +static size_t total_size(const vector*>& params) { + size_t size = 0; + for (int i = 0; i < params.size(); ++i) + size += params[i]->count(); + return size; +} + +template +Params::Params(shared_ptr > root_solver) + : size_(total_size(root_solver->net()->learnable_params())), + data_(), + diff_() { +} + +template +GPUParams::GPUParams(shared_ptr > root_solver, int device) + : Params(root_solver) { +#ifndef CPU_ONLY + int initial_device; + CUDA_CHECK(cudaGetDevice(&initial_device)); + + // Allocate device buffers + CUDA_CHECK(cudaSetDevice(device)); + CUDA_CHECK(cudaMalloc(&data_, size_ * sizeof(Dtype))); + + // Copy blob values + const vector*>& net = + root_solver->net()->learnable_params(); + apply_buffers(net, data_, size_, copy); + + CUDA_CHECK(cudaMalloc(&diff_, size_ * sizeof(Dtype))); + caffe_gpu_set(size_, Dtype(0), diff_); + + CUDA_CHECK(cudaSetDevice(initial_device)); +#else + NO_GPU; +#endif +} + +template +GPUParams::~GPUParams() { +#ifndef CPU_ONLY + CUDA_CHECK(cudaFree(data_)); + CUDA_CHECK(cudaFree(diff_)); +#endif +} + +template +void GPUParams::configure(Solver* solver) const { + const vector*>& net = + solver->net()->learnable_params(); + apply_buffers(net, data_, size_, replace_gpu); + apply_buffers(net, diff_, size_, replace_gpu_diff); +} + +void DevicePair::compute(const vector devices, vector* pairs) { +#ifndef CPU_ONLY + vector remaining(devices); + + // Group GPUs by board + for (int i = 0; i < remaining.size(); ++i) { + for (int j = i + 1; j < remaining.size(); ++j) { + cudaDeviceProp a, b; + CUDA_CHECK(cudaGetDeviceProperties(&a, remaining[i])); + CUDA_CHECK(cudaGetDeviceProperties(&b, remaining[j])); + if (a.isMultiGpuBoard && b.isMultiGpuBoard) { + if (a.multiGpuBoardGroupID == b.multiGpuBoardGroupID) { + pairs->push_back(DevicePair(remaining[i], remaining[j])); + DLOG(INFO) << "GPU board: " << remaining[i] << ":" << remaining[j]; + remaining.erase(remaining.begin() + j); + break; + } + } + } + } + ostringstream s; + for (int i = 0; i < remaining.size(); ++i) { + s << (i ? ", " : "") << remaining[i]; + } + DLOG(INFO) << "GPUs paired by boards, remaining: " << s.str(); + + // Group by P2P accessibility + for (int i = 0; i < remaining.size(); ++i) { + for (int j = i + 1; j < remaining.size(); ++j) { + int access; + CUDA_CHECK(cudaDeviceCanAccessPeer(&access, remaining[i], remaining[j])); + if (access) { + pairs->push_back(DevicePair(remaining[i], remaining[j])); + DLOG(INFO) << "P2P pair: " << remaining[i] << ":" << remaining[j]; + remaining.erase(remaining.begin() + j); + break; + } + } + } + s.str(""); + for (int i = 0; i < remaining.size(); ++i) { + s << (i ? ", " : "") << remaining[i]; + } + DLOG(INFO) << "GPUs paired by P2P access, remaining: " << s.str(); + + // Group remaining + for (int i = 0; i < remaining.size(); ++i) { + for (int j = i + 1; j < remaining.size(); ++j) { + pairs->push_back(DevicePair(remaining[i], remaining[j])); + DLOG(INFO) << "Remaining pair: " << remaining[i] << ":" << remaining[j]; + remaining.erase(remaining.begin() + j); + break; + } + } + CHECK_EQ(remaining.size(), 1); + pairs->insert(pairs->begin(), DevicePair(-1, remaining[0])); + + CHECK(pairs->size() == devices.size()); + for (int i = 0; i < pairs->size(); ++i) { + CHECK((*pairs)[i].parent() != (*pairs)[i].device()); + for (int j = i + 1; j < pairs->size(); ++j) { + CHECK((*pairs)[i].device() != (*pairs)[j].device()); + } + } +#else + NO_GPU; +#endif +} + +// + +template +P2PSync::P2PSync(shared_ptr > root_solver, + P2PSync* parent, const SolverParameter& param) + : GPUParams(root_solver, param.device_id()), + parent_(parent), + children_(), + queue_(), + initial_iter_(root_solver->iter()), + solver_() { +#ifndef CPU_ONLY + int initial_device; + CUDA_CHECK(cudaGetDevice(&initial_device)); + const int self = param.device_id(); + CUDA_CHECK(cudaSetDevice(self)); + + if (parent == NULL) { + solver_ = root_solver; + } else { + Caffe::set_root_solver(false); + solver_.reset(new WorkerSolver(param)); + Caffe::set_root_solver(true); + } + this->configure(solver_.get()); + solver_->add_callback(this); + + if (parent) { + // Enable p2p access between devices + const int peer = parent->solver_->param().device_id(); + int access; + CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer)); + if (access) { + CUDA_CHECK(cudaDeviceEnablePeerAccess(peer, 0)); + } else { + LOG(INFO)<< "GPU " << self << " does not have p2p access to GPU " << peer; + } + // Allocate receiving buffer on parent + CUDA_CHECK(cudaSetDevice(peer)); + CUDA_CHECK(cudaMalloc(&parent_grads_, size_ * sizeof(Dtype))); + CUDA_CHECK(cudaSetDevice(self)); + } + + CUDA_CHECK(cudaSetDevice(initial_device)); +#else + NO_GPU; +#endif +} + +template +P2PSync::~P2PSync() { +#ifndef CPU_ONLY + int initial_device; + CUDA_CHECK(cudaGetDevice(&initial_device)); + const int self = solver_->param().device_id(); + CUDA_CHECK(cudaSetDevice(self)); + + if (parent_) { + CUDA_CHECK(cudaFree(parent_grads_)); + const int peer = parent_->solver_->param().device_id(); + int access; + CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer)); + if (access) { + CUDA_CHECK(cudaDeviceDisablePeerAccess(peer)); + } + } + + CUDA_CHECK(cudaSetDevice(initial_device)); +#endif +} + +template +void P2PSync::InternalThreadEntry() { + Caffe::SetDevice(solver_->param().device_id()); + CHECK(Caffe::root_solver()); + Caffe::set_root_solver(false); + // See if there is a defined seed and reset random state if so + if (solver_->param().random_seed() >= 0) { + // Fetch random seed and modulate by device ID to make sure + // everyone doesn't have the same seed. We seem to have some + // solver instability if we have everyone with the same seed + Caffe::set_random_seed( + solver_->param().random_seed() + solver_->param().device_id()); + } + solver_->Step(solver_->param().max_iter() - initial_iter_); +} + +template +void P2PSync::on_start() { +#ifndef CPU_ONLY +#ifdef DEBUG + int device; + CUDA_CHECK(cudaGetDevice(&device)); + CHECK(device == solver_->param().device_id()); +#else +// CHECK(false); +#endif + + // Wait for update from parent + if (parent_) { + P2PSync *parent = queue_.pop(); + CHECK(parent == parent_); + } + + // Update children + for (int i = 0; i < children_.size(); ++i) { + Dtype* src = data_; + Dtype* dst = children_[i]->data_; + +#ifdef DEBUG + cudaPointerAttributes attributes; + CUDA_CHECK(cudaPointerGetAttributes(&attributes, src)); + CHECK(attributes.device == device); + CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst)); + CHECK(attributes.device == children_[i]->solver_->param().device_id()); +#endif + + CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype), // + cudaMemcpyDeviceToDevice, cudaStreamDefault)); + } + if (children_.size()) { + CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault)); + } + for (int i = 0; i < children_.size(); ++i) { + children_[i]->queue_.push(this); + } +#endif +} + +template +void P2PSync::on_gradients_ready() { +#ifndef CPU_ONLY +#ifdef DEBUG + int device; + CUDA_CHECK(cudaGetDevice(&device)); + CHECK(device == solver_->param().device_id()); +#endif + + // Sum children gradients as they appear in the queue + for (int i = 0; i < children_.size(); ++i) { + P2PSync *child = queue_.pop(); + Dtype* src = child->parent_grads_; + Dtype* dst = diff_; + +#ifdef DEBUG + bool ok = false; + for (int j = 0; j < children_.size(); ++j) { + if (child == children_[j]) { + ok = true; + } + } + CHECK(ok); + cudaPointerAttributes attributes; + CUDA_CHECK(cudaPointerGetAttributes(&attributes, src)); + CHECK(attributes.device == device); + CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst)); + CHECK(attributes.device == device); +#endif + + caffe_gpu_add(size_, src, dst, dst); + } + + // Send gradients to parent + if (parent_) { + Dtype* src = diff_; + Dtype* dst = parent_grads_; + +#ifdef DEBUG + cudaPointerAttributes attributes; + CUDA_CHECK(cudaPointerGetAttributes(&attributes, src)); + CHECK(attributes.device == device); + CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst)); + CHECK(attributes.device == parent_->solver_->param().device_id()); +#endif + + CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype), // + cudaMemcpyDeviceToDevice, cudaStreamDefault)); + CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault)); + parent_->queue_.push(this); + } else { + // Loss functions divide gradients by the batch size, so to compensate + // for split batch, the root solver divides by number of solvers. + caffe_gpu_scal(size_, Dtype(1.0 / Caffe::solver_count()), diff_); + } +#endif +} + +template +void P2PSync::run(const vector& gpus) { + // Pair devices for map-reduce synchronization + vector pairs; + DevicePair::compute(gpus, &pairs); + ostringstream s; + for (int i = 1; i < pairs.size(); ++i) { + s << (i == 1 ? "" : ", ") << pairs[i].parent() << ":" << pairs[i].device(); + } + LOG(INFO)<< "GPUs pairs " << s.str(); + + SolverParameter param(solver_->param()); + vector > > syncs(gpus.size()); + + // Build the GPU tree by finding the parent for each solver + for (int attempts = 0; attempts < pairs.size(); ++attempts) { + for (int i = 1; i < pairs.size(); ++i) { + if (!syncs[i].get()) { + P2PSync* parent = NULL; + for (int j = 0; j < syncs.size(); ++j) { + P2PSync* sync = j == 0 ? this : syncs[j].get(); + if (sync) { + const SolverParameter& p = sync->solver()->param(); + if (p.device_id() == pairs[i].parent()) { + parent = sync; + } + } + } + if (parent) { + param.set_device_id(pairs[i].device()); + syncs[i].reset(new P2PSync(solver_, parent, param)); + parent->children_.push_back((P2PSync*) syncs[i].get()); + } + } + } + } + + LOG(INFO)<< "Starting Optimization"; + + for (int i = 1; i < syncs.size(); ++i) { + syncs[i]->StartInternalThread(); + } + + // Run root solver on current thread + solver_->Solve(); + + for (int i = 1; i < syncs.size(); ++i) { + syncs[i]->StopInternalThread(); + } +} + +INSTANTIATE_CLASS(Params); +INSTANTIATE_CLASS(GPUParams); +INSTANTIATE_CLASS(P2PSync); + +} // namespace caffe + diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 54e085a63e5..b6fd6b642f1 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -19,13 +19,13 @@ namespace caffe { template Solver::Solver(const SolverParameter& param) - : net_() { + : net_(), callbacks_() { Init(param); } template Solver::Solver(const string& param_file) - : net_() { + : net_(), callbacks_() { SolverParameter param; ReadProtoFromTextFileOrDie(param_file, ¶m); Init(param); @@ -33,17 +33,19 @@ Solver::Solver(const string& param_file) template void Solver::Init(const SolverParameter& param) { - LOG(INFO) << "Initializing solver from parameters: " << std::endl - << param.DebugString(); + LOG_IF(INFO, Caffe::root_solver()) << "Initializing solver from parameters: " + << std::endl << param.DebugString(); param_ = param; CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative."; - if (param_.random_seed() >= 0) { + if (Caffe::root_solver() && param_.random_seed() >= 0) { Caffe::set_random_seed(param_.random_seed()); } // Scaffolding code InitTrainNet(); - InitTestNets(); - LOG(INFO) << "Solver scaffolding done."; + if (Caffe::root_solver()) { + InitTestNets(); + LOG(INFO) << "Solver scaffolding done."; + } iter_ = 0; current_step_ = 0; } @@ -59,19 +61,22 @@ void Solver::InitTrainNet() { << "one of these fields specifying a train_net: " << field_names; NetParameter net_param; if (param_.has_train_net_param()) { - LOG(INFO) << "Creating training net specified in train_net_param."; + LOG_IF(INFO, Caffe::root_solver()) + << "Creating training net specified in train_net_param."; net_param.CopyFrom(param_.train_net_param()); } else if (param_.has_train_net()) { - LOG(INFO) << "Creating training net from train_net file: " - << param_.train_net(); + LOG_IF(INFO, Caffe::root_solver()) + << "Creating training net from train_net file: " << param_.train_net(); ReadNetParamsFromTextFileOrDie(param_.train_net(), &net_param); } if (param_.has_net_param()) { - LOG(INFO) << "Creating training net specified in net_param."; + LOG_IF(INFO, Caffe::root_solver()) + << "Creating training net specified in net_param."; net_param.CopyFrom(param_.net_param()); } if (param_.has_net()) { - LOG(INFO) << "Creating training net from net file: " << param_.net(); + LOG_IF(INFO, Caffe::root_solver()) + << "Creating training net from net file: " << param_.net(); ReadNetParamsFromTextFileOrDie(param_.net(), &net_param); } // Set the correct NetState. We start with the solver defaults (lowest @@ -88,6 +93,7 @@ void Solver::InitTrainNet() { template void Solver::InitTestNets() { + CHECK(Caffe::root_solver()); const bool has_net_param = param_.has_net_param(); const bool has_net_file = param_.has_net(); const int num_generic_nets = has_net_param + has_net_file; @@ -175,10 +181,14 @@ void Solver::Step(int iters) { // zero-init the params net_->ClearParamDiffs(); if (param_.test_interval() && iter_ % param_.test_interval() == 0 - && (iter_ > 0 || param_.test_initialization())) { + && (iter_ > 0 || param_.test_initialization()) + && Caffe::root_solver()) { TestAll(); } + for (int i = 0; i < callbacks_.size(); ++i) { + callbacks_[i]->on_start(); + } const bool display = param_.display() && iter_ % param_.display() == 0; net_->set_debug_info(display && param_.debug_info()); // accumulate the loss and gradient @@ -198,7 +208,8 @@ void Solver::Step(int iters) { losses[idx] = loss; } if (display) { - LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss; + LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_ + << ", loss = " << smoothed_loss; const vector*>& result = net_->output_blobs(); int score_index = 0; for (int j = 0; j < result.size(); ++j) { @@ -213,12 +224,15 @@ void Solver::Step(int iters) { loss_msg_stream << " (* " << loss_weight << " = " << loss_weight * result_vec[k] << " loss)"; } - LOG(INFO) << " Train net output #" + LOG_IF(INFO, Caffe::root_solver()) << " Train net output #" << score_index++ << ": " << output_name << " = " << result_vec[k] << loss_msg_stream.str(); } } } + for (int i = 0; i < callbacks_.size(); ++i) { + callbacks_[i]->on_gradients_ready(); + } ApplyUpdate(); // Increment the internal iter_ counter -- its value should always indicate @@ -226,7 +240,9 @@ void Solver::Step(int iters) { ++iter_; // Save a snapshot if needed. - if (param_.snapshot() && iter_ % param_.snapshot() == 0) { + if (param_.snapshot() + && iter_ % param_.snapshot() == 0 + && Caffe::root_solver()) { Snapshot(); } } @@ -234,6 +250,7 @@ void Solver::Step(int iters) { template void Solver::Solve(const char* resume_file) { + CHECK(Caffe::root_solver()); LOG(INFO) << "Solving " << net_->name(); LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy(); @@ -278,6 +295,7 @@ void Solver::TestAll() { template void Solver::Test(const int test_net_id) { + CHECK(Caffe::root_solver()); LOG(INFO) << "Iteration " << iter_ << ", Testing net (#" << test_net_id << ")"; CHECK_NOTNULL(test_nets_[test_net_id].get())-> @@ -328,13 +346,14 @@ void Solver::Test(const int test_net_id) { << " = " << loss_weight * mean_score << " loss)"; } LOG(INFO) << " Test net output #" << i << ": " << output_name << " = " - << mean_score << loss_msg_stream.str(); + << mean_score << loss_msg_stream.str(); } } template void Solver::Snapshot() { + CHECK(Caffe::root_solver()); string model_filename; switch (param_.snapshot_format()) { case caffe::SolverParameter_SnapshotFormat_BINARYPROTO: @@ -379,6 +398,7 @@ string Solver::SnapshotToHDF5() { template void Solver::Restore(const char* state_file) { + CHECK(Caffe::root_solver()); string state_filename(state_file); if (state_filename.size() >= 3 && state_filename.compare(state_filename.size() - 3, 3, ".h5") == 0) { @@ -480,6 +500,7 @@ void SGDSolver::ClipGradients() { template void SGDSolver::ApplyUpdate() { + CHECK(Caffe::root_solver()); Dtype rate = GetLearningRate(); if (this->param_.display() && this->iter_ % this->param_.display() == 0) { LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate; @@ -723,6 +744,7 @@ void SGDSolver::RestoreSolverStateFromHDF5(const string& state_file) { template void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { + CHECK(Caffe::root_solver()); const vector*>& net_params = this->net_->learnable_params(); const vector& net_params_lr = this->net_->params_lr(); Dtype momentum = this->param_.momentum(); @@ -783,6 +805,7 @@ void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { template void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { + CHECK(Caffe::root_solver()); const vector*>& net_params = this->net_->learnable_params(); const vector& net_params_lr = this->net_->params_lr(); Dtype delta = this->param_.delta(); diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp index 0da7a3bac79..a667a867af0 100644 --- a/src/caffe/syncedmem.cpp +++ b/src/caffe/syncedmem.cpp @@ -12,8 +12,14 @@ SyncedMemory::~SyncedMemory() { } #ifndef CPU_ONLY - if (gpu_ptr_) { + if (gpu_ptr_ && own_gpu_data_) { + int initial_device; + cudaGetDevice(&initial_device); + if (gpu_device_ != -1) { + CUDA_CHECK(cudaSetDevice(gpu_device_)); + } CUDA_CHECK(cudaFree(gpu_ptr_)); + cudaSetDevice(initial_device); } #endif // CPU_ONLY } @@ -48,13 +54,17 @@ inline void SyncedMemory::to_gpu() { #ifndef CPU_ONLY switch (head_) { case UNINITIALIZED: + CUDA_CHECK(cudaGetDevice(&gpu_device_)); CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_)); caffe_gpu_memset(size_, 0, gpu_ptr_); head_ = HEAD_AT_GPU; + own_gpu_data_ = true; break; case HEAD_AT_CPU: if (gpu_ptr_ == NULL) { + CUDA_CHECK(cudaGetDevice(&gpu_device_)); CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_)); + own_gpu_data_ = true; } caffe_gpu_memcpy(size_, cpu_ptr_, gpu_ptr_); head_ = SYNCED; @@ -92,6 +102,26 @@ const void* SyncedMemory::gpu_data() { #endif } +void SyncedMemory::set_gpu_data(void* data) { +#ifndef CPU_ONLY + CHECK(data); + if (own_gpu_data_) { + int initial_device; + cudaGetDevice(&initial_device); + if (gpu_device_ != -1) { + CUDA_CHECK(cudaSetDevice(gpu_device_)); + } + CUDA_CHECK(cudaFree(gpu_ptr_)); + cudaSetDevice(initial_device); + } + gpu_ptr_ = data; + head_ = HEAD_AT_GPU; + own_gpu_data_ = false; +#else + NO_GPU; +#endif +} + void* SyncedMemory::mutable_cpu_data() { to_cpu(); head_ = HEAD_AT_CPU; @@ -112,7 +142,9 @@ void* SyncedMemory::mutable_gpu_data() { void SyncedMemory::async_gpu_push(const cudaStream_t& stream) { CHECK(head_ == HEAD_AT_CPU); if (gpu_ptr_ == NULL) { + CUDA_CHECK(cudaGetDevice(&gpu_device_)); CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_)); + own_gpu_data_ = true; } const cudaMemcpyKind put = cudaMemcpyHostToDevice; CUDA_CHECK(cudaMemcpyAsync(gpu_ptr_, cpu_ptr_, size_, put, stream)); diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index eaa7a759b9b..1cede07f774 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -8,6 +8,7 @@ #include "gtest/gtest.h" #include "caffe/common.hpp" +#include "caffe/parallel.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/solver.hpp" #include "caffe/util/io.hpp" @@ -35,6 +36,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { string snapshot_prefix_; shared_ptr > solver_; + shared_ptr > sync_; int seed_; // Dimensions are determined by generate_sample_data.py // TODO this is brittle and the hdf5 file should be checked instead. @@ -70,8 +72,8 @@ class GradientBasedSolverTest : public MultiDeviceTest { string RunLeastSquaresSolver(const Dtype learning_rate, const Dtype weight_decay, const Dtype momentum, const int num_iters, - const int iter_size = 1, const bool snapshot = false, - const char* from_snapshot = NULL) { + const int iter_size = 1, const int devices = 1, + const bool snapshot = false, const char* from_snapshot = NULL) { ostringstream proto; proto << "snapshot_after_train: " << snapshot << " " @@ -184,7 +186,20 @@ class GradientBasedSolverTest : public MultiDeviceTest { this->solver_->net()->Forward(empty_bottom_vec); } } - this->solver_->Solve(); + if (devices == 1) { + this->solver_->Solve(); + } else { + LOG(INFO) << "Multi-GPU test on " << devices << " devices"; + vector gpus; + for (int i = 0; i < devices; ++i) { + gpus.push_back(i); + } + Caffe::set_solver_count(gpus.size()); + this->sync_.reset(new P2PSync( + this->solver_, NULL, this->solver_->param())); + this->sync_->run(gpus); + Caffe::set_solver_count(1); + } if (snapshot) { ostringstream resume_file; resume_file << snapshot_prefix_ << "/_iter_" << num_iters @@ -410,20 +425,38 @@ class GradientBasedSolverTest : public MultiDeviceTest { void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0, const Dtype weight_decay = 0.0, const Dtype momentum = 0.0, const int iter_to_check = 0) { - // Initialize the solver and run K (= iter_to_check) solver iterations. - RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check); - - // Compute the (K+1)th update using the analytic least squares gradient. - vector > > updated_params; - ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum, - &updated_params); - - // Reinitialize the solver and run K+1 solver iterations. - RunLeastSquaresSolver(learning_rate, weight_decay, momentum, - iter_to_check + 1); - - // Check that the solver's solution matches ours. - CheckLeastSquaresUpdate(updated_params); + const int kNum = num_; + const int kIterSize = 1; + // Test over all numbers of devices. + int available_devices = 1; +#ifndef CPU_ONLY + if (Caffe::mode() == Caffe::GPU) { + CUDA_CHECK(cudaGetDeviceCount(&available_devices)); + } +#endif + for (int devices = 1; devices <= available_devices; ++devices) { + // Configure batch size for single / multi device equivalence. + // Constant data is needed for multi device as for accumulation. + num_ = kNum * devices; + + // Initialize the solver and run K (= iter_to_check) solver iterations + // (on single device). + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, + iter_to_check, kIterSize, 1); + + // Compute the (K+1)th update using the analytic least squares gradient. + vector > > updated_params; + ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum, + &updated_params); + + // Reinitialize the solver and run K+1 solver iterations. + num_ = kNum; + RunLeastSquaresSolver(learning_rate, weight_decay, momentum, + iter_to_check + 1, kIterSize, devices); + + // Check that the solver's solution matches ours. + CheckLeastSquaresUpdate(updated_params); + } } void TestSnapshot(const Dtype learning_rate = 1.0, @@ -433,8 +466,9 @@ class GradientBasedSolverTest : public MultiDeviceTest { const int total_num_iters = num_iters * 2; bool snapshot = false; const int kIterSize = 1; + const int kDevices = 1; RunLeastSquaresSolver(learning_rate, weight_decay, momentum, - total_num_iters, kIterSize, snapshot); + total_num_iters, kIterSize, kDevices, snapshot); // Save the resulting param values. vector > > param_copies; @@ -464,12 +498,13 @@ class GradientBasedSolverTest : public MultiDeviceTest { // Run the solver for num_iters iterations and snapshot. snapshot = true; string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay, - momentum, num_iters, kIterSize, snapshot); + momentum, num_iters, kIterSize, kDevices, snapshot); // Reinitialize the solver and run for num_iters more iterations. snapshot = false; RunLeastSquaresSolver(learning_rate, weight_decay, momentum, - total_num_iters, kIterSize, snapshot, snapshot_name.c_str()); + total_num_iters, kIterSize, kDevices, + snapshot, snapshot_name.c_str()); // Check that params now match. const vector*>& params = solver_->net()->learnable_params(); diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp index f7c53f22225..d1d1fa864c3 100644 --- a/src/caffe/util/blocking_queue.cpp +++ b/src/caffe/util/blocking_queue.cpp @@ -3,6 +3,7 @@ #include "caffe/data_layers.hpp" #include "caffe/data_reader.hpp" +#include "caffe/parallel.hpp" #include "caffe/util/blocking_queue.hpp" namespace caffe { @@ -89,5 +90,7 @@ template class BlockingQueue*>; template class BlockingQueue*>; template class BlockingQueue; template class BlockingQueue >; +template class BlockingQueue*>; +template class BlockingQueue*>; } // namespace caffe diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 46f99594800..9f31b37ac2b 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -17,13 +17,17 @@ using caffe::Blob; using caffe::Caffe; using caffe::Net; using caffe::Layer; +using caffe::Solver; using caffe::shared_ptr; +using caffe::string; using caffe::Timer; using caffe::vector; +using std::ostringstream; - -DEFINE_int32(gpu, -1, - "Run in GPU mode on given device ID."); +DEFINE_string(gpu, "", + "Optional; run in GPU mode on given device IDs separated by ','." + "Use '-gpu all' to run on all available GPUs. The effective training " + "batch size is multiplied by the number of devices."); DEFINE_string(solver, "", "The solver definition protocol buffer text file."); DEFINE_string(model, "", @@ -31,8 +35,8 @@ DEFINE_string(model, "", DEFINE_string(snapshot, "", "Optional; the snapshot solver state to resume training."); DEFINE_string(weights, "", - "Optional; the pretrained weights to initialize finetuning. " - "Cannot be set simultaneously with snapshot."); + "Optional; the pretrained weights to initialize finetuning, " + "separated by ','. Cannot be set simultaneously with snapshot."); DEFINE_int32(iterations, 50, "The number of iterations to run."); @@ -66,6 +70,29 @@ static BrewFunction GetBrewFunction(const caffe::string& name) { } } +// Parse GPU ids or use all available devices +static void get_gpus(vector* gpus) { + if (FLAGS_gpu == "all") { + int count = 0; +#ifndef CPU_ONLY + CUDA_CHECK(cudaGetDeviceCount(&count)); +#else + NO_GPU; +#endif + for (int i = 0; i < count; ++i) { + gpus->push_back(i); + } + } else if (FLAGS_gpu.size()) { + vector strings; + boost::split(strings, FLAGS_gpu, boost::is_any_of(",")); + for (int i = 0; i < strings.size(); ++i) { + gpus->push_back(boost::lexical_cast(strings[i])); + } + } else { + CHECK_EQ(gpus->size(), 0); + } +} + // caffe commands to call by // caffe // @@ -74,10 +101,13 @@ static BrewFunction GetBrewFunction(const caffe::string& name) { // Device Query: show diagnostic information for a GPU device. int device_query() { - CHECK_GT(FLAGS_gpu, -1) << "Need a device ID to query."; - LOG(INFO) << "Querying device ID = " << FLAGS_gpu; - caffe::Caffe::SetDevice(FLAGS_gpu); - caffe::Caffe::DeviceQuery(); + LOG(INFO) << "Querying GPUs " << FLAGS_gpu; + vector gpus; + get_gpus(&gpus); + for (int i = 0; i < gpus.size(); ++i) { + caffe::Caffe::SetDevice(gpus[i]); + caffe::Caffe::DeviceQuery(); + } return 0; } RegisterBrewFunction(device_query); @@ -106,34 +136,49 @@ int train() { caffe::SolverParameter solver_param; caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param); - // If the gpu flag is not provided, allow the mode and device to be set + // If the gpus flag is not provided, allow the mode and device to be set // in the solver prototxt. - if (FLAGS_gpu < 0 + if (FLAGS_gpu.size() == 0 && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) { - FLAGS_gpu = solver_param.device_id(); + if (solver_param.has_device_id()) { + FLAGS_gpu = "" + + boost::lexical_cast(solver_param.device_id()); + } else { // Set default GPU if unspecified + FLAGS_gpu = "" + boost::lexical_cast(0); + } } - // Set device id and mode - if (FLAGS_gpu >= 0) { - LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu; - Caffe::SetDevice(FLAGS_gpu); - Caffe::set_mode(Caffe::GPU); - } else { - LOG(INFO) << "Use CPU."; + vector gpus; + get_gpus(&gpus); + if (gpus.size() == 0) { Caffe::set_mode(Caffe::CPU); + } else { + ostringstream s; + for (int i = 0; i < gpus.size(); ++i) { + s << (i ? ", " : "") << gpus[i]; + } + LOG(INFO) << "Using GPUs " << s.str(); + + solver_param.set_device_id(gpus[0]); + Caffe::SetDevice(gpus[0]); + Caffe::set_mode(Caffe::GPU); + Caffe::set_solver_count(gpus.size()); } - LOG(INFO) << "Starting Optimization"; - shared_ptr > - solver(caffe::GetSolver(solver_param)); + shared_ptr > solver(caffe::GetSolver(solver_param)); if (FLAGS_snapshot.size()) { LOG(INFO) << "Resuming from " << FLAGS_snapshot; - solver->Solve(FLAGS_snapshot); + solver->Restore(FLAGS_snapshot.c_str()); } else if (FLAGS_weights.size()) { - CopyLayers(&*solver, FLAGS_weights); - solver->Solve(); + CopyLayers(solver.get(), FLAGS_weights); + } + + if (gpus.size() > 1) { + caffe::P2PSync sync(solver, NULL, solver->param()); + sync.run(gpus); } else { + LOG(INFO) << "Starting Optimization"; solver->Solve(); } LOG(INFO) << "Optimization Done."; @@ -148,9 +193,11 @@ int test() { CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score."; // Set device id and mode - if (FLAGS_gpu >= 0) { - LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu; - Caffe::SetDevice(FLAGS_gpu); + vector gpus; + get_gpus(&gpus); + if (gpus.size() != 0) { + LOG(INFO) << "Use GPU with device ID " << gpus[0]; + Caffe::SetDevice(gpus[0]); Caffe::set_mode(Caffe::GPU); } else { LOG(INFO) << "Use CPU."; @@ -213,9 +260,11 @@ int time() { CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time."; // Set device id and mode - if (FLAGS_gpu >= 0) { - LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu; - Caffe::SetDevice(FLAGS_gpu); + vector gpus; + get_gpus(&gpus); + if (gpus.size() != 0) { + LOG(INFO) << "Use GPU with device ID " << gpus[0]; + Caffe::SetDevice(gpus[0]); Caffe::set_mode(Caffe::GPU); } else { LOG(INFO) << "Use CPU."; From 335bee737cb2e715abe685e6029afc83ccd8f404 Mon Sep 17 00:00:00 2001 From: mhouston Date: Fri, 10 Jul 2015 16:05:48 -0700 Subject: [PATCH 192/446] Detect topology corner cases and improve broadcast order - Start with distant nodes in broadcast - Fix outside loop to loop for full tree depth --- src/caffe/parallel.cpp | 73 ++++++++++++++++++++++++------------------ 1 file changed, 41 insertions(+), 32 deletions(-) diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp index 3fef8cfdb33..5a08df6c1c8 100644 --- a/src/caffe/parallel.cpp +++ b/src/caffe/parallel.cpp @@ -119,18 +119,23 @@ void DevicePair::compute(const vector devices, vector* pairs) { #ifndef CPU_ONLY vector remaining(devices); + // Depth for reduction tree + int remaining_depth = static_cast(ceil(log2(remaining.size()))); + // Group GPUs by board - for (int i = 0; i < remaining.size(); ++i) { - for (int j = i + 1; j < remaining.size(); ++j) { - cudaDeviceProp a, b; - CUDA_CHECK(cudaGetDeviceProperties(&a, remaining[i])); - CUDA_CHECK(cudaGetDeviceProperties(&b, remaining[j])); - if (a.isMultiGpuBoard && b.isMultiGpuBoard) { - if (a.multiGpuBoardGroupID == b.multiGpuBoardGroupID) { - pairs->push_back(DevicePair(remaining[i], remaining[j])); - DLOG(INFO) << "GPU board: " << remaining[i] << ":" << remaining[j]; - remaining.erase(remaining.begin() + j); - break; + for (int d = 0; d < remaining_depth; ++d) { + for (int i = 0; i < remaining.size(); ++i) { + for (int j = i + 1; j < remaining.size(); ++j) { + cudaDeviceProp a, b; + CUDA_CHECK(cudaGetDeviceProperties(&a, remaining[i])); + CUDA_CHECK(cudaGetDeviceProperties(&b, remaining[j])); + if (a.isMultiGpuBoard && b.isMultiGpuBoard) { + if (a.multiGpuBoardGroupID == b.multiGpuBoardGroupID) { + pairs->push_back(DevicePair(remaining[i], remaining[j])); + DLOG(INFO) << "GPU board: " << remaining[i] << ":" << remaining[j]; + remaining.erase(remaining.begin() + j); + break; + } } } } @@ -142,15 +147,19 @@ void DevicePair::compute(const vector devices, vector* pairs) { DLOG(INFO) << "GPUs paired by boards, remaining: " << s.str(); // Group by P2P accessibility - for (int i = 0; i < remaining.size(); ++i) { - for (int j = i + 1; j < remaining.size(); ++j) { - int access; - CUDA_CHECK(cudaDeviceCanAccessPeer(&access, remaining[i], remaining[j])); - if (access) { - pairs->push_back(DevicePair(remaining[i], remaining[j])); - DLOG(INFO) << "P2P pair: " << remaining[i] << ":" << remaining[j]; - remaining.erase(remaining.begin() + j); - break; + remaining_depth = ceil(log2(remaining.size())); + for (int d = 0; d < remaining_depth; ++d) { + for (int i = 0; i < remaining.size(); ++i) { + for (int j = i + 1; j < remaining.size(); ++j) { + int access; + CUDA_CHECK( + cudaDeviceCanAccessPeer(&access, remaining[i], remaining[j])); + if (access) { + pairs->push_back(DevicePair(remaining[i], remaining[j])); + DLOG(INFO) << "P2P pair: " << remaining[i] << ":" << remaining[j]; + remaining.erase(remaining.begin() + j); + break; + } } } } @@ -161,15 +170,19 @@ void DevicePair::compute(const vector devices, vector* pairs) { DLOG(INFO) << "GPUs paired by P2P access, remaining: " << s.str(); // Group remaining - for (int i = 0; i < remaining.size(); ++i) { - for (int j = i + 1; j < remaining.size(); ++j) { - pairs->push_back(DevicePair(remaining[i], remaining[j])); - DLOG(INFO) << "Remaining pair: " << remaining[i] << ":" << remaining[j]; - remaining.erase(remaining.begin() + j); - break; + remaining_depth = ceil(log2(remaining.size())); + for (int d = 0; d < remaining_depth; ++d) { + for (int i = 0; i < remaining.size(); ++i) { + pairs->push_back(DevicePair(remaining[i], remaining[i + 1])); + DLOG(INFO) << "Remaining pair: " << remaining[i] << ":" + << remaining[i + 1]; + remaining.erase(remaining.begin() + i + 1); } } + + // Should only be the parent node remaining CHECK_EQ(remaining.size(), 1); + pairs->insert(pairs->begin(), DevicePair(-1, remaining[0])); CHECK(pairs->size() == devices.size()); @@ -289,7 +302,7 @@ void P2PSync::on_start() { } // Update children - for (int i = 0; i < children_.size(); ++i) { + for (int i = children_.size() - 1; i >= 0; i--) { Dtype* src = data_; Dtype* dst = children_[i]->data_; @@ -301,13 +314,9 @@ void P2PSync::on_start() { CHECK(attributes.device == children_[i]->solver_->param().device_id()); #endif - CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype), // + CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype), cudaMemcpyDeviceToDevice, cudaStreamDefault)); - } - if (children_.size()) { CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault)); - } - for (int i = 0; i < children_.size(); ++i) { children_[i]->queue_.push(this); } #endif From 8771d0f4317fc0081d86b7637f5f5ceef5b92dfb Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 7 Aug 2015 13:56:49 -0700 Subject: [PATCH 193/446] [docs] add multi-gpu usage note to interfaces --- docs/tutorial/interfaces.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md index 40602948cc3..9006179d0f1 100644 --- a/docs/tutorial/interfaces.md +++ b/docs/tutorial/interfaces.md @@ -50,6 +50,13 @@ For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but # query the first device caffe device_query -gpu 0 +**Parallelism**: the `-gpu` flag to the `caffe` tool can take a comma separated list of IDs to run on multiple GPUs. A solver and net will be instantiated for each GPU so the batch size is effectively multiplied by the number of GPUs. To reproduce single GPU training, reduce the batch size in the network definition accordingly. + + # train on GPUs 0 & 1 (doubling the batch size) + caffe train -solver examples/mnist/lenet_solver.prototxt -gpu 0,1 + # train on all GPUs (multiplying batch size by number of devices) + caffe train -solver examples/mnist/lenet_solver.prototxt -gpu all + ## Python The Python interface -- pycaffe -- is the `caffe` module and its scripts in caffe/python. `import caffe` to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing. From 1ce3380f172336cadaa649a6e077a42a246a534d Mon Sep 17 00:00:00 2001 From: Mohamed Omran Date: Sat, 20 Sep 2014 19:01:28 +0200 Subject: [PATCH 194/446] Implement AdaDelta; add test cases; add mnist examples --- examples/mnist/lenet_adadelta_solver.prototxt | 22 ++ ...mnist_autoencoder_solver_adadelta.prototxt | 17 ++ .../mnist/train_mnist_autoencoder_adadelta.sh | 4 + include/caffe/solver.hpp | 23 ++ src/caffe/proto/caffe.proto | 1 + src/caffe/solver.cpp | 199 ++++++++++++++++++ src/caffe/test/test_gradient_based_solver.cpp | 100 ++++++++- 7 files changed, 364 insertions(+), 2 deletions(-) create mode 100644 examples/mnist/lenet_adadelta_solver.prototxt create mode 100644 examples/mnist/mnist_autoencoder_solver_adadelta.prototxt create mode 100755 examples/mnist/train_mnist_autoencoder_adadelta.sh diff --git a/examples/mnist/lenet_adadelta_solver.prototxt b/examples/mnist/lenet_adadelta_solver.prototxt new file mode 100644 index 00000000000..b77b451d56a --- /dev/null +++ b/examples/mnist/lenet_adadelta_solver.prototxt @@ -0,0 +1,22 @@ +# The train/test net protocol buffer definition +net: "examples/mnist/lenet_train_test.prototxt" +# test_iter specifies how many forward passes the test should carry out. +# In the case of MNIST, we have test batch size 100 and 100 test iterations, +# covering the full 10,000 testing images. +test_iter: 100 +# Carry out testing every 500 training iterations. +test_interval: 500 +# The base learning rate, momentum and the weight decay of the network. +momentum: 0.95 +weight_decay: 0.0005 +# Display every 100 iterations +display: 100 +# The maximum number of iterations +max_iter: 10000 +# snapshot intermediate results +snapshot: 5000 +snapshot_prefix: "examples/mnist/lenet_adadelta" +# solver mode: CPU or GPU +solver_mode: GPU +solver_type: ADADELTA +delta: 1e-6 diff --git a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt new file mode 100644 index 00000000000..cc4f0bbb4a7 --- /dev/null +++ b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt @@ -0,0 +1,17 @@ +net: "examples/mnist/mnist_autoencoder.prototxt" +test_state: { stage: 'test-on-train' } +test_iter: 500 +test_state: { stage: 'test-on-test' } +test_iter: 100 +test_interval: 500 +test_compute_loss: true +momentum: 0.95 +display: 100 +max_iter: 65000 +weight_decay: 0.0005 +snapshot: 10000 +snapshot_prefix: "examples/mnist/mnist_autoencoder_adadelta_train" +# solver mode: CPU or GPU +solver_mode: GPU +solver_type: ADADELTA +delta: 1e-8 diff --git a/examples/mnist/train_mnist_autoencoder_adadelta.sh b/examples/mnist/train_mnist_autoencoder_adadelta.sh new file mode 100755 index 00000000000..4be0ebddedc --- /dev/null +++ b/examples/mnist/train_mnist_autoencoder_adadelta.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +./build/tools/caffe train \ + --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index fbade9389ff..4b408380119 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -158,6 +158,27 @@ class RMSPropSolver : public SGDSolver { DISABLE_COPY_AND_ASSIGN(RMSPropSolver); }; +template +class AdaDeltaSolver : public SGDSolver { + public: + explicit AdaDeltaSolver(const SolverParameter& param) + : SGDSolver(param) { constructor_sanity_check(); } + explicit AdaDeltaSolver(const string& param_file) + : SGDSolver(param_file) { constructor_sanity_check(); } + + protected: + virtual void PreSolve(); + virtual void ComputeUpdateValue(); + void constructor_sanity_check() { + CHECK_EQ(0, this->param_.base_lr()) + << "Learning rate cannot be used with AdaDelta."; + CHECK_EQ("", this->param_.lr_policy()) + << "Learning rate policy cannot be applied to AdaDelta."; + } + + DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver); +}; + template Solver* GetSolver(const SolverParameter& param) { SolverParameter_SolverType type = param.solver_type(); @@ -171,6 +192,8 @@ Solver* GetSolver(const SolverParameter& param) { return new AdaGradSolver(param); case SolverParameter_SolverType_RMSPROP: return new RMSPropSolver(param); + case SolverParameter_SolverType_ADADELTA: + return new AdaDeltaSolver(param); default: LOG(FATAL) << "Unknown SolverType: " << type; } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 89f14595ba6..7cfcaa8bac7 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -215,6 +215,7 @@ message SolverParameter { NESTEROV = 1; ADAGRAD = 2; RMSPROP = 3; + ADADELTA = 4; } optional SolverType solver_type = 30 [default = SGD]; // numerical stability for AdaGrad diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 54e085a63e5..d8749a1b939 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -934,10 +934,209 @@ void RMSPropSolver::ComputeUpdateValue(int param_id, Dtype rate) { } } +template +void AdaDeltaSolver::PreSolve() { + // Initialize the history + vector > >& net_params = this->net_->params(); + this->history_.clear(); + this->update_.clear(); + this->temp_.clear(); + for (int i = 0; i < net_params.size(); ++i) { + const Blob* net_param = net_params[i].get(); + this->history_.push_back(shared_ptr >(new Blob( + net_param->num(), net_param->channels(), net_param->height(), + net_param->width()))); + this->update_.push_back(shared_ptr >(new Blob( + net_param->num(), net_param->channels(), net_param->height(), + net_param->width()))); + this->temp_.push_back(shared_ptr >(new Blob( + net_param->num(), net_param->channels(), net_param->height(), + net_param->width()))); + } + for (int i = 0; i < net_params.size(); ++i) { + const Blob* net_param = net_params[i].get(); + this->history_.push_back(shared_ptr >(new Blob( + net_param->num(), net_param->channels(), net_param->height(), + net_param->width()))); + } +} + +template +void AdaDeltaSolver::ComputeUpdateValue() { + vector > >& net_params = this->net_->params(); + vector& net_params_weight_decay = this->net_->params_weight_decay(); + Dtype delta = this->param_.delta(); + Dtype momentum = this->param_.momentum(); + Dtype weight_decay = this->param_.weight_decay(); + string regularization_type = this->param_.regularization_type(); + size_t update_history_offset = net_params.size(); + switch (Caffe::mode()) { + case Caffe::CPU: + for (int param_id = 0; param_id < net_params.size(); ++param_id) { + Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; + + if (local_decay) { + if (regularization_type == "L2") { + // add weight decay + caffe_axpy(net_params[param_id]->count(), + local_decay, + net_params[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + } else if (regularization_type == "L1") { + caffe_cpu_sign(net_params[param_id]->count(), + net_params[param_id]->cpu_data(), + this->temp_[param_id]->mutable_cpu_data()); + caffe_axpy(net_params[param_id]->count(), + local_decay, + this->temp_[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + } else { + LOG(FATAL) << "Unknown regularization type: " << regularization_type; + } + } + + // compute square of gradient in update + caffe_powx(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), Dtype(2), + this->update_[param_id]->mutable_cpu_data()); + + // update history of gradients + caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->cpu_data(), momentum, + this->history_[param_id]->mutable_cpu_data()); + + // add delta to history to guard against dividing by zero later + caffe_set(net_params[param_id]->count(), delta, + this->temp_[param_id]->mutable_cpu_data()); + + caffe_add(net_params[param_id]->count(), + this->temp_[param_id]->cpu_data(), + this->history_[update_history_offset + param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + caffe_add(net_params[param_id]->count(), + this->temp_[param_id]->cpu_data(), + this->history_[param_id]->cpu_data(), + this->temp_[param_id]->mutable_cpu_data()); + + // divide history of updates by history of gradients + caffe_div(net_params[param_id]->count(), + this->update_[param_id]->cpu_data(), + this->temp_[param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + // jointly compute the RMS of both for update and gradient history + caffe_powx(net_params[param_id]->count(), + this->update_[param_id]->cpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_cpu_data()); + + // compute the update + caffe_mul(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), + this->update_[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + + // compute square of update + caffe_powx(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), Dtype(2), + this->update_[param_id]->mutable_cpu_data()); + + // update history of updates + caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->cpu_data(), momentum, + this->history_[update_history_offset + param_id]->mutable_cpu_data()); + } + break; + case Caffe::GPU: +#ifndef CPU_ONLY + for (int param_id = 0; param_id < net_params.size(); ++param_id) { + Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; + + if (local_decay) { + if (regularization_type == "L2") { + // add weight decay + caffe_gpu_axpy(net_params[param_id]->count(), + local_decay, + net_params[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); + } else if (regularization_type == "L1") { + caffe_gpu_sign(net_params[param_id]->count(), + net_params[param_id]->gpu_data(), + this->temp_[param_id]->mutable_gpu_data()); + caffe_gpu_axpy(net_params[param_id]->count(), + local_decay, + this->temp_[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); + } else { + LOG(FATAL) << "Unknown regularization type: " << regularization_type; + } + } + + // compute square of gradient in update + caffe_gpu_powx(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), Dtype(2), + this->update_[param_id]->mutable_gpu_data()); + + // update history of gradients + caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->gpu_data(), momentum, + this->history_[param_id]->mutable_gpu_data()); + + // add delta to history to guard against dividing by zero later + caffe_gpu_set(net_params[param_id]->count(), delta, + this->temp_[param_id]->mutable_gpu_data()); + + caffe_gpu_add(net_params[param_id]->count(), + this->temp_[param_id]->gpu_data(), + this->history_[update_history_offset + param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_add(net_params[param_id]->count(), + this->temp_[param_id]->gpu_data(), + this->history_[param_id]->gpu_data(), + this->temp_[param_id]->mutable_gpu_data()); + + // divide history of updates by history of gradients + caffe_gpu_div(net_params[param_id]->count(), + this->update_[param_id]->gpu_data(), + this->temp_[param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + // jointly compute the RMS of both for update and gradient history + caffe_gpu_powx(net_params[param_id]->count(), + this->update_[param_id]->gpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_gpu_data()); + + // compute the update and copy to net_diff + caffe_gpu_mul(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), + this->update_[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); + + // compute square of update + caffe_gpu_powx(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), Dtype(2), + this->update_[param_id]->mutable_gpu_data()); + + // update history of updates + caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->gpu_data(), momentum, + this->history_[update_history_offset + param_id]->mutable_gpu_data()); + } +#else + NO_GPU; +#endif + break; + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + INSTANTIATE_CLASS(Solver); INSTANTIATE_CLASS(SGDSolver); INSTANTIATE_CLASS(NesterovSolver); INSTANTIATE_CLASS(AdaGradSolver); INSTANTIATE_CLASS(RMSPropSolver); +INSTANTIATE_CLASS(AdaDeltaSolver); } // namespace caffe diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index eaa7a759b9b..db89e285a9f 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -64,7 +64,8 @@ class GradientBasedSolverTest : public MultiDeviceTest { } InitSolver(param); delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD || - solver_type() == SolverParameter_SolverType_RMSPROP) ? + solver_type() == SolverParameter_SolverType_RMSPROP || + solver_type() == SolverParameter_SolverType_ADADELTA) ? param.delta() : 0; } @@ -164,6 +165,10 @@ class GradientBasedSolverTest : public MultiDeviceTest { " bottom: 'targets' " " } " "} "; + if (learning_rate != 0) { + proto << "base_lr: " << learning_rate << " "; + proto << "lr_policy: 'fixed' "; + } if (weight_decay != 0) { proto << "weight_decay: " << weight_decay << " "; } @@ -266,7 +271,11 @@ class GradientBasedSolverTest : public MultiDeviceTest { ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]); // Finally, compute update. const vector > >& history = solver_->history(); - ASSERT_EQ(2, history.size()); // 1 blob for weights, 1 for bias + if (solver_type() != SolverParameter_SolverType_ADADELTA) { + ASSERT_EQ(2, history.size()); // 1 blob for weights, 1 for bias + } else { + ASSERT_EQ(4, history.size()); // additional blobs for update history + } Dtype update_value = learning_rate * grad; const Dtype history_value = (i == D) ? history[1]->cpu_data()[0] : history[0]->cpu_data()[i]; @@ -289,6 +298,19 @@ class GradientBasedSolverTest : public MultiDeviceTest { + grad * grad * (1 - rms_decay)) + delta_; } break; + case SolverParameter_SolverType_ADADELTA: + { + const Dtype update_history_value = (i == D) ? + history[3]->cpu_data()[0] : history[2]->cpu_data()[i]; + const Dtype weighted_gradient_average = + momentum * history_value + (1 - momentum) * (grad * grad); + update_value = grad * std::sqrt((update_history_value + delta_) / + (weighted_gradient_average + delta_)); + // not actually needed, just here for illustrative purposes + // const Dtype weighted_update_average = + // momentum * update_history_value + (1 - momentum) * (update_value); + break; + } default: LOG(FATAL) << "Unknown solver type: " << solver_type(); } @@ -981,4 +1003,78 @@ TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) { } } +template +class AdaDeltaSolverTest : public GradientBasedSolverTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + virtual void InitSolver(const SolverParameter& param) { + this->solver_.reset(new AdaDeltaSolver(param)); + } + + virtual SolverParameter_SolverType solver_type() { + return SolverParameter_SolverType_ADADELTA; + } +}; + +TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices); + +TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.0; + this->TestLeastSquaresUpdate(kLearningRate); +} + +TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.0; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.95; + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); +} + +TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.0; + const Dtype kWeightDecay = 0.0; + const Dtype kMomentum = 0.5; + const int kNumIters = 1; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); + } +} + +TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.0; + const Dtype kWeightDecay = 0.0; + const Dtype kMomentum = 0.95; + const int kNumIters = 1; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); + } +} + +TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.0; + const Dtype kWeightDecay = 0.0; + const Dtype kMomentum = 0.95; + const int kNumIters = 500; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + +TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.0; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.95; + const int kNumIters = 500; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + } // namespace caffe From 4c58741ce2e031b61aef53914128801e6edd673d Mon Sep 17 00:00:00 2001 From: Kevin Bache Date: Thu, 19 Mar 2015 15:56:51 -0700 Subject: [PATCH 195/446] Updated AdaDelta for modern Caffe; reduced iterations on multi-iter tests --- ...mnist_autoencoder_solver_adadelta.prototxt | 2 +- include/caffe/solver.hpp | 6 ++-- src/caffe/solver.cpp | 32 ++++++------------- src/caffe/test/test_gradient_based_solver.cpp | 4 +-- 4 files changed, 15 insertions(+), 29 deletions(-) diff --git a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt index cc4f0bbb4a7..4e43468a71f 100644 --- a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt +++ b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt @@ -6,6 +6,7 @@ test_iter: 100 test_interval: 500 test_compute_loss: true momentum: 0.95 +delta: 1e-8 display: 100 max_iter: 65000 weight_decay: 0.0005 @@ -14,4 +15,3 @@ snapshot_prefix: "examples/mnist/mnist_autoencoder_adadelta_train" # solver mode: CPU or GPU solver_mode: GPU solver_type: ADADELTA -delta: 1e-8 diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 4b408380119..495cd4f159e 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -82,12 +82,12 @@ class SGDSolver : public Solver { const vector > >& history() { return history_; } protected: - void PreSolve(); Dtype GetLearningRate(); virtual void ApplyUpdate(); virtual void Normalize(int param_id); virtual void Regularize(int param_id); virtual void ComputeUpdateValue(int param_id, Dtype rate); + virtual void PreSolve(); virtual void ClipGradients(); virtual void SnapshotSolverState(const string& model_filename); virtual void SnapshotSolverStateToBinaryProto(const string& model_filename); @@ -162,9 +162,9 @@ template class AdaDeltaSolver : public SGDSolver { public: explicit AdaDeltaSolver(const SolverParameter& param) - : SGDSolver(param) { constructor_sanity_check(); } + : SGDSolver(param) { PreSolve(); constructor_sanity_check(); } explicit AdaDeltaSolver(const string& param_file) - : SGDSolver(param_file) { constructor_sanity_check(); } + : SGDSolver(param_file) { PreSolve(); constructor_sanity_check(); } protected: virtual void PreSolve(); diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index d8749a1b939..34a290ffe3d 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -936,35 +936,21 @@ void RMSPropSolver::ComputeUpdateValue(int param_id, Dtype rate) { template void AdaDeltaSolver::PreSolve() { - // Initialize the history - vector > >& net_params = this->net_->params(); - this->history_.clear(); - this->update_.clear(); - this->temp_.clear(); - for (int i = 0; i < net_params.size(); ++i) { - const Blob* net_param = net_params[i].get(); - this->history_.push_back(shared_ptr >(new Blob( - net_param->num(), net_param->channels(), net_param->height(), - net_param->width()))); - this->update_.push_back(shared_ptr >(new Blob( - net_param->num(), net_param->channels(), net_param->height(), - net_param->width()))); - this->temp_.push_back(shared_ptr >(new Blob( - net_param->num(), net_param->channels(), net_param->height(), - net_param->width()))); - } + // Add the extra history entries for AdaDelta after those from + // SGDSolver::PreSolve + const vector > >& net_params = this->net_->params(); for (int i = 0; i < net_params.size(); ++i) { - const Blob* net_param = net_params[i].get(); - this->history_.push_back(shared_ptr >(new Blob( - net_param->num(), net_param->channels(), net_param->height(), - net_param->width()))); + const vector& shape = net_params[i]->shape(); + this->history_.push_back( + shared_ptr >(new Blob(shape))); } } template void AdaDeltaSolver::ComputeUpdateValue() { - vector > >& net_params = this->net_->params(); - vector& net_params_weight_decay = this->net_->params_weight_decay(); + const vector > >& net_params = this->net_->params(); + const vector& net_params_weight_decay = + this->net_->params_weight_decay(); Dtype delta = this->param_.delta(); Dtype momentum = this->param_.momentum(); Dtype weight_decay = this->param_.weight_decay(); diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index db89e285a9f..277aa3a5c8e 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -1060,7 +1060,7 @@ TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { const Dtype kLearningRate = 0.0; const Dtype kWeightDecay = 0.0; const Dtype kMomentum = 0.95; - const int kNumIters = 500; + const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } @@ -1071,7 +1071,7 @@ TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) { const Dtype kLearningRate = 0.0; const Dtype kWeightDecay = 0.1; const Dtype kMomentum = 0.95; - const int kNumIters = 500; + const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } From f2e523e479b89902b644f3a8bb2ac51a6dc28eee Mon Sep 17 00:00:00 2001 From: Matthias Plappert Date: Sat, 18 Jul 2015 18:46:51 +0200 Subject: [PATCH 196/446] Clean up and modernize AdaDelta code; add learning rate support; add additional test cases --- examples/mnist/lenet_adadelta_solver.prototxt | 2 + ...mnist_autoencoder_solver_adadelta.prototxt | 2 + include/caffe/solver.hpp | 16 +- src/caffe/solver.cpp | 274 ++++++++---------- src/caffe/test/test_gradient_based_solver.cpp | 211 +++++++++----- 5 files changed, 260 insertions(+), 245 deletions(-) diff --git a/examples/mnist/lenet_adadelta_solver.prototxt b/examples/mnist/lenet_adadelta_solver.prototxt index b77b451d56a..776d1e06139 100644 --- a/examples/mnist/lenet_adadelta_solver.prototxt +++ b/examples/mnist/lenet_adadelta_solver.prototxt @@ -7,6 +7,8 @@ test_iter: 100 # Carry out testing every 500 training iterations. test_interval: 500 # The base learning rate, momentum and the weight decay of the network. +base_lr: 1.0 +lr_policy: "fixed" momentum: 0.95 weight_decay: 0.0005 # Display every 100 iterations diff --git a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt index 4e43468a71f..065647df31b 100644 --- a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt +++ b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt @@ -5,6 +5,8 @@ test_state: { stage: 'test-on-test' } test_iter: 100 test_interval: 500 test_compute_loss: true +base_lr: 1.0 +lr_policy: "fixed" momentum: 0.95 delta: 1e-8 display: 100 diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 495cd4f159e..5fefd01e549 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -82,12 +82,12 @@ class SGDSolver : public Solver { const vector > >& history() { return history_; } protected: + void PreSolve(); Dtype GetLearningRate(); virtual void ApplyUpdate(); virtual void Normalize(int param_id); virtual void Regularize(int param_id); virtual void ComputeUpdateValue(int param_id, Dtype rate); - virtual void PreSolve(); virtual void ClipGradients(); virtual void SnapshotSolverState(const string& model_filename); virtual void SnapshotSolverStateToBinaryProto(const string& model_filename); @@ -162,19 +162,13 @@ template class AdaDeltaSolver : public SGDSolver { public: explicit AdaDeltaSolver(const SolverParameter& param) - : SGDSolver(param) { PreSolve(); constructor_sanity_check(); } + : SGDSolver(param) { AdaDeltaPreSolve(); } explicit AdaDeltaSolver(const string& param_file) - : SGDSolver(param_file) { PreSolve(); constructor_sanity_check(); } + : SGDSolver(param_file) { AdaDeltaPreSolve(); } protected: - virtual void PreSolve(); - virtual void ComputeUpdateValue(); - void constructor_sanity_check() { - CHECK_EQ(0, this->param_.base_lr()) - << "Learning rate cannot be used with AdaDelta."; - CHECK_EQ("", this->param_.lr_policy()) - << "Learning rate policy cannot be applied to AdaDelta."; - } + void AdaDeltaPreSolve(); + virtual void ComputeUpdateValue(int param_id, Dtype rate); DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver); }; diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 34a290ffe3d..78902ca0ebc 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -935,10 +935,10 @@ void RMSPropSolver::ComputeUpdateValue(int param_id, Dtype rate) { } template -void AdaDeltaSolver::PreSolve() { +void AdaDeltaSolver::AdaDeltaPreSolve() { // Add the extra history entries for AdaDelta after those from // SGDSolver::PreSolve - const vector > >& net_params = this->net_->params(); + const vector*>& net_params = this->net_->learnable_params(); for (int i = 0; i < net_params.size(); ++i) { const vector& shape = net_params[i]->shape(); this->history_.push_back( @@ -947,172 +947,134 @@ void AdaDeltaSolver::PreSolve() { } template -void AdaDeltaSolver::ComputeUpdateValue() { - const vector > >& net_params = this->net_->params(); - const vector& net_params_weight_decay = - this->net_->params_weight_decay(); +void AdaDeltaSolver::ComputeUpdateValue(int param_id, Dtype rate) { + const vector*>& net_params = this->net_->learnable_params(); + const vector& net_params_lr = this->net_->params_lr(); Dtype delta = this->param_.delta(); Dtype momentum = this->param_.momentum(); - Dtype weight_decay = this->param_.weight_decay(); - string regularization_type = this->param_.regularization_type(); + Dtype local_rate = rate * net_params_lr[param_id]; size_t update_history_offset = net_params.size(); switch (Caffe::mode()) { - case Caffe::CPU: - for (int param_id = 0; param_id < net_params.size(); ++param_id) { - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; - - if (local_decay) { - if (regularization_type == "L2") { - // add weight decay - caffe_axpy(net_params[param_id]->count(), - local_decay, - net_params[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } else if (regularization_type == "L1") { - caffe_cpu_sign(net_params[param_id]->count(), - net_params[param_id]->cpu_data(), - this->temp_[param_id]->mutable_cpu_data()); - caffe_axpy(net_params[param_id]->count(), - local_decay, - this->temp_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } else { - LOG(FATAL) << "Unknown regularization type: " << regularization_type; - } - } + case Caffe::CPU: { + // compute square of gradient in update + caffe_powx(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), Dtype(2), + this->update_[param_id]->mutable_cpu_data()); - // compute square of gradient in update - caffe_powx(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), Dtype(2), - this->update_[param_id]->mutable_cpu_data()); - - // update history of gradients - caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, - this->update_[param_id]->cpu_data(), momentum, - this->history_[param_id]->mutable_cpu_data()); - - // add delta to history to guard against dividing by zero later - caffe_set(net_params[param_id]->count(), delta, - this->temp_[param_id]->mutable_cpu_data()); - - caffe_add(net_params[param_id]->count(), - this->temp_[param_id]->cpu_data(), - this->history_[update_history_offset + param_id]->cpu_data(), - this->update_[param_id]->mutable_cpu_data()); - - caffe_add(net_params[param_id]->count(), - this->temp_[param_id]->cpu_data(), - this->history_[param_id]->cpu_data(), - this->temp_[param_id]->mutable_cpu_data()); - - // divide history of updates by history of gradients - caffe_div(net_params[param_id]->count(), - this->update_[param_id]->cpu_data(), - this->temp_[param_id]->cpu_data(), - this->update_[param_id]->mutable_cpu_data()); - - // jointly compute the RMS of both for update and gradient history - caffe_powx(net_params[param_id]->count(), - this->update_[param_id]->cpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_cpu_data()); - - // compute the update - caffe_mul(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), - this->update_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - - // compute square of update - caffe_powx(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), Dtype(2), - this->update_[param_id]->mutable_cpu_data()); - - // update history of updates - caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, - this->update_[param_id]->cpu_data(), momentum, - this->history_[update_history_offset + param_id]->mutable_cpu_data()); - } + // update history of gradients + caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->cpu_data(), momentum, + this->history_[param_id]->mutable_cpu_data()); + + // add delta to history to guard against dividing by zero later + caffe_set(net_params[param_id]->count(), delta, + this->temp_[param_id]->mutable_cpu_data()); + + caffe_add(net_params[param_id]->count(), + this->temp_[param_id]->cpu_data(), + this->history_[update_history_offset + param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + caffe_add(net_params[param_id]->count(), + this->temp_[param_id]->cpu_data(), + this->history_[param_id]->cpu_data(), + this->temp_[param_id]->mutable_cpu_data()); + + // divide history of updates by history of gradients + caffe_div(net_params[param_id]->count(), + this->update_[param_id]->cpu_data(), + this->temp_[param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + // jointly compute the RMS of both for update and gradient history + caffe_powx(net_params[param_id]->count(), + this->update_[param_id]->cpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_cpu_data()); + + // compute the update + caffe_mul(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), + this->update_[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + + // compute square of update + caffe_powx(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), Dtype(2), + this->update_[param_id]->mutable_cpu_data()); + + // update history of updates + caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->cpu_data(), momentum, + this->history_[update_history_offset + param_id]->mutable_cpu_data()); + + // apply learning rate + caffe_cpu_scale(net_params[param_id]->count(), local_rate, + net_params[param_id]->cpu_diff(), + net_params[param_id]->mutable_cpu_diff()); break; - case Caffe::GPU: + } + case Caffe::GPU: { #ifndef CPU_ONLY - for (int param_id = 0; param_id < net_params.size(); ++param_id) { - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; - - if (local_decay) { - if (regularization_type == "L2") { - // add weight decay - caffe_gpu_axpy(net_params[param_id]->count(), - local_decay, - net_params[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } else if (regularization_type == "L1") { - caffe_gpu_sign(net_params[param_id]->count(), - net_params[param_id]->gpu_data(), - this->temp_[param_id]->mutable_gpu_data()); - caffe_gpu_axpy(net_params[param_id]->count(), - local_decay, - this->temp_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } else { - LOG(FATAL) << "Unknown regularization type: " << regularization_type; - } - } + // compute square of gradient in update + caffe_gpu_powx(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), Dtype(2), + this->update_[param_id]->mutable_gpu_data()); - // compute square of gradient in update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history of gradients - caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, - this->update_[param_id]->gpu_data(), momentum, - this->history_[param_id]->mutable_gpu_data()); - - // add delta to history to guard against dividing by zero later - caffe_gpu_set(net_params[param_id]->count(), delta, - this->temp_[param_id]->mutable_gpu_data()); - - caffe_gpu_add(net_params[param_id]->count(), - this->temp_[param_id]->gpu_data(), - this->history_[update_history_offset + param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_add(net_params[param_id]->count(), - this->temp_[param_id]->gpu_data(), - this->history_[param_id]->gpu_data(), - this->temp_[param_id]->mutable_gpu_data()); - - // divide history of updates by history of gradients - caffe_gpu_div(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), - this->temp_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - // jointly compute the RMS of both for update and gradient history - caffe_gpu_powx(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_gpu_data()); - - // compute the update and copy to net_diff - caffe_gpu_mul(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), - this->update_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - - // compute square of update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history of updates - caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, - this->update_[param_id]->gpu_data(), momentum, - this->history_[update_history_offset + param_id]->mutable_gpu_data()); - } + // update history of gradients + caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->gpu_data(), momentum, + this->history_[param_id]->mutable_gpu_data()); + + // add delta to history to guard against dividing by zero later + caffe_gpu_set(net_params[param_id]->count(), delta, + this->temp_[param_id]->mutable_gpu_data()); + + caffe_gpu_add(net_params[param_id]->count(), + this->temp_[param_id]->gpu_data(), + this->history_[update_history_offset + param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_add(net_params[param_id]->count(), + this->temp_[param_id]->gpu_data(), + this->history_[param_id]->gpu_data(), + this->temp_[param_id]->mutable_gpu_data()); + + // divide history of updates by history of gradients + caffe_gpu_div(net_params[param_id]->count(), + this->update_[param_id]->gpu_data(), + this->temp_[param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + // jointly compute the RMS of both for update and gradient history + caffe_gpu_powx(net_params[param_id]->count(), + this->update_[param_id]->gpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_gpu_data()); + + // compute the update and copy to net_diff + caffe_gpu_mul(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), + this->update_[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); + + // compute square of update + caffe_gpu_powx(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), Dtype(2), + this->update_[param_id]->mutable_gpu_data()); + + // update history of updates + caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->gpu_data(), momentum, + this->history_[update_history_offset + param_id]->mutable_gpu_data()); + + // apply learning rate + caffe_gpu_scale(net_params[param_id]->count(), local_rate, + net_params[param_id]->gpu_diff(), + net_params[param_id]->mutable_gpu_diff()); #else NO_GPU; #endif break; + } default: LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); } diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 277aa3a5c8e..c97d4ede3b3 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -165,10 +165,6 @@ class GradientBasedSolverTest : public MultiDeviceTest { " bottom: 'targets' " " } " "} "; - if (learning_rate != 0) { - proto << "base_lr: " << learning_rate << " "; - proto << "lr_policy: 'fixed' "; - } if (weight_decay != 0) { proto << "weight_decay: " << weight_decay << " "; } @@ -897,6 +893,139 @@ TYPED_TEST(NesterovSolverTest, TestSnapshotShare) { } } +template +class AdaDeltaSolverTest : public GradientBasedSolverTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + virtual void InitSolver(const SolverParameter& param) { + this->solver_.reset(new AdaDeltaSolver(param)); + } + + virtual SolverParameter_SolverType solver_type() { + return SolverParameter_SolverType_ADADELTA; + } +}; + +TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices); + +TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + this->TestLeastSquaresUpdate(kLearningRate); +} + +TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.95; + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); +} + +TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.0; + const Dtype kMomentum = 0.5; + const int kNumIters = 1; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); + } +} + +TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.0; + const Dtype kMomentum = 0.95; + const int kNumIters = 1; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); + } +} + +TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.0; + const Dtype kMomentum = 0.95; + const int kNumIters = 4; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + +TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.95; + const int kNumIters = 4; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + +TYPED_TEST(AdaDeltaSolverTest, + TestAdaDeltaLeastSquaresUpdateWithEverythingShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.95; + const int kNumIters = 4; + this->share_ = true; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + +TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.95; + const int kNumIters = 4; + const int kIterSize = 2; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); +} + +TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.95; + const int kNumIters = 4; + const int kIterSize = 2; + this->share_ = true; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); +} + +TYPED_TEST(AdaDeltaSolverTest, TestSnapshot) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.95; + const int kNumIters = 4; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + } +} + +TYPED_TEST(AdaDeltaSolverTest, TestSnapshotShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 1.0; + const Dtype kWeightDecay = 0.1; + const Dtype kMomentum = 0.95; + const int kNumIters = 4; + this->share_ = true; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + } +} + template class RMSPropSolverTest : public GradientBasedSolverTest { typedef typename TypeParam::Dtype Dtype; @@ -1003,78 +1132,4 @@ TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) { } } -template -class AdaDeltaSolverTest : public GradientBasedSolverTest { - typedef typename TypeParam::Dtype Dtype; - - protected: - virtual void InitSolver(const SolverParameter& param) { - this->solver_.reset(new AdaDeltaSolver(param)); - } - - virtual SolverParameter_SolverType solver_type() { - return SolverParameter_SolverType_ADADELTA; - } -}; - -TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices); - -TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) { - typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 0.0; - this->TestLeastSquaresUpdate(kLearningRate); -} - -TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) { - typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 0.0; - const Dtype kWeightDecay = 0.5; - const Dtype kMomentum = 0.95; - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); -} - -TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) { - typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 0.0; - const Dtype kWeightDecay = 0.0; - const Dtype kMomentum = 0.5; - const int kNumIters = 1; - for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); - } -} - -TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) { - typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 0.0; - const Dtype kWeightDecay = 0.0; - const Dtype kMomentum = 0.95; - const int kNumIters = 1; - for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); - } -} - -TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { - typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 0.0; - const Dtype kWeightDecay = 0.0; - const Dtype kMomentum = 0.95; - const int kNumIters = 4; - for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); - } -} - -TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) { - typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 0.0; - const Dtype kWeightDecay = 0.1; - const Dtype kMomentum = 0.95; - const int kNumIters = 4; - for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); - } -} - } // namespace caffe From 918c2aca42a0e9f87dab6f7b40d648f2c41321cb Mon Sep 17 00:00:00 2001 From: mfigurnov Date: Mon, 10 Aug 2015 13:59:35 +0300 Subject: [PATCH 197/446] Fix truncation of value warning --- src/caffe/common.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp index af96cac40aa..b40760efe16 100644 --- a/src/caffe/common.cpp +++ b/src/caffe/common.cpp @@ -1,4 +1,5 @@ #include +#include #include #include @@ -25,7 +26,7 @@ int64_t cluster_seedgen(void) { pid = getpid(); s = time(NULL); - seed = abs(((s * 181) * ((pid - 83) * 359)) % 104729); + seed = std::abs(((s * 181) * ((pid - 83) * 359)) % 104729); return seed; } From dde32b4ee631a647776e33ea92096558a2b37cbd Mon Sep 17 00:00:00 2001 From: Russell Stewart Date: Mon, 10 Aug 2015 13:50:01 -0700 Subject: [PATCH 198/446] Update net_spec.py --- python/caffe/net_spec.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py index 31cde7ad946..77a0e0070ae 100644 --- a/python/caffe/net_spec.py +++ b/python/caffe/net_spec.py @@ -1,7 +1,7 @@ """Python net specification. This module provides a way to write nets directly in Python, using a natural, -functional style. See examples/python_nets/caffenet.py for an example. +functional style. See examples/pycaffe/caffenet.py for an example. Currently this works as a thin wrapper around the Python protobuf interface, with layers and parameters automatically generated for the "layers" and From bd2591e31e53e159c84874d26dcfe4a45d3c904e Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Tue, 11 Aug 2015 13:18:41 -0700 Subject: [PATCH 199/446] fix for learnable_param_ids_ --- src/caffe/net.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 0e5ed804b73..91883a10a0a 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -485,6 +485,7 @@ void Net::AppendParam(const NetParameter& param, const int layer_id, CHECK(this_blob->shape() == owner_blob->shape()); } const int learnable_param_id = learnable_param_ids_[owner_net_param_id]; + learnable_param_ids_.push_back(learnable_param_id); if (param_spec->has_lr_mult()) { if (has_params_lr_[learnable_param_id]) { CHECK_EQ(param_spec->lr_mult(), params_lr_[learnable_param_id]) From 0d34d5ba0fbdc09ac8f372cb581ccaec599f10bc Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Tue, 11 Aug 2015 21:38:06 -0700 Subject: [PATCH 200/446] Data Layers Parallel for Multi-GPU Allow data layers (and also PythonLayer when used as data layer) to be shared among worker solver's training net, and also test net for future-proof if one wants to do Multi-GPU testing. Data layers are locked during forward to ensure sequential forward. --- include/caffe/data_layers.hpp | 11 ++++++++++- include/caffe/layer.hpp | 15 +++++++++++++++ include/caffe/net.hpp | 8 +++++--- include/caffe/python_layer.hpp | 4 ++++ include/caffe/solver.hpp | 14 ++++++++++---- src/caffe/net.cpp | 32 ++++++++++++++++++++++++++++---- src/caffe/parallel.cpp | 3 +-- src/caffe/proto/caffe.proto | 4 ++++ src/caffe/solver.cpp | 23 +++++++++++++++++------ 9 files changed, 94 insertions(+), 20 deletions(-) diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp index 12e6c366620..552d814131e 100644 --- a/include/caffe/data_layers.hpp +++ b/include/caffe/data_layers.hpp @@ -34,6 +34,8 @@ class BaseDataLayer : public Layer { // This method may not be overridden except by the BasePrefetchingDataLayer. virtual void LayerSetUp(const vector*>& bottom, const vector*>& top); + // Data layers should be shared by multiple solvers in parallel + virtual inline bool ShareInParallel() const { return true; } virtual void DataLayerSetUp(const vector*>& bottom, const vector*>& top) {} // Data layers have no bottoms, so reshaping is trivial. @@ -94,7 +96,8 @@ class DataLayer : public BasePrefetchingDataLayer { virtual ~DataLayer(); virtual void DataLayerSetUp(const vector*>& bottom, const vector*>& top); - + // DataLayer uses DataReader instead for sharing for parallelism + virtual inline bool ShareInParallel() const { return false; } virtual inline const char* type() const { return "Data"; } virtual inline int ExactNumBottomBlobs() const { return 0; } virtual inline int MinTopBlobs() const { return 1; } @@ -118,6 +121,8 @@ class DummyDataLayer : public Layer { : Layer(param) {} virtual void LayerSetUp(const vector*>& bottom, const vector*>& top); + // Data layers should be shared by multiple solvers in parallel + virtual inline bool ShareInParallel() const { return true; } // Data layers have no bottoms, so reshaping is trivial. virtual void Reshape(const vector*>& bottom, const vector*>& top) {} @@ -151,6 +156,8 @@ class HDF5DataLayer : public Layer { virtual ~HDF5DataLayer(); virtual void LayerSetUp(const vector*>& bottom, const vector*>& top); + // Data layers should be shared by multiple solvers in parallel + virtual inline bool ShareInParallel() const { return true; } // Data layers have no bottoms, so reshaping is trivial. virtual void Reshape(const vector*>& bottom, const vector*>& top) {} @@ -192,6 +199,8 @@ class HDF5OutputLayer : public Layer { virtual ~HDF5OutputLayer(); virtual void LayerSetUp(const vector*>& bottom, const vector*>& top); + // Data layers should be shared by multiple solvers in parallel + virtual inline bool ShareInParallel() const { return true; } // Data layers have no bottoms, so reshaping is trivial. virtual void Reshape(const vector*>& bottom, const vector*>& top) {} diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp index 0771b6a8fb4..d82197a9c29 100644 --- a/include/caffe/layer.hpp +++ b/include/caffe/layer.hpp @@ -1,6 +1,7 @@ #ifndef CAFFE_LAYER_H_ #define CAFFE_LAYER_H_ +#include #include #include #include @@ -85,6 +86,14 @@ class Layer { virtual void LayerSetUp(const vector*>& bottom, const vector*>& top) {} + /** + * @brief Whether a layer should be shared by multiple nets during data + * parallelism. By default, all layers except for data layers should + * not be shared. data layers should be shared to ensure each worker + * solver access data sequentially during data parallelism. + */ + virtual inline bool ShareInParallel() const { return false; } + /** * @brief Adjust the shapes of top blobs and internal buffers to accommodate * the shapes of the bottom blobs. @@ -396,6 +405,10 @@ class Layer { } } + private: + // mutex to lock layer to ensure sequential forward + boost::mutex forward_mutex_; + DISABLE_COPY_AND_ASSIGN(Layer); }; // class Layer @@ -405,6 +418,8 @@ class Layer { template inline Dtype Layer::Forward(const vector*>& bottom, const vector*>& top) { + // Lock during forward to ensure sequential forward + boost::mutex::scoped_lock lock(forward_mutex_); Dtype loss = 0; Reshape(bottom, top); switch (Caffe::mode()) { diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index bf997553ee2..1bf07d28d13 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -23,8 +23,9 @@ namespace caffe { template class Net { public: - explicit Net(const NetParameter& param); - explicit Net(const string& param_file, Phase phase); + explicit Net(const NetParameter& param, const Net* root_net = NULL); + explicit Net(const string& param_file, Phase phase, + const Net* root_net = NULL); virtual ~Net() {} /// @brief Initialize a network with a NetParameter. @@ -291,7 +292,8 @@ class Net { size_t memory_used_; /// Whether to compute and display debug info for the net. bool debug_info_; - + /// The root net that actually holds the shared layers in data parallelism + const Net* const root_net_; DISABLE_COPY_AND_ASSIGN(Net); }; diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp index 2957e7426be..c43c1e8a91b 100644 --- a/include/caffe/python_layer.hpp +++ b/include/caffe/python_layer.hpp @@ -27,6 +27,10 @@ class PythonLayer : public Layer { self_.attr("reshape")(bottom, top); } + virtual inline bool ShareInParallel() const { + return this->layer_param_.python_param().share_in_parallel(); + } + virtual inline const char* type() const { return "Python"; } protected: diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 89a6c76d5f7..f583324acef 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -17,8 +17,9 @@ namespace caffe { template class Solver { public: - explicit Solver(const SolverParameter& param); - explicit Solver(const string& param_file); + explicit Solver(const SolverParameter& param, + const Solver* root_solver = NULL); + explicit Solver(const string& param_file, const Solver* root_solver = NULL); void Init(const SolverParameter& param); void InitTrainNet(); void InitTestNets(); @@ -79,6 +80,10 @@ class Solver { vector > > test_nets_; vector callbacks_; + // The root solver that holds root nets (actually containing shared layers) + // in data parallelism + const Solver* const root_solver_; + DISABLE_COPY_AND_ASSIGN(Solver); }; @@ -89,8 +94,9 @@ class Solver { template class WorkerSolver : public Solver { public: - explicit WorkerSolver(const SolverParameter& param) - : Solver(param) {} + explicit WorkerSolver(const SolverParameter& param, + const Solver* root_solver = NULL) + : Solver(param, root_solver) {} protected: void ApplyUpdate() {} diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 5d0f4322d19..14f8385c6ee 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -22,12 +22,14 @@ namespace caffe { template -Net::Net(const NetParameter& param) { +Net::Net(const NetParameter& param, const Net* root_net) + : root_net_(root_net) { Init(param); } template -Net::Net(const string& param_file, Phase phase) { +Net::Net(const string& param_file, Phase phase, const Net* root_net) + : root_net_(root_net) { NetParameter param; ReadNetParamsFromTextFileOrDie(param_file, ¶m); param.mutable_state()->set_phase(phase); @@ -36,6 +38,8 @@ Net::Net(const string& param_file, Phase phase) { template void Net::Init(const NetParameter& in_param) { + CHECK(Caffe::root_solver() || root_net_) + << "root_net_ needs to be set for all non-root solvers"; // Set phase from the state. phase_ = in_param.state().phase(); // Filter layers based on their include/exclude rules and @@ -79,6 +83,9 @@ void Net::Init(const NetParameter& in_param) { top_id_vecs_.resize(param.layer_size()); bottom_need_backward_.resize(param.layer_size()); for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) { + // For non-root solvers, whether this layer is shared from root_net_. + bool is_shared_layer = !Caffe::root_solver() + && root_net_->layers_[layer_id]->ShareInParallel(); // Inherit phase from net if unset. if (!param.layer(layer_id).has_phase()) { param.mutable_layer(layer_id)->set_phase(phase_); @@ -91,7 +98,12 @@ void Net::Init(const NetParameter& in_param) { << "propagate_down param must be specified " << "either 0 or bottom_size times "; } - layers_.push_back(LayerRegistry::CreateLayer(layer_param)); + if (is_shared_layer) { + LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net"; + layers_.push_back(root_net_->layers_[layer_id]); + } else { + layers_.push_back(LayerRegistry::CreateLayer(layer_param)); + } layer_names_.push_back(layer_param.name()); if (Caffe::root_solver()) { LOG(INFO) << "Creating Layer " << layer_param.name(); @@ -125,10 +137,22 @@ void Net::Init(const NetParameter& in_param) { } } // After this layer is connected, set it up. + if (is_shared_layer) { + // Set up size of top blobs using root_net_ + const vector*>& base_top = root_net_->top_vecs_[layer_id]; + const vector*>& this_top = this->top_vecs_[layer_id]; + for (int top_id = 0; top_id < base_top.size(); ++top_id) { + this_top[top_id]->ReshapeLike(*base_top[top_id]); + LOG(INFO) << "Created top blob " << top_id << " (shape: " + << this_top[top_id]->shape_string() << ") for shared layer " + << layer_param.name(); + } + } else { + layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]); + } if (Caffe::root_solver()) { LOG(INFO) << "Setting up " << layer_names_[layer_id]; } - layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]); for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) { if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) { blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0)); diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp index 5a08df6c1c8..6e7d802bb99 100644 --- a/src/caffe/parallel.cpp +++ b/src/caffe/parallel.cpp @@ -218,7 +218,7 @@ P2PSync::P2PSync(shared_ptr > root_solver, solver_ = root_solver; } else { Caffe::set_root_solver(false); - solver_.reset(new WorkerSolver(param)); + solver_.reset(new WorkerSolver(param, root_solver.get())); Caffe::set_root_solver(true); } this->configure(solver_.get()); @@ -436,4 +436,3 @@ INSTANTIATE_CLASS(GPUParams); INSTANTIATE_CLASS(P2PSync); } // namespace caffe - diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 41165410f33..e78c6686049 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -740,6 +740,10 @@ message PythonParameter { // string, dictionary in Python dict format, JSON, etc. You may parse this // string in `setup` method and use it in `forward` and `backward`. optional string param_str = 3 [default = '']; + // Whether this PythonLayer is shared among worker solvers during data parallelism. + // If true, each worker solver sequentially run forward from this layer. + // This value should be set true if you are using it as a data layer. + optional bool share_in_parallel = 4 [default = false]; } // Message that stores parameters used by ReductionLayer diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index b6fd6b642f1..a44ff88dfd6 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -18,14 +18,14 @@ namespace caffe { template -Solver::Solver(const SolverParameter& param) - : net_(), callbacks_() { +Solver::Solver(const SolverParameter& param, const Solver* root_solver) + : net_(), callbacks_(), root_solver_(root_solver) { Init(param); } template -Solver::Solver(const string& param_file) - : net_(), callbacks_() { +Solver::Solver(const string& param_file, const Solver* root_solver) + : net_(), callbacks_(), root_solver_(root_solver) { SolverParameter param; ReadProtoFromTextFileOrDie(param_file, ¶m); Init(param); @@ -33,6 +33,8 @@ Solver::Solver(const string& param_file) template void Solver::Init(const SolverParameter& param) { + CHECK(Caffe::root_solver() || root_solver_) + << "root_solver_ needs to be set for all non-root solvers"; LOG_IF(INFO, Caffe::root_solver()) << "Initializing solver from parameters: " << std::endl << param.DebugString(); param_ = param; @@ -88,7 +90,11 @@ void Solver::InitTrainNet() { net_state.MergeFrom(net_param.state()); net_state.MergeFrom(param_.train_state()); net_param.mutable_state()->CopyFrom(net_state); - net_.reset(new Net(net_param)); + if (Caffe::root_solver()) { + net_.reset(new Net(net_param)); + } else { + net_.reset(new Net(net_param, root_solver_->net_.get())); + } } template @@ -163,7 +169,12 @@ void Solver::InitTestNets() { net_params[i].mutable_state()->CopyFrom(net_state); LOG(INFO) << "Creating test net (#" << i << ") specified by " << sources[i]; - test_nets_[i].reset(new Net(net_params[i])); + if (Caffe::root_solver()) { + test_nets_[i].reset(new Net(net_params[i])); + } else { + test_nets_[i].reset(new Net(net_params[i], + root_solver_->test_nets_[i].get())); + } test_nets_[i]->set_debug_info(param_.debug_info()); } } From 6b50ed6fc1897ce1ccd673cf0287788b38b58a6d Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Wed, 12 Aug 2015 12:05:56 -0700 Subject: [PATCH 201/446] Apply mutex only to shared layers and fix NVCC warning --- include/caffe/layer.hpp | 43 ++++++++++++++++++++++++++++++++++++----- src/caffe/layer.cpp | 27 ++++++++++++++++++++++++++ src/caffe/net.cpp | 7 ++++--- 3 files changed, 69 insertions(+), 8 deletions(-) create mode 100644 src/caffe/layer.cpp diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp index d82197a9c29..a0d1d4ecc94 100644 --- a/include/caffe/layer.hpp +++ b/include/caffe/layer.hpp @@ -1,7 +1,6 @@ #ifndef CAFFE_LAYER_H_ #define CAFFE_LAYER_H_ -#include #include #include #include @@ -12,6 +11,12 @@ #include "caffe/proto/caffe.pb.h" #include "caffe/util/device_alternate.hpp" +/** + Forward declare boost::thread instead of including boost/thread.hpp + to avoid a boost/NVCC issues (#1009, #1010) on OSX. + */ +namespace boost { class mutex; } + namespace caffe { /** @@ -33,7 +38,7 @@ class Layer { * layer. */ explicit Layer(const LayerParameter& param) - : layer_param_(param) { + : layer_param_(param), is_shared_(false) { // Set phase and copy blobs (if there are any). phase_ = param.phase(); if (layer_param_.blobs_size() > 0) { @@ -61,6 +66,7 @@ class Layer { */ void SetUp(const vector*>& bottom, const vector*>& top) { + InitMutex(); CheckBlobCounts(bottom, top); LayerSetUp(bottom, top); Reshape(bottom, top); @@ -94,6 +100,22 @@ class Layer { */ virtual inline bool ShareInParallel() const { return false; } + /** @brief Return whether this layer is actually shared by other nets. + * If ShareInParallel() is true and using more than one GPU and the + * net has TRAIN phase, then this function is expected return true. + */ + inline bool IsShared() const { return is_shared_; } + + /** @brief Set whether this layer is actually shared by other nets + * If ShareInParallel() is true and using more than one GPU and the + * net has TRAIN phase, then is_shared should be set true. + */ + inline void SetShared(bool is_shared) { + CHECK(ShareInParallel() || !is_shared) + << type() << "Layer does not support sharing."; + is_shared_ = is_shared; + } + /** * @brief Adjust the shapes of top blobs and internal buffers to accommodate * the shapes of the bottom blobs. @@ -406,8 +428,18 @@ class Layer { } private: - // mutex to lock layer to ensure sequential forward - boost::mutex forward_mutex_; + /** Whether this layer is actually shared by other nets*/ + bool is_shared_; + + /** The mutex for sequential forward if this layer is shared */ + shared_ptr forward_mutex_; + + /** Initialize forward_mutex_ */ + void InitMutex(); + /** Lock forward_mutex_ if this layer is shared */ + void Lock(); + /** Unlock forward_mutex_ if this layer is shared */ + void Unlock(); DISABLE_COPY_AND_ASSIGN(Layer); }; // class Layer @@ -419,7 +451,7 @@ template inline Dtype Layer::Forward(const vector*>& bottom, const vector*>& top) { // Lock during forward to ensure sequential forward - boost::mutex::scoped_lock lock(forward_mutex_); + Lock(); Dtype loss = 0; Reshape(bottom, top); switch (Caffe::mode()) { @@ -450,6 +482,7 @@ inline Dtype Layer::Forward(const vector*>& bottom, default: LOG(FATAL) << "Unknown caffe mode."; } + Unlock(); return loss; } diff --git a/src/caffe/layer.cpp b/src/caffe/layer.cpp new file mode 100644 index 00000000000..3b9128986ae --- /dev/null +++ b/src/caffe/layer.cpp @@ -0,0 +1,27 @@ +#include +#include "caffe/layer.hpp" + +namespace caffe { + +template +void Layer::InitMutex() { + forward_mutex_.reset(new boost::mutex()); +} + +template +void Layer::Lock() { + if (IsShared()) { + forward_mutex_->lock(); + } +} + +template +void Layer::Unlock() { + if (IsShared()) { + forward_mutex_->unlock(); + } +} + +INSTANTIATE_CLASS(Layer); + +} // namespace caffe diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 14f8385c6ee..7f5bdf7e2ba 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -84,7 +84,7 @@ void Net::Init(const NetParameter& in_param) { bottom_need_backward_.resize(param.layer_size()); for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) { // For non-root solvers, whether this layer is shared from root_net_. - bool is_shared_layer = !Caffe::root_solver() + bool share_from_root = !Caffe::root_solver() && root_net_->layers_[layer_id]->ShareInParallel(); // Inherit phase from net if unset. if (!param.layer(layer_id).has_phase()) { @@ -98,9 +98,10 @@ void Net::Init(const NetParameter& in_param) { << "propagate_down param must be specified " << "either 0 or bottom_size times "; } - if (is_shared_layer) { + if (share_from_root) { LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net"; layers_.push_back(root_net_->layers_[layer_id]); + layers_[layer_id]->SetShared(true); } else { layers_.push_back(LayerRegistry::CreateLayer(layer_param)); } @@ -137,7 +138,7 @@ void Net::Init(const NetParameter& in_param) { } } // After this layer is connected, set it up. - if (is_shared_layer) { + if (share_from_root) { // Set up size of top blobs using root_net_ const vector*>& base_top = root_net_->top_vecs_[layer_id]; const vector*>& this_top = this->top_vecs_[layer_id]; From 3c6485a95e2c5653c601f07fd7f5875cf956f3e6 Mon Sep 17 00:00:00 2001 From: Ajinkya Kale Date: Thu, 13 Aug 2015 17:10:46 -0700 Subject: [PATCH 202/446] fixing the database param The example talks about LevelDB as the db backend but has lmdb as the param in the execution. --- examples/feature_extraction/readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md index a980b8b3203..f3ec3609859 100644 --- a/examples/feature_extraction/readme.md +++ b/examples/feature_extraction/readme.md @@ -51,7 +51,7 @@ Extract Features Now everything necessary is in place. - ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 lmdb + ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 leveldb The name of feature blob that you extract is `fc7`, which represents the highest level feature of the reference model. We can use any other layer, as well, such as `conv5` or `pool3`. From 4c7e58e201ab2a06bb3b08d7c148e3b67988f853 Mon Sep 17 00:00:00 2001 From: PatWie Date: Fri, 14 Aug 2015 13:06:13 +0200 Subject: [PATCH 203/446] information about new implemented solvers --- docs/tutorial/solver.md | 79 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 78 insertions(+), 1 deletion(-) diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md index 17f793ef778..b150f6487bc 100644 --- a/docs/tutorial/solver.md +++ b/docs/tutorial/solver.md @@ -6,7 +6,14 @@ title: Solver / Model Optimization The solver orchestrates model optimization by coordinating the network's forward inference and backward gradients to form parameter updates that attempt to improve the loss. The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients. -The Caffe solvers are Stochastic Gradient Descent (SGD), Adaptive Gradient (ADAGRAD), and Nesterov's Accelerated Gradient (NESTEROV). +The Caffe solvers are: + +- Stochastic Gradient Descent (`SGD`), +- AdaDelta (`ADADELTA`), +- Adaptive Gradient (`ADAGRAD`), +- Adam (`ADAM`), +- Nesterov's Accelerated Gradient (`NESTEROV`) and +- RMSprop (`RMSPROP`) The solver @@ -104,6 +111,32 @@ If learning diverges (e.g., you start to see very large or `NaN` or `inf` loss v [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). *Advances in Neural Information Processing Systems*, 2012. +### AdaDelta + +The **AdaDelta** (`solver_type: ADADELTA`) method (M. Zeiler [1]) is a "robust learning rate method". It is a gradient-based optimization method (like SGD). The update formulas are + +$$ +\begin{align} +(v_t)_i &= \frac{\operatorname{RMS}((v_{t-1})_i)}{\operatorname{RMS}\left( \nabla L(W_t) \right)_{i}} \left( \nabla L(W_{t'}) \right)_i +\\ +\operatorname{RMS}\left( \nabla L(W_t) \right)_{i} &= \sqrt{E[g^2] + \varepsilon} +\\ +E[g^2]_t &= \delta{E[g^2]_{t-1} } + (1-\delta)g_{t}^2 +\end{align} +$$ + +and + +$$ +(W_{t+1})_i = +(W_t)_i - \alpha +(v_t)_i. +$$ + +[1] M. Zeiler + [ADADELTA: AN ADAPTIVE LEARNING RATE METHOD](http://arxiv.org/pdf/1212.5701.pdf). + *arXiv preprint*, 2012. + ### AdaGrad The **adaptive gradient** (`solver_type: ADAGRAD`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words. @@ -124,6 +157,28 @@ Note that in practice, for weights $$ W \in \mathcal{R}^d $$, AdaGrad implementa [Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://www.magicbroom.info/Papers/DuchiHaSi10.pdf). *The Journal of Machine Learning Research*, 2011. +### Adam + +The **Adam** (`solver_type: ADAM`), proposed in Kingma et al. [1], is a gradient-based optimization method (like SGD). This includes an "adaptive moment estimation" ($$m_t, v_t$$) and can be regarded as a generalization of AdaGrad. The update formulas are + +$$ +(m_t)_i = \beta_1 (m_{t-1})_i + (1-\beta_1)(\nabla L(W_t))_i,\\ +(v_t)_i = \beta_2 (v_{t-1})_i + (1-\beta_2)(\nabla L(W_t))_i^2 +$$ + +and + +$$ +(W_{t+1})_i = +(W_t)_i - \alpha \frac{\sqrt{1-(\beta_2)_i^t}}{1-(\beta_1)_i^t}\frac{(m_t)_i}{\sqrt{(v_t)_i}+\varepsilon}. +$$ + +Kingma et al. [1] proposed to use $$\beta_1 = 0.9, \beta_2 = 0.999, \varepsilon = 10^{-8}$$ as default values. Caffe uses the values of `momemtum, momentum2, delta` for $$\beta_1, \beta_2, \varepsilon$$, respectively. + +[1] D. Kingma, J. Ba. + [Adam: A Method for Stochastic Optimization](http://arxiv.org/abs/1412.6980). + *International Conference for Learning Representations*, 2015. + ### NAG **Nesterov's accelerated gradient** (`solver_type: NESTEROV`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$. @@ -149,6 +204,28 @@ What distinguishes the method from SGD is the weight setting $$ W $$ on which we [On the Importance of Initialization and Momentum in Deep Learning](http://www.cs.toronto.edu/~fritz/absps/momentum.pdf). *Proceedings of the 30th International Conference on Machine Learning*, 2013. +### RMSprop + +The **RMSprop** (`solver_type: RMSPROP`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are + +$$ +(v_t)_i = +\begin{cases} +(v_{t-1})_i + \delta, &(\nabla L(W_t))_i(\nabla L(W_{t-1}))_i > 0\\ +(v_{t-1})_i \cdot (1-\delta), & \text{else} +\end{cases} +$$ + +$$ +(W_{t+1})_i =(W_t)_i - \alpha (v_t)_i, +$$ + +If the gradient updates results in oscillations the gradient is reduced by times $$1-\delta$$. Otherwise it will be increased by $$\delta$$. The default value of $$\delta$$ (`rms_decay`) is set to $$\delta = 0.02$$. + +[1] T. Tieleman, and G. Hinton. + [RMSProp: Divide the gradient by a running average of its recent magnitude](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf). + *COURSERA: Neural Networks for Machine Learning.Technical report*, 2012. + ## Scaffolding The solver scaffolding prepares the optimization method and initializes the model to be learned in `Solver::Presolve()`. From e696f85abe2c50107fbb67b211bf7dad0f87ade0 Mon Sep 17 00:00:00 2001 From: LI Yi Date: Fri, 14 Aug 2015 20:51:45 +0800 Subject: [PATCH 204/446] Destroy CUDA stream when finished --- src/caffe/layers/base_data_layer.cpp | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp index 20f76f62994..5303fe9c5d3 100644 --- a/src/caffe/layers/base_data_layer.cpp +++ b/src/caffe/layers/base_data_layer.cpp @@ -74,7 +74,7 @@ void BasePrefetchingDataLayer::InternalThreadEntry() { #ifndef CPU_ONLY cudaStream_t stream; if (Caffe::mode() == Caffe::GPU) { - cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking); + CUDA_CHECK(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking)); } #endif @@ -85,7 +85,7 @@ void BasePrefetchingDataLayer::InternalThreadEntry() { #ifndef CPU_ONLY if (Caffe::mode() == Caffe::GPU) { batch->data_.data().get()->async_gpu_push(stream); - cudaStreamSynchronize(stream); + CUDA_CHECK(cudaStreamSynchronize(stream)); } #endif prefetch_full_.push(batch); @@ -93,6 +93,11 @@ void BasePrefetchingDataLayer::InternalThreadEntry() { } catch (boost::thread_interrupted&) { // Interrupted exception is expected on shutdown } +#ifndef CPU_ONLY + if (Caffe::mode() == Caffe::GPU) { + CUDA_CHECK(cudaStreamDestroy(stream)); + } +#endif } template From 4e4c89b026cb0b7f296aae4dfbb45e2eb1654f43 Mon Sep 17 00:00:00 2001 From: PatWie Date: Mon, 3 Aug 2015 17:31:14 +0200 Subject: [PATCH 205/446] Adam solver This commit implements the Adam solver by Kingma et. al for CPU and GPU. All solver parameters are defined in the caffe.proto. This also adds an example for the MNIST dataset. --- examples/mnist/lenet_solver_adam.prototxt | 26 +++ examples/mnist/train_lenet_adam.sh | 3 + include/caffe/solver.hpp | 17 ++ src/caffe/proto/caffe.proto | 7 +- src/caffe/solver.cpp | 104 +++++++++++ src/caffe/test/test_gradient_based_solver.cpp | 170 +++++++++++++++--- 6 files changed, 299 insertions(+), 28 deletions(-) create mode 100644 examples/mnist/lenet_solver_adam.prototxt create mode 100755 examples/mnist/train_lenet_adam.sh diff --git a/examples/mnist/lenet_solver_adam.prototxt b/examples/mnist/lenet_solver_adam.prototxt new file mode 100644 index 00000000000..d22c5718f3f --- /dev/null +++ b/examples/mnist/lenet_solver_adam.prototxt @@ -0,0 +1,26 @@ +# The train/test net protocol buffer definition +# this follows "ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION" +net: "examples/mnist/lenet_train_test.prototxt" +# test_iter specifies how many forward passes the test should carry out. +# In the case of MNIST, we have test batch size 100 and 100 test iterations, +# covering the full 10,000 testing images. +test_iter: 100 +# Carry out testing every 500 training iterations. +test_interval: 500 +# All parameters are from the cited paper above +base_lr: 0.001 +momentum: 0.9 +momentum2: 0.999 +# since Adam dynamically changes the learning rate, we set the base learning +# rate to a fixed value +lr_policy: "fixed" +# Display every 100 iterations +display: 100 +# The maximum number of iterations +max_iter: 10000 +# snapshot intermediate results +snapshot: 5000 +snapshot_prefix: "examples/mnist/lenet" +# solver mode: CPU or GPU +solver_type: ADAM +solver_mode: GPU diff --git a/examples/mnist/train_lenet_adam.sh b/examples/mnist/train_lenet_adam.sh new file mode 100755 index 00000000000..a32ecf2d9c2 --- /dev/null +++ b/examples/mnist/train_lenet_adam.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env sh + +./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index d2b99923f23..582aa1427d3 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -217,6 +217,21 @@ class AdaDeltaSolver : public SGDSolver { DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver); }; +template +class AdamSolver : public SGDSolver { + public: + explicit AdamSolver(const SolverParameter& param) + : SGDSolver(param) { AdamPreSolve();} + explicit AdamSolver(const string& param_file) + : SGDSolver(param_file) { AdamPreSolve(); } + + protected: + void AdamPreSolve(); + virtual void ComputeUpdateValue(int param_id, Dtype rate); + + DISABLE_COPY_AND_ASSIGN(AdamSolver); +}; + template Solver* GetSolver(const SolverParameter& param) { SolverParameter_SolverType type = param.solver_type(); @@ -232,6 +247,8 @@ Solver* GetSolver(const SolverParameter& param) { return new RMSPropSolver(param); case SolverParameter_SolverType_ADADELTA: return new AdaDeltaSolver(param); + case SolverParameter_SolverType_ADAM: + return new AdamSolver(param); default: LOG(FATAL) << "Unknown SolverType: " << type; } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index fc0d961abda..d4c97d2bd06 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -98,7 +98,7 @@ message NetParameter { // NOTE // Update the next available ID when you add a new SolverParameter field. // -// SolverParameter next available ID: 39 (last added: rms_decay) +// SolverParameter next available ID: 40 (last added: momentum2) message SolverParameter { ////////////////////////////////////////////////////////////////////////////// // Specifying the train and test networks @@ -216,10 +216,13 @@ message SolverParameter { ADAGRAD = 2; RMSPROP = 3; ADADELTA = 4; + ADAM = 5; } optional SolverType solver_type = 30 [default = SGD]; - // numerical stability for AdaGrad + // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam optional float delta = 31 [default = 1e-8]; + // parameters for the Adam solver + optional float momentum2 = 39 [default = 0.999]; // RMSProp decay value // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t) diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 248f238eb76..9348e11c249 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -1114,11 +1114,115 @@ void AdaDeltaSolver::ComputeUpdateValue(int param_id, Dtype rate) { } } +template +void AdamSolver::AdamPreSolve() { + // Add the extra history entries for Adam after those from + // SGDSolver::PreSolve + const vector*>& net_params = this->net_->learnable_params(); + for (int i = 0; i < net_params.size(); ++i) { + const vector& shape = net_params[i]->shape(); + this->history_.push_back( + shared_ptr >(new Blob(shape))); + } +} + +template +void AdamSolver::ComputeUpdateValue(int param_id, Dtype rate) { + const vector*>& net_params = this->net_->learnable_params(); + const vector& net_params_lr = this->net_->params_lr(); + Dtype local_rate = rate * net_params_lr[param_id]; + const Dtype beta1 = this->param_.momentum(); + const Dtype beta2 = this->param_.momentum2(); + + // we create aliases for convenience + size_t update_history_offset = net_params.size(); + Blob* val_m = this->history_[param_id].get(); + Blob* val_v = this->history_[param_id + update_history_offset].get(); + Blob* val_t = this->temp_[param_id].get(); + + const int t = this->iter_ + 1; + const Dtype correction = std::sqrt(Dtype(1) - pow(beta2, t)) / + (Dtype(1.) - pow(beta1, t)); + const int N = net_params[param_id]->count(); + const Dtype eps_hat = this->param_.delta(); + + switch (Caffe::mode()) { + case Caffe::CPU: { + // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t + caffe_cpu_axpby(N, Dtype(1)-beta1, + net_params[param_id]->cpu_diff(), beta1, + val_m->mutable_cpu_data()); + + // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2 + caffe_mul(N, + net_params[param_id]->cpu_diff(), + net_params[param_id]->cpu_diff(), + val_t->mutable_cpu_data()); + caffe_cpu_axpby(N, Dtype(1)-beta2, + val_t->cpu_data(), beta2, + val_v->mutable_cpu_data()); + + // set update + caffe_powx(N, + val_v->cpu_data(), Dtype(0.5), + val_t->mutable_cpu_data()); + caffe_add_scalar(N, eps_hat, val_t->mutable_cpu_data()); + caffe_div(N, + val_m->cpu_data(), + val_t->cpu_data(), + val_t->mutable_cpu_data()); + + caffe_cpu_scale(N, local_rate*correction, + val_t->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + break; + } + case Caffe::GPU: { +#ifndef CPU_ONLY + // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t + caffe_gpu_axpby(N, Dtype(1)-beta1, + net_params[param_id]->gpu_diff(), beta1, + val_m->mutable_gpu_data()); + + // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2 + caffe_gpu_mul(N, + net_params[param_id]->gpu_diff(), + net_params[param_id]->gpu_diff(), + val_t->mutable_gpu_data()); + caffe_gpu_axpby(N, Dtype(1)-beta2, + val_t->gpu_data(), beta2, + val_v->mutable_gpu_data()); + + // set update + caffe_gpu_powx(N, + val_v->gpu_data(), Dtype(0.5), + val_t->mutable_gpu_data()); + caffe_gpu_add_scalar(N, eps_hat, + val_t->mutable_gpu_data()); + caffe_gpu_div(N, + val_m->gpu_data(), + val_t->gpu_data(), + val_t->mutable_gpu_data()); + + caffe_gpu_scale(N, local_rate*correction, + val_t->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + } + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + INSTANTIATE_CLASS(Solver); INSTANTIATE_CLASS(SGDSolver); INSTANTIATE_CLASS(NesterovSolver); INSTANTIATE_CLASS(AdaGradSolver); INSTANTIATE_CLASS(RMSPropSolver); INSTANTIATE_CLASS(AdaDeltaSolver); +INSTANTIATE_CLASS(AdamSolver); } // namespace caffe diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 1d255a86621..dcbfff1cad2 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -42,7 +42,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { // TODO this is brittle and the hdf5 file should be checked instead. int num_, channels_, height_, width_; bool share_; - Dtype delta_; // Stability constant for AdaGrad. + Dtype delta_; // Stability constant for RMSProp, AdaGrad, AdaDelta and Adam // Test data: check out generate_sample_data.py in the same directory. string* input_file_; @@ -65,10 +65,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode(); } InitSolver(param); - delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD || - solver_type() == SolverParameter_SolverType_RMSPROP || - solver_type() == SolverParameter_SolverType_ADADELTA) ? - param.delta() : 0; + delta_ = param.delta(); } string RunLeastSquaresSolver(const Dtype learning_rate, @@ -216,7 +213,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { // updated_params will store the updated weight and bias results, // using the blobs' diffs to hold the update values themselves. void ComputeLeastSquaresUpdate(const Dtype learning_rate, - const Dtype weight_decay, const Dtype momentum, + const Dtype weight_decay, const Dtype momentum, const int num_iters, vector > >* updated_params) { const int N = num_; const int D = channels_ * height_ * width_; @@ -282,7 +279,8 @@ class GradientBasedSolverTest : public MultiDeviceTest { ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]); // Finally, compute update. const vector > >& history = solver_->history(); - if (solver_type() != SolverParameter_SolverType_ADADELTA) { + if (solver_type() != SolverParameter_SolverType_ADADELTA + && solver_type() != SolverParameter_SolverType_ADAM) { ASSERT_EQ(2, history.size()); // 1 blob for weights, 1 for bias } else { ASSERT_EQ(4, history.size()); // additional blobs for update history @@ -312,16 +310,31 @@ class GradientBasedSolverTest : public MultiDeviceTest { case SolverParameter_SolverType_ADADELTA: { const Dtype update_history_value = (i == D) ? - history[3]->cpu_data()[0] : history[2]->cpu_data()[i]; + history[1 + num_param_blobs]->cpu_data()[0] : + history[0 + num_param_blobs]->cpu_data()[i]; const Dtype weighted_gradient_average = momentum * history_value + (1 - momentum) * (grad * grad); update_value = grad * std::sqrt((update_history_value + delta_) / - (weighted_gradient_average + delta_)); + (weighted_gradient_average + delta_)) * learning_rate; // not actually needed, just here for illustrative purposes // const Dtype weighted_update_average = // momentum * update_history_value + (1 - momentum) * (update_value); break; } + case SolverParameter_SolverType_ADAM: { + const Dtype momentum2 = 0.999; + const Dtype m = history_value; + const Dtype v = (i == D) ? + history[1 + num_param_blobs]->cpu_data()[0] : + history[0 + num_param_blobs]->cpu_data()[i]; + const Dtype val_m = (1 - momentum) * grad + momentum * m; + const Dtype val_v = (1 - momentum2) * grad * grad + momentum2 * v; + Dtype alpha_t = learning_rate * + std::sqrt(Dtype(1) - pow(momentum2, num_iters)) / + (Dtype(1.) - pow(momentum, num_iters)); + update_value = alpha_t * val_m / (std::sqrt(val_v) + delta_); + break; + } default: LOG(FATAL) << "Unknown solver type: " << solver_type(); } @@ -465,7 +478,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { // Compute the (K+1)th update using the analytic least squares gradient. vector > > updated_params; ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum, - &updated_params); + iter_to_check + 1, &updated_params); // Reinitialize the solver and run K+1 solver iterations. num_ = kNum; @@ -946,13 +959,13 @@ TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices); TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; this->TestLeastSquaresUpdate(kLearningRate); } TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; const Dtype kWeightDecay = 0.5; const Dtype kMomentum = 0.95; this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); @@ -960,64 +973,64 @@ TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) { TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; const Dtype kWeightDecay = 0.0; const Dtype kMomentum = 0.5; const int kNumIters = 1; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); } } TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; const Dtype kWeightDecay = 0.0; const Dtype kMomentum = 0.95; const int kNumIters = 1; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); } } TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; const Dtype kWeightDecay = 0.0; const Dtype kMomentum = 0.95; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; const Dtype kWeightDecay = 0.1; const Dtype kMomentum = 0.95; const int kNumIters = 4; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverythingShare) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; const Dtype kWeightDecay = 0.1; const Dtype kMomentum = 0.95; const int kNumIters = 4; this->share_ = true; for (int i = 0; i <= kNumIters; ++i) { - this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); } } TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; const Dtype kWeightDecay = 0.1; const Dtype kMomentum = 0.95; const int kNumIters = 4; @@ -1028,7 +1041,7 @@ TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; const Dtype kWeightDecay = 0.1; const Dtype kMomentum = 0.95; const int kNumIters = 4; @@ -1040,7 +1053,7 @@ TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { TYPED_TEST(AdaDeltaSolverTest, TestSnapshot) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; const Dtype kWeightDecay = 0.1; const Dtype kMomentum = 0.95; const int kNumIters = 4; @@ -1051,7 +1064,7 @@ TYPED_TEST(AdaDeltaSolverTest, TestSnapshot) { TYPED_TEST(AdaDeltaSolverTest, TestSnapshotShare) { typedef typename TypeParam::Dtype Dtype; - const Dtype kLearningRate = 1.0; + const Dtype kLearningRate = 0.1; const Dtype kWeightDecay = 0.1; const Dtype kMomentum = 0.95; const int kNumIters = 4; @@ -1061,6 +1074,111 @@ TYPED_TEST(AdaDeltaSolverTest, TestSnapshotShare) { } } +template +class AdamSolverTest : public GradientBasedSolverTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + virtual void InitSolver(const SolverParameter& param) { + SolverParameter new_param = param; + const Dtype momentum = 0.9; + new_param.set_momentum(momentum); + const Dtype momentum2 = 0.999; + new_param.set_momentum2(momentum2); + this->solver_.reset(new AdamSolver(new_param)); + } + virtual SolverParameter_SolverType solver_type() { + return SolverParameter_SolverType_ADAM; + } +}; + +TYPED_TEST_CASE(AdamSolverTest, TestDtypesAndDevices); + +TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdate) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0; + const Dtype kMomentum = 0.9; + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); +} + +TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithWeightDecay) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum); +} + +TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithEverything) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + +TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithEverythingShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + this->share_ = true; + for (int i = 0; i <= kNumIters; ++i) { + this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i); + } +} + +TYPED_TEST(AdamSolverTest, TestLeastSquaresUpdateWithEverythingAccum) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + const int kIterSize = 2; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); +} + +TYPED_TEST(AdamSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + const int kIterSize = 2; + this->share_ = true; + this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters, + kIterSize); +} + +TYPED_TEST(AdamSolverTest, TestSnapshot) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + } +} + +TYPED_TEST(AdamSolverTest, TestSnapshotShare) { + typedef typename TypeParam::Dtype Dtype; + const Dtype kLearningRate = 0.01; + const Dtype kWeightDecay = 0.5; + const Dtype kMomentum = 0.9; + const int kNumIters = 4; + this->share_ = true; + for (int i = 1; i <= kNumIters; ++i) { + this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i); + } +} + template class RMSPropSolverTest : public GradientBasedSolverTest { typedef typename TypeParam::Dtype Dtype; From bf42e6ebf7c56ff2f0d13bdcc7294d357d7592c6 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Thu, 13 Aug 2015 22:41:21 -0700 Subject: [PATCH 206/446] Cite Adam paper in solver.hpp --- include/caffe/solver.hpp | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 582aa1427d3..ab12ef1b1bd 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -217,6 +217,14 @@ class AdaDeltaSolver : public SGDSolver { DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver); }; +/** + * @brief AdamSolver, an algorithm for first-order gradient-based optimization + * of stochastic objective functions, based on adaptive estimates of + * lower-order moments. Described in [1]. + * + * [1] D. P. Kingma and J. L. Ba, "ADAM: A Method for Stochastic Optimization." + * arXiv preprint arXiv:1412.6980v8 (2014). + */ template class AdamSolver : public SGDSolver { public: From 1d820f7fa81e6b02a64140a93edbfd30dc529e8b Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Fri, 14 Aug 2015 09:19:48 -0700 Subject: [PATCH 207/446] Malloc at least one byte in Parallel --- src/caffe/parallel.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp index 6e7d802bb99..d48136c56b8 100644 --- a/src/caffe/parallel.cpp +++ b/src/caffe/parallel.cpp @@ -64,7 +64,9 @@ static size_t total_size(const vector*>& params) { size_t size = 0; for (int i = 0; i < params.size(); ++i) size += params[i]->count(); - return size; + // Size have at least one byte, otherwise cudaMalloc fails if net has no + // learnable parameters. + return (size > 0) ? size : 1; } template From 98593e3aa64d9a8f42723fb03fa46a1343e12c33 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Fri, 14 Aug 2015 11:15:56 -0700 Subject: [PATCH 208/446] Make classification.bin support models with less than 5 classes The example program would crash if the number of classes was less than 5, since it was still attempting to get the top 5 predictions. Close #2585 --- examples/cpp_classification/classification.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp index 1c6371e382b..dc8b863f53f 100644 --- a/examples/cpp_classification/classification.cpp +++ b/examples/cpp_classification/classification.cpp @@ -2,6 +2,7 @@ #include #include #include +#include #include #include #include @@ -101,6 +102,7 @@ static std::vector Argmax(const std::vector& v, int N) { std::vector Classifier::Classify(const cv::Mat& img, int N) { std::vector output = Predict(img); + N = std::min(labels_.size(), N); std::vector maxN = Argmax(output, N); std::vector predictions; for (int i = 0; i < N; ++i) { From e94be07e2d7e186821698df053ef77a35fe71c26 Mon Sep 17 00:00:00 2001 From: philkr Date: Thu, 23 Jul 2015 08:33:58 -0700 Subject: [PATCH 209/446] Exposing blob loss weight to python --- python/caffe/_caffe.cpp | 4 ++++ python/caffe/pycaffe.py | 10 ++++++++++ 2 files changed, 14 insertions(+) diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index bb5130fddd0..e1ae3ec73c7 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -211,6 +211,8 @@ BOOST_PYTHON_MODULE(_caffe) { .def("copy_from", static_cast::*)(const string)>( &Net::CopyTrainedLayersFrom)) .def("share_with", &Net::ShareTrainedLayersWith) + .add_property("_blob_loss_weights", bp::make_function( + &Net::blob_loss_weights, bp::return_internal_reference<>())) .add_property("_blobs", bp::make_function(&Net::blobs, bp::return_internal_reference<>())) .add_property("layers", bp::make_function(&Net::layers, @@ -293,6 +295,8 @@ BOOST_PYTHON_MODULE(_caffe) { .def(bp::vector_indexing_suite >()); bp::class_ >("IntVec") .def(bp::vector_indexing_suite >()); + bp::class_ >("DtypeVec") + .def(bp::vector_indexing_suite >()); bp::class_ > > >("NetVec") .def(bp::vector_indexing_suite > >, true>()); bp::class_ >("BoolVec") diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py index e8a676a26d2..4f980a92c38 100644 --- a/python/caffe/pycaffe.py +++ b/python/caffe/pycaffe.py @@ -27,6 +27,15 @@ def _Net_blobs(self): return OrderedDict(zip(self._blob_names, self._blobs)) +@property +def _Net_blob_loss_weights(self): + """ + An OrderedDict (bottom to top, i.e., input to output) of network + blob loss weights indexed by name + """ + return OrderedDict(zip(self._blob_names, self._blob_loss_weights)) + + @property def _Net_params(self): """ @@ -270,6 +279,7 @@ def _Net_batch(self, blobs): # Attach methods to Net. Net.blobs = _Net_blobs +Net.blob_loss_weights = _Net_blob_loss_weights Net.params = _Net_params Net.forward = _Net_forward Net.backward = _Net_backward From 7f4ffcd7c4d3896fd2a40cc4dd153ba04b1ba968 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Fri, 14 Aug 2015 12:52:39 -0700 Subject: [PATCH 210/446] [net] improve net config and shape mismatch error messages --- src/caffe/net.cpp | 37 ++++++++++++++++++++++++++------ src/caffe/util/insert_splits.cpp | 3 ++- 2 files changed, 33 insertions(+), 7 deletions(-) diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 7875285fea1..31644422e59 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -424,7 +424,8 @@ void Net::AppendTop(const NetParameter& param, const int layer_id, blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) { // If we are not doing in-place computation but have duplicated blobs, // raise an error. - LOG(FATAL) << "Duplicate blobs produced by multiple sources."; + LOG(FATAL) << "Top blob '" << blob_name + << "' produced by multiple sources."; } else { // Normal output. if (Caffe::root_solver()) { @@ -468,8 +469,8 @@ int Net::AppendBottom(const NetParameter& param, const int layer_id, const LayerParameter& layer_param = param.layer(layer_id); const string& blob_name = layer_param.bottom(bottom_id); if (available_blobs->find(blob_name) == available_blobs->end()) { - LOG(FATAL) << "Unknown blob input " << blob_name - << " (at index " << bottom_id << ") to layer " << layer_id; + LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '" + << layer_param.name() << "', bottom index " << bottom_id << ")"; } const int blob_id = (*blob_name_to_idx)[blob_name]; if (Caffe::root_solver()) { @@ -545,10 +546,19 @@ void Net::AppendParam(const NetParameter& param, const int layer_id, ParamSpec_DimCheckMode_PERMISSIVE)) { // Permissive dimension checking -- only check counts are the same. CHECK_EQ(this_blob->count(), owner_blob->count()) - << "Shared parameter blobs must have the same count."; + << "Cannot share param '" << param_name << "' owned by layer '" + << layer_names_[owner_layer_id] << "' with layer '" + << layer_names_[layer_id] << "'; count mismatch. Owner layer param " + << "shape is " << owner_blob->shape_string() << "; sharing layer " + << "shape is " << this_blob->shape_string(); } else { // Strict dimension checking -- all dims must be the same. - CHECK(this_blob->shape() == owner_blob->shape()); + CHECK(this_blob->shape() == owner_blob->shape()) + << "Cannot share param '" << param_name << "' owned by layer '" + << layer_names_[owner_layer_id] << "' with layer '" + << layer_names_[layer_id] << "'; shape mismatch. Owner layer param " + << "shape is " << owner_blob->shape_string() << "; sharing layer " + << "expects shape " << this_blob->shape_string(); } const int learnable_param_id = learnable_param_ids_[owner_net_param_id]; learnable_param_ids_.push_back(learnable_param_id); @@ -775,7 +785,11 @@ void Net::ShareTrainedLayersWith(const Net* other) { << "Incompatible number of blobs for layer " << source_layer_name; for (int j = 0; j < target_blobs.size(); ++j) { Blob* source_blob = source_layer->blobs()[j].get(); - CHECK(target_blobs[j]->shape() == source_blob->shape()); + CHECK(target_blobs[j]->shape() == source_blob->shape()) + << "Cannot share param " << j << " weights from layer '" + << source_layer_name << "'; shape mismatch. Source param shape is " + << source_blob->shape_string() << "; target param shape is " + << target_blobs[j]->shape_string(); target_blobs[j]->ShareData(*source_blob); } } @@ -839,6 +853,17 @@ void Net::CopyTrainedLayersFrom(const NetParameter& param) { CHECK_EQ(target_blobs.size(), source_layer.blobs_size()) << "Incompatible number of blobs for layer " << source_layer_name; for (int j = 0; j < target_blobs.size(); ++j) { + if (!target_blobs[j]->ShapeEquals(source_layer.blobs(j))) { + Blob source_blob; + const bool kReshape = true; + source_blob.FromProto(source_layer.blobs(j), kReshape); + LOG(FATAL) << "Cannot copy param " << j << " weights from layer '" + << source_layer_name << "'; shape mismatch. Source param shape is " + << source_blob.shape_string() << "; target param shape is " + << target_blobs[j]->shape_string() << ". " + << "To learn this layer's parameters from scratch rather than " + << "copying from a saved net, rename the layer."; + } const bool kReshape = false; target_blobs[j]->FromProto(source_layer.blobs(j), kReshape); } diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp index 416f80ab3c2..475a2a9f618 100644 --- a/src/caffe/util/insert_splits.cpp +++ b/src/caffe/util/insert_splits.cpp @@ -32,7 +32,8 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) { const string& blob_name = layer_param.bottom(j); if (blob_name_to_last_top_idx.find(blob_name) == blob_name_to_last_top_idx.end()) { - LOG(FATAL) << "Unknown blob input " << blob_name << " to layer " << j; + LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '" + << layer_param.name() << "', bottom index " << j << ")"; } const pair& bottom_idx = make_pair(i, j); const pair& top_idx = blob_name_to_last_top_idx[blob_name]; From 660cd1239038bac54942c1c4fefe694413e633a0 Mon Sep 17 00:00:00 2001 From: Cyprien Noel Date: Fri, 14 Aug 2015 16:22:34 -0700 Subject: [PATCH 211/446] New make target to only build the library. --- Makefile | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 05b783af35c..80bc37375be 100644 --- a/Makefile +++ b/Makefile @@ -386,11 +386,13 @@ endif ############################## # Define build targets ############################## -.PHONY: all test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \ +.PHONY: all lib test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \ py mat py$(PROJECT) mat$(PROJECT) proto runtest \ superclean supercleanlist supercleanfiles warn everything -all: $(STATIC_NAME) $(DYNAMIC_NAME) tools examples +all: lib tools examples + +lib: $(STATIC_NAME) $(DYNAMIC_NAME) everything: $(EVERYTHING_TARGETS) From 3778a2a6ca1c5d909f12add066478d04fe44b8cd Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Fri, 14 Aug 2015 13:52:01 -0700 Subject: [PATCH 212/446] Fix MultiGPU solver test with TEST_GPUID != 1 This is a patch for multi-gpu testing issue (#2926). The problem fixed in this commit is that when calling make runtest with TEST_GPUID != 0 on a MultiGPU machine, solver tests will crash because gpu ids in multi-gpu tests doesn't match that of single GPU test. --- src/caffe/test/test_gradient_based_solver.cpp | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index dcbfff1cad2..7ad7467f86f 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -73,12 +73,19 @@ class GradientBasedSolverTest : public MultiDeviceTest { const int iter_size = 1, const int devices = 1, const bool snapshot = false, const char* from_snapshot = NULL) { ostringstream proto; + int device_id = 0; +#ifndef CPU_ONLY + if (Caffe::mode() == Caffe::GPU) { + CUDA_CHECK(cudaGetDevice(&device_id)); + } +#endif proto << "snapshot_after_train: " << snapshot << " " "max_iter: " << num_iters << " " "base_lr: " << learning_rate << " " "lr_policy: 'fixed' " "iter_size: " << iter_size << " " + "device_id: " << device_id << " " "net_param { " " name: 'TestNetwork' " " layer { " @@ -189,8 +196,12 @@ class GradientBasedSolverTest : public MultiDeviceTest { } else { LOG(INFO) << "Multi-GPU test on " << devices << " devices"; vector gpus; - for (int i = 0; i < devices; ++i) { - gpus.push_back(i); + // put current device at the beginning + int device_id = solver_->param().device_id(); + gpus.push_back(device_id); + for (int i = 0; gpus.size() < devices; ++i) { + if (i != device_id) + gpus.push_back(i); } Caffe::set_solver_count(gpus.size()); this->sync_.reset(new P2PSync( From 09868aced72a20bc30198b84a225e58fdd6f13a6 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Sat, 15 Aug 2015 19:02:57 -0700 Subject: [PATCH 213/446] Malloc at least 1 byte for MultiGPU P2PSync buffers --- src/caffe/parallel.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp index d48136c56b8..a6d154e168e 100644 --- a/src/caffe/parallel.cpp +++ b/src/caffe/parallel.cpp @@ -55,7 +55,8 @@ static void apply_buffers(const vector*>& blobs, } ptr += size; } - CHECK_EQ(total_size, ptr - buffer); + // total_size is at least one byte + CHECK_EQ(total_size, (ptr == buffer ? 1 : ptr - buffer)); } // Buffer size necessary to store given blobs From 7453bbf6ea1aeb03330b5892a06276b69434f699 Mon Sep 17 00:00:00 2001 From: mhouston Date: Tue, 18 Aug 2015 13:40:00 -0700 Subject: [PATCH 214/446] Add some documentation on Multi-GPU support --- docs/multigpu.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) create mode 100644 docs/multigpu.md diff --git a/docs/multigpu.md b/docs/multigpu.md new file mode 100644 index 00000000000..4b202347417 --- /dev/null +++ b/docs/multigpu.md @@ -0,0 +1,24 @@ +--- +title: Multi-GPU Usage, Hardware Configuration Assumptions, and Performance +--- + +# Multi-GPU Usage + +Currently Multi-GPU is only supported via the C/C++ paths and only for training. + +The GPUs to be used for training can be set with the "-gpu" flag on the command line to the 'caffe' tool. e.g. "build/tools/caffe train --solver=models/bvlc_alexnet/solver.prototxt --gpu=0,1" will train on GPUs 0 and 1. + +**NOTE**: each GPU runs the batchsize specified in your train_val.prototxt. So if you go from 1 GPU to 2 GPU, your effective batchsize will double. e.g. if your train_val.prototxt specified a batchsize of 256, if you run 2 GPUs your effective batch size is now 512. So you need to adjust the batchsize when running multiple GPUs and/or adjust your solver params, specifically learning rate. + +# Hardware Configuration Assumptions + +The current implementation uses a tree reduction strategy. e.g. if there are 4 GPUs in the system, 0:1, 2:3 will exchange gradients, then 0:2 (top of the tree) will exchange gradients, 0 will calculate +updated model, 0\-\>2, and then 0\-\>1, 2\-\>3. + +For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced. + +Current implementation has a "soft" assumption that the devices being used are homogeneous. In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used. e.g. if you combine a TitanX and a GTX980, peformance will be limited by the 980. Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported. + +# Scaling Performance + +Performance is **heavily** dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers. Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset). In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet. 4 GPUs begins to have falloff in scaling. Generally with "weak scaling" where the batchsize increases with the number of GPUs you will see 3.5x scaling or so. With "strong scaling", the system can become communication bound, especially with layer performance optimizations like those in [cuDNNv3](http://nvidia.com/cudnn), and you will likely see closer to mid 2.x scaling in performance. Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance. \ No newline at end of file From 26a9880d72e81d415d1dc3bf449586ce54185ea4 Mon Sep 17 00:00:00 2001 From: mhouston Date: Tue, 18 Aug 2015 15:29:26 -0700 Subject: [PATCH 215/446] Add information about how to get GPU topology from nvidia-smi --- docs/multigpu.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/multigpu.md b/docs/multigpu.md index 4b202347417..01cfb8938b5 100644 --- a/docs/multigpu.md +++ b/docs/multigpu.md @@ -19,6 +19,8 @@ For best performance, P2P DMA access between devices is needed. Without P2P acce Current implementation has a "soft" assumption that the devices being used are homogeneous. In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used. e.g. if you combine a TitanX and a GTX980, peformance will be limited by the 980. Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported. +"nvidia-smi topo -m" will show you the connectivity matrix. You can do P2P through PCIe bridges, but not across socket level links at this time, e.g. across CPU sockets on a multi-socket motherboard. + # Scaling Performance Performance is **heavily** dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers. Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset). In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet. 4 GPUs begins to have falloff in scaling. Generally with "weak scaling" where the batchsize increases with the number of GPUs you will see 3.5x scaling or so. With "strong scaling", the system can become communication bound, especially with layer performance optimizations like those in [cuDNNv3](http://nvidia.com/cudnn), and you will likely see closer to mid 2.x scaling in performance. Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance. \ No newline at end of file From 3b00ca6d32647e683d9808d13243bd0240550901 Mon Sep 17 00:00:00 2001 From: max argus Date: Thu, 20 Aug 2015 09:01:58 +0000 Subject: [PATCH 216/446] In BasePrefetchingDataLayer::Forward_cpu hanged top[0]->Reshape to top[0]->ReshapeLike, in line with other calls. --- src/caffe/layers/base_data_layer.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp index 5303fe9c5d3..b90bd4e0caf 100644 --- a/src/caffe/layers/base_data_layer.cpp +++ b/src/caffe/layers/base_data_layer.cpp @@ -105,8 +105,7 @@ void BasePrefetchingDataLayer::Forward_cpu( const vector*>& bottom, const vector*>& top) { Batch* batch = prefetch_full_.pop("Data layer prefetch queue empty"); // Reshape to loaded data. - top[0]->Reshape(batch->data_.num(), batch->data_.channels(), - batch->data_.height(), batch->data_.width()); + top[0]->ReshapeLike(batch->data_); // Copy the data caffe_copy(batch->data_.count(), batch->data_.cpu_data(), top[0]->mutable_cpu_data()); From 51b172ce2fcd7f63aa7830389af54d353f53a3bc Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Fri, 14 Aug 2015 16:53:39 -0700 Subject: [PATCH 217/446] Expose LayerFactory::LayerTypeList in pycaffe Useful for validating NetParameters without crashing on SIGABRT --- include/caffe/layer_factory.hpp | 31 +++++++++++++++-------- python/caffe/__init__.py | 2 +- python/caffe/_caffe.cpp | 2 ++ python/caffe/test/test_layer_type_list.py | 10 ++++++++ 4 files changed, 34 insertions(+), 11 deletions(-) create mode 100644 python/caffe/test/test_layer_type_list.py diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp index 32e849de0d2..2c2fde4d979 100644 --- a/include/caffe/layer_factory.hpp +++ b/include/caffe/layer_factory.hpp @@ -41,6 +41,7 @@ #include #include +#include #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" @@ -77,26 +78,36 @@ class LayerRegistry { const string& type = param.type(); CreatorRegistry& registry = Registry(); CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type - << " (known types: " << LayerTypeList() << ")"; + << " (known types: " << LayerTypeListString() << ")"; return registry[type](param); } + static vector LayerTypeList() { + CreatorRegistry& registry = Registry(); + vector layer_types; + for (typename CreatorRegistry::iterator iter = registry.begin(); + iter != registry.end(); ++iter) { + layer_types.push_back(iter->first); + } + return layer_types; + } + private: // Layer registry should never be instantiated - everything is done with its // static variables. LayerRegistry() {} - static string LayerTypeList() { - CreatorRegistry& registry = Registry(); - string layer_types; - for (typename CreatorRegistry::iterator iter = registry.begin(); - iter != registry.end(); ++iter) { - if (iter != registry.begin()) { - layer_types += ", "; + static string LayerTypeListString() { + vector layer_types = LayerTypeList(); + string layer_types_str; + for (vector::iterator iter = layer_types.begin(); + iter != layer_types.end(); ++iter) { + if (iter != layer_types.begin()) { + layer_types_str += ", "; } - layer_types += iter->first; + layer_types_str += *iter; } - return layer_types; + return layer_types_str; } }; diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py index 1b2da510a90..6cc44e729f4 100644 --- a/python/caffe/__init__.py +++ b/python/caffe/__init__.py @@ -1,5 +1,5 @@ from .pycaffe import Net, SGDSolver -from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver +from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list from .proto.caffe_pb2 import TRAIN, TEST from .classifier import Classifier from .detector import Detector diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index bb5130fddd0..f9b2dba1d64 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -200,6 +200,8 @@ BOOST_PYTHON_MODULE(_caffe) { bp::def("set_mode_gpu", &set_mode_gpu); bp::def("set_device", &Caffe::SetDevice); + bp::def("layer_type_list", &LayerRegistry::LayerTypeList); + bp::class_, shared_ptr >, boost::noncopyable >("Net", bp::no_init) .def("__init__", bp::make_constructor(&Net_Init)) diff --git a/python/caffe/test/test_layer_type_list.py b/python/caffe/test/test_layer_type_list.py new file mode 100644 index 00000000000..7edc80df069 --- /dev/null +++ b/python/caffe/test/test_layer_type_list.py @@ -0,0 +1,10 @@ +import unittest + +import caffe + +class TestLayerTypeList(unittest.TestCase): + + def test_standard_types(self): + for type_name in ['Data', 'Convolution', 'InnerProduct']: + self.assertIn(type_name, caffe.layer_type_list(), + '%s not in layer_type_list()' % type_name) From 35657e31ad82f73a0682deb52cb1606b33a202be Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Thu, 20 Aug 2015 11:54:08 -0700 Subject: [PATCH 218/446] DeconvolutionLayer Backward_gpu fix: don't redo im2col --- src/caffe/layers/deconv_layer.cu | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu index 39bc4de8c66..8a1eed8aa16 100644 --- a/src/caffe/layers/deconv_layer.cu +++ b/src/caffe/layers/deconv_layer.cu @@ -52,7 +52,8 @@ void DeconvolutionLayer::Backward_gpu(const vector*>& top, // gradient w.r.t. bottom data, if necessary. if (propagate_down[i]) { this->forward_gpu_gemm(top_diff + top[i]->offset(n), weight, - bottom_diff + bottom[i]->offset(n)); + bottom_diff + bottom[i]->offset(n), + this->param_propagate_down_[0]); } } } From 2f5889cb84b8c7f8f64a6b7eb48e1dc624b18162 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Thu, 20 Aug 2015 14:29:02 -0700 Subject: [PATCH 219/446] Use input_shape instead of input_dim in examples --- examples/cifar10/cifar10_full.prototxt | 10 ++++++---- examples/cifar10/cifar10_quick.prototxt | 10 ++++++---- examples/mnist/lenet.prototxt | 10 ++++++---- examples/net_surgery/bvlc_caffenet_full_conv.prototxt | 10 ++++++---- examples/net_surgery/conv.prototxt | 10 ++++++---- examples/siamese/mnist_siamese.prototxt | 10 ++++++---- models/bvlc_alexnet/deploy.prototxt | 10 ++++++---- models/bvlc_googlenet/deploy.prototxt | 10 ++++++---- models/bvlc_reference_caffenet/deploy.prototxt | 10 ++++++---- models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt | 10 ++++++---- models/finetune_flickr_style/deploy.prototxt | 10 ++++++---- 11 files changed, 66 insertions(+), 44 deletions(-) diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt index c16f7dca49f..446479da961 100644 --- a/examples/cifar10/cifar10_full.prototxt +++ b/examples/cifar10/cifar10_full.prototxt @@ -2,10 +2,12 @@ name: "CIFAR10_full_deploy" # N.B. input image must be in CIFAR-10 format # as described at http://www.cs.toronto.edu/~kriz/cifar.html input: "data" -input_dim: 1 -input_dim: 3 -input_dim: 32 -input_dim: 32 +input_shape { + dim: 1 + dim: 3 + dim: 32 + dim: 32 +} layer { name: "conv1" type: "Convolution" diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt index 1ad190e185f..9352fbf65df 100644 --- a/examples/cifar10/cifar10_quick.prototxt +++ b/examples/cifar10/cifar10_quick.prototxt @@ -1,9 +1,11 @@ name: "CIFAR10_quick_test" input: "data" -input_dim: 1 -input_dim: 3 -input_dim: 32 -input_dim: 32 +input_shape { + dim: 1 + dim: 3 + dim: 32 + dim: 32 +} layer { name: "conv1" type: "Convolution" diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt index cb42610fe1e..dff7123bf73 100644 --- a/examples/mnist/lenet.prototxt +++ b/examples/mnist/lenet.prototxt @@ -1,9 +1,11 @@ name: "LeNet" input: "data" -input_dim: 64 -input_dim: 1 -input_dim: 28 -input_dim: 28 +input_shape { + dim: 64 + dim: 1 + dim: 28 + dim: 28 +} layer { name: "conv1" type: "Convolution" diff --git a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt index 3c951970fc1..0cadde9b58b 100644 --- a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt +++ b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt @@ -1,10 +1,12 @@ # Fully convolutional network version of CaffeNet. name: "CaffeNetConv" input: "data" -input_dim: 1 -input_dim: 3 -input_dim: 451 -input_dim: 451 +input_shape { + dim: 1 + dim: 3 + dim: 451 + dim: 451 +} layer { name: "conv1" type: "Convolution" diff --git a/examples/net_surgery/conv.prototxt b/examples/net_surgery/conv.prototxt index 9444c63ab74..6b3e5c768d5 100644 --- a/examples/net_surgery/conv.prototxt +++ b/examples/net_surgery/conv.prototxt @@ -1,10 +1,12 @@ # Simple single-layer network to showcase editing model parameters. name: "convolution" input: "data" -input_dim: 1 -input_dim: 1 -input_dim: 100 -input_dim: 100 +input_shape { + dim: 1 + dim: 1 + dim: 100 + dim: 100 +} layer { name: "conv" type: "Convolution" diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt index 0e903f85909..332731bd75f 100644 --- a/examples/siamese/mnist_siamese.prototxt +++ b/examples/siamese/mnist_siamese.prototxt @@ -1,9 +1,11 @@ name: "mnist_siamese" input: "data" -input_dim: 10000 -input_dim: 1 -input_dim: 28 -input_dim: 28 +input_shape { + dim: 10000 + dim: 1 + dim: 28 + dim: 28 +} layer { name: "conv1" type: "Convolution" diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt index ced055b85d0..ff10daa9399 100644 --- a/models/bvlc_alexnet/deploy.prototxt +++ b/models/bvlc_alexnet/deploy.prototxt @@ -1,9 +1,11 @@ name: "AlexNet" input: "data" -input_dim: 10 -input_dim: 3 -input_dim: 227 -input_dim: 227 +input_shape { + dim: 10 + dim: 3 + dim: 227 + dim: 227 +} layer { name: "conv1" type: "Convolution" diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt index 4648bf26efc..1f90ee21630 100644 --- a/models/bvlc_googlenet/deploy.prototxt +++ b/models/bvlc_googlenet/deploy.prototxt @@ -1,9 +1,11 @@ name: "GoogleNet" input: "data" -input_dim: 10 -input_dim: 3 -input_dim: 224 -input_dim: 224 +input_shape { + dim: 10 + dim: 3 + dim: 224 + dim: 224 +} layer { name: "conv1/7x7_s2" type: "Convolution" diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt index 29ccf1469f7..127f1e265fd 100644 --- a/models/bvlc_reference_caffenet/deploy.prototxt +++ b/models/bvlc_reference_caffenet/deploy.prototxt @@ -1,9 +1,11 @@ name: "CaffeNet" input: "data" -input_dim: 10 -input_dim: 3 -input_dim: 227 -input_dim: 227 +input_shape { + dim: 10 + dim: 3 + dim: 227 + dim: 227 +} layer { name: "conv1" type: "Convolution" diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt index ea9cf98a926..ae1df967742 100644 --- a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt +++ b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt @@ -1,9 +1,11 @@ name: "R-CNN-ilsvrc13" input: "data" -input_dim: 10 -input_dim: 3 -input_dim: 227 -input_dim: 227 +input_shape { + dim: 10 + dim: 3 + dim: 227 + dim: 227 +} layer { name: "conv1" type: "Convolution" diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt index 4a924f74927..0f07e47acab 100644 --- a/models/finetune_flickr_style/deploy.prototxt +++ b/models/finetune_flickr_style/deploy.prototxt @@ -1,9 +1,11 @@ name: "FlickrStyleCaffeNet" input: "data" -input_dim: 10 -input_dim: 3 -input_dim: 227 -input_dim: 227 +input_shape { + dim: 10 + dim: 3 + dim: 227 + dim: 227 +} layer { name: "conv1" type: "Convolution" From 7146e596347db81869b5bfa9b4cb014e80be9732 Mon Sep 17 00:00:00 2001 From: David Larson Date: Thu, 20 Aug 2015 15:18:03 -0700 Subject: [PATCH 220/446] [examples] fix link to feature visualization notebook --- examples/feature_extraction/readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md index a980b8b3203..2bc3dacbb69 100644 --- a/examples/feature_extraction/readme.md +++ b/examples/feature_extraction/readme.md @@ -64,7 +64,7 @@ If you meet with the error "Check failed: status.ok() Failed to open leveldb exa rm -rf examples/_temp/features/ -If you'd like to use the Python wrapper for extracting features, check out the [layer visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb). +If you'd like to use the Python wrapper for extracting features, check out the [filter visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb). Clean Up -------- From 08086c7580ddfd0d2b996157b3f3b4c1a52fd2b5 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Fri, 21 Aug 2015 21:23:22 -0700 Subject: [PATCH 221/446] remove superfluous code in Net::ToProto --- src/caffe/net.cpp | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index a18ee63818e..554343f9767 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -763,12 +763,6 @@ void Net::ToProto(NetParameter* param, bool write_diff) const { DLOG(INFO) << "Serializing " << layers_.size() << " layers"; for (int i = 0; i < layers_.size(); ++i) { LayerParameter* layer_param = param->add_layer(); - for (int j = 0; j < bottom_id_vecs_[i].size(); ++j) { - layer_param->add_bottom(blob_names_[bottom_id_vecs_[i][j]]); - } - for (int j = 0; j < top_id_vecs_[i].size(); ++j) { - layer_param->add_top(blob_names_[top_id_vecs_[i][j]]); - } layers_[i]->ToProto(layer_param, write_diff); } } From ff19d5f5c010dd8d6bfcf768b4fe27d0458f17df Mon Sep 17 00:00:00 2001 From: J Yegerlehner Date: Fri, 3 Apr 2015 16:11:23 -0500 Subject: [PATCH 222/446] Add signal handler and early exit/snapshot to Solver. Add signal handler and early exit/snapshot to Solver. Add signal handler and early exit/snapshot to Solver. Also check for exit and snapshot when testing. Skip running test after early exit. Fix more lint. Rebase on master. Finish rebase on master. Fixups per review comments. Redress review comments. Lint. Correct error message wording. --- include/caffe/solver.hpp | 37 ++++++++- include/caffe/util/signal_handler.h | 24 ++++++ src/caffe/solver.cpp | 70 +++++++++++++++-- src/caffe/util/signal_handler.cpp | 115 ++++++++++++++++++++++++++++ tools/caffe.cpp | 32 +++++++- 5 files changed, 268 insertions(+), 10 deletions(-) create mode 100644 include/caffe/util/signal_handler.h create mode 100644 src/caffe/util/signal_handler.cpp diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index ab12ef1b1bd..aba3e036004 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -1,6 +1,6 @@ #ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_ #define CAFFE_OPTIMIZATION_SOLVER_HPP_ - +#include #include #include @@ -8,6 +8,28 @@ namespace caffe { +/** + * @brief Enumeration of actions that a client of the Solver may request by + * implementing the Solver's action request function, which a + * a client may optionally provide in order to request early termination + * or saving a snapshot without exiting. In the executable caffe, this + * mechanism is used to allow the snapshot to be saved when stopping + * execution with a SIGINT (Ctrl-C). + */ + namespace SolverAction { + enum Enum { + NONE = 0, // Take no special action. + STOP = 1, // Stop training. snapshot_after_train controls whether a + // snapshot is created. + SNAPSHOT = 2 // Take a snapshot, and keep training. + }; + } + +/** + * @brief Type of a function that returns a Solver Action enumeration. + */ +typedef boost::function ActionCallback; + /** * @brief An interface for classes that perform optimization on Net%s. * @@ -23,6 +45,12 @@ class Solver { void Init(const SolverParameter& param); void InitTrainNet(); void InitTestNets(); + + // Client of the Solver optionally may call this in order to set the function + // that the solver uses to see what action it should take (e.g. snapshot or + // exit training early). + void SetActionFunction(ActionCallback func); + SolverAction::Enum GetRequestedAction(); // The main entry of the solver function. In default, iter will be zero. Pass // in a non-zero iter number to resume training for a pre-trained net. virtual void Solve(const char* resume_file = NULL); @@ -84,6 +112,13 @@ class Solver { // in data parallelism const Solver* const root_solver_; + // A function that can be set by a client of the Solver to provide indication + // that it wants a snapshot saved and/or to exit early. + ActionCallback action_request_function_; + + // True iff a request to stop early was received. + bool requested_early_exit_; + DISABLE_COPY_AND_ASSIGN(Solver); }; diff --git a/include/caffe/util/signal_handler.h b/include/caffe/util/signal_handler.h new file mode 100644 index 00000000000..fb84c65bd2e --- /dev/null +++ b/include/caffe/util/signal_handler.h @@ -0,0 +1,24 @@ +#ifndef INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ +#define INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ + +#include "caffe/proto/caffe.pb.h" +#include "caffe/solver.hpp" + +namespace caffe { + +class SignalHandler { + public: + // Contructor. Specify what action to take when a signal is received. + SignalHandler(SolverAction::Enum SIGINT_action, + SolverAction::Enum SIGHUP_action); + ~SignalHandler(); + ActionCallback GetActionFunction(); + private: + SolverAction::Enum CheckForSignals() const; + SolverAction::Enum SIGINT_action_; + SolverAction::Enum SIGHUP_action_; +}; + +} // namespace caffe + +#endif // INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 9348e11c249..394ec3b3ad7 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -17,15 +17,31 @@ namespace caffe { +template +void Solver::SetActionFunction(ActionCallback func) { + action_request_function_ = func; +} + +template +SolverAction::Enum Solver::GetRequestedAction() { + if (action_request_function_) { + // If the external request function has been set, call it. + return action_request_function_(); + } + return SolverAction::NONE; +} + template Solver::Solver(const SolverParameter& param, const Solver* root_solver) - : net_(), callbacks_(), root_solver_(root_solver) { + : net_(), callbacks_(), root_solver_(root_solver), + requested_early_exit_(false) { Init(param); } template Solver::Solver(const string& param_file, const Solver* root_solver) - : net_(), callbacks_(), root_solver_(root_solver) { + : net_(), callbacks_(), root_solver_(root_solver), + requested_early_exit_(false) { SolverParameter param; ReadProtoFromTextFileOrDie(param_file, ¶m); Init(param); @@ -195,6 +211,10 @@ void Solver::Step(int iters) { && (iter_ > 0 || param_.test_initialization()) && Caffe::root_solver()) { TestAll(); + if (requested_early_exit_) { + // Break out of the while loop because stop was requested while testing. + break; + } } for (int i = 0; i < callbacks_.size(); ++i) { @@ -250,12 +270,20 @@ void Solver::Step(int iters) { // the number of times the weights have been updated. ++iter_; + SolverAction::Enum request = GetRequestedAction(); + // Save a snapshot if needed. - if (param_.snapshot() - && iter_ % param_.snapshot() == 0 - && Caffe::root_solver()) { + if ((param_.snapshot() + && iter_ % param_.snapshot() == 0 + && Caffe::root_solver()) || + (request == SolverAction::SNAPSHOT)) { Snapshot(); } + if (SolverAction::STOP == request) { + requested_early_exit_ = true; + // Break out of training loop. + break; + } } } @@ -265,6 +293,9 @@ void Solver::Solve(const char* resume_file) { LOG(INFO) << "Solving " << net_->name(); LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy(); + // Initialize to false every time we start solving. + requested_early_exit_ = false; + if (resume_file) { LOG(INFO) << "Restoring previous solver status from " << resume_file; Restore(resume_file); @@ -279,6 +310,10 @@ void Solver::Solve(const char* resume_file) { && (!param_.snapshot() || iter_ % param_.snapshot() != 0)) { Snapshot(); } + if (requested_early_exit_) { + LOG(INFO) << "Optimization stopped early."; + return; + } // After the optimization is done, run an additional train and test pass to // display the train and test loss/outputs if appropriate (based on the // display and test_interval settings, respectively). Unlike in the rest of @@ -296,10 +331,11 @@ void Solver::Solve(const char* resume_file) { LOG(INFO) << "Optimization Done."; } - template void Solver::TestAll() { - for (int test_net_id = 0; test_net_id < test_nets_.size(); ++test_net_id) { + for (int test_net_id = 0; + test_net_id < test_nets_.size() && !requested_early_exit_; + ++test_net_id) { Test(test_net_id); } } @@ -317,6 +353,21 @@ void Solver::Test(const int test_net_id) { const shared_ptr >& test_net = test_nets_[test_net_id]; Dtype loss = 0; for (int i = 0; i < param_.test_iter(test_net_id); ++i) { + SolverAction::Enum request = GetRequestedAction(); + // Check to see if stoppage of testing/training has been requested. + while (request != SolverAction::NONE) { + if (SolverAction::SNAPSHOT == request) { + Snapshot(); + } else if (SolverAction::STOP == request) { + requested_early_exit_ = true; + } + request = GetRequestedAction(); + } + if (requested_early_exit_) { + // break out of test loop. + break; + } + Dtype iter_loss; const vector*>& result = test_net->Forward(bottom_vec, &iter_loss); @@ -341,6 +392,10 @@ void Solver::Test(const int test_net_id) { } } } + if (requested_early_exit_) { + LOG(INFO) << "Test interrupted."; + return; + } if (param_.test_compute_loss()) { loss /= param_.test_iter(test_net_id); LOG(INFO) << "Test loss: " << loss; @@ -361,7 +416,6 @@ void Solver::Test(const int test_net_id) { } } - template void Solver::Snapshot() { CHECK(Caffe::root_solver()); diff --git a/src/caffe/util/signal_handler.cpp b/src/caffe/util/signal_handler.cpp new file mode 100644 index 00000000000..5d764ec524f --- /dev/null +++ b/src/caffe/util/signal_handler.cpp @@ -0,0 +1,115 @@ +#include +#include + +#include +#include + +#include "caffe/util/signal_handler.h" + +namespace { + static volatile sig_atomic_t got_sigint = false; + static volatile sig_atomic_t got_sighup = false; + static bool already_hooked_up = false; + + void handle_signal(int signal) { + switch (signal) { + case SIGHUP: + got_sighup = true; + break; + case SIGINT: + got_sigint = true; + break; + } + } + + void HookupHandler() { + if (already_hooked_up) { + LOG(FATAL) << "Tried to hookup signal handlers more than once."; + } + already_hooked_up = true; + + struct sigaction sa; + // Setup the handler + sa.sa_handler = &handle_signal; + // Restart the system call, if at all possible + sa.sa_flags = SA_RESTART; + // Block every signal during the handler + sigfillset(&sa.sa_mask); + // Intercept SIGHUP and SIGINT + if (sigaction(SIGHUP, &sa, NULL) == -1) { + LOG(FATAL) << "Cannot install SIGHUP handler."; + } + if (sigaction(SIGINT, &sa, NULL) == -1) { + LOG(FATAL) << "Cannot install SIGINT handler."; + } + } + + // Set the signal handlers to the default. + void UnhookHandler() { + if (already_hooked_up) { + struct sigaction sa; + // Setup the sighub handler + sa.sa_handler = SIG_DFL; + // Restart the system call, if at all possible + sa.sa_flags = SA_RESTART; + // Block every signal during the handler + sigfillset(&sa.sa_mask); + // Intercept SIGHUP and SIGINT + if (sigaction(SIGHUP, &sa, NULL) == -1) { + LOG(FATAL) << "Cannot uninstall SIGHUP handler."; + } + if (sigaction(SIGINT, &sa, NULL) == -1) { + LOG(FATAL) << "Cannot uninstall SIGINT handler."; + } + + already_hooked_up = false; + } + } + + // Return true iff a SIGINT has been received since the last time this + // function was called. + bool GotSIGINT() { + bool result = got_sigint; + got_sigint = false; + return result; + } + + // Return true iff a SIGHUP has been received since the last time this + // function was called. + bool GotSIGHUP() { + bool result = got_sighup; + got_sighup = false; + return result; + } +} // namespace + +namespace caffe { + +SignalHandler::SignalHandler(SolverAction::Enum SIGINT_action, + SolverAction::Enum SIGHUP_action): + SIGINT_action_(SIGINT_action), + SIGHUP_action_(SIGHUP_action) { + HookupHandler(); +} + +SignalHandler::~SignalHandler() { + UnhookHandler(); +} + +SolverAction::Enum SignalHandler::CheckForSignals() const { + if (GotSIGHUP()) { + return SIGHUP_action_; + } + if (GotSIGINT()) { + return SIGINT_action_; + } + return SolverAction::NONE; +} + +// Return the function that the solver can use to find out if a snapshot or +// early exit is being requested. +ActionCallback SignalHandler::GetActionFunction() { + return boost::bind(&SignalHandler::CheckForSignals, this); +} + +} // namespace caffe diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 9f31b37ac2b..ff63860a3c1 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -12,6 +12,7 @@ namespace bp = boost::python; #include "boost/algorithm/string.hpp" #include "caffe/caffe.hpp" +#include "caffe/util/signal_handler.h" using caffe::Blob; using caffe::Caffe; @@ -39,6 +40,12 @@ DEFINE_string(weights, "", "separated by ','. Cannot be set simultaneously with snapshot."); DEFINE_int32(iterations, 50, "The number of iterations to run."); +DEFINE_string(sigint_effect, "stop", + "Optional; action to take when a SIGINT signal is received: " + "snapshot, stop or none."); +DEFINE_string(sighup_effect, "snapshot", + "Optional; action to take when a SIGHUP signal is received: " + "snapshot, stop or none."); // A simple registry for caffe commands. typedef int (*BrewFunction)(); @@ -126,6 +133,22 @@ void CopyLayers(caffe::Solver* solver, const std::string& model_list) { } } +// Translate the signal effect the user specified on the command-line to the +// corresponding enumeration. +caffe::SolverAction::Enum GetRequestedAction( + const std::string& flag_value) { + if (flag_value == "stop") { + return caffe::SolverAction::STOP; + } + if (flag_value == "snapshot") { + return caffe::SolverAction::SNAPSHOT; + } + if (flag_value == "none") { + return caffe::SolverAction::NONE; + } + LOG(FATAL) << "Invalid signal effect \""<< flag_value << "\" was specified"; +} + // Train / Finetune a model. int train() { CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train."; @@ -165,7 +188,14 @@ int train() { Caffe::set_solver_count(gpus.size()); } - shared_ptr > solver(caffe::GetSolver(solver_param)); + caffe::SignalHandler signal_handler( + GetRequestedAction(FLAGS_sigint_effect), + GetRequestedAction(FLAGS_sighup_effect)); + + shared_ptr > + solver(caffe::GetSolver(solver_param)); + + solver->SetActionFunction(signal_handler.GetActionFunction()); if (FLAGS_snapshot.size()) { LOG(INFO) << "Resuming from " << FLAGS_snapshot; From 374fb8c79c3f23ee36c46d0bcaeb2176037aa4b8 Mon Sep 17 00:00:00 2001 From: Ran Date: Sat, 15 Aug 2015 20:09:43 +0300 Subject: [PATCH 223/446] Output accuracies per class. Fixed case where number of samples in class can be zero. - Fixed ignore_label case, also added a test. - Two other fixes. Fixed lint errors. Small fix. --- include/caffe/loss_layers.hpp | 8 +- src/caffe/layers/accuracy_layer.cpp | 20 +++++ src/caffe/test/test_accuracy_layer.cpp | 107 +++++++++++++++++++++++++ 3 files changed, 134 insertions(+), 1 deletion(-) diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp index 528266394f1..02687a944fb 100644 --- a/include/caffe/loss_layers.hpp +++ b/include/caffe/loss_layers.hpp @@ -39,7 +39,11 @@ class AccuracyLayer : public Layer { virtual inline const char* type() const { return "Accuracy"; } virtual inline int ExactNumBottomBlobs() const { return 2; } - virtual inline int ExactNumTopBlobs() const { return 1; } + + // If there are two top blobs, then the second blob will contain + // accuracies per class. + virtual inline int MinTopBlobs() const { return 1; } + virtual inline int MaxTopBlos() const { return 2; } protected: /** @@ -86,6 +90,8 @@ class AccuracyLayer : public Layer { bool has_ignore_label_; /// The label indicating that an instance should be ignored. int ignore_label_; + /// Keeps counts of the number of samples per class. + Blob nums_buffer_; }; /** diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp index 90aad675ed3..e2d8d9f8a24 100644 --- a/src/caffe/layers/accuracy_layer.cpp +++ b/src/caffe/layers/accuracy_layer.cpp @@ -38,6 +38,13 @@ void AccuracyLayer::Reshape( << "with integer values in {0, 1, ..., C-1}."; vector top_shape(0); // Accuracy is a scalar; 0 axes. top[0]->Reshape(top_shape); + if (top.size() > 1) { + // Per-class accuracy is a vector; 1 axes. + vector top_shape_per_class(1); + top_shape_per_class[0] = bottom[0]->shape(label_axis_); + top[1]->Reshape(top_shape_per_class); + nums_buffer_.Reshape(top_shape_per_class); + } } template @@ -50,6 +57,10 @@ void AccuracyLayer::Forward_cpu(const vector*>& bottom, const int num_labels = bottom[0]->shape(label_axis_); vector maxval(top_k_+1); vector max_id(top_k_+1); + if (top.size() > 1) { + caffe_set(nums_buffer_.count(), Dtype(0), nums_buffer_.mutable_cpu_data()); + caffe_set(top[1]->count(), Dtype(0), top[1]->mutable_cpu_data()); + } int count = 0; for (int i = 0; i < outer_num_; ++i) { for (int j = 0; j < inner_num_; ++j) { @@ -58,6 +69,7 @@ void AccuracyLayer::Forward_cpu(const vector*>& bottom, if (has_ignore_label_ && label_value == ignore_label_) { continue; } + if (top.size() > 1) ++nums_buffer_.mutable_cpu_data()[label_value]; DCHECK_GE(label_value, 0); DCHECK_LT(label_value, num_labels); // Top-k accuracy @@ -73,6 +85,7 @@ void AccuracyLayer::Forward_cpu(const vector*>& bottom, for (int k = 0; k < top_k_; k++) { if (bottom_data_vector[k].second == label_value) { ++accuracy; + if (top.size() > 1) ++top[1]->mutable_cpu_data()[label_value]; break; } } @@ -82,6 +95,13 @@ void AccuracyLayer::Forward_cpu(const vector*>& bottom, // LOG(INFO) << "Accuracy: " << accuracy; top[0]->mutable_cpu_data()[0] = accuracy / count; + if (top.size() > 1) { + for (int i = 0; i < top[1]->count(); ++i) { + top[1]->mutable_cpu_data()[i] = + nums_buffer_.cpu_data()[i] == 0 ? 0 + : top[1]->cpu_data()[i] / nums_buffer_.cpu_data()[i]; + } + } // Accuracy layer should not be used as a loss function. } diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp index c14b67cc0e9..94e529b5eee 100644 --- a/src/caffe/test/test_accuracy_layer.cpp +++ b/src/caffe/test/test_accuracy_layer.cpp @@ -22,6 +22,7 @@ class AccuracyLayerTest : public CPUDeviceTest { : blob_bottom_data_(new Blob()), blob_bottom_label_(new Blob()), blob_top_(new Blob()), + blob_top_per_class_(new Blob()), top_k_(3) { vector shape(2); shape[0] = 100; @@ -34,6 +35,8 @@ class AccuracyLayerTest : public CPUDeviceTest { blob_bottom_vec_.push_back(blob_bottom_data_); blob_bottom_vec_.push_back(blob_bottom_label_); blob_top_vec_.push_back(blob_top_); + blob_top_per_class_vec_.push_back(blob_top_); + blob_top_per_class_vec_.push_back(blob_top_per_class_); } virtual void FillBottoms() { @@ -56,12 +59,15 @@ class AccuracyLayerTest : public CPUDeviceTest { delete blob_bottom_data_; delete blob_bottom_label_; delete blob_top_; + delete blob_top_per_class_; } Blob* const blob_bottom_data_; Blob* const blob_bottom_label_; Blob* const blob_top_; + Blob* const blob_top_per_class_; vector*> blob_bottom_vec_; vector*> blob_top_vec_; + vector*> blob_top_per_class_vec_; int top_k_; }; @@ -90,6 +96,20 @@ TYPED_TEST(AccuracyLayerTest, TestSetupTopK) { EXPECT_EQ(this->blob_top_->width(), 1); } +TYPED_TEST(AccuracyLayerTest, TestSetupOutputPerClass) { + LayerParameter layer_param; + AccuracyLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_); + EXPECT_EQ(this->blob_top_->num(), 1); + EXPECT_EQ(this->blob_top_->channels(), 1); + EXPECT_EQ(this->blob_top_->height(), 1); + EXPECT_EQ(this->blob_top_->width(), 1); + EXPECT_EQ(this->blob_top_per_class_->num(), 10); + EXPECT_EQ(this->blob_top_per_class_->channels(), 1); + EXPECT_EQ(this->blob_top_per_class_->height(), 1); + EXPECT_EQ(this->blob_top_per_class_->width(), 1); +} + TYPED_TEST(AccuracyLayerTest, TestForwardCPU) { LayerParameter layer_param; AccuracyLayer layer(layer_param); @@ -228,4 +248,91 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) { num_correct_labels / 100.0, 1e-4); } +TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClass) { + LayerParameter layer_param; + Caffe::set_mode(Caffe::CPU); + AccuracyLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_); + + TypeParam max_value; + int max_id; + int num_correct_labels = 0; + const int num_class = this->blob_top_per_class_->num(); + vector correct_per_class(num_class, 0); + vector num_per_class(num_class, 0); + for (int i = 0; i < 100; ++i) { + max_value = -FLT_MAX; + max_id = 0; + for (int j = 0; j < 10; ++j) { + if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) { + max_value = this->blob_bottom_data_->data_at(i, j, 0, 0); + max_id = j; + } + } + ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)]; + if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) { + ++num_correct_labels; + ++correct_per_class[max_id]; + } + } + EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0), + num_correct_labels / 100.0, 1e-4); + for (int i = 0; i < num_class; ++i) { + EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0), + static_cast(correct_per_class[i]) / num_per_class[i], + 1e-4); + } +} + + +TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClassWithIgnoreLabel) { + LayerParameter layer_param; + Caffe::set_mode(Caffe::CPU); + const TypeParam kIgnoreLabelValue = -1; + layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue); + AccuracyLayer layer(layer_param); + // Manually set some labels to the ignore label value (-1). + this->blob_bottom_label_->mutable_cpu_data()[2] = kIgnoreLabelValue; + this->blob_bottom_label_->mutable_cpu_data()[5] = kIgnoreLabelValue; + this->blob_bottom_label_->mutable_cpu_data()[32] = kIgnoreLabelValue; + layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_); + + TypeParam max_value; + int max_id; + int num_correct_labels = 0; + const int num_class = this->blob_top_per_class_->num(); + vector correct_per_class(num_class, 0); + vector num_per_class(num_class, 0); + int count = 0; + for (int i = 0; i < 100; ++i) { + if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) { + continue; + } + ++count; + max_value = -FLT_MAX; + max_id = 0; + for (int j = 0; j < 10; ++j) { + if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) { + max_value = this->blob_bottom_data_->data_at(i, j, 0, 0); + max_id = j; + } + } + ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)]; + if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) { + ++num_correct_labels; + ++correct_per_class[max_id]; + } + } + EXPECT_EQ(count, 97); + EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0), + num_correct_labels / TypeParam(count), 1e-4); + for (int i = 0; i < 10; ++i) { + EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0), + TypeParam(correct_per_class[i]) / num_per_class[i], + 1e-4); + } +} + } // namespace caffe From 4bed0ac9bab45246183184d84ff1b742e60574c7 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Mon, 24 Aug 2015 19:44:18 -0700 Subject: [PATCH 224/446] TestConcatLayer: add gradient check for bottom[1] only (to verify propagate_down[0] == false correctness) --- src/caffe/test/test_concat_layer.cpp | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp index 662a50fa23b..088e0a41685 100644 --- a/src/caffe/test/test_concat_layer.cpp +++ b/src/caffe/test/test_concat_layer.cpp @@ -173,4 +173,13 @@ TYPED_TEST(ConcatLayerTest, TestGradientChannels) { this->blob_top_vec_); } +TYPED_TEST(ConcatLayerTest, TestGradientChannelsBottomOneOnly) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ConcatLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + checker.CheckGradient(&layer, this->blob_bottom_vec_0_, + this->blob_top_vec_, 1); +} + } // namespace caffe From 6a7d4d6652018245f7bde1499d5208996912f3fb Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Mon, 24 Aug 2015 19:22:54 -0700 Subject: [PATCH 225/446] bugfix for ConcatLayer with propagate_down set if propagate_down[i] was set, offset_concat_axis was not correctly updated for subsequent bottoms i+1, i+2, ... --- src/caffe/layers/concat_layer.cpp | 13 +++++++------ src/caffe/layers/concat_layer.cu | 17 +++++++++-------- 2 files changed, 16 insertions(+), 14 deletions(-) diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp index 1cac8fc3387..95fba105b9a 100644 --- a/src/caffe/layers/concat_layer.cpp +++ b/src/caffe/layers/concat_layer.cpp @@ -76,13 +76,14 @@ void ConcatLayer::Backward_cpu(const vector*>& top, int offset_concat_axis = 0; const int top_concat_axis = top[0]->shape(concat_axis_); for (int i = 0; i < bottom.size(); ++i) { - if (!propagate_down[i]) { continue; } - Dtype* bottom_diff = bottom[i]->mutable_cpu_diff(); const int bottom_concat_axis = bottom[i]->shape(concat_axis_); - for (int n = 0; n < num_concats_; ++n) { - caffe_copy(bottom_concat_axis * concat_input_size_, top_diff + - (n * top_concat_axis + offset_concat_axis) * concat_input_size_, - bottom_diff + n * bottom_concat_axis * concat_input_size_); + if (propagate_down[i]) { + Dtype* bottom_diff = bottom[i]->mutable_cpu_diff(); + for (int n = 0; n < num_concats_; ++n) { + caffe_copy(bottom_concat_axis * concat_input_size_, top_diff + + (n * top_concat_axis + offset_concat_axis) * concat_input_size_, + bottom_diff + n * bottom_concat_axis * concat_input_size_); + } } offset_concat_axis += bottom_concat_axis; } diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu index 8f2e85d8f52..3c64c7ef224 100644 --- a/src/caffe/layers/concat_layer.cu +++ b/src/caffe/layers/concat_layer.cu @@ -53,15 +53,16 @@ void ConcatLayer::Backward_gpu(const vector*>& top, const int top_concat_axis = top[0]->shape(concat_axis_); const bool kForward = false; for (int i = 0; i < bottom.size(); ++i) { - if (!propagate_down[i]) { continue; } - Dtype* bottom_diff = bottom[i]->mutable_gpu_diff(); const int bottom_concat_axis = bottom[i]->shape(concat_axis_); - const int bottom_concat_size = bottom_concat_axis * concat_input_size_; - const int nthreads = bottom_concat_size * num_concats_; - Concat // NOLINT_NEXT_LINE(whitespace/operators) - <<>>( - nthreads, top_diff, kForward, num_concats_, concat_input_size_, - top_concat_axis, bottom_concat_axis, offset_concat_axis, bottom_diff); + if (propagate_down[i]) { + Dtype* bottom_diff = bottom[i]->mutable_gpu_diff(); + const int bottom_concat_size = bottom_concat_axis * concat_input_size_; + const int nthreads = bottom_concat_size * num_concats_; + Concat // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + nthreads, top_diff, kForward, num_concats_, concat_input_size_, + top_concat_axis, bottom_concat_axis, offset_concat_axis, bottom_diff); + } offset_concat_axis += bottom_concat_axis; } } From 6aecb431319df1e6c97a6d15cda77ed62cb04148 Mon Sep 17 00:00:00 2001 From: philkr Date: Wed, 19 Aug 2015 15:11:30 -0700 Subject: [PATCH 226/446] Allow the python layer have weight/parameter blobs. --- python/caffe/_caffe.cpp | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index 020a5bee16b..1638e9d7cd2 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -189,7 +189,18 @@ bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) { // We need to explicitly return None to use bp::raw_function. return bp::object(); } - +bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) { + if (bp::len(kwargs) > 0) + throw std::runtime_error("BlobVec.add_blob takes no kwargs"); + typedef vector > > BlobVec; + BlobVec* self = bp::extract(args[0]); + vector shape(bp::len(args) - 1); + for (int i = 1; i < bp::len(args); ++i) + shape[i - 1] = bp::extract(args[i]); + self->push_back(shared_ptr >(new Blob(shape))); + // We need to explicitly return None to use bp::raw_function. + return bp::object(); +} BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1); BOOST_PYTHON_MODULE(_caffe) { @@ -288,7 +299,8 @@ BOOST_PYTHON_MODULE(_caffe) { // vector wrappers for all the vector types we use bp::class_ > > >("BlobVec") - .def(bp::vector_indexing_suite > >, true>()); + .def(bp::vector_indexing_suite > >, true>()) + .def("add_blob", bp::raw_function(&BlobVec_add_blob)); bp::class_*> >("RawBlobVec") .def(bp::vector_indexing_suite*>, true>()); bp::class_ > > >("LayerVec") From 60c0d58baab7be6c770d81f4c5a7cc1fce0ef7af Mon Sep 17 00:00:00 2001 From: philkr Date: Tue, 25 Aug 2015 10:20:53 -0700 Subject: [PATCH 227/446] Python parameter test added --- python/caffe/_caffe.cpp | 8 +++- python/caffe/test/test_python_layer.py | 54 ++++++++++++++++++++++++++ 2 files changed, 60 insertions(+), 2 deletions(-) diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index 1638e9d7cd2..cc49f60ab13 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -189,18 +189,22 @@ bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) { // We need to explicitly return None to use bp::raw_function. return bp::object(); } + bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) { - if (bp::len(kwargs) > 0) + if (bp::len(kwargs) > 0) { throw std::runtime_error("BlobVec.add_blob takes no kwargs"); + } typedef vector > > BlobVec; BlobVec* self = bp::extract(args[0]); vector shape(bp::len(args) - 1); - for (int i = 1; i < bp::len(args); ++i) + for (int i = 1; i < bp::len(args); ++i) { shape[i - 1] = bp::extract(args[i]); + } self->push_back(shared_ptr >(new Blob(shape))); // We need to explicitly return None to use bp::raw_function. return bp::object(); } + BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1); BOOST_PYTHON_MODULE(_caffe) { diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py index a1e11bc248d..8ed86655ec3 100644 --- a/python/caffe/test/test_python_layer.py +++ b/python/caffe/test/test_python_layer.py @@ -28,6 +28,21 @@ class ExceptionLayer(caffe.Layer): def setup(self, bottom, top): raise RuntimeError +class ParameterLayer(caffe.Layer): + """A layer that just multiplies by ten""" + + def setup(self, bottom, top): + self.blobs.add_blob(1) + self.blobs[0].data[0] = 0 + + def reshape(self, bottom, top): + top[0].reshape(*bottom[0].data.shape) + + def forward(self, bottom, top): + pass + + def backward(self, top, propagate_down, bottom): + self.blobs[0].diff[0] = 1 def python_net_file(): with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f: @@ -52,6 +67,16 @@ def exception_net_file(): return f.name +def parameter_net_file(): + with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f: + f.write("""name: 'pythonnet' force_backward: true + input: 'data' input_shape { dim: 10 dim: 9 dim: 8 } + layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top' + python_param { module: 'test_python_layer' layer: 'ParameterLayer' } } + """) + return f.name + + class TestPythonLayer(unittest.TestCase): def setUp(self): net_file = python_net_file() @@ -84,3 +109,32 @@ def test_exception(self): net_file = exception_net_file() self.assertRaises(RuntimeError, caffe.Net, net_file, caffe.TEST) os.remove(net_file) + + def test_parameter(self): + net_file = parameter_net_file() + net = caffe.Net(net_file, caffe.TRAIN) + # Test forward and backward + net.forward() + net.backward() + layer = net.layers[list(net._layer_names).index('layer')] + self.assertEqual(layer.blobs[0].data[0], 0) + self.assertEqual(layer.blobs[0].diff[0], 1) + layer.blobs[0].data[0] += layer.blobs[0].diff[0] + self.assertEqual(layer.blobs[0].data[0], 1) + + # Test saving and loading + h, caffemodel_file = tempfile.mkstemp() + net.save(caffemodel_file) + layer.blobs[0].data[0] = -1 + self.assertEqual(layer.blobs[0].data[0], -1) + net.copy_from(caffemodel_file) + self.assertEqual(layer.blobs[0].data[0], 1) + os.remove(caffemodel_file) + + # Test weight sharing + net2 = caffe.Net(net_file, caffe.TRAIN) + net2.share_with(net) + layer = net.layers[list(net2._layer_names).index('layer')] + self.assertEqual(layer.blobs[0].data[0], 1) + + os.remove(net_file) From 251e67ab3141bc8ac2adf97ea4e961e5664ae008 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 31 Dec 2014 14:07:00 -0800 Subject: [PATCH 228/446] Add TileLayer --- include/caffe/common_layers.hpp | 29 ++++++ src/caffe/layers/tile_layer.cpp | 62 +++++++++++ src/caffe/layers/tile_layer.cu | 42 ++++++++ src/caffe/proto/caffe.proto | 13 ++- src/caffe/test/test_tile_layer.cpp | 162 +++++++++++++++++++++++++++++ 5 files changed, 307 insertions(+), 1 deletion(-) create mode 100644 src/caffe/layers/tile_layer.cpp create mode 100644 src/caffe/layers/tile_layer.cu create mode 100644 src/caffe/test/test_tile_layer.cpp diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 691e755f88f..8e64b3e5dc5 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -644,6 +644,35 @@ class SliceLayer : public Layer { vector slice_point_; }; +/** + * @brief Copy a Blob along specified dimensions. + */ +template +class TileLayer : public Layer { + public: + explicit TileLayer(const LayerParameter& param) + : Layer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Tile"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + unsigned int axis_, tiles_, outer_dim_, inner_dim_; +}; + } // namespace caffe #endif // CAFFE_COMMON_LAYERS_HPP_ diff --git a/src/caffe/layers/tile_layer.cpp b/src/caffe/layers/tile_layer.cpp new file mode 100644 index 00000000000..f55008cc53a --- /dev/null +++ b/src/caffe/layers/tile_layer.cpp @@ -0,0 +1,62 @@ +#include + +#include "caffe/common_layers.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void TileLayer::Reshape( + const vector*>& bottom, const vector*>& top) { + const TileParameter& tile_param = this->layer_param_.tile_param(); + axis_ = bottom[0]->CanonicalAxisIndex(tile_param.axis()); + CHECK(tile_param.has_tiles()) << "Number of tiles must be specified"; + tiles_ = tile_param.tiles(); + CHECK_GT(tiles_, 0) << "Number of tiles must be positive."; + vector top_shape = bottom[0]->shape(); + top_shape[axis_] = bottom[0]->shape(axis_) * tiles_; + top[0]->Reshape(top_shape); + outer_dim_ = bottom[0]->count(0, axis_); + inner_dim_ = bottom[0]->count(axis_); +} + +template +void TileLayer::Forward_cpu( + const vector*>& bottom, const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + for (int i = 0; i < outer_dim_; ++i) { + for (int t = 0; t < tiles_; ++t) { + caffe_copy(inner_dim_, bottom_data, top_data); + top_data += inner_dim_; + } + bottom_data += inner_dim_; + } +} + +template +void TileLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (!propagate_down[0]) { return; } + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + for (int i = 0; i < outer_dim_; ++i) { + caffe_copy(inner_dim_, top_diff, bottom_diff); + top_diff += inner_dim_; + for (int t = 1; t < tiles_; ++t) { + caffe_axpy(inner_dim_, Dtype(1), top_diff, bottom_diff); + top_diff += inner_dim_; + } + bottom_diff += inner_dim_; + } +} + +#ifdef CPU_ONLY +STUB_GPU(TileLayer); +#endif + +INSTANTIATE_CLASS(TileLayer); +REGISTER_LAYER_CLASS(Tile); + +} // namespace caffe diff --git a/src/caffe/layers/tile_layer.cu b/src/caffe/layers/tile_layer.cu new file mode 100644 index 00000000000..3af8e2eb72f --- /dev/null +++ b/src/caffe/layers/tile_layer.cu @@ -0,0 +1,42 @@ +#include + +#include "caffe/common_layers.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void TileLayer::Forward_gpu( + const vector*>& bottom, const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + for (int i = 0; i < outer_dim_; ++i) { + for (int t = 0; t < tiles_; ++t) { + caffe_copy(inner_dim_, bottom_data, top_data); + top_data += inner_dim_; + } + bottom_data += inner_dim_; + } +} + +template +void TileLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (!propagate_down[0]) { return; } + const Dtype* top_diff = top[0]->gpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + for (int i = 0; i < outer_dim_; ++i) { + caffe_copy(inner_dim_, top_diff, bottom_diff); + top_diff += inner_dim_; + for (int t = 1; t < tiles_; ++t) { + caffe_gpu_axpy(inner_dim_, Dtype(1), top_diff, bottom_diff); + top_diff += inner_dim_; + } + bottom_diff += inner_dim_; + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(TileLayer); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 35264610bdd..aa299f8660b 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -301,7 +301,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 138 (last added: embed_param) +// LayerParameter next available layer-specific ID: 139 (last added: tile_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -383,6 +383,7 @@ message LayerParameter { optional SliceParameter slice_param = 126; optional TanHParameter tanh_param = 127; optional ThresholdParameter threshold_param = 128; + optional TileParameter tile_param = 138; optional WindowDataParameter window_data_param = 129; } @@ -919,6 +920,16 @@ message TanHParameter { optional Engine engine = 1 [default = DEFAULT]; } +// Message that stores parameters used by TileLayer +message TileParameter { + // The index of the axis to tile. + optional int32 axis = 1 [default = 1]; + + // The number of copies (tiles) of the blob to output. + optional int32 tiles = 2; +} + +// Message that stores parameters used by ThresholdLayer message ThresholdParameter { optional float threshold = 1 [default = 0]; // Strictly positive values } diff --git a/src/caffe/test/test_tile_layer.cpp b/src/caffe/test/test_tile_layer.cpp new file mode 100644 index 00000000000..540aac3c2d3 --- /dev/null +++ b/src/caffe/test/test_tile_layer.cpp @@ -0,0 +1,162 @@ +#include +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/vision_layers.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class TileLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + TileLayerTest() + : blob_bottom_(new Blob(2, 3, 4, 5)), + blob_top_(new Blob()) {} + virtual void SetUp() { + blob_bottom_vec_.push_back(blob_bottom_); + blob_top_vec_.push_back(blob_top_); + FillerParameter filler_param; + filler_param.set_mean(0.0); + filler_param.set_std(1.0); + GaussianFiller filler(filler_param); + filler.Fill(blob_bottom_); + } + + virtual ~TileLayerTest() { + delete blob_bottom_; + delete blob_top_; + } + + Blob* const blob_bottom_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(TileLayerTest, TestDtypesAndDevices); + +TYPED_TEST(TileLayerTest, TestTrivialSetup) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + const int kNumTiles = 1; + layer_param.mutable_tile_param()->set_tiles(kNumTiles); + for (int i = 0; i < this->blob_bottom_->num_axes(); ++i) { + layer_param.mutable_tile_param()->set_axis(i); + TileLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_top_->num_axes(), this->blob_bottom_->num_axes()); + for (int j = 0; j < this->blob_bottom_->num_axes(); ++j) { + EXPECT_EQ(this->blob_top_->shape(j), this->blob_bottom_->shape(j)); + } + } +} + +TYPED_TEST(TileLayerTest, TestSetup) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + const int kNumTiles = 3; + layer_param.mutable_tile_param()->set_tiles(kNumTiles); + for (int i = 0; i < this->blob_bottom_->num_axes(); ++i) { + layer_param.mutable_tile_param()->set_axis(i); + TileLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_top_->num_axes(), this->blob_bottom_->num_axes()); + for (int j = 0; j < this->blob_bottom_->num_axes(); ++j) { + const int top_dim = + ((i == j) ? kNumTiles : 1) * this->blob_bottom_->shape(j); + EXPECT_EQ(top_dim, this->blob_top_->shape(j)); + } + } +} + +TYPED_TEST(TileLayerTest, TestForwardNum) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + const int kTileAxis = 0; + const int kNumTiles = 3; + layer_param.mutable_tile_param()->set_axis(kTileAxis); + layer_param.mutable_tile_param()->set_tiles(kNumTiles); + TileLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_top_->num(); ++n) { + for (int c = 0; c < this->blob_top_->channels(); ++c) { + for (int h = 0; h < this->blob_top_->height(); ++h) { + for (int w = 0; w < this->blob_top_->width(); ++w) { + const int bottom_n = n % this->blob_bottom_->num(); + EXPECT_EQ(this->blob_bottom_->data_at(bottom_n, c, h, w), + this->blob_top_->data_at(n, c, h, w)); + } + } + } + } +} + +TYPED_TEST(TileLayerTest, TestForwardChannels) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + const int kNumTiles = 3; + layer_param.mutable_tile_param()->set_tiles(kNumTiles); + TileLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_top_->num(); ++n) { + for (int c = 0; c < this->blob_top_->channels(); ++c) { + for (int h = 0; h < this->blob_top_->height(); ++h) { + for (int w = 0; w < this->blob_top_->width(); ++w) { + const int bottom_c = c % this->blob_bottom_->channels(); + EXPECT_EQ(this->blob_bottom_->data_at(n, bottom_c, h, w), + this->blob_top_->data_at(n, c, h, w)); + } + } + } + } +} + +TYPED_TEST(TileLayerTest, TestTrivialGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + const int kNumTiles = 1; + layer_param.mutable_tile_param()->set_tiles(kNumTiles); + TileLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(TileLayerTest, TestGradientNum) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + const int kTileAxis = 0; + const int kNumTiles = 3; + layer_param.mutable_tile_param()->set_axis(kTileAxis); + layer_param.mutable_tile_param()->set_tiles(kNumTiles); + TileLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(TileLayerTest, TestGradientChannels) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + const int kTileAxis = 1; + const int kNumTiles = 3; + layer_param.mutable_tile_param()->set_axis(kTileAxis); + layer_param.mutable_tile_param()->set_tiles(kNumTiles); + TileLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +} // namespace caffe From cbff2255bc8470299e15cc155ae7957a3acdd688 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Tue, 18 Aug 2015 18:15:20 -0700 Subject: [PATCH 229/446] TileLayer: add CUDA kernels --- src/caffe/layers/tile_layer.cu | 53 +++++++++++++++++++++++++--------- 1 file changed, 39 insertions(+), 14 deletions(-) diff --git a/src/caffe/layers/tile_layer.cu b/src/caffe/layers/tile_layer.cu index 3af8e2eb72f..7fd3bc47d0f 100644 --- a/src/caffe/layers/tile_layer.cu +++ b/src/caffe/layers/tile_layer.cu @@ -6,17 +6,45 @@ namespace caffe { +template +__global__ void Tile(const int nthreads, const Dtype* bottom_data, + const int tile_size, const int num_tiles, const int bottom_tile_axis, + Dtype* top_data) { + CUDA_KERNEL_LOOP(index, nthreads) { + const int d = index % tile_size; + const int b = (index / tile_size / num_tiles) % bottom_tile_axis; + const int n = index / tile_size / num_tiles / bottom_tile_axis; + const int bottom_index = (n * bottom_tile_axis + b) * tile_size + d; + top_data[index] = bottom_data[bottom_index]; + } +} + template void TileLayer::Forward_gpu( const vector*>& bottom, const vector*>& top) { const Dtype* bottom_data = bottom[0]->gpu_data(); Dtype* top_data = top[0]->mutable_gpu_data(); - for (int i = 0; i < outer_dim_; ++i) { - for (int t = 0; t < tiles_; ++t) { - caffe_copy(inner_dim_, bottom_data, top_data); - top_data += inner_dim_; + const int bottom_tile_axis = bottom[0]->shape(axis_); + const int nthreads = top[0]->count(); + Tile // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + nthreads, bottom_data, inner_dim_, tiles_, bottom_tile_axis, top_data); +} + +template +__global__ void TileBackward(const int nthreads, const Dtype* top_diff, + const int tile_size, const int num_tiles, const int bottom_tile_axis, + Dtype* bottom_diff) { + CUDA_KERNEL_LOOP(index, nthreads) { + const int d = index % tile_size; + const int b = (index / tile_size) % bottom_tile_axis; + const int n = index / tile_size / bottom_tile_axis; + bottom_diff[index] = 0; + int top_index = (n * num_tiles * bottom_tile_axis + b) * tile_size + d; + for (int t = 0; t < num_tiles; ++t) { + bottom_diff[index] += top_diff[top_index]; + top_index += bottom_tile_axis * tile_size; } - bottom_data += inner_dim_; } } @@ -26,15 +54,12 @@ void TileLayer::Backward_gpu(const vector*>& top, if (!propagate_down[0]) { return; } const Dtype* top_diff = top[0]->gpu_diff(); Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); - for (int i = 0; i < outer_dim_; ++i) { - caffe_copy(inner_dim_, top_diff, bottom_diff); - top_diff += inner_dim_; - for (int t = 1; t < tiles_; ++t) { - caffe_gpu_axpy(inner_dim_, Dtype(1), top_diff, bottom_diff); - top_diff += inner_dim_; - } - bottom_diff += inner_dim_; - } + const int bottom_tile_axis = bottom[0]->shape(axis_); + const int tile_size = inner_dim_ / bottom_tile_axis; + const int nthreads = bottom[0]->count(); + TileBackward // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + nthreads, top_diff, tile_size, tiles_, bottom_tile_axis, bottom_diff); } INSTANTIATE_LAYER_GPU_FUNCS(TileLayer); From 1f3f9529df6285a5be5f8e72bd1922a6a0cec4d8 Mon Sep 17 00:00:00 2001 From: J Yegerlehner Date: Sun, 23 Aug 2015 18:57:16 -0500 Subject: [PATCH 230/446] MVNLayer fixes. Fix the MVNLayer tests so they actually test what they claim. MVNLayer fixes: sum_multiplier_ sized correctly; backward gradient calculation. Gradient calculation per analysis of seanbell, found here: https://github.com/BVLC/caffe/issues/1938 Fixes according to review comments. --- src/caffe/layers/mvn_layer.cpp | 15 ++++++++++++--- src/caffe/layers/mvn_layer.cu | 7 ++++++- src/caffe/test/test_mvn_layer.cpp | 13 +++++++++---- 3 files changed, 27 insertions(+), 8 deletions(-) diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp index 3e79bddcdde..325691b1875 100644 --- a/src/caffe/layers/mvn_layer.cpp +++ b/src/caffe/layers/mvn_layer.cpp @@ -18,8 +18,12 @@ void MVNLayer::Reshape(const vector*>& bottom, 1, 1); temp_.Reshape(bottom[0]->num(), bottom[0]->channels(), bottom[0]->height(), bottom[0]->width()); - sum_multiplier_.Reshape(1, 1, - bottom[0]->height(), bottom[0]->width()); + if ( this->layer_param_.mvn_param().across_channels() ) { + sum_multiplier_.Reshape(1, bottom[0]->channels(), bottom[0]->height(), + bottom[0]->width()); + } else { + sum_multiplier_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width()); + } Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data(); caffe_set(sum_multiplier_.count(), Dtype(1), multiplier_data); eps_ = this->layer_param_.mvn_param().eps(); @@ -130,7 +134,12 @@ void MVNLayer::Backward_cpu(const vector*>& top, caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff); } else { - caffe_copy(temp_.count(), top_diff, bottom_diff); + caffe_cpu_gemv(CblasNoTrans, num, dim, 1. / dim, top_diff, + sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, -1., + mean_.cpu_data(), sum_multiplier_.cpu_data(), 0., + temp_.mutable_cpu_data()); + caffe_add(temp_.count(), top_diff, temp_.cpu_data(), bottom_diff); } } diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu index 3888a0c7106..d86a2e73fc2 100644 --- a/src/caffe/layers/mvn_layer.cu +++ b/src/caffe/layers/mvn_layer.cu @@ -113,7 +113,12 @@ void MVNLayer::Backward_gpu(const vector*>& top, caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff); } else { - caffe_copy(temp_.count(), top_diff, bottom_diff); + caffe_gpu_gemv(CblasNoTrans, num, dim, 1. / dim, top_diff, + sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, -1., + mean_.gpu_data(), sum_multiplier_.gpu_data(), 0., + temp_.mutable_gpu_data()); + caffe_gpu_add(temp_.count(), top_diff, temp_.gpu_data(), bottom_diff); } } diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp index 933b4326417..be23d86e9c3 100644 --- a/src/caffe/test/test_mvn_layer.cpp +++ b/src/caffe/test/test_mvn_layer.cpp @@ -6,6 +6,7 @@ #include "caffe/common.hpp" #include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "google/protobuf/text_format.h" #include "gtest/gtest.h" #include "caffe/test/test_caffe_main.hpp" @@ -73,7 +74,8 @@ TYPED_TEST(MVNLayerTest, TestForward) { TYPED_TEST(MVNLayerTest, TestForwardMeanOnly) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; - layer_param.ParseFromString("mvn_param{normalize_variance: false}"); + CHECK(google::protobuf::TextFormat::ParseFromString( + "mvn_param{normalize_variance: false}", &layer_param)); MVNLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); @@ -105,7 +107,8 @@ TYPED_TEST(MVNLayerTest, TestForwardMeanOnly) { TYPED_TEST(MVNLayerTest, TestForwardAcrossChannels) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; - layer_param.ParseFromString("mvn_param{across_channels: true}"); + CHECK(google::protobuf::TextFormat::ParseFromString( + "mvn_param{across_channels: true}", &layer_param)); MVNLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); @@ -149,7 +152,8 @@ TYPED_TEST(MVNLayerTest, TestGradient) { TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; - layer_param.ParseFromString("mvn_param{normalize_variance: false}"); + CHECK(google::protobuf::TextFormat::ParseFromString( + "mvn_param{normalize_variance: false}", &layer_param)); MVNLayer layer(layer_param); GradientChecker checker(1e-2, 1e-3); checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, @@ -159,7 +163,8 @@ TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) { TYPED_TEST(MVNLayerTest, TestGradientAcrossChannels) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; - layer_param.ParseFromString("mvn_param{across_channels: true}"); + CHECK(google::protobuf::TextFormat::ParseFromString( + "mvn_param{across_channels: true}", &layer_param)); MVNLayer layer(layer_param); GradientChecker checker(1e-2, 1e-3); checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, From c548a7972b554b3ababaa0dc52db15a5f5a28be8 Mon Sep 17 00:00:00 2001 From: Jonas Maaskola Date: Sun, 2 Aug 2015 23:47:12 +0200 Subject: [PATCH 231/446] Draw Deconvolution layers like Convolution layers --- python/caffe/draw.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python/caffe/draw.py b/python/caffe/draw.py index 324929deca4..a002b60b59a 100644 --- a/python/caffe/draw.py +++ b/python/caffe/draw.py @@ -40,7 +40,7 @@ def get_edge_label(layer): if layer.type == 'Data': edge_label = 'Batch ' + str(layer.data_param.batch_size) - elif layer.type == 'Convolution': + elif layer.type == 'Convolution' or layer.type == 'Deconvolution': edge_label = str(layer.convolution_param.num_output) elif layer.type == 'InnerProduct': edge_label = str(layer.inner_product_param.num_output) @@ -74,7 +74,7 @@ def get_layer_label(layer, rankdir): # horizontal space is not; separate words with newlines separator = '\\n' - if layer.type == 'Convolution': + if layer.type == 'Convolution' or layer.type == 'Deconvolution': # Outer double quotes needed or else colon characters don't parse # properly node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\ @@ -109,7 +109,7 @@ def choose_color_by_layertype(layertype): """Define colors for nodes based on the layer type. """ color = '#6495ED' # Default - if layertype == 'Convolution': + if layertype == 'Convolution' or layertype == 'Deconvolution': color = '#FF5050' elif layertype == 'Pooling': color = '#FF9900' From 292dbc5866c0b0b2ad56278591dce8b519166b20 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Mon, 24 Aug 2015 14:46:23 -0700 Subject: [PATCH 232/446] Fix previous mistake on unimplemented top and address pyramid_height_==1 in SPPLayer also, do nothing in SPPLayer Reshape if already reshaped once and bottom size unchanged --- include/caffe/vision_layers.hpp | 10 +++------ src/caffe/layers/spp_layer.cpp | 36 +++++++++++++++++++++++++++++++++ 2 files changed, 39 insertions(+), 7 deletions(-) diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp index a6bd86a93f5..211e3d9042d 100644 --- a/include/caffe/vision_layers.hpp +++ b/include/caffe/vision_layers.hpp @@ -471,13 +471,7 @@ class SPPLayer : public Layer { virtual inline const char* type() const { return "SPP"; } virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int MinTopBlobs() const { return 1; } - // MAX POOL layers can output an extra top blob for the mask; - // others can only output the pooled inputs. - virtual inline int MaxTopBlobs() const { - return (this->layer_param_.pooling_param().pool() == - PoolingParameter_PoolMethod_MAX) ? 2 : 1; - } + virtual inline int ExactNumTopBlobs() const { return 1; } protected: virtual void Forward_cpu(const vector*>& bottom, @@ -491,9 +485,11 @@ class SPPLayer : public Layer { int pyramid_height_; int bottom_h_, bottom_w_; + int num_; int channels_; int kernel_h_, kernel_w_; int pad_h_, pad_w_; + bool reshaped_first_time_; /// the internal Split layer that feeds the pooling layers shared_ptr > split_layer_; diff --git a/src/caffe/layers/spp_layer.cpp b/src/caffe/layers/spp_layer.cpp index 795dd71693e..d7622910495 100644 --- a/src/caffe/layers/spp_layer.cpp +++ b/src/caffe/layers/spp_layer.cpp @@ -66,8 +66,11 @@ void SPPLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { SPPParameter spp_param = this->layer_param_.spp_param(); + num_ = bottom[0]->num(); + channels_ = bottom[0]->channels(); bottom_h_ = bottom[0]->height(); bottom_w_ = bottom[0]->width(); + reshaped_first_time_ = false; CHECK_GT(bottom_h_, 0) << "Input dimensions cannot be zero."; CHECK_GT(bottom_w_, 0) << "Input dimensions cannot be zero."; @@ -82,6 +85,15 @@ void SPPLayer::LayerSetUp(const vector*>& bottom, flatten_outputs_.clear(); concat_bottom_vec_.clear(); + if (pyramid_height_ == 1) { + // pooling layer setup + LayerParameter pooling_param = GetPoolingParam(0, bottom_h_, bottom_w_, + spp_param); + pooling_layers_.push_back(shared_ptr > ( + new PoolingLayer(pooling_param))); + pooling_layers_[0]->SetUp(bottom, top); + return; + } // split layer output holders setup for (int i = 0; i < pyramid_height_; i++) { split_top_vec_.push_back(new Blob()); @@ -135,10 +147,26 @@ void SPPLayer::Reshape(const vector*>& bottom, const vector*>& top) { CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, " << "corresponding to (num, channels, height, width)"; + // Do nothing if bottom shape is unchanged since last Reshape + if (num_ == bottom[0]->num() && channels_ == bottom[0]->channels() && + bottom_h_ == bottom[0]->height() && bottom_w_ == bottom[0]->width() && + reshaped_first_time_) { + return; + } + num_ = bottom[0]->num(); channels_ = bottom[0]->channels(); bottom_h_ = bottom[0]->height(); bottom_w_ = bottom[0]->width(); + reshaped_first_time_ = true; SPPParameter spp_param = this->layer_param_.spp_param(); + if (pyramid_height_ == 1) { + LayerParameter pooling_param = GetPoolingParam(0, bottom_h_, bottom_w_, + spp_param); + pooling_layers_[0].reset(new PoolingLayer(pooling_param)); + pooling_layers_[0]->SetUp(bottom, top); + pooling_layers_[0]->Reshape(bottom, top); + return; + } split_layer_->Reshape(bottom, split_top_vec_); for (int i = 0; i < pyramid_height_; i++) { LayerParameter pooling_param = GetPoolingParam( @@ -159,6 +187,10 @@ void SPPLayer::Reshape(const vector*>& bottom, template void SPPLayer::Forward_cpu(const vector*>& bottom, const vector*>& top) { + if (pyramid_height_ == 1) { + pooling_layers_[0]->Forward(bottom, top); + return; + } split_layer_->Forward(bottom, split_top_vec_); for (int i = 0; i < pyramid_height_; i++) { pooling_layers_[i]->Forward( @@ -175,6 +207,10 @@ void SPPLayer::Backward_cpu(const vector*>& top, if (!propagate_down[0]) { return; } + if (pyramid_height_ == 1) { + pooling_layers_[0]->Backward(top, propagate_down, bottom); + return; + } vector concat_propagate_down(pyramid_height_, true); concat_layer_->Backward(top, concat_propagate_down, concat_bottom_vec_); for (int i = 0; i < pyramid_height_; i++) { From 4f64b9ee3ed6c1267c4252cf79b2ccf0d042f0b2 Mon Sep 17 00:00:00 2001 From: Matt Dawkins Date: Thu, 27 Aug 2015 10:51:36 -0400 Subject: [PATCH 233/446] Add extra openblas search path --- cmake/Modules/FindOpenBLAS.cmake | 2 ++ 1 file changed, 2 insertions(+) diff --git a/cmake/Modules/FindOpenBLAS.cmake b/cmake/Modules/FindOpenBLAS.cmake index b8434927a4d..a6512ae7e4e 100644 --- a/cmake/Modules/FindOpenBLAS.cmake +++ b/cmake/Modules/FindOpenBLAS.cmake @@ -2,8 +2,10 @@ SET(Open_BLAS_INCLUDE_SEARCH_PATHS /usr/include + /usr/include/openblas /usr/include/openblas-base /usr/local/include + /usr/local/include/openblas /usr/local/include/openblas-base /opt/OpenBLAS/include $ENV{OpenBLAS_HOME} From 4d7fe4de7e7bddfe107f4a37a7ec85c6f6178469 Mon Sep 17 00:00:00 2001 From: J Yegerlehner Date: Thu, 27 Aug 2015 10:47:14 -0500 Subject: [PATCH 234/446] Fix EmbedLayer compiler warning for unused variable. --- src/caffe/layers/embed_layer.cu | 1 - 1 file changed, 1 deletion(-) diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu index 672fb9c608c..62a4db81336 100644 --- a/src/caffe/layers/embed_layer.cu +++ b/src/caffe/layers/embed_layer.cu @@ -64,7 +64,6 @@ void EmbedLayer::Backward_gpu(const vector*>& top, CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input."; if (this->param_propagate_down_[0]) { const int top_count = top[0]->count(); - const int count = this->blobs_[0]->count(); const Dtype* top_diff = top[0]->gpu_diff(); const Dtype* bottom_data = bottom[0]->gpu_data(); Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff(); From 846f2c3cce8b937637e0b46a7f62be068b835ade Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Fri, 28 Aug 2015 21:27:11 -0700 Subject: [PATCH 235/446] fix GPU data race Previously, the prefetch GPU -> top GPU and prefetch CPU -> prefetch GPU copies were launched concurrently in separate streams, allowing the next batch to be copied in before the current one is read. This patch explicitly synchronizes the prefetch -> top copy wrt the host, preventing the CPU -> GPU from being launched until its completion. --- src/caffe/layers/base_data_layer.cu | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu index 56439bc506a..ff6e412aba6 100644 --- a/src/caffe/layers/base_data_layer.cu +++ b/src/caffe/layers/base_data_layer.cu @@ -20,7 +20,9 @@ void BasePrefetchingDataLayer::Forward_gpu( caffe_copy(batch->label_.count(), batch->label_.gpu_data(), top[1]->mutable_gpu_data()); } - + // Ensure the copy is synchronous wrt the host, so that the next batch isn't + // copied in meanwhile. + CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault)); prefetch_free_.push(batch); } From a6751723234926bdd03b6167ea6414da109854a3 Mon Sep 17 00:00:00 2001 From: philkr Date: Tue, 1 Sep 2015 13:11:26 -0700 Subject: [PATCH 236/446] Compute backward for negative lr_mult --- src/caffe/net.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index f1fc63aba95..89d14013dc9 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -182,7 +182,7 @@ void Net::Init(const NetParameter& in_param) { for (int param_id = 0; param_id < num_param_blobs; ++param_id) { const ParamSpec* param_spec = (param_id < param_size) ? &layer_param.param(param_id) : &default_param_spec; - const bool param_need_backward = param_spec->lr_mult() > 0; + const bool param_need_backward = param_spec->lr_mult() != 0; need_backward |= param_need_backward; layers_[layer_id]->set_param_propagate_down(param_id, param_need_backward); From b04000e4204753803f4cdead66af8a830bc7c4ca Mon Sep 17 00:00:00 2001 From: Darren Garvey Date: Tue, 1 Sep 2015 15:42:26 +0100 Subject: [PATCH 237/446] Cleanup: Fixup capitalisation of Caffe_POSTFIX. Replaces CAffe_POSTFIX -> Caffe_POSTFIX. --- cmake/Misc.cmake | 4 ++-- examples/CMakeLists.txt | 2 +- matlab/CMakeLists.txt | 4 ++-- python/CMakeLists.txt | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake index 7676754fe04..9dd2609b36a 100644 --- a/cmake/Misc.cmake +++ b/cmake/Misc.cmake @@ -46,7 +46,7 @@ endif() # ---[ Set debug postfix set(Caffe_DEBUG_POSTFIX "-d") -set(CAffe_POSTFIX "") +set(Caffe_POSTFIX "") if(CMAKE_BUILD_TYPE MATCHES "Debug") - set(CAffe_POSTFIX ${Caffe_DEBUG_POSTFIX}) + set(Caffe_POSTFIX ${Caffe_DEBUG_POSTFIX}) endif() diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index f29fc7e5522..663d7360b7d 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -24,7 +24,7 @@ foreach(source_file ${examples_srcs}) if(UNIX OR APPLE) # Funny command to make tutorials work # TODO: remove in future as soon as naming is standartaized everywhere - set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${CAffe_POSTFIX}) + set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${Caffe_POSTFIX}) add_custom_command(TARGET ${name} POST_BUILD COMMAND ln -sf "${__outname}" "${__outname}.bin") endif() diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt index 4b0d549f07f..f420df8d412 100644 --- a/matlab/CMakeLists.txt +++ b/matlab/CMakeLists.txt @@ -43,7 +43,7 @@ string(REPLACE ";" ";-L" link_folders "-L${folders}") string(REPLACE ";" ":" rpath_folders "${folders}") if(build_using MATCHES "Matlab") - set(libflags -lcaffe${CAffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive + set(libflags -lcaffe${Caffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive caffe_fetch_and_set_proper_mexext(Matlab_caffe_mex) add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Matlab_mex} @@ -56,7 +56,7 @@ elseif(build_using MATCHES "Octave") if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") set(libflags -Wl,-force_load,$ ${libflags}) elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU") - set(libflags -Wl,--whole-archive -lcaffe${CAffe_POSTFIX} -Wl,--no-whole-archive ${libflags}) + set(libflags -Wl,--whole-archive -lcaffe${Caffe_POSTFIX} -Wl,--no-whole-archive ${libflags}) endif() add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Octave_compiler} diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index df0401daa1c..0e2bc7e66a8 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -18,7 +18,7 @@ if(UNIX OR APPLE) COMMAND ${CMAKE_COMMAND} -E make_directory ${PROJECT_SOURCE_DIR}/python/caffe/proto COMMAND touch ${PROJECT_SOURCE_DIR}/python/caffe/proto/__init__.py COMMAND cp ${proto_gen_folder}/*.py ${PROJECT_SOURCE_DIR}/python/caffe/proto/ - COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so") + COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${Caffe_POSTFIX}.so") endif() # ---[ Install From e8f96f58aa6b64726f62f7304964d1c0a82b5c38 Mon Sep 17 00:00:00 2001 From: Darren Garvey Date: Mon, 10 Aug 2015 02:16:20 +0100 Subject: [PATCH 238/446] Fix memory leak in convert_mnist_siamese_data. This fixes a memory leak by using delete[] rather than plain delete. --- examples/siamese/convert_mnist_siamese_data.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/siamese/convert_mnist_siamese_data.cpp b/examples/siamese/convert_mnist_siamese_data.cpp index 71c56a0ae61..8008b4439c5 100644 --- a/examples/siamese/convert_mnist_siamese_data.cpp +++ b/examples/siamese/convert_mnist_siamese_data.cpp @@ -102,7 +102,7 @@ void convert_dataset(const char* image_filename, const char* label_filename, } delete db; - delete pixels; + delete [] pixels; } int main(int argc, char** argv) { From 583194a00811a312e0bdfa151a8ad6a222c2dd4d Mon Sep 17 00:00:00 2001 From: Darren Garvey Date: Tue, 1 Sep 2015 20:09:25 +0100 Subject: [PATCH 239/446] cifar10: Fix examples by setting snapshot_format. Commit 4227828a set the default binary format from HDF5 to BINARYPROTO to fix #2885. This broke the cifar10 examples which relied on this default. This commit specifies the snapshot_format explicitly since the rest of the example relies on this being HDF5. --- examples/cifar10/cifar10_full_solver.prototxt | 1 + examples/cifar10/cifar10_full_solver_lr1.prototxt | 1 + examples/cifar10/cifar10_full_solver_lr2.prototxt | 1 + examples/cifar10/cifar10_quick_solver.prototxt | 1 + examples/cifar10/cifar10_quick_solver_lr1.prototxt | 1 + 5 files changed, 5 insertions(+) diff --git a/examples/cifar10/cifar10_full_solver.prototxt b/examples/cifar10/cifar10_full_solver.prototxt index f30b3986142..882daa2d2b5 100644 --- a/examples/cifar10/cifar10_full_solver.prototxt +++ b/examples/cifar10/cifar10_full_solver.prototxt @@ -21,6 +21,7 @@ display: 200 max_iter: 60000 # snapshot intermediate results snapshot: 10000 +snapshot_format: HDF5 snapshot_prefix: "examples/cifar10/cifar10_full" # solver mode: CPU or GPU solver_mode: GPU diff --git a/examples/cifar10/cifar10_full_solver_lr1.prototxt b/examples/cifar10/cifar10_full_solver_lr1.prototxt index 59bc5721f4c..55f4be44053 100644 --- a/examples/cifar10/cifar10_full_solver_lr1.prototxt +++ b/examples/cifar10/cifar10_full_solver_lr1.prototxt @@ -21,6 +21,7 @@ display: 200 max_iter: 65000 # snapshot intermediate results snapshot: 5000 +snapshot_format: HDF5 snapshot_prefix: "examples/cifar10/cifar10_full" # solver mode: CPU or GPU solver_mode: GPU diff --git a/examples/cifar10/cifar10_full_solver_lr2.prototxt b/examples/cifar10/cifar10_full_solver_lr2.prototxt index d4ed5d8e041..7c3d2da31de 100644 --- a/examples/cifar10/cifar10_full_solver_lr2.prototxt +++ b/examples/cifar10/cifar10_full_solver_lr2.prototxt @@ -21,6 +21,7 @@ display: 200 max_iter: 70000 # snapshot intermediate results snapshot: 5000 +snapshot_format: HDF5 snapshot_prefix: "examples/cifar10/cifar10_full" # solver mode: CPU or GPU solver_mode: GPU diff --git a/examples/cifar10/cifar10_quick_solver.prototxt b/examples/cifar10/cifar10_quick_solver.prototxt index 14b4401ba16..5de276f722f 100644 --- a/examples/cifar10/cifar10_quick_solver.prototxt +++ b/examples/cifar10/cifar10_quick_solver.prototxt @@ -20,6 +20,7 @@ display: 100 max_iter: 4000 # snapshot intermediate results snapshot: 4000 +snapshot_format: HDF5 snapshot_prefix: "examples/cifar10/cifar10_quick" # solver mode: CPU or GPU solver_mode: GPU diff --git a/examples/cifar10/cifar10_quick_solver_lr1.prototxt b/examples/cifar10/cifar10_quick_solver_lr1.prototxt index d3af70c05e7..f8f1efd54af 100644 --- a/examples/cifar10/cifar10_quick_solver_lr1.prototxt +++ b/examples/cifar10/cifar10_quick_solver_lr1.prototxt @@ -20,6 +20,7 @@ display: 100 max_iter: 5000 # snapshot intermediate results snapshot: 5000 +snapshot_format: HDF5 snapshot_prefix: "examples/cifar10/cifar10_quick" # solver mode: CPU or GPU solver_mode: GPU From 6f5812c4547dd912dd0569330ebdd44a5afd278e Mon Sep 17 00:00:00 2001 From: Darren Garvey Date: Wed, 2 Sep 2015 00:54:06 +0100 Subject: [PATCH 240/446] Fix up documentation errors. Fix some doxygen warnings about an undocumented argument in Blob and incorrect documentation for SoftmaxWithLossLayer::Forward_cpu(). --- include/caffe/blob.hpp | 2 +- include/caffe/loss_layers.hpp | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp index 9b813e739e9..dda7b1f8372 100644 --- a/include/caffe/blob.hpp +++ b/include/caffe/blob.hpp @@ -109,7 +109,7 @@ class Blob { * @brief Returns the 'canonical' version of a (usually) user-specified axis, * allowing for negative indexing (e.g., -1 for the last axis). * - * @param index the axis index. + * @param axis_index the axis index. * If 0 <= index < num_axes(), return index. * If -num_axes <= index <= -1, return (num_axes() - (-index)), * e.g., the last axis index (num_axes() - 1) if index == -1, diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp index 02687a944fb..8d41af34e88 100644 --- a/include/caffe/loss_layers.hpp +++ b/include/caffe/loss_layers.hpp @@ -712,7 +712,6 @@ class SoftmaxWithLossLayer : public LossLayer { virtual inline int MaxTopBlobs() const { return 2; } protected: - /// @copydoc SoftmaxWithLossLayer virtual void Forward_cpu(const vector*>& bottom, const vector*>& top); virtual void Forward_gpu(const vector*>& bottom, From 6ca0ab66077c578dff14aa775858035b2a69fed6 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Tue, 1 Sep 2015 17:20:37 -0700 Subject: [PATCH 241/446] Show output from convert_imageset tool --- tools/convert_imageset.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp index 816a91f971b..aad1f1fe216 100644 --- a/tools/convert_imageset.cpp +++ b/tools/convert_imageset.cpp @@ -44,6 +44,8 @@ DEFINE_string(encode_type, "", int main(int argc, char** argv) { ::google::InitGoogleLogging(argv[0]); + // Print output to stderr (while still logging) + FLAGS_alsologtostderr = 1; #ifndef GFLAGS_GFLAGS_H_ namespace gflags = google; @@ -140,13 +142,13 @@ int main(int argc, char** argv) { // Commit db txn->Commit(); txn.reset(db->NewTransaction()); - LOG(ERROR) << "Processed " << count << " files."; + LOG(INFO) << "Processed " << count << " files."; } } // write the last batch if (count % 1000 != 0) { txn->Commit(); - LOG(ERROR) << "Processed " << count << " files."; + LOG(INFO) << "Processed " << count << " files."; } return 0; } From d1a0345eaaff501d11a1705986cac8f124dd2545 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Tue, 7 Oct 2014 11:55:54 -0700 Subject: [PATCH 242/446] SliceLayer: allow trivial operation with single top Blob --- include/caffe/common_layers.hpp | 2 +- src/caffe/layers/slice_layer.cpp | 7 ++++++- src/caffe/layers/slice_layer.cu | 3 ++- src/caffe/test/test_slice_layer.cpp | 27 +++++++++++++++++++++++++++ 4 files changed, 36 insertions(+), 3 deletions(-) diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 8e64b3e5dc5..6d4a9e3cb81 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -625,7 +625,7 @@ class SliceLayer : public Layer { virtual inline const char* type() const { return "Slice"; } virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int MinTopBlobs() const { return 2; } + virtual inline int MinTopBlobs() const { return 1; } protected: virtual void Forward_cpu(const vector*>& bottom, diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp index e4418c9cf9c..0a059ae88fe 100644 --- a/src/caffe/layers/slice_layer.cpp +++ b/src/caffe/layers/slice_layer.cpp @@ -67,11 +67,16 @@ void SliceLayer::Reshape(const vector*>& bottom, } } CHECK_EQ(count, bottom[0]->count()); + if (top.size() == 1) { + top[0]->ShareData(*bottom[0]); + top[0]->ShareDiff(*bottom[0]); + } } template void SliceLayer::Forward_cpu(const vector*>& bottom, const vector*>& top) { + if (top.size() == 1) { return; } int offset_slice_axis = 0; const Dtype* bottom_data = bottom[0]->cpu_data(); const int bottom_slice_axis = bottom[0]->shape(slice_axis_); @@ -92,7 +97,7 @@ void SliceLayer::Forward_cpu(const vector*>& bottom, template void SliceLayer::Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { - if (!propagate_down[0]) { return; } + if (!propagate_down[0] || top.size() == 1) { return; } int offset_slice_axis = 0; Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); const int bottom_slice_axis = bottom[0]->shape(slice_axis_); diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu index 796841d3f52..e8dc6cd98fc 100644 --- a/src/caffe/layers/slice_layer.cu +++ b/src/caffe/layers/slice_layer.cu @@ -28,6 +28,7 @@ __global__ void Slice(const int nthreads, const Dtype* in_data, template void SliceLayer::Forward_gpu(const vector*>& bottom, const vector*>& top) { + if (top.size() == 1) { return; } int offset_slice_axis = 0; const Dtype* bottom_data = bottom[0]->gpu_data(); const int bottom_slice_axis = bottom[0]->shape(slice_axis_); @@ -48,7 +49,7 @@ void SliceLayer::Forward_gpu(const vector*>& bottom, template void SliceLayer::Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { - if (!propagate_down[0]) { return; } + if (!propagate_down[0] || top.size() == 1) { return; } int offset_slice_axis = 0; Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); const int bottom_slice_axis = bottom[0]->shape(slice_axis_); diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp index ccd03646d19..2d2d0fdc005 100644 --- a/src/caffe/test/test_slice_layer.cpp +++ b/src/caffe/test/test_slice_layer.cpp @@ -88,6 +88,21 @@ TYPED_TEST(SliceLayerTest, TestSetupChannels) { EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0_->width()); } +TYPED_TEST(SliceLayerTest, TestTrivialSlice) { + // Test the trivial (single output) "slice" operation -- + // should be the identity. + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + SliceLayer layer(layer_param); + this->blob_top_vec_0_.resize(1); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_0_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_0_->shape()); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_EQ(this->blob_bottom_->cpu_data()[i], + this->blob_top_0_->cpu_data()[i]); + } +} + TYPED_TEST(SliceLayerTest, TestSliceAcrossNum) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; @@ -161,6 +176,18 @@ TYPED_TEST(SliceLayerTest, TestSliceAcrossChannels) { } } +TYPED_TEST(SliceLayerTest, TestGradientTrivial) { + // Test the trivial (single output) "slice" operation -- + // should be the identity. + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + SliceLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + this->blob_top_vec_0_.resize(1); + checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_, + this->blob_top_vec_0_); +} + TYPED_TEST(SliceLayerTest, TestGradientAcrossNum) { typedef typename TypeParam::Dtype Dtype; // Gradient checks are slow; reduce blob size. From 784dfddd42cc787fd9661a954f2a360990867df2 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Fri, 23 Jan 2015 12:52:44 -0800 Subject: [PATCH 243/446] ConcatLayer: allow trivial operation with single bottom Blob --- include/caffe/common_layers.hpp | 2 +- src/caffe/layers/concat_layer.cpp | 6 ++++++ src/caffe/layers/concat_layer.cu | 2 ++ src/caffe/test/test_concat_layer.cpp | 23 +++++++++++++++++++++++ 4 files changed, 32 insertions(+), 1 deletion(-) diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 6d4a9e3cb81..89bab8d6f3a 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -85,7 +85,7 @@ class ConcatLayer : public Layer { const vector*>& top); virtual inline const char* type() const { return "Concat"; } - virtual inline int MinBottomBlobs() const { return 2; } + virtual inline int MinBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } protected: diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp index 95fba105b9a..86b500de859 100644 --- a/src/caffe/layers/concat_layer.cpp +++ b/src/caffe/layers/concat_layer.cpp @@ -48,11 +48,16 @@ void ConcatLayer::Reshape(const vector*>& bottom, } top[0]->Reshape(top_shape); CHECK_EQ(bottom_count_sum, top[0]->count()); + if (bottom.size() == 1) { + top[0]->ShareData(*bottom[0]); + top[0]->ShareDiff(*bottom[0]); + } } template void ConcatLayer::Forward_cpu(const vector*>& bottom, const vector*>& top) { + if (bottom.size() == 1) { return; } Dtype* top_data = top[0]->mutable_cpu_data(); int offset_concat_axis = 0; const int top_concat_axis = top[0]->shape(concat_axis_); @@ -72,6 +77,7 @@ void ConcatLayer::Forward_cpu(const vector*>& bottom, template void ConcatLayer::Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { + if (bottom.size() == 1) { return; } const Dtype* top_diff = top[0]->cpu_diff(); int offset_concat_axis = 0; const int top_concat_axis = top[0]->shape(concat_axis_); diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu index 3c64c7ef224..617701e2621 100644 --- a/src/caffe/layers/concat_layer.cu +++ b/src/caffe/layers/concat_layer.cu @@ -28,6 +28,7 @@ __global__ void Concat(const int nthreads, const Dtype* in_data, template void ConcatLayer::Forward_gpu(const vector*>& bottom, const vector*>& top) { + if (bottom.size() == 1) { return; } Dtype* top_data = top[0]->mutable_gpu_data(); int offset_concat_axis = 0; const int top_concat_axis = top[0]->shape(concat_axis_); @@ -48,6 +49,7 @@ void ConcatLayer::Forward_gpu(const vector*>& bottom, template void ConcatLayer::Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { + if (bottom.size() == 1) { return; } const Dtype* top_diff = top[0]->gpu_diff(); int offset_concat_axis = 0; const int top_concat_axis = top[0]->shape(concat_axis_); diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp index 088e0a41685..ccd97eb1d66 100644 --- a/src/caffe/test/test_concat_layer.cpp +++ b/src/caffe/test/test_concat_layer.cpp @@ -99,6 +99,19 @@ TYPED_TEST(ConcatLayerTest, TestSetupChannelsNegativeIndexing) { EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width()); } +TYPED_TEST(ConcatLayerTest, TestForwardTrivial) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ConcatLayer layer(layer_param); + this->blob_bottom_vec_0_.resize(1); + layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_); + for (int i = 0; i < this->blob_bottom_0_->count(); ++i) { + EXPECT_EQ(this->blob_bottom_0_->cpu_data()[i], + this->blob_top_->cpu_data()[i]); + } +} + TYPED_TEST(ConcatLayerTest, TestForwardNum) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; @@ -154,6 +167,16 @@ TYPED_TEST(ConcatLayerTest, TestForwardChannels) { } } +TYPED_TEST(ConcatLayerTest, TestGradientTrivial) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ConcatLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + this->blob_bottom_vec_0_.resize(1); + checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_0_, + this->blob_top_vec_); +} + TYPED_TEST(ConcatLayerTest, TestGradientNum) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; From cf1516634d677cb8d2b2068e2b795c9b58a7c098 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Sun, 15 Feb 2015 15:18:56 -0800 Subject: [PATCH 244/446] Net: expose param_display_names_ --- include/caffe/net.hpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index 1bf07d28d13..bed241d2a6c 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -179,6 +179,9 @@ class Net { return param_names_index_; } inline const vector& param_owners() const { return param_owners_; } + inline const vector& param_display_names() const { + return param_display_names_; + } /// @brief Input and output blob numbers inline int num_inputs() const { return net_input_blobs_.size(); } inline int num_outputs() const { return net_output_blobs_.size(); } From 46c3f00bc91819668fb384b7982706d19b2a9fd6 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Thu, 3 Sep 2015 14:57:19 -0700 Subject: [PATCH 245/446] net.cpp fix debug_info params -> learnable_params --- src/caffe/net.cpp | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 89d14013dc9..ebb8b5d28c2 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -810,12 +810,11 @@ void Net::Backward() { BackwardFromTo(layers_.size() - 1, 0); if (debug_info_) { Dtype asum_data = 0, asum_diff = 0, sumsq_data = 0, sumsq_diff = 0; - for (int i = 0; i < params_.size(); ++i) { - if (param_owners_[i] >= 0) { continue; } - asum_data += params_[i]->asum_data(); - asum_diff += params_[i]->asum_diff(); - sumsq_data += params_[i]->sumsq_data(); - sumsq_diff += params_[i]->sumsq_diff(); + for (int i = 0; i < learnable_params_.size(); ++i) { + asum_data += learnable_params_[i]->asum_data(); + asum_diff += learnable_params_[i]->asum_diff(); + sumsq_data += learnable_params_[i]->sumsq_data(); + sumsq_diff += learnable_params_[i]->sumsq_diff(); } const Dtype l2norm_data = std::sqrt(sumsq_data); const Dtype l2norm_diff = std::sqrt(sumsq_diff); From c2484747d813d616bcb504d97f93071b26bb372d Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Fri, 21 Aug 2015 17:29:06 -0700 Subject: [PATCH 246/446] NetSpec: don't require lists to specify single-element repeated fields --- python/caffe/net_spec.py | 10 ++++++++-- python/caffe/test/test_net_spec.py | 3 +-- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py index 77a0e0070ae..93fc01927db 100644 --- a/python/caffe/net_spec.py +++ b/python/caffe/net_spec.py @@ -56,8 +56,14 @@ def to_proto(*tops): def assign_proto(proto, name, val): """Assign a Python object to a protobuf message, based on the Python type (in recursive fashion). Lists become repeated fields/messages, dicts - become messages, and other types are assigned directly.""" - + become messages, and other types are assigned directly. For convenience, + repeated fields whose values are not lists are converted to single-element + lists; e.g., `my_repeated_int_field=3` is converted to + `my_repeated_int_field=[3]`.""" + + is_repeated_field = hasattr(getattr(proto, name), 'extend') + if is_repeated_field and not isinstance(val, list): + val = [val] if isinstance(val, list): if isinstance(val[0], dict): for item in val: diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py index b4595e6531a..fee3c0aaebe 100644 --- a/python/caffe/test/test_net_spec.py +++ b/python/caffe/test/test_net_spec.py @@ -43,8 +43,7 @@ def anon_lenet(batch_size): def silent_net(): n = caffe.NetSpec() - n.data, n.data2 = L.DummyData(shape=[dict(dim=[3]), dict(dim=[4, 2])], - ntop=2) + n.data, n.data2 = L.DummyData(shape=dict(dim=3), ntop=2) n.silence_data = L.Silence(n.data, ntop=0) n.silence_data2 = L.Silence(n.data2, ntop=0) return n.to_proto() From 1bdc18c5beb4c6e679ed359eb707be8822306ea5 Mon Sep 17 00:00:00 2001 From: Lumin Zhou Date: Fri, 4 Sep 2015 04:38:43 +0000 Subject: [PATCH 247/446] Update extract_features.cpp --- tools/extract_features.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp index 365dd495bbf..084c9bf88df 100644 --- a/tools/extract_features.cpp +++ b/tools/extract_features.cpp @@ -42,7 +42,7 @@ int feature_extraction_pipeline(int argc, char** argv) { " save_feature_dataset_name1[,name2,...] num_mini_batches db_type" " [CPU/GPU] [DEVICE_ID=0]\n" "Note: you can extract multiple features in one pass by specifying" - " multiple feature blob names and dataset names seperated by ','." + " multiple feature blob names and dataset names separated by ','." " The names cannot contain white space characters and the number of blobs" " and datasets must be equal."; return 1; From aa40ab98717507a60c23fb9cd4bf61c8b0bbb38d Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Thu, 3 Sep 2015 21:44:45 -0700 Subject: [PATCH 248/446] Fix AccuracyLayerTest for per-class accuracy. Fix AccuracyLayerTest for per-class accuracy. Previously in #2935, it crashes since the test accuracy is nan (0/0) when a class never appear. --- src/caffe/test/test_accuracy_layer.cpp | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp index 94e529b5eee..ef0e57a37a1 100644 --- a/src/caffe/test/test_accuracy_layer.cpp +++ b/src/caffe/test/test_accuracy_layer.cpp @@ -250,7 +250,6 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) { TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClass) { LayerParameter layer_param; - Caffe::set_mode(Caffe::CPU); AccuracyLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_); @@ -279,16 +278,16 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClass) { EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0), num_correct_labels / 100.0, 1e-4); for (int i = 0; i < num_class; ++i) { + TypeParam accuracy_per_class = (num_per_class[i] > 0 ? + static_cast(correct_per_class[i]) / num_per_class[i] : 0); EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0), - static_cast(correct_per_class[i]) / num_per_class[i], - 1e-4); + accuracy_per_class, 1e-4); } } TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClassWithIgnoreLabel) { LayerParameter layer_param; - Caffe::set_mode(Caffe::CPU); const TypeParam kIgnoreLabelValue = -1; layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue); AccuracyLayer layer(layer_param); @@ -329,9 +328,10 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClassWithIgnoreLabel) { EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0), num_correct_labels / TypeParam(count), 1e-4); for (int i = 0; i < 10; ++i) { + TypeParam accuracy_per_class = (num_per_class[i] > 0 ? + static_cast(correct_per_class[i]) / num_per_class[i] : 0); EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0), - TypeParam(correct_per_class[i]) / num_per_class[i], - 1e-4); + accuracy_per_class, 1e-4); } } From 5cc76ad2e38f19a140497ff09c475500da9d76cf Mon Sep 17 00:00:00 2001 From: Daniel Gordon Date: Fri, 4 Sep 2015 10:12:00 -0700 Subject: [PATCH 249/446] enabling the alternate solvers to be accessed by the python interface --- python/caffe/__init__.py | 2 +- python/caffe/_caffe.cpp | 9 +++++++++ python/caffe/pycaffe.py | 3 ++- 3 files changed, 12 insertions(+), 2 deletions(-) diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py index 6cc44e729f4..ccda1bcae4f 100644 --- a/python/caffe/__init__.py +++ b/python/caffe/__init__.py @@ -1,4 +1,4 @@ -from .pycaffe import Net, SGDSolver +from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list from .proto.caffe_pb2 import TRAIN, TEST from .classifier import Classifier diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index cc49f60ab13..ccd5776ac40 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -297,6 +297,15 @@ BOOST_PYTHON_MODULE(_caffe) { bp::class_, bp::bases >, shared_ptr >, boost::noncopyable>( "AdaGradSolver", bp::init()); + bp::class_, bp::bases >, + shared_ptr >, boost::noncopyable>( + "RMSPropSolver", bp::init()); + bp::class_, bp::bases >, + shared_ptr >, boost::noncopyable>( + "AdaDeltaSolver", bp::init()); + bp::class_, bp::bases >, + shared_ptr >, boost::noncopyable>( + "AdamSolver", bp::init()); bp::def("get_solver", &GetSolverFromFile, bp::return_value_policy()); diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py index 4f980a92c38..8ea24da4fdd 100644 --- a/python/caffe/pycaffe.py +++ b/python/caffe/pycaffe.py @@ -10,7 +10,8 @@ from itertools import zip_longest as izip_longest import numpy as np -from ._caffe import Net, SGDSolver +from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \ + RMSPropSolver, AdaDeltaSolver, AdamSolver import caffe.io # We directly update methods from Net here (rather than using composition or From 1394cdc383e2f41d7435862442b15151e8ac1237 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Fri, 4 Sep 2015 14:16:31 -0700 Subject: [PATCH 250/446] disallow PythonLayer in Multi-GPU training --- include/caffe/python_layer.hpp | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/include/caffe/python_layer.hpp b/include/caffe/python_layer.hpp index c43c1e8a91b..b839d52684e 100644 --- a/include/caffe/python_layer.hpp +++ b/include/caffe/python_layer.hpp @@ -18,6 +18,12 @@ class PythonLayer : public Layer { virtual void LayerSetUp(const vector*>& bottom, const vector*>& top) { + // Disallow PythonLayer in MultiGPU training stage, due to GIL issues + // Details: https://github.com/BVLC/caffe/issues/2936 + if (this->phase_ == TRAIN && Caffe::solver_count() > 1 + && !ShareInParallel()) { + LOG(FATAL) << "PythonLayer is not implemented in Multi-GPU training"; + } self_.attr("param_str") = bp::str( this->layer_param_.python_param().param_str()); self_.attr("setup")(bottom, top); From 8fbac04ac32672ae8a97f8a1171f1d39456b97ca Mon Sep 17 00:00:00 2001 From: Sean Bell Date: Wed, 9 Sep 2015 12:52:52 -0400 Subject: [PATCH 251/446] Minor: missing space in string formatting --- src/caffe/solver.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 394ec3b3ad7..2e59a881d82 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -732,7 +732,7 @@ void SGDSolver::SnapshotSolverStateToBinaryProto( } string snapshot_filename = Solver::SnapshotFilename(".solverstate"); LOG(INFO) - << "Snapshotting solver state to binary proto file" << snapshot_filename; + << "Snapshotting solver state to binary proto file " << snapshot_filename; WriteProtoToBinaryFile(state, snapshot_filename.c_str()); } From 3456259d400f7eef27e07c15c34f22b8d5e13bdd Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Sun, 13 Sep 2015 20:46:24 -0700 Subject: [PATCH 252/446] Use EXPECT_NEAR in EltwiseLayer test Otherwise there seem to be some numerical issues causing BLAS results not exactly same as evaluated results in test code. --- src/caffe/test/test_eltwise_layer.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp index be0c1347709..8031f6e9022 100644 --- a/src/caffe/test/test_eltwise_layer.cpp +++ b/src/caffe/test/test_eltwise_layer.cpp @@ -80,7 +80,7 @@ TYPED_TEST(EltwiseLayerTest, TestProd) { const Dtype* in_data_b = this->blob_bottom_b_->cpu_data(); const Dtype* in_data_c = this->blob_bottom_c_->cpu_data(); for (int i = 0; i < count; ++i) { - EXPECT_EQ(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i]); + EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i], 1e-4); } } @@ -99,7 +99,7 @@ TYPED_TEST(EltwiseLayerTest, TestSum) { const Dtype* in_data_b = this->blob_bottom_b_->cpu_data(); const Dtype* in_data_c = this->blob_bottom_c_->cpu_data(); for (int i = 0; i < count; ++i) { - EXPECT_EQ(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i]); + EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i], 1e-4); } } From ab554cb4918cf7bccfada00339b4d1d5ccf3b4af Mon Sep 17 00:00:00 2001 From: Sean Bell Date: Wed, 9 Sep 2015 12:49:27 -0400 Subject: [PATCH 253/446] Check that the snapshot directory is writeable before starting training --- include/caffe/solver.hpp | 2 ++ src/caffe/solver.cpp | 19 +++++++++++++++++++ 2 files changed, 21 insertions(+) diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index aba3e036004..8d52785ac6e 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -82,6 +82,8 @@ class Solver { callbacks_.push_back(value); } + void CheckSnapshotWritePermissions(); + protected: // Make and apply the update value for the current iteration. virtual void ApplyUpdate() = 0; diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 394ec3b3ad7..47493174f46 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -55,6 +55,7 @@ void Solver::Init(const SolverParameter& param) { << std::endl << param.DebugString(); param_ = param; CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative."; + CheckSnapshotWritePermissions(); if (Caffe::root_solver() && param_.random_seed() >= 0) { Caffe::set_random_seed(param_.random_seed()); } @@ -434,6 +435,24 @@ void Solver::Snapshot() { SnapshotSolverState(model_filename); } +template +void Solver::CheckSnapshotWritePermissions() { + if (Caffe::root_solver() && param_.snapshot()) { + CHECK(param_.has_snapshot_prefix()) + << "In solver params, snapshot is specified but snapshot_prefix is not"; + string probe_filename = SnapshotFilename(".tempfile"); + std::ofstream probe_ofs(probe_filename.c_str()); + if (probe_ofs.good()) { + probe_ofs.close(); + std::remove(probe_filename.c_str()); + } else { + LOG(FATAL) << "Cannot write to snapshot prefix '" + << param_.snapshot_prefix() << "'. Make sure " + << "that the directory exists and is writeable."; + } + } +} + template string Solver::SnapshotFilename(const string extension) { string filename(param_.snapshot_prefix()); From b7f9cba875c6db5c4ae33446dc80cd010c1c392c Mon Sep 17 00:00:00 2001 From: Mohamed Omran Date: Tue, 15 Sep 2015 17:18:32 +0200 Subject: [PATCH 254/446] removed bug in caffe.io.resize_image when applied to Nd images --- python/caffe/io.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/caffe/io.py b/python/caffe/io.py index fc96266085f..0cad7211291 100644 --- a/python/caffe/io.py +++ b/python/caffe/io.py @@ -329,7 +329,7 @@ def resize_image(im, new_dims, interp_order=1): return ret else: # ndimage interpolates anything but more slowly. - scale = tuple(np.array(new_dims) / np.array(im.shape[:2])) + scale = tuple(np.array(new_dims, dtype=float) / np.array(im.shape[:2])) resized_im = zoom(im, scale + (1,), order=interp_order) return resized_im.astype(np.float32) From 3d3a8b2ca09b64d94836652d0c9b5ffbb31551f6 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Wed, 16 Sep 2015 12:06:16 -0700 Subject: [PATCH 255/446] Get back 'USE CPU' print for caffe train --- tools/caffe.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/tools/caffe.cpp b/tools/caffe.cpp index ff63860a3c1..e3f684b5ab3 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -174,6 +174,7 @@ int train() { vector gpus; get_gpus(&gpus); if (gpus.size() == 0) { + LOG(INFO) << "Use CPU."; Caffe::set_mode(Caffe::CPU); } else { ostringstream s; From f3a933a620b8b089a7fe29ba76ec27f5408ff68d Mon Sep 17 00:00:00 2001 From: Tea Date: Sat, 27 Jun 2015 11:44:56 +0800 Subject: [PATCH 256/446] Separate IO dependencies OpenCV, LMDB, LevelDB and Snappy are made optional via switches (USE_OPENCV, USE_LMDB, USE_LEVELDB) available for Make and CMake builds. Since Snappy is a LevelDB dependency, its use is determined by USE_LEVELDB. HDF5 is left bundled because it is used for serializing weights and solverstates. --- .travis.yml | 11 ++--- CMakeLists.txt | 7 +++- Makefile | 28 +++++++++++-- Makefile.config.example | 5 +++ cmake/ConfigGen.cmake | 12 ++++++ cmake/Dependencies.cmake | 41 ++++++++++++------- cmake/Summary.cmake | 18 +++++--- cmake/Templates/CaffeConfig.cmake.in | 26 ++++++------ cmake/Templates/caffe_config.h.in | 5 +++ docs/installation.md | 9 ++-- .../cpp_classification/classification.cpp | 8 ++++ examples/mnist/convert_mnist_data.cpp | 12 ++++++ .../siamese/convert_mnist_siamese_data.cpp | 9 +++- include/caffe/data_layers.hpp | 3 +- include/caffe/data_transformer.hpp | 5 ++- include/caffe/util/db_leveldb.hpp | 2 + include/caffe/util/db_lmdb.hpp | 2 + include/caffe/util/io.hpp | 2 + python/caffe/test/test_layer_type_list.py | 1 + scripts/travis/travis_build_and_test.sh | 14 ++++++- .../travis/travis_setup_makefile_config.sh | 6 +++ src/caffe/data_transformer.cpp | 16 +++++++- src/caffe/layers/data_layer.cpp | 3 +- src/caffe/layers/image_data_layer.cpp | 2 + src/caffe/layers/memory_data_layer.cpp | 4 ++ src/caffe/layers/window_data_layer.cpp | 2 + src/caffe/test/test_data_layer.cpp | 6 +++ src/caffe/test/test_data_transformer.cpp | 2 + src/caffe/test/test_db.cpp | 2 + src/caffe/test/test_image_data_layer.cpp | 2 + src/caffe/test/test_io.cpp | 2 + src/caffe/test/test_layer_factory.cpp | 4 ++ src/caffe/test/test_memory_data_layer.cpp | 5 ++- src/caffe/test/test_upgrade_proto.cpp | 12 +++++- src/caffe/util/db.cpp | 14 +++++-- src/caffe/util/db_leveldb.cpp | 2 + src/caffe/util/db_lmdb.cpp | 2 + src/caffe/util/io.cpp | 10 ++++- tools/compute_image_mean.cpp | 4 ++ tools/convert_imageset.cpp | 4 ++ 40 files changed, 264 insertions(+), 60 deletions(-) diff --git a/.travis.yml b/.travis.yml index b920a935d0d..4dc7ed72d6c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -2,11 +2,12 @@ # one using CMake, and one using make. env: matrix: - - WITH_CUDA=false WITH_CMAKE=false - - WITH_CUDA=false WITH_CMAKE=true - - WITH_CUDA=true WITH_CMAKE=false - - WITH_CUDA=true WITH_CMAKE=true - - WITH_CUDA=false WITH_CMAKE=true PYTHON_VERSION=3 + - WITH_CUDA=false WITH_CMAKE=false WITH_IO=true + - WITH_CUDA=false WITH_CMAKE=true WITH_IO=true PYTHON_VERSION=3 + - WITH_CUDA=true WITH_CMAKE=false WITH_IO=true + - WITH_CUDA=true WITH_CMAKE=true WITH_IO=true + - WITH_CUDA=false WITH_CMAKE=false WITH_IO=false + - WITH_CUDA=false WITH_CMAKE=true WITH_IO=false PYTHON_VERSION=3 language: cpp diff --git a/CMakeLists.txt b/CMakeLists.txt index ef599b68922..838723beca2 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -16,13 +16,16 @@ include(cmake/ConfigGen.cmake) # ---[ Options caffe_option(CPU_ONLY "Build Caffe without CUDA support" OFF) # TODO: rename to USE_CUDA -caffe_option(USE_CUDNN "Build Caffe with cuDNN libary support" ON IF NOT CPU_ONLY) +caffe_option(USE_CUDNN "Build Caffe with cuDNN library support" ON IF NOT CPU_ONLY) caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON) caffe_option(BUILD_python "Build Python wrapper" ON) set(python_version "2" CACHE STRING "Specify which python version to use") caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE) caffe_option(BUILD_docs "Build documentation" ON IF UNIX OR APPLE) -caffe_option(BUILD_python_layer "Build the Caffe python layer" ON) +caffe_option(BUILD_python_layer "Build the caffe python layer" ON) +caffe_option(USE_LMDB "Build with lmdb" ON) +caffe_option(USE_LEVELDB "Build with levelDB" ON) +caffe_option(USE_OPENCV "Build with OpenCV support" ON) # ---[ Dependencies include(cmake/Dependencies.cmake) diff --git a/Makefile b/Makefile index 80bc37375be..ddaed59b0a9 100644 --- a/Makefile +++ b/Makefile @@ -169,9 +169,18 @@ ifneq ($(CPU_ONLY), 1) LIBRARY_DIRS += $(CUDA_LIB_DIR) LIBRARIES := cudart cublas curand endif -LIBRARIES += glog gflags protobuf leveldb snappy \ - lmdb boost_system hdf5_hl hdf5 m \ - opencv_core opencv_highgui opencv_imgproc + +LIBRARIES += glog gflags protobuf boost_system m hdf5_hl hdf5 + +ifeq ($(USE_LEVELDB), 1) + LIBRARIES += leveldb snappy +endif +ifeq ($(USE_LMDB), 1) + LIBRARIES += lmdb +endif +ifeq ($(USE_OPENCV), 1) + LIBRARIES += opencv_core opencv_highgui opencv_imgproc +endif PYTHON_LIBRARIES := boost_python python2.7 WARNINGS := -Wall -Wno-sign-compare @@ -290,6 +299,17 @@ ifeq ($(USE_CUDNN), 1) COMMON_FLAGS += -DUSE_CUDNN endif +# i/o libraries configuration +ifeq ($(USE_OPENCV), 1) + COMMON_FLAGS += -DUSE_OPENCV +endif +ifeq ($(USE_LEVELDB), 1) + COMMON_FLAGS += -DUSE_LEVELDB +endif +ifeq ($(USE_LMDB), 1) + COMMON_FLAGS += -DUSE_LMDB +endif + # CPU-only configuration ifeq ($(CPU_ONLY), 1) OBJS := $(PROTO_OBJS) $(CXX_OBJS) @@ -472,7 +492,7 @@ runtest: $(TEST_ALL_BIN) pytest: py cd python; python -m unittest discover -s caffe/test - + mattest: mat cd matlab; $(MATLAB_DIR)/bin/matlab -nodisplay -r 'caffe.run_tests(), exit()' diff --git a/Makefile.config.example b/Makefile.config.example index a873502559f..32e67ee493e 100644 --- a/Makefile.config.example +++ b/Makefile.config.example @@ -7,6 +7,11 @@ # CPU-only switch (uncomment to build without GPU support). # CPU_ONLY := 1 +# comment out to disable IO dependencies +USE_LEVELDB := 1 +USE_LMDB := 1 +USE_OPENCV := 1 + # To customize your choice of compiler, uncomment and set the following. # N.B. the default for Linux is g++ and the default for OSX is clang++ # CUSTOM_CXX := g++ diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake index 566d6ca0aa7..8b259965359 100644 --- a/cmake/ConfigGen.cmake +++ b/cmake/ConfigGen.cmake @@ -56,6 +56,18 @@ function(caffe_generate_export_configs) list(APPEND Caffe_DEFINITIONS -DCPU_ONLY) endif() + if(USE_OPENCV) + list(APPEND Caffe_DEFINITIONS -DUSE_OPENCV) + endif() + + if(USE_LMDB) + list(APPEND Caffe_DEFINITIONS -DUSE_LMDB) + endif() + + if(USE_LEVELDB) + list(APPEND Caffe_DEFINITIONS -DUSE_LEVELDB) + endif() + if(NOT HAVE_CUDNN) set(HAVE_CUDNN FALSE) else() diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake index 7c86dd55a30..d68d7bfba66 100644 --- a/cmake/Dependencies.cmake +++ b/cmake/Dependencies.cmake @@ -29,19 +29,27 @@ include_directories(SYSTEM ${HDF5_INCLUDE_DIRS} ${HDF5_HL_INCLUDE_DIR}) list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES}) # ---[ LMDB -find_package(LMDB REQUIRED) -include_directories(SYSTEM ${LMDB_INCLUDE_DIR}) -list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES}) +if(USE_LMDB) + find_package(LMDB REQUIRED) + include_directories(SYSTEM ${LMDB_INCLUDE_DIR}) + list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES}) + add_definitions(-DUSE_LMDB) +endif() # ---[ LevelDB -find_package(LevelDB REQUIRED) -include_directories(SYSTEM ${LevelDB_INCLUDE}) -list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES}) +if(USE_LEVELDB) + find_package(LevelDB REQUIRED) + include_directories(SYSTEM ${LevelDB_INCLUDE}) + list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES}) + add_definitions(-DUSE_LEVELDB) +endif() # ---[ Snappy -find_package(Snappy REQUIRED) -include_directories(SYSTEM ${Snappy_INCLUDE_DIR}) -list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES}) +if(USE_LEVELDB) + find_package(Snappy REQUIRED) + include_directories(SYSTEM ${Snappy_INCLUDE_DIR}) + list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES}) +endif() # ---[ CUDA include(cmake/Cuda.cmake) @@ -57,13 +65,16 @@ if(NOT HAVE_CUDA) endif() # ---[ OpenCV -find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs) -if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found - find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc) +if(USE_OPENCV) + find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs) + if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found + find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc) + endif() + include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS}) + list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS}) + message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})") + add_definitions(-DUSE_OPENCV) endif() -include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS}) -list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS}) -message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})") # ---[ BLAS if(NOT APPLE) diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake index e094ac0040e..3d12e81a130 100644 --- a/cmake/Summary.cmake +++ b/cmake/Summary.cmake @@ -114,6 +114,9 @@ function(caffe_print_configuration_summary) caffe_status(" BUILD_matlab : ${BUILD_matlab}") caffe_status(" BUILD_docs : ${BUILD_docs}") caffe_status(" CPU_ONLY : ${CPU_ONLY}") + caffe_status(" USE_LMDB : ${USE_LMDB}") + caffe_status(" USE_LEVELDB : ${USE_LEVELDB}") + caffe_status(" USE_OPENCV : ${USE_OPENCV}") caffe_status("") caffe_status("Dependencies:") caffe_status(" BLAS : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})") @@ -121,10 +124,16 @@ function(caffe_print_configuration_summary) caffe_status(" glog : Yes") caffe_status(" gflags : Yes") caffe_status(" protobuf : " PROTOBUF_FOUND THEN "Yes (ver. ${PROTOBUF_VERSION})" ELSE "No" ) - caffe_status(" lmdb : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No") - caffe_status(" Snappy : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" ) - caffe_status(" LevelDB : " LEVELDB_FOUND THEN "Yes (ver. ${LEVELDB_VERSION})" ELSE "No") - caffe_status(" OpenCV : Yes (ver. ${OpenCV_VERSION})") + if(USE_LMDB) + caffe_status(" lmdb : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No") + endif() + if(USE_LEVELDB) + caffe_status(" LevelDB : " LEVELDB_FOUND THEN "Yes (ver. ${LEVELDB_VERSION})" ELSE "No") + caffe_status(" Snappy : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" ) + endif() + if(USE_OPENCV) + caffe_status(" OpenCV : Yes (ver. ${OpenCV_VERSION})") + endif() caffe_status(" CUDA : " HAVE_CUDA THEN "Yes (ver. ${CUDA_VERSION})" ELSE "No" ) caffe_status("") if(HAVE_CUDA) @@ -165,4 +174,3 @@ function(caffe_print_configuration_summary) caffe_status(" Install path : ${CMAKE_INSTALL_PREFIX}") caffe_status("") endfunction() - diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in index 8f23742e52e..73f57ac2d74 100644 --- a/cmake/Templates/CaffeConfig.cmake.in +++ b/cmake/Templates/CaffeConfig.cmake.in @@ -17,22 +17,24 @@ # Caffe_HAVE_CUDNN - signals about cuDNN support -# OpenCV dependency +# OpenCV dependency (optional) -if(NOT OpenCV_FOUND) - set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@") - if(Caffe_OpenCV_CONFIG_PATH) - get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE) +if(@USE_OPENCV@) + if(NOT OpenCV_FOUND) + set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@") + if(Caffe_OpenCV_CONFIG_PATH) + get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE) - if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core) - message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}") - include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake) - endif() + if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core) + message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}") + include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake) + endif() - else() - find_package(OpenCV REQUIRED) + else() + find_package(OpenCV REQUIRED) + endif() + unset(Caffe_OpenCV_CONFIG_PATH) endif() - unset(Caffe_OpenCV_CONFIG_PATH) endif() # Compute paths diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in index 6039e8f6b21..9302022d7da 100644 --- a/cmake/Templates/caffe_config.h.in +++ b/cmake/Templates/caffe_config.h.in @@ -30,3 +30,8 @@ /* Matlab */ #cmakedefine HAVE_MATLAB + +/* IO libraries */ +#cmakedefine USE_OPENCV +#cmakedefine USE_LMDB +#cmakedefine USE_LEVELDB diff --git a/docs/installation.md b/docs/installation.md index d535c6d093d..89a8c71c71a 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -17,16 +17,19 @@ When updating Caffe, it's best to `make clean` before re-compiling. ## Prerequisites -Caffe has several dependencies. +Caffe has several dependencies: * [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode. * library version 7.0 and the latest driver version are recommended, but 6.* is fine too * 5.5, and 5.0 are compatible but considered legacy * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS. * [Boost](http://www.boost.org/) >= 1.55 +* `protobuf`, `glog`, `gflags`, `hdf5` + +Optional dependencies: + * [OpenCV](http://opencv.org/) >= 2.4 including 3.0 -* `protobuf`, `glog`, `gflags` -* IO libraries `hdf5`, `leveldb`, `snappy`, `lmdb` +* IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`) Pycaffe and Matcaffe interfaces have their own natural needs. diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp index dc8b863f53f..de48fb692c8 100644 --- a/examples/cpp_classification/classification.cpp +++ b/examples/cpp_classification/classification.cpp @@ -1,7 +1,9 @@ #include +#ifdef USE_OPENCV #include #include #include +#endif // USE_OPENCV #include #include #include @@ -9,6 +11,7 @@ #include #include +#ifdef USE_OPENCV using namespace caffe; // NOLINT(build/namespaces) using std::string; @@ -255,3 +258,8 @@ int main(int argc, char** argv) { << p.first << "\"" << std::endl; } } +#else +int main(int argc, char** argv) { + LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV."; +} +#endif // USE_OPENCV diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp index 54443f11dd3..8f29bafde85 100644 --- a/examples/mnist/convert_mnist_data.cpp +++ b/examples/mnist/convert_mnist_data.cpp @@ -9,9 +9,13 @@ #include #include #include + +#if defined(USE_LEVELDB) && defined(USE_LMDB) #include #include #include +#endif + #include #include @@ -20,6 +24,8 @@ #include "caffe/proto/caffe.pb.h" +#if defined(USE_LEVELDB) && defined(USE_LMDB) + using namespace caffe; // NOLINT(build/namespaces) using std::string; @@ -196,3 +202,9 @@ int main(int argc, char** argv) { } return 0; } +#else +int main(int argc, char** argv) { + LOG(FATAL) << "This example requires LevelDB and LMDB; " << + "compile with USE_LEVELDB and USE_LMDB."; +} +#endif // USE_LEVELDB and USE_LMDB diff --git a/examples/siamese/convert_mnist_siamese_data.cpp b/examples/siamese/convert_mnist_siamese_data.cpp index 8008b4439c5..ad08036fb08 100644 --- a/examples/siamese/convert_mnist_siamese_data.cpp +++ b/examples/siamese/convert_mnist_siamese_data.cpp @@ -10,12 +10,14 @@ #include "glog/logging.h" #include "google/protobuf/text_format.h" -#include "leveldb/db.h" #include "stdint.h" #include "caffe/proto/caffe.pb.h" #include "caffe/util/math_functions.hpp" +#ifdef USE_LEVELDB +#include "leveldb/db.h" + uint32_t swap_endian(uint32_t val) { val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF); return (val << 16) | (val >> 16); @@ -121,3 +123,8 @@ int main(int argc, char** argv) { } return 0; } +#else +int main(int argc, char** argv) { + LOG(FATAL) << "This example requires LevelDB; compile with USE_LEVELDB."; +} +#endif // USE_LEVELDB diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp index 552d814131e..90fd0d19917 100644 --- a/include/caffe/data_layers.hpp +++ b/include/caffe/data_layers.hpp @@ -4,7 +4,6 @@ #include #include #include - #include "hdf5.h" #include "caffe/blob.hpp" @@ -275,8 +274,10 @@ class MemoryDataLayer : public BaseDataLayer { virtual inline int ExactNumTopBlobs() const { return 2; } virtual void AddDatumVector(const vector& datum_vector); +#ifdef USE_OPENCV virtual void AddMatVector(const vector& mat_vector, const vector& labels); +#endif // USE_OPENCV // Reset should accept const pointers, but can't, because the memory // will be given to Blob, which is mutable diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp index 0ad68c80216..97b4ee6a8c4 100644 --- a/include/caffe/data_transformer.hpp +++ b/include/caffe/data_transformer.hpp @@ -50,6 +50,7 @@ class DataTransformer { void Transform(const vector & datum_vector, Blob* transformed_blob); +#ifdef USE_OPENCV /** * @brief Applies the transformation defined in the data layer's * transform_param block to a vector of Mat. @@ -74,6 +75,7 @@ class DataTransformer { * set_cpu_data() is used. See image_data_layer.cpp for an example. */ void Transform(const cv::Mat& cv_img, Blob* transformed_blob); +#endif // USE_OPENCV /** * @brief Applies the same transformation defined in the data layer's @@ -113,6 +115,7 @@ class DataTransformer { * @param mat_vector * A vector of Mat containing the data to be transformed. */ +#ifdef USE_OPENCV vector InferBlobShape(const vector & mat_vector); /** * @brief Infers the shape of transformed_blob will have when @@ -122,6 +125,7 @@ class DataTransformer { * cv::Mat containing the data to be transformed. */ vector InferBlobShape(const cv::Mat& cv_img); +#endif // USE_OPENCV protected: /** @@ -148,4 +152,3 @@ class DataTransformer { } // namespace caffe #endif // CAFFE_DATA_TRANSFORMER_HPP_ - diff --git a/include/caffe/util/db_leveldb.hpp b/include/caffe/util/db_leveldb.hpp index 10623554b67..e9fa0d32b66 100644 --- a/include/caffe/util/db_leveldb.hpp +++ b/include/caffe/util/db_leveldb.hpp @@ -1,3 +1,4 @@ +#ifdef USE_LEVELDB #ifndef CAFFE_UTIL_DB_LEVELDB_HPP #define CAFFE_UTIL_DB_LEVELDB_HPP @@ -71,3 +72,4 @@ class LevelDB : public DB { } // namespace caffe #endif // CAFFE_UTIL_DB_LEVELDB_HPP +#endif // USE_LEVELDB diff --git a/include/caffe/util/db_lmdb.hpp b/include/caffe/util/db_lmdb.hpp index cc7c90afc4c..4e1568ace50 100644 --- a/include/caffe/util/db_lmdb.hpp +++ b/include/caffe/util/db_lmdb.hpp @@ -1,3 +1,4 @@ +#ifdef USE_LMDB #ifndef CAFFE_UTIL_DB_LMDB_HPP #define CAFFE_UTIL_DB_LMDB_HPP @@ -89,3 +90,4 @@ class LMDB : public DB { } // namespace caffe #endif // CAFFE_UTIL_DB_LMDB_HPP +#endif // USE_LMDB diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp index c0938ad0625..6070b4c7f3a 100644 --- a/include/caffe/util/io.hpp +++ b/include/caffe/util/io.hpp @@ -120,6 +120,7 @@ inline bool ReadImageToDatum(const string& filename, const int label, bool DecodeDatumNative(Datum* datum); bool DecodeDatum(Datum* datum, bool is_color); +#ifdef USE_OPENCV cv::Mat ReadImageToCVMat(const string& filename, const int height, const int width, const bool is_color); @@ -135,6 +136,7 @@ cv::Mat DecodeDatumToCVMatNative(const Datum& datum); cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color); void CVMatToDatum(const cv::Mat& cv_img, Datum* datum); +#endif // USE_OPENCV } // namespace caffe diff --git a/python/caffe/test/test_layer_type_list.py b/python/caffe/test/test_layer_type_list.py index 7edc80df069..47f4cf6d008 100644 --- a/python/caffe/test/test_layer_type_list.py +++ b/python/caffe/test/test_layer_type_list.py @@ -5,6 +5,7 @@ class TestLayerTypeList(unittest.TestCase): def test_standard_types(self): + #removing 'Data' from list for type_name in ['Data', 'Convolution', 'InnerProduct']: self.assertIn(type_name, caffe.layer_type_list(), '%s not in layer_type_list()' % type_name) diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh index 9ba737e28a9..bbc8213347b 100755 --- a/scripts/travis/travis_build_and_test.sh +++ b/scripts/travis/travis_build_and_test.sh @@ -1,5 +1,5 @@ #!/bin/bash -# Script called by Travis to do a CPU-only build of and test Caffe. +# Script called by Travis to build and test Caffe. set -e MAKE="make --jobs=$NUM_THREADS --keep-going" @@ -15,7 +15,12 @@ if $WITH_CMAKE; then if [ "$PYTHON_VERSION" = "3" ]; then PYTHON_ARGS="$PYTHON_ARGS -Dpython_version=3 -DBOOST_LIBRARYDIR=$CONDA_DIR/lib/" fi - cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" .. + if $WITH_IO; then + IO_ARGS="-DUSE_OPENCV=ON -DUSE_LMDB=ON -DUSE_LEVELDB=ON" + else + IO_ARGS="-DUSE_OPENCV=OFF -DUSE_LMDB=OFF -DUSE_LEVELDB=OFF" + fi + cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" $IO_ARGS .. $MAKE $MAKE pytest if ! $WITH_CUDA; then @@ -28,6 +33,11 @@ else if ! $WITH_CUDA; then export CPU_ONLY=1 fi + if $WITH_IO; then + export USE_LMDB=1 + export USE_LEVELDB=1 + export USE_OPENCV=1 + fi $MAKE all test pycaffe warn lint || true if ! $WITH_CUDA; then $MAKE runtest diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh index 1440be2af8b..83aacf11fb0 100755 --- a/scripts/travis/travis_setup_makefile_config.sh +++ b/scripts/travis/travis_setup_makefile_config.sh @@ -11,6 +11,12 @@ if $WITH_CUDA; then echo "CUDA_ARCH := $GENCODE" >> Makefile.config fi +# Remove IO library settings from Makefile.config +# to avoid conflicts with CI configuration +sed -i -e '/USE_LMDB/d' Makefile.config +sed -i -e '/USE_LEVELDB/d' Makefile.config +sed -i -e '/USE_OPENCV/d' Makefile.config + cat << 'EOF' >> Makefile.config # Travis' nvcc doesn't like newer boost versions NVCCFLAGS := -Xcudafe --diag_suppress=cc_clobber_ignored -Xcudafe --diag_suppress=useless_using_declaration -Xcudafe --diag_suppress=set_but_not_used diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp index 4666d9bd881..7189d67e289 100644 --- a/src/caffe/data_transformer.cpp +++ b/src/caffe/data_transformer.cpp @@ -1,4 +1,6 @@ +#ifdef USE_OPENCV #include +#endif // USE_OPENCV #include #include @@ -124,11 +126,13 @@ void DataTransformer::Transform(const Datum& datum, } } + template void DataTransformer::Transform(const Datum& datum, Blob* transformed_blob) { // If datum is encoded, decoded and transform the cv::image. if (datum.encoded()) { +#ifdef USE_OPENCV CHECK(!(param_.force_color() && param_.force_gray())) << "cannot set both force_color and force_gray"; cv::Mat cv_img; @@ -140,6 +144,9 @@ void DataTransformer::Transform(const Datum& datum, } // Transform the cv::image into blob. return Transform(cv_img, transformed_blob); +#else + LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV."; +#endif // USE_OPENCV } else { if (param_.force_color() || param_.force_gray()) { LOG(ERROR) << "force_color and force_gray only for encoded datum"; @@ -194,6 +201,7 @@ void DataTransformer::Transform(const vector & datum_vector, } } +#ifdef USE_OPENCV template void DataTransformer::Transform(const vector & mat_vector, Blob* transformed_blob) { @@ -315,6 +323,7 @@ void DataTransformer::Transform(const cv::Mat& cv_img, } } } +#endif // USE_OPENCV template void DataTransformer::Transform(Blob* input_blob, @@ -432,6 +441,7 @@ void DataTransformer::Transform(Blob* input_blob, template vector DataTransformer::InferBlobShape(const Datum& datum) { if (datum.encoded()) { +#ifdef USE_OPENCV CHECK(!(param_.force_color() && param_.force_gray())) << "cannot set both force_color and force_gray"; cv::Mat cv_img; @@ -443,8 +453,10 @@ vector DataTransformer::InferBlobShape(const Datum& datum) { } // InferBlobShape using the cv::image. return InferBlobShape(cv_img); +#else + LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV."; +#endif // USE_OPENCV } - const int crop_size = param_.crop_size(); const int datum_channels = datum.channels(); const int datum_height = datum.height(); @@ -474,6 +486,7 @@ vector DataTransformer::InferBlobShape( return shape; } +#ifdef USE_OPENCV template vector DataTransformer::InferBlobShape(const cv::Mat& cv_img) { const int crop_size = param_.crop_size(); @@ -504,6 +517,7 @@ vector DataTransformer::InferBlobShape( shape[0] = num; return shape; } +#endif // USE_OPENCV template void DataTransformer::InitRand() { diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp index 0932d9feff3..71f8cb099e8 100644 --- a/src/caffe/layers/data_layer.cpp +++ b/src/caffe/layers/data_layer.cpp @@ -1,5 +1,6 @@ +#ifdef USE_OPENCV #include - +#endif // USE_OPENCV #include #include diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp index 223ba3a75ca..3d2190f8bbb 100644 --- a/src/caffe/layers/image_data_layer.cpp +++ b/src/caffe/layers/image_data_layer.cpp @@ -1,3 +1,4 @@ +#ifdef USE_OPENCV #include #include // NOLINT(readability/streams) @@ -164,3 +165,4 @@ INSTANTIATE_CLASS(ImageDataLayer); REGISTER_LAYER_CLASS(ImageData); } // namespace caffe +#endif // USE_OPENCV diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp index 42de4198bc4..2370aa04d3b 100644 --- a/src/caffe/layers/memory_data_layer.cpp +++ b/src/caffe/layers/memory_data_layer.cpp @@ -1,4 +1,6 @@ +#ifdef USE_OPENCV #include +#endif // USE_OPENCV #include @@ -53,6 +55,7 @@ void MemoryDataLayer::AddDatumVector(const vector& datum_vector) { has_new_data_ = true; } +#ifdef USE_OPENCV template void MemoryDataLayer::AddMatVector(const vector& mat_vector, const vector& labels) { @@ -76,6 +79,7 @@ void MemoryDataLayer::AddMatVector(const vector& mat_vector, Reset(top_data, top_label, num); has_new_data_ = true; } +#endif // USE_OPENCV template void MemoryDataLayer::Reset(Dtype* data, Dtype* labels, int n) { diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp index f637f2ec6d4..f8db61c9258 100644 --- a/src/caffe/layers/window_data_layer.cpp +++ b/src/caffe/layers/window_data_layer.cpp @@ -1,3 +1,4 @@ +#ifdef USE_OPENCV #include #include @@ -468,3 +469,4 @@ INSTANTIATE_CLASS(WindowDataLayer); REGISTER_LAYER_CLASS(WindowData); } // namespace caffe +#endif // USE_OPENCV diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp index afe2a40d227..9e03954a543 100644 --- a/src/caffe/test/test_data_layer.cpp +++ b/src/caffe/test/test_data_layer.cpp @@ -1,3 +1,4 @@ +#ifdef USE_OPENCV #include #include @@ -348,6 +349,7 @@ class DataLayerTest : public MultiDeviceTest { TYPED_TEST_CASE(DataLayerTest, TestDtypesAndDevices); +#ifdef USE_LEVELDB TYPED_TEST(DataLayerTest, TestReadLevelDB) { const bool unique_pixels = false; // all pixels the same; images different this->Fill(unique_pixels, DataParameter_DB_LEVELDB); @@ -385,7 +387,9 @@ TYPED_TEST(DataLayerTest, TestReadCropTestLevelDB) { this->Fill(unique_pixels, DataParameter_DB_LEVELDB); this->TestReadCrop(TEST); } +#endif // USE_LEVELDB +#ifdef USE_LMDB TYPED_TEST(DataLayerTest, TestReadLMDB) { const bool unique_pixels = false; // all pixels the same; images different this->Fill(unique_pixels, DataParameter_DB_LMDB); @@ -424,4 +428,6 @@ TYPED_TEST(DataLayerTest, TestReadCropTestLMDB) { this->TestReadCrop(TEST); } +#endif // USE_LMDB } // namespace caffe +#endif // USE_OPENCV diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp index 16570e20356..8a1013744e8 100644 --- a/src/caffe/test/test_data_transformer.cpp +++ b/src/caffe/test/test_data_transformer.cpp @@ -1,3 +1,4 @@ +#ifdef USE_OPENCV #include #include @@ -353,3 +354,4 @@ TYPED_TEST(DataTransformTest, TestMeanFile) { } } // namespace caffe +#endif // USE_OPENCV diff --git a/src/caffe/test/test_db.cpp b/src/caffe/test/test_db.cpp index 5b2ac230a0b..1b487b14c58 100644 --- a/src/caffe/test/test_db.cpp +++ b/src/caffe/test/test_db.cpp @@ -1,3 +1,4 @@ +#if defined(USE_LEVELDB) && defined(USE_LMDB) && defined(USE_OPENCV) #include #include "boost/scoped_ptr.hpp" @@ -132,3 +133,4 @@ TYPED_TEST(DBTest, TestWrite) { } } // namespace caffe +#endif // USE_LEVELDB, USE_LMDB and USE_OPENCV diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp index 931a5ebf137..481fcef7b27 100644 --- a/src/caffe/test/test_image_data_layer.cpp +++ b/src/caffe/test/test_image_data_layer.cpp @@ -1,3 +1,4 @@ +#ifdef USE_OPENCV #include #include #include @@ -177,3 +178,4 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) { } } // namespace caffe +#endif // USE_OPENCV diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp index 4ab96311bbc..c2c919e90dc 100644 --- a/src/caffe/test/test_io.cpp +++ b/src/caffe/test/test_io.cpp @@ -1,3 +1,4 @@ +#ifdef USE_OPENCV #include #include #include @@ -420,3 +421,4 @@ TEST_F(IOTest, TestDecodeDatumToCVMatContentNative) { } } // namespace caffe +#endif // USE_OPENCV diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp index c86fafd000c..7d5d39d8b91 100644 --- a/src/caffe/test/test_layer_factory.cpp +++ b/src/caffe/test/test_layer_factory.cpp @@ -31,12 +31,16 @@ TYPED_TEST(LayerFactoryTest, TestCreateLayer) { LayerParameter layer_param; // Data layers expect a DB if (iter->first == "Data") { +#ifdef USE_LEVELDB string tmp; MakeTempDir(&tmp); boost::scoped_ptr db(db::GetDB(DataParameter_DB_LEVELDB)); db->Open(tmp, db::NEW); db->Close(); layer_param.mutable_data_param()->set_source(tmp); +#else + continue; +#endif // USE_LEVELDB } layer_param.set_type(iter->first); layer = LayerRegistry::CreateLayer(layer_param); diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp index a79033f59f1..7269a4d441b 100644 --- a/src/caffe/test/test_memory_data_layer.cpp +++ b/src/caffe/test/test_memory_data_layer.cpp @@ -1,4 +1,6 @@ +#ifdef USE_OPENCV #include +#endif // USE_OPENCV #include #include @@ -113,6 +115,7 @@ TYPED_TEST(MemoryDataLayerTest, TestForward) { } } +#ifdef USE_OPENCV TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) { typedef typename TypeParam::Dtype Dtype; @@ -292,5 +295,5 @@ TYPED_TEST(MemoryDataLayerTest, TestSetBatchSize) { } } } - +#endif // USE_OPENCV } // namespace caffe diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp index 006720231a5..ee05b151e72 100644 --- a/src/caffe/test/test_upgrade_proto.cpp +++ b/src/caffe/test/test_upgrade_proto.cpp @@ -2892,6 +2892,7 @@ TEST_F(NetUpgradeTest, TestImageNet) { this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto); } // NOLINT(readability/fn_size) +#ifdef USE_OPENCV TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) { LayerParameter layer_param; shared_ptr > layer; @@ -2906,16 +2907,25 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) { layer_param.set_type(v2_layer_type); // Data layers expect a DB if (v2_layer_type == "Data") { + #ifdef USE_LEVELDB string tmp; MakeTempDir(&tmp); boost::scoped_ptr db(db::GetDB(DataParameter_DB_LEVELDB)); db->Open(tmp, db::NEW); db->Close(); layer_param.mutable_data_param()->set_source(tmp); + #else + continue; + #endif // USE_LEVELDB } + #ifndef USE_OPENCV + if (v2_layer_type == "ImageData" || v2_layer_type == "WindowData") { + continue; + } + #endif // !USE_OPENCV layer = LayerRegistry::CreateLayer(layer_param); EXPECT_EQ(v2_layer_type, layer->type()); } } - +#endif // USE_OPENCV } // NOLINT(readability/fn_size) // namespace caffe diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp index f55420e9840..ccda054d881 100644 --- a/src/caffe/util/db.cpp +++ b/src/caffe/util/db.cpp @@ -8,23 +8,31 @@ namespace caffe { namespace db { DB* GetDB(DataParameter::DB backend) { switch (backend) { +#ifdef USE_LEVELDB case DataParameter_DB_LEVELDB: return new LevelDB(); +#endif // USE_LEVELDB +#ifdef USE_LMDB case DataParameter_DB_LMDB: return new LMDB(); +#endif // USE_LMDB default: LOG(FATAL) << "Unknown database backend"; } } DB* GetDB(const string& backend) { +#ifdef USE_LEVELDB if (backend == "leveldb") { return new LevelDB(); - } else if (backend == "lmdb") { + } +#endif // USE_LEVELDB +#ifdef USE_LMDB + if (backend == "lmdb") { return new LMDB(); - } else { - LOG(FATAL) << "Unknown database backend"; } +#endif // USE_LMDB + LOG(FATAL) << "Unknown database backend"; } } // namespace db diff --git a/src/caffe/util/db_leveldb.cpp b/src/caffe/util/db_leveldb.cpp index 06c46627d31..f5c4d8a660d 100644 --- a/src/caffe/util/db_leveldb.cpp +++ b/src/caffe/util/db_leveldb.cpp @@ -1,3 +1,4 @@ +#ifdef USE_LEVELDB #include "caffe/util/db_leveldb.hpp" #include @@ -19,3 +20,4 @@ void LevelDB::Open(const string& source, Mode mode) { } // namespace db } // namespace caffe +#endif // USE_LEVELDB diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp index a054b796806..78dd880ac41 100644 --- a/src/caffe/util/db_lmdb.cpp +++ b/src/caffe/util/db_lmdb.cpp @@ -1,3 +1,4 @@ +#ifdef USE_LMDB #include "caffe/util/db_lmdb.hpp" #include @@ -49,3 +50,4 @@ void LMDBTransaction::Put(const string& key, const string& value) { } // namespace db } // namespace caffe +#endif // USE_LMDB diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp index 6f03314202c..f2b1dd98423 100644 --- a/src/caffe/util/io.cpp +++ b/src/caffe/util/io.cpp @@ -3,9 +3,11 @@ #include #include #include +#ifdef USE_OPENCV #include #include #include +#endif // USE_OPENCV #include #include @@ -67,6 +69,7 @@ void WriteProtoToBinaryFile(const Message& proto, const char* filename) { CHECK(proto.SerializeToOstream(&output)); } +#ifdef USE_OPENCV cv::Mat ReadImageToCVMat(const string& filename, const int height, const int width, const bool is_color) { cv::Mat cv_img; @@ -98,6 +101,7 @@ cv::Mat ReadImageToCVMat(const string& filename, cv::Mat ReadImageToCVMat(const string& filename) { return ReadImageToCVMat(filename, 0, 0, true); } + // Do the file extension and encoding match? static bool matchExt(const std::string & fn, std::string en) { @@ -111,6 +115,7 @@ static bool matchExt(const std::string & fn, return true; return false; } + bool ReadImageToDatum(const string& filename, const int label, const int height, const int width, const bool is_color, const std::string & encoding, Datum* datum) { @@ -135,6 +140,7 @@ bool ReadImageToDatum(const string& filename, const int label, return false; } } +#endif // USE_OPENCV bool ReadFileToDatum(const string& filename, const int label, Datum* datum) { @@ -156,6 +162,7 @@ bool ReadFileToDatum(const string& filename, const int label, } } +#ifdef USE_OPENCV cv::Mat DecodeDatumToCVMatNative(const Datum& datum) { cv::Mat cv_img; CHECK(datum.encoded()) << "Datum not encoded"; @@ -227,6 +234,5 @@ void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) { } datum->set_data(buffer); } - - +#endif // USE_OPENCV } // namespace caffe diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp index b1fc7cae38f..2035d515195 100644 --- a/tools/compute_image_mean.cpp +++ b/tools/compute_image_mean.cpp @@ -24,6 +24,7 @@ DEFINE_string(backend, "lmdb", int main(int argc, char** argv) { ::google::InitGoogleLogging(argv[0]); +#ifdef USE_OPENCV #ifndef GFLAGS_GFLAGS_H_ namespace gflags = google; #endif @@ -115,5 +116,8 @@ int main(int argc, char** argv) { } LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim; } +#else + LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV."; +#endif // USE_OPENCV return 0; } diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp index aad1f1fe216..e51a2631077 100644 --- a/tools/convert_imageset.cpp +++ b/tools/convert_imageset.cpp @@ -43,6 +43,7 @@ DEFINE_string(encode_type, "", "Optional: What type should we encode the image as ('png','jpg',...)."); int main(int argc, char** argv) { +#ifdef USE_OPENCV ::google::InitGoogleLogging(argv[0]); // Print output to stderr (while still logging) FLAGS_alsologtostderr = 1; @@ -150,5 +151,8 @@ int main(int argc, char** argv) { txn->Commit(); LOG(INFO) << "Processed " << count << " files."; } +#else + LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV."; +#endif // USE_OPENCV return 0; } From 2349c6de69bf5043508cde41bb1d337fdb78e188 Mon Sep 17 00:00:00 2001 From: Tea Date: Thu, 17 Sep 2015 15:02:45 +0800 Subject: [PATCH 257/446] Fix case in CMake notices --- CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 838723beca2..37f937fe489 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -19,10 +19,10 @@ caffe_option(CPU_ONLY "Build Caffe without CUDA support" OFF) # TODO: rename to caffe_option(USE_CUDNN "Build Caffe with cuDNN library support" ON IF NOT CPU_ONLY) caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON) caffe_option(BUILD_python "Build Python wrapper" ON) -set(python_version "2" CACHE STRING "Specify which python version to use") +set(python_version "2" CACHE STRING "Specify which Python version to use") caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE) caffe_option(BUILD_docs "Build documentation" ON IF UNIX OR APPLE) -caffe_option(BUILD_python_layer "Build the caffe python layer" ON) +caffe_option(BUILD_python_layer "Build the Caffe Python layer" ON) caffe_option(USE_LMDB "Build with lmdb" ON) caffe_option(USE_LEVELDB "Build with levelDB" ON) caffe_option(USE_OPENCV "Build with OpenCV support" ON) From 68c9e2b4703ce18fd9a7ab541addf701129a8080 Mon Sep 17 00:00:00 2001 From: "T.E.A de Souza" Date: Tue, 8 Sep 2015 12:20:40 +0800 Subject: [PATCH 258/446] Add a comment indicating that Travis CI tests are CPU only --- scripts/travis/travis_build_and_test.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh index bbc8213347b..174f1ee5a0a 100755 --- a/scripts/travis/travis_build_and_test.sh +++ b/scripts/travis/travis_build_and_test.sh @@ -1,5 +1,6 @@ #!/bin/bash # Script called by Travis to build and test Caffe. +# Travis CI tests are CPU-only for lack of compatible hardware. set -e MAKE="make --jobs=$NUM_THREADS --keep-going" From e75ae965519444fb64d67c0aa6323bc2ef4049ef Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 17 Sep 2015 15:05:12 -0700 Subject: [PATCH 259/446] [build] include IO dependencies by default keep old behavior by including leveldb, lmdb, and opencv by default --- Makefile | 7 ++++++- Makefile.config.example | 8 ++++---- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/Makefile b/Makefile index ddaed59b0a9..a911133661f 100644 --- a/Makefile +++ b/Makefile @@ -172,6 +172,11 @@ endif LIBRARIES += glog gflags protobuf boost_system m hdf5_hl hdf5 +# handle IO dependencies +USE_LEVELDB ?= 1 +USE_LMDB ?= 1 +USE_OPENCV ?= 1 + ifeq ($(USE_LEVELDB), 1) LIBRARIES += leveldb snappy endif @@ -299,7 +304,7 @@ ifeq ($(USE_CUDNN), 1) COMMON_FLAGS += -DUSE_CUDNN endif -# i/o libraries configuration +# configure IO libraries ifeq ($(USE_OPENCV), 1) COMMON_FLAGS += -DUSE_OPENCV endif diff --git a/Makefile.config.example b/Makefile.config.example index 32e67ee493e..a20bad2f5ce 100644 --- a/Makefile.config.example +++ b/Makefile.config.example @@ -7,10 +7,10 @@ # CPU-only switch (uncomment to build without GPU support). # CPU_ONLY := 1 -# comment out to disable IO dependencies -USE_LEVELDB := 1 -USE_LMDB := 1 -USE_OPENCV := 1 +# uncomment to disable IO dependencies and corresponding data layers +# USE_LEVELDB := 0 +# USE_LMDB := 0 +# USE_OPENCV := 0 # To customize your choice of compiler, uncomment and set the following. # N.B. the default for Linux is g++ and the default for OSX is clang++ From b4f9add57fa468ab43aa40f0a95badf3e9ace243 Mon Sep 17 00:00:00 2001 From: Gustav Larsson Date: Thu, 17 Sep 2015 20:32:33 -0500 Subject: [PATCH 260/446] Expose `Snapshot` to pycaffe - Solver::Snapshot is made public - It is also added as `snapshot` to pycaffe Addressing #3077 --- include/caffe/solver.hpp | 10 +++++----- python/caffe/_caffe.cpp | 3 ++- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 8d52785ac6e..51f8d495c37 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -60,6 +60,11 @@ class Solver { // RestoreSolverStateFrom___ protected methods. You should implement these // methods to restore the state from the appropriate snapshot type. void Restore(const char* resume_file); + // The Solver::Snapshot function implements the basic snapshotting utility + // that stores the learned net. You should implement the SnapshotSolverState() + // function that produces a SolverState protocol buffer that needs to be + // written to disk together with the learned net. + void Snapshot(); virtual ~Solver() {} inline const SolverParameter& param() const { return param_; } inline shared_ptr > net() { return net_; } @@ -87,11 +92,6 @@ class Solver { protected: // Make and apply the update value for the current iteration. virtual void ApplyUpdate() = 0; - // The Solver::Snapshot function implements the basic snapshotting utility - // that stores the learned net. You should implement the SnapshotSolverState() - // function that produces a SolverState protocol buffer that needs to be - // written to disk together with the learned net. - void Snapshot(); string SnapshotFilename(const string extension); string SnapshotToBinaryProto(); string SnapshotToHDF5(); diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index ccd5776ac40..6c2ccaa5794 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -286,7 +286,8 @@ BOOST_PYTHON_MODULE(_caffe) { .def("solve", static_cast::*)(const char*)>( &Solver::Solve), SolveOverloads()) .def("step", &Solver::Step) - .def("restore", &Solver::Restore); + .def("restore", &Solver::Restore) + .def("snapshot", &Solver::Snapshot); bp::class_, bp::bases >, shared_ptr >, boost::noncopyable>( From f75d594bbec1efab69cdc09c04bed1762aebd0e1 Mon Sep 17 00:00:00 2001 From: Yan Chen Date: Fri, 18 Sep 2015 17:02:16 +0800 Subject: [PATCH 261/446] refine format of switch case in solver --- include/caffe/solver.hpp | 14 +++++++------- src/caffe/solver.cpp | 16 ++++++++-------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 8d52785ac6e..2ecf539baef 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -283,19 +283,19 @@ Solver* GetSolver(const SolverParameter& param) { switch (type) { case SolverParameter_SolverType_SGD: - return new SGDSolver(param); + return new SGDSolver(param); case SolverParameter_SolverType_NESTEROV: - return new NesterovSolver(param); + return new NesterovSolver(param); case SolverParameter_SolverType_ADAGRAD: - return new AdaGradSolver(param); + return new AdaGradSolver(param); case SolverParameter_SolverType_RMSPROP: - return new RMSPropSolver(param); + return new RMSPropSolver(param); case SolverParameter_SolverType_ADADELTA: - return new AdaDeltaSolver(param); + return new AdaDeltaSolver(param); case SolverParameter_SolverType_ADAM: - return new AdamSolver(param); + return new AdamSolver(param); default: - LOG(FATAL) << "Unknown SolverType: " << type; + LOG(FATAL) << "Unknown SolverType: " << type; } return (Solver*) NULL; } diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 3574ce75046..12c13dd8385 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -422,14 +422,14 @@ void Solver::Snapshot() { CHECK(Caffe::root_solver()); string model_filename; switch (param_.snapshot_format()) { - case caffe::SolverParameter_SnapshotFormat_BINARYPROTO: - model_filename = SnapshotToBinaryProto(); - break; - case caffe::SolverParameter_SnapshotFormat_HDF5: - model_filename = SnapshotToHDF5(); - break; - default: - LOG(FATAL) << "Unsupported snapshot format."; + case caffe::SolverParameter_SnapshotFormat_BINARYPROTO: + model_filename = SnapshotToBinaryProto(); + break; + case caffe::SolverParameter_SnapshotFormat_HDF5: + model_filename = SnapshotToHDF5(); + break; + default: + LOG(FATAL) << "Unsupported snapshot format."; } SnapshotSolverState(model_filename); From 4c2ff1693ea509dc4758e73b913f4cbec6c1ac3a Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 4 Mar 2015 19:27:56 -0800 Subject: [PATCH 262/446] caffe.proto: generalize ConvolutionParameter to N spatial axes --- src/caffe/proto/caffe.proto | 37 +++++++++++++++++++++++++++---------- 1 file changed, 27 insertions(+), 10 deletions(-) diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index aa299f8660b..86683eb45da 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -471,18 +471,24 @@ message ContrastiveLossParameter { message ConvolutionParameter { optional uint32 num_output = 1; // The number of outputs for the layer optional bool bias_term = 2 [default = true]; // whether to have bias terms + // Pad, kernel size, and stride are all given as a single value for equal - // dimensions in height and width or as Y, X pairs. - optional uint32 pad = 3 [default = 0]; // The padding size (equal in Y, X) - optional uint32 pad_h = 9 [default = 0]; // The padding height - optional uint32 pad_w = 10 [default = 0]; // The padding width - optional uint32 kernel_size = 4; // The kernel size (square) - optional uint32 kernel_h = 11; // The kernel height - optional uint32 kernel_w = 12; // The kernel width + // dimensions in all spatial dimensions, or once per spatial dimension. + repeated uint32 pad = 3; // The padding size; defaults to 0 + repeated uint32 kernel_size = 4; // The kernel size + repeated uint32 stride = 6; // The stride; defaults to 1 + + // For 2D convolution only, the *_h and *_w versions may also be used to + // specify both spatial dimensions. + optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only) + optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only) + optional uint32 kernel_h = 11; // The kernel height (2D only) + optional uint32 kernel_w = 12; // The kernel width (2D only) + optional uint32 stride_h = 13; // The stride height (2D only) + optional uint32 stride_w = 14; // The stride width (2D only) + optional uint32 group = 5 [default = 1]; // The group size for group conv - optional uint32 stride = 6 [default = 1]; // The stride (equal in Y, X) - optional uint32 stride_h = 13; // The stride height - optional uint32 stride_w = 14; // The stride width + optional FillerParameter weight_filler = 7; // The filler for the weight optional FillerParameter bias_filler = 8; // The filler for the bias enum Engine { @@ -491,6 +497,17 @@ message ConvolutionParameter { CUDNN = 2; } optional Engine engine = 15 [default = DEFAULT]; + + // The axis to interpret as "channels" when performing convolution. + // Preceding dimensions are treated as independent inputs; + // succeeding dimensions are treated as "spatial". + // With (N, C, H, W) inputs, and axis == 1 (the default), we perform + // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for + // groups g>1) filters across the spatial axes (H, W) of the input. + // With (N, C, D, H, W) inputs, and axis == 1, we perform + // N independent 3D convolutions, sliding (C/g)-channels + // filters across the spatial axes (D, H, W) of the input. + optional int32 axis = 16 [default = 1]; } message DataParameter { From 0813f32038bf7477d343ae369981166cfed783b5 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 4 Mar 2015 21:31:34 -0800 Subject: [PATCH 263/446] Blob: add SyncedMemory shape accessor for GPU shape access --- include/caffe/blob.hpp | 2 ++ src/caffe/blob.cpp | 11 +++++++++++ 2 files changed, 13 insertions(+) diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp index dda7b1f8372..fea5117ef10 100644 --- a/include/caffe/blob.hpp +++ b/include/caffe/blob.hpp @@ -219,6 +219,7 @@ class Blob { const Dtype* cpu_data() const; void set_cpu_data(Dtype* data); + const int* gpu_shape() const; const Dtype* gpu_data() const; const Dtype* cpu_diff() const; const Dtype* gpu_diff() const; @@ -268,6 +269,7 @@ class Blob { protected: shared_ptr data_; shared_ptr diff_; + shared_ptr shape_data_; vector shape_; int count_; int capacity_; diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp index 8450aa140be..c86fd5d1d94 100644 --- a/src/caffe/blob.cpp +++ b/src/caffe/blob.cpp @@ -24,11 +24,16 @@ void Blob::Reshape(const vector& shape) { CHECK_LE(shape.size(), kMaxBlobAxes); count_ = 1; shape_.resize(shape.size()); + if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) { + shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int))); + } + int* shape_data = static_cast(shape_data_->mutable_cpu_data()); for (int i = 0; i < shape.size(); ++i) { CHECK_GE(shape[i], 0); CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX"; count_ *= shape[i]; shape_[i] = shape[i]; + shape_data[i] = shape[i]; } if (count_ > capacity_) { capacity_ = count_; @@ -67,6 +72,12 @@ Blob::Blob(const vector& shape) Reshape(shape); } +template +const int* Blob::gpu_shape() const { + CHECK(shape_data_); + return (const int*)shape_data_->gpu_data(); +} + template const Dtype* Blob::cpu_data() const { CHECK(data_); From 9d8206e0f906069e7c04f08dfddefa1357f3915c Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Wed, 4 Mar 2015 19:30:17 -0800 Subject: [PATCH 264/446] Im2col and Convolution layers support N spatial axes --- include/caffe/util/im2col.hpp | 24 ++ include/caffe/vision_layers.hpp | 108 +++++- src/caffe/layers/base_conv_layer.cpp | 241 ++++++++---- src/caffe/layers/conv_layer.cpp | 32 +- src/caffe/layers/conv_layer.cu | 16 +- src/caffe/layers/cudnn_conv_layer.cpp | 46 ++- src/caffe/layers/cudnn_conv_layer.cu | 18 +- src/caffe/layers/deconv_layer.cpp | 32 +- src/caffe/layers/deconv_layer.cu | 16 +- src/caffe/layers/im2col_layer.cpp | 171 +++++--- src/caffe/layers/im2col_layer.cu | 41 +- src/caffe/proto/caffe.proto | 7 + src/caffe/test/test_convolution_layer.cpp | 409 ++++++++++++++++---- src/caffe/test/test_deconvolution_layer.cpp | 159 +++++++- src/caffe/test/test_im2col_kernel.cu | 87 ++++- src/caffe/test/test_im2col_layer.cpp | 30 +- src/caffe/util/im2col.cpp | 116 ++++++ src/caffe/util/im2col.cu | 306 ++++++++++++++- src/caffe/util/upgrade_proto.cpp | 6 +- 19 files changed, 1554 insertions(+), 311 deletions(-) diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp index 0051e2fa067..531fd29c57a 100644 --- a/include/caffe/util/im2col.hpp +++ b/include/caffe/util/im2col.hpp @@ -3,24 +3,48 @@ namespace caffe { +template +void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_col); + template void im2col_cpu(const Dtype* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, Dtype* data_col); +template +void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_im); + template void col2im_cpu(const Dtype* data_col, const int channels, const int height, const int width, const int patch_h, const int patch_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, Dtype* data_im); +template +void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes, + const int col_size, const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_col); + template void im2col_gpu(const Dtype* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, Dtype* data_col); +template +void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes, + const int im_size, const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_im); + template void col2im_gpu(const Dtype* data_col, const int channels, const int height, const int width, const int patch_h, const int patch_w, diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp index 211e3d9042d..eae65820c40 100644 --- a/include/caffe/vision_layers.hpp +++ b/include/caffe/vision_layers.hpp @@ -64,46 +64,101 @@ class BaseConvolutionLayer : public Layer { // Compute height_out_ and width_out_ from other parameters. virtual void compute_output_shape() = 0; - int kernel_h_, kernel_w_; - int stride_h_, stride_w_; + /// @brief The spatial dimensions of a filter kernel. + Blob kernel_shape_; + /// @brief The spatial dimensions of the stride. + Blob stride_; + /// @brief The spatial dimensions of the padding. + Blob pad_; + /// @brief The spatial dimensions of the convolution input. + Blob conv_input_shape_; + /// @brief The spatial dimensions of the input. + Blob input_shape_; + /// @brief The spatial dimensions of the col_buffer. + vector col_buffer_shape_; + /// @brief The spatial dimensions of the output. + vector output_shape_; + + int num_spatial_axes_; + int bottom_dim_; + int top_dim_; + + int channel_axis_; int num_; int channels_; - int pad_h_, pad_w_; - int height_, width_; int group_; + int out_spatial_dim_; + int weight_offset_; int num_output_; - int height_out_, width_out_; bool bias_term_; bool is_1x1_; + bool force_nd_im2col_; private: // wrap im2col/col2im so we don't have to remember the (long) argument lists inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) { - im2col_cpu(data, conv_in_channels_, conv_in_height_, conv_in_width_, - kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff); + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + im2col_cpu(data, conv_in_channels_, + conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff); + } else { + im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(), + col_buffer_shape_.data(), kernel_shape_.cpu_data(), + pad_.cpu_data(), stride_.cpu_data(), col_buff); + } } inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) { - col2im_cpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_, - kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data); + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + col2im_cpu(col_buff, conv_in_channels_, + conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], data); + } else { + col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(), + col_buffer_shape_.data(), kernel_shape_.cpu_data(), + pad_.cpu_data(), stride_.cpu_data(), data); + } } #ifndef CPU_ONLY inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) { - im2col_gpu(data, conv_in_channels_, conv_in_height_, conv_in_width_, - kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff); + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + im2col_gpu(data, conv_in_channels_, + conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff); + } else { + im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_, + conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(), + kernel_shape_.gpu_data(), pad_.gpu_data(), + stride_.gpu_data(), col_buff); + } } inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) { - col2im_gpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_, - kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data); + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + col2im_gpu(col_buff, conv_in_channels_, + conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], data); + } else { + col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_, + conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(), + kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(), + data); + } } #endif + int num_kernels_im2col_; + int num_kernels_col2im_; int conv_out_channels_; int conv_in_channels_; int conv_out_spatial_dim_; - int conv_in_height_; - int conv_in_width_; int kernel_dim_; - int weight_offset_; int col_offset_; int output_offset_; @@ -250,7 +305,7 @@ class CuDNNConvolutionLayer : public ConvolutionLayer { cudnnTensorDescriptor_t bias_desc_; cudnnFilterDescriptor_t filter_desc_; vector conv_descs_; - int bottom_offset_, top_offset_, weight_offset_, bias_offset_; + int bottom_offset_, top_offset_, bias_offset_; size_t workspaceSizeInBytes; void *workspace; }; @@ -287,11 +342,22 @@ class Im2colLayer : public Layer { virtual void Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom); - int kernel_h_, kernel_w_; - int stride_h_, stride_w_; + /// @brief The spatial dimensions of a filter kernel. + Blob kernel_shape_; + /// @brief The spatial dimensions of the stride. + Blob stride_; + /// @brief The spatial dimensions of the padding. + Blob pad_; + + int num_spatial_axes_; + int bottom_dim_; + int top_dim_; + + int channel_axis_; + int num_; int channels_; - int height_, width_; - int pad_h_, pad_w_; + + bool force_nd_im2col_; }; // Forward declare PoolingLayer and SplitLayer for use in LRNLayer. diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp index ccb3adc7e89..a5b90a549bb 100644 --- a/src/caffe/layers/base_conv_layer.cpp +++ b/src/caffe/layers/base_conv_layer.cpp @@ -1,3 +1,4 @@ +#include #include #include "caffe/filler.hpp" @@ -11,50 +12,103 @@ namespace caffe { template void BaseConvolutionLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { - CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, " - << "corresponding to (num, channels, height, width)"; // Configure the kernel size, padding, stride, and inputs. ConvolutionParameter conv_param = this->layer_param_.convolution_param(); - CHECK(!conv_param.has_kernel_size() != - !(conv_param.has_kernel_h() && conv_param.has_kernel_w())) - << "Filter size is kernel_size OR kernel_h and kernel_w; not both"; - CHECK(conv_param.has_kernel_size() || - (conv_param.has_kernel_h() && conv_param.has_kernel_w())) - << "For non-square filters both kernel_h and kernel_w are required."; - CHECK((!conv_param.has_pad() && conv_param.has_pad_h() - && conv_param.has_pad_w()) - || (!conv_param.has_pad_h() && !conv_param.has_pad_w())) - << "pad is pad OR pad_h and pad_w are required."; - CHECK((!conv_param.has_stride() && conv_param.has_stride_h() - && conv_param.has_stride_w()) - || (!conv_param.has_stride_h() && !conv_param.has_stride_w())) - << "Stride is stride OR stride_h and stride_w are required."; - if (conv_param.has_kernel_size()) { - kernel_h_ = kernel_w_ = conv_param.kernel_size(); + force_nd_im2col_ = conv_param.force_nd_im2col(); + channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis()); + const int first_spatial_axis = channel_axis_ + 1; + const int num_axes = bottom[0]->num_axes(); + num_spatial_axes_ = num_axes - first_spatial_axis; + CHECK_GE(num_spatial_axes_, 0); + // Setup input dimensions (input_shape_). + vector bottom_dim_blob_shape(1, num_spatial_axes_ + 1); + input_shape_.Reshape(bottom_dim_blob_shape); + int* input_shape_data = input_shape_.mutable_cpu_data(); + for (int i = 0; i < num_spatial_axes_ + 1; ++i) { + input_shape_data[i] = bottom[0]->shape(channel_axis_ + i); + } + vector spatial_dim_blob_shape(1, std::max(num_spatial_axes_, 1)); + // Setup filter kernel dimensions (kernel_shape_). + kernel_shape_.Reshape(spatial_dim_blob_shape); + int* kernel_shape_data = kernel_shape_.mutable_cpu_data(); + if (conv_param.has_kernel_h() || conv_param.has_kernel_w()) { + CHECK_EQ(num_spatial_axes_, 2) + << "kernel_h & kernel_w can only be used for 2D convolution."; + CHECK_EQ(0, conv_param.kernel_size_size()) + << "Either kernel_size or kernel_h/w should be specified; not both."; + kernel_shape_data[0] = conv_param.kernel_h(); + kernel_shape_data[1] = conv_param.kernel_w(); } else { - kernel_h_ = conv_param.kernel_h(); - kernel_w_ = conv_param.kernel_w(); + const int num_kernel_dims = conv_param.kernel_size_size(); + CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_) + << "kernel_size must be specified once, or once per spatial dimension " + << "(kernel_size specified " << num_kernel_dims << " times; " + << num_spatial_axes_ << " spatial dims);"; + for (int i = 0; i < num_spatial_axes_; ++i) { + kernel_shape_data[i] = + conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i); + } + } + for (int i = 0; i < num_spatial_axes_; ++i) { + CHECK_GT(kernel_shape_data[i], 0) << "Filter dimensions must be nonzero."; } - CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero."; - CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero."; - if (!conv_param.has_pad_h()) { - pad_h_ = pad_w_ = conv_param.pad(); + // Setup stride dimensions (stride_). + stride_.Reshape(spatial_dim_blob_shape); + int* stride_data = stride_.mutable_cpu_data(); + if (conv_param.has_stride_h() || conv_param.has_stride_w()) { + CHECK_EQ(num_spatial_axes_, 2) + << "stride_h & stride_w can only be used for 2D convolution."; + CHECK_EQ(0, conv_param.stride_size()) + << "Either stride or stride_h/w should be specified; not both."; + stride_data[0] = conv_param.stride_h(); + stride_data[1] = conv_param.stride_w(); } else { - pad_h_ = conv_param.pad_h(); - pad_w_ = conv_param.pad_w(); + const int num_stride_dims = conv_param.stride_size(); + CHECK(num_stride_dims == 0 || num_stride_dims == 1 || + num_stride_dims == num_spatial_axes_) + << "stride must be specified once, or once per spatial dimension " + << "(stride specified " << num_stride_dims << " times; " + << num_spatial_axes_ << " spatial dims);"; + const int kDefaultStride = 1; + for (int i = 0; i < num_spatial_axes_; ++i) { + stride_data[i] = (num_stride_dims == 0) ? kDefaultStride : + conv_param.stride((num_stride_dims == 1) ? 0 : i); + CHECK_GT(stride_data[i], 0) << "Stride dimensions must be nonzero."; + } } - if (!conv_param.has_stride_h()) { - stride_h_ = stride_w_ = conv_param.stride(); + // Setup pad dimensions (pad_). + pad_.Reshape(spatial_dim_blob_shape); + int* pad_data = pad_.mutable_cpu_data(); + if (conv_param.has_pad_h() || conv_param.has_pad_w()) { + CHECK_EQ(num_spatial_axes_, 2) + << "pad_h & pad_w can only be used for 2D convolution."; + CHECK_EQ(0, conv_param.pad_size()) + << "Either pad or pad_h/w should be specified; not both."; + pad_data[0] = conv_param.pad_h(); + pad_data[1] = conv_param.pad_w(); } else { - stride_h_ = conv_param.stride_h(); - stride_w_ = conv_param.stride_w(); + const int num_pad_dims = conv_param.pad_size(); + CHECK(num_pad_dims == 0 || num_pad_dims == 1 || + num_pad_dims == num_spatial_axes_) + << "pad must be specified once, or once per spatial dimension " + << "(pad specified " << num_pad_dims << " times; " + << num_spatial_axes_ << " spatial dims);"; + const int kDefaultPad = 0; + for (int i = 0; i < num_spatial_axes_; ++i) { + pad_data[i] = (num_pad_dims == 0) ? kDefaultPad : + conv_param.pad((num_pad_dims == 1) ? 0 : i); + } } // Special case: im2col is the identity for 1x1 convolution with stride 1 // and no padding, so flag for skipping the buffer and transformation. - is_1x1_ = kernel_w_ == 1 && kernel_h_ == 1 - && stride_h_ == 1 && stride_w_ == 1 && pad_h_ == 0 && pad_w_ == 0; + is_1x1_ = true; + for (int i = 0; i < num_spatial_axes_; ++i) { + is_1x1_ &= + kernel_shape_data[i] == 1 && stride_data[i] == 1 && pad_data[i] == 0; + if (!is_1x1_) { break; } + } // Configure output channels and groups. - channels_ = bottom[0]->channels(); + channels_ = bottom[0]->shape(channel_axis_); num_output_ = this->layer_param_.convolution_param().num_output(); CHECK_GT(num_output_, 0); group_ = this->layer_param_.convolution_param().group(); @@ -71,8 +125,29 @@ void BaseConvolutionLayer::LayerSetUp(const vector*>& bottom, // Handle the parameters: weights and biases. // - blobs_[0] holds the filter weights // - blobs_[1] holds the biases (optional) + vector weight_shape(2); + weight_shape[0] = conv_out_channels_; + weight_shape[1] = conv_in_channels_ / group_; + for (int i = 0; i < num_spatial_axes_; ++i) { + weight_shape.push_back(kernel_shape_data[i]); + } bias_term_ = this->layer_param_.convolution_param().bias_term(); + vector bias_shape(bias_term_, num_output_); if (this->blobs_.size() > 0) { + CHECK_EQ(1 + bias_term_, this->blobs_.size()) + << "Incorrect number of weight blobs."; + if (weight_shape != this->blobs_[0]->shape()) { + Blob weight_shaped_blob(weight_shape); + LOG(FATAL) << "Incorrect weight shape: expected shape " + << weight_shaped_blob.shape_string() << "; instead, shape was " + << this->blobs_[0]->shape_string(); + } + if (bias_term_ && bias_shape != this->blobs_[1]->shape()) { + Blob bias_shaped_blob(bias_shape); + LOG(FATAL) << "Incorrect bias shape: expected shape " + << bias_shaped_blob.shape_string() << "; instead, shape was " + << this->blobs_[1]->shape_string(); + } LOG(INFO) << "Skipping parameter initialization"; } else { if (bias_term_) { @@ -82,20 +157,20 @@ void BaseConvolutionLayer::LayerSetUp(const vector*>& bottom, } // Initialize and fill the weights: // output channels x input channels per-group x kernel height x kernel width - this->blobs_[0].reset(new Blob( - conv_out_channels_, conv_in_channels_ / group_, kernel_h_, kernel_w_)); + this->blobs_[0].reset(new Blob(weight_shape)); shared_ptr > weight_filler(GetFiller( this->layer_param_.convolution_param().weight_filler())); weight_filler->Fill(this->blobs_[0].get()); // If necessary, initialize and fill the biases. if (bias_term_) { - vector bias_shape(1, num_output_); this->blobs_[1].reset(new Blob(bias_shape)); shared_ptr > bias_filler(GetFiller( this->layer_param_.convolution_param().bias_filler())); bias_filler->Fill(this->blobs_[1].get()); } } + kernel_dim_ = this->blobs_[0]->count(1); + weight_offset_ = conv_out_channels_ * kernel_dim_ / group_; // Propagate gradients to the parameters (as directed by backward pass). this->param_propagate_down_.resize(this->blobs_.size(), true); } @@ -103,52 +178,68 @@ void BaseConvolutionLayer::LayerSetUp(const vector*>& bottom, template void BaseConvolutionLayer::Reshape(const vector*>& bottom, const vector*>& top) { - CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, " - << "corresponding to (num, channels, height, width)"; - num_ = bottom[0]->num(); - height_ = bottom[0]->height(); - width_ = bottom[0]->width(); - CHECK_EQ(bottom[0]->channels(), channels_) << "Input size incompatible with" - " convolution kernel."; + const int first_spatial_axis = channel_axis_ + 1; + CHECK_EQ(bottom[0]->num_axes(), first_spatial_axis + num_spatial_axes_) + << "bottom num_axes may not change."; + num_ = bottom[0]->count(0, channel_axis_); + CHECK_EQ(bottom[0]->shape(channel_axis_), channels_) + << "Input size incompatible with convolution kernel."; // TODO: generalize to handle inputs of different shapes. for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) { - CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num."; - CHECK_EQ(channels_, bottom[bottom_id]->channels()) - << "Inputs must have same channels."; - CHECK_EQ(height_, bottom[bottom_id]->height()) - << "Inputs must have same height."; - CHECK_EQ(width_, bottom[bottom_id]->width()) - << "Inputs must have same width."; + CHECK(bottom[0]->shape() == bottom[bottom_id]->shape()) + << "All inputs must have the same shape."; } // Shape the tops. compute_output_shape(); + vector top_shape(bottom[0]->shape().begin(), + bottom[0]->shape().begin() + channel_axis_); + top_shape.push_back(num_output_); + for (int i = 0; i < num_spatial_axes_; ++i) { + top_shape.push_back(output_shape_[i]); + } for (int top_id = 0; top_id < top.size(); ++top_id) { - top[top_id]->Reshape(num_, num_output_, height_out_, width_out_); + top[top_id]->Reshape(top_shape); } if (reverse_dimensions()) { - conv_in_height_ = height_out_; - conv_in_width_ = width_out_; - conv_out_spatial_dim_ = height_ * width_; + conv_out_spatial_dim_ = bottom[0]->count(first_spatial_axis); } else { - conv_in_height_ = height_; - conv_in_width_ = width_; - conv_out_spatial_dim_ = height_out_ * width_out_; + conv_out_spatial_dim_ = top[0]->count(first_spatial_axis); } - kernel_dim_ = conv_in_channels_ * kernel_h_ * kernel_w_; - weight_offset_ = conv_out_channels_ * kernel_dim_ / group_ / group_; - col_offset_ = kernel_dim_ * conv_out_spatial_dim_ / group_; + col_offset_ = kernel_dim_ * conv_out_spatial_dim_; output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_; + // Setup input dimensions (conv_input_shape_). + vector bottom_dim_blob_shape(1, num_spatial_axes_ + 1); + conv_input_shape_.Reshape(bottom_dim_blob_shape); + int* conv_input_shape_data = conv_input_shape_.mutable_cpu_data(); + for (int i = 0; i < num_spatial_axes_ + 1; ++i) { + if (reverse_dimensions()) { + conv_input_shape_data[i] = top[0]->shape(channel_axis_ + i); + } else { + conv_input_shape_data[i] = bottom[0]->shape(channel_axis_ + i); + } + } // The im2col result buffer will only hold one image at a time to avoid // overly large memory usage. In the special case of 1x1 convolution // it goes lazily unused to save memory. - if (reverse_dimensions()) { - col_buffer_.Reshape(1, kernel_dim_, height_, width_); - } else { - col_buffer_.Reshape(1, kernel_dim_, height_out_, width_out_); + col_buffer_shape_.clear(); + col_buffer_shape_.push_back(kernel_dim_ * group_); + const int* input_shape_data = input_shape_.cpu_data() + 1; + for (int i = 0; i < num_spatial_axes_; ++i) { + if (reverse_dimensions()) { + col_buffer_shape_.push_back(input_shape_data[i]); + } else { + col_buffer_shape_.push_back(output_shape_[i]); + } } + col_buffer_.Reshape(col_buffer_shape_); + bottom_dim_ = bottom[0]->count(channel_axis_); + top_dim_ = top[0]->count(channel_axis_); + num_kernels_im2col_ = conv_in_channels_ * conv_out_spatial_dim_; + num_kernels_col2im_ = reverse_dimensions() ? top_dim_ : bottom_dim_; // Set up the all ones "bias multiplier" for adding biases by BLAS + out_spatial_dim_ = top[0]->count(first_spatial_axis); if (bias_term_) { - vector bias_multiplier_shape(1, height_out_ * width_out_); + vector bias_multiplier_shape(1, out_spatial_dim_); bias_multiplier_.Reshape(bias_multiplier_shape); caffe_set(bias_multiplier_.count(), Dtype(1), bias_multiplier_.mutable_cpu_data()); @@ -167,7 +258,7 @@ void BaseConvolutionLayer::forward_cpu_gemm(const Dtype* input, } for (int g = 0; g < group_; ++g) { caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, conv_out_channels_ / - group_, conv_out_spatial_dim_, kernel_dim_ / group_, + group_, conv_out_spatial_dim_, kernel_dim_, (Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g, (Dtype)0., output + output_offset_ * g); } @@ -177,7 +268,7 @@ template void BaseConvolutionLayer::forward_cpu_bias(Dtype* output, const Dtype* bias) { caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num_output_, - height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.cpu_data(), + out_spatial_dim_, 1, (Dtype)1., bias, bias_multiplier_.cpu_data(), (Dtype)1., output); } @@ -189,7 +280,7 @@ void BaseConvolutionLayer::backward_cpu_gemm(const Dtype* output, col_buff = input; } for (int g = 0; g < group_; ++g) { - caffe_cpu_gemm(CblasTrans, CblasNoTrans, kernel_dim_ / group_, + caffe_cpu_gemm(CblasTrans, CblasNoTrans, kernel_dim_, conv_out_spatial_dim_, conv_out_channels_ / group_, (Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g, (Dtype)0., col_buff + col_offset_ * g); @@ -209,7 +300,7 @@ void BaseConvolutionLayer::weight_cpu_gemm(const Dtype* input, } for (int g = 0; g < group_; ++g) { caffe_cpu_gemm(CblasNoTrans, CblasTrans, conv_out_channels_ / group_, - kernel_dim_ / group_, conv_out_spatial_dim_, + kernel_dim_, conv_out_spatial_dim_, (Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g, (Dtype)1., weights + weight_offset_ * g); } @@ -218,7 +309,7 @@ void BaseConvolutionLayer::weight_cpu_gemm(const Dtype* input, template void BaseConvolutionLayer::backward_cpu_bias(Dtype* bias, const Dtype* input) { - caffe_cpu_gemv(CblasNoTrans, num_output_, height_out_ * width_out_, 1., + caffe_cpu_gemv(CblasNoTrans, num_output_, out_spatial_dim_, 1., input, bias_multiplier_.cpu_data(), 1., bias); } @@ -236,7 +327,7 @@ void BaseConvolutionLayer::forward_gpu_gemm(const Dtype* input, } for (int g = 0; g < group_; ++g) { caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, conv_out_channels_ / - group_, conv_out_spatial_dim_, kernel_dim_ / group_, + group_, conv_out_spatial_dim_, kernel_dim_, (Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g, (Dtype)0., output + output_offset_ * g); } @@ -246,7 +337,7 @@ template void BaseConvolutionLayer::forward_gpu_bias(Dtype* output, const Dtype* bias) { caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num_output_, - height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.gpu_data(), + out_spatial_dim_, 1, (Dtype)1., bias, bias_multiplier_.gpu_data(), (Dtype)1., output); } @@ -258,7 +349,7 @@ void BaseConvolutionLayer::backward_gpu_gemm(const Dtype* output, col_buff = input; } for (int g = 0; g < group_; ++g) { - caffe_gpu_gemm(CblasTrans, CblasNoTrans, kernel_dim_ / group_, + caffe_gpu_gemm(CblasTrans, CblasNoTrans, kernel_dim_, conv_out_spatial_dim_, conv_out_channels_ / group_, (Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g, (Dtype)0., col_buff + col_offset_ * g); @@ -278,7 +369,7 @@ void BaseConvolutionLayer::weight_gpu_gemm(const Dtype* input, } for (int g = 0; g < group_; ++g) { caffe_gpu_gemm(CblasNoTrans, CblasTrans, conv_out_channels_ / group_, - kernel_dim_ / group_, conv_out_spatial_dim_, + kernel_dim_, conv_out_spatial_dim_, (Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g, (Dtype)1., weights + weight_offset_ * g); } @@ -287,7 +378,7 @@ void BaseConvolutionLayer::weight_gpu_gemm(const Dtype* input, template void BaseConvolutionLayer::backward_gpu_bias(Dtype* bias, const Dtype* input) { - caffe_gpu_gemv(CblasNoTrans, num_output_, height_out_ * width_out_, 1., + caffe_gpu_gemv(CblasNoTrans, num_output_, out_spatial_dim_, 1., input, bias_multiplier_.gpu_data(), 1., bias); } diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp index 928ef5ee468..5cf26970a0b 100644 --- a/src/caffe/layers/conv_layer.cpp +++ b/src/caffe/layers/conv_layer.cpp @@ -10,10 +10,18 @@ namespace caffe { template void ConvolutionLayer::compute_output_shape() { - this->height_out_ = (this->height_ + 2 * this->pad_h_ - this->kernel_h_) - / this->stride_h_ + 1; - this->width_out_ = (this->width_ + 2 * this->pad_w_ - this->kernel_w_) - / this->stride_w_ + 1; + // input_shape_ + 1 to skip channel axis + const int* input_shape_data = this->input_shape_.cpu_data() + 1; + const int* kernel_shape_data = this->kernel_shape_.cpu_data(); + const int* stride_data = this->stride_.cpu_data(); + const int* pad_data = this->pad_.cpu_data(); + this->output_shape_.clear(); + for (int i = 0; i < this->num_spatial_axes_; ++i) { + const int input_dim = input_shape_data[i]; + const int output_dim = (input_dim + 2 * pad_data[i] - kernel_shape_data[i]) + / stride_data[i] + 1; + this->output_shape_.push_back(output_dim); + } } template @@ -24,11 +32,11 @@ void ConvolutionLayer::Forward_cpu(const vector*>& bottom, const Dtype* bottom_data = bottom[i]->cpu_data(); Dtype* top_data = top[i]->mutable_cpu_data(); for (int n = 0; n < this->num_; ++n) { - this->forward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight, - top_data + top[i]->offset(n)); + this->forward_cpu_gemm(bottom_data + n * this->bottom_dim_, weight, + top_data + n * this->top_dim_); if (this->bias_term_) { const Dtype* bias = this->blobs_[1]->cpu_data(); - this->forward_cpu_bias(top_data + top[i]->offset(n), bias); + this->forward_cpu_bias(top_data + n * this->top_dim_, bias); } } } @@ -47,20 +55,20 @@ void ConvolutionLayer::Backward_cpu(const vector*>& top, if (this->bias_term_ && this->param_propagate_down_[1]) { Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff(); for (int n = 0; n < this->num_; ++n) { - this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n)); + this->backward_cpu_bias(bias_diff, top_diff + n * this->top_dim_); } } if (this->param_propagate_down_[0] || propagate_down[i]) { for (int n = 0; n < this->num_; ++n) { // gradient w.r.t. weight. Note that we will accumulate diffs. if (this->param_propagate_down_[0]) { - this->weight_cpu_gemm(bottom_data + bottom[i]->offset(n), - top_diff + top[i]->offset(n), weight_diff); + this->weight_cpu_gemm(bottom_data + n * this->bottom_dim_, + top_diff + n * this->top_dim_, weight_diff); } // gradient w.r.t. bottom data, if necessary. if (propagate_down[i]) { - this->backward_cpu_gemm(top_diff + top[i]->offset(n), weight, - bottom_diff + bottom[i]->offset(n)); + this->backward_cpu_gemm(top_diff + n * this->top_dim_, weight, + bottom_diff + n * this->bottom_dim_); } } } diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu index b8a98ff7cc9..b429d2b47d0 100644 --- a/src/caffe/layers/conv_layer.cu +++ b/src/caffe/layers/conv_layer.cu @@ -16,11 +16,11 @@ void ConvolutionLayer::Forward_gpu(const vector*>& bottom, const Dtype* bottom_data = bottom[i]->gpu_data(); Dtype* top_data = top[i]->mutable_gpu_data(); for (int n = 0; n < this->num_; ++n) { - this->forward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight, - top_data + top[i]->offset(n)); + this->forward_gpu_gemm(bottom_data + n * this->bottom_dim_, weight, + top_data + n * this->top_dim_); if (this->bias_term_) { const Dtype* bias = this->blobs_[1]->gpu_data(); - this->forward_gpu_bias(top_data + top[i]->offset(n), bias); + this->forward_gpu_bias(top_data + n * this->top_dim_, bias); } } } @@ -37,7 +37,7 @@ void ConvolutionLayer::Backward_gpu(const vector*>& top, if (this->bias_term_ && this->param_propagate_down_[1]) { Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff(); for (int n = 0; n < this->num_; ++n) { - this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n)); + this->backward_gpu_bias(bias_diff, top_diff + n * this->top_dim_); } } if (this->param_propagate_down_[0] || propagate_down[i]) { @@ -46,13 +46,13 @@ void ConvolutionLayer::Backward_gpu(const vector*>& top, for (int n = 0; n < this->num_; ++n) { // gradient w.r.t. weight. Note that we will accumulate diffs. if (this->param_propagate_down_[0]) { - this->weight_gpu_gemm(bottom_data + bottom[i]->offset(n), - top_diff + top[i]->offset(n), weight_diff); + this->weight_gpu_gemm(bottom_data + n * this->bottom_dim_, + top_diff + n * this->top_dim_, weight_diff); } // gradient w.r.t. bottom data, if necessary. if (propagate_down[i]) { - this->backward_gpu_gemm(top_diff + top[i]->offset(n), weight, - bottom_diff + bottom[i]->offset(n)); + this->backward_gpu_gemm(top_diff + n * this->top_dim_, weight, + bottom_diff + n * this->bottom_dim_); } } } diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp index 104d2b9d669..3514fe2aba5 100644 --- a/src/caffe/layers/cudnn_conv_layer.cpp +++ b/src/caffe/layers/cudnn_conv_layer.cpp @@ -34,14 +34,15 @@ void CuDNNConvolutionLayer::LayerSetUp( } // Set the indexing parameters. - weight_offset_ = (this->num_output_ / this->group_) - * (this->channels_ / this->group_) * this->kernel_h_ * this->kernel_w_; bias_offset_ = (this->num_output_ / this->group_); // Create filter descriptor. + const int* kernel_shape_data = this->kernel_shape_.cpu_data(); + const int kernel_h = kernel_shape_data[0]; + const int kernel_w = kernel_shape_data[1]; cudnn::createFilterDesc(&filter_desc_, this->num_output_ / this->group_, this->channels_ / this->group_, - this->kernel_h_, this->kernel_w_); + kernel_h, kernel_w); // Create tensor descriptor(s) for data and corresponding convolution(s). for (int i = 0; i < bottom.size(); i++) { @@ -68,29 +69,36 @@ template void CuDNNConvolutionLayer::Reshape( const vector*>& bottom, const vector*>& top) { ConvolutionLayer::Reshape(bottom, top); - bottom_offset_ = (this->channels_ / this->group_) - * this->height_ * this->width_; - top_offset_ = (this->num_output_ / this->group_) - * this->height_out_ * this->width_out_; + CHECK_EQ(2, this->num_spatial_axes_) + << "CuDNNConvolution input must have 2 spatial axes " + << "(e.g., height and width). " + << "Use 'engine: CAFFE' for general ND convolution."; + bottom_offset_ = this->bottom_dim_ / this->group_; + top_offset_ = this->top_dim_ / this->group_; + const int height = bottom[0]->shape(this->channel_axis_ + 1); + const int width = bottom[0]->shape(this->channel_axis_ + 2); + const int height_out = top[0]->shape(this->channel_axis_ + 1); + const int width_out = top[0]->shape(this->channel_axis_ + 2); + const int* pad_data = this->pad_.cpu_data(); + const int pad_h = pad_data[0]; + const int pad_w = pad_data[1]; + const int* stride_data = this->stride_.cpu_data(); + const int stride_h = stride_data[0]; + const int stride_w = stride_data[1]; for (int i = 0; i < bottom.size(); i++) { cudnn::setTensor4dDesc(&bottom_descs_[i], this->num_, - this->channels_ / this->group_, - this->height_, this->width_, - this->channels_ * this->height_ * this->width_, - this->height_ * this->width_, - this->width_, 1); + this->channels_ / this->group_, height, width, + this->channels_ * height * width, + height * width, width, 1); cudnn::setTensor4dDesc(&top_descs_[i], this->num_, - this->num_output_ / this->group_, - this->height_out_, this->width_out_, - this->num_output_ * this->height_out_ * this->width_out_, - this->height_out_ * this->width_out_, - this->width_out_, 1); + this->num_output_ / this->group_, height_out, width_out, + this->num_output_ * this->out_spatial_dim_, + this->out_spatial_dim_, width_out, 1); cudnn::setConvolutionDesc(&conv_descs_[i], bottom_descs_[i], - filter_desc_, this->pad_h_, this->pad_w_, - this->stride_h_, this->stride_w_); + filter_desc_, pad_h, pad_w, stride_h, stride_w); } // Tensor descriptor for bias. diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu index b4e802e13d1..691152021a3 100644 --- a/src/caffe/layers/cudnn_conv_layer.cu +++ b/src/caffe/layers/cudnn_conv_layer.cu @@ -14,15 +14,15 @@ __global__ void sync_conv_groups() { } template void CuDNNConvolutionLayer::Forward_gpu( const vector*>& bottom, const vector*>& top) { + const int* kernel_shape_data = this->kernel_shape_.cpu_data(); + const int kernel_h = kernel_shape_data[0]; + const int kernel_w = kernel_shape_data[1]; + const size_t workspace_limit_bytes = + kernel_h * kernel_w * this->channels_ * sizeof(int) + 1; + const Dtype* weight = this->blobs_[0]->gpu_data(); for (int i = 0; i < bottom.size(); ++i) { const Dtype* bottom_data = bottom[i]->gpu_data(); Dtype* top_data = top[i]->mutable_gpu_data(); - const Dtype* weight = this->blobs_[0]->gpu_data(); - - size_t workspace_limit_bytes = this->kernel_h_ * - this->kernel_w_ * - this->channels_ * - sizeof(int) + 1; // Forward through cuDNN in parallel over groups. for (int g = 0; g < this->group_; g++) { @@ -69,7 +69,7 @@ void CuDNNConvolutionLayer::Forward_gpu( CUDNN_CHECK(cudnnConvolutionForward(handle_[g], cudnn::dataType::one, bottom_descs_[i], bottom_data + bottom_offset_ * g, - filter_desc_, weight + weight_offset_ * g, + filter_desc_, weight + this->weight_offset_ * g, conv_descs_[i], algo, workspace, workspaceSizeInBytes, cudnn::dataType::zero, @@ -128,7 +128,7 @@ void CuDNNConvolutionLayer::Backward_gpu(const vector*>& top, top_descs_[i], top_diff + top_offset_ * g, conv_descs_[i], cudnn::dataType::one, - filter_desc_, weight_diff + weight_offset_ * g)); + filter_desc_, weight_diff + this->weight_offset_ * g)); } // Gradient w.r.t. bottom data. @@ -139,7 +139,7 @@ void CuDNNConvolutionLayer::Backward_gpu(const vector*>& top, Dtype* bottom_diff = bottom[i]->mutable_gpu_diff(); CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g], cudnn::dataType::one, - filter_desc_, weight + weight_offset_ * g, + filter_desc_, weight + this->weight_offset_ * g, top_descs_[i], top_diff + top_offset_ * g, conv_descs_[i], cudnn::dataType::zero, diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp index a4612963b6b..f1d1abf288a 100644 --- a/src/caffe/layers/deconv_layer.cpp +++ b/src/caffe/layers/deconv_layer.cpp @@ -10,10 +10,18 @@ namespace caffe { template void DeconvolutionLayer::compute_output_shape() { - this->height_out_ = this->stride_h_ * (this->height_ - 1) + this->kernel_h_ - - 2 * this->pad_h_; - this->width_out_ = this->stride_w_ * (this->width_ - 1) + this->kernel_w_ - - 2 * this->pad_w_; + // input_shape_ + 1 to skip channel axis + const int* input_shape_data = this->input_shape_.cpu_data() + 1; + const int* kernel_shape_data = this->kernel_shape_.cpu_data(); + const int* stride_data = this->stride_.cpu_data(); + const int* pad_data = this->pad_.cpu_data(); + this->output_shape_.clear(); + for (int i = 0; i < this->num_spatial_axes_; ++i) { + const int input_dim = input_shape_data[i]; + const int output_dim = stride_data[i] * (input_dim - 1) + + kernel_shape_data[i] - 2 * pad_data[i]; + this->output_shape_.push_back(output_dim); + } } template @@ -24,11 +32,11 @@ void DeconvolutionLayer::Forward_cpu(const vector*>& bottom, const Dtype* bottom_data = bottom[i]->cpu_data(); Dtype* top_data = top[i]->mutable_cpu_data(); for (int n = 0; n < this->num_; ++n) { - this->backward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight, - top_data + top[i]->offset(n)); + this->backward_cpu_gemm(bottom_data + n * this->bottom_dim_, weight, + top_data + n * this->top_dim_); if (this->bias_term_) { const Dtype* bias = this->blobs_[1]->cpu_data(); - this->forward_cpu_bias(top_data + top[i]->offset(n), bias); + this->forward_cpu_bias(top_data + n * this->top_dim_, bias); } } } @@ -47,21 +55,21 @@ void DeconvolutionLayer::Backward_cpu(const vector*>& top, if (this->bias_term_ && this->param_propagate_down_[1]) { Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff(); for (int n = 0; n < this->num_; ++n) { - this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n)); + this->backward_cpu_bias(bias_diff, top_diff + n * this->top_dim_); } } if (this->param_propagate_down_[0] || propagate_down[i]) { for (int n = 0; n < this->num_; ++n) { // Gradient w.r.t. weight. Note that we will accumulate diffs. if (this->param_propagate_down_[0]) { - this->weight_cpu_gemm(top_diff + top[i]->offset(n), - bottom_data + bottom[i]->offset(n), weight_diff); + this->weight_cpu_gemm(top_diff + n * this->top_dim_, + bottom_data + n * this->bottom_dim_, weight_diff); } // Gradient w.r.t. bottom data, if necessary, reusing the column buffer // we might have just computed above. if (propagate_down[i]) { - this->forward_cpu_gemm(top_diff + top[i]->offset(n), weight, - bottom_diff + bottom[i]->offset(n), + this->forward_cpu_gemm(top_diff + n * this->top_dim_, weight, + bottom_diff + n * this->bottom_dim_, this->param_propagate_down_[0]); } } diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu index 8a1eed8aa16..ea83f56f1b2 100644 --- a/src/caffe/layers/deconv_layer.cu +++ b/src/caffe/layers/deconv_layer.cu @@ -16,11 +16,11 @@ void DeconvolutionLayer::Forward_gpu(const vector*>& bottom, const Dtype* bottom_data = bottom[i]->gpu_data(); Dtype* top_data = top[i]->mutable_gpu_data(); for (int n = 0; n < this->num_; ++n) { - this->backward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight, - top_data + top[i]->offset(n)); + this->backward_gpu_gemm(bottom_data + n * this->bottom_dim_, weight, + top_data + n * this->top_dim_); if (this->bias_term_) { const Dtype* bias = this->blobs_[1]->gpu_data(); - this->forward_gpu_bias(top_data + top[i]->offset(n), bias); + this->forward_gpu_bias(top_data + n * this->top_dim_, bias); } } } @@ -39,20 +39,20 @@ void DeconvolutionLayer::Backward_gpu(const vector*>& top, if (this->bias_term_ && this->param_propagate_down_[1]) { Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff(); for (int n = 0; n < this->num_; ++n) { - this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n)); + this->backward_gpu_bias(bias_diff, top_diff + n * this->top_dim_); } } if (this->param_propagate_down_[0] || propagate_down[i]) { for (int n = 0; n < this->num_; ++n) { // gradient w.r.t. weight. Note that we will accumulate diffs. if (this->param_propagate_down_[0]) { - this->weight_gpu_gemm(top_diff + top[i]->offset(n), - bottom_data + bottom[i]->offset(n), weight_diff); + this->weight_gpu_gemm(top_diff + n * this->top_dim_, + bottom_data + n * this->bottom_dim_, weight_diff); } // gradient w.r.t. bottom data, if necessary. if (propagate_down[i]) { - this->forward_gpu_gemm(top_diff + top[i]->offset(n), weight, - bottom_diff + bottom[i]->offset(n), + this->forward_gpu_gemm(top_diff + this->top_dim_, weight, + bottom_diff + n * this->bottom_dim_, this->param_propagate_down_[0]); } } diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp index 1c802714e33..595c9dbbe5e 100644 --- a/src/caffe/layers/im2col_layer.cpp +++ b/src/caffe/layers/im2col_layer.cpp @@ -11,54 +11,106 @@ template void Im2colLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { ConvolutionParameter conv_param = this->layer_param_.convolution_param(); - CHECK(!conv_param.has_kernel_size() != - !(conv_param.has_kernel_h() && conv_param.has_kernel_w())) - << "Filter size is kernel_size OR kernel_h and kernel_w; not both"; - CHECK(conv_param.has_kernel_size() || - (conv_param.has_kernel_h() && conv_param.has_kernel_w())) - << "For non-square filters both kernel_h and kernel_w are required."; - CHECK((!conv_param.has_pad() && conv_param.has_pad_h() - && conv_param.has_pad_w()) - || (!conv_param.has_pad_h() && !conv_param.has_pad_w())) - << "pad is pad OR pad_h and pad_w are required."; - CHECK((!conv_param.has_stride() && conv_param.has_stride_h() - && conv_param.has_stride_w()) - || (!conv_param.has_stride_h() && !conv_param.has_stride_w())) - << "Stride is stride OR stride_h and stride_w are required."; - if (conv_param.has_kernel_size()) { - kernel_h_ = kernel_w_ = conv_param.kernel_size(); + force_nd_im2col_ = conv_param.force_nd_im2col(); + const int input_num_dims = bottom[0]->shape().size(); + channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis()); + const int first_spatial_dim = channel_axis_ + 1; + num_spatial_axes_ = input_num_dims - first_spatial_dim; + CHECK_GE(num_spatial_axes_, 1); + vector dim_blob_shape(1, num_spatial_axes_); + // Setup filter kernel dimensions (kernel_shape_). + kernel_shape_.Reshape(dim_blob_shape); + int* kernel_shape_data = kernel_shape_.mutable_cpu_data(); + if (conv_param.has_kernel_h() || conv_param.has_kernel_w()) { + CHECK_EQ(num_spatial_axes_, 2) + << "kernel_h & kernel_w can only be used for 2D convolution."; + CHECK_EQ(0, conv_param.kernel_size_size()) + << "Either kernel_size or kernel_h/w should be specified; not both."; + kernel_shape_data[0] = conv_param.kernel_h(); + kernel_shape_data[1] = conv_param.kernel_w(); } else { - kernel_h_ = conv_param.kernel_h(); - kernel_w_ = conv_param.kernel_w(); + const int num_kernel_dims = conv_param.kernel_size_size(); + CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_) + << "kernel_size must be specified once, or once per spatial dimension " + << "(kernel_size specified " << num_kernel_dims << " times; " + << num_spatial_axes_ << " spatial dims);"; + for (int i = 0; i < num_spatial_axes_; ++i) { + kernel_shape_data[i] = + conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i); + } } - CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero."; - CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero."; - if (!conv_param.has_pad_h()) { - pad_h_ = pad_w_ = conv_param.pad(); + for (int i = 0; i < num_spatial_axes_; ++i) { + CHECK_GT(kernel_shape_data[i], 0) << "Filter dimensions must be nonzero."; + } + // Setup stride dimensions (stride_). + stride_.Reshape(dim_blob_shape); + int* stride_data = stride_.mutable_cpu_data(); + if (conv_param.has_stride_h() || conv_param.has_stride_w()) { + CHECK_EQ(num_spatial_axes_, 2) + << "stride_h & stride_w can only be used for 2D convolution."; + CHECK_EQ(0, conv_param.stride_size()) + << "Either stride or stride_h/w should be specified; not both."; + stride_data[0] = conv_param.stride_h(); + stride_data[1] = conv_param.stride_w(); } else { - pad_h_ = conv_param.pad_h(); - pad_w_ = conv_param.pad_w(); + const int num_stride_dims = conv_param.stride_size(); + CHECK(num_stride_dims == 0 || num_stride_dims == 1 || + num_stride_dims == num_spatial_axes_) + << "stride must be specified once, or once per spatial dimension " + << "(stride specified " << num_stride_dims << " times; " + << num_spatial_axes_ << " spatial dims);"; + const int kDefaultStride = 1; + for (int i = 0; i < num_spatial_axes_; ++i) { + stride_data[i] = (num_stride_dims == 0) ? kDefaultStride : + conv_param.stride((num_stride_dims == 1) ? 0 : i); + CHECK_GT(stride_data[i], 0) << "Stride dimensions must be nonzero."; + } } - if (!conv_param.has_stride_h()) { - stride_h_ = stride_w_ = conv_param.stride(); + // Setup pad dimensions (pad_). + pad_.Reshape(dim_blob_shape); + int* pad_data = pad_.mutable_cpu_data(); + if (conv_param.has_pad_h() || conv_param.has_pad_w()) { + CHECK_EQ(num_spatial_axes_, 2) + << "pad_h & pad_w can only be used for 2D convolution."; + CHECK_EQ(0, conv_param.pad_size()) + << "Either pad or pad_h/w should be specified; not both."; + pad_data[0] = conv_param.pad_h(); + pad_data[1] = conv_param.pad_w(); } else { - stride_h_ = conv_param.stride_h(); - stride_w_ = conv_param.stride_w(); + const int num_pad_dims = conv_param.pad_size(); + CHECK(num_pad_dims == 0 || num_pad_dims == 1 || + num_pad_dims == num_spatial_axes_) + << "pad must be specified once, or once per spatial dimension " + << "(pad specified " << num_pad_dims << " times; " + << num_spatial_axes_ << " spatial dims);"; + const int kDefaultPad = 0; + for (int i = 0; i < num_spatial_axes_; ++i) { + pad_data[i] = (num_pad_dims == 0) ? kDefaultPad : + conv_param.pad((num_pad_dims == 1) ? 0 : i); + } } } template void Im2colLayer::Reshape(const vector*>& bottom, const vector*>& top) { - CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, " - << "corresponding to (num, channels, height, width)"; - channels_ = bottom[0]->channels(); - height_ = bottom[0]->height(); - width_ = bottom[0]->width(); - top[0]->Reshape( - bottom[0]->num(), channels_ * kernel_h_ * kernel_w_, - (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1, - (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1); + vector top_shape = bottom[0]->shape(); + const int* kernel_shape_data = kernel_shape_.cpu_data(); + const int* stride_data = stride_.cpu_data(); + const int* pad_data = pad_.cpu_data(); + for (int i = 0; i < num_spatial_axes_; ++i) { + top_shape[channel_axis_] *= kernel_shape_data[i]; + const int input_dim = bottom[0]->shape(channel_axis_ + i + 1); + const int output_dim = (input_dim + 2 * pad_data[i] - kernel_shape_data[i]) + / stride_data[i] + 1; + top_shape[channel_axis_ + i + 1] = output_dim; + } + top[0]->Reshape(top_shape); + num_ = bottom[0]->count(0, channel_axis_); + bottom_dim_ = bottom[0]->count(channel_axis_); + top_dim_ = top[0]->count(channel_axis_); + + channels_ = bottom[0]->shape(channel_axis_); } template @@ -66,10 +118,27 @@ void Im2colLayer::Forward_cpu(const vector*>& bottom, const vector*>& top) { const Dtype* bottom_data = bottom[0]->cpu_data(); Dtype* top_data = top[0]->mutable_cpu_data(); - for (int n = 0; n < bottom[0]->num(); ++n) { - im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_, - width_, kernel_h_, kernel_w_, pad_h_, pad_w_, - stride_h_, stride_w_, top_data + top[0]->offset(n)); + for (int n = 0; n < num_; ++n) { + DCHECK_EQ(bottom[0]->shape().size() - channel_axis_, num_spatial_axes_ + 1); + DCHECK_EQ(top[0]->shape().size() - channel_axis_, num_spatial_axes_ + 1); + DCHECK_EQ(kernel_shape_.count(), num_spatial_axes_); + DCHECK_EQ(pad_.count(), num_spatial_axes_); + DCHECK_EQ(stride_.count(), num_spatial_axes_); + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + im2col_cpu(bottom_data + n * bottom_dim_, channels_, + bottom[0]->shape(channel_axis_ + 1), + bottom[0]->shape(channel_axis_ + 2), + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], + top_data + n * top_dim_); + } else { + im2col_nd_cpu(bottom_data + n * bottom_dim_, num_spatial_axes_, + bottom[0]->shape().data() + channel_axis_, + top[0]->shape().data() + channel_axis_, + kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(), + top_data + n * top_dim_); + } } } @@ -78,10 +147,22 @@ void Im2colLayer::Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { const Dtype* top_diff = top[0]->cpu_diff(); Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); - for (int n = 0; n < top[0]->num(); ++n) { - col2im_cpu(top_diff + top[0]->offset(n), channels_, height_, width_, - kernel_h_, kernel_w_, pad_h_, pad_w_, - stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n)); + for (int n = 0; n < num_; ++n) { + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + col2im_cpu(top_diff + n * top_dim_, channels_, + bottom[0]->shape(channel_axis_ + 1), + bottom[0]->shape(channel_axis_ + 2), + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], + bottom_diff + n * bottom_dim_); + } else { + col2im_nd_cpu(top_diff + n * top_dim_, num_spatial_axes_, + bottom[0]->shape().data() + channel_axis_, + top[0]->shape().data() + channel_axis_, + kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(), + bottom_diff + n * bottom_dim_); + } } } diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu index 9c338b14cb7..cd507623c78 100644 --- a/src/caffe/layers/im2col_layer.cu +++ b/src/caffe/layers/im2col_layer.cu @@ -12,10 +12,23 @@ void Im2colLayer::Forward_gpu(const vector*>& bottom, const vector*>& top) { const Dtype* bottom_data = bottom[0]->gpu_data(); Dtype* top_data = top[0]->mutable_gpu_data(); - for (int n = 0; n < bottom[0]->num(); ++n) { - im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_, - width_, kernel_h_, kernel_w_, pad_h_, pad_w_, - stride_h_, stride_w_, top_data + top[0]->offset(n)); + const int num_kernels = channels_ * top[0]->count(channel_axis_ + 1); + for (int n = 0; n < num_; ++n) { + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + im2col_gpu(bottom_data + n * bottom_dim_, channels_, + bottom[0]->shape(channel_axis_ + 1), + bottom[0]->shape(channel_axis_ + 2), + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], + top_data + n * top_dim_); + } else { + im2col_nd_gpu(bottom_data + n * bottom_dim_, num_spatial_axes_, + num_kernels, bottom[0]->gpu_shape() + channel_axis_, + top[0]->gpu_shape() + channel_axis_, + kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(), + top_data + n * top_dim_); + } } } @@ -24,10 +37,22 @@ void Im2colLayer::Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { const Dtype* top_diff = top[0]->gpu_diff(); Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); - for (int n = 0; n < top[0]->num(); ++n) { - col2im_gpu(top_diff + top[0]->offset(n), channels_, height_, width_, - kernel_h_, kernel_w_, pad_h_, pad_w_, - stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n)); + for (int n = 0; n < num_; ++n) { + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + col2im_gpu(top_diff + n * top_dim_, channels_, + bottom[0]->shape(channel_axis_ + 1), + bottom[0]->shape(channel_axis_ + 2), + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], + bottom_diff + n * bottom_dim_); + } else { + col2im_nd_gpu(top_diff + n * top_dim_, num_spatial_axes_, bottom_dim_, + bottom[0]->gpu_shape() + channel_axis_, + top[0]->gpu_shape() + channel_axis_, + kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(), + bottom_diff + n * bottom_dim_); + } } } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 86683eb45da..f52c941b05e 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -508,6 +508,13 @@ message ConvolutionParameter { // N independent 3D convolutions, sliding (C/g)-channels // filters across the spatial axes (D, H, W) of the input. optional int32 axis = 16 [default = 1]; + + // Whether to force use of the general ND convolution, even if a specific + // implementation for blobs of the appropriate number of spatial dimensions + // is available. (Currently, there is only a 2D-specific convolution + // implementation; for input blobs with num_axes != 2, this option is + // ignored and the ND implementation will be used.) + optional bool force_nd_im2col = 17 [default = false]; } message DataParameter { diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp index 67d41fff844..9df979a2d27 100644 --- a/src/caffe/test/test_convolution_layer.cpp +++ b/src/caffe/test/test_convolution_layer.cpp @@ -19,54 +19,87 @@ template void caffe_conv(const Blob* in, ConvolutionParameter* conv_param, const vector > >& weights, Blob* out) { + const bool has_depth = (out->num_axes() == 5); + if (!has_depth) { CHECK_EQ(4, out->num_axes()); } // Kernel size, stride, and pad int kernel_h, kernel_w; - if (conv_param->has_kernel_size()) { - kernel_h = kernel_w = conv_param->kernel_size(); - } else { + if (conv_param->has_kernel_h() || conv_param->has_kernel_w()) { kernel_h = conv_param->kernel_h(); kernel_w = conv_param->kernel_w(); + } else { + kernel_h = kernel_w = conv_param->kernel_size(0); } int pad_h, pad_w; - if (!conv_param->has_pad_h()) { - pad_h = pad_w = conv_param->pad(); - } else { + if (conv_param->has_pad_h() || conv_param->has_pad_w()) { pad_h = conv_param->pad_h(); pad_w = conv_param->pad_w(); + } else { + pad_h = pad_w = conv_param->pad_size() ? conv_param->pad(0) : 0; } int stride_h, stride_w; - if (!conv_param->has_stride_h()) { - stride_h = stride_w = conv_param->stride(); - } else { + if (conv_param->has_stride_h() || conv_param->has_stride_w()) { stride_h = conv_param->stride_h(); stride_w = conv_param->stride_w(); + } else { + stride_h = stride_w = conv_param->stride_size() ? conv_param->stride(0) : 1; + } + int kernel_d, pad_d, stride_d; + if (has_depth) { + kernel_d = kernel_h; + stride_d = stride_h; + pad_d = pad_h; + } else { + kernel_d = stride_d = 1; + pad_d = 0; } // Groups int groups = conv_param->group(); - int o_g = out->channels() / groups; - int k_g = in->channels() / groups; + int o_g = out->shape(1) / groups; + int k_g = in->shape(1) / groups; int o_head, k_head; // Convolution - const Dtype* in_data = in->cpu_data(); - const Dtype* weight_data = weights[0]->cpu_data(); + vector weight_offset(4 + has_depth); + vector in_offset(4 + has_depth); + vector out_offset(4 + has_depth); Dtype* out_data = out->mutable_cpu_data(); - for (int n = 0; n < out->num(); n++) { + for (int n = 0; n < out->shape(0); n++) { for (int g = 0; g < groups; g++) { o_head = o_g * g; k_head = k_g * g; for (int o = 0; o < o_g; o++) { for (int k = 0; k < k_g; k++) { - for (int y = 0; y < out->height(); y++) { - for (int x = 0; x < out->width(); x++) { - for (int p = 0; p < kernel_h; p++) { - for (int q = 0; q < kernel_w; q++) { - int in_y = y * stride_h - pad_h + p; - int in_x = x * stride_w - pad_w + q; - if (in_y >= 0 && in_y < in->height() - && in_x >= 0 && in_x < in->width()) { - out_data[out->offset(n, o + o_head, y, x)] += - in_data[in->offset(n, k + k_head, in_y, in_x)] - * weight_data[weights[0]->offset(o + o_head, k, p, q)]; + for (int z = 0; z < (has_depth ? out->shape(2) : 1); z++) { + for (int y = 0; y < out->shape(2 + has_depth); y++) { + for (int x = 0; x < out->shape(3 + has_depth); x++) { + for (int r = 0; r < kernel_d; r++) { + for (int p = 0; p < kernel_h; p++) { + for (int q = 0; q < kernel_w; q++) { + int in_z = z * stride_d - pad_d + r; + int in_y = y * stride_h - pad_h + p; + int in_x = x * stride_w - pad_w + q; + if (in_z >= 0 && in_z < (has_depth ? in->shape(2) : 1) + && in_y >= 0 && in_y < in->shape(2 + has_depth) + && in_x >= 0 && in_x < in->shape(3 + has_depth)) { + weight_offset[0] = o + o_head; + weight_offset[1] = k; + if (has_depth) { weight_offset[2] = r; } + weight_offset[2 + has_depth] = p; + weight_offset[3 + has_depth] = q; + in_offset[0] = n; + in_offset[1] = k + k_head; + if (has_depth) { in_offset[2] = in_z; } + in_offset[2 + has_depth] = in_y; + in_offset[3 + has_depth] = in_x; + out_offset[0] = n; + out_offset[1] = o + o_head; + if (has_depth) { out_offset[2] = z; } + out_offset[2 + has_depth] = y; + out_offset[3 + has_depth] = x; + out_data[out->offset(out_offset)] += + in->data_at(in_offset) + * weights[0]->data_at(weight_offset); + } + } } } } @@ -79,11 +112,18 @@ void caffe_conv(const Blob* in, ConvolutionParameter* conv_param, // Bias if (conv_param->bias_term()) { const Dtype* bias_data = weights[1]->cpu_data(); - for (int n = 0; n < out->num(); n++) { - for (int o = 0; o < out->channels(); o++) { - for (int y = 0; y < out->height(); y++) { - for (int x = 0; x < out->width(); x++) { - out_data[out->offset(n, o, y, x)] += bias_data[o]; + for (int n = 0; n < out->shape(0); n++) { + for (int o = 0; o < out->shape(1); o++) { + for (int z = 0; z < (has_depth ? out->shape(2) : 1); z++) { + for (int y = 0; y < out->shape(2 + has_depth); y++) { + for (int x = 0; x < out->shape(3 + has_depth); x++) { + out_offset[0] = n; + out_offset[1] = o; + if (has_depth) { out_offset[2] = z; } + out_offset[2 + has_depth] = y; + out_offset[3 + has_depth] = x; + out_data[out->offset(out_offset)] += bias_data[o]; + } } } } @@ -150,8 +190,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(4); this->blob_bottom_vec_.push_back(this->blob_bottom_2_); this->blob_top_vec_.push_back(this->blob_top_2_); @@ -188,8 +228,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(4); convolution_param->mutable_weight_filler()->set_type("gaussian"); convolution_param->mutable_bias_filler()->set_type("constant"); @@ -217,13 +257,98 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) { } } +TYPED_TEST(ConvolutionLayerTest, Test0DConvolution) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + const int kNumOutput = 3; + convolution_param->set_num_output(kNumOutput); + convolution_param->set_axis(3); + convolution_param->mutable_weight_filler()->set_type("gaussian"); + convolution_param->mutable_bias_filler()->set_type("gaussian"); + shared_ptr > layer( + new ConvolutionLayer(layer_param)); + vector top_shape = this->blob_bottom_->shape(); + top_shape[3] = kNumOutput; + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + EXPECT_EQ(top_shape, this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Check against reference convolution. + vector weight_offset(2); + const Blob* weight = layer->blobs()[0].get(); + const Blob* bias = layer->blobs()[1].get(); + const int num = this->blob_top_->count(3); + const int dim = this->blob_top_->shape(3); + const int bottom_dim = this->blob_bottom_->shape(3); + for (int n = 0; n < num; ++n) { + for (int d = 0; d < dim; ++d) { + weight_offset[0] = d; + Dtype value = bias->cpu_data()[d]; + for (int bottom_d = 0; bottom_d < bottom_dim; ++bottom_d) { + weight_offset[1] = bottom_d; + value += weight->data_at(weight_offset) * + this->blob_bottom_->cpu_data()[n * bottom_dim + bottom_d]; + } + EXPECT_NEAR(value, this->blob_top_->cpu_data()[n * dim + d], 1e-4); + } + } +} + +TYPED_TEST(ConvolutionLayerTest, TestSimple3DConvolution) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_2_); + this->blob_top_vec_.push_back(this->blob_top_2_); + vector bottom_shape(5); + bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0); + bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1); + bottom_shape[2] = 5; + bottom_shape[3] = this->blob_bottom_vec_[0]->shape(2); + bottom_shape[4] = this->blob_bottom_vec_[0]->shape(3); + FillerParameter filler_param; + GaussianFiller filler(filler_param); + for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) { + this->blob_bottom_vec_[i]->Reshape(bottom_shape); + filler.Fill(this->blob_bottom_vec_[i]); + } + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); + convolution_param->set_num_output(4); + convolution_param->mutable_weight_filler()->set_type("gaussian"); + convolution_param->mutable_bias_filler()->set_type("gaussian"); + shared_ptr > layer( + new ConvolutionLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Check against reference convolution. + const Dtype* top_data; + const Dtype* ref_top_data; + caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(), + this->MakeReferenceTop(this->blob_top_)); + top_data = this->blob_top_->cpu_data(); + ref_top_data = this->ref_blob_top_->cpu_data(); + for (int i = 0; i < this->blob_top_->count(); ++i) { + EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4); + } + caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(), + this->MakeReferenceTop(this->blob_top_2_)); + top_data = this->blob_top_2_->cpu_data(); + ref_top_data = this->ref_blob_top_->cpu_data(); + for (int i = 0; i < this->blob_top_->count(); ++i) { + EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4); + } +} + TYPED_TEST(ConvolutionLayerTest, Test1x1Convolution) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(1); - convolution_param->set_stride(1); + convolution_param->add_kernel_size(1); + convolution_param->add_stride(1); convolution_param->set_num_output(4); convolution_param->mutable_weight_filler()->set_type("gaussian"); convolution_param->mutable_bias_filler()->set_type("constant"); @@ -249,8 +374,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(3); convolution_param->set_group(3); convolution_param->mutable_weight_filler()->set_type("gaussian"); @@ -288,8 +413,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(1); convolution_param->set_bias_term(false); shared_ptr > layer( @@ -350,14 +475,11 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) { convolution_param->set_bias_term(false); layer.reset(new ConvolutionLayer(layer_param)); layer->blobs().resize(1); - layer->blobs()[0].reset(new Blob(1, 3, 1, 3)); + layer->blobs()[0].reset(new Blob(1, 1, 1, 3)); Dtype* weights_2 = layer->blobs()[0]->mutable_cpu_data(); - for (int c = 0; c < 3; ++c) { - int i = c * 3; // 1 x 3 filter - weights_2[i + 0] = -1; - weights_2[i + 1] = 0; - weights_2[i + 2] = 1; - } + weights_2[0] = -1; + weights_2[1] = 0; + weights_2[2] = 1; layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec); layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec); // Test equivalence of full and separable filters. @@ -368,6 +490,124 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) { } } +TYPED_TEST(ConvolutionLayerTest, TestNDAgainst2D) { + typedef typename TypeParam::Dtype Dtype; + const int kernel_h = 11; + const int kernel_w = 13; + vector bottom_shape(4); + bottom_shape[0] = 15; + bottom_shape[1] = 18; + bottom_shape[2] = kernel_h * 2; + bottom_shape[3] = kernel_w * 2; + FillerParameter filler_param; + GaussianFiller filler(filler_param); + for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) { + this->blob_bottom_vec_[i]->Reshape(bottom_shape); + filler.Fill(this->blob_bottom_vec_[i]); + } + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + convolution_param->set_num_output(12); + convolution_param->set_bias_term(false); + convolution_param->set_group(6); + convolution_param->set_kernel_h(kernel_h); + convolution_param->set_kernel_w(kernel_w); + convolution_param->mutable_weight_filler()->set_type("gaussian"); + Blob weights; + Blob top_diff; + // Shape and fill weights and top_diff. + bool copy_diff; + bool reshape; + { + ConvolutionLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + top_diff.ReshapeLike(*this->blob_top_); + filler.Fill(&top_diff); + ASSERT_EQ(1, layer.blobs().size()); + copy_diff = false; reshape = true; + weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape); + } + vector propagate_down(1, true); + Blob result_2d; + Blob backward_result_2d; + Blob backward_weight_result_2d; + // Test with 2D im2col + { + caffe_set(this->blob_top_->count(), Dtype(0), + this->blob_top_->mutable_cpu_data()); + caffe_set(this->blob_bottom_->count(), Dtype(0), + this->blob_bottom_->mutable_cpu_diff()); + caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff()); + // Do SetUp and Forward; save Forward result in result_2d. + convolution_param->set_force_nd_im2col(false); + ConvolutionLayer layer_2d(layer_param); + layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(1, layer_2d.blobs().size()); + copy_diff = false; reshape = false; + layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape); + layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + copy_diff = false; reshape = true; + result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape); + // Copy pre-generated top diff into actual top diff; + // do Backward and save result in backward_result_2d. + ASSERT_EQ(this->blob_top_->shape(), top_diff.shape()); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_top_->mutable_cpu_diff()); + layer_2d.Backward(this->blob_top_vec_, propagate_down, + this->blob_bottom_vec_); + copy_diff = true; reshape = true; + backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape); + backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape); + } + Blob result_nd; + Blob backward_result_nd; + Blob backward_weight_result_nd; + // Test with ND im2col + { + caffe_set(this->blob_top_->count(), Dtype(0), + this->blob_top_->mutable_cpu_data()); + caffe_set(this->blob_bottom_->count(), Dtype(0), + this->blob_bottom_->mutable_cpu_diff()); + caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff()); + // Do SetUp and Forward; save Forward result in result_nd. + convolution_param->set_force_nd_im2col(true); + ConvolutionLayer layer_nd(layer_param); + layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(1, layer_nd.blobs().size()); + copy_diff = false; reshape = false; + layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape); + layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + copy_diff = false; reshape = true; + result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape); + // Copy pre-generated top diff into actual top diff; + // do Backward and save result in backward_result_nd. + ASSERT_EQ(this->blob_top_->shape(), top_diff.shape()); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_top_->mutable_cpu_diff()); + layer_nd.Backward(this->blob_top_vec_, propagate_down, + this->blob_bottom_vec_); + copy_diff = true; reshape = true; + backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape); + backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape); + } + ASSERT_EQ(result_nd.count(), result_2d.count()); + for (int i = 0; i < result_2d.count(); ++i) { + EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]); + } + ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count()); + for (int i = 0; i < backward_result_2d.count(); ++i) { + EXPECT_EQ(backward_result_2d.cpu_diff()[i], + backward_result_nd.cpu_diff()[i]); + } + ASSERT_EQ(backward_weight_result_nd.count(), + backward_weight_result_2d.count()); + for (int i = 0; i < backward_weight_result_2d.count(); ++i) { + EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i], + backward_weight_result_nd.cpu_diff()[i]); + } +} + TYPED_TEST(ConvolutionLayerTest, TestGradient) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; @@ -375,8 +615,36 @@ TYPED_TEST(ConvolutionLayerTest, TestGradient) { layer_param.mutable_convolution_param(); this->blob_bottom_vec_.push_back(this->blob_bottom_2_); this->blob_top_vec_.push_back(this->blob_top_2_); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); + convolution_param->set_num_output(2); + convolution_param->mutable_weight_filler()->set_type("gaussian"); + convolution_param->mutable_bias_filler()->set_type("gaussian"); + ConvolutionLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(ConvolutionLayerTest, TestGradient3D) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + vector bottom_shape(5); + bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0); + bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1); + bottom_shape[2] = 5; + bottom_shape[3] = this->blob_bottom_vec_[0]->shape(2); + bottom_shape[4] = this->blob_bottom_vec_[0]->shape(3); + FillerParameter filler_param; + GaussianFiller filler(filler_param); + for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) { + this->blob_bottom_vec_[i]->Reshape(bottom_shape); + filler.Fill(this->blob_bottom_vec_[i]); + } + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(2); convolution_param->mutable_weight_filler()->set_type("gaussian"); convolution_param->mutable_bias_filler()->set_type("gaussian"); @@ -393,8 +661,8 @@ TYPED_TEST(ConvolutionLayerTest, Test1x1Gradient) { layer_param.mutable_convolution_param(); this->blob_bottom_vec_.push_back(this->blob_bottom_2_); this->blob_top_vec_.push_back(this->blob_top_2_); - convolution_param->set_kernel_size(1); - convolution_param->set_stride(1); + convolution_param->add_kernel_size(1); + convolution_param->add_stride(1); convolution_param->set_num_output(2); convolution_param->mutable_weight_filler()->set_type("gaussian"); convolution_param->mutable_bias_filler()->set_type("gaussian"); @@ -409,8 +677,8 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(3); convolution_param->set_group(3); convolution_param->mutable_weight_filler()->set_type("gaussian"); @@ -472,8 +740,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(4); this->blob_bottom_vec_.push_back(this->blob_bottom_2_); this->blob_top_vec_.push_back(this->blob_top_2_); @@ -509,8 +777,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(4); convolution_param->mutable_weight_filler()->set_type("gaussian"); convolution_param->mutable_bias_filler()->set_type("constant"); @@ -542,8 +810,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(3); convolution_param->set_group(3); convolution_param->mutable_weight_filler()->set_type("gaussian"); @@ -581,8 +849,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(1); convolution_param->set_bias_term(false); shared_ptr > layer( @@ -643,14 +911,11 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) { convolution_param->set_bias_term(false); layer.reset(new CuDNNConvolutionLayer(layer_param)); layer->blobs().resize(1); - layer->blobs()[0].reset(new Blob(1, 3, 1, 3)); + layer->blobs()[0].reset(new Blob(1, 1, 1, 3)); TypeParam* weights_2 = layer->blobs()[0]->mutable_cpu_data(); - for (int c = 0; c < 3; ++c) { - int i = c * 3; // 1 x 3 filter - weights_2[i + 0] = -1; - weights_2[i + 1] = 0; - weights_2[i + 2] = 1; - } + weights_2[0] = -1; + weights_2[1] = 0; + weights_2[2] = 1; layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec); layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec); // Test equivalence of full and separable filters. @@ -667,8 +932,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) { layer_param.mutable_convolution_param(); this->blob_bottom_vec_.push_back(this->blob_bottom_2_); this->blob_top_vec_.push_back(this->blob_top_2_); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(2); convolution_param->mutable_weight_filler()->set_type("gaussian"); convolution_param->mutable_bias_filler()->set_type("gaussian"); @@ -682,8 +947,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(3); convolution_param->set_group(3); convolution_param->mutable_weight_filler()->set_type("gaussian"); diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp index fc63d5efbe3..770e7b277ee 100644 --- a/src/caffe/test/test_deconvolution_layer.cpp +++ b/src/caffe/test/test_deconvolution_layer.cpp @@ -58,8 +58,8 @@ TYPED_TEST(DeconvolutionLayerTest, TestSetup) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(4); this->blob_bottom_vec_.push_back(this->blob_bottom_2_); this->blob_top_vec_.push_back(this->blob_top_2_); @@ -96,8 +96,8 @@ TYPED_TEST(DeconvolutionLayerTest, TestSimpleDeconvolution) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_num_output(4); convolution_param->mutable_weight_filler()->set_type("constant"); convolution_param->mutable_weight_filler()->set_value(1); @@ -144,8 +144,8 @@ TYPED_TEST(DeconvolutionLayerTest, TestGradient) { layer_param.mutable_convolution_param(); this->blob_bottom_vec_.push_back(this->blob_bottom_2_); this->blob_top_vec_.push_back(this->blob_top_2_); - convolution_param->set_kernel_size(2); - convolution_param->set_stride(1); + convolution_param->add_kernel_size(2); + convolution_param->add_stride(1); convolution_param->set_num_output(1); convolution_param->mutable_weight_filler()->set_type("gaussian"); convolution_param->mutable_bias_filler()->set_type("gaussian"); @@ -155,4 +155,151 @@ TYPED_TEST(DeconvolutionLayerTest, TestGradient) { this->blob_top_vec_); } +TYPED_TEST(DeconvolutionLayerTest, TestNDAgainst2D) { + typedef typename TypeParam::Dtype Dtype; + const int kernel_h = 11; + const int kernel_w = 13; + vector bottom_shape(4); + bottom_shape[0] = 15; + bottom_shape[1] = 12; + bottom_shape[2] = kernel_h * 2; + bottom_shape[3] = kernel_w * 2; + FillerParameter filler_param; + GaussianFiller filler(filler_param); + for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) { + this->blob_bottom_vec_[i]->Reshape(bottom_shape); + filler.Fill(this->blob_bottom_vec_[i]); + } + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + convolution_param->set_num_output(18); + convolution_param->set_bias_term(false); + convolution_param->set_group(6); + convolution_param->set_kernel_h(kernel_h); + convolution_param->set_kernel_w(kernel_w); + convolution_param->mutable_weight_filler()->set_type("gaussian"); + Blob weights; + Blob top_diff; + // Shape and fill weights and top_diff. + bool copy_diff; + bool reshape; + { + DeconvolutionLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + top_diff.ReshapeLike(*this->blob_top_); + filler.Fill(&top_diff); + ASSERT_EQ(1, layer.blobs().size()); + copy_diff = false; reshape = true; + weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape); + } + vector propagate_down(1, true); + Blob result_2d; + Blob backward_result_2d; + Blob backward_weight_result_2d; + // Test with 2D im2col + { + caffe_set(this->blob_top_->count(), Dtype(0), + this->blob_top_->mutable_cpu_data()); + caffe_set(this->blob_bottom_->count(), Dtype(0), + this->blob_bottom_->mutable_cpu_diff()); + caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff()); + // Do SetUp and Forward; save Forward result in result_2d. + convolution_param->set_force_nd_im2col(false); + DeconvolutionLayer layer_2d(layer_param); + layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(1, layer_2d.blobs().size()); + copy_diff = false; reshape = false; + layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape); + layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + copy_diff = false; reshape = true; + result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape); + // Copy pre-generated top diff into actual top diff; + // do Backward and save result in backward_result_2d. + ASSERT_EQ(this->blob_top_->shape(), top_diff.shape()); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_top_->mutable_cpu_diff()); + layer_2d.Backward(this->blob_top_vec_, propagate_down, + this->blob_bottom_vec_); + copy_diff = true; reshape = true; + backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape); + backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape); + } + Blob result_nd; + Blob backward_result_nd; + Blob backward_weight_result_nd; + // Test with ND im2col + { + caffe_set(this->blob_top_->count(), Dtype(0), + this->blob_top_->mutable_cpu_data()); + caffe_set(this->blob_bottom_->count(), Dtype(0), + this->blob_bottom_->mutable_cpu_diff()); + caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff()); + // Do SetUp and Forward; save Forward result in result_nd. + convolution_param->set_force_nd_im2col(true); + DeconvolutionLayer layer_nd(layer_param); + layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(1, layer_nd.blobs().size()); + copy_diff = false; reshape = false; + layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape); + layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + copy_diff = false; reshape = true; + result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape); + // Copy pre-generated top diff into actual top diff; + // do Backward and save result in backward_result_nd. + ASSERT_EQ(this->blob_top_->shape(), top_diff.shape()); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_top_->mutable_cpu_diff()); + layer_nd.Backward(this->blob_top_vec_, propagate_down, + this->blob_bottom_vec_); + copy_diff = true; reshape = true; + backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape); + backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape); + } + ASSERT_EQ(result_nd.count(), result_2d.count()); + for (int i = 0; i < result_2d.count(); ++i) { + EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]); + } + ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count()); + for (int i = 0; i < backward_result_2d.count(); ++i) { + EXPECT_EQ(backward_result_2d.cpu_diff()[i], + backward_result_nd.cpu_diff()[i]); + } + ASSERT_EQ(backward_weight_result_nd.count(), + backward_weight_result_2d.count()); + for (int i = 0; i < backward_weight_result_2d.count(); ++i) { + EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i], + backward_weight_result_nd.cpu_diff()[i]); + } +} + +TYPED_TEST(DeconvolutionLayerTest, TestGradient3D) { + typedef typename TypeParam::Dtype Dtype; + vector bottom_shape(5); + bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0); + bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1); + bottom_shape[2] = 2; + bottom_shape[3] = 3; + bottom_shape[4] = 2; + FillerParameter filler_param; + GaussianFiller filler(filler_param); + for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) { + this->blob_bottom_vec_[i]->Reshape(bottom_shape); + filler.Fill(this->blob_bottom_vec_[i]); + } + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + convolution_param->add_kernel_size(2); + convolution_param->add_stride(2); + convolution_param->add_pad(1); + convolution_param->set_num_output(2); + convolution_param->mutable_weight_filler()->set_type("gaussian"); + convolution_param->mutable_bias_filler()->set_type("gaussian"); + DeconvolutionLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + } // namespace caffe diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu index 0017ac23e69..f0b75fcc68d 100644 --- a/src/caffe/test/test_im2col_kernel.cu +++ b/src/caffe/test/test_im2col_kernel.cu @@ -22,6 +22,12 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im, const int height_col, const int width_col, Dtype* data_col); +template +__global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_col); + extern cudaDeviceProp CAFFE_TEST_CUDA_PROP; template @@ -30,11 +36,18 @@ class Im2colKernelTest : public GPUDeviceTest { Im2colKernelTest() // big so launches > 1024 threads : blob_bottom_(new Blob(5, 500, 10, 10)), + blob_kernel_shape_(new Blob()), + blob_stride_(new Blob()), + blob_pad_(new Blob()), blob_top_(new Blob()), blob_top_cpu_(new Blob()) { FillerParameter filler_param; GaussianFiller filler(filler_param); filler.Fill(this->blob_bottom_); + vector dim_blob_shape(1, 2); + blob_kernel_shape_->Reshape(dim_blob_shape); + blob_stride_->Reshape(dim_blob_shape); + blob_pad_->Reshape(dim_blob_shape); height_ = blob_bottom_->height(); width_ = blob_bottom_->width(); @@ -44,14 +57,26 @@ class Im2colKernelTest : public GPUDeviceTest { kernel_size_ = 3; height_col_ = (height_ + 2 * pad_ - kernel_size_) / stride_ + 1; width_col_ = (width_ + 2 * pad_ - kernel_size_) / stride_ + 1; + + for (int i = 0; i < 2; ++i) { + blob_kernel_shape_->mutable_cpu_data()[i] = kernel_size_; + blob_stride_->mutable_cpu_data()[i] = stride_; + blob_pad_->mutable_cpu_data()[i] = pad_; + } } virtual ~Im2colKernelTest() { - delete blob_bottom_; - delete blob_top_; - delete blob_top_cpu_; + delete blob_bottom_; + delete blob_top_; + delete blob_top_cpu_; + delete blob_kernel_shape_; + delete blob_stride_; + delete blob_pad_; } + Blob* const blob_kernel_shape_; + Blob* const blob_stride_; + Blob* const blob_pad_; Blob* const blob_bottom_; Blob* const blob_top_; Blob* const blob_top_cpu_; @@ -67,7 +92,7 @@ class Im2colKernelTest : public GPUDeviceTest { TYPED_TEST_CASE(Im2colKernelTest, TestDtypes); -TYPED_TEST(Im2colKernelTest, TestGPU) { +TYPED_TEST(Im2colKernelTest, Test2D) { // Reshape the blobs to correct size for im2col output this->blob_top_->Reshape(this->blob_bottom_->num(), this->channels_ * this->kernel_size_ * this->kernel_size_, @@ -122,4 +147,58 @@ TYPED_TEST(Im2colKernelTest, TestGPU) { } } +TYPED_TEST(Im2colKernelTest, TestND) { + // Reshape the blobs to correct size for im2col output + this->blob_top_->Reshape(this->blob_bottom_->num(), + this->channels_ * this->kernel_size_ * this->kernel_size_, + this->height_col_, + this->width_col_); + + this->blob_top_cpu_->ReshapeLike(*this->blob_top_); + + const TypeParam* bottom_data_cpu = this->blob_bottom_->cpu_data(); + TypeParam* top_data_cpu = this->blob_top_cpu_->mutable_cpu_data(); + + // CPU Version + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + im2col_nd_cpu(bottom_data_cpu + this->blob_bottom_->offset(n), 2, + this->blob_bottom_->shape().data() + 1, + this->blob_top_cpu_->shape().data() + 1, + this->blob_kernel_shape_->cpu_data(), + this->blob_pad_->cpu_data(), this->blob_stride_->cpu_data(), + top_data_cpu + this->blob_top_cpu_->offset(n)); + } + + // GPU version + int num_kernels = this->channels_ * this->height_col_ * this->width_col_; + int default_grid_dim = CAFFE_GET_BLOCKS(num_kernels); + const TypeParam* bottom_data_gpu = this->blob_bottom_->gpu_data(); + + // Launch with different grid sizes + for (int grid_div = 2; grid_div <= 8; grid_div++) { + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + const int grid_dim = default_grid_dim / grid_div; + TypeParam* top_data_gpu = this->blob_top_->mutable_gpu_data(); + // NOLINT_NEXT_LINE(whitespace/operators) + im2col_nd_gpu_kernel<<>>( + num_kernels, bottom_data_gpu + this->blob_bottom_->offset(n), + this->blob_bottom_->gpu_shape() + 1, this->blob_top_->gpu_shape() + 1, + this->blob_kernel_shape_->gpu_data(), this->blob_pad_->gpu_data(), + this->blob_stride_->gpu_data(), + top_data_gpu + this->blob_top_->offset(n)); + CUDA_POST_KERNEL_CHECK; + } + + // Compare results against CPU version + for (int i = 0; i < this->blob_top_->count(); ++i) { + TypeParam cpuval = top_data_cpu[i]; + TypeParam gpuval = this->blob_top_->cpu_data()[i]; + EXPECT_EQ(cpuval, gpuval); + if (cpuval != gpuval) { + break; + } + } + } +} + } // namespace caffe diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp index f50abe103f8..293aa262059 100644 --- a/src/caffe/test/test_im2col_layer.cpp +++ b/src/caffe/test/test_im2col_layer.cpp @@ -21,6 +21,7 @@ class Im2colLayerTest : public MultiDeviceTest { : blob_bottom_(new Blob(2, 3, 6, 5)), blob_top_(new Blob()) { // fill the values + Caffe::set_random_seed(1701); FillerParameter filler_param; GaussianFiller filler(filler_param); filler.Fill(this->blob_bottom_); @@ -41,8 +42,8 @@ TYPED_TEST(Im2colLayerTest, TestSetup) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); Im2colLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); EXPECT_EQ(this->blob_top_->num(), 2); @@ -56,8 +57,8 @@ TYPED_TEST(Im2colLayerTest, TestForward) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); Im2colLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); @@ -73,14 +74,27 @@ TYPED_TEST(Im2colLayerTest, TestGradient) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); - convolution_param->set_kernel_size(3); - convolution_param->set_stride(2); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); Im2colLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, this->blob_top_vec_); } +TYPED_TEST(Im2colLayerTest, TestGradientForceND) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); + convolution_param->set_force_nd_im2col(true); + Im2colLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} TYPED_TEST(Im2colLayerTest, TestRect) { typedef typename TypeParam::Dtype Dtype; @@ -89,7 +103,7 @@ TYPED_TEST(Im2colLayerTest, TestRect) { layer_param.mutable_convolution_param(); convolution_param->set_kernel_h(5); convolution_param->set_kernel_w(3); - convolution_param->set_stride(2); + convolution_param->add_stride(2); Im2colLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); @@ -108,7 +122,7 @@ TYPED_TEST(Im2colLayerTest, TestRectGradient) { layer_param.mutable_convolution_param(); convolution_param->set_kernel_h(5); convolution_param->set_kernel_w(3); - convolution_param->set_stride(2); + convolution_param->add_stride(2); Im2colLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp index c48f31f35d4..b0a7be50e5c 100644 --- a/src/caffe/util/im2col.cpp +++ b/src/caffe/util/im2col.cpp @@ -1,6 +1,7 @@ #include #include #include +#include #include "caffe/util/im2col.hpp" #include "caffe/util/math_functions.hpp" @@ -44,6 +45,98 @@ template void im2col_cpu(const double* data_im, const int channels, const int pad_h, const int pad_w, const int stride_h, const int stride_w, double* data_col); +template +inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col, + const int num_spatial_axes, const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_output) { + if (!im2col) { + int im_size = im_shape[0]; + for (int i = 0; i < num_spatial_axes; ++i) { + im_size *= im_shape[1 + i]; + } + caffe_set(im_size, Dtype(0), data_output); + } + int kernel_size = 1; + for (int i = 0; i < num_spatial_axes; ++i) { + kernel_size *= kernel_shape[i]; + } + const int channels_col = col_shape[0]; + vector d_offset(num_spatial_axes, 0); + vector d_iter(num_spatial_axes, 0); + for (int c = 0; c < channels_col; ++c) { + // Loop over spatial axes in reverse order to compute a per-axis offset. + int offset = c; + for (int d_i = num_spatial_axes - 1; d_i >= 0; --d_i) { + if (d_i < num_spatial_axes - 1) { + offset /= kernel_shape[d_i + 1]; + } + d_offset[d_i] = offset % kernel_shape[d_i]; + } + for (bool incremented = true; incremented; ) { + // Loop over spatial axes in forward order to compute the indices in the + // image and column, and whether the index lies in the padding. + int index_col = c; + int index_im = c / kernel_size; + bool is_padding = false; + for (int d_i = 0; d_i < num_spatial_axes; ++d_i) { + const int d = d_iter[d_i]; + const int d_pad = d * stride[d_i] - pad[d_i] + d_offset[d_i]; + is_padding |= d_pad < 0 || d_pad >= im_shape[d_i + 1]; + index_col *= col_shape[d_i + 1]; + index_col += d; + index_im *= im_shape[d_i + 1]; + index_im += d_pad; + } + if (im2col) { + if (is_padding) { + data_output[index_col] = 0; + } else { + data_output[index_col] = data_input[index_im]; + } + } else if (!is_padding) { // col2im + data_output[index_im] += data_input[index_col]; + } + // Loop over spatial axes in reverse order to choose an index, + // like counting. + incremented = false; + for (int d_i = num_spatial_axes - 1; d_i >= 0; --d_i) { + const int d_max = col_shape[d_i + 1]; + DCHECK_LT(d_iter[d_i], d_max); + if (d_iter[d_i] == d_max - 1) { + d_iter[d_i] = 0; + } else { // d_iter[d_i] < d_max - 1 + ++d_iter[d_i]; + incremented = true; + break; + } + } + } // while(incremented) { + } // for (int c = 0; c < channels_col; ++c) { +} + +template +void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_col) { + const bool kIm2Col = true; + im2col_nd_core_cpu(data_im, kIm2Col, num_spatial_axes, im_shape, col_shape, + kernel_shape, pad, stride, data_col); +} + +// Explicit instantiation +template void im2col_nd_cpu(const float* data_im, + const int num_spatial_axes, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + float* data_col); +template void im2col_nd_cpu(const double* data_im, + const int num_spatial_axes, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + double* data_col); + template void col2im_cpu(const Dtype* data_col, const int channels, const int height, const int width, const int patch_h, const int patch_w, @@ -80,4 +173,27 @@ template void col2im_cpu(const double* data_col, const int channels, const int pad_h, const int pad_w, const int stride_h, const int stride_w, double* data_im); +template +void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_im) { + const bool kIm2Col = false; + im2col_nd_core_cpu(data_col, kIm2Col, num_spatial_axes, im_shape, col_shape, + kernel_shape, pad, stride, data_im); +} + +// Explicit instantiation +template void col2im_nd_cpu(const float* data_col, + const int num_spatial_axes, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + float* data_im); +template void col2im_nd_cpu(const double* data_col, + const int num_spatial_axes, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + double* data_im); + + } // namespace caffe diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu index c90f93eb67b..5a478ba62d2 100644 --- a/src/caffe/util/im2col.cu +++ b/src/caffe/util/im2col.cu @@ -59,7 +59,6 @@ void im2col_gpu(const Dtype* data_im, const int channels, CUDA_POST_KERNEL_CHECK; } - // Explicit instantiation template void im2col_gpu(const float* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, @@ -70,6 +69,156 @@ template void im2col_gpu(const double* data_im, const int channels, const int pad_h, const int pad_w, const int stride_h, const int stride_w, double* data_col); +template +__global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_col) { + int d_temp[num_axes]; // NOLINT(runtime/arrays) + int d_iter[num_axes]; // NOLINT(runtime/arrays) + int i; + CUDA_KERNEL_LOOP(index, n) { + // Initialize channel_in, computed in the loop below, with intermediate + // computations used to compute the spatial indices. + int channel_in = index; + int channel_out = 1; + for (i = num_axes - 1; i >= 0; --i) { + d_temp[i] = channel_in % col_shape[i + 1]; + channel_in /= col_shape[i + 1]; + channel_out *= kernel_shape[i]; + } + channel_out *= channel_in; + int data_col_inc = 1; + for (i = 0; i < num_axes; ++i) { + channel_out *= col_shape[i + 1]; + channel_out += d_temp[i]; + d_temp[i] = d_temp[i] * stride[i] - pad[i]; + channel_in *= im_shape[i + 1]; + channel_in += d_temp[i]; + data_col_inc *= col_shape[i + 1]; + d_iter[i] = 0; + } + Dtype* data_col_ptr = data_col + channel_out; + const Dtype* data_im_ptr = data_im + channel_in; + bool incremented; + do { + bool in_range = true; + for (i = 0; i < num_axes; ++i) { + const int d_iter_im = d_iter[i] + d_temp[i]; + in_range &= d_iter_im >= 0 && d_iter_im < im_shape[i + 1]; + if (!in_range) { break; } + } + if (in_range) { + int data_im_offset = d_iter[0]; + for (i = 1; i < num_axes; ++i) { + data_im_offset *= im_shape[i + 1]; + data_im_offset += d_iter[i]; + } + *data_col_ptr = data_im_ptr[data_im_offset]; + } else { + *data_col_ptr = 0; + } + data_col_ptr += data_col_inc; + incremented = false; + for (i = num_axes - 1; i >= 0; --i) { + const int d_max = kernel_shape[i]; + if (d_iter[i] == d_max - 1) { + d_iter[i] = 0; + } else { // d_iter[i] < d_max - 1 + ++d_iter[i]; + incremented = true; + break; + } + } // for (int i = num_axes - 1; i >= 0; --i) + } while (incremented); // do + } // CUDA_KERNEL_LOOP(index, n) +} + +template +void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes, + const int num_kernels, const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_col) { + switch (num_spatial_axes) { + case 1: + im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + num_kernels, data_im, im_shape, col_shape, + kernel_shape, pad, stride, data_col); + break; + case 2: + im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + num_kernels, data_im, im_shape, col_shape, + kernel_shape, pad, stride, data_col); + break; + case 3: + im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + num_kernels, data_im, im_shape, col_shape, + kernel_shape, pad, stride, data_col); + break; + case 4: + im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + num_kernels, data_im, im_shape, col_shape, + kernel_shape, pad, stride, data_col); + break; + case 5: + im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + num_kernels, data_im, im_shape, col_shape, + kernel_shape, pad, stride, data_col); + break; + case 6: + im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + num_kernels, data_im, im_shape, col_shape, + kernel_shape, pad, stride, data_col); + break; + case 7: + im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + num_kernels, data_im, im_shape, col_shape, + kernel_shape, pad, stride, data_col); + break; + case 8: + im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + num_kernels, data_im, im_shape, col_shape, + kernel_shape, pad, stride, data_col); + break; + case 9: + im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + num_kernels, data_im, im_shape, col_shape, + kernel_shape, pad, stride, data_col); + break; + case 10: + im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + num_kernels, data_im, im_shape, col_shape, + kernel_shape, pad, stride, data_col); + break; + default: + LOG(FATAL) << "im2col_nd_gpu does not support computation with " + << num_spatial_axes << " spatial axes"; + } + CUDA_POST_KERNEL_CHECK; +} + +// Explicit instantiation +template void im2col_nd_gpu(const float* data_im, + const int num_spatial_axes, const int col_size, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + float* data_col); +template void im2col_nd_gpu(const double* data_im, + const int num_spatial_axes, const int col_size, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + double* data_col); + template __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col, const int height, const int width, const int channels, @@ -141,4 +290,159 @@ template void col2im_gpu(const double* data_col, const int channels, const int pad_h, const int pad_w, const int stride_h, const int stride_w, double* data_im); +template +__global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_im) { + int d_im[num_axes]; // NOLINT(runtime/arrays) + int d_col_iter[num_axes]; // NOLINT(runtime/arrays) + int d_col_start[num_axes]; // NOLINT(runtime/arrays) + int d_col_end[num_axes]; // NOLINT(runtime/arrays) + CUDA_KERNEL_LOOP(index, n) { + // Initialize channel_in, computed in the loop below, with intermediate + // computations used to compute the spatial indices. + int channel_im = index; + // Calculate d_im (image dimensions). + for (int i = num_axes - 1; i >= 0; --i) { + d_im[i] = channel_im % im_shape[i + 1] + pad[i]; + channel_im /= im_shape[i + 1]; + } + // Calculate col start/end indices. + bool done = false; + for (int i = 0; i < num_axes; ++i) { + d_col_start[i] = d_col_iter[i] = + (d_im[i] < kernel_shape[i]) ? + 0 : (d_im[i] - kernel_shape[i]) / stride[i] + 1; + d_col_end[i] = min(d_im[i] / stride[i] + 1, col_shape[i + 1]); + if (d_col_start[i] >= d_col_end[i]) { + // Skip computation if the dimension is 0 at any spatial axis -- + // final val will be 0. + data_im[index] = 0; + done = true; + break; // for (int i = 0; i < num_axes; ++i) + } + } + if (done) { + continue; // CUDA_KERNEL_LOOP(index, n) + } + // Loop over the col to compute the output val. + Dtype val = 0; + bool incremented = true; + do { + // Compute the final offset. + int final_offset = 0; + int kernel_shape_prod = 1; + for (int i = num_axes - 1; i >= 0; --i) { + final_offset += + (d_im[i] - d_col_iter[i] * stride[i]) * kernel_shape_prod; + kernel_shape_prod *= kernel_shape[i]; + } + final_offset += kernel_shape_prod * channel_im; + for (int i = 0; i < num_axes; ++i) { + final_offset *= col_shape[i + 1]; + final_offset += d_col_iter[i]; + } + val += data_col[final_offset]; + incremented = false; + for (int i = num_axes - 1; i >= 0; --i) { + const int d_max = d_col_end[i]; + if (d_col_iter[i] == d_max - 1) { + d_col_iter[i] = d_col_start[i]; + } else { // d_col_iter[i] < d_max - 1 + ++d_col_iter[i]; + incremented = true; + break; // for (int i = num_axes - 1; i >= 0; --i) + } + } // for (int i = num_axes - 1; i >= 0; --i) + } while (incremented); + data_im[index] = val; + } // CUDA_KERNEL_LOOP(index, n) +} + +template +void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes, + const int im_size, const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + Dtype* data_im) { + switch (num_spatial_axes) { + case 1: + col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + im_size, data_col, im_shape, col_shape, + kernel_shape, pad, stride, data_im); + break; + case 2: + col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + im_size, data_col, im_shape, col_shape, + kernel_shape, pad, stride, data_im); + break; + case 3: + col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + im_size, data_col, im_shape, col_shape, + kernel_shape, pad, stride, data_im); + break; + case 4: + col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + im_size, data_col, im_shape, col_shape, + kernel_shape, pad, stride, data_im); + break; + case 5: + col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + im_size, data_col, im_shape, col_shape, + kernel_shape, pad, stride, data_im); + break; + case 6: + col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + im_size, data_col, im_shape, col_shape, + kernel_shape, pad, stride, data_im); + break; + case 7: + col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + im_size, data_col, im_shape, col_shape, + kernel_shape, pad, stride, data_im); + break; + case 8: + col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + im_size, data_col, im_shape, col_shape, + kernel_shape, pad, stride, data_im); + break; + case 9: + col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + im_size, data_col, im_shape, col_shape, + kernel_shape, pad, stride, data_im); + break; + case 10: + col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + im_size, data_col, im_shape, col_shape, + kernel_shape, pad, stride, data_im); + break; + default: + LOG(FATAL) << "col2im_nd_gpu does not support computation with " + << num_spatial_axes << " spatial axes"; + } + CUDA_POST_KERNEL_CHECK; +} + +// Explicit instantiation +template void col2im_nd_gpu(const float* data_col, + const int num_spatial_axes, const int im_size, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + float* data_im); +template void col2im_nd_gpu(const double* data_col, + const int num_spatial_axes, const int im_size, + const int* im_shape, const int* col_shape, + const int* kernel_shape, const int* pad, const int* stride, + double* data_im); + } // namespace caffe diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp index 92e5cf55fa9..ac379e50f4f 100644 --- a/src/caffe/util/upgrade_proto.cpp +++ b/src/caffe/util/upgrade_proto.cpp @@ -193,7 +193,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection, } if (v0_layer_param.has_pad()) { if (type == "conv") { - layer_param->mutable_convolution_param()->set_pad(v0_layer_param.pad()); + layer_param->mutable_convolution_param()->add_pad(v0_layer_param.pad()); } else if (type == "pool") { layer_param->mutable_pooling_param()->set_pad(v0_layer_param.pad()); } else { @@ -203,7 +203,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection, } if (v0_layer_param.has_kernelsize()) { if (type == "conv") { - layer_param->mutable_convolution_param()->set_kernel_size( + layer_param->mutable_convolution_param()->add_kernel_size( v0_layer_param.kernelsize()); } else if (type == "pool") { layer_param->mutable_pooling_param()->set_kernel_size( @@ -224,7 +224,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection, } if (v0_layer_param.has_stride()) { if (type == "conv") { - layer_param->mutable_convolution_param()->set_stride( + layer_param->mutable_convolution_param()->add_stride( v0_layer_param.stride()); } else if (type == "pool") { layer_param->mutable_pooling_param()->set_stride( From 328df2450c534119f239ce1d606f8502922c6825 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Sat, 19 Sep 2015 13:50:57 -0700 Subject: [PATCH 265/446] clarify im2col + col2im var names - clarify indices by naming *_im for indices in image and *_col for indices in column - mark corresonding im2col + col2im quantities by renaming patch_* -> kernel_* - fix out-of-date names in equivalent col2im loop --- include/caffe/util/im2col.hpp | 4 +- src/caffe/util/im2col.cpp | 72 +++++++++++++++++------------------ src/caffe/util/im2col.cu | 69 ++++++++++++++++----------------- 3 files changed, 73 insertions(+), 72 deletions(-) diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp index 531fd29c57a..d3eb6ccd6fc 100644 --- a/include/caffe/util/im2col.hpp +++ b/include/caffe/util/im2col.hpp @@ -23,7 +23,7 @@ void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes, template void col2im_cpu(const Dtype* data_col, const int channels, - const int height, const int width, const int patch_h, const int patch_w, + const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, Dtype* data_im); @@ -47,7 +47,7 @@ void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes, template void col2im_gpu(const Dtype* data_col, const int channels, - const int height, const int width, const int patch_h, const int patch_w, + const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, Dtype* data_im); diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp index b0a7be50e5c..afeb5e5d9b3 100644 --- a/src/caffe/util/im2col.cpp +++ b/src/caffe/util/im2col.cpp @@ -17,19 +17,19 @@ void im2col_cpu(const Dtype* data_im, const int channels, int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; int channels_col = channels * kernel_h * kernel_w; - for (int c = 0; c < channels_col; ++c) { - int w_offset = c % kernel_w; - int h_offset = (c / kernel_w) % kernel_h; - int c_im = c / kernel_h / kernel_w; - for (int h = 0; h < height_col; ++h) { - for (int w = 0; w < width_col; ++w) { - int h_pad = h * stride_h - pad_h + h_offset; - int w_pad = w * stride_w - pad_w + w_offset; - if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width) - data_col[(c * height_col + h) * width_col + w] = - data_im[(c_im * height + h_pad) * width + w_pad]; + for (int c_col = 0; c_col < channels_col; ++c_col) { + int w_offset = c_col % kernel_w; + int h_offset = (c_col / kernel_w) % kernel_h; + int c_im = c_col / kernel_h / kernel_w; + for (int h_col = 0; h_col < height_col; ++h_col) { + for (int w_col = 0; w_col < width_col; ++w_col) { + int h_im = h_col * stride_h - pad_h + h_offset; + int w_im = w_col * stride_w - pad_w + w_offset; + if (h_im >= 0 && h_im < height && w_im >= 0 && w_im < width) + data_col[(c_col * height_col + h_col) * width_col + w_col] = + data_im[(c_im * height + h_im) * width + w_im]; else - data_col[(c * height_col + h) * width_col + w] = 0; + data_col[(c_col * height_col + h_im) * width_col + w_im] = 0; } } } @@ -64,9 +64,9 @@ inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col, const int channels_col = col_shape[0]; vector d_offset(num_spatial_axes, 0); vector d_iter(num_spatial_axes, 0); - for (int c = 0; c < channels_col; ++c) { + for (int c_col = 0; c_col < channels_col; ++c_col) { // Loop over spatial axes in reverse order to compute a per-axis offset. - int offset = c; + int offset = c_col; for (int d_i = num_spatial_axes - 1; d_i >= 0; --d_i) { if (d_i < num_spatial_axes - 1) { offset /= kernel_shape[d_i + 1]; @@ -76,17 +76,17 @@ inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col, for (bool incremented = true; incremented; ) { // Loop over spatial axes in forward order to compute the indices in the // image and column, and whether the index lies in the padding. - int index_col = c; - int index_im = c / kernel_size; + int index_col = c_col; + int index_im = c_col / kernel_size; bool is_padding = false; for (int d_i = 0; d_i < num_spatial_axes; ++d_i) { const int d = d_iter[d_i]; - const int d_pad = d * stride[d_i] - pad[d_i] + d_offset[d_i]; - is_padding |= d_pad < 0 || d_pad >= im_shape[d_i + 1]; + const int d_im = d * stride[d_i] - pad[d_i] + d_offset[d_i]; + is_padding |= d_im < 0 || d_im >= im_shape[d_i + 1]; index_col *= col_shape[d_i + 1]; index_col += d; index_im *= im_shape[d_i + 1]; - index_im += d_pad; + index_im += d_im; } if (im2col) { if (is_padding) { @@ -139,25 +139,25 @@ template void im2col_nd_cpu(const double* data_im, template void col2im_cpu(const Dtype* data_col, const int channels, - const int height, const int width, const int patch_h, const int patch_w, + const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, Dtype* data_im) { caffe_set(height * width * channels, Dtype(0), data_im); - int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1; - int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1; - int channels_col = channels * patch_h * patch_w; - for (int c = 0; c < channels_col; ++c) { - int w_offset = c % patch_w; - int h_offset = (c / patch_w) % patch_h; - int c_im = c / patch_h / patch_w; - for (int h = 0; h < height_col; ++h) { - for (int w = 0; w < width_col; ++w) { - int h_pad = h * stride_h - pad_h + h_offset; - int w_pad = w * stride_w - pad_w + w_offset; - if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width) - data_im[(c_im * height + h_pad) * width + w_pad] += - data_col[(c * height_col + h) * width_col + w]; + int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; + int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; + int channels_col = channels * kernel_h * kernel_w; + for (int c_col = 0; c_col < channels_col; ++c_col) { + int w_offset = c_col % kernel_w; + int h_offset = (c_col / kernel_w) % kernel_h; + int c_im = c_col / kernel_h / kernel_w; + for (int h_col = 0; h_col < height_col; ++h_col) { + for (int w_col = 0; w_col < width_col; ++w_col) { + int h_im = h_col * stride_h - pad_h + h_offset; + int w_im = w_col * stride_w - pad_w + w_offset; + if (h_im >= 0 && h_im < height && w_im >= 0 && w_im < width) + data_im[(c_im * height + h_im) * width + w_im] += + data_col[(c_col * height_col + h_col) * width_col + w_col]; } } } @@ -165,11 +165,11 @@ void col2im_cpu(const Dtype* data_col, const int channels, // Explicit instantiation template void col2im_cpu(const float* data_col, const int channels, - const int height, const int width, const int patch_h, const int patch_w, + const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, float* data_im); template void col2im_cpu(const double* data_col, const int channels, - const int height, const int width, const int patch_h, const int patch_w, + const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, double* data_im); diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu index 5a478ba62d2..897e3c92c1f 100644 --- a/src/caffe/util/im2col.cu +++ b/src/caffe/util/im2col.cu @@ -16,22 +16,23 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im, const int height_col, const int width_col, Dtype* data_col) { CUDA_KERNEL_LOOP(index, n) { - int w_out = index % width_col; int h_index = index / width_col; - int h_out = h_index % height_col; - int channel_in = h_index / height_col; - int channel_out = channel_in * kernel_h * kernel_w; - int h_in = h_out * stride_h - pad_h; - int w_in = w_out * stride_w - pad_w; + int h_col = h_index % height_col; + int w_col = index % width_col; + int c_im = h_index / height_col; + int c_col = c_im * kernel_h * kernel_w; + int h_offset = h_col * stride_h - pad_h; + int w_offset = w_col * stride_w - pad_w; Dtype* data_col_ptr = data_col; - data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out; + data_col_ptr += (c_col * height_col + h_col) * width_col + w_col; const Dtype* data_im_ptr = data_im; - data_im_ptr += (channel_in * height + h_in) * width + w_in; + data_im_ptr += (c_im * height + h_offset) * width + w_offset; for (int i = 0; i < kernel_h; ++i) { for (int j = 0; j < kernel_w; ++j) { - int h = h_in + i; - int w = w_in + j; - *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ? + int h_im = h_offset + i; + int w_im = w_offset + j; + *data_col_ptr = + (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ? data_im_ptr[i * width + j] : 0; data_col_ptr += height_col * width_col; } @@ -222,35 +223,35 @@ template void im2col_nd_gpu(const double* data_im, template __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col, const int height, const int width, const int channels, - const int patch_h, const int patch_w, + const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, const int height_col, const int width_col, Dtype* data_im) { CUDA_KERNEL_LOOP(index, n) { Dtype val = 0; - int w = index % width + pad_w; - int h = (index / width) % height + pad_h; - int c = index / (width * height); + int w_im = index % width + pad_w; + int h_im = (index / width) % height + pad_h; + int c_im = index / (width * height); // compute the start and end of the output - int w_col_start = (w < patch_w) ? 0 : (w - patch_w) / stride_w + 1; - int w_col_end = min(w / stride_w + 1, width_col); - int h_col_start = (h < patch_h) ? 0 : (h - patch_h) / stride_h + 1; - int h_col_end = min(h / stride_h + 1, height_col); + int w_col_start = (w_im < kernel_w) ? 0 : (w_im - kernel_w) / stride_w + 1; + int w_col_end = min(w_im / stride_w + 1, width_col); + int h_col_start = (h_im < kernel_h) ? 0 : (h_im - kernel_h) / stride_h + 1; + int h_col_end = min(h_im / stride_h + 1, height_col); /* for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { // the col location: [c * width * height + h_out, w_out] - int c_col = c * patch_h * patch_w + (h - h_col * stride_h) * ksize - + (w - w_col * stride_w); + int c_col = c_im * kernel_h * kernel_w + + (h_im - h_col * stride_h) * kernel_w + (w_im - w_col * stride_w); val += data_col[(c_col * height_col + h_col) * width_col + w_col]; } } */ // equivalent implementation - int offset = - (c * patch_h * patch_w + h * patch_w + w) * height_col * width_col; - int coeff_h_col = (1 - stride_h * patch_w * height_col) * width_col; + int offset = (c_im * kernel_h * kernel_w + h_im * kernel_w + w_im) + * height_col * width_col; + int coeff_h_col = (1 - stride_h * kernel_w * height_col) * width_col; int coeff_w_col = (1 - stride_w * height_col * width_col); for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { @@ -263,18 +264,18 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col, template void col2im_gpu(const Dtype* data_col, const int channels, - const int height, const int width, const int patch_h, const int patch_w, + const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, Dtype* data_im) { - int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1; - int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1; + int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; + int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; int num_kernels = channels * height * width; // To avoid involving atomic operations, we will launch one kernel per // bottom dimension, and then in the kernel add up the top dimensions. // NOLINT_NEXT_LINE(whitespace/operators) col2im_gpu_kernel<<>>( - num_kernels, data_col, height, width, channels, patch_h, patch_w, + num_kernels, data_col, height, width, channels, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, height_col, width_col, data_im); CUDA_POST_KERNEL_CHECK; @@ -282,11 +283,11 @@ void col2im_gpu(const Dtype* data_col, const int channels, // Explicit instantiation template void col2im_gpu(const float* data_col, const int channels, - const int height, const int width, const int patch_h, const int patch_w, + const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, float* data_im); template void col2im_gpu(const double* data_col, const int channels, - const int height, const int width, const int patch_h, const int patch_w, + const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, double* data_im); @@ -302,11 +303,11 @@ __global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col, CUDA_KERNEL_LOOP(index, n) { // Initialize channel_in, computed in the loop below, with intermediate // computations used to compute the spatial indices. - int channel_im = index; + int c_im = index; // Calculate d_im (image dimensions). for (int i = num_axes - 1; i >= 0; --i) { - d_im[i] = channel_im % im_shape[i + 1] + pad[i]; - channel_im /= im_shape[i + 1]; + d_im[i] = c_im % im_shape[i + 1] + pad[i]; + c_im /= im_shape[i + 1]; } // Calculate col start/end indices. bool done = false; @@ -338,7 +339,7 @@ __global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col, (d_im[i] - d_col_iter[i] * stride[i]) * kernel_shape_prod; kernel_shape_prod *= kernel_shape[i]; } - final_offset += kernel_shape_prod * channel_im; + final_offset += kernel_shape_prod * c_im; for (int i = 0; i < num_axes; ++i) { final_offset *= col_shape[i + 1]; final_offset += d_col_iter[i]; From ec77358c2d2e05b3aa39221bd3ec093789bd40f6 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Sat, 19 Sep 2015 14:00:14 -0700 Subject: [PATCH 266/446] harmonize the im2col_{cpu,gpu} assignment --- src/caffe/util/im2col.cpp | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp index afeb5e5d9b3..018ff0cdc8c 100644 --- a/src/caffe/util/im2col.cpp +++ b/src/caffe/util/im2col.cpp @@ -25,11 +25,9 @@ void im2col_cpu(const Dtype* data_im, const int channels, for (int w_col = 0; w_col < width_col; ++w_col) { int h_im = h_col * stride_h - pad_h + h_offset; int w_im = w_col * stride_w - pad_w + w_offset; - if (h_im >= 0 && h_im < height && w_im >= 0 && w_im < width) - data_col[(c_col * height_col + h_col) * width_col + w_col] = - data_im[(c_im * height + h_im) * width + w_im]; - else - data_col[(c_col * height_col + h_im) * width_col + w_im] = 0; + data_col[(c_col * height_col + h_col) * width_col + w_col] = + (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ? + data_im[(c_im * height + h_im) * width + w_im] : 0; } } } From d292a162b3659685b5f4399b8adf743bdcac49a1 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Sat, 19 Sep 2015 14:07:10 -0700 Subject: [PATCH 267/446] mark const im2col + col2im terms --- src/caffe/util/im2col.cpp | 12 ++++++------ src/caffe/util/im2col.cu | 32 ++++++++++++++++++-------------- 2 files changed, 24 insertions(+), 20 deletions(-) diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp index 018ff0cdc8c..09da23d490f 100644 --- a/src/caffe/util/im2col.cpp +++ b/src/caffe/util/im2col.cpp @@ -14,9 +14,9 @@ void im2col_cpu(const Dtype* data_im, const int channels, const int pad_h, const int pad_w, const int stride_h, const int stride_w, Dtype* data_col) { - int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; - int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; - int channels_col = channels * kernel_h * kernel_w; + const int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; + const int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; + const int channels_col = channels * kernel_h * kernel_w; for (int c_col = 0; c_col < channels_col; ++c_col) { int w_offset = c_col % kernel_w; int h_offset = (c_col / kernel_w) % kernel_h; @@ -142,9 +142,9 @@ void col2im_cpu(const Dtype* data_col, const int channels, const int stride_h, const int stride_w, Dtype* data_im) { caffe_set(height * width * channels, Dtype(0), data_im); - int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; - int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; - int channels_col = channels * kernel_h * kernel_w; + const int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; + const int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; + const int channels_col = channels * kernel_h * kernel_w; for (int c_col = 0; c_col < channels_col; ++c_col) { int w_offset = c_col % kernel_w; int h_offset = (c_col / kernel_w) % kernel_h; diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu index 897e3c92c1f..451097f8a1b 100644 --- a/src/caffe/util/im2col.cu +++ b/src/caffe/util/im2col.cu @@ -16,13 +16,13 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im, const int height_col, const int width_col, Dtype* data_col) { CUDA_KERNEL_LOOP(index, n) { - int h_index = index / width_col; - int h_col = h_index % height_col; - int w_col = index % width_col; - int c_im = h_index / height_col; - int c_col = c_im * kernel_h * kernel_w; - int h_offset = h_col * stride_h - pad_h; - int w_offset = w_col * stride_w - pad_w; + const int h_index = index / width_col; + const int h_col = h_index % height_col; + const int w_col = index % width_col; + const int c_im = h_index / height_col; + const int c_col = c_im * kernel_h * kernel_w; + const int h_offset = h_col * stride_h - pad_h; + const int w_offset = w_col * stride_w - pad_w; Dtype* data_col_ptr = data_col; data_col_ptr += (c_col * height_col + h_col) * width_col + w_col; const Dtype* data_im_ptr = data_im; @@ -230,14 +230,18 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col, Dtype* data_im) { CUDA_KERNEL_LOOP(index, n) { Dtype val = 0; - int w_im = index % width + pad_w; - int h_im = (index / width) % height + pad_h; - int c_im = index / (width * height); + const int w_im = index % width + pad_w; + const int h_im = (index / width) % height + pad_h; + const int c_im = index / (width * height); // compute the start and end of the output - int w_col_start = (w_im < kernel_w) ? 0 : (w_im - kernel_w) / stride_w + 1; - int w_col_end = min(w_im / stride_w + 1, width_col); - int h_col_start = (h_im < kernel_h) ? 0 : (h_im - kernel_h) / stride_h + 1; - int h_col_end = min(h_im / stride_h + 1, height_col); + const int w_col_start = + (w_im < kernel_w) ? 0 : (w_im - kernel_w) / stride_w + 1; + const int w_col_end = + min(w_im / stride_w + 1, width_col); + const int h_col_start = + (h_im < kernel_h) ? 0 : (h_im - kernel_h) / stride_h + 1; + const int h_col_end = + min(h_im / stride_h + 1, height_col); /* for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { From da75a0e715f3d434c6b4c23d55947e114b332337 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Sat, 19 Sep 2015 20:38:18 -0700 Subject: [PATCH 268/446] [build] check xcode command line tools version >= 6 future-proof version check for BLAS libraries on OS X fix #3092 --- Makefile | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index a911133661f..5fb6394e947 100644 --- a/Makefile +++ b/Makefile @@ -354,8 +354,9 @@ else # OS X packages atlas as the vecLib framework LIBRARIES += cblas # 10.10 has accelerate while 10.9 has veclib - XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep -o 'version: 6') - ifneq (,$(findstring version: 6,$(XCODE_CLT_VER))) + XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep 'version' | sed 's/[^0-9]*\([0-9]\).*/\1/') + XCODE_CLT_GEQ_6 := $(shell [ $(XCODE_CLT_VER) -gt 5 ] && echo 1) + ifeq ($(XCODE_CLT_GEQ_6), 1) BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/ LDFLAGS += -framework Accelerate else From 84eb44e6cf9623e09c354a863e201971270ba25b Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Sat, 19 Sep 2015 14:14:03 -0700 Subject: [PATCH 269/446] [tools] add Python script for at-a-glance prototxt summary --- tools/extra/summarize.py | 140 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100755 tools/extra/summarize.py diff --git a/tools/extra/summarize.py b/tools/extra/summarize.py new file mode 100755 index 00000000000..7e2d22fd364 --- /dev/null +++ b/tools/extra/summarize.py @@ -0,0 +1,140 @@ +#!/usr/bin/env python + +"""Net summarization tool. + +This tool summarizes the structure of a net in a concise but comprehensive +tabular listing, taking a prototxt file as input. + +Use this tool to check at a glance that the computation you've specified is the +computation you expect. +""" + +from caffe.proto import caffe_pb2 +from google import protobuf +import re +import argparse + +# ANSI codes for coloring blobs (used cyclically) +COLORS = ['92', '93', '94', '95', '97', '96', '42', '43;30', '100', + '444', '103;30', '107;30'] +DISCONNECTED_COLOR = '41' + +def read_net(filename): + net = caffe_pb2.NetParameter() + with open(filename) as f: + protobuf.text_format.Parse(f.read(), net) + return net + +def format_param(param): + out = [] + if len(param.name) > 0: + out.append(param.name) + if param.lr_mult != 1: + out.append('x{}'.format(param.lr_mult)) + if param.decay_mult != 1: + out.append('Dx{}'.format(param.decay_mult)) + return ' '.join(out) + +def printed_len(s): + return len(re.sub(r'\033\[[\d;]+m', '', s)) + +def print_table(table, max_width): + """Print a simple nicely-aligned table. + + table must be a list of (equal-length) lists. Columns are space-separated, + and as narrow as possible, but no wider than max_width. Text may overflow + columns; note that unlike string.format, this will not affect subsequent + columns, if possible.""" + + max_widths = [max_width] * len(table[0]) + column_widths = [max(printed_len(row[j]) + 1 for row in table) + for j in range(len(table[0]))] + column_widths = [min(w, max_w) for w, max_w in zip(column_widths, max_widths)] + + for row in table: + row_str = '' + right_col = 0 + for cell, width in zip(row, column_widths): + right_col += width + row_str += cell + ' ' + row_str += ' ' * max(right_col - printed_len(row_str), 0) + print row_str + +def summarize_net(net): + disconnected_tops = set() + for lr in net.layer: + disconnected_tops |= set(lr.top) + disconnected_tops -= set(lr.bottom) + + table = [] + colors = {} + for lr in net.layer: + tops = [] + for ind, top in enumerate(lr.top): + color = colors.setdefault(top, COLORS[len(colors) % len(COLORS)]) + if top in disconnected_tops: + top = '\033[1;4m' + top + if len(lr.loss_weight) > 0: + top = '{} * {}'.format(lr.loss_weight[ind], top) + tops.append('\033[{}m{}\033[0m'.format(color, top)) + top_str = ', '.join(tops) + + bottoms = [] + for bottom in lr.bottom: + color = colors.get(bottom, DISCONNECTED_COLOR) + bottoms.append('\033[{}m{}\033[0m'.format(color, bottom)) + bottom_str = ', '.join(bottoms) + + if lr.type == 'Python': + type_str = lr.python_param.module + '.' + lr.python_param.layer + else: + type_str = lr.type + + # Summarize conv/pool parameters. + # TODO support rectangular/ND parameters + conv_param = lr.convolution_param + if (lr.type in ['Convolution', 'Deconvolution'] + and len(conv_param.kernel_size) == 1): + arg_str = str(conv_param.kernel_size[0]) + if len(conv_param.stride) > 0 and conv_param.stride[0] != 1: + arg_str += '/' + str(conv_param.stride[0]) + if len(conv_param.pad) > 0 and conv_param.pad[0] != 0: + arg_str += '+' + str(conv_param.pad[0]) + arg_str += ' ' + str(conv_param.num_output) + if conv_param.group != 1: + arg_str += '/' + str(conv_param.group) + elif lr.type == 'Pooling': + arg_str = str(lr.pooling_param.kernel_size) + if lr.pooling_param.stride != 1: + arg_str += '/' + str(lr.pooling_param.stride) + if lr.pooling_param.pad != 0: + arg_str += '+' + str(lr.pooling_param.pad) + else: + arg_str = '' + + if len(lr.param) > 0: + param_strs = map(format_param, lr.param) + if max(map(len, param_strs)) > 0: + param_str = '({})'.format(', '.join(param_strs)) + else: + param_str = '' + else: + param_str = '' + + table.append([lr.name, type_str, param_str, bottom_str, '->', top_str, + arg_str]) + return table + +def main(): + parser = argparse.ArgumentParser(description="Print a concise summary of net computation.") + parser.add_argument('filename', help='net prototxt file to summarize') + parser.add_argument('-w', '--max-width', help='maximum field width', + type=int, default=30) + args = parser.parse_args() + + net = read_net(args.filename) + table = summarize_net(net) + print_table(table, max_width=args.max_width) + +if __name__ == '__main__': + main() From a40c2a08421ebf9a164e198a70752f2d5cb1c93d Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Sun, 20 Sep 2015 14:20:28 -0700 Subject: [PATCH 270/446] fix broken DeconvolutionLayer GPU backward caused by typo --- src/caffe/layers/deconv_layer.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu index ea83f56f1b2..5dbdcc3149f 100644 --- a/src/caffe/layers/deconv_layer.cu +++ b/src/caffe/layers/deconv_layer.cu @@ -51,7 +51,7 @@ void DeconvolutionLayer::Backward_gpu(const vector*>& top, } // gradient w.r.t. bottom data, if necessary. if (propagate_down[i]) { - this->forward_gpu_gemm(top_diff + this->top_dim_, weight, + this->forward_gpu_gemm(top_diff + n * this->top_dim_, weight, bottom_diff + n * this->bottom_dim_, this->param_propagate_down_[0]); } From 6a00ecae67a95cf39e1961aaddc3be1f5a828bb4 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Sun, 20 Sep 2015 15:31:59 -0700 Subject: [PATCH 271/446] fix broken conv/deconv reshaping caused by reading bottom shape in LayerSetUp This also eliminates the extra copying of bottom's shape. --- include/caffe/vision_layers.hpp | 7 +++++-- src/caffe/layers/base_conv_layer.cpp | 10 ++-------- src/caffe/layers/conv_layer.cpp | 5 ++--- src/caffe/layers/deconv_layer.cpp | 5 ++--- 4 files changed, 11 insertions(+), 16 deletions(-) diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp index eae65820c40..06bc0457e2d 100644 --- a/include/caffe/vision_layers.hpp +++ b/include/caffe/vision_layers.hpp @@ -58,6 +58,10 @@ class BaseConvolutionLayer : public Layer { void backward_gpu_bias(Dtype* bias, const Dtype* input); #endif + /// @brief The spatial dimensions of the input. + inline int input_shape(int i) { + return (*bottom_shape_)[channel_axis_ + i]; + } // reverse_dimensions should return true iff we are implementing deconv, so // that conv helpers know which dimensions are which. virtual bool reverse_dimensions() = 0; @@ -72,12 +76,11 @@ class BaseConvolutionLayer : public Layer { Blob pad_; /// @brief The spatial dimensions of the convolution input. Blob conv_input_shape_; - /// @brief The spatial dimensions of the input. - Blob input_shape_; /// @brief The spatial dimensions of the col_buffer. vector col_buffer_shape_; /// @brief The spatial dimensions of the output. vector output_shape_; + const vector* bottom_shape_; int num_spatial_axes_; int bottom_dim_; diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp index a5b90a549bb..c6b47550292 100644 --- a/src/caffe/layers/base_conv_layer.cpp +++ b/src/caffe/layers/base_conv_layer.cpp @@ -20,13 +20,7 @@ void BaseConvolutionLayer::LayerSetUp(const vector*>& bottom, const int num_axes = bottom[0]->num_axes(); num_spatial_axes_ = num_axes - first_spatial_axis; CHECK_GE(num_spatial_axes_, 0); - // Setup input dimensions (input_shape_). vector bottom_dim_blob_shape(1, num_spatial_axes_ + 1); - input_shape_.Reshape(bottom_dim_blob_shape); - int* input_shape_data = input_shape_.mutable_cpu_data(); - for (int i = 0; i < num_spatial_axes_ + 1; ++i) { - input_shape_data[i] = bottom[0]->shape(channel_axis_ + i); - } vector spatial_dim_blob_shape(1, std::max(num_spatial_axes_, 1)); // Setup filter kernel dimensions (kernel_shape_). kernel_shape_.Reshape(spatial_dim_blob_shape); @@ -190,6 +184,7 @@ void BaseConvolutionLayer::Reshape(const vector*>& bottom, << "All inputs must have the same shape."; } // Shape the tops. + bottom_shape_ = &bottom[0]->shape(); compute_output_shape(); vector top_shape(bottom[0]->shape().begin(), bottom[0]->shape().begin() + channel_axis_); @@ -223,10 +218,9 @@ void BaseConvolutionLayer::Reshape(const vector*>& bottom, // it goes lazily unused to save memory. col_buffer_shape_.clear(); col_buffer_shape_.push_back(kernel_dim_ * group_); - const int* input_shape_data = input_shape_.cpu_data() + 1; for (int i = 0; i < num_spatial_axes_; ++i) { if (reverse_dimensions()) { - col_buffer_shape_.push_back(input_shape_data[i]); + col_buffer_shape_.push_back(input_shape(i + 1)); } else { col_buffer_shape_.push_back(output_shape_[i]); } diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp index 5cf26970a0b..fb50bb095ed 100644 --- a/src/caffe/layers/conv_layer.cpp +++ b/src/caffe/layers/conv_layer.cpp @@ -10,14 +10,13 @@ namespace caffe { template void ConvolutionLayer::compute_output_shape() { - // input_shape_ + 1 to skip channel axis - const int* input_shape_data = this->input_shape_.cpu_data() + 1; const int* kernel_shape_data = this->kernel_shape_.cpu_data(); const int* stride_data = this->stride_.cpu_data(); const int* pad_data = this->pad_.cpu_data(); this->output_shape_.clear(); for (int i = 0; i < this->num_spatial_axes_; ++i) { - const int input_dim = input_shape_data[i]; + // i + 1 to skip channel axis + const int input_dim = this->input_shape(i + 1); const int output_dim = (input_dim + 2 * pad_data[i] - kernel_shape_data[i]) / stride_data[i] + 1; this->output_shape_.push_back(output_dim); diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp index f1d1abf288a..91aabb315b2 100644 --- a/src/caffe/layers/deconv_layer.cpp +++ b/src/caffe/layers/deconv_layer.cpp @@ -10,14 +10,13 @@ namespace caffe { template void DeconvolutionLayer::compute_output_shape() { - // input_shape_ + 1 to skip channel axis - const int* input_shape_data = this->input_shape_.cpu_data() + 1; const int* kernel_shape_data = this->kernel_shape_.cpu_data(); const int* stride_data = this->stride_.cpu_data(); const int* pad_data = this->pad_.cpu_data(); this->output_shape_.clear(); for (int i = 0; i < this->num_spatial_axes_; ++i) { - const int input_dim = input_shape_data[i]; + // i + 1 to skip channel axis + const int input_dim = this->input_shape(i + 1); const int output_dim = stride_data[i] * (input_dim - 1) + kernel_shape_data[i] - 2 * pad_data[i]; this->output_shape_.push_back(output_dim); From 74e174537418b6a3c0c8708e444edf45ab491e94 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Wed, 23 Sep 2015 13:38:29 -0700 Subject: [PATCH 272/446] [test] TestReshape: check small then large checking large then small can mask failure since the smaller shape memory will fit within the larger shape. --- src/caffe/test/test_net.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp index 12998d8912f..ec01053c1f7 100644 --- a/src/caffe/test/test_net.cpp +++ b/src/caffe/test/test_net.cpp @@ -2269,8 +2269,10 @@ TYPED_TEST(NetTest, TestReshape) { FillerParameter filler_param; filler_param.set_std(1); GaussianFiller filler(filler_param); - Blob blob1(4, 3, 9, 11); - Blob blob2(2, 3, 12, 10); + // Check smaller shape first as larger first could hide realloc failures. + Blob blob1(2, 3, 12, 10); + Blob blob2(4, 3, 9, 11); + ASSERT_LT(blob1.count(), blob2.count()); filler.Fill(&blob1); filler.Fill(&blob2); From ae77b15495d4c2a83202c49991bfc0885765de03 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Wed, 23 Sep 2015 13:40:16 -0700 Subject: [PATCH 273/446] [test] TestReshape: expect instead of check --- src/caffe/test/test_net.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp index ec01053c1f7..16c1d35f3fc 100644 --- a/src/caffe/test/test_net.cpp +++ b/src/caffe/test/test_net.cpp @@ -2306,7 +2306,7 @@ TYPED_TEST(NetTest, TestReshape) { this->net_->ForwardPrefilled(); this->net_->Backward(); for (int i = 0; i < output1.count(); ++i) { - CHECK_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i)); + EXPECT_FLOAT_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i)); } input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(), @@ -2315,7 +2315,7 @@ TYPED_TEST(NetTest, TestReshape) { this->net_->ForwardPrefilled(); this->net_->Backward(); for (int i = 0; i < output2.count(); ++i) { - CHECK_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i)); + EXPECT_FLOAT_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i)); } } From b8c81bd2bfbc5bc2e394395bf2c1f435cb32b2a1 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Wed, 23 Sep 2015 13:40:24 -0700 Subject: [PATCH 274/446] [test] TestReshape: check that shapes actually change Check that output spatial shape varies with input shape while the output num matches the input num. --- src/caffe/test/test_net.cpp | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp index 16c1d35f3fc..ab4afba1a93 100644 --- a/src/caffe/test/test_net.cpp +++ b/src/caffe/test/test_net.cpp @@ -2262,8 +2262,8 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) { TYPED_TEST(NetTest, TestReshape) { typedef typename TypeParam::Dtype Dtype; // We set up bottom blobs of two different sizes, switch between - // them, and check that forward and backward both run and the results - // are the same. + // them, check that forward and backward both run and the results + // are the same, and check that the output shapes change. Caffe::set_random_seed(this->seed_); Caffe::set_mode(Caffe::CPU); FillerParameter filler_param; @@ -2317,6 +2317,18 @@ TYPED_TEST(NetTest, TestReshape) { for (int i = 0; i < output2.count(); ++i) { EXPECT_FLOAT_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i)); } + + EXPECT_EQ(output1.num(), blob1.num()); + EXPECT_EQ(output2.num(), blob2.num()); + bool same_spatial_shape = true; + const int kFirstSpatialAxis = 2; + for (int i = kFirstSpatialAxis; i < output1.num_axes(); ++i) { + if (output1.shape(i) != output2.shape(i)) { + same_spatial_shape = false; + break; + } + } + EXPECT_FALSE(same_spatial_shape); } TYPED_TEST(NetTest, TestSkipPropagateDown) { From 84e390c5a16347c7369f6c92cb62526e42ce73ac Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Thu, 24 Sep 2015 12:35:35 -0700 Subject: [PATCH 275/446] Allow H5T_INTEGER in HDF5 files --- src/caffe/util/hdf5.cpp | 29 ++++++++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/src/caffe/util/hdf5.cpp b/src/caffe/util/hdf5.cpp index d0d05f70f8f..7730e76ab87 100644 --- a/src/caffe/util/hdf5.cpp +++ b/src/caffe/util/hdf5.cpp @@ -27,7 +27,34 @@ void hdf5_load_nd_dataset_helper( status = H5LTget_dataset_info( file_id, dataset_name_, dims.data(), &class_, NULL); CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_; - CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data"; + switch (class_) { + case H5T_FLOAT: + LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_FLOAT"; + break; + case H5T_INTEGER: + LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_INTEGER"; + break; + case H5T_TIME: + LOG(FATAL) << "Unsupported datatype class: H5T_TIME"; + case H5T_STRING: + LOG(FATAL) << "Unsupported datatype class: H5T_STRING"; + case H5T_BITFIELD: + LOG(FATAL) << "Unsupported datatype class: H5T_BITFIELD"; + case H5T_OPAQUE: + LOG(FATAL) << "Unsupported datatype class: H5T_OPAQUE"; + case H5T_COMPOUND: + LOG(FATAL) << "Unsupported datatype class: H5T_COMPOUND"; + case H5T_REFERENCE: + LOG(FATAL) << "Unsupported datatype class: H5T_REFERENCE"; + case H5T_ENUM: + LOG(FATAL) << "Unsupported datatype class: H5T_ENUM"; + case H5T_VLEN: + LOG(FATAL) << "Unsupported datatype class: H5T_VLEN"; + case H5T_ARRAY: + LOG(FATAL) << "Unsupported datatype class: H5T_ARRAY"; + default: + LOG(FATAL) << "Datatype class unknown"; + } vector blob_dims(dims.size()); for (int i = 0; i < dims.size(); ++i) { From ebc9963fea7b72f397c446a10a9aeab576979566 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Tue, 25 Aug 2015 18:58:45 -0700 Subject: [PATCH 276/446] Modify HDF5DataLayerTest to test H5T_INTEGER data --- .../test/test_data/generate_sample_data.py | 14 ++++++++------ .../test/test_data/sample_data_2_gzip.h5 | Bin 15446 -> 15446 bytes 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py index 3703b41823b..8349dbbc8e6 100644 --- a/src/caffe/test/test_data/generate_sample_data.py +++ b/src/caffe/test/test_data/generate_sample_data.py @@ -36,23 +36,25 @@ f['label'] = label f['label2'] = label2 -with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f: +with h5py.File(script_dir + '/sample_data_uint8_gzip.h5', 'w') as f: f.create_dataset( 'data', data=data + total_size, compression='gzip', compression_opts=1 ) f.create_dataset( 'label', data=label, - compression='gzip', compression_opts=1 + compression='gzip', compression_opts=1, + dtype='uint8', ) f.create_dataset( 'label2', data=label2, - compression='gzip', compression_opts=1 + compression='gzip', compression_opts=1, + dtype='uint8', ) with open(script_dir + '/sample_data_list.txt', 'w') as f: - f.write(script_dir + '/sample_data.h5\n') - f.write(script_dir + '/sample_data_2_gzip.h5\n') + f.write('src/caffe/test/test_data/sample_data.h5\n') + f.write('src/caffe/test/test_data/sample_uint8_gzip.h5\n') # Generate GradientBasedSolver solver_data.h5 @@ -76,4 +78,4 @@ f['targets'] = targets with open(script_dir + '/solver_data_list.txt', 'w') as f: - f.write(script_dir + '/solver_data.h5\n') + f.write('src/caffe/test/test_data/solver_data.h5\n') diff --git a/src/caffe/test/test_data/sample_data_2_gzip.h5 b/src/caffe/test/test_data/sample_data_2_gzip.h5 index a138e0367be3d4b4ce4b51dcf0d7895056018883..0cb9ef92241d049b699b65f87e800f97337cae54 100644 GIT binary patch delta 225 zcmcasajjwl4+~4C%-zt<0xUly1qB!w85kG@fEYwGFmOy(l#7r6vxOKqz(ODnNCN|d z$Ha}klMPrT7=H51ueFFg#LWgGis1!kei3Wf$yr%6iSb&3kmDU2qQ`Q_!o GjsXC!dpCsu From 859f93891e4bf47d02899f03f0620fd1f29ca224 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Thu, 24 Sep 2015 13:33:11 -0700 Subject: [PATCH 277/446] Fix generate_sample_data.py - bug from #2978 --- src/caffe/test/test_data/generate_sample_data.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py index 8349dbbc8e6..2645073575f 100644 --- a/src/caffe/test/test_data/generate_sample_data.py +++ b/src/caffe/test/test_data/generate_sample_data.py @@ -36,7 +36,7 @@ f['label'] = label f['label2'] = label2 -with h5py.File(script_dir + '/sample_data_uint8_gzip.h5', 'w') as f: +with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f: f.create_dataset( 'data', data=data + total_size, compression='gzip', compression_opts=1 @@ -54,7 +54,7 @@ with open(script_dir + '/sample_data_list.txt', 'w') as f: f.write('src/caffe/test/test_data/sample_data.h5\n') - f.write('src/caffe/test/test_data/sample_uint8_gzip.h5\n') + f.write('src/caffe/test/test_data/sample_data_2_gzip.h5\n') # Generate GradientBasedSolver solver_data.h5 From 200bd40391bc1c072730ea4bd80a6fe42b7a3901 Mon Sep 17 00:00:00 2001 From: Dmytro Mishkin Date: Fri, 25 Sep 2015 10:00:23 +0300 Subject: [PATCH 278/446] Fix parse_log.sh against "prefetch queue empty" messages --- tools/extra/parse_log.sh | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/tools/extra/parse_log.sh b/tools/extra/parse_log.sh index 98ef0a05002..9892c897682 100755 --- a/tools/extra/parse_log.sh +++ b/tools/extra/parse_log.sh @@ -14,7 +14,12 @@ echo "Usage parse_log.sh /path/to/your.log" exit fi LOG=`basename $1` -grep -B 1 'Test ' $1 > aux.txt +sed -n '/Iteration .* Testing net/,/Iteration *. loss/p' $1 > aux.txt +sed -i '/Waiting for data/d' aux.txt +sed -i '/prefetch queue empty/d' aux.txt +sed -i '/Iteration .* loss/d' aux.txt +sed -i '/Iteration .* lr/d' aux.txt +sed -i '/Train net/d' aux.txt grep 'Iteration ' aux.txt | sed 's/.*Iteration \([[:digit:]]*\).*/\1/g' > aux0.txt grep 'Test net output #0' aux.txt | awk '{print $11}' > aux1.txt grep 'Test net output #1' aux.txt | awk '{print $11}' > aux2.txt From 6c02c8b7daf123f64b944ede407d0022e98d6e0b Mon Sep 17 00:00:00 2001 From: Tim Meinhardt Date: Tue, 15 Sep 2015 16:55:26 +0200 Subject: [PATCH 279/446] Add argmax_param axis --- include/caffe/common_layers.hpp | 2 ++ src/caffe/layers/argmax_layer.cpp | 22 +++++++++++++++++----- src/caffe/proto/caffe.proto | 5 +++++ 3 files changed, 24 insertions(+), 5 deletions(-) diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 89bab8d6f3a..491f9edbf19 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -68,6 +68,8 @@ class ArgMaxLayer : public Layer { } bool out_max_val_; size_t top_k_; + bool has_axis_; + int axis_; }; /** diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp index c4040cdcaaa..dad3d08bd66 100644 --- a/src/caffe/layers/argmax_layer.cpp +++ b/src/caffe/layers/argmax_layer.cpp @@ -11,11 +11,23 @@ namespace caffe { template void ArgMaxLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { - out_max_val_ = this->layer_param_.argmax_param().out_max_val(); - top_k_ = this->layer_param_.argmax_param().top_k(); - CHECK_GE(top_k_, 1) << " top k must not be less than 1."; - CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num()) - << "top_k must be less than or equal to the number of classes."; + const ArgMaxParameter& argmax_param = this->layer_param_.argmax_param(); + out_max_val_ = argmax_param.out_max_val(); + top_k_ = argmax_param.top_k(); + has_axis_ = argmax_param.has_axis(); + CHECK_GE(top_k_, 1) << "top k must not be less than 1."; + if (has_axis_) { + axis_ = bottom[0]->CanonicalAxisIndex(argmax_param.axis()); + CHECK_GE(axis_, 0) << "axis must not be less than 0."; + CHECK_LE(axis_, bottom[0]->num_axes()) << + "axis must be less than or equal to the number of axis."; + CHECK_LE(top_k_, bottom[0]->shape(axis_)) + << "top_k must be less than or equal to the dimension of the axis."; + } else { + CHECK_LE(top_k_, bottom[0]->count(1)) + << "top_k must be less than or equal to" + " the dimension of the flattened bottom blob per instance."; + } } template diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index f52c941b05e..a8747c12b37 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -443,6 +443,11 @@ message ArgMaxParameter { // If true produce pairs (argmax, maxval) optional bool out_max_val = 1 [default = false]; optional uint32 top_k = 2 [default = 1]; + // The axis along which to maximise -- may be negative to index from the + // end (e.g., -1 for the last axis). + // By default ArgMaxLayer maximizes over the flattened trailing dimensions + // for each index of the first / num dimension. + optional int32 axis = 3; } message ConcatParameter { From c77d5e5156f94720c1decd13f7f87fe78df9d4eb Mon Sep 17 00:00:00 2001 From: Tim Meinhardt Date: Tue, 15 Sep 2015 16:56:16 +0200 Subject: [PATCH 280/446] Implement ArgMaxLayer forward_cpu and reshape for axis param --- src/caffe/layers/argmax_layer.cpp | 53 ++++++++++++++++++++++--------- 1 file changed, 38 insertions(+), 15 deletions(-) diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp index dad3d08bd66..18ff5f5a639 100644 --- a/src/caffe/layers/argmax_layer.cpp +++ b/src/caffe/layers/argmax_layer.cpp @@ -33,13 +33,19 @@ void ArgMaxLayer::LayerSetUp(const vector*>& bottom, template void ArgMaxLayer::Reshape(const vector*>& bottom, const vector*>& top) { - if (out_max_val_) { + std::vector shape(4, 1); + shape[0] = bottom[0]->shape(0); + // Produces max_ind + shape[2] = top_k_; + if (has_axis_) { + // Produces max_ind or max_val per axis + shape = bottom[0]->shape(); + shape[axis_] = top_k_; + } else if (out_max_val_) { // Produces max_ind and max_val - top[0]->Reshape(bottom[0]->num(), 2, top_k_, 1); - } else { - // Produces only max_ind - top[0]->Reshape(bottom[0]->num(), 1, top_k_, 1); + shape[1] = 2; } + top[0]->Reshape(shape); } template @@ -47,23 +53,40 @@ void ArgMaxLayer::Forward_cpu(const vector*>& bottom, const vector*>& top) { const Dtype* bottom_data = bottom[0]->cpu_data(); Dtype* top_data = top[0]->mutable_cpu_data(); - int num = bottom[0]->num(); - int dim = bottom[0]->count() / bottom[0]->num(); + int dim, axis_dist; + if (has_axis_) { + dim = bottom[0]->shape(axis_); + // Distance between values of axis in blob + axis_dist = bottom[0]->count(axis_) / dim; + } else { + dim = bottom[0]->count(1); + axis_dist = 1; + } + int num = bottom[0]->count() / dim; + std::vector > bottom_data_vector(dim); for (int i = 0; i < num; ++i) { - std::vector > bottom_data_vector; for (int j = 0; j < dim; ++j) { - bottom_data_vector.push_back( - std::make_pair(bottom_data[i * dim + j], j)); + bottom_data_vector[j] = std::make_pair( + bottom_data[(i / axis_dist * dim + j) * axis_dist + i % axis_dist], j); } std::partial_sort( bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_, bottom_data_vector.end(), std::greater >()); for (int j = 0; j < top_k_; ++j) { - top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second; - } - if (out_max_val_) { - for (int j = 0; j < top_k_; ++j) { - top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first; + if (out_max_val_) { + if (has_axis_) { + // Produces max_val per axis + top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist] = + bottom_data_vector[j].first; + } else { + // Produces max_ind and max_val + top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second; + top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first; + } + } else { + // Produces max_ind per axis + top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist] = + bottom_data_vector[j].second; } } } From 9b2d267941411d9727a88ead18e3531bad50d14d Mon Sep 17 00:00:00 2001 From: Tim Meinhardt Date: Tue, 15 Sep 2015 16:56:45 +0200 Subject: [PATCH 281/446] Update ArgMaxLayer documentation for axis param --- include/caffe/common_layers.hpp | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 491f9edbf19..d1ddaee4f19 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -21,7 +21,8 @@ namespace caffe { * * Intended for use after a classification layer to produce a prediction. * If parameter out_max_val is set to true, output is a vector of pairs - * (max_ind, max_val) for each image. + * (max_ind, max_val) for each image. The axis parameter specifies an axis + * along which to maximise. * * NOTE: does not implement Backwards operation. */ @@ -34,7 +35,11 @@ class ArgMaxLayer : public Layer { * - top_k (\b optional uint, default 1). * the number @f$ K @f$ of maximal items to output. * - out_max_val (\b optional bool, default false). - * if set, output a vector of pairs (max_ind, max_val) for each image. + * if set, output a vector of pairs (max_ind, max_val) unless axis is set then + * output max_val along the specified axis. + * - axis (\b optional int). + * if set, maximise along the specified axis else maximise the flattened + * trailing dimensions for each index of the first / num dimension. */ explicit ArgMaxLayer(const LayerParameter& param) : Layer(param) {} @@ -54,7 +59,8 @@ class ArgMaxLayer : public Layer { * the inputs @f$ x @f$ * @param top output Blob vector (length 1) * -# @f$ (N \times 1 \times K \times 1) @f$ or, if out_max_val - * @f$ (N \times 2 \times K \times 1) @f$ + * @f$ (N \times 2 \times K \times 1) @f$ unless axis set than e.g. + * @f$ (N \times K \times H \times W) @f$ if axis == 1 * the computed outputs @f$ * y_n = \arg\max\limits_i x_{ni} * @f$ (for @f$ K = 1 @f$). From a2a5e22d0b7d44a5e577edd53181d4802f057740 Mon Sep 17 00:00:00 2001 From: Tim Meinhardt Date: Tue, 15 Sep 2015 16:57:37 +0200 Subject: [PATCH 282/446] Generalise ArgMaxLayerTest bottom blob shape --- src/caffe/test/test_argmax_layer.cpp | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp index 895c3d372ff..d3018f90c9d 100644 --- a/src/caffe/test/test_argmax_layer.cpp +++ b/src/caffe/test/test_argmax_layer.cpp @@ -16,7 +16,7 @@ template class ArgMaxLayerTest : public CPUDeviceTest { protected: ArgMaxLayerTest() - : blob_bottom_(new Blob(10, 20, 1, 1)), + : blob_bottom_(new Blob(10, 10, 20, 20)), blob_top_(new Blob()), top_k_(5) { Caffe::set_random_seed(1701); @@ -112,6 +112,7 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) { layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); // Now, check values + const TypeParam* bottom_data = this->blob_bottom_->cpu_data(); int max_ind; TypeParam max_val; int num = this->blob_bottom_->num(); @@ -121,10 +122,10 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) { EXPECT_LE(this->blob_top_->data_at(i, 0, 0, 0), dim); for (int j = 0; j < this->top_k_; ++j) { max_ind = this->blob_top_->data_at(i, 0, j, 0); - max_val = this->blob_bottom_->data_at(i, max_ind, 0, 0); + max_val = bottom_data[i * dim + max_ind]; int count = 0; for (int k = 0; k < dim; ++k) { - if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) { + if (bottom_data[i * dim + k] > max_val) { ++count; } } @@ -142,6 +143,7 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) { layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); // Now, check values + const TypeParam* bottom_data = this->blob_bottom_->cpu_data(); int max_ind; TypeParam max_val; int num = this->blob_bottom_->num(); @@ -152,10 +154,10 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) { for (int j = 0; j < this->top_k_; ++j) { max_ind = this->blob_top_->data_at(i, 0, j, 0); max_val = this->blob_top_->data_at(i, 1, j, 0); - EXPECT_EQ(this->blob_bottom_->data_at(i, max_ind, 0, 0), max_val); + EXPECT_EQ(bottom_data[i * dim + max_ind], max_val); int count = 0; for (int k = 0; k < dim; ++k) { - if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) { + if (bottom_data[i * dim + k] > max_val) { ++count; } } From def3d3cc49b908e54f787be377c299e6e6cbf16c Mon Sep 17 00:00:00 2001 From: Tim Meinhardt Date: Tue, 15 Sep 2015 16:57:55 +0200 Subject: [PATCH 283/446] Implement ArgMaxLayerTest for axis param --- src/caffe/layers/argmax_layer.cpp | 28 +++--- src/caffe/test/test_argmax_layer.cpp | 125 +++++++++++++++++++++++++++ 2 files changed, 140 insertions(+), 13 deletions(-) diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp index 18ff5f5a639..0c0a932dac7 100644 --- a/src/caffe/layers/argmax_layer.cpp +++ b/src/caffe/layers/argmax_layer.cpp @@ -33,17 +33,19 @@ void ArgMaxLayer::LayerSetUp(const vector*>& bottom, template void ArgMaxLayer::Reshape(const vector*>& bottom, const vector*>& top) { - std::vector shape(4, 1); - shape[0] = bottom[0]->shape(0); - // Produces max_ind - shape[2] = top_k_; + std::vector shape(bottom[0]->num_axes(), 1); if (has_axis_) { // Produces max_ind or max_val per axis shape = bottom[0]->shape(); shape[axis_] = top_k_; - } else if (out_max_val_) { - // Produces max_ind and max_val - shape[1] = 2; + } else { + shape[0] = bottom[0]->shape(0); + // Produces max_ind + shape[2] = top_k_; + if (out_max_val_) { + // Produces max_ind and max_val + shape[1] = 2; + } } top[0]->Reshape(shape); } @@ -76,17 +78,17 @@ void ArgMaxLayer::Forward_cpu(const vector*>& bottom, if (out_max_val_) { if (has_axis_) { // Produces max_val per axis - top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist] = - bottom_data_vector[j].first; + top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist] + = bottom_data_vector[j].first; } else { // Produces max_ind and max_val - top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second; - top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first; + top_data[2 * i * top_k_ + j] = bottom_data_vector[j].second; + top_data[2 * i * top_k_ + top_k_ + j] = bottom_data_vector[j].first; } } else { // Produces max_ind per axis - top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist] = - bottom_data_vector[j].second; + top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist] + = bottom_data_vector[j].second; } } } diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp index d3018f90c9d..bbf19099905 100644 --- a/src/caffe/test/test_argmax_layer.cpp +++ b/src/caffe/test/test_argmax_layer.cpp @@ -55,6 +55,43 @@ TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) { EXPECT_EQ(this->blob_top_->channels(), 2); } +TYPED_TEST(ArgMaxLayerTest, TestSetupAxis) { + LayerParameter layer_param; + ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param(); + argmax_param->set_axis(0); + ArgMaxLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + EXPECT_EQ(this->blob_top_->shape(0), argmax_param->top_k()); + EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(0)); + EXPECT_EQ(this->blob_top_->shape(2), this->blob_bottom_->shape(2)); + EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3)); +} + +TYPED_TEST(ArgMaxLayerTest, TestSetupAxisNegativeIndexing) { + LayerParameter layer_param; + ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param(); + argmax_param->set_axis(-2); + ArgMaxLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + EXPECT_EQ(this->blob_top_->shape(0), this->blob_bottom_->shape(0)); + EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(1)); + EXPECT_EQ(this->blob_top_->shape(2), argmax_param->top_k()); + EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3)); +} + +TYPED_TEST(ArgMaxLayerTest, TestSetupAxisMaxVal) { + LayerParameter layer_param; + ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param(); + argmax_param->set_axis(2); + argmax_param->set_out_max_val(true); + ArgMaxLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + EXPECT_EQ(this->blob_top_->shape(0), this->blob_bottom_->shape(0)); + EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(1)); + EXPECT_EQ(this->blob_top_->shape(2), argmax_param->top_k()); + EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3)); +} + TYPED_TEST(ArgMaxLayerTest, TestCPU) { LayerParameter layer_param; ArgMaxLayer layer(layer_param); @@ -166,5 +203,93 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) { } } +TYPED_TEST(ArgMaxLayerTest, TestCPUAxis) { + LayerParameter layer_param; + ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param(); + argmax_param->set_axis(0); + ArgMaxLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Now, check values + int max_ind; + TypeParam max_val; + std::vector shape = this->blob_bottom_->shape(); + for (int i = 0; i < shape[1]; ++i) { + for (int j = 0; j < shape[2]; ++j) { + for (int k = 0; k < shape[3]; ++k) { + max_ind = this->blob_top_->data_at(0, i, j, k); + max_val = this->blob_bottom_->data_at(max_ind, i, j, k); + EXPECT_GE(max_ind, 0); + EXPECT_LE(max_ind, shape[0]); + for (int l = 0; l < shape[0]; ++l) { + EXPECT_LE(this->blob_bottom_->data_at(l, i, j, k), max_val); + } + } + } + } +} + +TYPED_TEST(ArgMaxLayerTest, TestCPUAxisTopK) { + LayerParameter layer_param; + ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param(); + argmax_param->set_axis(2); + argmax_param->set_top_k(this->top_k_); + ArgMaxLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Now, check values + int max_ind; + TypeParam max_val; + std::vector shape = this->blob_bottom_->shape(); + for (int i = 0; i < shape[0]; ++i) { + for (int j = 0; j < shape[1]; ++j) { + for (int k = 0; k < shape[3]; ++k) { + for (int m = 0; m < this->top_k_; ++m) { + max_ind = this->blob_top_->data_at(i, j, m, k); + max_val = this->blob_bottom_->data_at(i, j, max_ind, k); + EXPECT_GE(max_ind, 0); + EXPECT_LE(max_ind, shape[2]); + int count = 0; + for (int l = 0; l < shape[2]; ++l) { + if (this->blob_bottom_->data_at(i, j, l, k) > max_val) { + ++count; + } + } + EXPECT_EQ(m, count); + } + } + } + } +} + +TYPED_TEST(ArgMaxLayerTest, TestCPUAxisMaxValTopK) { + LayerParameter layer_param; + ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param(); + argmax_param->set_axis(-1); + argmax_param->set_top_k(this->top_k_); + argmax_param->set_out_max_val(true); + ArgMaxLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Now, check values + TypeParam max_val; + std::vector shape = this->blob_bottom_->shape(); + for (int i = 0; i < shape[0]; ++i) { + for (int j = 0; j < shape[1]; ++j) { + for (int k = 0; k < shape[2]; ++k) { + for (int m = 0; m < this->top_k_; ++m) { + max_val = this->blob_top_->data_at(i, j, k, m); + int count = 0; + for (int l = 0; l < shape[3]; ++l) { + if (this->blob_bottom_->data_at(i, j, k, l) > max_val) { + ++count; + } + } + EXPECT_EQ(m, count); + } + } + } + } +} } // namespace caffe From bd5f15427cc2f008f80378a5948ce379d93ebde6 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Wed, 16 Sep 2015 13:39:23 -0700 Subject: [PATCH 284/446] Add flag on how host memory is allocated Add a bool flag to record whether a host memory is allocated using malloc or cudaMallocHost, and free correspondingly using this flag, instead of depending on Caffe::mode(), which is mutable during runtime. --- include/caffe/syncedmem.hpp | 15 ++++++++++----- src/caffe/syncedmem.cpp | 8 ++++---- 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp index 62aadef498d..3d92a0eaf3e 100644 --- a/include/caffe/syncedmem.hpp +++ b/include/caffe/syncedmem.hpp @@ -13,20 +13,22 @@ namespace caffe { // The improvement in performance seems negligible in the single GPU case, // but might be more significant for parallel training. Most importantly, // it improved stability for large models on many GPUs. -inline void CaffeMallocHost(void** ptr, size_t size) { +inline void CaffeMallocHost(void** ptr, size_t size, bool* use_cuda) { #ifndef CPU_ONLY if (Caffe::mode() == Caffe::GPU) { CUDA_CHECK(cudaMallocHost(ptr, size)); + *use_cuda = true; return; } #endif *ptr = malloc(size); + *use_cuda = false; CHECK(*ptr) << "host allocation of size " << size << " failed"; } -inline void CaffeFreeHost(void* ptr) { +inline void CaffeFreeHost(void* ptr, bool use_cuda) { #ifndef CPU_ONLY - if (Caffe::mode() == Caffe::GPU) { + if (use_cuda) { CUDA_CHECK(cudaFreeHost(ptr)); return; } @@ -45,10 +47,12 @@ class SyncedMemory { public: SyncedMemory() : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED), - own_cpu_data_(false), own_gpu_data_(false), gpu_device_(-1) {} + own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false), + gpu_device_(-1) {} explicit SyncedMemory(size_t size) : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED), - own_cpu_data_(false), own_gpu_data_(false), gpu_device_(-1) {} + own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false), + gpu_device_(-1) {} ~SyncedMemory(); const void* cpu_data(); void set_cpu_data(void* data); @@ -72,6 +76,7 @@ class SyncedMemory { size_t size_; SyncedHead head_; bool own_cpu_data_; + bool cpu_malloc_use_cuda_; bool own_gpu_data_; int gpu_device_; diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp index a667a867af0..632bf1f12d3 100644 --- a/src/caffe/syncedmem.cpp +++ b/src/caffe/syncedmem.cpp @@ -8,7 +8,7 @@ namespace caffe { SyncedMemory::~SyncedMemory() { if (cpu_ptr_ && own_cpu_data_) { - CaffeFreeHost(cpu_ptr_); + CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_); } #ifndef CPU_ONLY @@ -27,7 +27,7 @@ SyncedMemory::~SyncedMemory() { inline void SyncedMemory::to_cpu() { switch (head_) { case UNINITIALIZED: - CaffeMallocHost(&cpu_ptr_, size_); + CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_); caffe_memset(size_, 0, cpu_ptr_); head_ = HEAD_AT_CPU; own_cpu_data_ = true; @@ -35,7 +35,7 @@ inline void SyncedMemory::to_cpu() { case HEAD_AT_GPU: #ifndef CPU_ONLY if (cpu_ptr_ == NULL) { - CaffeMallocHost(&cpu_ptr_, size_); + CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_); own_cpu_data_ = true; } caffe_gpu_memcpy(size_, gpu_ptr_, cpu_ptr_); @@ -86,7 +86,7 @@ const void* SyncedMemory::cpu_data() { void SyncedMemory::set_cpu_data(void* data) { CHECK(data); if (own_cpu_data_) { - CaffeFreeHost(cpu_ptr_); + CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_); } cpu_ptr_ = data; head_ = HEAD_AT_CPU; From aaf4a4557668dfb75c540903ec02ed5821f75835 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Fri, 25 Sep 2015 15:43:47 -0700 Subject: [PATCH 285/446] Re-ordering some lines in build files Enforcing a consistent ordering - OpenCV, LevelDB, LMDB This will allow me to add the ALLOW_LMDB_NOLOCK option just after the USE_LMDB option, while keeping the IO dependency options together. --- CMakeLists.txt | 4 ++-- Makefile.config.example | 2 +- cmake/Summary.cmake | 4 ++-- cmake/Templates/caffe_config.h.in | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 37f937fe489..277c3dc49f8 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -23,9 +23,9 @@ set(python_version "2" CACHE STRING "Specify which Python version to use") caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE) caffe_option(BUILD_docs "Build documentation" ON IF UNIX OR APPLE) caffe_option(BUILD_python_layer "Build the Caffe Python layer" ON) -caffe_option(USE_LMDB "Build with lmdb" ON) -caffe_option(USE_LEVELDB "Build with levelDB" ON) caffe_option(USE_OPENCV "Build with OpenCV support" ON) +caffe_option(USE_LEVELDB "Build with levelDB" ON) +caffe_option(USE_LMDB "Build with lmdb" ON) # ---[ Dependencies include(cmake/Dependencies.cmake) diff --git a/Makefile.config.example b/Makefile.config.example index a20bad2f5ce..42f86db4595 100644 --- a/Makefile.config.example +++ b/Makefile.config.example @@ -8,9 +8,9 @@ # CPU_ONLY := 1 # uncomment to disable IO dependencies and corresponding data layers +# USE_OPENCV := 0 # USE_LEVELDB := 0 # USE_LMDB := 0 -# USE_OPENCV := 0 # To customize your choice of compiler, uncomment and set the following. # N.B. the default for Linux is g++ and the default for OSX is clang++ diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake index 3d12e81a130..703e22acaf6 100644 --- a/cmake/Summary.cmake +++ b/cmake/Summary.cmake @@ -114,9 +114,9 @@ function(caffe_print_configuration_summary) caffe_status(" BUILD_matlab : ${BUILD_matlab}") caffe_status(" BUILD_docs : ${BUILD_docs}") caffe_status(" CPU_ONLY : ${CPU_ONLY}") - caffe_status(" USE_LMDB : ${USE_LMDB}") - caffe_status(" USE_LEVELDB : ${USE_LEVELDB}") caffe_status(" USE_OPENCV : ${USE_OPENCV}") + caffe_status(" USE_LEVELDB : ${USE_LEVELDB}") + caffe_status(" USE_LMDB : ${USE_LMDB}") caffe_status("") caffe_status("Dependencies:") caffe_status(" BLAS : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})") diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in index 9302022d7da..84377493f48 100644 --- a/cmake/Templates/caffe_config.h.in +++ b/cmake/Templates/caffe_config.h.in @@ -33,5 +33,5 @@ /* IO libraries */ #cmakedefine USE_OPENCV -#cmakedefine USE_LMDB #cmakedefine USE_LEVELDB +#cmakedefine USE_LMDB From b93afe8378cd66d9bf375a0f492a30f9db77e8ae Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Fri, 25 Sep 2015 15:53:54 -0700 Subject: [PATCH 286/446] Add ALLOW_LMDB_NOLOCK build option This option lets you open LMDB files with the MDB_NOLOCK flag. You should not set this flag if you will be reading LMDBs with any possibility of simultaneous read and write. --- CMakeLists.txt | 1 + Makefile | 3 +++ Makefile.config.example | 5 +++++ cmake/ConfigGen.cmake | 3 +++ cmake/Dependencies.cmake | 3 +++ cmake/Summary.cmake | 1 + cmake/Templates/caffe_config.h.in | 1 + src/caffe/util/db_lmdb.cpp | 17 ++++++++++++++++- 8 files changed, 33 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 277c3dc49f8..f8f75305f2c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -26,6 +26,7 @@ caffe_option(BUILD_python_layer "Build the Caffe Python layer" ON) caffe_option(USE_OPENCV "Build with OpenCV support" ON) caffe_option(USE_LEVELDB "Build with levelDB" ON) caffe_option(USE_LMDB "Build with lmdb" ON) +caffe_option(ALLOW_LMDB_NOLOCK "Allow MDB_NOLOCK when reading LMDB files (only if necessary)" OFF) # ---[ Dependencies include(cmake/Dependencies.cmake) diff --git a/Makefile b/Makefile index 5fb6394e947..7cc73931349 100644 --- a/Makefile +++ b/Makefile @@ -313,6 +313,9 @@ ifeq ($(USE_LEVELDB), 1) endif ifeq ($(USE_LMDB), 1) COMMON_FLAGS += -DUSE_LMDB +ifeq ($(ALLOW_LMDB_NOLOCK), 1) + COMMON_FLAGS += -DALLOW_LMDB_NOLOCK +endif endif # CPU-only configuration diff --git a/Makefile.config.example b/Makefile.config.example index 42f86db4595..bda66ea101b 100644 --- a/Makefile.config.example +++ b/Makefile.config.example @@ -12,6 +12,11 @@ # USE_LEVELDB := 0 # USE_LMDB := 0 +# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary) +# You should not set this flag if you will be reading LMDBs with any +# possibility of simultaneous read and write +# ALLOW_LMDB_NOLOCK := 1 + # To customize your choice of compiler, uncomment and set the following. # N.B. the default for Linux is g++ and the default for OSX is clang++ # CUSTOM_CXX := g++ diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake index 8b259965359..056371110b5 100644 --- a/cmake/ConfigGen.cmake +++ b/cmake/ConfigGen.cmake @@ -62,6 +62,9 @@ function(caffe_generate_export_configs) if(USE_LMDB) list(APPEND Caffe_DEFINITIONS -DUSE_LMDB) + if (ALLOW_LMDB_NOLOCK) + list(APPEND Caffe_DEFINITIONS -DALLOW_LMDB_NOLOCK) + endif() endif() if(USE_LEVELDB) diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake index d68d7bfba66..a77ac6df6a0 100644 --- a/cmake/Dependencies.cmake +++ b/cmake/Dependencies.cmake @@ -34,6 +34,9 @@ if(USE_LMDB) include_directories(SYSTEM ${LMDB_INCLUDE_DIR}) list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES}) add_definitions(-DUSE_LMDB) + if(ALLOW_LMDB_NOLOCK) + add_definitions(-DALLOW_LMDB_NOLOCK) + endif() endif() # ---[ LevelDB diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake index 703e22acaf6..6984f417e71 100644 --- a/cmake/Summary.cmake +++ b/cmake/Summary.cmake @@ -117,6 +117,7 @@ function(caffe_print_configuration_summary) caffe_status(" USE_OPENCV : ${USE_OPENCV}") caffe_status(" USE_LEVELDB : ${USE_LEVELDB}") caffe_status(" USE_LMDB : ${USE_LMDB}") + caffe_status(" ALLOW_LMDB_NOLOCK : ${ALLOW_LMDB_NOLOCK}") caffe_status("") caffe_status("Dependencies:") caffe_status(" BLAS : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})") diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in index 84377493f48..8a31b43cabf 100644 --- a/cmake/Templates/caffe_config.h.in +++ b/cmake/Templates/caffe_config.h.in @@ -35,3 +35,4 @@ #cmakedefine USE_OPENCV #cmakedefine USE_LEVELDB #cmakedefine USE_LMDB +#cmakedefine ALLOW_LMDB_NOLOCK diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp index 78dd880ac41..0bc82b53e2b 100644 --- a/src/caffe/util/db_lmdb.cpp +++ b/src/caffe/util/db_lmdb.cpp @@ -19,7 +19,22 @@ void LMDB::Open(const string& source, Mode mode) { if (mode == READ) { flags = MDB_RDONLY | MDB_NOTLS; } - MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664)); + int rc = mdb_env_open(mdb_env_, source.c_str(), flags, 0664); +#ifndef ALLOW_LMDB_NOLOCK + MDB_CHECK(rc); +#else + if (rc == EACCES) { + LOG(WARNING) << "Permission denied. Trying with MDB_NOLOCK ..."; + // Close and re-open environment handle + mdb_env_close(mdb_env_); + MDB_CHECK(mdb_env_create(&mdb_env_)); + // Try again with MDB_NOLOCK + flags |= MDB_NOLOCK; + MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664)); + } else { + MDB_CHECK(rc); + } +#endif LOG(INFO) << "Opened lmdb " << source; } From e98b84762fb55daed5092225f71c3b76015aa4a4 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Mon, 28 Sep 2015 15:35:24 -0700 Subject: [PATCH 287/446] Install libs as non-executable files According to the Debian policy manual, "Shared libraries should not be installed executable, since the dynamic linker does not require this and trying to execute a shared library usually results in a core dump." https://www.debian.org/doc/debian-policy/ch-sharedlibs.html#s-sharedlibs-runtime --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 5fb6394e947..fbda44befd7 100644 --- a/Makefile +++ b/Makefile @@ -652,7 +652,7 @@ $(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS) cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin # add libraries cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib - cp $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib + install -m 644 $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib # add python - it's not the standard way, indeed... cp -r python $(DISTRIBUTE_DIR)/python From 96ba513f54ac7bfc62c40a2481c1556c2f743120 Mon Sep 17 00:00:00 2001 From: Yang Song Date: Tue, 29 Sep 2015 20:07:52 +0800 Subject: [PATCH 288/446] Fix a typo Fix a typo in the message. --- python/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index 0e2bc7e66a8..a22641401f0 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -1,5 +1,5 @@ if(NOT HAVE_PYTHON) - message(STATUS "Python interface is disabled or not all required dependecies found. Building without it...") + message(STATUS "Python interface is disabled or not all required dependencies found. Building without it...") return() endif() From 98cc023939641482432d4082db061306a7ab1654 Mon Sep 17 00:00:00 2001 From: Youssef Kashef Date: Thu, 1 Oct 2015 18:20:23 +0200 Subject: [PATCH 289/446] add badge for travis build and license --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index ebec286d550..44b9e62c157 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,8 @@ # Caffe +[![Build Status](https://travis-ci.org/BVLC/caffe.svg?branch=master)](https://travis-ci.org/BVLC/caffe) +[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE) + Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors. From 552a84aaddeabc074f4a0184b90b7194e7f7a44b Mon Sep 17 00:00:00 2001 From: zoharby Date: Fri, 11 Sep 2015 15:06:28 +0300 Subject: [PATCH 290/446] Add a caffe.io.write_mean function to the MATLAB interface Useful for exporting models from MATLAB (e.g. MatConvNet) to Caffe --- matlab/+caffe/io.m | 8 ++++++++ matlab/+caffe/private/caffe_.cpp | 24 ++++++++++++++++++++++++ 2 files changed, 32 insertions(+) diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m index af8369ddfab..4b072fecdab 100644 --- a/matlab/+caffe/io.m +++ b/matlab/+caffe/io.m @@ -29,5 +29,13 @@ CHECK_FILE_EXIST(mean_proto_file); mean_data = caffe_('read_mean', mean_proto_file); end + function write_mean(mean_data, mean_proto_file) + % write_mean(mean_data, mean_proto_file) + % write image mean data to binaryproto file + % mean_data should be W x H x C with BGR channels + CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string'); + CHECK(isa(mean_data, 'single'), 'mean_data must be a SINGLE matrix'); + caffe_('write_mean', mean_data, mean_proto_file); + end end end diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp index 4e0ebc1c00a..7883f79ebd9 100644 --- a/matlab/+caffe/private/caffe_.cpp +++ b/matlab/+caffe/private/caffe_.cpp @@ -478,6 +478,29 @@ static void read_mean(MEX_ARGS) { mxFree(mean_proto_file); } +// Usage: caffe_('write_mean', mean_data, mean_proto_file) +static void write_mean(MEX_ARGS) { + mxCHECK(nrhs == 2 && mxIsSingle(prhs[0]) && mxIsChar(prhs[1]), + "Usage: caffe_('write_mean', mean_data, mean_proto_file)"); + char* mean_proto_file = mxArrayToString(prhs[1]); + int ndims = mxGetNumberOfDimensions(prhs[0]); + mxCHECK(ndims >= 2 && ndims <= 3, "mean_data must have at 2 or 3 dimensions"); + const mwSize *dims = mxGetDimensions(prhs[0]); + int width = dims[0]; + int height = dims[1]; + int channels; + if (ndims == 3) + channels = dims[2]; + else + channels = 1; + Blob data_mean(1, channels, height, width); + mx_mat_to_blob(prhs[0], &data_mean, DATA); + BlobProto blob_proto; + data_mean.ToProto(&blob_proto, false); + WriteProtoToBinaryFile(blob_proto, mean_proto_file); + mxFree(mean_proto_file); +} + /** ----------------------------------------------------------------- ** Available commands. **/ @@ -515,6 +538,7 @@ static handler_registry handlers[] = { { "get_init_key", get_init_key }, { "reset", reset }, { "read_mean", read_mean }, + { "write_mean", write_mean }, // The end. { "END", NULL }, }; From 30dfb864f0e7ad8f39bcdb48200eccc2a0efa7d3 Mon Sep 17 00:00:00 2001 From: Jan Issac Date: Mon, 5 Oct 2015 11:13:26 +0200 Subject: [PATCH 291/446] minor typo fix --- cmake/Cuda.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake index ff58d31c166..98aef268cf4 100644 --- a/cmake/Cuda.cmake +++ b/cmake/Cuda.cmake @@ -132,7 +132,7 @@ function(caffe_select_nvcc_arch_flags out_variable) endfunction() ################################################################################################ -# Short command for cuda comnpilation +# Short command for cuda compilation # Usage: # caffe_cuda_compile( ) macro(caffe_cuda_compile objlist_variable) From 64f948a6829c53031632d87f78183dd87d5d6f71 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Mon, 5 Oct 2015 14:15:08 -0700 Subject: [PATCH 292/446] SilenceLayer Backward bugfix (fixes #3151) --- src/caffe/layers/silence_layer.cpp | 2 +- src/caffe/layers/silence_layer.cu | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp index 4abf9eff4a2..7e70ab4329e 100644 --- a/src/caffe/layers/silence_layer.cpp +++ b/src/caffe/layers/silence_layer.cpp @@ -12,7 +12,7 @@ void SilenceLayer::Backward_cpu(const vector*>& top, for (int i = 0; i < bottom.size(); ++i) { if (propagate_down[i]) { caffe_set(bottom[i]->count(), Dtype(0), - bottom[i]->mutable_cpu_data()); + bottom[i]->mutable_cpu_diff()); } } } diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu index 8d044ee7307..34faef22bda 100644 --- a/src/caffe/layers/silence_layer.cu +++ b/src/caffe/layers/silence_layer.cu @@ -18,7 +18,7 @@ void SilenceLayer::Backward_gpu(const vector*>& top, for (int i = 0; i < bottom.size(); ++i) { if (propagate_down[i]) { caffe_gpu_set(bottom[i]->count(), Dtype(0), - bottom[i]->mutable_gpu_data()); + bottom[i]->mutable_gpu_diff()); } } } From 19d9927d76d6655a3efc090611e59aa2ea0f25a5 Mon Sep 17 00:00:00 2001 From: Gustav Larsson Date: Mon, 5 Oct 2015 21:55:00 -0500 Subject: [PATCH 293/446] Add pycaffe test for solver.snapshot() --- python/caffe/test/test_solver.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py index 9cfc10d29a9..f618fded8cd 100644 --- a/python/caffe/test/test_solver.py +++ b/python/caffe/test/test_solver.py @@ -16,7 +16,8 @@ def setUp(self): f.write("""net: '""" + net_f + """' test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9 weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75 - display: 100 max_iter: 100 snapshot_after_train: false""") + display: 100 max_iter: 100 snapshot_after_train: false + snapshot_prefix: "model" """) f.close() self.solver = caffe.SGDSolver(f.name) # also make sure get_solver runs @@ -51,3 +52,11 @@ def test_net_memory(self): total += p.data.sum() + p.diff.sum() for bl in six.itervalues(net.blobs): total += bl.data.sum() + bl.diff.sum() + + def test_snapshot(self): + self.solver.snapshot() + # Check that these files exist and then remove them + files = ['model_iter_0.caffemodel', 'model_iter_0.solverstate'] + for fn in files: + assert os.path.isfile(fn) + os.remove(fn) From e0615464ddf550ee57c17733ba9c5a0fa71b8edb Mon Sep 17 00:00:00 2001 From: e3 Date: Wed, 7 Oct 2015 11:52:45 -0700 Subject: [PATCH 294/446] fixes BVLC/caffe#3163 --- docs/tutorial/layers.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md index eabc792b704..7362aac298a 100644 --- a/docs/tutorial/layers.md +++ b/docs/tutorial/layers.md @@ -39,7 +39,7 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of - `n * c_i * h_i * w_i` * Output - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise. -* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`) +* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`) layer { name: "conv1" @@ -83,7 +83,7 @@ The `Convolution` layer convolves the input image with a set of learnable filter - `n * c * h_i * w_i` * Output - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution. -* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`) +* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`) layer { name: "pool1" @@ -197,7 +197,7 @@ In general, activation / Neuron layers are element-wise operators, taking one bo * Parameters (`ReLUParameter relu_param`) - Optional - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0. -* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`) +* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`) layer { name: "relu1" From bda1a633ec874e313f5d5dddfc0afc70573847d7 Mon Sep 17 00:00:00 2001 From: Carl Doersch Date: Sun, 23 Aug 2015 20:47:25 -0700 Subject: [PATCH 295/446] BatchReindexLayer to shuffle, subsample, and replicate examples in a batch --- include/caffe/common_layers.hpp | 69 ++++++++++++ src/caffe/layers/batch_reindex_layer.cpp | 79 +++++++++++++ src/caffe/layers/batch_reindex_layer.cu | 107 ++++++++++++++++++ src/caffe/test/test_batch_reindex_layer.cpp | 119 ++++++++++++++++++++ 4 files changed, 374 insertions(+) create mode 100644 src/caffe/layers/batch_reindex_layer.cpp create mode 100644 src/caffe/layers/batch_reindex_layer.cu create mode 100644 src/caffe/test/test_batch_reindex_layer.cpp diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index d2c0ce6d0c6..5d68e865e10 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -70,6 +70,75 @@ class ArgMaxLayer : public Layer { size_t top_k_; }; +/** + * @brief Index into the input blob along its first axis. + * + * This layer can be used to select, reorder, and even replicate examples in a + * batch. The second blob is cast to int and treated as an index into the + * first axis of the first blob. + */ +template +class BatchReindexLayer : public Layer { + public: + explicit BatchReindexLayer(const LayerParameter& param) + : Layer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "BatchReindex"; } + virtual inline int ExactNumBottomBlobs() const { return 2; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + /** + * @param bottom input Blob vector (length 2+) + * -# @f$ (N \times ...) @f$ + * the inputs @f$ x_1 @f$ + * -# @f$ (M) @f$ + * the inputs @f$ x_2 @f$ + * @param top output Blob vector (length 1) + * -# @f$ (M \times ...) @f$: + * the reindexed array @f$ + * y = x_1[x_2] + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the reordered input. + * + * @param top output Blob vector (length 1), providing the error gradient + * with respect to the outputs + * -# @f$ (M \times ...) @f$: + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to concatenated outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 2): + * - @f$ \frac{\partial E}{\partial y} @f$ is de-indexed (summing where + * required) back to the input x_1 + * - This layer cannot backprop to x_2, i.e. propagate_down[1] must be + * false. + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + private: + struct pair_sort_first { + bool operator()(const std::pair &left, + const std::pair &right) { + return left.first < right.first; + } + }; + void check_batch_reindex(int initial_num, int final_num, + const Dtype* ridx_data); +}; + + /** * @brief Takes at least two Blob%s and concatenates them along either the num * or channel dimension, outputting the result. diff --git a/src/caffe/layers/batch_reindex_layer.cpp b/src/caffe/layers/batch_reindex_layer.cpp new file mode 100644 index 00000000000..3bf757c718d --- /dev/null +++ b/src/caffe/layers/batch_reindex_layer.cpp @@ -0,0 +1,79 @@ +#include + +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +template +void BatchReindexLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + CHECK_EQ(1, bottom[1]->num_axes()); + vector newshape; + newshape.push_back(bottom[1]->shape(0)); + for (int i = 1; i < bottom[0]->shape().size(); ++i) { + newshape.push_back(bottom[0]->shape()[i]); + } + top[0]->Reshape(newshape); +} + +template +void BatchReindexLayer::check_batch_reindex(int initial_num, + int final_num, + const Dtype* ridx_data) { + for (int i = 0; i < final_num; ++i) { + CHECK_GE(ridx_data[i], 0) + << "Index specified for reindex layer was negative."; + CHECK_LT(ridx_data[i], initial_num) + << "Index specified for reindex layer was greater than batch size."; + } +} + +template +void BatchReindexLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + check_batch_reindex(bottom[0]->shape(0), bottom[1]->count(), + bottom[1]->cpu_data()); + if (top[0]->count() == 0) { + return; + } + int inner_dim = bottom[0]->count() / bottom[0]->shape(0); + const Dtype* in = bottom[0]->cpu_data(); + const Dtype* permut = bottom[1]->cpu_data(); + Dtype* out = top[0]->mutable_cpu_data(); + for (int index = 0; index < top[0]->count(); ++index) { + int n = index / (inner_dim); + int in_n = static_cast(permut[n]); + out[index] = in[in_n * (inner_dim) + index % (inner_dim)]; + } +} + +template +void BatchReindexLayer::Backward_cpu( + const vector*>& top, const vector& propagate_down, + const vector*>& bottom) { + CHECK(!propagate_down[1]) << "Cannot backprop to index."; + if (!propagate_down[0]) { + return; + } + int inner_dim = bottom[0]->count() / bottom[0]->shape(0); + Dtype* bot_diff = bottom[0]->mutable_cpu_diff(); + const Dtype* permut = bottom[1]->cpu_data(); + const Dtype* top_diff = top[0]->cpu_diff(); + caffe_set(bottom[0]->count(), Dtype(0), bot_diff); + for (int index = 0; index < top[0]->count(); ++index) { + int n = index / (inner_dim); + int in_n = static_cast(permut[n]); + bot_diff[in_n * (inner_dim) + index % (inner_dim)] += top_diff[index]; + } +} + +#ifdef CPU_ONLY +STUB_GPU(BatchReindexLayer); +#endif + +INSTANTIATE_CLASS(BatchReindexLayer); +REGISTER_LAYER_CLASS(BatchReindex); + +} // namespace caffe diff --git a/src/caffe/layers/batch_reindex_layer.cu b/src/caffe/layers/batch_reindex_layer.cu new file mode 100644 index 00000000000..c418cab9042 --- /dev/null +++ b/src/caffe/layers/batch_reindex_layer.cu @@ -0,0 +1,107 @@ +#include +#include +#include + +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +template +__global__ void BRForward(const int count, const int inner_dim, const Dtype* in, + const Dtype* permut, Dtype* out) { + CUDA_KERNEL_LOOP(index, count) { + int n = index / (inner_dim); + int in_n = static_cast(permut[n]); + out[index] = in[in_n * (inner_dim) + index % (inner_dim)]; + } +} + +template +void BatchReindexLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + check_batch_reindex(bottom[0]->shape(0), bottom[1]->count(), + bottom[1]->cpu_data()); + if (top[0]->count() == 0) { + return; + } + int threads = top[0]->count(); + // NOLINT_NEXT_LINE(whitespace/operators) + BRForward <<>>( + top[0]->count(), bottom[0]->count() / bottom[0]->shape(0), + bottom[0]->gpu_data(), bottom[1]->gpu_data(), top[0]->mutable_gpu_data()); + CUDA_POST_KERNEL_CHECK; +} + +template +__global__ void BRBackward(const int count, const int inner_dim, + const Dtype* in, const Dtype* top_indexes, + const Dtype* begins, const Dtype* counts, + Dtype* out) { + CUDA_KERNEL_LOOP(index, count) { + int n = index / (inner_dim); + out[index] = 0; + int lower = static_cast(begins[n]); + int upper = lower + static_cast(counts[n]); + for (int i = lower; i < upper; ++i) { + int in_n = static_cast(top_indexes[i]); + out[index] += in[in_n * (inner_dim) + index % (inner_dim)]; + } + } +} + +template +void BatchReindexLayer::Backward_gpu( + const vector*>& top, const vector& propagate_down, + const vector*>& bottom) { + CHECK(!propagate_down[1]) << "Cannot backprop to index."; + if (!propagate_down[0]) { + return; + } + + vector > mapping; + const Dtype* perm = bottom[1]->cpu_data(); + for (int i = 0; i < bottom[1]->count(); ++i) { + mapping.push_back(pair(static_cast(perm[i]), i)); + } + std::sort(mapping.begin(), mapping.end(), pair_sort_first()); + + // Each element of the bottom diff is potentially the sum of many top diffs. + // However, we'd like each CUDA thread to handle exactly one output. Hence, + // we first pre-compute a list of lists of indices that need to be summed for + // each output. `top_indexes` holds the data of this list of lists. The + // k'th element of `begins` points to the location in `top_indexes` where the + // list for the k'th example begin, and the k'th element of `counts` is the + // length of that list. + vector shape; + shape.push_back(bottom[1]->count()); + Blob top_indexes(shape); + shape[0] = bottom[0]->shape(0); + Blob counts(shape); + Blob begins(shape); + Dtype* t_i_data = top_indexes.mutable_cpu_data(); + Dtype* c_data = counts.mutable_cpu_data(); + Dtype* b_data = begins.mutable_cpu_data(); + caffe_set(begins.count(), Dtype(-1), b_data); + caffe_set(counts.count(), Dtype(0), c_data); + for (int i = 0; i < mapping.size(); ++i) { + t_i_data[i] = mapping[i].second; + if (b_data[mapping[i].first] == -1) { + b_data[mapping[i].first] = i; + } + c_data[mapping[i].first] += 1; + } + + int threads = bottom[0]->count(); + // NOLINT_NEXT_LINE(whitespace/operators) + BRBackward <<>>( + bottom[0]->count(), bottom[0]->count() / bottom[0]->shape(0), + top[0]->gpu_diff(), top_indexes.gpu_data(), begins.gpu_data(), + counts.gpu_data(), bottom[0]->mutable_gpu_diff()); + CUDA_POST_KERNEL_CHECK; +} + +INSTANTIATE_LAYER_GPU_FUNCS(BatchReindexLayer); + +} // namespace caffe diff --git a/src/caffe/test/test_batch_reindex_layer.cpp b/src/caffe/test/test_batch_reindex_layer.cpp new file mode 100644 index 00000000000..985db343d12 --- /dev/null +++ b/src/caffe/test/test_batch_reindex_layer.cpp @@ -0,0 +1,119 @@ +#include +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/vision_layers.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class BatchReindexLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + BatchReindexLayerTest() + : blob_bottom_(new Blob()), + blob_bottom_permute_(new Blob()), + blob_top_(new Blob()) { + } + virtual void SetUp() { + Caffe::set_random_seed(1701); + vector sz; + sz.push_back(5); + sz.push_back(4); + sz.push_back(3); + sz.push_back(2); + blob_bottom_->Reshape(sz); + vector permsz; + permsz.push_back(6); + blob_bottom_permute_->Reshape(permsz); + + // fill the values + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + int perm[] = { 4, 0, 4, 0, 1, 2 }; + for (int i = 0; i < blob_bottom_permute_->count(); ++i) { + blob_bottom_permute_->mutable_cpu_data()[i] = perm[i]; + } + + blob_bottom_vec_.push_back(blob_bottom_); + blob_bottom_vec_.push_back(blob_bottom_permute_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~BatchReindexLayerTest() { + delete blob_bottom_permute_; + delete blob_bottom_; + delete blob_top_; + } + Blob* const blob_bottom_; + Blob* const blob_bottom_permute_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; + + void TestForward() { + LayerParameter layer_param; + + vector sz; + sz.push_back(5); + sz.push_back(4); + sz.push_back(3); + sz.push_back(2); + blob_bottom_->Reshape(sz); + for (int i = 0; i < blob_bottom_->count(); ++i) { + blob_bottom_->mutable_cpu_data()[i] = i; + } + + vector permsz; + permsz.push_back(6); + blob_bottom_permute_->Reshape(permsz); + int perm[] = { 4, 0, 4, 0, 1, 2 }; + for (int i = 0; i < blob_bottom_permute_->count(); ++i) { + blob_bottom_permute_->mutable_cpu_data()[i] = perm[i]; + } + BatchReindexLayer layer(layer_param); + layer.SetUp(blob_bottom_vec_, blob_top_vec_); + EXPECT_EQ(blob_top_->num(), blob_bottom_permute_->num()); + EXPECT_EQ(blob_top_->channels(), blob_bottom_->channels()); + EXPECT_EQ(blob_top_->height(), blob_bottom_->height()); + EXPECT_EQ(blob_top_->width(), blob_bottom_->width()); + + layer.Forward(blob_bottom_vec_, blob_top_vec_); + int channels = blob_top_->channels(); + int height = blob_top_->height(); + int width = blob_top_->width(); + for (int i = 0; i < blob_top_->count(); ++i) { + int n = i / (channels * width * height); + int inner_idx = (i % (channels * width * height)); + EXPECT_EQ( + blob_top_->cpu_data()[i], + blob_bottom_->cpu_data()[perm[n] * channels * width * height + + inner_idx]); + } + } +}; + +TYPED_TEST_CASE(BatchReindexLayerTest, TestDtypesAndDevices); + +TYPED_TEST(BatchReindexLayerTest, TestForward) { + this->TestForward(); +} + +TYPED_TEST(BatchReindexLayerTest, TestGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + BatchReindexLayer layer(layer_param); + GradientChecker checker(1e-4, 1e-2); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_, 0); + } + +} // namespace caffe From c65ba61bdf273604c3edcd24ba7a80cc3835441a Mon Sep 17 00:00:00 2001 From: sh1r0 Date: Fri, 9 Oct 2015 00:31:05 +0800 Subject: [PATCH 296/446] Remove the 4D constraint of blobproto IO in python --- python/caffe/io.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/python/caffe/io.py b/python/caffe/io.py index 0cad7211291..40b7ac1ed78 100644 --- a/python/caffe/io.py +++ b/python/caffe/io.py @@ -21,22 +21,18 @@ def blobproto_to_array(blob, return_diff=False): unless return_diff is True, in which case we will return the diff. """ if return_diff: - return np.array(blob.diff).reshape( - blob.num, blob.channels, blob.height, blob.width) + return np.array(blob.diff).reshape(*blob.shape.dim) else: - return np.array(blob.data).reshape( - blob.num, blob.channels, blob.height, blob.width) + return np.array(blob.data).reshape(*blob.shape.dim) def array_to_blobproto(arr, diff=None): - """Converts a 4-dimensional array to blob proto. If diff is given, also + """Converts a N-dimensional array to blob proto. If diff is given, also convert the diff. You need to make sure that arr and diff have the same shape, and this function does not do sanity check. """ - if arr.ndim != 4: - raise ValueError('Incorrect array shape.') blob = caffe_pb2.BlobProto() - blob.num, blob.channels, blob.height, blob.width = arr.shape + blob.shape.dim.extend(arr.shape) blob.data.extend(arr.astype(float).flat) if diff is not None: blob.diff.extend(diff.astype(float).flat) From ee5191b3e41fddae73653e0d61172360b89526ca Mon Sep 17 00:00:00 2001 From: Kang Kim Date: Thu, 8 Oct 2015 01:26:25 +0900 Subject: [PATCH 297/446] Improve numerical stability of variance computation in MVNLayer --- src/caffe/layers/mvn_layer.cpp | 42 ++++++++++----------------------- src/caffe/layers/mvn_layer.cu | 43 ++++++++++------------------------ 2 files changed, 25 insertions(+), 60 deletions(-) diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp index 325691b1875..61c2141ecd9 100644 --- a/src/caffe/layers/mvn_layer.cpp +++ b/src/caffe/layers/mvn_layer.cpp @@ -42,29 +42,21 @@ void MVNLayer::Forward_cpu(const vector*>& bottom, int dim = bottom[0]->count() / num; - if (this->layer_param_.mvn_param().normalize_variance()) { - // put the squares of bottom into temp_ - caffe_powx(bottom[0]->count(), bottom_data, Dtype(2), - temp_.mutable_cpu_data()); + // subtract mean + caffe_cpu_gemv(CblasNoTrans, num, dim, 1. / dim, bottom_data, + sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, -1., + mean_.cpu_data(), sum_multiplier_.cpu_data(), 0., + temp_.mutable_cpu_data()); + caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data); // X-EX - // computes variance using var(X) = E(X^2) - (EX)^2 - caffe_cpu_gemv(CblasNoTrans, num, dim, 1. / dim, bottom_data, - sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX + if (this->layer_param_.mvn_param().normalize_variance()) { + // compute variance using var(X) = E((X-EX)^2) + caffe_powx(bottom[0]->count(), top_data, Dtype(2), + temp_.mutable_cpu_data()); // (X-EX)^2 caffe_cpu_gemv(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(), sum_multiplier_.cpu_data(), 0., - variance_.mutable_cpu_data()); // E(X^2) - caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2), - temp_.mutable_cpu_data()); // (EX)^2 - caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(), - variance_.mutable_cpu_data()); // variance - - // do mean and variance normalization - // subtract mean - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, -1., - mean_.cpu_data(), sum_multiplier_.cpu_data(), 0., - temp_.mutable_cpu_data()); - - caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data); + variance_.mutable_cpu_data()); // E((X-EX)^2) // normalize variance caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5), @@ -77,16 +69,6 @@ void MVNLayer::Forward_cpu(const vector*>& bottom, temp_.mutable_cpu_data()); caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data); - } else { - caffe_cpu_gemv(CblasNoTrans, num, dim, 1. / dim, bottom_data, - sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); // EX - - // subtract mean - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, -1., - mean_.cpu_data(), sum_multiplier_.cpu_data(), 0., - temp_.mutable_cpu_data()); - - caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data); } } diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu index d86a2e73fc2..5cbb112de4d 100644 --- a/src/caffe/layers/mvn_layer.cu +++ b/src/caffe/layers/mvn_layer.cu @@ -20,29 +20,22 @@ void MVNLayer::Forward_gpu(const vector*>& bottom, int dim = bottom[0]->count() / num; - if (this->layer_param_.mvn_param().normalize_variance()) { - // put the squares of bottom into temp_ - caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2), - temp_.mutable_gpu_data()); + // subtract mean + caffe_gpu_gemv(CblasNoTrans, num, dim, 1. / dim, bottom_data, + sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, -1., + mean_.gpu_data(), sum_multiplier_.gpu_data(), 0., + temp_.mutable_gpu_data()); + caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), + top_data); // X-EX - // computes variance using var(X) = E(X^2) - (EX)^2 - caffe_gpu_gemv(CblasNoTrans, num, dim, 1. / dim, bottom_data, - sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX + if (this->layer_param_.mvn_param().normalize_variance()) { + // compute variance using var(X) = E((X-EX)^2) + caffe_gpu_powx(bottom[0]->count(), top_data, Dtype(2), + temp_.mutable_gpu_data()); // (X-EX)^2 caffe_gpu_gemv(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(), sum_multiplier_.gpu_data(), 0., - variance_.mutable_gpu_data()); // E(X^2) - caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2), - temp_.mutable_gpu_data()); // (EX)^2 - caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(), - variance_.mutable_gpu_data()); // variance - - // do mean and variance normalization - // subtract mean - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, -1., - mean_.gpu_data(), sum_multiplier_.gpu_data(), 0., - temp_.mutable_gpu_data()); - - caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data); + variance_.mutable_gpu_data()); // E((X-EX)^2) // normalize variance caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5), @@ -55,16 +48,6 @@ void MVNLayer::Forward_gpu(const vector*>& bottom, temp_.mutable_gpu_data()); caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data); - } else { - caffe_gpu_gemv(CblasNoTrans, num, dim, 1. / dim, bottom_data, - sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); // EX - - // subtract mean - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, dim, 1, -1., - mean_.gpu_data(), sum_multiplier_.gpu_data(), 0., - temp_.mutable_gpu_data()); - - caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data); } } From e5990b3dafa2b95fae7b7bfaac4dcd309a20d151 Mon Sep 17 00:00:00 2001 From: Brian Chu Date: Tue, 13 Oct 2015 03:50:53 -0700 Subject: [PATCH 298/446] In 00-classification example, get correct class label index --- examples/00-classification.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/00-classification.ipynb b/examples/00-classification.ipynb index 46bbb193fe7..89b7dd34f0e 100644 --- a/examples/00-classification.ipynb +++ b/examples/00-classification.ipynb @@ -119,7 +119,7 @@ "source": [ "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n", "out = net.forward()\n", - "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))" + "print(\"Predicted class is #{}.\".format(out['prob'][0].argmax()))" ] }, { From ec94055a6c5f0f86b88f98f1659cc9f317df2e3e Mon Sep 17 00:00:00 2001 From: Alessandro Giusti Date: Tue, 13 Oct 2015 14:30:45 +0200 Subject: [PATCH 299/446] Update store2hdf5.m Fixed a bug in two assertions (the condition input argument must be a scalar logical) --- matlab/hdf5creation/store2hdf5.m | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/matlab/hdf5creation/store2hdf5.m b/matlab/hdf5creation/store2hdf5.m index 0a0016dca40..4e8c81d9de8 100644 --- a/matlab/hdf5creation/store2hdf5.m +++ b/matlab/hdf5creation/store2hdf5.m @@ -39,8 +39,8 @@ info=h5info(filename); prev_dat_sz=info.Datasets(1).Dataspace.Size; prev_lab_sz=info.Datasets(2).Dataspace.Size; - assert(prev_dat_sz(1:end-1)==dat_dims(1:end-1), 'Data dimensions must match existing dimensions in dataset'); - assert(prev_lab_sz(1:end-1)==lab_dims(1:end-1), 'Label dimensions must match existing dimensions in dataset'); + assert(all(prev_dat_sz(1:end-1)==dat_dims(1:end-1)), 'Data dimensions must match existing dimensions in dataset'); + assert(all(prev_lab_sz(1:end-1)==lab_dims(1:end-1)), 'Label dimensions must match existing dimensions in dataset'); startloc.dat=[ones(1,length(dat_dims)-1), prev_dat_sz(end)+1]; startloc.lab=[ones(1,length(lab_dims)-1), prev_lab_sz(end)+1]; end From a8839dbcb3b16f8f5d3f8d17209a3c8c0142a51b Mon Sep 17 00:00:00 2001 From: Akash A Date: Tue, 13 Oct 2015 17:53:35 +0100 Subject: [PATCH 300/446] Add pyyaml as a requirement In getting the [web demo](http://caffe.berkeleyvision.org/gathered/examples/web_demo.html) started I get an `ImportError: No module named yaml` error when running `./scripts/download_model_binary.py models/bvlc_reference_caffenet`. --- examples/web_demo/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/web_demo/requirements.txt b/examples/web_demo/requirements.txt index 8fb1d2ccbb2..43e1b98cc34 100644 --- a/examples/web_demo/requirements.txt +++ b/examples/web_demo/requirements.txt @@ -4,3 +4,4 @@ tornado numpy pandas pillow +pyyaml From ca4c6fb4e2106bbd3d6a2c09c34567558edde891 Mon Sep 17 00:00:00 2001 From: Brian Chu Date: Tue, 13 Oct 2015 13:24:42 -0700 Subject: [PATCH 301/446] Set CaffeNet train_val test mirroring to false --- models/bvlc_reference_caffenet/train_val.prototxt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/bvlc_reference_caffenet/train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt index c79472e09ab..e3e427968ab 100644 --- a/models/bvlc_reference_caffenet/train_val.prototxt +++ b/models/bvlc_reference_caffenet/train_val.prototxt @@ -45,7 +45,7 @@ layer { # mean_value: 104 # mean_value: 117 # mean_value: 123 -# mirror: true +# mirror: false # } data_param { source: "examples/imagenet/ilsvrc12_val_lmdb" From e0c34cedde6e0d12d420a51cea7a98df50069559 Mon Sep 17 00:00:00 2001 From: Vladimir Date: Wed, 14 Oct 2015 12:00:14 +0900 Subject: [PATCH 302/446] Fixed drawing problems with repeated convolution --- python/caffe/draw.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python/caffe/draw.py b/python/caffe/draw.py index a002b60b59a..f8bf5722aba 100644 --- a/python/caffe/draw.py +++ b/python/caffe/draw.py @@ -82,11 +82,11 @@ def get_layer_label(layer, rankdir): separator, layer.type, separator, - layer.convolution_param.kernel_size, + layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size._values) else 1, separator, - layer.convolution_param.stride, + layer.convolution_param.stride[0] if len(layer.convolution_param.stride._values) else 1, separator, - layer.convolution_param.pad) + layer.convolution_param.pad[0] if len(layer.convolution_param.pad._values) else 0) elif layer.type == 'Pooling': pooling_types_dict = get_pooling_types_dict() node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\ From 75e859a522fdbf78a2ea58393500af6103bcce56 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Thu, 15 Oct 2015 11:03:09 -0700 Subject: [PATCH 303/446] Allow old-style shape in blobproto_to_array Fixes #3199 Bug introduced in #3170 --- python/caffe/io.py | 11 ++++++++-- python/caffe/test/test_io.py | 41 ++++++++++++++++++++++++++++++++++++ 2 files changed, 50 insertions(+), 2 deletions(-) create mode 100644 python/caffe/test/test_io.py diff --git a/python/caffe/io.py b/python/caffe/io.py index 40b7ac1ed78..11c84260f1a 100644 --- a/python/caffe/io.py +++ b/python/caffe/io.py @@ -20,11 +20,18 @@ def blobproto_to_array(blob, return_diff=False): Convert a blob proto to an array. In default, we will just return the data, unless return_diff is True, in which case we will return the diff. """ + # Read the data into an array if return_diff: - return np.array(blob.diff).reshape(*blob.shape.dim) + data = np.array(blob.diff) else: - return np.array(blob.data).reshape(*blob.shape.dim) + data = np.array(blob.data) + # Reshape the array + if blob.HasField('num') or blob.HasField('channels') or blob.HasField('height') or blob.HasField('width'): + # Use legacy 4D shape + return data.reshape(blob.num, blob.channels, blob.height, blob.width) + else: + return data.reshape(blob.shape.dim) def array_to_blobproto(arr, diff=None): """Converts a N-dimensional array to blob proto. If diff is given, also diff --git a/python/caffe/test/test_io.py b/python/caffe/test/test_io.py new file mode 100644 index 00000000000..8c86ef75fb2 --- /dev/null +++ b/python/caffe/test/test_io.py @@ -0,0 +1,41 @@ +import numpy as np +import unittest + +import caffe + +class TestBlobProtoToArray(unittest.TestCase): + + def test_old_format(self): + data = np.zeros((10,10)) + blob = caffe.proto.caffe_pb2.BlobProto() + blob.data.extend(list(data.flatten())) + shape = (1,1,10,10) + blob.num, blob.channels, blob.height, blob.width = shape + + arr = caffe.io.blobproto_to_array(blob) + self.assertEqual(arr.shape, shape) + + def test_new_format(self): + data = np.zeros((10,10)) + blob = caffe.proto.caffe_pb2.BlobProto() + blob.data.extend(list(data.flatten())) + blob.shape.dim.extend(list(data.shape)) + + arr = caffe.io.blobproto_to_array(blob) + self.assertEqual(arr.shape, data.shape) + + def test_no_shape(self): + data = np.zeros((10,10)) + blob = caffe.proto.caffe_pb2.BlobProto() + blob.data.extend(list(data.flatten())) + + with self.assertRaises(ValueError): + caffe.io.blobproto_to_array(blob) + + def test_scalar(self): + data = np.ones((1)) * 123 + blob = caffe.proto.caffe_pb2.BlobProto() + blob.data.extend(list(data.flatten())) + + arr = caffe.io.blobproto_to_array(blob) + self.assertEqual(arr, 123) From ecac7ff6286642420eb5db723c382e74bf82c9d7 Mon Sep 17 00:00:00 2001 From: Simon Layton Date: Wed, 8 Jul 2015 15:35:55 -0400 Subject: [PATCH 304/446] Initial cuDNN v3 support --- include/caffe/vision_layers.hpp | 74 +++++++++- src/caffe/layer_factory.cpp | 43 +++++- src/caffe/layers/cudnn_conv_layer.cpp | 138 +++++++++++++++++- src/caffe/layers/cudnn_conv_layer.cu | 58 ++------ src/caffe/layers/cudnn_lcn_layer.cpp | 77 ++++++++++ src/caffe/layers/cudnn_lcn_layer.cu | 50 +++++++ src/caffe/layers/cudnn_lrn_layer.cpp | 57 ++++++++ src/caffe/layers/cudnn_lrn_layer.cu | 48 +++++++ src/caffe/layers/lrn_layer.cpp | 1 - src/caffe/proto/caffe.proto | 6 + src/caffe/test/test_lrn_layer.cpp | 196 ++++++++++++++++++++++++++ 11 files changed, 692 insertions(+), 56 deletions(-) create mode 100644 src/caffe/layers/cudnn_lcn_layer.cpp create mode 100644 src/caffe/layers/cudnn_lcn_layer.cu create mode 100644 src/caffe/layers/cudnn_lrn_layer.cpp create mode 100644 src/caffe/layers/cudnn_lrn_layer.cu diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp index 06bc0457e2d..237b05d6fa6 100644 --- a/include/caffe/vision_layers.hpp +++ b/include/caffe/vision_layers.hpp @@ -304,13 +304,24 @@ class CuDNNConvolutionLayer : public ConvolutionLayer { bool handles_setup_; cudnnHandle_t* handle_; cudaStream_t* stream_; + + // algorithms for forward and backwards convolutions + cudnnConvolutionFwdAlgo_t *fwd_algo_; + cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_; + cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_; + vector bottom_descs_, top_descs_; cudnnTensorDescriptor_t bias_desc_; cudnnFilterDescriptor_t filter_desc_; vector conv_descs_; int bottom_offset_, top_offset_, bias_offset_; - size_t workspaceSizeInBytes; - void *workspace; + + size_t *workspace_fwd_sizes_; + size_t *workspace_bwd_data_sizes_; + size_t *workspace_bwd_filter_sizes_; + size_t workspaceSizeInBytes; // size of underlying storage + void *workspaceData; // underlying storage + void **workspace; // aliases into workspaceData }; #endif @@ -442,6 +453,65 @@ class LRNLayer : public Layer { vector*> product_bottom_vec_; }; +#ifdef USE_CUDNN + +template +class CuDNNLRNLayer : public LRNLayer { + public: + explicit CuDNNLRNLayer(const LayerParameter& param) + : LRNLayer(param), handles_setup_(false) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual ~CuDNNLRNLayer(); + + protected: + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool handles_setup_; + cudnnHandle_t handle_; + cudnnLRNDescriptor_t norm_desc_; + cudnnTensorDescriptor_t bottom_desc_, top_desc_; + + int size_; + Dtype alpha_, beta_, k_; +}; + +template +class CuDNNLCNLayer : public LRNLayer { + public: + explicit CuDNNLCNLayer(const LayerParameter& param) + : LRNLayer(param), handles_setup_(false), tempDataSize(0), + tempData1(NULL), tempData2(NULL) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual ~CuDNNLCNLayer(); + + protected: + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool handles_setup_; + cudnnHandle_t handle_; + cudnnLRNDescriptor_t norm_desc_; + cudnnTensorDescriptor_t bottom_desc_, top_desc_; + + int size_, pre_pad_; + Dtype alpha_, beta_, k_; + + size_t tempDataSize; + void *tempData1, *tempData2; +}; + +#endif /** * @brief Pools the input image by taking the max, average, etc. within regions. diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp index 926c7d8ff78..417ffe986df 100644 --- a/src/caffe/layer_factory.cpp +++ b/src/caffe/layer_factory.cpp @@ -54,10 +54,8 @@ shared_ptr > GetPoolingLayer(const LayerParameter& param) { return shared_ptr >(new PoolingLayer(param)); #ifdef USE_CUDNN } else if (engine == PoolingParameter_Engine_CUDNN) { - PoolingParameter p_param = param.pooling_param(); - if (p_param.pad() || p_param.pad_h() || p_param.pad_w() || - param.top_size() > 1) { - LOG(INFO) << "CUDNN does not support padding or multiple tops. " + if (param.top_size() > 1) { + LOG(INFO) << "cuDNN does not support multiple tops. " << "Using Caffe's own pooling layer."; return shared_ptr >(new PoolingLayer(param)); } @@ -70,6 +68,43 @@ shared_ptr > GetPoolingLayer(const LayerParameter& param) { REGISTER_LAYER_CREATOR(Pooling, GetPoolingLayer); +// Get LRN layer according to engine +template +shared_ptr > GetLRNLayer(const LayerParameter& param) { + LRNParameter_Engine engine = param.lrn_param().engine(); + + if (engine == LRNParameter_Engine_DEFAULT) { +#ifdef USE_CUDNN + engine = LRNParameter_Engine_CUDNN; +#else + engine = LRNParameter_Engine_CAFFE; +#endif + } + + if (engine == LRNParameter_Engine_CAFFE) { + return shared_ptr >(new LRNLayer(param)); +#ifdef USE_CUDNN + } else if (engine == LRNParameter_Engine_CUDNN) { + LRNParameter lrn_param = param.lrn_param(); + + if (lrn_param.norm_region() ==LRNParameter_NormRegion_WITHIN_CHANNEL) { + return shared_ptr >(new CuDNNLCNLayer(param)); + } else { + // local size is too big to be handled through cuDNN + if (param.lrn_param().local_size() > CUDNN_LRN_MAX_N) { + return shared_ptr >(new LRNLayer(param)); + } else { + return shared_ptr >(new CuDNNLRNLayer(param)); + } + } +#endif + } else { + LOG(FATAL) << "Layer " << param.name() << " has unknown engine."; + } +} + +REGISTER_LAYER_CREATOR(LRN, GetLRNLayer); + // Get relu layer according to engine. template shared_ptr > GetReLULayer(const LayerParameter& param) { diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp index 3514fe2aba5..d7b1e0d651f 100644 --- a/src/caffe/layers/cudnn_conv_layer.cpp +++ b/src/caffe/layers/cudnn_conv_layer.cpp @@ -1,4 +1,5 @@ #ifdef USE_CUDNN +#include #include #include "caffe/filler.hpp" @@ -24,13 +25,38 @@ void CuDNNConvolutionLayer::LayerSetUp( // Initialize CUDA streams and cuDNN. stream_ = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP]; handle_ = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP]; + + // Initialize algorithm arrays + fwd_algo_ = new cudnnConvolutionFwdAlgo_t[bottom.size()]; + bwd_filter_algo_= new cudnnConvolutionBwdFilterAlgo_t[bottom.size()]; + bwd_data_algo_ = new cudnnConvolutionBwdDataAlgo_t[bottom.size()]; + + // initialize size arrays + workspace_fwd_sizes_ = new size_t[bottom.size()]; + workspace_bwd_filter_sizes_ = new size_t[bottom.size()]; + workspace_bwd_data_sizes_ = new size_t[bottom.size()]; + + // workspace data workspaceSizeInBytes = 0; - workspace = NULL; + workspaceData = NULL; + workspace = new void*[this->group_ * CUDNN_STREAMS_PER_GROUP]; + + for (size_t i = 0; i < bottom.size(); ++i) { + // initialize all to default algorithms + fwd_algo_[i] = (cudnnConvolutionFwdAlgo_t)0; + bwd_filter_algo_[i] = (cudnnConvolutionBwdFilterAlgo_t)0; + bwd_data_algo_[i] = (cudnnConvolutionBwdDataAlgo_t)0; + // default algorithms don't require workspace + workspace_fwd_sizes_[i] = 0; + workspace_bwd_data_sizes_[i] = 0; + workspace_bwd_filter_sizes_[i] = 0; + } for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) { CUDA_CHECK(cudaStreamCreate(&stream_[g])); CUDNN_CHECK(cudnnCreate(&handle_[g])); CUDNN_CHECK(cudnnSetStream(handle_[g], stream_[g])); + workspace[g] = NULL; } // Set the indexing parameters. @@ -86,6 +112,10 @@ void CuDNNConvolutionLayer::Reshape( const int stride_h = stride_data[0]; const int stride_w = stride_data[1]; + // Specify workspace limit for kernels directly until we have a + // planning strategy and a rewrite of Caffe's GPU memory mangagement + size_t workspace_limit_bytes = 8*1024*1024; + for (int i = 0; i < bottom.size(); i++) { cudnn::setTensor4dDesc(&bottom_descs_[i], this->num_, @@ -98,7 +128,104 @@ void CuDNNConvolutionLayer::Reshape( this->num_output_ * this->out_spatial_dim_, this->out_spatial_dim_, width_out, 1); cudnn::setConvolutionDesc(&conv_descs_[i], bottom_descs_[i], - filter_desc_, pad_h, pad_w, stride_h, stride_w); + filter_desc_, pad_h, pad_w, + stride_h, stride_w); + + // choose forward and backward algorithms + workspace(s) + CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[0], + bottom_descs_[i], + filter_desc_, + conv_descs_[i], + top_descs_[i], + CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, + workspace_limit_bytes, + &fwd_algo_[i])); + + CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[0], + bottom_descs_[i], + filter_desc_, + conv_descs_[i], + top_descs_[i], + fwd_algo_[i], + &(workspace_fwd_sizes_[i]))); + + // choose backward algorithm for filter + CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(handle_[0], + bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_, + CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, + workspace_limit_bytes, &bwd_filter_algo_[i]) ); + + // get workspace for backwards filter algorithm + CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(handle_[0], + bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_, + bwd_filter_algo_[i], &workspace_bwd_filter_sizes_[i])); + + // choose backward algo for data + CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm(handle_[0], + filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i], + CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, + workspace_limit_bytes, &bwd_data_algo_[i])); + + // get workspace size + CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(handle_[0], + filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i], + bwd_data_algo_[i], &workspace_bwd_data_sizes_[i]) ); + } + + // reduce over all workspace sizes to get a maximum to allocate / reallocate + size_t total_workspace_fwd = 0; + size_t total_workspace_bwd_data = 0; + size_t total_workspace_bwd_filter = 0; + + for (size_t i = 0; i < bottom.size(); i++) { + total_workspace_fwd = std::max(total_workspace_fwd, + workspace_fwd_sizes_[i]); + total_workspace_bwd_data = std::max(total_workspace_bwd_data, + workspace_bwd_data_sizes_[i]); + total_workspace_bwd_filter = std::max(total_workspace_bwd_filter, + workspace_bwd_filter_sizes_[i]); + } + // get max over all operations + size_t max_workspace = std::max(total_workspace_fwd, + total_workspace_bwd_data); + max_workspace = std::max(max_workspace, total_workspace_bwd_filter); + // ensure all groups have enough workspace + size_t total_max_workspace = max_workspace * + (this->group_ * CUDNN_STREAMS_PER_GROUP); + + // this is the total amount of storage needed over all groups + streams + if (total_max_workspace > workspaceSizeInBytes) { + LOG(INFO) << "Reallocating workspace storage: " << total_max_workspace; + workspaceSizeInBytes = total_max_workspace; + + // free the existing workspace and allocate a new (larger) one + cudaFree(this->workspaceData); + + cudaError_t err = cudaMalloc(&(this->workspaceData), workspaceSizeInBytes); + if (err != cudaSuccess) { + // force zero memory path + for (int i = 0; i < bottom.size(); i++) { + workspace_fwd_sizes_[i] = 0; + workspace_bwd_filter_sizes_[i] = 0; + workspace_bwd_data_sizes_[i] = 0; + fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM; + bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0; + bwd_data_algo_[i] = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0; + } + + // NULL out all workspace pointers + for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) { + workspace[g] = NULL; + } + // NULL out underlying data + workspaceData = NULL; + workspaceSizeInBytes = 0; + } + + // if we succeed in the allocation, set pointer aliases for workspaces + for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) { + workspace[g] = reinterpret_cast(workspaceData) + g*max_workspace; + } } // Tensor descriptor for bias. @@ -128,8 +255,15 @@ CuDNNConvolutionLayer::~CuDNNConvolutionLayer() { cudnnDestroy(handle_[g]); } + cudaFree(workspaceData); delete [] stream_; delete [] handle_; + delete [] fwd_algo_; + delete [] bwd_filter_algo_; + delete [] bwd_data_algo_; + delete [] workspace_fwd_sizes_; + delete [] workspace_bwd_data_sizes_; + delete [] workspace_bwd_filter_sizes_; } INSTANTIATE_CLASS(CuDNNConvolutionLayer); diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu index 691152021a3..e88e4dd3281 100644 --- a/src/caffe/layers/cudnn_conv_layer.cu +++ b/src/caffe/layers/cudnn_conv_layer.cu @@ -14,11 +14,6 @@ __global__ void sync_conv_groups() { } template void CuDNNConvolutionLayer::Forward_gpu( const vector*>& bottom, const vector*>& top) { - const int* kernel_shape_data = this->kernel_shape_.cpu_data(); - const int kernel_h = kernel_shape_data[0]; - const int kernel_w = kernel_shape_data[1]; - const size_t workspace_limit_bytes = - kernel_h * kernel_w * this->channels_ * sizeof(int) + 1; const Dtype* weight = this->blobs_[0]->gpu_data(); for (int i = 0; i < bottom.size(); ++i) { const Dtype* bottom_data = bottom[i]->gpu_data(); @@ -26,52 +21,13 @@ void CuDNNConvolutionLayer::Forward_gpu( // Forward through cuDNN in parallel over groups. for (int g = 0; g < this->group_; g++) { - cudnnConvolutionFwdAlgo_t algo; - - // pick the convolution algorithm - // TODO(shelhamer) this should be done during reshape - // TODO(shelhamer) the choice of automatic or manual algorithm picking - // should be exposed in proto - CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[g], - bottom_descs_[i], - filter_desc_, - conv_descs_[i], - top_descs_[i], - CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, - workspace_limit_bytes, // memoryLimitInBytes, - &algo)); - - // get minimum size of the workspace needed for the desired algorithm - size_t workspaceSizeInBytes_temp = 0; - - CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[g], - bottom_descs_[i], - filter_desc_, - conv_descs_[i], - top_descs_[i], - algo, - &workspaceSizeInBytes_temp)); - - if (workspaceSizeInBytes_temp > workspaceSizeInBytes) { - workspaceSizeInBytes = workspaceSizeInBytes_temp; - // free the existing workspace and allocate a new (larger) one - cudaFree(this->workspace); - cudaError_t err = cudaMalloc(&(this->workspace), workspaceSizeInBytes); - if (err != cudaSuccess) { - // force zero memory path - algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM; - workspace = NULL; - workspaceSizeInBytes = 0; - } - } - // Filters. CUDNN_CHECK(cudnnConvolutionForward(handle_[g], cudnn::dataType::one, bottom_descs_[i], bottom_data + bottom_offset_ * g, filter_desc_, weight + this->weight_offset_ * g, conv_descs_[i], - algo, workspace, workspaceSizeInBytes, + fwd_algo_[i], workspace[g], workspace_fwd_sizes_[i], cudnn::dataType::zero, top_descs_[i], top_data + top_offset_ * g)); @@ -101,10 +57,12 @@ void CuDNNConvolutionLayer::Backward_gpu(const vector*>& top, if (this->param_propagate_down_[0]) { weight = this->blobs_[0]->gpu_data(); weight_diff = this->blobs_[0]->mutable_gpu_diff(); + caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff); } Dtype* bias_diff = NULL; if (this->bias_term_ && this->param_propagate_down_[1]) { bias_diff = this->blobs_[1]->mutable_gpu_diff(); + caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff); } for (int i = 0; i < top.size(); ++i) { const Dtype* top_diff = top[i]->gpu_diff(); @@ -122,11 +80,14 @@ void CuDNNConvolutionLayer::Backward_gpu(const vector*>& top, // Gradient w.r.t. weights. if (this->param_propagate_down_[0]) { const Dtype* bottom_data = bottom[i]->gpu_data(); - CUDNN_CHECK(cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g], + CUDNN_CHECK(cudnnConvolutionBackwardFilter_v3( + handle_[1*this->group_ + g], cudnn::dataType::one, bottom_descs_[i], bottom_data + bottom_offset_ * g, top_descs_[i], top_diff + top_offset_ * g, conv_descs_[i], + bwd_filter_algo_[i], workspace[1*this->group_ + g], + workspace_bwd_filter_sizes_[i], cudnn::dataType::one, filter_desc_, weight_diff + this->weight_offset_ * g)); } @@ -137,11 +98,14 @@ void CuDNNConvolutionLayer::Backward_gpu(const vector*>& top, weight = this->blobs_[0]->gpu_data(); } Dtype* bottom_diff = bottom[i]->mutable_gpu_diff(); - CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g], + CUDNN_CHECK(cudnnConvolutionBackwardData_v3( + handle_[2*this->group_ + g], cudnn::dataType::one, filter_desc_, weight + this->weight_offset_ * g, top_descs_[i], top_diff + top_offset_ * g, conv_descs_[i], + bwd_data_algo_[i], workspace[2*this->group_ + g], + workspace_bwd_data_sizes_[i], cudnn::dataType::zero, bottom_descs_[i], bottom_diff + bottom_offset_ * g)); } diff --git a/src/caffe/layers/cudnn_lcn_layer.cpp b/src/caffe/layers/cudnn_lcn_layer.cpp new file mode 100644 index 00000000000..866d810b9f9 --- /dev/null +++ b/src/caffe/layers/cudnn_lcn_layer.cpp @@ -0,0 +1,77 @@ +#ifdef USE_CUDNN +#include + +#include "caffe/filler.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/im2col.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +template +void CuDNNLCNLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + LRNLayer::LayerSetUp(bottom, top); + + CUDNN_CHECK(cudnnCreate(&handle_)); + CUDNN_CHECK(cudnnCreateLRNDescriptor(&norm_desc_)); + cudnn::createTensor4dDesc(&bottom_desc_); + cudnn::createTensor4dDesc(&top_desc_); + + // create a LRN handle + handles_setup_ = true; + + size_ = this->layer_param().lrn_param().local_size(); + pre_pad_ = (size_ - 1) / 2; + alpha_ = this->layer_param().lrn_param().alpha(); + beta_ = this->layer_param().lrn_param().beta(); + k_ = this->layer_param().lrn_param().k(); +} + +template +void CuDNNLCNLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + LRNLayer::Reshape(bottom, top); + cudnn::setTensor4dDesc(&bottom_desc_, bottom[0]->num(), + this->channels_, this->height_, this->width_); + cudnn::setTensor4dDesc(&top_desc_, bottom[0]->num(), + this->channels_, this->height_, this->width_); + CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc_, size_, alpha_, beta_, k_)); + + // allocate / reallocate tempData buffers + size_t totalSizeInBytes = sizeof(Dtype)*bottom[0]->num()* \ + this->channels_*this->height_*this->width_; + + if (totalSizeInBytes > tempDataSize) { + tempDataSize = totalSizeInBytes; + + cudaFree(tempData1); + cudaFree(tempData2); + + // allocate new buffers + CUDA_CHECK(cudaMalloc(&tempData1, totalSizeInBytes)); + CUDA_CHECK(cudaMalloc(&tempData2, totalSizeInBytes)); + } +} + +template +CuDNNLCNLayer::~CuDNNLCNLayer() { + // Check that handles have been setup before destroying. + if (!handles_setup_) { return; } + + cudnnDestroyTensorDescriptor(bottom_desc_); + cudnnDestroyTensorDescriptor(top_desc_); + + // destroy LRN handle + cudnnDestroy(handle_); + + // free temp buffers + cudaFree(tempData1); + cudaFree(tempData2); +} + +INSTANTIATE_CLASS(CuDNNLCNLayer); + +} // namespace caffe +#endif diff --git a/src/caffe/layers/cudnn_lcn_layer.cu b/src/caffe/layers/cudnn_lcn_layer.cu new file mode 100644 index 00000000000..c07ade72066 --- /dev/null +++ b/src/caffe/layers/cudnn_lcn_layer.cu @@ -0,0 +1,50 @@ +#ifdef USE_CUDNN +#include + +#include "caffe/filler.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/im2col.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +template +void CuDNNLCNLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + + CUDNN_CHECK(cudnnDivisiveNormalizationForward( + handle_, norm_desc_, CUDNN_DIVNORM_PRECOMPUTED_MEANS, + cudnn::dataType::one, + bottom_desc_, bottom_data, + NULL, // srcMeansData + this->tempData1, this->tempData2, + cudnn::dataType::zero, + top_desc_, top_data) ); +} + +template +void CuDNNLCNLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + const Dtype* top_diff = top[0]->gpu_diff(); + const Dtype* top_data = top[0]->gpu_data(); + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + + CUDNN_CHECK(cudnnDivisiveNormalizationBackward( + handle_, norm_desc_, CUDNN_DIVNORM_PRECOMPUTED_MEANS, + cudnn::dataType::one, + bottom_desc_, bottom_data, + NULL, top_diff, // NULL - srcMeansData + this->tempData1, this->tempData2, + cudnn::dataType::zero, + bottom_desc_, bottom_diff, + NULL) ); +} + +INSTANTIATE_LAYER_GPU_FUNCS(CuDNNLCNLayer); + +} // namespace caffe +#endif diff --git a/src/caffe/layers/cudnn_lrn_layer.cpp b/src/caffe/layers/cudnn_lrn_layer.cpp new file mode 100644 index 00000000000..6e9921490f0 --- /dev/null +++ b/src/caffe/layers/cudnn_lrn_layer.cpp @@ -0,0 +1,57 @@ +#ifdef USE_CUDNN +#include + +#include "caffe/filler.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/im2col.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +template +void CuDNNLRNLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + LRNLayer::LayerSetUp(bottom, top); + + CUDNN_CHECK(cudnnCreate(&handle_)); + CUDNN_CHECK(cudnnCreateLRNDescriptor(&norm_desc_)); + cudnn::createTensor4dDesc(&bottom_desc_); + cudnn::createTensor4dDesc(&top_desc_); + + // create a LRN handle + handles_setup_ = true; + + size_ = this->layer_param().lrn_param().local_size(); + alpha_ = this->layer_param().lrn_param().alpha(); + beta_ = this->layer_param().lrn_param().beta(); + k_ = this->layer_param().lrn_param().k(); +} + +template +void CuDNNLRNLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + LRNLayer::Reshape(bottom, top); + cudnn::setTensor4dDesc(&bottom_desc_, bottom[0]->num(), + this->channels_, this->height_, this->width_); + cudnn::setTensor4dDesc(&top_desc_, bottom[0]->num(), + this->channels_, this->height_, this->width_); + CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc_, size_, alpha_, beta_, k_)); +} + +template +CuDNNLRNLayer::~CuDNNLRNLayer() { + // Check that handles have been setup before destroying. + if (!handles_setup_) { return; } + + cudnnDestroyTensorDescriptor(bottom_desc_); + cudnnDestroyTensorDescriptor(top_desc_); + + // destroy LRN handle + cudnnDestroy(handle_); +} + +INSTANTIATE_CLASS(CuDNNLRNLayer); + +} // namespace caffe +#endif diff --git a/src/caffe/layers/cudnn_lrn_layer.cu b/src/caffe/layers/cudnn_lrn_layer.cu new file mode 100644 index 00000000000..f9923033011 --- /dev/null +++ b/src/caffe/layers/cudnn_lrn_layer.cu @@ -0,0 +1,48 @@ +#ifdef USE_CUDNN +#include + +#include "caffe/filler.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/im2col.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/vision_layers.hpp" + +namespace caffe { + +template +void CuDNNLRNLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + + CUDNN_CHECK(cudnnLRNCrossChannelForward( + handle_, norm_desc_, CUDNN_LRN_CROSS_CHANNEL_DIM1, + cudnn::dataType::one, + bottom_desc_, bottom_data, + cudnn::dataType::zero, + top_desc_, top_data) ); +} + +template +void CuDNNLRNLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + const Dtype* top_diff = top[0]->gpu_diff(); + const Dtype* top_data = top[0]->gpu_data(); + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + + CUDNN_CHECK(cudnnLRNCrossChannelBackward( + handle_, norm_desc_, CUDNN_LRN_CROSS_CHANNEL_DIM1, + cudnn::dataType::one, + top_desc_, top_data, + top_desc_, top_diff, + bottom_desc_, bottom_data, + cudnn::dataType::zero, + bottom_desc_, bottom_diff) ); +} + +INSTANTIATE_LAYER_GPU_FUNCS(CuDNNLRNLayer); + +}; // namespace caffe + +#endif diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp index 36c1ace4c99..d18a04ef58d 100644 --- a/src/caffe/layers/lrn_layer.cpp +++ b/src/caffe/layers/lrn_layer.cpp @@ -254,6 +254,5 @@ STUB_GPU_BACKWARD(LRNLayer, CrossChannelBackward); #endif INSTANTIATE_CLASS(LRNLayer); -REGISTER_LAYER_CLASS(LRN); } // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index f52c941b05e..af01b4721c9 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -721,6 +721,12 @@ message LRNParameter { } optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS]; optional float k = 5 [default = 1.]; + enum Engine { + DEFAULT = 0; + CAFFE = 1; + CUDNN = 2; + } + optional Engine engine = 6 [default = DEFAULT]; } message MemoryDataParameter { diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp index c4e2f8ea7f2..78cf2d9df9b 100644 --- a/src/caffe/test/test_lrn_layer.cpp +++ b/src/caffe/test/test_lrn_layer.cpp @@ -246,5 +246,201 @@ TYPED_TEST(LRNLayerTest, TestGradientWithinChannel) { this->blob_top_vec_); } +#ifdef USE_CUDNN +template +class CuDNNLRNLayerTest : public GPUDeviceTest { + protected: + CuDNNLRNLayerTest() + : epsilon_(Dtype(1e-5)), + blob_bottom_(new Blob()), + blob_top_(new Blob()) {} + virtual void SetUp() { + Caffe::set_random_seed(1701); + blob_bottom_->Reshape(2, 7, 3, 3); + // fill the values + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + blob_bottom_vec_.push_back(blob_bottom_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~CuDNNLRNLayerTest() { delete blob_bottom_; delete blob_top_; } + void ReferenceLRNForward(const Blob& blob_bottom, + const LayerParameter& layer_param, Blob* blob_top); + + Dtype epsilon_; + Blob* const blob_bottom_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +template +void CuDNNLRNLayerTest::ReferenceLRNForward( + const Blob& blob_bottom, const LayerParameter& layer_param, + Blob* blob_top) { + typedef TypeParam Dtype; + blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(), + blob_bottom.height(), blob_bottom.width()); + Dtype* top_data = blob_top->mutable_cpu_data(); + LRNParameter lrn_param = layer_param.lrn_param(); + Dtype alpha = lrn_param.alpha(); + Dtype beta = lrn_param.beta(); + int size = lrn_param.local_size(); + switch (lrn_param.norm_region()) { + case LRNParameter_NormRegion_ACROSS_CHANNELS: + for (int n = 0; n < blob_bottom.num(); ++n) { + for (int c = 0; c < blob_bottom.channels(); ++c) { + for (int h = 0; h < blob_bottom.height(); ++h) { + for (int w = 0; w < blob_bottom.width(); ++w) { + int c_start = c - (size - 1) / 2; + int c_end = min(c_start + size, blob_bottom.channels()); + c_start = max(c_start, 0); + Dtype scale = 1.; + for (int i = c_start; i < c_end; ++i) { + Dtype value = blob_bottom.data_at(n, i, h, w); + scale += value * value * alpha / size; + } + *(top_data + blob_top->offset(n, c, h, w)) = + blob_bottom.data_at(n, c, h, w) / pow(scale, beta); + } + } + } + } + break; + case LRNParameter_NormRegion_WITHIN_CHANNEL: + for (int n = 0; n < blob_bottom.num(); ++n) { + for (int c = 0; c < blob_bottom.channels(); ++c) { + for (int h = 0; h < blob_bottom.height(); ++h) { + int h_start = h - (size - 1) / 2; + int h_end = min(h_start + size, blob_bottom.height()); + h_start = max(h_start, 0); + for (int w = 0; w < blob_bottom.width(); ++w) { + Dtype scale = 1.; + int w_start = w - (size - 1) / 2; + int w_end = min(w_start + size, blob_bottom.width()); + w_start = max(w_start, 0); + for (int nh = h_start; nh < h_end; ++nh) { + for (int nw = w_start; nw < w_end; ++nw) { + Dtype value = blob_bottom.data_at(n, c, nh, nw); + scale += value * value * alpha / (size * size); + } + } + *(top_data + blob_top->offset(n, c, h, w)) = + blob_bottom.data_at(n, c, h, w) / pow(scale, beta); + } + } + } + } + break; + default: + LOG(FATAL) << "Unknown normalization region."; + } +} + +TYPED_TEST_CASE(CuDNNLRNLayerTest, TestDtypes); + +TYPED_TEST(CuDNNLRNLayerTest, TestForwardAcrossChannelsCuDNN) { + // typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + CuDNNLRNLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + Blob top_reference; + this->ReferenceLRNForward(*(this->blob_bottom_), layer_param, + &top_reference); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i], + this->epsilon_); + } +} + +TYPED_TEST(CuDNNLRNLayerTest, TestForwardAcrossChannelsLargeRegionCuDNN) { + typedef TypeParam Dtype; + LayerParameter layer_param; + layer_param.mutable_lrn_param()->set_local_size(15); + CuDNNLRNLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + Blob top_reference; + this->ReferenceLRNForward(*(this->blob_bottom_), layer_param, + &top_reference); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i], + this->epsilon_); + } +} + +TYPED_TEST(CuDNNLRNLayerTest, TestGradientAcrossChannelsCuDNN) { + typedef TypeParam Dtype; + LayerParameter layer_param; + CuDNNLRNLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int i = 0; i < this->blob_top_->count(); ++i) { + this->blob_top_->mutable_cpu_diff()[i] = 1.; + } + vector propagate_down(this->blob_bottom_vec_.size(), true); + layer.Backward(this->blob_top_vec_, propagate_down, + this->blob_bottom_vec_); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(CuDNNLRNLayerTest, TestForwardWithinChannel) { + typedef TypeParam Dtype; + LayerParameter layer_param; + layer_param.mutable_lrn_param()->set_norm_region( + LRNParameter_NormRegion_WITHIN_CHANNEL); + layer_param.mutable_lrn_param()->set_local_size(3); + CuDNNLCNLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + Blob top_reference; + this->ReferenceLRNForward(*(this->blob_bottom_), layer_param, + &top_reference); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i], + this->epsilon_); + } +} + +TYPED_TEST(CuDNNLRNLayerTest, TestGradientWithinChannel) { + typedef TypeParam Dtype; + LayerParameter layer_param; + layer_param.mutable_lrn_param()->set_norm_region( + LRNParameter_NormRegion_WITHIN_CHANNEL); + layer_param.mutable_lrn_param()->set_local_size(3); + CuDNNLCNLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int i = 0; i < this->blob_top_->count(); ++i) { + this->blob_top_->mutable_cpu_diff()[i] = 1.; + } + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(CuDNNLRNLayerTest, TestGradientAcrossChannelsLargeRegionCuDNN) { + typedef TypeParam Dtype; + LayerParameter layer_param; + layer_param.mutable_lrn_param()->set_local_size(15); + CuDNNLRNLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int i = 0; i < this->blob_top_->count(); ++i) { + this->blob_top_->mutable_cpu_diff()[i] = 1.; + } + vector propagate_down(this->blob_bottom_vec_.size(), true); + layer.Backward(this->blob_top_vec_, propagate_down, + this->blob_bottom_vec_); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +#endif } // namespace caffe From 1e75fb922f968a92071232c7e6b3332475141d47 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 16 Oct 2015 16:33:06 -0700 Subject: [PATCH 305/446] rearrange upgrade helpers order from general helpers to specific upgrades in chronological order. --- include/caffe/util/upgrade_proto.hpp | 18 ++-- src/caffe/util/upgrade_proto.cpp | 122 +++++++++++++-------------- 2 files changed, 70 insertions(+), 70 deletions(-) diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp index c1f21a0d4d8..6a1418434a6 100644 --- a/include/caffe/util/upgrade_proto.hpp +++ b/include/caffe/util/upgrade_proto.hpp @@ -10,6 +10,15 @@ namespace caffe { // Return true iff the net is not the current version. bool NetNeedsUpgrade(const NetParameter& net_param); +// Check for deprecations and upgrade the NetParameter as needed. +bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param); + +// Read parameters from a file into a NetParameter proto message. +void ReadNetParamsFromTextFileOrDie(const string& param_file, + NetParameter* param); +void ReadNetParamsFromBinaryFileOrDie(const string& param_file, + NetParameter* param); + // Return true iff any layer contains parameters specified using // deprecated V0LayerParameter. bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param); @@ -50,15 +59,6 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param, const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type); -// Check for deprecations and upgrade the NetParameter as needed. -bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param); - -// Read parameters from a file into a NetParameter proto message. -void ReadNetParamsFromTextFileOrDie(const string& param_file, - NetParameter* param); -void ReadNetParamsFromBinaryFileOrDie(const string& param_file, - NetParameter* param); - } // namespace caffe #endif // CAFFE_UTIL_UPGRADE_PROTO_H_ diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp index ac379e50f4f..6eae9fec00a 100644 --- a/src/caffe/util/upgrade_proto.cpp +++ b/src/caffe/util/upgrade_proto.cpp @@ -16,6 +16,67 @@ bool NetNeedsUpgrade(const NetParameter& net_param) { return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param); } +bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) { + bool success = true; + if (NetNeedsV0ToV1Upgrade(*param)) { + // NetParameter was specified using the old style (V0LayerParameter); try to + // upgrade it. + LOG(INFO) << "Attempting to upgrade input file specified using deprecated " + << "V0LayerParameter: " << param_file; + NetParameter original_param(*param); + if (!UpgradeV0Net(original_param, param)) { + success = false; + LOG(ERROR) << "Warning: had one or more problems upgrading " + << "V0NetParameter to NetParameter (see above); continuing anyway."; + } else { + LOG(INFO) << "Successfully upgraded file specified using deprecated " + << "V0LayerParameter"; + } + LOG(WARNING) << "Note that future Caffe releases will not support " + << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for " + << "prototxt and ./build/tools/upgrade_net_proto_binary for model " + << "weights upgrade this and any other net protos to the new format."; + } + // NetParameter uses old style data transformation fields; try to upgrade it. + if (NetNeedsDataUpgrade(*param)) { + LOG(INFO) << "Attempting to upgrade input file specified using deprecated " + << "transformation parameters: " << param_file; + UpgradeNetDataTransformation(param); + LOG(INFO) << "Successfully upgraded file specified using deprecated " + << "data transformation parameters."; + LOG(WARNING) << "Note that future Caffe releases will only support " + << "transform_param messages for transformation fields."; + } + if (NetNeedsV1ToV2Upgrade(*param)) { + LOG(INFO) << "Attempting to upgrade input file specified using deprecated " + << "V1LayerParameter: " << param_file; + NetParameter original_param(*param); + if (!UpgradeV1Net(original_param, param)) { + success = false; + LOG(ERROR) << "Warning: had one or more problems upgrading " + << "V1LayerParameter (see above); continuing anyway."; + } else { + LOG(INFO) << "Successfully upgraded file specified using deprecated " + << "V1LayerParameter"; + } + } + return success; +} + +void ReadNetParamsFromTextFileOrDie(const string& param_file, + NetParameter* param) { + CHECK(ReadProtoFromTextFile(param_file, param)) + << "Failed to parse NetParameter file: " << param_file; + UpgradeNetAsNeeded(param_file, param); +} + +void ReadNetParamsFromBinaryFileOrDie(const string& param_file, + NetParameter* param) { + CHECK(ReadProtoFromBinaryFile(param_file, param)) + << "Failed to parse NetParameter file: " << param_file; + UpgradeNetAsNeeded(param_file, param); +} + bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param) { for (int i = 0; i < net_param.layers_size(); ++i) { if (net_param.layers(i).has_layer()) { @@ -583,53 +644,6 @@ void UpgradeNetDataTransformation(NetParameter* net_param) { } } -bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) { - bool success = true; - if (NetNeedsV0ToV1Upgrade(*param)) { - // NetParameter was specified using the old style (V0LayerParameter); try to - // upgrade it. - LOG(INFO) << "Attempting to upgrade input file specified using deprecated " - << "V0LayerParameter: " << param_file; - NetParameter original_param(*param); - if (!UpgradeV0Net(original_param, param)) { - success = false; - LOG(ERROR) << "Warning: had one or more problems upgrading " - << "V0NetParameter to NetParameter (see above); continuing anyway."; - } else { - LOG(INFO) << "Successfully upgraded file specified using deprecated " - << "V0LayerParameter"; - } - LOG(WARNING) << "Note that future Caffe releases will not support " - << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for " - << "prototxt and ./build/tools/upgrade_net_proto_binary for model " - << "weights upgrade this and any other net protos to the new format."; - } - // NetParameter uses old style data transformation fields; try to upgrade it. - if (NetNeedsDataUpgrade(*param)) { - LOG(INFO) << "Attempting to upgrade input file specified using deprecated " - << "transformation parameters: " << param_file; - UpgradeNetDataTransformation(param); - LOG(INFO) << "Successfully upgraded file specified using deprecated " - << "data transformation parameters."; - LOG(WARNING) << "Note that future Caffe releases will only support " - << "transform_param messages for transformation fields."; - } - if (NetNeedsV1ToV2Upgrade(*param)) { - LOG(INFO) << "Attempting to upgrade input file specified using deprecated " - << "V1LayerParameter: " << param_file; - NetParameter original_param(*param); - if (!UpgradeV1Net(original_param, param)) { - success = false; - LOG(ERROR) << "Warning: had one or more problems upgrading " - << "V1LayerParameter (see above); continuing anyway."; - } else { - LOG(INFO) << "Successfully upgraded file specified using deprecated " - << "V1LayerParameter"; - } - } - return success; -} - bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) { bool is_fully_compatible = true; if (v1_net_param.layer_size() > 0) { @@ -923,18 +937,4 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) { } } -void ReadNetParamsFromTextFileOrDie(const string& param_file, - NetParameter* param) { - CHECK(ReadProtoFromTextFile(param_file, param)) - << "Failed to parse NetParameter file: " << param_file; - UpgradeNetAsNeeded(param_file, param); -} - -void ReadNetParamsFromBinaryFileOrDie(const string& param_file, - NetParameter* param) { - CHECK(ReadProtoFromBinaryFile(param_file, param)) - << "Failed to parse NetParameter file: " << param_file; - UpgradeNetAsNeeded(param_file, param); -} - } // namespace caffe From e5a74b282efb2293a05d91635a5b26837adc2aa3 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Fri, 16 Oct 2015 21:19:59 -0700 Subject: [PATCH 306/446] Test reading and writing mean proto in matlab --- matlab/+caffe/+test/test_io.m | 18 ++++++++++++++++++ matlab/+caffe/run_tests.m | 3 ++- 2 files changed, 20 insertions(+), 1 deletion(-) create mode 100644 matlab/+caffe/+test/test_io.m diff --git a/matlab/+caffe/+test/test_io.m b/matlab/+caffe/+test/test_io.m new file mode 100644 index 00000000000..2c34bd1e938 --- /dev/null +++ b/matlab/+caffe/+test/test_io.m @@ -0,0 +1,18 @@ +classdef test_io < matlab.unittest.TestCase + methods (Test) + function test_read_write_mean(self) + % randomly generate mean data + width = 200; + height = 300; + channels = 3; + mean_data_write = 255 * rand(width, height, channels, 'single'); + % write mean data to binary proto + mean_proto_file = tempname(); + caffe.io.write_mean(mean_data_write, mean_proto_file); + % read mean data from saved binary proto and test whether they are equal + mean_data_read = caffe.io.read_mean(mean_proto_file); + self.verifyEqual(mean_data_write, mean_data_read) + delete(mean_proto_file); + end + end +end diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m index 93896855ac2..6dbf6b23151 100644 --- a/matlab/+caffe/run_tests.m +++ b/matlab/+caffe/run_tests.m @@ -11,7 +11,8 @@ % put all test cases here results = [... run(caffe.test.test_net) ... - run(caffe.test.test_solver) ]; + run(caffe.test.test_solver) ... + run(caffe.test.test_io) ]; % reset caffe after testing caffe.reset_all(); From b822a702d19d4fbebbc91198a991f91c34e60650 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Thu, 24 Sep 2015 17:11:07 -0700 Subject: [PATCH 307/446] Split solver code into one file per solver class --- include/caffe/sgd_solvers.hpp | 142 +++ include/caffe/solver.hpp | 158 +--- python/caffe/_caffe.cpp | 1 + src/caffe/solver.cpp | 811 ------------------ src/caffe/solver_factory.cpp | 32 + src/caffe/solvers/adadelta_solver.cpp | 155 ++++ src/caffe/solvers/adagrad_solver.cpp | 88 ++ src/caffe/solvers/adam_solver.cpp | 112 +++ src/caffe/solvers/nesterov_solver.cpp | 70 ++ src/caffe/solvers/rmsprop_solver.cpp | 84 ++ src/caffe/solvers/sgd_solver.cpp | 347 ++++++++ src/caffe/test/test_gradient_based_solver.cpp | 2 +- src/caffe/test/test_solver.cpp | 1 + 13 files changed, 1038 insertions(+), 965 deletions(-) create mode 100644 include/caffe/sgd_solvers.hpp create mode 100644 src/caffe/solver_factory.cpp create mode 100644 src/caffe/solvers/adadelta_solver.cpp create mode 100644 src/caffe/solvers/adagrad_solver.cpp create mode 100644 src/caffe/solvers/adam_solver.cpp create mode 100644 src/caffe/solvers/nesterov_solver.cpp create mode 100644 src/caffe/solvers/rmsprop_solver.cpp create mode 100644 src/caffe/solvers/sgd_solver.cpp diff --git a/include/caffe/sgd_solvers.hpp b/include/caffe/sgd_solvers.hpp new file mode 100644 index 00000000000..6bf1d70c752 --- /dev/null +++ b/include/caffe/sgd_solvers.hpp @@ -0,0 +1,142 @@ +#ifndef CAFFE_SGD_SOLVERS_HPP_ +#define CAFFE_SGD_SOLVERS_HPP_ + +#include +#include + +#include "caffe/solver.hpp" + +namespace caffe { + +/** + * @brief Optimizes the parameters of a Net using + * stochastic gradient descent (SGD) with momentum. + */ +template +class SGDSolver : public Solver { + public: + explicit SGDSolver(const SolverParameter& param) + : Solver(param) { PreSolve(); } + explicit SGDSolver(const string& param_file) + : Solver(param_file) { PreSolve(); } + + const vector > >& history() { return history_; } + + protected: + void PreSolve(); + Dtype GetLearningRate(); + virtual void ApplyUpdate(); + virtual void Normalize(int param_id); + virtual void Regularize(int param_id); + virtual void ComputeUpdateValue(int param_id, Dtype rate); + virtual void ClipGradients(); + virtual void SnapshotSolverState(const string& model_filename); + virtual void SnapshotSolverStateToBinaryProto(const string& model_filename); + virtual void SnapshotSolverStateToHDF5(const string& model_filename); + virtual void RestoreSolverStateFromHDF5(const string& state_file); + virtual void RestoreSolverStateFromBinaryProto(const string& state_file); + // history maintains the historical momentum data. + // update maintains update related data and is not needed in snapshots. + // temp maintains other information that might be needed in computation + // of gradients/updates and is not needed in snapshots + vector > > history_, update_, temp_; + + DISABLE_COPY_AND_ASSIGN(SGDSolver); +}; + +template +class NesterovSolver : public SGDSolver { + public: + explicit NesterovSolver(const SolverParameter& param) + : SGDSolver(param) {} + explicit NesterovSolver(const string& param_file) + : SGDSolver(param_file) {} + + protected: + virtual void ComputeUpdateValue(int param_id, Dtype rate); + + DISABLE_COPY_AND_ASSIGN(NesterovSolver); +}; + +template +class AdaGradSolver : public SGDSolver { + public: + explicit AdaGradSolver(const SolverParameter& param) + : SGDSolver(param) { constructor_sanity_check(); } + explicit AdaGradSolver(const string& param_file) + : SGDSolver(param_file) { constructor_sanity_check(); } + + protected: + virtual void ComputeUpdateValue(int param_id, Dtype rate); + void constructor_sanity_check() { + CHECK_EQ(0, this->param_.momentum()) + << "Momentum cannot be used with AdaGrad."; + } + + DISABLE_COPY_AND_ASSIGN(AdaGradSolver); +}; + + +template +class RMSPropSolver : public SGDSolver { + public: + explicit RMSPropSolver(const SolverParameter& param) + : SGDSolver(param) { constructor_sanity_check(); } + explicit RMSPropSolver(const string& param_file) + : SGDSolver(param_file) { constructor_sanity_check(); } + + protected: + virtual void ComputeUpdateValue(int param_id, Dtype rate); + void constructor_sanity_check() { + CHECK_EQ(0, this->param_.momentum()) + << "Momentum cannot be used with RMSProp."; + CHECK_GE(this->param_.rms_decay(), 0) + << "rms_decay should lie between 0 and 1."; + CHECK_LT(this->param_.rms_decay(), 1) + << "rms_decay should lie between 0 and 1."; + } + + DISABLE_COPY_AND_ASSIGN(RMSPropSolver); +}; + +template +class AdaDeltaSolver : public SGDSolver { + public: + explicit AdaDeltaSolver(const SolverParameter& param) + : SGDSolver(param) { AdaDeltaPreSolve(); } + explicit AdaDeltaSolver(const string& param_file) + : SGDSolver(param_file) { AdaDeltaPreSolve(); } + + protected: + void AdaDeltaPreSolve(); + virtual void ComputeUpdateValue(int param_id, Dtype rate); + + DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver); +}; + +/** + * @brief AdamSolver, an algorithm for first-order gradient-based optimization + * of stochastic objective functions, based on adaptive estimates of + * lower-order moments. Described in [1]. + * + * [1] D. P. Kingma and J. L. Ba, "ADAM: A Method for Stochastic Optimization." + * arXiv preprint arXiv:1412.6980v8 (2014). + */ +template +class AdamSolver : public SGDSolver { + public: + explicit AdamSolver(const SolverParameter& param) + : SGDSolver(param) { AdamPreSolve();} + explicit AdamSolver(const string& param_file) + : SGDSolver(param_file) { AdamPreSolve(); } + + protected: + void AdamPreSolve(); + virtual void ComputeUpdateValue(int param_id, Dtype rate); + + DISABLE_COPY_AND_ASSIGN(AdamSolver); +}; + +} // namespace caffe + +#endif // CAFFE_SGD_SOLVERS_HPP_ diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 2ecf539baef..a045ccf254c 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -1,5 +1,5 @@ -#ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_ -#define CAFFE_OPTIMIZATION_SOLVER_HPP_ +#ifndef CAFFE_SOLVER_HPP_ +#define CAFFE_SOLVER_HPP_ #include #include #include @@ -148,158 +148,10 @@ class WorkerSolver : public Solver { } }; -/** - * @brief Optimizes the parameters of a Net using - * stochastic gradient descent (SGD) with momentum. - */ -template -class SGDSolver : public Solver { - public: - explicit SGDSolver(const SolverParameter& param) - : Solver(param) { PreSolve(); } - explicit SGDSolver(const string& param_file) - : Solver(param_file) { PreSolve(); } - - const vector > >& history() { return history_; } - - protected: - void PreSolve(); - Dtype GetLearningRate(); - virtual void ApplyUpdate(); - virtual void Normalize(int param_id); - virtual void Regularize(int param_id); - virtual void ComputeUpdateValue(int param_id, Dtype rate); - virtual void ClipGradients(); - virtual void SnapshotSolverState(const string& model_filename); - virtual void SnapshotSolverStateToBinaryProto(const string& model_filename); - virtual void SnapshotSolverStateToHDF5(const string& model_filename); - virtual void RestoreSolverStateFromHDF5(const string& state_file); - virtual void RestoreSolverStateFromBinaryProto(const string& state_file); - // history maintains the historical momentum data. - // update maintains update related data and is not needed in snapshots. - // temp maintains other information that might be needed in computation - // of gradients/updates and is not needed in snapshots - vector > > history_, update_, temp_; - - DISABLE_COPY_AND_ASSIGN(SGDSolver); -}; - +// The solver factory function template -class NesterovSolver : public SGDSolver { - public: - explicit NesterovSolver(const SolverParameter& param) - : SGDSolver(param) {} - explicit NesterovSolver(const string& param_file) - : SGDSolver(param_file) {} - - protected: - virtual void ComputeUpdateValue(int param_id, Dtype rate); - - DISABLE_COPY_AND_ASSIGN(NesterovSolver); -}; - -template -class AdaGradSolver : public SGDSolver { - public: - explicit AdaGradSolver(const SolverParameter& param) - : SGDSolver(param) { constructor_sanity_check(); } - explicit AdaGradSolver(const string& param_file) - : SGDSolver(param_file) { constructor_sanity_check(); } - - protected: - virtual void ComputeUpdateValue(int param_id, Dtype rate); - void constructor_sanity_check() { - CHECK_EQ(0, this->param_.momentum()) - << "Momentum cannot be used with AdaGrad."; - } - - DISABLE_COPY_AND_ASSIGN(AdaGradSolver); -}; - - -template -class RMSPropSolver : public SGDSolver { - public: - explicit RMSPropSolver(const SolverParameter& param) - : SGDSolver(param) { constructor_sanity_check(); } - explicit RMSPropSolver(const string& param_file) - : SGDSolver(param_file) { constructor_sanity_check(); } - - protected: - virtual void ComputeUpdateValue(int param_id, Dtype rate); - void constructor_sanity_check() { - CHECK_EQ(0, this->param_.momentum()) - << "Momentum cannot be used with RMSProp."; - CHECK_GE(this->param_.rms_decay(), 0) - << "rms_decay should lie between 0 and 1."; - CHECK_LT(this->param_.rms_decay(), 1) - << "rms_decay should lie between 0 and 1."; - } - - DISABLE_COPY_AND_ASSIGN(RMSPropSolver); -}; - -template -class AdaDeltaSolver : public SGDSolver { - public: - explicit AdaDeltaSolver(const SolverParameter& param) - : SGDSolver(param) { AdaDeltaPreSolve(); } - explicit AdaDeltaSolver(const string& param_file) - : SGDSolver(param_file) { AdaDeltaPreSolve(); } - - protected: - void AdaDeltaPreSolve(); - virtual void ComputeUpdateValue(int param_id, Dtype rate); - - DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver); -}; - -/** - * @brief AdamSolver, an algorithm for first-order gradient-based optimization - * of stochastic objective functions, based on adaptive estimates of - * lower-order moments. Described in [1]. - * - * [1] D. P. Kingma and J. L. Ba, "ADAM: A Method for Stochastic Optimization." - * arXiv preprint arXiv:1412.6980v8 (2014). - */ -template -class AdamSolver : public SGDSolver { - public: - explicit AdamSolver(const SolverParameter& param) - : SGDSolver(param) { AdamPreSolve();} - explicit AdamSolver(const string& param_file) - : SGDSolver(param_file) { AdamPreSolve(); } - - protected: - void AdamPreSolve(); - virtual void ComputeUpdateValue(int param_id, Dtype rate); - - DISABLE_COPY_AND_ASSIGN(AdamSolver); -}; - -template -Solver* GetSolver(const SolverParameter& param) { - SolverParameter_SolverType type = param.solver_type(); - - switch (type) { - case SolverParameter_SolverType_SGD: - return new SGDSolver(param); - case SolverParameter_SolverType_NESTEROV: - return new NesterovSolver(param); - case SolverParameter_SolverType_ADAGRAD: - return new AdaGradSolver(param); - case SolverParameter_SolverType_RMSPROP: - return new RMSPropSolver(param); - case SolverParameter_SolverType_ADADELTA: - return new AdaDeltaSolver(param); - case SolverParameter_SolverType_ADAM: - return new AdamSolver(param); - default: - LOG(FATAL) << "Unknown SolverType: " << type; - } - return (Solver*) NULL; -} +Solver* GetSolver(const SolverParameter& param); } // namespace caffe -#endif // CAFFE_OPTIMIZATION_SOLVER_HPP_ +#endif // CAFFE_SOLVER_HPP_ diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index ccd5776ac40..0e38dee70d1 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -16,6 +16,7 @@ #include "caffe/caffe.hpp" #include "caffe/python_layer.hpp" +#include "caffe/sgd_solvers.hpp" // Temporary solution for numpy < 1.7 versions: old macro, no promises. // You're strongly advised to upgrade to >= 1.7. diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 12c13dd8385..016a02888d8 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -1,18 +1,11 @@ #include -#include #include #include -#include "hdf5.h" -#include "hdf5_hl.h" - -#include "caffe/net.hpp" -#include "caffe/proto/caffe.pb.h" #include "caffe/solver.hpp" #include "caffe/util/hdf5.hpp" #include "caffe/util/io.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/util/upgrade_proto.hpp" namespace caffe { @@ -492,810 +485,6 @@ void Solver::Restore(const char* state_file) { } } -// Return the current learning rate. The currently implemented learning rate -// policies are as follows: -// - fixed: always return base_lr. -// - step: return base_lr * gamma ^ (floor(iter / step)) -// - exp: return base_lr * gamma ^ iter -// - inv: return base_lr * (1 + gamma * iter) ^ (- power) -// - multistep: similar to step but it allows non uniform steps defined by -// stepvalue -// - poly: the effective learning rate follows a polynomial decay, to be -// zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power) -// - sigmoid: the effective learning rate follows a sigmod decay -// return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize)))) -// -// where base_lr, max_iter, gamma, step, stepvalue and power are defined -// in the solver parameter protocol buffer, and iter is the current iteration. -template -Dtype SGDSolver::GetLearningRate() { - Dtype rate; - const string& lr_policy = this->param_.lr_policy(); - if (lr_policy == "fixed") { - rate = this->param_.base_lr(); - } else if (lr_policy == "step") { - this->current_step_ = this->iter_ / this->param_.stepsize(); - rate = this->param_.base_lr() * - pow(this->param_.gamma(), this->current_step_); - } else if (lr_policy == "exp") { - rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_); - } else if (lr_policy == "inv") { - rate = this->param_.base_lr() * - pow(Dtype(1) + this->param_.gamma() * this->iter_, - - this->param_.power()); - } else if (lr_policy == "multistep") { - if (this->current_step_ < this->param_.stepvalue_size() && - this->iter_ >= this->param_.stepvalue(this->current_step_)) { - this->current_step_++; - LOG(INFO) << "MultiStep Status: Iteration " << - this->iter_ << ", step = " << this->current_step_; - } - rate = this->param_.base_lr() * - pow(this->param_.gamma(), this->current_step_); - } else if (lr_policy == "poly") { - rate = this->param_.base_lr() * pow(Dtype(1.) - - (Dtype(this->iter_) / Dtype(this->param_.max_iter())), - this->param_.power()); - } else if (lr_policy == "sigmoid") { - rate = this->param_.base_lr() * (Dtype(1.) / - (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) - - Dtype(this->param_.stepsize()))))); - } else { - LOG(FATAL) << "Unknown learning rate policy: " << lr_policy; - } - return rate; -} - -template -void SGDSolver::PreSolve() { - // Initialize the history - const vector*>& net_params = this->net_->learnable_params(); - history_.clear(); - update_.clear(); - temp_.clear(); - for (int i = 0; i < net_params.size(); ++i) { - const vector& shape = net_params[i]->shape(); - history_.push_back(shared_ptr >(new Blob(shape))); - update_.push_back(shared_ptr >(new Blob(shape))); - temp_.push_back(shared_ptr >(new Blob(shape))); - } -} - -template -void SGDSolver::ClipGradients() { - const Dtype clip_gradients = this->param_.clip_gradients(); - if (clip_gradients < 0) { return; } - const vector*>& net_params = this->net_->learnable_params(); - Dtype sumsq_diff = 0; - for (int i = 0; i < net_params.size(); ++i) { - sumsq_diff += net_params[i]->sumsq_diff(); - } - const Dtype l2norm_diff = std::sqrt(sumsq_diff); - if (l2norm_diff > clip_gradients) { - Dtype scale_factor = clip_gradients / l2norm_diff; - LOG(INFO) << "Gradient clipping: scaling down gradients (L2 norm " - << l2norm_diff << " > " << clip_gradients << ") " - << "by scale factor " << scale_factor; - for (int i = 0; i < net_params.size(); ++i) { - net_params[i]->scale_diff(scale_factor); - } - } -} - -template -void SGDSolver::ApplyUpdate() { - CHECK(Caffe::root_solver()); - Dtype rate = GetLearningRate(); - if (this->param_.display() && this->iter_ % this->param_.display() == 0) { - LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate; - } - ClipGradients(); - for (int param_id = 0; param_id < this->net_->learnable_params().size(); - ++param_id) { - Normalize(param_id); - Regularize(param_id); - ComputeUpdateValue(param_id, rate); - } - this->net_->Update(); -} - -template -void SGDSolver::Normalize(int param_id) { - if (this->param_.iter_size() == 1) { return; } - // Scale gradient to counterbalance accumulation. - const vector*>& net_params = this->net_->learnable_params(); - const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size(); - switch (Caffe::mode()) { - case Caffe::CPU: { - caffe_scal(net_params[param_id]->count(), accum_normalization, - net_params[param_id]->mutable_cpu_diff()); - break; - } - case Caffe::GPU: { -#ifndef CPU_ONLY - caffe_gpu_scal(net_params[param_id]->count(), accum_normalization, - net_params[param_id]->mutable_gpu_diff()); -#else - NO_GPU; -#endif - break; - } - default: - LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); - } -} - -template -void SGDSolver::Regularize(int param_id) { - const vector*>& net_params = this->net_->learnable_params(); - const vector& net_params_weight_decay = - this->net_->params_weight_decay(); - Dtype weight_decay = this->param_.weight_decay(); - string regularization_type = this->param_.regularization_type(); - Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; - switch (Caffe::mode()) { - case Caffe::CPU: { - if (local_decay) { - if (regularization_type == "L2") { - // add weight decay - caffe_axpy(net_params[param_id]->count(), - local_decay, - net_params[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } else if (regularization_type == "L1") { - caffe_cpu_sign(net_params[param_id]->count(), - net_params[param_id]->cpu_data(), - temp_[param_id]->mutable_cpu_data()); - caffe_axpy(net_params[param_id]->count(), - local_decay, - temp_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - } else { - LOG(FATAL) << "Unknown regularization type: " << regularization_type; - } - } - break; - } - case Caffe::GPU: { -#ifndef CPU_ONLY - if (local_decay) { - if (regularization_type == "L2") { - // add weight decay - caffe_gpu_axpy(net_params[param_id]->count(), - local_decay, - net_params[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } else if (regularization_type == "L1") { - caffe_gpu_sign(net_params[param_id]->count(), - net_params[param_id]->gpu_data(), - temp_[param_id]->mutable_gpu_data()); - caffe_gpu_axpy(net_params[param_id]->count(), - local_decay, - temp_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - } else { - LOG(FATAL) << "Unknown regularization type: " << regularization_type; - } - } -#else - NO_GPU; -#endif - break; - } - default: - LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); - } -} - -template -void SGDSolver::ComputeUpdateValue(int param_id, Dtype rate) { - const vector*>& net_params = this->net_->learnable_params(); - const vector& net_params_lr = this->net_->params_lr(); - Dtype momentum = this->param_.momentum(); - Dtype local_rate = rate * net_params_lr[param_id]; - // Compute the update to history, then copy it to the parameter diff. - switch (Caffe::mode()) { - case Caffe::CPU: { - caffe_cpu_axpby(net_params[param_id]->count(), local_rate, - net_params[param_id]->cpu_diff(), momentum, - history_[param_id]->mutable_cpu_data()); - caffe_copy(net_params[param_id]->count(), - history_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - break; - } - case Caffe::GPU: { -#ifndef CPU_ONLY - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - net_params[param_id]->gpu_diff(), momentum, - history_[param_id]->mutable_gpu_data()); - caffe_copy(net_params[param_id]->count(), - history_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); -#else - NO_GPU; -#endif - break; - } - default: - LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); - } -} - -template -void SGDSolver::SnapshotSolverState(const string& model_filename) { - switch (this->param_.snapshot_format()) { - case caffe::SolverParameter_SnapshotFormat_BINARYPROTO: - SnapshotSolverStateToBinaryProto(model_filename); - break; - case caffe::SolverParameter_SnapshotFormat_HDF5: - SnapshotSolverStateToHDF5(model_filename); - break; - default: - LOG(FATAL) << "Unsupported snapshot format."; - } -} - -template -void SGDSolver::SnapshotSolverStateToBinaryProto( - const string& model_filename) { - SolverState state; - state.set_iter(this->iter_); - state.set_learned_net(model_filename); - state.set_current_step(this->current_step_); - state.clear_history(); - for (int i = 0; i < history_.size(); ++i) { - // Add history - BlobProto* history_blob = state.add_history(); - history_[i]->ToProto(history_blob); - } - string snapshot_filename = Solver::SnapshotFilename(".solverstate"); - LOG(INFO) - << "Snapshotting solver state to binary proto file " << snapshot_filename; - WriteProtoToBinaryFile(state, snapshot_filename.c_str()); -} - -template -void SGDSolver::SnapshotSolverStateToHDF5( - const string& model_filename) { - string snapshot_filename = - Solver::SnapshotFilename(".solverstate.h5"); - LOG(INFO) << "Snapshotting solver state to HDF5 file " << snapshot_filename; - hid_t file_hid = H5Fcreate(snapshot_filename.c_str(), H5F_ACC_TRUNC, - H5P_DEFAULT, H5P_DEFAULT); - CHECK_GE(file_hid, 0) - << "Couldn't open " << snapshot_filename << " to save solver state."; - hdf5_save_int(file_hid, "iter", this->iter_); - hdf5_save_string(file_hid, "learned_net", model_filename); - hdf5_save_int(file_hid, "current_step", this->current_step_); - hid_t history_hid = H5Gcreate2(file_hid, "history", H5P_DEFAULT, H5P_DEFAULT, - H5P_DEFAULT); - CHECK_GE(history_hid, 0) - << "Error saving solver state to " << snapshot_filename << "."; - for (int i = 0; i < history_.size(); ++i) { - ostringstream oss; - oss << i; - hdf5_save_nd_dataset(history_hid, oss.str(), *history_[i]); - } - H5Gclose(history_hid); - H5Fclose(file_hid); -} - -template -void SGDSolver::RestoreSolverStateFromBinaryProto( - const string& state_file) { - SolverState state; - ReadProtoFromBinaryFile(state_file, &state); - this->iter_ = state.iter(); - if (state.has_learned_net()) { - NetParameter net_param; - ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param); - this->net_->CopyTrainedLayersFrom(net_param); - } - this->current_step_ = state.current_step(); - CHECK_EQ(state.history_size(), history_.size()) - << "Incorrect length of history blobs."; - LOG(INFO) << "SGDSolver: restoring history"; - for (int i = 0; i < history_.size(); ++i) { - history_[i]->FromProto(state.history(i)); - } -} - -template -void SGDSolver::RestoreSolverStateFromHDF5(const string& state_file) { - hid_t file_hid = H5Fopen(state_file.c_str(), H5F_ACC_RDONLY, H5P_DEFAULT); - CHECK_GE(file_hid, 0) << "Couldn't open solver state file " << state_file; - this->iter_ = hdf5_load_int(file_hid, "iter"); - if (H5LTfind_dataset(file_hid, "learned_net")) { - string learned_net = hdf5_load_string(file_hid, "learned_net"); - this->net_->CopyTrainedLayersFrom(learned_net); - } - this->current_step_ = hdf5_load_int(file_hid, "current_step"); - hid_t history_hid = H5Gopen2(file_hid, "history", H5P_DEFAULT); - CHECK_GE(history_hid, 0) << "Error reading history from " << state_file; - int state_history_size = hdf5_get_num_links(history_hid); - CHECK_EQ(state_history_size, history_.size()) - << "Incorrect length of history blobs."; - for (int i = 0; i < history_.size(); ++i) { - ostringstream oss; - oss << i; - hdf5_load_nd_dataset(history_hid, oss.str().c_str(), 0, - kMaxBlobAxes, history_[i].get()); - } - H5Gclose(history_hid); - H5Fclose(file_hid); -} - -template -void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { - CHECK(Caffe::root_solver()); - const vector*>& net_params = this->net_->learnable_params(); - const vector& net_params_lr = this->net_->params_lr(); - Dtype momentum = this->param_.momentum(); - Dtype local_rate = rate * net_params_lr[param_id]; - switch (Caffe::mode()) { - case Caffe::CPU: { - // save history momentum for stepping back - caffe_copy(net_params[param_id]->count(), - this->history_[param_id]->cpu_data(), - this->update_[param_id]->mutable_cpu_data()); - - // update history - caffe_cpu_axpby(net_params[param_id]->count(), local_rate, - net_params[param_id]->cpu_diff(), momentum, - this->history_[param_id]->mutable_cpu_data()); - - // compute update: step back then over step - caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum, - this->history_[param_id]->cpu_data(), -momentum, - this->update_[param_id]->mutable_cpu_data()); - - // copy - caffe_copy(net_params[param_id]->count(), - this->update_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - break; - } - case Caffe::GPU: { -#ifndef CPU_ONLY - // save history momentum for stepping back - caffe_copy(net_params[param_id]->count(), - this->history_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - // update history - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - net_params[param_id]->gpu_diff(), momentum, - this->history_[param_id]->mutable_gpu_data()); - - // compute update: step back then over step - caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum, - this->history_[param_id]->gpu_data(), -momentum, - this->update_[param_id]->mutable_gpu_data()); - - // copy - caffe_copy(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); -#else - NO_GPU; -#endif - break; - } - default: - LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); - } -} - -template -void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { - CHECK(Caffe::root_solver()); - const vector*>& net_params = this->net_->learnable_params(); - const vector& net_params_lr = this->net_->params_lr(); - Dtype delta = this->param_.delta(); - Dtype local_rate = rate * net_params_lr[param_id]; - switch (Caffe::mode()) { - case Caffe::CPU: { - // compute square of gradient in update - caffe_powx(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), Dtype(2), - this->update_[param_id]->mutable_cpu_data()); - - // update history - caffe_add(net_params[param_id]->count(), - this->update_[param_id]->cpu_data(), - this->history_[param_id]->cpu_data(), - this->history_[param_id]->mutable_cpu_data()); - - // prepare update - caffe_powx(net_params[param_id]->count(), - this->history_[param_id]->cpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_cpu_data()); - - caffe_add_scalar(net_params[param_id]->count(), - delta, this->update_[param_id]->mutable_cpu_data()); - - caffe_div(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), - this->update_[param_id]->cpu_data(), - this->update_[param_id]->mutable_cpu_data()); - - // scale and copy - caffe_cpu_axpby(net_params[param_id]->count(), local_rate, - this->update_[param_id]->cpu_data(), Dtype(0), - net_params[param_id]->mutable_cpu_diff()); - break; - } - case Caffe::GPU: { -#ifndef CPU_ONLY - // compute square of gradient in update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history - caffe_gpu_add(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), - this->history_[param_id]->gpu_data(), - this->history_[param_id]->mutable_gpu_data()); - - // prepare update - caffe_gpu_powx(net_params[param_id]->count(), - this->history_[param_id]->gpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_add_scalar(net_params[param_id]->count(), - delta, this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_div(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), - this->update_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - // scale and copy - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - this->update_[param_id]->gpu_data(), Dtype(0), - net_params[param_id]->mutable_gpu_diff()); -#else - NO_GPU; -#endif - break; - } - default: - LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); - } -} - -template -void RMSPropSolver::ComputeUpdateValue(int param_id, Dtype rate) { - const vector*>& net_params = this->net_->learnable_params(); - const vector& net_params_lr = this->net_->params_lr(); - - // get the learning rate - Dtype delta = this->param_.delta(); - Dtype rms_decay = this->param_.rms_decay(); - Dtype local_rate = rate * net_params_lr[param_id]; - - switch (Caffe::mode()) { - case Caffe::CPU: - // compute square of gradient in update - caffe_powx(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), Dtype(2), - this->update_[param_id]->mutable_cpu_data()); - - // update history - caffe_cpu_axpby(net_params[param_id] -> count(), - Dtype(1-rms_decay), this->update_[param_id]->cpu_data(), - rms_decay, this->history_[param_id]-> mutable_cpu_data()); - - // prepare update - caffe_powx(net_params[param_id]->count(), - this->history_[param_id]->cpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_cpu_data()); - - caffe_add_scalar(net_params[param_id]->count(), - delta, this->update_[param_id]->mutable_cpu_data()); - - caffe_div(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), this->update_[param_id]->cpu_data(), - this->update_[param_id]->mutable_cpu_data()); - - // scale and copy - caffe_cpu_axpby(net_params[param_id]->count(), local_rate, - this->update_[param_id]->cpu_data(), Dtype(0), - net_params[param_id]->mutable_cpu_diff()); - break; - case Caffe::GPU: -#ifndef CPU_ONLY - // compute square of gradient in update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history - caffe_gpu_axpby(net_params[param_id] -> count(), - Dtype(1-rms_decay), this->update_[param_id]->gpu_data(), - rms_decay, this->history_[param_id]-> mutable_gpu_data()); - - // prepare update - caffe_gpu_powx(net_params[param_id]->count(), - this->history_[param_id]->gpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_add_scalar(net_params[param_id]->count(), - delta, this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_div(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), this->update_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - this->update_[param_id]->gpu_data(), Dtype(0), - net_params[param_id]->mutable_gpu_diff()); -#else - NO_GPU; -#endif - break; - default: - LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); - } -} - -template -void AdaDeltaSolver::AdaDeltaPreSolve() { - // Add the extra history entries for AdaDelta after those from - // SGDSolver::PreSolve - const vector*>& net_params = this->net_->learnable_params(); - for (int i = 0; i < net_params.size(); ++i) { - const vector& shape = net_params[i]->shape(); - this->history_.push_back( - shared_ptr >(new Blob(shape))); - } -} - -template -void AdaDeltaSolver::ComputeUpdateValue(int param_id, Dtype rate) { - const vector*>& net_params = this->net_->learnable_params(); - const vector& net_params_lr = this->net_->params_lr(); - Dtype delta = this->param_.delta(); - Dtype momentum = this->param_.momentum(); - Dtype local_rate = rate * net_params_lr[param_id]; - size_t update_history_offset = net_params.size(); - switch (Caffe::mode()) { - case Caffe::CPU: { - // compute square of gradient in update - caffe_powx(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), Dtype(2), - this->update_[param_id]->mutable_cpu_data()); - - // update history of gradients - caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, - this->update_[param_id]->cpu_data(), momentum, - this->history_[param_id]->mutable_cpu_data()); - - // add delta to history to guard against dividing by zero later - caffe_set(net_params[param_id]->count(), delta, - this->temp_[param_id]->mutable_cpu_data()); - - caffe_add(net_params[param_id]->count(), - this->temp_[param_id]->cpu_data(), - this->history_[update_history_offset + param_id]->cpu_data(), - this->update_[param_id]->mutable_cpu_data()); - - caffe_add(net_params[param_id]->count(), - this->temp_[param_id]->cpu_data(), - this->history_[param_id]->cpu_data(), - this->temp_[param_id]->mutable_cpu_data()); - - // divide history of updates by history of gradients - caffe_div(net_params[param_id]->count(), - this->update_[param_id]->cpu_data(), - this->temp_[param_id]->cpu_data(), - this->update_[param_id]->mutable_cpu_data()); - - // jointly compute the RMS of both for update and gradient history - caffe_powx(net_params[param_id]->count(), - this->update_[param_id]->cpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_cpu_data()); - - // compute the update - caffe_mul(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), - this->update_[param_id]->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - - // compute square of update - caffe_powx(net_params[param_id]->count(), - net_params[param_id]->cpu_diff(), Dtype(2), - this->update_[param_id]->mutable_cpu_data()); - - // update history of updates - caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, - this->update_[param_id]->cpu_data(), momentum, - this->history_[update_history_offset + param_id]->mutable_cpu_data()); - - // apply learning rate - caffe_cpu_scale(net_params[param_id]->count(), local_rate, - net_params[param_id]->cpu_diff(), - net_params[param_id]->mutable_cpu_diff()); - break; - } - case Caffe::GPU: { -#ifndef CPU_ONLY - // compute square of gradient in update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history of gradients - caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, - this->update_[param_id]->gpu_data(), momentum, - this->history_[param_id]->mutable_gpu_data()); - - // add delta to history to guard against dividing by zero later - caffe_gpu_set(net_params[param_id]->count(), delta, - this->temp_[param_id]->mutable_gpu_data()); - - caffe_gpu_add(net_params[param_id]->count(), - this->temp_[param_id]->gpu_data(), - this->history_[update_history_offset + param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_add(net_params[param_id]->count(), - this->temp_[param_id]->gpu_data(), - this->history_[param_id]->gpu_data(), - this->temp_[param_id]->mutable_gpu_data()); - - // divide history of updates by history of gradients - caffe_gpu_div(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), - this->temp_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - // jointly compute the RMS of both for update and gradient history - caffe_gpu_powx(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_gpu_data()); - - // compute the update and copy to net_diff - caffe_gpu_mul(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), - this->update_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - - // compute square of update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history of updates - caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, - this->update_[param_id]->gpu_data(), momentum, - this->history_[update_history_offset + param_id]->mutable_gpu_data()); - - // apply learning rate - caffe_gpu_scale(net_params[param_id]->count(), local_rate, - net_params[param_id]->gpu_diff(), - net_params[param_id]->mutable_gpu_diff()); -#else - NO_GPU; -#endif - break; - } - default: - LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); - } -} - -template -void AdamSolver::AdamPreSolve() { - // Add the extra history entries for Adam after those from - // SGDSolver::PreSolve - const vector*>& net_params = this->net_->learnable_params(); - for (int i = 0; i < net_params.size(); ++i) { - const vector& shape = net_params[i]->shape(); - this->history_.push_back( - shared_ptr >(new Blob(shape))); - } -} - -template -void AdamSolver::ComputeUpdateValue(int param_id, Dtype rate) { - const vector*>& net_params = this->net_->learnable_params(); - const vector& net_params_lr = this->net_->params_lr(); - Dtype local_rate = rate * net_params_lr[param_id]; - const Dtype beta1 = this->param_.momentum(); - const Dtype beta2 = this->param_.momentum2(); - - // we create aliases for convenience - size_t update_history_offset = net_params.size(); - Blob* val_m = this->history_[param_id].get(); - Blob* val_v = this->history_[param_id + update_history_offset].get(); - Blob* val_t = this->temp_[param_id].get(); - - const int t = this->iter_ + 1; - const Dtype correction = std::sqrt(Dtype(1) - pow(beta2, t)) / - (Dtype(1.) - pow(beta1, t)); - const int N = net_params[param_id]->count(); - const Dtype eps_hat = this->param_.delta(); - - switch (Caffe::mode()) { - case Caffe::CPU: { - // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t - caffe_cpu_axpby(N, Dtype(1)-beta1, - net_params[param_id]->cpu_diff(), beta1, - val_m->mutable_cpu_data()); - - // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2 - caffe_mul(N, - net_params[param_id]->cpu_diff(), - net_params[param_id]->cpu_diff(), - val_t->mutable_cpu_data()); - caffe_cpu_axpby(N, Dtype(1)-beta2, - val_t->cpu_data(), beta2, - val_v->mutable_cpu_data()); - - // set update - caffe_powx(N, - val_v->cpu_data(), Dtype(0.5), - val_t->mutable_cpu_data()); - caffe_add_scalar(N, eps_hat, val_t->mutable_cpu_data()); - caffe_div(N, - val_m->cpu_data(), - val_t->cpu_data(), - val_t->mutable_cpu_data()); - - caffe_cpu_scale(N, local_rate*correction, - val_t->cpu_data(), - net_params[param_id]->mutable_cpu_diff()); - break; - } - case Caffe::GPU: { -#ifndef CPU_ONLY - // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t - caffe_gpu_axpby(N, Dtype(1)-beta1, - net_params[param_id]->gpu_diff(), beta1, - val_m->mutable_gpu_data()); - - // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2 - caffe_gpu_mul(N, - net_params[param_id]->gpu_diff(), - net_params[param_id]->gpu_diff(), - val_t->mutable_gpu_data()); - caffe_gpu_axpby(N, Dtype(1)-beta2, - val_t->gpu_data(), beta2, - val_v->mutable_gpu_data()); - - // set update - caffe_gpu_powx(N, - val_v->gpu_data(), Dtype(0.5), - val_t->mutable_gpu_data()); - caffe_gpu_add_scalar(N, eps_hat, - val_t->mutable_gpu_data()); - caffe_gpu_div(N, - val_m->gpu_data(), - val_t->gpu_data(), - val_t->mutable_gpu_data()); - - caffe_gpu_scale(N, local_rate*correction, - val_t->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); -#else - NO_GPU; -#endif - break; - } - default: - LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); - } -} - INSTANTIATE_CLASS(Solver); -INSTANTIATE_CLASS(SGDSolver); -INSTANTIATE_CLASS(NesterovSolver); -INSTANTIATE_CLASS(AdaGradSolver); -INSTANTIATE_CLASS(RMSPropSolver); -INSTANTIATE_CLASS(AdaDeltaSolver); -INSTANTIATE_CLASS(AdamSolver); } // namespace caffe diff --git a/src/caffe/solver_factory.cpp b/src/caffe/solver_factory.cpp new file mode 100644 index 00000000000..f78fab28720 --- /dev/null +++ b/src/caffe/solver_factory.cpp @@ -0,0 +1,32 @@ +#include "caffe/solver.hpp" +#include "caffe/sgd_solvers.hpp" + +namespace caffe { + +template +Solver* GetSolver(const SolverParameter& param) { + SolverParameter_SolverType type = param.solver_type(); + + switch (type) { + case SolverParameter_SolverType_SGD: + return new SGDSolver(param); + case SolverParameter_SolverType_NESTEROV: + return new NesterovSolver(param); + case SolverParameter_SolverType_ADAGRAD: + return new AdaGradSolver(param); + case SolverParameter_SolverType_RMSPROP: + return new RMSPropSolver(param); + case SolverParameter_SolverType_ADADELTA: + return new AdaDeltaSolver(param); + case SolverParameter_SolverType_ADAM: + return new AdamSolver(param); + default: + LOG(FATAL) << "Unknown SolverType: " << type; + } + return (Solver*) NULL; +} + +template Solver* GetSolver(const SolverParameter& param); +template Solver* GetSolver(const SolverParameter& param); + +} // namespace caffe diff --git a/src/caffe/solvers/adadelta_solver.cpp b/src/caffe/solvers/adadelta_solver.cpp new file mode 100644 index 00000000000..45cd4eb2988 --- /dev/null +++ b/src/caffe/solvers/adadelta_solver.cpp @@ -0,0 +1,155 @@ +#include + +#include "caffe/sgd_solvers.hpp" + +namespace caffe { + +template +void AdaDeltaSolver::AdaDeltaPreSolve() { + // Add the extra history entries for AdaDelta after those from + // SGDSolver::PreSolve + const vector*>& net_params = this->net_->learnable_params(); + for (int i = 0; i < net_params.size(); ++i) { + const vector& shape = net_params[i]->shape(); + this->history_.push_back( + shared_ptr >(new Blob(shape))); + } +} + +template +void AdaDeltaSolver::ComputeUpdateValue(int param_id, Dtype rate) { + const vector*>& net_params = this->net_->learnable_params(); + const vector& net_params_lr = this->net_->params_lr(); + Dtype delta = this->param_.delta(); + Dtype momentum = this->param_.momentum(); + Dtype local_rate = rate * net_params_lr[param_id]; + size_t update_history_offset = net_params.size(); + switch (Caffe::mode()) { + case Caffe::CPU: { + // compute square of gradient in update + caffe_powx(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), Dtype(2), + this->update_[param_id]->mutable_cpu_data()); + + // update history of gradients + caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->cpu_data(), momentum, + this->history_[param_id]->mutable_cpu_data()); + + // add delta to history to guard against dividing by zero later + caffe_set(net_params[param_id]->count(), delta, + this->temp_[param_id]->mutable_cpu_data()); + + caffe_add(net_params[param_id]->count(), + this->temp_[param_id]->cpu_data(), + this->history_[update_history_offset + param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + caffe_add(net_params[param_id]->count(), + this->temp_[param_id]->cpu_data(), + this->history_[param_id]->cpu_data(), + this->temp_[param_id]->mutable_cpu_data()); + + // divide history of updates by history of gradients + caffe_div(net_params[param_id]->count(), + this->update_[param_id]->cpu_data(), + this->temp_[param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + // jointly compute the RMS of both for update and gradient history + caffe_powx(net_params[param_id]->count(), + this->update_[param_id]->cpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_cpu_data()); + + // compute the update + caffe_mul(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), + this->update_[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + + // compute square of update + caffe_powx(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), Dtype(2), + this->update_[param_id]->mutable_cpu_data()); + + // update history of updates + caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->cpu_data(), momentum, + this->history_[update_history_offset + param_id]->mutable_cpu_data()); + + // apply learning rate + caffe_cpu_scale(net_params[param_id]->count(), local_rate, + net_params[param_id]->cpu_diff(), + net_params[param_id]->mutable_cpu_diff()); + break; + } + case Caffe::GPU: { +#ifndef CPU_ONLY + // compute square of gradient in update + caffe_gpu_powx(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), Dtype(2), + this->update_[param_id]->mutable_gpu_data()); + + // update history of gradients + caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->gpu_data(), momentum, + this->history_[param_id]->mutable_gpu_data()); + + // add delta to history to guard against dividing by zero later + caffe_gpu_set(net_params[param_id]->count(), delta, + this->temp_[param_id]->mutable_gpu_data()); + + caffe_gpu_add(net_params[param_id]->count(), + this->temp_[param_id]->gpu_data(), + this->history_[update_history_offset + param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_add(net_params[param_id]->count(), + this->temp_[param_id]->gpu_data(), + this->history_[param_id]->gpu_data(), + this->temp_[param_id]->mutable_gpu_data()); + + // divide history of updates by history of gradients + caffe_gpu_div(net_params[param_id]->count(), + this->update_[param_id]->gpu_data(), + this->temp_[param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + // jointly compute the RMS of both for update and gradient history + caffe_gpu_powx(net_params[param_id]->count(), + this->update_[param_id]->gpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_gpu_data()); + + // compute the update and copy to net_diff + caffe_gpu_mul(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), + this->update_[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); + + // compute square of update + caffe_gpu_powx(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), Dtype(2), + this->update_[param_id]->mutable_gpu_data()); + + // update history of updates + caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, + this->update_[param_id]->gpu_data(), momentum, + this->history_[update_history_offset + param_id]->mutable_gpu_data()); + + // apply learning rate + caffe_gpu_scale(net_params[param_id]->count(), local_rate, + net_params[param_id]->gpu_diff(), + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + } + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + +INSTANTIATE_CLASS(AdaDeltaSolver); + +} // namespace caffe diff --git a/src/caffe/solvers/adagrad_solver.cpp b/src/caffe/solvers/adagrad_solver.cpp new file mode 100644 index 00000000000..627d816a470 --- /dev/null +++ b/src/caffe/solvers/adagrad_solver.cpp @@ -0,0 +1,88 @@ +#include + +#include "caffe/sgd_solvers.hpp" + +namespace caffe { + +template +void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { + CHECK(Caffe::root_solver()); + const vector*>& net_params = this->net_->learnable_params(); + const vector& net_params_lr = this->net_->params_lr(); + Dtype delta = this->param_.delta(); + Dtype local_rate = rate * net_params_lr[param_id]; + switch (Caffe::mode()) { + case Caffe::CPU: { + // compute square of gradient in update + caffe_powx(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), Dtype(2), + this->update_[param_id]->mutable_cpu_data()); + + // update history + caffe_add(net_params[param_id]->count(), + this->update_[param_id]->cpu_data(), + this->history_[param_id]->cpu_data(), + this->history_[param_id]->mutable_cpu_data()); + + // prepare update + caffe_powx(net_params[param_id]->count(), + this->history_[param_id]->cpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_cpu_data()); + + caffe_add_scalar(net_params[param_id]->count(), + delta, this->update_[param_id]->mutable_cpu_data()); + + caffe_div(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), + this->update_[param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + // scale and copy + caffe_cpu_axpby(net_params[param_id]->count(), local_rate, + this->update_[param_id]->cpu_data(), Dtype(0), + net_params[param_id]->mutable_cpu_diff()); + break; + } + case Caffe::GPU: { +#ifndef CPU_ONLY + // compute square of gradient in update + caffe_gpu_powx(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), Dtype(2), + this->update_[param_id]->mutable_gpu_data()); + + // update history + caffe_gpu_add(net_params[param_id]->count(), + this->update_[param_id]->gpu_data(), + this->history_[param_id]->gpu_data(), + this->history_[param_id]->mutable_gpu_data()); + + // prepare update + caffe_gpu_powx(net_params[param_id]->count(), + this->history_[param_id]->gpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_add_scalar(net_params[param_id]->count(), + delta, this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_div(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), + this->update_[param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + // scale and copy + caffe_gpu_axpby(net_params[param_id]->count(), local_rate, + this->update_[param_id]->gpu_data(), Dtype(0), + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + } + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + +INSTANTIATE_CLASS(AdaGradSolver); + +} // namespace caffe diff --git a/src/caffe/solvers/adam_solver.cpp b/src/caffe/solvers/adam_solver.cpp new file mode 100644 index 00000000000..8c334f665dd --- /dev/null +++ b/src/caffe/solvers/adam_solver.cpp @@ -0,0 +1,112 @@ +#include + +#include "caffe/sgd_solvers.hpp" + +namespace caffe { + +template +void AdamSolver::AdamPreSolve() { + // Add the extra history entries for Adam after those from + // SGDSolver::PreSolve + const vector*>& net_params = this->net_->learnable_params(); + for (int i = 0; i < net_params.size(); ++i) { + const vector& shape = net_params[i]->shape(); + this->history_.push_back( + shared_ptr >(new Blob(shape))); + } +} + +template +void AdamSolver::ComputeUpdateValue(int param_id, Dtype rate) { + const vector*>& net_params = this->net_->learnable_params(); + const vector& net_params_lr = this->net_->params_lr(); + Dtype local_rate = rate * net_params_lr[param_id]; + const Dtype beta1 = this->param_.momentum(); + const Dtype beta2 = this->param_.momentum2(); + + // we create aliases for convenience + size_t update_history_offset = net_params.size(); + Blob* val_m = this->history_[param_id].get(); + Blob* val_v = this->history_[param_id + update_history_offset].get(); + Blob* val_t = this->temp_[param_id].get(); + + const int t = this->iter_ + 1; + const Dtype correction = std::sqrt(Dtype(1) - pow(beta2, t)) / + (Dtype(1.) - pow(beta1, t)); + const int N = net_params[param_id]->count(); + const Dtype eps_hat = this->param_.delta(); + + switch (Caffe::mode()) { + case Caffe::CPU: { + // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t + caffe_cpu_axpby(N, Dtype(1)-beta1, + net_params[param_id]->cpu_diff(), beta1, + val_m->mutable_cpu_data()); + + // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2 + caffe_mul(N, + net_params[param_id]->cpu_diff(), + net_params[param_id]->cpu_diff(), + val_t->mutable_cpu_data()); + caffe_cpu_axpby(N, Dtype(1)-beta2, + val_t->cpu_data(), beta2, + val_v->mutable_cpu_data()); + + // set update + caffe_powx(N, + val_v->cpu_data(), Dtype(0.5), + val_t->mutable_cpu_data()); + caffe_add_scalar(N, eps_hat, val_t->mutable_cpu_data()); + caffe_div(N, + val_m->cpu_data(), + val_t->cpu_data(), + val_t->mutable_cpu_data()); + + caffe_cpu_scale(N, local_rate*correction, + val_t->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + break; + } + case Caffe::GPU: { +#ifndef CPU_ONLY + // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t + caffe_gpu_axpby(N, Dtype(1)-beta1, + net_params[param_id]->gpu_diff(), beta1, + val_m->mutable_gpu_data()); + + // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2 + caffe_gpu_mul(N, + net_params[param_id]->gpu_diff(), + net_params[param_id]->gpu_diff(), + val_t->mutable_gpu_data()); + caffe_gpu_axpby(N, Dtype(1)-beta2, + val_t->gpu_data(), beta2, + val_v->mutable_gpu_data()); + + // set update + caffe_gpu_powx(N, + val_v->gpu_data(), Dtype(0.5), + val_t->mutable_gpu_data()); + caffe_gpu_add_scalar(N, eps_hat, + val_t->mutable_gpu_data()); + caffe_gpu_div(N, + val_m->gpu_data(), + val_t->gpu_data(), + val_t->mutable_gpu_data()); + + caffe_gpu_scale(N, local_rate*correction, + val_t->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + } + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + +INSTANTIATE_CLASS(AdamSolver); + +} // namespace caffe diff --git a/src/caffe/solvers/nesterov_solver.cpp b/src/caffe/solvers/nesterov_solver.cpp new file mode 100644 index 00000000000..8135ee2c657 --- /dev/null +++ b/src/caffe/solvers/nesterov_solver.cpp @@ -0,0 +1,70 @@ +#include + +#include "caffe/sgd_solvers.hpp" + +namespace caffe { + +template +void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { + CHECK(Caffe::root_solver()); + const vector*>& net_params = this->net_->learnable_params(); + const vector& net_params_lr = this->net_->params_lr(); + Dtype momentum = this->param_.momentum(); + Dtype local_rate = rate * net_params_lr[param_id]; + switch (Caffe::mode()) { + case Caffe::CPU: { + // save history momentum for stepping back + caffe_copy(net_params[param_id]->count(), + this->history_[param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + // update history + caffe_cpu_axpby(net_params[param_id]->count(), local_rate, + net_params[param_id]->cpu_diff(), momentum, + this->history_[param_id]->mutable_cpu_data()); + + // compute update: step back then over step + caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum, + this->history_[param_id]->cpu_data(), -momentum, + this->update_[param_id]->mutable_cpu_data()); + + // copy + caffe_copy(net_params[param_id]->count(), + this->update_[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + break; + } + case Caffe::GPU: { +#ifndef CPU_ONLY + // save history momentum for stepping back + caffe_copy(net_params[param_id]->count(), + this->history_[param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + // update history + caffe_gpu_axpby(net_params[param_id]->count(), local_rate, + net_params[param_id]->gpu_diff(), momentum, + this->history_[param_id]->mutable_gpu_data()); + + // compute update: step back then over step + caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum, + this->history_[param_id]->gpu_data(), -momentum, + this->update_[param_id]->mutable_gpu_data()); + + // copy + caffe_copy(net_params[param_id]->count(), + this->update_[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + } + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + +INSTANTIATE_CLASS(NesterovSolver); + +} // namespace caffe diff --git a/src/caffe/solvers/rmsprop_solver.cpp b/src/caffe/solvers/rmsprop_solver.cpp new file mode 100644 index 00000000000..96d1b3dda0b --- /dev/null +++ b/src/caffe/solvers/rmsprop_solver.cpp @@ -0,0 +1,84 @@ +#include + +#include "caffe/sgd_solvers.hpp" + +namespace caffe { + +template +void RMSPropSolver::ComputeUpdateValue(int param_id, Dtype rate) { + const vector*>& net_params = this->net_->learnable_params(); + const vector& net_params_lr = this->net_->params_lr(); + + // get the learning rate + Dtype delta = this->param_.delta(); + Dtype rms_decay = this->param_.rms_decay(); + Dtype local_rate = rate * net_params_lr[param_id]; + + switch (Caffe::mode()) { + case Caffe::CPU: + // compute square of gradient in update + caffe_powx(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), Dtype(2), + this->update_[param_id]->mutable_cpu_data()); + + // update history + caffe_cpu_axpby(net_params[param_id] -> count(), + Dtype(1-rms_decay), this->update_[param_id]->cpu_data(), + rms_decay, this->history_[param_id]-> mutable_cpu_data()); + + // prepare update + caffe_powx(net_params[param_id]->count(), + this->history_[param_id]->cpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_cpu_data()); + + caffe_add_scalar(net_params[param_id]->count(), + delta, this->update_[param_id]->mutable_cpu_data()); + + caffe_div(net_params[param_id]->count(), + net_params[param_id]->cpu_diff(), this->update_[param_id]->cpu_data(), + this->update_[param_id]->mutable_cpu_data()); + + // scale and copy + caffe_cpu_axpby(net_params[param_id]->count(), local_rate, + this->update_[param_id]->cpu_data(), Dtype(0), + net_params[param_id]->mutable_cpu_diff()); + break; + case Caffe::GPU: +#ifndef CPU_ONLY + // compute square of gradient in update + caffe_gpu_powx(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), Dtype(2), + this->update_[param_id]->mutable_gpu_data()); + + // update history + caffe_gpu_axpby(net_params[param_id] -> count(), + Dtype(1-rms_decay), this->update_[param_id]->gpu_data(), + rms_decay, this->history_[param_id]-> mutable_gpu_data()); + + // prepare update + caffe_gpu_powx(net_params[param_id]->count(), + this->history_[param_id]->gpu_data(), Dtype(0.5), + this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_add_scalar(net_params[param_id]->count(), + delta, this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_div(net_params[param_id]->count(), + net_params[param_id]->gpu_diff(), this->update_[param_id]->gpu_data(), + this->update_[param_id]->mutable_gpu_data()); + + caffe_gpu_axpby(net_params[param_id]->count(), local_rate, + this->update_[param_id]->gpu_data(), Dtype(0), + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + +INSTANTIATE_CLASS(RMSPropSolver); + +} // namespace caffe diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp new file mode 100644 index 00000000000..89ef5ec451d --- /dev/null +++ b/src/caffe/solvers/sgd_solver.cpp @@ -0,0 +1,347 @@ +#include +#include + +#include "caffe/sgd_solvers.hpp" +#include "caffe/util/hdf5.hpp" +#include "caffe/util/io.hpp" +#include "caffe/util/upgrade_proto.hpp" + +namespace caffe { + +// Return the current learning rate. The currently implemented learning rate +// policies are as follows: +// - fixed: always return base_lr. +// - step: return base_lr * gamma ^ (floor(iter / step)) +// - exp: return base_lr * gamma ^ iter +// - inv: return base_lr * (1 + gamma * iter) ^ (- power) +// - multistep: similar to step but it allows non uniform steps defined by +// stepvalue +// - poly: the effective learning rate follows a polynomial decay, to be +// zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power) +// - sigmoid: the effective learning rate follows a sigmod decay +// return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize)))) +// +// where base_lr, max_iter, gamma, step, stepvalue and power are defined +// in the solver parameter protocol buffer, and iter is the current iteration. +template +Dtype SGDSolver::GetLearningRate() { + Dtype rate; + const string& lr_policy = this->param_.lr_policy(); + if (lr_policy == "fixed") { + rate = this->param_.base_lr(); + } else if (lr_policy == "step") { + this->current_step_ = this->iter_ / this->param_.stepsize(); + rate = this->param_.base_lr() * + pow(this->param_.gamma(), this->current_step_); + } else if (lr_policy == "exp") { + rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_); + } else if (lr_policy == "inv") { + rate = this->param_.base_lr() * + pow(Dtype(1) + this->param_.gamma() * this->iter_, + - this->param_.power()); + } else if (lr_policy == "multistep") { + if (this->current_step_ < this->param_.stepvalue_size() && + this->iter_ >= this->param_.stepvalue(this->current_step_)) { + this->current_step_++; + LOG(INFO) << "MultiStep Status: Iteration " << + this->iter_ << ", step = " << this->current_step_; + } + rate = this->param_.base_lr() * + pow(this->param_.gamma(), this->current_step_); + } else if (lr_policy == "poly") { + rate = this->param_.base_lr() * pow(Dtype(1.) - + (Dtype(this->iter_) / Dtype(this->param_.max_iter())), + this->param_.power()); + } else if (lr_policy == "sigmoid") { + rate = this->param_.base_lr() * (Dtype(1.) / + (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) - + Dtype(this->param_.stepsize()))))); + } else { + LOG(FATAL) << "Unknown learning rate policy: " << lr_policy; + } + return rate; +} + +template +void SGDSolver::PreSolve() { + // Initialize the history + const vector*>& net_params = this->net_->learnable_params(); + history_.clear(); + update_.clear(); + temp_.clear(); + for (int i = 0; i < net_params.size(); ++i) { + const vector& shape = net_params[i]->shape(); + history_.push_back(shared_ptr >(new Blob(shape))); + update_.push_back(shared_ptr >(new Blob(shape))); + temp_.push_back(shared_ptr >(new Blob(shape))); + } +} + +template +void SGDSolver::ClipGradients() { + const Dtype clip_gradients = this->param_.clip_gradients(); + if (clip_gradients < 0) { return; } + const vector*>& net_params = this->net_->learnable_params(); + Dtype sumsq_diff = 0; + for (int i = 0; i < net_params.size(); ++i) { + sumsq_diff += net_params[i]->sumsq_diff(); + } + const Dtype l2norm_diff = std::sqrt(sumsq_diff); + if (l2norm_diff > clip_gradients) { + Dtype scale_factor = clip_gradients / l2norm_diff; + LOG(INFO) << "Gradient clipping: scaling down gradients (L2 norm " + << l2norm_diff << " > " << clip_gradients << ") " + << "by scale factor " << scale_factor; + for (int i = 0; i < net_params.size(); ++i) { + net_params[i]->scale_diff(scale_factor); + } + } +} + +template +void SGDSolver::ApplyUpdate() { + CHECK(Caffe::root_solver()); + Dtype rate = GetLearningRate(); + if (this->param_.display() && this->iter_ % this->param_.display() == 0) { + LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate; + } + ClipGradients(); + for (int param_id = 0; param_id < this->net_->learnable_params().size(); + ++param_id) { + Normalize(param_id); + Regularize(param_id); + ComputeUpdateValue(param_id, rate); + } + this->net_->Update(); +} + +template +void SGDSolver::Normalize(int param_id) { + if (this->param_.iter_size() == 1) { return; } + // Scale gradient to counterbalance accumulation. + const vector*>& net_params = this->net_->learnable_params(); + const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size(); + switch (Caffe::mode()) { + case Caffe::CPU: { + caffe_scal(net_params[param_id]->count(), accum_normalization, + net_params[param_id]->mutable_cpu_diff()); + break; + } + case Caffe::GPU: { +#ifndef CPU_ONLY + caffe_gpu_scal(net_params[param_id]->count(), accum_normalization, + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + } + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + +template +void SGDSolver::Regularize(int param_id) { + const vector*>& net_params = this->net_->learnable_params(); + const vector& net_params_weight_decay = + this->net_->params_weight_decay(); + Dtype weight_decay = this->param_.weight_decay(); + string regularization_type = this->param_.regularization_type(); + Dtype local_decay = weight_decay * net_params_weight_decay[param_id]; + switch (Caffe::mode()) { + case Caffe::CPU: { + if (local_decay) { + if (regularization_type == "L2") { + // add weight decay + caffe_axpy(net_params[param_id]->count(), + local_decay, + net_params[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + } else if (regularization_type == "L1") { + caffe_cpu_sign(net_params[param_id]->count(), + net_params[param_id]->cpu_data(), + temp_[param_id]->mutable_cpu_data()); + caffe_axpy(net_params[param_id]->count(), + local_decay, + temp_[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + } else { + LOG(FATAL) << "Unknown regularization type: " << regularization_type; + } + } + break; + } + case Caffe::GPU: { +#ifndef CPU_ONLY + if (local_decay) { + if (regularization_type == "L2") { + // add weight decay + caffe_gpu_axpy(net_params[param_id]->count(), + local_decay, + net_params[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); + } else if (regularization_type == "L1") { + caffe_gpu_sign(net_params[param_id]->count(), + net_params[param_id]->gpu_data(), + temp_[param_id]->mutable_gpu_data()); + caffe_gpu_axpy(net_params[param_id]->count(), + local_decay, + temp_[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); + } else { + LOG(FATAL) << "Unknown regularization type: " << regularization_type; + } + } +#else + NO_GPU; +#endif + break; + } + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + +template +void SGDSolver::ComputeUpdateValue(int param_id, Dtype rate) { + const vector*>& net_params = this->net_->learnable_params(); + const vector& net_params_lr = this->net_->params_lr(); + Dtype momentum = this->param_.momentum(); + Dtype local_rate = rate * net_params_lr[param_id]; + // Compute the update to history, then copy it to the parameter diff. + switch (Caffe::mode()) { + case Caffe::CPU: { + caffe_cpu_axpby(net_params[param_id]->count(), local_rate, + net_params[param_id]->cpu_diff(), momentum, + history_[param_id]->mutable_cpu_data()); + caffe_copy(net_params[param_id]->count(), + history_[param_id]->cpu_data(), + net_params[param_id]->mutable_cpu_diff()); + break; + } + case Caffe::GPU: { +#ifndef CPU_ONLY + caffe_gpu_axpby(net_params[param_id]->count(), local_rate, + net_params[param_id]->gpu_diff(), momentum, + history_[param_id]->mutable_gpu_data()); + caffe_copy(net_params[param_id]->count(), + history_[param_id]->gpu_data(), + net_params[param_id]->mutable_gpu_diff()); +#else + NO_GPU; +#endif + break; + } + default: + LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode(); + } +} + +template +void SGDSolver::SnapshotSolverState(const string& model_filename) { + switch (this->param_.snapshot_format()) { + case caffe::SolverParameter_SnapshotFormat_BINARYPROTO: + SnapshotSolverStateToBinaryProto(model_filename); + break; + case caffe::SolverParameter_SnapshotFormat_HDF5: + SnapshotSolverStateToHDF5(model_filename); + break; + default: + LOG(FATAL) << "Unsupported snapshot format."; + } +} + +template +void SGDSolver::SnapshotSolverStateToBinaryProto( + const string& model_filename) { + SolverState state; + state.set_iter(this->iter_); + state.set_learned_net(model_filename); + state.set_current_step(this->current_step_); + state.clear_history(); + for (int i = 0; i < history_.size(); ++i) { + // Add history + BlobProto* history_blob = state.add_history(); + history_[i]->ToProto(history_blob); + } + string snapshot_filename = Solver::SnapshotFilename(".solverstate"); + LOG(INFO) + << "Snapshotting solver state to binary proto file " << snapshot_filename; + WriteProtoToBinaryFile(state, snapshot_filename.c_str()); +} + +template +void SGDSolver::SnapshotSolverStateToHDF5( + const string& model_filename) { + string snapshot_filename = + Solver::SnapshotFilename(".solverstate.h5"); + LOG(INFO) << "Snapshotting solver state to HDF5 file " << snapshot_filename; + hid_t file_hid = H5Fcreate(snapshot_filename.c_str(), H5F_ACC_TRUNC, + H5P_DEFAULT, H5P_DEFAULT); + CHECK_GE(file_hid, 0) + << "Couldn't open " << snapshot_filename << " to save solver state."; + hdf5_save_int(file_hid, "iter", this->iter_); + hdf5_save_string(file_hid, "learned_net", model_filename); + hdf5_save_int(file_hid, "current_step", this->current_step_); + hid_t history_hid = H5Gcreate2(file_hid, "history", H5P_DEFAULT, H5P_DEFAULT, + H5P_DEFAULT); + CHECK_GE(history_hid, 0) + << "Error saving solver state to " << snapshot_filename << "."; + for (int i = 0; i < history_.size(); ++i) { + ostringstream oss; + oss << i; + hdf5_save_nd_dataset(history_hid, oss.str(), *history_[i]); + } + H5Gclose(history_hid); + H5Fclose(file_hid); +} + +template +void SGDSolver::RestoreSolverStateFromBinaryProto( + const string& state_file) { + SolverState state; + ReadProtoFromBinaryFile(state_file, &state); + this->iter_ = state.iter(); + if (state.has_learned_net()) { + NetParameter net_param; + ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param); + this->net_->CopyTrainedLayersFrom(net_param); + } + this->current_step_ = state.current_step(); + CHECK_EQ(state.history_size(), history_.size()) + << "Incorrect length of history blobs."; + LOG(INFO) << "SGDSolver: restoring history"; + for (int i = 0; i < history_.size(); ++i) { + history_[i]->FromProto(state.history(i)); + } +} + +template +void SGDSolver::RestoreSolverStateFromHDF5(const string& state_file) { + hid_t file_hid = H5Fopen(state_file.c_str(), H5F_ACC_RDONLY, H5P_DEFAULT); + CHECK_GE(file_hid, 0) << "Couldn't open solver state file " << state_file; + this->iter_ = hdf5_load_int(file_hid, "iter"); + if (H5LTfind_dataset(file_hid, "learned_net")) { + string learned_net = hdf5_load_string(file_hid, "learned_net"); + this->net_->CopyTrainedLayersFrom(learned_net); + } + this->current_step_ = hdf5_load_int(file_hid, "current_step"); + hid_t history_hid = H5Gopen2(file_hid, "history", H5P_DEFAULT); + CHECK_GE(history_hid, 0) << "Error reading history from " << state_file; + int state_history_size = hdf5_get_num_links(history_hid); + CHECK_EQ(state_history_size, history_.size()) + << "Incorrect length of history blobs."; + for (int i = 0; i < history_.size(); ++i) { + ostringstream oss; + oss << i; + hdf5_load_nd_dataset(history_hid, oss.str().c_str(), 0, + kMaxBlobAxes, history_[i].get()); + } + H5Gclose(history_hid); + H5Fclose(file_hid); +} + +INSTANTIATE_CLASS(SGDSolver); + +} // namespace caffe diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 7ad7467f86f..1767ad3f666 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -10,7 +10,7 @@ #include "caffe/common.hpp" #include "caffe/parallel.hpp" #include "caffe/proto/caffe.pb.h" -#include "caffe/solver.hpp" +#include "caffe/sgd_solvers.hpp" #include "caffe/util/io.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp index ceabc9cdd2c..b181642681c 100644 --- a/src/caffe/test/test_solver.cpp +++ b/src/caffe/test/test_solver.cpp @@ -7,6 +7,7 @@ #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" +#include "caffe/sgd_solvers.hpp" #include "caffe/solver.hpp" #include "caffe/test/test_caffe_main.hpp" From 0eea815ad6fa3313888b6229499a237820258deb Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Thu, 24 Sep 2015 19:40:45 -0700 Subject: [PATCH 308/446] Change solver type to string and provide solver registry --- include/caffe/caffe.hpp | 1 + include/caffe/sgd_solvers.hpp | 6 + include/caffe/solver.hpp | 9 +- include/caffe/solver_factory.hpp | 137 ++++++++++++++++++ src/caffe/proto/caffe.proto | 27 ++-- src/caffe/solver_factory.cpp | 32 ---- src/caffe/solvers/adadelta_solver.cpp | 1 + src/caffe/solvers/adagrad_solver.cpp | 1 + src/caffe/solvers/adam_solver.cpp | 1 + src/caffe/solvers/nesterov_solver.cpp | 1 + src/caffe/solvers/rmsprop_solver.cpp | 1 + src/caffe/solvers/sgd_solver.cpp | 1 + src/caffe/test/test_gradient_based_solver.cpp | 54 ++----- src/caffe/test/test_solver_factory.cpp | 50 +++++++ tools/caffe.cpp | 2 +- 15 files changed, 233 insertions(+), 91 deletions(-) create mode 100644 include/caffe/solver_factory.hpp delete mode 100644 src/caffe/solver_factory.cpp create mode 100644 src/caffe/test/test_solver_factory.cpp diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp index 68a5e1d1d1a..bd772830b40 100644 --- a/include/caffe/caffe.hpp +++ b/include/caffe/caffe.hpp @@ -13,6 +13,7 @@ #include "caffe/parallel.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/solver.hpp" +#include "caffe/solver_factory.hpp" #include "caffe/util/benchmark.hpp" #include "caffe/util/io.hpp" #include "caffe/vision_layers.hpp" diff --git a/include/caffe/sgd_solvers.hpp b/include/caffe/sgd_solvers.hpp index 6bf1d70c752..1fc52d87137 100644 --- a/include/caffe/sgd_solvers.hpp +++ b/include/caffe/sgd_solvers.hpp @@ -19,6 +19,7 @@ class SGDSolver : public Solver { : Solver(param) { PreSolve(); } explicit SGDSolver(const string& param_file) : Solver(param_file) { PreSolve(); } + virtual inline const char* type() const { return "SGD"; } const vector > >& history() { return history_; } @@ -51,6 +52,7 @@ class NesterovSolver : public SGDSolver { : SGDSolver(param) {} explicit NesterovSolver(const string& param_file) : SGDSolver(param_file) {} + virtual inline const char* type() const { return "Nesterov"; } protected: virtual void ComputeUpdateValue(int param_id, Dtype rate); @@ -65,6 +67,7 @@ class AdaGradSolver : public SGDSolver { : SGDSolver(param) { constructor_sanity_check(); } explicit AdaGradSolver(const string& param_file) : SGDSolver(param_file) { constructor_sanity_check(); } + virtual inline const char* type() const { return "AdaGrad"; } protected: virtual void ComputeUpdateValue(int param_id, Dtype rate); @@ -84,6 +87,7 @@ class RMSPropSolver : public SGDSolver { : SGDSolver(param) { constructor_sanity_check(); } explicit RMSPropSolver(const string& param_file) : SGDSolver(param_file) { constructor_sanity_check(); } + virtual inline const char* type() const { return "RMSProp"; } protected: virtual void ComputeUpdateValue(int param_id, Dtype rate); @@ -106,6 +110,7 @@ class AdaDeltaSolver : public SGDSolver { : SGDSolver(param) { AdaDeltaPreSolve(); } explicit AdaDeltaSolver(const string& param_file) : SGDSolver(param_file) { AdaDeltaPreSolve(); } + virtual inline const char* type() const { return "AdaDelta"; } protected: void AdaDeltaPreSolve(); @@ -129,6 +134,7 @@ class AdamSolver : public SGDSolver { : SGDSolver(param) { AdamPreSolve();} explicit AdamSolver(const string& param_file) : SGDSolver(param_file) { AdamPreSolve(); } + virtual inline const char* type() const { return "Adam"; } protected: void AdamPreSolve(); diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index a045ccf254c..298a68f37df 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -5,6 +5,7 @@ #include #include "caffe/net.hpp" +#include "caffe/solver_factory.hpp" namespace caffe { @@ -83,6 +84,10 @@ class Solver { } void CheckSnapshotWritePermissions(); + /** + * @brief Returns the solver type. + */ + virtual inline const char* type() const { return ""; } protected: // Make and apply the update value for the current iteration. @@ -148,10 +153,6 @@ class WorkerSolver : public Solver { } }; -// The solver factory function -template -Solver* GetSolver(const SolverParameter& param); - } // namespace caffe #endif // CAFFE_SOLVER_HPP_ diff --git a/include/caffe/solver_factory.hpp b/include/caffe/solver_factory.hpp new file mode 100644 index 00000000000..cfff721af40 --- /dev/null +++ b/include/caffe/solver_factory.hpp @@ -0,0 +1,137 @@ +/** + * @brief A solver factory that allows one to register solvers, similar to + * layer factory. During runtime, registered solvers could be called by passing + * a SolverParameter protobuffer to the CreateSolver function: + * + * SolverRegistry::CreateSolver(param); + * + * There are two ways to register a solver. Assuming that we have a solver like: + * + * template + * class MyAwesomeSolver : public Solver { + * // your implementations + * }; + * + * and its type is its C++ class name, but without the "Solver" at the end + * ("MyAwesomeSolver" -> "MyAwesome"). + * + * If the solver is going to be created simply by its constructor, in your c++ + * file, add the following line: + * + * REGISTER_SOLVER_CLASS(MyAwesome); + * + * Or, if the solver is going to be created by another creator function, in the + * format of: + * + * template + * Solver GetMyAwesomeSolver(const SolverParameter& param) { + * // your implementation + * } + * + * then you can register the creator function instead, like + * + * REGISTER_SOLVER_CREATOR(MyAwesome, GetMyAwesomeSolver) + * + * Note that each solver type should only be registered once. + */ + +#ifndef CAFFE_SOLVER_FACTORY_H_ +#define CAFFE_SOLVER_FACTORY_H_ + +#include +#include +#include + +#include "caffe/common.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +template +class Solver; + +template +class SolverRegistry { + public: + typedef Solver* (*Creator)(const SolverParameter&); + typedef std::map CreatorRegistry; + + static CreatorRegistry& Registry() { + static CreatorRegistry* g_registry_ = new CreatorRegistry(); + return *g_registry_; + } + + // Adds a creator. + static void AddCreator(const string& type, Creator creator) { + CreatorRegistry& registry = Registry(); + CHECK_EQ(registry.count(type), 0) + << "Solver type " << type << " already registered."; + registry[type] = creator; + } + + // Get a solver using a SolverParameter. + static Solver* CreateSolver(const SolverParameter& param) { + const string& type = param.type(); + CreatorRegistry& registry = Registry(); + CHECK_EQ(registry.count(type), 1) << "Unknown solver type: " << type + << " (known types: " << SolverTypeListString() << ")"; + return registry[type](param); + } + + static vector SolverTypeList() { + CreatorRegistry& registry = Registry(); + vector solver_types; + for (typename CreatorRegistry::iterator iter = registry.begin(); + iter != registry.end(); ++iter) { + solver_types.push_back(iter->first); + } + return solver_types; + } + + private: + // Solver registry should never be instantiated - everything is done with its + // static variables. + SolverRegistry() {} + + static string SolverTypeListString() { + vector solver_types = SolverTypeList(); + string solver_types_str; + for (vector::iterator iter = solver_types.begin(); + iter != solver_types.end(); ++iter) { + if (iter != solver_types.begin()) { + solver_types_str += ", "; + } + solver_types_str += *iter; + } + return solver_types_str; + } +}; + + +template +class SolverRegisterer { + public: + SolverRegisterer(const string& type, + Solver* (*creator)(const SolverParameter&)) { + // LOG(INFO) << "Registering solver type: " << type; + SolverRegistry::AddCreator(type, creator); + } +}; + + +#define REGISTER_SOLVER_CREATOR(type, creator) \ + static SolverRegisterer g_creator_f_##type(#type, creator); \ + static SolverRegisterer g_creator_d_##type(#type, creator) \ + +#define REGISTER_SOLVER_CLASS(type) \ + template \ + Solver* Creator_##type##Solver( \ + const SolverParameter& param) \ + { \ + return new type##Solver(param); \ + } \ + REGISTER_SOLVER_CREATOR(type, Creator_##type##Solver) + +} // namespace caffe + +#endif // CAFFE_SOLVER_FACTORY_H_ diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 4794991f917..76c869c127e 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -98,7 +98,7 @@ message NetParameter { // NOTE // Update the next available ID when you add a new SolverParameter field. // -// SolverParameter next available ID: 40 (last added: momentum2) +// SolverParameter next available ID: 41 (last added: type) message SolverParameter { ////////////////////////////////////////////////////////////////////////////// // Specifying the train and test networks @@ -209,16 +209,9 @@ message SolverParameter { // (and by default) initialize using a seed derived from the system clock. optional int64 random_seed = 20 [default = -1]; - // Solver type - enum SolverType { - SGD = 0; - NESTEROV = 1; - ADAGRAD = 2; - RMSPROP = 3; - ADADELTA = 4; - ADAM = 5; - } - optional SolverType solver_type = 30 [default = SGD]; + // type of the solver + optional string type = 40 [default = "SGD"]; + // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam optional float delta = 31 [default = 1e-8]; // parameters for the Adam solver @@ -234,6 +227,18 @@ message SolverParameter { // If false, don't save a snapshot after training finishes. optional bool snapshot_after_train = 28 [default = true]; + + // DEPRECATED: old solver enum types, use string instead + enum SolverType { + SGD = 0; + NESTEROV = 1; + ADAGRAD = 2; + RMSPROP = 3; + ADADELTA = 4; + ADAM = 5; + } + // DEPRECATED: use type instead of solver_type + optional SolverType solver_type = 30 [default = SGD]; } // A message that stores the solver snapshots diff --git a/src/caffe/solver_factory.cpp b/src/caffe/solver_factory.cpp deleted file mode 100644 index f78fab28720..00000000000 --- a/src/caffe/solver_factory.cpp +++ /dev/null @@ -1,32 +0,0 @@ -#include "caffe/solver.hpp" -#include "caffe/sgd_solvers.hpp" - -namespace caffe { - -template -Solver* GetSolver(const SolverParameter& param) { - SolverParameter_SolverType type = param.solver_type(); - - switch (type) { - case SolverParameter_SolverType_SGD: - return new SGDSolver(param); - case SolverParameter_SolverType_NESTEROV: - return new NesterovSolver(param); - case SolverParameter_SolverType_ADAGRAD: - return new AdaGradSolver(param); - case SolverParameter_SolverType_RMSPROP: - return new RMSPropSolver(param); - case SolverParameter_SolverType_ADADELTA: - return new AdaDeltaSolver(param); - case SolverParameter_SolverType_ADAM: - return new AdamSolver(param); - default: - LOG(FATAL) << "Unknown SolverType: " << type; - } - return (Solver*) NULL; -} - -template Solver* GetSolver(const SolverParameter& param); -template Solver* GetSolver(const SolverParameter& param); - -} // namespace caffe diff --git a/src/caffe/solvers/adadelta_solver.cpp b/src/caffe/solvers/adadelta_solver.cpp index 45cd4eb2988..a37899ebbb4 100644 --- a/src/caffe/solvers/adadelta_solver.cpp +++ b/src/caffe/solvers/adadelta_solver.cpp @@ -151,5 +151,6 @@ void AdaDeltaSolver::ComputeUpdateValue(int param_id, Dtype rate) { } INSTANTIATE_CLASS(AdaDeltaSolver); +REGISTER_SOLVER_CLASS(AdaDelta); } // namespace caffe diff --git a/src/caffe/solvers/adagrad_solver.cpp b/src/caffe/solvers/adagrad_solver.cpp index 627d816a470..5e406326095 100644 --- a/src/caffe/solvers/adagrad_solver.cpp +++ b/src/caffe/solvers/adagrad_solver.cpp @@ -84,5 +84,6 @@ void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { } INSTANTIATE_CLASS(AdaGradSolver); +REGISTER_SOLVER_CLASS(AdaGrad); } // namespace caffe diff --git a/src/caffe/solvers/adam_solver.cpp b/src/caffe/solvers/adam_solver.cpp index 8c334f665dd..cb0fbfe2f78 100644 --- a/src/caffe/solvers/adam_solver.cpp +++ b/src/caffe/solvers/adam_solver.cpp @@ -108,5 +108,6 @@ void AdamSolver::ComputeUpdateValue(int param_id, Dtype rate) { } INSTANTIATE_CLASS(AdamSolver); +REGISTER_SOLVER_CLASS(Adam); } // namespace caffe diff --git a/src/caffe/solvers/nesterov_solver.cpp b/src/caffe/solvers/nesterov_solver.cpp index 8135ee2c657..34bf01ebf29 100644 --- a/src/caffe/solvers/nesterov_solver.cpp +++ b/src/caffe/solvers/nesterov_solver.cpp @@ -66,5 +66,6 @@ void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { } INSTANTIATE_CLASS(NesterovSolver); +REGISTER_SOLVER_CLASS(Nesterov); } // namespace caffe diff --git a/src/caffe/solvers/rmsprop_solver.cpp b/src/caffe/solvers/rmsprop_solver.cpp index 96d1b3dda0b..c6247676094 100644 --- a/src/caffe/solvers/rmsprop_solver.cpp +++ b/src/caffe/solvers/rmsprop_solver.cpp @@ -80,5 +80,6 @@ void RMSPropSolver::ComputeUpdateValue(int param_id, Dtype rate) { } INSTANTIATE_CLASS(RMSPropSolver); +REGISTER_SOLVER_CLASS(RMSProp); } // namespace caffe diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp index 89ef5ec451d..32bf19b17c8 100644 --- a/src/caffe/solvers/sgd_solver.cpp +++ b/src/caffe/solvers/sgd_solver.cpp @@ -343,5 +343,6 @@ void SGDSolver::RestoreSolverStateFromHDF5(const string& state_file) { } INSTANTIATE_CLASS(SGDSolver); +REGISTER_SOLVER_CLASS(SGD); } // namespace caffe diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 1767ad3f666..84c6747f61a 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -47,7 +47,6 @@ class GradientBasedSolverTest : public MultiDeviceTest { // Test data: check out generate_sample_data.py in the same directory. string* input_file_; - virtual SolverParameter_SolverType solver_type() = 0; virtual void InitSolver(const SolverParameter& param) = 0; virtual void InitSolverFromProtoString(const string& proto) { @@ -290,8 +289,8 @@ class GradientBasedSolverTest : public MultiDeviceTest { ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]); // Finally, compute update. const vector > >& history = solver_->history(); - if (solver_type() != SolverParameter_SolverType_ADADELTA - && solver_type() != SolverParameter_SolverType_ADAM) { + if (solver_->type() != string("AdaDelta") + && solver_->type() != string("Adam")) { ASSERT_EQ(2, history.size()); // 1 blob for weights, 1 for bias } else { ASSERT_EQ(4, history.size()); // additional blobs for update history @@ -300,26 +299,19 @@ class GradientBasedSolverTest : public MultiDeviceTest { const Dtype history_value = (i == D) ? history[1]->cpu_data()[0] : history[0]->cpu_data()[i]; const Dtype temp = momentum * history_value; - switch (solver_type()) { - case SolverParameter_SolverType_SGD: + if (solver_->type() == string("SGD")) { update_value += temp; - break; - case SolverParameter_SolverType_NESTEROV: + } else if (solver_->type() == string("Nesterov")) { update_value += temp; // step back then over-step update_value = (1 + momentum) * update_value - temp; - break; - case SolverParameter_SolverType_ADAGRAD: + } else if (solver_->type() == string("AdaGrad")) { update_value /= std::sqrt(history_value + grad * grad) + delta_; - break; - case SolverParameter_SolverType_RMSPROP: { + } else if (solver_->type() == string("RMSProp")) { const Dtype rms_decay = 0.95; update_value /= std::sqrt(rms_decay*history_value + grad * grad * (1 - rms_decay)) + delta_; - } - break; - case SolverParameter_SolverType_ADADELTA: - { + } else if (solver_->type() == string("AdaDelta")) { const Dtype update_history_value = (i == D) ? history[1 + num_param_blobs]->cpu_data()[0] : history[0 + num_param_blobs]->cpu_data()[i]; @@ -330,9 +322,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { // not actually needed, just here for illustrative purposes // const Dtype weighted_update_average = // momentum * update_history_value + (1 - momentum) * (update_value); - break; - } - case SolverParameter_SolverType_ADAM: { + } else if (solver_->type() == string("Adam")) { const Dtype momentum2 = 0.999; const Dtype m = history_value; const Dtype v = (i == D) ? @@ -344,10 +334,8 @@ class GradientBasedSolverTest : public MultiDeviceTest { std::sqrt(Dtype(1) - pow(momentum2, num_iters)) / (Dtype(1.) - pow(momentum, num_iters)); update_value = alpha_t * val_m / (std::sqrt(val_v) + delta_); - break; - } - default: - LOG(FATAL) << "Unknown solver type: " << solver_type(); + } else { + LOG(FATAL) << "Unknown solver type: " << solver_->type(); } if (i == D) { updated_bias.mutable_cpu_diff()[0] = update_value; @@ -392,7 +380,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin); // Check the solver's history -- should contain the previous update value. - if (solver_type() == SolverParameter_SolverType_SGD) { + if (solver_->type() == string("SGD")) { const vector > >& history = solver_->history(); ASSERT_EQ(2, history.size()); for (int i = 0; i < D; ++i) { @@ -581,10 +569,6 @@ class SGDSolverTest : public GradientBasedSolverTest { virtual void InitSolver(const SolverParameter& param) { this->solver_.reset(new SGDSolver(param)); } - - virtual SolverParameter_SolverType solver_type() { - return SolverParameter_SolverType_SGD; - } }; TYPED_TEST_CASE(SGDSolverTest, TestDtypesAndDevices); @@ -721,9 +705,6 @@ class AdaGradSolverTest : public GradientBasedSolverTest { virtual void InitSolver(const SolverParameter& param) { this->solver_.reset(new AdaGradSolver(param)); } - virtual SolverParameter_SolverType solver_type() { - return SolverParameter_SolverType_ADAGRAD; - } }; TYPED_TEST_CASE(AdaGradSolverTest, TestDtypesAndDevices); @@ -824,9 +805,6 @@ class NesterovSolverTest : public GradientBasedSolverTest { virtual void InitSolver(const SolverParameter& param) { this->solver_.reset(new NesterovSolver(param)); } - virtual SolverParameter_SolverType solver_type() { - return SolverParameter_SolverType_NESTEROV; - } }; TYPED_TEST_CASE(NesterovSolverTest, TestDtypesAndDevices); @@ -960,10 +938,6 @@ class AdaDeltaSolverTest : public GradientBasedSolverTest { virtual void InitSolver(const SolverParameter& param) { this->solver_.reset(new AdaDeltaSolver(param)); } - - virtual SolverParameter_SolverType solver_type() { - return SolverParameter_SolverType_ADADELTA; - } }; TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices); @@ -1098,9 +1072,6 @@ class AdamSolverTest : public GradientBasedSolverTest { new_param.set_momentum2(momentum2); this->solver_.reset(new AdamSolver(new_param)); } - virtual SolverParameter_SolverType solver_type() { - return SolverParameter_SolverType_ADAM; - } }; TYPED_TEST_CASE(AdamSolverTest, TestDtypesAndDevices); @@ -1201,9 +1172,6 @@ class RMSPropSolverTest : public GradientBasedSolverTest { new_param.set_rms_decay(rms_decay); this->solver_.reset(new RMSPropSolver(new_param)); } - virtual SolverParameter_SolverType solver_type() { - return SolverParameter_SolverType_RMSPROP; - } }; TYPED_TEST_CASE(RMSPropSolverTest, TestDtypesAndDevices); diff --git a/src/caffe/test/test_solver_factory.cpp b/src/caffe/test/test_solver_factory.cpp new file mode 100644 index 00000000000..eef5290fe2e --- /dev/null +++ b/src/caffe/test/test_solver_factory.cpp @@ -0,0 +1,50 @@ +#include +#include + +#include "boost/scoped_ptr.hpp" +#include "google/protobuf/text_format.h" +#include "gtest/gtest.h" + +#include "caffe/common.hpp" +#include "caffe/solver.hpp" +#include "caffe/solver_factory.hpp" + +#include "caffe/test/test_caffe_main.hpp" + +namespace caffe { + +template +class SolverFactoryTest : public MultiDeviceTest { + protected: + SolverParameter simple_solver_param() { + const string solver_proto = + "train_net_param { " + " layer { " + " name: 'data' type: 'DummyData' top: 'data' " + " dummy_data_param { shape { dim: 1 } } " + " } " + "} "; + SolverParameter solver_param; + CHECK(google::protobuf::TextFormat::ParseFromString( + solver_proto, &solver_param)); + return solver_param; + } +}; + +TYPED_TEST_CASE(SolverFactoryTest, TestDtypesAndDevices); + +TYPED_TEST(SolverFactoryTest, TestCreateSolver) { + typedef typename TypeParam::Dtype Dtype; + typename SolverRegistry::CreatorRegistry& registry = + SolverRegistry::Registry(); + shared_ptr > solver; + SolverParameter solver_param = this->simple_solver_param(); + for (typename SolverRegistry::CreatorRegistry::iterator iter = + registry.begin(); iter != registry.end(); ++iter) { + solver_param.set_type(iter->first); + solver.reset(SolverRegistry::CreateSolver(solver_param)); + EXPECT_EQ(iter->first, solver->type()); + } +} + +} // namespace caffe diff --git a/tools/caffe.cpp b/tools/caffe.cpp index e3f684b5ab3..1cb6ad895da 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -194,7 +194,7 @@ int train() { GetRequestedAction(FLAGS_sighup_effect)); shared_ptr > - solver(caffe::GetSolver(solver_param)); + solver(caffe::SolverRegistry::CreateSolver(solver_param)); solver->SetActionFunction(signal_handler.GetActionFunction()); From c1f7fe1cffa4388886b735f49cd915fad905fca4 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Sat, 26 Sep 2015 11:47:02 -0700 Subject: [PATCH 309/446] Add automatic upgrade for solver type --- include/caffe/caffe.hpp | 1 + include/caffe/util/upgrade_proto.hpp | 12 +++++ matlab/+caffe/private/caffe_.cpp | 5 +- python/caffe/_caffe.cpp | 4 +- src/caffe/solver.cpp | 2 +- src/caffe/test/test_upgrade_proto.cpp | 61 ++++++++++++++++++++++ src/caffe/util/upgrade_proto.cpp | 74 +++++++++++++++++++++++++++ tools/caffe.cpp | 2 +- tools/upgrade_solver_proto_text.cpp | 50 ++++++++++++++++++ 9 files changed, 206 insertions(+), 5 deletions(-) create mode 100644 tools/upgrade_solver_proto_text.cpp diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp index bd772830b40..a339efba5c0 100644 --- a/include/caffe/caffe.hpp +++ b/include/caffe/caffe.hpp @@ -16,6 +16,7 @@ #include "caffe/solver_factory.hpp" #include "caffe/util/benchmark.hpp" #include "caffe/util/io.hpp" +#include "caffe/util/upgrade_proto.hpp" #include "caffe/vision_layers.hpp" #endif // CAFFE_CAFFE_HPP_ diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp index 6a1418434a6..c94bb3caaa3 100644 --- a/include/caffe/util/upgrade_proto.hpp +++ b/include/caffe/util/upgrade_proto.hpp @@ -59,6 +59,18 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param, const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type); +// Return true iff the solver contains any old solver_type specified as enums +bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param); + +bool UpgradeSolverType(SolverParameter* solver_param); + +// Check for deprecations and upgrade the SolverParameter as needed. +bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param); + +// Read parameters from a file into a SolverParameter proto message. +void ReadSolverParamsFromTextFileOrDie(const string& param_file, + SolverParameter* param); + } // namespace caffe #endif // CAFFE_UTIL_UPGRADE_PROTO_H_ diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp index 7883f79ebd9..1641e14b534 100644 --- a/matlab/+caffe/private/caffe_.cpp +++ b/matlab/+caffe/private/caffe_.cpp @@ -188,7 +188,10 @@ static void get_solver(MEX_ARGS) { "Usage: caffe_('get_solver', solver_file)"); char* solver_file = mxArrayToString(prhs[0]); mxCHECK_FILE_EXIST(solver_file); - shared_ptr > solver(new caffe::SGDSolver(solver_file)); + SolverParameter solver_param; + ReadSolverParamsFromTextFileOrDie(solver_file, &solver_param); + shared_ptr > solver( + SolverRegistry::CreateSolver(solver_param)); solvers_.push_back(solver); plhs[0] = ptr_to_handle >(solver.get()); mxFree(solver_file); diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index 0e38dee70d1..8687dd872eb 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -134,8 +134,8 @@ void Net_SetInputArrays(Net* net, bp::object data_obj, Solver* GetSolverFromFile(const string& filename) { SolverParameter param; - ReadProtoFromTextFileOrDie(filename, ¶m); - return GetSolver(param); + ReadSolverParamsFromTextFileOrDie(filename, ¶m); + return SolverRegistry::CreateSolver(param); } struct NdarrayConverterGenerator { diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index 016a02888d8..d3bc7361dd5 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -36,7 +36,7 @@ Solver::Solver(const string& param_file, const Solver* root_solver) : net_(), callbacks_(), root_solver_(root_solver), requested_early_exit_(false) { SolverParameter param; - ReadProtoFromTextFileOrDie(param_file, ¶m); + ReadSolverParamsFromTextFileOrDie(param_file, ¶m); Init(param); } diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp index ee05b151e72..df9aeb62464 100644 --- a/src/caffe/test/test_upgrade_proto.cpp +++ b/src/caffe/test/test_upgrade_proto.cpp @@ -2928,4 +2928,65 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) { } } #endif // USE_OPENCV + +class SolverTypeUpgradeTest : public ::testing::Test { + protected: + void RunSolverTypeUpgradeTest( + const string& input_param_string, const string& output_param_string) { + // Test upgrading old solver_type field (enum) to new type field (string) + SolverParameter input_param; + CHECK(google::protobuf::TextFormat::ParseFromString( + input_param_string, &input_param)); + SolverParameter expected_output_param; + CHECK(google::protobuf::TextFormat::ParseFromString( + output_param_string, &expected_output_param)); + SolverParameter actual_output_param = input_param; + UpgradeSolverType(&actual_output_param); + EXPECT_EQ(expected_output_param.DebugString(), + actual_output_param.DebugString()); + } +}; + +TEST_F(SolverTypeUpgradeTest, TestSimple) { + const char* old_type_vec[6] = { "SGD", "ADAGRAD", "NESTEROV", "RMSPROP", + "ADADELTA", "ADAM" }; + const char* new_type_vec[6] = { "SGD", "AdaGrad", "Nesterov", "RMSProp", + "AdaDelta", "Adam" }; + for (int i = 0; i < 6; ++i) { + const string& input_proto = + "net: 'examples/mnist/lenet_train_test.prototxt' " + "test_iter: 100 " + "test_interval: 500 " + "base_lr: 0.01 " + "momentum: 0.0 " + "weight_decay: 0.0005 " + "lr_policy: 'inv' " + "gamma: 0.0001 " + "power: 0.75 " + "display: 100 " + "max_iter: 10000 " + "snapshot: 5000 " + "snapshot_prefix: 'examples/mnist/lenet_rmsprop' " + "solver_mode: GPU " + "solver_type: " + std::string(old_type_vec[i]) + " "; + const string& expected_output_proto = + "net: 'examples/mnist/lenet_train_test.prototxt' " + "test_iter: 100 " + "test_interval: 500 " + "base_lr: 0.01 " + "momentum: 0.0 " + "weight_decay: 0.0005 " + "lr_policy: 'inv' " + "gamma: 0.0001 " + "power: 0.75 " + "display: 100 " + "max_iter: 10000 " + "snapshot: 5000 " + "snapshot_prefix: 'examples/mnist/lenet_rmsprop' " + "solver_mode: GPU " + "type: '" + std::string(new_type_vec[i]) + "' "; + this->RunSolverTypeUpgradeTest(input_proto, expected_output_proto); + } +} + } // NOLINT(readability/fn_size) // namespace caffe diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp index 6eae9fec00a..ff3f8ffc4f0 100644 --- a/src/caffe/util/upgrade_proto.cpp +++ b/src/caffe/util/upgrade_proto.cpp @@ -937,4 +937,78 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) { } } +// Return true iff the solver contains any old solver_type specified as enums +bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param) { + if (solver_param.has_solver_type()) { + return true; + } + return false; +} + +bool UpgradeSolverType(SolverParameter* solver_param) { + CHECK(!solver_param->has_solver_type() || !solver_param->has_type()) + << "Failed to upgrade solver: old solver_type field (enum) and new type " + << "field (string) cannot be both specified in solver proto text."; + if (solver_param->has_solver_type()) { + string type; + switch (solver_param->solver_type()) { + case SolverParameter_SolverType_SGD: + type = "SGD"; + break; + case SolverParameter_SolverType_NESTEROV: + type = "Nesterov"; + break; + case SolverParameter_SolverType_ADAGRAD: + type = "AdaGrad"; + break; + case SolverParameter_SolverType_RMSPROP: + type = "RMSProp"; + break; + case SolverParameter_SolverType_ADADELTA: + type = "AdaDelta"; + break; + case SolverParameter_SolverType_ADAM: + type = "Adam"; + break; + default: + LOG(FATAL) << "Unknown SolverParameter solver_type: " << type; + } + solver_param->set_type(type); + solver_param->clear_solver_type(); + } else { + LOG(ERROR) << "Warning: solver type already up to date. "; + return false; + } + return true; +} + +// Check for deprecations and upgrade the SolverParameter as needed. +bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param) { + bool success = true; + // Try to upgrade old style solver_type enum fields into new string type + if (SolverNeedsTypeUpgrade(*param)) { + LOG(INFO) << "Attempting to upgrade input file specified using deprecated " + << "'solver_type' field (enum)': " << param_file; + if (!UpgradeSolverType(param)) { + success = false; + LOG(ERROR) << "Warning: had one or more problems upgrading " + << "SolverType (see above)."; + } else { + LOG(INFO) << "Successfully upgraded file specified using deprecated " + << "'solver_type' field (enum) to 'type' field (string)."; + LOG(WARNING) << "Note that future Caffe releases will only support " + << "'type' field (string) for a solver's type."; + } + } + return success; +} + +// Read parameters from a file into a SolverParameter proto message. +void ReadSolverParamsFromTextFileOrDie(const string& param_file, + SolverParameter* param) { + CHECK(ReadProtoFromTextFile(param_file, param)) + << "Failed to parse SolverParameter file: " << param_file; + UpgradeSolverAsNeeded(param_file, param); +} + } // namespace caffe diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 1cb6ad895da..305cfc3635d 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -157,7 +157,7 @@ int train() { "but not both."; caffe::SolverParameter solver_param; - caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param); + caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param); // If the gpus flag is not provided, allow the mode and device to be set // in the solver prototxt. diff --git a/tools/upgrade_solver_proto_text.cpp b/tools/upgrade_solver_proto_text.cpp new file mode 100644 index 00000000000..7130232aed7 --- /dev/null +++ b/tools/upgrade_solver_proto_text.cpp @@ -0,0 +1,50 @@ +// This is a script to upgrade old solver prototxts to the new format. +// Usage: +// upgrade_solver_proto_text old_solver_proto_file_in solver_proto_file_out + +#include +#include // NOLINT(readability/streams) +#include // NOLINT(readability/streams) +#include + +#include "caffe/caffe.hpp" +#include "caffe/util/io.hpp" +#include "caffe/util/upgrade_proto.hpp" + +using std::ofstream; + +using namespace caffe; // NOLINT(build/namespaces) + +int main(int argc, char** argv) { + ::google::InitGoogleLogging(argv[0]); + if (argc != 3) { + LOG(ERROR) << "Usage: upgrade_solver_proto_text " + << "old_solver_proto_file_in solver_proto_file_out"; + return 1; + } + + SolverParameter solver_param; + string input_filename(argv[1]); + if (!ReadProtoFromTextFile(input_filename, &solver_param)) { + LOG(ERROR) << "Failed to parse input text file as SolverParameter: " + << input_filename; + return 2; + } + bool need_upgrade = SolverNeedsTypeUpgrade(solver_param); + bool success = true; + if (need_upgrade) { + success = UpgradeSolverAsNeeded(input_filename, &solver_param); + if (!success) { + LOG(ERROR) << "Encountered error(s) while upgrading prototxt; " + << "see details above."; + } + } else { + LOG(ERROR) << "File already in latest proto format: " << input_filename; + } + + // Save new format prototxt. + WriteProtoToTextFile(solver_param, argv[2]); + + LOG(ERROR) << "Wrote upgraded SolverParameter text proto to " << argv[2]; + return !success; +} From 9563537e86363fac2768200f5748000ec6b3a911 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Sat, 26 Sep 2015 11:47:32 -0700 Subject: [PATCH 310/446] Update examples and docs --- docs/tutorial/solver.md | 28 +++++++++---------- examples/mnist/lenet_adadelta_solver.prototxt | 2 +- examples/mnist/lenet_solver_adam.prototxt | 2 +- examples/mnist/lenet_solver_rmsprop.prototxt | 2 +- ...mnist_autoencoder_solver_adadelta.prototxt | 2 +- .../mnist_autoencoder_solver_adagrad.prototxt | 2 +- ...mnist_autoencoder_solver_nesterov.prototxt | 2 +- 7 files changed, 20 insertions(+), 20 deletions(-) diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md index b150f6487bc..b719f715a4b 100644 --- a/docs/tutorial/solver.md +++ b/docs/tutorial/solver.md @@ -8,12 +8,12 @@ The responsibilities of learning are divided between the Solver for overseeing t The Caffe solvers are: -- Stochastic Gradient Descent (`SGD`), -- AdaDelta (`ADADELTA`), -- Adaptive Gradient (`ADAGRAD`), -- Adam (`ADAM`), -- Nesterov's Accelerated Gradient (`NESTEROV`) and -- RMSprop (`RMSPROP`) +- Stochastic Gradient Descent (`type: "SGD"`), +- AdaDelta (`type: "AdaDelta"`), +- Adaptive Gradient (`type: "AdaGrad"`), +- Adam (`type: "Adam"`), +- Nesterov's Accelerated Gradient (`type: "Nesterov"`) and +- RMSprop (`type: "RMSProp"`) The solver @@ -51,7 +51,7 @@ The parameter update $$\Delta W$$ is formed by the solver from the error gradien ### SGD -**Stochastic gradient descent** (`solver_type: SGD`) updates the weights $$ W $$ by a linear combination of the negative gradient $$ \nabla L(W) $$ and the previous weight update $$ V_t $$. +**Stochastic gradient descent** (`type: "SGD"`) updates the weights $$ W $$ by a linear combination of the negative gradient $$ \nabla L(W) $$ and the previous weight update $$ V_t $$. The **learning rate** $$ \alpha $$ is the weight of the negative gradient. The **momentum** $$ \mu $$ is the weight of the previous update. @@ -113,7 +113,7 @@ If learning diverges (e.g., you start to see very large or `NaN` or `inf` loss v ### AdaDelta -The **AdaDelta** (`solver_type: ADADELTA`) method (M. Zeiler [1]) is a "robust learning rate method". It is a gradient-based optimization method (like SGD). The update formulas are +The **AdaDelta** (`type: "AdaDelta"`) method (M. Zeiler [1]) is a "robust learning rate method". It is a gradient-based optimization method (like SGD). The update formulas are $$ \begin{align} @@ -125,7 +125,7 @@ E[g^2]_t &= \delta{E[g^2]_{t-1} } + (1-\delta)g_{t}^2 \end{align} $$ -and +and $$ (W_{t+1})_i = @@ -139,7 +139,7 @@ $$ ### AdaGrad -The **adaptive gradient** (`solver_type: ADAGRAD`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words. +The **adaptive gradient** (`type: "AdaGrad"`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words. Given the update information from all previous iterations $$ \left( \nabla L(W) \right)_{t'} $$ for $$ t' \in \{1, 2, ..., t\} $$, the update formulas proposed by [1] are as follows, specified for each component $$i$$ of the weights $$W$$: @@ -159,7 +159,7 @@ Note that in practice, for weights $$ W \in \mathcal{R}^d $$, AdaGrad implementa ### Adam -The **Adam** (`solver_type: ADAM`), proposed in Kingma et al. [1], is a gradient-based optimization method (like SGD). This includes an "adaptive moment estimation" ($$m_t, v_t$$) and can be regarded as a generalization of AdaGrad. The update formulas are +The **Adam** (`type: "Adam"`), proposed in Kingma et al. [1], is a gradient-based optimization method (like SGD). This includes an "adaptive moment estimation" ($$m_t, v_t$$) and can be regarded as a generalization of AdaGrad. The update formulas are $$ (m_t)_i = \beta_1 (m_{t-1})_i + (1-\beta_1)(\nabla L(W_t))_i,\\ @@ -181,7 +181,7 @@ Kingma et al. [1] proposed to use $$\beta_1 = 0.9, \beta_2 = 0.999, \varepsilon ### NAG -**Nesterov's accelerated gradient** (`solver_type: NESTEROV`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$. +**Nesterov's accelerated gradient** (`type: "Nesterov"`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$. Though the required assumptions to achieve the $$ \mathcal{O}(1/t^2) $$ convergence typically will not hold for deep networks trained with Caffe (e.g., due to non-smoothness and non-convexity), in practice NAG can be a very effective method for optimizing certain types of deep learning architectures, as demonstrated for deep MNIST autoencoders by Sutskever et al. [2]. The weight update formulas look very similar to the SGD updates given above: @@ -206,10 +206,10 @@ What distinguishes the method from SGD is the weight setting $$ W $$ on which we ### RMSprop -The **RMSprop** (`solver_type: RMSPROP`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are +The **RMSprop** (`type: "RMSProp"`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are $$ -(v_t)_i = +(v_t)_i = \begin{cases} (v_{t-1})_i + \delta, &(\nabla L(W_t))_i(\nabla L(W_{t-1}))_i > 0\\ (v_{t-1})_i \cdot (1-\delta), & \text{else} diff --git a/examples/mnist/lenet_adadelta_solver.prototxt b/examples/mnist/lenet_adadelta_solver.prototxt index 776d1e06139..16176c0ffae 100644 --- a/examples/mnist/lenet_adadelta_solver.prototxt +++ b/examples/mnist/lenet_adadelta_solver.prototxt @@ -20,5 +20,5 @@ snapshot: 5000 snapshot_prefix: "examples/mnist/lenet_adadelta" # solver mode: CPU or GPU solver_mode: GPU -solver_type: ADADELTA +type: "AdaDelta" delta: 1e-6 diff --git a/examples/mnist/lenet_solver_adam.prototxt b/examples/mnist/lenet_solver_adam.prototxt index d22c5718f3f..4b5336b1a04 100644 --- a/examples/mnist/lenet_solver_adam.prototxt +++ b/examples/mnist/lenet_solver_adam.prototxt @@ -22,5 +22,5 @@ max_iter: 10000 snapshot: 5000 snapshot_prefix: "examples/mnist/lenet" # solver mode: CPU or GPU -solver_type: ADAM +type: "Adam" solver_mode: GPU diff --git a/examples/mnist/lenet_solver_rmsprop.prototxt b/examples/mnist/lenet_solver_rmsprop.prototxt index 74dadc51069..924b72d306e 100644 --- a/examples/mnist/lenet_solver_rmsprop.prototxt +++ b/examples/mnist/lenet_solver_rmsprop.prototxt @@ -23,5 +23,5 @@ snapshot: 5000 snapshot_prefix: "examples/mnist/lenet_rmsprop" # solver mode: CPU or GPU solver_mode: GPU -solver_type: RMSPROP +type: "RMSProp" rms_decay: 0.98 diff --git a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt index 065647df31b..26c4084a374 100644 --- a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt +++ b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt @@ -16,4 +16,4 @@ snapshot: 10000 snapshot_prefix: "examples/mnist/mnist_autoencoder_adadelta_train" # solver mode: CPU or GPU solver_mode: GPU -solver_type: ADADELTA +type: "AdaDelta" diff --git a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt index cc0ed9e310a..065cdb20ddc 100644 --- a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt +++ b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt @@ -14,4 +14,4 @@ snapshot: 10000 snapshot_prefix: "examples/mnist/mnist_autoencoder_adagrad_train" # solver mode: CPU or GPU solver_mode: GPU -solver_type: ADAGRAD +type: "AdaGrad" diff --git a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt index 2a59fd45c8d..c95e3fe7e49 100644 --- a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt +++ b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt @@ -17,4 +17,4 @@ snapshot_prefix: "examples/mnist/mnist_autoencoder_nesterov_train" momentum: 0.95 # solver mode: CPU or GPU solver_mode: GPU -solver_type: NESTEROV +type: "Nesterov" From 6f8370a1f3917b525e60896586cac41bb829ac2b Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 16 Oct 2015 17:32:27 -0700 Subject: [PATCH 311/446] clean up logging for Net init - condense conditions by `LOG_IF` - only log memory use once after all tops --- src/caffe/net.cpp | 182 +++++++++++++++++++--------------------------- 1 file changed, 76 insertions(+), 106 deletions(-) diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index ebb8b5d28c2..1ad93e6af5f 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -46,10 +46,9 @@ void Net::Init(const NetParameter& in_param) { // the current NetState. NetParameter filtered_param; FilterNet(in_param, &filtered_param); - if (Caffe::root_solver()) { - LOG(INFO) << "Initializing net from parameters: " << std::endl - << filtered_param.DebugString(); - } + LOG_IF(INFO, Caffe::root_solver()) + << "Initializing net from parameters: " << std::endl + << filtered_param.DebugString(); // Create a copy of filtered_param with splits added where necessary. NetParameter param; InsertSplits(filtered_param, ¶m); @@ -73,8 +72,6 @@ void Net::Init(const NetParameter& in_param) { const int layer_id = -1; // inputs have fake layer ID -1 AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx); } - DLOG_IF(INFO, Caffe::root_solver()) - << "Memory required for data: " << memory_used_ * sizeof(Dtype); // For each layer, set up its input and output bottom_vecs_.resize(param.layer_size()); top_vecs_.resize(param.layer_size()); @@ -106,9 +103,8 @@ void Net::Init(const NetParameter& in_param) { layers_.push_back(LayerRegistry::CreateLayer(layer_param)); } layer_names_.push_back(layer_param.name()); - if (Caffe::root_solver()) { - LOG(INFO) << "Creating Layer " << layer_param.name(); - } + LOG_IF(INFO, Caffe::root_solver()) + << "Creating Layer " << layer_param.name(); bool need_backward = false; // Figure out this layer's input and output @@ -151,29 +147,23 @@ void Net::Init(const NetParameter& in_param) { } else { layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]); } - if (Caffe::root_solver()) { - LOG(INFO) << "Setting up " << layer_names_[layer_id]; - } + LOG_IF(INFO, Caffe::root_solver()) + << "Setting up " << layer_names_[layer_id]; for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) { if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) { blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0)); } blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id); - if (Caffe::root_solver()) { - LOG(INFO) << "Top shape: " - << top_vecs_[layer_id][top_id]->shape_string(); - } + LOG_IF(INFO, Caffe::root_solver()) + << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string(); if (layer->loss(top_id)) { - if (Caffe::root_solver()) { - LOG(INFO) << " with loss weight " << layer->loss(top_id); - } + LOG_IF(INFO, Caffe::root_solver()) + << " with loss weight " << layer->loss(top_id); } memory_used_ += top_vecs_[layer_id][top_id]->count(); } - if (Caffe::root_solver()) { - DLOG(INFO) << "Memory required for data: " - << memory_used_ * sizeof(Dtype); - } + LOG_IF(INFO, Caffe::root_solver()) + << "Memory required for data: " << memory_used_ * sizeof(Dtype); const int param_size = layer_param.param_size(); const int num_param_blobs = layers_[layer_id]->blobs().size(); CHECK_LE(param_size, num_param_blobs) @@ -231,14 +221,12 @@ void Net::Init(const NetParameter& in_param) { } } if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; } - if (layer_need_backward_[layer_id]) { - if (Caffe::root_solver()) { + if (Caffe::root_solver()) { + if (layer_need_backward_[layer_id]) { LOG(INFO) << layer_names_[layer_id] << " needs backward computation."; - } - } else { - if (Caffe::root_solver()) { + } else { LOG(INFO) << layer_names_[layer_id] - << " does not need backward computation."; + << " does not need backward computation."; } } for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size(); @@ -279,9 +267,8 @@ void Net::Init(const NetParameter& in_param) { // In the end, all remaining blobs are considered output blobs. for (set::iterator it = available_blobs.begin(); it != available_blobs.end(); ++it) { - if (Caffe::root_solver()) { - LOG(INFO) << "This network produces output " << *it; - } + LOG_IF(INFO, Caffe::root_solver()) + << "This network produces output " << *it; net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get()); net_output_blob_indices_.push_back(blob_name_to_idx[*it]); } @@ -293,10 +280,7 @@ void Net::Init(const NetParameter& in_param) { } ShareWeights(); debug_info_ = param.debug_info(); - if (Caffe::root_solver()) { - LOG(INFO) << "Network initialization done."; - LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype); - } + LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done."; } template @@ -335,33 +319,30 @@ bool Net::StateMeetsRule(const NetState& state, // Check whether the rule is broken due to phase. if (rule.has_phase()) { if (rule.phase() != state.phase()) { - if (Caffe::root_solver()) { - LOG(INFO) << "The NetState phase (" << state.phase() - << ") differed from the phase (" << rule.phase() - << ") specified by a rule in layer " << layer_name; - } + LOG_IF(INFO, Caffe::root_solver()) + << "The NetState phase (" << state.phase() + << ") differed from the phase (" << rule.phase() + << ") specified by a rule in layer " << layer_name; return false; } } // Check whether the rule is broken due to min level. if (rule.has_min_level()) { if (state.level() < rule.min_level()) { - if (Caffe::root_solver()) { - LOG(INFO) << "The NetState level (" << state.level() - << ") is above the min_level (" << rule.min_level() - << ") specified by a rule in layer " << layer_name; - } + LOG_IF(INFO, Caffe::root_solver()) + << "The NetState level (" << state.level() + << ") is above the min_level (" << rule.min_level() + << ") specified by a rule in layer " << layer_name; return false; } } // Check whether the rule is broken due to max level. if (rule.has_max_level()) { if (state.level() > rule.max_level()) { - if (Caffe::root_solver()) { - LOG(INFO) << "The NetState level (" << state.level() - << ") is above the max_level (" << rule.max_level() - << ") specified by a rule in layer " << layer_name; - } + LOG_IF(INFO, Caffe::root_solver()) + << "The NetState level (" << state.level() + << ") is above the max_level (" << rule.max_level() + << ") specified by a rule in layer " << layer_name; return false; } } @@ -374,10 +355,9 @@ bool Net::StateMeetsRule(const NetState& state, if (rule.stage(i) == state.stage(j)) { has_stage = true; } } if (!has_stage) { - if (Caffe::root_solver()) { - LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i) - << "' specified by a rule in layer " << layer_name; - } + LOG_IF(INFO, Caffe::root_solver()) + << "The NetState did not contain stage '" << rule.stage(i) + << "' specified by a rule in layer " << layer_name; return false; } } @@ -390,10 +370,9 @@ bool Net::StateMeetsRule(const NetState& state, if (rule.not_stage(i) == state.stage(j)) { has_stage = true; } } if (has_stage) { - if (Caffe::root_solver()) { - LOG(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i) - << "' specified by a rule in layer " << layer_name; - } + LOG_IF(INFO, Caffe::root_solver()) + << "The NetState contained a not_stage '" << rule.not_stage(i) + << "' specified by a rule in layer " << layer_name; return false; } } @@ -415,9 +394,8 @@ void Net::AppendTop(const NetParameter& param, const int layer_id, if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id && blob_name == layer_param->bottom(top_id)) { // In-place computation - if (Caffe::root_solver()) { - LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)"; - } + LOG_IF(INFO, Caffe::root_solver()) + << layer_param->name() << " -> " << blob_name << " (in-place)"; top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get()); top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]); } else if (blob_name_to_idx && @@ -473,9 +451,8 @@ int Net::AppendBottom(const NetParameter& param, const int layer_id, << layer_param.name() << "', bottom index " << bottom_id << ")"; } const int blob_id = (*blob_name_to_idx)[blob_name]; - if (Caffe::root_solver()) { - LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name; - } + LOG_IF(INFO, Caffe::root_solver()) + << layer_names_[layer_id] << " <- " << blob_name; bottom_vecs_[layer_id].push_back(blobs_[blob_id].get()); bottom_id_vecs_[layer_id].push_back(blob_id); available_blobs->erase(blob_name); @@ -672,10 +649,9 @@ void Net::InputDebugInfo(const int input_id) { const Blob& blob = *net_input_blobs_[input_id]; const string& blob_name = blob_names_[net_input_blob_indices_[input_id]]; const Dtype data_abs_val_mean = blob.asum_data() / blob.count(); - if (Caffe::root_solver()) { - LOG(INFO) << " [Forward] " - << "Input " << blob_name << " data: " << data_abs_val_mean; - } + LOG_IF(INFO, Caffe::root_solver()) + << " [Forward] " + << "Input " << blob_name << " data: " << data_abs_val_mean; } template @@ -684,12 +660,11 @@ void Net::ForwardDebugInfo(const int layer_id) { const Blob& blob = *top_vecs_[layer_id][top_id]; const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]]; const Dtype data_abs_val_mean = blob.asum_data() / blob.count(); - if (Caffe::root_solver()) { - LOG(INFO) << " [Forward] " - << "Layer " << layer_names_[layer_id] - << ", top blob " << blob_name - << " data: " << data_abs_val_mean; - } + LOG_IF(INFO, Caffe::root_solver()) + << " [Forward] " + << "Layer " << layer_names_[layer_id] + << ", top blob " << blob_name + << " data: " << data_abs_val_mean; } for (int param_id = 0; param_id < layers_[layer_id]->blobs().size(); ++param_id) { @@ -697,12 +672,11 @@ void Net::ForwardDebugInfo(const int layer_id) { const int net_param_id = param_id_vecs_[layer_id][param_id]; const string& blob_name = param_display_names_[net_param_id]; const Dtype data_abs_val_mean = blob.asum_data() / blob.count(); - if (Caffe::root_solver()) { - LOG(INFO) << " [Forward] " - << "Layer " << layer_names_[layer_id] - << ", param blob " << blob_name - << " data: " << data_abs_val_mean; - } + LOG_IF(INFO, Caffe::root_solver()) + << " [Forward] " + << "Layer " << layer_names_[layer_id] + << ", param blob " << blob_name + << " data: " << data_abs_val_mean; } } @@ -714,24 +688,22 @@ void Net::BackwardDebugInfo(const int layer_id) { const Blob& blob = *bottom_vec[bottom_id]; const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]]; const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count(); - if (Caffe::root_solver()) { - LOG(INFO) << " [Backward] " - << "Layer " << layer_names_[layer_id] - << ", bottom blob " << blob_name - << " diff: " << diff_abs_val_mean; - } + LOG_IF(INFO, Caffe::root_solver()) + << " [Backward] " + << "Layer " << layer_names_[layer_id] + << ", bottom blob " << blob_name + << " diff: " << diff_abs_val_mean; } for (int param_id = 0; param_id < layers_[layer_id]->blobs().size(); ++param_id) { if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; } const Blob& blob = *layers_[layer_id]->blobs()[param_id]; const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count(); - if (Caffe::root_solver()) { - LOG(INFO) << " [Backward] " - << "Layer " << layer_names_[layer_id] - << ", param blob " << param_id - << " diff: " << diff_abs_val_mean; - } + LOG_IF(INFO, Caffe::root_solver()) + << " [Backward] " + << "Layer " << layer_names_[layer_id] + << ", param blob " << param_id + << " diff: " << diff_abs_val_mean; } } @@ -744,22 +716,20 @@ void Net::UpdateDebugInfo(const int param_id) { const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count(); if (param_owner < 0) { const Dtype data_abs_val_mean = blob.asum_data() / blob.count(); - if (Caffe::root_solver()) { - LOG(INFO) << " [Update] Layer " << layer_name - << ", param " << param_display_name - << " data: " << data_abs_val_mean - << "; diff: " << diff_abs_val_mean; - } + LOG_IF(INFO, Caffe::root_solver()) + << " [Update] Layer " << layer_name + << ", param " << param_display_name + << " data: " << data_abs_val_mean + << "; diff: " << diff_abs_val_mean; } else { const string& owner_layer_name = layer_names_[param_layer_indices_[param_owner].first]; - if (Caffe::root_solver()) { - LOG(INFO) << " [Update] Layer " << layer_name - << ", param blob " << param_display_name - << " (owned by layer " << owner_layer_name << ", " << "param " - << param_display_names_[param_owners_[param_id]] << ")" - << " diff: " << diff_abs_val_mean; - } + LOG_IF(INFO, Caffe::root_solver()) + << " [Update] Layer " << layer_name + << ", param blob " << param_display_name + << " (owned by layer " << owner_layer_name << ", " << "param " + << param_display_names_[param_owners_[param_id]] << ")" + << " diff: " << diff_abs_val_mean; } } From 4c93b3dc555891ae0ad75092b6c0f77508740ecf Mon Sep 17 00:00:00 2001 From: Mausoom Sarkar Date: Tue, 13 Oct 2015 18:35:32 +0530 Subject: [PATCH 312/446] Moved the loop inside PReLUParamBackward to do the reduction inside the kernel Now PReLU backward is taking the same time as forward Code cleanup Removed unnecessary code Fixed indent merge if(channed_shared_) --- src/caffe/layers/prelu_layer.cu | 44 ++++++++++++++++----------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu index e1f20048f60..1225334f335 100644 --- a/src/caffe/layers/prelu_layer.cu +++ b/src/caffe/layers/prelu_layer.cu @@ -31,10 +31,15 @@ __global__ void PReLUBackward(const int n, const int channels, const int dim, // CUDA kernel for element-wise parameter backward template -__global__ void PReLUParamBackward(const int n, const Dtype* in_diff, +__global__ void PReLUParamBackward(const int n, + const int rows, const int rowPitch, const Dtype* in_diff, const Dtype* in_data, Dtype* out_diff) { CUDA_KERNEL_LOOP(index, n) { out_diff[index] = in_diff[index] * in_data[index] * (in_data[index] <= 0); + for ( int k = 1; k < rows; k++ ) { + out_diff[index] += in_diff[index + k*rowPitch] + * in_data[index + k*rowPitch] * (in_data[index + k*rowPitch] <= 0); + } } } @@ -82,29 +87,24 @@ void PReLULayer::Backward_gpu(const vector*>& top, if (this->param_propagate_down_[0]) { Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff(); int cdim = channels * dim; - Dtype dsum = 0.; - for (int n = 0; n < bottom[0]->num(); ++n) { - // compute element-wise diff - // NOLINT_NEXT_LINE(whitespace/operators) - PReLUParamBackward<<>>( - cdim, top_diff + top[0]->offset(n), - bottom_data + bottom[0]->offset(n), - backward_buff_.mutable_gpu_diff()); - CUDA_POST_KERNEL_CHECK; - if (channel_shared_) { - Dtype d; - caffe_gpu_dot(channels * dim, backward_buff_.gpu_diff(), - multiplier_.gpu_data(), &d); - dsum += d; - } else { - caffe_gpu_gemv(CblasNoTrans, channels, dim, 1., - backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1., - slope_diff); - } - } + + // compute element-wise diff + // NOLINT_NEXT_LINE(whitespace/operators) + PReLUParamBackward<<>>( + cdim, bottom[0]->num(), top[0]->offset(1), top_diff , + bottom_data , + backward_buff_.mutable_gpu_diff()); + CUDA_POST_KERNEL_CHECK; if (channel_shared_) { + Dtype dsum; + caffe_gpu_dot(channels * dim, backward_buff_.gpu_diff(), + multiplier_.gpu_data(), &dsum); caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(dsum), slope_diff); + } else { + caffe_gpu_gemv(CblasNoTrans, channels, dim, 1., + backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1., + slope_diff); } } // Propagate to bottom From a7d84f3c7e2db7f400c933349edcd4bcf46903b8 Mon Sep 17 00:00:00 2001 From: "T.E.A de Souza" Date: Mon, 19 Oct 2015 18:19:38 +0800 Subject: [PATCH 313/446] Qualify messages issued by CMake when CUDA is unavailable --- cmake/Dependencies.cmake | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake index d68d7bfba66..2005b9927b8 100644 --- a/cmake/Dependencies.cmake +++ b/cmake/Dependencies.cmake @@ -55,9 +55,9 @@ endif() include(cmake/Cuda.cmake) if(NOT HAVE_CUDA) if(CPU_ONLY) - message("-- CUDA is disabled. Building without it...") + message(STATUS "-- CUDA is disabled. Building without it...") else() - message("-- CUDA is not detected by cmake. Building without it...") + message(WARNING "-- CUDA is not detected by cmake. Building without it...") endif() # TODO: remove this not cross platform define in future. Use caffe_config.h instead. From 52429c77cb84b06bf7564f5df619f9f489fe5f72 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Mon, 19 Oct 2015 11:36:38 -0700 Subject: [PATCH 314/446] installation questions -> caffe-users --- INSTALL.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/INSTALL.md b/INSTALL.md index 42fcf027ec2..05c714dbda8 100644 --- a/INSTALL.md +++ b/INSTALL.md @@ -3,5 +3,5 @@ See http://caffe.berkeleyvision.org/installation.html for the latest installation instructions. -Check the issue tracker in case you need help: -https://github.com/BVLC/caffe/issues +Check the users group in case you need help: +https://groups.google.com/forum/#!forum/caffe-users From 2aabba4f8e33a1d0d474a17fff445e9d12201be4 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Mon, 19 Oct 2015 11:39:29 -0700 Subject: [PATCH 315/446] [docs] cuDNN v3 compatible --- docs/installation.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/installation.md b/docs/installation.md index 89a8c71c71a..cce7ec358f5 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -30,13 +30,14 @@ Optional dependencies: * [OpenCV](http://opencv.org/) >= 2.4 including 3.0 * IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`) +* cuDNN for GPU acceleration (v3) Pycaffe and Matcaffe interfaces have their own natural needs. * For Python Caffe: `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python` * For MATLAB Caffe: MATLAB with the `mex` compiler. -**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. For now cuDNN v1 is integrated but see [PR #1731](https://github.com/BVLC/caffe/pull/1731) for v2. +**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe. **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment. From 33a8ba64145e308aefefd5997d06ad53038c4f21 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 16 Oct 2015 17:13:24 -0700 Subject: [PATCH 316/446] [test] drop bogus OpenCV guard for layer type --- src/caffe/test/test_upgrade_proto.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp index df9aeb62464..23deddd453d 100644 --- a/src/caffe/test/test_upgrade_proto.cpp +++ b/src/caffe/test/test_upgrade_proto.cpp @@ -2892,7 +2892,6 @@ TEST_F(NetUpgradeTest, TestImageNet) { this->RunV1UpgradeTest(expected_v1_proto, expected_v2_proto); } // NOLINT(readability/fn_size) -#ifdef USE_OPENCV TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) { LayerParameter layer_param; shared_ptr > layer; @@ -2927,7 +2926,6 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) { EXPECT_EQ(v2_layer_type, layer->type()); } } -#endif // USE_OPENCV class SolverTypeUpgradeTest : public ::testing::Test { protected: From 1caaf38370a6dd1bd7bc91fe3b5242ae63be6a22 Mon Sep 17 00:00:00 2001 From: "T.E.A de Souza" Date: Mon, 19 Oct 2015 18:13:57 +0800 Subject: [PATCH 317/446] Endorse CMP0046, CMP0054 Set policies to NEW to silence warnings in CMake 3.02 and later. --- CMakeLists.txt | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 37f937fe489..82742dafc99 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,4 +1,10 @@ cmake_minimum_required(VERSION 2.8.7) +if(POLICY CMP0046) + cmake_policy(SET CMP0046 NEW) +endif() +if(POLICY CMP0054) + cmake_policy(SET CMP0054 NEW) +endif() # ---[ Caffe project project(Caffe C CXX) @@ -66,8 +72,10 @@ add_subdirectory(docs) add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${PROJECT_SOURCE_DIR}/cmake/lint.cmake) # ---[ pytest target -add_custom_target(pytest COMMAND python${python_version} -m unittest discover -s caffe/test WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python ) -add_dependencies(pytest pycaffe) +if(BUILD_python) + add_custom_target(pytest COMMAND python${python_version} -m unittest discover -s caffe/test WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python ) + add_dependencies(pytest pycaffe) +endif() # ---[ Configuration summary caffe_print_configuration_summary() From 93212e61aa9382762954a01c62f9f0a96d9ff00d Mon Sep 17 00:00:00 2001 From: Kang Kim Date: Sun, 18 Oct 2015 16:52:19 +0900 Subject: [PATCH 318/446] Move HDF5 defines to data_layers header --- include/caffe/data_layers.hpp | 3 +++ include/caffe/neuron_layers.hpp | 3 --- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp index 90fd0d19917..aa0ab7df390 100644 --- a/include/caffe/data_layers.hpp +++ b/include/caffe/data_layers.hpp @@ -17,6 +17,9 @@ #include "caffe/util/blocking_queue.hpp" #include "caffe/util/db.hpp" +#define HDF5_DATA_DATASET_NAME "data" +#define HDF5_DATA_LABEL_NAME "label" + namespace caffe { /** diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp index c2e0774aaa2..4fa330ec783 100644 --- a/include/caffe/neuron_layers.hpp +++ b/include/caffe/neuron_layers.hpp @@ -10,9 +10,6 @@ #include "caffe/layer.hpp" #include "caffe/proto/caffe.pb.h" -#define HDF5_DATA_DATASET_NAME "data" -#define HDF5_DATA_LABEL_NAME "label" - namespace caffe { /** From 80d045263f26c41a1e886906a30d649a5c812038 Mon Sep 17 00:00:00 2001 From: Kang Kim Date: Mon, 19 Oct 2015 00:58:55 +0900 Subject: [PATCH 319/446] Clean redundant/unnecessary headers --- include/caffe/blob.hpp | 1 - include/caffe/common_layers.hpp | 5 ----- include/caffe/filler.hpp | 1 - include/caffe/layer.hpp | 2 +- include/caffe/loss_layers.hpp | 1 - include/caffe/syncedmem.hpp | 1 - include/caffe/util/blocking_queue.hpp | 2 -- include/caffe/util/io.hpp | 1 - src/caffe/layers/absval_layer.cpp | 1 - src/caffe/layers/absval_layer.cu | 3 +-- src/caffe/layers/accuracy_layer.cpp | 5 +---- src/caffe/layers/argmax_layer.cpp | 3 +-- src/caffe/layers/base_conv_layer.cpp | 1 - src/caffe/layers/base_data_layer.cpp | 3 --- src/caffe/layers/batch_reindex_layer.cpp | 3 +-- src/caffe/layers/batch_reindex_layer.cu | 3 +-- src/caffe/layers/bnll_layer.cpp | 3 +-- src/caffe/layers/bnll_layer.cu | 3 +-- src/caffe/layers/concat_layer.cpp | 3 +-- src/caffe/layers/concat_layer.cu | 3 +-- src/caffe/layers/contrastive_loss_layer.cpp | 2 -- src/caffe/layers/contrastive_loss_layer.cu | 4 +--- src/caffe/layers/conv_layer.cpp | 4 ---- src/caffe/layers/conv_layer.cu | 4 ---- src/caffe/layers/cudnn_conv_layer.cpp | 4 ---- src/caffe/layers/cudnn_conv_layer.cu | 4 ---- src/caffe/layers/cudnn_lcn_layer.cpp | 4 ---- src/caffe/layers/cudnn_lcn_layer.cu | 4 ---- src/caffe/layers/cudnn_lrn_layer.cpp | 4 ---- src/caffe/layers/cudnn_lrn_layer.cu | 4 ---- src/caffe/layers/cudnn_pooling_layer.cpp | 4 ---- src/caffe/layers/cudnn_pooling_layer.cu | 4 ---- src/caffe/layers/cudnn_relu_layer.cpp | 2 -- src/caffe/layers/cudnn_relu_layer.cu | 2 -- src/caffe/layers/cudnn_sigmoid_layer.cpp | 2 -- src/caffe/layers/cudnn_sigmoid_layer.cu | 2 -- src/caffe/layers/cudnn_softmax_layer.cpp | 4 ---- src/caffe/layers/cudnn_softmax_layer.cu | 4 ---- src/caffe/layers/cudnn_tanh_layer.cpp | 4 +--- src/caffe/layers/cudnn_tanh_layer.cu | 4 +--- src/caffe/layers/data_layer.cpp | 4 ---- src/caffe/layers/deconv_layer.cpp | 4 ---- src/caffe/layers/deconv_layer.cu | 4 ---- src/caffe/layers/dropout_layer.cpp | 5 +---- src/caffe/layers/dropout_layer.cu | 7 +------ src/caffe/layers/dummy_data_layer.cpp | 3 +-- src/caffe/layers/eltwise_layer.cpp | 3 +-- src/caffe/layers/eltwise_layer.cu | 3 +-- src/caffe/layers/embed_layer.cpp | 3 --- src/caffe/layers/embed_layer.cu | 3 --- src/caffe/layers/euclidean_loss_layer.cpp | 4 +--- src/caffe/layers/euclidean_loss_layer.cu | 4 +--- src/caffe/layers/exp_layer.cpp | 4 +--- src/caffe/layers/exp_layer.cu | 4 +--- src/caffe/layers/filter_layer.cpp | 4 +--- src/caffe/layers/filter_layer.cu | 3 +-- src/caffe/layers/flatten_layer.cpp | 4 +--- src/caffe/layers/hdf5_data_layer.cpp | 1 - src/caffe/layers/hdf5_data_layer.cu | 3 --- src/caffe/layers/hdf5_output_layer.cpp | 5 +---- src/caffe/layers/hdf5_output_layer.cu | 5 +---- src/caffe/layers/hinge_loss_layer.cpp | 6 +----- src/caffe/layers/im2col_layer.cpp | 2 -- src/caffe/layers/im2col_layer.cu | 2 -- src/caffe/layers/image_data_layer.cpp | 1 - src/caffe/layers/infogain_loss_layer.cpp | 5 +---- src/caffe/layers/inner_product_layer.cpp | 5 +---- src/caffe/layers/inner_product_layer.cu | 5 +---- src/caffe/layers/log_layer.cpp | 2 -- src/caffe/layers/log_layer.cu | 2 -- src/caffe/layers/loss_layer.cpp | 8 +------- src/caffe/layers/lrn_layer.cpp | 1 - src/caffe/layers/lrn_layer.cu | 1 - src/caffe/layers/memory_data_layer.cpp | 2 -- src/caffe/layers/multinomial_logistic_loss_layer.cpp | 5 +---- src/caffe/layers/mvn_layer.cpp | 2 -- src/caffe/layers/mvn_layer.cu | 2 -- src/caffe/layers/neuron_layer.cpp | 3 +-- src/caffe/layers/pooling_layer.cpp | 3 --- src/caffe/layers/pooling_layer.cu | 1 - src/caffe/layers/power_layer.cpp | 4 +--- src/caffe/layers/power_layer.cu | 4 +--- src/caffe/layers/prelu_layer.cpp | 3 +-- src/caffe/layers/prelu_layer.cu | 3 +-- src/caffe/layers/reduction_layer.cpp | 5 +---- src/caffe/layers/reduction_layer.cu | 4 +--- src/caffe/layers/relu_layer.cpp | 3 +-- src/caffe/layers/relu_layer.cu | 3 +-- src/caffe/layers/reshape_layer.cpp | 1 - src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp | 5 +---- src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu | 5 +---- src/caffe/layers/sigmoid_layer.cpp | 4 +--- src/caffe/layers/sigmoid_layer.cu | 4 +--- src/caffe/layers/silence_layer.cpp | 1 - src/caffe/layers/silence_layer.cu | 1 - src/caffe/layers/slice_layer.cpp | 3 +-- src/caffe/layers/slice_layer.cu | 3 +-- src/caffe/layers/softmax_layer.cpp | 3 +-- src/caffe/layers/softmax_layer.cu | 3 +-- src/caffe/layers/softmax_loss_layer.cpp | 4 +--- src/caffe/layers/softmax_loss_layer.cu | 3 +-- src/caffe/layers/split_layer.cpp | 3 +-- src/caffe/layers/split_layer.cu | 3 +-- src/caffe/layers/spp_layer.cpp | 5 ----- src/caffe/layers/tanh_layer.cpp | 4 +--- src/caffe/layers/tanh_layer.cu | 4 +--- src/caffe/layers/threshold_layer.cpp | 3 +-- src/caffe/layers/threshold_layer.cu | 4 +--- src/caffe/layers/tile_layer.cpp | 1 - src/caffe/layers/tile_layer.cu | 1 - src/caffe/layers/window_data_layer.cpp | 2 -- src/caffe/parallel.cpp | 1 - src/caffe/syncedmem.cpp | 2 -- src/caffe/test/test_accuracy_layer.cpp | 4 +--- src/caffe/test/test_argmax_layer.cpp | 2 +- src/caffe/test/test_batch_reindex_layer.cpp | 3 +-- src/caffe/test/test_blob.cpp | 1 - src/caffe/test/test_common.cpp | 2 -- src/caffe/test/test_concat_layer.cpp | 3 +-- src/caffe/test/test_contrastive_loss_layer.cpp | 4 +--- src/caffe/test/test_convolution_layer.cpp | 1 - src/caffe/test/test_deconvolution_layer.cpp | 1 - src/caffe/test/test_eltwise_layer.cpp | 2 +- src/caffe/test/test_embed_layer.cpp | 3 +-- src/caffe/test/test_euclidean_loss_layer.cpp | 4 +--- src/caffe/test/test_filler.cpp | 2 -- src/caffe/test/test_filter_layer.cpp | 4 +--- src/caffe/test/test_flatten_layer.cpp | 3 +-- src/caffe/test/test_hdf5_output_layer.cpp | 2 +- src/caffe/test/test_hdf5data_layer.cpp | 3 +-- src/caffe/test/test_hinge_loss_layer.cpp | 4 +--- src/caffe/test/test_im2col_kernel.cu | 1 - src/caffe/test/test_im2col_layer.cpp | 1 - src/caffe/test/test_image_data_layer.cpp | 2 +- src/caffe/test/test_infogain_loss_layer.cpp | 3 --- src/caffe/test/test_inner_product_layer.cpp | 3 +-- src/caffe/test/test_lrn_layer.cpp | 1 - src/caffe/test/test_math_functions.cpp | 2 -- src/caffe/test/test_maxpool_dropout_layers.cpp | 1 - src/caffe/test/test_multinomial_logistic_loss_layer.cpp | 5 +---- src/caffe/test/test_mvn_layer.cpp | 2 -- src/caffe/test/test_neuron_layer.cpp | 4 ++-- src/caffe/test/test_pooling_layer.cpp | 1 - src/caffe/test/test_power_layer.cpp | 2 +- src/caffe/test/test_random_number_generator.cpp | 1 - src/caffe/test/test_reduction_layer.cpp | 3 +-- src/caffe/test/test_reshape_layer.cpp | 1 - src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp | 4 +--- src/caffe/test/test_slice_layer.cpp | 3 +-- src/caffe/test/test_softmax_layer.cpp | 3 +-- src/caffe/test/test_softmax_with_loss_layer.cpp | 4 +--- src/caffe/test/test_split_layer.cpp | 3 +-- src/caffe/test/test_spp_layer.cpp | 2 -- src/caffe/test/test_stochastic_pooling.cpp | 1 - src/caffe/test/test_syncedmem.cpp | 1 - src/caffe/test/test_tanh_layer.cpp | 2 +- src/caffe/test/test_threshold_layer.cpp | 2 +- src/caffe/test/test_tile_layer.cpp | 3 +-- src/caffe/test/test_upgrade_proto.cpp | 1 - src/caffe/test/test_util_blas.cpp | 2 -- src/caffe/util/im2col.cpp | 3 --- src/caffe/util/im2col.cu | 3 --- src/caffe/util/math_functions.cu | 2 -- 163 files changed, 86 insertions(+), 392 deletions(-) diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp index fea5117ef10..af360ac24bd 100644 --- a/include/caffe/blob.hpp +++ b/include/caffe/blob.hpp @@ -8,7 +8,6 @@ #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/syncedmem.hpp" -#include "caffe/util/math_functions.hpp" const int kMaxBlobAxes = 32; diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 21a27d759a8..95358d4cd6f 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -1,16 +1,11 @@ #ifndef CAFFE_COMMON_LAYERS_HPP_ #define CAFFE_COMMON_LAYERS_HPP_ -#include #include #include #include "caffe/blob.hpp" -#include "caffe/common.hpp" -#include "caffe/data_layers.hpp" #include "caffe/layer.hpp" -#include "caffe/loss_layers.hpp" -#include "caffe/neuron_layers.hpp" #include "caffe/proto/caffe.pb.h" namespace caffe { diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp index 888f4a4ba3b..dad9ad46b3b 100644 --- a/include/caffe/filler.hpp +++ b/include/caffe/filler.hpp @@ -8,7 +8,6 @@ #include #include "caffe/blob.hpp" -#include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/syncedmem.hpp" #include "caffe/util/math_functions.hpp" diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp index a0d1d4ecc94..10f353f94f9 100644 --- a/include/caffe/layer.hpp +++ b/include/caffe/layer.hpp @@ -9,7 +9,7 @@ #include "caffe/common.hpp" #include "caffe/layer_factory.hpp" #include "caffe/proto/caffe.pb.h" -#include "caffe/util/device_alternate.hpp" +#include "caffe/util/math_functions.hpp" /** Forward declare boost::thread instead of including boost/thread.hpp diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp index 8d41af34e88..d08ad9b6894 100644 --- a/include/caffe/loss_layers.hpp +++ b/include/caffe/loss_layers.hpp @@ -6,7 +6,6 @@ #include #include "caffe/blob.hpp" -#include "caffe/common.hpp" #include "caffe/layer.hpp" #include "caffe/neuron_layers.hpp" #include "caffe/proto/caffe.pb.h" diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp index 3d92a0eaf3e..38ee4664028 100644 --- a/include/caffe/syncedmem.hpp +++ b/include/caffe/syncedmem.hpp @@ -4,7 +4,6 @@ #include #include "caffe/common.hpp" -#include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/include/caffe/util/blocking_queue.hpp b/include/caffe/util/blocking_queue.hpp index 955e12cc567..d3de2e59b80 100644 --- a/include/caffe/util/blocking_queue.hpp +++ b/include/caffe/util/blocking_queue.hpp @@ -4,8 +4,6 @@ #include #include -#include "caffe/common.hpp" - namespace caffe { template diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp index 6070b4c7f3a..d6cfa442fca 100644 --- a/include/caffe/util/io.hpp +++ b/include/caffe/util/io.hpp @@ -6,7 +6,6 @@ #include "google/protobuf/message.h" -#include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp index 5ce28c9e2b4..7e552352608 100644 --- a/src/caffe/layers/absval_layer.cpp +++ b/src/caffe/layers/absval_layer.cpp @@ -1,6 +1,5 @@ #include -#include "caffe/layer.hpp" #include "caffe/neuron_layers.hpp" #include "caffe/util/math_functions.hpp" diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu index bb310e1afbb..b5a6c25a85a 100644 --- a/src/caffe/layers/absval_layer.cu +++ b/src/caffe/layers/absval_layer.cu @@ -1,8 +1,7 @@ #include -#include "caffe/layer.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp index e2d8d9f8a24..ae2df1f1bad 100644 --- a/src/caffe/layers/accuracy_layer.cpp +++ b/src/caffe/layers/accuracy_layer.cpp @@ -1,12 +1,9 @@ -#include #include #include #include -#include "caffe/layer.hpp" -#include "caffe/util/io.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp index 0c0a932dac7..44df8d4e2e4 100644 --- a/src/caffe/layers/argmax_layer.cpp +++ b/src/caffe/layers/argmax_layer.cpp @@ -3,8 +3,7 @@ #include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/common_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp index c6b47550292..316cb0fdf98 100644 --- a/src/caffe/layers/base_conv_layer.cpp +++ b/src/caffe/layers/base_conv_layer.cpp @@ -2,7 +2,6 @@ #include #include "caffe/filler.hpp" -#include "caffe/layer.hpp" #include "caffe/util/im2col.hpp" #include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp index b90bd4e0caf..d77f91c913b 100644 --- a/src/caffe/layers/base_data_layer.cpp +++ b/src/caffe/layers/base_data_layer.cpp @@ -1,10 +1,7 @@ #include -#include #include #include "caffe/data_layers.hpp" -#include "caffe/net.hpp" -#include "caffe/util/io.hpp" namespace caffe { diff --git a/src/caffe/layers/batch_reindex_layer.cpp b/src/caffe/layers/batch_reindex_layer.cpp index 3bf757c718d..3d3ce32c90d 100644 --- a/src/caffe/layers/batch_reindex_layer.cpp +++ b/src/caffe/layers/batch_reindex_layer.cpp @@ -1,8 +1,7 @@ #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/batch_reindex_layer.cu b/src/caffe/layers/batch_reindex_layer.cu index c418cab9042..0b5ccf099fe 100644 --- a/src/caffe/layers/batch_reindex_layer.cu +++ b/src/caffe/layers/batch_reindex_layer.cu @@ -2,9 +2,8 @@ #include #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp index 9ba0ea9a715..1e422a54653 100644 --- a/src/caffe/layers/bnll_layer.cpp +++ b/src/caffe/layers/bnll_layer.cpp @@ -1,8 +1,7 @@ #include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu index d963d0687d2..3e328ef70fa 100644 --- a/src/caffe/layers/bnll_layer.cu +++ b/src/caffe/layers/bnll_layer.cu @@ -1,8 +1,7 @@ #include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp index 86b500de859..14cbfb11f8b 100644 --- a/src/caffe/layers/concat_layer.cpp +++ b/src/caffe/layers/concat_layer.cpp @@ -1,8 +1,7 @@ #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu index 617701e2621..e1e9449e406 100644 --- a/src/caffe/layers/concat_layer.cu +++ b/src/caffe/layers/concat_layer.cu @@ -1,8 +1,7 @@ #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp index 25e167819d3..74002087ec9 100644 --- a/src/caffe/layers/contrastive_loss_layer.cpp +++ b/src/caffe/layers/contrastive_loss_layer.cpp @@ -1,9 +1,7 @@ #include #include -#include "caffe/layer.hpp" #include "caffe/loss_layers.hpp" -#include "caffe/util/io.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu index 931239316ac..ee2784077e4 100644 --- a/src/caffe/layers/contrastive_loss_layer.cu +++ b/src/caffe/layers/contrastive_loss_layer.cu @@ -1,10 +1,8 @@ #include #include -#include "caffe/layer.hpp" -#include "caffe/util/io.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp index fb50bb095ed..efd69d45edf 100644 --- a/src/caffe/layers/conv_layer.cpp +++ b/src/caffe/layers/conv_layer.cpp @@ -1,9 +1,5 @@ #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu index b429d2b47d0..a534b3560f5 100644 --- a/src/caffe/layers/conv_layer.cu +++ b/src/caffe/layers/conv_layer.cu @@ -1,9 +1,5 @@ #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp index d7b1e0d651f..8b61249a4f8 100644 --- a/src/caffe/layers/cudnn_conv_layer.cpp +++ b/src/caffe/layers/cudnn_conv_layer.cpp @@ -2,10 +2,6 @@ #include #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu index e88e4dd3281..63b6ab9c388 100644 --- a/src/caffe/layers/cudnn_conv_layer.cu +++ b/src/caffe/layers/cudnn_conv_layer.cu @@ -1,10 +1,6 @@ #ifdef USE_CUDNN #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_lcn_layer.cpp b/src/caffe/layers/cudnn_lcn_layer.cpp index 866d810b9f9..4c700786e6b 100644 --- a/src/caffe/layers/cudnn_lcn_layer.cpp +++ b/src/caffe/layers/cudnn_lcn_layer.cpp @@ -1,10 +1,6 @@ #ifdef USE_CUDNN #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_lcn_layer.cu b/src/caffe/layers/cudnn_lcn_layer.cu index c07ade72066..e79c745894a 100644 --- a/src/caffe/layers/cudnn_lcn_layer.cu +++ b/src/caffe/layers/cudnn_lcn_layer.cu @@ -1,10 +1,6 @@ #ifdef USE_CUDNN #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_lrn_layer.cpp b/src/caffe/layers/cudnn_lrn_layer.cpp index 6e9921490f0..a03db3bdd9f 100644 --- a/src/caffe/layers/cudnn_lrn_layer.cpp +++ b/src/caffe/layers/cudnn_lrn_layer.cpp @@ -1,10 +1,6 @@ #ifdef USE_CUDNN #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_lrn_layer.cu b/src/caffe/layers/cudnn_lrn_layer.cu index f9923033011..327e44b4d31 100644 --- a/src/caffe/layers/cudnn_lrn_layer.cu +++ b/src/caffe/layers/cudnn_lrn_layer.cu @@ -1,10 +1,6 @@ #ifdef USE_CUDNN #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp index c92c4e477b5..5f995d45e0c 100644 --- a/src/caffe/layers/cudnn_pooling_layer.cpp +++ b/src/caffe/layers/cudnn_pooling_layer.cpp @@ -1,10 +1,6 @@ #ifdef USE_CUDNN #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu index a952b855a48..9aa39ed8818 100644 --- a/src/caffe/layers/cudnn_pooling_layer.cu +++ b/src/caffe/layers/cudnn_pooling_layer.cu @@ -1,10 +1,6 @@ #ifdef USE_CUDNN #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp index 759d83984ef..e6b6d5a9715 100644 --- a/src/caffe/layers/cudnn_relu_layer.cpp +++ b/src/caffe/layers/cudnn_relu_layer.cpp @@ -1,8 +1,6 @@ #ifdef USE_CUDNN -#include #include -#include "caffe/layer.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu index 21d14857dd2..2a53a49b91f 100644 --- a/src/caffe/layers/cudnn_relu_layer.cu +++ b/src/caffe/layers/cudnn_relu_layer.cu @@ -1,8 +1,6 @@ #ifdef USE_CUDNN -#include #include -#include "caffe/layer.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp index 32637873d46..4b489fa5b16 100644 --- a/src/caffe/layers/cudnn_sigmoid_layer.cpp +++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp @@ -1,8 +1,6 @@ #ifdef USE_CUDNN -#include #include -#include "caffe/layer.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu index 7a06cf721da..9de5c742c8e 100644 --- a/src/caffe/layers/cudnn_sigmoid_layer.cu +++ b/src/caffe/layers/cudnn_sigmoid_layer.cu @@ -1,8 +1,6 @@ #ifdef USE_CUDNN -#include #include -#include "caffe/layer.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp index 77a3225adcd..f5cd04505b8 100644 --- a/src/caffe/layers/cudnn_softmax_layer.cpp +++ b/src/caffe/layers/cudnn_softmax_layer.cpp @@ -1,12 +1,8 @@ #ifdef USE_CUDNN -#include -#include #include #include "thrust/device_vector.h" -#include "caffe/layer.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu index a9e2fcefaf7..c270202f0c9 100644 --- a/src/caffe/layers/cudnn_softmax_layer.cu +++ b/src/caffe/layers/cudnn_softmax_layer.cu @@ -1,12 +1,8 @@ #ifdef USE_CUDNN -#include -#include #include #include "thrust/device_vector.h" -#include "caffe/layer.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp index 376faad324d..4629681807d 100644 --- a/src/caffe/layers/cudnn_tanh_layer.cpp +++ b/src/caffe/layers/cudnn_tanh_layer.cpp @@ -1,9 +1,7 @@ #ifdef USE_CUDNN -#include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu index d287f6fee85..84f784b37c4 100644 --- a/src/caffe/layers/cudnn_tanh_layer.cu +++ b/src/caffe/layers/cudnn_tanh_layer.cu @@ -1,9 +1,7 @@ #ifdef USE_CUDNN -#include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp index 71f8cb099e8..49ac858efc9 100644 --- a/src/caffe/layers/data_layer.cpp +++ b/src/caffe/layers/data_layer.cpp @@ -3,15 +3,11 @@ #endif // USE_OPENCV #include -#include #include -#include "caffe/common.hpp" #include "caffe/data_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/benchmark.hpp" -#include "caffe/util/io.hpp" namespace caffe { diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp index 91aabb315b2..5038b6389fa 100644 --- a/src/caffe/layers/deconv_layer.cpp +++ b/src/caffe/layers/deconv_layer.cpp @@ -1,9 +1,5 @@ #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu index 5dbdcc3149f..0e8e2edea1e 100644 --- a/src/caffe/layers/deconv_layer.cu +++ b/src/caffe/layers/deconv_layer.cu @@ -1,9 +1,5 @@ #include -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/im2col.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp index ec1256fd2fa..eb7a8a9a20b 100644 --- a/src/caffe/layers/dropout_layer.cpp +++ b/src/caffe/layers/dropout_layer.cpp @@ -2,11 +2,8 @@ #include -#include "caffe/common.hpp" -#include "caffe/layer.hpp" -#include "caffe/syncedmem.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu index f9ea04f4acf..028fc026d51 100644 --- a/src/caffe/layers/dropout_layer.cu +++ b/src/caffe/layers/dropout_layer.cu @@ -1,12 +1,7 @@ -#include -#include #include -#include "caffe/common.hpp" -#include "caffe/layer.hpp" -#include "caffe/syncedmem.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp index 6b0d617464c..ab0478c860c 100644 --- a/src/caffe/layers/dummy_data_layer.cpp +++ b/src/caffe/layers/dummy_data_layer.cpp @@ -1,8 +1,7 @@ #include +#include "caffe/data_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp index a80700736bd..7924fbeec7d 100644 --- a/src/caffe/layers/eltwise_layer.cpp +++ b/src/caffe/layers/eltwise_layer.cpp @@ -1,9 +1,8 @@ #include #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu index 2247870d97f..01404209834 100644 --- a/src/caffe/layers/eltwise_layer.cu +++ b/src/caffe/layers/eltwise_layer.cu @@ -1,9 +1,8 @@ #include #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/embed_layer.cpp b/src/caffe/layers/embed_layer.cpp index be6b2cd2727..52704a06d29 100644 --- a/src/caffe/layers/embed_layer.cpp +++ b/src/caffe/layers/embed_layer.cpp @@ -1,10 +1,7 @@ #include -#include "caffe/blob.hpp" -#include "caffe/common.hpp" #include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu index 62a4db81336..cd4b40f58fe 100644 --- a/src/caffe/layers/embed_layer.cu +++ b/src/caffe/layers/embed_layer.cu @@ -1,10 +1,7 @@ #include -#include "caffe/blob.hpp" -#include "caffe/common.hpp" #include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/layer.hpp" #include "caffe/util/gpu_util.cuh" #include "caffe/util/math_functions.hpp" diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp index 80efa31b22c..7338953d340 100644 --- a/src/caffe/layers/euclidean_loss_layer.cpp +++ b/src/caffe/layers/euclidean_loss_layer.cpp @@ -1,9 +1,7 @@ #include -#include "caffe/layer.hpp" -#include "caffe/util/io.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu index 5b1de3ad2d9..1aa79bd542d 100644 --- a/src/caffe/layers/euclidean_loss_layer.cu +++ b/src/caffe/layers/euclidean_loss_layer.cu @@ -1,9 +1,7 @@ #include -#include "caffe/layer.hpp" -#include "caffe/util/io.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp index c7e7c60cfad..f85692d6c5d 100644 --- a/src/caffe/layers/exp_layer.cpp +++ b/src/caffe/layers/exp_layer.cpp @@ -1,9 +1,7 @@ -#include #include -#include "caffe/layer.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/exp_layer.cu b/src/caffe/layers/exp_layer.cu index 2d75d8dd6c7..9e24bbeec9d 100644 --- a/src/caffe/layers/exp_layer.cu +++ b/src/caffe/layers/exp_layer.cu @@ -1,9 +1,7 @@ -#include #include -#include "caffe/layer.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/filter_layer.cpp b/src/caffe/layers/filter_layer.cpp index be1db32dbaa..e8b62a5d5fc 100644 --- a/src/caffe/layers/filter_layer.cpp +++ b/src/caffe/layers/filter_layer.cpp @@ -1,9 +1,7 @@ -#include #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/filter_layer.cu b/src/caffe/layers/filter_layer.cu index cf929eeeadf..746e91c9e95 100644 --- a/src/caffe/layers/filter_layer.cu +++ b/src/caffe/layers/filter_layer.cu @@ -1,8 +1,7 @@ #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp index f7e5c9c2172..d831fb5c6b5 100644 --- a/src/caffe/layers/flatten_layer.cpp +++ b/src/caffe/layers/flatten_layer.cpp @@ -1,8 +1,6 @@ #include -#include "caffe/layer.hpp" -#include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/common_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp index 8ced51039cf..c765fa02c82 100644 --- a/src/caffe/layers/hdf5_data_layer.cpp +++ b/src/caffe/layers/hdf5_data_layer.cpp @@ -15,7 +15,6 @@ #include "stdint.h" #include "caffe/data_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/hdf5.hpp" namespace caffe { diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu index 5e3e4ced141..6ac499c6fbd 100644 --- a/src/caffe/layers/hdf5_data_layer.cu +++ b/src/caffe/layers/hdf5_data_layer.cu @@ -4,15 +4,12 @@ TODO: */ #include -#include #include #include "hdf5.h" #include "hdf5_hl.h" #include "caffe/data_layers.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/io.hpp" namespace caffe { diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp index 56788c21e5e..dbde65da14f 100644 --- a/src/caffe/layers/hdf5_output_layer.cpp +++ b/src/caffe/layers/hdf5_output_layer.cpp @@ -3,11 +3,8 @@ #include "hdf5.h" #include "hdf5_hl.h" -#include "caffe/blob.hpp" -#include "caffe/common.hpp" -#include "caffe/layer.hpp" +#include "caffe/data_layers.hpp" #include "caffe/util/hdf5.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu index eb6d0e470b0..ca8f2616548 100644 --- a/src/caffe/layers/hdf5_output_layer.cu +++ b/src/caffe/layers/hdf5_output_layer.cu @@ -3,10 +3,7 @@ #include "hdf5.h" #include "hdf5_hl.h" -#include "caffe/blob.hpp" -#include "caffe/common.hpp" -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/data_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp index a2fb2a18309..a88c8775205 100644 --- a/src/caffe/layers/hinge_loss_layer.cpp +++ b/src/caffe/layers/hinge_loss_layer.cpp @@ -1,12 +1,8 @@ #include -#include -#include #include -#include "caffe/layer.hpp" -#include "caffe/util/io.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp index 595c9dbbe5e..f3b0f7101b8 100644 --- a/src/caffe/layers/im2col_layer.cpp +++ b/src/caffe/layers/im2col_layer.cpp @@ -1,7 +1,5 @@ #include -#include "caffe/common.hpp" -#include "caffe/layer.hpp" #include "caffe/util/im2col.hpp" #include "caffe/vision_layers.hpp" diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu index cd507623c78..4633628b4c0 100644 --- a/src/caffe/layers/im2col_layer.cu +++ b/src/caffe/layers/im2col_layer.cu @@ -1,7 +1,5 @@ #include -#include "caffe/common.hpp" -#include "caffe/layer.hpp" #include "caffe/util/im2col.hpp" #include "caffe/vision_layers.hpp" diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp index 3d2190f8bbb..9a7df5a78ca 100644 --- a/src/caffe/layers/image_data_layer.cpp +++ b/src/caffe/layers/image_data_layer.cpp @@ -8,7 +8,6 @@ #include #include "caffe/data_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/benchmark.hpp" #include "caffe/util/io.hpp" #include "caffe/util/math_functions.hpp" diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp index a1e0b40de0e..88bd8aaf54f 100644 --- a/src/caffe/layers/infogain_loss_layer.cpp +++ b/src/caffe/layers/infogain_loss_layer.cpp @@ -1,12 +1,9 @@ #include -#include #include #include -#include "caffe/layer.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/io.hpp" -#include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp index 83c3235eb71..274744eaaf8 100644 --- a/src/caffe/layers/inner_product_layer.cpp +++ b/src/caffe/layers/inner_product_layer.cpp @@ -1,11 +1,8 @@ #include -#include "caffe/blob.hpp" -#include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu index c0ebd2c47da..e91e94fc9c6 100644 --- a/src/caffe/layers/inner_product_layer.cu +++ b/src/caffe/layers/inner_product_layer.cu @@ -1,11 +1,8 @@ #include -#include "caffe/blob.hpp" -#include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp index 55a227f6226..a1876b9da76 100644 --- a/src/caffe/layers/log_layer.cpp +++ b/src/caffe/layers/log_layer.cpp @@ -1,7 +1,5 @@ -#include #include -#include "caffe/layer.hpp" #include "caffe/neuron_layers.hpp" #include "caffe/util/math_functions.hpp" diff --git a/src/caffe/layers/log_layer.cu b/src/caffe/layers/log_layer.cu index 847c86cd10c..055b713bef3 100644 --- a/src/caffe/layers/log_layer.cu +++ b/src/caffe/layers/log_layer.cu @@ -1,7 +1,5 @@ -#include #include -#include "caffe/layer.hpp" #include "caffe/neuron_layers.hpp" #include "caffe/util/math_functions.hpp" diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp index 3496a5c2a8a..c10466dbdb0 100644 --- a/src/caffe/layers/loss_layer.cpp +++ b/src/caffe/layers/loss_layer.cpp @@ -1,12 +1,6 @@ -#include -#include -#include #include -#include "caffe/layer.hpp" -#include "caffe/util/io.hpp" -#include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/loss_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp index d18a04ef58d..cc561811e95 100644 --- a/src/caffe/layers/lrn_layer.cpp +++ b/src/caffe/layers/lrn_layer.cpp @@ -1,6 +1,5 @@ #include -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu index 001b3c34ac1..4523d41095d 100644 --- a/src/caffe/layers/lrn_layer.cu +++ b/src/caffe/layers/lrn_layer.cu @@ -1,6 +1,5 @@ #include -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp index 2370aa04d3b..13a3d9f61b9 100644 --- a/src/caffe/layers/memory_data_layer.cpp +++ b/src/caffe/layers/memory_data_layer.cpp @@ -5,8 +5,6 @@ #include #include "caffe/data_layers.hpp" -#include "caffe/layer.hpp" -#include "caffe/util/io.hpp" namespace caffe { diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp index 4267a594a0f..59745923825 100644 --- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp +++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp @@ -1,12 +1,9 @@ #include -#include #include #include -#include "caffe/layer.hpp" -#include "caffe/util/io.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp index 61c2141ecd9..0e7301442ba 100644 --- a/src/caffe/layers/mvn_layer.cpp +++ b/src/caffe/layers/mvn_layer.cpp @@ -1,8 +1,6 @@ -#include #include #include "caffe/common_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu index 5cbb112de4d..b7e3b3ceb39 100644 --- a/src/caffe/layers/mvn_layer.cu +++ b/src/caffe/layers/mvn_layer.cu @@ -1,8 +1,6 @@ -#include #include #include "caffe/common_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp index ba67b43878e..1dcb2c06419 100644 --- a/src/caffe/layers/neuron_layer.cpp +++ b/src/caffe/layers/neuron_layer.cpp @@ -1,7 +1,6 @@ #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp index c8d41499455..3a7de42c91a 100644 --- a/src/caffe/layers/pooling_layer.cpp +++ b/src/caffe/layers/pooling_layer.cpp @@ -2,9 +2,6 @@ #include #include -#include "caffe/common.hpp" -#include "caffe/layer.hpp" -#include "caffe/syncedmem.hpp" #include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu index ca4b13f7c41..5e94ce2bc3f 100644 --- a/src/caffe/layers/pooling_layer.cu +++ b/src/caffe/layers/pooling_layer.cu @@ -2,7 +2,6 @@ #include #include -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp index 4fe34c49f32..6304fadd489 100644 --- a/src/caffe/layers/power_layer.cpp +++ b/src/caffe/layers/power_layer.cpp @@ -1,9 +1,7 @@ -#include #include -#include "caffe/layer.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu index 90d944059b6..680faad4bca 100644 --- a/src/caffe/layers/power_layer.cu +++ b/src/caffe/layers/power_layer.cu @@ -1,9 +1,7 @@ -#include #include -#include "caffe/layer.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp index 81831755512..b5a294e1c5a 100644 --- a/src/caffe/layers/prelu_layer.cpp +++ b/src/caffe/layers/prelu_layer.cpp @@ -2,8 +2,7 @@ #include #include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu index 1225334f335..992cd885a95 100644 --- a/src/caffe/layers/prelu_layer.cu +++ b/src/caffe/layers/prelu_layer.cu @@ -1,8 +1,7 @@ #include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/reduction_layer.cpp b/src/caffe/layers/reduction_layer.cpp index 8ae6329ebe4..6b7925e37b0 100644 --- a/src/caffe/layers/reduction_layer.cpp +++ b/src/caffe/layers/reduction_layer.cpp @@ -1,10 +1,7 @@ -#include -#include #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/reduction_layer.cu b/src/caffe/layers/reduction_layer.cu index 2dbd3bc9f94..a9a8c8d9633 100644 --- a/src/caffe/layers/reduction_layer.cu +++ b/src/caffe/layers/reduction_layer.cu @@ -1,9 +1,7 @@ -#include #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp index cc00319a578..93d090263c7 100644 --- a/src/caffe/layers/relu_layer.cpp +++ b/src/caffe/layers/relu_layer.cpp @@ -1,8 +1,7 @@ #include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu index b8924c855e5..c18ab61f2ed 100644 --- a/src/caffe/layers/relu_layer.cu +++ b/src/caffe/layers/relu_layer.cu @@ -1,8 +1,7 @@ #include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp index ffe970f2689..8659049b528 100644 --- a/src/caffe/layers/reshape_layer.cpp +++ b/src/caffe/layers/reshape_layer.cpp @@ -1,7 +1,6 @@ #include #include "caffe/common_layers.hpp" -#include "caffe/layer.hpp" namespace caffe { diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp index cc236fe1e8e..98588637831 100644 --- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp +++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp @@ -1,10 +1,7 @@ -#include -#include #include -#include "caffe/layer.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu index 547fa80c72f..48dbec41be1 100644 --- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu +++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu @@ -1,10 +1,7 @@ -#include -#include #include -#include "caffe/layer.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp index 48c384905bf..d4a3f8773d6 100644 --- a/src/caffe/layers/sigmoid_layer.cpp +++ b/src/caffe/layers/sigmoid_layer.cpp @@ -1,9 +1,7 @@ -#include #include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu index e1af0657ec1..5730636ef11 100644 --- a/src/caffe/layers/sigmoid_layer.cu +++ b/src/caffe/layers/sigmoid_layer.cu @@ -1,9 +1,7 @@ -#include #include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp index 7e70ab4329e..3974f5d4e65 100644 --- a/src/caffe/layers/silence_layer.cpp +++ b/src/caffe/layers/silence_layer.cpp @@ -1,7 +1,6 @@ #include #include "caffe/common_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu index 34faef22bda..c49ecb2338a 100644 --- a/src/caffe/layers/silence_layer.cu +++ b/src/caffe/layers/silence_layer.cu @@ -1,7 +1,6 @@ #include #include "caffe/common_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp index 0a059ae88fe..f368a249a5e 100644 --- a/src/caffe/layers/slice_layer.cpp +++ b/src/caffe/layers/slice_layer.cpp @@ -1,9 +1,8 @@ #include #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu index e8dc6cd98fc..d555f7d0dec 100644 --- a/src/caffe/layers/slice_layer.cu +++ b/src/caffe/layers/slice_layer.cu @@ -1,8 +1,7 @@ #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp index 04712c9e653..8ae7d49cfaa 100644 --- a/src/caffe/layers/softmax_layer.cpp +++ b/src/caffe/layers/softmax_layer.cpp @@ -1,9 +1,8 @@ #include #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu index 1f9c3a41203..a620fcc8601 100644 --- a/src/caffe/layers/softmax_layer.cu +++ b/src/caffe/layers/softmax_layer.cu @@ -4,9 +4,8 @@ #include "thrust/device_vector.h" -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp index ba312f67fbc..dee50ac6355 100644 --- a/src/caffe/layers/softmax_loss_layer.cpp +++ b/src/caffe/layers/softmax_loss_layer.cpp @@ -2,10 +2,8 @@ #include #include -#include "caffe/layer.hpp" -#include "caffe/layer_factory.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu index 7e0f3da4552..42e91fa9e38 100644 --- a/src/caffe/layers/softmax_loss_layer.cu +++ b/src/caffe/layers/softmax_loss_layer.cu @@ -2,9 +2,8 @@ #include #include -#include "caffe/layer.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp index 272cb59cd37..5333e578f76 100644 --- a/src/caffe/layers/split_layer.cpp +++ b/src/caffe/layers/split_layer.cpp @@ -1,8 +1,7 @@ #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu index a4f5df26452..73d04c98fc1 100644 --- a/src/caffe/layers/split_layer.cu +++ b/src/caffe/layers/split_layer.cu @@ -1,8 +1,7 @@ #include -#include "caffe/layer.hpp" +#include "caffe/common_layers.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/spp_layer.cpp b/src/caffe/layers/spp_layer.cpp index d7622910495..2ef4ac7ab95 100644 --- a/src/caffe/layers/spp_layer.cpp +++ b/src/caffe/layers/spp_layer.cpp @@ -1,11 +1,6 @@ #include -#include #include -#include "caffe/common.hpp" -#include "caffe/layer.hpp" -#include "caffe/syncedmem.hpp" -#include "caffe/util/math_functions.hpp" #include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp index ee5ed773c74..9d1cac76184 100644 --- a/src/caffe/layers/tanh_layer.cpp +++ b/src/caffe/layers/tanh_layer.cpp @@ -1,11 +1,9 @@ // TanH neuron activation function layer. // Adapted from ReLU layer code written by Yangqing Jia -#include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu index ccd6e63ee7c..d87bcceced7 100644 --- a/src/caffe/layers/tanh_layer.cu +++ b/src/caffe/layers/tanh_layer.cu @@ -1,11 +1,9 @@ // TanH neuron activation function layer. // Adapted from ReLU layer code written by Yangqing Jia -#include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp index 2365e7b9c72..d65147368fc 100644 --- a/src/caffe/layers/threshold_layer.cpp +++ b/src/caffe/layers/threshold_layer.cpp @@ -1,7 +1,6 @@ #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu index bfa7f159460..1cd62d99482 100644 --- a/src/caffe/layers/threshold_layer.cu +++ b/src/caffe/layers/threshold_layer.cu @@ -1,8 +1,6 @@ -#include #include -#include "caffe/layer.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/tile_layer.cpp b/src/caffe/layers/tile_layer.cpp index f55008cc53a..581546c4fdb 100644 --- a/src/caffe/layers/tile_layer.cpp +++ b/src/caffe/layers/tile_layer.cpp @@ -1,7 +1,6 @@ #include #include "caffe/common_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/tile_layer.cu b/src/caffe/layers/tile_layer.cu index 7fd3bc47d0f..fdf960901a3 100644 --- a/src/caffe/layers/tile_layer.cu +++ b/src/caffe/layers/tile_layer.cu @@ -1,7 +1,6 @@ #include #include "caffe/common_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp index f8db61c9258..3f937bc9dd8 100644 --- a/src/caffe/layers/window_data_layer.cpp +++ b/src/caffe/layers/window_data_layer.cpp @@ -12,9 +12,7 @@ #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" -#include "caffe/common.hpp" #include "caffe/data_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/benchmark.hpp" #include "caffe/util/io.hpp" #include "caffe/util/math_functions.hpp" diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp index a6d154e168e..9abc92b612d 100644 --- a/src/caffe/parallel.cpp +++ b/src/caffe/parallel.cpp @@ -7,7 +7,6 @@ #include #include -#include #include #include #include diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp index 632bf1f12d3..ec4665ecdda 100644 --- a/src/caffe/syncedmem.cpp +++ b/src/caffe/syncedmem.cpp @@ -1,5 +1,3 @@ -#include - #include "caffe/common.hpp" #include "caffe/syncedmem.hpp" #include "caffe/util/math_functions.hpp" diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp index ef0e57a37a1..5960a666734 100644 --- a/src/caffe/test/test_accuracy_layer.cpp +++ b/src/caffe/test/test_accuracy_layer.cpp @@ -1,6 +1,4 @@ #include -#include -#include #include #include "gtest/gtest.h" @@ -8,8 +6,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/util/rng.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp index bbf19099905..f3f2094edeb 100644 --- a/src/caffe/test/test_argmax_layer.cpp +++ b/src/caffe/test/test_argmax_layer.cpp @@ -5,8 +5,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_batch_reindex_layer.cpp b/src/caffe/test/test_batch_reindex_layer.cpp index 985db343d12..17e47f05066 100644 --- a/src/caffe/test/test_batch_reindex_layer.cpp +++ b/src/caffe/test/test_batch_reindex_layer.cpp @@ -1,12 +1,11 @@ -#include #include #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp index 7da6423b67c..a9d7d519e45 100644 --- a/src/caffe/test/test_blob.cpp +++ b/src/caffe/test/test_blob.cpp @@ -1,4 +1,3 @@ -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp index b3a61b0fd25..58ae5c60a4f 100644 --- a/src/caffe/test/test_common.cpp +++ b/src/caffe/test/test_common.cpp @@ -1,5 +1,3 @@ -#include - #include "gtest/gtest.h" #include "caffe/common.hpp" diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp index ccd97eb1d66..8ba51f4f7f7 100644 --- a/src/caffe/test/test_concat_layer.cpp +++ b/src/caffe/test/test_concat_layer.cpp @@ -1,12 +1,11 @@ -#include #include #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp index 1e9447cbc51..592997e4578 100644 --- a/src/caffe/test/test_contrastive_loss_layer.cpp +++ b/src/caffe/test/test_contrastive_loss_layer.cpp @@ -1,7 +1,5 @@ #include #include -#include -#include #include #include "gtest/gtest.h" @@ -9,7 +7,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp index 9df979a2d27..b474735716d 100644 --- a/src/caffe/test/test_convolution_layer.cpp +++ b/src/caffe/test/test_convolution_layer.cpp @@ -1,4 +1,3 @@ -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp index 770e7b277ee..b473dbb9c51 100644 --- a/src/caffe/test/test_deconvolution_layer.cpp +++ b/src/caffe/test/test_deconvolution_layer.cpp @@ -1,4 +1,3 @@ -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp index 8031f6e9022..3b56c5cae36 100644 --- a/src/caffe/test/test_eltwise_layer.cpp +++ b/src/caffe/test/test_eltwise_layer.cpp @@ -5,8 +5,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp index 7a4fb9800f2..0f4caf15742 100644 --- a/src/caffe/test/test_embed_layer.cpp +++ b/src/caffe/test/test_embed_layer.cpp @@ -1,12 +1,11 @@ -#include #include #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp index 1949742bbcb..9dc14de4149 100644 --- a/src/caffe/test/test_euclidean_loss_layer.cpp +++ b/src/caffe/test/test_euclidean_loss_layer.cpp @@ -1,6 +1,4 @@ #include -#include -#include #include #include "gtest/gtest.h" @@ -8,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp index 728b8dc5f0d..26e9b217e35 100644 --- a/src/caffe/test/test_filler.cpp +++ b/src/caffe/test/test_filler.cpp @@ -1,5 +1,3 @@ -#include - #include "gtest/gtest.h" #include "caffe/filler.hpp" diff --git a/src/caffe/test/test_filter_layer.cpp b/src/caffe/test/test_filter_layer.cpp index c641b6ef6e8..a2d0c293644 100644 --- a/src/caffe/test/test_filter_layer.cpp +++ b/src/caffe/test/test_filter_layer.cpp @@ -1,13 +1,11 @@ -#include -#include #include #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp index 7b6757cba32..5d1caac2aa7 100644 --- a/src/caffe/test/test_flatten_layer.cpp +++ b/src/caffe/test/test_flatten_layer.cpp @@ -1,12 +1,11 @@ -#include #include #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp index b56277b53ae..adc27df4ad0 100644 --- a/src/caffe/test/test_hdf5_output_layer.cpp +++ b/src/caffe/test/test_hdf5_output_layer.cpp @@ -5,10 +5,10 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/data_layers.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/hdf5.hpp" #include "caffe/util/io.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp index c9b027f88cf..7169e7bfc41 100644 --- a/src/caffe/test/test_hdf5data_layer.cpp +++ b/src/caffe/test/test_hdf5data_layer.cpp @@ -5,9 +5,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/filler.hpp" +#include "caffe/data_layers.hpp" #include "caffe/proto/caffe.pb.h" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp index b6a99022905..dfdd01d0291 100644 --- a/src/caffe/test/test_hinge_loss_layer.cpp +++ b/src/caffe/test/test_hinge_loss_layer.cpp @@ -1,6 +1,4 @@ #include -#include -#include #include #include "gtest/gtest.h" @@ -8,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu index f0b75fcc68d..bafcacf784e 100644 --- a/src/caffe/test/test_im2col_kernel.cu +++ b/src/caffe/test/test_im2col_kernel.cu @@ -1,4 +1,3 @@ -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp index 293aa262059..ec055b20176 100644 --- a/src/caffe/test/test_im2col_layer.cpp +++ b/src/caffe/test/test_im2col_layer.cpp @@ -1,4 +1,3 @@ -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp index 481fcef7b27..77690245111 100644 --- a/src/caffe/test/test_image_data_layer.cpp +++ b/src/caffe/test/test_image_data_layer.cpp @@ -7,10 +7,10 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/data_layers.hpp" #include "caffe/filler.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/io.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp index 7ec2f8073c1..b2a6754fd40 100644 --- a/src/caffe/test/test_infogain_loss_layer.cpp +++ b/src/caffe/test/test_infogain_loss_layer.cpp @@ -1,6 +1,3 @@ -#include -#include -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp index fbf0c851220..1ad2c97e75a 100644 --- a/src/caffe/test/test_inner_product_layer.cpp +++ b/src/caffe/test/test_inner_product_layer.cpp @@ -1,12 +1,11 @@ -#include #include #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp index 78cf2d9df9b..bd1c4fe8810 100644 --- a/src/caffe/test/test_lrn_layer.cpp +++ b/src/caffe/test/test_lrn_layer.cpp @@ -1,5 +1,4 @@ #include -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp index a095b544e17..fbee3f9c32d 100644 --- a/src/caffe/test/test_math_functions.cpp +++ b/src/caffe/test/test_math_functions.cpp @@ -1,8 +1,6 @@ #include // for uint32_t & uint64_t #include -#include #include // for std::fabs -#include // for rand_r #include "gtest/gtest.h" diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp index 611d9790863..8fc944f3250 100644 --- a/src/caffe/test/test_maxpool_dropout_layers.cpp +++ b/src/caffe/test/test_maxpool_dropout_layers.cpp @@ -1,4 +1,3 @@ -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp index b2db984feb1..0404aa25af6 100644 --- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp +++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp @@ -1,6 +1,3 @@ -#include -#include -#include #include #include "gtest/gtest.h" @@ -8,7 +5,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp index be23d86e9c3..e9a7d54ce4a 100644 --- a/src/caffe/test/test_mvn_layer.cpp +++ b/src/caffe/test/test_mvn_layer.cpp @@ -1,5 +1,3 @@ -#include -#include #include #include "caffe/blob.hpp" diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp index c6e4d27b903..b333fdee802 100644 --- a/src/caffe/test/test_neuron_layer.cpp +++ b/src/caffe/test/test_neuron_layer.cpp @@ -1,5 +1,4 @@ #include -#include #include #include "google/protobuf/text_format.h" @@ -7,8 +6,9 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp index 69f2d5c1135..9e986e66528 100644 --- a/src/caffe/test/test_pooling_layer.cpp +++ b/src/caffe/test/test_pooling_layer.cpp @@ -1,4 +1,3 @@ -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp index 76c9e857f36..1041ddd4ee8 100644 --- a/src/caffe/test/test_power_layer.cpp +++ b/src/caffe/test/test_power_layer.cpp @@ -6,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_random_number_generator.cpp b/src/caffe/test/test_random_number_generator.cpp index 98424c06bfc..833b0047b5d 100644 --- a/src/caffe/test/test_random_number_generator.cpp +++ b/src/caffe/test/test_random_number_generator.cpp @@ -1,5 +1,4 @@ #include -#include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_reduction_layer.cpp b/src/caffe/test/test_reduction_layer.cpp index f568a18089a..a8d43727113 100644 --- a/src/caffe/test/test_reduction_layer.cpp +++ b/src/caffe/test/test_reduction_layer.cpp @@ -1,12 +1,11 @@ -#include #include #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp index 9d08ec60d4e..e0f4ba42851 100644 --- a/src/caffe/test/test_reshape_layer.cpp +++ b/src/caffe/test/test_reshape_layer.cpp @@ -1,4 +1,3 @@ -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp index e5737e43f6e..b4f831c8f2d 100644 --- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp +++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp @@ -1,6 +1,4 @@ #include -#include -#include #include #include "gtest/gtest.h" @@ -8,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp index 2d2d0fdc005..45fbcffda3f 100644 --- a/src/caffe/test/test_slice_layer.cpp +++ b/src/caffe/test/test_slice_layer.cpp @@ -1,12 +1,11 @@ -#include #include #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp index 996da4b8f7c..4b01f5cfbab 100644 --- a/src/caffe/test/test_softmax_layer.cpp +++ b/src/caffe/test/test_softmax_layer.cpp @@ -1,13 +1,12 @@ #include -#include #include #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp index 1498d5c5ce1..0ae4cd6815a 100644 --- a/src/caffe/test/test_softmax_with_loss_layer.cpp +++ b/src/caffe/test/test_softmax_with_loss_layer.cpp @@ -1,6 +1,4 @@ #include -#include -#include #include #include "boost/scoped_ptr.hpp" @@ -9,7 +7,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/loss_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp index be5204bfc3e..e27e355c77e 100644 --- a/src/caffe/test/test_split_layer.cpp +++ b/src/caffe/test/test_split_layer.cpp @@ -1,4 +1,3 @@ -#include #include #include @@ -7,10 +6,10 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/insert_splits.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_spp_layer.cpp b/src/caffe/test/test_spp_layer.cpp index b2585f1a5fa..1b48a842d3f 100644 --- a/src/caffe/test/test_spp_layer.cpp +++ b/src/caffe/test/test_spp_layer.cpp @@ -1,5 +1,3 @@ -#include -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp index f84464c322c..5a412bd4e17 100644 --- a/src/caffe/test/test_stochastic_pooling.cpp +++ b/src/caffe/test/test_stochastic_pooling.cpp @@ -1,5 +1,4 @@ #include -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp index b946233d07c..16dfb58230f 100644 --- a/src/caffe/test/test_syncedmem.cpp +++ b/src/caffe/test/test_syncedmem.cpp @@ -1,4 +1,3 @@ -#include #include #include "gtest/gtest.h" diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp index 5dc92832fc8..f31579cac40 100644 --- a/src/caffe/test/test_tanh_layer.cpp +++ b/src/caffe/test/test_tanh_layer.cpp @@ -5,8 +5,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp index 05ce82120e6..903a9bc8157 100644 --- a/src/caffe/test/test_threshold_layer.cpp +++ b/src/caffe/test/test_threshold_layer.cpp @@ -5,7 +5,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/neuron_layers.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_tile_layer.cpp b/src/caffe/test/test_tile_layer.cpp index 540aac3c2d3..5c459604a6b 100644 --- a/src/caffe/test/test_tile_layer.cpp +++ b/src/caffe/test/test_tile_layer.cpp @@ -1,12 +1,11 @@ -#include #include #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp index 23deddd453d..9dcc2aa55ec 100644 --- a/src/caffe/test/test_upgrade_proto.cpp +++ b/src/caffe/test/test_upgrade_proto.cpp @@ -1,4 +1,3 @@ -#include #include #include diff --git a/src/caffe/test/test_util_blas.cpp b/src/caffe/test/test_util_blas.cpp index 8770f309951..9ee8818ff1d 100644 --- a/src/caffe/test/test_util_blas.cpp +++ b/src/caffe/test/test_util_blas.cpp @@ -1,7 +1,5 @@ #ifndef CPU_ONLY // CPU-GPU test -#include - #include "gtest/gtest.h" #include "caffe/blob.hpp" diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp index 09da23d490f..27e5b7c0928 100644 --- a/src/caffe/util/im2col.cpp +++ b/src/caffe/util/im2col.cpp @@ -1,6 +1,3 @@ -#include -#include -#include #include #include "caffe/util/im2col.hpp" diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu index 451097f8a1b..49354ab7aa1 100644 --- a/src/caffe/util/im2col.cu +++ b/src/caffe/util/im2col.cu @@ -1,7 +1,4 @@ #include -#include -#include -#include #include "caffe/common.hpp" #include "caffe/util/im2col.hpp" diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu index 2631a0740d6..e4d0c4b04bb 100644 --- a/src/caffe/util/math_functions.cu +++ b/src/caffe/util/math_functions.cu @@ -4,8 +4,6 @@ #include #include -#include -#include #include "caffe/common.hpp" #include "caffe/util/math_functions.hpp" From 2f05b03371e5936a478c7ad2946d0cd7c013920c Mon Sep 17 00:00:00 2001 From: Dmytro Mishkin Date: Wed, 25 Feb 2015 17:00:22 +0200 Subject: [PATCH 320/446] Added batch normalization layer with test and examples --- .../cifar10_full_sigmoid_solver.prototxt | 28 ++ .../cifar10_full_sigmoid_solver_bn.prototxt | 28 ++ .../cifar10_full_sigmoid_train_test.prototxt | 212 +++++++++++ ...ifar10_full_sigmoid_train_test_bn.prototxt | 284 ++++++++++++++ examples/cifar10/train_full_sigmoid.sh | 7 + examples/cifar10/train_full_sigmoid_bn.sh | 7 + include/caffe/common_layers.hpp | 50 ++- src/caffe/layers/batch_norm_layer.cpp | 351 ++++++++++++++++++ src/caffe/layers/batch_norm_layer.cu | 228 ++++++++++++ src/caffe/test/test_batch_norm_layer.cpp | 90 +++++ 10 files changed, 1284 insertions(+), 1 deletion(-) create mode 100644 examples/cifar10/cifar10_full_sigmoid_solver.prototxt create mode 100644 examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt create mode 100644 examples/cifar10/cifar10_full_sigmoid_train_test.prototxt create mode 100644 examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt create mode 100755 examples/cifar10/train_full_sigmoid.sh create mode 100755 examples/cifar10/train_full_sigmoid_bn.sh create mode 100644 src/caffe/layers/batch_norm_layer.cpp create mode 100644 src/caffe/layers/batch_norm_layer.cu create mode 100644 src/caffe/test/test_batch_norm_layer.cpp diff --git a/examples/cifar10/cifar10_full_sigmoid_solver.prototxt b/examples/cifar10/cifar10_full_sigmoid_solver.prototxt new file mode 100644 index 00000000000..7dd3ecb9d8e --- /dev/null +++ b/examples/cifar10/cifar10_full_sigmoid_solver.prototxt @@ -0,0 +1,28 @@ +# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10 +# then another factor of 10 after 10 more epochs (5000 iters) + +# The train/test net protocol buffer definition +net: "examples/cifar10/cifar10_full_sigmoid_train_test.prototxt" +# test_iter specifies how many forward passes the test should carry out. +# In the case of CIFAR10, we have test batch size 100 and 100 test iterations, +# covering the full 10,000 testing images. +test_iter: 10 +# Carry out testing every 1000 training iterations. +test_interval: 1000 +# The base learning rate, momentum and the weight decay of the network. +base_lr: 0.001 +momentum: 0.9 +#weight_decay: 0.004 +# The learning rate policy +lr_policy: "step" +gamma: 1 +stepsize: 5000 +# Display every 200 iterations +display: 100 +# The maximum number of iterations +max_iter: 60000 +# snapshot intermediate results +snapshot: 10000 +snapshot_prefix: "examples/cifar10_full_sigmoid" +# solver mode: CPU or GPU +solver_mode: GPU diff --git a/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt b/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt new file mode 100644 index 00000000000..a57b280fd1e --- /dev/null +++ b/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt @@ -0,0 +1,28 @@ +# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10 +# then another factor of 10 after 10 more epochs (5000 iters) + +# The train/test net protocol buffer definition +net: "examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt" +# test_iter specifies how many forward passes the test should carry out. +# In the case of CIFAR10, we have test batch size 100 and 100 test iterations, +# covering the full 10,000 testing images. +test_iter: 10 +# Carry out testing every 1000 training iterations. +test_interval: 1000 +# The base learning rate, momentum and the weight decay of the network. +base_lr: 0.001 +momentum: 0.9 +#weight_decay: 0.004 +# The learning rate policy +lr_policy: "step" +gamma: 1 +stepsize: 5000 +# Display every 200 iterations +display: 100 +# The maximum number of iterations +max_iter: 60000 +# snapshot intermediate results +snapshot: 10000 +snapshot_prefix: "examples/cifar10_full_sigmoid_bn" +# solver mode: CPU or GPU +solver_mode: GPU diff --git a/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt b/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt new file mode 100644 index 00000000000..6f5bf26bf99 --- /dev/null +++ b/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt @@ -0,0 +1,212 @@ +name: "CIFAR10_full" +layer { + name: "cifar" + type: "Data" + top: "data" + top: "label" + include { + phase: TRAIN + } + transform_param { + mean_file: "examples/cifar10/mean.binaryproto" + } + data_param { + source: "examples/cifar10/cifar10_train_lmdb" + batch_size: 111 + backend: LMDB + } +} +layer { + name: "cifar" + type: "Data" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + mean_file: "examples/cifar10/mean.binaryproto" + } + data_param { + source: "examples/cifar10/cifar10_test_lmdb" + batch_size: 1000 + backend: LMDB + } +} +layer { + name: "conv1" + type: "Convolution" + bottom: "data" + top: "conv1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 32 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.0001 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} + + + +layer { + name: "Sigmoid1" + type: "Sigmoid" + bottom: "pool1" + top: "Sigmoid1" +} + +layer { + name: "conv2" + type: "Convolution" + bottom: "Sigmoid1" + top: "conv2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 32 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} + + +layer { + name: "Sigmoid2" + type: "Sigmoid" + bottom: "conv2" + top: "Sigmoid2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "Sigmoid2" + top: "pool2" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 2 + } +} +layer { + name: "conv3" + type: "Convolution" + bottom: "pool2" + top: "conv3" + convolution_param { + num_output: 64 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } + param { + lr_mult: 1 + } + param { + lr_mult: 1 + } + +} + +layer { + name: "Sigmoid3" + type: "Sigmoid" + bottom: "conv3" + top: "Sigmoid3" +} + +layer { + name: "pool3" + type: "Pooling" + bottom: "Sigmoid3" + top: "pool3" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 2 + } +} + +layer { + name: "ip1" + type: "InnerProduct" + bottom: "pool3" + top: "ip1" + param { + lr_mult: 1 + decay_mult: 250 + } + param { + lr_mult: 0.2 + decay_mult: 0 + } + inner_product_param { + num_output: 10 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "accuracy" + type: "Accuracy" + bottom: "ip1" + bottom: "label" + top: "accuracy" + include { + phase: TEST + } +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "ip1" + bottom: "label" + top: "loss" +} diff --git a/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt b/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt new file mode 100644 index 00000000000..85c2dffe3f6 --- /dev/null +++ b/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt @@ -0,0 +1,284 @@ +name: "CIFAR10_full" +layer { + name: "cifar" + type: "Data" + top: "data" + top: "label" + include { + phase: TRAIN + } + transform_param { + mean_file: "examples/cifar10/mean.binaryproto" + } + data_param { + source: "examples/cifar10/cifar10_train_lmdb" + batch_size: 111 + backend: LMDB + } +} +layer { + name: "cifar" + type: "Data" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + mean_file: "examples/cifar10/mean.binaryproto" + } + data_param { + source: "examples/cifar10/cifar10_test_lmdb" + batch_size: 1000 + backend: LMDB + } +} +layer { + name: "conv1" + type: "Convolution" + bottom: "data" + top: "conv1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 32 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.0001 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} + +layer { + name: "bn1" + type: "BatchNorm" + bottom: "pool1" + top: "bn1" + bn_param { + scale_filler { + type: "constant" + value: 1 + } + shift_filler { + type: "constant" + value: 0.001 + } + } + param { + lr_mult: 1.00001 + decay_mult: 0 + } + param { + lr_mult: 1.00001 + decay_mult: 0 + } +} + +layer { + name: "Sigmoid1" + type: "Sigmoid" + bottom: "bn1" + top: "Sigmoid1" +} + +layer { + name: "conv2" + type: "Convolution" + bottom: "Sigmoid1" + top: "conv2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 32 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} + + + +layer { + name: "bn2" + type: "BatchNorm" + bottom: "conv2" + top: "bn2" + bn_param { + scale_filler { + type: "constant" + value: 1 + } + shift_filler { + type: "constant" + value: 0.001 + } + } + param { + lr_mult: 1.00001 + decay_mult: 0 + } + param { + lr_mult: 1.00001 + decay_mult: 0 + } +} +layer { + name: "Sigmoid2" + type: "Sigmoid" + bottom: "bn2" + top: "Sigmoid2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "Sigmoid2" + top: "pool2" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 2 + } +} +layer { + name: "conv3" + type: "Convolution" + bottom: "pool2" + top: "conv3" + convolution_param { + num_output: 64 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } + param { + lr_mult: 1 + } + param { + lr_mult: 1 + } + +} + + +layer { + name: "bn3" + type: "BatchNorm" + bottom: "conv3" + top: "bn3" + bn_param { + scale_filler { + type: "constant" + value: 1 + } + shift_filler { + type: "constant" + value: 0.001 + } + } + param { + lr_mult: 1.00001 + decay_mult: 0 + } + param { + lr_mult: 1.00001 + decay_mult: 0 + } +} +layer { + name: "Sigmoid3" + type: "Sigmoid" + bottom: "bn3" + top: "Sigmoid3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "Sigmoid3" + top: "pool3" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 2 + } +} + +layer { + name: "ip1" + type: "InnerProduct" + bottom: "pool3" + top: "ip1" + param { + lr_mult: 1 + decay_mult: 250 + } + param { + lr_mult: 0.2 + decay_mult: 0 + } + inner_product_param { + num_output: 10 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + } + } +} +layer { + name: "accuracy" + type: "Accuracy" + bottom: "ip1" + bottom: "label" + top: "accuracy" + include { + phase: TEST + } +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "ip1" + bottom: "label" + top: "loss" +} diff --git a/examples/cifar10/train_full_sigmoid.sh b/examples/cifar10/train_full_sigmoid.sh new file mode 100755 index 00000000000..9cff06d3e34 --- /dev/null +++ b/examples/cifar10/train_full_sigmoid.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env sh + +TOOLS=./build/tools + +$TOOLS/caffe train \ + --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt + diff --git a/examples/cifar10/train_full_sigmoid_bn.sh b/examples/cifar10/train_full_sigmoid_bn.sh new file mode 100755 index 00000000000..011387c996e --- /dev/null +++ b/examples/cifar10/train_full_sigmoid_bn.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env sh + +TOOLS=./build/tools + +$TOOLS/caffe train \ + --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt + diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 21a27d759a8..09605db9a53 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -78,6 +78,55 @@ class ArgMaxLayer : public Layer { int axis_; }; +/** +* @brief Batch Normalization per-channel with scale & shift linear transform. +* +*/ +template +class BatchNormLayer : public Layer { + public: + explicit BatchNormLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "BN"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + // spatial mean & variance + Blob spatial_mean_, spatial_variance_; + // batch mean & variance + Blob batch_mean_, batch_variance_; + // buffer blob + Blob buffer_blob_; + + Blob x_norm_; + // x_sum_multiplier is used to carry out sum using BLAS + Blob spatial_sum_multiplier_, batch_sum_multiplier_; + + // dimension + int N_; + int C_; + int H_; + int W_; + // eps + Dtype var_eps_; +}; + /** * @brief Index into the input blob along its first axis. * @@ -146,7 +195,6 @@ class BatchReindexLayer : public Layer { const Dtype* ridx_data); }; - /** * @brief Takes at least two Blob%s and concatenates them along either the num * or channel dimension, outputting the result. diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp new file mode 100644 index 00000000000..8dea34932f3 --- /dev/null +++ b/src/caffe/layers/batch_norm_layer.cpp @@ -0,0 +1,351 @@ +#include +#include + +#include "caffe/common_layers.hpp" +#include "caffe/filler.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + template + void BatchNormLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), + bottom[0]->height(), bottom[0]->width()); + + x_norm_.Reshape(bottom[0]->num(), bottom[0]->channels(), + bottom[0]->height(), bottom[0]->width()); + + // Figure out the dimensions + N_ = bottom[0]->num(); + C_ = bottom[0]->channels(); + H_ = bottom[0]->height(); + W_ = bottom[0]->width(); + + // mean + spatial_mean_.Reshape(N_, C_, 1, 1); + batch_mean_.Reshape(1, C_, 1, 1); + // variance + spatial_variance_.Reshape(N_, C_, 1, 1); + batch_variance_.Reshape(1, C_, 1, 1); + // buffer blod + buffer_blob_.Reshape(N_, C_, H_, W_); + + // fill spatial multiplier + spatial_sum_multiplier_.Reshape(1, 1, H_, W_); + Dtype* spatial_multipl_data = spatial_sum_multiplier_.mutable_cpu_data(); + caffe_set(spatial_sum_multiplier_.count(), Dtype(1), + spatial_multipl_data); + caffe_set(spatial_sum_multiplier_.count(), Dtype(0), + spatial_sum_multiplier_.mutable_cpu_diff()); + // fill batch multiplier + batch_sum_multiplier_.Reshape(N_, 1, 1, 1); + Dtype* batch_multiplier_data = batch_sum_multiplier_.mutable_cpu_data(); + caffe_set(batch_sum_multiplier_.count(), Dtype(1), + batch_multiplier_data); + caffe_set(batch_sum_multiplier_.count(), Dtype(0), + batch_sum_multiplier_.mutable_cpu_diff()); + this->param_propagate_down_.resize(this->blobs_.size(), true); + } + template + void BatchNormLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not " + "allow in-place computation."; + + top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), + bottom[0]->height(), bottom[0]->width()); + + x_norm_.Reshape(bottom[0]->num(), bottom[0]->channels(), + bottom[0]->height(), bottom[0]->width()); + // Figure out the dimensions + N_ = bottom[0]->num(); + C_ = bottom[0]->channels(); + H_ = bottom[0]->height(); + W_ = bottom[0]->width(); + var_eps_ = 1e-9; + + // mean + spatial_mean_.Reshape(N_, C_, 1, 1); + batch_mean_.Reshape(1, C_, 1, 1); + // variance + spatial_variance_.Reshape(N_, C_, 1, 1); + batch_variance_.Reshape(1, C_, 1, 1); + // buffer blod + buffer_blob_.Reshape(N_, C_, H_, W_); + + // fill spatial multiplier + spatial_sum_multiplier_.Reshape(1, 1, H_, W_); + Dtype* spatial_multipl_data = spatial_sum_multiplier_.mutable_cpu_data(); + caffe_set(spatial_sum_multiplier_.count(), Dtype(1), + spatial_multipl_data); + caffe_set(spatial_sum_multiplier_.count(), Dtype(0), + spatial_sum_multiplier_.mutable_cpu_diff()); + + // fill batch multiplier + batch_sum_multiplier_.Reshape(N_, 1, 1, 1); + Dtype* batch_multiplier_data = batch_sum_multiplier_.mutable_cpu_data(); + caffe_set(batch_sum_multiplier_.count(), Dtype(1), + batch_multiplier_data); + caffe_set(batch_sum_multiplier_.count(), Dtype(0), + batch_sum_multiplier_.mutable_cpu_diff()); + + // Check if we need to set up the weights + if (this->blobs_.size() > 0) { + LOG(INFO) << "Skipping parameter initialization"; + } else { + this->blobs_.resize(2); + + // fill scale with scale_filler + this->blobs_[0].reset(new Blob(1, C_, 1, 1)); + caffe_set(this->blobs_[0]->count(), Dtype(1), + this->blobs_[0]->mutable_cpu_data()); + + // fill shift with shift_filler + this->blobs_[1].reset(new Blob(1, C_, 1, 1)); + caffe_set(this->blobs_[1]->count(), Dtype(0), + this->blobs_[1]->mutable_cpu_data()); + } // parameter initialization + this->param_propagate_down_.resize(this->blobs_.size(), true); + } + + template + void BatchNormLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + const Dtype* const_top_data = top[0]->cpu_data(); + + const Dtype* scale_data = this->blobs_[0]->cpu_data(); + const Dtype* shift_data = this->blobs_[1]->cpu_data(); + + // put the squares of bottom into buffer_blob_ + caffe_powx(bottom[0]->count(), bottom_data, Dtype(2), + buffer_blob_.mutable_cpu_data()); + + // computes variance using var(X) = E(X^2) - (EX)^2 + // EX across spatial + caffe_cpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, + Dtype(1. / (H_ * W_)), bottom_data, + spatial_sum_multiplier_.cpu_data(), Dtype(0), + spatial_mean_.mutable_cpu_data()); + // EX across batch + caffe_cpu_gemv(CblasTrans, N_, C_, Dtype(1. / N_), + spatial_mean_.cpu_data(), + batch_sum_multiplier_.cpu_data(), Dtype(0), + batch_mean_.mutable_cpu_data()); + + // E(X^2) across spatial + caffe_cpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, + Dtype(1. / (H_ * W_)), buffer_blob_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(0), + spatial_variance_.mutable_cpu_data()); + // E(X^2) across batch + caffe_cpu_gemv(CblasTrans, N_, C_, Dtype(1. / N_), + spatial_variance_.cpu_data(), + batch_sum_multiplier_.cpu_data(), Dtype(0), + batch_variance_.mutable_cpu_data()); + + caffe_powx(batch_mean_.count(), batch_mean_.cpu_data(), Dtype(2), + buffer_blob_.mutable_cpu_data()); // (EX)^2 + caffe_sub(batch_mean_.count(), batch_variance_.cpu_data(), + buffer_blob_.cpu_data(), + batch_variance_.mutable_cpu_data()); // variance + + // do mean and variance normalization + // subtract mean + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, + C_, 1, Dtype(1), + batch_sum_multiplier_.cpu_data(), + batch_mean_.cpu_data(), Dtype(0), + spatial_mean_.mutable_cpu_data()); + + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, + H_ * W_, 1, Dtype(-1), + spatial_mean_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(0), + buffer_blob_.mutable_cpu_data()); + + caffe_add(buffer_blob_.count(), bottom_data, + buffer_blob_.cpu_data(), top_data); + + // normalize variance + caffe_add_scalar(batch_variance_.count(), var_eps_, + batch_variance_.mutable_cpu_data()); + caffe_powx(batch_variance_.count(), + batch_variance_.cpu_data(), Dtype(0.5), + batch_variance_.mutable_cpu_data()); + + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, + C_, 1, Dtype(1), + batch_sum_multiplier_.cpu_data(), + batch_variance_.cpu_data(), Dtype(0), + spatial_variance_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, + N_ * C_, H_ * W_, 1, Dtype(1), + spatial_variance_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(0), + buffer_blob_.mutable_cpu_data()); + + caffe_div(buffer_blob_.count(), const_top_data, + buffer_blob_.cpu_data(), top_data); + + // Saving x_norm + caffe_copy(buffer_blob_.count(), const_top_data, + x_norm_.mutable_cpu_data()); + // scale + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), + batch_sum_multiplier_.cpu_data(), scale_data, Dtype(0), + spatial_variance_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, + H_ * W_, 1, Dtype(1), + spatial_variance_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(0), + buffer_blob_.mutable_cpu_data()); + caffe_mul(buffer_blob_.count(), top_data, + buffer_blob_.cpu_data(), top_data); + + // shift + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), + batch_sum_multiplier_.cpu_data(), shift_data, Dtype(0), + spatial_mean_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, + N_ * C_, H_ * W_, 1, Dtype(1), + spatial_mean_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(0), + buffer_blob_.mutable_cpu_data()); + caffe_add(buffer_blob_.count(), const_top_data, + buffer_blob_.cpu_data(), top_data); + } + + template + void BatchNormLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, + const vector*>& bottom) { + const Dtype* top_diff = top[0]->cpu_diff(); + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + + Dtype* scale_diff = this->blobs_[0]->mutable_cpu_diff(); + Dtype* shift_diff = this->blobs_[1]->mutable_cpu_diff(); + const Dtype* scale_data = this->blobs_[0]->cpu_data(); + +// Propagate layer to parameters + // gradient w.r.t. scale + caffe_mul(buffer_blob_.count(), x_norm_.cpu_data(), + top_diff, buffer_blob_.mutable_cpu_data()); + // EX across spatial + caffe_cpu_gemv(CblasNoTrans, N_ * C_, + H_ * W_, Dtype(1), buffer_blob_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(0), + spatial_variance_.mutable_cpu_diff()); + // EX across batch + caffe_cpu_gemv(CblasTrans, N_, C_, Dtype(1), + spatial_variance_.cpu_diff(), + batch_sum_multiplier_.cpu_data(), Dtype(0), scale_diff); + + // gradient w.r.t. shift + // EX across spatial + caffe_cpu_gemv(CblasNoTrans, N_ * C_, + H_ * W_, Dtype(1), top_diff, + spatial_sum_multiplier_.cpu_data(), + Dtype(0), spatial_mean_.mutable_cpu_diff()); + // EX across batch + caffe_cpu_gemv(CblasTrans, N_, C_, + Dtype(1), spatial_mean_.cpu_diff(), + batch_sum_multiplier_.cpu_data(), + Dtype(0), shift_diff); + +// Propagate down + + // put scale * top_diff to buffer_blob_ + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), + batch_sum_multiplier_.cpu_data(), scale_data, Dtype(0), + spatial_variance_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, + H_ * W_, 1, Dtype(1), + spatial_variance_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(0), + buffer_blob_.mutable_cpu_data()); + caffe_mul(buffer_blob_.count(), top_diff, buffer_blob_.cpu_data(), + buffer_blob_.mutable_cpu_data()); + + // use new top diff for computation + caffe_mul(buffer_blob_.count(), x_norm_.cpu_data(), + buffer_blob_.cpu_data(), bottom_diff); + // EX across spatial + caffe_cpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, + Dtype(1), bottom_diff, + spatial_sum_multiplier_.cpu_data(), Dtype(0), + spatial_mean_.mutable_cpu_data()); + // EX across batch + caffe_cpu_gemv(CblasTrans, N_, C_, Dtype(1), + spatial_mean_.cpu_data(), + batch_sum_multiplier_.cpu_data(), Dtype(0), + batch_mean_.mutable_cpu_data()); + + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, + N_, C_, 1, Dtype(1), + batch_sum_multiplier_.cpu_data(), + batch_mean_.cpu_data(), Dtype(0), + spatial_mean_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, + H_ * W_, 1, Dtype(1), + spatial_mean_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(0), + bottom_diff); + + caffe_mul(buffer_blob_.count(), + x_norm_.cpu_data(), bottom_diff, bottom_diff); + + // EX across spatial + caffe_cpu_gemv(CblasNoTrans, N_ * C_, + H_ * W_, Dtype(1), buffer_blob_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(0), + spatial_mean_.mutable_cpu_data()); + // EX across batch + caffe_cpu_gemv(CblasTrans, N_, C_, Dtype(1), + spatial_mean_.cpu_data(), + batch_sum_multiplier_.cpu_data(), Dtype(0), + batch_mean_.mutable_cpu_data()); + + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, + N_, C_, 1, Dtype(1), + batch_sum_multiplier_.cpu_data(), + batch_mean_.cpu_data(), Dtype(0), + spatial_mean_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, + N_ * C_, H_ * W_, 1, Dtype(1), + spatial_mean_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(1), bottom_diff); + + caffe_cpu_axpby(buffer_blob_.count(), Dtype(1), + buffer_blob_.cpu_data(), Dtype(-1. / (N_ * H_ * W_)), + bottom_diff); + + // put the squares of bottom into buffer_blob_ +// caffe_powx(buffer_blob_.count(), bottom_data, Dtype(2), +// buffer_blob_.mutable_cpu_data()); + + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, + N_, C_, 1, Dtype(1), + batch_sum_multiplier_.cpu_data(), + batch_variance_.cpu_data(), Dtype(0), + spatial_variance_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, + N_ * C_, H_ * W_, 1, Dtype(1), + spatial_variance_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), Dtype(0), + buffer_blob_.mutable_cpu_data()); + + caffe_div(buffer_blob_.count(), bottom_diff, + buffer_blob_.cpu_data(), bottom_diff); + } +#ifdef CPU_ONLY +STUB_GPU(BatchNormLayer); +#endif + + INSTANTIATE_CLASS(BatchNormLayer); + REGISTER_LAYER_CLASS(BatchNorm); +} // namespace caffe + diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu new file mode 100644 index 00000000000..e87f8c62f43 --- /dev/null +++ b/src/caffe/layers/batch_norm_layer.cu @@ -0,0 +1,228 @@ +#include +#include + +#include "caffe/common_layers.hpp" +#include "caffe/filler.hpp" +#include "caffe/layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + template + void BatchNormLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + const Dtype* const_top_data = top[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + Dtype* spatial_mean_data = spatial_mean_.mutable_gpu_data(); + Dtype* buffer_data = buffer_blob_.mutable_gpu_data(); + const Dtype* const_buffer_data = buffer_blob_.gpu_data(); + + + // put the squares of bottom into buffer_blob_ + caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2), + buffer_blob_.mutable_gpu_data()); + + // computes variance using var(X) = E(X^2) - (EX)^2 + // EX across spatial + caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, + Dtype(1. / (H_ * W_)), + bottom_data, spatial_sum_multiplier_.gpu_data(), + Dtype(0), spatial_mean_data); + // EX across batch + caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1. / N_), + spatial_mean_.gpu_data(), + batch_sum_multiplier_.gpu_data(), Dtype(0), + batch_mean_.mutable_gpu_data()); + + // E(X^2) across spatial + caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, + Dtype(1. / (H_ * W_)), buffer_data, + spatial_sum_multiplier_.gpu_data(), Dtype(0), + spatial_variance_.mutable_gpu_data()); + // E(X^2) across batch + caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1. / N_), + spatial_variance_.gpu_data(), + batch_sum_multiplier_.gpu_data(), Dtype(0), + batch_variance_.mutable_gpu_data()); + + caffe_gpu_powx(batch_mean_.count(), batch_mean_.gpu_data(), + Dtype(2), buffer_blob_.mutable_gpu_data()); // (EX)^2 + caffe_gpu_sub(batch_mean_.count(), batch_variance_.gpu_data(), + buffer_data, batch_variance_.mutable_gpu_data()); // variance + + // do mean and variance normalization + // subtract mean + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), + batch_sum_multiplier_.gpu_data(), batch_mean_.gpu_data(), Dtype(0), + spatial_mean_data); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, + 1, -Dtype(1), + spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(), Dtype(0), + buffer_blob_.mutable_gpu_data()); + + caffe_gpu_add(buffer_blob_.count(), bottom_data, buffer_data, top_data); + + // normalize variance + caffe_gpu_add_scalar(batch_variance_.count(), var_eps_, + batch_variance_.mutable_gpu_data()); + caffe_gpu_powx(batch_variance_.count(), batch_variance_.gpu_data(), + Dtype(0.5), batch_variance_.mutable_gpu_data()); + + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), + batch_sum_multiplier_.gpu_data(), batch_variance_.gpu_data(), Dtype(0), + spatial_variance_.mutable_gpu_data()); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, + H_ * W_, 1, Dtype(1), + spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(), + Dtype(0), buffer_blob_.mutable_gpu_data()); + + caffe_gpu_div(buffer_blob_.count(), top_data, buffer_data, top_data); + + // Saving x_norm + caffe_copy(top[0]->count(), const_top_data, x_norm_.mutable_gpu_data()); + + // scale + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), + batch_sum_multiplier_.gpu_data(), this->blobs_[0]->gpu_data(), + Dtype(0), spatial_variance_.mutable_gpu_data()); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, + H_ * W_, 1, Dtype(1), + spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(), + Dtype(0), buffer_blob_.mutable_gpu_data()); + + caffe_gpu_mul(buffer_blob_.count(), top_data, buffer_data, top_data); + + // shift + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), + batch_sum_multiplier_.gpu_data(), + this->blobs_[1]->gpu_data(), Dtype(0), + spatial_mean_data); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, + Dtype(1), + spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(), Dtype(0), + buffer_blob_.mutable_gpu_data()); + caffe_gpu_add(buffer_blob_.count(), top_data, buffer_data, top_data); + } + + template + void BatchNormLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, + const vector*>& bottom) { + const Dtype* top_diff = top[0]->gpu_diff(); + const Dtype* top_data = top[0]->gpu_data(); + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + const Dtype* const_bottom_diff = bottom[0]->gpu_diff(); + Dtype* spatial_mean_data = spatial_mean_.mutable_gpu_data(); + Dtype* buffer_data = buffer_blob_.mutable_gpu_data(); + const Dtype* const_buffer_data = buffer_blob_.gpu_data(); + + // Propage to layer params + // gradient w.r.t. scale + caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(), + top_diff, buffer_blob_.mutable_gpu_data()); + // EX across spatial + caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), + buffer_data, spatial_sum_multiplier_.gpu_data(), Dtype(0), + spatial_variance_.mutable_gpu_data()); + // EX across batch + caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1), + spatial_variance_.gpu_data(), + batch_sum_multiplier_.gpu_data(), Dtype(0), + this->blobs_[0]->mutable_gpu_diff()); + + // gradient w.r.t. shift + // EX across spatial + caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), + top_diff, spatial_sum_multiplier_.gpu_data(), + Dtype(0), spatial_mean_data); + // EX across batch + caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1), + spatial_mean_.gpu_data(), + batch_sum_multiplier_.gpu_data(), Dtype(0), + this->blobs_[1]->mutable_gpu_diff()); + + // Propagate down + // scale top diff + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), + batch_sum_multiplier_.gpu_data(), this->blobs_[0]->gpu_data(), + Dtype(0), spatial_variance_.mutable_gpu_data()); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, + H_ * W_, 1, Dtype(1), + spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(), + Dtype(0), + buffer_blob_.mutable_gpu_data()); + caffe_gpu_mul(buffer_blob_.count(), top_diff, buffer_data, + buffer_blob_.mutable_gpu_data()); + + // use new top diff for computation + caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(), + buffer_data, bottom_diff); + // EX across spatial + caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, + Dtype(1), bottom_diff, + spatial_sum_multiplier_.gpu_data(), Dtype(0), spatial_mean_data); + // EX across batch + caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1), + spatial_mean_.gpu_data(), + batch_sum_multiplier_.gpu_data(), Dtype(0), + batch_mean_.mutable_gpu_data()); + + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), + batch_sum_multiplier_.gpu_data(), + batch_mean_.gpu_data(), Dtype(0), + spatial_mean_data); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, + H_ * W_, 1, Dtype(1), spatial_mean_.gpu_data(), + spatial_sum_multiplier_.gpu_data(), Dtype(0), + bottom_diff); + + caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(), + bottom_diff, bottom_diff); + + // EX across spatial + caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), + buffer_data, spatial_sum_multiplier_.gpu_data(), + Dtype(0), spatial_mean_data); + + // EX across batch + caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1), + spatial_mean_.gpu_data(), + batch_sum_multiplier_.gpu_data(), Dtype(0), + batch_mean_.mutable_gpu_data()); + + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, + C_, 1, Dtype(1), + batch_sum_multiplier_.gpu_data(), + batch_mean_.gpu_data(), Dtype(0), + spatial_mean_data); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, + H_ * W_, 1, Dtype(1), + spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(), + Dtype(1), + bottom_diff); + + caffe_gpu_axpby(buffer_blob_.count(), Dtype(1), buffer_data, + Dtype(-1. / (N_ * H_ * W_)), + bottom_diff); + + // put the squares of bottom into buffer_blob_ +// caffe_gpu_powx(buffer_blob_.count(), bottom_data, Dtype(2), +// buffer_blob_.mutable_gpu_data()); + + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), + batch_sum_multiplier_.gpu_data(), batch_variance_.gpu_data(), Dtype(0), + spatial_variance_.mutable_gpu_data()); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, + H_ * W_, 1, Dtype(1), + spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(), + Dtype(0), + buffer_blob_.mutable_gpu_data()); + + caffe_gpu_div(buffer_blob_.count(), const_bottom_diff, + const_buffer_data, bottom_diff); + } + + INSTANTIATE_LAYER_GPU_FUNCS(BatchNormLayer); +} // namespace caffe + diff --git a/src/caffe/test/test_batch_norm_layer.cpp b/src/caffe/test/test_batch_norm_layer.cpp new file mode 100644 index 00000000000..704efd5df3d --- /dev/null +++ b/src/caffe/test/test_batch_norm_layer.cpp @@ -0,0 +1,90 @@ +#include +#include +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/common_layers.hpp" +#include "caffe/filler.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +#define BATCH_SIZE 2 +#define INPUT_DATA_SIZE 3 + +namespace caffe { + + template + class BatchNormLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + protected: + BatchNormLayerTest() + : blob_bottom_(new Blob(5, 2, 3, 4)), + blob_top_(new Blob()) { + // fill the values + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + blob_bottom_vec_.push_back(blob_bottom_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~BatchNormLayerTest() { delete blob_bottom_; delete blob_top_; } + Blob* const blob_bottom_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; + }; + + TYPED_TEST_CASE(BatchNormLayerTest, TestDtypesAndDevices); + + TYPED_TEST(BatchNormLayerTest, TestForward) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + + BatchNormLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + + // Test mean + int num = this->blob_bottom_->num(); + int channels = this->blob_bottom_->channels(); + int height = this->blob_bottom_->height(); + int width = this->blob_bottom_->width(); + + for (int j = 0; j < channels; ++j) { + Dtype sum = 0, var = 0; + for (int i = 0; i < num; ++i) { + for ( int k = 0; k < height; ++k ) { + for ( int l = 0; l < width; ++l ) { + Dtype data = this->blob_top_->data_at(i, j, k, l); + Dtype bottom_data = this->blob_bottom_->data_at(i, j, k, l); + sum += data; + var += data * data; + } + } + } + sum /= height * width * num; + var /= height * width * num; + + const Dtype kErrorBound = 0.001; + // expect zero mean + EXPECT_NEAR(0, sum, kErrorBound); + // expect unit variance + EXPECT_NEAR(1, var, kErrorBound); + } + } + + TYPED_TEST(BatchNormLayerTest, TestGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + + BatchNormLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-4); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); + } + +} // namespace caffe From a52ee656a589313901560c87b65a570ee41c9fee Mon Sep 17 00:00:00 2001 From: Carl Doersch Date: Tue, 6 Oct 2015 14:19:59 -0700 Subject: [PATCH 321/446] Cleanup batch norm layer, include global stats computation --- .../cifar10_full_sigmoid_train_test.prototxt | 4 +- ...ifar10_full_sigmoid_train_test_bn.prototxt | 90 +-- include/caffe/common_layers.hpp | 64 ++- src/caffe/layers/batch_norm_layer.cpp | 535 +++++++----------- src/caffe/layers/batch_norm_layer.cu | 365 +++++------- src/caffe/proto/caffe.proto | 15 +- src/caffe/test/test_batch_norm_layer.cpp | 45 +- 7 files changed, 486 insertions(+), 632 deletions(-) diff --git a/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt b/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt index 6f5bf26bf99..fba69b814ad 100644 --- a/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt +++ b/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt @@ -176,10 +176,10 @@ layer { top: "ip1" param { lr_mult: 1 - decay_mult: 250 + decay_mult: 0 } param { - lr_mult: 0.2 + lr_mult: 2 decay_mult: 0 } inner_product_param { diff --git a/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt b/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt index 85c2dffe3f6..1a810751177 100644 --- a/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt +++ b/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt @@ -12,7 +12,7 @@ layer { } data_param { source: "examples/cifar10/cifar10_train_lmdb" - batch_size: 111 + batch_size: 100 backend: LMDB } } @@ -41,21 +41,16 @@ layer { param { lr_mult: 1 } - param { - lr_mult: 2 - } convolution_param { num_output: 32 pad: 2 kernel_size: 5 stride: 1 + bias_term: false weight_filler { type: "gaussian" std: 0.0001 } - bias_filler { - type: "constant" - } } } layer { @@ -75,23 +70,14 @@ layer { type: "BatchNorm" bottom: "pool1" top: "bn1" - bn_param { - scale_filler { - type: "constant" - value: 1 - } - shift_filler { - type: "constant" - value: 0.001 - } + param { + lr_mult: 0 } param { - lr_mult: 1.00001 - decay_mult: 0 + lr_mult: 0 } param { - lr_mult: 1.00001 - decay_mult: 0 + lr_mult: 0 } } @@ -110,50 +96,35 @@ layer { param { lr_mult: 1 } - param { - lr_mult: 2 - } convolution_param { num_output: 32 pad: 2 kernel_size: 5 stride: 1 + bias_term: false weight_filler { type: "gaussian" std: 0.01 } - bias_filler { - type: "constant" - } } } - - layer { name: "bn2" type: "BatchNorm" bottom: "conv2" top: "bn2" - bn_param { - scale_filler { - type: "constant" - value: 1 - } - shift_filler { - type: "constant" - value: 0.001 - } + param { + lr_mult: 0 } param { - lr_mult: 1.00001 - decay_mult: 0 + lr_mult: 0 } param { - lr_mult: 1.00001 - decay_mult: 0 + lr_mult: 0 } } + layer { name: "Sigmoid2" type: "Sigmoid" @@ -176,53 +147,38 @@ layer { type: "Convolution" bottom: "pool2" top: "conv3" + param { + lr_mult: 1 + } convolution_param { num_output: 64 pad: 2 kernel_size: 5 stride: 1 + bias_term: false weight_filler { type: "gaussian" std: 0.01 } - bias_filler { - type: "constant" - } - } - param { - lr_mult: 1 } - param { - lr_mult: 1 - } - } - layer { name: "bn3" type: "BatchNorm" bottom: "conv3" top: "bn3" - bn_param { - scale_filler { - type: "constant" - value: 1 - } - shift_filler { - type: "constant" - value: 0.001 - } + param { + lr_mult: 0 } param { - lr_mult: 1.00001 - decay_mult: 0 + lr_mult: 0 } param { - lr_mult: 1.00001 - decay_mult: 0 + lr_mult: 0 } } + layer { name: "Sigmoid3" type: "Sigmoid" @@ -248,10 +204,10 @@ layer { top: "ip1" param { lr_mult: 1 - decay_mult: 250 + decay_mult: 1 } param { - lr_mult: 0.2 + lr_mult: 1 decay_mult: 0 } inner_product_param { diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 09605db9a53..da38f1227ba 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -79,9 +79,35 @@ class ArgMaxLayer : public Layer { }; /** -* @brief Batch Normalization per-channel with scale & shift linear transform. -* -*/ + * @brief Normalizes the input to have 0-mean and/or unit (1) variance across + * the batch. + * + * This layer computes Batch Normalization described in [1]. For + * each channel in the data (i.e. axis 1), it subtracts the mean and divides + * by the variance, where both statistics are computed across both spatial + * dimensions and across the different examples in the batch. + * + * By default, during training time, the network is computing global mean/ + * variance statistics via a running average, which is then used at test + * time to allow deterministic outputs for each input. You can manually + * toggle whether the network is accumulating or using the statistics via the + * use_global_stats option. IMPORTANT: for this feature to work, you MUST + * set the learning rate to zero for all three parameter blobs, i.e., + * param {lr_mult: 0} three times in the layer definition. + * + * Note that the original paper also included a per-channel learned bias and + * scaling factor. It is possible (though a bit cumbersome) to implement + * this in caffe using a single-channel DummyDataLayer filled with zeros, + * followed by a Convolution layer with output the same size as the current. + * This produces a channel-specific value that can be added or multiplied by + * the BatchNorm layer's output. + * + * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network + * Training by Reducing Internal Covariate Shift." arXiv preprint + * arXiv:1502.03167 (2015). + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ template class BatchNormLayer : public Layer { public: @@ -89,11 +115,10 @@ class BatchNormLayer : public Layer { : Layer(param) {} virtual void LayerSetUp(const vector*>& bottom, const vector*>& top); - virtual void Reshape(const vector*>& bottom, const vector*>& top); - virtual inline const char* type() const { return "BN"; } + virtual inline const char* type() const { return "BatchNorm"; } virtual inline int ExactNumBottomBlobs() const { return 1; } virtual inline int ExactNumTopBlobs() const { return 1; } @@ -105,26 +130,19 @@ class BatchNormLayer : public Layer { virtual void Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom); virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - // spatial mean & variance - Blob spatial_mean_, spatial_variance_; - // batch mean & variance - Blob batch_mean_, batch_variance_; - // buffer blob - Blob buffer_blob_; + const vector& propagate_down, const vector*>& bottom); - Blob x_norm_; - // x_sum_multiplier is used to carry out sum using BLAS - Blob spatial_sum_multiplier_, batch_sum_multiplier_; + Blob mean_, variance_, temp_, x_norm_; + bool use_global_stats_; + Dtype moving_average_fraction_; + int channels_; + Dtype eps_; - // dimension - int N_; - int C_; - int H_; - int W_; - // eps - Dtype var_eps_; + // extra temporarary variables is used to carry out sums/broadcasting + // using BLAS + Blob batch_sum_multiplier_; + Blob num_by_chans_; + Blob spatial_sum_multiplier_; }; /** diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp index 8dea34932f3..94c2b96b9cd 100644 --- a/src/caffe/layers/batch_norm_layer.cpp +++ b/src/caffe/layers/batch_norm_layer.cpp @@ -2,350 +2,235 @@ #include #include "caffe/common_layers.hpp" -#include "caffe/filler.hpp" #include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { - template - void BatchNormLayer::Reshape(const vector*>& bottom, - const vector*>& top) { - top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), - bottom[0]->height(), bottom[0]->width()); - - x_norm_.Reshape(bottom[0]->num(), bottom[0]->channels(), - bottom[0]->height(), bottom[0]->width()); - - // Figure out the dimensions - N_ = bottom[0]->num(); - C_ = bottom[0]->channels(); - H_ = bottom[0]->height(); - W_ = bottom[0]->width(); - // mean - spatial_mean_.Reshape(N_, C_, 1, 1); - batch_mean_.Reshape(1, C_, 1, 1); - // variance - spatial_variance_.Reshape(N_, C_, 1, 1); - batch_variance_.Reshape(1, C_, 1, 1); - // buffer blod - buffer_blob_.Reshape(N_, C_, H_, W_); - - // fill spatial multiplier - spatial_sum_multiplier_.Reshape(1, 1, H_, W_); - Dtype* spatial_multipl_data = spatial_sum_multiplier_.mutable_cpu_data(); - caffe_set(spatial_sum_multiplier_.count(), Dtype(1), - spatial_multipl_data); - caffe_set(spatial_sum_multiplier_.count(), Dtype(0), - spatial_sum_multiplier_.mutable_cpu_diff()); - // fill batch multiplier - batch_sum_multiplier_.Reshape(N_, 1, 1, 1); - Dtype* batch_multiplier_data = batch_sum_multiplier_.mutable_cpu_data(); - caffe_set(batch_sum_multiplier_.count(), Dtype(1), - batch_multiplier_data); - caffe_set(batch_sum_multiplier_.count(), Dtype(0), - batch_sum_multiplier_.mutable_cpu_diff()); - this->param_propagate_down_.resize(this->blobs_.size(), true); - } - template - void BatchNormLayer::LayerSetUp(const vector*>& bottom, +template +void BatchNormLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { - CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not " - "allow in-place computation."; - - top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), - bottom[0]->height(), bottom[0]->width()); - - x_norm_.Reshape(bottom[0]->num(), bottom[0]->channels(), - bottom[0]->height(), bottom[0]->width()); - // Figure out the dimensions - N_ = bottom[0]->num(); - C_ = bottom[0]->channels(); - H_ = bottom[0]->height(); - W_ = bottom[0]->width(); - var_eps_ = 1e-9; - - // mean - spatial_mean_.Reshape(N_, C_, 1, 1); - batch_mean_.Reshape(1, C_, 1, 1); - // variance - spatial_variance_.Reshape(N_, C_, 1, 1); - batch_variance_.Reshape(1, C_, 1, 1); - // buffer blod - buffer_blob_.Reshape(N_, C_, H_, W_); - - // fill spatial multiplier - spatial_sum_multiplier_.Reshape(1, 1, H_, W_); - Dtype* spatial_multipl_data = spatial_sum_multiplier_.mutable_cpu_data(); - caffe_set(spatial_sum_multiplier_.count(), Dtype(1), - spatial_multipl_data); - caffe_set(spatial_sum_multiplier_.count(), Dtype(0), - spatial_sum_multiplier_.mutable_cpu_diff()); - - // fill batch multiplier - batch_sum_multiplier_.Reshape(N_, 1, 1, 1); - Dtype* batch_multiplier_data = batch_sum_multiplier_.mutable_cpu_data(); - caffe_set(batch_sum_multiplier_.count(), Dtype(1), - batch_multiplier_data); - caffe_set(batch_sum_multiplier_.count(), Dtype(0), - batch_sum_multiplier_.mutable_cpu_diff()); - - // Check if we need to set up the weights - if (this->blobs_.size() > 0) { - LOG(INFO) << "Skipping parameter initialization"; - } else { - this->blobs_.resize(2); - - // fill scale with scale_filler - this->blobs_[0].reset(new Blob(1, C_, 1, 1)); - caffe_set(this->blobs_[0]->count(), Dtype(1), - this->blobs_[0]->mutable_cpu_data()); - - // fill shift with shift_filler - this->blobs_[1].reset(new Blob(1, C_, 1, 1)); - caffe_set(this->blobs_[1]->count(), Dtype(0), - this->blobs_[1]->mutable_cpu_data()); - } // parameter initialization - this->param_propagate_down_.resize(this->blobs_.size(), true); + BatchNormParameter param = this->layer_param_.batch_norm_param(); + moving_average_fraction_ = param.moving_average_fraction(); + use_global_stats_ = this->phase_ == TEST; + if (param.has_use_global_stats()) + use_global_stats_ = param.use_global_stats(); + if (bottom[0]->num_axes() == 1) + channels_ = 1; + else + channels_ = bottom[0]->shape(1); + eps_ = param.eps(); + if (this->blobs_.size() > 0) { + LOG(INFO) << "Skipping parameter initialization"; + } else { + this->blobs_.resize(3); + vector sz; + sz.push_back(channels_); + this->blobs_[0].reset(new Blob(sz)); + this->blobs_[1].reset(new Blob(sz)); + sz[0]=1; + this->blobs_[2].reset(new Blob(sz)); + for (int i = 0; i < 3; ++i) { + caffe_set(this->blobs_[i]->count(), Dtype(0), + this->blobs_[i]->mutable_cpu_data()); + } } +} - template - void BatchNormLayer::Forward_cpu(const vector*>& bottom, +template +void BatchNormLayer::Reshape(const vector*>& bottom, const vector*>& top) { - const Dtype* bottom_data = bottom[0]->cpu_data(); - Dtype* top_data = top[0]->mutable_cpu_data(); - const Dtype* const_top_data = top[0]->cpu_data(); - - const Dtype* scale_data = this->blobs_[0]->cpu_data(); - const Dtype* shift_data = this->blobs_[1]->cpu_data(); - - // put the squares of bottom into buffer_blob_ - caffe_powx(bottom[0]->count(), bottom_data, Dtype(2), - buffer_blob_.mutable_cpu_data()); + if (bottom[0]->num_axes() >= 1) + CHECK_EQ(bottom[0]->shape(1), channels_); + top[0]->ReshapeLike(*bottom[0]); + + vector sz; + sz.push_back(channels_); + mean_.Reshape(sz); + variance_.Reshape(sz); + temp_.ReshapeLike(*bottom[0]); + x_norm_.ReshapeLike(*bottom[0]); + sz[0]=bottom[0]->shape(0); + batch_sum_multiplier_.Reshape(sz); + + int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0)); + if (spatial_sum_multiplier_.num_axes() == 0 || + spatial_sum_multiplier_.shape(0) != spatial_dim) { + sz[0] = spatial_dim; + spatial_sum_multiplier_.Reshape(sz); + Dtype* multiplier_data = spatial_sum_multiplier_.mutable_cpu_data(); + caffe_set(spatial_sum_multiplier_.count(), Dtype(1), multiplier_data); + } + int numbychans = channels_*bottom[0]->shape(0); + if (num_by_chans_.num_axes() == 0 || + num_by_chans_.shape(0) != numbychans) { + sz[0] = numbychans; + num_by_chans_.Reshape(sz); + caffe_set(batch_sum_multiplier_.count(), Dtype(1), + batch_sum_multiplier_.mutable_cpu_data()); + } +} + +template +void BatchNormLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + int num = bottom[0]->shape(0); + int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_); + + // elementwise square + caffe_powx(bottom[0]->count(), bottom_data, Dtype(2), + temp_.mutable_cpu_data()); + + if (use_global_stats_) { + // use the stored mean/variance estimates. TODO(cdoersch): allow an option + // to use an unbiased variance estimate, like the paper does. + const Dtype scale_factor = 1 / this->blobs_[2]->cpu_data()[0]; + caffe_cpu_scale(variance_.count(), scale_factor, + this->blobs_[0]->cpu_data(), mean_.mutable_cpu_data()); + caffe_cpu_scale(variance_.count(), scale_factor, + this->blobs_[1]->cpu_data(), variance_.mutable_cpu_data()); + } else { // computes variance using var(X) = E(X^2) - (EX)^2 - // EX across spatial - caffe_cpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, - Dtype(1. / (H_ * W_)), bottom_data, - spatial_sum_multiplier_.cpu_data(), Dtype(0), - spatial_mean_.mutable_cpu_data()); - // EX across batch - caffe_cpu_gemv(CblasTrans, N_, C_, Dtype(1. / N_), - spatial_mean_.cpu_data(), - batch_sum_multiplier_.cpu_data(), Dtype(0), - batch_mean_.mutable_cpu_data()); - - // E(X^2) across spatial - caffe_cpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, - Dtype(1. / (H_ * W_)), buffer_blob_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(0), - spatial_variance_.mutable_cpu_data()); - // E(X^2) across batch - caffe_cpu_gemv(CblasTrans, N_, C_, Dtype(1. / N_), - spatial_variance_.cpu_data(), - batch_sum_multiplier_.cpu_data(), Dtype(0), - batch_variance_.mutable_cpu_data()); - - caffe_powx(batch_mean_.count(), batch_mean_.cpu_data(), Dtype(2), - buffer_blob_.mutable_cpu_data()); // (EX)^2 - caffe_sub(batch_mean_.count(), batch_variance_.cpu_data(), - buffer_blob_.cpu_data(), - batch_variance_.mutable_cpu_data()); // variance - - // do mean and variance normalization - // subtract mean - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, - C_, 1, Dtype(1), - batch_sum_multiplier_.cpu_data(), - batch_mean_.cpu_data(), Dtype(0), - spatial_mean_.mutable_cpu_data()); - - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, - H_ * W_, 1, Dtype(-1), - spatial_mean_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(0), - buffer_blob_.mutable_cpu_data()); - - caffe_add(buffer_blob_.count(), bottom_data, - buffer_blob_.cpu_data(), top_data); - - // normalize variance - caffe_add_scalar(batch_variance_.count(), var_eps_, - batch_variance_.mutable_cpu_data()); - caffe_powx(batch_variance_.count(), - batch_variance_.cpu_data(), Dtype(0.5), - batch_variance_.mutable_cpu_data()); - - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, - C_, 1, Dtype(1), - batch_sum_multiplier_.cpu_data(), - batch_variance_.cpu_data(), Dtype(0), - spatial_variance_.mutable_cpu_data()); - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, - N_ * C_, H_ * W_, 1, Dtype(1), - spatial_variance_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(0), - buffer_blob_.mutable_cpu_data()); - - caffe_div(buffer_blob_.count(), const_top_data, - buffer_blob_.cpu_data(), top_data); - - // Saving x_norm - caffe_copy(buffer_blob_.count(), const_top_data, - x_norm_.mutable_cpu_data()); - // scale - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), - batch_sum_multiplier_.cpu_data(), scale_data, Dtype(0), - spatial_variance_.mutable_cpu_data()); - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, - H_ * W_, 1, Dtype(1), - spatial_variance_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(0), - buffer_blob_.mutable_cpu_data()); - caffe_mul(buffer_blob_.count(), top_data, - buffer_blob_.cpu_data(), top_data); - - // shift - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), - batch_sum_multiplier_.cpu_data(), shift_data, Dtype(0), - spatial_mean_.mutable_cpu_data()); - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, - N_ * C_, H_ * W_, 1, Dtype(1), - spatial_mean_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(0), - buffer_blob_.mutable_cpu_data()); - caffe_add(buffer_blob_.count(), const_top_data, - buffer_blob_.cpu_data(), top_data); + caffe_cpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, + 1. / (num * spatial_dim), bottom_data, + spatial_sum_multiplier_.cpu_data(), 0., + num_by_chans_.mutable_cpu_data()); + caffe_cpu_gemv(CblasTrans, num, channels_, 1., + num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0., + mean_.mutable_cpu_data()); + caffe_cpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, + 1. / (num * spatial_dim), temp_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), 0., + num_by_chans_.mutable_cpu_data()); + caffe_cpu_gemv(CblasTrans, num, channels_, 1., + num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0., + variance_.mutable_cpu_data()); + this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_; + this->blobs_[2]->mutable_cpu_data()[0] += 1; + caffe_cpu_axpby(mean_.count(), Dtype(1), mean_.cpu_data(), + moving_average_fraction_, this->blobs_[0]->mutable_cpu_data()); + Dtype m = Dtype(bottom[0]->count()/channels_); + caffe_cpu_axpby(variance_.count(), m/(m-1), variance_.cpu_data(), + moving_average_fraction_, this->blobs_[1]->mutable_cpu_data()); } + // elementwise square of mean + caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2), + temp_.mutable_cpu_data()); - template - void BatchNormLayer::Backward_cpu(const vector*>& top, - const vector& propagate_down, - const vector*>& bottom) { - const Dtype* top_diff = top[0]->cpu_diff(); - const Dtype* bottom_data = bottom[0]->cpu_data(); - Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); - - Dtype* scale_diff = this->blobs_[0]->mutable_cpu_diff(); - Dtype* shift_diff = this->blobs_[1]->mutable_cpu_diff(); - const Dtype* scale_data = this->blobs_[0]->cpu_data(); - -// Propagate layer to parameters - // gradient w.r.t. scale - caffe_mul(buffer_blob_.count(), x_norm_.cpu_data(), - top_diff, buffer_blob_.mutable_cpu_data()); - // EX across spatial - caffe_cpu_gemv(CblasNoTrans, N_ * C_, - H_ * W_, Dtype(1), buffer_blob_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(0), - spatial_variance_.mutable_cpu_diff()); - // EX across batch - caffe_cpu_gemv(CblasTrans, N_, C_, Dtype(1), - spatial_variance_.cpu_diff(), - batch_sum_multiplier_.cpu_data(), Dtype(0), scale_diff); - - // gradient w.r.t. shift - // EX across spatial - caffe_cpu_gemv(CblasNoTrans, N_ * C_, - H_ * W_, Dtype(1), top_diff, - spatial_sum_multiplier_.cpu_data(), - Dtype(0), spatial_mean_.mutable_cpu_diff()); - // EX across batch - caffe_cpu_gemv(CblasTrans, N_, C_, - Dtype(1), spatial_mean_.cpu_diff(), - batch_sum_multiplier_.cpu_data(), - Dtype(0), shift_diff); + caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(), + variance_.mutable_cpu_data()); // variance -// Propagate down + // normalize variance + caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data()); + caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5), + variance_.mutable_cpu_data()); - // put scale * top_diff to buffer_blob_ - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), - batch_sum_multiplier_.cpu_data(), scale_data, Dtype(0), - spatial_variance_.mutable_cpu_data()); - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, - H_ * W_, 1, Dtype(1), - spatial_variance_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(0), - buffer_blob_.mutable_cpu_data()); - caffe_mul(buffer_blob_.count(), top_diff, buffer_blob_.cpu_data(), - buffer_blob_.mutable_cpu_data()); - - // use new top diff for computation - caffe_mul(buffer_blob_.count(), x_norm_.cpu_data(), - buffer_blob_.cpu_data(), bottom_diff); - // EX across spatial - caffe_cpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, - Dtype(1), bottom_diff, - spatial_sum_multiplier_.cpu_data(), Dtype(0), - spatial_mean_.mutable_cpu_data()); - // EX across batch - caffe_cpu_gemv(CblasTrans, N_, C_, Dtype(1), - spatial_mean_.cpu_data(), - batch_sum_multiplier_.cpu_data(), Dtype(0), - batch_mean_.mutable_cpu_data()); - - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, - N_, C_, 1, Dtype(1), - batch_sum_multiplier_.cpu_data(), - batch_mean_.cpu_data(), Dtype(0), - spatial_mean_.mutable_cpu_data()); - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, - H_ * W_, 1, Dtype(1), - spatial_mean_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(0), - bottom_diff); - - caffe_mul(buffer_blob_.count(), - x_norm_.cpu_data(), bottom_diff, bottom_diff); - - // EX across spatial - caffe_cpu_gemv(CblasNoTrans, N_ * C_, - H_ * W_, Dtype(1), buffer_blob_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(0), - spatial_mean_.mutable_cpu_data()); - // EX across batch - caffe_cpu_gemv(CblasTrans, N_, C_, Dtype(1), - spatial_mean_.cpu_data(), - batch_sum_multiplier_.cpu_data(), Dtype(0), - batch_mean_.mutable_cpu_data()); - - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, - N_, C_, 1, Dtype(1), - batch_sum_multiplier_.cpu_data(), - batch_mean_.cpu_data(), Dtype(0), - spatial_mean_.mutable_cpu_data()); - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, - N_ * C_, H_ * W_, 1, Dtype(1), - spatial_mean_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(1), bottom_diff); - - caffe_cpu_axpby(buffer_blob_.count(), Dtype(1), - buffer_blob_.cpu_data(), Dtype(-1. / (N_ * H_ * W_)), - bottom_diff); - - // put the squares of bottom into buffer_blob_ -// caffe_powx(buffer_blob_.count(), bottom_data, Dtype(2), -// buffer_blob_.mutable_cpu_data()); + // do mean and variance normalization + if (bottom[0] != top[0]) { + caffe_copy(bottom[0]->count(), bottom_data, top_data); + } + // subtract mean + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, + batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0., + num_by_chans_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels_ * num, + spatial_dim, 1, -1, num_by_chans_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), 1., top_data); + // replicate variance to input size + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, + batch_sum_multiplier_.cpu_data(), variance_.cpu_data(), 0., + num_by_chans_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels_ * num, + spatial_dim, 1, 1., num_by_chans_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), 0., temp_.mutable_cpu_data()); + caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data); + // TODO(cdoersch): The caching is only needed because later in-place layers + // might clobber the data. Can we skip this if they won't? + caffe_copy(x_norm_.count(), top_data, + x_norm_.mutable_cpu_data()); +} + +template +void BatchNormLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, + const vector*>& bottom) { + CHECK(!use_global_stats_); + const Dtype* top_diff; + if (bottom[0] != top[0]) { + top_diff = top[0]->cpu_diff(); + } else { + caffe_copy(x_norm_.count(), top[0]->cpu_diff(), x_norm_.mutable_cpu_diff()); + top_diff = x_norm_.cpu_diff(); + } + const Dtype* top_data = x_norm_.cpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + int num = bottom[0]->shape()[0]; + int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_); + // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then + // + // dE(Y)/dX = + // (dE/dY - mean(dE/dY) - mean(dE/dY \cdot Y) \cdot Y) + // ./ sqrt(var(X) + eps) + // + // where \cdot and ./ are hadamard product and elementwise division, + // respectively, dE/dY is the top diff, and mean/var/sum are all computed + // along all dimensions except the channels dimension. In the above + // equation, the operations allow for expansion (i.e. broadcast) along all + // dimensions except the channels dimension where required. + + // sum(dE/dY \cdot Y) + caffe_mul(temp_.count(), top_data, top_diff, bottom_diff); + caffe_cpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, 1., + bottom_diff, spatial_sum_multiplier_.cpu_data(), 0., + num_by_chans_.mutable_cpu_data()); + caffe_cpu_gemv(CblasTrans, num, channels_, 1., + num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0., + mean_.mutable_cpu_data()); + + // reshape (broadcast) the above + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, + batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0., + num_by_chans_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels_ * num, + spatial_dim, 1, 1., num_by_chans_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), 0., bottom_diff); + + // sum(dE/dY \cdot Y) \cdot Y + caffe_mul(temp_.count(), top_data, bottom_diff, bottom_diff); + + // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y + caffe_cpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, 1., + top_diff, spatial_sum_multiplier_.cpu_data(), 0., + num_by_chans_.mutable_cpu_data()); + caffe_cpu_gemv(CblasTrans, num, channels_, 1., + num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0., + mean_.mutable_cpu_data()); + // reshape (broadcast) the above to make + // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, + batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0., + num_by_chans_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num * channels_, + spatial_dim, 1, 1., num_by_chans_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), 1., bottom_diff); + + // dE/dY - mean(dE/dY)-mean(dE/dY \cdot Y) \cdot Y + caffe_cpu_axpby(temp_.count(), Dtype(1), top_diff, + Dtype(-1. / (num * spatial_dim)), bottom_diff); + + // note: temp_ still contains sqrt(var(X)+eps), computed during the forward + // pass. + caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff); +} - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, - N_, C_, 1, Dtype(1), - batch_sum_multiplier_.cpu_data(), - batch_variance_.cpu_data(), Dtype(0), - spatial_variance_.mutable_cpu_data()); - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, - N_ * C_, H_ * W_, 1, Dtype(1), - spatial_variance_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), Dtype(0), - buffer_blob_.mutable_cpu_data()); - caffe_div(buffer_blob_.count(), bottom_diff, - buffer_blob_.cpu_data(), bottom_diff); - } #ifdef CPU_ONLY STUB_GPU(BatchNormLayer); #endif - INSTANTIATE_CLASS(BatchNormLayer); - REGISTER_LAYER_CLASS(BatchNorm); +INSTANTIATE_CLASS(BatchNormLayer); +REGISTER_LAYER_CLASS(BatchNorm); } // namespace caffe - diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu index e87f8c62f43..cd8924a451d 100644 --- a/src/caffe/layers/batch_norm_layer.cu +++ b/src/caffe/layers/batch_norm_layer.cu @@ -2,227 +2,166 @@ #include #include "caffe/common_layers.hpp" -#include "caffe/filler.hpp" #include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { - template - void BatchNormLayer::Forward_gpu(const vector*>& bottom, - const vector*>& top) { - const Dtype* bottom_data = bottom[0]->gpu_data(); - const Dtype* const_top_data = top[0]->gpu_data(); - Dtype* top_data = top[0]->mutable_gpu_data(); - Dtype* spatial_mean_data = spatial_mean_.mutable_gpu_data(); - Dtype* buffer_data = buffer_blob_.mutable_gpu_data(); - const Dtype* const_buffer_data = buffer_blob_.gpu_data(); - - - // put the squares of bottom into buffer_blob_ - caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2), - buffer_blob_.mutable_gpu_data()); +template +void BatchNormLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + int num = bottom[0]->shape(0); + int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0)); + + // elementwise square + caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2), + temp_.mutable_gpu_data()); + + if (use_global_stats_) { + // use the stored mean/variance estimates. TODO(cdoersch): allow an option + // to use an unbiased variance estimate, like the paper does. + const Dtype scale_factor = 1 / this->blobs_[2]->cpu_data()[0]; + caffe_gpu_scale(variance_.count(), scale_factor, + this->blobs_[0]->gpu_data(), mean_.mutable_gpu_data()); + caffe_gpu_scale(variance_.count(), scale_factor, + this->blobs_[1]->gpu_data(), variance_.mutable_gpu_data()); + } else { // computes variance using var(X) = E(X^2) - (EX)^2 - // EX across spatial - caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, - Dtype(1. / (H_ * W_)), - bottom_data, spatial_sum_multiplier_.gpu_data(), - Dtype(0), spatial_mean_data); - // EX across batch - caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1. / N_), - spatial_mean_.gpu_data(), - batch_sum_multiplier_.gpu_data(), Dtype(0), - batch_mean_.mutable_gpu_data()); - - // E(X^2) across spatial - caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, - Dtype(1. / (H_ * W_)), buffer_data, - spatial_sum_multiplier_.gpu_data(), Dtype(0), - spatial_variance_.mutable_gpu_data()); - // E(X^2) across batch - caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1. / N_), - spatial_variance_.gpu_data(), - batch_sum_multiplier_.gpu_data(), Dtype(0), - batch_variance_.mutable_gpu_data()); - - caffe_gpu_powx(batch_mean_.count(), batch_mean_.gpu_data(), - Dtype(2), buffer_blob_.mutable_gpu_data()); // (EX)^2 - caffe_gpu_sub(batch_mean_.count(), batch_variance_.gpu_data(), - buffer_data, batch_variance_.mutable_gpu_data()); // variance - - // do mean and variance normalization - // subtract mean - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), - batch_sum_multiplier_.gpu_data(), batch_mean_.gpu_data(), Dtype(0), - spatial_mean_data); - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, - 1, -Dtype(1), - spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(), Dtype(0), - buffer_blob_.mutable_gpu_data()); - - caffe_gpu_add(buffer_blob_.count(), bottom_data, buffer_data, top_data); - - // normalize variance - caffe_gpu_add_scalar(batch_variance_.count(), var_eps_, - batch_variance_.mutable_gpu_data()); - caffe_gpu_powx(batch_variance_.count(), batch_variance_.gpu_data(), - Dtype(0.5), batch_variance_.mutable_gpu_data()); - - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), - batch_sum_multiplier_.gpu_data(), batch_variance_.gpu_data(), Dtype(0), - spatial_variance_.mutable_gpu_data()); - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, - H_ * W_, 1, Dtype(1), - spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(), - Dtype(0), buffer_blob_.mutable_gpu_data()); - - caffe_gpu_div(buffer_blob_.count(), top_data, buffer_data, top_data); - - // Saving x_norm - caffe_copy(top[0]->count(), const_top_data, x_norm_.mutable_gpu_data()); - - // scale - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), - batch_sum_multiplier_.gpu_data(), this->blobs_[0]->gpu_data(), - Dtype(0), spatial_variance_.mutable_gpu_data()); - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, - H_ * W_, 1, Dtype(1), - spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(), - Dtype(0), buffer_blob_.mutable_gpu_data()); - - caffe_gpu_mul(buffer_blob_.count(), top_data, buffer_data, top_data); - - // shift - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), - batch_sum_multiplier_.gpu_data(), - this->blobs_[1]->gpu_data(), Dtype(0), - spatial_mean_data); - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, H_ * W_, 1, - Dtype(1), - spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(), Dtype(0), - buffer_blob_.mutable_gpu_data()); - caffe_gpu_add(buffer_blob_.count(), top_data, buffer_data, top_data); + caffe_gpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, + 1. / (num * spatial_dim), bottom_data, + spatial_sum_multiplier_.gpu_data(), 0., + num_by_chans_.mutable_gpu_data()); + caffe_gpu_gemv(CblasTrans, num, channels_, 1., + num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0., + mean_.mutable_gpu_data()); + caffe_gpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, + 1. / (num * spatial_dim), temp_.gpu_data(), + spatial_sum_multiplier_.gpu_data(), 0., + num_by_chans_.mutable_gpu_data()); + caffe_gpu_gemv(CblasTrans, num, channels_, 1., + num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0., + variance_.mutable_gpu_data()); + this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_; + this->blobs_[2]->mutable_cpu_data()[0] += 1; + caffe_gpu_axpby(mean_.count(), Dtype(1), mean_.gpu_data(), + moving_average_fraction_, this->blobs_[0]->mutable_gpu_data()); + Dtype m = Dtype(bottom[0]->count()/channels_); + caffe_gpu_axpby(variance_.count(), m/(m-1), variance_.gpu_data(), + moving_average_fraction_, this->blobs_[1]->mutable_gpu_data()); } + // elementwise square of mean + caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2), + temp_.mutable_gpu_data()); + + caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(), + variance_.mutable_gpu_data()); // variance + + // normalize variance + caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data()); + caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5), + variance_.mutable_gpu_data()); - template - void BatchNormLayer::Backward_gpu(const vector*>& top, - const vector& propagate_down, - const vector*>& bottom) { - const Dtype* top_diff = top[0]->gpu_diff(); - const Dtype* top_data = top[0]->gpu_data(); - const Dtype* bottom_data = bottom[0]->gpu_data(); - Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); - const Dtype* const_bottom_diff = bottom[0]->gpu_diff(); - Dtype* spatial_mean_data = spatial_mean_.mutable_gpu_data(); - Dtype* buffer_data = buffer_blob_.mutable_gpu_data(); - const Dtype* const_buffer_data = buffer_blob_.gpu_data(); - - // Propage to layer params - // gradient w.r.t. scale - caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(), - top_diff, buffer_blob_.mutable_gpu_data()); - // EX across spatial - caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), - buffer_data, spatial_sum_multiplier_.gpu_data(), Dtype(0), - spatial_variance_.mutable_gpu_data()); - // EX across batch - caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1), - spatial_variance_.gpu_data(), - batch_sum_multiplier_.gpu_data(), Dtype(0), - this->blobs_[0]->mutable_gpu_diff()); - - // gradient w.r.t. shift - // EX across spatial - caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), - top_diff, spatial_sum_multiplier_.gpu_data(), - Dtype(0), spatial_mean_data); - // EX across batch - caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1), - spatial_mean_.gpu_data(), - batch_sum_multiplier_.gpu_data(), Dtype(0), - this->blobs_[1]->mutable_gpu_diff()); - - // Propagate down - // scale top diff - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), - batch_sum_multiplier_.gpu_data(), this->blobs_[0]->gpu_data(), - Dtype(0), spatial_variance_.mutable_gpu_data()); - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, - H_ * W_, 1, Dtype(1), - spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(), - Dtype(0), - buffer_blob_.mutable_gpu_data()); - caffe_gpu_mul(buffer_blob_.count(), top_diff, buffer_data, - buffer_blob_.mutable_gpu_data()); - - // use new top diff for computation - caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(), - buffer_data, bottom_diff); - // EX across spatial - caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, - Dtype(1), bottom_diff, - spatial_sum_multiplier_.gpu_data(), Dtype(0), spatial_mean_data); - // EX across batch - caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1), - spatial_mean_.gpu_data(), - batch_sum_multiplier_.gpu_data(), Dtype(0), - batch_mean_.mutable_gpu_data()); - - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), - batch_sum_multiplier_.gpu_data(), - batch_mean_.gpu_data(), Dtype(0), - spatial_mean_data); - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, - H_ * W_, 1, Dtype(1), spatial_mean_.gpu_data(), - spatial_sum_multiplier_.gpu_data(), Dtype(0), - bottom_diff); - - caffe_gpu_mul(buffer_blob_.count(), x_norm_.gpu_data(), - bottom_diff, bottom_diff); - - // EX across spatial - caffe_gpu_gemv(CblasNoTrans, N_ * C_, H_ * W_, Dtype(1), - buffer_data, spatial_sum_multiplier_.gpu_data(), - Dtype(0), spatial_mean_data); - - // EX across batch - caffe_gpu_gemv(CblasTrans, N_, C_, Dtype(1), - spatial_mean_.gpu_data(), - batch_sum_multiplier_.gpu_data(), Dtype(0), - batch_mean_.mutable_gpu_data()); - - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, - C_, 1, Dtype(1), - batch_sum_multiplier_.gpu_data(), - batch_mean_.gpu_data(), Dtype(0), - spatial_mean_data); - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, - H_ * W_, 1, Dtype(1), - spatial_mean_.gpu_data(), spatial_sum_multiplier_.gpu_data(), - Dtype(1), - bottom_diff); - - caffe_gpu_axpby(buffer_blob_.count(), Dtype(1), buffer_data, - Dtype(-1. / (N_ * H_ * W_)), - bottom_diff); - - // put the squares of bottom into buffer_blob_ -// caffe_gpu_powx(buffer_blob_.count(), bottom_data, Dtype(2), -// buffer_blob_.mutable_gpu_data()); - - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_, C_, 1, Dtype(1), - batch_sum_multiplier_.gpu_data(), batch_variance_.gpu_data(), Dtype(0), - spatial_variance_.mutable_gpu_data()); - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, N_ * C_, - H_ * W_, 1, Dtype(1), - spatial_variance_.gpu_data(), spatial_sum_multiplier_.gpu_data(), - Dtype(0), - buffer_blob_.mutable_gpu_data()); - - caffe_gpu_div(buffer_blob_.count(), const_bottom_diff, - const_buffer_data, bottom_diff); + // do mean and variance normalization + if (bottom[0] != top[0]) { + caffe_copy(bottom[0]->count(), bottom_data, top_data); } + // subtract mean + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, + batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0., + num_by_chans_.mutable_gpu_data()); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, channels_ * num, + spatial_dim, 1, -1, num_by_chans_.gpu_data(), + spatial_sum_multiplier_.gpu_data(), 1., top_data); + // replicate variance to input size + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, + batch_sum_multiplier_.gpu_data(), variance_.gpu_data(), 0., + num_by_chans_.mutable_gpu_data()); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, channels_ * num, + spatial_dim, 1, 1., num_by_chans_.gpu_data(), + spatial_sum_multiplier_.gpu_data(), 0., temp_.mutable_gpu_data()); + caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data); + // TODO(cdoersch): The caching is only needed because later in-place layers + // might clobber the data. Can we skip this if they won't? + caffe_copy(x_norm_.count(), top_data, + x_norm_.mutable_gpu_data()); +} + +template +void BatchNormLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, + const vector*>& bottom) { + CHECK(!use_global_stats_); + const Dtype* top_diff; + if (bottom[0] != top[0]) { + top_diff = top[0]->gpu_diff(); + } else { + caffe_copy(x_norm_.count(), top[0]->gpu_diff(), x_norm_.mutable_gpu_diff()); + top_diff = x_norm_.gpu_diff(); + } + const Dtype* top_data = x_norm_.gpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + int num = bottom[0]->shape()[0]; + int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0)); + // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then + // + // dE(Y)/dX = + // (dE/dY - mean(dE/dY) - mean(dE/dY \cdot Y) \cdot Y) + // ./ sqrt(var(X) + eps) + // + // where \cdot and ./ are hadamard product and elementwise division, + // respectively, dE/dY is the top diff, and mean/var/sum are all computed + // along all dimensions except the channels dimension. In the above + // equation, the operations allow for expansion (i.e. broadcast) along all + // dimensions except the channels dimension where required. + + // sum(dE/dY \cdot Y) + caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff); + caffe_gpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, 1., + bottom_diff, spatial_sum_multiplier_.gpu_data(), 0., + num_by_chans_.mutable_gpu_data()); + caffe_gpu_gemv(CblasTrans, num, channels_, 1., + num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0., + mean_.mutable_gpu_data()); + + // reshape (broadcast) the above + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, + batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0., + num_by_chans_.mutable_gpu_data()); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, channels_ * num, + spatial_dim, 1, 1., num_by_chans_.gpu_data(), + spatial_sum_multiplier_.gpu_data(), 0., bottom_diff); + + // sum(dE/dY \cdot Y) \cdot Y + caffe_gpu_mul(temp_.count(), top_data, bottom_diff, bottom_diff); + + // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y + caffe_gpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, 1., + top_diff, spatial_sum_multiplier_.gpu_data(), 0., + num_by_chans_.mutable_gpu_data()); + caffe_gpu_gemv(CblasTrans, num, channels_, 1., + num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0., + mean_.mutable_gpu_data()); + // reshape (broadcast) the above to make + // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, + batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0., + num_by_chans_.mutable_gpu_data()); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num * channels_, + spatial_dim, 1, 1., num_by_chans_.gpu_data(), + spatial_sum_multiplier_.gpu_data(), 1., bottom_diff); + + // dE/dY - mean(dE/dY)-mean(dE/dY \cdot Y) \cdot Y + caffe_gpu_axpby(temp_.count(), Dtype(1), top_diff, + Dtype(-1. / (num * spatial_dim)), bottom_diff); + + // note: temp_ still contains sqrt(var(X)+eps), computed during the forward + // pass. + caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff); +} + +INSTANTIATE_LAYER_GPU_FUNCS(BatchNormLayer); - INSTANTIATE_LAYER_GPU_FUNCS(BatchNormLayer); -} // namespace caffe +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index a8747c12b37..99dd3c90eef 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -301,7 +301,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 139 (last added: tile_param) +// LayerParameter next available layer-specific ID: 140 (last added: batch_norm_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -350,6 +350,7 @@ message LayerParameter { // The default for the engine is set by the ENGINE switch at compile-time. optional AccuracyParameter accuracy_param = 102; optional ArgMaxParameter argmax_param = 103; + optional BatchNormParameter batch_norm_param = 139; optional ConcatParameter concat_param = 104; optional ContrastiveLossParameter contrastive_loss_param = 105; optional ConvolutionParameter convolution_param = 106; @@ -461,6 +462,18 @@ message ConcatParameter { optional uint32 concat_dim = 1 [default = 1]; } +message BatchNormParameter { + // If false, accumulate global mean/variance values via a moving average. If + // true, use those accumulated values instead of computing mean/variance + // across the batch. + optional bool use_global_stats = 1; + // How much does the moving average decay each iteration? + optional float moving_average_fraction = 2 [default = .999]; + // Small value to add to the variance estimate so that we don't divide by + // zero. + optional float eps = 3 [default = 1e-5]; +} + message ContrastiveLossParameter { // margin for dissimilar pair optional float margin = 1 [default = 1.0]; diff --git a/src/caffe/test/test_batch_norm_layer.cpp b/src/caffe/test/test_batch_norm_layer.cpp index 704efd5df3d..22b9667f31b 100644 --- a/src/caffe/test/test_batch_norm_layer.cpp +++ b/src/caffe/test/test_batch_norm_layer.cpp @@ -60,7 +60,50 @@ namespace caffe { for ( int k = 0; k < height; ++k ) { for ( int l = 0; l < width; ++l ) { Dtype data = this->blob_top_->data_at(i, j, k, l); - Dtype bottom_data = this->blob_bottom_->data_at(i, j, k, l); + sum += data; + var += data * data; + } + } + } + sum /= height * width * num; + var /= height * width * num; + + const Dtype kErrorBound = 0.001; + // expect zero mean + EXPECT_NEAR(0, sum, kErrorBound); + // expect unit variance + EXPECT_NEAR(1, var, kErrorBound); + } + } + + TYPED_TEST(BatchNormLayerTest, TestForwardInplace) { + typedef typename TypeParam::Dtype Dtype; + Blob blob_inplace(5, 2, 3, 4); + vector*> blob_bottom_vec; + vector*> blob_top_vec; + LayerParameter layer_param; + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(&blob_inplace); + blob_bottom_vec.push_back(&blob_inplace); + blob_top_vec.push_back(&blob_inplace); + + BatchNormLayer layer(layer_param); + layer.SetUp(blob_bottom_vec, blob_top_vec); + layer.Forward(blob_bottom_vec, blob_top_vec); + + // Test mean + int num = blob_inplace.num(); + int channels = blob_inplace.channels(); + int height = blob_inplace.height(); + int width = blob_inplace.width(); + + for (int j = 0; j < channels; ++j) { + Dtype sum = 0, var = 0; + for (int i = 0; i < num; ++i) { + for ( int k = 0; k < height; ++k ) { + for ( int l = 0; l < width; ++l ) { + Dtype data = blob_inplace.data_at(i, j, k, l); sum += data; var += data * data; } From 09b8738d73ebc37dda09e8c6dd05e35609999c77 Mon Sep 17 00:00:00 2001 From: Rodrigo Benenson Date: Thu, 22 Oct 2015 18:18:08 +0200 Subject: [PATCH 322/446] diff.ndim != 4 is outdated this code seems not to apply to the caffe head. ``` if diff.ndim != 4: raise Exception('{} diff is not 4-d'.format(top)) ``` --- python/caffe/pycaffe.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py index 8ea24da4fdd..7bd4f411b6a 100644 --- a/python/caffe/pycaffe.py +++ b/python/caffe/pycaffe.py @@ -146,8 +146,6 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs): # Set top diffs according to defined shapes and make arrays single and # C-contiguous as Caffe expects. for top, diff in kwargs.iteritems(): - if diff.ndim != 4: - raise Exception('{} diff is not 4-d'.format(top)) if diff.shape[0] != self.blobs[top].num: raise Exception('Diff is not batch sized') self.blobs[top].diff[...] = diff From 3e5f49435f95de57bffbde53d745dcb4a8f1f870 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andreas=20L=C3=B8ve=20Selvik?= Date: Tue, 13 Oct 2015 16:32:52 +0200 Subject: [PATCH 323/446] Add opencv_imgcodecs to library path in Makefile Project does not compile without opencv_imgcodecs in the library path if you're using OpenCV 3. This introduces a OPENCV_VERSION flag in Makefile.config that includes the library if set to 3. (Trying to include it with OpenCV 2 also breaks the build) --- Makefile | 7 ++++++- Makefile.config.example | 3 +++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 5fb6394e947..43cb15fe491 100644 --- a/Makefile +++ b/Makefile @@ -184,7 +184,12 @@ ifeq ($(USE_LMDB), 1) LIBRARIES += lmdb endif ifeq ($(USE_OPENCV), 1) - LIBRARIES += opencv_core opencv_highgui opencv_imgproc + LIBRARIES += opencv_core opencv_highgui opencv_imgproc + + ifeq ($(OPENCV_VERSION), 3) + LIBRARIES += opencv_imgcodecs + endif + endif PYTHON_LIBRARIES := boost_python python2.7 WARNINGS := -Wall -Wno-sign-compare diff --git a/Makefile.config.example b/Makefile.config.example index a20bad2f5ce..8e2c4fb46a0 100644 --- a/Makefile.config.example +++ b/Makefile.config.example @@ -12,6 +12,9 @@ # USE_LMDB := 0 # USE_OPENCV := 0 +# Uncomment if you're using OpenCV 3 +# OPENCV_VERSION := 3 + # To customize your choice of compiler, uncomment and set the following. # N.B. the default for Linux is g++ and the default for OSX is clang++ # CUSTOM_CXX := g++ From 9898794172b7def7a91d925d97e11dd0878ddb61 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 22 Oct 2015 19:12:48 -0700 Subject: [PATCH 324/446] cuDNN: only log conv workspace in debug mode --- src/caffe/layers/cudnn_conv_layer.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp index 8b61249a4f8..c82cb7efd6c 100644 --- a/src/caffe/layers/cudnn_conv_layer.cpp +++ b/src/caffe/layers/cudnn_conv_layer.cpp @@ -191,7 +191,7 @@ void CuDNNConvolutionLayer::Reshape( // this is the total amount of storage needed over all groups + streams if (total_max_workspace > workspaceSizeInBytes) { - LOG(INFO) << "Reallocating workspace storage: " << total_max_workspace; + DLOG(INFO) << "Reallocating workspace storage: " << total_max_workspace; workspaceSizeInBytes = total_max_workspace; // free the existing workspace and allocate a new (larger) one From 8e455850bf398dd16dffa5e7591480d013b8e573 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Tue, 27 Oct 2015 22:49:28 -0700 Subject: [PATCH 325/446] CuDNNConvolutionLayer accumulate gradients --- src/caffe/layers/cudnn_conv_layer.cu | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu index 63b6ab9c388..f2df4aa502f 100644 --- a/src/caffe/layers/cudnn_conv_layer.cu +++ b/src/caffe/layers/cudnn_conv_layer.cu @@ -53,12 +53,10 @@ void CuDNNConvolutionLayer::Backward_gpu(const vector*>& top, if (this->param_propagate_down_[0]) { weight = this->blobs_[0]->gpu_data(); weight_diff = this->blobs_[0]->mutable_gpu_diff(); - caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff); } Dtype* bias_diff = NULL; if (this->bias_term_ && this->param_propagate_down_[1]) { bias_diff = this->blobs_[1]->mutable_gpu_diff(); - caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff); } for (int i = 0; i < top.size(); ++i) { const Dtype* top_diff = top[i]->gpu_diff(); From 5925fa8ed94e9af02449f853b06f252ac5f4c364 Mon Sep 17 00:00:00 2001 From: Kai Li <1196594711@qq.com> Date: Fri, 30 Oct 2015 00:46:08 +0800 Subject: [PATCH 326/446] Update plot_training_log.py.example I find there is no plot_log.sh file --- tools/extra/plot_training_log.py.example | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/extra/plot_training_log.py.example b/tools/extra/plot_training_log.py.example index b6fda54e01c..4d3ed0d15a9 100755 --- a/tools/extra/plot_training_log.py.example +++ b/tools/extra/plot_training_log.py.example @@ -150,7 +150,7 @@ Be warned that the fields in the training log may change in the future. You had better check the data files and change the mapping from field name to field index in create_field_index before designing your own plots. Usage: - ./plot_log.sh chart_type[0-%s] /where/to/save.png /path/to/first.log ... + ./plot_training_log.py chart_type[0-%s] /where/to/save.png /path/to/first.log ... Notes: 1. Supporting multiple logs. 2. Log file name must end with the lower-cased "%s". From 54f0c08ca144c498c835baa017887a64bc8fbbf2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ren=C3=A9=20Scheibe?= Date: Tue, 3 Nov 2015 19:27:07 +0100 Subject: [PATCH 327/446] fix detect.py (invalid model path) --- python/detect.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/detect.py b/python/detect.py index 691098f5c53..1aba964a9d8 100755 --- a/python/detect.py +++ b/python/detect.py @@ -46,7 +46,7 @@ def main(argv): parser.add_argument( "--model_def", default=os.path.join(pycaffe_dir, - "../models/bvlc_reference_caffenet/deploy.prototxt.prototxt"), + "../models/bvlc_reference_caffenet/deploy.prototxt"), help="Model definition file." ) parser.add_argument( From b2339716fd3c6ebb050be0241ba2a34f804ae904 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Tue, 3 Nov 2015 14:42:24 -0800 Subject: [PATCH 328/446] TravisCI: wget cmake with --no-check-certificate ``` --2015-11-03 22:31:11-- http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh Resolving www.cmake.org (www.cmake.org)... 66.194.253.19 Connecting to www.cmake.org (www.cmake.org)|66.194.253.19|:80... connected. HTTP request sent, awaiting response... 301 Moved Permanently Location: http://cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh [following] --2015-11-03 22:31:11-- http://cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh Resolving cmake.org (cmake.org)... 66.194.253.19 Connecting to cmake.org (cmake.org)|66.194.253.19|:80... connected. HTTP request sent, awaiting response... 301 Moved Permanently Location: https://cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh [following] --2015-11-03 22:31:11-- https://cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh Connecting to cmake.org (cmake.org)|66.194.253.19|:443... connected. ERROR: no certificate subject alternative name matches requested host name `cmake.org'. To connect to cmake.org insecurely, use `--no-check-certificate'. ``` --- scripts/travis/travis_install.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh index d6c6e228b58..d78c4d2f7b2 100755 --- a/scripts/travis/travis_install.sh +++ b/scripts/travis/travis_install.sh @@ -23,7 +23,7 @@ apt-get install \ # Caffe requires a minimum CMake version of 2.8.8. if $WITH_CMAKE; then # cmake 3 will make sure that the python interpreter and libraries match - wget http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh + wget --no-check-certificate http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh chmod +x cmake3.sh ./cmake3.sh --prefix=/usr/ --skip-license --exclude-subdir fi From 5196926a7cca1a85aecbd97e78452352fc5d2b3d Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Wed, 4 Nov 2015 22:10:25 -0800 Subject: [PATCH 329/446] [travis] fix boost/python3 conda conflict --- scripts/travis/travis_install.sh | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh index d78c4d2f7b2..432c81dc6ec 100755 --- a/scripts/travis/travis_install.sh +++ b/scripts/travis/travis_install.sh @@ -70,6 +70,10 @@ if [ ! -d $CONDA_DIR ]; then ./miniconda.sh -b -p $CONDA_DIR conda update --yes conda + # The version of boost we're using for Python 3 depends on 3.4 for now. + if [ "$PYTHON_VERSION" -eq "3" ]; then + conda install --yes python=3.4 + fi conda install --yes numpy scipy matplotlib scikit-image pip # Let conda install boost (so that boost_python matches) conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0 From 4137c093bd6ca018c5953a1e069069ab96f4f91d Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Wed, 4 Nov 2015 23:54:41 -0800 Subject: [PATCH 330/446] [style] fix whitespace in travis_install.sh --- scripts/travis/travis_install.sh | 54 ++++++++++++++++---------------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh index 432c81dc6ec..d18dc223a06 100755 --- a/scripts/travis/travis_install.sh +++ b/scripts/travis/travis_install.sh @@ -61,39 +61,39 @@ rm -f $LMDB_FILE # than using pip for everything). export PATH=$CONDA_DIR/bin:$PATH if [ ! -d $CONDA_DIR ]; then - if [ "$PYTHON_VERSION" -eq "3" ]; then - wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - else - wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh - fi - chmod +x miniconda.sh - ./miniconda.sh -b -p $CONDA_DIR - - conda update --yes conda - # The version of boost we're using for Python 3 depends on 3.4 for now. - if [ "$PYTHON_VERSION" -eq "3" ]; then - conda install --yes python=3.4 - fi - conda install --yes numpy scipy matplotlib scikit-image pip - # Let conda install boost (so that boost_python matches) - conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0 + if [ "$PYTHON_VERSION" -eq "3" ]; then + wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh + else + wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh + fi + chmod +x miniconda.sh + ./miniconda.sh -b -p $CONDA_DIR + + conda update --yes conda + # The version of boost we're using for Python 3 depends on 3.4 for now. + if [ "$PYTHON_VERSION" -eq "3" ]; then + conda install --yes python=3.4 + fi + conda install --yes numpy scipy matplotlib scikit-image pip + # Let conda install boost (so that boost_python matches) + conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0 fi # install protobuf 3 (just use the miniconda3 directory to avoid having to setup the path again) if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then - pushd . - wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz - tar -C /tmp -xzvf protobuf-3.tar.gz - cd /tmp/protobuf-3*/ - ./autogen.sh - ./configure --prefix=$CONDA_DIR - $MAKE - $MAKE install - popd + pushd . + wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz + tar -C /tmp -xzvf protobuf-3.tar.gz + cd /tmp/protobuf-3*/ + ./autogen.sh + ./configure --prefix=$CONDA_DIR + $MAKE + $MAKE install + popd fi if [ "$PYTHON_VERSION" -eq "3" ]; then - pip install --pre protobuf + pip install --pre protobuf else - pip install protobuf + pip install protobuf fi From bc1aa41af7d0ba46da4d7c71fc9109baea651ce0 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Wed, 4 Nov 2015 20:48:43 -0800 Subject: [PATCH 331/446] remove dead cpp code for number of CUDA threads __CUDA_ARCH__ is not defined in host code; the #if was vacuous and misleading. --- include/caffe/util/device_alternate.hpp | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp index 6ea595dba2d..e3fe4fe29fd 100644 --- a/include/caffe/util/device_alternate.hpp +++ b/include/caffe/util/device_alternate.hpp @@ -81,14 +81,8 @@ namespace caffe { const char* cublasGetErrorString(cublasStatus_t error); const char* curandGetErrorString(curandStatus_t error); -// CUDA: thread number configuration. -// Use 1024 threads per block, which requires cuda sm_2x or above, -// or fall back to attempt compatibility (best of luck to you). -#if __CUDA_ARCH__ >= 200 - const int CAFFE_CUDA_NUM_THREADS = 1024; -#else - const int CAFFE_CUDA_NUM_THREADS = 512; -#endif +// CUDA: use 512 threads per block +const int CAFFE_CUDA_NUM_THREADS = 512; // CUDA: number of blocks for threads. inline int CAFFE_GET_BLOCKS(const int N) { From 32dc03f14c36d1df46f37a7d13ad528e52c6f786 Mon Sep 17 00:00:00 2001 From: ernest-tg Date: Thu, 5 Nov 2015 15:47:28 +0100 Subject: [PATCH 332/446] Correct transposition & channel_swap in deprocess MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The deprocess( ) function should invert the preprocess( ) function, however it only worked when the permutation of your channel_swap is of order 2 and the permutation of your transpose were of order 3. This is usually the case, which is why this bug went unnoticed for a long time. To reproduce it (on former version), try to preprocess and then deprocess with transformer.set_transpose('data', (0,2,1)) (or (1,0,2) or (2,1,0)) Or with transformer.set_channel_swap('data', (2,0,1)) (or (1,2,0) ) Indeed, we had L152 (in preprocess) caffe_in = caffe_in[channel_swap, :, :] L181 (in deprocess) decaf_in = decaf_in[channel_swap, :, :] So we applied [channel_swap,:,:] twice to the initial data => not always the identity L154 (in preprocess) caffe_in = caffe_in.transpose(transpose) L183 (in deprocess) decaf_in = decaf_in.transpose([transpose[t] for t in transpose]) The transposition [transpose[t] for t in transpose] is (tranpsose)² so we applied transpose[t] three times which is not always the identity. --- python/caffe/io.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/caffe/io.py b/python/caffe/io.py index 11c84260f1a..14942bed542 100644 --- a/python/caffe/io.py +++ b/python/caffe/io.py @@ -178,9 +178,9 @@ def deprocess(self, in_, data): if raw_scale is not None: decaf_in /= raw_scale if channel_swap is not None: - decaf_in = decaf_in[channel_swap, :, :] + decaf_in = decaf_in[np.argsort(channel_swap), :, :] if transpose is not None: - decaf_in = decaf_in.transpose([transpose[t] for t in transpose]) + decaf_in = decaf_in.transpose(np.argsort(transpose)) return decaf_in def set_transpose(self, in_, order): From 987b3d8794e3fe27b4402d52fb3921555104b451 Mon Sep 17 00:00:00 2001 From: Tim Meinhardt Date: Fri, 6 Nov 2015 14:51:46 +0100 Subject: [PATCH 333/446] Fix ArgMaxLayer::Reshape for any num of bottom axes --- include/caffe/common_layers.hpp | 14 +++++++------- src/caffe/layers/argmax_layer.cpp | 4 +++- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp index 72f39ee082b..d42d15c47e3 100644 --- a/include/caffe/common_layers.hpp +++ b/include/caffe/common_layers.hpp @@ -53,8 +53,8 @@ class ArgMaxLayer : public Layer { * -# @f$ (N \times C \times H \times W) @f$ * the inputs @f$ x @f$ * @param top output Blob vector (length 1) - * -# @f$ (N \times 1 \times K \times 1) @f$ or, if out_max_val - * @f$ (N \times 2 \times K \times 1) @f$ unless axis set than e.g. + * -# @f$ (N \times 1 \times K) @f$ or, if out_max_val + * @f$ (N \times 2 \times K) @f$ unless axis set than e.g. * @f$ (N \times K \times H \times W) @f$ if axis == 1 * the computed outputs @f$ * y_n = \arg\max\limits_i x_{ni} @@ -81,13 +81,13 @@ class ArgMaxLayer : public Layer { * each channel in the data (i.e. axis 1), it subtracts the mean and divides * by the variance, where both statistics are computed across both spatial * dimensions and across the different examples in the batch. - * + * * By default, during training time, the network is computing global mean/ * variance statistics via a running average, which is then used at test * time to allow deterministic outputs for each input. You can manually * toggle whether the network is accumulating or using the statistics via the * use_global_stats option. IMPORTANT: for this feature to work, you MUST - * set the learning rate to zero for all three parameter blobs, i.e., + * set the learning rate to zero for all three parameter blobs, i.e., * param {lr_mult: 0} three times in the layer definition. * * Note that the original paper also included a per-channel learned bias and @@ -96,10 +96,10 @@ class ArgMaxLayer : public Layer { * followed by a Convolution layer with output the same size as the current. * This produces a channel-specific value that can be added or multiplied by * the BatchNorm layer's output. - * + * * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network - * Training by Reducing Internal Covariate Shift." arXiv preprint - * arXiv:1502.03167 (2015). + * Training by Reducing Internal Covariate Shift." arXiv preprint + * arXiv:1502.03167 (2015). * * TODO(dox): thorough documentation for Forward, Backward, and proto params. */ diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp index 44df8d4e2e4..354d83f7061 100644 --- a/src/caffe/layers/argmax_layer.cpp +++ b/src/caffe/layers/argmax_layer.cpp @@ -32,7 +32,9 @@ void ArgMaxLayer::LayerSetUp(const vector*>& bottom, template void ArgMaxLayer::Reshape(const vector*>& bottom, const vector*>& top) { - std::vector shape(bottom[0]->num_axes(), 1); + int num_top_axes = bottom[0]->num_axes(); + if ( num_top_axes < 3 ) num_top_axes = 3; + std::vector shape(num_top_axes, 1); if (has_axis_) { // Produces max_ind or max_val per axis shape = bottom[0]->shape(); From 0f1e4e5ddd884325df82db00ae0fc531481e9c60 Mon Sep 17 00:00:00 2001 From: Shandy Brown Date: Fri, 6 Nov 2015 20:01:57 -0800 Subject: [PATCH 334/446] Add a -c to wget so that it continues interrupted downloads This would've saved me an overnight download (slow connection here) I tested it, and it worked for me. --- data/ilsvrc12/get_ilsvrc_aux.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data/ilsvrc12/get_ilsvrc_aux.sh b/data/ilsvrc12/get_ilsvrc_aux.sh index b9b85d21e2d..90935f25099 100755 --- a/data/ilsvrc12/get_ilsvrc_aux.sh +++ b/data/ilsvrc12/get_ilsvrc_aux.sh @@ -12,7 +12,7 @@ cd $DIR echo "Downloading..." -wget http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz +wget -c http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz echo "Unzipping..." From c42eb9c4f7d18f1ba16d1d5cb0646296679d936c Mon Sep 17 00:00:00 2001 From: "T.E.A de Souza" Date: Sun, 8 Nov 2015 18:50:29 +0800 Subject: [PATCH 335/446] GetDB must return a value As noted by @danst18, when USE_LEVELDB and USE_LMDB are disabled, a compiler error is issued since GetDB no longer returns a value. At runtime a fatal error would be issued anyways. However to help users who don't need a DB backend, NULL should be returned here. --- src/caffe/util/db.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp index ccda054d881..d0a2b0b5c07 100644 --- a/src/caffe/util/db.cpp +++ b/src/caffe/util/db.cpp @@ -33,6 +33,7 @@ DB* GetDB(const string& backend) { } #endif // USE_LMDB LOG(FATAL) << "Unknown database backend"; + return NULL; } } // namespace db From 0eea94a0e02dd6d28175538a29720456f5213da9 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Sun, 8 Nov 2015 11:20:32 -0800 Subject: [PATCH 336/446] display 'ignore source layer' when initializing from existing parameters This helps in the case to see which layer is initialized from existing parameters, and which layer is ignored. This helps identify the cases where the user types a error mismatch layer name. --- src/caffe/net.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 1ad93e6af5f..05bee7987da 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -745,7 +745,7 @@ void Net::ShareTrainedLayersWith(const Net* other) { ++target_layer_id; } if (target_layer_id == layer_names_.size()) { - DLOG(INFO) << "Ignoring source layer " << source_layer_name; + LOG(INFO) << "Ignoring source layer " << source_layer_name; continue; } DLOG(INFO) << "Copying source layer " << source_layer_name; @@ -813,7 +813,7 @@ void Net::CopyTrainedLayersFrom(const NetParameter& param) { ++target_layer_id; } if (target_layer_id == layer_names_.size()) { - DLOG(INFO) << "Ignoring source layer " << source_layer_name; + LOG(INFO) << "Ignoring source layer " << source_layer_name; continue; } DLOG(INFO) << "Copying source layer " << source_layer_name; @@ -868,7 +868,7 @@ void Net::CopyTrainedLayersFromHDF5(const string trained_filename) { for (int i = 0; i < num_layers; ++i) { string source_layer_name = hdf5_get_name_by_idx(data_hid, i); if (!layer_names_index_.count(source_layer_name)) { - DLOG(INFO) << "Ignoring source layer " << source_layer_name; + LOG(INFO) << "Ignoring source layer " << source_layer_name; continue; } int target_layer_id = layer_names_index_[source_layer_name]; From 50a44b05f87d6c5b734e2b172f5120898c6e3e47 Mon Sep 17 00:00:00 2001 From: panmari Date: Fri, 6 Nov 2015 12:53:16 +0100 Subject: [PATCH 337/446] Switched order of two layers for simpler diff with untuned file Untuned file is in models/bvlc_reference_caffenet/train_val.prototxt. --- models/finetune_flickr_style/train_val.prototxt | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/models/finetune_flickr_style/train_val.prototxt b/models/finetune_flickr_style/train_val.prototxt index 848a426c914..985353be369 100644 --- a/models/finetune_flickr_style/train_val.prototxt +++ b/models/finetune_flickr_style/train_val.prototxt @@ -369,13 +369,6 @@ layer { } } } -layer { - name: "loss" - type: "SoftmaxWithLoss" - bottom: "fc8_flickr" - bottom: "label" - top: "loss" -} layer { name: "accuracy" type: "Accuracy" @@ -386,3 +379,10 @@ layer { phase: TEST } } +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "fc8_flickr" + bottom: "label" + top: "loss" +} From 96e95fb24dc53bed1e46f2404a2f79f1cf870472 Mon Sep 17 00:00:00 2001 From: Gustav Larsson Date: Mon, 9 Nov 2015 14:32:37 -0600 Subject: [PATCH 338/446] DOC: Fix consistent typo in contrastive loss If a pair is similar, it should take the squared distance and not the distance. This is clearly what the code is doing. --- include/caffe/loss_layers.hpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp index d08ad9b6894..1591c0fe151 100644 --- a/include/caffe/loss_layers.hpp +++ b/include/caffe/loss_layers.hpp @@ -132,7 +132,7 @@ class LossLayer : public Layer { /** * @brief Computes the contrastive loss @f$ - * E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d + + * E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + * \left(1-y\right) \max \left(margin-d, 0\right)^2 * @f$ where @f$ * d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be @@ -148,7 +148,7 @@ class LossLayer : public Layer { * @param top output Blob vector (length 1) * -# @f$ (1 \times 1 \times 1 \times 1) @f$ * the computed contrastive loss: @f$ E = - * \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d + + * \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + * \left(1-y\right) \max \left(margin-d, 0\right)^2 * @f$ where @f$ * d = \left| \left| a_n - b_n \right| \right|_2 @f$. From 29f6670f11c4ac505cad0f779430dea01358c025 Mon Sep 17 00:00:00 2001 From: Tea Date: Sat, 7 Nov 2015 14:09:59 +0800 Subject: [PATCH 339/446] Replace unistd functions with cross platform counterparts --- Makefile | 2 +- cmake/Dependencies.cmake | 2 +- include/caffe/util/io.hpp | 30 +++++++++++------------------- src/caffe/test/test_benchmark.cpp | 6 +++--- 4 files changed, 16 insertions(+), 24 deletions(-) diff --git a/Makefile b/Makefile index 4a1d41d5a82..f5dbf432f75 100644 --- a/Makefile +++ b/Makefile @@ -170,7 +170,7 @@ ifneq ($(CPU_ONLY), 1) LIBRARIES := cudart cublas curand endif -LIBRARIES += glog gflags protobuf boost_system m hdf5_hl hdf5 +LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5 # handle IO dependencies USE_LEVELDB ?= 1 diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake index 5651e2b086d..51a803c1a73 100644 --- a/cmake/Dependencies.cmake +++ b/cmake/Dependencies.cmake @@ -2,7 +2,7 @@ set(Caffe_LINKER_LIBS "") # ---[ Boost -find_package(Boost 1.46 REQUIRED COMPONENTS system thread) +find_package(Boost 1.46 REQUIRED COMPONENTS system thread filesystem) include_directories(SYSTEM ${Boost_INCLUDE_DIR}) list(APPEND Caffe_LINKER_LIBS ${Boost_LIBRARIES}) diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp index d6cfa442fca..6b7332548b0 100644 --- a/include/caffe/util/io.hpp +++ b/include/caffe/util/io.hpp @@ -1,7 +1,7 @@ #ifndef CAFFE_UTIL_IO_H_ #define CAFFE_UTIL_IO_H_ -#include +#include #include #include "google/protobuf/message.h" @@ -12,31 +12,23 @@ namespace caffe { using ::google::protobuf::Message; +using ::boost::filesystem::path; inline void MakeTempFilename(string* temp_filename) { temp_filename->clear(); - *temp_filename = "/tmp/caffe_test.XXXXXX"; - char* temp_filename_cstr = new char[temp_filename->size() + 1]; - // NOLINT_NEXT_LINE(runtime/printf) - strcpy(temp_filename_cstr, temp_filename->c_str()); - int fd = mkstemp(temp_filename_cstr); - CHECK_GE(fd, 0) << "Failed to open a temporary file at: " << *temp_filename; - close(fd); - *temp_filename = temp_filename_cstr; - delete[] temp_filename_cstr; + const path& model = boost::filesystem::temp_directory_path() + /"caffe_test.%%%%%%"; + *temp_filename = boost::filesystem::unique_path(model).string(); } inline void MakeTempDir(string* temp_dirname) { temp_dirname->clear(); - *temp_dirname = "/tmp/caffe_test.XXXXXX"; - char* temp_dirname_cstr = new char[temp_dirname->size() + 1]; - // NOLINT_NEXT_LINE(runtime/printf) - strcpy(temp_dirname_cstr, temp_dirname->c_str()); - char* mkdtemp_result = mkdtemp(temp_dirname_cstr); - CHECK(mkdtemp_result != NULL) - << "Failed to create a temporary directory at: " << *temp_dirname; - *temp_dirname = temp_dirname_cstr; - delete[] temp_dirname_cstr; + const path& model = boost::filesystem::temp_directory_path() + /"caffe_test.%%%%%%"; + const path& dir = boost::filesystem::unique_path(model).string(); + bool directoryCreated = boost::filesystem::create_directory(dir); + CHECK(directoryCreated); + *temp_dirname = dir.string(); } bool ReadProtoFromTextFile(const char* filename, Message* proto); diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp index 43aaa639b3c..b03fdf69a8a 100644 --- a/src/caffe/test/test_benchmark.cpp +++ b/src/caffe/test/test_benchmark.cpp @@ -1,4 +1,4 @@ -#include // for usleep +#include #include "gtest/gtest.h" @@ -64,7 +64,7 @@ TYPED_TEST(BenchmarkTest, TestTimerMilliSeconds) { EXPECT_FALSE(timer.running()); EXPECT_FALSE(timer.has_run_at_least_once()); timer.Start(); - usleep(300 * 1000); + boost::this_thread::sleep(boost::posix_time::milliseconds(300)); EXPECT_GE(timer.MilliSeconds(), 300 - kMillisecondsThreshold); EXPECT_LE(timer.MilliSeconds(), 300 + kMillisecondsThreshold); EXPECT_TRUE(timer.initted()); @@ -79,7 +79,7 @@ TYPED_TEST(BenchmarkTest, TestTimerSeconds) { EXPECT_FALSE(timer.running()); EXPECT_FALSE(timer.has_run_at_least_once()); timer.Start(); - usleep(300 * 1000); + boost::this_thread::sleep(boost::posix_time::milliseconds(300)); EXPECT_GE(timer.Seconds(), 0.3 - kMillisecondsThreshold / 1000.); EXPECT_LE(timer.Seconds(), 0.3 + kMillisecondsThreshold / 1000.); EXPECT_TRUE(timer.initted()); From f9970c83264b43722bd9f97376580cc3dbf61227 Mon Sep 17 00:00:00 2001 From: gdh1995 Date: Tue, 10 Nov 2015 22:41:55 +0800 Subject: [PATCH 340/446] fix a bug that time duration may be 0 when downloading model binary --- scripts/download_model_binary.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/download_model_binary.py b/scripts/download_model_binary.py index 03a50f6776a..66f72f2477e 100755 --- a/scripts/download_model_binary.py +++ b/scripts/download_model_binary.py @@ -18,7 +18,7 @@ def reporthook(count, block_size, total_size): if count == 0: start_time = time.time() return - duration = time.time() - start_time + duration = (time.time() - start_time) or 0.01 progress_size = int(count * block_size) speed = int(progress_size / (1024 * duration)) percent = int(count * block_size * 100 / total_size) From 9ff2baf8e06e4809ad668e5c355ad76c36d9674d Mon Sep 17 00:00:00 2001 From: "T.E.A de Souza" Date: Thu, 12 Nov 2015 15:32:28 +0800 Subject: [PATCH 341/446] Remove un-necessary includes --- src/caffe/parallel.cpp | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp index 9abc92b612d..62f5d738593 100644 --- a/src/caffe/parallel.cpp +++ b/src/caffe/parallel.cpp @@ -3,9 +3,6 @@ #endif #include #include -#include -#include -#include #include #include From 3682fde8a9a4a7b20e6ceb2d95a9abeab5227561 Mon Sep 17 00:00:00 2001 From: Tea Date: Thu, 12 Nov 2015 15:55:06 +0800 Subject: [PATCH 342/446] Functions shall return a value in syncedmem --- src/caffe/syncedmem.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp index ec4665ecdda..4d3564172ab 100644 --- a/src/caffe/syncedmem.cpp +++ b/src/caffe/syncedmem.cpp @@ -97,6 +97,7 @@ const void* SyncedMemory::gpu_data() { return (const void*)gpu_ptr_; #else NO_GPU; + return NULL; #endif } @@ -133,6 +134,7 @@ void* SyncedMemory::mutable_gpu_data() { return gpu_ptr_; #else NO_GPU; + return NULL; #endif } From dc48870d7f8e823138594c794789ac3156cc0798 Mon Sep 17 00:00:00 2001 From: Benedikt Wilbertz Date: Wed, 30 Sep 2015 23:02:34 +0200 Subject: [PATCH 343/446] Fix loss of last iteration when average_loss > 1 refactor duplicate code into separate update function for smoothed loss fix naming convention --- include/caffe/solver.hpp | 3 +++ src/caffe/solver.cpp | 37 ++++++++++++++++++++++++------------- 2 files changed, 27 insertions(+), 13 deletions(-) diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 26b8e8e2038..38259edad9f 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -107,6 +107,7 @@ class Solver { virtual void RestoreSolverStateFromHDF5(const string& state_file) = 0; virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0; void DisplayOutputBlobs(const int net_id); + void UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss); SolverParameter param_; int iter_; @@ -114,6 +115,8 @@ class Solver { shared_ptr > net_; vector > > test_nets_; vector callbacks_; + vector losses_; + Dtype smoothed_loss_; // The root solver that holds root nets (actually containing shared layers) // in data parallelism diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index d3bc7361dd5..5b31c7d81a3 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -195,8 +195,8 @@ void Solver::Step(int iters) { const int start_iter = iter_; const int stop_iter = iter_ + iters; int average_loss = this->param_.average_loss(); - vector losses; - Dtype smoothed_loss = 0; + losses_.clear(); + smoothed_loss_ = 0; while (iter_ < stop_iter) { // zero-init the params @@ -223,18 +223,10 @@ void Solver::Step(int iters) { } loss /= param_.iter_size(); // average the loss across iterations for smoothed reporting - if (losses.size() < average_loss) { - losses.push_back(loss); - int size = losses.size(); - smoothed_loss = (smoothed_loss * (size - 1) + loss) / size; - } else { - int idx = (iter_ - start_iter) % average_loss; - smoothed_loss += (loss - losses[idx]) / average_loss; - losses[idx] = loss; - } + UpdateSmoothedLoss(loss, start_iter, average_loss); if (display) { LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_ - << ", loss = " << smoothed_loss; + << ", loss = " << smoothed_loss_; const vector*>& result = net_->output_blobs(); int score_index = 0; for (int j = 0; j < result.size(); ++j) { @@ -297,6 +289,7 @@ void Solver::Solve(const char* resume_file) { // For a network that is trained by the solver, no bottom or top vecs // should be given, and we will just provide dummy vecs. + int start_iter = iter_; Step(param_.max_iter() - iter_); // If we haven't already, save a snapshot after optimization, unless // overridden by setting snapshot_after_train := false @@ -315,9 +308,13 @@ void Solver::Solve(const char* resume_file) { // updated the parameters "max_iter" times -- this final pass is only done to // display the loss, which is computed in the forward pass. if (param_.display() && iter_ % param_.display() == 0) { + int average_loss = this->param_.average_loss(); Dtype loss; net_->ForwardPrefilled(&loss); - LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss; + + UpdateSmoothedLoss(loss, start_iter, average_loss); + + LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss_; } if (param_.test_interval() && iter_ % param_.test_interval() == 0) { TestAll(); @@ -485,6 +482,20 @@ void Solver::Restore(const char* state_file) { } } +template +void Solver::UpdateSmoothedLoss(Dtype loss, int start_iter, + int average_loss) { + if (losses_.size() < average_loss) { + losses_.push_back(loss); + int size = losses_.size(); + smoothed_loss_ = (smoothed_loss_ * (size - 1) + loss) / size; + } else { + int idx = (iter_ - start_iter) % average_loss; + smoothed_loss_ += (loss - losses_[idx]) / average_loss; + losses_[idx] = loss; + } +} + INSTANTIATE_CLASS(Solver); } // namespace caffe From 0ad1d8ab3f8e3d0bd4d9a7e8b65c7a5f9f28d60a Mon Sep 17 00:00:00 2001 From: Kang Kim Date: Sat, 7 Nov 2015 12:49:15 +0900 Subject: [PATCH 344/446] Update computation of variance and global stats in BatchNormLayer --- src/caffe/layers/batch_norm_layer.cpp | 55 +++++++++++++------------- src/caffe/layers/batch_norm_layer.cu | 56 ++++++++++++++------------- 2 files changed, 57 insertions(+), 54 deletions(-) diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp index 94c2b96b9cd..5eba25e9024 100644 --- a/src/caffe/layers/batch_norm_layer.cpp +++ b/src/caffe/layers/batch_norm_layer.cpp @@ -2,7 +2,6 @@ #include #include "caffe/common_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { @@ -80,20 +79,21 @@ void BatchNormLayer::Forward_cpu(const vector*>& bottom, int num = bottom[0]->shape(0); int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_); - // elementwise square - caffe_powx(bottom[0]->count(), bottom_data, Dtype(2), - temp_.mutable_cpu_data()); + if (bottom[0] != top[0]) { + caffe_copy(bottom[0]->count(), bottom_data, top_data); + } if (use_global_stats_) { // use the stored mean/variance estimates. TODO(cdoersch): allow an option // to use an unbiased variance estimate, like the paper does. - const Dtype scale_factor = 1 / this->blobs_[2]->cpu_data()[0]; + const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ? + 0 : 1 / this->blobs_[2]->cpu_data()[0]; caffe_cpu_scale(variance_.count(), scale_factor, this->blobs_[0]->cpu_data(), mean_.mutable_cpu_data()); caffe_cpu_scale(variance_.count(), scale_factor, this->blobs_[1]->cpu_data(), variance_.mutable_cpu_data()); } else { - // computes variance using var(X) = E(X^2) - (EX)^2 + // compute mean caffe_cpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, 1. / (num * spatial_dim), bottom_data, spatial_sum_multiplier_.cpu_data(), 0., @@ -101,44 +101,45 @@ void BatchNormLayer::Forward_cpu(const vector*>& bottom, caffe_cpu_gemv(CblasTrans, num, channels_, 1., num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data()); + } + + // subtract mean + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, + batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0., + num_by_chans_.mutable_cpu_data()); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels_ * num, + spatial_dim, 1, -1, num_by_chans_.cpu_data(), + spatial_sum_multiplier_.cpu_data(), 1., top_data); + + if (!use_global_stats_) { + // compute variance using var(X) = E((X-EX)^2) + caffe_powx(top[0]->count(), top_data, Dtype(2), + temp_.mutable_cpu_data()); // (X-EX)^2 caffe_cpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, 1. / (num * spatial_dim), temp_.cpu_data(), spatial_sum_multiplier_.cpu_data(), 0., num_by_chans_.mutable_cpu_data()); caffe_cpu_gemv(CblasTrans, num, channels_, 1., num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0., - variance_.mutable_cpu_data()); + variance_.mutable_cpu_data()); // E((X_EX)^2) + + // compute and save moving average this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_; this->blobs_[2]->mutable_cpu_data()[0] += 1; caffe_cpu_axpby(mean_.count(), Dtype(1), mean_.cpu_data(), moving_average_fraction_, this->blobs_[0]->mutable_cpu_data()); - Dtype m = Dtype(bottom[0]->count()/channels_); - caffe_cpu_axpby(variance_.count(), m/(m-1), variance_.cpu_data(), - moving_average_fraction_, this->blobs_[1]->mutable_cpu_data()); + int m = bottom[0]->count()/channels_; + Dtype bias_correction_factor = m > 1 ? Dtype(m)/(m-1) : 1; + caffe_cpu_axpby(variance_.count(), bias_correction_factor, + variance_.cpu_data(), moving_average_fraction_, + this->blobs_[1]->mutable_cpu_data()); } - // elementwise square of mean - caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2), - temp_.mutable_cpu_data()); - - caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(), - variance_.mutable_cpu_data()); // variance // normalize variance caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data()); caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5), variance_.mutable_cpu_data()); - // do mean and variance normalization - if (bottom[0] != top[0]) { - caffe_copy(bottom[0]->count(), bottom_data, top_data); - } - // subtract mean - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, - batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0., - num_by_chans_.mutable_cpu_data()); - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels_ * num, - spatial_dim, 1, -1, num_by_chans_.cpu_data(), - spatial_sum_multiplier_.cpu_data(), 1., top_data); // replicate variance to input size caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, batch_sum_multiplier_.cpu_data(), variance_.cpu_data(), 0., diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu index cd8924a451d..921a58f07a9 100644 --- a/src/caffe/layers/batch_norm_layer.cu +++ b/src/caffe/layers/batch_norm_layer.cu @@ -2,7 +2,6 @@ #include #include "caffe/common_layers.hpp" -#include "caffe/layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { @@ -15,20 +14,22 @@ void BatchNormLayer::Forward_gpu(const vector*>& bottom, int num = bottom[0]->shape(0); int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0)); - // elementwise square - caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2), - temp_.mutable_gpu_data()); + if (bottom[0] != top[0]) { + caffe_copy(bottom[0]->count(), bottom_data, top_data); + } + if (use_global_stats_) { // use the stored mean/variance estimates. TODO(cdoersch): allow an option // to use an unbiased variance estimate, like the paper does. - const Dtype scale_factor = 1 / this->blobs_[2]->cpu_data()[0]; + const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ? + 0 : 1 / this->blobs_[2]->cpu_data()[0]; caffe_gpu_scale(variance_.count(), scale_factor, this->blobs_[0]->gpu_data(), mean_.mutable_gpu_data()); caffe_gpu_scale(variance_.count(), scale_factor, this->blobs_[1]->gpu_data(), variance_.mutable_gpu_data()); } else { - // computes variance using var(X) = E(X^2) - (EX)^2 + // compute mean caffe_gpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, 1. / (num * spatial_dim), bottom_data, spatial_sum_multiplier_.gpu_data(), 0., @@ -36,44 +37,45 @@ void BatchNormLayer::Forward_gpu(const vector*>& bottom, caffe_gpu_gemv(CblasTrans, num, channels_, 1., num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data()); + } + + // subtract mean + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, + batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0., + num_by_chans_.mutable_gpu_data()); + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, channels_ * num, + spatial_dim, 1, -1, num_by_chans_.gpu_data(), + spatial_sum_multiplier_.gpu_data(), 1., top_data); + + if (!use_global_stats_) { + // compute variance using var(X) = E((X-EX)^2) + caffe_gpu_powx(top[0]->count(), top_data, Dtype(2), + temp_.mutable_gpu_data()); // (X-EX)^2 caffe_gpu_gemv(CblasNoTrans, channels_ * num, spatial_dim, 1. / (num * spatial_dim), temp_.gpu_data(), spatial_sum_multiplier_.gpu_data(), 0., num_by_chans_.mutable_gpu_data()); caffe_gpu_gemv(CblasTrans, num, channels_, 1., num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0., - variance_.mutable_gpu_data()); + variance_.mutable_gpu_data()); // E((X_EX)^2) + + // compute and save moving average this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_; this->blobs_[2]->mutable_cpu_data()[0] += 1; caffe_gpu_axpby(mean_.count(), Dtype(1), mean_.gpu_data(), moving_average_fraction_, this->blobs_[0]->mutable_gpu_data()); - Dtype m = Dtype(bottom[0]->count()/channels_); - caffe_gpu_axpby(variance_.count(), m/(m-1), variance_.gpu_data(), - moving_average_fraction_, this->blobs_[1]->mutable_gpu_data()); + int m = bottom[0]->count()/channels_; + Dtype bias_correction_factor = m > 1 ? Dtype(m)/(m-1) : 1; + caffe_gpu_axpby(variance_.count(), bias_correction_factor, + variance_.gpu_data(), moving_average_fraction_, + this->blobs_[1]->mutable_gpu_data()); } - // elementwise square of mean - caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2), - temp_.mutable_gpu_data()); - - caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(), - variance_.mutable_gpu_data()); // variance // normalize variance caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data()); caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5), variance_.mutable_gpu_data()); - // do mean and variance normalization - if (bottom[0] != top[0]) { - caffe_copy(bottom[0]->count(), bottom_data, top_data); - } - // subtract mean - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, - batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0., - num_by_chans_.mutable_gpu_data()); - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, channels_ * num, - spatial_dim, 1, -1, num_by_chans_.gpu_data(), - spatial_sum_multiplier_.gpu_data(), 1., top_data); // replicate variance to input size caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1, batch_sum_multiplier_.gpu_data(), variance_.gpu_data(), 0., From f6e582a38deee8db0904460cbf7aaeb143c682f5 Mon Sep 17 00:00:00 2001 From: Kang Kim Date: Fri, 13 Nov 2015 02:20:02 +0900 Subject: [PATCH 345/446] Make backward pass work when global stats is active for BatchNormLayer including minor code cleaning --- src/caffe/layers/batch_norm_layer.cpp | 10 ++++++---- src/caffe/layers/batch_norm_layer.cu | 10 ++++++---- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp index 5eba25e9024..b5c91b5e1b3 100644 --- a/src/caffe/layers/batch_norm_layer.cpp +++ b/src/caffe/layers/batch_norm_layer.cpp @@ -84,8 +84,7 @@ void BatchNormLayer::Forward_cpu(const vector*>& bottom, } if (use_global_stats_) { - // use the stored mean/variance estimates. TODO(cdoersch): allow an option - // to use an unbiased variance estimate, like the paper does. + // use the stored mean/variance estimates. const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ? 0 : 1 / this->blobs_[2]->cpu_data()[0]; caffe_cpu_scale(variance_.count(), scale_factor, @@ -158,7 +157,6 @@ template void BatchNormLayer::Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { - CHECK(!use_global_stats_); const Dtype* top_diff; if (bottom[0] != top[0]) { top_diff = top[0]->cpu_diff(); @@ -166,8 +164,12 @@ void BatchNormLayer::Backward_cpu(const vector*>& top, caffe_copy(x_norm_.count(), top[0]->cpu_diff(), x_norm_.mutable_cpu_diff()); top_diff = x_norm_.cpu_diff(); } - const Dtype* top_data = x_norm_.cpu_data(); Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + if (use_global_stats_) { + caffe_div(temp_.count(), top_diff, temp_.cpu_data(), bottom_diff); + return; + } + const Dtype* top_data = x_norm_.cpu_data(); int num = bottom[0]->shape()[0]; int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_); // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu index 921a58f07a9..2a6cac54168 100644 --- a/src/caffe/layers/batch_norm_layer.cu +++ b/src/caffe/layers/batch_norm_layer.cu @@ -20,8 +20,7 @@ void BatchNormLayer::Forward_gpu(const vector*>& bottom, if (use_global_stats_) { - // use the stored mean/variance estimates. TODO(cdoersch): allow an option - // to use an unbiased variance estimate, like the paper does. + // use the stored mean/variance estimates. const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ? 0 : 1 / this->blobs_[2]->cpu_data()[0]; caffe_gpu_scale(variance_.count(), scale_factor, @@ -94,7 +93,6 @@ template void BatchNormLayer::Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { - CHECK(!use_global_stats_); const Dtype* top_diff; if (bottom[0] != top[0]) { top_diff = top[0]->gpu_diff(); @@ -102,8 +100,12 @@ void BatchNormLayer::Backward_gpu(const vector*>& top, caffe_copy(x_norm_.count(), top[0]->gpu_diff(), x_norm_.mutable_gpu_diff()); top_diff = x_norm_.gpu_diff(); } - const Dtype* top_data = x_norm_.gpu_data(); Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + if (use_global_stats_) { + caffe_gpu_div(temp_.count(), top_diff, temp_.gpu_data(), bottom_diff); + return; + } + const Dtype* top_data = x_norm_.gpu_data(); int num = bottom[0]->shape()[0]; int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0)); // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then From d81ffbff8cda56de1fe6c41b7156d781f775c7b3 Mon Sep 17 00:00:00 2001 From: Adam Siembida Date: Thu, 12 Nov 2015 16:03:41 -0500 Subject: [PATCH 346/446] Add parentheses to backward_{cpu,gpu} method. --- docs/tutorial/forward_backward.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial/forward_backward.md b/docs/tutorial/forward_backward.md index a645f002f61..528b993ba07 100644 --- a/docs/tutorial/forward_backward.md +++ b/docs/tutorial/forward_backward.md @@ -29,7 +29,7 @@ The backward pass begins with the loss and computes the gradient with respect to These computations follow immediately from defining the model: Caffe plans and carries out the forward and backward passes for you. - The `Net::Forward()` and `Net::Backward()` methods carry out the respective passes while `Layer::Forward()` and `Layer::Backward()` compute each step. -- Every layer type has `forward_{cpu,gpu}()` and `backward_{cpu,gpu}` methods to compute its steps according to the mode of computation. A layer may only implement CPU or GPU mode due to constraints or convenience. +- Every layer type has `forward_{cpu,gpu}()` and `backward_{cpu,gpu}()` methods to compute its steps according to the mode of computation. A layer may only implement CPU or GPU mode due to constraints or convenience. The [Solver](solver.html) optimizes a model by first calling forward to yield the output and loss, then calling backward to generate the gradient of the model, and then incorporating the gradient into a weight update that attempts to minimize the loss. Division of labor between the Solver, Net, and Layer keep Caffe modular and open to development. From a6f14f6e3d03caf8242ed5aa7e224a9ea8ef740d Mon Sep 17 00:00:00 2001 From: Balint Cristian Date: Fri, 13 Nov 2015 13:58:49 +0200 Subject: [PATCH 347/446] Display and store cuDNN version numbers during cmake. --- cmake/Cuda.cmake | 33 +++++++++++++++++++++++++++++++-- cmake/Summary.cmake | 2 +- 2 files changed, 32 insertions(+), 3 deletions(-) diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake index 98aef268cf4..286a42802b4 100644 --- a/cmake/Cuda.cmake +++ b/cmake/Cuda.cmake @@ -183,12 +183,41 @@ function(detect_cuDNN) set(HAVE_CUDNN TRUE PARENT_SCOPE) set(CUDNN_FOUND TRUE PARENT_SCOPE) + file(READ ${CUDNN_INCLUDE}/cudnn.h CUDNN_VERSION_FILE_CONTENTS) + + # cuDNN v3 and beyond + string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)" + CUDNN_VERSION_MAJOR "${CUDNN_VERSION_FILE_CONTENTS}") + string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1" + CUDNN_VERSION_MAJOR "${CUDNN_VERSION_MAJOR}") + string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)" + CUDNN_VERSION_MINOR "${CUDNN_VERSION_FILE_CONTENTS}") + string(REGEX REPLACE "define CUDNN_MINOR * +([0-9]+)" "\\1" + CUDNN_VERSION_MINOR "${CUDNN_VERSION_MINOR}") + string(REGEX MATCH "define CUDNN_PATCHLEVEL * +([0-9]+)" + CUDNN_VERSION_PATCH "${CUDNN_VERSION_FILE_CONTENTS}") + string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1" + CUDNN_VERSION_PATCH "${CUDNN_VERSION_PATCH}") + + if(NOT CUDNN_VERSION_MAJOR) + set(CUDNN_VERSION "???") + else() + set(CUDNN_VERSION "${CUDNN_VERSION_MAJOR}.${CUDNN_VERSION_MINOR}.${CUDNN_VERSION_PATCH}") + endif() + + message(STATUS "Found cuDNN: ver. ${CUDNN_VERSION} found (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})") + + string(COMPARE LESS "${CUDNN_VERSION_MAJOR}" 3 cuDNNVersionIncompatible) + if(cuDNNVersionIncompatible) + message(FATAL_ERROR "cuDNN version >3 is required.") + endif() + + set(CUDNN_VERSION "${CUDNN_VERSION}" PARENT_SCOPE) mark_as_advanced(CUDNN_INCLUDE CUDNN_LIBRARY CUDNN_ROOT) - message(STATUS "Found cuDNN (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})") + endif() endfunction() - ################################################################################################ ### Non macro section ################################################################################################ diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake index 6984f417e71..557a6f04e4b 100644 --- a/cmake/Summary.cmake +++ b/cmake/Summary.cmake @@ -142,7 +142,7 @@ function(caffe_print_configuration_summary) caffe_status(" Target GPU(s) : ${CUDA_ARCH_NAME}" ) caffe_status(" GPU arch(s) : ${NVCC_FLAGS_EXTRA_readable}") if(USE_CUDNN) - caffe_status(" cuDNN : " HAVE_CUDNN THEN "Yes" ELSE "Not found") + caffe_status(" cuDNN : " HAVE_CUDNN THEN "Yes (ver. ${CUDNN_VERSION})" ELSE "Not found") else() caffe_status(" cuDNN : Disabled") endif() From a29c2f7a0ff2ff4278a2e498f0b686b5d5cb88cd Mon Sep 17 00:00:00 2001 From: Alex Lee Date: Sat, 14 Nov 2015 12:49:05 -0800 Subject: [PATCH 348/446] Fix outs and diffs being overwritten in forward_backward_all. --- python/caffe/pycaffe.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py index 7bd4f411b6a..31dc702f647 100644 --- a/python/caffe/pycaffe.py +++ b/python/caffe/pycaffe.py @@ -216,9 +216,9 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs): batch_blobs = self.forward(blobs=blobs, **fb) batch_diffs = self.backward(diffs=diffs, **bb) for out, out_blobs in batch_blobs.iteritems(): - all_outs[out].extend(out_blobs) + all_outs[out].extend(out_blobs.copy()) for diff, out_diffs in batch_diffs.iteritems(): - all_diffs[diff].extend(out_diffs) + all_diffs[diff].extend(out_diffs.copy()) # Package in ndarray. for out, diff in zip(all_outs, all_diffs): all_outs[out] = np.asarray(all_outs[out]) From c4190a56ab62b1a63c1c55bcef3860701a322bed Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Wed, 18 Nov 2015 10:38:32 -0800 Subject: [PATCH 349/446] Skip python layer tests if WITH_PYTHON_LAYER unset --- python/caffe/test/test_python_layer.py | 2 ++ python/caffe/test/test_python_layer_with_param_str.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py index 8ed86655ec3..e46b7118014 100644 --- a/python/caffe/test/test_python_layer.py +++ b/python/caffe/test/test_python_layer.py @@ -77,6 +77,8 @@ def parameter_net_file(): return f.name +@unittest.skipIf('Python' not in caffe.layer_type_list(), + 'Caffe built without Python layer support') class TestPythonLayer(unittest.TestCase): def setUp(self): net_file = python_net_file() diff --git a/python/caffe/test/test_python_layer_with_param_str.py b/python/caffe/test/test_python_layer_with_param_str.py index 3d0f107b3bb..c36048ae9f0 100644 --- a/python/caffe/test/test_python_layer_with_param_str.py +++ b/python/caffe/test/test_python_layer_with_param_str.py @@ -38,6 +38,8 @@ def python_param_net_file(): return f.name +@unittest.skipIf('Python' not in caffe.layer_type_list(), + 'Caffe built without Python layer support') class TestLayerWithParam(unittest.TestCase): def setUp(self): net_file = python_param_net_file() From 1b0716cfd761cec547c85b19fc8f6f971e9236ac Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Thu, 19 Nov 2015 10:05:48 -0800 Subject: [PATCH 350/446] Fix MaxTopBlobs in Accuracy Layer Fix the typo "MaxTopBlos" to "MaxTopBlobs". This typo causes maximum top number to be incorrect. --- include/caffe/loss_layers.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp index 1591c0fe151..e2e3e48ceb5 100644 --- a/include/caffe/loss_layers.hpp +++ b/include/caffe/loss_layers.hpp @@ -42,7 +42,7 @@ class AccuracyLayer : public Layer { // If there are two top blobs, then the second blob will contain // accuracies per class. virtual inline int MinTopBlobs() const { return 1; } - virtual inline int MaxTopBlos() const { return 2; } + virtual inline int MaxTopBlobs() const { return 2; } protected: /** From 41d0c77e5849f97744a3ca5933fd20887bb97f43 Mon Sep 17 00:00:00 2001 From: Tea Date: Thu, 12 Nov 2015 15:15:22 +0800 Subject: [PATCH 351/446] Convert std::max args to Dtype --- include/caffe/test/test_gradient_check_util.hpp | 5 +++-- src/caffe/layers/contrastive_loss_layer.cpp | 3 ++- src/caffe/test/test_contrastive_loss_layer.cpp | 2 +- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp index 25f35d1589e..b25a84875ef 100644 --- a/include/caffe/test/test_gradient_check_util.hpp +++ b/include/caffe/test/test_gradient_check_util.hpp @@ -169,8 +169,9 @@ void GradientChecker::CheckGradientSingle(Layer* layer, || fabs(feature) > kink_ + kink_range_) { // We check relative accuracy, but for too small values, we threshold // the scale factor by 1. - Dtype scale = std::max( - std::max(fabs(computed_gradient), fabs(estimated_gradient)), 1.); + Dtype scale = std::max( + std::max(fabs(computed_gradient), fabs(estimated_gradient)), + Dtype(1.)); EXPECT_NEAR(computed_gradient, estimated_gradient, threshold_ * scale) << "debug: (top_id, top_data_id, blob_id, feat_id)=" << top_id << "," << top_data_id << "," << blob_id << "," << feat_id diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp index 74002087ec9..45facd4a4f5 100644 --- a/src/caffe/layers/contrastive_loss_layer.cpp +++ b/src/caffe/layers/contrastive_loss_layer.cpp @@ -51,7 +51,8 @@ void ContrastiveLossLayer::Forward_cpu( if (legacy_version) { loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0)); } else { - Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), 0.0); + Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]), + Dtype(0.0)); loss += dist*dist; } } diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp index 592997e4578..95901f14297 100644 --- a/src/caffe/test/test_contrastive_loss_layer.cpp +++ b/src/caffe/test/test_contrastive_loss_layer.cpp @@ -77,7 +77,7 @@ TYPED_TEST(ContrastiveLossLayerTest, TestForward) { if (this->blob_bottom_y_->cpu_data()[i]) { // similar pairs loss += dist_sq; } else { - Dtype dist = std::max(margin - sqrt(dist_sq), 0.0); + Dtype dist = std::max(margin - sqrt(dist_sq), 0.0); loss += dist*dist; } } From 23e4e4621b0199684d6d7a8535fb7628f5609952 Mon Sep 17 00:00:00 2001 From: "T.E.A de Souza" Date: Fri, 20 Nov 2015 16:36:29 +0800 Subject: [PATCH 352/446] Function must return a value Currently compilation will fail with some compilers when LevelDB and LMDB are disabled. Very similar to a recently fixed issue. --- src/caffe/util/db.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp index d0a2b0b5c07..7f22509b56e 100644 --- a/src/caffe/util/db.cpp +++ b/src/caffe/util/db.cpp @@ -18,6 +18,7 @@ DB* GetDB(DataParameter::DB backend) { #endif // USE_LMDB default: LOG(FATAL) << "Unknown database backend"; + return NULL; } } From e09329077d7612d7d1a185ea120be6be91bf03d2 Mon Sep 17 00:00:00 2001 From: "T.E.A de Souza" Date: Fri, 20 Nov 2015 16:52:25 +0800 Subject: [PATCH 353/446] Exclude core.hpp when building without OpenCV --- src/caffe/util/io.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp index f2b1dd98423..835d2d4e4ff 100644 --- a/src/caffe/util/io.cpp +++ b/src/caffe/util/io.cpp @@ -2,8 +2,8 @@ #include #include #include -#include #ifdef USE_OPENCV +#include #include #include #include From 8b2aa7093cba002a5f286d47658de72a961d1299 Mon Sep 17 00:00:00 2001 From: Carl Doersch Date: Fri, 6 Nov 2015 14:41:30 -0800 Subject: [PATCH 354/446] Better normalization options for SoftmaxWithLoss layer. --- include/caffe/loss_layers.hpp | 11 +++-- src/caffe/layers/softmax_loss_layer.cpp | 54 +++++++++++++++++++------ src/caffe/layers/softmax_loss_layer.cu | 32 ++++++++------- src/caffe/proto/caffe.proto | 24 +++++++++-- 4 files changed, 89 insertions(+), 32 deletions(-) diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp index d08ad9b6894..d6569c4a574 100644 --- a/include/caffe/loss_layers.hpp +++ b/include/caffe/loss_layers.hpp @@ -747,6 +747,12 @@ class SoftmaxWithLossLayer : public LossLayer { virtual void Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom); + /// Read the normalization mode parameter and compute the normalizer based + /// on the blob size. If normalization_mode is VALID, the count of valid + /// outputs will be read from valid_count, unless it is -1 in which case + /// all outputs are assumed to be valid. + virtual Dtype get_normalizer( + LossParameter_NormalizationMode normalization_mode, int valid_count); /// The internal SoftmaxLayer used to map predictions to a distribution. shared_ptr > softmax_layer_; @@ -760,9 +766,8 @@ class SoftmaxWithLossLayer : public LossLayer { bool has_ignore_label_; /// The label indicating that an instance should be ignored. int ignore_label_; - /// Whether to normalize the loss by the total number of values present - /// (otherwise just by the batch size). - bool normalize_; + /// How to normalize the output loss. + LossParameter_NormalizationMode normalization_; int softmax_axis_, outer_num_, inner_num_; }; diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp index dee50ac6355..3cdef82afd2 100644 --- a/src/caffe/layers/softmax_loss_layer.cpp +++ b/src/caffe/layers/softmax_loss_layer.cpp @@ -25,7 +25,14 @@ void SoftmaxWithLossLayer::LayerSetUp( if (has_ignore_label_) { ignore_label_ = this->layer_param_.loss_param().ignore_label(); } - normalize_ = this->layer_param_.loss_param().normalize(); + if (!this->layer_param_.loss_param().has_normalization() && + this->layer_param_.loss_param().has_normalize()) { + normalization_ = this->layer_param_.loss_param().normalize() ? + LossParameter_NormalizationMode_VALID : + LossParameter_NormalizationMode_BATCH_SIZE; + } else { + normalization_ = this->layer_param_.loss_param().normalization(); + } } template @@ -48,6 +55,36 @@ void SoftmaxWithLossLayer::Reshape( } } +template +Dtype SoftmaxWithLossLayer::get_normalizer( + LossParameter_NormalizationMode normalization_mode, int valid_count) { + Dtype normalizer; + switch (normalization_mode) { + case LossParameter_NormalizationMode_FULL: + normalizer = Dtype(outer_num_ * inner_num_); + break; + case LossParameter_NormalizationMode_VALID: + if (valid_count == -1) { + normalizer = Dtype(outer_num_ * inner_num_); + } else { + normalizer = Dtype(valid_count); + } + break; + case LossParameter_NormalizationMode_BATCH_SIZE: + normalizer = Dtype(outer_num_); + break; + case LossParameter_NormalizationMode_NONE: + normalizer = Dtype(1); + break; + default: + LOG(FATAL) << "Unknown normalization mode: " + << LossParameter_NormalizationMode_Name(normalization_mode); + } + // Some users will have no labels for some examples in order to 'turn off' a + // particular loss in a multi-task setup. The max prevents NaNs in that case. + return std::max(Dtype(1.0), normalizer); +} + template void SoftmaxWithLossLayer::Forward_cpu( const vector*>& bottom, const vector*>& top) { @@ -71,11 +108,7 @@ void SoftmaxWithLossLayer::Forward_cpu( ++count; } } - if (normalize_) { - top[0]->mutable_cpu_data()[0] = loss / count; - } else { - top[0]->mutable_cpu_data()[0] = loss / outer_num_; - } + top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count); if (top.size() == 2) { top[1]->ShareData(prob_); } @@ -109,12 +142,9 @@ void SoftmaxWithLossLayer::Backward_cpu(const vector*>& top, } } // Scale gradient - const Dtype loss_weight = top[0]->cpu_diff()[0]; - if (normalize_) { - caffe_scal(prob_.count(), loss_weight / count, bottom_diff); - } else { - caffe_scal(prob_.count(), loss_weight / outer_num_, bottom_diff); - } + Dtype loss_weight = top[0]->cpu_diff()[0] / + get_normalizer(normalization_, count); + caffe_scal(prob_.count(), loss_weight, bottom_diff); } } diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu index 42e91fa9e38..4753a1ec24b 100644 --- a/src/caffe/layers/softmax_loss_layer.cu +++ b/src/caffe/layers/softmax_loss_layer.cu @@ -49,14 +49,15 @@ void SoftmaxWithLossLayer::Forward_gpu( outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts); Dtype loss; caffe_gpu_asum(nthreads, loss_data, &loss); - if (normalize_) { - Dtype count; - caffe_gpu_asum(nthreads, counts, &count); - loss /= count; - } else { - loss /= outer_num_; + Dtype valid_count = -1; + // Only launch another CUDA kernel if we actually need the count of valid + // outputs. + if (normalization_ == LossParameter_NormalizationMode_VALID && + has_ignore_label_) { + caffe_gpu_asum(nthreads, counts, &valid_count); } - top[0]->mutable_cpu_data()[0] = loss; + top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, + valid_count); if (top.size() == 2) { top[1]->ShareData(prob_); } @@ -108,14 +109,17 @@ void SoftmaxWithLossLayer::Backward_gpu(const vector*>& top, SoftmaxLossBackwardGPU<<>>(nthreads, top_data, label, bottom_diff, outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts); - const Dtype loss_weight = top[0]->cpu_diff()[0]; - if (normalize_) { - Dtype count; - caffe_gpu_asum(nthreads, counts, &count); - caffe_gpu_scal(prob_.count(), loss_weight / count, bottom_diff); - } else { - caffe_gpu_scal(prob_.count(), loss_weight / outer_num_, bottom_diff); + + Dtype valid_count = -1; + // Only launch another CUDA kernel if we actually need the count of valid + // outputs. + if (normalization_ == LossParameter_NormalizationMode_VALID && + has_ignore_label_) { + caffe_gpu_asum(nthreads, counts, &valid_count); } + const Dtype loss_weight = top[0]->cpu_diff()[0] / + get_normalizer(normalization_, valid_count); + caffe_gpu_scal(prob_.count(), loss_weight , bottom_diff); } } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 39873cf7f50..787369f7cff 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -420,9 +420,27 @@ message TransformationParameter { message LossParameter { // If specified, ignore instances with the given label. optional int32 ignore_label = 1; - // If true, normalize each batch across all instances (including spatial - // dimesions, but not ignored instances); else, divide by batch size only. - optional bool normalize = 2 [default = true]; + // How to normalize the loss for loss layers that aggregate across batches, + // spatial dimensions, or other dimensions. Currently only implemented in + // SoftmaxWithLoss layer. + enum NormalizationMode { + // Divide by the number of examples in the batch times spatial dimensions. + // Outputs that receive the ignore label will NOT be ignored in computing + // the normalization factor. + FULL = 0; + // Divide by the total number of output locations that do not take the + // ignore_label. If ignore_label is not set, this behaves like FULL. + VALID = 1; + // Divide by the batch size. + BATCH_SIZE = 2; + // Do not normalize the loss. + NONE = 3; + } + optional NormalizationMode normalization = 3 [default = VALID]; + // Deprecated. Ignored if normalization is specified. If normalization + // is not specified, then setting this to false will be equivalent to + // normalization = BATCH_SIZE to be consistent with previous behavior. + optional bool normalize = 2; } // Messages that store parameters used by individual layer types follow, in From b72b0318e2802785c17be1fe8ed1b6899961df19 Mon Sep 17 00:00:00 2001 From: Tea Date: Tue, 17 Nov 2015 17:05:56 +0800 Subject: [PATCH 355/446] replace snprintf with a C++98 equivalent --- examples/cifar10/convert_cifar_data.cpp | 13 ++++++------- examples/mnist/convert_mnist_data.cpp | 12 +++++------- .../siamese/convert_mnist_siamese_data.cpp | 7 +++---- include/caffe/util/format.hpp | 18 ++++++++++++++++++ src/caffe/solver.cpp | 8 +++----- tools/convert_imageset.cpp | 8 +++----- tools/extract_features.cpp | 11 ++++------- 7 files changed, 42 insertions(+), 35 deletions(-) create mode 100644 include/caffe/util/format.hpp diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp index f4c42e4d2e7..e1b89f42fb6 100644 --- a/examples/cifar10/convert_cifar_data.cpp +++ b/examples/cifar10/convert_cifar_data.cpp @@ -16,6 +16,7 @@ #include "caffe/proto/caffe.pb.h" #include "caffe/util/db.hpp" +#include "caffe/util/format.hpp" using caffe::Datum; using boost::scoped_ptr; @@ -52,19 +53,18 @@ void convert_dataset(const string& input_folder, const string& output_folder, for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) { // Open files LOG(INFO) << "Training Batch " << fileid + 1; - snprintf(str_buffer, kCIFARImageNBytes, "/data_batch_%d.bin", fileid + 1); - std::ifstream data_file((input_folder + str_buffer).c_str(), + string batchFileName = input_folder + "/data_batch_" + + caffe::format_int(fileid+1) + ".bin"; + std::ifstream data_file(batchFileName.c_str(), std::ios::in | std::ios::binary); CHECK(data_file) << "Unable to open train file #" << fileid + 1; for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) { read_image(&data_file, &label, str_buffer); datum.set_label(label); datum.set_data(str_buffer, kCIFARImageNBytes); - int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", - fileid * kCIFARBatchSize + itemid); string out; CHECK(datum.SerializeToString(&out)); - txn->Put(string(str_buffer, length), out); + txn->Put(caffe::format_int(fileid * kCIFARBatchSize + itemid, 5), out); } } txn->Commit(); @@ -82,10 +82,9 @@ void convert_dataset(const string& input_folder, const string& output_folder, read_image(&data_file, &label, str_buffer); datum.set_label(label); datum.set_data(str_buffer, kCIFARImageNBytes); - int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid); string out; CHECK(datum.SerializeToString(&out)); - txn->Put(string(str_buffer, length), out); + txn->Put(caffe::format_int(itemid, 5), out); } txn->Commit(); test_db->Close(); diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp index 8f29bafde85..16d28093dd5 100644 --- a/examples/mnist/convert_mnist_data.cpp +++ b/examples/mnist/convert_mnist_data.cpp @@ -23,6 +23,7 @@ #include #include "caffe/proto/caffe.pb.h" +#include "caffe/util/format.hpp" #if defined(USE_LEVELDB) && defined(USE_LMDB) @@ -108,8 +109,6 @@ void convert_dataset(const char* image_filename, const char* label_filename, char label; char* pixels = new char[rows * cols]; int count = 0; - const int kMaxKeyLength = 10; - char key_cstr[kMaxKeyLength]; string value; Datum datum; @@ -123,18 +122,17 @@ void convert_dataset(const char* image_filename, const char* label_filename, label_file.read(&label, 1); datum.set_data(pixels, rows*cols); datum.set_label(label); - snprintf(key_cstr, kMaxKeyLength, "%08d", item_id); + string key_str = caffe::format_int(item_id, 8); datum.SerializeToString(&value); - string keystr(key_cstr); // Put in db if (db_backend == "leveldb") { // leveldb - batch->Put(keystr, value); + batch->Put(key_str, value); } else if (db_backend == "lmdb") { // lmdb mdb_data.mv_size = value.size(); mdb_data.mv_data = reinterpret_cast(&value[0]); - mdb_key.mv_size = keystr.size(); - mdb_key.mv_data = reinterpret_cast(&keystr[0]); + mdb_key.mv_size = key_str.size(); + mdb_key.mv_data = reinterpret_cast(&key_str[0]); CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS) << "mdb_put failed"; } else { diff --git a/examples/siamese/convert_mnist_siamese_data.cpp b/examples/siamese/convert_mnist_siamese_data.cpp index ad08036fb08..928b3fbf4d5 100644 --- a/examples/siamese/convert_mnist_siamese_data.cpp +++ b/examples/siamese/convert_mnist_siamese_data.cpp @@ -13,6 +13,7 @@ #include "stdint.h" #include "caffe/proto/caffe.pb.h" +#include "caffe/util/format.hpp" #include "caffe/util/math_functions.hpp" #ifdef USE_LEVELDB @@ -75,8 +76,6 @@ void convert_dataset(const char* image_filename, const char* label_filename, char label_i; char label_j; char* pixels = new char[2 * rows * cols]; - const int kMaxKeyLength = 10; - char key[kMaxKeyLength]; std::string value; caffe::Datum datum; @@ -99,8 +98,8 @@ void convert_dataset(const char* image_filename, const char* label_filename, datum.set_label(0); } datum.SerializeToString(&value); - snprintf(key, kMaxKeyLength, "%08d", itemid); - db->Put(leveldb::WriteOptions(), std::string(key), value); + std::string key_str = caffe::format_int(itemid, 8); + db->Put(leveldb::WriteOptions(), key_str, value); } delete db; diff --git a/include/caffe/util/format.hpp b/include/caffe/util/format.hpp new file mode 100644 index 00000000000..925ad2e0479 --- /dev/null +++ b/include/caffe/util/format.hpp @@ -0,0 +1,18 @@ +#ifndef CAFFE_UTIL_FORMAT_H_ +#define CAFFE_UTIL_FORMAT_H_ + +#include // NOLINT(readability/streams) +#include // NOLINT(readability/streams) +#include + +namespace caffe { + +inline std::string format_int(int n, int numberOfLeadingZeros = 0 ) { + std::ostringstream s; + s << std::setw(numberOfLeadingZeros) << std::setfill('0') << n; + return s.str(); +} + +} + +#endif // CAFFE_UTIL_FORMAT_H_ diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index d3bc7361dd5..95d7506635e 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -4,6 +4,7 @@ #include #include "caffe/solver.hpp" +#include "caffe/util/format.hpp" #include "caffe/util/hdf5.hpp" #include "caffe/util/io.hpp" #include "caffe/util/upgrade_proto.hpp" @@ -448,11 +449,8 @@ void Solver::CheckSnapshotWritePermissions() { template string Solver::SnapshotFilename(const string extension) { - string filename(param_.snapshot_prefix()); - const int kBufferSize = 20; - char iter_str_buffer[kBufferSize]; - snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_); - return filename + iter_str_buffer + extension; + return param_.snapshot_prefix() + "_iter_" + caffe::format_int(iter_) + + extension; } template diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp index e51a2631077..9c52bfa0ef8 100644 --- a/tools/convert_imageset.cpp +++ b/tools/convert_imageset.cpp @@ -20,6 +20,7 @@ #include "caffe/proto/caffe.pb.h" #include "caffe/util/db.hpp" +#include "caffe/util/format.hpp" #include "caffe/util/io.hpp" #include "caffe/util/rng.hpp" @@ -99,8 +100,6 @@ int main(int argc, char** argv) { std::string root_folder(argv[1]); Datum datum; int count = 0; - const int kMaxKeyLength = 256; - char key_cstr[kMaxKeyLength]; int data_size = 0; bool data_size_initialized = false; @@ -131,13 +130,12 @@ int main(int argc, char** argv) { } } // sequential - int length = snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id, - lines[line_id].first.c_str()); + string key_str = caffe::format_int(line_id, 8) + "_" + lines[line_id].first; // Put in db string out; CHECK(datum.SerializeToString(&out)); - txn->Put(string(key_cstr, length), out); + txn->Put(key_str, out); if (++count % 1000 == 0) { // Commit db diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp index 084c9bf88df..b94dbb980fd 100644 --- a/tools/extract_features.cpp +++ b/tools/extract_features.cpp @@ -1,4 +1,3 @@ -#include // for snprintf #include #include @@ -10,6 +9,7 @@ #include "caffe/net.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/db.hpp" +#include "caffe/util/format.hpp" #include "caffe/util/io.hpp" #include "caffe/vision_layers.hpp" @@ -135,8 +135,6 @@ int feature_extraction_pipeline(int argc, char** argv) { LOG(ERROR)<< "Extacting Features"; Datum datum; - const int kMaxKeyStrLength = 100; - char key_str[kMaxKeyStrLength]; std::vector*> input_vec; std::vector image_indices(num_features, 0); for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) { @@ -158,11 +156,11 @@ int feature_extraction_pipeline(int argc, char** argv) { for (int d = 0; d < dim_features; ++d) { datum.add_float_data(feature_blob_data[d]); } - int length = snprintf(key_str, kMaxKeyStrLength, "%010d", - image_indices[i]); + string key_str = caffe::format_int(image_indices[i], 10); + string out; CHECK(datum.SerializeToString(&out)); - txns.at(i)->Put(std::string(key_str, length), out); + txns.at(i)->Put(key_str, out); ++image_indices[i]; if (image_indices[i] % 1000 == 0) { txns.at(i)->Commit(); @@ -186,4 +184,3 @@ int feature_extraction_pipeline(int argc, char** argv) { LOG(ERROR)<< "Successfully extracted the features!"; return 0; } - From d3025f5ffb731ef2f7e796f67f6fd6bd43f601b9 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Wed, 25 Nov 2015 21:02:02 -0800 Subject: [PATCH 356/446] Remove bogus stepearly in MNIST example This `examples/lenet/lenet_stepearly_solver.prototxt` is introduced in #190 by mistake, since stepearly is never actually merged. --- .../mnist/lenet_stepearly_solver.prototxt | 28 ------------------- 1 file changed, 28 deletions(-) delete mode 100644 examples/mnist/lenet_stepearly_solver.prototxt diff --git a/examples/mnist/lenet_stepearly_solver.prototxt b/examples/mnist/lenet_stepearly_solver.prototxt deleted file mode 100644 index efc6a335d8f..00000000000 --- a/examples/mnist/lenet_stepearly_solver.prototxt +++ /dev/null @@ -1,28 +0,0 @@ -# The training protocol buffer definition -train_net: "lenet_train.prototxt" -# The testing protocol buffer definition -test_net: "lenet_test.prototxt" -# test_iter specifies how many forward passes the test should carry out. -# In the case of MNIST, we have test batch size 100 and 100 test iterations, -# covering the full 10,000 testing images. -test_iter: 100 -# Carry out testing every 500 training iterations. -test_interval: 500 -# The base learning rate, momentum and the weight decay of the network. -base_lr: 0.01 -momentum: 0.9 -weight_decay: 0.0005 -# The learning rate policy -lr_policy: "stepearly" -gamma: 0.9 -stepearly: 1 -# Display every 100 iterations -display: 100 -# The maximum number of iterations -max_iter: 10000 -# snapshot intermediate results -snapshot: 5000 -snapshot_prefix: "lenet" -# solver mode: 0 for CPU and 1 for GPU -solver_mode: 1 -device_id: 1 From 34ee5df55dc11dfc8afff60cf64cd479b639e5a8 Mon Sep 17 00:00:00 2001 From: "T.E.A de Souza" Date: Tue, 24 Nov 2015 14:33:27 +0800 Subject: [PATCH 357/446] Secure implementation of MakeTempDir --- include/caffe/util/io.hpp | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp index 6b7332548b0..f9f0f55a5d4 100644 --- a/include/caffe/util/io.hpp +++ b/include/caffe/util/io.hpp @@ -9,6 +9,10 @@ #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" +#ifndef CAFFE_TMP_DIR_RETRIES +#define CAFFE_TMP_DIR_RETRIES 100 +#endif + namespace caffe { using ::google::protobuf::Message; @@ -23,12 +27,17 @@ inline void MakeTempFilename(string* temp_filename) { inline void MakeTempDir(string* temp_dirname) { temp_dirname->clear(); - const path& model = boost::filesystem::temp_directory_path() - /"caffe_test.%%%%%%"; - const path& dir = boost::filesystem::unique_path(model).string(); - bool directoryCreated = boost::filesystem::create_directory(dir); - CHECK(directoryCreated); - *temp_dirname = dir.string(); + const path& model = + boost::filesystem::temp_directory_path()/"caffe_test.%%%%-%%%%"; + for ( int i = 0; i < CAFFE_TMP_DIR_RETRIES; i++ ) { + const path& dir = boost::filesystem::unique_path(model).string(); + bool done = boost::filesystem::create_directory(dir); + if ( done ) { + *temp_dirname = dir.string(); + return; + } + } + LOG(FATAL) << "Failed to create a temporary directory."; } bool ReadProtoFromTextFile(const char* filename, Message* proto); From 33905d5a8023c3dbac514dac680060dc608145e8 Mon Sep 17 00:00:00 2001 From: Tea Date: Wed, 25 Nov 2015 11:43:45 +0800 Subject: [PATCH 358/446] Secure temporary file creation --- include/caffe/util/io.hpp | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp index f9f0f55a5d4..1a599883ca3 100644 --- a/include/caffe/util/io.hpp +++ b/include/caffe/util/io.hpp @@ -2,12 +2,15 @@ #define CAFFE_UTIL_IO_H_ #include +#include +#include // NOLINT(readability/streams) #include #include "google/protobuf/message.h" #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" +#include "caffe/util/format.hpp" #ifndef CAFFE_TMP_DIR_RETRIES #define CAFFE_TMP_DIR_RETRIES 100 @@ -18,13 +21,6 @@ namespace caffe { using ::google::protobuf::Message; using ::boost::filesystem::path; -inline void MakeTempFilename(string* temp_filename) { - temp_filename->clear(); - const path& model = boost::filesystem::temp_directory_path() - /"caffe_test.%%%%%%"; - *temp_filename = boost::filesystem::unique_path(model).string(); -} - inline void MakeTempDir(string* temp_dirname) { temp_dirname->clear(); const path& model = @@ -40,6 +36,19 @@ inline void MakeTempDir(string* temp_dirname) { LOG(FATAL) << "Failed to create a temporary directory."; } +inline void MakeTempFilename(string* temp_filename) { + static path temp_files_subpath; + static uint64_t next_temp_file = 0; + temp_filename->clear(); + if ( temp_files_subpath.empty() ) { + string path_string=""; + MakeTempDir(&path_string); + temp_files_subpath = path_string; + } + *temp_filename = + (temp_files_subpath/caffe::format_int(next_temp_file++, 9)).string(); +} + bool ReadProtoFromTextFile(const char* filename, Message* proto); inline bool ReadProtoFromTextFile(const string& filename, Message* proto) { From 300f43f3ae6347ac8e01093f9a57ee99e551ed74 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Tue, 10 Nov 2015 00:22:58 -0800 Subject: [PATCH 359/446] dismantle layer headers No more monolithic includes: split layers into their own headers for modular inclusion and build. --- Makefile | 2 +- include/caffe/caffe.hpp | 1 - include/caffe/common_layers.hpp | 816 ------------------ include/caffe/data_layers.hpp | 347 -------- include/caffe/layer_factory.hpp | 1 + include/caffe/layers/absval_layer.hpp | 68 ++ include/caffe/layers/accuracy_layer.hpp | 95 ++ include/caffe/layers/argmax_layer.hpp | 77 ++ include/caffe/layers/base_conv_layer.hpp | 168 ++++ include/caffe/layers/base_data_layer.hpp | 86 ++ include/caffe/layers/batch_norm_layer.hpp | 81 ++ include/caffe/layers/batch_reindex_layer.hpp | 83 ++ include/caffe/layers/bnll_layer.hpp | 70 ++ include/caffe/layers/concat_layer.hpp | 87 ++ .../caffe/layers/contrastive_loss_layer.hpp | 101 +++ include/caffe/layers/conv_layer.hpp | 81 ++ include/caffe/layers/cudnn_conv_layer.hpp | 72 ++ include/caffe/layers/cudnn_lcn_layer.hpp | 49 ++ include/caffe/layers/cudnn_lrn_layer.hpp | 44 + include/caffe/layers/cudnn_pooling_layer.hpp | 49 ++ include/caffe/layers/cudnn_relu_layer.hpp | 45 + include/caffe/layers/cudnn_sigmoid_layer.hpp | 45 + include/caffe/layers/cudnn_softmax_layer.hpp | 45 + include/caffe/layers/cudnn_tanh_layer.hpp | 45 + include/caffe/layers/data_layer.hpp | 39 + include/caffe/layers/deconv_layer.hpp | 51 ++ include/caffe/layers/dropout_layer.hpp | 80 ++ include/caffe/layers/dummy_data_layer.hpp | 49 ++ include/caffe/layers/eltwise_layer.hpp | 51 ++ include/caffe/layers/embed_layer.hpp | 52 ++ include/caffe/layers/euclidean_loss_layer.hpp | 107 +++ include/caffe/layers/exp_layer.hpp | 80 ++ include/caffe/layers/filter_layer.hpp | 77 ++ include/caffe/layers/flatten_layer.hpp | 61 ++ include/caffe/layers/hdf5_data_layer.hpp | 62 ++ include/caffe/layers/hdf5_output_layer.hpp | 64 ++ include/caffe/layers/hinge_loss_layer.hpp | 104 +++ include/caffe/layers/im2col_layer.hpp | 63 ++ include/caffe/layers/image_data_layer.hpp | 47 + include/caffe/layers/infogain_loss_layer.hpp | 110 +++ include/caffe/layers/inner_product_layer.hpp | 51 ++ include/caffe/layers/log_layer.hpp | 82 ++ include/caffe/layers/loss_layer.hpp | 53 ++ include/caffe/layers/lrn_layer.hpp | 94 ++ include/caffe/layers/memory_data_layer.hpp | 63 ++ .../multinomial_logistic_loss_layer.hpp | 92 ++ include/caffe/layers/mvn_layer.hpp | 48 ++ include/caffe/layers/neuron_layer.hpp | 32 + include/caffe/layers/pooling_layer.hpp | 60 ++ include/caffe/layers/power_layer.hpp | 89 ++ include/caffe/layers/prelu_layer.hpp | 101 +++ include/caffe/{ => layers}/python_layer.hpp | 0 include/caffe/layers/reduction_layer.hpp | 59 ++ include/caffe/layers/relu_layer.hpp | 85 ++ include/caffe/layers/reshape_layer.hpp | 52 ++ .../sigmoid_cross_entropy_loss_layer.hpp | 110 +++ include/caffe/layers/sigmoid_layer.hpp | 71 ++ include/caffe/layers/silence_layer.hpp | 43 + include/caffe/layers/slice_layer.hpp | 51 ++ include/caffe/layers/softmax_layer.hpp | 50 ++ include/caffe/layers/softmax_loss_layer.hpp | 130 +++ include/caffe/layers/split_layer.hpp | 45 + include/caffe/layers/spp_layer.hpp | 76 ++ include/caffe/layers/tanh_layer.hpp | 73 ++ include/caffe/layers/threshold_layer.hpp | 64 ++ include/caffe/layers/tile_layer.hpp | 43 + include/caffe/layers/window_data_layer.hpp | 55 ++ include/caffe/loss_layers.hpp | 777 ----------------- include/caffe/neuron_layers.hpp | 806 ----------------- include/caffe/vision_layers.hpp | 659 -------------- python/caffe/_caffe.cpp | 3 +- src/caffe/data_reader.cpp | 2 +- src/caffe/layer_factory.cpp | 21 +- src/caffe/layers/absval_layer.cpp | 2 +- src/caffe/layers/absval_layer.cu | 2 +- src/caffe/layers/accuracy_layer.cpp | 2 +- src/caffe/layers/argmax_layer.cpp | 2 +- src/caffe/layers/base_conv_layer.cpp | 2 +- src/caffe/layers/base_data_layer.cpp | 8 +- src/caffe/layers/base_data_layer.cu | 2 +- src/caffe/layers/batch_norm_layer.cpp | 2 +- src/caffe/layers/batch_norm_layer.cu | 2 +- src/caffe/layers/batch_reindex_layer.cpp | 2 +- src/caffe/layers/batch_reindex_layer.cu | 2 +- src/caffe/layers/bnll_layer.cpp | 2 +- src/caffe/layers/bnll_layer.cu | 2 +- src/caffe/layers/concat_layer.cpp | 2 +- src/caffe/layers/concat_layer.cu | 2 +- src/caffe/layers/contrastive_loss_layer.cpp | 2 +- src/caffe/layers/contrastive_loss_layer.cu | 2 +- src/caffe/layers/conv_layer.cpp | 2 +- src/caffe/layers/conv_layer.cu | 2 +- src/caffe/layers/cudnn_conv_layer.cpp | 2 +- src/caffe/layers/cudnn_conv_layer.cu | 2 +- src/caffe/layers/cudnn_lcn_layer.cpp | 2 +- src/caffe/layers/cudnn_lcn_layer.cu | 2 +- src/caffe/layers/cudnn_lrn_layer.cpp | 2 +- src/caffe/layers/cudnn_lrn_layer.cu | 2 +- src/caffe/layers/cudnn_pooling_layer.cpp | 2 +- src/caffe/layers/cudnn_pooling_layer.cu | 2 +- src/caffe/layers/cudnn_relu_layer.cpp | 2 +- src/caffe/layers/cudnn_relu_layer.cu | 2 +- src/caffe/layers/cudnn_sigmoid_layer.cpp | 2 +- src/caffe/layers/cudnn_sigmoid_layer.cu | 2 +- src/caffe/layers/cudnn_softmax_layer.cpp | 2 +- src/caffe/layers/cudnn_softmax_layer.cu | 2 +- src/caffe/layers/cudnn_tanh_layer.cpp | 2 +- src/caffe/layers/cudnn_tanh_layer.cu | 2 +- src/caffe/layers/data_layer.cpp | 4 +- src/caffe/layers/deconv_layer.cpp | 2 +- src/caffe/layers/deconv_layer.cu | 2 +- src/caffe/layers/dropout_layer.cpp | 2 +- src/caffe/layers/dropout_layer.cu | 4 +- src/caffe/layers/dummy_data_layer.cpp | 2 +- src/caffe/layers/eltwise_layer.cpp | 2 +- src/caffe/layers/eltwise_layer.cu | 2 +- src/caffe/layers/embed_layer.cpp | 2 +- src/caffe/layers/embed_layer.cu | 2 +- src/caffe/layers/euclidean_loss_layer.cpp | 2 +- src/caffe/layers/euclidean_loss_layer.cu | 2 +- src/caffe/layers/exp_layer.cpp | 2 +- src/caffe/layers/exp_layer.cu | 2 +- src/caffe/layers/filter_layer.cpp | 2 +- src/caffe/layers/filter_layer.cu | 2 +- src/caffe/layers/flatten_layer.cpp | 2 +- src/caffe/layers/hdf5_data_layer.cpp | 2 +- src/caffe/layers/hdf5_data_layer.cu | 2 +- src/caffe/layers/hdf5_output_layer.cpp | 2 +- src/caffe/layers/hdf5_output_layer.cu | 2 +- src/caffe/layers/hinge_loss_layer.cpp | 2 +- src/caffe/layers/im2col_layer.cpp | 2 +- src/caffe/layers/im2col_layer.cu | 2 +- src/caffe/layers/image_data_layer.cpp | 4 +- src/caffe/layers/infogain_loss_layer.cpp | 2 +- src/caffe/layers/inner_product_layer.cpp | 2 +- src/caffe/layers/inner_product_layer.cu | 2 +- src/caffe/layers/log_layer.cpp | 2 +- src/caffe/layers/log_layer.cu | 2 +- src/caffe/layers/loss_layer.cpp | 2 +- src/caffe/layers/lrn_layer.cpp | 2 +- src/caffe/layers/lrn_layer.cu | 2 +- src/caffe/layers/memory_data_layer.cpp | 2 +- .../multinomial_logistic_loss_layer.cpp | 2 +- src/caffe/layers/mvn_layer.cpp | 2 +- src/caffe/layers/mvn_layer.cu | 2 +- src/caffe/layers/neuron_layer.cpp | 2 +- src/caffe/layers/pooling_layer.cpp | 2 +- src/caffe/layers/pooling_layer.cu | 2 +- src/caffe/layers/power_layer.cpp | 2 +- src/caffe/layers/power_layer.cu | 2 +- src/caffe/layers/prelu_layer.cpp | 4 +- src/caffe/layers/prelu_layer.cu | 3 +- src/caffe/layers/reduction_layer.cpp | 2 +- src/caffe/layers/reduction_layer.cu | 2 +- src/caffe/layers/relu_layer.cpp | 2 +- src/caffe/layers/relu_layer.cu | 2 +- src/caffe/layers/reshape_layer.cpp | 2 +- .../sigmoid_cross_entropy_loss_layer.cpp | 2 +- .../sigmoid_cross_entropy_loss_layer.cu | 2 +- src/caffe/layers/sigmoid_layer.cpp | 2 +- src/caffe/layers/sigmoid_layer.cu | 2 +- src/caffe/layers/silence_layer.cpp | 2 +- src/caffe/layers/silence_layer.cu | 2 +- src/caffe/layers/slice_layer.cpp | 2 +- src/caffe/layers/slice_layer.cu | 2 +- src/caffe/layers/softmax_layer.cpp | 2 +- src/caffe/layers/softmax_layer.cu | 2 +- src/caffe/layers/softmax_loss_layer.cpp | 2 +- src/caffe/layers/softmax_loss_layer.cu | 2 +- src/caffe/layers/split_layer.cpp | 2 +- src/caffe/layers/split_layer.cu | 2 +- src/caffe/layers/spp_layer.cpp | 8 +- src/caffe/layers/tanh_layer.cpp | 2 +- src/caffe/layers/tanh_layer.cu | 2 +- src/caffe/layers/threshold_layer.cpp | 3 +- src/caffe/layers/threshold_layer.cu | 2 +- src/caffe/layers/tile_layer.cpp | 2 +- src/caffe/layers/tile_layer.cu | 2 +- src/caffe/layers/window_data_layer.cpp | 5 +- src/caffe/test/test_accuracy_layer.cpp | 2 +- src/caffe/test/test_argmax_layer.cpp | 2 +- src/caffe/test/test_batch_norm_layer.cpp | 2 +- src/caffe/test/test_batch_reindex_layer.cpp | 2 +- src/caffe/test/test_concat_layer.cpp | 2 +- .../test/test_contrastive_loss_layer.cpp | 2 +- src/caffe/test/test_convolution_layer.cpp | 6 +- src/caffe/test/test_data_layer.cpp | 2 +- src/caffe/test/test_deconvolution_layer.cpp | 2 +- src/caffe/test/test_dummy_data_layer.cpp | 2 +- src/caffe/test/test_eltwise_layer.cpp | 2 +- src/caffe/test/test_embed_layer.cpp | 2 +- src/caffe/test/test_euclidean_loss_layer.cpp | 2 +- src/caffe/test/test_filter_layer.cpp | 2 +- src/caffe/test/test_flatten_layer.cpp | 2 +- src/caffe/test/test_hdf5_output_layer.cpp | 2 +- src/caffe/test/test_hdf5data_layer.cpp | 4 +- src/caffe/test/test_hinge_loss_layer.cpp | 2 +- src/caffe/test/test_im2col_kernel.cu | 2 +- src/caffe/test/test_im2col_layer.cpp | 2 +- src/caffe/test/test_image_data_layer.cpp | 2 +- src/caffe/test/test_infogain_loss_layer.cpp | 2 +- src/caffe/test/test_inner_product_layer.cpp | 2 +- src/caffe/test/test_lrn_layer.cpp | 7 +- .../test/test_maxpool_dropout_layers.cpp | 3 +- src/caffe/test/test_memory_data_layer.cpp | 2 +- .../test_multinomial_logistic_loss_layer.cpp | 2 +- src/caffe/test/test_mvn_layer.cpp | 2 +- src/caffe/test/test_neuron_layer.cpp | 21 +- src/caffe/test/test_pooling_layer.cpp | 6 +- src/caffe/test/test_power_layer.cpp | 2 +- src/caffe/test/test_reduction_layer.cpp | 2 +- src/caffe/test/test_reshape_layer.cpp | 2 +- .../test_sigmoid_cross_entropy_loss_layer.cpp | 2 +- src/caffe/test/test_slice_layer.cpp | 2 +- src/caffe/test/test_softmax_layer.cpp | 6 +- .../test/test_softmax_with_loss_layer.cpp | 2 +- src/caffe/test/test_split_layer.cpp | 2 +- src/caffe/test/test_spp_layer.cpp | 7 +- src/caffe/test/test_stochastic_pooling.cpp | 2 +- src/caffe/test/test_tanh_layer.cpp | 2 +- src/caffe/test/test_threshold_layer.cpp | 2 +- src/caffe/test/test_tile_layer.cpp | 2 +- src/caffe/util/blocking_queue.cpp | 2 +- tools/extract_features.cpp | 1 - 224 files changed, 4497 insertions(+), 3568 deletions(-) delete mode 100644 include/caffe/common_layers.hpp delete mode 100644 include/caffe/data_layers.hpp create mode 100644 include/caffe/layers/absval_layer.hpp create mode 100644 include/caffe/layers/accuracy_layer.hpp create mode 100644 include/caffe/layers/argmax_layer.hpp create mode 100644 include/caffe/layers/base_conv_layer.hpp create mode 100644 include/caffe/layers/base_data_layer.hpp create mode 100644 include/caffe/layers/batch_norm_layer.hpp create mode 100644 include/caffe/layers/batch_reindex_layer.hpp create mode 100644 include/caffe/layers/bnll_layer.hpp create mode 100644 include/caffe/layers/concat_layer.hpp create mode 100644 include/caffe/layers/contrastive_loss_layer.hpp create mode 100644 include/caffe/layers/conv_layer.hpp create mode 100644 include/caffe/layers/cudnn_conv_layer.hpp create mode 100644 include/caffe/layers/cudnn_lcn_layer.hpp create mode 100644 include/caffe/layers/cudnn_lrn_layer.hpp create mode 100644 include/caffe/layers/cudnn_pooling_layer.hpp create mode 100644 include/caffe/layers/cudnn_relu_layer.hpp create mode 100644 include/caffe/layers/cudnn_sigmoid_layer.hpp create mode 100644 include/caffe/layers/cudnn_softmax_layer.hpp create mode 100644 include/caffe/layers/cudnn_tanh_layer.hpp create mode 100644 include/caffe/layers/data_layer.hpp create mode 100644 include/caffe/layers/deconv_layer.hpp create mode 100644 include/caffe/layers/dropout_layer.hpp create mode 100644 include/caffe/layers/dummy_data_layer.hpp create mode 100644 include/caffe/layers/eltwise_layer.hpp create mode 100644 include/caffe/layers/embed_layer.hpp create mode 100644 include/caffe/layers/euclidean_loss_layer.hpp create mode 100644 include/caffe/layers/exp_layer.hpp create mode 100644 include/caffe/layers/filter_layer.hpp create mode 100644 include/caffe/layers/flatten_layer.hpp create mode 100644 include/caffe/layers/hdf5_data_layer.hpp create mode 100644 include/caffe/layers/hdf5_output_layer.hpp create mode 100644 include/caffe/layers/hinge_loss_layer.hpp create mode 100644 include/caffe/layers/im2col_layer.hpp create mode 100644 include/caffe/layers/image_data_layer.hpp create mode 100644 include/caffe/layers/infogain_loss_layer.hpp create mode 100644 include/caffe/layers/inner_product_layer.hpp create mode 100644 include/caffe/layers/log_layer.hpp create mode 100644 include/caffe/layers/loss_layer.hpp create mode 100644 include/caffe/layers/lrn_layer.hpp create mode 100644 include/caffe/layers/memory_data_layer.hpp create mode 100644 include/caffe/layers/multinomial_logistic_loss_layer.hpp create mode 100644 include/caffe/layers/mvn_layer.hpp create mode 100644 include/caffe/layers/neuron_layer.hpp create mode 100644 include/caffe/layers/pooling_layer.hpp create mode 100644 include/caffe/layers/power_layer.hpp create mode 100644 include/caffe/layers/prelu_layer.hpp rename include/caffe/{ => layers}/python_layer.hpp (100%) create mode 100644 include/caffe/layers/reduction_layer.hpp create mode 100644 include/caffe/layers/relu_layer.hpp create mode 100644 include/caffe/layers/reshape_layer.hpp create mode 100644 include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp create mode 100644 include/caffe/layers/sigmoid_layer.hpp create mode 100644 include/caffe/layers/silence_layer.hpp create mode 100644 include/caffe/layers/slice_layer.hpp create mode 100644 include/caffe/layers/softmax_layer.hpp create mode 100644 include/caffe/layers/softmax_loss_layer.hpp create mode 100644 include/caffe/layers/split_layer.hpp create mode 100644 include/caffe/layers/spp_layer.hpp create mode 100644 include/caffe/layers/tanh_layer.hpp create mode 100644 include/caffe/layers/threshold_layer.hpp create mode 100644 include/caffe/layers/tile_layer.hpp create mode 100644 include/caffe/layers/window_data_layer.hpp delete mode 100644 include/caffe/loss_layers.hpp delete mode 100644 include/caffe/neuron_layers.hpp delete mode 100644 include/caffe/vision_layers.hpp diff --git a/Makefile b/Makefile index 3dc76ae5697..985fffd6c0b 100644 --- a/Makefile +++ b/Makefile @@ -78,7 +78,7 @@ NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT) # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT) PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so -PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp +PY$(PROJECT)_HXX := include/$(PROJECT)/layers/python_layer.hpp # MAT$(PROJECT)_SRC is the mex entrance point of matlab package for $(PROJECT) MAT$(PROJECT)_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp ifneq ($(MATLAB_DIR),) diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp index a339efba5c0..06882096c55 100644 --- a/include/caffe/caffe.hpp +++ b/include/caffe/caffe.hpp @@ -17,6 +17,5 @@ #include "caffe/util/benchmark.hpp" #include "caffe/util/io.hpp" #include "caffe/util/upgrade_proto.hpp" -#include "caffe/vision_layers.hpp" #endif // CAFFE_CAFFE_HPP_ diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp deleted file mode 100644 index d42d15c47e3..00000000000 --- a/include/caffe/common_layers.hpp +++ /dev/null @@ -1,816 +0,0 @@ -#ifndef CAFFE_COMMON_LAYERS_HPP_ -#define CAFFE_COMMON_LAYERS_HPP_ - -#include -#include - -#include "caffe/blob.hpp" -#include "caffe/layer.hpp" -#include "caffe/proto/caffe.pb.h" - -namespace caffe { - -/** - * @brief Compute the index of the @f$ K @f$ max values for each datum across - * all dimensions @f$ (C \times H \times W) @f$. - * - * Intended for use after a classification layer to produce a prediction. - * If parameter out_max_val is set to true, output is a vector of pairs - * (max_ind, max_val) for each image. The axis parameter specifies an axis - * along which to maximise. - * - * NOTE: does not implement Backwards operation. - */ -template -class ArgMaxLayer : public Layer { - public: - /** - * @param param provides ArgMaxParameter argmax_param, - * with ArgMaxLayer options: - * - top_k (\b optional uint, default 1). - * the number @f$ K @f$ of maximal items to output. - * - out_max_val (\b optional bool, default false). - * if set, output a vector of pairs (max_ind, max_val) unless axis is set then - * output max_val along the specified axis. - * - axis (\b optional int). - * if set, maximise along the specified axis else maximise the flattened - * trailing dimensions for each index of the first / num dimension. - */ - explicit ArgMaxLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "ArgMax"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times 1 \times K) @f$ or, if out_max_val - * @f$ (N \times 2 \times K) @f$ unless axis set than e.g. - * @f$ (N \times K \times H \times W) @f$ if axis == 1 - * the computed outputs @f$ - * y_n = \arg\max\limits_i x_{ni} - * @f$ (for @f$ K = 1 @f$). - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - /// @brief Not implemented (non-differentiable function) - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) { - NOT_IMPLEMENTED; - } - bool out_max_val_; - size_t top_k_; - bool has_axis_; - int axis_; -}; - -/** - * @brief Normalizes the input to have 0-mean and/or unit (1) variance across - * the batch. - * - * This layer computes Batch Normalization described in [1]. For - * each channel in the data (i.e. axis 1), it subtracts the mean and divides - * by the variance, where both statistics are computed across both spatial - * dimensions and across the different examples in the batch. - * - * By default, during training time, the network is computing global mean/ - * variance statistics via a running average, which is then used at test - * time to allow deterministic outputs for each input. You can manually - * toggle whether the network is accumulating or using the statistics via the - * use_global_stats option. IMPORTANT: for this feature to work, you MUST - * set the learning rate to zero for all three parameter blobs, i.e., - * param {lr_mult: 0} three times in the layer definition. - * - * Note that the original paper also included a per-channel learned bias and - * scaling factor. It is possible (though a bit cumbersome) to implement - * this in caffe using a single-channel DummyDataLayer filled with zeros, - * followed by a Convolution layer with output the same size as the current. - * This produces a channel-specific value that can be added or multiplied by - * the BatchNorm layer's output. - * - * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network - * Training by Reducing Internal Covariate Shift." arXiv preprint - * arXiv:1502.03167 (2015). - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class BatchNormLayer : public Layer { - public: - explicit BatchNormLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "BatchNorm"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - Blob mean_, variance_, temp_, x_norm_; - bool use_global_stats_; - Dtype moving_average_fraction_; - int channels_; - Dtype eps_; - - // extra temporarary variables is used to carry out sums/broadcasting - // using BLAS - Blob batch_sum_multiplier_; - Blob num_by_chans_; - Blob spatial_sum_multiplier_; -}; - -/** - * @brief Index into the input blob along its first axis. - * - * This layer can be used to select, reorder, and even replicate examples in a - * batch. The second blob is cast to int and treated as an index into the - * first axis of the first blob. - */ -template -class BatchReindexLayer : public Layer { - public: - explicit BatchReindexLayer(const LayerParameter& param) - : Layer(param) {} - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "BatchReindex"; } - virtual inline int ExactNumBottomBlobs() const { return 2; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - /** - * @param bottom input Blob vector (length 2+) - * -# @f$ (N \times ...) @f$ - * the inputs @f$ x_1 @f$ - * -# @f$ (M) @f$ - * the inputs @f$ x_2 @f$ - * @param top output Blob vector (length 1) - * -# @f$ (M \times ...) @f$: - * the reindexed array @f$ - * y = x_1[x_2] - * @f$ - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the reordered input. - * - * @param top output Blob vector (length 1), providing the error gradient - * with respect to the outputs - * -# @f$ (M \times ...) @f$: - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to concatenated outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 2): - * - @f$ \frac{\partial E}{\partial y} @f$ is de-indexed (summing where - * required) back to the input x_1 - * - This layer cannot backprop to x_2, i.e. propagate_down[1] must be - * false. - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - private: - struct pair_sort_first { - bool operator()(const std::pair &left, - const std::pair &right) { - return left.first < right.first; - } - }; - void check_batch_reindex(int initial_num, int final_num, - const Dtype* ridx_data); -}; - -/** - * @brief Takes at least two Blob%s and concatenates them along either the num - * or channel dimension, outputting the result. - */ -template -class ConcatLayer : public Layer { - public: - explicit ConcatLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Concat"; } - virtual inline int MinBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - /** - * @param bottom input Blob vector (length 2+) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x_1 @f$ - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x_2 @f$ - * -# ... - * - K @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x_K @f$ - * @param top output Blob vector (length 1) - * -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or - * @f$ (N \times KC \times H \times W) @f$ if axis == 1: - * the concatenated output @f$ - * y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}] - * @f$ - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the concatenate inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or - * @f$ (N \times KC \times H \times W) @f$ if axis == 1: - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to concatenated outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length K), into which the top gradient - * @f$ \frac{\partial E}{\partial y} @f$ is deconcatenated back to the - * inputs @f$ - * \left[ \begin{array}{cccc} - * \frac{\partial E}{\partial x_1} & - * \frac{\partial E}{\partial x_2} & - * ... & - * \frac{\partial E}{\partial x_K} - * \end{array} \right] = - * \frac{\partial E}{\partial y} - * @f$ - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - int count_; - int num_concats_; - int concat_input_size_; - int concat_axis_; -}; - -/** - * @brief Compute elementwise operations, such as product and sum, - * along multiple input Blobs. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class EltwiseLayer : public Layer { - public: - explicit EltwiseLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Eltwise"; } - virtual inline int MinBottomBlobs() const { return 2; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - EltwiseParameter_EltwiseOp op_; - vector coeffs_; - Blob max_idx_; - - bool stable_prod_grad_; -}; - -/** - * @brief A layer for learning "embeddings" of one-hot vector input. - * Equivalent to an InnerProductLayer with one-hot vectors as input, but - * for efficiency the input is the "hot" index of each column itself. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class EmbedLayer : public Layer { - public: - explicit EmbedLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Embed"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - int M_; - int K_; - int N_; - bool bias_term_; - Blob bias_multiplier_; -}; - -/** - * @brief Takes two+ Blobs, interprets last Blob as a selector and - * filter remaining Blobs accordingly with selector data (0 means that - * the corresponding item has to be filtered, non-zero means that corresponding - * item needs to stay). - */ -template -class FilterLayer : public Layer { - public: - explicit FilterLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Filter"; } - virtual inline int MinBottomBlobs() const { return 2; } - virtual inline int MinTopBlobs() const { return 1; } - - protected: - /** - * @param bottom input Blob vector (length 2+) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs to be filtered @f$ x_1 @f$ - * -# ... - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs to be filtered @f$ x_K @f$ - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the selector blob - * @param top output Blob vector (length 1+) - * -# @f$ (S \times C \times H \times W) @f$ () - * the filtered output @f$ x_1 @f$ - * where S is the number of items - * that haven't been filtered - * @f$ (S \times C \times H \times W) @f$ - * the filtered output @f$ x_K @f$ - * where S is the number of items - * that haven't been filtered - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the forwarded inputs. - * - * @param top output Blob vector (length 1+), providing the error gradient with - * respect to the outputs - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 2+), into which the top error - * gradient is copied - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - bool first_reshape_; - vector indices_to_forward_; -}; - -/** - * @brief Reshapes the input Blob into flat vectors. - * - * Note: because this layer does not change the input values -- merely the - * dimensions -- it can simply copy the input. The copy happens "virtually" - * (thus taking effectively 0 real time) by setting, in Forward, the data - * pointer of the top Blob to that of the bottom Blob (see Blob::ShareData), - * and in Backward, the diff pointer of the bottom Blob to that of the top Blob - * (see Blob::ShareDiff). - */ -template -class FlattenLayer : public Layer { - public: - explicit FlattenLayer(const LayerParameter& param) - : Layer(param) {} - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Flatten"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - /** - * @param bottom input Blob vector (length 2+) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs - * @param top output Blob vector (length 1) - * -# @f$ (N \times CHW \times 1 \times 1) @f$ - * the outputs -- i.e., the (virtually) copied, flattened inputs - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the concatenate inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length K), into which the top error - * gradient is (virtually) copied - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); -}; - -/** - * @brief Also known as a "fully-connected" layer, computes an inner product - * with a set of learned weights, and (optionally) adds biases. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class InnerProductLayer : public Layer { - public: - explicit InnerProductLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "InnerProduct"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - int M_; - int K_; - int N_; - bool bias_term_; - Blob bias_multiplier_; -}; - -/** - * @brief Normalizes the input to have 0-mean and/or unit (1) variance. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class MVNLayer : public Layer { - public: - explicit MVNLayer(const LayerParameter& param) - : Layer(param) {} - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "MVN"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - Blob mean_, variance_, temp_; - - /// sum_multiplier is used to carry out sum using BLAS - Blob sum_multiplier_; - Dtype eps_; -}; - -/* - * @brief Reshapes the input Blob into an arbitrary-sized output Blob. - * - * Note: similarly to FlattenLayer, this layer does not change the input values - * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff). - */ -template -class ReshapeLayer : public Layer { - public: - explicit ReshapeLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Reshape"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top) {} - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) {} - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top) {} - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) {} - - /// @brief vector of axes indices whose dimensions we'll copy from the bottom - vector copy_axes_; - /// @brief the index of the axis whose dimension we infer, or -1 if none - int inferred_axis_; - /// @brief the product of the "constant" output dimensions - int constant_count_; -}; - -/** - * @brief Compute "reductions" -- operations that return a scalar output Blob - * for an input Blob of arbitrary size, such as the sum, absolute sum, - * and sum of squares. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class ReductionLayer : public Layer { - public: - explicit ReductionLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Reduction"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - /// @brief the reduction operation performed by the layer - ReductionParameter_ReductionOp op_; - /// @brief a scalar coefficient applied to all outputs - Dtype coeff_; - /// @brief the index of the first input axis to reduce - int axis_; - /// @brief the number of reductions performed - int num_; - /// @brief the input size of each reduction - int dim_; - /// @brief a helper Blob used for summation (op_ == SUM) - Blob sum_multiplier_; -}; - -/** - * @brief Ignores bottom blobs while producing no top blobs. (This is useful - * to suppress outputs during testing.) - */ -template -class SilenceLayer : public Layer { - public: - explicit SilenceLayer(const LayerParameter& param) - : Layer(param) {} - virtual void Reshape(const vector*>& bottom, - const vector*>& top) {} - - virtual inline const char* type() const { return "Silence"; } - virtual inline int MinBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 0; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top) {} - // We can't define Forward_gpu here, since STUB_GPU will provide - // its own definition for CPU_ONLY mode. - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); -}; - -/** - * @brief Computes the softmax function. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class SoftmaxLayer : public Layer { - public: - explicit SoftmaxLayer(const LayerParameter& param) - : Layer(param) {} - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Softmax"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - int outer_num_; - int inner_num_; - int softmax_axis_; - /// sum_multiplier is used to carry out sum using BLAS - Blob sum_multiplier_; - /// scale is an intermediate Blob to hold temporary results. - Blob scale_; -}; - -#ifdef USE_CUDNN -/** - * @brief cuDNN implementation of SoftmaxLayer. - * Fallback to SoftmaxLayer for CPU mode. - */ -template -class CuDNNSoftmaxLayer : public SoftmaxLayer { - public: - explicit CuDNNSoftmaxLayer(const LayerParameter& param) - : SoftmaxLayer(param), handles_setup_(false) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - virtual ~CuDNNSoftmaxLayer(); - - protected: - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - bool handles_setup_; - cudnnHandle_t handle_; - cudnnTensorDescriptor_t bottom_desc_; - cudnnTensorDescriptor_t top_desc_; -}; -#endif - -/** - * @brief Creates a "split" path in the network by copying the bottom Blob - * into multiple top Blob%s to be used by multiple consuming layers. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class SplitLayer : public Layer { - public: - explicit SplitLayer(const LayerParameter& param) - : Layer(param) {} - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Split"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int MinTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - int count_; -}; - -/** - * @brief Takes a Blob and slices it along either the num or channel dimension, - * outputting multiple sliced Blob results. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class SliceLayer : public Layer { - public: - explicit SliceLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Slice"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int MinTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - int count_; - int num_slices_; - int slice_size_; - int slice_axis_; - vector slice_point_; -}; - -/** - * @brief Copy a Blob along specified dimensions. - */ -template -class TileLayer : public Layer { - public: - explicit TileLayer(const LayerParameter& param) - : Layer(param) {} - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Tile"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - unsigned int axis_, tiles_, outer_dim_, inner_dim_; -}; - -} // namespace caffe - -#endif // CAFFE_COMMON_LAYERS_HPP_ diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp deleted file mode 100644 index aa0ab7df390..00000000000 --- a/include/caffe/data_layers.hpp +++ /dev/null @@ -1,347 +0,0 @@ -#ifndef CAFFE_DATA_LAYERS_HPP_ -#define CAFFE_DATA_LAYERS_HPP_ - -#include -#include -#include -#include "hdf5.h" - -#include "caffe/blob.hpp" -#include "caffe/common.hpp" -#include "caffe/data_reader.hpp" -#include "caffe/data_transformer.hpp" -#include "caffe/filler.hpp" -#include "caffe/internal_thread.hpp" -#include "caffe/layer.hpp" -#include "caffe/proto/caffe.pb.h" -#include "caffe/util/blocking_queue.hpp" -#include "caffe/util/db.hpp" - -#define HDF5_DATA_DATASET_NAME "data" -#define HDF5_DATA_LABEL_NAME "label" - -namespace caffe { - -/** - * @brief Provides base for data layers that feed blobs to the Net. - * - * TODO(dox): thorough documentation for Forward and proto params. - */ -template -class BaseDataLayer : public Layer { - public: - explicit BaseDataLayer(const LayerParameter& param); - // LayerSetUp: implements common data layer setup functionality, and calls - // DataLayerSetUp to do special data layer setup for individual layer types. - // This method may not be overridden except by the BasePrefetchingDataLayer. - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - // Data layers should be shared by multiple solvers in parallel - virtual inline bool ShareInParallel() const { return true; } - virtual void DataLayerSetUp(const vector*>& bottom, - const vector*>& top) {} - // Data layers have no bottoms, so reshaping is trivial. - virtual void Reshape(const vector*>& bottom, - const vector*>& top) {} - - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) {} - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) {} - - protected: - TransformationParameter transform_param_; - shared_ptr > data_transformer_; - bool output_labels_; -}; - -template -class Batch { - public: - Blob data_, label_; -}; - -template -class BasePrefetchingDataLayer : - public BaseDataLayer, public InternalThread { - public: - explicit BasePrefetchingDataLayer(const LayerParameter& param); - // LayerSetUp: implements common data layer setup functionality, and calls - // DataLayerSetUp to do special data layer setup for individual layer types. - // This method may not be overridden. - void LayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - // Prefetches batches (asynchronously if to GPU memory) - static const int PREFETCH_COUNT = 3; - - protected: - virtual void InternalThreadEntry(); - virtual void load_batch(Batch* batch) = 0; - - Batch prefetch_[PREFETCH_COUNT]; - BlockingQueue*> prefetch_free_; - BlockingQueue*> prefetch_full_; - - Blob transformed_data_; -}; - -template -class DataLayer : public BasePrefetchingDataLayer { - public: - explicit DataLayer(const LayerParameter& param); - virtual ~DataLayer(); - virtual void DataLayerSetUp(const vector*>& bottom, - const vector*>& top); - // DataLayer uses DataReader instead for sharing for parallelism - virtual inline bool ShareInParallel() const { return false; } - virtual inline const char* type() const { return "Data"; } - virtual inline int ExactNumBottomBlobs() const { return 0; } - virtual inline int MinTopBlobs() const { return 1; } - virtual inline int MaxTopBlobs() const { return 2; } - - protected: - virtual void load_batch(Batch* batch); - - DataReader reader_; -}; - -/** - * @brief Provides data to the Net generated by a Filler. - * - * TODO(dox): thorough documentation for Forward and proto params. - */ -template -class DummyDataLayer : public Layer { - public: - explicit DummyDataLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - // Data layers should be shared by multiple solvers in parallel - virtual inline bool ShareInParallel() const { return true; } - // Data layers have no bottoms, so reshaping is trivial. - virtual void Reshape(const vector*>& bottom, - const vector*>& top) {} - - virtual inline const char* type() const { return "DummyData"; } - virtual inline int ExactNumBottomBlobs() const { return 0; } - virtual inline int MinTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) {} - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) {} - - vector > > fillers_; - vector refill_; -}; - -/** - * @brief Provides data to the Net from HDF5 files. - * - * TODO(dox): thorough documentation for Forward and proto params. - */ -template -class HDF5DataLayer : public Layer { - public: - explicit HDF5DataLayer(const LayerParameter& param) - : Layer(param) {} - virtual ~HDF5DataLayer(); - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - // Data layers should be shared by multiple solvers in parallel - virtual inline bool ShareInParallel() const { return true; } - // Data layers have no bottoms, so reshaping is trivial. - virtual void Reshape(const vector*>& bottom, - const vector*>& top) {} - - virtual inline const char* type() const { return "HDF5Data"; } - virtual inline int ExactNumBottomBlobs() const { return 0; } - virtual inline int MinTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) {} - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) {} - virtual void LoadHDF5FileData(const char* filename); - - std::vector hdf_filenames_; - unsigned int num_files_; - unsigned int current_file_; - hsize_t current_row_; - std::vector > > hdf_blobs_; - std::vector data_permutation_; - std::vector file_permutation_; -}; - -/** - * @brief Write blobs to disk as HDF5 files. - * - * TODO(dox): thorough documentation for Forward and proto params. - */ -template -class HDF5OutputLayer : public Layer { - public: - explicit HDF5OutputLayer(const LayerParameter& param) - : Layer(param), file_opened_(false) {} - virtual ~HDF5OutputLayer(); - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - // Data layers should be shared by multiple solvers in parallel - virtual inline bool ShareInParallel() const { return true; } - // Data layers have no bottoms, so reshaping is trivial. - virtual void Reshape(const vector*>& bottom, - const vector*>& top) {} - - virtual inline const char* type() const { return "HDF5Output"; } - // TODO: no limit on the number of blobs - virtual inline int ExactNumBottomBlobs() const { return 2; } - virtual inline int ExactNumTopBlobs() const { return 0; } - - inline std::string file_name() const { return file_name_; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void SaveBlobs(); - - bool file_opened_; - std::string file_name_; - hid_t file_id_; - Blob data_blob_; - Blob label_blob_; -}; - -/** - * @brief Provides data to the Net from image files. - * - * TODO(dox): thorough documentation for Forward and proto params. - */ -template -class ImageDataLayer : public BasePrefetchingDataLayer { - public: - explicit ImageDataLayer(const LayerParameter& param) - : BasePrefetchingDataLayer(param) {} - virtual ~ImageDataLayer(); - virtual void DataLayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "ImageData"; } - virtual inline int ExactNumBottomBlobs() const { return 0; } - virtual inline int ExactNumTopBlobs() const { return 2; } - - protected: - shared_ptr prefetch_rng_; - virtual void ShuffleImages(); - virtual void load_batch(Batch* batch); - - vector > lines_; - int lines_id_; -}; - -/** - * @brief Provides data to the Net from memory. - * - * TODO(dox): thorough documentation for Forward and proto params. - */ -template -class MemoryDataLayer : public BaseDataLayer { - public: - explicit MemoryDataLayer(const LayerParameter& param) - : BaseDataLayer(param), has_new_data_(false) {} - virtual void DataLayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "MemoryData"; } - virtual inline int ExactNumBottomBlobs() const { return 0; } - virtual inline int ExactNumTopBlobs() const { return 2; } - - virtual void AddDatumVector(const vector& datum_vector); -#ifdef USE_OPENCV - virtual void AddMatVector(const vector& mat_vector, - const vector& labels); -#endif // USE_OPENCV - - // Reset should accept const pointers, but can't, because the memory - // will be given to Blob, which is mutable - void Reset(Dtype* data, Dtype* label, int n); - void set_batch_size(int new_size); - - int batch_size() { return batch_size_; } - int channels() { return channels_; } - int height() { return height_; } - int width() { return width_; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - - int batch_size_, channels_, height_, width_, size_; - Dtype* data_; - Dtype* labels_; - int n_; - size_t pos_; - Blob added_data_; - Blob added_label_; - bool has_new_data_; -}; - -/** - * @brief Provides data to the Net from windows of images files, specified - * by a window data file. - * - * TODO(dox): thorough documentation for Forward and proto params. - */ -template -class WindowDataLayer : public BasePrefetchingDataLayer { - public: - explicit WindowDataLayer(const LayerParameter& param) - : BasePrefetchingDataLayer(param) {} - virtual ~WindowDataLayer(); - virtual void DataLayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "WindowData"; } - virtual inline int ExactNumBottomBlobs() const { return 0; } - virtual inline int ExactNumTopBlobs() const { return 2; } - - protected: - virtual unsigned int PrefetchRand(); - virtual void load_batch(Batch* batch); - - shared_ptr prefetch_rng_; - vector > > image_database_; - enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM }; - vector > fg_windows_; - vector > bg_windows_; - Blob data_mean_; - vector mean_values_; - bool has_mean_file_; - bool has_mean_values_; - bool cache_images_; - vector > image_database_cache_; -}; - -} // namespace caffe - -#endif // CAFFE_DATA_LAYERS_HPP_ diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp index 2c2fde4d979..f385afccfee 100644 --- a/include/caffe/layer_factory.hpp +++ b/include/caffe/layer_factory.hpp @@ -44,6 +44,7 @@ #include #include "caffe/common.hpp" +#include "caffe/layer.hpp" #include "caffe/proto/caffe.pb.h" namespace caffe { diff --git a/include/caffe/layers/absval_layer.hpp b/include/caffe/layers/absval_layer.hpp new file mode 100644 index 00000000000..9b5305dceb4 --- /dev/null +++ b/include/caffe/layers/absval_layer.hpp @@ -0,0 +1,68 @@ +#ifndef CAFFE_ABSVAL_LAYER_HPP_ +#define CAFFE_ABSVAL_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief Computes @f$ y = |x| @f$ + * + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ y = |x| @f$ + */ +template +class AbsValLayer : public NeuronLayer { + public: + explicit AbsValLayer(const LayerParameter& param) + : NeuronLayer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "AbsVal"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + /// @copydoc AbsValLayer + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the absolute value inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} = + * \mathrm{sign}(x) \frac{\partial E}{\partial y} + * @f$ if propagate_down[0] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); +}; + +} // namespace caffe + +#endif // CAFFE_ABSVAL_LAYER_HPP_ diff --git a/include/caffe/layers/accuracy_layer.hpp b/include/caffe/layers/accuracy_layer.hpp new file mode 100644 index 00000000000..fe2adb939e4 --- /dev/null +++ b/include/caffe/layers/accuracy_layer.hpp @@ -0,0 +1,95 @@ +#ifndef CAFFE_ACCURACY_LAYER_HPP_ +#define CAFFE_ACCURACY_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/loss_layer.hpp" + +namespace caffe { + +/** + * @brief Computes the classification accuracy for a one-of-many + * classification task. + */ +template +class AccuracyLayer : public Layer { + public: + /** + * @param param provides AccuracyParameter accuracy_param, + * with AccuracyLayer options: + * - top_k (\b optional, default 1). + * Sets the maximum rank @f$ k @f$ at which a prediction is considered + * correct. For example, if @f$ k = 5 @f$, a prediction is counted + * correct if the correct label is among the top 5 predicted labels. + */ + explicit AccuracyLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Accuracy"; } + virtual inline int ExactNumBottomBlobs() const { return 2; } + + // If there are two top blobs, then the second blob will contain + // accuracies per class. + virtual inline int MinTopBlobs() const { return 1; } + virtual inline int MaxTopBlos() const { return 2; } + + protected: + /** + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$ x @f$, a Blob with values in + * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of + * the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted + * label @f$ \hat{l}_n @f$ given by its maximal index: + * @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$ + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the labels @f$ l @f$, an integer-valued Blob with values + * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ + * indicating the correct class label among the @f$ K @f$ classes + * @param top output Blob vector (length 1) + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * the computed accuracy: @f$ + * \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \} + * @f$, where @f$ + * \delta\{\mathrm{condition}\} = \left\{ + * \begin{array}{lr} + * 1 & \mbox{if condition} \\ + * 0 & \mbox{otherwise} + * \end{array} \right. + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + + + /// @brief Not implemented -- AccuracyLayer cannot be used as a loss. + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + for (int i = 0; i < propagate_down.size(); ++i) { + if (propagate_down[i]) { NOT_IMPLEMENTED; } + } + } + + int label_axis_, outer_num_, inner_num_; + + int top_k_; + + /// Whether to ignore instances with a certain label. + bool has_ignore_label_; + /// The label indicating that an instance should be ignored. + int ignore_label_; + /// Keeps counts of the number of samples per class. + Blob nums_buffer_; +}; + +} // namespace caffe + +#endif // CAFFE_ACCURACY_LAYER_HPP_ diff --git a/include/caffe/layers/argmax_layer.hpp b/include/caffe/layers/argmax_layer.hpp new file mode 100644 index 00000000000..4fef363e850 --- /dev/null +++ b/include/caffe/layers/argmax_layer.hpp @@ -0,0 +1,77 @@ +#ifndef CAFFE_ARGMAX_LAYER_HPP_ +#define CAFFE_ARGMAX_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Compute the index of the @f$ K @f$ max values for each datum across + * all dimensions @f$ (C \times H \times W) @f$. + * + * Intended for use after a classification layer to produce a prediction. + * If parameter out_max_val is set to true, output is a vector of pairs + * (max_ind, max_val) for each image. The axis parameter specifies an axis + * along which to maximise. + * + * NOTE: does not implement Backwards operation. + */ +template +class ArgMaxLayer : public Layer { + public: + /** + * @param param provides ArgMaxParameter argmax_param, + * with ArgMaxLayer options: + * - top_k (\b optional uint, default 1). + * the number @f$ K @f$ of maximal items to output. + * - out_max_val (\b optional bool, default false). + * if set, output a vector of pairs (max_ind, max_val) unless axis is set then + * output max_val along the specified axis. + * - axis (\b optional int). + * if set, maximise along the specified axis else maximise the flattened + * trailing dimensions for each index of the first / num dimension. + */ + explicit ArgMaxLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "ArgMax"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times 1 \times K) @f$ or, if out_max_val + * @f$ (N \times 2 \times K) @f$ unless axis set than e.g. + * @f$ (N \times K \times H \times W) @f$ if axis == 1 + * the computed outputs @f$ + * y_n = \arg\max\limits_i x_{ni} + * @f$ (for @f$ K = 1 @f$). + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + /// @brief Not implemented (non-differentiable function) + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + NOT_IMPLEMENTED; + } + bool out_max_val_; + size_t top_k_; + bool has_axis_; + int axis_; +}; + +} // namespace caffe + +#endif // CAFFE_ARGMAX_LAYER_HPP_ diff --git a/include/caffe/layers/base_conv_layer.hpp b/include/caffe/layers/base_conv_layer.hpp new file mode 100644 index 00000000000..f3def16c039 --- /dev/null +++ b/include/caffe/layers/base_conv_layer.hpp @@ -0,0 +1,168 @@ +#ifndef CAFFE_BASE_CONVOLUTION_LAYER_HPP_ +#define CAFFE_BASE_CONVOLUTION_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" +#include "caffe/util/im2col.hpp" + +namespace caffe { + +/** + * @brief Abstract base class that factors out the BLAS code common to + * ConvolutionLayer and DeconvolutionLayer. + */ +template +class BaseConvolutionLayer : public Layer { + public: + explicit BaseConvolutionLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline int MinBottomBlobs() const { return 1; } + virtual inline int MinTopBlobs() const { return 1; } + virtual inline bool EqualNumBottomTopBlobs() const { return true; } + + protected: + // Helper functions that abstract away the column buffer and gemm arguments. + // The last argument in forward_cpu_gemm is so that we can skip the im2col if + // we just called weight_cpu_gemm with the same input. + void forward_cpu_gemm(const Dtype* input, const Dtype* weights, + Dtype* output, bool skip_im2col = false); + void forward_cpu_bias(Dtype* output, const Dtype* bias); + void backward_cpu_gemm(const Dtype* input, const Dtype* weights, + Dtype* output); + void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype* + weights); + void backward_cpu_bias(Dtype* bias, const Dtype* input); + +#ifndef CPU_ONLY + void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights, + Dtype* output, bool skip_im2col = false); + void forward_gpu_bias(Dtype* output, const Dtype* bias); + void backward_gpu_gemm(const Dtype* input, const Dtype* weights, + Dtype* col_output); + void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype* + weights); + void backward_gpu_bias(Dtype* bias, const Dtype* input); +#endif + + /// @brief The spatial dimensions of the input. + inline int input_shape(int i) { + return (*bottom_shape_)[channel_axis_ + i]; + } + // reverse_dimensions should return true iff we are implementing deconv, so + // that conv helpers know which dimensions are which. + virtual bool reverse_dimensions() = 0; + // Compute height_out_ and width_out_ from other parameters. + virtual void compute_output_shape() = 0; + + /// @brief The spatial dimensions of a filter kernel. + Blob kernel_shape_; + /// @brief The spatial dimensions of the stride. + Blob stride_; + /// @brief The spatial dimensions of the padding. + Blob pad_; + /// @brief The spatial dimensions of the convolution input. + Blob conv_input_shape_; + /// @brief The spatial dimensions of the col_buffer. + vector col_buffer_shape_; + /// @brief The spatial dimensions of the output. + vector output_shape_; + const vector* bottom_shape_; + + int num_spatial_axes_; + int bottom_dim_; + int top_dim_; + + int channel_axis_; + int num_; + int channels_; + int group_; + int out_spatial_dim_; + int weight_offset_; + int num_output_; + bool bias_term_; + bool is_1x1_; + bool force_nd_im2col_; + + private: + // wrap im2col/col2im so we don't have to remember the (long) argument lists + inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) { + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + im2col_cpu(data, conv_in_channels_, + conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff); + } else { + im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(), + col_buffer_shape_.data(), kernel_shape_.cpu_data(), + pad_.cpu_data(), stride_.cpu_data(), col_buff); + } + } + inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) { + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + col2im_cpu(col_buff, conv_in_channels_, + conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], data); + } else { + col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(), + col_buffer_shape_.data(), kernel_shape_.cpu_data(), + pad_.cpu_data(), stride_.cpu_data(), data); + } + } +#ifndef CPU_ONLY + inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) { + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + im2col_gpu(data, conv_in_channels_, + conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff); + } else { + im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_, + conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(), + kernel_shape_.gpu_data(), pad_.gpu_data(), + stride_.gpu_data(), col_buff); + } + } + inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) { + if (!force_nd_im2col_ && num_spatial_axes_ == 2) { + col2im_gpu(col_buff, conv_in_channels_, + conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], + kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], + pad_.cpu_data()[0], pad_.cpu_data()[1], + stride_.cpu_data()[0], stride_.cpu_data()[1], data); + } else { + col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_, + conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(), + kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(), + data); + } + } +#endif + + int num_kernels_im2col_; + int num_kernels_col2im_; + int conv_out_channels_; + int conv_in_channels_; + int conv_out_spatial_dim_; + int kernel_dim_; + int col_offset_; + int output_offset_; + + Blob col_buffer_; + Blob bias_multiplier_; +}; + +} // namespace caffe + +#endif // CAFFE_BASE_CONVOLUTION_LAYER_HPP_ diff --git a/include/caffe/layers/base_data_layer.hpp b/include/caffe/layers/base_data_layer.hpp new file mode 100644 index 00000000000..2c49b73184b --- /dev/null +++ b/include/caffe/layers/base_data_layer.hpp @@ -0,0 +1,86 @@ +#ifndef CAFFE_DATA_LAYERS_HPP_ +#define CAFFE_DATA_LAYERS_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/data_transformer.hpp" +#include "caffe/internal_thread.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" +#include "caffe/util/blocking_queue.hpp" + +namespace caffe { + +/** + * @brief Provides base for data layers that feed blobs to the Net. + * + * TODO(dox): thorough documentation for Forward and proto params. + */ +template +class BaseDataLayer : public Layer { + public: + explicit BaseDataLayer(const LayerParameter& param); + // LayerSetUp: implements common data layer setup functionality, and calls + // DataLayerSetUp to do special data layer setup for individual layer types. + // This method may not be overridden except by the BasePrefetchingDataLayer. + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + // Data layers should be shared by multiple solvers in parallel + virtual inline bool ShareInParallel() const { return true; } + virtual void DataLayerSetUp(const vector*>& bottom, + const vector*>& top) {} + // Data layers have no bottoms, so reshaping is trivial. + virtual void Reshape(const vector*>& bottom, + const vector*>& top) {} + + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) {} + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) {} + + protected: + TransformationParameter transform_param_; + shared_ptr > data_transformer_; + bool output_labels_; +}; + +template +class Batch { + public: + Blob data_, label_; +}; + +template +class BasePrefetchingDataLayer : + public BaseDataLayer, public InternalThread { + public: + explicit BasePrefetchingDataLayer(const LayerParameter& param); + // LayerSetUp: implements common data layer setup functionality, and calls + // DataLayerSetUp to do special data layer setup for individual layer types. + // This method may not be overridden. + void LayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + // Prefetches batches (asynchronously if to GPU memory) + static const int PREFETCH_COUNT = 3; + + protected: + virtual void InternalThreadEntry(); + virtual void load_batch(Batch* batch) = 0; + + Batch prefetch_[PREFETCH_COUNT]; + BlockingQueue*> prefetch_free_; + BlockingQueue*> prefetch_full_; + + Blob transformed_data_; +}; + +} // namespace caffe + +#endif // CAFFE_DATA_LAYERS_HPP_ diff --git a/include/caffe/layers/batch_norm_layer.hpp b/include/caffe/layers/batch_norm_layer.hpp new file mode 100644 index 00000000000..9b2d5126efb --- /dev/null +++ b/include/caffe/layers/batch_norm_layer.hpp @@ -0,0 +1,81 @@ +#ifndef CAFFE_BATCHNORM_LAYER_HPP_ +#define CAFFE_BATCHNORM_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Normalizes the input to have 0-mean and/or unit (1) variance across + * the batch. + * + * This layer computes Batch Normalization described in [1]. For + * each channel in the data (i.e. axis 1), it subtracts the mean and divides + * by the variance, where both statistics are computed across both spatial + * dimensions and across the different examples in the batch. + * + * By default, during training time, the network is computing global mean/ + * variance statistics via a running average, which is then used at test + * time to allow deterministic outputs for each input. You can manually + * toggle whether the network is accumulating or using the statistics via the + * use_global_stats option. IMPORTANT: for this feature to work, you MUST + * set the learning rate to zero for all three parameter blobs, i.e., + * param {lr_mult: 0} three times in the layer definition. + * + * Note that the original paper also included a per-channel learned bias and + * scaling factor. It is possible (though a bit cumbersome) to implement + * this in caffe using a single-channel DummyDataLayer filled with zeros, + * followed by a Convolution layer with output the same size as the current. + * This produces a channel-specific value that can be added or multiplied by + * the BatchNorm layer's output. + * + * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network + * Training by Reducing Internal Covariate Shift." arXiv preprint + * arXiv:1502.03167 (2015). + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class BatchNormLayer : public Layer { + public: + explicit BatchNormLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "BatchNorm"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + Blob mean_, variance_, temp_, x_norm_; + bool use_global_stats_; + Dtype moving_average_fraction_; + int channels_; + Dtype eps_; + + // extra temporarary variables is used to carry out sums/broadcasting + // using BLAS + Blob batch_sum_multiplier_; + Blob num_by_chans_; + Blob spatial_sum_multiplier_; +}; + +} // namespace caffe + +#endif // CAFFE_BATCHNORM_LAYER_HPP_ diff --git a/include/caffe/layers/batch_reindex_layer.hpp b/include/caffe/layers/batch_reindex_layer.hpp new file mode 100644 index 00000000000..ebb3a567bc4 --- /dev/null +++ b/include/caffe/layers/batch_reindex_layer.hpp @@ -0,0 +1,83 @@ +#ifndef CAFFE_BATCHREINDEX_LAYER_HPP_ +#define CAFFE_BATCHREINDEX_LAYER_HPP_ + +#include +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Index into the input blob along its first axis. + * + * This layer can be used to select, reorder, and even replicate examples in a + * batch. The second blob is cast to int and treated as an index into the + * first axis of the first blob. + */ +template +class BatchReindexLayer : public Layer { + public: + explicit BatchReindexLayer(const LayerParameter& param) + : Layer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "BatchReindex"; } + virtual inline int ExactNumBottomBlobs() const { return 2; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + /** + * @param bottom input Blob vector (length 2+) + * -# @f$ (N \times ...) @f$ + * the inputs @f$ x_1 @f$ + * -# @f$ (M) @f$ + * the inputs @f$ x_2 @f$ + * @param top output Blob vector (length 1) + * -# @f$ (M \times ...) @f$: + * the reindexed array @f$ + * y = x_1[x_2] + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the reordered input. + * + * @param top output Blob vector (length 1), providing the error gradient + * with respect to the outputs + * -# @f$ (M \times ...) @f$: + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to concatenated outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 2): + * - @f$ \frac{\partial E}{\partial y} @f$ is de-indexed (summing where + * required) back to the input x_1 + * - This layer cannot backprop to x_2, i.e. propagate_down[1] must be + * false. + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + private: + struct pair_sort_first { + bool operator()(const std::pair &left, + const std::pair &right) { + return left.first < right.first; + } + }; + void check_batch_reindex(int initial_num, int final_num, + const Dtype* ridx_data); +}; + +} // namespace caffe + +#endif // CAFFE_BATCHREINDEX_LAYER_HPP_ diff --git a/include/caffe/layers/bnll_layer.hpp b/include/caffe/layers/bnll_layer.hpp new file mode 100644 index 00000000000..be07c748364 --- /dev/null +++ b/include/caffe/layers/bnll_layer.hpp @@ -0,0 +1,70 @@ +#ifndef CAFFE_BNLL_LAYER_HPP_ +#define CAFFE_BNLL_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief Computes @f$ y = x + \log(1 + \exp(-x)) @f$ if @f$ x > 0 @f$; + * @f$ y = \log(1 + \exp(x)) @f$ otherwise. + * + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = \left\{ + * \begin{array}{ll} + * x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ + * \log(1 + \exp(x)) & \mbox{otherwise} + * \end{array} \right. + * @f$ + */ +template +class BNLLLayer : public NeuronLayer { + public: + explicit BNLLLayer(const LayerParameter& param) + : NeuronLayer(param) {} + + virtual inline const char* type() const { return "BNLL"; } + + protected: + /// @copydoc BNLLLayer + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the BNLL inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} + * @f$ if propagate_down[0] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); +}; + +} // namespace caffe + +#endif // CAFFE_BNLL_LAYER_HPP_ diff --git a/include/caffe/layers/concat_layer.hpp b/include/caffe/layers/concat_layer.hpp new file mode 100644 index 00000000000..a1570249197 --- /dev/null +++ b/include/caffe/layers/concat_layer.hpp @@ -0,0 +1,87 @@ +#ifndef CAFFE_CONCAT_LAYER_HPP_ +#define CAFFE_CONCAT_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Takes at least two Blob%s and concatenates them along either the num + * or channel dimension, outputting the result. + */ +template +class ConcatLayer : public Layer { + public: + explicit ConcatLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Concat"; } + virtual inline int MinBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + /** + * @param bottom input Blob vector (length 2+) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x_1 @f$ + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x_2 @f$ + * -# ... + * - K @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x_K @f$ + * @param top output Blob vector (length 1) + * -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or + * @f$ (N \times KC \times H \times W) @f$ if axis == 1: + * the concatenated output @f$ + * y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}] + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the concatenate inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or + * @f$ (N \times KC \times H \times W) @f$ if axis == 1: + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to concatenated outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length K), into which the top gradient + * @f$ \frac{\partial E}{\partial y} @f$ is deconcatenated back to the + * inputs @f$ + * \left[ \begin{array}{cccc} + * \frac{\partial E}{\partial x_1} & + * \frac{\partial E}{\partial x_2} & + * ... & + * \frac{\partial E}{\partial x_K} + * \end{array} \right] = + * \frac{\partial E}{\partial y} + * @f$ + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + int count_; + int num_concats_; + int concat_input_size_; + int concat_axis_; +}; + +} // namespace caffe + +#endif // CAFFE_CONCAT_LAYER_HPP_ diff --git a/include/caffe/layers/contrastive_loss_layer.hpp b/include/caffe/layers/contrastive_loss_layer.hpp new file mode 100644 index 00000000000..e890afb8207 --- /dev/null +++ b/include/caffe/layers/contrastive_loss_layer.hpp @@ -0,0 +1,101 @@ +#ifndef CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_ +#define CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/loss_layer.hpp" + +namespace caffe { + +/** + * @brief Computes the contrastive loss @f$ + * E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + + * \left(1-y\right) \max \left(margin-d, 0\right)^2 + * @f$ where @f$ + * d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be + * used to train siamese networks. + * + * @param bottom input Blob vector (length 3) + * -# @f$ (N \times C \times 1 \times 1) @f$ + * the features @f$ a \in [-\infty, +\infty]@f$ + * -# @f$ (N \times C \times 1 \times 1) @f$ + * the features @f$ b \in [-\infty, +\infty]@f$ + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the binary similarity @f$ s \in [0, 1]@f$ + * @param top output Blob vector (length 1) + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * the computed contrastive loss: @f$ E = + * \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + + * \left(1-y\right) \max \left(margin-d, 0\right)^2 + * @f$ where @f$ + * d = \left| \left| a_n - b_n \right| \right|_2 @f$. + * This can be used to train siamese networks. + */ +template +class ContrastiveLossLayer : public LossLayer { + public: + explicit ContrastiveLossLayer(const LayerParameter& param) + : LossLayer(param), diff_() {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual inline int ExactNumBottomBlobs() const { return 3; } + virtual inline const char* type() const { return "ContrastiveLoss"; } + /** + * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate + * to the first two inputs. + */ + virtual inline bool AllowForceBackward(const int bottom_index) const { + return bottom_index != 2; + } + + protected: + /// @copydoc ContrastiveLossLayer + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the Contrastive error gradient w.r.t. the inputs. + * + * Computes the gradients with respect to the two input vectors (bottom[0] and + * bottom[1]), but not the similarity label (bottom[2]). + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, + * as @f$ \lambda @f$ is the coefficient of this layer's output + * @f$\ell_i@f$ in the overall Net loss + * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence + * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. + * (*Assuming that this top Blob is not used as a bottom (input) by any + * other layer of the Net.) + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times 1 \times 1) @f$ + * the features @f$a@f$; Backward fills their diff with + * gradients if propagate_down[0] + * -# @f$ (N \times C \times 1 \times 1) @f$ + * the features @f$b@f$; Backward fills their diff with gradients if + * propagate_down[1] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + Blob diff_; // cached for backward pass + Blob dist_sq_; // cached for backward pass + Blob diff_sq_; // tmp storage for gpu forward pass + Blob summer_vec_; // tmp storage for gpu forward pass +}; + +} // namespace caffe + +#endif // CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_ diff --git a/include/caffe/layers/conv_layer.hpp b/include/caffe/layers/conv_layer.hpp new file mode 100644 index 00000000000..15574766de5 --- /dev/null +++ b/include/caffe/layers/conv_layer.hpp @@ -0,0 +1,81 @@ +#ifndef CAFFE_CONV_LAYER_HPP_ +#define CAFFE_CONV_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/base_conv_layer.hpp" + +namespace caffe { + +/** + * @brief Convolves the input image with a bank of learned filters, + * and (optionally) adds biases. + * + * Caffe convolves by reduction to matrix multiplication. This achieves + * high-throughput and generality of input and filter dimensions but comes at + * the cost of memory for matrices. This makes use of efficiency in BLAS. + * + * The input is "im2col" transformed to a channel K' x H x W data matrix + * for multiplication with the N x K' x H x W filter matrix to yield a + * N' x H x W output matrix that is then "col2im" restored. K' is the + * input channel * kernel height * kernel width dimension of the unrolled + * inputs so that the im2col matrix has a column for each input region to + * be filtered. col2im restores the output spatial structure by rolling up + * the output channel N' columns of the output matrix. + */ +template +class ConvolutionLayer : public BaseConvolutionLayer { + public: + /** + * @param param provides ConvolutionParameter convolution_param, + * with ConvolutionLayer options: + * - num_output. The number of filters. + * - kernel_size / kernel_h / kernel_w. The filter dimensions, given by + * kernel_size for square filters or kernel_h and kernel_w for rectangular + * filters. + * - stride / stride_h / stride_w (\b optional, default 1). The filter + * stride, given by stride_size for equal dimensions or stride_h and stride_w + * for different strides. By default the convolution is dense with stride 1. + * - pad / pad_h / pad_w (\b optional, default 0). The zero-padding for + * convolution, given by pad for equal dimensions or pad_h and pad_w for + * different padding. Input padding is computed implicitly instead of + * actually padding. + * - group (\b optional, default 1). The number of filter groups. Group + * convolution is a method for reducing parameterization by selectively + * connecting input and output channels. The input and output channel dimensions must be divisible + * by the number of groups. For group @f$ \geq 1 @f$, the + * convolutional filters' input and output channels are separated s.t. each + * group takes 1 / group of the input channels and makes 1 / group of the + * output channels. Concretely 4 input channels, 8 output channels, and + * 2 groups separate input channels 1-2 and output channels 1-4 into the + * first group and input channels 3-4 and output channels 5-8 into the second + * group. + * - bias_term (\b optional, default true). Whether to have a bias. + * - engine: convolution has CAFFE (matrix multiplication) and CUDNN (library + * kernels + stream parallelism) engines. + */ + explicit ConvolutionLayer(const LayerParameter& param) + : BaseConvolutionLayer(param) {} + + virtual inline const char* type() const { return "Convolution"; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual inline bool reverse_dimensions() { return false; } + virtual void compute_output_shape(); +}; + +} // namespace caffe + +#endif // CAFFE_CONV_LAYER_HPP_ diff --git a/include/caffe/layers/cudnn_conv_layer.hpp b/include/caffe/layers/cudnn_conv_layer.hpp new file mode 100644 index 00000000000..31fe49a71fa --- /dev/null +++ b/include/caffe/layers/cudnn_conv_layer.hpp @@ -0,0 +1,72 @@ +#ifndef CAFFE_CUDNN_CONV_LAYER_HPP_ +#define CAFFE_CUDNN_CONV_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/conv_layer.hpp" + +namespace caffe { + +#ifdef USE_CUDNN +/* + * @brief cuDNN implementation of ConvolutionLayer. + * Fallback to ConvolutionLayer for CPU mode. + * + * cuDNN accelerates convolution through forward kernels for filtering and bias + * plus backward kernels for the gradient w.r.t. the filters, biases, and + * inputs. Caffe + cuDNN further speeds up the computation through forward + * parallelism across groups and backward parallelism across gradients. + * + * The CUDNN engine does not have memory overhead for matrix buffers. For many + * input and filter regimes the CUDNN engine is faster than the CAFFE engine, + * but for fully-convolutional models and large inputs the CAFFE engine can be + * faster as long as it fits in memory. +*/ +template +class CuDNNConvolutionLayer : public ConvolutionLayer { + public: + explicit CuDNNConvolutionLayer(const LayerParameter& param) + : ConvolutionLayer(param), handles_setup_(false) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual ~CuDNNConvolutionLayer(); + + protected: + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool handles_setup_; + cudnnHandle_t* handle_; + cudaStream_t* stream_; + + // algorithms for forward and backwards convolutions + cudnnConvolutionFwdAlgo_t *fwd_algo_; + cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_; + cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_; + + vector bottom_descs_, top_descs_; + cudnnTensorDescriptor_t bias_desc_; + cudnnFilterDescriptor_t filter_desc_; + vector conv_descs_; + int bottom_offset_, top_offset_, bias_offset_; + + size_t *workspace_fwd_sizes_; + size_t *workspace_bwd_data_sizes_; + size_t *workspace_bwd_filter_sizes_; + size_t workspaceSizeInBytes; // size of underlying storage + void *workspaceData; // underlying storage + void **workspace; // aliases into workspaceData +}; +#endif + +} // namespace caffe + +#endif // CAFFE_CUDNN_CONV_LAYER_HPP_ diff --git a/include/caffe/layers/cudnn_lcn_layer.hpp b/include/caffe/layers/cudnn_lcn_layer.hpp new file mode 100644 index 00000000000..74cf4775e51 --- /dev/null +++ b/include/caffe/layers/cudnn_lcn_layer.hpp @@ -0,0 +1,49 @@ +#ifndef CAFFE_CUDNN_LCN_LAYER_HPP_ +#define CAFFE_CUDNN_LCN_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/lrn_layer.hpp" +#include "caffe/layers/power_layer.hpp" + +namespace caffe { + +#ifdef USE_CUDNN +template +class CuDNNLCNLayer : public LRNLayer { + public: + explicit CuDNNLCNLayer(const LayerParameter& param) + : LRNLayer(param), handles_setup_(false), tempDataSize(0), + tempData1(NULL), tempData2(NULL) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual ~CuDNNLCNLayer(); + + protected: + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool handles_setup_; + cudnnHandle_t handle_; + cudnnLRNDescriptor_t norm_desc_; + cudnnTensorDescriptor_t bottom_desc_, top_desc_; + + int size_, pre_pad_; + Dtype alpha_, beta_, k_; + + size_t tempDataSize; + void *tempData1, *tempData2; +}; +#endif + +} // namespace caffe + +#endif // CAFFE_CUDNN_LCN_LAYER_HPP_ diff --git a/include/caffe/layers/cudnn_lrn_layer.hpp b/include/caffe/layers/cudnn_lrn_layer.hpp new file mode 100644 index 00000000000..000ccc36507 --- /dev/null +++ b/include/caffe/layers/cudnn_lrn_layer.hpp @@ -0,0 +1,44 @@ +#ifndef CAFFE_CUDNN_LRN_LAYER_HPP_ +#define CAFFE_CUDNN_LRN_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/lrn_layer.hpp" + +namespace caffe { + +#ifdef USE_CUDNN +template +class CuDNNLRNLayer : public LRNLayer { + public: + explicit CuDNNLRNLayer(const LayerParameter& param) + : LRNLayer(param), handles_setup_(false) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual ~CuDNNLRNLayer(); + + protected: + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool handles_setup_; + cudnnHandle_t handle_; + cudnnLRNDescriptor_t norm_desc_; + cudnnTensorDescriptor_t bottom_desc_, top_desc_; + + int size_; + Dtype alpha_, beta_, k_; +}; +#endif + +} // namespace caffe + +#endif // CAFFE_CUDNN_LRN_LAYER_HPP_ diff --git a/include/caffe/layers/cudnn_pooling_layer.hpp b/include/caffe/layers/cudnn_pooling_layer.hpp new file mode 100644 index 00000000000..6d0db47d660 --- /dev/null +++ b/include/caffe/layers/cudnn_pooling_layer.hpp @@ -0,0 +1,49 @@ +#ifndef CAFFE_CUDNN_POOLING_LAYER_HPP_ +#define CAFFE_CUDNN_POOLING_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/pooling_layer.hpp" + +namespace caffe { + +#ifdef USE_CUDNN +/* + * @brief cuDNN implementation of PoolingLayer. + * Fallback to PoolingLayer for CPU mode. +*/ +template +class CuDNNPoolingLayer : public PoolingLayer { + public: + explicit CuDNNPoolingLayer(const LayerParameter& param) + : PoolingLayer(param), handles_setup_(false) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual ~CuDNNPoolingLayer(); + // Currently, cuDNN does not support the extra top blob. + virtual inline int MinTopBlobs() const { return -1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool handles_setup_; + cudnnHandle_t handle_; + cudnnTensorDescriptor_t bottom_desc_, top_desc_; + cudnnPoolingDescriptor_t pooling_desc_; + cudnnPoolingMode_t mode_; +}; +#endif + +} // namespace caffe + +#endif // CAFFE_CUDNN_POOLING_LAYER_HPP_ diff --git a/include/caffe/layers/cudnn_relu_layer.hpp b/include/caffe/layers/cudnn_relu_layer.hpp new file mode 100644 index 00000000000..e01f568abc9 --- /dev/null +++ b/include/caffe/layers/cudnn_relu_layer.hpp @@ -0,0 +1,45 @@ +#ifndef CAFFE_CUDNN_RELU_LAYER_HPP_ +#define CAFFE_CUDNN_RELU_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" +#include "caffe/layers/relu_layer.hpp" + +namespace caffe { + +#ifdef USE_CUDNN +/** + * @brief CuDNN acceleration of ReLULayer. + */ +template +class CuDNNReLULayer : public ReLULayer { + public: + explicit CuDNNReLULayer(const LayerParameter& param) + : ReLULayer(param), handles_setup_(false) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual ~CuDNNReLULayer(); + + protected: + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool handles_setup_; + cudnnHandle_t handle_; + cudnnTensorDescriptor_t bottom_desc_; + cudnnTensorDescriptor_t top_desc_; +}; +#endif + +} // namespace caffe + +#endif // CAFFE_CUDNN_RELU_LAYER_HPP_ diff --git a/include/caffe/layers/cudnn_sigmoid_layer.hpp b/include/caffe/layers/cudnn_sigmoid_layer.hpp new file mode 100644 index 00000000000..9c597958b0b --- /dev/null +++ b/include/caffe/layers/cudnn_sigmoid_layer.hpp @@ -0,0 +1,45 @@ +#ifndef CAFFE_CUDNN_SIGMOID_LAYER_HPP_ +#define CAFFE_CUDNN_SIGMOID_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" +#include "caffe/layers/sigmoid_layer.hpp" + +namespace caffe { + +#ifdef USE_CUDNN +/** + * @brief CuDNN acceleration of SigmoidLayer. + */ +template +class CuDNNSigmoidLayer : public SigmoidLayer { + public: + explicit CuDNNSigmoidLayer(const LayerParameter& param) + : SigmoidLayer(param), handles_setup_(false) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual ~CuDNNSigmoidLayer(); + + protected: + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool handles_setup_; + cudnnHandle_t handle_; + cudnnTensorDescriptor_t bottom_desc_; + cudnnTensorDescriptor_t top_desc_; +}; +#endif + +} // namespace caffe + +#endif // CAFFE_CUDNN_SIGMOID_LAYER_HPP_ diff --git a/include/caffe/layers/cudnn_softmax_layer.hpp b/include/caffe/layers/cudnn_softmax_layer.hpp new file mode 100644 index 00000000000..174368e413d --- /dev/null +++ b/include/caffe/layers/cudnn_softmax_layer.hpp @@ -0,0 +1,45 @@ +#ifndef CAFFE_CUDNN_SOFTMAX_LAYER_HPP_ +#define CAFFE_CUDNN_SOFTMAX_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/softmax_layer.hpp" + +namespace caffe { + +#ifdef USE_CUDNN +/** + * @brief cuDNN implementation of SoftmaxLayer. + * Fallback to SoftmaxLayer for CPU mode. + */ +template +class CuDNNSoftmaxLayer : public SoftmaxLayer { + public: + explicit CuDNNSoftmaxLayer(const LayerParameter& param) + : SoftmaxLayer(param), handles_setup_(false) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual ~CuDNNSoftmaxLayer(); + + protected: + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool handles_setup_; + cudnnHandle_t handle_; + cudnnTensorDescriptor_t bottom_desc_; + cudnnTensorDescriptor_t top_desc_; +}; +#endif + +} // namespace caffe + +#endif // CAFFE_CUDNN_SOFTMAX_LAYER_HPP_ diff --git a/include/caffe/layers/cudnn_tanh_layer.hpp b/include/caffe/layers/cudnn_tanh_layer.hpp new file mode 100644 index 00000000000..c0f0053f71e --- /dev/null +++ b/include/caffe/layers/cudnn_tanh_layer.hpp @@ -0,0 +1,45 @@ +#ifndef CAFFE_CUDNN_TANH_LAYER_HPP_ +#define CAFFE_CUDNN_TANH_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" +#include "caffe/layers/tanh_layer.hpp" + +namespace caffe { + +#ifdef USE_CUDNN +/** + * @brief CuDNN acceleration of TanHLayer. + */ +template +class CuDNNTanHLayer : public TanHLayer { + public: + explicit CuDNNTanHLayer(const LayerParameter& param) + : TanHLayer(param), handles_setup_(false) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual ~CuDNNTanHLayer(); + + protected: + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool handles_setup_; + cudnnHandle_t handle_; + cudnnTensorDescriptor_t bottom_desc_; + cudnnTensorDescriptor_t top_desc_; +}; +#endif + +} // namespace caffe + +#endif // CAFFE_CUDNN_TANH_LAYER_HPP_ diff --git a/include/caffe/layers/data_layer.hpp b/include/caffe/layers/data_layer.hpp new file mode 100644 index 00000000000..6c361791a0c --- /dev/null +++ b/include/caffe/layers/data_layer.hpp @@ -0,0 +1,39 @@ +#ifndef CAFFE_DATA_LAYER_HPP_ +#define CAFFE_DATA_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/data_reader.hpp" +#include "caffe/data_transformer.hpp" +#include "caffe/internal_thread.hpp" +#include "caffe/layer.hpp" +#include "caffe/layers/base_data_layer.hpp" +#include "caffe/proto/caffe.pb.h" +#include "caffe/util/db.hpp" + +namespace caffe { + +template +class DataLayer : public BasePrefetchingDataLayer { + public: + explicit DataLayer(const LayerParameter& param); + virtual ~DataLayer(); + virtual void DataLayerSetUp(const vector*>& bottom, + const vector*>& top); + // DataLayer uses DataReader instead for sharing for parallelism + virtual inline bool ShareInParallel() const { return false; } + virtual inline const char* type() const { return "Data"; } + virtual inline int ExactNumBottomBlobs() const { return 0; } + virtual inline int MinTopBlobs() const { return 1; } + virtual inline int MaxTopBlobs() const { return 2; } + + protected: + virtual void load_batch(Batch* batch); + + DataReader reader_; +}; + +} // namespace caffe + +#endif // CAFFE_DATA_LAYER_HPP_ diff --git a/include/caffe/layers/deconv_layer.hpp b/include/caffe/layers/deconv_layer.hpp new file mode 100644 index 00000000000..23ae887e61e --- /dev/null +++ b/include/caffe/layers/deconv_layer.hpp @@ -0,0 +1,51 @@ +#ifndef CAFFE_DECONV_LAYER_HPP_ +#define CAFFE_DECONV_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/base_conv_layer.hpp" + +namespace caffe { + +/** + * @brief Convolve the input with a bank of learned filters, and (optionally) + * add biases, treating filters and convolution parameters in the + * opposite sense as ConvolutionLayer. + * + * ConvolutionLayer computes each output value by dotting an input window with + * a filter; DeconvolutionLayer multiplies each input value by a filter + * elementwise, and sums over the resulting output windows. In other words, + * DeconvolutionLayer is ConvolutionLayer with the forward and backward passes + * reversed. DeconvolutionLayer reuses ConvolutionParameter for its + * parameters, but they take the opposite sense as in ConvolutionLayer (so + * padding is removed from the output rather than added to the input, and + * stride results in upsampling rather than downsampling). + */ +template +class DeconvolutionLayer : public BaseConvolutionLayer { + public: + explicit DeconvolutionLayer(const LayerParameter& param) + : BaseConvolutionLayer(param) {} + + virtual inline const char* type() const { return "Deconvolution"; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual inline bool reverse_dimensions() { return true; } + virtual void compute_output_shape(); +}; + +} // namespace caffe + +#endif // CAFFE_DECONV_LAYER_HPP_ diff --git a/include/caffe/layers/dropout_layer.hpp b/include/caffe/layers/dropout_layer.hpp new file mode 100644 index 00000000000..e83143bc3cc --- /dev/null +++ b/include/caffe/layers/dropout_layer.hpp @@ -0,0 +1,80 @@ +#ifndef CAFFE_DROPOUT_LAYER_HPP_ +#define CAFFE_DROPOUT_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting + * the rest of the vector magnitude accordingly. + * + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ y = |x| @f$ + */ +template +class DropoutLayer : public NeuronLayer { + public: + /** + * @param param provides DropoutParameter dropout_param, + * with DropoutLayer options: + * - dropout_ratio (\b optional, default 0.5). + * Sets the probability @f$ p @f$ that any given unit is dropped. + */ + explicit DropoutLayer(const LayerParameter& param) + : NeuronLayer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Dropout"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs. At training time, we have @f$ + * y_{\mbox{train}} = \left\{ + * \begin{array}{ll} + * \frac{x}{1 - p} & \mbox{if } u > p \\ + * 0 & \mbox{otherwise} + * \end{array} \right. + * @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each + * input at each iteration. At test time, we simply have + * @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$. + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$ + Blob rand_vec_; + /// the probability @f$ p @f$ of dropping any input + Dtype threshold_; + /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$ + Dtype scale_; + unsigned int uint_thres_; +}; + +} // namespace caffe + +#endif // CAFFE_DROPOUT_LAYER_HPP_ diff --git a/include/caffe/layers/dummy_data_layer.hpp b/include/caffe/layers/dummy_data_layer.hpp new file mode 100644 index 00000000000..4180f1d01e4 --- /dev/null +++ b/include/caffe/layers/dummy_data_layer.hpp @@ -0,0 +1,49 @@ +#ifndef CAFFE_DUMMY_DATA_LAYER_HPP_ +#define CAFFE_DUMMY_DATA_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/filler.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Provides data to the Net generated by a Filler. + * + * TODO(dox): thorough documentation for Forward and proto params. + */ +template +class DummyDataLayer : public Layer { + public: + explicit DummyDataLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + // Data layers should be shared by multiple solvers in parallel + virtual inline bool ShareInParallel() const { return true; } + // Data layers have no bottoms, so reshaping is trivial. + virtual void Reshape(const vector*>& bottom, + const vector*>& top) {} + + virtual inline const char* type() const { return "DummyData"; } + virtual inline int ExactNumBottomBlobs() const { return 0; } + virtual inline int MinTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) {} + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) {} + + vector > > fillers_; + vector refill_; +}; + +} // namespace caffe + +#endif // CAFFE_DUMMY_DATA_LAYER_HPP_ diff --git a/include/caffe/layers/eltwise_layer.hpp b/include/caffe/layers/eltwise_layer.hpp new file mode 100644 index 00000000000..091de834362 --- /dev/null +++ b/include/caffe/layers/eltwise_layer.hpp @@ -0,0 +1,51 @@ +#ifndef CAFFE_ELTWISE_LAYER_HPP_ +#define CAFFE_ELTWISE_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Compute elementwise operations, such as product and sum, + * along multiple input Blobs. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class EltwiseLayer : public Layer { + public: + explicit EltwiseLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Eltwise"; } + virtual inline int MinBottomBlobs() const { return 2; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + EltwiseParameter_EltwiseOp op_; + vector coeffs_; + Blob max_idx_; + + bool stable_prod_grad_; +}; + +} // namespace caffe + +#endif // CAFFE_ELTWISE_LAYER_HPP_ diff --git a/include/caffe/layers/embed_layer.hpp b/include/caffe/layers/embed_layer.hpp new file mode 100644 index 00000000000..36137a625b6 --- /dev/null +++ b/include/caffe/layers/embed_layer.hpp @@ -0,0 +1,52 @@ +#ifndef CAFFE_EMBED_LAYER_HPP_ +#define CAFFE_EMBED_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief A layer for learning "embeddings" of one-hot vector input. + * Equivalent to an InnerProductLayer with one-hot vectors as input, but + * for efficiency the input is the "hot" index of each column itself. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class EmbedLayer : public Layer { + public: + explicit EmbedLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Embed"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + int M_; + int K_; + int N_; + bool bias_term_; + Blob bias_multiplier_; +}; + +} // namespace caffe + +#endif // CAFFE_EMBED_LAYER_HPP_ diff --git a/include/caffe/layers/euclidean_loss_layer.hpp b/include/caffe/layers/euclidean_loss_layer.hpp new file mode 100644 index 00000000000..f564569e27a --- /dev/null +++ b/include/caffe/layers/euclidean_loss_layer.hpp @@ -0,0 +1,107 @@ +#ifndef CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_ +#define CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/loss_layer.hpp" + +namespace caffe { + +/** + * @brief Computes the Euclidean (L2) loss @f$ + * E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n + * \right| \right|_2^2 @f$ for real-valued regression tasks. + * + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$ \hat{y} \in [-\infty, +\infty]@f$ + * -# @f$ (N \times C \times H \times W) @f$ + * the targets @f$ y \in [-\infty, +\infty]@f$ + * @param top output Blob vector (length 1) + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * the computed Euclidean loss: @f$ E = + * \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n + * \right| \right|_2^2 @f$ + * + * This can be used for least-squares regression tasks. An InnerProductLayer + * input to a EuclideanLossLayer exactly formulates a linear least squares + * regression problem. With non-zero weight decay the problem becomes one of + * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete + * example wherein we check that the gradients computed for a Net with exactly + * this structure match hand-computed gradient formulas for ridge regression. + * + * (Note: Caffe, and SGD in general, is certainly \b not the best way to solve + * linear least squares problems! We use it only as an instructive example.) + */ +template +class EuclideanLossLayer : public LossLayer { + public: + explicit EuclideanLossLayer(const LayerParameter& param) + : LossLayer(param), diff_() {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "EuclideanLoss"; } + /** + * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate + * to both inputs -- override to return true and always allow force_backward. + */ + virtual inline bool AllowForceBackward(const int bottom_index) const { + return true; + } + + protected: + /// @copydoc EuclideanLossLayer + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the Euclidean error gradient w.r.t. the inputs. + * + * Unlike other children of LossLayer, EuclideanLossLayer \b can compute + * gradients with respect to the label inputs bottom[1] (but still only will + * if propagate_down[1] is set, due to being produced by learnable parameters + * or if force_backward is set). In fact, this layer is "commutative" -- the + * result is the same regardless of the order of the two bottoms. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, + * as @f$ \lambda @f$ is the coefficient of this layer's output + * @f$\ell_i@f$ in the overall Net loss + * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence + * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. + * (*Assuming that this top Blob is not used as a bottom (input) by any + * other layer of the Net.) + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$\hat{y}@f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial \hat{y}} = + * \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n) + * @f$ if propagate_down[0] + * -# @f$ (N \times C \times H \times W) @f$ + * the targets @f$y@f$; Backward fills their diff with gradients + * @f$ \frac{\partial E}{\partial y} = + * \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n) + * @f$ if propagate_down[1] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + Blob diff_; +}; + +} // namespace caffe + +#endif // CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_ diff --git a/include/caffe/layers/exp_layer.hpp b/include/caffe/layers/exp_layer.hpp new file mode 100644 index 00000000000..9fc8c396a74 --- /dev/null +++ b/include/caffe/layers/exp_layer.hpp @@ -0,0 +1,80 @@ +#ifndef CAFFE_EXP_LAYER_HPP_ +#define CAFFE_EXP_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief Computes @f$ y = \gamma ^ {\alpha x + \beta} @f$, + * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$, + * and base @f$ \gamma @f$. + */ +template +class ExpLayer : public NeuronLayer { + public: + /** + * @param param provides ExpParameter exp_param, + * with ExpLayer options: + * - scale (\b optional, default 1) the scale @f$ \alpha @f$ + * - shift (\b optional, default 0) the shift @f$ \beta @f$ + * - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$) + * the base @f$ \gamma @f$ + */ + explicit ExpLayer(const LayerParameter& param) + : NeuronLayer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Exp"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = \gamma ^ {\alpha x + \beta} + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the exp inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} = + * \frac{\partial E}{\partial y} y \alpha \log_e(gamma) + * @f$ if propagate_down[0] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + Dtype inner_scale_, outer_scale_; +}; + +} // namespace caffe + +#endif // CAFFE_EXP_LAYER_HPP_ diff --git a/include/caffe/layers/filter_layer.hpp b/include/caffe/layers/filter_layer.hpp new file mode 100644 index 00000000000..e040e66612b --- /dev/null +++ b/include/caffe/layers/filter_layer.hpp @@ -0,0 +1,77 @@ +#ifndef CAFFE_FILTER_LAYER_HPP_ +#define CAFFE_FILTER_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Takes two+ Blobs, interprets last Blob as a selector and + * filter remaining Blobs accordingly with selector data (0 means that + * the corresponding item has to be filtered, non-zero means that corresponding + * item needs to stay). + */ +template +class FilterLayer : public Layer { + public: + explicit FilterLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Filter"; } + virtual inline int MinBottomBlobs() const { return 2; } + virtual inline int MinTopBlobs() const { return 1; } + + protected: + /** + * @param bottom input Blob vector (length 2+) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs to be filtered @f$ x_1 @f$ + * -# ... + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs to be filtered @f$ x_K @f$ + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the selector blob + * @param top output Blob vector (length 1+) + * -# @f$ (S \times C \times H \times W) @f$ () + * the filtered output @f$ x_1 @f$ + * where S is the number of items + * that haven't been filtered + * @f$ (S \times C \times H \times W) @f$ + * the filtered output @f$ x_K @f$ + * where S is the number of items + * that haven't been filtered + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the forwarded inputs. + * + * @param top output Blob vector (length 1+), providing the error gradient with + * respect to the outputs + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 2+), into which the top error + * gradient is copied + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool first_reshape_; + vector indices_to_forward_; +}; + +} // namespace caffe + +#endif // CAFFE_FILTER_LAYER_HPP_ diff --git a/include/caffe/layers/flatten_layer.hpp b/include/caffe/layers/flatten_layer.hpp new file mode 100644 index 00000000000..e494bbb588f --- /dev/null +++ b/include/caffe/layers/flatten_layer.hpp @@ -0,0 +1,61 @@ +#ifndef CAFFE_FLATTEN_LAYER_HPP_ +#define CAFFE_FLATTEN_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Reshapes the input Blob into flat vectors. + * + * Note: because this layer does not change the input values -- merely the + * dimensions -- it can simply copy the input. The copy happens "virtually" + * (thus taking effectively 0 real time) by setting, in Forward, the data + * pointer of the top Blob to that of the bottom Blob (see Blob::ShareData), + * and in Backward, the diff pointer of the bottom Blob to that of the top Blob + * (see Blob::ShareDiff). + */ +template +class FlattenLayer : public Layer { + public: + explicit FlattenLayer(const LayerParameter& param) + : Layer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Flatten"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + /** + * @param bottom input Blob vector (length 2+) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs + * @param top output Blob vector (length 1) + * -# @f$ (N \times CHW \times 1 \times 1) @f$ + * the outputs -- i.e., the (virtually) copied, flattened inputs + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the concatenate inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length K), into which the top error + * gradient is (virtually) copied + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); +}; + +} // namespace caffe + +#endif // CAFFE_FLATTEN_LAYER_HPP_ diff --git a/include/caffe/layers/hdf5_data_layer.hpp b/include/caffe/layers/hdf5_data_layer.hpp new file mode 100644 index 00000000000..b04cf8e1940 --- /dev/null +++ b/include/caffe/layers/hdf5_data_layer.hpp @@ -0,0 +1,62 @@ +#ifndef CAFFE_HDF5_DATA_LAYER_HPP_ +#define CAFFE_HDF5_DATA_LAYER_HPP_ + +#include "hdf5.h" + +#include +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/base_data_layer.hpp" + +namespace caffe { + +/** + * @brief Provides data to the Net from HDF5 files. + * + * TODO(dox): thorough documentation for Forward and proto params. + */ +template +class HDF5DataLayer : public Layer { + public: + explicit HDF5DataLayer(const LayerParameter& param) + : Layer(param) {} + virtual ~HDF5DataLayer(); + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + // Data layers should be shared by multiple solvers in parallel + virtual inline bool ShareInParallel() const { return true; } + // Data layers have no bottoms, so reshaping is trivial. + virtual void Reshape(const vector*>& bottom, + const vector*>& top) {} + + virtual inline const char* type() const { return "HDF5Data"; } + virtual inline int ExactNumBottomBlobs() const { return 0; } + virtual inline int MinTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) {} + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) {} + virtual void LoadHDF5FileData(const char* filename); + + std::vector hdf_filenames_; + unsigned int num_files_; + unsigned int current_file_; + hsize_t current_row_; + std::vector > > hdf_blobs_; + std::vector data_permutation_; + std::vector file_permutation_; +}; + +} // namespace caffe + +#endif // CAFFE_HDF5_DATA_LAYER_HPP_ diff --git a/include/caffe/layers/hdf5_output_layer.hpp b/include/caffe/layers/hdf5_output_layer.hpp new file mode 100644 index 00000000000..487d08fc06c --- /dev/null +++ b/include/caffe/layers/hdf5_output_layer.hpp @@ -0,0 +1,64 @@ +#ifndef CAFFE_HDF5_OUTPUT_LAYER_HPP_ +#define CAFFE_HDF5_OUTPUT_LAYER_HPP_ + +#include "hdf5.h" + +#include +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +#define HDF5_DATA_DATASET_NAME "data" +#define HDF5_DATA_LABEL_NAME "label" + +/** + * @brief Write blobs to disk as HDF5 files. + * + * TODO(dox): thorough documentation for Forward and proto params. + */ +template +class HDF5OutputLayer : public Layer { + public: + explicit HDF5OutputLayer(const LayerParameter& param) + : Layer(param), file_opened_(false) {} + virtual ~HDF5OutputLayer(); + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + // Data layers should be shared by multiple solvers in parallel + virtual inline bool ShareInParallel() const { return true; } + // Data layers have no bottoms, so reshaping is trivial. + virtual void Reshape(const vector*>& bottom, + const vector*>& top) {} + + virtual inline const char* type() const { return "HDF5Output"; } + // TODO: no limit on the number of blobs + virtual inline int ExactNumBottomBlobs() const { return 2; } + virtual inline int ExactNumTopBlobs() const { return 0; } + + inline std::string file_name() const { return file_name_; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void SaveBlobs(); + + bool file_opened_; + std::string file_name_; + hid_t file_id_; + Blob data_blob_; + Blob label_blob_; +}; + +} // namespace caffe + +#endif // CAFFE_HDF5_OUTPUT_LAYER_HPP_ diff --git a/include/caffe/layers/hinge_loss_layer.hpp b/include/caffe/layers/hinge_loss_layer.hpp new file mode 100644 index 00000000000..54e42bd44da --- /dev/null +++ b/include/caffe/layers/hinge_loss_layer.hpp @@ -0,0 +1,104 @@ +#ifndef CAFFE_HINGE_LOSS_LAYER_HPP_ +#define CAFFE_HINGE_LOSS_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/loss_layer.hpp" + +namespace caffe { + +/** + * @brief Computes the hinge loss for a one-of-many classification task. + * + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$ t @f$, a Blob with values in + * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of + * the @f$ K = CHW @f$ classes. In an SVM, @f$ t @f$ is the result of + * taking the inner product @f$ X^T W @f$ of the D-dimensional features + * @f$ X \in \mathcal{R}^{D \times N} @f$ and the learned hyperplane + * parameters @f$ W \in \mathcal{R}^{D \times K} @f$, so a Net with just + * an InnerProductLayer (with num_output = D) providing predictions to a + * HingeLossLayer and no other learnable parameters or losses is + * equivalent to an SVM. + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the labels @f$ l @f$, an integer-valued Blob with values + * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ + * indicating the correct class label among the @f$ K @f$ classes + * @param top output Blob vector (length 1) + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * the computed hinge loss: @f$ E = + * \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K + * [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p + * @f$, for the @f$ L^p @f$ norm + * (defaults to @f$ p = 1 @f$, the L1 norm; L2 norm, as in L2-SVM, + * is also available), and @f$ + * \delta\{\mathrm{condition}\} = \left\{ + * \begin{array}{lr} + * 1 & \mbox{if condition} \\ + * -1 & \mbox{otherwise} + * \end{array} \right. + * @f$ + * + * In an SVM, @f$ t \in \mathcal{R}^{N \times K} @f$ is the result of taking + * the inner product @f$ X^T W @f$ of the features + * @f$ X \in \mathcal{R}^{D \times N} @f$ + * and the learned hyperplane parameters + * @f$ W \in \mathcal{R}^{D \times K} @f$. So, a Net with just an + * InnerProductLayer (with num_output = @f$k@f$) providing predictions to a + * HingeLossLayer is equivalent to an SVM (assuming it has no other learned + * outside the InnerProductLayer and no other losses outside the + * HingeLossLayer). + */ +template +class HingeLossLayer : public LossLayer { + public: + explicit HingeLossLayer(const LayerParameter& param) + : LossLayer(param) {} + + virtual inline const char* type() const { return "HingeLoss"; } + + protected: + /// @copydoc HingeLossLayer + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the hinge loss error gradient w.r.t. the predictions. + * + * Gradients cannot be computed with respect to the label inputs (bottom[1]), + * so this method ignores bottom[1] and requires !propagate_down[1], crashing + * if propagate_down[1] is set. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, + * as @f$ \lambda @f$ is the coefficient of this layer's output + * @f$\ell_i@f$ in the overall Net loss + * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence + * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. + * (*Assuming that this top Blob is not used as a bottom (input) by any + * other layer of the Net.) + * @param propagate_down see Layer::Backward. + * propagate_down[1] must be false as we can't compute gradients with + * respect to the labels. + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$t@f$; Backward computes diff + * @f$ \frac{\partial E}{\partial t} @f$ + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the labels -- ignored as we can't compute their error gradients + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); +}; + + +} // namespace caffe + +#endif // CAFFE_HINGE_LOSS_LAYER_HPP_ diff --git a/include/caffe/layers/im2col_layer.hpp b/include/caffe/layers/im2col_layer.hpp new file mode 100644 index 00000000000..1d3b2eb67d1 --- /dev/null +++ b/include/caffe/layers/im2col_layer.hpp @@ -0,0 +1,63 @@ +#ifndef CAFFE_IM2COL_LAYER_HPP_ +#define CAFFE_IM2COL_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief A helper for image operations that rearranges image regions into + * column vectors. Used by ConvolutionLayer to perform convolution + * by matrix multiplication. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class Im2colLayer : public Layer { + public: + explicit Im2colLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Im2col"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + /// @brief The spatial dimensions of a filter kernel. + Blob kernel_shape_; + /// @brief The spatial dimensions of the stride. + Blob stride_; + /// @brief The spatial dimensions of the padding. + Blob pad_; + + int num_spatial_axes_; + int bottom_dim_; + int top_dim_; + + int channel_axis_; + int num_; + int channels_; + + bool force_nd_im2col_; +}; + +} // namespace caffe + +#endif // CAFFE_IM2COL_LAYER_HPP_ diff --git a/include/caffe/layers/image_data_layer.hpp b/include/caffe/layers/image_data_layer.hpp new file mode 100644 index 00000000000..a0d3384e4c9 --- /dev/null +++ b/include/caffe/layers/image_data_layer.hpp @@ -0,0 +1,47 @@ +#ifndef CAFFE_IMAGE_DATA_LAYER_HPP_ +#define CAFFE_IMAGE_DATA_LAYER_HPP_ + +#include +#include +#include + +#include "caffe/blob.hpp" +#include "caffe/data_transformer.hpp" +#include "caffe/internal_thread.hpp" +#include "caffe/layer.hpp" +#include "caffe/layers/base_data_layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Provides data to the Net from image files. + * + * TODO(dox): thorough documentation for Forward and proto params. + */ +template +class ImageDataLayer : public BasePrefetchingDataLayer { + public: + explicit ImageDataLayer(const LayerParameter& param) + : BasePrefetchingDataLayer(param) {} + virtual ~ImageDataLayer(); + virtual void DataLayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "ImageData"; } + virtual inline int ExactNumBottomBlobs() const { return 0; } + virtual inline int ExactNumTopBlobs() const { return 2; } + + protected: + shared_ptr prefetch_rng_; + virtual void ShuffleImages(); + virtual void load_batch(Batch* batch); + + vector > lines_; + int lines_id_; +}; + + +} // namespace caffe + +#endif // CAFFE_IMAGE_DATA_LAYER_HPP_ diff --git a/include/caffe/layers/infogain_loss_layer.hpp b/include/caffe/layers/infogain_loss_layer.hpp new file mode 100644 index 00000000000..633f339a28e --- /dev/null +++ b/include/caffe/layers/infogain_loss_layer.hpp @@ -0,0 +1,110 @@ +#ifndef CAFFE_INFOGAIN_LOSS_LAYER_HPP_ +#define CAFFE_INFOGAIN_LOSS_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/loss_layer.hpp" + +namespace caffe { + +/** + * @brief A generalization of MultinomialLogisticLossLayer that takes an + * "information gain" (infogain) matrix specifying the "value" of all label + * pairs. + * + * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the + * identity. + * + * @param bottom input Blob vector (length 2-3) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$ \hat{p} @f$, a Blob with values in + * @f$ [0, 1] @f$ indicating the predicted probability of each of the + * @f$ K = CHW @f$ classes. Each prediction vector @f$ \hat{p}_n @f$ + * should sum to 1 as in a probability distribution: @f$ + * \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$. + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the labels @f$ l @f$, an integer-valued Blob with values + * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ + * indicating the correct class label among the @f$ K @f$ classes + * -# @f$ (1 \times 1 \times K \times K) @f$ + * (\b optional) the infogain matrix @f$ H @f$. This must be provided as + * the third bottom blob input if not provided as the infogain_mat in the + * InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the + * MultinomialLogisticLossLayer. + * @param top output Blob vector (length 1) + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * the computed infogain multinomial logistic loss: @f$ E = + * \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = + * \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} + * \log(\hat{p}_{n,k}) + * @f$, where @f$ H_{l_n} @f$ denotes row @f$l_n@f$ of @f$H@f$. + */ +template +class InfogainLossLayer : public LossLayer { + public: + explicit InfogainLossLayer(const LayerParameter& param) + : LossLayer(param), infogain_() {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should + // be the infogain matrix. (Otherwise the infogain matrix is loaded from a + // file specified by LayerParameter.) + virtual inline int ExactNumBottomBlobs() const { return -1; } + virtual inline int MinBottomBlobs() const { return 2; } + virtual inline int MaxBottomBlobs() const { return 3; } + + virtual inline const char* type() const { return "InfogainLoss"; } + + protected: + /// @copydoc InfogainLossLayer + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the infogain loss error gradient w.r.t. the predictions. + * + * Gradients cannot be computed with respect to the label inputs (bottom[1]), + * so this method ignores bottom[1] and requires !propagate_down[1], crashing + * if propagate_down[1] is set. (The same applies to the infogain matrix, if + * provided as bottom[2] rather than in the layer_param.) + * + * @param top output Blob vector (length 1), providing the error gradient + * with respect to the outputs + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, + * as @f$ \lambda @f$ is the coefficient of this layer's output + * @f$\ell_i@f$ in the overall Net loss + * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence + * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. + * (*Assuming that this top Blob is not used as a bottom (input) by any + * other layer of the Net.) + * @param propagate_down see Layer::Backward. + * propagate_down[1] must be false as we can't compute gradients with + * respect to the labels (similarly for propagate_down[2] and the + * infogain matrix, if provided as bottom[2]) + * @param bottom input Blob vector (length 2-3) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$ \hat{p} @f$; Backward computes diff + * @f$ \frac{\partial E}{\partial \hat{p}} @f$ + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the labels -- ignored as we can't compute their error gradients + * -# @f$ (1 \times 1 \times K \times K) @f$ + * (\b optional) the information gain matrix -- ignored as its error + * gradient computation is not implemented. + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + Blob infogain_; +}; + +} // namespace caffe + +#endif // CAFFE_INFOGAIN_LOSS_LAYER_HPP_ diff --git a/include/caffe/layers/inner_product_layer.hpp b/include/caffe/layers/inner_product_layer.hpp new file mode 100644 index 00000000000..250576a4817 --- /dev/null +++ b/include/caffe/layers/inner_product_layer.hpp @@ -0,0 +1,51 @@ +#ifndef CAFFE_INNER_PRODUCT_LAYER_HPP_ +#define CAFFE_INNER_PRODUCT_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Also known as a "fully-connected" layer, computes an inner product + * with a set of learned weights, and (optionally) adds biases. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class InnerProductLayer : public Layer { + public: + explicit InnerProductLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "InnerProduct"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + int M_; + int K_; + int N_; + bool bias_term_; + Blob bias_multiplier_; +}; + +} // namespace caffe + +#endif // CAFFE_INNER_PRODUCT_LAYER_HPP_ diff --git a/include/caffe/layers/log_layer.hpp b/include/caffe/layers/log_layer.hpp new file mode 100644 index 00000000000..7d037d2bdca --- /dev/null +++ b/include/caffe/layers/log_layer.hpp @@ -0,0 +1,82 @@ +#ifndef CAFFE_LOG_LAYER_HPP_ +#define CAFFE_LOG_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief Computes @f$ y = log_{\gamma}(\alpha x + \beta) @f$, + * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$, + * and base @f$ \gamma @f$. + */ +template +class LogLayer : public NeuronLayer { + public: + /** + * @param param provides LogParameter log_param, + * with LogLayer options: + * - scale (\b optional, default 1) the scale @f$ \alpha @f$ + * - shift (\b optional, default 0) the shift @f$ \beta @f$ + * - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$) + * the base @f$ \gamma @f$ + */ + explicit LogLayer(const LayerParameter& param) + : NeuronLayer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Log"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = log_{\gamma}(\alpha x + \beta) + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the exp inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} = + * \frac{\partial E}{\partial y} y \alpha \log_e(gamma) + * @f$ if propagate_down[0] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + Dtype base_scale_; + Dtype input_scale_, input_shift_; + Dtype backward_num_scale_; +}; + +} // namespace caffe + +#endif // CAFFE_LOG_LAYER_HPP_ diff --git a/include/caffe/layers/loss_layer.hpp b/include/caffe/layers/loss_layer.hpp new file mode 100644 index 00000000000..dbdf612c062 --- /dev/null +++ b/include/caffe/layers/loss_layer.hpp @@ -0,0 +1,53 @@ +#ifndef CAFFE_LOSS_LAYER_HPP_ +#define CAFFE_LOSS_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +const float kLOG_THRESHOLD = 1e-20; + +/** + * @brief An interface for Layer%s that take two Blob%s as input -- usually + * (1) predictions and (2) ground-truth labels -- and output a + * singleton Blob representing the loss. + * + * LossLayers are typically only capable of backpropagating to their first input + * -- the predictions. + */ +template +class LossLayer : public Layer { + public: + explicit LossLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp( + const vector*>& bottom, const vector*>& top); + virtual void Reshape( + const vector*>& bottom, const vector*>& top); + + virtual inline int ExactNumBottomBlobs() const { return 2; } + + /** + * @brief For convenience and backwards compatibility, instruct the Net to + * automatically allocate a single top Blob for LossLayers, into which + * they output their singleton loss, (even if the user didn't specify + * one in the prototxt, etc.). + */ + virtual inline bool AutoTopBlobs() const { return true; } + virtual inline int ExactNumTopBlobs() const { return 1; } + /** + * We usually cannot backpropagate to the labels; ignore force_backward for + * these inputs. + */ + virtual inline bool AllowForceBackward(const int bottom_index) const { + return bottom_index != 1; + } +}; + +} // namespace caffe + +#endif // CAFFE_LOSS_LAYER_HPP_ diff --git a/include/caffe/layers/lrn_layer.hpp b/include/caffe/layers/lrn_layer.hpp new file mode 100644 index 00000000000..06cf71a94cb --- /dev/null +++ b/include/caffe/layers/lrn_layer.hpp @@ -0,0 +1,94 @@ +#ifndef CAFFE_LRN_LAYER_HPP_ +#define CAFFE_LRN_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/eltwise_layer.hpp" +#include "caffe/layers/pooling_layer.hpp" +#include "caffe/layers/power_layer.hpp" +#include "caffe/layers/split_layer.hpp" + +namespace caffe { + +/** + * @brief Normalize the input in a local region across or within feature maps. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class LRNLayer : public Layer { + public: + explicit LRNLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "LRN"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + virtual void CrossChannelForward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void CrossChannelForward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void WithinChannelForward(const vector*>& bottom, + const vector*>& top); + virtual void CrossChannelBackward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void CrossChannelBackward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void WithinChannelBackward(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + int size_; + int pre_pad_; + Dtype alpha_; + Dtype beta_; + Dtype k_; + int num_; + int channels_; + int height_; + int width_; + + // Fields used for normalization ACROSS_CHANNELS + // scale_ stores the intermediate summing results + Blob scale_; + + // Fields used for normalization WITHIN_CHANNEL + shared_ptr > split_layer_; + vector*> split_top_vec_; + shared_ptr > square_layer_; + Blob square_input_; + Blob square_output_; + vector*> square_bottom_vec_; + vector*> square_top_vec_; + shared_ptr > pool_layer_; + Blob pool_output_; + vector*> pool_top_vec_; + shared_ptr > power_layer_; + Blob power_output_; + vector*> power_top_vec_; + shared_ptr > product_layer_; + Blob product_input_; + vector*> product_bottom_vec_; +}; + +} // namespace caffe + +#endif // CAFFE_LRN_LAYER_HPP_ diff --git a/include/caffe/layers/memory_data_layer.hpp b/include/caffe/layers/memory_data_layer.hpp new file mode 100644 index 00000000000..8abcc8c1b68 --- /dev/null +++ b/include/caffe/layers/memory_data_layer.hpp @@ -0,0 +1,63 @@ +#ifndef CAFFE_MEMORY_DATA_LAYER_HPP_ +#define CAFFE_MEMORY_DATA_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/base_data_layer.hpp" + +namespace caffe { + +/** + * @brief Provides data to the Net from memory. + * + * TODO(dox): thorough documentation for Forward and proto params. + */ +template +class MemoryDataLayer : public BaseDataLayer { + public: + explicit MemoryDataLayer(const LayerParameter& param) + : BaseDataLayer(param), has_new_data_(false) {} + virtual void DataLayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "MemoryData"; } + virtual inline int ExactNumBottomBlobs() const { return 0; } + virtual inline int ExactNumTopBlobs() const { return 2; } + + virtual void AddDatumVector(const vector& datum_vector); +#ifdef USE_OPENCV + virtual void AddMatVector(const vector& mat_vector, + const vector& labels); +#endif // USE_OPENCV + + // Reset should accept const pointers, but can't, because the memory + // will be given to Blob, which is mutable + void Reset(Dtype* data, Dtype* label, int n); + void set_batch_size(int new_size); + + int batch_size() { return batch_size_; } + int channels() { return channels_; } + int height() { return height_; } + int width() { return width_; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + + int batch_size_, channels_, height_, width_, size_; + Dtype* data_; + Dtype* labels_; + int n_; + size_t pos_; + Blob added_data_; + Blob added_label_; + bool has_new_data_; +}; + +} // namespace caffe + +#endif // CAFFE_MEMORY_DATA_LAYER_HPP_ diff --git a/include/caffe/layers/multinomial_logistic_loss_layer.hpp b/include/caffe/layers/multinomial_logistic_loss_layer.hpp new file mode 100644 index 00000000000..3977cf9ea57 --- /dev/null +++ b/include/caffe/layers/multinomial_logistic_loss_layer.hpp @@ -0,0 +1,92 @@ +#ifndef CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_ +#define CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/loss_layer.hpp" + +namespace caffe { + +/** + * @brief Computes the multinomial logistic loss for a one-of-many + * classification task, directly taking a predicted probability + * distribution as input. + * + * When predictions are not already a probability distribution, you should + * instead use the SoftmaxWithLossLayer, which maps predictions to a + * distribution using the SoftmaxLayer, before computing the multinomial + * logistic loss. The SoftmaxWithLossLayer should be preferred over separate + * SoftmaxLayer + MultinomialLogisticLossLayer + * as its gradient computation is more numerically stable. + * + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$ \hat{p} @f$, a Blob with values in + * @f$ [0, 1] @f$ indicating the predicted probability of each of the + * @f$ K = CHW @f$ classes. Each prediction vector @f$ \hat{p}_n @f$ + * should sum to 1 as in a probability distribution: @f$ + * \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$. + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the labels @f$ l @f$, an integer-valued Blob with values + * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ + * indicating the correct class label among the @f$ K @f$ classes + * @param top output Blob vector (length 1) + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * the computed multinomial logistic loss: @f$ E = + * \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) + * @f$ + */ +template +class MultinomialLogisticLossLayer : public LossLayer { + public: + explicit MultinomialLogisticLossLayer(const LayerParameter& param) + : LossLayer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "MultinomialLogisticLoss"; } + + protected: + /// @copydoc MultinomialLogisticLossLayer + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the multinomial logistic loss error gradient w.r.t. the + * predictions. + * + * Gradients cannot be computed with respect to the label inputs (bottom[1]), + * so this method ignores bottom[1] and requires !propagate_down[1], crashing + * if propagate_down[1] is set. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, + * as @f$ \lambda @f$ is the coefficient of this layer's output + * @f$\ell_i@f$ in the overall Net loss + * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence + * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. + * (*Assuming that this top Blob is not used as a bottom (input) by any + * other layer of the Net.) + * @param propagate_down see Layer::Backward. + * propagate_down[1] must be false as we can't compute gradients with + * respect to the labels. + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$ \hat{p} @f$; Backward computes diff + * @f$ \frac{\partial E}{\partial \hat{p}} @f$ + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the labels -- ignored as we can't compute their error gradients + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); +}; + +} // namespace caffe + +#endif // CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_ diff --git a/include/caffe/layers/mvn_layer.hpp b/include/caffe/layers/mvn_layer.hpp new file mode 100644 index 00000000000..3a235ceca64 --- /dev/null +++ b/include/caffe/layers/mvn_layer.hpp @@ -0,0 +1,48 @@ +#ifndef CAFFE_MVN_LAYER_HPP_ +#define CAFFE_MVN_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Normalizes the input to have 0-mean and/or unit (1) variance. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class MVNLayer : public Layer { + public: + explicit MVNLayer(const LayerParameter& param) + : Layer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "MVN"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + Blob mean_, variance_, temp_; + + /// sum_multiplier is used to carry out sum using BLAS + Blob sum_multiplier_; + Dtype eps_; +}; + +} // namespace caffe + +#endif // CAFFE_MVN_LAYER_HPP_ diff --git a/include/caffe/layers/neuron_layer.hpp b/include/caffe/layers/neuron_layer.hpp new file mode 100644 index 00000000000..10c108ce682 --- /dev/null +++ b/include/caffe/layers/neuron_layer.hpp @@ -0,0 +1,32 @@ +#ifndef CAFFE_NEURON_LAYER_HPP_ +#define CAFFE_NEURON_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief An interface for layers that take one blob as input (@f$ x @f$) + * and produce one equally-sized blob as output (@f$ y @f$), where + * each element of the output depends only on the corresponding input + * element. + */ +template +class NeuronLayer : public Layer { + public: + explicit NeuronLayer(const LayerParameter& param) + : Layer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } +}; + +} // namespace caffe + +#endif // CAFFE_NEURON_LAYER_HPP_ diff --git a/include/caffe/layers/pooling_layer.hpp b/include/caffe/layers/pooling_layer.hpp new file mode 100644 index 00000000000..f4d6803ba8e --- /dev/null +++ b/include/caffe/layers/pooling_layer.hpp @@ -0,0 +1,60 @@ +#ifndef CAFFE_POOLING_LAYER_HPP_ +#define CAFFE_POOLING_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Pools the input image by taking the max, average, etc. within regions. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class PoolingLayer : public Layer { + public: + explicit PoolingLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Pooling"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int MinTopBlobs() const { return 1; } + // MAX POOL layers can output an extra top blob for the mask; + // others can only output the pooled inputs. + virtual inline int MaxTopBlobs() const { + return (this->layer_param_.pooling_param().pool() == + PoolingParameter_PoolMethod_MAX) ? 2 : 1; + } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + int kernel_h_, kernel_w_; + int stride_h_, stride_w_; + int pad_h_, pad_w_; + int channels_; + int height_, width_; + int pooled_height_, pooled_width_; + bool global_pooling_; + Blob rand_idx_; + Blob max_idx_; +}; + +} // namespace caffe + +#endif // CAFFE_POOLING_LAYER_HPP_ diff --git a/include/caffe/layers/power_layer.hpp b/include/caffe/layers/power_layer.hpp new file mode 100644 index 00000000000..6ecbafcaca8 --- /dev/null +++ b/include/caffe/layers/power_layer.hpp @@ -0,0 +1,89 @@ +#ifndef CAFFE_POWER_LAYER_HPP_ +#define CAFFE_POWER_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$, + * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$, + * and power @f$ \gamma @f$. + */ +template +class PowerLayer : public NeuronLayer { + public: + /** + * @param param provides PowerParameter power_param, + * with PowerLayer options: + * - scale (\b optional, default 1) the scale @f$ \alpha @f$ + * - shift (\b optional, default 0) the shift @f$ \beta @f$ + * - power (\b optional, default 1) the power @f$ \gamma @f$ + */ + explicit PowerLayer(const LayerParameter& param) + : NeuronLayer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Power"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = (\alpha x + \beta) ^ \gamma + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the power inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} = + * \frac{\partial E}{\partial y} + * \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} = + * \frac{\partial E}{\partial y} + * \frac{\alpha \gamma y}{\alpha x + \beta} + * @f$ if propagate_down[0] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + /// @brief @f$ \gamma @f$ from layer_param_.power_param() + Dtype power_; + /// @brief @f$ \alpha @f$ from layer_param_.power_param() + Dtype scale_; + /// @brief @f$ \beta @f$ from layer_param_.power_param() + Dtype shift_; + /// @brief Result of @f$ \alpha \gamma @f$ + Dtype diff_scale_; +}; + +} // namespace caffe + +#endif // CAFFE_POWER_LAYER_HPP_ diff --git a/include/caffe/layers/prelu_layer.hpp b/include/caffe/layers/prelu_layer.hpp new file mode 100644 index 00000000000..3ddfb484b66 --- /dev/null +++ b/include/caffe/layers/prelu_layer.hpp @@ -0,0 +1,101 @@ +#ifndef CAFFE_PRELU_LAYER_HPP_ +#define CAFFE_PRELU_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief Parameterized Rectified Linear Unit non-linearity @f$ + * y_i = \max(0, x_i) + a_i \min(0, x_i) + * @f$. The differences from ReLULayer are 1) negative slopes are + * learnable though backprop and 2) negative slopes can vary across + * channels. The number of axes of input blob should be greater than or + * equal to 2. The 1st axis (0-based) is seen as channels. + */ +template +class PReLULayer : public NeuronLayer { + public: + /** + * @param param provides PReLUParameter prelu_param, + * with PReLULayer options: + * - filler (\b optional, FillerParameter, + * default {'type': constant 'value':0.25}). + * - channel_shared (\b optional, default false). + * negative slopes are shared across channels. + */ + explicit PReLULayer(const LayerParameter& param) + : NeuronLayer(param) {} + + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "PReLU"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times ...) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times ...) @f$ + * the computed outputs for each channel @f$i@f$ @f$ + * y_i = \max(0, x_i) + a_i \min(0, x_i) + * @f$. + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the PReLU inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times ...) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times ...) @f$ + * the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their + * diff with gradients @f$ + * \frac{\partial E}{\partial x_i} = \left\{ + * \begin{array}{lr} + * a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ + * \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0 + * \end{array} \right. + * @f$. + * If param_propagate_down_[0] is true, it fills the diff with gradients + * @f$ + * \frac{\partial E}{\partial a_i} = \left\{ + * \begin{array}{lr} + * \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ + * 0 & \mathrm{if} \; x_i > 0 + * \end{array} \right. + * @f$. + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + bool channel_shared_; + Blob multiplier_; // dot multiplier for backward computation of params + Blob backward_buff_; // temporary buffer for backward computation + Blob bottom_memory_; // memory for in-place computation +}; + +} // namespace caffe + +#endif // CAFFE_PRELU_LAYER_HPP_ diff --git a/include/caffe/python_layer.hpp b/include/caffe/layers/python_layer.hpp similarity index 100% rename from include/caffe/python_layer.hpp rename to include/caffe/layers/python_layer.hpp diff --git a/include/caffe/layers/reduction_layer.hpp b/include/caffe/layers/reduction_layer.hpp new file mode 100644 index 00000000000..804a495b11c --- /dev/null +++ b/include/caffe/layers/reduction_layer.hpp @@ -0,0 +1,59 @@ +#ifndef CAFFE_REDUCTION_LAYER_HPP_ +#define CAFFE_REDUCTION_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Compute "reductions" -- operations that return a scalar output Blob + * for an input Blob of arbitrary size, such as the sum, absolute sum, + * and sum of squares. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class ReductionLayer : public Layer { + public: + explicit ReductionLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Reduction"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + /// @brief the reduction operation performed by the layer + ReductionParameter_ReductionOp op_; + /// @brief a scalar coefficient applied to all outputs + Dtype coeff_; + /// @brief the index of the first input axis to reduce + int axis_; + /// @brief the number of reductions performed + int num_; + /// @brief the input size of each reduction + int dim_; + /// @brief a helper Blob used for summation (op_ == SUM) + Blob sum_multiplier_; +}; + +} // namespace caffe + +#endif // CAFFE_REDUCTION_LAYER_HPP_ diff --git a/include/caffe/layers/relu_layer.hpp b/include/caffe/layers/relu_layer.hpp new file mode 100644 index 00000000000..d7a73f7a8d1 --- /dev/null +++ b/include/caffe/layers/relu_layer.hpp @@ -0,0 +1,85 @@ +#ifndef CAFFE_RELU_LAYER_HPP_ +#define CAFFE_RELU_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief Rectified Linear Unit non-linearity @f$ y = \max(0, x) @f$. + * The simple max is fast to compute, and the function does not saturate. + */ +template +class ReLULayer : public NeuronLayer { + public: + /** + * @param param provides ReLUParameter relu_param, + * with ReLULayer options: + * - negative_slope (\b optional, default 0). + * the value @f$ \nu @f$ by which negative values are multiplied. + */ + explicit ReLULayer(const LayerParameter& param) + : NeuronLayer(param) {} + + virtual inline const char* type() const { return "ReLU"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = \max(0, x) + * @f$ by default. If a non-zero negative_slope @f$ \nu @f$ is provided, + * the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$. + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the ReLU inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} = \left\{ + * \begin{array}{lr} + * 0 & \mathrm{if} \; x \le 0 \\ + * \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 + * \end{array} \right. + * @f$ if propagate_down[0], by default. + * If a non-zero negative_slope @f$ \nu @f$ is provided, + * the computed gradients are @f$ + * \frac{\partial E}{\partial x} = \left\{ + * \begin{array}{lr} + * \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\ + * \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 + * \end{array} \right. + * @f$. + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); +}; + +} // namespace caffe + +#endif // CAFFE_RELU_LAYER_HPP_ diff --git a/include/caffe/layers/reshape_layer.hpp b/include/caffe/layers/reshape_layer.hpp new file mode 100644 index 00000000000..d11e06384ce --- /dev/null +++ b/include/caffe/layers/reshape_layer.hpp @@ -0,0 +1,52 @@ +#ifndef CAFFE_XXX_LAYER_HPP_ +#define CAFFE_XXX_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/* + * @brief Reshapes the input Blob into an arbitrary-sized output Blob. + * + * Note: similarly to FlattenLayer, this layer does not change the input values + * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff). + */ +template +class ReshapeLayer : public Layer { + public: + explicit ReshapeLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Reshape"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top) {} + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) {} + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top) {} + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) {} + + /// @brief vector of axes indices whose dimensions we'll copy from the bottom + vector copy_axes_; + /// @brief the index of the axis whose dimension we infer, or -1 if none + int inferred_axis_; + /// @brief the product of the "constant" output dimensions + int constant_count_; +}; + +} // namespace caffe + +#endif // CAFFE_XXX_LAYER_HPP_ diff --git a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp new file mode 100644 index 00000000000..598dca5ff2c --- /dev/null +++ b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp @@ -0,0 +1,110 @@ +#ifndef CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_ +#define CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/loss_layer.hpp" +#include "caffe/layers/sigmoid_layer.hpp" + +namespace caffe { + +/** + * @brief Computes the cross-entropy (logistic) loss @f$ + * E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ + * p_n \log \hat{p}_n + + * (1 - p_n) \log(1 - \hat{p}_n) + * \right] + * @f$, often used for predicting targets interpreted as probabilities. + * + * This layer is implemented rather than separate + * SigmoidLayer + CrossEntropyLayer + * as its gradient computation is more numerically stable. + * At test time, this layer can be replaced simply by a SigmoidLayer. + * + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the scores @f$ x \in [-\infty, +\infty]@f$, + * which this layer maps to probability predictions + * @f$ \hat{p}_n = \sigma(x_n) \in [0, 1] @f$ + * using the sigmoid function @f$ \sigma(.) @f$ (see SigmoidLayer). + * -# @f$ (N \times C \times H \times W) @f$ + * the targets @f$ y \in [0, 1] @f$ + * @param top output Blob vector (length 1) + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * the computed cross-entropy loss: @f$ + * E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ + * p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) + * \right] + * @f$ + */ +template +class SigmoidCrossEntropyLossLayer : public LossLayer { + public: + explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param) + : LossLayer(param), + sigmoid_layer_(new SigmoidLayer(param)), + sigmoid_output_(new Blob()) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; } + + protected: + /// @copydoc SigmoidCrossEntropyLossLayer + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the + * predictions. + * + * Gradients cannot be computed with respect to the target inputs (bottom[1]), + * so this method ignores bottom[1] and requires !propagate_down[1], crashing + * if propagate_down[1] is set. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, + * as @f$ \lambda @f$ is the coefficient of this layer's output + * @f$\ell_i@f$ in the overall Net loss + * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence + * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. + * (*Assuming that this top Blob is not used as a bottom (input) by any + * other layer of the Net.) + * @param propagate_down see Layer::Backward. + * propagate_down[1] must be false as gradient computation with respect + * to the targets is not implemented. + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$x@f$; Backward computes diff + * @f$ \frac{\partial E}{\partial x} = + * \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n) + * @f$ + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the labels -- ignored as we can't compute their error gradients + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + /// The internal SigmoidLayer used to map predictions to probabilities. + shared_ptr > sigmoid_layer_; + /// sigmoid_output stores the output of the SigmoidLayer. + shared_ptr > sigmoid_output_; + /// bottom vector holder to call the underlying SigmoidLayer::Forward + vector*> sigmoid_bottom_vec_; + /// top vector holder to call the underlying SigmoidLayer::Forward + vector*> sigmoid_top_vec_; +}; + +} // namespace caffe + +#endif // CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_ diff --git a/include/caffe/layers/sigmoid_layer.hpp b/include/caffe/layers/sigmoid_layer.hpp new file mode 100644 index 00000000000..ac0f6927feb --- /dev/null +++ b/include/caffe/layers/sigmoid_layer.hpp @@ -0,0 +1,71 @@ +#ifndef CAFFE_SIGMOID_LAYER_HPP_ +#define CAFFE_SIGMOID_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief Sigmoid function non-linearity @f$ + * y = (1 + \exp(-x))^{-1} + * @f$, a classic choice in neural networks. + * + * Note that the gradient vanishes as the values move away from 0. + * The ReLULayer is often a better choice for this reason. + */ +template +class SigmoidLayer : public NeuronLayer { + public: + explicit SigmoidLayer(const LayerParameter& param) + : NeuronLayer(param) {} + + virtual inline const char* type() const { return "Sigmoid"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = (1 + \exp(-x))^{-1} + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the sigmoid inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} + * = \frac{\partial E}{\partial y} y (1 - y) + * @f$ if propagate_down[0] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); +}; + +} // namespace caffe + +#endif // CAFFE_SIGMOID_LAYER_HPP_ diff --git a/include/caffe/layers/silence_layer.hpp b/include/caffe/layers/silence_layer.hpp new file mode 100644 index 00000000000..fba087fcef0 --- /dev/null +++ b/include/caffe/layers/silence_layer.hpp @@ -0,0 +1,43 @@ +#ifndef CAFFE_SILENCE_LAYER_HPP_ +#define CAFFE_SILENCE_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Ignores bottom blobs while producing no top blobs. (This is useful + * to suppress outputs during testing.) + */ +template +class SilenceLayer : public Layer { + public: + explicit SilenceLayer(const LayerParameter& param) + : Layer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top) {} + + virtual inline const char* type() const { return "Silence"; } + virtual inline int MinBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 0; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top) {} + // We can't define Forward_gpu here, since STUB_GPU will provide + // its own definition for CPU_ONLY mode. + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); +}; + +} // namespace caffe + +#endif // CAFFE_SILENCE_LAYER_HPP_ diff --git a/include/caffe/layers/slice_layer.hpp b/include/caffe/layers/slice_layer.hpp new file mode 100644 index 00000000000..10a0abb6eeb --- /dev/null +++ b/include/caffe/layers/slice_layer.hpp @@ -0,0 +1,51 @@ +#ifndef CAFFE_SLICE_LAYER_HPP_ +#define CAFFE_SLICE_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Takes a Blob and slices it along either the num or channel dimension, + * outputting multiple sliced Blob results. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class SliceLayer : public Layer { + public: + explicit SliceLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Slice"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int MinTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + int count_; + int num_slices_; + int slice_size_; + int slice_axis_; + vector slice_point_; +}; + +} // namespace caffe + +#endif // CAFFE_SLICE_LAYER_HPP_ diff --git a/include/caffe/layers/softmax_layer.hpp b/include/caffe/layers/softmax_layer.hpp new file mode 100644 index 00000000000..c65b8703e43 --- /dev/null +++ b/include/caffe/layers/softmax_layer.hpp @@ -0,0 +1,50 @@ +#ifndef CAFFE_SOFTMAX_LAYER_HPP_ +#define CAFFE_SOFTMAX_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Computes the softmax function. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class SoftmaxLayer : public Layer { + public: + explicit SoftmaxLayer(const LayerParameter& param) + : Layer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Softmax"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + int outer_num_; + int inner_num_; + int softmax_axis_; + /// sum_multiplier is used to carry out sum using BLAS + Blob sum_multiplier_; + /// scale is an intermediate Blob to hold temporary results. + Blob scale_; +}; + +} // namespace caffe + +#endif // CAFFE_SOFTMAX_LAYER_HPP_ diff --git a/include/caffe/layers/softmax_loss_layer.hpp b/include/caffe/layers/softmax_loss_layer.hpp new file mode 100644 index 00000000000..f07e8a02cf1 --- /dev/null +++ b/include/caffe/layers/softmax_loss_layer.hpp @@ -0,0 +1,130 @@ +#ifndef CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_ +#define CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/loss_layer.hpp" +#include "caffe/layers/softmax_layer.hpp" + +namespace caffe { + +/** + * @brief Computes the multinomial logistic loss for a one-of-many + * classification task, passing real-valued predictions through a + * softmax to get a probability distribution over classes. + * + * This layer should be preferred over separate + * SoftmaxLayer + MultinomialLogisticLossLayer + * as its gradient computation is more numerically stable. + * At test time, this layer can be replaced simply by a SoftmaxLayer. + * + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$ x @f$, a Blob with values in + * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of + * the @f$ K = CHW @f$ classes. This layer maps these scores to a + * probability distribution over classes using the softmax function + * @f$ \hat{p}_{nk} = \exp(x_{nk}) / + * \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer). + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the labels @f$ l @f$, an integer-valued Blob with values + * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ + * indicating the correct class label among the @f$ K @f$ classes + * @param top output Blob vector (length 1) + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * the computed cross-entropy classification loss: @f$ E = + * \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) + * @f$, for softmax output class probabilites @f$ \hat{p} @f$ + */ +template +class SoftmaxWithLossLayer : public LossLayer { + public: + /** + * @param param provides LossParameter loss_param, with options: + * - ignore_label (optional) + * Specify a label value that should be ignored when computing the loss. + * - normalize (optional, default true) + * If true, the loss is normalized by the number of (nonignored) labels + * present; otherwise the loss is simply summed over spatial locations. + */ + explicit SoftmaxWithLossLayer(const LayerParameter& param) + : LossLayer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "SoftmaxWithLoss"; } + virtual inline int ExactNumTopBlobs() const { return -1; } + virtual inline int MinTopBlobs() const { return 1; } + virtual inline int MaxTopBlobs() const { return 2; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + /** + * @brief Computes the softmax loss error gradient w.r.t. the predictions. + * + * Gradients cannot be computed with respect to the label inputs (bottom[1]), + * so this method ignores bottom[1] and requires !propagate_down[1], crashing + * if propagate_down[1] is set. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (1 \times 1 \times 1 \times 1) @f$ + * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, + * as @f$ \lambda @f$ is the coefficient of this layer's output + * @f$\ell_i@f$ in the overall Net loss + * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence + * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. + * (*Assuming that this top Blob is not used as a bottom (input) by any + * other layer of the Net.) + * @param propagate_down see Layer::Backward. + * propagate_down[1] must be false as we can't compute gradients with + * respect to the labels. + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times C \times H \times W) @f$ + * the predictions @f$ x @f$; Backward computes diff + * @f$ \frac{\partial E}{\partial x} @f$ + * -# @f$ (N \times 1 \times 1 \times 1) @f$ + * the labels -- ignored as we can't compute their error gradients + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + /// Read the normalization mode parameter and compute the normalizer based + /// on the blob size. If normalization_mode is VALID, the count of valid + /// outputs will be read from valid_count, unless it is -1 in which case + /// all outputs are assumed to be valid. + virtual Dtype get_normalizer( + LossParameter_NormalizationMode normalization_mode, int valid_count); + + /// The internal SoftmaxLayer used to map predictions to a distribution. + shared_ptr > softmax_layer_; + /// prob stores the output probability predictions from the SoftmaxLayer. + Blob prob_; + /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward + vector*> softmax_bottom_vec_; + /// top vector holder used in call to the underlying SoftmaxLayer::Forward + vector*> softmax_top_vec_; + /// Whether to ignore instances with a certain label. + bool has_ignore_label_; + /// The label indicating that an instance should be ignored. + int ignore_label_; + /// How to normalize the output loss. + LossParameter_NormalizationMode normalization_; + + int softmax_axis_, outer_num_, inner_num_; +}; + +} // namespace caffe + +#endif // CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_ diff --git a/include/caffe/layers/split_layer.hpp b/include/caffe/layers/split_layer.hpp new file mode 100644 index 00000000000..8140dfc7c40 --- /dev/null +++ b/include/caffe/layers/split_layer.hpp @@ -0,0 +1,45 @@ +#ifndef CAFFE_SPLIT_LAYER_HPP_ +#define CAFFE_SPLIT_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Creates a "split" path in the network by copying the bottom Blob + * into multiple top Blob%s to be used by multiple consuming layers. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class SplitLayer : public Layer { + public: + explicit SplitLayer(const LayerParameter& param) + : Layer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Split"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int MinTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + int count_; +}; + +} // namespace caffe + +#endif // CAFFE_SPLIT_LAYER_HPP_ diff --git a/include/caffe/layers/spp_layer.hpp b/include/caffe/layers/spp_layer.hpp new file mode 100644 index 00000000000..9f145cc77e3 --- /dev/null +++ b/include/caffe/layers/spp_layer.hpp @@ -0,0 +1,76 @@ +#ifndef CAFFE_SPP_LAYER_HPP_ +#define CAFFE_SPP_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Does spatial pyramid pooling on the input image + * by taking the max, average, etc. within regions + * so that the result vector of different sized + * images are of the same size. + */ +template +class SPPLayer : public Layer { + public: + explicit SPPLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "SPP"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + // calculates the kernel and stride dimensions for the pooling layer, + // returns a correctly configured LayerParameter for a PoolingLayer + virtual LayerParameter GetPoolingParam(const int pyramid_level, + const int bottom_h, const int bottom_w, const SPPParameter spp_param); + + int pyramid_height_; + int bottom_h_, bottom_w_; + int num_; + int channels_; + int kernel_h_, kernel_w_; + int pad_h_, pad_w_; + bool reshaped_first_time_; + + /// the internal Split layer that feeds the pooling layers + shared_ptr > split_layer_; + /// top vector holder used in call to the underlying SplitLayer::Forward + vector*> split_top_vec_; + /// bottom vector holder used in call to the underlying PoolingLayer::Forward + vector*>*> pooling_bottom_vecs_; + /// the internal Pooling layers of different kernel sizes + vector > > pooling_layers_; + /// top vector holders used in call to the underlying PoolingLayer::Forward + vector*>*> pooling_top_vecs_; + /// pooling_outputs stores the outputs of the PoolingLayers + vector*> pooling_outputs_; + /// the internal Flatten layers that the Pooling layers feed into + vector*> flatten_layers_; + /// top vector holders used in call to the underlying FlattenLayer::Forward + vector*>*> flatten_top_vecs_; + /// flatten_outputs stores the outputs of the FlattenLayers + vector*> flatten_outputs_; + /// bottom vector holder used in call to the underlying ConcatLayer::Forward + vector*> concat_bottom_vec_; + /// the internal Concat layers that the Flatten layers feed into + shared_ptr > concat_layer_; +}; + +} // namespace caffe + +#endif // CAFFE_SPP_LAYER_HPP_ diff --git a/include/caffe/layers/tanh_layer.hpp b/include/caffe/layers/tanh_layer.hpp new file mode 100644 index 00000000000..8f95e9322d9 --- /dev/null +++ b/include/caffe/layers/tanh_layer.hpp @@ -0,0 +1,73 @@ +#ifndef CAFFE_TANH_LAYER_HPP_ +#define CAFFE_TANH_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief TanH hyperbolic tangent non-linearity @f$ + * y = \frac{\exp(2x) - 1}{\exp(2x) + 1} + * @f$, popular in auto-encoders. + * + * Note that the gradient vanishes as the values move away from 0. + * The ReLULayer is often a better choice for this reason. + */ +template +class TanHLayer : public NeuronLayer { + public: + explicit TanHLayer(const LayerParameter& param) + : NeuronLayer(param) {} + + virtual inline const char* type() const { return "TanH"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = \frac{\exp(2x) - 1}{\exp(2x) + 1} + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the sigmoid inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} + * = \frac{\partial E}{\partial y} + * \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right) + * = \frac{\partial E}{\partial y} (1 - y^2) + * @f$ if propagate_down[0] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); +}; + +} // namespace caffe + +#endif // CAFFE_TANH_LAYER_HPP_ diff --git a/include/caffe/layers/threshold_layer.hpp b/include/caffe/layers/threshold_layer.hpp new file mode 100644 index 00000000000..3bf4db63e5c --- /dev/null +++ b/include/caffe/layers/threshold_layer.hpp @@ -0,0 +1,64 @@ +#ifndef CAFFE_THRESHOLD_LAYER_HPP_ +#define CAFFE_THRESHOLD_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief Tests whether the input exceeds a threshold: outputs 1 for inputs + * above threshold; 0 otherwise. + */ +template +class ThresholdLayer : public NeuronLayer { + public: + /** + * @param param provides ThresholdParameter threshold_param, + * with ThresholdLayer options: + * - threshold (\b optional, default 0). + * the threshold value @f$ t @f$ to which the input values are compared. + */ + explicit ThresholdLayer(const LayerParameter& param) + : NeuronLayer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Threshold"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = \left\{ + * \begin{array}{lr} + * 0 & \mathrm{if} \; x \le t \\ + * 1 & \mathrm{if} \; x > t + * \end{array} \right. + * @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + /// @brief Not implemented (non-differentiable function) + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + NOT_IMPLEMENTED; + } + + Dtype threshold_; +}; + +} // namespace caffe + +#endif // CAFFE_THRESHOLD_LAYER_HPP_ diff --git a/include/caffe/layers/tile_layer.hpp b/include/caffe/layers/tile_layer.hpp new file mode 100644 index 00000000000..fbdbe2f0c53 --- /dev/null +++ b/include/caffe/layers/tile_layer.hpp @@ -0,0 +1,43 @@ +#ifndef CAFFE_TILE_LAYER_HPP_ +#define CAFFE_TILE_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Copy a Blob along specified dimensions. + */ +template +class TileLayer : public Layer { + public: + explicit TileLayer(const LayerParameter& param) + : Layer(param) {} + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Tile"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + unsigned int axis_, tiles_, outer_dim_, inner_dim_; +}; + +} // namespace caffe + +#endif // CAFFE_TILE_LAYER_HPP_ diff --git a/include/caffe/layers/window_data_layer.hpp b/include/caffe/layers/window_data_layer.hpp new file mode 100644 index 00000000000..35f41b80e63 --- /dev/null +++ b/include/caffe/layers/window_data_layer.hpp @@ -0,0 +1,55 @@ +#ifndef CAFFE_WINDOW_DATA_LAYER_HPP_ +#define CAFFE_WINDOW_DATA_LAYER_HPP_ + +#include +#include +#include + +#include "caffe/blob.hpp" +#include "caffe/data_transformer.hpp" +#include "caffe/internal_thread.hpp" +#include "caffe/layer.hpp" +#include "caffe/layers/base_data_layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Provides data to the Net from windows of images files, specified + * by a window data file. + * + * TODO(dox): thorough documentation for Forward and proto params. + */ +template +class WindowDataLayer : public BasePrefetchingDataLayer { + public: + explicit WindowDataLayer(const LayerParameter& param) + : BasePrefetchingDataLayer(param) {} + virtual ~WindowDataLayer(); + virtual void DataLayerSetUp(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "WindowData"; } + virtual inline int ExactNumBottomBlobs() const { return 0; } + virtual inline int ExactNumTopBlobs() const { return 2; } + + protected: + virtual unsigned int PrefetchRand(); + virtual void load_batch(Batch* batch); + + shared_ptr prefetch_rng_; + vector > > image_database_; + enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM }; + vector > fg_windows_; + vector > bg_windows_; + Blob data_mean_; + vector mean_values_; + bool has_mean_file_; + bool has_mean_values_; + bool cache_images_; + vector > image_database_cache_; +}; + +} // namespace caffe + +#endif // CAFFE_WINDOW_DATA_LAYER_HPP_ diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp deleted file mode 100644 index 53d07025f0c..00000000000 --- a/include/caffe/loss_layers.hpp +++ /dev/null @@ -1,777 +0,0 @@ -#ifndef CAFFE_LOSS_LAYERS_HPP_ -#define CAFFE_LOSS_LAYERS_HPP_ - -#include -#include -#include - -#include "caffe/blob.hpp" -#include "caffe/layer.hpp" -#include "caffe/neuron_layers.hpp" -#include "caffe/proto/caffe.pb.h" - -namespace caffe { - -const float kLOG_THRESHOLD = 1e-20; - -/** - * @brief Computes the classification accuracy for a one-of-many - * classification task. - */ -template -class AccuracyLayer : public Layer { - public: - /** - * @param param provides AccuracyParameter accuracy_param, - * with AccuracyLayer options: - * - top_k (\b optional, default 1). - * Sets the maximum rank @f$ k @f$ at which a prediction is considered - * correct. For example, if @f$ k = 5 @f$, a prediction is counted - * correct if the correct label is among the top 5 predicted labels. - */ - explicit AccuracyLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Accuracy"; } - virtual inline int ExactNumBottomBlobs() const { return 2; } - - // If there are two top blobs, then the second blob will contain - // accuracies per class. - virtual inline int MinTopBlobs() const { return 1; } - virtual inline int MaxTopBlobs() const { return 2; } - - protected: - /** - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ x @f$, a Blob with values in - * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of - * the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted - * label @f$ \hat{l}_n @f$ given by its maximal index: - * @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$ - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the labels @f$ l @f$, an integer-valued Blob with values - * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ - * indicating the correct class label among the @f$ K @f$ classes - * @param top output Blob vector (length 1) - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * the computed accuracy: @f$ - * \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \} - * @f$, where @f$ - * \delta\{\mathrm{condition}\} = \left\{ - * \begin{array}{lr} - * 1 & \mbox{if condition} \\ - * 0 & \mbox{otherwise} - * \end{array} \right. - * @f$ - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - - - /// @brief Not implemented -- AccuracyLayer cannot be used as a loss. - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) { - for (int i = 0; i < propagate_down.size(); ++i) { - if (propagate_down[i]) { NOT_IMPLEMENTED; } - } - } - - int label_axis_, outer_num_, inner_num_; - - int top_k_; - - /// Whether to ignore instances with a certain label. - bool has_ignore_label_; - /// The label indicating that an instance should be ignored. - int ignore_label_; - /// Keeps counts of the number of samples per class. - Blob nums_buffer_; -}; - -/** - * @brief An interface for Layer%s that take two Blob%s as input -- usually - * (1) predictions and (2) ground-truth labels -- and output a - * singleton Blob representing the loss. - * - * LossLayers are typically only capable of backpropagating to their first input - * -- the predictions. - */ -template -class LossLayer : public Layer { - public: - explicit LossLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp( - const vector*>& bottom, const vector*>& top); - virtual void Reshape( - const vector*>& bottom, const vector*>& top); - - virtual inline int ExactNumBottomBlobs() const { return 2; } - - /** - * @brief For convenience and backwards compatibility, instruct the Net to - * automatically allocate a single top Blob for LossLayers, into which - * they output their singleton loss, (even if the user didn't specify - * one in the prototxt, etc.). - */ - virtual inline bool AutoTopBlobs() const { return true; } - virtual inline int ExactNumTopBlobs() const { return 1; } - /** - * We usually cannot backpropagate to the labels; ignore force_backward for - * these inputs. - */ - virtual inline bool AllowForceBackward(const int bottom_index) const { - return bottom_index != 1; - } -}; - -/** - * @brief Computes the contrastive loss @f$ - * E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + - * \left(1-y\right) \max \left(margin-d, 0\right)^2 - * @f$ where @f$ - * d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be - * used to train siamese networks. - * - * @param bottom input Blob vector (length 3) - * -# @f$ (N \times C \times 1 \times 1) @f$ - * the features @f$ a \in [-\infty, +\infty]@f$ - * -# @f$ (N \times C \times 1 \times 1) @f$ - * the features @f$ b \in [-\infty, +\infty]@f$ - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the binary similarity @f$ s \in [0, 1]@f$ - * @param top output Blob vector (length 1) - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * the computed contrastive loss: @f$ E = - * \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + - * \left(1-y\right) \max \left(margin-d, 0\right)^2 - * @f$ where @f$ - * d = \left| \left| a_n - b_n \right| \right|_2 @f$. - * This can be used to train siamese networks. - */ -template -class ContrastiveLossLayer : public LossLayer { - public: - explicit ContrastiveLossLayer(const LayerParameter& param) - : LossLayer(param), diff_() {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual inline int ExactNumBottomBlobs() const { return 3; } - virtual inline const char* type() const { return "ContrastiveLoss"; } - /** - * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate - * to the first two inputs. - */ - virtual inline bool AllowForceBackward(const int bottom_index) const { - return bottom_index != 2; - } - - protected: - /// @copydoc ContrastiveLossLayer - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the Contrastive error gradient w.r.t. the inputs. - * - * Computes the gradients with respect to the two input vectors (bottom[0] and - * bottom[1]), but not the similarity label (bottom[2]). - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, - * as @f$ \lambda @f$ is the coefficient of this layer's output - * @f$\ell_i@f$ in the overall Net loss - * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence - * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. - * (*Assuming that this top Blob is not used as a bottom (input) by any - * other layer of the Net.) - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times 1 \times 1) @f$ - * the features @f$a@f$; Backward fills their diff with - * gradients if propagate_down[0] - * -# @f$ (N \times C \times 1 \times 1) @f$ - * the features @f$b@f$; Backward fills their diff with gradients if - * propagate_down[1] - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - Blob diff_; // cached for backward pass - Blob dist_sq_; // cached for backward pass - Blob diff_sq_; // tmp storage for gpu forward pass - Blob summer_vec_; // tmp storage for gpu forward pass -}; - -/** - * @brief Computes the Euclidean (L2) loss @f$ - * E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n - * \right| \right|_2^2 @f$ for real-valued regression tasks. - * - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ \hat{y} \in [-\infty, +\infty]@f$ - * -# @f$ (N \times C \times H \times W) @f$ - * the targets @f$ y \in [-\infty, +\infty]@f$ - * @param top output Blob vector (length 1) - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * the computed Euclidean loss: @f$ E = - * \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n - * \right| \right|_2^2 @f$ - * - * This can be used for least-squares regression tasks. An InnerProductLayer - * input to a EuclideanLossLayer exactly formulates a linear least squares - * regression problem. With non-zero weight decay the problem becomes one of - * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete - * example wherein we check that the gradients computed for a Net with exactly - * this structure match hand-computed gradient formulas for ridge regression. - * - * (Note: Caffe, and SGD in general, is certainly \b not the best way to solve - * linear least squares problems! We use it only as an instructive example.) - */ -template -class EuclideanLossLayer : public LossLayer { - public: - explicit EuclideanLossLayer(const LayerParameter& param) - : LossLayer(param), diff_() {} - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "EuclideanLoss"; } - /** - * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate - * to both inputs -- override to return true and always allow force_backward. - */ - virtual inline bool AllowForceBackward(const int bottom_index) const { - return true; - } - - protected: - /// @copydoc EuclideanLossLayer - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the Euclidean error gradient w.r.t. the inputs. - * - * Unlike other children of LossLayer, EuclideanLossLayer \b can compute - * gradients with respect to the label inputs bottom[1] (but still only will - * if propagate_down[1] is set, due to being produced by learnable parameters - * or if force_backward is set). In fact, this layer is "commutative" -- the - * result is the same regardless of the order of the two bottoms. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, - * as @f$ \lambda @f$ is the coefficient of this layer's output - * @f$\ell_i@f$ in the overall Net loss - * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence - * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. - * (*Assuming that this top Blob is not used as a bottom (input) by any - * other layer of the Net.) - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$\hat{y}@f$; Backward fills their diff with - * gradients @f$ - * \frac{\partial E}{\partial \hat{y}} = - * \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n) - * @f$ if propagate_down[0] - * -# @f$ (N \times C \times H \times W) @f$ - * the targets @f$y@f$; Backward fills their diff with gradients - * @f$ \frac{\partial E}{\partial y} = - * \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n) - * @f$ if propagate_down[1] - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - Blob diff_; -}; - -/** - * @brief Computes the hinge loss for a one-of-many classification task. - * - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ t @f$, a Blob with values in - * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of - * the @f$ K = CHW @f$ classes. In an SVM, @f$ t @f$ is the result of - * taking the inner product @f$ X^T W @f$ of the D-dimensional features - * @f$ X \in \mathcal{R}^{D \times N} @f$ and the learned hyperplane - * parameters @f$ W \in \mathcal{R}^{D \times K} @f$, so a Net with just - * an InnerProductLayer (with num_output = D) providing predictions to a - * HingeLossLayer and no other learnable parameters or losses is - * equivalent to an SVM. - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the labels @f$ l @f$, an integer-valued Blob with values - * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ - * indicating the correct class label among the @f$ K @f$ classes - * @param top output Blob vector (length 1) - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * the computed hinge loss: @f$ E = - * \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K - * [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p - * @f$, for the @f$ L^p @f$ norm - * (defaults to @f$ p = 1 @f$, the L1 norm; L2 norm, as in L2-SVM, - * is also available), and @f$ - * \delta\{\mathrm{condition}\} = \left\{ - * \begin{array}{lr} - * 1 & \mbox{if condition} \\ - * -1 & \mbox{otherwise} - * \end{array} \right. - * @f$ - * - * In an SVM, @f$ t \in \mathcal{R}^{N \times K} @f$ is the result of taking - * the inner product @f$ X^T W @f$ of the features - * @f$ X \in \mathcal{R}^{D \times N} @f$ - * and the learned hyperplane parameters - * @f$ W \in \mathcal{R}^{D \times K} @f$. So, a Net with just an - * InnerProductLayer (with num_output = @f$k@f$) providing predictions to a - * HingeLossLayer is equivalent to an SVM (assuming it has no other learned - * outside the InnerProductLayer and no other losses outside the - * HingeLossLayer). - */ -template -class HingeLossLayer : public LossLayer { - public: - explicit HingeLossLayer(const LayerParameter& param) - : LossLayer(param) {} - - virtual inline const char* type() const { return "HingeLoss"; } - - protected: - /// @copydoc HingeLossLayer - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the hinge loss error gradient w.r.t. the predictions. - * - * Gradients cannot be computed with respect to the label inputs (bottom[1]), - * so this method ignores bottom[1] and requires !propagate_down[1], crashing - * if propagate_down[1] is set. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, - * as @f$ \lambda @f$ is the coefficient of this layer's output - * @f$\ell_i@f$ in the overall Net loss - * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence - * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. - * (*Assuming that this top Blob is not used as a bottom (input) by any - * other layer of the Net.) - * @param propagate_down see Layer::Backward. - * propagate_down[1] must be false as we can't compute gradients with - * respect to the labels. - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$t@f$; Backward computes diff - * @f$ \frac{\partial E}{\partial t} @f$ - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the labels -- ignored as we can't compute their error gradients - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); -}; - -/** - * @brief A generalization of MultinomialLogisticLossLayer that takes an - * "information gain" (infogain) matrix specifying the "value" of all label - * pairs. - * - * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the - * identity. - * - * @param bottom input Blob vector (length 2-3) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ \hat{p} @f$, a Blob with values in - * @f$ [0, 1] @f$ indicating the predicted probability of each of the - * @f$ K = CHW @f$ classes. Each prediction vector @f$ \hat{p}_n @f$ - * should sum to 1 as in a probability distribution: @f$ - * \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$. - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the labels @f$ l @f$, an integer-valued Blob with values - * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ - * indicating the correct class label among the @f$ K @f$ classes - * -# @f$ (1 \times 1 \times K \times K) @f$ - * (\b optional) the infogain matrix @f$ H @f$. This must be provided as - * the third bottom blob input if not provided as the infogain_mat in the - * InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the - * MultinomialLogisticLossLayer. - * @param top output Blob vector (length 1) - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * the computed infogain multinomial logistic loss: @f$ E = - * \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) = - * \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k} - * \log(\hat{p}_{n,k}) - * @f$, where @f$ H_{l_n} @f$ denotes row @f$l_n@f$ of @f$H@f$. - */ -template -class InfogainLossLayer : public LossLayer { - public: - explicit InfogainLossLayer(const LayerParameter& param) - : LossLayer(param), infogain_() {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should - // be the infogain matrix. (Otherwise the infogain matrix is loaded from a - // file specified by LayerParameter.) - virtual inline int ExactNumBottomBlobs() const { return -1; } - virtual inline int MinBottomBlobs() const { return 2; } - virtual inline int MaxBottomBlobs() const { return 3; } - - virtual inline const char* type() const { return "InfogainLoss"; } - - protected: - /// @copydoc InfogainLossLayer - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the infogain loss error gradient w.r.t. the predictions. - * - * Gradients cannot be computed with respect to the label inputs (bottom[1]), - * so this method ignores bottom[1] and requires !propagate_down[1], crashing - * if propagate_down[1] is set. (The same applies to the infogain matrix, if - * provided as bottom[2] rather than in the layer_param.) - * - * @param top output Blob vector (length 1), providing the error gradient - * with respect to the outputs - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, - * as @f$ \lambda @f$ is the coefficient of this layer's output - * @f$\ell_i@f$ in the overall Net loss - * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence - * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. - * (*Assuming that this top Blob is not used as a bottom (input) by any - * other layer of the Net.) - * @param propagate_down see Layer::Backward. - * propagate_down[1] must be false as we can't compute gradients with - * respect to the labels (similarly for propagate_down[2] and the - * infogain matrix, if provided as bottom[2]) - * @param bottom input Blob vector (length 2-3) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ \hat{p} @f$; Backward computes diff - * @f$ \frac{\partial E}{\partial \hat{p}} @f$ - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the labels -- ignored as we can't compute their error gradients - * -# @f$ (1 \times 1 \times K \times K) @f$ - * (\b optional) the information gain matrix -- ignored as its error - * gradient computation is not implemented. - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - Blob infogain_; -}; - -/** - * @brief Computes the multinomial logistic loss for a one-of-many - * classification task, directly taking a predicted probability - * distribution as input. - * - * When predictions are not already a probability distribution, you should - * instead use the SoftmaxWithLossLayer, which maps predictions to a - * distribution using the SoftmaxLayer, before computing the multinomial - * logistic loss. The SoftmaxWithLossLayer should be preferred over separate - * SoftmaxLayer + MultinomialLogisticLossLayer - * as its gradient computation is more numerically stable. - * - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ \hat{p} @f$, a Blob with values in - * @f$ [0, 1] @f$ indicating the predicted probability of each of the - * @f$ K = CHW @f$ classes. Each prediction vector @f$ \hat{p}_n @f$ - * should sum to 1 as in a probability distribution: @f$ - * \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$. - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the labels @f$ l @f$, an integer-valued Blob with values - * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ - * indicating the correct class label among the @f$ K @f$ classes - * @param top output Blob vector (length 1) - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * the computed multinomial logistic loss: @f$ E = - * \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) - * @f$ - */ -template -class MultinomialLogisticLossLayer : public LossLayer { - public: - explicit MultinomialLogisticLossLayer(const LayerParameter& param) - : LossLayer(param) {} - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "MultinomialLogisticLoss"; } - - protected: - /// @copydoc MultinomialLogisticLossLayer - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the multinomial logistic loss error gradient w.r.t. the - * predictions. - * - * Gradients cannot be computed with respect to the label inputs (bottom[1]), - * so this method ignores bottom[1] and requires !propagate_down[1], crashing - * if propagate_down[1] is set. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, - * as @f$ \lambda @f$ is the coefficient of this layer's output - * @f$\ell_i@f$ in the overall Net loss - * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence - * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. - * (*Assuming that this top Blob is not used as a bottom (input) by any - * other layer of the Net.) - * @param propagate_down see Layer::Backward. - * propagate_down[1] must be false as we can't compute gradients with - * respect to the labels. - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ \hat{p} @f$; Backward computes diff - * @f$ \frac{\partial E}{\partial \hat{p}} @f$ - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the labels -- ignored as we can't compute their error gradients - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); -}; - -/** - * @brief Computes the cross-entropy (logistic) loss @f$ - * E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ - * p_n \log \hat{p}_n + - * (1 - p_n) \log(1 - \hat{p}_n) - * \right] - * @f$, often used for predicting targets interpreted as probabilities. - * - * This layer is implemented rather than separate - * SigmoidLayer + CrossEntropyLayer - * as its gradient computation is more numerically stable. - * At test time, this layer can be replaced simply by a SigmoidLayer. - * - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the scores @f$ x \in [-\infty, +\infty]@f$, - * which this layer maps to probability predictions - * @f$ \hat{p}_n = \sigma(x_n) \in [0, 1] @f$ - * using the sigmoid function @f$ \sigma(.) @f$ (see SigmoidLayer). - * -# @f$ (N \times C \times H \times W) @f$ - * the targets @f$ y \in [0, 1] @f$ - * @param top output Blob vector (length 1) - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * the computed cross-entropy loss: @f$ - * E = \frac{-1}{n} \sum\limits_{n=1}^N \left[ - * p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n) - * \right] - * @f$ - */ -template -class SigmoidCrossEntropyLossLayer : public LossLayer { - public: - explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param) - : LossLayer(param), - sigmoid_layer_(new SigmoidLayer(param)), - sigmoid_output_(new Blob()) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; } - - protected: - /// @copydoc SigmoidCrossEntropyLossLayer - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the - * predictions. - * - * Gradients cannot be computed with respect to the target inputs (bottom[1]), - * so this method ignores bottom[1] and requires !propagate_down[1], crashing - * if propagate_down[1] is set. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, - * as @f$ \lambda @f$ is the coefficient of this layer's output - * @f$\ell_i@f$ in the overall Net loss - * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence - * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. - * (*Assuming that this top Blob is not used as a bottom (input) by any - * other layer of the Net.) - * @param propagate_down see Layer::Backward. - * propagate_down[1] must be false as gradient computation with respect - * to the targets is not implemented. - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$x@f$; Backward computes diff - * @f$ \frac{\partial E}{\partial x} = - * \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n) - * @f$ - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the labels -- ignored as we can't compute their error gradients - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - /// The internal SigmoidLayer used to map predictions to probabilities. - shared_ptr > sigmoid_layer_; - /// sigmoid_output stores the output of the SigmoidLayer. - shared_ptr > sigmoid_output_; - /// bottom vector holder to call the underlying SigmoidLayer::Forward - vector*> sigmoid_bottom_vec_; - /// top vector holder to call the underlying SigmoidLayer::Forward - vector*> sigmoid_top_vec_; -}; - -// Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer. -template class SoftmaxLayer; - -/** - * @brief Computes the multinomial logistic loss for a one-of-many - * classification task, passing real-valued predictions through a - * softmax to get a probability distribution over classes. - * - * This layer should be preferred over separate - * SoftmaxLayer + MultinomialLogisticLossLayer - * as its gradient computation is more numerically stable. - * At test time, this layer can be replaced simply by a SoftmaxLayer. - * - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ x @f$, a Blob with values in - * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of - * the @f$ K = CHW @f$ classes. This layer maps these scores to a - * probability distribution over classes using the softmax function - * @f$ \hat{p}_{nk} = \exp(x_{nk}) / - * \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer). - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the labels @f$ l @f$, an integer-valued Blob with values - * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ - * indicating the correct class label among the @f$ K @f$ classes - * @param top output Blob vector (length 1) - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * the computed cross-entropy classification loss: @f$ E = - * \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n}) - * @f$, for softmax output class probabilites @f$ \hat{p} @f$ - */ -template -class SoftmaxWithLossLayer : public LossLayer { - public: - /** - * @param param provides LossParameter loss_param, with options: - * - ignore_label (optional) - * Specify a label value that should be ignored when computing the loss. - * - normalize (optional, default true) - * If true, the loss is normalized by the number of (nonignored) labels - * present; otherwise the loss is simply summed over spatial locations. - */ - explicit SoftmaxWithLossLayer(const LayerParameter& param) - : LossLayer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "SoftmaxWithLoss"; } - virtual inline int ExactNumTopBlobs() const { return -1; } - virtual inline int MinTopBlobs() const { return 1; } - virtual inline int MaxTopBlobs() const { return 2; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - /** - * @brief Computes the softmax loss error gradient w.r.t. the predictions. - * - * Gradients cannot be computed with respect to the label inputs (bottom[1]), - * so this method ignores bottom[1] and requires !propagate_down[1], crashing - * if propagate_down[1] is set. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (1 \times 1 \times 1 \times 1) @f$ - * This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$, - * as @f$ \lambda @f$ is the coefficient of this layer's output - * @f$\ell_i@f$ in the overall Net loss - * @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence - * @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$. - * (*Assuming that this top Blob is not used as a bottom (input) by any - * other layer of the Net.) - * @param propagate_down see Layer::Backward. - * propagate_down[1] must be false as we can't compute gradients with - * respect to the labels. - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ x @f$; Backward computes diff - * @f$ \frac{\partial E}{\partial x} @f$ - * -# @f$ (N \times 1 \times 1 \times 1) @f$ - * the labels -- ignored as we can't compute their error gradients - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - /// Read the normalization mode parameter and compute the normalizer based - /// on the blob size. If normalization_mode is VALID, the count of valid - /// outputs will be read from valid_count, unless it is -1 in which case - /// all outputs are assumed to be valid. - virtual Dtype get_normalizer( - LossParameter_NormalizationMode normalization_mode, int valid_count); - - /// The internal SoftmaxLayer used to map predictions to a distribution. - shared_ptr > softmax_layer_; - /// prob stores the output probability predictions from the SoftmaxLayer. - Blob prob_; - /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward - vector*> softmax_bottom_vec_; - /// top vector holder used in call to the underlying SoftmaxLayer::Forward - vector*> softmax_top_vec_; - /// Whether to ignore instances with a certain label. - bool has_ignore_label_; - /// The label indicating that an instance should be ignored. - int ignore_label_; - /// How to normalize the output loss. - LossParameter_NormalizationMode normalization_; - - int softmax_axis_, outer_num_, inner_num_; -}; - -} // namespace caffe - -#endif // CAFFE_LOSS_LAYERS_HPP_ diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp deleted file mode 100644 index 4fa330ec783..00000000000 --- a/include/caffe/neuron_layers.hpp +++ /dev/null @@ -1,806 +0,0 @@ -#ifndef CAFFE_NEURON_LAYERS_HPP_ -#define CAFFE_NEURON_LAYERS_HPP_ - -#include -#include -#include - -#include "caffe/blob.hpp" -#include "caffe/common.hpp" -#include "caffe/layer.hpp" -#include "caffe/proto/caffe.pb.h" - -namespace caffe { - -/** - * @brief An interface for layers that take one blob as input (@f$ x @f$) - * and produce one equally-sized blob as output (@f$ y @f$), where - * each element of the output depends only on the corresponding input - * element. - */ -template -class NeuronLayer : public Layer { - public: - explicit NeuronLayer(const LayerParameter& param) - : Layer(param) {} - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } -}; - -/** - * @brief Computes @f$ y = |x| @f$ - * - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs @f$ y = |x| @f$ - */ -template -class AbsValLayer : public NeuronLayer { - public: - explicit AbsValLayer(const LayerParameter& param) - : NeuronLayer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "AbsVal"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - /// @copydoc AbsValLayer - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the absolute value inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (N \times C \times H \times W) @f$ - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to computed outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$; Backward fills their diff with - * gradients @f$ - * \frac{\partial E}{\partial x} = - * \mathrm{sign}(x) \frac{\partial E}{\partial y} - * @f$ if propagate_down[0] - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); -}; - -/** - * @brief Computes @f$ y = x + \log(1 + \exp(-x)) @f$ if @f$ x > 0 @f$; - * @f$ y = \log(1 + \exp(x)) @f$ otherwise. - * - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs @f$ - * y = \left\{ - * \begin{array}{ll} - * x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\ - * \log(1 + \exp(x)) & \mbox{otherwise} - * \end{array} \right. - * @f$ - */ -template -class BNLLLayer : public NeuronLayer { - public: - explicit BNLLLayer(const LayerParameter& param) - : NeuronLayer(param) {} - - virtual inline const char* type() const { return "BNLL"; } - - protected: - /// @copydoc BNLLLayer - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the BNLL inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (N \times C \times H \times W) @f$ - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to computed outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 2) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$; Backward fills their diff with - * gradients @f$ - * \frac{\partial E}{\partial x} - * @f$ if propagate_down[0] - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); -}; - -/** - * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting - * the rest of the vector magnitude accordingly. - * - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs @f$ y = |x| @f$ - */ -template -class DropoutLayer : public NeuronLayer { - public: - /** - * @param param provides DropoutParameter dropout_param, - * with DropoutLayer options: - * - dropout_ratio (\b optional, default 0.5). - * Sets the probability @f$ p @f$ that any given unit is dropped. - */ - explicit DropoutLayer(const LayerParameter& param) - : NeuronLayer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Dropout"; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs. At training time, we have @f$ - * y_{\mbox{train}} = \left\{ - * \begin{array}{ll} - * \frac{x}{1 - p} & \mbox{if } u > p \\ - * 0 & \mbox{otherwise} - * \end{array} \right. - * @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each - * input at each iteration. At test time, we simply have - * @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$. - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$ - Blob rand_vec_; - /// the probability @f$ p @f$ of dropping any input - Dtype threshold_; - /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$ - Dtype scale_; - unsigned int uint_thres_; -}; - -/** - * @brief Computes @f$ y = \gamma ^ {\alpha x + \beta} @f$, - * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$, - * and base @f$ \gamma @f$. - */ -template -class ExpLayer : public NeuronLayer { - public: - /** - * @param param provides ExpParameter exp_param, - * with ExpLayer options: - * - scale (\b optional, default 1) the scale @f$ \alpha @f$ - * - shift (\b optional, default 0) the shift @f$ \beta @f$ - * - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$) - * the base @f$ \gamma @f$ - */ - explicit ExpLayer(const LayerParameter& param) - : NeuronLayer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Exp"; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs @f$ - * y = \gamma ^ {\alpha x + \beta} - * @f$ - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the exp inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (N \times C \times H \times W) @f$ - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to computed outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$; Backward fills their diff with - * gradients @f$ - * \frac{\partial E}{\partial x} = - * \frac{\partial E}{\partial y} y \alpha \log_e(gamma) - * @f$ if propagate_down[0] - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - Dtype inner_scale_, outer_scale_; -}; - -/** - * @brief Computes @f$ y = log_{\gamma}(\alpha x + \beta) @f$, - * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$, - * and base @f$ \gamma @f$. - */ -template -class LogLayer : public NeuronLayer { - public: - /** - * @param param provides LogParameter log_param, - * with LogLayer options: - * - scale (\b optional, default 1) the scale @f$ \alpha @f$ - * - shift (\b optional, default 0) the shift @f$ \beta @f$ - * - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$) - * the base @f$ \gamma @f$ - */ - explicit LogLayer(const LayerParameter& param) - : NeuronLayer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Log"; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs @f$ - * y = log_{\gamma}(\alpha x + \beta) - * @f$ - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the exp inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (N \times C \times H \times W) @f$ - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to computed outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$; Backward fills their diff with - * gradients @f$ - * \frac{\partial E}{\partial x} = - * \frac{\partial E}{\partial y} y \alpha \log_e(gamma) - * @f$ if propagate_down[0] - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - Dtype base_scale_; - Dtype input_scale_, input_shift_; - Dtype backward_num_scale_; -}; - -/** - * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$, - * as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$, - * and power @f$ \gamma @f$. - */ -template -class PowerLayer : public NeuronLayer { - public: - /** - * @param param provides PowerParameter power_param, - * with PowerLayer options: - * - scale (\b optional, default 1) the scale @f$ \alpha @f$ - * - shift (\b optional, default 0) the shift @f$ \beta @f$ - * - power (\b optional, default 1) the power @f$ \gamma @f$ - */ - explicit PowerLayer(const LayerParameter& param) - : NeuronLayer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Power"; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs @f$ - * y = (\alpha x + \beta) ^ \gamma - * @f$ - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the power inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (N \times C \times H \times W) @f$ - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to computed outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$; Backward fills their diff with - * gradients @f$ - * \frac{\partial E}{\partial x} = - * \frac{\partial E}{\partial y} - * \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} = - * \frac{\partial E}{\partial y} - * \frac{\alpha \gamma y}{\alpha x + \beta} - * @f$ if propagate_down[0] - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - /// @brief @f$ \gamma @f$ from layer_param_.power_param() - Dtype power_; - /// @brief @f$ \alpha @f$ from layer_param_.power_param() - Dtype scale_; - /// @brief @f$ \beta @f$ from layer_param_.power_param() - Dtype shift_; - /// @brief Result of @f$ \alpha \gamma @f$ - Dtype diff_scale_; -}; - -/** - * @brief Rectified Linear Unit non-linearity @f$ y = \max(0, x) @f$. - * The simple max is fast to compute, and the function does not saturate. - */ -template -class ReLULayer : public NeuronLayer { - public: - /** - * @param param provides ReLUParameter relu_param, - * with ReLULayer options: - * - negative_slope (\b optional, default 0). - * the value @f$ \nu @f$ by which negative values are multiplied. - */ - explicit ReLULayer(const LayerParameter& param) - : NeuronLayer(param) {} - - virtual inline const char* type() const { return "ReLU"; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs @f$ - * y = \max(0, x) - * @f$ by default. If a non-zero negative_slope @f$ \nu @f$ is provided, - * the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$. - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the ReLU inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (N \times C \times H \times W) @f$ - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to computed outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$; Backward fills their diff with - * gradients @f$ - * \frac{\partial E}{\partial x} = \left\{ - * \begin{array}{lr} - * 0 & \mathrm{if} \; x \le 0 \\ - * \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 - * \end{array} \right. - * @f$ if propagate_down[0], by default. - * If a non-zero negative_slope @f$ \nu @f$ is provided, - * the computed gradients are @f$ - * \frac{\partial E}{\partial x} = \left\{ - * \begin{array}{lr} - * \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\ - * \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0 - * \end{array} \right. - * @f$. - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); -}; - -#ifdef USE_CUDNN -/** - * @brief CuDNN acceleration of ReLULayer. - */ -template -class CuDNNReLULayer : public ReLULayer { - public: - explicit CuDNNReLULayer(const LayerParameter& param) - : ReLULayer(param), handles_setup_(false) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - virtual ~CuDNNReLULayer(); - - protected: - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - bool handles_setup_; - cudnnHandle_t handle_; - cudnnTensorDescriptor_t bottom_desc_; - cudnnTensorDescriptor_t top_desc_; -}; -#endif - -/** - * @brief Sigmoid function non-linearity @f$ - * y = (1 + \exp(-x))^{-1} - * @f$, a classic choice in neural networks. - * - * Note that the gradient vanishes as the values move away from 0. - * The ReLULayer is often a better choice for this reason. - */ -template -class SigmoidLayer : public NeuronLayer { - public: - explicit SigmoidLayer(const LayerParameter& param) - : NeuronLayer(param) {} - - virtual inline const char* type() const { return "Sigmoid"; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs @f$ - * y = (1 + \exp(-x))^{-1} - * @f$ - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the sigmoid inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (N \times C \times H \times W) @f$ - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to computed outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$; Backward fills their diff with - * gradients @f$ - * \frac{\partial E}{\partial x} - * = \frac{\partial E}{\partial y} y (1 - y) - * @f$ if propagate_down[0] - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); -}; - -#ifdef USE_CUDNN -/** - * @brief CuDNN acceleration of SigmoidLayer. - */ -template -class CuDNNSigmoidLayer : public SigmoidLayer { - public: - explicit CuDNNSigmoidLayer(const LayerParameter& param) - : SigmoidLayer(param), handles_setup_(false) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - virtual ~CuDNNSigmoidLayer(); - - protected: - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - bool handles_setup_; - cudnnHandle_t handle_; - cudnnTensorDescriptor_t bottom_desc_; - cudnnTensorDescriptor_t top_desc_; -}; -#endif - -/** - * @brief TanH hyperbolic tangent non-linearity @f$ - * y = \frac{\exp(2x) - 1}{\exp(2x) + 1} - * @f$, popular in auto-encoders. - * - * Note that the gradient vanishes as the values move away from 0. - * The ReLULayer is often a better choice for this reason. - */ -template -class TanHLayer : public NeuronLayer { - public: - explicit TanHLayer(const LayerParameter& param) - : NeuronLayer(param) {} - - virtual inline const char* type() const { return "TanH"; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs @f$ - * y = \frac{\exp(2x) - 1}{\exp(2x) + 1} - * @f$ - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the sigmoid inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (N \times C \times H \times W) @f$ - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to computed outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$; Backward fills their diff with - * gradients @f$ - * \frac{\partial E}{\partial x} - * = \frac{\partial E}{\partial y} - * \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right) - * = \frac{\partial E}{\partial y} (1 - y^2) - * @f$ if propagate_down[0] - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); -}; - -#ifdef USE_CUDNN -/** - * @brief CuDNN acceleration of TanHLayer. - */ -template -class CuDNNTanHLayer : public TanHLayer { - public: - explicit CuDNNTanHLayer(const LayerParameter& param) - : TanHLayer(param), handles_setup_(false) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - virtual ~CuDNNTanHLayer(); - - protected: - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - bool handles_setup_; - cudnnHandle_t handle_; - cudnnTensorDescriptor_t bottom_desc_; - cudnnTensorDescriptor_t top_desc_; -}; -#endif - -/** - * @brief Tests whether the input exceeds a threshold: outputs 1 for inputs - * above threshold; 0 otherwise. - */ -template -class ThresholdLayer : public NeuronLayer { - public: - /** - * @param param provides ThresholdParameter threshold_param, - * with ThresholdLayer options: - * - threshold (\b optional, default 0). - * the threshold value @f$ t @f$ to which the input values are compared. - */ - explicit ThresholdLayer(const LayerParameter& param) - : NeuronLayer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Threshold"; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times H \times W) @f$ - * the computed outputs @f$ - * y = \left\{ - * \begin{array}{lr} - * 0 & \mathrm{if} \; x \le t \\ - * 1 & \mathrm{if} \; x > t - * \end{array} \right. - * @f$ - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - /// @brief Not implemented (non-differentiable function) - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) { - NOT_IMPLEMENTED; - } - - Dtype threshold_; -}; - -/** - * @brief Parameterized Rectified Linear Unit non-linearity @f$ - * y_i = \max(0, x_i) + a_i \min(0, x_i) - * @f$. The differences from ReLULayer are 1) negative slopes are - * learnable though backprop and 2) negative slopes can vary across - * channels. The number of axes of input blob should be greater than or - * equal to 2. The 1st axis (0-based) is seen as channels. - */ -template -class PReLULayer : public NeuronLayer { - public: - /** - * @param param provides PReLUParameter prelu_param, - * with PReLULayer options: - * - filler (\b optional, FillerParameter, - * default {'type': constant 'value':0.25}). - * - channel_shared (\b optional, default false). - * negative slopes are shared across channels. - */ - explicit PReLULayer(const LayerParameter& param) - : NeuronLayer(param) {} - - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "PReLU"; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times ...) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times ...) @f$ - * the computed outputs for each channel @f$i@f$ @f$ - * y_i = \max(0, x_i) + a_i \min(0, x_i) - * @f$. - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - - /** - * @brief Computes the error gradient w.r.t. the PReLU inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (N \times C \times ...) @f$ - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to computed outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times ...) @f$ - * the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their - * diff with gradients @f$ - * \frac{\partial E}{\partial x_i} = \left\{ - * \begin{array}{lr} - * a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ - * \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0 - * \end{array} \right. - * @f$. - * If param_propagate_down_[0] is true, it fills the diff with gradients - * @f$ - * \frac{\partial E}{\partial a_i} = \left\{ - * \begin{array}{lr} - * \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\ - * 0 & \mathrm{if} \; x_i > 0 - * \end{array} \right. - * @f$. - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - bool channel_shared_; - Blob multiplier_; // dot multiplier for backward computation of params - Blob backward_buff_; // temporary buffer for backward computation - Blob bottom_memory_; // memory for in-place computation -}; - -} // namespace caffe - -#endif // CAFFE_NEURON_LAYERS_HPP_ diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp deleted file mode 100644 index 237b05d6fa6..00000000000 --- a/include/caffe/vision_layers.hpp +++ /dev/null @@ -1,659 +0,0 @@ -#ifndef CAFFE_VISION_LAYERS_HPP_ -#define CAFFE_VISION_LAYERS_HPP_ - -#include -#include -#include - -#include "caffe/blob.hpp" -#include "caffe/common.hpp" -#include "caffe/common_layers.hpp" -#include "caffe/data_layers.hpp" -#include "caffe/layer.hpp" -#include "caffe/loss_layers.hpp" -#include "caffe/neuron_layers.hpp" -#include "caffe/proto/caffe.pb.h" - -namespace caffe { - -/** - * @brief Abstract base class that factors out the BLAS code common to - * ConvolutionLayer and DeconvolutionLayer. - */ -template -class BaseConvolutionLayer : public Layer { - public: - explicit BaseConvolutionLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline int MinBottomBlobs() const { return 1; } - virtual inline int MinTopBlobs() const { return 1; } - virtual inline bool EqualNumBottomTopBlobs() const { return true; } - - protected: - // Helper functions that abstract away the column buffer and gemm arguments. - // The last argument in forward_cpu_gemm is so that we can skip the im2col if - // we just called weight_cpu_gemm with the same input. - void forward_cpu_gemm(const Dtype* input, const Dtype* weights, - Dtype* output, bool skip_im2col = false); - void forward_cpu_bias(Dtype* output, const Dtype* bias); - void backward_cpu_gemm(const Dtype* input, const Dtype* weights, - Dtype* output); - void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype* - weights); - void backward_cpu_bias(Dtype* bias, const Dtype* input); - -#ifndef CPU_ONLY - void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights, - Dtype* output, bool skip_im2col = false); - void forward_gpu_bias(Dtype* output, const Dtype* bias); - void backward_gpu_gemm(const Dtype* input, const Dtype* weights, - Dtype* col_output); - void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype* - weights); - void backward_gpu_bias(Dtype* bias, const Dtype* input); -#endif - - /// @brief The spatial dimensions of the input. - inline int input_shape(int i) { - return (*bottom_shape_)[channel_axis_ + i]; - } - // reverse_dimensions should return true iff we are implementing deconv, so - // that conv helpers know which dimensions are which. - virtual bool reverse_dimensions() = 0; - // Compute height_out_ and width_out_ from other parameters. - virtual void compute_output_shape() = 0; - - /// @brief The spatial dimensions of a filter kernel. - Blob kernel_shape_; - /// @brief The spatial dimensions of the stride. - Blob stride_; - /// @brief The spatial dimensions of the padding. - Blob pad_; - /// @brief The spatial dimensions of the convolution input. - Blob conv_input_shape_; - /// @brief The spatial dimensions of the col_buffer. - vector col_buffer_shape_; - /// @brief The spatial dimensions of the output. - vector output_shape_; - const vector* bottom_shape_; - - int num_spatial_axes_; - int bottom_dim_; - int top_dim_; - - int channel_axis_; - int num_; - int channels_; - int group_; - int out_spatial_dim_; - int weight_offset_; - int num_output_; - bool bias_term_; - bool is_1x1_; - bool force_nd_im2col_; - - private: - // wrap im2col/col2im so we don't have to remember the (long) argument lists - inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) { - if (!force_nd_im2col_ && num_spatial_axes_ == 2) { - im2col_cpu(data, conv_in_channels_, - conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], - kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], - pad_.cpu_data()[0], pad_.cpu_data()[1], - stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff); - } else { - im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(), - col_buffer_shape_.data(), kernel_shape_.cpu_data(), - pad_.cpu_data(), stride_.cpu_data(), col_buff); - } - } - inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) { - if (!force_nd_im2col_ && num_spatial_axes_ == 2) { - col2im_cpu(col_buff, conv_in_channels_, - conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], - kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], - pad_.cpu_data()[0], pad_.cpu_data()[1], - stride_.cpu_data()[0], stride_.cpu_data()[1], data); - } else { - col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(), - col_buffer_shape_.data(), kernel_shape_.cpu_data(), - pad_.cpu_data(), stride_.cpu_data(), data); - } - } -#ifndef CPU_ONLY - inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) { - if (!force_nd_im2col_ && num_spatial_axes_ == 2) { - im2col_gpu(data, conv_in_channels_, - conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], - kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], - pad_.cpu_data()[0], pad_.cpu_data()[1], - stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff); - } else { - im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_, - conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(), - kernel_shape_.gpu_data(), pad_.gpu_data(), - stride_.gpu_data(), col_buff); - } - } - inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) { - if (!force_nd_im2col_ && num_spatial_axes_ == 2) { - col2im_gpu(col_buff, conv_in_channels_, - conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], - kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], - pad_.cpu_data()[0], pad_.cpu_data()[1], - stride_.cpu_data()[0], stride_.cpu_data()[1], data); - } else { - col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_, - conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(), - kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(), - data); - } - } -#endif - - int num_kernels_im2col_; - int num_kernels_col2im_; - int conv_out_channels_; - int conv_in_channels_; - int conv_out_spatial_dim_; - int kernel_dim_; - int col_offset_; - int output_offset_; - - Blob col_buffer_; - Blob bias_multiplier_; -}; - -/** - * @brief Convolves the input image with a bank of learned filters, - * and (optionally) adds biases. - * - * Caffe convolves by reduction to matrix multiplication. This achieves - * high-throughput and generality of input and filter dimensions but comes at - * the cost of memory for matrices. This makes use of efficiency in BLAS. - * - * The input is "im2col" transformed to a channel K' x H x W data matrix - * for multiplication with the N x K' x H x W filter matrix to yield a - * N' x H x W output matrix that is then "col2im" restored. K' is the - * input channel * kernel height * kernel width dimension of the unrolled - * inputs so that the im2col matrix has a column for each input region to - * be filtered. col2im restores the output spatial structure by rolling up - * the output channel N' columns of the output matrix. - */ -template -class ConvolutionLayer : public BaseConvolutionLayer { - public: - /** - * @param param provides ConvolutionParameter convolution_param, - * with ConvolutionLayer options: - * - num_output. The number of filters. - * - kernel_size / kernel_h / kernel_w. The filter dimensions, given by - * kernel_size for square filters or kernel_h and kernel_w for rectangular - * filters. - * - stride / stride_h / stride_w (\b optional, default 1). The filter - * stride, given by stride_size for equal dimensions or stride_h and stride_w - * for different strides. By default the convolution is dense with stride 1. - * - pad / pad_h / pad_w (\b optional, default 0). The zero-padding for - * convolution, given by pad for equal dimensions or pad_h and pad_w for - * different padding. Input padding is computed implicitly instead of - * actually padding. - * - group (\b optional, default 1). The number of filter groups. Group - * convolution is a method for reducing parameterization by selectively - * connecting input and output channels. The input and output channel dimensions must be divisible - * by the number of groups. For group @f$ \geq 1 @f$, the - * convolutional filters' input and output channels are separated s.t. each - * group takes 1 / group of the input channels and makes 1 / group of the - * output channels. Concretely 4 input channels, 8 output channels, and - * 2 groups separate input channels 1-2 and output channels 1-4 into the - * first group and input channels 3-4 and output channels 5-8 into the second - * group. - * - bias_term (\b optional, default true). Whether to have a bias. - * - engine: convolution has CAFFE (matrix multiplication) and CUDNN (library - * kernels + stream parallelism) engines. - */ - explicit ConvolutionLayer(const LayerParameter& param) - : BaseConvolutionLayer(param) {} - - virtual inline const char* type() const { return "Convolution"; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual inline bool reverse_dimensions() { return false; } - virtual void compute_output_shape(); -}; - -/** - * @brief Convolve the input with a bank of learned filters, and (optionally) - * add biases, treating filters and convolution parameters in the - * opposite sense as ConvolutionLayer. - * - * ConvolutionLayer computes each output value by dotting an input window with - * a filter; DeconvolutionLayer multiplies each input value by a filter - * elementwise, and sums over the resulting output windows. In other words, - * DeconvolutionLayer is ConvolutionLayer with the forward and backward passes - * reversed. DeconvolutionLayer reuses ConvolutionParameter for its - * parameters, but they take the opposite sense as in ConvolutionLayer (so - * padding is removed from the output rather than added to the input, and - * stride results in upsampling rather than downsampling). - */ -template -class DeconvolutionLayer : public BaseConvolutionLayer { - public: - explicit DeconvolutionLayer(const LayerParameter& param) - : BaseConvolutionLayer(param) {} - - virtual inline const char* type() const { return "Deconvolution"; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual inline bool reverse_dimensions() { return true; } - virtual void compute_output_shape(); -}; - -#ifdef USE_CUDNN -/* - * @brief cuDNN implementation of ConvolutionLayer. - * Fallback to ConvolutionLayer for CPU mode. - * - * cuDNN accelerates convolution through forward kernels for filtering and bias - * plus backward kernels for the gradient w.r.t. the filters, biases, and - * inputs. Caffe + cuDNN further speeds up the computation through forward - * parallelism across groups and backward parallelism across gradients. - * - * The CUDNN engine does not have memory overhead for matrix buffers. For many - * input and filter regimes the CUDNN engine is faster than the CAFFE engine, - * but for fully-convolutional models and large inputs the CAFFE engine can be - * faster as long as it fits in memory. -*/ -template -class CuDNNConvolutionLayer : public ConvolutionLayer { - public: - explicit CuDNNConvolutionLayer(const LayerParameter& param) - : ConvolutionLayer(param), handles_setup_(false) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - virtual ~CuDNNConvolutionLayer(); - - protected: - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - bool handles_setup_; - cudnnHandle_t* handle_; - cudaStream_t* stream_; - - // algorithms for forward and backwards convolutions - cudnnConvolutionFwdAlgo_t *fwd_algo_; - cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_; - cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_; - - vector bottom_descs_, top_descs_; - cudnnTensorDescriptor_t bias_desc_; - cudnnFilterDescriptor_t filter_desc_; - vector conv_descs_; - int bottom_offset_, top_offset_, bias_offset_; - - size_t *workspace_fwd_sizes_; - size_t *workspace_bwd_data_sizes_; - size_t *workspace_bwd_filter_sizes_; - size_t workspaceSizeInBytes; // size of underlying storage - void *workspaceData; // underlying storage - void **workspace; // aliases into workspaceData -}; -#endif - -/** - * @brief A helper for image operations that rearranges image regions into - * column vectors. Used by ConvolutionLayer to perform convolution - * by matrix multiplication. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class Im2colLayer : public Layer { - public: - explicit Im2colLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Im2col"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - /// @brief The spatial dimensions of a filter kernel. - Blob kernel_shape_; - /// @brief The spatial dimensions of the stride. - Blob stride_; - /// @brief The spatial dimensions of the padding. - Blob pad_; - - int num_spatial_axes_; - int bottom_dim_; - int top_dim_; - - int channel_axis_; - int num_; - int channels_; - - bool force_nd_im2col_; -}; - -// Forward declare PoolingLayer and SplitLayer for use in LRNLayer. -template class PoolingLayer; -template class SplitLayer; - -/** - * @brief Normalize the input in a local region across or within feature maps. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class LRNLayer : public Layer { - public: - explicit LRNLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "LRN"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - virtual void CrossChannelForward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void CrossChannelForward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void WithinChannelForward(const vector*>& bottom, - const vector*>& top); - virtual void CrossChannelBackward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void CrossChannelBackward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void WithinChannelBackward(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - int size_; - int pre_pad_; - Dtype alpha_; - Dtype beta_; - Dtype k_; - int num_; - int channels_; - int height_; - int width_; - - // Fields used for normalization ACROSS_CHANNELS - // scale_ stores the intermediate summing results - Blob scale_; - - // Fields used for normalization WITHIN_CHANNEL - shared_ptr > split_layer_; - vector*> split_top_vec_; - shared_ptr > square_layer_; - Blob square_input_; - Blob square_output_; - vector*> square_bottom_vec_; - vector*> square_top_vec_; - shared_ptr > pool_layer_; - Blob pool_output_; - vector*> pool_top_vec_; - shared_ptr > power_layer_; - Blob power_output_; - vector*> power_top_vec_; - shared_ptr > product_layer_; - Blob product_input_; - vector*> product_bottom_vec_; -}; - -#ifdef USE_CUDNN - -template -class CuDNNLRNLayer : public LRNLayer { - public: - explicit CuDNNLRNLayer(const LayerParameter& param) - : LRNLayer(param), handles_setup_(false) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - virtual ~CuDNNLRNLayer(); - - protected: - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - bool handles_setup_; - cudnnHandle_t handle_; - cudnnLRNDescriptor_t norm_desc_; - cudnnTensorDescriptor_t bottom_desc_, top_desc_; - - int size_; - Dtype alpha_, beta_, k_; -}; - -template -class CuDNNLCNLayer : public LRNLayer { - public: - explicit CuDNNLCNLayer(const LayerParameter& param) - : LRNLayer(param), handles_setup_(false), tempDataSize(0), - tempData1(NULL), tempData2(NULL) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - virtual ~CuDNNLCNLayer(); - - protected: - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - bool handles_setup_; - cudnnHandle_t handle_; - cudnnLRNDescriptor_t norm_desc_; - cudnnTensorDescriptor_t bottom_desc_, top_desc_; - - int size_, pre_pad_; - Dtype alpha_, beta_, k_; - - size_t tempDataSize; - void *tempData1, *tempData2; -}; - -#endif - -/** - * @brief Pools the input image by taking the max, average, etc. within regions. - * - * TODO(dox): thorough documentation for Forward, Backward, and proto params. - */ -template -class PoolingLayer : public Layer { - public: - explicit PoolingLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "Pooling"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int MinTopBlobs() const { return 1; } - // MAX POOL layers can output an extra top blob for the mask; - // others can only output the pooled inputs. - virtual inline int MaxTopBlobs() const { - return (this->layer_param_.pooling_param().pool() == - PoolingParameter_PoolMethod_MAX) ? 2 : 1; - } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - int kernel_h_, kernel_w_; - int stride_h_, stride_w_; - int pad_h_, pad_w_; - int channels_; - int height_, width_; - int pooled_height_, pooled_width_; - bool global_pooling_; - Blob rand_idx_; - Blob max_idx_; -}; - -#ifdef USE_CUDNN -/* - * @brief cuDNN implementation of PoolingLayer. - * Fallback to PoolingLayer for CPU mode. -*/ -template -class CuDNNPoolingLayer : public PoolingLayer { - public: - explicit CuDNNPoolingLayer(const LayerParameter& param) - : PoolingLayer(param), handles_setup_(false) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - virtual ~CuDNNPoolingLayer(); - // Currently, cuDNN does not support the extra top blob. - virtual inline int MinTopBlobs() const { return -1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - - bool handles_setup_; - cudnnHandle_t handle_; - cudnnTensorDescriptor_t bottom_desc_, top_desc_; - cudnnPoolingDescriptor_t pooling_desc_; - cudnnPoolingMode_t mode_; -}; -#endif - -/** - * @brief Does spatial pyramid pooling on the input image - * by taking the max, average, etc. within regions - * so that the result vector of different sized - * images are of the same size. - */ -template -class SPPLayer : public Layer { - public: - explicit SPPLayer(const LayerParameter& param) - : Layer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - - virtual inline const char* type() const { return "SPP"; } - virtual inline int ExactNumBottomBlobs() const { return 1; } - virtual inline int ExactNumTopBlobs() const { return 1; } - - protected: - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom); - // calculates the kernel and stride dimensions for the pooling layer, - // returns a correctly configured LayerParameter for a PoolingLayer - virtual LayerParameter GetPoolingParam(const int pyramid_level, - const int bottom_h, const int bottom_w, const SPPParameter spp_param); - - int pyramid_height_; - int bottom_h_, bottom_w_; - int num_; - int channels_; - int kernel_h_, kernel_w_; - int pad_h_, pad_w_; - bool reshaped_first_time_; - - /// the internal Split layer that feeds the pooling layers - shared_ptr > split_layer_; - /// top vector holder used in call to the underlying SplitLayer::Forward - vector*> split_top_vec_; - /// bottom vector holder used in call to the underlying PoolingLayer::Forward - vector*>*> pooling_bottom_vecs_; - /// the internal Pooling layers of different kernel sizes - vector > > pooling_layers_; - /// top vector holders used in call to the underlying PoolingLayer::Forward - vector*>*> pooling_top_vecs_; - /// pooling_outputs stores the outputs of the PoolingLayers - vector*> pooling_outputs_; - /// the internal Flatten layers that the Pooling layers feed into - vector*> flatten_layers_; - /// top vector holders used in call to the underlying FlattenLayer::Forward - vector*>*> flatten_top_vecs_; - /// flatten_outputs stores the outputs of the FlattenLayers - vector*> flatten_outputs_; - /// bottom vector holder used in call to the underlying ConcatLayer::Forward - vector*> concat_bottom_vec_; - /// the internal Concat layers that the Flatten layers feed into - shared_ptr > concat_layer_; -}; - -} // namespace caffe - -#endif // CAFFE_VISION_LAYERS_HPP_ diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index 1a318f8d36d..69d55332841 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -15,7 +15,8 @@ #include // NOLINT #include "caffe/caffe.hpp" -#include "caffe/python_layer.hpp" +#include "caffe/layers/memory_data_layer.hpp" +#include "caffe/layers/python_layer.hpp" #include "caffe/sgd_solvers.hpp" // Temporary solution for numpy < 1.7 versions: old macro, no promises. diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp index 16378203a88..9f019bbfcb7 100644 --- a/src/caffe/data_reader.cpp +++ b/src/caffe/data_reader.cpp @@ -4,8 +4,8 @@ #include #include "caffe/common.hpp" -#include "caffe/data_layers.hpp" #include "caffe/data_reader.hpp" +#include "caffe/layers/data_layer.hpp" #include "caffe/proto/caffe.pb.h" namespace caffe { diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp index 417ffe986df..76d851af9a2 100644 --- a/src/caffe/layer_factory.cpp +++ b/src/caffe/layer_factory.cpp @@ -7,11 +7,28 @@ #include "caffe/layer.hpp" #include "caffe/layer_factory.hpp" +#include "caffe/layers/conv_layer.hpp" +#include "caffe/layers/lrn_layer.hpp" +#include "caffe/layers/pooling_layer.hpp" +#include "caffe/layers/relu_layer.hpp" +#include "caffe/layers/sigmoid_layer.hpp" +#include "caffe/layers/softmax_layer.hpp" +#include "caffe/layers/tanh_layer.hpp" #include "caffe/proto/caffe.pb.h" -#include "caffe/vision_layers.hpp" + +#ifdef USE_CUDNN +#include "caffe/layers/cudnn_conv_layer.hpp" +#include "caffe/layers/cudnn_lcn_layer.hpp" +#include "caffe/layers/cudnn_lrn_layer.hpp" +#include "caffe/layers/cudnn_pooling_layer.hpp" +#include "caffe/layers/cudnn_relu_layer.hpp" +#include "caffe/layers/cudnn_sigmoid_layer.hpp" +#include "caffe/layers/cudnn_softmax_layer.hpp" +#include "caffe/layers/cudnn_tanh_layer.hpp" +#endif #ifdef WITH_PYTHON_LAYER -#include "caffe/python_layer.hpp" +#include "caffe/layers/python_layer.hpp" #endif namespace caffe { diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp index 7e552352608..855bf0bfacb 100644 --- a/src/caffe/layers/absval_layer.cpp +++ b/src/caffe/layers/absval_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/absval_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu index b5a6c25a85a..6c927e6fabc 100644 --- a/src/caffe/layers/absval_layer.cu +++ b/src/caffe/layers/absval_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/absval_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp index ae2df1f1bad..4eddbb5c850 100644 --- a/src/caffe/layers/accuracy_layer.cpp +++ b/src/caffe/layers/accuracy_layer.cpp @@ -2,7 +2,7 @@ #include #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/accuracy_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp index 354d83f7061..2d3d6f2d3ff 100644 --- a/src/caffe/layers/argmax_layer.cpp +++ b/src/caffe/layers/argmax_layer.cpp @@ -3,7 +3,7 @@ #include #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/argmax_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp index 316cb0fdf98..f6f14cd0f17 100644 --- a/src/caffe/layers/base_conv_layer.cpp +++ b/src/caffe/layers/base_conv_layer.cpp @@ -2,9 +2,9 @@ #include #include "caffe/filler.hpp" +#include "caffe/layers/base_conv_layer.hpp" #include "caffe/util/im2col.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp index d77f91c913b..989319f1a07 100644 --- a/src/caffe/layers/base_data_layer.cpp +++ b/src/caffe/layers/base_data_layer.cpp @@ -1,7 +1,13 @@ #include #include -#include "caffe/data_layers.hpp" +#include "caffe/blob.hpp" +#include "caffe/data_transformer.hpp" +#include "caffe/internal_thread.hpp" +#include "caffe/layer.hpp" +#include "caffe/layers/base_data_layer.hpp" +#include "caffe/proto/caffe.pb.h" +#include "caffe/util/blocking_queue.hpp" namespace caffe { diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu index ff6e412aba6..4056d36a7b4 100644 --- a/src/caffe/layers/base_data_layer.cu +++ b/src/caffe/layers/base_data_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/data_layers.hpp" +#include "caffe/layers/base_data_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp index b5c91b5e1b3..a69d8f99316 100644 --- a/src/caffe/layers/batch_norm_layer.cpp +++ b/src/caffe/layers/batch_norm_layer.cpp @@ -1,7 +1,7 @@ #include #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/batch_norm_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu index 2a6cac54168..c21713c81d9 100644 --- a/src/caffe/layers/batch_norm_layer.cu +++ b/src/caffe/layers/batch_norm_layer.cu @@ -1,7 +1,7 @@ #include #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/batch_norm_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/batch_reindex_layer.cpp b/src/caffe/layers/batch_reindex_layer.cpp index 3d3ce32c90d..b14e56f7c6b 100644 --- a/src/caffe/layers/batch_reindex_layer.cpp +++ b/src/caffe/layers/batch_reindex_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/batch_reindex_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/batch_reindex_layer.cu b/src/caffe/layers/batch_reindex_layer.cu index 0b5ccf099fe..83054d36d33 100644 --- a/src/caffe/layers/batch_reindex_layer.cu +++ b/src/caffe/layers/batch_reindex_layer.cu @@ -2,7 +2,7 @@ #include #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/batch_reindex_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp index 1e422a54653..448d86d752d 100644 --- a/src/caffe/layers/bnll_layer.cpp +++ b/src/caffe/layers/bnll_layer.cpp @@ -1,7 +1,7 @@ #include #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/bnll_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu index 3e328ef70fa..8df8ef09afe 100644 --- a/src/caffe/layers/bnll_layer.cu +++ b/src/caffe/layers/bnll_layer.cu @@ -1,7 +1,7 @@ #include #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/bnll_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp index 14cbfb11f8b..580bd47977d 100644 --- a/src/caffe/layers/concat_layer.cpp +++ b/src/caffe/layers/concat_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/concat_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu index e1e9449e406..a3a0bf6f6ea 100644 --- a/src/caffe/layers/concat_layer.cu +++ b/src/caffe/layers/concat_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/concat_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp index 45facd4a4f5..599e178e9c4 100644 --- a/src/caffe/layers/contrastive_loss_layer.cpp +++ b/src/caffe/layers/contrastive_loss_layer.cpp @@ -1,7 +1,7 @@ #include #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/contrastive_loss_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu index ee2784077e4..fd7d67cca94 100644 --- a/src/caffe/layers/contrastive_loss_layer.cu +++ b/src/caffe/layers/contrastive_loss_layer.cu @@ -1,7 +1,7 @@ #include #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/contrastive_loss_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp index efd69d45edf..cff09783945 100644 --- a/src/caffe/layers/conv_layer.cpp +++ b/src/caffe/layers/conv_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/conv_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu index a534b3560f5..d06e4b6244e 100644 --- a/src/caffe/layers/conv_layer.cu +++ b/src/caffe/layers/conv_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/conv_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp index c82cb7efd6c..1987fb096b0 100644 --- a/src/caffe/layers/cudnn_conv_layer.cpp +++ b/src/caffe/layers/cudnn_conv_layer.cpp @@ -2,7 +2,7 @@ #include #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_conv_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu index f2df4aa502f..1990e932a70 100644 --- a/src/caffe/layers/cudnn_conv_layer.cu +++ b/src/caffe/layers/cudnn_conv_layer.cu @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_conv_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_lcn_layer.cpp b/src/caffe/layers/cudnn_lcn_layer.cpp index 4c700786e6b..9c09bf26b4d 100644 --- a/src/caffe/layers/cudnn_lcn_layer.cpp +++ b/src/caffe/layers/cudnn_lcn_layer.cpp @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_lcn_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_lcn_layer.cu b/src/caffe/layers/cudnn_lcn_layer.cu index e79c745894a..b44ef4730ef 100644 --- a/src/caffe/layers/cudnn_lcn_layer.cu +++ b/src/caffe/layers/cudnn_lcn_layer.cu @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_lcn_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_lrn_layer.cpp b/src/caffe/layers/cudnn_lrn_layer.cpp index a03db3bdd9f..0495b802baf 100644 --- a/src/caffe/layers/cudnn_lrn_layer.cpp +++ b/src/caffe/layers/cudnn_lrn_layer.cpp @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_lrn_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_lrn_layer.cu b/src/caffe/layers/cudnn_lrn_layer.cu index 327e44b4d31..ca647f3c64d 100644 --- a/src/caffe/layers/cudnn_lrn_layer.cu +++ b/src/caffe/layers/cudnn_lrn_layer.cu @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_lrn_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp index 5f995d45e0c..24f14780b4f 100644 --- a/src/caffe/layers/cudnn_pooling_layer.cpp +++ b/src/caffe/layers/cudnn_pooling_layer.cpp @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_pooling_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu index 9aa39ed8818..6f00195fa2d 100644 --- a/src/caffe/layers/cudnn_pooling_layer.cu +++ b/src/caffe/layers/cudnn_pooling_layer.cu @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_pooling_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp index e6b6d5a9715..c86c6907113 100644 --- a/src/caffe/layers/cudnn_relu_layer.cpp +++ b/src/caffe/layers/cudnn_relu_layer.cpp @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_relu_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu index 2a53a49b91f..9f617183baa 100644 --- a/src/caffe/layers/cudnn_relu_layer.cu +++ b/src/caffe/layers/cudnn_relu_layer.cu @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_relu_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp index 4b489fa5b16..ccb955cdaff 100644 --- a/src/caffe/layers/cudnn_sigmoid_layer.cpp +++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_sigmoid_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu index 9de5c742c8e..e2a4b460c6c 100644 --- a/src/caffe/layers/cudnn_sigmoid_layer.cu +++ b/src/caffe/layers/cudnn_sigmoid_layer.cu @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_sigmoid_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp index f5cd04505b8..6440df9805b 100644 --- a/src/caffe/layers/cudnn_softmax_layer.cpp +++ b/src/caffe/layers/cudnn_softmax_layer.cpp @@ -3,7 +3,7 @@ #include "thrust/device_vector.h" -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_softmax_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu index c270202f0c9..7283eb71558 100644 --- a/src/caffe/layers/cudnn_softmax_layer.cu +++ b/src/caffe/layers/cudnn_softmax_layer.cu @@ -3,7 +3,7 @@ #include "thrust/device_vector.h" -#include "caffe/vision_layers.hpp" +#include "caffe/layers/cudnn_softmax_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp index 4629681807d..1a56418227c 100644 --- a/src/caffe/layers/cudnn_tanh_layer.cpp +++ b/src/caffe/layers/cudnn_tanh_layer.cpp @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/cudnn_tanh_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu index 84f784b37c4..89df28a3e8b 100644 --- a/src/caffe/layers/cudnn_tanh_layer.cu +++ b/src/caffe/layers/cudnn_tanh_layer.cu @@ -1,7 +1,7 @@ #ifdef USE_CUDNN #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/cudnn_tanh_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp index 49ac858efc9..66e6301fd45 100644 --- a/src/caffe/layers/data_layer.cpp +++ b/src/caffe/layers/data_layer.cpp @@ -5,8 +5,8 @@ #include -#include "caffe/data_layers.hpp" -#include "caffe/proto/caffe.pb.h" +#include "caffe/data_transformer.hpp" +#include "caffe/layers/data_layer.hpp" #include "caffe/util/benchmark.hpp" namespace caffe { diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp index 5038b6389fa..275c05626c8 100644 --- a/src/caffe/layers/deconv_layer.cpp +++ b/src/caffe/layers/deconv_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/deconv_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu index 0e8e2edea1e..226763223fa 100644 --- a/src/caffe/layers/deconv_layer.cu +++ b/src/caffe/layers/deconv_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/vision_layers.hpp" +#include "caffe/layers/deconv_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp index eb7a8a9a20b..9cb64d9735f 100644 --- a/src/caffe/layers/dropout_layer.cpp +++ b/src/caffe/layers/dropout_layer.cpp @@ -2,7 +2,7 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/dropout_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu index 028fc026d51..186c10ca489 100644 --- a/src/caffe/layers/dropout_layer.cu +++ b/src/caffe/layers/dropout_layer.cu @@ -1,11 +1,10 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/dropout_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { - template __global__ void DropoutForward(const int n, const Dtype* in, const unsigned int* mask, const unsigned int threshold, const float scale, @@ -68,5 +67,4 @@ void DropoutLayer::Backward_gpu(const vector*>& top, INSTANTIATE_LAYER_GPU_FUNCS(DropoutLayer); - } // namespace caffe diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp index ab0478c860c..e382bfea802 100644 --- a/src/caffe/layers/dummy_data_layer.cpp +++ b/src/caffe/layers/dummy_data_layer.cpp @@ -1,7 +1,7 @@ #include -#include "caffe/data_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/dummy_data_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp index 7924fbeec7d..21256166bfa 100644 --- a/src/caffe/layers/eltwise_layer.cpp +++ b/src/caffe/layers/eltwise_layer.cpp @@ -1,7 +1,7 @@ #include #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/eltwise_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu index 01404209834..c142852e03d 100644 --- a/src/caffe/layers/eltwise_layer.cu +++ b/src/caffe/layers/eltwise_layer.cu @@ -1,7 +1,7 @@ #include #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/eltwise_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/embed_layer.cpp b/src/caffe/layers/embed_layer.cpp index 52704a06d29..36b40d700fd 100644 --- a/src/caffe/layers/embed_layer.cpp +++ b/src/caffe/layers/embed_layer.cpp @@ -1,7 +1,7 @@ #include -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/embed_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu index cd4b40f58fe..6324a3a8937 100644 --- a/src/caffe/layers/embed_layer.cu +++ b/src/caffe/layers/embed_layer.cu @@ -1,7 +1,7 @@ #include -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/embed_layer.hpp" #include "caffe/util/gpu_util.cuh" #include "caffe/util/math_functions.hpp" diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp index 7338953d340..300d991e765 100644 --- a/src/caffe/layers/euclidean_loss_layer.cpp +++ b/src/caffe/layers/euclidean_loss_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/euclidean_loss_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu index 1aa79bd542d..4c221b64faf 100644 --- a/src/caffe/layers/euclidean_loss_layer.cu +++ b/src/caffe/layers/euclidean_loss_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/euclidean_loss_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp index f85692d6c5d..1f4a309fe25 100644 --- a/src/caffe/layers/exp_layer.cpp +++ b/src/caffe/layers/exp_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/exp_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/exp_layer.cu b/src/caffe/layers/exp_layer.cu index 9e24bbeec9d..61f7f11dd46 100644 --- a/src/caffe/layers/exp_layer.cu +++ b/src/caffe/layers/exp_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/exp_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/filter_layer.cpp b/src/caffe/layers/filter_layer.cpp index e8b62a5d5fc..e226c0b6c9b 100644 --- a/src/caffe/layers/filter_layer.cpp +++ b/src/caffe/layers/filter_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/filter_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/filter_layer.cu b/src/caffe/layers/filter_layer.cu index 746e91c9e95..b01b16f840c 100644 --- a/src/caffe/layers/filter_layer.cu +++ b/src/caffe/layers/filter_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/filter_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp index d831fb5c6b5..651507e2e7d 100644 --- a/src/caffe/layers/flatten_layer.cpp +++ b/src/caffe/layers/flatten_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/flatten_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp index c765fa02c82..2f13dc641df 100644 --- a/src/caffe/layers/hdf5_data_layer.cpp +++ b/src/caffe/layers/hdf5_data_layer.cpp @@ -14,7 +14,7 @@ #include "hdf5_hl.h" #include "stdint.h" -#include "caffe/data_layers.hpp" +#include "caffe/layers/hdf5_data_layer.hpp" #include "caffe/util/hdf5.hpp" namespace caffe { diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu index 6ac499c6fbd..595d2230220 100644 --- a/src/caffe/layers/hdf5_data_layer.cu +++ b/src/caffe/layers/hdf5_data_layer.cu @@ -9,7 +9,7 @@ TODO: #include "hdf5.h" #include "hdf5_hl.h" -#include "caffe/data_layers.hpp" +#include "caffe/layers/hdf5_data_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp index dbde65da14f..f8f1edcd18e 100644 --- a/src/caffe/layers/hdf5_output_layer.cpp +++ b/src/caffe/layers/hdf5_output_layer.cpp @@ -3,7 +3,7 @@ #include "hdf5.h" #include "hdf5_hl.h" -#include "caffe/data_layers.hpp" +#include "caffe/layers/hdf5_output_layer.hpp" #include "caffe/util/hdf5.hpp" namespace caffe { diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu index ca8f2616548..c1685cd34a7 100644 --- a/src/caffe/layers/hdf5_output_layer.cu +++ b/src/caffe/layers/hdf5_output_layer.cu @@ -3,7 +3,7 @@ #include "hdf5.h" #include "hdf5_hl.h" -#include "caffe/data_layers.hpp" +#include "caffe/layers/hdf5_output_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp index a88c8775205..374aed3c98f 100644 --- a/src/caffe/layers/hinge_loss_layer.cpp +++ b/src/caffe/layers/hinge_loss_layer.cpp @@ -1,7 +1,7 @@ #include #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/hinge_loss_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp index f3b0f7101b8..c12e4f52a10 100644 --- a/src/caffe/layers/im2col_layer.cpp +++ b/src/caffe/layers/im2col_layer.cpp @@ -1,7 +1,7 @@ #include +#include "caffe/layers/im2col_layer.hpp" #include "caffe/util/im2col.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu index 4633628b4c0..517b4220cb9 100644 --- a/src/caffe/layers/im2col_layer.cu +++ b/src/caffe/layers/im2col_layer.cu @@ -1,7 +1,7 @@ #include +#include "caffe/layers/im2col_layer.hpp" #include "caffe/util/im2col.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp index 9a7df5a78ca..62fda4accce 100644 --- a/src/caffe/layers/image_data_layer.cpp +++ b/src/caffe/layers/image_data_layer.cpp @@ -7,7 +7,9 @@ #include #include -#include "caffe/data_layers.hpp" +#include "caffe/data_transformer.hpp" +#include "caffe/layers/base_data_layer.hpp" +#include "caffe/layers/image_data_layer.hpp" #include "caffe/util/benchmark.hpp" #include "caffe/util/io.hpp" #include "caffe/util/math_functions.hpp" diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp index 88bd8aaf54f..624d3118124 100644 --- a/src/caffe/layers/infogain_loss_layer.cpp +++ b/src/caffe/layers/infogain_loss_layer.cpp @@ -2,7 +2,7 @@ #include #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/infogain_loss_layer.hpp" #include "caffe/util/io.hpp" namespace caffe { diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp index 274744eaaf8..d9088805501 100644 --- a/src/caffe/layers/inner_product_layer.cpp +++ b/src/caffe/layers/inner_product_layer.cpp @@ -1,7 +1,7 @@ #include -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/inner_product_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu index e91e94fc9c6..dc25aa33bd1 100644 --- a/src/caffe/layers/inner_product_layer.cu +++ b/src/caffe/layers/inner_product_layer.cu @@ -1,7 +1,7 @@ #include -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/inner_product_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp index a1876b9da76..c70a795cf53 100644 --- a/src/caffe/layers/log_layer.cpp +++ b/src/caffe/layers/log_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/log_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/log_layer.cu b/src/caffe/layers/log_layer.cu index 055b713bef3..db466dbac29 100644 --- a/src/caffe/layers/log_layer.cu +++ b/src/caffe/layers/log_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/log_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp index c10466dbdb0..c0b7a862181 100644 --- a/src/caffe/layers/loss_layer.cpp +++ b/src/caffe/layers/loss_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/loss_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp index cc561811e95..210525e20f3 100644 --- a/src/caffe/layers/lrn_layer.cpp +++ b/src/caffe/layers/lrn_layer.cpp @@ -1,7 +1,7 @@ #include +#include "caffe/layers/lrn_layer.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu index 4523d41095d..26e619c7569 100644 --- a/src/caffe/layers/lrn_layer.cu +++ b/src/caffe/layers/lrn_layer.cu @@ -1,7 +1,7 @@ #include +#include "caffe/layers/lrn_layer.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp index 13a3d9f61b9..82909874054 100644 --- a/src/caffe/layers/memory_data_layer.cpp +++ b/src/caffe/layers/memory_data_layer.cpp @@ -4,7 +4,7 @@ #include -#include "caffe/data_layers.hpp" +#include "caffe/layers/memory_data_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp index 59745923825..65664998d2c 100644 --- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp +++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp @@ -2,7 +2,7 @@ #include #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/multinomial_logistic_loss_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp index 0e7301442ba..8fe4ef8c0a8 100644 --- a/src/caffe/layers/mvn_layer.cpp +++ b/src/caffe/layers/mvn_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/mvn_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu index b7e3b3ceb39..739293be00e 100644 --- a/src/caffe/layers/mvn_layer.cu +++ b/src/caffe/layers/mvn_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/mvn_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp index 1dcb2c06419..d7b5f389310 100644 --- a/src/caffe/layers/neuron_layer.cpp +++ b/src/caffe/layers/neuron_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/neuron_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp index 3a7de42c91a..90897db0f45 100644 --- a/src/caffe/layers/pooling_layer.cpp +++ b/src/caffe/layers/pooling_layer.cpp @@ -2,8 +2,8 @@ #include #include +#include "caffe/layers/pooling_layer.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu index 5e94ce2bc3f..1ea46cc81b1 100644 --- a/src/caffe/layers/pooling_layer.cu +++ b/src/caffe/layers/pooling_layer.cu @@ -2,8 +2,8 @@ #include #include +#include "caffe/layers/pooling_layer.hpp" #include "caffe/util/math_functions.hpp" -#include "caffe/vision_layers.hpp" namespace caffe { diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp index 6304fadd489..d99b77ca839 100644 --- a/src/caffe/layers/power_layer.cpp +++ b/src/caffe/layers/power_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/power_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu index 680faad4bca..07711c4213d 100644 --- a/src/caffe/layers/power_layer.cu +++ b/src/caffe/layers/power_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/power_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp index b5a294e1c5a..853181bd5a2 100644 --- a/src/caffe/layers/prelu_layer.cpp +++ b/src/caffe/layers/prelu_layer.cpp @@ -2,7 +2,9 @@ #include #include "caffe/filler.hpp" -#include "caffe/neuron_layers.hpp" + +#include "caffe/layers/neuron_layer.hpp" +#include "caffe/layers/prelu_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu index 992cd885a95..aeb80eacd03 100644 --- a/src/caffe/layers/prelu_layer.cu +++ b/src/caffe/layers/prelu_layer.cu @@ -1,7 +1,8 @@ #include #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/neuron_layer.hpp" +#include "caffe/layers/prelu_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/reduction_layer.cpp b/src/caffe/layers/reduction_layer.cpp index 6b7925e37b0..fa46487e6a3 100644 --- a/src/caffe/layers/reduction_layer.cpp +++ b/src/caffe/layers/reduction_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/reduction_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/reduction_layer.cu b/src/caffe/layers/reduction_layer.cu index a9a8c8d9633..4a6b2b73fc7 100644 --- a/src/caffe/layers/reduction_layer.cu +++ b/src/caffe/layers/reduction_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/reduction_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp index 93d090263c7..92a729c81bd 100644 --- a/src/caffe/layers/relu_layer.cpp +++ b/src/caffe/layers/relu_layer.cpp @@ -1,7 +1,7 @@ #include #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/relu_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu index c18ab61f2ed..4bf15b3aad3 100644 --- a/src/caffe/layers/relu_layer.cu +++ b/src/caffe/layers/relu_layer.cu @@ -1,7 +1,7 @@ #include #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/relu_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp index 8659049b528..82339f76d8f 100644 --- a/src/caffe/layers/reshape_layer.cpp +++ b/src/caffe/layers/reshape_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/reshape_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp index 98588637831..10ac9470832 100644 --- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp +++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu index 48dbec41be1..046cb9d3a31 100644 --- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu +++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp index d4a3f8773d6..85fd9676812 100644 --- a/src/caffe/layers/sigmoid_layer.cpp +++ b/src/caffe/layers/sigmoid_layer.cpp @@ -1,7 +1,7 @@ #include #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/sigmoid_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu index 5730636ef11..184c61ede83 100644 --- a/src/caffe/layers/sigmoid_layer.cu +++ b/src/caffe/layers/sigmoid_layer.cu @@ -1,7 +1,7 @@ #include #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/sigmoid_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp index 3974f5d4e65..b2f85c52a0f 100644 --- a/src/caffe/layers/silence_layer.cpp +++ b/src/caffe/layers/silence_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/silence_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu index c49ecb2338a..3494f6f6731 100644 --- a/src/caffe/layers/silence_layer.cu +++ b/src/caffe/layers/silence_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/silence_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp index f368a249a5e..759beafe0d9 100644 --- a/src/caffe/layers/slice_layer.cpp +++ b/src/caffe/layers/slice_layer.cpp @@ -1,7 +1,7 @@ #include #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/slice_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu index d555f7d0dec..1be3a797d3e 100644 --- a/src/caffe/layers/slice_layer.cu +++ b/src/caffe/layers/slice_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/slice_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp index 8ae7d49cfaa..f60e9b03ebf 100644 --- a/src/caffe/layers/softmax_layer.cpp +++ b/src/caffe/layers/softmax_layer.cpp @@ -1,7 +1,7 @@ #include #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/softmax_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu index a620fcc8601..7a9e6833bf6 100644 --- a/src/caffe/layers/softmax_layer.cu +++ b/src/caffe/layers/softmax_layer.cu @@ -4,7 +4,7 @@ #include "thrust/device_vector.h" -#include "caffe/common_layers.hpp" +#include "caffe/layers/softmax_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp index 3cdef82afd2..dddb7606573 100644 --- a/src/caffe/layers/softmax_loss_layer.cpp +++ b/src/caffe/layers/softmax_loss_layer.cpp @@ -2,7 +2,7 @@ #include #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/softmax_loss_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu index 4753a1ec24b..660e1b39fe0 100644 --- a/src/caffe/layers/softmax_loss_layer.cu +++ b/src/caffe/layers/softmax_loss_layer.cu @@ -2,7 +2,7 @@ #include #include -#include "caffe/loss_layers.hpp" +#include "caffe/layers/softmax_loss_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp index 5333e578f76..1a27a9af0a1 100644 --- a/src/caffe/layers/split_layer.cpp +++ b/src/caffe/layers/split_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/split_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu index 73d04c98fc1..bec9987c7cc 100644 --- a/src/caffe/layers/split_layer.cu +++ b/src/caffe/layers/split_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/split_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/spp_layer.cpp b/src/caffe/layers/spp_layer.cpp index 2ef4ac7ab95..b9af8e8af0e 100644 --- a/src/caffe/layers/spp_layer.cpp +++ b/src/caffe/layers/spp_layer.cpp @@ -1,7 +1,12 @@ #include #include -#include "caffe/vision_layers.hpp" +#include "caffe/layer.hpp" +#include "caffe/layers/concat_layer.hpp" +#include "caffe/layers/flatten_layer.hpp" +#include "caffe/layers/pooling_layer.hpp" +#include "caffe/layers/split_layer.hpp" +#include "caffe/layers/spp_layer.hpp" namespace caffe { @@ -217,7 +222,6 @@ void SPPLayer::Backward_cpu(const vector*>& top, split_layer_->Backward(split_top_vec_, propagate_down, bottom); } - INSTANTIATE_CLASS(SPPLayer); REGISTER_LAYER_CLASS(SPP); diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp index 9d1cac76184..184e926d22a 100644 --- a/src/caffe/layers/tanh_layer.cpp +++ b/src/caffe/layers/tanh_layer.cpp @@ -3,7 +3,7 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/tanh_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu index d87bcceced7..cbfc178e6db 100644 --- a/src/caffe/layers/tanh_layer.cu +++ b/src/caffe/layers/tanh_layer.cu @@ -3,7 +3,7 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/tanh_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp index d65147368fc..63822ee5520 100644 --- a/src/caffe/layers/threshold_layer.cpp +++ b/src/caffe/layers/threshold_layer.cpp @@ -1,7 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" - +#include "caffe/layers/threshold_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu index 1cd62d99482..b0b0665589f 100644 --- a/src/caffe/layers/threshold_layer.cu +++ b/src/caffe/layers/threshold_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/threshold_layer.hpp" namespace caffe { diff --git a/src/caffe/layers/tile_layer.cpp b/src/caffe/layers/tile_layer.cpp index 581546c4fdb..cf0c187005c 100644 --- a/src/caffe/layers/tile_layer.cpp +++ b/src/caffe/layers/tile_layer.cpp @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/tile_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/tile_layer.cu b/src/caffe/layers/tile_layer.cu index fdf960901a3..282049ebd7b 100644 --- a/src/caffe/layers/tile_layer.cu +++ b/src/caffe/layers/tile_layer.cu @@ -1,6 +1,6 @@ #include -#include "caffe/common_layers.hpp" +#include "caffe/layers/tile_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp index 3f937bc9dd8..4ca8315d791 100644 --- a/src/caffe/layers/window_data_layer.cpp +++ b/src/caffe/layers/window_data_layer.cpp @@ -12,7 +12,10 @@ #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" -#include "caffe/data_layers.hpp" +#include "caffe/data_transformer.hpp" +#include "caffe/internal_thread.hpp" +#include "caffe/layers/base_data_layer.hpp" +#include "caffe/layers/window_data_layer.hpp" #include "caffe/util/benchmark.hpp" #include "caffe/util/io.hpp" #include "caffe/util/math_functions.hpp" diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp index 5960a666734..6fe808bd5c5 100644 --- a/src/caffe/test/test_accuracy_layer.cpp +++ b/src/caffe/test/test_accuracy_layer.cpp @@ -6,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/loss_layers.hpp" +#include "caffe/layers/accuracy_layer.hpp" #include "caffe/util/rng.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp index f3f2094edeb..472e6652239 100644 --- a/src/caffe/test/test_argmax_layer.cpp +++ b/src/caffe/test/test_argmax_layer.cpp @@ -5,8 +5,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/argmax_layer.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_batch_norm_layer.cpp b/src/caffe/test/test_batch_norm_layer.cpp index 22b9667f31b..936b93a1756 100644 --- a/src/caffe/test/test_batch_norm_layer.cpp +++ b/src/caffe/test/test_batch_norm_layer.cpp @@ -6,8 +6,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/batch_norm_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_batch_reindex_layer.cpp b/src/caffe/test/test_batch_reindex_layer.cpp index 17e47f05066..9ea1a2f6f47 100644 --- a/src/caffe/test/test_batch_reindex_layer.cpp +++ b/src/caffe/test/test_batch_reindex_layer.cpp @@ -4,8 +4,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/batch_reindex_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp index 8ba51f4f7f7..23c1e8c1d29 100644 --- a/src/caffe/test/test_concat_layer.cpp +++ b/src/caffe/test/test_concat_layer.cpp @@ -4,8 +4,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/concat_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp index 95901f14297..2fa055ee0de 100644 --- a/src/caffe/test/test_contrastive_loss_layer.cpp +++ b/src/caffe/test/test_contrastive_loss_layer.cpp @@ -7,7 +7,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/loss_layers.hpp" +#include "caffe/layers/contrastive_loss_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp index b474735716d..e2d43f31b6a 100644 --- a/src/caffe/test/test_convolution_layer.cpp +++ b/src/caffe/test/test_convolution_layer.cpp @@ -5,7 +5,11 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/layers/conv_layer.hpp" + +#ifdef USE_CUDNN +#include "caffe/layers/cudnn_conv_layer.hpp" +#endif #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp index 9e03954a543..3e8d113d918 100644 --- a/src/caffe/test/test_data_layer.cpp +++ b/src/caffe/test/test_data_layer.cpp @@ -7,8 +7,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/data_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/data_layer.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/db.hpp" #include "caffe/util/io.hpp" diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp index b473dbb9c51..c4b09ad555a 100644 --- a/src/caffe/test/test_deconvolution_layer.cpp +++ b/src/caffe/test/test_deconvolution_layer.cpp @@ -5,7 +5,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/layers/deconv_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp index c9ed38db3a5..1a01ca85f89 100644 --- a/src/caffe/test/test_dummy_data_layer.cpp +++ b/src/caffe/test/test_dummy_data_layer.cpp @@ -5,8 +5,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" +#include "caffe/layers/dummy_data_layer.hpp" #include "caffe/proto/caffe.pb.h" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp index 3b56c5cae36..c06e3baab15 100644 --- a/src/caffe/test/test_eltwise_layer.cpp +++ b/src/caffe/test/test_eltwise_layer.cpp @@ -5,8 +5,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/eltwise_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp index 0f4caf15742..acd4b0f636b 100644 --- a/src/caffe/test/test_embed_layer.cpp +++ b/src/caffe/test/test_embed_layer.cpp @@ -4,8 +4,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/embed_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp index 9dc14de4149..f253f9fd393 100644 --- a/src/caffe/test/test_euclidean_loss_layer.cpp +++ b/src/caffe/test/test_euclidean_loss_layer.cpp @@ -6,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/loss_layers.hpp" +#include "caffe/layers/euclidean_loss_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_filter_layer.cpp b/src/caffe/test/test_filter_layer.cpp index a2d0c293644..9ea2b8b2168 100644 --- a/src/caffe/test/test_filter_layer.cpp +++ b/src/caffe/test/test_filter_layer.cpp @@ -4,8 +4,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/filter_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp index 5d1caac2aa7..d929ac7a720 100644 --- a/src/caffe/test/test_flatten_layer.cpp +++ b/src/caffe/test/test_flatten_layer.cpp @@ -4,8 +4,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/flatten_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp index adc27df4ad0..3833ebff78e 100644 --- a/src/caffe/test/test_hdf5_output_layer.cpp +++ b/src/caffe/test/test_hdf5_output_layer.cpp @@ -5,7 +5,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/data_layers.hpp" +#include "caffe/layers/hdf5_output_layer.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/hdf5.hpp" #include "caffe/util/io.hpp" diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp index 7169e7bfc41..8884ce95a23 100644 --- a/src/caffe/test/test_hdf5data_layer.cpp +++ b/src/caffe/test/test_hdf5data_layer.cpp @@ -1,11 +1,13 @@ #include #include +#include "hdf5.h" + #include "gtest/gtest.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/data_layers.hpp" +#include "caffe/layers/hdf5_data_layer.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp index dfdd01d0291..8bf89fa6387 100644 --- a/src/caffe/test/test_hinge_loss_layer.cpp +++ b/src/caffe/test/test_hinge_loss_layer.cpp @@ -6,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/loss_layers.hpp" +#include "caffe/layers/hinge_loss_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu index bafcacf784e..3f97cf6d5ae 100644 --- a/src/caffe/test/test_im2col_kernel.cu +++ b/src/caffe/test/test_im2col_kernel.cu @@ -5,8 +5,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/im2col_layer.hpp" #include "caffe/util/im2col.hpp" -#include "caffe/vision_layers.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp index ec055b20176..8274dd48971 100644 --- a/src/caffe/test/test_im2col_layer.cpp +++ b/src/caffe/test/test_im2col_layer.cpp @@ -5,7 +5,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/layers/im2col_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp index 77690245111..a4080ccd145 100644 --- a/src/caffe/test/test_image_data_layer.cpp +++ b/src/caffe/test/test_image_data_layer.cpp @@ -7,8 +7,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/data_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/image_data_layer.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/io.hpp" diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp index b2a6754fd40..a24ac683dc5 100644 --- a/src/caffe/test/test_infogain_loss_layer.cpp +++ b/src/caffe/test/test_infogain_loss_layer.cpp @@ -5,7 +5,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/loss_layers.hpp" +#include "caffe/layers/infogain_loss_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp index 1ad2c97e75a..b888b510318 100644 --- a/src/caffe/test/test_inner_product_layer.cpp +++ b/src/caffe/test/test_inner_product_layer.cpp @@ -4,8 +4,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/inner_product_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp index bd1c4fe8810..4c97b1ae07b 100644 --- a/src/caffe/test/test_lrn_layer.cpp +++ b/src/caffe/test/test_lrn_layer.cpp @@ -6,7 +6,12 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/layers/lrn_layer.hpp" + +#ifdef USE_CUDNN +#include "caffe/layers/cudnn_lcn_layer.hpp" +#include "caffe/layers/cudnn_lrn_layer.hpp" +#endif #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp index 8fc944f3250..4f0e20ac3a7 100644 --- a/src/caffe/test/test_maxpool_dropout_layers.cpp +++ b/src/caffe/test/test_maxpool_dropout_layers.cpp @@ -5,7 +5,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/layers/dropout_layer.hpp" +#include "caffe/layers/pooling_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp index 7269a4d441b..7998bc18262 100644 --- a/src/caffe/test/test_memory_data_layer.cpp +++ b/src/caffe/test/test_memory_data_layer.cpp @@ -5,8 +5,8 @@ #include #include -#include "caffe/data_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/memory_data_layer.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp index 0404aa25af6..8cc21022305 100644 --- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp +++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp @@ -5,7 +5,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/loss_layers.hpp" +#include "caffe/layers/multinomial_logistic_loss_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp index e9a7d54ce4a..28a762d2741 100644 --- a/src/caffe/test/test_mvn_layer.cpp +++ b/src/caffe/test/test_mvn_layer.cpp @@ -2,8 +2,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/mvn_layer.hpp" #include "google/protobuf/text_format.h" #include "gtest/gtest.h" diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp index b333fdee802..21441b4121e 100644 --- a/src/caffe/test/test_neuron_layer.cpp +++ b/src/caffe/test/test_neuron_layer.cpp @@ -6,9 +6,26 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" -#include "caffe/neuron_layers.hpp" + +#include "caffe/layers/absval_layer.hpp" +#include "caffe/layers/bnll_layer.hpp" +#include "caffe/layers/dropout_layer.hpp" +#include "caffe/layers/exp_layer.hpp" +#include "caffe/layers/inner_product_layer.hpp" +#include "caffe/layers/log_layer.hpp" +#include "caffe/layers/power_layer.hpp" +#include "caffe/layers/prelu_layer.hpp" +#include "caffe/layers/relu_layer.hpp" +#include "caffe/layers/sigmoid_layer.hpp" +#include "caffe/layers/tanh_layer.hpp" +#include "caffe/layers/threshold_layer.hpp" + +#ifdef USE_CUDNN +#include "caffe/layers/cudnn_relu_layer.hpp" +#include "caffe/layers/cudnn_sigmoid_layer.hpp" +#include "caffe/layers/cudnn_tanh_layer.hpp" +#endif #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp index 9e986e66528..bb95cae032d 100644 --- a/src/caffe/test/test_pooling_layer.cpp +++ b/src/caffe/test/test_pooling_layer.cpp @@ -5,7 +5,11 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/layers/pooling_layer.hpp" + +#ifdef USE_CUDNN +#include "caffe/layers/cudnn_pooling_layer.hpp" +#endif #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp index 1041ddd4ee8..1aa587ac97a 100644 --- a/src/caffe/test/test_power_layer.cpp +++ b/src/caffe/test/test_power_layer.cpp @@ -6,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/power_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_reduction_layer.cpp b/src/caffe/test/test_reduction_layer.cpp index a8d43727113..6ed7cda6adc 100644 --- a/src/caffe/test/test_reduction_layer.cpp +++ b/src/caffe/test/test_reduction_layer.cpp @@ -4,8 +4,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/reduction_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp index e0f4ba42851..4f2613868d4 100644 --- a/src/caffe/test/test_reshape_layer.cpp +++ b/src/caffe/test/test_reshape_layer.cpp @@ -4,8 +4,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/reshape_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp index b4f831c8f2d..5dfd7656db2 100644 --- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp +++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp @@ -6,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/loss_layers.hpp" +#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp index 45fbcffda3f..c2b231e1ef4 100644 --- a/src/caffe/test/test_slice_layer.cpp +++ b/src/caffe/test/test_slice_layer.cpp @@ -4,8 +4,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/slice_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp index 4b01f5cfbab..94443576724 100644 --- a/src/caffe/test/test_softmax_layer.cpp +++ b/src/caffe/test/test_softmax_layer.cpp @@ -5,8 +5,12 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/softmax_layer.hpp" + +#ifdef USE_CUDNN +#include "caffe/layers/cudnn_softmax_layer.hpp" +#endif #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp index 0ae4cd6815a..c67f3e0d907 100644 --- a/src/caffe/test/test_softmax_with_loss_layer.cpp +++ b/src/caffe/test/test_softmax_with_loss_layer.cpp @@ -7,7 +7,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/loss_layers.hpp" +#include "caffe/layers/softmax_loss_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp index e27e355c77e..ba2ccbb2b18 100644 --- a/src/caffe/test/test_split_layer.cpp +++ b/src/caffe/test/test_split_layer.cpp @@ -6,8 +6,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/split_layer.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/insert_splits.hpp" diff --git a/src/caffe/test/test_spp_layer.cpp b/src/caffe/test/test_spp_layer.cpp index 1b48a842d3f..59a3af2aec1 100644 --- a/src/caffe/test/test_spp_layer.cpp +++ b/src/caffe/test/test_spp_layer.cpp @@ -5,7 +5,12 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/layers/concat_layer.hpp" +#include "caffe/layers/flatten_layer.hpp" +#include "caffe/layers/pooling_layer.hpp" +#include "caffe/layers/split_layer.hpp" +#include "caffe/layers/spp_layer.hpp" + #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp index 5a412bd4e17..cd5db8383ab 100644 --- a/src/caffe/test/test_stochastic_pooling.cpp +++ b/src/caffe/test/test_stochastic_pooling.cpp @@ -6,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/vision_layers.hpp" +#include "caffe/layers/pooling_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp index f31579cac40..bb8699a8e91 100644 --- a/src/caffe/test/test_tanh_layer.cpp +++ b/src/caffe/test/test_tanh_layer.cpp @@ -6,7 +6,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/tanh_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp index 903a9bc8157..1e84cc5ab84 100644 --- a/src/caffe/test/test_threshold_layer.cpp +++ b/src/caffe/test/test_threshold_layer.cpp @@ -5,7 +5,7 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" -#include "caffe/neuron_layers.hpp" +#include "caffe/layers/threshold_layer.hpp" #include "caffe/test/test_caffe_main.hpp" diff --git a/src/caffe/test/test_tile_layer.cpp b/src/caffe/test/test_tile_layer.cpp index 5c459604a6b..7ff75520e8e 100644 --- a/src/caffe/test/test_tile_layer.cpp +++ b/src/caffe/test/test_tile_layer.cpp @@ -4,8 +4,8 @@ #include "caffe/blob.hpp" #include "caffe/common.hpp" -#include "caffe/common_layers.hpp" #include "caffe/filler.hpp" +#include "caffe/layers/tile_layer.hpp" #include "caffe/test/test_caffe_main.hpp" #include "caffe/test/test_gradient_check_util.hpp" diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp index d1d1fa864c3..058668fe28c 100644 --- a/src/caffe/util/blocking_queue.cpp +++ b/src/caffe/util/blocking_queue.cpp @@ -1,8 +1,8 @@ #include #include -#include "caffe/data_layers.hpp" #include "caffe/data_reader.hpp" +#include "caffe/layers/base_data_layer.hpp" #include "caffe/parallel.hpp" #include "caffe/util/blocking_queue.hpp" diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp index b94dbb980fd..1ef132660b8 100644 --- a/tools/extract_features.cpp +++ b/tools/extract_features.cpp @@ -11,7 +11,6 @@ #include "caffe/util/db.hpp" #include "caffe/util/format.hpp" #include "caffe/util/io.hpp" -#include "caffe/vision_layers.hpp" using caffe::Blob; using caffe::Caffe; From 36bf811574a0787910d80d12fd9ea186481d7939 Mon Sep 17 00:00:00 2001 From: Tea Date: Wed, 2 Dec 2015 15:39:19 +0800 Subject: [PATCH 360/446] Remove hamming_distance and popcount --- include/caffe/util/math_functions.hpp | 7 ---- src/caffe/test/test_math_functions.cpp | 41 ----------------------- src/caffe/util/math_functions.cpp | 22 ------------- src/caffe/util/math_functions.cu | 45 -------------------------- 4 files changed, 115 deletions(-) diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp index 2cacd8e72cd..6f6d3feeae2 100644 --- a/include/caffe/util/math_functions.hpp +++ b/include/caffe/util/math_functions.hpp @@ -101,9 +101,6 @@ template Dtype caffe_cpu_strided_dot(const int n, const Dtype* x, const int incx, const Dtype* y, const int incy); -template -int caffe_cpu_hamming_distance(const int n, const Dtype* x, const Dtype* y); - // Returns the sum of the absolute values of the elements of vector x template Dtype caffe_cpu_asum(const int n, const Dtype* x); @@ -234,10 +231,6 @@ void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r); template void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out); -template -uint32_t caffe_gpu_hamming_distance(const int n, const Dtype* x, - const Dtype* y); - template void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y); diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp index fbee3f9c32d..efc5a2784eb 100644 --- a/src/caffe/test/test_math_functions.cpp +++ b/src/caffe/test/test_math_functions.cpp @@ -39,27 +39,6 @@ class MathFunctionsTest : public MultiDeviceTest { delete blob_top_; } - // http://en.wikipedia.org/wiki/Hamming_distance - int ReferenceHammingDistance(const int n, const Dtype* x, const Dtype* y) { - int dist = 0; - uint64_t val; - for (int i = 0; i < n; ++i) { - if (sizeof(Dtype) == 8) { - val = static_cast(x[i]) ^ static_cast(y[i]); - } else if (sizeof(Dtype) == 4) { - val = static_cast(x[i]) ^ static_cast(y[i]); - } else { - LOG(FATAL) << "Unrecognized Dtype size: " << sizeof(Dtype); - } - // Count the number of set bits - while (val) { - ++dist; - val &= val - 1; - } - } - return dist; - } - Blob* const blob_bottom_; Blob* const blob_top_; }; @@ -76,14 +55,6 @@ TYPED_TEST(CPUMathFunctionsTest, TestNothing) { // due to the set up overhead. } -TYPED_TEST(CPUMathFunctionsTest, TestHammingDistance) { - int n = this->blob_bottom_->count(); - const TypeParam* x = this->blob_bottom_->cpu_data(); - const TypeParam* y = this->blob_top_->cpu_data(); - EXPECT_EQ(this->ReferenceHammingDistance(n, x, y), - caffe_cpu_hamming_distance(n, x, y)); -} - TYPED_TEST(CPUMathFunctionsTest, TestAsum) { int n = this->blob_bottom_->count(); const TypeParam* x = this->blob_bottom_->cpu_data(); @@ -156,18 +127,6 @@ class GPUMathFunctionsTest : public MathFunctionsTest > { TYPED_TEST_CASE(GPUMathFunctionsTest, TestDtypes); -// TODO: Fix caffe_gpu_hamming_distance and re-enable this test. -TYPED_TEST(GPUMathFunctionsTest, DISABLED_TestHammingDistance) { - int n = this->blob_bottom_->count(); - const TypeParam* x = this->blob_bottom_->cpu_data(); - const TypeParam* y = this->blob_top_->cpu_data(); - int reference_distance = this->ReferenceHammingDistance(n, x, y); - x = this->blob_bottom_->gpu_data(); - y = this->blob_top_->gpu_data(); - int computed_distance = caffe_gpu_hamming_distance(n, x, y); - EXPECT_EQ(reference_distance, computed_distance); -} - TYPED_TEST(GPUMathFunctionsTest, TestAsum) { int n = this->blob_bottom_->count(); const TypeParam* x = this->blob_bottom_->cpu_data(); diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp index 0aab6b17b85..71c02274a75 100644 --- a/src/caffe/util/math_functions.cpp +++ b/src/caffe/util/math_functions.cpp @@ -348,28 +348,6 @@ float caffe_cpu_dot(const int n, const float* x, const float* y); template double caffe_cpu_dot(const int n, const double* x, const double* y); -template <> -int caffe_cpu_hamming_distance(const int n, const float* x, - const float* y) { - int dist = 0; - for (int i = 0; i < n; ++i) { - dist += __builtin_popcount(static_cast(x[i]) ^ - static_cast(y[i])); - } - return dist; -} - -template <> -int caffe_cpu_hamming_distance(const int n, const double* x, - const double* y) { - int dist = 0; - for (int i = 0; i < n; ++i) { - dist += __builtin_popcountl(static_cast(x[i]) ^ - static_cast(y[i])); - } - return dist; -} - template <> float caffe_cpu_asum(const int n, const float* x) { return cblas_sasum(n, x, 1); diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu index e4d0c4b04bb..4c587537435 100644 --- a/src/caffe/util/math_functions.cu +++ b/src/caffe/util/math_functions.cu @@ -371,51 +371,6 @@ DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sign, y[index] = (Dtype(0) < x[index]) - (x[index] < Dtype(0))); DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = signbit(x[index])); -__global__ void popc_kernel(const int n, const float* a, - const float* b, uint8_t* y) { - CUDA_KERNEL_LOOP(index, n) { - y[index] = __popc(static_cast(a[index]) ^ - static_cast(b[index])); - } -} - -__global__ void popcll_kernel(const int n, const double* a, - const double* b, uint8_t* y) { - CUDA_KERNEL_LOOP(index, n) { - y[index] = __popcll(static_cast(a[index]) ^ - static_cast(b[index])); - } -} - -template <> -uint32_t caffe_gpu_hamming_distance(const int n, const float* x, - const float* y) { - // TODO: Fix caffe_gpu_hamming_distance (see failing unit test - // TestHammingDistanceGPU in test_math_functions.cpp). - NOT_IMPLEMENTED; - thrust::device_vector popcounts(n); - // NOLINT_NEXT_LINE(whitespace/operators) - popc_kernel<<>>( - n, x, y, thrust::raw_pointer_cast(popcounts.data())); - return thrust::reduce(popcounts.begin(), popcounts.end(), - (uint32_t) 0, thrust::plus()); -} - -template <> -uint32_t caffe_gpu_hamming_distance(const int n, const double* x, - const double* y) { - // TODO: Fix caffe_gpu_hamming_distance (see failing unit test - // TestHammingDistanceGPU in test_math_functions.cpp). - NOT_IMPLEMENTED; - thrust::device_vector popcounts(n); - // NOLINT_NEXT_LINE(whitespace/operators) - popcll_kernel<<>>( - n, x, y, thrust::raw_pointer_cast(popcounts.data())); - return thrust::reduce(popcounts.begin(), popcounts.end(), - /* NOLINT_NEXT_LINE(build/include_what_you_use) */ - (uint32_t) 0, thrust::plus()); -} - void caffe_gpu_rng_uniform(const int n, unsigned int* r) { CURAND_CHECK(curandGenerate(Caffe::curand_generator(), r, n)); } From 99571c471d493c650c53be1416bb26d5b984f178 Mon Sep 17 00:00:00 2001 From: "T.E.A de Souza" Date: Sun, 29 Nov 2015 14:24:09 +0800 Subject: [PATCH 361/446] Correct type of device_id; disambiguate shared_ptr --- tools/extract_features.cpp | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp index 1ef132660b8..d6562f98059 100644 --- a/tools/extract_features.cpp +++ b/tools/extract_features.cpp @@ -16,7 +16,6 @@ using caffe::Blob; using caffe::Caffe; using caffe::Datum; using caffe::Net; -using boost::shared_ptr; using std::string; namespace db = caffe::db; @@ -51,7 +50,7 @@ int feature_extraction_pipeline(int argc, char** argv) { arg_pos = num_required_args; if (argc > arg_pos && strcmp(argv[arg_pos], "GPU") == 0) { LOG(ERROR)<< "Using GPU"; - uint device_id = 0; + int device_id = 0; if (argc > arg_pos + 1) { device_id = atoi(argv[arg_pos + 1]); CHECK_GE(device_id, 0); @@ -95,7 +94,7 @@ int feature_extraction_pipeline(int argc, char** argv) { } */ std::string feature_extraction_proto(argv[++arg_pos]); - shared_ptr > feature_extraction_net( + boost::shared_ptr > feature_extraction_net( new Net(feature_extraction_proto, caffe::TEST)); feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto); @@ -119,15 +118,15 @@ int feature_extraction_pipeline(int argc, char** argv) { int num_mini_batches = atoi(argv[++arg_pos]); - std::vector > feature_dbs; - std::vector > txns; + std::vector > feature_dbs; + std::vector > txns; const char* db_type = argv[++arg_pos]; for (size_t i = 0; i < num_features; ++i) { LOG(INFO)<< "Opening dataset " << dataset_names[i]; - shared_ptr db(db::GetDB(db_type)); + boost::shared_ptr db(db::GetDB(db_type)); db->Open(dataset_names.at(i), db::NEW); feature_dbs.push_back(db); - shared_ptr txn(db->NewTransaction()); + boost::shared_ptr txn(db->NewTransaction()); txns.push_back(txn); } @@ -139,8 +138,8 @@ int feature_extraction_pipeline(int argc, char** argv) { for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) { feature_extraction_net->Forward(input_vec); for (int i = 0; i < num_features; ++i) { - const shared_ptr > feature_blob = feature_extraction_net - ->blob_by_name(blob_names[i]); + const boost::shared_ptr > feature_blob = + feature_extraction_net->blob_by_name(blob_names[i]); int batch_size = feature_blob->num(); int dim_features = feature_blob->count() / batch_size; const Dtype* feature_blob_data; From a6681945be4736a584adadfaf2bffe43ad31422e Mon Sep 17 00:00:00 2001 From: Mohamed Omran Date: Thu, 26 Nov 2015 01:46:42 +0100 Subject: [PATCH 362/446] ELU layer with basic tests --- include/caffe/layers/elu_layer.hpp | 86 ++++++++++++++++++++++++++++ src/caffe/layers/elu_layer.cpp | 47 +++++++++++++++ src/caffe/layers/elu_layer.cu | 62 ++++++++++++++++++++ src/caffe/proto/caffe.proto | 11 +++- src/caffe/test/test_neuron_layer.cpp | 59 +++++++++++++++++++ 5 files changed, 264 insertions(+), 1 deletion(-) create mode 100644 include/caffe/layers/elu_layer.hpp create mode 100644 src/caffe/layers/elu_layer.cpp create mode 100644 src/caffe/layers/elu_layer.cu diff --git a/include/caffe/layers/elu_layer.hpp b/include/caffe/layers/elu_layer.hpp new file mode 100644 index 00000000000..0796e898007 --- /dev/null +++ b/include/caffe/layers/elu_layer.hpp @@ -0,0 +1,86 @@ +#ifndef CAFFE_ELU_LAYER_HPP_ +#define CAFFE_ELU_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" + +namespace caffe { + +/** + * @brief Exponential Linear Unit non-linearity @f$ + * y = \left\{ + * \begin{array}{lr} + * x & \mathrm{if} \; x > 0 \\ + * \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 + * \end{array} \right. + * @f$. + */ +template +class ELULayer : public NeuronLayer { + public: + /** + * @param param provides ELUParameter elu_param, + * with ELULayer options: + * - alpha (\b optional, default 1). + * the value @f$ \alpha @f$ by which controls saturation for negative inputs. + */ + explicit ELULayer(const LayerParameter& param) + : NeuronLayer(param) {} + + virtual inline const char* type() const { return "ELU"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = \left\{ + * \begin{array}{lr} + * x & \mathrm{if} \; x > 0 \\ + * \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0 + * \end{array} \right. + * @f$. + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the ELU inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} = \left\{ + * \begin{array}{lr} + * 1 & \mathrm{if} \; x > 0 \\ + * y + \alpha & \mathrm{if} \; x \le 0 + * \end{array} \right. + * @f$ if propagate_down[0]. + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); +}; + + +} // namespace caffe + +#endif // CAFFE_ELU_LAYER_HPP_ diff --git a/src/caffe/layers/elu_layer.cpp b/src/caffe/layers/elu_layer.cpp new file mode 100644 index 00000000000..a0f87635a5a --- /dev/null +++ b/src/caffe/layers/elu_layer.cpp @@ -0,0 +1,47 @@ +#include +#include + +#include "caffe/layers/elu_layer.hpp" + +namespace caffe { + +template +void ELULayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + const int count = bottom[0]->count(); + Dtype alpha = this->layer_param_.elu_param().alpha(); + for (int i = 0; i < count; ++i) { + top_data[i] = std::max(bottom_data[i], Dtype(0)) + + alpha * (exp(std::min(bottom_data[i], Dtype(0))) - Dtype(1)); + } +} + +template +void ELULayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, + const vector*>& bottom) { + if (propagate_down[0]) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + const Dtype* top_data = top[0]->cpu_data(); + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + const int count = bottom[0]->count(); + Dtype alpha = this->layer_param_.elu_param().alpha(); + for (int i = 0; i < count; ++i) { + bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0) + + (alpha + top_data[i]) * (bottom_data[i] <= 0)); + } + } +} + + +#ifdef CPU_ONLY +STUB_GPU(ELULayer); +#endif + +INSTANTIATE_CLASS(ELULayer); +REGISTER_LAYER_CLASS(ELU); + +} // namespace caffe diff --git a/src/caffe/layers/elu_layer.cu b/src/caffe/layers/elu_layer.cu new file mode 100644 index 00000000000..12545aa8253 --- /dev/null +++ b/src/caffe/layers/elu_layer.cu @@ -0,0 +1,62 @@ +#include +#include + +#include "caffe/layers/elu_layer.hpp" + +namespace caffe { + +template +__global__ void ELUForward(const int n, const Dtype* in, Dtype* out, + Dtype alpha) { + CUDA_KERNEL_LOOP(index, n) { + out[index] = in[index] > 0 ? in[index] : + alpha * (exp(in[index]) - 1); + } +} + +template +void ELULayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + const int count = bottom[0]->count(); + Dtype alpha = this->layer_param_.elu_param().alpha(); + // NOLINT_NEXT_LINE(whitespace/operators) + ELUForward<<>>( + count, bottom_data, top_data, alpha); + CUDA_POST_KERNEL_CHECK; +} + +template +__global__ void ELUBackward(const int n, const Dtype* in_diff, + const Dtype* out_data, const Dtype* in_data, + Dtype* out_diff, Dtype alpha) { + CUDA_KERNEL_LOOP(index, n) { + out_diff[index] = in_data[index] > 0 ? in_diff[index] : + in_diff[index] * (out_data[index] + alpha); + } +} + +template +void ELULayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, + const vector*>& bottom) { + if (propagate_down[0]) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + const Dtype* top_diff = top[0]->gpu_diff(); + const Dtype* top_data = top[0]->gpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + const int count = bottom[0]->count(); + Dtype alpha = this->layer_param_.elu_param().alpha(); + // NOLINT_NEXT_LINE(whitespace/operators) + ELUBackward<<>>( + count, top_diff, top_data, bottom_data, bottom_diff, alpha); + CUDA_POST_KERNEL_CHECK; + } +} + + +INSTANTIATE_LAYER_GPU_FUNCS(ELULayer); + + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 787369f7cff..1daf148d3e4 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -306,7 +306,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 140 (last added: batch_norm_param) +// LayerParameter next available layer-specific ID: 141 (last added: elu_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -363,6 +363,7 @@ message LayerParameter { optional DropoutParameter dropout_param = 108; optional DummyDataParameter dummy_data_param = 109; optional EltwiseParameter eltwise_param = 110; + optional ELUParameter elu_param = 140; optional EmbedParameter embed_param = 137; optional ExpParameter exp_param = 111; optional FlattenParameter flatten_param = 135; @@ -629,6 +630,14 @@ message EltwiseParameter { optional bool stable_prod_grad = 3 [default = true]; } +// Message that stores parameters used by ELULayer +message ELUParameter { + // Described in: + // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate + // Deep Network Learning by Exponential Linear Units (ELUs). arXiv + optional float alpha = 1 [default = 1]; +} + // Message that stores parameters used by EmbedLayer message EmbedParameter { optional uint32 num_output = 1; // The number of outputs for the layer diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp index 21441b4121e..dd591f7d204 100644 --- a/src/caffe/test/test_neuron_layer.cpp +++ b/src/caffe/test/test_neuron_layer.cpp @@ -11,6 +11,7 @@ #include "caffe/layers/absval_layer.hpp" #include "caffe/layers/bnll_layer.hpp" #include "caffe/layers/dropout_layer.hpp" +#include "caffe/layers/elu_layer.hpp" #include "caffe/layers/exp_layer.hpp" #include "caffe/layers/inner_product_layer.hpp" #include "caffe/layers/log_layer.hpp" @@ -259,6 +260,64 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) { this->blob_top_vec_); } +TYPED_TEST(NeuronLayerTest, TestELU) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + CHECK(google::protobuf::TextFormat::ParseFromString( + "elu_param { alpha: 0.5 }", &layer_param)); + ELULayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype kDelta = 2e-4; + // Now, check values + const Dtype* bottom_data = this->blob_bottom_->cpu_data(); + const Dtype* top_data = this->blob_top_->cpu_data(); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + if (bottom_data[i] > 0) { + EXPECT_FLOAT_EQ(top_data[i], bottom_data[i]); + } else { + EXPECT_NEAR(top_data[i], 0.5 * (exp(bottom_data[i]) - 1), kDelta); + } + } +} + +TYPED_TEST(NeuronLayerTest, TestELUasReLU) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + CHECK(google::protobuf::TextFormat::ParseFromString( + "elu_param { alpha: 0 }", &layer_param)); + ELULayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Now, check values + const Dtype* bottom_data = this->blob_bottom_->cpu_data(); + const Dtype* top_data = this->blob_top_->cpu_data(); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_GE(top_data[i], 0.); + EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]); + } +} + +TYPED_TEST(NeuronLayerTest, TestELUGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ELULayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3, 1701, 0., 0.01); + checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(NeuronLayerTest, TestELUasReLUGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + CHECK(google::protobuf::TextFormat::ParseFromString( + "elu_param { alpha: 0 }", &layer_param)); + ELULayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3, 1701, 0., 0.01); + checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + TYPED_TEST(NeuronLayerTest, TestSigmoid) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; From b13bda2fcc984ce916d4911c90f5466056f25092 Mon Sep 17 00:00:00 2001 From: Ian Hunter Date: Wed, 9 Dec 2015 13:29:01 +0000 Subject: [PATCH 363/446] Update interfaces.md typo --- docs/tutorial/interfaces.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md index 9006179d0f1..d7ff378239d 100644 --- a/docs/tutorial/interfaces.md +++ b/docs/tutorial/interfaces.md @@ -61,7 +61,7 @@ For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but The Python interface -- pycaffe -- is the `caffe` module and its scripts in caffe/python. `import caffe` to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing. -- `caffe.Net` is the central interface for loading, configuring, and running models. `caffe.Classsifier` and `caffe.Detector` provide convenience interfaces for common tasks. +- `caffe.Net` is the central interface for loading, configuring, and running models. `caffe.Classifier` and `caffe.Detector` provide convenience interfaces for common tasks. - `caffe.SGDSolver` exposes the solving interface. - `caffe.io` handles input / output with preprocessing and protocol buffers. - `caffe.draw` visualizes network architectures. From eb2b848df173f7a07eb0d76a432c5d4badca7ba6 Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Thu, 10 Dec 2015 15:11:51 -0800 Subject: [PATCH 364/446] Fix CuDNNConvolutionLayer for cuDNN v4 Add a macro to check the current cuDNN version --- include/caffe/util/cudnn.hpp | 3 +++ src/caffe/layers/cudnn_conv_layer.cu | 8 ++++++++ 2 files changed, 11 insertions(+) diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp index b531dd5fa7a..8a7e17c6cd4 100644 --- a/include/caffe/util/cudnn.hpp +++ b/include/caffe/util/cudnn.hpp @@ -7,6 +7,9 @@ #include "caffe/common.hpp" #include "caffe/proto/caffe.pb.h" +#define CUDNN_VERSION_MIN(major, minor, patch) \ + (CUDNN_VERSION >= (major * 1000 + minor * 100 + patch)) + #define CUDNN_CHECK(condition) \ do { \ cudnnStatus_t status = condition; \ diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu index 1990e932a70..42c4fd0260c 100644 --- a/src/caffe/layers/cudnn_conv_layer.cu +++ b/src/caffe/layers/cudnn_conv_layer.cu @@ -30,11 +30,19 @@ void CuDNNConvolutionLayer::Forward_gpu( // Bias. if (this->bias_term_) { const Dtype* bias_data = this->blobs_[1]->gpu_data(); +#if CUDNN_VERSION_MIN(4, 0, 0) + CUDNN_CHECK(cudnnAddTensor(handle_[g], + cudnn::dataType::one, + bias_desc_, bias_data + bias_offset_ * g, + cudnn::dataType::one, + top_descs_[i], top_data + top_offset_ * g)); +#else CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C, cudnn::dataType::one, bias_desc_, bias_data + bias_offset_ * g, cudnn::dataType::one, top_descs_[i], top_data + top_offset_ * g)); +#endif } } From 12f85982c4599734a43d95e2c4b7565aad410530 Mon Sep 17 00:00:00 2001 From: Jacek Czaja Date: Mon, 14 Dec 2015 16:45:59 +0100 Subject: [PATCH 365/446] - Fix to cmake build for clang --- CMakeLists.txt | 2 ++ cmake/Targets.cmake | 19 ++++++++++--------- 2 files changed, 12 insertions(+), 9 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index c446c608952..f1ab1936f86 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -42,6 +42,8 @@ if(UNIX OR APPLE) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall") endif() +caffe_set_caffe_link() + if(USE_libstdcpp) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libstdc++") message("-- Warning: forcing libstdc++ (controlled by USE_libstdcpp option in cmake)") diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake index 2401f252e93..a796d00548f 100644 --- a/cmake/Targets.cmake +++ b/cmake/Targets.cmake @@ -1,16 +1,17 @@ ################################################################################################ # Defines global Caffe_LINK flag, This flag is required to prevent linker from excluding # some objects which are not addressed directly but are registered via static constructors -if(BUILD_SHARED_LIBS) - set(Caffe_LINK caffe) -else() - if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") - set(Caffe_LINK -Wl,-force_load caffe) - elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU") - set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive) +macro(caffe_set_caffe_link) + if(BUILD_SHARED_LIBS) + set(Caffe_LINK caffe) + else() + if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") + set(Caffe_LINK -Wl,-force_load caffe) + elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU") + set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive) + endif() endif() -endif() - +endmacro() ################################################################################################ # Convenient command to setup source group for IDEs that support this feature (VS, XCode) # Usage: From f19896ccca23f091abb82d77a2f281a9c954a147 Mon Sep 17 00:00:00 2001 From: Muneyuki Noguchi Date: Sun, 20 Dec 2015 19:12:09 +0900 Subject: [PATCH 366/446] Replace blobs_lr with lr_mult in readme.md. models/finetune_flickr_style/deploy.prototxt uses lr_mult now. --- examples/finetune_flickr_style/readme.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md index ecb9d3d2e6d..4e9d41f13cc 100644 --- a/examples/finetune_flickr_style/readme.md +++ b/examples/finetune_flickr_style/readme.md @@ -22,10 +22,10 @@ Because we are predicting 20 classes instead of a 1,000, we do need to change th Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt. Since there is no layer named that in the `bvlc_reference_caffenet`, that layer will begin training with random weights. -We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `blobs_lr` on the newly introduced layer. +We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `lr_mult` on the newly introduced layer. The idea is to have the rest of the model change very slowly with new data, but let the new layer learn fast. Additionally, we set `stepsize` in the solver to a lower value than if we were training from scratch, since we're virtually far along in training and therefore want the learning rate to go down faster. -Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `blobs_lr` to 0. +Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `lr_mult` to 0. ## Procedure From 93bfcb53120416255d6d7261b638f0b38ff9e9bf Mon Sep 17 00:00:00 2001 From: Fisher Yu Date: Sun, 6 Dec 2015 20:04:43 -0500 Subject: [PATCH 367/446] add support for 2D dilated convolution --- include/caffe/layers/base_conv_layer.hpp | 14 ++-- include/caffe/layers/conv_layer.hpp | 3 + include/caffe/layers/im2col_layer.hpp | 2 + include/caffe/util/im2col.hpp | 12 ++-- src/caffe/layer_factory.cpp | 17 ++++- src/caffe/layers/base_conv_layer.cpp | 20 +++++- src/caffe/layers/conv_layer.cpp | 4 +- src/caffe/layers/im2col_layer.cpp | 21 +++++- src/caffe/layers/im2col_layer.cu | 2 + src/caffe/proto/caffe.proto | 1 + src/caffe/test/test_convolution_layer.cpp | 14 ++-- src/caffe/test/test_im2col_kernel.cu | 17 ++++- src/caffe/test/test_im2col_layer.cpp | 3 +- src/caffe/util/im2col.cpp | 34 ++++++---- src/caffe/util/im2col.cu | 80 ++++++++++++----------- 15 files changed, 170 insertions(+), 74 deletions(-) diff --git a/include/caffe/layers/base_conv_layer.hpp b/include/caffe/layers/base_conv_layer.hpp index f3def16c039..db471b586da 100644 --- a/include/caffe/layers/base_conv_layer.hpp +++ b/include/caffe/layers/base_conv_layer.hpp @@ -68,6 +68,8 @@ class BaseConvolutionLayer : public Layer { Blob stride_; /// @brief The spatial dimensions of the padding. Blob pad_; + /// @brief The spatial dimensions of the dilation. + Blob dilation_; /// @brief The spatial dimensions of the convolution input. Blob conv_input_shape_; /// @brief The spatial dimensions of the col_buffer. @@ -99,7 +101,8 @@ class BaseConvolutionLayer : public Layer { conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], pad_.cpu_data()[0], pad_.cpu_data()[1], - stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff); + stride_.cpu_data()[0], stride_.cpu_data()[1], + dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff); } else { im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(), col_buffer_shape_.data(), kernel_shape_.cpu_data(), @@ -112,7 +115,8 @@ class BaseConvolutionLayer : public Layer { conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], pad_.cpu_data()[0], pad_.cpu_data()[1], - stride_.cpu_data()[0], stride_.cpu_data()[1], data); + stride_.cpu_data()[0], stride_.cpu_data()[1], + dilation_.cpu_data()[0], dilation_.cpu_data()[1], data); } else { col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(), col_buffer_shape_.data(), kernel_shape_.cpu_data(), @@ -126,7 +130,8 @@ class BaseConvolutionLayer : public Layer { conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], pad_.cpu_data()[0], pad_.cpu_data()[1], - stride_.cpu_data()[0], stride_.cpu_data()[1], col_buff); + stride_.cpu_data()[0], stride_.cpu_data()[1], + dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff); } else { im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_, conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(), @@ -140,7 +145,8 @@ class BaseConvolutionLayer : public Layer { conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2], kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], pad_.cpu_data()[0], pad_.cpu_data()[1], - stride_.cpu_data()[0], stride_.cpu_data()[1], data); + stride_.cpu_data()[0], stride_.cpu_data()[1], + dilation_.cpu_data()[0], dilation_.cpu_data()[1], data); } else { col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_, conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(), diff --git a/include/caffe/layers/conv_layer.hpp b/include/caffe/layers/conv_layer.hpp index 15574766de5..93a618ddd72 100644 --- a/include/caffe/layers/conv_layer.hpp +++ b/include/caffe/layers/conv_layer.hpp @@ -44,6 +44,9 @@ class ConvolutionLayer : public BaseConvolutionLayer { * convolution, given by pad for equal dimensions or pad_h and pad_w for * different padding. Input padding is computed implicitly instead of * actually padding. + * - dilation (\b optional, default 1). The filter + * dilation, given by dilation_size for equal dimensions for different + * dilation. By default the convolution has dilation 1. * - group (\b optional, default 1). The number of filter groups. Group * convolution is a method for reducing parameterization by selectively * connecting input and output channels. The input and output channel dimensions must be divisible diff --git a/include/caffe/layers/im2col_layer.hpp b/include/caffe/layers/im2col_layer.hpp index 1d3b2eb67d1..71e32f7427f 100644 --- a/include/caffe/layers/im2col_layer.hpp +++ b/include/caffe/layers/im2col_layer.hpp @@ -46,6 +46,8 @@ class Im2colLayer : public Layer { Blob stride_; /// @brief The spatial dimensions of the padding. Blob pad_; + /// @brief The spatial dimensions of the dilation. + Blob dilation_; int num_spatial_axes_; int bottom_dim_; diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp index d3eb6ccd6fc..748b65c4f36 100644 --- a/include/caffe/util/im2col.hpp +++ b/include/caffe/util/im2col.hpp @@ -13,7 +13,8 @@ template void im2col_cpu(const Dtype* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, Dtype* data_col); + const int stride_w, const int dilation_h, const int dilation_w, + Dtype* data_col); template void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes, @@ -25,7 +26,8 @@ template void col2im_cpu(const Dtype* data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, Dtype* data_im); + const int stride_w, const int dilation_h, const int dilation_w, + Dtype* data_im); template void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes, @@ -37,7 +39,8 @@ template void im2col_gpu(const Dtype* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, Dtype* data_col); + const int stride_w, const int dilation_h, const int dilation_w, + Dtype* data_col); template void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes, @@ -49,7 +52,8 @@ template void col2im_gpu(const Dtype* data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, Dtype* data_im); + const int stride_w, const int dilation_h, const int dilation_w, + Dtype* data_im); } // namespace caffe diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp index 76d851af9a2..6b1d1c1a5f5 100644 --- a/src/caffe/layer_factory.cpp +++ b/src/caffe/layer_factory.cpp @@ -37,17 +37,30 @@ namespace caffe { template shared_ptr > GetConvolutionLayer( const LayerParameter& param) { - ConvolutionParameter_Engine engine = param.convolution_param().engine(); + ConvolutionParameter conv_param = param.convolution_param(); + ConvolutionParameter_Engine engine = conv_param.engine(); + bool use_dilation = false; + for (int i = 0; i < conv_param.dilation_size(); ++i) { + if (conv_param.dilation(i) > 1) { + use_dilation = true; + } + } if (engine == ConvolutionParameter_Engine_DEFAULT) { engine = ConvolutionParameter_Engine_CAFFE; #ifdef USE_CUDNN - engine = ConvolutionParameter_Engine_CUDNN; + if (!use_dilation) { + engine = ConvolutionParameter_Engine_CUDNN; + } #endif } if (engine == ConvolutionParameter_Engine_CAFFE) { return shared_ptr >(new ConvolutionLayer(param)); #ifdef USE_CUDNN } else if (engine == ConvolutionParameter_Engine_CUDNN) { + if (use_dilation) { + LOG(FATAL) << "CuDNN doesn't support the dilated convolution at Layer " + << param.name(); + } return shared_ptr >(new CuDNNConvolutionLayer(param)); #endif } else { diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp index f6f14cd0f17..4a4c68e009a 100644 --- a/src/caffe/layers/base_conv_layer.cpp +++ b/src/caffe/layers/base_conv_layer.cpp @@ -36,7 +36,7 @@ void BaseConvolutionLayer::LayerSetUp(const vector*>& bottom, CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_) << "kernel_size must be specified once, or once per spatial dimension " << "(kernel_size specified " << num_kernel_dims << " times; " - << num_spatial_axes_ << " spatial dims);"; + << num_spatial_axes_ << " spatial dims)."; for (int i = 0; i < num_spatial_axes_; ++i) { kernel_shape_data[i] = conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i); @@ -61,7 +61,7 @@ void BaseConvolutionLayer::LayerSetUp(const vector*>& bottom, num_stride_dims == num_spatial_axes_) << "stride must be specified once, or once per spatial dimension " << "(stride specified " << num_stride_dims << " times; " - << num_spatial_axes_ << " spatial dims);"; + << num_spatial_axes_ << " spatial dims)."; const int kDefaultStride = 1; for (int i = 0; i < num_spatial_axes_; ++i) { stride_data[i] = (num_stride_dims == 0) ? kDefaultStride : @@ -85,13 +85,27 @@ void BaseConvolutionLayer::LayerSetUp(const vector*>& bottom, num_pad_dims == num_spatial_axes_) << "pad must be specified once, or once per spatial dimension " << "(pad specified " << num_pad_dims << " times; " - << num_spatial_axes_ << " spatial dims);"; + << num_spatial_axes_ << " spatial dims)."; const int kDefaultPad = 0; for (int i = 0; i < num_spatial_axes_; ++i) { pad_data[i] = (num_pad_dims == 0) ? kDefaultPad : conv_param.pad((num_pad_dims == 1) ? 0 : i); } } + // Setup dilation dimensions (dilation_). + dilation_.Reshape(spatial_dim_blob_shape); + int* dilation_data = dilation_.mutable_cpu_data(); + const int num_dilation_dims = conv_param.dilation_size(); + CHECK(num_dilation_dims == 0 || num_dilation_dims == 1 || + num_dilation_dims == num_spatial_axes_) + << "dilation must be specified once, or once per spatial dimension " + << "(dilation specified " << num_dilation_dims << " times; " + << num_spatial_axes_ << " spatial dims)."; + const int kDefaultDilation = 1; + for (int i = 0; i < num_spatial_axes_; ++i) { + dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation : + conv_param.dilation((num_dilation_dims == 1) ? 0 : i); + } // Special case: im2col is the identity for 1x1 convolution with stride 1 // and no padding, so flag for skipping the buffer and transformation. is_1x1_ = true; diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp index cff09783945..5d522ab31f2 100644 --- a/src/caffe/layers/conv_layer.cpp +++ b/src/caffe/layers/conv_layer.cpp @@ -9,11 +9,13 @@ void ConvolutionLayer::compute_output_shape() { const int* kernel_shape_data = this->kernel_shape_.cpu_data(); const int* stride_data = this->stride_.cpu_data(); const int* pad_data = this->pad_.cpu_data(); + const int* dilation_data = this->dilation_.cpu_data(); this->output_shape_.clear(); for (int i = 0; i < this->num_spatial_axes_; ++i) { // i + 1 to skip channel axis const int input_dim = this->input_shape(i + 1); - const int output_dim = (input_dim + 2 * pad_data[i] - kernel_shape_data[i]) + const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1; + const int output_dim = (input_dim + 2 * pad_data[i] - kernel_extent) / stride_data[i] + 1; this->output_shape_.push_back(output_dim); } diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp index c12e4f52a10..19ae3019593 100644 --- a/src/caffe/layers/im2col_layer.cpp +++ b/src/caffe/layers/im2col_layer.cpp @@ -87,6 +87,20 @@ void Im2colLayer::LayerSetUp(const vector*>& bottom, conv_param.pad((num_pad_dims == 1) ? 0 : i); } } + // Setup dilation dimensions (dilation_). + dilation_.Reshape(dim_blob_shape); + int* dilation_data = dilation_.mutable_cpu_data(); + const int num_dilation_dims = conv_param.dilation_size(); + CHECK(num_dilation_dims == 0 || num_dilation_dims == 1 || + num_dilation_dims == num_spatial_axes_) + << "dilation must be specified once, or once per spatial dimension " + << "(dilation specified " << num_dilation_dims << " times; " + << num_spatial_axes_ << " spatial dims)."; + const int kDefaultDilation = 1; + for (int i = 0; i < num_spatial_axes_; ++i) { + dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation : + conv_param.dilation((num_dilation_dims == 1) ? 0 : i); + } } template @@ -96,10 +110,12 @@ void Im2colLayer::Reshape(const vector*>& bottom, const int* kernel_shape_data = kernel_shape_.cpu_data(); const int* stride_data = stride_.cpu_data(); const int* pad_data = pad_.cpu_data(); + const int* dilation_data = dilation_.cpu_data(); for (int i = 0; i < num_spatial_axes_; ++i) { top_shape[channel_axis_] *= kernel_shape_data[i]; const int input_dim = bottom[0]->shape(channel_axis_ + i + 1); - const int output_dim = (input_dim + 2 * pad_data[i] - kernel_shape_data[i]) + const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1; + const int output_dim = (input_dim + 2 * pad_data[i] - kernel_extent) / stride_data[i] + 1; top_shape[channel_axis_ + i + 1] = output_dim; } @@ -122,6 +138,7 @@ void Im2colLayer::Forward_cpu(const vector*>& bottom, DCHECK_EQ(kernel_shape_.count(), num_spatial_axes_); DCHECK_EQ(pad_.count(), num_spatial_axes_); DCHECK_EQ(stride_.count(), num_spatial_axes_); + DCHECK_EQ(dilation_.count(), num_spatial_axes_); if (!force_nd_im2col_ && num_spatial_axes_ == 2) { im2col_cpu(bottom_data + n * bottom_dim_, channels_, bottom[0]->shape(channel_axis_ + 1), @@ -129,6 +146,7 @@ void Im2colLayer::Forward_cpu(const vector*>& bottom, kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], pad_.cpu_data()[0], pad_.cpu_data()[1], stride_.cpu_data()[0], stride_.cpu_data()[1], + dilation_.cpu_data()[0], dilation_.cpu_data()[1], top_data + n * top_dim_); } else { im2col_nd_cpu(bottom_data + n * bottom_dim_, num_spatial_axes_, @@ -153,6 +171,7 @@ void Im2colLayer::Backward_cpu(const vector*>& top, kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], pad_.cpu_data()[0], pad_.cpu_data()[1], stride_.cpu_data()[0], stride_.cpu_data()[1], + dilation_.cpu_data()[0], dilation_.cpu_data()[1], bottom_diff + n * bottom_dim_); } else { col2im_nd_cpu(top_diff + n * top_dim_, num_spatial_axes_, diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu index 517b4220cb9..d90075d4304 100644 --- a/src/caffe/layers/im2col_layer.cu +++ b/src/caffe/layers/im2col_layer.cu @@ -19,6 +19,7 @@ void Im2colLayer::Forward_gpu(const vector*>& bottom, kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], pad_.cpu_data()[0], pad_.cpu_data()[1], stride_.cpu_data()[0], stride_.cpu_data()[1], + dilation_.cpu_data()[0], dilation_.cpu_data()[1], top_data + n * top_dim_); } else { im2col_nd_gpu(bottom_data + n * bottom_dim_, num_spatial_axes_, @@ -43,6 +44,7 @@ void Im2colLayer::Backward_gpu(const vector*>& top, kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1], pad_.cpu_data()[0], pad_.cpu_data()[1], stride_.cpu_data()[0], stride_.cpu_data()[1], + dilation_.cpu_data()[0], dilation_.cpu_data()[1], bottom_diff + n * bottom_dim_); } else { col2im_nd_gpu(top_diff + n * top_dim_, num_spatial_axes_, bottom_dim_, diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 787369f7cff..87c46629baf 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -518,6 +518,7 @@ message ConvolutionParameter { repeated uint32 pad = 3; // The padding size; defaults to 0 repeated uint32 kernel_size = 4; // The kernel size repeated uint32 stride = 6; // The stride; defaults to 1 + repeated uint32 dilation = 18; // The dilation; defaults to 1 // For 2D convolution only, the *_h and *_w versions may also be used to // specify both spatial dimensions. diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp index e2d43f31b6a..95c3c80c59d 100644 --- a/src/caffe/test/test_convolution_layer.cpp +++ b/src/caffe/test/test_convolution_layer.cpp @@ -46,13 +46,17 @@ void caffe_conv(const Blob* in, ConvolutionParameter* conv_param, } else { stride_h = stride_w = conv_param->stride_size() ? conv_param->stride(0) : 1; } - int kernel_d, pad_d, stride_d; + int dilation_h, dilation_w; + dilation_h = dilation_w = conv_param->dilation_size() ? + conv_param->dilation(0) : 1; + int kernel_d, pad_d, stride_d, dilation_d; if (has_depth) { kernel_d = kernel_h; stride_d = stride_h; pad_d = pad_h; + dilation_d = dilation_h; } else { - kernel_d = stride_d = 1; + kernel_d = stride_d = dilation_d = 1; pad_d = 0; } // Groups @@ -77,9 +81,9 @@ void caffe_conv(const Blob* in, ConvolutionParameter* conv_param, for (int r = 0; r < kernel_d; r++) { for (int p = 0; p < kernel_h; p++) { for (int q = 0; q < kernel_w; q++) { - int in_z = z * stride_d - pad_d + r; - int in_y = y * stride_h - pad_h + p; - int in_x = x * stride_w - pad_w + q; + int in_z = z * stride_d - pad_d + r * dilation_d; + int in_y = y * stride_h - pad_h + p * dilation_h; + int in_x = x * stride_w - pad_w + q * dilation_w; if (in_z >= 0 && in_z < (has_depth ? in->shape(2) : 1) && in_y >= 0 && in_y < in->shape(2 + has_depth) && in_x >= 0 && in_x < in->shape(3 + has_depth)) { diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu index 3f97cf6d5ae..15e06aa8583 100644 --- a/src/caffe/test/test_im2col_kernel.cu +++ b/src/caffe/test/test_im2col_kernel.cu @@ -18,6 +18,7 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int height_col, const int width_col, Dtype* data_col); @@ -38,6 +39,7 @@ class Im2colKernelTest : public GPUDeviceTest { blob_kernel_shape_(new Blob()), blob_stride_(new Blob()), blob_pad_(new Blob()), + blob_dilation_(new Blob()), blob_top_(new Blob()), blob_top_cpu_(new Blob()) { FillerParameter filler_param; @@ -47,20 +49,25 @@ class Im2colKernelTest : public GPUDeviceTest { blob_kernel_shape_->Reshape(dim_blob_shape); blob_stride_->Reshape(dim_blob_shape); blob_pad_->Reshape(dim_blob_shape); + blob_dilation_->Reshape(dim_blob_shape); height_ = blob_bottom_->height(); width_ = blob_bottom_->width(); channels_ = blob_bottom_->channels(); pad_ = 0; stride_ = 2; + dilation_ = 1; kernel_size_ = 3; - height_col_ = (height_ + 2 * pad_ - kernel_size_) / stride_ + 1; - width_col_ = (width_ + 2 * pad_ - kernel_size_) / stride_ + 1; + height_col_ = (height_ + 2 * pad_ - + (dilation_ * (kernel_size_ - 1) + 1)) / stride_ + 1; + width_col_ = (width_ + 2 * pad_ - + (dilation_ * (kernel_size_ - 1) + 1)) / stride_ + 1; for (int i = 0; i < 2; ++i) { blob_kernel_shape_->mutable_cpu_data()[i] = kernel_size_; blob_stride_->mutable_cpu_data()[i] = stride_; blob_pad_->mutable_cpu_data()[i] = pad_; + blob_dilation_->mutable_cpu_data()[i] = dilation_; } } @@ -71,11 +78,13 @@ class Im2colKernelTest : public GPUDeviceTest { delete blob_kernel_shape_; delete blob_stride_; delete blob_pad_; + delete blob_dilation_; } Blob* const blob_kernel_shape_; Blob* const blob_stride_; Blob* const blob_pad_; + Blob* const blob_dilation_; Blob* const blob_bottom_; Blob* const blob_top_; Blob* const blob_top_cpu_; @@ -84,6 +93,7 @@ class Im2colKernelTest : public GPUDeviceTest { int channels_; int pad_; int stride_; + int dilation_; int kernel_size_; int height_col_; int width_col_; @@ -112,7 +122,7 @@ TYPED_TEST(Im2colKernelTest, Test2D) { im2col_cpu(this->blob_bottom_->cpu_data() + this->blob_bottom_->offset(n), this->channels_, this->height_, this->width_, this->kernel_size_, this->kernel_size_, this->pad_, this->pad_, - this->stride_, this->stride_, + this->stride_, this->stride_, this->dilation_, this->dilation_, cpu_data + this->blob_top_cpu_->offset(n)); } @@ -129,6 +139,7 @@ TYPED_TEST(Im2colKernelTest, Test2D) { num_kernels, bottom_data + this->blob_bottom_->offset(n), this->height_, this->width_, this->kernel_size_, this->kernel_size_, this->pad_, this->pad_, this->stride_, this->stride_, + this->dilation_, this->dilation_, this->height_col_, this->width_col_, top_data + this->blob_top_->offset(n)); CUDA_POST_KERNEL_CHECK; diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp index 8274dd48971..932d3f21ae9 100644 --- a/src/caffe/test/test_im2col_layer.cpp +++ b/src/caffe/test/test_im2col_layer.cpp @@ -17,7 +17,7 @@ class Im2colLayerTest : public MultiDeviceTest { typedef typename TypeParam::Dtype Dtype; protected: Im2colLayerTest() - : blob_bottom_(new Blob(2, 3, 6, 5)), + : blob_bottom_(new Blob(2, 3, 10, 9)), blob_top_(new Blob()) { // fill the values Caffe::set_random_seed(1701); @@ -75,6 +75,7 @@ TYPED_TEST(Im2colLayerTest, TestGradient) { layer_param.mutable_convolution_param(); convolution_param->add_kernel_size(3); convolution_param->add_stride(2); + convolution_param->add_dilation(3); Im2colLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp index 27e5b7c0928..1e578e7c9dc 100644 --- a/src/caffe/util/im2col.cpp +++ b/src/caffe/util/im2col.cpp @@ -10,9 +10,12 @@ void im2col_cpu(const Dtype* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, Dtype* data_col) { - const int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; - const int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; + const int height_col = (height + 2 * pad_h - + (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_col = (width + 2 * pad_w - + (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; const int channels_col = channels * kernel_h * kernel_w; for (int c_col = 0; c_col < channels_col; ++c_col) { int w_offset = c_col % kernel_w; @@ -20,8 +23,8 @@ void im2col_cpu(const Dtype* data_im, const int channels, int c_im = c_col / kernel_h / kernel_w; for (int h_col = 0; h_col < height_col; ++h_col) { for (int w_col = 0; w_col < width_col; ++w_col) { - int h_im = h_col * stride_h - pad_h + h_offset; - int w_im = w_col * stride_w - pad_w + w_offset; + int h_im = h_col * stride_h - pad_h + h_offset * dilation_h; + int w_im = w_col * stride_w - pad_w + w_offset * dilation_w; data_col[(c_col * height_col + h_col) * width_col + w_col] = (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ? data_im[(c_im * height + h_im) * width + w_im] : 0; @@ -34,11 +37,13 @@ void im2col_cpu(const Dtype* data_im, const int channels, template void im2col_cpu(const float* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, float* data_col); + const int stride_w, const int dilation_h, const int dilation_w, + float* data_col); template void im2col_cpu(const double* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, double* data_col); + const int stride_w, const int dilation_h, const int dilation_w, + double* data_col); template inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col, @@ -137,10 +142,13 @@ void col2im_cpu(const Dtype* data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, Dtype* data_im) { caffe_set(height * width * channels, Dtype(0), data_im); - const int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; - const int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; + const int height_col = (height + 2 * pad_h - + (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_col = (width + 2 * pad_w - + (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; const int channels_col = channels * kernel_h * kernel_w; for (int c_col = 0; c_col < channels_col; ++c_col) { int w_offset = c_col % kernel_w; @@ -148,8 +156,8 @@ void col2im_cpu(const Dtype* data_col, const int channels, int c_im = c_col / kernel_h / kernel_w; for (int h_col = 0; h_col < height_col; ++h_col) { for (int w_col = 0; w_col < width_col; ++w_col) { - int h_im = h_col * stride_h - pad_h + h_offset; - int w_im = w_col * stride_w - pad_w + w_offset; + int h_im = h_col * stride_h - pad_h + h_offset * dilation_h; + int w_im = w_col * stride_w - pad_w + w_offset * dilation_w; if (h_im >= 0 && h_im < height && w_im >= 0 && w_im < width) data_im[(c_im * height + h_im) * width + w_im] += data_col[(c_col * height_col + h_col) * width_col + w_col]; @@ -162,11 +170,13 @@ void col2im_cpu(const Dtype* data_col, const int channels, template void col2im_cpu(const float* data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, float* data_im); + const int stride_w, const int dilation_h, const int dilation_w, + float* data_im); template void col2im_cpu(const double* data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, double* data_im); + const int stride_w, const int dilation_h, const int dilation_w, + double* data_im); template void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes, diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu index 49354ab7aa1..cdcaac5bcc5 100644 --- a/src/caffe/util/im2col.cu +++ b/src/caffe/util/im2col.cu @@ -10,6 +10,7 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int height_col, const int width_col, Dtype* data_col) { CUDA_KERNEL_LOOP(index, n) { @@ -26,11 +27,11 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im, data_im_ptr += (c_im * height + h_offset) * width + w_offset; for (int i = 0; i < kernel_h; ++i) { for (int j = 0; j < kernel_w; ++j) { - int h_im = h_offset + i; - int w_im = w_offset + j; + int h_im = h_offset + i * dilation_h; + int w_im = w_offset + j * dilation_w; *data_col_ptr = (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ? - data_im_ptr[i * width + j] : 0; + data_im_ptr[i * dilation_h * width + j * dilation_w] : 0; data_col_ptr += height_col * width_col; } } @@ -42,17 +43,20 @@ void im2col_gpu(const Dtype* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, Dtype* data_col) { // We are going to launch channels * height_col * width_col kernels, each // kernel responsible for copying a single-channel grid. - int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; - int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; + int height_col = (height + 2 * pad_h - + (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + int width_col = (width + 2 * pad_w - + (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; int num_kernels = channels * height_col * width_col; // NOLINT_NEXT_LINE(whitespace/operators) im2col_gpu_kernel<<>>( num_kernels, data_im, height, width, kernel_h, kernel_w, pad_h, - pad_w, stride_h, stride_w, height_col, + pad_w, stride_h, stride_w, dilation_h, dilation_w, height_col, width_col, data_col); CUDA_POST_KERNEL_CHECK; } @@ -61,11 +65,11 @@ void im2col_gpu(const Dtype* data_im, const int channels, template void im2col_gpu(const float* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, - float* data_col); + const int dilation_h, const int dilation_w, float* data_col); template void im2col_gpu(const double* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, - double* data_col); + const int dilation_h, const int dilation_w, double* data_col); template __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im, @@ -223,6 +227,7 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int height_col, const int width_col, Dtype* data_im) { CUDA_KERNEL_LOOP(index, n) { @@ -230,33 +235,27 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col, const int w_im = index % width + pad_w; const int h_im = (index / width) % height + pad_h; const int c_im = index / (width * height); + int kernel_extent_w = (kernel_w - 1) * dilation_w + 1; + int kernel_extent_h = (kernel_h - 1) * dilation_h + 1; // compute the start and end of the output const int w_col_start = - (w_im < kernel_w) ? 0 : (w_im - kernel_w) / stride_w + 1; - const int w_col_end = - min(w_im / stride_w + 1, width_col); + (w_im < kernel_extent_w) ? 0 : (w_im - kernel_extent_w) / stride_w + 1; + const int w_col_end = min(w_im / stride_w + 1, width_col); const int h_col_start = - (h_im < kernel_h) ? 0 : (h_im - kernel_h) / stride_h + 1; - const int h_col_end = - min(h_im / stride_h + 1, height_col); - /* - for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { - for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { - // the col location: [c * width * height + h_out, w_out] - int c_col = c_im * kernel_h * kernel_w - + (h_im - h_col * stride_h) * kernel_w + (w_im - w_col * stride_w); - val += data_col[(c_col * height_col + h_col) * width_col + w_col]; - } - } - */ - // equivalent implementation - int offset = (c_im * kernel_h * kernel_w + h_im * kernel_w + w_im) - * height_col * width_col; - int coeff_h_col = (1 - stride_h * kernel_w * height_col) * width_col; - int coeff_w_col = (1 - stride_w * height_col * width_col); - for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { - for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { - val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col]; + (h_im < kernel_extent_h) ? 0 : (h_im - kernel_extent_h) / stride_h + 1; + const int h_col_end = min(h_im / stride_h + 1, height_col); + // TODO: use LCM of stride and dilation to avoid unnecessary loops + for (int h_col = h_col_start; h_col < h_col_end; h_col += 1) { + for (int w_col = w_col_start; w_col < w_col_end; w_col += 1) { + int h_k = (h_im - h_col * stride_h); + int w_k = (w_im - w_col * stride_w); + if (h_k % dilation_h == 0 && w_k % dilation_w == 0) { + h_k /= dilation_h; + w_k /= dilation_w; + int data_col_index = (((c_im * kernel_h + h_k) * kernel_w + w_k) * + height_col + h_col) * width_col + w_col; + val += data_col[data_col_index]; + } } } data_im[index] = val; @@ -267,9 +266,12 @@ template void col2im_gpu(const Dtype* data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, Dtype* data_im) { - int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1; - int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1; + const int stride_w, const int dilation_h, const int dilation_w, + Dtype* data_im) { + int height_col = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / + stride_h + 1; + int width_col = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / + stride_w + 1; int num_kernels = channels * height * width; // To avoid involving atomic operations, we will launch one kernel per // bottom dimension, and then in the kernel add up the top dimensions. @@ -277,7 +279,7 @@ void col2im_gpu(const Dtype* data_col, const int channels, col2im_gpu_kernel<<>>( num_kernels, data_col, height, width, channels, kernel_h, kernel_w, - pad_h, pad_w, stride_h, stride_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, height_col, width_col, data_im); CUDA_POST_KERNEL_CHECK; } @@ -286,11 +288,13 @@ void col2im_gpu(const Dtype* data_col, const int channels, template void col2im_gpu(const float* data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, float* data_im); + const int stride_w, const int dilation_h, const int dilation_w, + float* data_im); template void col2im_gpu(const double* data_col, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, double* data_im); + const int stride_w, const int dilation_h, const int dilation_w, + double* data_im); template __global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col, From 18c795ebe8401cb82c9f8350664de665f1ec8733 Mon Sep 17 00:00:00 2001 From: Fisher Yu Date: Sun, 27 Dec 2015 20:48:30 -0800 Subject: [PATCH 368/446] add support for N-D dilated convolution --- include/caffe/layers/base_conv_layer.hpp | 8 +- include/caffe/util/im2col.hpp | 8 +- src/caffe/layer_factory.cpp | 2 + src/caffe/layers/im2col_layer.cpp | 4 +- src/caffe/layers/im2col_layer.cu | 4 +- src/caffe/test/test_im2col_kernel.cu | 9 +- src/caffe/test/test_im2col_layer.cpp | 8 +- src/caffe/util/im2col.cpp | 21 +-- src/caffe/util/im2col.cu | 166 +++++++++++++++-------- 9 files changed, 148 insertions(+), 82 deletions(-) diff --git a/include/caffe/layers/base_conv_layer.hpp b/include/caffe/layers/base_conv_layer.hpp index db471b586da..0160a833dd2 100644 --- a/include/caffe/layers/base_conv_layer.hpp +++ b/include/caffe/layers/base_conv_layer.hpp @@ -106,7 +106,7 @@ class BaseConvolutionLayer : public Layer { } else { im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(), col_buffer_shape_.data(), kernel_shape_.cpu_data(), - pad_.cpu_data(), stride_.cpu_data(), col_buff); + pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), col_buff); } } inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) { @@ -120,7 +120,7 @@ class BaseConvolutionLayer : public Layer { } else { col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(), col_buffer_shape_.data(), kernel_shape_.cpu_data(), - pad_.cpu_data(), stride_.cpu_data(), data); + pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), data); } } #ifndef CPU_ONLY @@ -136,7 +136,7 @@ class BaseConvolutionLayer : public Layer { im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_, conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(), kernel_shape_.gpu_data(), pad_.gpu_data(), - stride_.gpu_data(), col_buff); + stride_.gpu_data(), dilation_.gpu_data(), col_buff); } } inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) { @@ -151,7 +151,7 @@ class BaseConvolutionLayer : public Layer { col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_, conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(), kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(), - data); + dilation_.gpu_data(), data); } } #endif diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp index 748b65c4f36..a35bc6e0b1c 100644 --- a/include/caffe/util/im2col.hpp +++ b/include/caffe/util/im2col.hpp @@ -7,7 +7,7 @@ template void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_col); + const int* dilation, Dtype* data_col); template void im2col_cpu(const Dtype* data_im, const int channels, @@ -20,7 +20,7 @@ template void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_im); + const int* dilation, Dtype* data_im); template void col2im_cpu(const Dtype* data_col, const int channels, @@ -33,7 +33,7 @@ template void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes, const int col_size, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_col); + const int* dilation, Dtype* data_col); template void im2col_gpu(const Dtype* data_im, const int channels, @@ -46,7 +46,7 @@ template void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes, const int im_size, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_im); + const int* dilation, Dtype* data_im); template void col2im_gpu(const Dtype* data_col, const int channels, diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp index 6b1d1c1a5f5..4d912d28351 100644 --- a/src/caffe/layer_factory.cpp +++ b/src/caffe/layer_factory.cpp @@ -39,12 +39,14 @@ shared_ptr > GetConvolutionLayer( const LayerParameter& param) { ConvolutionParameter conv_param = param.convolution_param(); ConvolutionParameter_Engine engine = conv_param.engine(); +#ifdef USE_CUDNN bool use_dilation = false; for (int i = 0; i < conv_param.dilation_size(); ++i) { if (conv_param.dilation(i) > 1) { use_dilation = true; } } +#endif if (engine == ConvolutionParameter_Engine_DEFAULT) { engine = ConvolutionParameter_Engine_CAFFE; #ifdef USE_CUDNN diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp index 19ae3019593..2fb9b3c1099 100644 --- a/src/caffe/layers/im2col_layer.cpp +++ b/src/caffe/layers/im2col_layer.cpp @@ -153,7 +153,7 @@ void Im2colLayer::Forward_cpu(const vector*>& bottom, bottom[0]->shape().data() + channel_axis_, top[0]->shape().data() + channel_axis_, kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(), - top_data + n * top_dim_); + dilation_.cpu_data(), top_data + n * top_dim_); } } } @@ -178,7 +178,7 @@ void Im2colLayer::Backward_cpu(const vector*>& top, bottom[0]->shape().data() + channel_axis_, top[0]->shape().data() + channel_axis_, kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(), - bottom_diff + n * bottom_dim_); + dilation_.cpu_data(), bottom_diff + n * bottom_dim_); } } } diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu index d90075d4304..792c97f70f9 100644 --- a/src/caffe/layers/im2col_layer.cu +++ b/src/caffe/layers/im2col_layer.cu @@ -26,7 +26,7 @@ void Im2colLayer::Forward_gpu(const vector*>& bottom, num_kernels, bottom[0]->gpu_shape() + channel_axis_, top[0]->gpu_shape() + channel_axis_, kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(), - top_data + n * top_dim_); + dilation_.gpu_data(), top_data + n * top_dim_); } } } @@ -51,7 +51,7 @@ void Im2colLayer::Backward_gpu(const vector*>& top, bottom[0]->gpu_shape() + channel_axis_, top[0]->gpu_shape() + channel_axis_, kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(), - bottom_diff + n * bottom_dim_); + dilation_.gpu_data(), bottom_diff + n * bottom_dim_); } } } diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu index 15e06aa8583..5d8f01f1713 100644 --- a/src/caffe/test/test_im2col_kernel.cu +++ b/src/caffe/test/test_im2col_kernel.cu @@ -26,7 +26,7 @@ template __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_col); + const int* dilation, Dtype* data_col); extern cudaDeviceProp CAFFE_TEST_CUDA_PROP; @@ -35,7 +35,7 @@ class Im2colKernelTest : public GPUDeviceTest { protected: Im2colKernelTest() // big so launches > 1024 threads - : blob_bottom_(new Blob(5, 500, 10, 10)), + : blob_bottom_(new Blob(5, 500, 15, 15)), blob_kernel_shape_(new Blob()), blob_stride_(new Blob()), blob_pad_(new Blob()), @@ -56,7 +56,7 @@ class Im2colKernelTest : public GPUDeviceTest { channels_ = blob_bottom_->channels(); pad_ = 0; stride_ = 2; - dilation_ = 1; + dilation_ = 3; kernel_size_ = 3; height_col_ = (height_ + 2 * pad_ - (dilation_ * (kernel_size_ - 1) + 1)) / stride_ + 1; @@ -176,6 +176,7 @@ TYPED_TEST(Im2colKernelTest, TestND) { this->blob_top_cpu_->shape().data() + 1, this->blob_kernel_shape_->cpu_data(), this->blob_pad_->cpu_data(), this->blob_stride_->cpu_data(), + this->blob_dilation_->cpu_data(), top_data_cpu + this->blob_top_cpu_->offset(n)); } @@ -194,7 +195,7 @@ TYPED_TEST(Im2colKernelTest, TestND) { num_kernels, bottom_data_gpu + this->blob_bottom_->offset(n), this->blob_bottom_->gpu_shape() + 1, this->blob_top_->gpu_shape() + 1, this->blob_kernel_shape_->gpu_data(), this->blob_pad_->gpu_data(), - this->blob_stride_->gpu_data(), + this->blob_stride_->gpu_data(), this->blob_dilation_->gpu_data(), top_data_gpu + this->blob_top_->offset(n)); CUDA_POST_KERNEL_CHECK; } diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp index 932d3f21ae9..24885e6b706 100644 --- a/src/caffe/test/test_im2col_layer.cpp +++ b/src/caffe/test/test_im2col_layer.cpp @@ -17,7 +17,7 @@ class Im2colLayerTest : public MultiDeviceTest { typedef typename TypeParam::Dtype Dtype; protected: Im2colLayerTest() - : blob_bottom_(new Blob(2, 3, 10, 9)), + : blob_bottom_(new Blob(2, 3, 10, 11)), blob_top_(new Blob()) { // fill the values Caffe::set_random_seed(1701); @@ -43,12 +43,13 @@ TYPED_TEST(Im2colLayerTest, TestSetup) { layer_param.mutable_convolution_param(); convolution_param->add_kernel_size(3); convolution_param->add_stride(2); + convolution_param->add_dilation(3); Im2colLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); EXPECT_EQ(this->blob_top_->num(), 2); EXPECT_EQ(this->blob_top_->channels(), 27); EXPECT_EQ(this->blob_top_->height(), 2); - EXPECT_EQ(this->blob_top_->width(), 2); + EXPECT_EQ(this->blob_top_->width(), 3); } TYPED_TEST(Im2colLayerTest, TestForward) { @@ -89,6 +90,7 @@ TYPED_TEST(Im2colLayerTest, TestGradientForceND) { layer_param.mutable_convolution_param(); convolution_param->add_kernel_size(3); convolution_param->add_stride(2); + convolution_param->add_dilation(3); convolution_param->set_force_nd_im2col(true); Im2colLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); @@ -123,6 +125,8 @@ TYPED_TEST(Im2colLayerTest, TestRectGradient) { convolution_param->set_kernel_h(5); convolution_param->set_kernel_w(3); convolution_param->add_stride(2); + convolution_param->add_dilation(1); + convolution_param->add_dilation(3); Im2colLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp index 1e578e7c9dc..6e5ea875757 100644 --- a/src/caffe/util/im2col.cpp +++ b/src/caffe/util/im2col.cpp @@ -49,7 +49,7 @@ template inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col, const int num_spatial_axes, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_output) { + const int* dilation, Dtype* data_output) { if (!im2col) { int im_size = im_shape[0]; for (int i = 0; i < num_spatial_axes; ++i) { @@ -81,7 +81,8 @@ inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col, bool is_padding = false; for (int d_i = 0; d_i < num_spatial_axes; ++d_i) { const int d = d_iter[d_i]; - const int d_im = d * stride[d_i] - pad[d_i] + d_offset[d_i]; + const int d_im = d * stride[d_i] - pad[d_i] + + d_offset[d_i] * dilation[d_i]; is_padding |= d_im < 0 || d_im >= im_shape[d_i + 1]; index_col *= col_shape[d_i + 1]; index_col += d; @@ -119,10 +120,10 @@ template void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_col) { + const int* dilation, Dtype* data_col) { const bool kIm2Col = true; im2col_nd_core_cpu(data_im, kIm2Col, num_spatial_axes, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); } // Explicit instantiation @@ -130,12 +131,12 @@ template void im2col_nd_cpu(const float* data_im, const int num_spatial_axes, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - float* data_col); + const int* dilation, float* data_col); template void im2col_nd_cpu(const double* data_im, const int num_spatial_axes, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - double* data_col); + const int* dilation, double* data_col); template void col2im_cpu(const Dtype* data_col, const int channels, @@ -182,10 +183,10 @@ template void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_im) { + const int* dilation, Dtype* data_im) { const bool kIm2Col = false; im2col_nd_core_cpu(data_col, kIm2Col, num_spatial_axes, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); } // Explicit instantiation @@ -193,12 +194,12 @@ template void col2im_nd_cpu(const float* data_col, const int num_spatial_axes, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - float* data_im); + const int* dilation, float* data_im); template void col2im_nd_cpu(const double* data_col, const int num_spatial_axes, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - double* data_im); + const int* dilation, double* data_im); } // namespace caffe diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu index cdcaac5bcc5..a8f30a02484 100644 --- a/src/caffe/util/im2col.cu +++ b/src/caffe/util/im2col.cu @@ -75,9 +75,29 @@ template __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_col) { + const int* dilation, Dtype* data_col) { int d_temp[num_axes]; // NOLINT(runtime/arrays) int d_iter[num_axes]; // NOLINT(runtime/arrays) + + __shared__ int shared_dilation[num_axes]; + __shared__ int shared_kernel_shape[num_axes]; + __shared__ int shared_pad[num_axes]; + __shared__ int shared_stride[num_axes]; + __shared__ int shared_col_shape[num_axes + 1]; + __shared__ int shared_im_shape[num_axes + 1]; + + if (threadIdx.x < num_axes) { + shared_dilation[threadIdx.x] = dilation[threadIdx.x]; + shared_kernel_shape[threadIdx.x] = kernel_shape[threadIdx.x]; + shared_pad[threadIdx.x] = pad[threadIdx.x]; + shared_stride[threadIdx.x] = stride[threadIdx.x]; + } + if (threadIdx.x < num_axes + 1) { + shared_col_shape[threadIdx.x] = col_shape[threadIdx.x]; + shared_im_shape[threadIdx.x] = im_shape[threadIdx.x]; + } + __syncthreads(); + int i; CUDA_KERNEL_LOOP(index, n) { // Initialize channel_in, computed in the loop below, with intermediate @@ -85,19 +105,19 @@ __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im, int channel_in = index; int channel_out = 1; for (i = num_axes - 1; i >= 0; --i) { - d_temp[i] = channel_in % col_shape[i + 1]; - channel_in /= col_shape[i + 1]; - channel_out *= kernel_shape[i]; + d_temp[i] = channel_in % shared_col_shape[i + 1]; + channel_in /= shared_col_shape[i + 1]; + channel_out *= shared_kernel_shape[i]; } channel_out *= channel_in; int data_col_inc = 1; for (i = 0; i < num_axes; ++i) { - channel_out *= col_shape[i + 1]; + channel_out *= shared_col_shape[i + 1]; channel_out += d_temp[i]; - d_temp[i] = d_temp[i] * stride[i] - pad[i]; - channel_in *= im_shape[i + 1]; + d_temp[i] = d_temp[i] * shared_stride[i] - shared_pad[i]; + channel_in *= shared_im_shape[i + 1]; channel_in += d_temp[i]; - data_col_inc *= col_shape[i + 1]; + data_col_inc *= shared_col_shape[i + 1]; d_iter[i] = 0; } Dtype* data_col_ptr = data_col + channel_out; @@ -106,15 +126,15 @@ __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im, do { bool in_range = true; for (i = 0; i < num_axes; ++i) { - const int d_iter_im = d_iter[i] + d_temp[i]; - in_range &= d_iter_im >= 0 && d_iter_im < im_shape[i + 1]; + const int d_iter_im = d_iter[i] * shared_dilation[i] + d_temp[i]; + in_range &= d_iter_im >= 0 && d_iter_im < shared_im_shape[i + 1]; if (!in_range) { break; } } if (in_range) { - int data_im_offset = d_iter[0]; + int data_im_offset = d_iter[0] * shared_dilation[0]; for (i = 1; i < num_axes; ++i) { - data_im_offset *= im_shape[i + 1]; - data_im_offset += d_iter[i]; + data_im_offset *= shared_im_shape[i + 1]; + data_im_offset += d_iter[i] * shared_dilation[i]; } *data_col_ptr = data_im_ptr[data_im_offset]; } else { @@ -123,7 +143,7 @@ __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im, data_col_ptr += data_col_inc; incremented = false; for (i = num_axes - 1; i >= 0; --i) { - const int d_max = kernel_shape[i]; + const int d_max = shared_kernel_shape[i]; if (d_iter[i] == d_max - 1) { d_iter[i] = 0; } else { // d_iter[i] < d_max - 1 @@ -140,67 +160,69 @@ template void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes, const int num_kernels, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_col) { + const int* dilation, Dtype* data_col) { + // num_axes should be smaller than block size + DCHECK_LT(num_spatial_axes, CAFFE_CUDA_NUM_THREADS); switch (num_spatial_axes) { case 1: im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( num_kernels, data_im, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); break; case 2: im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( num_kernels, data_im, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); break; case 3: im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( num_kernels, data_im, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); break; case 4: im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( num_kernels, data_im, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); break; case 5: im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( num_kernels, data_im, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); break; case 6: im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( num_kernels, data_im, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); break; case 7: im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( num_kernels, data_im, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); break; case 8: im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( num_kernels, data_im, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); break; case 9: im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( num_kernels, data_im, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); break; case 10: im2col_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( num_kernels, data_im, im_shape, col_shape, - kernel_shape, pad, stride, data_col); + kernel_shape, pad, stride, dilation, data_col); break; default: LOG(FATAL) << "im2col_nd_gpu does not support computation with " @@ -214,12 +236,12 @@ template void im2col_nd_gpu(const float* data_im, const int num_spatial_axes, const int col_size, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - float* data_col); + const int* dilation, float* data_col); template void im2col_nd_gpu(const double* data_im, const int num_spatial_axes, const int col_size, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - double* data_col); + const int* dilation, double* data_col); template __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col, @@ -300,27 +322,50 @@ template __global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_im) { + const int* dilation, Dtype* data_im) { int d_im[num_axes]; // NOLINT(runtime/arrays) int d_col_iter[num_axes]; // NOLINT(runtime/arrays) int d_col_start[num_axes]; // NOLINT(runtime/arrays) int d_col_end[num_axes]; // NOLINT(runtime/arrays) + + __shared__ int shared_dilation[num_axes]; + __shared__ int shared_kernel_shape[num_axes]; + __shared__ int shared_pad[num_axes]; + __shared__ int shared_stride[num_axes]; + __shared__ int shared_col_shape[num_axes + 1]; + __shared__ int shared_im_shape[num_axes + 1]; + + if (threadIdx.x < num_axes) { + shared_dilation[threadIdx.x] = dilation[threadIdx.x]; + shared_kernel_shape[threadIdx.x] = kernel_shape[threadIdx.x]; + shared_pad[threadIdx.x] = pad[threadIdx.x]; + shared_stride[threadIdx.x] = stride[threadIdx.x]; + } + if (threadIdx.x < num_axes + 1) { + shared_col_shape[threadIdx.x] = col_shape[threadIdx.x]; + shared_im_shape[threadIdx.x] = im_shape[threadIdx.x]; + } + __syncthreads(); + CUDA_KERNEL_LOOP(index, n) { // Initialize channel_in, computed in the loop below, with intermediate // computations used to compute the spatial indices. int c_im = index; // Calculate d_im (image dimensions). for (int i = num_axes - 1; i >= 0; --i) { - d_im[i] = c_im % im_shape[i + 1] + pad[i]; - c_im /= im_shape[i + 1]; + d_im[i] = c_im % shared_im_shape[i + 1] + shared_pad[i]; + c_im /= shared_im_shape[i + 1]; } // Calculate col start/end indices. bool done = false; for (int i = 0; i < num_axes; ++i) { + const int kernel_extent = + shared_dilation[i] * (shared_kernel_shape[i] - 1) + 1; d_col_start[i] = d_col_iter[i] = - (d_im[i] < kernel_shape[i]) ? - 0 : (d_im[i] - kernel_shape[i]) / stride[i] + 1; - d_col_end[i] = min(d_im[i] / stride[i] + 1, col_shape[i + 1]); + (d_im[i] < kernel_extent) ? 0 : + (d_im[i] - kernel_extent) / shared_stride[i] + 1; + d_col_end[i] = + min(d_im[i] / shared_stride[i] + 1, shared_col_shape[i + 1]); if (d_col_start[i] >= d_col_end[i]) { // Skip computation if the dimension is 0 at any spatial axis -- // final val will be 0. @@ -335,21 +380,32 @@ __global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col, // Loop over the col to compute the output val. Dtype val = 0; bool incremented = true; + bool skip = false; do { // Compute the final offset. int final_offset = 0; int kernel_shape_prod = 1; + int kernel_index; for (int i = num_axes - 1; i >= 0; --i) { - final_offset += - (d_im[i] - d_col_iter[i] * stride[i]) * kernel_shape_prod; - kernel_shape_prod *= kernel_shape[i]; + kernel_index = d_im[i] - d_col_iter[i] * shared_stride[i]; + if (kernel_index % shared_dilation[i]) { + skip = true; + break; + } else { + kernel_index /= shared_dilation[i]; + final_offset += kernel_index * kernel_shape_prod; + kernel_shape_prod *= shared_kernel_shape[i]; + } } - final_offset += kernel_shape_prod * c_im; - for (int i = 0; i < num_axes; ++i) { - final_offset *= col_shape[i + 1]; - final_offset += d_col_iter[i]; + if (!skip) { + final_offset += kernel_shape_prod * c_im; + for (int i = 0; i < num_axes; ++i) { + final_offset *= shared_col_shape[i + 1]; + final_offset += d_col_iter[i]; + } + val += data_col[final_offset]; } - val += data_col[final_offset]; + skip = false; incremented = false; for (int i = num_axes - 1; i >= 0; --i) { const int d_max = d_col_end[i]; @@ -370,67 +426,69 @@ template void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes, const int im_size, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - Dtype* data_im) { + const int* dilation, Dtype* data_im) { + // num_axes should be smaller than block size + DCHECK_LT(num_spatial_axes, CAFFE_CUDA_NUM_THREADS); switch (num_spatial_axes) { case 1: col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( im_size, data_col, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); break; case 2: col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( im_size, data_col, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); break; case 3: col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( im_size, data_col, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); break; case 4: col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( im_size, data_col, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); break; case 5: col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( im_size, data_col, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); break; case 6: col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( im_size, data_col, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); break; case 7: col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( im_size, data_col, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); break; case 8: col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( im_size, data_col, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); break; case 9: col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( im_size, data_col, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); break; case 10: col2im_nd_gpu_kernel // NOLINT_NEXT_LINE(whitespace/operators) <<>>( im_size, data_col, im_shape, col_shape, - kernel_shape, pad, stride, data_im); + kernel_shape, pad, stride, dilation, data_im); break; default: LOG(FATAL) << "col2im_nd_gpu does not support computation with " @@ -444,11 +502,11 @@ template void col2im_nd_gpu(const float* data_col, const int num_spatial_axes, const int im_size, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - float* data_im); + const int* dilation, float* data_im); template void col2im_nd_gpu(const double* data_col, const int num_spatial_axes, const int im_size, const int* im_shape, const int* col_shape, const int* kernel_shape, const int* pad, const int* stride, - double* data_im); + const int* dilation, double* data_im); } // namespace caffe From 7674799475598fcb0494c83d93a46b41f8261a11 Mon Sep 17 00:00:00 2001 From: Fisher Yu Date: Sat, 26 Dec 2015 13:04:25 -0800 Subject: [PATCH 369/446] add and improve tests for dilated convolution/im2col --- src/caffe/test/test_convolution_layer.cpp | 115 ++++++++++++++++++++++ src/caffe/test/test_im2col_layer.cpp | 54 ++++++++-- 2 files changed, 163 insertions(+), 6 deletions(-) diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp index 95c3c80c59d..9bb19d13592 100644 --- a/src/caffe/test/test_convolution_layer.cpp +++ b/src/caffe/test/test_convolution_layer.cpp @@ -264,6 +264,50 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) { } } +TYPED_TEST(ConvolutionLayerTest, TestDilatedConvolution) { + typedef typename TypeParam::Dtype Dtype; + vector bottom_shape; + bottom_shape.push_back(2); + bottom_shape.push_back(3); + bottom_shape.push_back(8); + bottom_shape.push_back(7); + this->blob_bottom_vec_.push_back(this->blob_bottom_2_); + this->blob_top_vec_.push_back(this->blob_top_2_); + for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) { + this->blob_bottom_vec_[i]->Reshape(bottom_shape); + } + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + convolution_param->add_kernel_size(3); + convolution_param->add_dilation(2); + convolution_param->set_num_output(4); + convolution_param->mutable_weight_filler()->set_type("gaussian"); + convolution_param->mutable_bias_filler()->set_type("constant"); + convolution_param->mutable_bias_filler()->set_value(0.1); + shared_ptr > layer( + new ConvolutionLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Check against reference convolution. + const Dtype* top_data; + const Dtype* ref_top_data; + caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(), + this->MakeReferenceTop(this->blob_top_)); + top_data = this->blob_top_->cpu_data(); + ref_top_data = this->ref_blob_top_->cpu_data(); + for (int i = 0; i < this->blob_top_->count(); ++i) { + EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4); + } + caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(), + this->MakeReferenceTop(this->blob_top_2_)); + top_data = this->blob_top_2_->cpu_data(); + ref_top_data = this->ref_blob_top_->cpu_data(); + for (int i = 0; i < this->blob_top_->count(); ++i) { + EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4); + } +} + TYPED_TEST(ConvolutionLayerTest, Test0DConvolution) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; @@ -349,6 +393,53 @@ TYPED_TEST(ConvolutionLayerTest, TestSimple3DConvolution) { } } +TYPED_TEST(ConvolutionLayerTest, TestDilated3DConvolution) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_2_); + this->blob_top_vec_.push_back(this->blob_top_2_); + vector bottom_shape(5); + bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0); + bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1); + bottom_shape[2] = 6; + bottom_shape[3] = 7; + bottom_shape[4] = 8; + FillerParameter filler_param; + GaussianFiller filler(filler_param); + for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) { + this->blob_bottom_vec_[i]->Reshape(bottom_shape); + filler.Fill(this->blob_bottom_vec_[i]); + } + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + convolution_param->add_kernel_size(3); + convolution_param->add_dilation(2); + convolution_param->set_num_output(4); + convolution_param->mutable_weight_filler()->set_type("gaussian"); + convolution_param->mutable_bias_filler()->set_type("gaussian"); + shared_ptr > layer( + new ConvolutionLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Check against reference convolution. + const Dtype* top_data; + const Dtype* ref_top_data; + caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(), + this->MakeReferenceTop(this->blob_top_)); + top_data = this->blob_top_->cpu_data(); + ref_top_data = this->ref_blob_top_->cpu_data(); + for (int i = 0; i < this->blob_top_->count(); ++i) { + EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4); + } + caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(), + this->MakeReferenceTop(this->blob_top_2_)); + top_data = this->blob_top_2_->cpu_data(); + ref_top_data = this->ref_blob_top_->cpu_data(); + for (int i = 0; i < this->blob_top_->count(); ++i) { + EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4); + } +} + TYPED_TEST(ConvolutionLayerTest, Test1x1Convolution) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; @@ -633,6 +724,30 @@ TYPED_TEST(ConvolutionLayerTest, TestGradient) { this->blob_top_vec_); } +TYPED_TEST(ConvolutionLayerTest, TestDilatedGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + vector bottom_shape; + bottom_shape.push_back(2); + bottom_shape.push_back(3); + bottom_shape.push_back(5); + bottom_shape.push_back(6); + for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) { + this->blob_bottom_vec_[i]->Reshape(bottom_shape); + } + convolution_param->add_kernel_size(3); + convolution_param->add_dilation(2); + convolution_param->set_num_output(2); + convolution_param->mutable_weight_filler()->set_type("gaussian"); + convolution_param->mutable_bias_filler()->set_type("gaussian"); + ConvolutionLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + TYPED_TEST(ConvolutionLayerTest, TestGradient3D) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp index 24885e6b706..a7faf18f972 100644 --- a/src/caffe/test/test_im2col_layer.cpp +++ b/src/caffe/test/test_im2col_layer.cpp @@ -17,7 +17,7 @@ class Im2colLayerTest : public MultiDeviceTest { typedef typename TypeParam::Dtype Dtype; protected: Im2colLayerTest() - : blob_bottom_(new Blob(2, 3, 10, 11)), + : blob_bottom_(new Blob(2, 3, 6, 5)), blob_top_(new Blob()) { // fill the values Caffe::set_random_seed(1701); @@ -41,6 +41,12 @@ TYPED_TEST(Im2colLayerTest, TestSetup) { LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); + vector bottom_shape; + bottom_shape.push_back(2); + bottom_shape.push_back(3); + bottom_shape.push_back(10); + bottom_shape.push_back(11); + this->blob_bottom_->Reshape(bottom_shape); convolution_param->add_kernel_size(3); convolution_param->add_stride(2); convolution_param->add_dilation(3); @@ -76,21 +82,39 @@ TYPED_TEST(Im2colLayerTest, TestGradient) { layer_param.mutable_convolution_param(); convolution_param->add_kernel_size(3); convolution_param->add_stride(2); - convolution_param->add_dilation(3); Im2colLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, this->blob_top_vec_); } -TYPED_TEST(Im2colLayerTest, TestGradientForceND) { +TYPED_TEST(Im2colLayerTest, TestDilatedGradient) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; ConvolutionParameter* convolution_param = layer_param.mutable_convolution_param(); + vector bottom_shape; + bottom_shape.push_back(2); + bottom_shape.push_back(3); + bottom_shape.push_back(10); + bottom_shape.push_back(9); + this->blob_bottom_->Reshape(bottom_shape); convolution_param->add_kernel_size(3); convolution_param->add_stride(2); convolution_param->add_dilation(3); + Im2colLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(Im2colLayerTest, TestGradientForceND) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); convolution_param->set_force_nd_im2col(true); Im2colLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); @@ -98,6 +122,27 @@ TYPED_TEST(Im2colLayerTest, TestGradientForceND) { this->blob_top_vec_); } +TYPED_TEST(Im2colLayerTest, TestDilatedGradientForceND) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ConvolutionParameter* convolution_param = + layer_param.mutable_convolution_param(); + vector bottom_shape; + bottom_shape.push_back(2); + bottom_shape.push_back(3); + bottom_shape.push_back(10); + bottom_shape.push_back(9); + this->blob_bottom_->Reshape(bottom_shape); + convolution_param->add_kernel_size(3); + convolution_param->add_stride(2); + convolution_param->add_dilation(3); + convolution_param->set_force_nd_im2col(true); + Im2colLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-2); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + TYPED_TEST(Im2colLayerTest, TestRect) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; @@ -116,7 +161,6 @@ TYPED_TEST(Im2colLayerTest, TestRect) { } } - TYPED_TEST(Im2colLayerTest, TestRectGradient) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; @@ -125,8 +169,6 @@ TYPED_TEST(Im2colLayerTest, TestRectGradient) { convolution_param->set_kernel_h(5); convolution_param->set_kernel_w(3); convolution_param->add_stride(2); - convolution_param->add_dilation(1); - convolution_param->add_dilation(3); Im2colLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, From c25c5796385eda485a743cae6222845ca8eb52bb Mon Sep 17 00:00:00 2001 From: Fisher Yu Date: Sat, 26 Dec 2015 13:10:02 -0800 Subject: [PATCH 370/446] disable dilated deconvolution --- src/caffe/layers/base_conv_layer.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp index 4a4c68e009a..deb58a714a4 100644 --- a/src/caffe/layers/base_conv_layer.cpp +++ b/src/caffe/layers/base_conv_layer.cpp @@ -105,6 +105,9 @@ void BaseConvolutionLayer::LayerSetUp(const vector*>& bottom, for (int i = 0; i < num_spatial_axes_; ++i) { dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation : conv_param.dilation((num_dilation_dims == 1) ? 0 : i); + if (reverse_dimensions()) { + CHECK_EQ(dilation_data[i], 1) << "Deconvolution doesn't support dilation"; + } } // Special case: im2col is the identity for 1x1 convolution with stride 1 // and no padding, so flag for skipping the buffer and transformation. From 3e3e9ce17636f813c80b5b22afc069d3c1c802cb Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Sat, 26 Dec 2015 13:10:11 -0800 Subject: [PATCH 371/446] add short description of dilation to caffe.proto --- src/caffe/proto/caffe.proto | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 87c46629baf..019aa614373 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -518,6 +518,9 @@ message ConvolutionParameter { repeated uint32 pad = 3; // The padding size; defaults to 0 repeated uint32 kernel_size = 4; // The kernel size repeated uint32 stride = 6; // The stride; defaults to 1 + // Factor used to dilate the kernel, (implicitly) zero-filling the resulting + // holes. (Kernel dilation is sometimes referred to by its use in the + // algorithme à trous from Holschneider et al. 1987.) repeated uint32 dilation = 18; // The dilation; defaults to 1 // For 2D convolution only, the *_h and *_w versions may also be used to From bbc4e578a54546bbc41ce9e959386dbba6e269c2 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Sun, 27 Dec 2015 20:56:24 -0800 Subject: [PATCH 372/446] enable dilated deconvolution Since the underlying routines are shared, we need only upgrade compute_output_shape. --- src/caffe/layers/base_conv_layer.cpp | 3 --- src/caffe/layers/deconv_layer.cpp | 4 +++- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp index deb58a714a4..4a4c68e009a 100644 --- a/src/caffe/layers/base_conv_layer.cpp +++ b/src/caffe/layers/base_conv_layer.cpp @@ -105,9 +105,6 @@ void BaseConvolutionLayer::LayerSetUp(const vector*>& bottom, for (int i = 0; i < num_spatial_axes_; ++i) { dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation : conv_param.dilation((num_dilation_dims == 1) ? 0 : i); - if (reverse_dimensions()) { - CHECK_EQ(dilation_data[i], 1) << "Deconvolution doesn't support dilation"; - } } // Special case: im2col is the identity for 1x1 convolution with stride 1 // and no padding, so flag for skipping the buffer and transformation. diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp index 275c05626c8..20a460fbdea 100644 --- a/src/caffe/layers/deconv_layer.cpp +++ b/src/caffe/layers/deconv_layer.cpp @@ -9,12 +9,14 @@ void DeconvolutionLayer::compute_output_shape() { const int* kernel_shape_data = this->kernel_shape_.cpu_data(); const int* stride_data = this->stride_.cpu_data(); const int* pad_data = this->pad_.cpu_data(); + const int* dilation_data = this->dilation_.cpu_data(); this->output_shape_.clear(); for (int i = 0; i < this->num_spatial_axes_; ++i) { // i + 1 to skip channel axis const int input_dim = this->input_shape(i + 1); + const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1; const int output_dim = stride_data[i] * (input_dim - 1) - + kernel_shape_data[i] - 2 * pad_data[i]; + + kernel_extent - 2 * pad_data[i]; this->output_shape_.push_back(output_dim); } } From 708c1a122c33bd35b0d53630fb74965488e1947a Mon Sep 17 00:00:00 2001 From: Fisher Yu Date: Mon, 28 Dec 2015 22:46:49 -0500 Subject: [PATCH 373/446] remove extra space before + --- src/caffe/solvers/adam_solver.cpp | 2 +- tools/caffe.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/caffe/solvers/adam_solver.cpp b/src/caffe/solvers/adam_solver.cpp index cb0fbfe2f78..c3378d3890a 100644 --- a/src/caffe/solvers/adam_solver.cpp +++ b/src/caffe/solvers/adam_solver.cpp @@ -30,7 +30,7 @@ void AdamSolver::ComputeUpdateValue(int param_id, Dtype rate) { Blob* val_v = this->history_[param_id + update_history_offset].get(); Blob* val_t = this->temp_[param_id].get(); - const int t = this->iter_ + 1; + const int t = this->iter_ + 1; const Dtype correction = std::sqrt(Dtype(1) - pow(beta2, t)) / (Dtype(1.) - pow(beta1, t)); const int N = net_params[param_id]->count(); diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 305cfc3635d..6b342ace0c5 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -164,7 +164,7 @@ int train() { if (FLAGS_gpu.size() == 0 && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) { if (solver_param.has_device_id()) { - FLAGS_gpu = "" + + FLAGS_gpu = "" + boost::lexical_cast(solver_param.device_id()); } else { // Set default GPU if unspecified FLAGS_gpu = "" + boost::lexical_cast(0); From 6320d8d2663aa80b54e74e374a34441124f88c24 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Tue, 29 Dec 2015 21:10:14 -0800 Subject: [PATCH 374/446] TestDataTransformer: fix some memory leaks caused by use of 'new' --- src/caffe/test/test_data_transformer.cpp | 136 +++++++++++------------ 1 file changed, 62 insertions(+), 74 deletions(-) diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp index 8a1013744e8..6103918fda1 100644 --- a/src/caffe/test/test_data_transformer.cpp +++ b/src/caffe/test/test_data_transformer.cpp @@ -40,23 +40,21 @@ class DataTransformTest : public ::testing::Test { int NumSequenceMatches(const TransformationParameter transform_param, const Datum& datum, Phase phase) { // Get crop sequence with Caffe seed 1701. - DataTransformer* transformer = - new DataTransformer(transform_param, phase); + DataTransformer transformer(transform_param, phase); const int crop_size = transform_param.crop_size(); Caffe::set_random_seed(seed_); - transformer->InitRand(); - Blob* blob = - new Blob(1, datum.channels(), datum.height(), datum.width()); + transformer.InitRand(); + Blob blob(1, datum.channels(), datum.height(), datum.width()); if (transform_param.crop_size() > 0) { - blob->Reshape(1, datum.channels(), crop_size, crop_size); + blob.Reshape(1, datum.channels(), crop_size, crop_size); } vector > crop_sequence; for (int iter = 0; iter < this->num_iter_; ++iter) { vector iter_crop_sequence; - transformer->Transform(datum, blob); - for (int j = 0; j < blob->count(); ++j) { - iter_crop_sequence.push_back(blob->cpu_data()[j]); + transformer.Transform(datum, &blob); + for (int j = 0; j < blob.count(); ++j) { + iter_crop_sequence.push_back(blob.cpu_data()[j]); } crop_sequence.push_back(iter_crop_sequence); } @@ -64,17 +62,14 @@ class DataTransformTest : public ::testing::Test { int num_sequence_matches = 0; for (int iter = 0; iter < this->num_iter_; ++iter) { vector iter_crop_sequence = crop_sequence[iter]; - transformer->Transform(datum, blob); - for (int j = 0; j < blob->count(); ++j) { - num_sequence_matches += - (crop_sequence[iter][j] == blob->cpu_data()[j]); + transformer.Transform(datum, &blob); + for (int j = 0; j < blob.count(); ++j) { + num_sequence_matches += (crop_sequence[iter][j] == blob.cpu_data()[j]); } } return num_sequence_matches; } - virtual ~DataTransformTest() { } - int seed_; int num_iter_; }; @@ -91,17 +86,16 @@ TYPED_TEST(DataTransformTest, TestEmptyTransform) { Datum datum; FillDatum(label, channels, height, width, unique_pixels, &datum); - Blob* blob = new Blob(1, channels, height, width); - DataTransformer* transformer = - new DataTransformer(transform_param, TEST); - transformer->InitRand(); - transformer->Transform(datum, blob); - EXPECT_EQ(blob->num(), 1); - EXPECT_EQ(blob->channels(), datum.channels()); - EXPECT_EQ(blob->height(), datum.height()); - EXPECT_EQ(blob->width(), datum.width()); - for (int j = 0; j < blob->count(); ++j) { - EXPECT_EQ(blob->cpu_data()[j], label); + Blob blob(1, channels, height, width); + DataTransformer transformer(transform_param, TEST); + transformer.InitRand(); + transformer.Transform(datum, &blob); + EXPECT_EQ(blob.num(), 1); + EXPECT_EQ(blob.channels(), datum.channels()); + EXPECT_EQ(blob.height(), datum.height()); + EXPECT_EQ(blob.width(), datum.width()); + for (int j = 0; j < blob.count(); ++j) { + EXPECT_EQ(blob.cpu_data()[j], label); } } @@ -115,17 +109,16 @@ TYPED_TEST(DataTransformTest, TestEmptyTransformUniquePixels) { Datum datum; FillDatum(label, channels, height, width, unique_pixels, &datum); - Blob* blob = new Blob(1, 3, 4, 5); - DataTransformer* transformer = - new DataTransformer(transform_param, TEST); - transformer->InitRand(); - transformer->Transform(datum, blob); - EXPECT_EQ(blob->num(), 1); - EXPECT_EQ(blob->channels(), datum.channels()); - EXPECT_EQ(blob->height(), datum.height()); - EXPECT_EQ(blob->width(), datum.width()); - for (int j = 0; j < blob->count(); ++j) { - EXPECT_EQ(blob->cpu_data()[j], j); + Blob blob(1, 3, 4, 5); + DataTransformer transformer(transform_param, TEST); + transformer.InitRand(); + transformer.Transform(datum, &blob); + EXPECT_EQ(blob.num(), 1); + EXPECT_EQ(blob.channels(), datum.channels()); + EXPECT_EQ(blob.height(), datum.height()); + EXPECT_EQ(blob.width(), datum.width()); + for (int j = 0; j < blob.count(); ++j) { + EXPECT_EQ(blob.cpu_data()[j], j); } } @@ -141,19 +134,17 @@ TYPED_TEST(DataTransformTest, TestCropSize) { transform_param.set_crop_size(crop_size); Datum datum; FillDatum(label, channels, height, width, unique_pixels, &datum); - DataTransformer* transformer = - new DataTransformer(transform_param, TEST); - transformer->InitRand(); - Blob* blob = - new Blob(1, channels, crop_size, crop_size); + DataTransformer transformer(transform_param, TEST); + transformer.InitRand(); + Blob blob(1, channels, crop_size, crop_size); for (int iter = 0; iter < this->num_iter_; ++iter) { - transformer->Transform(datum, blob); - EXPECT_EQ(blob->num(), 1); - EXPECT_EQ(blob->channels(), datum.channels()); - EXPECT_EQ(blob->height(), crop_size); - EXPECT_EQ(blob->width(), crop_size); - for (int j = 0; j < blob->count(); ++j) { - EXPECT_EQ(blob->cpu_data()[j], label); + transformer.Transform(datum, &blob); + EXPECT_EQ(blob.num(), 1); + EXPECT_EQ(blob.channels(), datum.channels()); + EXPECT_EQ(blob.height(), crop_size); + EXPECT_EQ(blob.width(), crop_size); + for (int j = 0; j < blob.count(); ++j) { + EXPECT_EQ(blob.cpu_data()[j], label); } } } @@ -280,13 +271,12 @@ TYPED_TEST(DataTransformTest, TestMeanValue) { transform_param.add_mean_value(mean_value); Datum datum; FillDatum(label, channels, height, width, unique_pixels, &datum); - Blob* blob = new Blob(1, channels, height, width); - DataTransformer* transformer = - new DataTransformer(transform_param, TEST); - transformer->InitRand(); - transformer->Transform(datum, blob); - for (int j = 0; j < blob->count(); ++j) { - EXPECT_EQ(blob->cpu_data()[j], label - mean_value); + Blob blob(1, channels, height, width); + DataTransformer transformer(transform_param, TEST); + transformer.InitRand(); + transformer.Transform(datum, &blob); + for (int j = 0; j < blob.count(); ++j) { + EXPECT_EQ(blob.cpu_data()[j], label - mean_value); } } @@ -303,14 +293,13 @@ TYPED_TEST(DataTransformTest, TestMeanValues) { transform_param.add_mean_value(2); Datum datum; FillDatum(label, channels, height, width, unique_pixels, &datum); - Blob* blob = new Blob(1, channels, height, width); - DataTransformer* transformer = - new DataTransformer(transform_param, TEST); - transformer->InitRand(); - transformer->Transform(datum, blob); + Blob blob(1, channels, height, width); + DataTransformer transformer(transform_param, TEST); + transformer.InitRand(); + transformer.Transform(datum, &blob); for (int c = 0; c < channels; ++c) { for (int j = 0; j < height * width; ++j) { - EXPECT_EQ(blob->cpu_data()[blob->offset(0, c) + j], label - c); + EXPECT_EQ(blob.cpu_data()[blob.offset(0, c) + j], label - c); } } } @@ -325,8 +314,8 @@ TYPED_TEST(DataTransformTest, TestMeanFile) { const int size = channels * height * width; // Create a mean file - string* mean_file = new string(); - MakeTempFilename(mean_file); + string mean_file; + MakeTempFilename(&mean_file); BlobProto blob_mean; blob_mean.set_num(1); blob_mean.set_channels(channels); @@ -337,19 +326,18 @@ TYPED_TEST(DataTransformTest, TestMeanFile) { blob_mean.add_data(j); } - LOG(INFO) << "Using temporary mean_file " << *mean_file; - WriteProtoToBinaryFile(blob_mean, *mean_file); + LOG(INFO) << "Using temporary mean_file " << mean_file; + WriteProtoToBinaryFile(blob_mean, mean_file); - transform_param.set_mean_file(*mean_file); + transform_param.set_mean_file(mean_file); Datum datum; FillDatum(label, channels, height, width, unique_pixels, &datum); - Blob* blob = new Blob(1, channels, height, width); - DataTransformer* transformer = - new DataTransformer(transform_param, TEST); - transformer->InitRand(); - transformer->Transform(datum, blob); - for (int j = 0; j < blob->count(); ++j) { - EXPECT_EQ(blob->cpu_data()[j], 0); + Blob blob(1, channels, height, width); + DataTransformer transformer(transform_param, TEST); + transformer.InitRand(); + transformer.Transform(datum, &blob); + for (int j = 0; j < blob.count(); ++j) { + EXPECT_EQ(blob.cpu_data()[j], 0); } } From 1137e89fef767c68e9368779a57dfc61c6d8d834 Mon Sep 17 00:00:00 2001 From: philkr Date: Wed, 5 Aug 2015 11:54:08 -0700 Subject: [PATCH 375/446] Exposing layer top and bottom names to python --- include/caffe/net.hpp | 12 ++++++++++++ python/caffe/_caffe.cpp | 4 ++++ python/caffe/pycaffe.py | 18 ++++++++++++++++++ 3 files changed, 34 insertions(+) diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index 1bf07d28d13..3b56f3070f7 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -149,6 +149,18 @@ class Net { inline const vector*> >& top_vecs() const { return top_vecs_; } + /// @brief returns the ids of the top blobs of layer i + inline const vector & top_ids(int i) const { + CHECK_GE(i, 0) << "Invalid layer id"; + CHECK_LT(i, top_id_vecs_.size()) << "Invalid layer id"; + return top_id_vecs_[i]; + } + /// @brief returns the ids of the bottom blobs of layer i + inline const vector & bottom_ids(int i) const { + CHECK_GE(i, 0) << "Invalid layer id"; + CHECK_LT(i, bottom_id_vecs_.size()) << "Invalid layer id"; + return bottom_id_vecs_[i]; + } inline const vector >& bottom_need_backward() const { return bottom_need_backward_; } diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index 69d55332841..4ea2ec60b7e 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -232,6 +232,10 @@ BOOST_PYTHON_MODULE(_caffe) { .def("share_with", &Net::ShareTrainedLayersWith) .add_property("_blob_loss_weights", bp::make_function( &Net::blob_loss_weights, bp::return_internal_reference<>())) + .def("_bottom_ids", bp::make_function(&Net::bottom_ids, + bp::return_value_policy())) + .def("_top_ids", bp::make_function(&Net::top_ids, + bp::return_value_policy())) .add_property("_blobs", bp::make_function(&Net::blobs, bp::return_internal_reference<>())) .add_property("layers", bp::make_function(&Net::layers, diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py index 31dc702f647..3054110771c 100644 --- a/python/caffe/pycaffe.py +++ b/python/caffe/pycaffe.py @@ -276,6 +276,22 @@ def _Net_batch(self, blobs): padding]) yield padded_batch + +class _Net_IdNameWrapper: + """ + A simple wrapper that allows the ids propery to be accessed as a dict + indexed by names. Used for top and bottom names + """ + def __init__(self, net, func): + self.net, self.func = net, func + + def __getitem__(self, name): + # Map the layer name to id + ids = self.func(self.net, list(self.net._layer_names).index(name)) + # Map the blob id to name + id_to_name = list(self.net.blobs) + return [id_to_name[i] for i in ids] + # Attach methods to Net. Net.blobs = _Net_blobs Net.blob_loss_weights = _Net_blob_loss_weights @@ -288,3 +304,5 @@ def _Net_batch(self, blobs): Net._batch = _Net_batch Net.inputs = _Net_inputs Net.outputs = _Net_outputs +Net.top_names = property(lambda n: _Net_IdNameWrapper(n, Net._top_ids)) +Net.bottom_names = property(lambda n: _Net_IdNameWrapper(n, Net._bottom_ids)) From 6d09ca2829dad0c7ae4ba1474fd351f41125ab2a Mon Sep 17 00:00:00 2001 From: philkr Date: Tue, 5 Jan 2016 12:45:52 -0800 Subject: [PATCH 376/446] Speeding up the GPU solvers --- src/caffe/solvers/adadelta_solver.cpp | 66 +++++---------------------- src/caffe/solvers/adadelta_solver.cu | 30 ++++++++++++ src/caffe/solvers/adagrad_solver.cpp | 37 ++++----------- src/caffe/solvers/adagrad_solver.cu | 26 +++++++++++ src/caffe/solvers/adam_solver.cpp | 37 ++++----------- src/caffe/solvers/adam_solver.cu | 29 ++++++++++++ src/caffe/solvers/nesterov_solver.cpp | 29 ++++-------- src/caffe/solvers/nesterov_solver.cu | 27 +++++++++++ src/caffe/solvers/rmsprop_solver.cpp | 35 ++++---------- src/caffe/solvers/rmsprop_solver.cu | 28 ++++++++++++ src/caffe/solvers/sgd_solver.cpp | 16 ++++--- src/caffe/solvers/sgd_solver.cu | 24 ++++++++++ 12 files changed, 223 insertions(+), 161 deletions(-) create mode 100644 src/caffe/solvers/adadelta_solver.cu create mode 100644 src/caffe/solvers/adagrad_solver.cu create mode 100644 src/caffe/solvers/adam_solver.cu create mode 100644 src/caffe/solvers/nesterov_solver.cu create mode 100644 src/caffe/solvers/rmsprop_solver.cu create mode 100644 src/caffe/solvers/sgd_solver.cu diff --git a/src/caffe/solvers/adadelta_solver.cpp b/src/caffe/solvers/adadelta_solver.cpp index a37899ebbb4..fd30f19acac 100644 --- a/src/caffe/solvers/adadelta_solver.cpp +++ b/src/caffe/solvers/adadelta_solver.cpp @@ -16,6 +16,12 @@ void AdaDeltaSolver::AdaDeltaPreSolve() { } } +#ifndef CPU_ONLY +template +void adadelta_update_gpu(int N, Dtype* g, Dtype* h, Dtype* h2, Dtype momentum, + Dtype delta, Dtype local_rate); +#endif + template void AdaDeltaSolver::ComputeUpdateValue(int param_id, Dtype rate) { const vector*>& net_params = this->net_->learnable_params(); @@ -85,61 +91,11 @@ void AdaDeltaSolver::ComputeUpdateValue(int param_id, Dtype rate) { } case Caffe::GPU: { #ifndef CPU_ONLY - // compute square of gradient in update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history of gradients - caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, - this->update_[param_id]->gpu_data(), momentum, - this->history_[param_id]->mutable_gpu_data()); - - // add delta to history to guard against dividing by zero later - caffe_gpu_set(net_params[param_id]->count(), delta, - this->temp_[param_id]->mutable_gpu_data()); - - caffe_gpu_add(net_params[param_id]->count(), - this->temp_[param_id]->gpu_data(), - this->history_[update_history_offset + param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_add(net_params[param_id]->count(), - this->temp_[param_id]->gpu_data(), - this->history_[param_id]->gpu_data(), - this->temp_[param_id]->mutable_gpu_data()); - - // divide history of updates by history of gradients - caffe_gpu_div(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), - this->temp_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - // jointly compute the RMS of both for update and gradient history - caffe_gpu_powx(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_gpu_data()); - - // compute the update and copy to net_diff - caffe_gpu_mul(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), - this->update_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); - - // compute square of update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history of updates - caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum, - this->update_[param_id]->gpu_data(), momentum, - this->history_[update_history_offset + param_id]->mutable_gpu_data()); - - // apply learning rate - caffe_gpu_scale(net_params[param_id]->count(), local_rate, - net_params[param_id]->gpu_diff(), - net_params[param_id]->mutable_gpu_diff()); + adadelta_update_gpu(net_params[param_id]->count(), + net_params[param_id]->mutable_gpu_diff(), + this->history_[param_id]->mutable_gpu_data(), + this->history_[update_history_offset + param_id]->mutable_gpu_data(), + momentum, delta, local_rate); #else NO_GPU; #endif diff --git a/src/caffe/solvers/adadelta_solver.cu b/src/caffe/solvers/adadelta_solver.cu new file mode 100644 index 00000000000..6c94585b89e --- /dev/null +++ b/src/caffe/solvers/adadelta_solver.cu @@ -0,0 +1,30 @@ +#include "caffe/util/math_functions.hpp" + + +namespace caffe { + +template +__global__ void AdaDeltaUpdate(int N, Dtype* g, Dtype* h, Dtype* h2, + Dtype momentum, Dtype delta, Dtype local_rate) { + CUDA_KERNEL_LOOP(i, N) { + float gi = g[i]; + float hi = h[i] = momentum * h[i] + (1-momentum) * gi * gi; + gi = gi * sqrt((h2[i] + delta) / (hi + delta)); + h2[i] = momentum * h2[i] + (1-momentum) * gi * gi; + g[i] = local_rate * gi; + } +} +template +void adadelta_update_gpu(int N, Dtype* g, Dtype* h, Dtype* h2, Dtype momentum, + Dtype delta, Dtype local_rate) { + AdaDeltaUpdate // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + N, g, h, h2, momentum, delta, local_rate); + CUDA_POST_KERNEL_CHECK; +} +template void adadelta_update_gpu(int , float*, float*, float*, + float, float, float); +template void adadelta_update_gpu(int, double*, double*, double*, + double, double, double); + +} // namespace caffe diff --git a/src/caffe/solvers/adagrad_solver.cpp b/src/caffe/solvers/adagrad_solver.cpp index 5e406326095..e78eadca141 100644 --- a/src/caffe/solvers/adagrad_solver.cpp +++ b/src/caffe/solvers/adagrad_solver.cpp @@ -4,6 +4,12 @@ namespace caffe { +#ifndef CPU_ONLY +template +void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta, + Dtype local_rate); +#endif + template void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { CHECK(Caffe::root_solver()); @@ -45,34 +51,9 @@ void AdaGradSolver::ComputeUpdateValue(int param_id, Dtype rate) { } case Caffe::GPU: { #ifndef CPU_ONLY - // compute square of gradient in update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history - caffe_gpu_add(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), - this->history_[param_id]->gpu_data(), - this->history_[param_id]->mutable_gpu_data()); - - // prepare update - caffe_gpu_powx(net_params[param_id]->count(), - this->history_[param_id]->gpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_add_scalar(net_params[param_id]->count(), - delta, this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_div(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), - this->update_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - // scale and copy - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - this->update_[param_id]->gpu_data(), Dtype(0), - net_params[param_id]->mutable_gpu_diff()); + adagrad_update_gpu(net_params[param_id]->count(), + net_params[param_id]->mutable_gpu_diff(), + this->history_[param_id]->mutable_gpu_data(), delta, local_rate); #else NO_GPU; #endif diff --git a/src/caffe/solvers/adagrad_solver.cu b/src/caffe/solvers/adagrad_solver.cu new file mode 100644 index 00000000000..adefd554bbd --- /dev/null +++ b/src/caffe/solvers/adagrad_solver.cu @@ -0,0 +1,26 @@ +#include "caffe/util/math_functions.hpp" + + +namespace caffe { + +template +__global__ void AdaGradUpdate(int N, Dtype* g, Dtype* h, Dtype delta, + Dtype local_rate) { + CUDA_KERNEL_LOOP(i, N) { + float gi = g[i]; + float hi = h[i] = h[i] + gi*gi; + g[i] = local_rate * gi / (sqrt(hi) + delta); + } +} +template +void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta, + Dtype local_rate) { + AdaGradUpdate // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + N, g, h, delta, local_rate); + CUDA_POST_KERNEL_CHECK; +} +template void adagrad_update_gpu(int, float*, float*, float, float); +template void adagrad_update_gpu(int, double*, double*, double, double); + +} // namespace caffe diff --git a/src/caffe/solvers/adam_solver.cpp b/src/caffe/solvers/adam_solver.cpp index c3378d3890a..4a91f00bd49 100644 --- a/src/caffe/solvers/adam_solver.cpp +++ b/src/caffe/solvers/adam_solver.cpp @@ -16,6 +16,12 @@ void AdamSolver::AdamPreSolve() { } } +#ifndef CPU_ONLY +template +void adam_update_gpu(int N, Dtype* g, Dtype* m, Dtype* v, Dtype beta1, + Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate); +#endif + template void AdamSolver::ComputeUpdateValue(int param_id, Dtype rate) { const vector*>& net_params = this->net_->learnable_params(); @@ -69,34 +75,9 @@ void AdamSolver::ComputeUpdateValue(int param_id, Dtype rate) { } case Caffe::GPU: { #ifndef CPU_ONLY - // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t - caffe_gpu_axpby(N, Dtype(1)-beta1, - net_params[param_id]->gpu_diff(), beta1, - val_m->mutable_gpu_data()); - - // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2 - caffe_gpu_mul(N, - net_params[param_id]->gpu_diff(), - net_params[param_id]->gpu_diff(), - val_t->mutable_gpu_data()); - caffe_gpu_axpby(N, Dtype(1)-beta2, - val_t->gpu_data(), beta2, - val_v->mutable_gpu_data()); - - // set update - caffe_gpu_powx(N, - val_v->gpu_data(), Dtype(0.5), - val_t->mutable_gpu_data()); - caffe_gpu_add_scalar(N, eps_hat, - val_t->mutable_gpu_data()); - caffe_gpu_div(N, - val_m->gpu_data(), - val_t->gpu_data(), - val_t->mutable_gpu_data()); - - caffe_gpu_scale(N, local_rate*correction, - val_t->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); + adam_update_gpu(N, net_params[param_id]->mutable_gpu_diff(), + val_m->mutable_gpu_data(), val_v->mutable_gpu_data(), beta1, beta2, + eps_hat, local_rate*correction); #else NO_GPU; #endif diff --git a/src/caffe/solvers/adam_solver.cu b/src/caffe/solvers/adam_solver.cu new file mode 100644 index 00000000000..917ae100246 --- /dev/null +++ b/src/caffe/solvers/adam_solver.cu @@ -0,0 +1,29 @@ +#include "caffe/util/math_functions.hpp" + + +namespace caffe { + +template +__global__ void AdamUpdate(int N, Dtype* g, Dtype* m, Dtype* v, + Dtype beta1, Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate) { + CUDA_KERNEL_LOOP(i, N) { + float gi = g[i]; + float mi = m[i] = m[i]*beta1 + gi*(1-beta1); + float vi = v[i] = v[i]*beta2 + gi*gi*(1-beta2); + g[i] = corrected_local_rate * mi / (sqrt(vi) + eps_hat); + } +} +template +void adam_update_gpu(int N, Dtype* g, Dtype* m, Dtype* v, Dtype beta1, + Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate) { + AdamUpdate // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + N, g, m, v, beta1, beta2, eps_hat, corrected_local_rate); + CUDA_POST_KERNEL_CHECK; +} +template void adam_update_gpu(int, float*, float*, float*, + float, float, float, float); +template void adam_update_gpu(int, double*, double*, double*, + double, double, double, double); + +} // namespace caffe diff --git a/src/caffe/solvers/nesterov_solver.cpp b/src/caffe/solvers/nesterov_solver.cpp index 34bf01ebf29..23ab2d4369a 100644 --- a/src/caffe/solvers/nesterov_solver.cpp +++ b/src/caffe/solvers/nesterov_solver.cpp @@ -4,6 +4,12 @@ namespace caffe { +#ifndef CPU_ONLY +template +void nesterov_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum, + Dtype local_rate); +#endif + template void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { CHECK(Caffe::root_solver()); @@ -36,25 +42,10 @@ void NesterovSolver::ComputeUpdateValue(int param_id, Dtype rate) { } case Caffe::GPU: { #ifndef CPU_ONLY - // save history momentum for stepping back - caffe_copy(net_params[param_id]->count(), - this->history_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - // update history - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - net_params[param_id]->gpu_diff(), momentum, - this->history_[param_id]->mutable_gpu_data()); - - // compute update: step back then over step - caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum, - this->history_[param_id]->gpu_data(), -momentum, - this->update_[param_id]->mutable_gpu_data()); - - // copy - caffe_copy(net_params[param_id]->count(), - this->update_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); + nesterov_update_gpu(net_params[param_id]->count(), + net_params[param_id]->mutable_gpu_diff(), + this->history_[param_id]->mutable_gpu_data(), + momentum, local_rate); #else NO_GPU; #endif diff --git a/src/caffe/solvers/nesterov_solver.cu b/src/caffe/solvers/nesterov_solver.cu new file mode 100644 index 00000000000..57a456b8252 --- /dev/null +++ b/src/caffe/solvers/nesterov_solver.cu @@ -0,0 +1,27 @@ +#include "caffe/util/math_functions.hpp" + + +namespace caffe { + +template +__global__ void NesterovUpdate(int N, Dtype* g, Dtype* h, + Dtype momentum, Dtype local_rate) { + CUDA_KERNEL_LOOP(i, N) { + float hi = h[i]; + float hi_new = h[i] = momentum * hi + local_rate * g[i]; + g[i] = (1+momentum) * hi_new - momentum * hi; + } +} +template +void nesterov_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum, + Dtype local_rate) { + NesterovUpdate // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + N, g, h, momentum, local_rate); + CUDA_POST_KERNEL_CHECK; +} +template void nesterov_update_gpu(int, float*, float*, float, float); +template void nesterov_update_gpu(int, double*, double*, double, + double); + +} // namespace caffe diff --git a/src/caffe/solvers/rmsprop_solver.cpp b/src/caffe/solvers/rmsprop_solver.cpp index c6247676094..3251ee423a7 100644 --- a/src/caffe/solvers/rmsprop_solver.cpp +++ b/src/caffe/solvers/rmsprop_solver.cpp @@ -4,6 +4,12 @@ namespace caffe { +#ifndef CPU_ONLY +template +void rmsprop_update_gpu(int N, Dtype* g, Dtype* h, Dtype rms_decay, + Dtype delta, Dtype local_rate); +#endif + template void RMSPropSolver::ComputeUpdateValue(int param_id, Dtype rate) { const vector*>& net_params = this->net_->learnable_params(); @@ -45,31 +51,10 @@ void RMSPropSolver::ComputeUpdateValue(int param_id, Dtype rate) { break; case Caffe::GPU: #ifndef CPU_ONLY - // compute square of gradient in update - caffe_gpu_powx(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), Dtype(2), - this->update_[param_id]->mutable_gpu_data()); - - // update history - caffe_gpu_axpby(net_params[param_id] -> count(), - Dtype(1-rms_decay), this->update_[param_id]->gpu_data(), - rms_decay, this->history_[param_id]-> mutable_gpu_data()); - - // prepare update - caffe_gpu_powx(net_params[param_id]->count(), - this->history_[param_id]->gpu_data(), Dtype(0.5), - this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_add_scalar(net_params[param_id]->count(), - delta, this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_div(net_params[param_id]->count(), - net_params[param_id]->gpu_diff(), this->update_[param_id]->gpu_data(), - this->update_[param_id]->mutable_gpu_data()); - - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - this->update_[param_id]->gpu_data(), Dtype(0), - net_params[param_id]->mutable_gpu_diff()); + rmsprop_update_gpu(net_params[param_id]->count(), + net_params[param_id]->mutable_gpu_diff(), + this->history_[param_id]->mutable_gpu_data(), + rms_decay, delta, local_rate); #else NO_GPU; #endif diff --git a/src/caffe/solvers/rmsprop_solver.cu b/src/caffe/solvers/rmsprop_solver.cu new file mode 100644 index 00000000000..c5ffd329d77 --- /dev/null +++ b/src/caffe/solvers/rmsprop_solver.cu @@ -0,0 +1,28 @@ +#include "caffe/util/math_functions.hpp" + + +namespace caffe { + +template +__global__ void RMSPropUpdate(int N, Dtype* g, Dtype* h, + Dtype rms_decay, Dtype delta, Dtype local_rate) { + CUDA_KERNEL_LOOP(i, N) { + float gi = g[i]; + float hi = h[i] = rms_decay*h[i] + (1-rms_decay)*gi*gi; + g[i] = local_rate * g[i] / (sqrt(hi) + delta); + } +} +template +void rmsprop_update_gpu(int N, Dtype* g, Dtype* h, Dtype rms_decay, + Dtype delta, Dtype local_rate) { + RMSPropUpdate // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + N, g, h, rms_decay, delta, local_rate); + CUDA_POST_KERNEL_CHECK; +} +template void rmsprop_update_gpu(int, float*, float*, float, float, + float); +template void rmsprop_update_gpu(int, double*, double*, double, double, + double); + +} // namespace caffe diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp index 32bf19b17c8..f30f316d1a0 100644 --- a/src/caffe/solvers/sgd_solver.cpp +++ b/src/caffe/solvers/sgd_solver.cpp @@ -203,6 +203,12 @@ void SGDSolver::Regularize(int param_id) { } } +#ifndef CPU_ONLY +template +void sgd_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum, + Dtype local_rate); +#endif + template void SGDSolver::ComputeUpdateValue(int param_id, Dtype rate) { const vector*>& net_params = this->net_->learnable_params(); @@ -222,12 +228,10 @@ void SGDSolver::ComputeUpdateValue(int param_id, Dtype rate) { } case Caffe::GPU: { #ifndef CPU_ONLY - caffe_gpu_axpby(net_params[param_id]->count(), local_rate, - net_params[param_id]->gpu_diff(), momentum, - history_[param_id]->mutable_gpu_data()); - caffe_copy(net_params[param_id]->count(), - history_[param_id]->gpu_data(), - net_params[param_id]->mutable_gpu_diff()); + sgd_update_gpu(net_params[param_id]->count(), + net_params[param_id]->mutable_gpu_diff(), + history_[param_id]->mutable_gpu_data(), + momentum, local_rate); #else NO_GPU; #endif diff --git a/src/caffe/solvers/sgd_solver.cu b/src/caffe/solvers/sgd_solver.cu new file mode 100644 index 00000000000..e5410352140 --- /dev/null +++ b/src/caffe/solvers/sgd_solver.cu @@ -0,0 +1,24 @@ +#include "caffe/util/math_functions.hpp" + + +namespace caffe { + +template +__global__ void SGDUpdate(int N, Dtype* g, Dtype* h, + Dtype momentum, Dtype local_rate) { + CUDA_KERNEL_LOOP(i, N) { + g[i] = h[i] = momentum*h[i] + local_rate*g[i]; + } +} +template +void sgd_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum, + Dtype local_rate) { + SGDUpdate // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + N, g, h, momentum, local_rate); + CUDA_POST_KERNEL_CHECK; +} +template void sgd_update_gpu(int, float*, float*, float, float); +template void sgd_update_gpu(int, double*, double*, double, double); + +} // namespace caffe From 672f30ece38b41c0133d83501882551c53610885 Mon Sep 17 00:00:00 2001 From: philkr Date: Wed, 6 Jan 2016 07:23:35 -0800 Subject: [PATCH 377/446] CMake python version fix --- cmake/Dependencies.cmake | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake index 51a803c1a73..c7b6a17aa69 100644 --- a/cmake/Dependencies.cmake +++ b/cmake/Dependencies.cmake @@ -114,14 +114,14 @@ if(BUILD_python) # Find the matching boost python implementation set(version ${PYTHONLIBS_VERSION_STRING}) - STRING( REPLACE "." "" boost_py_version ${version} ) + STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} ) find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}") set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND}) while(NOT "${version}" STREQUAL "" AND NOT Boost_PYTHON_FOUND) STRING( REGEX REPLACE "([0-9.]+).[0-9]+" "\\1" version ${version} ) - STRING( REPLACE "." "" boost_py_version ${version} ) + STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} ) find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}") set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND}) From 581c1cc3fd6c04640c4b89e5ed003a40cd67e855 Mon Sep 17 00:00:00 2001 From: Mariusz Moczala Date: Wed, 20 Jan 2016 09:28:35 +0100 Subject: [PATCH 378/446] Performance related update of im2col() and col2im() functions --- src/caffe/util/im2col.cpp | 93 +++++++++++++++++++++++++-------------- 1 file changed, 61 insertions(+), 32 deletions(-) diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp index 6e5ea875757..114a86cb81e 100644 --- a/src/caffe/util/im2col.cpp +++ b/src/caffe/util/im2col.cpp @@ -5,6 +5,16 @@ namespace caffe { +// Function uses casting from int to unsigned to compare if value of +// parameter a is greater or equal to zero and lower than value of +// parameter b. The b parameter is of type signed and is always positive, +// therefore its value is always lower than 0x800... where casting +// negative value of a parameter converts it to value higher than 0x800... +// The casting allows to use one condition instead of two. +inline bool is_a_ge_zero_and_a_lt_b(int a, int b) { + return static_cast(a) < static_cast(b); +} + template void im2col_cpu(const Dtype* data_im, const int channels, const int height, const int width, const int kernel_h, const int kernel_w, @@ -12,22 +22,33 @@ void im2col_cpu(const Dtype* data_im, const int channels, const int stride_h, const int stride_w, const int dilation_h, const int dilation_w, Dtype* data_col) { - const int height_col = (height + 2 * pad_h - - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; - const int width_col = (width + 2 * pad_w - - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - const int channels_col = channels * kernel_h * kernel_w; - for (int c_col = 0; c_col < channels_col; ++c_col) { - int w_offset = c_col % kernel_w; - int h_offset = (c_col / kernel_w) % kernel_h; - int c_im = c_col / kernel_h / kernel_w; - for (int h_col = 0; h_col < height_col; ++h_col) { - for (int w_col = 0; w_col < width_col; ++w_col) { - int h_im = h_col * stride_h - pad_h + h_offset * dilation_h; - int w_im = w_col * stride_w - pad_w + w_offset * dilation_w; - data_col[(c_col * height_col + h_col) * width_col + w_col] = - (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ? - data_im[(c_im * height + h_im) * width + w_im] : 0; + const int output_h = (height + 2 * pad_h - + (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int output_w = (width + 2 * pad_w - + (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + const int channel_size = height * width; + for (int channel = channels; channel--; data_im += channel_size) { + for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) { + for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) { + int input_row = -pad_h + kernel_row * dilation_h; + for (int output_rows = output_h; output_rows; output_rows--) { + if (!is_a_ge_zero_and_a_lt_b(input_row, height)) { + for (int output_cols = output_w; output_cols; output_cols--) { + *(data_col++) = 0; + } + } else { + int input_col = -pad_w + kernel_col * dilation_w; + for (int output_col = output_w; output_col; output_col--) { + if (is_a_ge_zero_and_a_lt_b(input_col, width)) { + *(data_col++) = data_im[input_row * width + input_col]; + } else { + *(data_col++) = 0; + } + input_col += stride_w; + } + } + input_row += stride_h; + } } } } @@ -146,22 +167,30 @@ void col2im_cpu(const Dtype* data_col, const int channels, const int dilation_h, const int dilation_w, Dtype* data_im) { caffe_set(height * width * channels, Dtype(0), data_im); - const int height_col = (height + 2 * pad_h - - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; - const int width_col = (width + 2 * pad_w - - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - const int channels_col = channels * kernel_h * kernel_w; - for (int c_col = 0; c_col < channels_col; ++c_col) { - int w_offset = c_col % kernel_w; - int h_offset = (c_col / kernel_w) % kernel_h; - int c_im = c_col / kernel_h / kernel_w; - for (int h_col = 0; h_col < height_col; ++h_col) { - for (int w_col = 0; w_col < width_col; ++w_col) { - int h_im = h_col * stride_h - pad_h + h_offset * dilation_h; - int w_im = w_col * stride_w - pad_w + w_offset * dilation_w; - if (h_im >= 0 && h_im < height && w_im >= 0 && w_im < width) - data_im[(c_im * height + h_im) * width + w_im] += - data_col[(c_col * height_col + h_col) * width_col + w_col]; + const int output_h = (height + 2 * pad_h - + (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int output_w = (width + 2 * pad_w - + (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + const int channel_size = height * width; + for (int channel = channels; channel--; data_im += channel_size) { + for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) { + for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) { + int input_row = -pad_h + kernel_row * dilation_h; + for (int output_rows = output_h; output_rows; output_rows--) { + if (!is_a_ge_zero_and_a_lt_b(input_row, height)) { + data_col += output_w; + } else { + int input_col = -pad_w + kernel_col * dilation_w; + for (int output_col = output_w; output_col; output_col--) { + if (is_a_ge_zero_and_a_lt_b(input_col, width)) { + data_im[input_row * width + input_col] += *data_col; + } + data_col++; + input_col += stride_w; + } + } + input_row += stride_h; + } } } } From de31e034e5570056666d161ce10078011b0f1601 Mon Sep 17 00:00:00 2001 From: biluochun Date: Wed, 20 Jan 2016 17:53:24 +0800 Subject: [PATCH 379/446] fixbug #issues/3494 No to_python (by-value) converter found for C++ type: boost::shared_ptr > --- python/caffe/_caffe.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index 4ea2ec60b7e..bd3128cbde7 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -271,6 +271,7 @@ BOOST_PYTHON_MODULE(_caffe) { NdarrayCallPolicies())) .add_property("diff", bp::make_function(&Blob::mutable_cpu_diff, NdarrayCallPolicies())); + bp::register_ptr_to_python > >(); bp::class_, shared_ptr >, boost::noncopyable>("Layer", bp::init()) From d0100ba632b767d2242c10fd1bd3e5782494c079 Mon Sep 17 00:00:00 2001 From: thatguymike Date: Tue, 19 Jan 2016 17:01:34 -0800 Subject: [PATCH 380/446] Workaround for inplace max pooling issue --- src/caffe/layer_factory.cpp | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp index 4d912d28351..e967bd6181c 100644 --- a/src/caffe/layer_factory.cpp +++ b/src/caffe/layer_factory.cpp @@ -91,7 +91,16 @@ shared_ptr > GetPoolingLayer(const LayerParameter& param) { << "Using Caffe's own pooling layer."; return shared_ptr >(new PoolingLayer(param)); } - return shared_ptr >(new CuDNNPoolingLayer(param)); + // CuDNN assumes layers are not being modified in place, thus + // breaking our index tracking for updates in some cases in Caffe. + // Until there is a workaround in Caffe (index management) or + // cuDNN, use Caffe layer to max pooling, or don't use in place + // layers after max pooling layers + if (param.pooling_param().pool() == PoolingParameter_PoolMethod_MAX) { + return shared_ptr >(new PoolingLayer(param)); + } else { + return shared_ptr >(new CuDNNPoolingLayer(param)); + } #endif } else { LOG(FATAL) << "Layer " << param.name() << " has unknown engine."; From 8fea1f580933f9e1130d3713e993f156dda3116d Mon Sep 17 00:00:00 2001 From: biluochun Date: Thu, 21 Jan 2016 13:16:47 +0800 Subject: [PATCH 381/446] add register Net and Solver --- python/caffe/_caffe.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index bd3128cbde7..0421c1c657d 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -252,6 +252,7 @@ BOOST_PYTHON_MODULE(_caffe) { .def("_set_input_arrays", &Net_SetInputArrays, bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >()) .def("save", &Net_Save); + bp::register_ptr_to_python > >(); bp::class_, shared_ptr >, boost::noncopyable>( "Blob", bp::no_init) @@ -295,6 +296,7 @@ BOOST_PYTHON_MODULE(_caffe) { .def("step", &Solver::Step) .def("restore", &Solver::Restore) .def("snapshot", &Solver::Snapshot); + bp::register_ptr_to_python > >(); bp::class_, bp::bases >, shared_ptr >, boost::noncopyable>( From d95998129d4a306693a7228905688ddfcffa2f49 Mon Sep 17 00:00:00 2001 From: Robbie Cooper Date: Thu, 21 Jan 2016 14:11:00 -0500 Subject: [PATCH 382/446] Add makefile config option for linking Python 3 libraries --- Makefile | 2 +- Makefile.config.example | 5 +++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 985fffd6c0b..ac7d12e298c 100644 --- a/Makefile +++ b/Makefile @@ -191,7 +191,7 @@ ifeq ($(USE_OPENCV), 1) endif endif -PYTHON_LIBRARIES := boost_python python2.7 +PYTHON_LIBRARIES ?= boost_python python2.7 WARNINGS := -Wall -Wno-sign-compare ############################## diff --git a/Makefile.config.example b/Makefile.config.example index 1dd6a8f7c7c..8fd49c9c1a7 100644 --- a/Makefile.config.example +++ b/Makefile.config.example @@ -70,6 +70,11 @@ PYTHON_INCLUDE := /usr/include/python2.7 \ # $(ANACONDA_HOME)/include/python2.7 \ # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \ +# Uncomment to use Python 3 (default is Python 2) +# PYTHON_LIBRARIES := boost_python3 python3.5m +# PYTHON_INCLUDE := /usr/include/python3.5m \ +# /usr/lib/python3.5/dist-packages/numpy/core/include + # We need to be able to find libpythonX.X.so or .dylib. PYTHON_LIB := /usr/lib # PYTHON_LIB := $(ANACONDA_HOME)/lib From 1954f0f76eb9129c8bf9f34814750dbd5b5e46c9 Mon Sep 17 00:00:00 2001 From: Jun Shi Date: Fri, 22 Jan 2016 05:09:21 -0800 Subject: [PATCH 383/446] copy proto to distribute directory --- Makefile | 2 ++ 1 file changed, 2 insertions(+) diff --git a/Makefile b/Makefile index 985fffd6c0b..5a6e74f617f 100644 --- a/Makefile +++ b/Makefile @@ -651,6 +651,8 @@ superclean: clean supercleanfiles $(DIST_ALIASES): $(DISTRIBUTE_DIR) $(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS) + # add proto + cp -r src/caffe/proto $(DISTRIBUTE_DIR)/ # add include cp -r include $(DISTRIBUTE_DIR)/ mkdir -p $(DISTRIBUTE_DIR)/include/caffe/proto From ec04197479d263d1c4801639f5635ceb3e7dcef1 Mon Sep 17 00:00:00 2001 From: Dmytro Mishkin Date: Thu, 14 Jan 2016 17:10:11 +0200 Subject: [PATCH 384/446] Add ChannelwiseAffine for batch norm --- .../caffe/layers/channelwise_affine_layer.hpp | 103 ++++++++++ src/caffe/layers/channelwise_affine_layer.cpp | 189 ++++++++++++++++++ src/caffe/layers/channelwise_affine_layer.cu | 144 +++++++++++++ src/caffe/proto/caffe.proto | 14 +- .../test/test_channelwise_affine_layer.cpp | 105 ++++++++++ 5 files changed, 554 insertions(+), 1 deletion(-) create mode 100644 include/caffe/layers/channelwise_affine_layer.hpp create mode 100644 src/caffe/layers/channelwise_affine_layer.cpp create mode 100644 src/caffe/layers/channelwise_affine_layer.cu create mode 100644 src/caffe/test/test_channelwise_affine_layer.cpp diff --git a/include/caffe/layers/channelwise_affine_layer.hpp b/include/caffe/layers/channelwise_affine_layer.hpp new file mode 100644 index 00000000000..6d8ac98b6ed --- /dev/null +++ b/include/caffe/layers/channelwise_affine_layer.hpp @@ -0,0 +1,103 @@ +#ifndef CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_ +#define CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_ + +#include +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/layers/neuron_layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + /** + * @brief Affine non-linearity function @f$ + * y = ax+b + * @f$, could be used after batch normalization layer + * + */ +template +class ChannelwiseAffineLayer : public NeuronLayer { + public: + /** + * @param param provides ChannelwiseAffineParameter ChannelwiseAffine_param, + * with ChannelwiseAffineLayer options: + * - slope_filler (\b optional, FillerParameter, + * default {'type': constant 'value':1.0001}). + * - bias_filler (\b optional, FillerParameter, + * default {'type': constant 'value':0.0001}). + * - channel_shared (\b optional, default false). + * slopes and biases are shared across channels. + */ + explicit ChannelwiseAffineLayer(const LayerParameter& param) + : NeuronLayer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + virtual inline const char* type() const { return "ChannelwiseAffine"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times ...) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times ...) @f$ + * the computed outputs for each channel @f$i@f$ @f$ + * y_i = a_i x_i + b_i + * @f$. + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + /** + * @brief Computes the error gradient w.r.t. the ChannelwiseAffine inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times ...) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times ...) @f$ + * the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their + * diff with gradients @f$ + * \frac{\partial E}{\partial x_i} = \left\{ + * \begin{array}{lr} + * a_i \frac{\partial E}{\partial y_i} + * \end{array} \right. + * @f$. + * If param_propagate_down_[0] is true, it fills the diff with gradients + * @f$ + * \frac{\partial E}{\partial a_i} = \left\{ + * \begin{array}{lr} + * \sum_{x_i} x_i \frac{\partial E}{\partial y_i} + * \end{array} \right. + * @f$. + * If param_propagate_down_[1] is true, it fills the diff with gradients + * @f$ + * \frac{\partial E}{\partial b_i} = \left\{ + * \begin{array}{lr} + * frac{\partial E}{\partial y_i} + * \end{array} \right. + * @f$. + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, + const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, + const vector*>& bottom); + bool channel_shared_; + Blob multiplier_; + // dot multiplier for backward computation of params + Blob bias_multiplier_; + Blob backward_buff_; + // temporary buffer for backward computation + Blob bottom_memory_; + // memory for in-place computation +}; +} // namespace caffe + +#endif // CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_ diff --git a/src/caffe/layers/channelwise_affine_layer.cpp b/src/caffe/layers/channelwise_affine_layer.cpp new file mode 100644 index 00000000000..e9f31fb10e3 --- /dev/null +++ b/src/caffe/layers/channelwise_affine_layer.cpp @@ -0,0 +1,189 @@ +#include +#include + +#include "caffe/filler.hpp" +#include "caffe/layer.hpp" +#include "caffe/layers/channelwise_affine_layer.hpp" + +namespace caffe { + +template +void ChannelwiseAffineLayer::LayerSetUp( + const vector*>& bottom, + const vector*>& top) { + CHECK_GE(bottom[0]->num_axes(), 2) + << "Number of axes of bottom blob must be >=2."; + ChannelwiseAffineParameter channelwise_affine_param = + this->layer_param().channelwise_affine_param(); + int channels = bottom[0]->channels(); + channel_shared_ = channelwise_affine_param.channel_shared(); + if (this->blobs_.size() > 0) { + LOG(INFO) << "Skipping parameter initialization"; + } else { + this->blobs_.resize(2); + if (channel_shared_) { + this->blobs_[0].reset(new Blob(vector(0))); + this->blobs_[1].reset(new Blob(vector(0))); + + } else { + this->blobs_[0].reset(new Blob(vector(1, channels))); + this->blobs_[1].reset(new Blob(vector(1, channels))); + } + shared_ptr > filler; + if (channelwise_affine_param.has_slope_filler()) { + filler.reset(GetFiller(channelwise_affine_param.slope_filler())); + } else { + FillerParameter filler_param; + filler_param.set_type("constant"); + filler_param.set_value(1.0001); + filler.reset(GetFiller(filler_param)); + } + filler->Fill(this->blobs_[0].get()); + + if (channelwise_affine_param.has_bias_filler()) { + filler.reset(GetFiller(channelwise_affine_param.bias_filler())); + } else { + FillerParameter filler_param; + filler_param.set_type("constant"); + filler_param.set_value(0.0001); + filler.reset(GetFiller(filler_param)); + } + filler->Fill(this->blobs_[1].get()); + } + if (channel_shared_) { + CHECK_EQ(this->blobs_[0]->count(), 1) + << "Slope size is inconsistent with prototxt config"; + } else { + CHECK_EQ(this->blobs_[0]->count(), channels) + << "Slope size is inconsistent with prototxt config"; + } + + // Propagate gradients to the parameters (as directed by backward pass). + this->param_propagate_down_.resize(this->blobs_.size(), true); + multiplier_.Reshape(vector(1, bottom[0]->count(1))); + bias_multiplier_.Reshape(vector(1, bottom[0]->count(1))); + backward_buff_.Reshape(vector(1, bottom[0]->count(1))); + caffe_set(multiplier_.count(), Dtype(1.0), + multiplier_.mutable_cpu_data()); + caffe_set(bias_multiplier_.count(), Dtype(1.0), + bias_multiplier_.mutable_cpu_data()); +} + +template +void ChannelwiseAffineLayer::Reshape( + const vector*>& bottom, + const vector*>& top) { + CHECK_GE(bottom[0]->num_axes(), 2) + << "Number of axes of bottom blob must be >=2."; + top[0]->ReshapeLike(*bottom[0]); + if (bottom[0] == top[0]) { + // For in-place computation + bottom_memory_.ReshapeLike(*bottom[0]); + } + int height = 1; + int width = 1; + if (bottom[0]->num_axes() > 2) { + height = bottom[0]->shape(2); + width = bottom[0]->shape(3); + } + vector bias_multiplier_shape(1, height * width); + bias_multiplier_.Reshape(bias_multiplier_shape); + caffe_set(bias_multiplier_.count(), Dtype(1), + bias_multiplier_.mutable_cpu_data()); +} + +template +void ChannelwiseAffineLayer::Forward_cpu( + const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + const int count = bottom[0]->count(); + const int dim = bottom[0]->count(2); + const int channels = bottom[0]->channels(); + const Dtype* slope_data = this->blobs_[0]->cpu_data(); + const Dtype* bias_data = this->blobs_[1]->cpu_data(); + // For in-place computation + if (bottom[0] == top[0]) { + caffe_copy(count, bottom_data, bottom_memory_.mutable_cpu_data()); + } + // if channel_shared, channel index in the following computation becomes + // always zero. + const int div_factor = channel_shared_ ? channels : 1; + for (int i = 0; i < count; ++i) { + int c = (i / dim) % channels / div_factor; + top_data[i] = bottom_data[i] * slope_data[c] + bias_data[c]; + } +} + +template +void ChannelwiseAffineLayer::Backward_cpu( + const vector*>& top, + const vector& propagate_down, + const vector*>& bottom) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + const Dtype* slope_data = this->blobs_[0]->cpu_data(); + + const Dtype* top_diff = top[0]->cpu_diff(); + const int count = bottom[0]->count(); + const int dim = bottom[0]->count(2); + const int channels = bottom[0]->shape(1); + const int num = bottom[0]->shape(0); + int height = 1; + int width = 1; + if (bottom[0]->num_axes() > 2) { + height = bottom[0]->shape(2); + width = bottom[0]->shape(3); + } + + // For in-place computation + if (top[0] == bottom[0]) { + bottom_data = bottom_memory_.cpu_data(); + } + + // if channel_shared, channel index in the following computation becomes + // always zero. + const int div_factor = channel_shared_ ? channels : 1; + + // Propagte to param + // Since to write bottom diff will affect top diff if top and bottom blobs + // are identical (in-place computaion), we first compute param backward to + // keep top_diff unchanged. + + if (this->param_propagate_down_[1]) { + Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff(); + caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff); + for (int n = 0; n < num; ++n) { + caffe_cpu_gemv(CblasNoTrans, channels, height * width, 1., + top_diff + top[0]->offset(n), + bias_multiplier_.cpu_data(), 1., bias_diff); + } + } + if (this->param_propagate_down_[0]) { + Dtype* slope_diff = this->blobs_[0]->mutable_cpu_diff(); + caffe_set(this->blobs_[0]->count(), Dtype(0), slope_diff); + for (int i = 0; i < count; ++i) { + int c = (i / dim) % channels / div_factor; + slope_diff[c] += top_diff[i] * bottom_data[i]; + } + } + + // Propagate to bottom + if (propagate_down[0]) { + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + for (int i = 0; i < count; ++i) { + int c = (i / dim) % channels / div_factor; + bottom_diff[i] = slope_data[c] * top_diff[i]; + } + } +} + + +#ifdef CPU_ONLY +STUB_GPU(ChannelwiseAffineLayer); +#endif + +INSTANTIATE_CLASS(ChannelwiseAffineLayer); +REGISTER_LAYER_CLASS(ChannelwiseAffine); + +} // namespace caffe diff --git a/src/caffe/layers/channelwise_affine_layer.cu b/src/caffe/layers/channelwise_affine_layer.cu new file mode 100644 index 00000000000..2066b26560b --- /dev/null +++ b/src/caffe/layers/channelwise_affine_layer.cu @@ -0,0 +1,144 @@ +#include +#include + +#include "caffe/layer.hpp" +#include "caffe/layers/channelwise_affine_layer.hpp" + +namespace caffe { + +// CUDA kernel for forward +template +__global__ void ChannelwiseAffineForward(const int n, const int channels, + const int dim, const Dtype* in, Dtype* out, const Dtype* slope_data, + const Dtype* bias_data, const int div_factor) { + CUDA_KERNEL_LOOP(index, n) { + int c = (index / dim) % channels / div_factor; + out[index] = in[index] * slope_data[c] + bias_data[c]; + } +} + +// CUDA kernel for bottom backward +template +__global__ void ChannelwiseAffineBackward(const int n, + const int channels, const int dim, const Dtype* in_diff, + Dtype* out_diff, const Dtype* slope_data, const int div_factor) { + CUDA_KERNEL_LOOP(index, n) { + int c = (index / dim) % channels / div_factor; + out_diff[index] = slope_data[c] * in_diff[index]; + } +} + +// CUDA kernel for element-wise parameter backward +template +__global__ void ChannelwiseAffineParamSlopeBackward(const int n, + const int rows, const int rowPitch, const Dtype* in_diff, + const Dtype* in_data, Dtype* out_diff) { + CUDA_KERNEL_LOOP(index, n) { + out_diff[index] = in_diff[index] * in_data[index]; + for ( int k = 1; k < rows; k++ ) { + out_diff[index] += in_diff[index + k*rowPitch] + * in_data[index + k*rowPitch]; + } + } +} + +template +void ChannelwiseAffineLayer::Forward_gpu( + const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + const int count = bottom[0]->count(); + const int dim = bottom[0]->count(2); + const int channels = bottom[0]->channels(); + const Dtype* slope_data = this->blobs_[0]->gpu_data(); + const Dtype* bias_data = this->blobs_[1]->gpu_data(); + const int div_factor = channel_shared_ ? channels : 1; + + // For in-place computation + if (top[0] == bottom[0]) { + caffe_copy(count, bottom_data, bottom_memory_.mutable_gpu_data()); + } + // NOLINT_NEXT_LINE(whitespace/operators) + ChannelwiseAffineForward<<>>( + count, channels, dim, bottom_data, top_data, + slope_data, bias_data, div_factor); + CUDA_POST_KERNEL_CHECK; +} + +template +void ChannelwiseAffineLayer::Backward_gpu( + const vector*>& top, + const vector& propagate_down, + const vector*>& bottom) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + const Dtype* top_diff = top[0]->gpu_diff(); + const int count = bottom[0]->count(); + const int num = bottom[0]->shape(0); + const int dim = bottom[0]->count(2); + const int channels = bottom[0]->shape(1); + int height = 1; + int width = 1; + if (bottom[0]->num_axes() > 2) { + height = bottom[0]->shape(2); + width = bottom[0]->shape(3); + } + + // For in-place computation + if (top[0] == bottom[0]) { + bottom_data = bottom_memory_.gpu_data(); + } + // Propagate to param + // Since to write bottom diff will affect top diff if top and bottom blobs + // are identical (in-place computaion), we first compute param backward to + // keep top_diff unchanged. + if (this->param_propagate_down_[1]) { + Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff(); + caffe_gpu_set(this->blobs_[1]->count(), Dtype(0.0), bias_diff); + // Gradient with respect to bias + for (int n = 0; n < num; ++n) { + caffe_gpu_gemv( + CblasNoTrans, channels, height * width, (Dtype)1., + top_diff + top[0]->offset(n), bias_multiplier_.gpu_data(), + (Dtype)1., bias_diff); + } + } + if (this->param_propagate_down_[0]) { + Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff(); + int cdim = channels * dim; + // compute element-wise diff + // NOLINT_NEXT_LINE(whitespace/operators) + ChannelwiseAffineParamSlopeBackward<<>>( + cdim, num, top[0]->offset(1), top_diff , + bottom_data, + backward_buff_.mutable_gpu_diff()); + CUDA_POST_KERNEL_CHECK; + if (channel_shared_) { + Dtype d = 0; + caffe_gpu_dot(cdim, backward_buff_.gpu_diff(), + multiplier_.gpu_data(), &d); + caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(d), slope_diff); + } else { + caffe_gpu_gemv(CblasNoTrans, channels, dim, Dtype(1.), + backward_buff_.gpu_diff(), multiplier_.gpu_data(), Dtype(1.), + slope_diff); + } + } + // Propagate to bottom + if (propagate_down[0]) { + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + const Dtype* slope_data = this->blobs_[0]->gpu_data(); + int div_factor = channel_shared_ ? channels : 1; + // NOLINT_NEXT_LINE(whitespace/operators) + ChannelwiseAffineBackward<<>>( + count, channels, dim, top_diff, bottom_diff, slope_data, div_factor); + CUDA_POST_KERNEL_CHECK; + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(ChannelwiseAffineLayer); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index f873deba10c..fe6209cf673 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -306,7 +306,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 141 (last added: elu_param) +// LayerParameter next available layer-specific ID: 142 (last added: channelwise_affine_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -356,6 +356,7 @@ message LayerParameter { optional AccuracyParameter accuracy_param = 102; optional ArgMaxParameter argmax_param = 103; optional BatchNormParameter batch_norm_param = 139; + optional ChannelwiseAffineParameter channelwise_affine_param = 141; optional ConcatParameter concat_param = 104; optional ContrastiveLossParameter contrastive_loss_param = 105; optional ConvolutionParameter convolution_param = 106; @@ -498,6 +499,17 @@ message BatchNormParameter { optional float eps = 3 [default = 1e-5]; } +message ChannelwiseAffineParameter { + + // Initial value of a_i. Default is a_i=1.0 for all i. + optional FillerParameter slope_filler = 1; + + optional FillerParameter bias_filler = 2; + + // Whether or not slope paramters are shared across channels. + optional bool channel_shared = 3 [default = false]; +} + message ContrastiveLossParameter { // margin for dissimilar pair optional float margin = 1 [default = 1.0]; diff --git a/src/caffe/test/test_channelwise_affine_layer.cpp b/src/caffe/test/test_channelwise_affine_layer.cpp new file mode 100644 index 00000000000..a3e2544f77a --- /dev/null +++ b/src/caffe/test/test_channelwise_affine_layer.cpp @@ -0,0 +1,105 @@ +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/layers/channelwise_affine_layer.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class ChannelwiseAffineLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + ChannelwiseAffineLayerTest() + : blob_bottom_(new Blob(2, 3, 4, 5)), + blob_top_(new Blob()) { + Caffe::set_random_seed(1701); + // fill the values + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + blob_bottom_vec_.push_back(blob_bottom_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~ChannelwiseAffineLayerTest() { + delete blob_bottom_; delete blob_top_; } + Blob* const blob_bottom_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; + + void TestChannelwiseAffine(ChannelwiseAffineLayer *layer) { + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Now, check values + const Dtype* bottom_data = this->blob_bottom_->cpu_data(); + const Dtype* top_data = this->blob_top_->cpu_data(); + const Dtype* slope_data = layer->blobs()[0]->cpu_data(); + const Dtype* bias_data = layer->blobs()[1]->cpu_data(); + const Dtype kDelta = 2e-5; + int hw = this->blob_bottom_->height() * this->blob_bottom_->width(); + int channels = this->blob_bottom_->channels(); + bool channel_shared = + layer->layer_param().channelwise_affine_param().channel_shared(); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + int c = channel_shared ? 0 : (i / hw) % channels; + EXPECT_NEAR(top_data[i], + bottom_data[i]* slope_data[c] + bias_data[c], kDelta); + } + } +}; +TYPED_TEST_CASE(ChannelwiseAffineLayerTest, TestDtypesAndDevices); + + +TYPED_TEST(ChannelwiseAffineLayerTest, TestChannelwiseAffineForward) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ChannelwiseAffineLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(layer.blobs()[0].get()); + filler.Fill(layer.blobs()[1].get()); + this->TestChannelwiseAffine(&layer); +} + +TYPED_TEST(ChannelwiseAffineLayerTest, + TestChannelwiseAffineForwardChannelShared) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_channelwise_affine_param()->set_channel_shared(true); + ChannelwiseAffineLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + this->TestChannelwiseAffine(&layer); +} + +TYPED_TEST(ChannelwiseAffineLayerTest, TestChannelwiseAffineGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_channelwise_affine_param()->set_channel_shared(false); + ChannelwiseAffineLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + GradientChecker checker(1e-2, 1e-3, 1701, 0., 0.01); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(ChannelwiseAffineLayerTest, + TestChannelwiseAffineGradientChannelShared) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_channelwise_affine_param()->set_channel_shared(true); + ChannelwiseAffineLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + GradientChecker checker(1e-2, 1e-3, 1701, 0., 0.01); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +} // namespace caffe From 67b497d4ec70018b168639df1e4342f78fb44bb0 Mon Sep 17 00:00:00 2001 From: Luke Yeager Date: Fri, 22 Jan 2016 15:30:35 -0800 Subject: [PATCH 385/446] Version 1.0.0-rc3 --- CMakeLists.txt | 5 +++++ Makefile | 30 +++++++++++++++++++++--------- cmake/Summary.cmake | 2 +- include/caffe/common.hpp | 4 ++++ python/caffe/__init__.py | 1 + python/caffe/_caffe.cpp | 3 +++ src/caffe/CMakeLists.txt | 4 ++++ tools/caffe.cpp | 3 +++ 8 files changed, 42 insertions(+), 10 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index c446c608952..32cc42ac9de 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -9,6 +9,11 @@ endif() # ---[ Caffe project project(Caffe C CXX) +# ---[ Caffe version +set(CAFFE_TARGET_VERSION "1.0.0-rc3") +set(CAFFE_TARGET_SOVERSION "1.0.0-rc3") +add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION}) + # ---[ Using cmake scripts and modules list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules) diff --git a/Makefile b/Makefile index 985fffd6c0b..f3135d35018 100644 --- a/Makefile +++ b/Makefile @@ -29,9 +29,17 @@ SRC_DIRS := $(shell find * -type d -exec bash -c "find {} -maxdepth 1 \ \( -name '*.cpp' -o -name '*.proto' \) | grep -q ." \; -print) # The target shared library name +LIBRARY_NAME := $(PROJECT) LIB_BUILD_DIR := $(BUILD_DIR)/lib -STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a -DYNAMIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so +STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a +DYNAMIC_VERSION_MAJOR := 1 +DYNAMIC_VERSION_MINOR := 0 +DYNAMIC_VERSION_REVISION := 0-rc3 +DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so +#DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR) +DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION) +DYNAMIC_NAME := $(LIB_BUILD_DIR)/$(DYNAMIC_VERSIONED_NAME_SHORT) +COMMON_FLAGS += -DCAFFE_VERSION=$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION) ############################## # Get all source files @@ -253,6 +261,7 @@ ifeq ($(LINUX), 1) # boost::thread is reasonably called boost_thread (compare OS X) # We will also explicitly add stdc++ to the link target. LIBRARIES += boost_thread stdc++ + VERSIONFLAGS += -Wl,-soname,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../lib endif # OS X: @@ -276,6 +285,7 @@ ifeq ($(OSX), 1) # we need to explicitly ask for the rpath to be obeyed DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so ORIGIN := @loader_path + VERSIONFLAGS += -Wl,-install_name,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib else ORIGIN := \$$ORIGIN endif @@ -478,7 +488,7 @@ py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY) $(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME) @ echo CXX/LD -o $@ $< $(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \ - -o $@ $(LINKFLAGS) -l$(PROJECT) $(PYTHON_LDFLAGS) \ + -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(PYTHON_LDFLAGS) \ -Wl,-rpath,$(ORIGIN)/../../build/lib mat$(PROJECT): mat @@ -542,7 +552,8 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK) $(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR) @ echo LD -o $@ - $(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS) + $(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS) + @ cd $(BUILD_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT); ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT) $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR) @ echo AR -o $@ @@ -573,19 +584,19 @@ $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \ | $(DYNAMIC_NAME) $(TEST_BIN_DIR) @ echo CXX/LD -o $@ $< $(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \ - -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib + -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib $(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \ $(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR) @ echo LD $< $(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \ - -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib + -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib $(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \ $(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR) @ echo LD $< $(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \ - -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib + -o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib # Target for extension-less symlinks to tool binaries with extension '*.bin'. $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR) @@ -594,12 +605,12 @@ $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR) $(TOOL_BINS): %.bin : %.o | $(DYNAMIC_NAME) @ echo CXX/LD -o $@ - $(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \ + $(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \ -Wl,-rpath,$(ORIGIN)/../lib $(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME) @ echo CXX/LD -o $@ - $(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \ + $(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \ -Wl,-rpath,$(ORIGIN)/../../lib proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER) @@ -661,6 +672,7 @@ $(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS) # add libraries cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib install -m 644 $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib + cd $(DISTRIBUTE_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT); ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT) # add python - it's not the standard way, indeed... cp -r python $(DISTRIBUTE_DIR)/python diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake index 557a6f04e4b..ba025cf81e0 100644 --- a/cmake/Summary.cmake +++ b/cmake/Summary.cmake @@ -101,7 +101,7 @@ function(caffe_print_configuration_summary) caffe_status("") caffe_status("******************* Caffe Configuration Summary *******************") caffe_status("General:") - caffe_status(" Version : ${Caffe_VERSION}") + caffe_status(" Version : ${CAFFE_TARGET_VERSION}") caffe_status(" Git : ${Caffe_GIT_VERSION}") caffe_status(" System : ${CMAKE_SYSTEM_NAME}") caffe_status(" C++ compiler : ${CMAKE_CXX_COMPILER}") diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp index 1df6b9a14fb..6b902a42e2d 100644 --- a/include/caffe/common.hpp +++ b/include/caffe/common.hpp @@ -18,6 +18,10 @@ #include "caffe/util/device_alternate.hpp" +// Convert macro to string +#define STRINGIFY(m) #m +#define AS_STRING(m) STRINGIFY(m) + // gflags 2.1 issue: namespace google was changed to gflags without warning. // Luckily we will be able to use GFLAGS_GFLAGS_H_ to detect if it is version // 2.1. If yes, we will add a temporary solution to redirect the namespace. diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py index ccda1bcae4f..e2881b89c1b 100644 --- a/python/caffe/__init__.py +++ b/python/caffe/__init__.py @@ -1,5 +1,6 @@ from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list +from ._caffe import __version__ from .proto.caffe_pb2 import TRAIN, TEST from .classifier import Classifier from .detector import Detector diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index 4ea2ec60b7e..12a574556c3 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -212,6 +212,9 @@ BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1); BOOST_PYTHON_MODULE(_caffe) { // below, we prepend an underscore to methods that will be replaced // in Python + + bp::scope().attr("__version__") = AS_STRING(CAFFE_VERSION); + // Caffe utility functions bp::def("set_mode_cpu", &set_mode_cpu); bp::def("set_mode_gpu", &set_mode_gpu); diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt index 40e6c11f5b0..8a80c940488 100644 --- a/src/caffe/CMakeLists.txt +++ b/src/caffe/CMakeLists.txt @@ -20,6 +20,10 @@ endif() add_library(caffe ${srcs}) target_link_libraries(caffe proto ${Caffe_LINKER_LIBS}) caffe_default_properties(caffe) +set_target_properties(caffe PROPERTIES + VERSION ${CAFFE_TARGET_VERSION} + SOVERSION ${CAFFE_TARGET_SOVERSION} + ) # ---[ Tests add_subdirectory(test) diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 6b342ace0c5..470165add7f 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -3,6 +3,7 @@ namespace bp = boost::python; #endif +#include #include #include @@ -378,6 +379,8 @@ RegisterBrewFunction(time); int main(int argc, char** argv) { // Print output to stderr (while still logging). FLAGS_alsologtostderr = 1; + // Set version + gflags::SetVersionString(AS_STRING(CAFFE_VERSION)); // Usage message. gflags::SetUsageMessage("command line brew\n" "usage: caffe \n\n" From 081690709e4a199824f433cc196c55c47731073f Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Fri, 22 Jan 2016 15:57:47 -0800 Subject: [PATCH 386/446] Separation and generalization of ChannelwiseAffineLayer into BiasLayer and ScaleLayer. The behavior of ChannelwiseAffineLayer can be reproduced by a ScaleLayer with `scale_param { bias_term: true }`. BiasLayer and ScaleLayer each take 1 or 2 bottoms, with the output having the same shape as the first. The second input -- either another bottom or a learned parameter -- will have its axes (virtually) broadcast and tiled to have the same shape as the first, after which elementwise addition (Bias) or multiplication (Scale) is performed. --- include/caffe/layers/bias_layer.hpp | 54 ++ .../caffe/layers/channelwise_affine_layer.hpp | 103 ---- include/caffe/layers/scale_layer.hpp | 83 +++ src/caffe/layers/bias_layer.cpp | 121 +++++ src/caffe/layers/bias_layer.cu | 59 ++ src/caffe/layers/channelwise_affine_layer.cpp | 189 ------- src/caffe/layers/channelwise_affine_layer.cu | 144 ----- src/caffe/layers/scale_layer.cpp | 219 ++++++++ src/caffe/layers/scale_layer.cu | 135 +++++ src/caffe/proto/caffe.proto | 79 ++- src/caffe/test/test_bias_layer.cpp | 467 ++++++++++++++++ .../test/test_channelwise_affine_layer.cpp | 105 ---- src/caffe/test/test_scale_layer.cpp | 507 ++++++++++++++++++ 13 files changed, 1714 insertions(+), 551 deletions(-) create mode 100644 include/caffe/layers/bias_layer.hpp delete mode 100644 include/caffe/layers/channelwise_affine_layer.hpp create mode 100644 include/caffe/layers/scale_layer.hpp create mode 100644 src/caffe/layers/bias_layer.cpp create mode 100644 src/caffe/layers/bias_layer.cu delete mode 100644 src/caffe/layers/channelwise_affine_layer.cpp delete mode 100644 src/caffe/layers/channelwise_affine_layer.cu create mode 100644 src/caffe/layers/scale_layer.cpp create mode 100644 src/caffe/layers/scale_layer.cu create mode 100644 src/caffe/test/test_bias_layer.cpp delete mode 100644 src/caffe/test/test_channelwise_affine_layer.cpp create mode 100644 src/caffe/test/test_scale_layer.cpp diff --git a/include/caffe/layers/bias_layer.hpp b/include/caffe/layers/bias_layer.hpp new file mode 100644 index 00000000000..eedc3aaa351 --- /dev/null +++ b/include/caffe/layers/bias_layer.hpp @@ -0,0 +1,54 @@ +#ifndef CAFFE_BIAS_LAYER_HPP_ +#define CAFFE_BIAS_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Computes a sum of two input Blobs, with the shape of the + * latter Blob "broadcast" to match the shape of the former. + * Equivalent to tiling the latter Blob, then computing the elementwise + * sum. + * + * The second input may be omitted, in which case it's learned as a parameter + * of the layer. + */ +template +class BiasLayer : public Layer { + public: + explicit BiasLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Bias"; } + virtual inline int MinBottomBlobs() const { return 1; } + virtual inline int MaxBottomBlobs() const { return 2; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + private: + Blob bias_multiplier_; + int outer_dim_, bias_dim_, inner_dim_, dim_; +}; + + + +} // namespace caffe + +#endif // CAFFE_BIAS_LAYER_HPP_ diff --git a/include/caffe/layers/channelwise_affine_layer.hpp b/include/caffe/layers/channelwise_affine_layer.hpp deleted file mode 100644 index 6d8ac98b6ed..00000000000 --- a/include/caffe/layers/channelwise_affine_layer.hpp +++ /dev/null @@ -1,103 +0,0 @@ -#ifndef CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_ -#define CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_ - -#include -#include "caffe/blob.hpp" -#include "caffe/layer.hpp" -#include "caffe/layers/neuron_layer.hpp" -#include "caffe/proto/caffe.pb.h" - -namespace caffe { - /** - * @brief Affine non-linearity function @f$ - * y = ax+b - * @f$, could be used after batch normalization layer - * - */ -template -class ChannelwiseAffineLayer : public NeuronLayer { - public: - /** - * @param param provides ChannelwiseAffineParameter ChannelwiseAffine_param, - * with ChannelwiseAffineLayer options: - * - slope_filler (\b optional, FillerParameter, - * default {'type': constant 'value':1.0001}). - * - bias_filler (\b optional, FillerParameter, - * default {'type': constant 'value':0.0001}). - * - channel_shared (\b optional, default false). - * slopes and biases are shared across channels. - */ - explicit ChannelwiseAffineLayer(const LayerParameter& param) - : NeuronLayer(param) {} - virtual void LayerSetUp(const vector*>& bottom, - const vector*>& top); - virtual void Reshape(const vector*>& bottom, - const vector*>& top); - virtual inline const char* type() const { return "ChannelwiseAffine"; } - - protected: - /** - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times ...) @f$ - * the inputs @f$ x @f$ - * @param top output Blob vector (length 1) - * -# @f$ (N \times C \times ...) @f$ - * the computed outputs for each channel @f$i@f$ @f$ - * y_i = a_i x_i + b_i - * @f$. - */ - virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top); - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top); - /** - * @brief Computes the error gradient w.r.t. the ChannelwiseAffine inputs. - * - * @param top output Blob vector (length 1), providing the error gradient with - * respect to the outputs - * -# @f$ (N \times C \times ...) @f$ - * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ - * with respect to computed outputs @f$ y @f$ - * @param propagate_down see Layer::Backward. - * @param bottom input Blob vector (length 1) - * -# @f$ (N \times C \times ...) @f$ - * the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their - * diff with gradients @f$ - * \frac{\partial E}{\partial x_i} = \left\{ - * \begin{array}{lr} - * a_i \frac{\partial E}{\partial y_i} - * \end{array} \right. - * @f$. - * If param_propagate_down_[0] is true, it fills the diff with gradients - * @f$ - * \frac{\partial E}{\partial a_i} = \left\{ - * \begin{array}{lr} - * \sum_{x_i} x_i \frac{\partial E}{\partial y_i} - * \end{array} \right. - * @f$. - * If param_propagate_down_[1] is true, it fills the diff with gradients - * @f$ - * \frac{\partial E}{\partial b_i} = \left\{ - * \begin{array}{lr} - * frac{\partial E}{\partial y_i} - * \end{array} \right. - * @f$. - */ - virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, - const vector*>& bottom); - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, - const vector*>& bottom); - bool channel_shared_; - Blob multiplier_; - // dot multiplier for backward computation of params - Blob bias_multiplier_; - Blob backward_buff_; - // temporary buffer for backward computation - Blob bottom_memory_; - // memory for in-place computation -}; -} // namespace caffe - -#endif // CAFFE_CHANNELWISE_AFFINE_LAYER_HPP_ diff --git a/include/caffe/layers/scale_layer.hpp b/include/caffe/layers/scale_layer.hpp new file mode 100644 index 00000000000..924df2e51ab --- /dev/null +++ b/include/caffe/layers/scale_layer.hpp @@ -0,0 +1,83 @@ +#ifndef CAFFE_SCALE_LAYER_HPP_ +#define CAFFE_SCALE_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/bias_layer.hpp" + +namespace caffe { + +/** + * @brief Computes a product of two input Blobs, with the shape of the + * latter Blob "broadcast" to match the shape of the former. + * Equivalent to tiling the latter Blob, then computing the elementwise + * product. + * + * The second input may be omitted, in which case it's learned as a parameter + * of the layer. + */ +template +class ScaleLayer: public Layer { + public: + explicit ScaleLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Scale"; } + // Scale + virtual inline int MinBottomBlobs() const { return 1; } + virtual inline int MaxBottomBlobs() const { return 2; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + /** + * In the below shape specifications, @f$ i @f$ denotes the value of the + * `axis` field given by `this->layer_param_.scale_param().axis()`, after + * canonicalization (i.e., conversion from negative to positive index, + * if applicable). + * + * @param bottom input Blob vector (length 2) + * -# @f$ (d_0 \times ... \times + * d_i \times ... \times d_j \times ... \times d_n) @f$ + * the first factor @f$ x @f$ + * -# @f$ (d_i \times ... \times d_j) @f$ + * the second factor @f$ y @f$ + * @param top output Blob vector (length 1) + * -# @f$ (d_0 \times ... \times + * d_i \times ... \times d_j \times ... \times d_n) @f$ + * the product @f$ z = x y @f$ computed after "broadcasting" y. + * Equivalent to tiling @f$ y @f$ to have the same shape as @f$ x @f$, + * then computing the elementwise product. + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + shared_ptr > bias_layer_; + vector*> bias_bottom_vec_; + vector bias_propagate_down_; + int bias_param_id_; + + Blob sum_multiplier_; + Blob sum_result_; + Blob temp_; + int axis_; + int outer_dim_, scale_dim_, inner_dim_; +}; + + +} // namespace caffe + +#endif // CAFFE_SCALE_LAYER_HPP_ diff --git a/src/caffe/layers/bias_layer.cpp b/src/caffe/layers/bias_layer.cpp new file mode 100644 index 00000000000..0a786b5db98 --- /dev/null +++ b/src/caffe/layers/bias_layer.cpp @@ -0,0 +1,121 @@ +#include + +#include "caffe/filler.hpp" +#include "caffe/layers/bias_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void BiasLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + if (bottom.size() == 1 && this->blobs_.size() > 0) { + LOG(INFO) << "Skipping parameter initialization"; + } else if (bottom.size() == 1) { + // bias is a learned parameter; initialize it + const BiasParameter& param = this->layer_param_.bias_param(); + const int axis = bottom[0]->CanonicalAxisIndex(param.axis()); + const int num_axes = param.num_axes(); + CHECK_GE(num_axes, -1) << "num_axes must be non-negative, " + << "or -1 to extend to the end of bottom[0]"; + if (num_axes >= 0) { + CHECK_GE(bottom[0]->num_axes(), axis + num_axes) + << "bias blob's shape extends past bottom[0]'s shape when applied " + << "starting with bottom[0] axis = " << axis; + } + this->blobs_.resize(1); + const vector::const_iterator& shape_start = + bottom[0]->shape().begin() + axis; + const vector::const_iterator& shape_end = + (num_axes == -1) ? bottom[0]->shape().end() : (shape_start + num_axes); + vector bias_shape(shape_start, shape_end); + this->blobs_[0].reset(new Blob(bias_shape)); + shared_ptr > filler(GetFiller(param.filler())); + filler->Fill(this->blobs_[0].get()); + } + this->param_propagate_down_.resize(this->blobs_.size(), true); +} + +template +void BiasLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + const BiasParameter& param = this->layer_param_.bias_param(); + Blob* bias = (bottom.size() > 1) ? bottom[1] : this->blobs_[0].get(); + // Always set axis == 0 in special case where bias is a scalar + // (num_axes == 0). Mathematically equivalent for any choice of axis, so the + // actual setting can be safely ignored; and computation is most efficient + // with axis == 0 and (therefore) outer_dim_ == 1. + const int axis = (bias->num_axes() == 0) ? + 0 : bottom[0]->CanonicalAxisIndex(param.axis()); + CHECK_GE(bottom[0]->num_axes(), axis + bias->num_axes()) + << "bias blob's shape extends past bottom[0]'s shape when applied " + << "starting with bottom[0] axis = " << axis; + for (int i = 0; i < bias->num_axes(); ++i) { + CHECK_EQ(bottom[0]->shape(axis + i), bias->shape(i)) + << "dimension mismatch between bottom[0]->shape(" << axis + i + << ") and bias->shape(" << i << ")"; + } + outer_dim_ = bottom[0]->count(0, axis); + bias_dim_ = bias->count(); + inner_dim_ = bottom[0]->count(axis + bias->num_axes()); + dim_ = bias_dim_ * inner_dim_; + if (bottom[0] != top[0]) { + top[0]->ReshapeLike(*bottom[0]); + } + bias_multiplier_.Reshape(vector(1, inner_dim_)); + if (bias_multiplier_.cpu_data()[inner_dim_ - 1] != Dtype(1)) { + caffe_set(inner_dim_, Dtype(1), bias_multiplier_.mutable_cpu_data()); + } +} + +template +void BiasLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bias_data = + ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + if (bottom[0] != top[0]) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + caffe_copy(bottom[0]->count(), bottom_data, top_data); + } + for (int n = 0; n < outer_dim_; ++n) { + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, bias_dim_, + inner_dim_, Dtype(1), Dtype(1), bias_data, + bias_multiplier_.cpu_data(), Dtype(1), top_data); + top_data += dim_; + } +} + +template +void BiasLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (propagate_down[0] && bottom[0] != top[0]) { + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + caffe_copy(bottom[0]->count(), top_diff, bottom_diff); + } + // in-place, we don't need to do anything with the data diff + const bool bias_param = (bottom.size() == 1); + if ((!bias_param && propagate_down[1]) || + (bias_param && this->param_propagate_down_[0])) { + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bias_diff = (bias_param ? this->blobs_[0].get() : bottom[1]) + ->mutable_cpu_diff(); + bool accum = bias_param; + for (int n = 0; n < outer_dim_; ++n) { + caffe_cpu_gemv(CblasNoTrans, bias_dim_, inner_dim_, Dtype(1), + top_diff, bias_multiplier_.cpu_data(), Dtype(accum), bias_diff); + top_diff += dim_; + accum = true; + } + } +} + +#ifdef CPU_ONLY +STUB_GPU(BiasLayer); +#endif + +INSTANTIATE_CLASS(BiasLayer); +REGISTER_LAYER_CLASS(Bias); + +} // namespace caffe diff --git a/src/caffe/layers/bias_layer.cu b/src/caffe/layers/bias_layer.cu new file mode 100644 index 00000000000..8ac913a5d7b --- /dev/null +++ b/src/caffe/layers/bias_layer.cu @@ -0,0 +1,59 @@ +#include + +#include "caffe/filler.hpp" +#include "caffe/layers/bias_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +__global__ void BiasForward(const int n, const Dtype* in, + const Dtype* bias, const int bias_dim, const int inner_dim, + Dtype* out) { + CUDA_KERNEL_LOOP(index, n) { + const int bias_index = (index / inner_dim) % bias_dim; + out[index] = in[index] + bias[bias_index]; + } +} + +template +void BiasLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const int count = top[0]->count(); + const Dtype* bottom_data = bottom[0]->gpu_data(); + const Dtype* bias_data = + ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + BiasForward // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, bottom_data, bias_data, bias_dim_, inner_dim_, top_data); +} + +template +void BiasLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (propagate_down[0] && bottom[0] != top[0]) { + const Dtype* top_diff = top[0]->gpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + caffe_copy(bottom[0]->count(), top_diff, bottom_diff); + } + // in-place, we don't need to do anything with the data diff + const bool bias_param = (bottom.size() == 1); + if ((!bias_param && propagate_down[1]) || + (bias_param && this->param_propagate_down_[0])) { + const Dtype* top_diff = top[0]->gpu_diff(); + Dtype* bias_diff = (bias_param ? this->blobs_[0].get() : bottom[1]) + ->mutable_gpu_diff(); + bool accum = bias_param; + for (int n = 0; n < outer_dim_; ++n) { + caffe_gpu_gemv(CblasNoTrans, bias_dim_, inner_dim_, Dtype(1), + top_diff, bias_multiplier_.gpu_data(), Dtype(accum), bias_diff); + top_diff += dim_; + accum = true; + } + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(BiasLayer); + +} // namespace caffe diff --git a/src/caffe/layers/channelwise_affine_layer.cpp b/src/caffe/layers/channelwise_affine_layer.cpp deleted file mode 100644 index e9f31fb10e3..00000000000 --- a/src/caffe/layers/channelwise_affine_layer.cpp +++ /dev/null @@ -1,189 +0,0 @@ -#include -#include - -#include "caffe/filler.hpp" -#include "caffe/layer.hpp" -#include "caffe/layers/channelwise_affine_layer.hpp" - -namespace caffe { - -template -void ChannelwiseAffineLayer::LayerSetUp( - const vector*>& bottom, - const vector*>& top) { - CHECK_GE(bottom[0]->num_axes(), 2) - << "Number of axes of bottom blob must be >=2."; - ChannelwiseAffineParameter channelwise_affine_param = - this->layer_param().channelwise_affine_param(); - int channels = bottom[0]->channels(); - channel_shared_ = channelwise_affine_param.channel_shared(); - if (this->blobs_.size() > 0) { - LOG(INFO) << "Skipping parameter initialization"; - } else { - this->blobs_.resize(2); - if (channel_shared_) { - this->blobs_[0].reset(new Blob(vector(0))); - this->blobs_[1].reset(new Blob(vector(0))); - - } else { - this->blobs_[0].reset(new Blob(vector(1, channels))); - this->blobs_[1].reset(new Blob(vector(1, channels))); - } - shared_ptr > filler; - if (channelwise_affine_param.has_slope_filler()) { - filler.reset(GetFiller(channelwise_affine_param.slope_filler())); - } else { - FillerParameter filler_param; - filler_param.set_type("constant"); - filler_param.set_value(1.0001); - filler.reset(GetFiller(filler_param)); - } - filler->Fill(this->blobs_[0].get()); - - if (channelwise_affine_param.has_bias_filler()) { - filler.reset(GetFiller(channelwise_affine_param.bias_filler())); - } else { - FillerParameter filler_param; - filler_param.set_type("constant"); - filler_param.set_value(0.0001); - filler.reset(GetFiller(filler_param)); - } - filler->Fill(this->blobs_[1].get()); - } - if (channel_shared_) { - CHECK_EQ(this->blobs_[0]->count(), 1) - << "Slope size is inconsistent with prototxt config"; - } else { - CHECK_EQ(this->blobs_[0]->count(), channels) - << "Slope size is inconsistent with prototxt config"; - } - - // Propagate gradients to the parameters (as directed by backward pass). - this->param_propagate_down_.resize(this->blobs_.size(), true); - multiplier_.Reshape(vector(1, bottom[0]->count(1))); - bias_multiplier_.Reshape(vector(1, bottom[0]->count(1))); - backward_buff_.Reshape(vector(1, bottom[0]->count(1))); - caffe_set(multiplier_.count(), Dtype(1.0), - multiplier_.mutable_cpu_data()); - caffe_set(bias_multiplier_.count(), Dtype(1.0), - bias_multiplier_.mutable_cpu_data()); -} - -template -void ChannelwiseAffineLayer::Reshape( - const vector*>& bottom, - const vector*>& top) { - CHECK_GE(bottom[0]->num_axes(), 2) - << "Number of axes of bottom blob must be >=2."; - top[0]->ReshapeLike(*bottom[0]); - if (bottom[0] == top[0]) { - // For in-place computation - bottom_memory_.ReshapeLike(*bottom[0]); - } - int height = 1; - int width = 1; - if (bottom[0]->num_axes() > 2) { - height = bottom[0]->shape(2); - width = bottom[0]->shape(3); - } - vector bias_multiplier_shape(1, height * width); - bias_multiplier_.Reshape(bias_multiplier_shape); - caffe_set(bias_multiplier_.count(), Dtype(1), - bias_multiplier_.mutable_cpu_data()); -} - -template -void ChannelwiseAffineLayer::Forward_cpu( - const vector*>& bottom, - const vector*>& top) { - const Dtype* bottom_data = bottom[0]->cpu_data(); - Dtype* top_data = top[0]->mutable_cpu_data(); - const int count = bottom[0]->count(); - const int dim = bottom[0]->count(2); - const int channels = bottom[0]->channels(); - const Dtype* slope_data = this->blobs_[0]->cpu_data(); - const Dtype* bias_data = this->blobs_[1]->cpu_data(); - // For in-place computation - if (bottom[0] == top[0]) { - caffe_copy(count, bottom_data, bottom_memory_.mutable_cpu_data()); - } - // if channel_shared, channel index in the following computation becomes - // always zero. - const int div_factor = channel_shared_ ? channels : 1; - for (int i = 0; i < count; ++i) { - int c = (i / dim) % channels / div_factor; - top_data[i] = bottom_data[i] * slope_data[c] + bias_data[c]; - } -} - -template -void ChannelwiseAffineLayer::Backward_cpu( - const vector*>& top, - const vector& propagate_down, - const vector*>& bottom) { - const Dtype* bottom_data = bottom[0]->cpu_data(); - const Dtype* slope_data = this->blobs_[0]->cpu_data(); - - const Dtype* top_diff = top[0]->cpu_diff(); - const int count = bottom[0]->count(); - const int dim = bottom[0]->count(2); - const int channels = bottom[0]->shape(1); - const int num = bottom[0]->shape(0); - int height = 1; - int width = 1; - if (bottom[0]->num_axes() > 2) { - height = bottom[0]->shape(2); - width = bottom[0]->shape(3); - } - - // For in-place computation - if (top[0] == bottom[0]) { - bottom_data = bottom_memory_.cpu_data(); - } - - // if channel_shared, channel index in the following computation becomes - // always zero. - const int div_factor = channel_shared_ ? channels : 1; - - // Propagte to param - // Since to write bottom diff will affect top diff if top and bottom blobs - // are identical (in-place computaion), we first compute param backward to - // keep top_diff unchanged. - - if (this->param_propagate_down_[1]) { - Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff(); - caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff); - for (int n = 0; n < num; ++n) { - caffe_cpu_gemv(CblasNoTrans, channels, height * width, 1., - top_diff + top[0]->offset(n), - bias_multiplier_.cpu_data(), 1., bias_diff); - } - } - if (this->param_propagate_down_[0]) { - Dtype* slope_diff = this->blobs_[0]->mutable_cpu_diff(); - caffe_set(this->blobs_[0]->count(), Dtype(0), slope_diff); - for (int i = 0; i < count; ++i) { - int c = (i / dim) % channels / div_factor; - slope_diff[c] += top_diff[i] * bottom_data[i]; - } - } - - // Propagate to bottom - if (propagate_down[0]) { - Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); - for (int i = 0; i < count; ++i) { - int c = (i / dim) % channels / div_factor; - bottom_diff[i] = slope_data[c] * top_diff[i]; - } - } -} - - -#ifdef CPU_ONLY -STUB_GPU(ChannelwiseAffineLayer); -#endif - -INSTANTIATE_CLASS(ChannelwiseAffineLayer); -REGISTER_LAYER_CLASS(ChannelwiseAffine); - -} // namespace caffe diff --git a/src/caffe/layers/channelwise_affine_layer.cu b/src/caffe/layers/channelwise_affine_layer.cu deleted file mode 100644 index 2066b26560b..00000000000 --- a/src/caffe/layers/channelwise_affine_layer.cu +++ /dev/null @@ -1,144 +0,0 @@ -#include -#include - -#include "caffe/layer.hpp" -#include "caffe/layers/channelwise_affine_layer.hpp" - -namespace caffe { - -// CUDA kernel for forward -template -__global__ void ChannelwiseAffineForward(const int n, const int channels, - const int dim, const Dtype* in, Dtype* out, const Dtype* slope_data, - const Dtype* bias_data, const int div_factor) { - CUDA_KERNEL_LOOP(index, n) { - int c = (index / dim) % channels / div_factor; - out[index] = in[index] * slope_data[c] + bias_data[c]; - } -} - -// CUDA kernel for bottom backward -template -__global__ void ChannelwiseAffineBackward(const int n, - const int channels, const int dim, const Dtype* in_diff, - Dtype* out_diff, const Dtype* slope_data, const int div_factor) { - CUDA_KERNEL_LOOP(index, n) { - int c = (index / dim) % channels / div_factor; - out_diff[index] = slope_data[c] * in_diff[index]; - } -} - -// CUDA kernel for element-wise parameter backward -template -__global__ void ChannelwiseAffineParamSlopeBackward(const int n, - const int rows, const int rowPitch, const Dtype* in_diff, - const Dtype* in_data, Dtype* out_diff) { - CUDA_KERNEL_LOOP(index, n) { - out_diff[index] = in_diff[index] * in_data[index]; - for ( int k = 1; k < rows; k++ ) { - out_diff[index] += in_diff[index + k*rowPitch] - * in_data[index + k*rowPitch]; - } - } -} - -template -void ChannelwiseAffineLayer::Forward_gpu( - const vector*>& bottom, - const vector*>& top) { - const Dtype* bottom_data = bottom[0]->gpu_data(); - Dtype* top_data = top[0]->mutable_gpu_data(); - const int count = bottom[0]->count(); - const int dim = bottom[0]->count(2); - const int channels = bottom[0]->channels(); - const Dtype* slope_data = this->blobs_[0]->gpu_data(); - const Dtype* bias_data = this->blobs_[1]->gpu_data(); - const int div_factor = channel_shared_ ? channels : 1; - - // For in-place computation - if (top[0] == bottom[0]) { - caffe_copy(count, bottom_data, bottom_memory_.mutable_gpu_data()); - } - // NOLINT_NEXT_LINE(whitespace/operators) - ChannelwiseAffineForward<<>>( - count, channels, dim, bottom_data, top_data, - slope_data, bias_data, div_factor); - CUDA_POST_KERNEL_CHECK; -} - -template -void ChannelwiseAffineLayer::Backward_gpu( - const vector*>& top, - const vector& propagate_down, - const vector*>& bottom) { - const Dtype* bottom_data = bottom[0]->gpu_data(); - const Dtype* top_diff = top[0]->gpu_diff(); - const int count = bottom[0]->count(); - const int num = bottom[0]->shape(0); - const int dim = bottom[0]->count(2); - const int channels = bottom[0]->shape(1); - int height = 1; - int width = 1; - if (bottom[0]->num_axes() > 2) { - height = bottom[0]->shape(2); - width = bottom[0]->shape(3); - } - - // For in-place computation - if (top[0] == bottom[0]) { - bottom_data = bottom_memory_.gpu_data(); - } - // Propagate to param - // Since to write bottom diff will affect top diff if top and bottom blobs - // are identical (in-place computaion), we first compute param backward to - // keep top_diff unchanged. - if (this->param_propagate_down_[1]) { - Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff(); - caffe_gpu_set(this->blobs_[1]->count(), Dtype(0.0), bias_diff); - // Gradient with respect to bias - for (int n = 0; n < num; ++n) { - caffe_gpu_gemv( - CblasNoTrans, channels, height * width, (Dtype)1., - top_diff + top[0]->offset(n), bias_multiplier_.gpu_data(), - (Dtype)1., bias_diff); - } - } - if (this->param_propagate_down_[0]) { - Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff(); - int cdim = channels * dim; - // compute element-wise diff - // NOLINT_NEXT_LINE(whitespace/operators) - ChannelwiseAffineParamSlopeBackward<<>>( - cdim, num, top[0]->offset(1), top_diff , - bottom_data, - backward_buff_.mutable_gpu_diff()); - CUDA_POST_KERNEL_CHECK; - if (channel_shared_) { - Dtype d = 0; - caffe_gpu_dot(cdim, backward_buff_.gpu_diff(), - multiplier_.gpu_data(), &d); - caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(d), slope_diff); - } else { - caffe_gpu_gemv(CblasNoTrans, channels, dim, Dtype(1.), - backward_buff_.gpu_diff(), multiplier_.gpu_data(), Dtype(1.), - slope_diff); - } - } - // Propagate to bottom - if (propagate_down[0]) { - Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); - const Dtype* slope_data = this->blobs_[0]->gpu_data(); - int div_factor = channel_shared_ ? channels : 1; - // NOLINT_NEXT_LINE(whitespace/operators) - ChannelwiseAffineBackward<<>>( - count, channels, dim, top_diff, bottom_diff, slope_data, div_factor); - CUDA_POST_KERNEL_CHECK; - } -} - -INSTANTIATE_LAYER_GPU_FUNCS(ChannelwiseAffineLayer); - -} // namespace caffe diff --git a/src/caffe/layers/scale_layer.cpp b/src/caffe/layers/scale_layer.cpp new file mode 100644 index 00000000000..ecdbb123e31 --- /dev/null +++ b/src/caffe/layers/scale_layer.cpp @@ -0,0 +1,219 @@ +#include +#include + +#include "caffe/filler.hpp" +#include "caffe/layer_factory.hpp" +#include "caffe/layers/scale_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void ScaleLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + const ScaleParameter& param = this->layer_param_.scale_param(); + if (bottom.size() == 1 && this->blobs_.size() > 0) { + LOG(INFO) << "Skipping parameter initialization"; + } else if (bottom.size() == 1) { + // scale is a learned parameter; initialize it + axis_ = bottom[0]->CanonicalAxisIndex(param.axis()); + const int num_axes = param.num_axes(); + CHECK_GE(num_axes, -1) << "num_axes must be non-negative, " + << "or -1 to extend to the end of bottom[0]"; + if (num_axes >= 0) { + CHECK_GE(bottom[0]->num_axes(), axis_ + num_axes) + << "scale blob's shape extends past bottom[0]'s shape when applied " + << "starting with bottom[0] axis = " << axis_; + } + this->blobs_.resize(1); + const vector::const_iterator& shape_start = + bottom[0]->shape().begin() + axis_; + const vector::const_iterator& shape_end = + (num_axes == -1) ? bottom[0]->shape().end() : (shape_start + num_axes); + vector scale_shape(shape_start, shape_end); + this->blobs_[0].reset(new Blob(scale_shape)); + FillerParameter filler_param(param.filler()); + if (!param.has_filler()) { + // Default to unit (1) filler for identity operation. + filler_param.set_type("constant"); + filler_param.set_value(1); + } + shared_ptr > filler(GetFiller(filler_param)); + filler->Fill(this->blobs_[0].get()); + } + if (param.bias_term()) { + LayerParameter layer_param(this->layer_param_); + layer_param.set_type("Bias"); + BiasParameter* bias_param = layer_param.mutable_bias_param(); + bias_param->set_axis(param.axis()); + if (bottom.size() > 1) { + bias_param->set_num_axes(bottom[1]->num_axes()); + } else { + bias_param->set_num_axes(param.num_axes()); + } + bias_param->mutable_filler()->CopyFrom(param.bias_filler()); + bias_layer_ = LayerRegistry::CreateLayer(layer_param); + bias_bottom_vec_.resize(1); + bias_bottom_vec_[0] = bottom[0]; + bias_layer_->SetUp(bias_bottom_vec_, top); + bias_param_id_ = this->blobs_.size(); + this->blobs_.resize(bias_param_id_ + 1); + this->blobs_[bias_param_id_] = bias_layer_->blobs()[0]; + bias_propagate_down_.resize(1, false); + } + this->param_propagate_down_.resize(this->blobs_.size(), true); +} + +template +void ScaleLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + const ScaleParameter& param = this->layer_param_.scale_param(); + Blob* scale = (bottom.size() > 1) ? bottom[1] : this->blobs_[0].get(); + // Always set axis_ == 0 in special case where scale is a scalar + // (num_axes == 0). Mathematically equivalent for any choice of axis_, so the + // actual setting can be safely ignored; and computation is most efficient + // with axis_ == 0 and (therefore) outer_dim_ == 1. (Setting axis_ to + // bottom[0]->num_axes() - 1, giving inner_dim_ == 1, would be equally + // performant.) + axis_ = (scale->num_axes() == 0) ? + 0 : bottom[0]->CanonicalAxisIndex(param.axis()); + CHECK_GE(bottom[0]->num_axes(), axis_ + scale->num_axes()) + << "scale blob's shape extends past bottom[0]'s shape when applied " + << "starting with bottom[0] axis = " << axis_; + for (int i = 0; i < scale->num_axes(); ++i) { + CHECK_EQ(bottom[0]->shape(axis_ + i), scale->shape(i)) + << "dimension mismatch between bottom[0]->shape(" << axis_ + i + << ") and scale->shape(" << i << ")"; + } + outer_dim_ = bottom[0]->count(0, axis_); + scale_dim_ = scale->count(); + inner_dim_ = bottom[0]->count(axis_ + scale->num_axes()); + if (bottom[0] == top[0]) { // in-place computation + temp_.ReshapeLike(*bottom[0]); + } else { + top[0]->ReshapeLike(*bottom[0]); + } + sum_result_.Reshape(vector(1, outer_dim_ * scale_dim_)); + const int sum_mult_size = std::max(outer_dim_, inner_dim_); + sum_multiplier_.Reshape(vector(1, sum_mult_size)); + if (sum_multiplier_.cpu_data()[sum_mult_size - 1] != Dtype(1)) { + caffe_set(sum_mult_size, Dtype(1), sum_multiplier_.mutable_cpu_data()); + } + if (bias_layer_) { + bias_bottom_vec_[0] = top[0]; + bias_layer_->Reshape(bias_bottom_vec_, top); + } +} + +template +void ScaleLayer::Forward_cpu( + const vector*>& bottom, const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + if (bottom[0] == top[0]) { + // In-place computation; need to store bottom data before overwriting it. + // Note that this is only necessary for Backward; we could skip this if not + // doing Backward, but Caffe currently provides no way of knowing whether + // we'll need to do Backward at the time of the Forward call. + caffe_copy(bottom[0]->count(), bottom[0]->cpu_data(), + temp_.mutable_cpu_data()); + } + const Dtype* scale_data = + ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + for (int n = 0; n < outer_dim_; ++n) { + for (int d = 0; d < scale_dim_; ++d) { + const Dtype factor = scale_data[d]; + caffe_cpu_scale(inner_dim_, factor, bottom_data, top_data); + bottom_data += inner_dim_; + top_data += inner_dim_; + } + } + if (bias_layer_) { + bias_layer_->Forward(bias_bottom_vec_, top); + } +} + +template +void ScaleLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (bias_layer_ && + this->param_propagate_down_[this->param_propagate_down_.size() - 1]) { + bias_layer_->Backward(top, bias_propagate_down_, bias_bottom_vec_); + } + const bool scale_param = (bottom.size() == 1); + Blob* scale = scale_param ? this->blobs_[0].get() : bottom[1]; + if ((!scale_param && propagate_down[1]) || + (scale_param && this->param_propagate_down_[0])) { + const Dtype* top_diff = top[0]->cpu_diff(); + const bool in_place = (bottom[0] == top[0]); + const Dtype* bottom_data = (in_place ? &temp_ : bottom[0])->cpu_data(); + // Hack: store big eltwise product in bottom[0] diff, except in the special + // case where this layer itself does the eltwise product, in which case we + // can store it directly in the scale diff, and we're done. + // If we're computing in-place (and not doing eltwise computation), this + // hack doesn't work and we store the product in temp_. + const bool is_eltwise = (bottom[0]->count() == scale->count()); + Dtype* product = (is_eltwise ? scale->mutable_cpu_diff() : + (in_place ? temp_.mutable_cpu_data() : bottom[0]->mutable_cpu_diff())); + caffe_mul(top[0]->count(), top_diff, bottom_data, product); + if (!is_eltwise) { + Dtype* sum_result = NULL; + if (inner_dim_ == 1) { + sum_result = product; + } else if (sum_result_.count() == 1) { + const Dtype* sum_mult = sum_multiplier_.cpu_data(); + Dtype* scale_diff = scale->mutable_cpu_diff(); + if (scale_param) { + Dtype result = caffe_cpu_dot(inner_dim_, product, sum_mult); + *scale_diff += result; + } else { + *scale_diff = caffe_cpu_dot(inner_dim_, product, sum_mult); + } + } else { + const Dtype* sum_mult = sum_multiplier_.cpu_data(); + sum_result = (outer_dim_ == 1) ? + scale->mutable_cpu_diff() : sum_result_.mutable_cpu_data(); + caffe_cpu_gemv(CblasNoTrans, sum_result_.count(), inner_dim_, + Dtype(1), product, sum_mult, Dtype(0), sum_result); + } + if (outer_dim_ != 1) { + const Dtype* sum_mult = sum_multiplier_.cpu_data(); + Dtype* scale_diff = scale->mutable_cpu_diff(); + if (scale_dim_ == 1) { + if (scale_param) { + Dtype result = caffe_cpu_dot(outer_dim_, sum_mult, sum_result); + *scale_diff += result; + } else { + *scale_diff = caffe_cpu_dot(outer_dim_, sum_mult, sum_result); + } + } else { + caffe_cpu_gemv(CblasTrans, outer_dim_, scale_dim_, + Dtype(1), sum_result, sum_mult, Dtype(scale_param), + scale_diff); + } + } + } + } + if (propagate_down[0]) { + const Dtype* top_diff = top[0]->cpu_diff(); + const Dtype* scale_data = scale->cpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + for (int n = 0; n < outer_dim_; ++n) { + for (int d = 0; d < scale_dim_; ++d) { + const Dtype factor = scale_data[d]; + caffe_cpu_scale(inner_dim_, factor, top_diff, bottom_diff); + bottom_diff += inner_dim_; + top_diff += inner_dim_; + } + } + } +} + +#ifdef CPU_ONLY +STUB_GPU(ScaleLayer); +#endif + +INSTANTIATE_CLASS(ScaleLayer); +REGISTER_LAYER_CLASS(Scale); + +} // namespace caffe diff --git a/src/caffe/layers/scale_layer.cu b/src/caffe/layers/scale_layer.cu new file mode 100644 index 00000000000..fc9a8064db5 --- /dev/null +++ b/src/caffe/layers/scale_layer.cu @@ -0,0 +1,135 @@ +#include +#include + +#include "caffe/layers/scale_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +__global__ void ScaleForward(const int n, const Dtype* in, + const Dtype* scale, const int scale_dim, const int inner_dim, + Dtype* out) { + CUDA_KERNEL_LOOP(index, n) { + const int scale_index = (index / inner_dim) % scale_dim; + out[index] = in[index] * scale[scale_index]; + } +} + +template +__global__ void ScaleBiasForward(const int n, const Dtype* in, + const Dtype* scale, const Dtype* bias, + const int scale_dim, const int inner_dim, Dtype* out) { + CUDA_KERNEL_LOOP(index, n) { + const int scale_index = (index / inner_dim) % scale_dim; + out[index] = in[index] * scale[scale_index] + bias[scale_index]; + } +} + +template +void ScaleLayer::Forward_gpu( + const vector*>& bottom, const vector*>& top) { + const int count = top[0]->count(); + const Dtype* bottom_data = bottom[0]->gpu_data(); + if (bottom[0] == top[0]) { + // in-place computation; need to store bottom data before overwriting it. + // Note that this is only necessary for Backward; we could skip this if not + // doing Backward, but Caffe currently provides no way of knowing whether + // we'll need to do Backward at the time of the Forward call. + caffe_copy(bottom[0]->count(), bottom[0]->gpu_data(), + temp_.mutable_gpu_data()); + } + const Dtype* scale_data = + ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + if (bias_layer_) { + const Dtype* bias_data = this->blobs_[bias_param_id_]->gpu_data(); + ScaleBiasForward // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, bottom_data, scale_data, bias_data, scale_dim_, inner_dim_, + top_data); + } else { + ScaleForward // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, bottom_data, scale_data, scale_dim_, inner_dim_, top_data); + } +} + +template +void ScaleLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (bias_layer_ && + this->param_propagate_down_[this->param_propagate_down_.size() - 1]) { + bias_layer_->Backward(top, bias_propagate_down_, bias_bottom_vec_); + } + const bool scale_param = (bottom.size() == 1); + Blob* scale = scale_param ? this->blobs_[0].get() : bottom[1]; + if ((!scale_param && propagate_down[1]) || + (scale_param && this->param_propagate_down_[0])) { + const Dtype* top_diff = top[0]->gpu_diff(); + const bool in_place = (bottom[0] == top[0]); + const Dtype* bottom_data = (in_place ? &temp_ : bottom[0])->gpu_data(); + // Hack: store big eltwise product in bottom[0] diff, except in the special + // case where this layer itself does the eltwise product, in which case we + // can store it directly in the scale diff, and we're done. + // If we're computing in-place (and not doing eltwise computation), this + // hack doesn't work and we store the product in temp_. + const bool is_eltwise = (bottom[0]->count() == scale->count()); + Dtype* product = (is_eltwise ? scale->mutable_gpu_diff() : + (in_place ? temp_.mutable_gpu_data() : bottom[0]->mutable_gpu_diff())); + caffe_gpu_mul(top[0]->count(), top_diff, bottom_data, product); + if (!is_eltwise) { + Dtype* sum_result = NULL; + if (inner_dim_ == 1) { + sum_result = product; + } else if (sum_result_.count() == 1) { + const Dtype* sum_mult = sum_multiplier_.gpu_data(); + Dtype* scale_diff = scale->mutable_cpu_diff(); + if (scale_param) { + Dtype result; + caffe_gpu_dot(inner_dim_, product, sum_mult, &result); + *scale_diff += result; + } else { + caffe_gpu_dot(inner_dim_, product, sum_mult, scale_diff); + } + } else { + const Dtype* sum_mult = sum_multiplier_.gpu_data(); + sum_result = (outer_dim_ == 1) ? + scale->mutable_gpu_diff() : sum_result_.mutable_gpu_data(); + caffe_gpu_gemv(CblasNoTrans, sum_result_.count(), inner_dim_, + Dtype(1), product, sum_mult, Dtype(0), sum_result); + } + if (outer_dim_ != 1) { + const Dtype* sum_mult = sum_multiplier_.gpu_data(); + if (scale_dim_ == 1) { + Dtype* scale_diff = scale->mutable_cpu_diff(); + if (scale_param) { + Dtype result; + caffe_gpu_dot(outer_dim_, sum_mult, sum_result, &result); + *scale_diff += result; + } else { + caffe_gpu_dot(outer_dim_, sum_mult, sum_result, scale_diff); + } + } else { + Dtype* scale_diff = scale->mutable_gpu_diff(); + caffe_gpu_gemv(CblasTrans, outer_dim_, scale_dim_, + Dtype(1), sum_result, sum_mult, Dtype(scale_param), + scale_diff); + } + } + } + } + if (propagate_down[0]) { + const int count = top[0]->count(); + const Dtype* top_diff = top[0]->gpu_diff(); + const Dtype* scale_data = scale->gpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + ScaleForward // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, top_diff, scale_data, scale_dim_, inner_dim_, bottom_diff); + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(ScaleLayer); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index fe6209cf673..6493a72d778 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -306,7 +306,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 142 (last added: channelwise_affine_param) +// LayerParameter next available layer-specific ID: 143 (last added: scale_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -356,7 +356,7 @@ message LayerParameter { optional AccuracyParameter accuracy_param = 102; optional ArgMaxParameter argmax_param = 103; optional BatchNormParameter batch_norm_param = 139; - optional ChannelwiseAffineParameter channelwise_affine_param = 141; + optional BiasParameter bias_param = 141; optional ConcatParameter concat_param = 104; optional ContrastiveLossParameter contrastive_loss_param = 105; optional ConvolutionParameter convolution_param = 106; @@ -385,6 +385,7 @@ message LayerParameter { optional ReductionParameter reduction_param = 136; optional ReLUParameter relu_param = 123; optional ReshapeParameter reshape_param = 133; + optional ScaleParameter scale_param = 142; optional SigmoidParameter sigmoid_param = 124; optional SoftmaxParameter softmax_param = 125; optional SPPParameter spp_param = 132; @@ -499,15 +500,36 @@ message BatchNormParameter { optional float eps = 3 [default = 1e-5]; } -message ChannelwiseAffineParameter { - - // Initial value of a_i. Default is a_i=1.0 for all i. - optional FillerParameter slope_filler = 1; - - optional FillerParameter bias_filler = 2; +message BiasParameter { + // The first axis of bottom[0] (the first input Blob) along which to apply + // bottom[1] (the second input Blob). May be negative to index from the end + // (e.g., -1 for the last axis). + // + // For example, if bottom[0] is 4D with shape 100x3x40x60, the output + // top[0] will have the same shape, and bottom[1] may have any of the + // following shapes (for the given value of axis): + // (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60 + // (axis == 1 == -3) 3; 3x40; 3x40x60 + // (axis == 2 == -2) 40; 40x60 + // (axis == 3 == -1) 60 + // Furthermore, bottom[1] may have the empty shape (regardless of the value of + // "axis") -- a scalar bias. + optional int32 axis = 1 [default = 1]; - // Whether or not slope paramters are shared across channels. - optional bool channel_shared = 3 [default = false]; + // (num_axes is ignored unless just one bottom is given and the bias is + // a learned parameter of the layer. Otherwise, num_axes is determined by the + // number of axes by the second bottom.) + // The number of axes of the input (bottom[0]) covered by the bias + // parameter, or -1 to cover all axes of bottom[0] starting from `axis`. + // Set num_axes := 0, to add a zero-axis Blob: a scalar. + optional int32 num_axes = 2 [default = 1]; + + // (filler is ignored unless just one bottom is given and the bias is + // a learned parameter of the layer.) + // The initialization for the learned bias parameter. + // Default is the zero (0) initialization, resulting in the BiasLayer + // initially performing the identity operation. + optional FillerParameter filler = 3; } message ContrastiveLossParameter { @@ -972,6 +994,43 @@ message ReshapeParameter { optional int32 num_axes = 3 [default = -1]; } +message ScaleParameter { + // The first axis of bottom[0] (the first input Blob) along which to apply + // bottom[1] (the second input Blob). May be negative to index from the end + // (e.g., -1 for the last axis). + // + // For example, if bottom[0] is 4D with shape 100x3x40x60, the output + // top[0] will have the same shape, and bottom[1] may have any of the + // following shapes (for the given value of axis): + // (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60 + // (axis == 1 == -3) 3; 3x40; 3x40x60 + // (axis == 2 == -2) 40; 40x60 + // (axis == 3 == -1) 60 + // Furthermore, bottom[1] may have the empty shape (regardless of the value of + // "axis") -- a scalar multiplier. + optional int32 axis = 1 [default = 1]; + + // (num_axes is ignored unless just one bottom is given and the scale is + // a learned parameter of the layer. Otherwise, num_axes is determined by the + // number of axes by the second bottom.) + // The number of axes of the input (bottom[0]) covered by the scale + // parameter, or -1 to cover all axes of bottom[0] starting from `axis`. + // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar. + optional int32 num_axes = 2 [default = 1]; + + // (filler is ignored unless just one bottom is given and the scale is + // a learned parameter of the layer.) + // The initialization for the learned scale parameter. + // Default is the unit (1) initialization, resulting in the ScaleLayer + // initially performing the identity operation. + optional FillerParameter filler = 3; + + // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but + // may be more efficient). Initialized with bias_filler (defaults to 0). + optional bool bias_term = 4 [default = false]; + optional FillerParameter bias_filler = 5; +} + message SigmoidParameter { enum Engine { DEFAULT = 0; diff --git a/src/caffe/test/test_bias_layer.cpp b/src/caffe/test/test_bias_layer.cpp new file mode 100644 index 00000000000..3862e763e28 --- /dev/null +++ b/src/caffe/test/test_bias_layer.cpp @@ -0,0 +1,467 @@ +#include +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/layers/bias_layer.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class BiasLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + BiasLayerTest() + : blob_bottom_(new Blob(2, 3, 4, 5)), + blob_bottom_eltwise_(new Blob(2, 3, 4, 5)), + blob_bottom_broadcast_0_(new Blob()), + blob_bottom_broadcast_1_(new Blob()), + blob_bottom_broadcast_2_(new Blob()), + blob_bottom_bias_(new Blob(vector())), + blob_top_(new Blob()) { + Caffe::set_random_seed(1701); + vector broadcast_shape(2); + broadcast_shape[0] = 2; broadcast_shape[1] = 3; + this->blob_bottom_broadcast_0_->Reshape(broadcast_shape); + broadcast_shape[0] = 3; broadcast_shape[1] = 4; + this->blob_bottom_broadcast_1_->Reshape(broadcast_shape); + broadcast_shape[0] = 4; broadcast_shape[1] = 5; + this->blob_bottom_broadcast_2_->Reshape(broadcast_shape); + FillerParameter filler_param; + filler_param.set_min(1); + filler_param.set_max(10); + UniformFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + filler.Fill(this->blob_bottom_eltwise_); + filler.Fill(this->blob_bottom_broadcast_0_); + filler.Fill(this->blob_bottom_broadcast_1_); + filler.Fill(this->blob_bottom_broadcast_2_); + filler.Fill(this->blob_bottom_bias_); + blob_bottom_vec_.push_back(blob_bottom_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~BiasLayerTest() { + delete blob_bottom_; + delete blob_bottom_eltwise_; + delete blob_bottom_broadcast_0_; + delete blob_bottom_broadcast_1_; + delete blob_bottom_broadcast_2_; + delete blob_bottom_bias_; + delete blob_top_; + } + Blob* const blob_bottom_; + Blob* const blob_bottom_eltwise_; + Blob* const blob_bottom_broadcast_0_; + Blob* const blob_bottom_broadcast_1_; + Blob* const blob_bottom_broadcast_2_; + Blob* const blob_bottom_bias_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(BiasLayerTest, TestDtypesAndDevices); + +TYPED_TEST(BiasLayerTest, TestForwardEltwise) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(0); + shared_ptr > layer(new BiasLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_top_->cpu_data(); + const int count = this->blob_top_->count(); + const Dtype* in_data_a = this->blob_bottom_->cpu_data(); + const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i], 1e-5); + } +} + +TYPED_TEST(BiasLayerTest, TestForwardEltwiseInPlace) { + typedef typename TypeParam::Dtype Dtype; + this->blob_top_vec_[0] = this->blob_bottom_; // in-place computation + Blob orig_bottom(this->blob_bottom_->shape()); + orig_bottom.CopyFrom(*this->blob_bottom_); + this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(0); + shared_ptr > layer(new BiasLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_bottom_->cpu_data(); + const int count = this->blob_bottom_->count(); + const Dtype* in_data_a = orig_bottom.cpu_data(); + const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i], 1e-5); + } +} + +TYPED_TEST(BiasLayerTest, TestBackwardEltwiseInPlace) { + typedef typename TypeParam::Dtype Dtype; + Blob orig_bottom(this->blob_bottom_->shape()); + orig_bottom.CopyFrom(*this->blob_bottom_); + this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(0); + shared_ptr > layer(new BiasLayer(layer_param)); + Blob top_diff(this->blob_bottom_->shape()); + FillerParameter filler_param; + filler_param.set_type("gaussian"); + filler_param.set_std(1); + GaussianFiller filler(filler_param); + filler.Fill(&top_diff); + vector propagate_down(2, true); + // Run forward + backward without in-place computation; + // save resulting bottom diffs. + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_top_->mutable_cpu_diff()); + layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_); + const bool kReshape = true; + const bool kCopyDiff = true; + Blob orig_bottom_diff; + orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape); + Blob orig_bias_diff; + orig_bias_diff.CopyFrom(*this->blob_bottom_eltwise_, + kCopyDiff, kReshape); + // Rerun forward + backward with in-place computation; + // check that resulting bottom diffs are the same. + this->blob_top_vec_[0] = this->blob_bottom_; // in-place computation + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_bottom_->mutable_cpu_diff()); + layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i], + this->blob_bottom_->cpu_diff()[i], 1e-5); + } + for (int i = 0; i < this->blob_bottom_eltwise_->count(); ++i) { + EXPECT_NEAR(orig_bias_diff.cpu_diff()[i], + this->blob_bottom_eltwise_->cpu_diff()[i], 1e-5); + } +} + +TYPED_TEST(BiasLayerTest, TestForwardEltwiseWithParam) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + BiasParameter* bias_param = layer_param.mutable_bias_param(); + bias_param->set_axis(0); + bias_param->set_num_axes(-1); + bias_param->mutable_filler()->set_type("gaussian"); + shared_ptr > layer(new BiasLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_top_->cpu_data(); + const int count = this->blob_top_->count(); + const Dtype* in_data_a = this->blob_bottom_->cpu_data(); + const Dtype* in_data_b = layer->blobs()[0]->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i], 1e-5); + } +} + +TYPED_TEST(BiasLayerTest, TestForwardBroadcastBegin) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(0); + shared_ptr > layer(new BiasLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_->data_at(n, c, h, w) + + this->blob_bottom_broadcast_0_->data_at(n, c, 0, 0), + 1e-5); + } + } + } + } +} + +TYPED_TEST(BiasLayerTest, TestForwardBroadcastMiddle) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(1); + shared_ptr > layer(new BiasLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_->data_at(n, c, h, w) + + this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0), + 1e-5); + } + } + } + } +} + +TYPED_TEST(BiasLayerTest, TestForwardBroadcastMiddleInPlace) { + typedef typename TypeParam::Dtype Dtype; + this->blob_top_vec_[0] = this->blob_bottom_; // in-place computation + Blob orig_bottom(this->blob_bottom_->shape()); + orig_bottom.CopyFrom(*this->blob_bottom_); + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(1); + shared_ptr > layer(new BiasLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_bottom_->data_at(n, c, h, w), + orig_bottom.data_at(n, c, h, w) + + this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0), + 1e-5); + } + } + } + } +} + +TYPED_TEST(BiasLayerTest, TestBackwardBroadcastMiddleInPlace) { + typedef typename TypeParam::Dtype Dtype; + Blob orig_bottom(this->blob_bottom_->shape()); + orig_bottom.CopyFrom(*this->blob_bottom_); + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(1); + shared_ptr > layer(new BiasLayer(layer_param)); + Blob top_diff(this->blob_bottom_->shape()); + FillerParameter filler_param; + filler_param.set_type("gaussian"); + filler_param.set_std(1); + GaussianFiller filler(filler_param); + filler.Fill(&top_diff); + vector propagate_down(2, true); + // Run forward + backward without in-place computation; + // save resulting bottom diffs. + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_top_->mutable_cpu_diff()); + layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_); + const bool kReshape = true; + const bool kCopyDiff = true; + Blob orig_bottom_diff; + orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape); + Blob orig_bias_diff; + orig_bias_diff.CopyFrom(*this->blob_bottom_broadcast_1_, + kCopyDiff, kReshape); + // Rerun forward + backward with in-place computation; + // check that resulting bottom diffs are the same. + this->blob_top_vec_[0] = this->blob_bottom_; // in-place computation + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_bottom_->mutable_cpu_diff()); + layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i], + this->blob_bottom_->cpu_diff()[i], 1e-5); + } + for (int i = 0; i < this->blob_bottom_broadcast_1_->count(); ++i) { + EXPECT_NEAR(orig_bias_diff.cpu_diff()[i], + this->blob_bottom_broadcast_1_->cpu_diff()[i], 1e-5); + } +} + +TYPED_TEST(BiasLayerTest, TestForwardBroadcastMiddleWithParam) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + BiasParameter* bias_param = layer_param.mutable_bias_param(); + bias_param->set_axis(1); + bias_param->set_num_axes(2); + bias_param->mutable_filler()->set_type("gaussian"); + shared_ptr > layer(new BiasLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_->data_at(n, c, h, w) + + layer->blobs()[0]->data_at(c, h, 0, 0), 1e-5); + } + } + } + } +} + +TYPED_TEST(BiasLayerTest, TestForwardBroadcastEnd) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(2); + shared_ptr > layer(new BiasLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_->data_at(n, c, h, w) + + this->blob_bottom_broadcast_2_->data_at(h, w, 0, 0), + 1e-5); + } + } + } + } +} + +TYPED_TEST(BiasLayerTest, TestForwardBias) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_bias_); + LayerParameter layer_param; + shared_ptr > layer(new BiasLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_top_->cpu_data(); + const int count = this->blob_top_->count(); + const Dtype* in_data = this->blob_bottom_->cpu_data(); + const Dtype bias = *this->blob_bottom_bias_->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_NEAR(data[i], in_data[i] + bias, 1e-5); + } +} + +TYPED_TEST(BiasLayerTest, TestForwardBiasAxis2) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_bias_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(2); + shared_ptr > layer(new BiasLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_top_->cpu_data(); + const int count = this->blob_top_->count(); + const Dtype* in_data = this->blob_bottom_->cpu_data(); + const Dtype bias = *this->blob_bottom_bias_->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_NEAR(data[i], in_data[i] + bias, 1e-5); + } +} + +TYPED_TEST(BiasLayerTest, TestGradientEltwise) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(0); + BiasLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(BiasLayerTest, TestGradientEltwiseWithParam) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + BiasParameter* bias_param = layer_param.mutable_bias_param(); + bias_param->set_axis(0); + bias_param->set_num_axes(-1); + bias_param->mutable_filler()->set_type("gaussian"); + BiasLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(BiasLayerTest, TestGradientBroadcastBegin) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(0); + BiasLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(BiasLayerTest, TestGradientBroadcastMiddle) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(1); + BiasLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(BiasLayerTest, TestGradientBroadcastMiddleWithParam) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_); + LayerParameter layer_param; + BiasParameter* bias_param = layer_param.mutable_bias_param(); + bias_param->set_axis(1); + bias_param->set_num_axes(2); + bias_param->mutable_filler()->set_type("gaussian"); + BiasLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(BiasLayerTest, TestGradientBroadcastEnd) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(2); + BiasLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(BiasLayerTest, TestGradientBias) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_bias_); + LayerParameter layer_param; + BiasLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(BiasLayerTest, TestGradientBiasAxis2) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_bias_); + LayerParameter layer_param; + layer_param.mutable_bias_param()->set_axis(2); + BiasLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +} // namespace caffe diff --git a/src/caffe/test/test_channelwise_affine_layer.cpp b/src/caffe/test/test_channelwise_affine_layer.cpp deleted file mode 100644 index a3e2544f77a..00000000000 --- a/src/caffe/test/test_channelwise_affine_layer.cpp +++ /dev/null @@ -1,105 +0,0 @@ -#include - -#include "gtest/gtest.h" - -#include "caffe/blob.hpp" -#include "caffe/common.hpp" -#include "caffe/filler.hpp" -#include "caffe/layers/channelwise_affine_layer.hpp" - -#include "caffe/test/test_caffe_main.hpp" -#include "caffe/test/test_gradient_check_util.hpp" - -namespace caffe { - -template -class ChannelwiseAffineLayerTest : public MultiDeviceTest { - typedef typename TypeParam::Dtype Dtype; - - protected: - ChannelwiseAffineLayerTest() - : blob_bottom_(new Blob(2, 3, 4, 5)), - blob_top_(new Blob()) { - Caffe::set_random_seed(1701); - // fill the values - FillerParameter filler_param; - GaussianFiller filler(filler_param); - filler.Fill(this->blob_bottom_); - blob_bottom_vec_.push_back(blob_bottom_); - blob_top_vec_.push_back(blob_top_); - } - virtual ~ChannelwiseAffineLayerTest() { - delete blob_bottom_; delete blob_top_; } - Blob* const blob_bottom_; - Blob* const blob_top_; - vector*> blob_bottom_vec_; - vector*> blob_top_vec_; - - void TestChannelwiseAffine(ChannelwiseAffineLayer *layer) { - layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); - // Now, check values - const Dtype* bottom_data = this->blob_bottom_->cpu_data(); - const Dtype* top_data = this->blob_top_->cpu_data(); - const Dtype* slope_data = layer->blobs()[0]->cpu_data(); - const Dtype* bias_data = layer->blobs()[1]->cpu_data(); - const Dtype kDelta = 2e-5; - int hw = this->blob_bottom_->height() * this->blob_bottom_->width(); - int channels = this->blob_bottom_->channels(); - bool channel_shared = - layer->layer_param().channelwise_affine_param().channel_shared(); - for (int i = 0; i < this->blob_bottom_->count(); ++i) { - int c = channel_shared ? 0 : (i / hw) % channels; - EXPECT_NEAR(top_data[i], - bottom_data[i]* slope_data[c] + bias_data[c], kDelta); - } - } -}; -TYPED_TEST_CASE(ChannelwiseAffineLayerTest, TestDtypesAndDevices); - - -TYPED_TEST(ChannelwiseAffineLayerTest, TestChannelwiseAffineForward) { - typedef typename TypeParam::Dtype Dtype; - LayerParameter layer_param; - ChannelwiseAffineLayer layer(layer_param); - layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); - FillerParameter filler_param; - GaussianFiller filler(filler_param); - filler.Fill(layer.blobs()[0].get()); - filler.Fill(layer.blobs()[1].get()); - this->TestChannelwiseAffine(&layer); -} - -TYPED_TEST(ChannelwiseAffineLayerTest, - TestChannelwiseAffineForwardChannelShared) { - typedef typename TypeParam::Dtype Dtype; - LayerParameter layer_param; - layer_param.mutable_channelwise_affine_param()->set_channel_shared(true); - ChannelwiseAffineLayer layer(layer_param); - layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); - this->TestChannelwiseAffine(&layer); -} - -TYPED_TEST(ChannelwiseAffineLayerTest, TestChannelwiseAffineGradient) { - typedef typename TypeParam::Dtype Dtype; - LayerParameter layer_param; - layer_param.mutable_channelwise_affine_param()->set_channel_shared(false); - ChannelwiseAffineLayer layer(layer_param); - layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); - GradientChecker checker(1e-2, 1e-3, 1701, 0., 0.01); - checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, - this->blob_top_vec_); -} - -TYPED_TEST(ChannelwiseAffineLayerTest, - TestChannelwiseAffineGradientChannelShared) { - typedef typename TypeParam::Dtype Dtype; - LayerParameter layer_param; - layer_param.mutable_channelwise_affine_param()->set_channel_shared(true); - ChannelwiseAffineLayer layer(layer_param); - layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); - GradientChecker checker(1e-2, 1e-3, 1701, 0., 0.01); - checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, - this->blob_top_vec_); -} - -} // namespace caffe diff --git a/src/caffe/test/test_scale_layer.cpp b/src/caffe/test/test_scale_layer.cpp new file mode 100644 index 00000000000..ad116795f44 --- /dev/null +++ b/src/caffe/test/test_scale_layer.cpp @@ -0,0 +1,507 @@ +#include +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/layers/scale_layer.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class ScaleLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + ScaleLayerTest() + : blob_bottom_(new Blob(2, 3, 4, 5)), + blob_bottom_eltwise_(new Blob(2, 3, 4, 5)), + blob_bottom_broadcast_0_(new Blob()), + blob_bottom_broadcast_1_(new Blob()), + blob_bottom_broadcast_2_(new Blob()), + blob_bottom_scale_(new Blob(vector())), + blob_top_(new Blob()) { + Caffe::set_random_seed(1701); + vector broadcast_shape(2); + broadcast_shape[0] = 2; broadcast_shape[1] = 3; + this->blob_bottom_broadcast_0_->Reshape(broadcast_shape); + broadcast_shape[0] = 3; broadcast_shape[1] = 4; + this->blob_bottom_broadcast_1_->Reshape(broadcast_shape); + broadcast_shape[0] = 4; broadcast_shape[1] = 5; + this->blob_bottom_broadcast_2_->Reshape(broadcast_shape); + FillerParameter filler_param; + filler_param.set_min(1); + filler_param.set_max(10); + UniformFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + filler.Fill(this->blob_bottom_eltwise_); + filler.Fill(this->blob_bottom_broadcast_0_); + filler.Fill(this->blob_bottom_broadcast_1_); + filler.Fill(this->blob_bottom_broadcast_2_); + filler.Fill(this->blob_bottom_scale_); + blob_bottom_vec_.push_back(blob_bottom_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~ScaleLayerTest() { + delete blob_bottom_; + delete blob_bottom_eltwise_; + delete blob_bottom_broadcast_0_; + delete blob_bottom_broadcast_1_; + delete blob_bottom_broadcast_2_; + delete blob_bottom_scale_; + delete blob_top_; + } + Blob* const blob_bottom_; + Blob* const blob_bottom_eltwise_; + Blob* const blob_bottom_broadcast_0_; + Blob* const blob_bottom_broadcast_1_; + Blob* const blob_bottom_broadcast_2_; + Blob* const blob_bottom_scale_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(ScaleLayerTest, TestDtypesAndDevices); + +TYPED_TEST(ScaleLayerTest, TestForwardEltwise) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(0); + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_top_->cpu_data(); + const int count = this->blob_top_->count(); + const Dtype* in_data_a = this->blob_bottom_->cpu_data(); + const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5); + } +} + +TYPED_TEST(ScaleLayerTest, TestForwardEltwiseInPlace) { + typedef typename TypeParam::Dtype Dtype; + this->blob_top_vec_[0] = this->blob_bottom_; // in-place computation + Blob orig_bottom(this->blob_bottom_->shape()); + orig_bottom.CopyFrom(*this->blob_bottom_); + this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(0); + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_bottom_->cpu_data(); + const int count = this->blob_bottom_->count(); + const Dtype* in_data_a = orig_bottom.cpu_data(); + const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5); + } +} + +TYPED_TEST(ScaleLayerTest, TestBackwardEltwiseInPlace) { + typedef typename TypeParam::Dtype Dtype; + Blob orig_bottom(this->blob_bottom_->shape()); + orig_bottom.CopyFrom(*this->blob_bottom_); + this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(0); + shared_ptr > layer(new ScaleLayer(layer_param)); + Blob top_diff(this->blob_bottom_->shape()); + FillerParameter filler_param; + filler_param.set_type("gaussian"); + filler_param.set_std(1); + GaussianFiller filler(filler_param); + filler.Fill(&top_diff); + vector propagate_down(2, true); + // Run forward + backward without in-place computation; + // save resulting bottom diffs. + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_top_->mutable_cpu_diff()); + layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_); + const bool kReshape = true; + const bool kCopyDiff = true; + Blob orig_bottom_diff; + orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape); + Blob orig_scale_diff; + orig_scale_diff.CopyFrom(*this->blob_bottom_eltwise_, + kCopyDiff, kReshape); + // Rerun forward + backward with in-place computation; + // check that resulting bottom diffs are the same. + this->blob_top_vec_[0] = this->blob_bottom_; // in-place computation + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_bottom_->mutable_cpu_diff()); + layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i], + this->blob_bottom_->cpu_diff()[i], 1e-5); + } + for (int i = 0; i < this->blob_bottom_eltwise_->count(); ++i) { + EXPECT_NEAR(orig_scale_diff.cpu_diff()[i], + this->blob_bottom_eltwise_->cpu_diff()[i], 1e-5); + } +} + +TYPED_TEST(ScaleLayerTest, TestForwardEltwiseWithParam) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ScaleParameter* scale_param = layer_param.mutable_scale_param(); + scale_param->set_axis(0); + scale_param->set_num_axes(-1); + scale_param->mutable_filler()->set_type("gaussian"); + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_top_->cpu_data(); + const int count = this->blob_top_->count(); + const Dtype* in_data_a = this->blob_bottom_->cpu_data(); + const Dtype* in_data_b = layer->blobs()[0]->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5); + } +} + +TYPED_TEST(ScaleLayerTest, TestForwardBroadcastBegin) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(0); + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_->data_at(n, c, h, w) * + this->blob_bottom_broadcast_0_->data_at(n, c, 0, 0), + 1e-5); + } + } + } + } +} + +TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddle) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(1); + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_->data_at(n, c, h, w) * + this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0), + 1e-5); + } + } + } + } +} + +TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleInPlace) { + typedef typename TypeParam::Dtype Dtype; + this->blob_top_vec_[0] = this->blob_bottom_; // in-place computation + Blob orig_bottom(this->blob_bottom_->shape()); + orig_bottom.CopyFrom(*this->blob_bottom_); + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(1); + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_bottom_->data_at(n, c, h, w), + orig_bottom.data_at(n, c, h, w) * + this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0), + 1e-5); + } + } + } + } +} + +TYPED_TEST(ScaleLayerTest, TestBackwardBroadcastMiddleInPlace) { + typedef typename TypeParam::Dtype Dtype; + Blob orig_bottom(this->blob_bottom_->shape()); + orig_bottom.CopyFrom(*this->blob_bottom_); + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(1); + shared_ptr > layer(new ScaleLayer(layer_param)); + Blob top_diff(this->blob_bottom_->shape()); + FillerParameter filler_param; + filler_param.set_type("gaussian"); + filler_param.set_std(1); + GaussianFiller filler(filler_param); + filler.Fill(&top_diff); + vector propagate_down(2, true); + // Run forward + backward without in-place computation; + // save resulting bottom diffs. + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_top_->mutable_cpu_diff()); + layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_); + const bool kReshape = true; + const bool kCopyDiff = true; + Blob orig_bottom_diff; + orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape); + Blob orig_scale_diff; + orig_scale_diff.CopyFrom(*this->blob_bottom_broadcast_1_, + kCopyDiff, kReshape); + // Rerun forward + backward with in-place computation; + // check that resulting bottom diffs are the same. + this->blob_top_vec_[0] = this->blob_bottom_; // in-place computation + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + caffe_copy(top_diff.count(), top_diff.cpu_data(), + this->blob_bottom_->mutable_cpu_diff()); + layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_); + for (int i = 0; i < this->blob_bottom_->count(); ++i) { + EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i], + this->blob_bottom_->cpu_diff()[i], 1e-5); + } + for (int i = 0; i < this->blob_bottom_broadcast_1_->count(); ++i) { + EXPECT_NEAR(orig_scale_diff.cpu_diff()[i], + this->blob_bottom_broadcast_1_->cpu_diff()[i], 1e-5); + } +} + +TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleWithParam) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ScaleParameter* scale_param = layer_param.mutable_scale_param(); + scale_param->set_axis(1); + scale_param->set_num_axes(2); + scale_param->mutable_filler()->set_type("gaussian"); + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_->data_at(n, c, h, w) * + layer->blobs()[0]->data_at(c, h, 0, 0), 1e-5); + } + } + } + } +} + +TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleWithParamAndBias) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ScaleParameter* scale_param = layer_param.mutable_scale_param(); + scale_param->set_axis(1); + scale_param->set_num_axes(2); + scale_param->mutable_filler()->set_type("gaussian"); + scale_param->set_bias_term(true); + scale_param->mutable_bias_filler()->set_type("gaussian"); + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_->data_at(n, c, h, w) * + layer->blobs()[0]->data_at(c, h, 0, 0) + + layer->blobs()[1]->data_at(c, h, 0, 0), 1e-5); + } + } + } + } +} + +TYPED_TEST(ScaleLayerTest, TestForwardBroadcastEnd) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(2); + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_->width(); ++w) { + EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_->data_at(n, c, h, w) * + this->blob_bottom_broadcast_2_->data_at(h, w, 0, 0), + 1e-5); + } + } + } + } +} + +TYPED_TEST(ScaleLayerTest, TestForwardScale) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_scale_); + LayerParameter layer_param; + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_top_->cpu_data(); + const int count = this->blob_top_->count(); + const Dtype* in_data = this->blob_bottom_->cpu_data(); + const Dtype scale = *this->blob_bottom_scale_->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_NEAR(data[i], in_data[i] * scale, 1e-5); + } +} + +TYPED_TEST(ScaleLayerTest, TestForwardScaleAxis2) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_scale_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(2); + shared_ptr > layer(new ScaleLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape()); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_top_->cpu_data(); + const int count = this->blob_top_->count(); + const Dtype* in_data = this->blob_bottom_->cpu_data(); + const Dtype scale = *this->blob_bottom_scale_->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_NEAR(data[i], in_data[i] * scale, 1e-5); + } +} + +TYPED_TEST(ScaleLayerTest, TestGradientEltwise) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(0); + ScaleLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(ScaleLayerTest, TestGradientEltwiseWithParam) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + ScaleParameter* scale_param = layer_param.mutable_scale_param(); + scale_param->set_axis(0); + scale_param->set_num_axes(-1); + scale_param->mutable_filler()->set_type("gaussian"); + ScaleLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(ScaleLayerTest, TestGradientBroadcastBegin) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(0); + ScaleLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(ScaleLayerTest, TestGradientBroadcastMiddle) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(1); + ScaleLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(ScaleLayerTest, TestGradientBroadcastMiddleWithParam) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_); + LayerParameter layer_param; + ScaleParameter* scale_param = layer_param.mutable_scale_param(); + scale_param->set_axis(1); + scale_param->set_num_axes(2); + scale_param->mutable_filler()->set_type("gaussian"); + ScaleLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(ScaleLayerTest, TestGradientBroadcastEnd) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(2); + ScaleLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(ScaleLayerTest, TestGradientScale) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_scale_); + LayerParameter layer_param; + ScaleLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(ScaleLayerTest, TestGradientScaleAndBias) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_scale_); + LayerParameter layer_param; + ScaleParameter* scale_param = layer_param.mutable_scale_param(); + scale_param->set_bias_term(true); + scale_param->mutable_bias_filler()->set_type("gaussian"); + ScaleLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(ScaleLayerTest, TestGradientScaleAxis2) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_scale_); + LayerParameter layer_param; + layer_param.mutable_scale_param()->set_axis(2); + ScaleLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +} // namespace caffe From e94065efd516668e5168ed82669063b69315595d Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Sat, 23 Jan 2016 01:22:04 -0800 Subject: [PATCH 387/446] show Caffe's version from MatCaffe --- matlab/+caffe/private/caffe_.cpp | 8 ++++++++ matlab/+caffe/version.m | 7 +++++++ 2 files changed, 15 insertions(+) create mode 100644 matlab/+caffe/version.m diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp index 1641e14b534..1b1b2bff861 100644 --- a/matlab/+caffe/private/caffe_.cpp +++ b/matlab/+caffe/private/caffe_.cpp @@ -504,6 +504,13 @@ static void write_mean(MEX_ARGS) { mxFree(mean_proto_file); } +// Usage: caffe_('version') +static void version(MEX_ARGS) { + mxCHECK(nrhs == 0, "Usage: caffe_('version')"); + // Return version string + plhs[0] = mxCreateString(AS_STRING(CAFFE_VERSION)); +} + /** ----------------------------------------------------------------- ** Available commands. **/ @@ -542,6 +549,7 @@ static handler_registry handlers[] = { { "reset", reset }, { "read_mean", read_mean }, { "write_mean", write_mean }, + { "version", version }, // The end. { "END", NULL }, }; diff --git a/matlab/+caffe/version.m b/matlab/+caffe/version.m new file mode 100644 index 00000000000..61cae4f76dc --- /dev/null +++ b/matlab/+caffe/version.m @@ -0,0 +1,7 @@ +function version_str = version() +% version() +% show Caffe's version. + +version_str = caffe_('version'); + +end From 407050e02790b3738cc42fbbf1b51c35ee7c3021 Mon Sep 17 00:00:00 2001 From: Hugo Serrat Date: Thu, 21 Jan 2016 14:34:01 +0100 Subject: [PATCH 388/446] Updated import to make it work with pydotplus --- python/caffe/draw.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/python/caffe/draw.py b/python/caffe/draw.py index f8bf5722aba..cfa3fc5b1fb 100644 --- a/python/caffe/draw.py +++ b/python/caffe/draw.py @@ -10,7 +10,16 @@ """ from caffe.proto import caffe_pb2 -import pydot + +""" +pydot is not supported under python 3 and pydot2 doesn't work properly. +pydotplus works nicely (pip install pydotplus) +""" +try: + # Try to load pydotplus + import pydotplus as pydot +except ImportError: + import pydot # Internal layer and blob styles. LAYER_STYLE_DEFAULT = {'shape': 'record', From ca402f6d15b8f36c2e53f7de7f9817a6b73ac04d Mon Sep 17 00:00:00 2001 From: Kang Kim Date: Fri, 27 Nov 2015 21:57:51 +0900 Subject: [PATCH 389/446] Prevent in-place computation in ReshapeLayer and FlattenLayer --- src/caffe/layers/flatten_layer.cpp | 2 ++ src/caffe/layers/reshape_layer.cpp | 2 ++ 2 files changed, 4 insertions(+) diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp index 651507e2e7d..d4ab3935760 100644 --- a/src/caffe/layers/flatten_layer.cpp +++ b/src/caffe/layers/flatten_layer.cpp @@ -7,6 +7,8 @@ namespace caffe { template void FlattenLayer::Reshape(const vector*>& bottom, const vector*>& top) { + CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not " + "allow in-place computation."; const int start_axis = bottom[0]->CanonicalAxisIndex( this->layer_param_.flatten_param().axis()); const int end_axis = bottom[0]->CanonicalAxisIndex( diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp index 82339f76d8f..45dd0902d6a 100644 --- a/src/caffe/layers/reshape_layer.cpp +++ b/src/caffe/layers/reshape_layer.cpp @@ -7,6 +7,8 @@ namespace caffe { template void ReshapeLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { + CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not " + "allow in-place computation."; inferred_axis_ = -1; copy_axes_.clear(); const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape(); From 9a43dcf0c738fa799256318162d29a3969446efb Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Tue, 26 Jan 2016 13:58:58 -0800 Subject: [PATCH 390/446] Remove unnecessary CAFFE_TEST_CUDA_PROP declarations --- src/caffe/test/test_embed_layer.cpp | 4 ---- src/caffe/test/test_im2col_kernel.cu | 2 -- 2 files changed, 6 deletions(-) diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp index acd4b0f636b..dc7f5c4aa47 100644 --- a/src/caffe/test/test_embed_layer.cpp +++ b/src/caffe/test/test_embed_layer.cpp @@ -12,10 +12,6 @@ namespace caffe { -#ifndef CPU_ONLY -extern cudaDeviceProp CAFFE_TEST_CUDA_PROP; -#endif - template class EmbedLayerTest : public MultiDeviceTest { typedef typename TypeParam::Dtype Dtype; diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu index 5d8f01f1713..e3a9791bcca 100644 --- a/src/caffe/test/test_im2col_kernel.cu +++ b/src/caffe/test/test_im2col_kernel.cu @@ -28,8 +28,6 @@ __global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im, const int* kernel_shape, const int* pad, const int* stride, const int* dilation, Dtype* data_col); -extern cudaDeviceProp CAFFE_TEST_CUDA_PROP; - template class Im2colKernelTest : public GPUDeviceTest { protected: From 91c02f3b3bc48af1ab24a4687331492cf0171815 Mon Sep 17 00:00:00 2001 From: Madan Ram Date: Thu, 23 Jul 2015 16:42:15 +0530 Subject: [PATCH 391/446] Update mnist readme.md: scale moved to transform_param --- examples/mnist/readme.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md index 413d4a1f40b..b87a0f53c7a 100644 --- a/examples/mnist/readme.md +++ b/examples/mnist/readme.md @@ -41,11 +41,13 @@ Currently, we will read the MNIST data from the lmdb we created earlier in the d layer { name: "mnist" type: "Data" + transform_param { + scale: 0.00390625 + } data_param { source: "mnist_train_lmdb" backend: LMDB batch_size: 64 - scale: 0.00390625 } top: "data" top: "label" From ae31adcdca0bc12e33e691ee7cd9c4ad75c229bb Mon Sep 17 00:00:00 2001 From: Keir Mierle Date: Fri, 26 Jun 2015 00:10:21 -0700 Subject: [PATCH 392/446] Make the two separate build systems clearer in the documentation --- docs/installation.md | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/docs/installation.md b/docs/installation.md index cce7ec358f5..ef781e8d638 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -87,15 +87,20 @@ There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https ## Compilation -Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup The defaults should work, but uncomment the relevant lines if using Anaconda Python. +Caffe can be compiled with either Make or CMake. Make is officially supported while CMake is supported by the community. + +### Compilation with Make + +Configure the build by copying and modifying the example `Makefile.config` for your setup. The defaults should work, but uncomment the relevant lines if using Anaconda Python. cp Makefile.config.example Makefile.config - # Adjust Makefile.config (for example, if using Anaconda Python) + # Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired) make all make test make runtest -- For cuDNN acceleration, you should uncomment the `USE_CUDNN := 1` switch in `Makefile.config`. +- For CPU & GPU accelerated Caffe, no changes are needed. +- For cuDNN acceleration using NVIDIA's proprietary cuDNN software, uncomment the `USE_CUDNN := 1` switch in `Makefile.config`. cuDNN is sometimes but not always faster than Caffe's GPU acceleration. - For CPU-only Caffe, uncomment `CPU_ONLY := 1` in `Makefile.config`. To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively. @@ -107,7 +112,7 @@ Be sure to set your MATLAB and Python paths in `Makefile.config` first! Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html). -### CMake Compilation +### Compilation with CMake In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7. The basic steps are as follows: @@ -116,6 +121,7 @@ The basic steps are as follows: cd build cmake .. make all + make install make runtest See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details. From afcaf253daa942821250db3f9a6afbe1d955bdf1 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Tue, 26 Jan 2016 23:09:27 -0800 Subject: [PATCH 393/446] Remove incorrect cast of gemm int arg to Dtype in BiasLayer --- src/caffe/layers/bias_layer.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/caffe/layers/bias_layer.cpp b/src/caffe/layers/bias_layer.cpp index 0a786b5db98..4726a729834 100644 --- a/src/caffe/layers/bias_layer.cpp +++ b/src/caffe/layers/bias_layer.cpp @@ -80,7 +80,7 @@ void BiasLayer::Forward_cpu(const vector*>& bottom, } for (int n = 0; n < outer_dim_; ++n) { caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, bias_dim_, - inner_dim_, Dtype(1), Dtype(1), bias_data, + inner_dim_, 1, Dtype(1), bias_data, bias_multiplier_.cpu_data(), Dtype(1), top_data); top_data += dim_; } From 14d0bb4767cba22b826eae03a6e5cfa4c1cd4287 Mon Sep 17 00:00:00 2001 From: gdh1995 Date: Wed, 13 Jan 2016 18:20:41 +0800 Subject: [PATCH 394/446] use relative paths on making build/tools/ links The old uses `abspath`, which I think is so harmful: * If I `cp -a` the whole project, `build/tools/caffe` still refer to the old file, until `make clean`, making debugging very hard * For `tar` and `scp`, the soft links can not work unless the target project folder has the same path --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 787b0e8d036..76d51ad8bd0 100644 --- a/Makefile +++ b/Makefile @@ -601,7 +601,7 @@ $(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \ # Target for extension-less symlinks to tool binaries with extension '*.bin'. $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR) @ $(RM) $@ - @ ln -s $(abspath $<) $@ + @ ln -s $(notdir $<) $@ $(TOOL_BINS): %.bin : %.o | $(DYNAMIC_NAME) @ echo CXX/LD -o $@ From dd2099786f11033ded6e9f46bc772ef9b2166399 Mon Sep 17 00:00:00 2001 From: Sergei Nikolaev Date: Tue, 2 Feb 2016 13:48:18 -0800 Subject: [PATCH 395/446] Nicely prints GPU names --- src/caffe/test/test_caffe_main.cpp | 1 + tools/caffe.cpp | 13 ++++++++++++- 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp index c8caf5ac58e..fccf6f1613b 100644 --- a/src/caffe/test/test_caffe_main.cpp +++ b/src/caffe/test/test_caffe_main.cpp @@ -34,6 +34,7 @@ int main(int argc, char** argv) { cudaGetDevice(&device); cout << "Current device id: " << device << endl; cudaGetDeviceProperties(&CAFFE_TEST_CUDA_PROP, device); + cout << "Current device name: " << CAFFE_TEST_CUDA_PROP.name << endl; #endif // invoke the test. return RUN_ALL_TESTS(); diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 470165add7f..ebe95d61ef1 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -183,7 +183,13 @@ int train() { s << (i ? ", " : "") << gpus[i]; } LOG(INFO) << "Using GPUs " << s.str(); - +#ifndef CPU_ONLY + cudaDeviceProp device_prop; + for (int i = 0; i < gpus.size(); ++i) { + cudaGetDeviceProperties(&device_prop, gpus[i]); + LOG(INFO) << "GPU " << gpus[i] << ": " << device_prop.name; + } +#endif solver_param.set_device_id(gpus[0]); Caffe::SetDevice(gpus[0]); Caffe::set_mode(Caffe::GPU); @@ -229,6 +235,11 @@ int test() { get_gpus(&gpus); if (gpus.size() != 0) { LOG(INFO) << "Use GPU with device ID " << gpus[0]; +#ifndef CPU_ONLY + cudaDeviceProp device_prop; + cudaGetDeviceProperties(&device_prop, gpus[0]); + LOG(INFO) << "GPU device name: " << device_prop.name; +#endif Caffe::SetDevice(gpus[0]); Caffe::set_mode(Caffe::GPU); } else { From 68c751c6f7a521994ccdc9330b89aef9c9024a0a Mon Sep 17 00:00:00 2001 From: Abhijit Kundu Date: Tue, 9 Feb 2016 02:45:46 -0500 Subject: [PATCH 396/446] bugfix for incorrect behaviour in caffe_parse_linker_libs function while extracting libflags from absolute library path with multiple (dots) --- cmake/Utils.cmake | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake index a1bde1ae95b..653de5fdf89 100644 --- a/cmake/Utils.cmake +++ b/cmake/Utils.cmake @@ -346,10 +346,11 @@ function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_va elseif(lib MATCHES "^-l.*") list(APPEND libflags ${lib}) elseif(IS_ABSOLUTE ${lib}) - get_filename_component(name_we ${lib} NAME_WE) get_filename_component(folder ${lib} PATH) + get_filename_component(filename ${lib} NAME) + string(REGEX REPLACE "\\.[^.]*$" "" filename_without_shortest_ext ${filename}) - string(REGEX MATCH "^lib(.*)" __match ${name_we}) + string(REGEX MATCH "^lib(.*)" __match ${filename_without_shortest_ext}) list(APPEND libflags -l${CMAKE_MATCH_1}) list(APPEND folders ${folder}) else() From 8800e4b42d621a843d99a431186bdfbc9271a3eb Mon Sep 17 00:00:00 2001 From: Felix Abecassis Date: Mon, 15 Feb 2016 16:52:32 -0800 Subject: [PATCH 397/446] Remove useless LevelDB include The tests could not compile with USE_LEVELDB=0 and LevelDB missing from the system --- src/caffe/test/test_data_transformer.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp index 6103918fda1..31bf1c1fb14 100644 --- a/src/caffe/test/test_data_transformer.cpp +++ b/src/caffe/test/test_data_transformer.cpp @@ -3,7 +3,6 @@ #include #include "gtest/gtest.h" -#include "leveldb/db.h" #include "caffe/blob.hpp" #include "caffe/common.hpp" From d957481d072a434dfd116ac2255180e09c86fac5 Mon Sep 17 00:00:00 2001 From: Prayag Verma Date: Thu, 18 Feb 2016 10:13:34 +0530 Subject: [PATCH 398/446] Fix a typo in docs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `peformance` → `performance` --- docs/multigpu.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/multigpu.md b/docs/multigpu.md index 01cfb8938b5..d91acef980d 100644 --- a/docs/multigpu.md +++ b/docs/multigpu.md @@ -17,7 +17,7 @@ updated model, 0\-\>2, and then 0\-\>1, 2\-\>3. For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced. -Current implementation has a "soft" assumption that the devices being used are homogeneous. In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used. e.g. if you combine a TitanX and a GTX980, peformance will be limited by the 980. Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported. +Current implementation has a "soft" assumption that the devices being used are homogeneous. In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used. e.g. if you combine a TitanX and a GTX980, performance will be limited by the 980. Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported. "nvidia-smi topo -m" will show you the connectivity matrix. You can do P2P through PCIe bridges, but not across socket level links at this time, e.g. across CPU sockets on a multi-socket motherboard. From 8f847fa8fae0460c6bf8e8d7a9bcf96a44305033 Mon Sep 17 00:00:00 2001 From: Youssef Kashef Date: Fri, 29 Jan 2016 19:21:48 +0100 Subject: [PATCH 399/446] tranpose parameter added to IP layer to support tied weights in an autoencoder. Arguments to matrix multiplication function are conditioned on this parameter, no actual transposing takes place. test ip gradient computation with transpose on --- include/caffe/layers/inner_product_layer.hpp | 1 + src/caffe/layers/inner_product_layer.cpp | 42 +++- src/caffe/layers/inner_product_layer.cu | 31 ++- src/caffe/proto/caffe.proto | 5 + src/caffe/test/test_inner_product_layer.cpp | 240 +++++++++++++++++++ 5 files changed, 304 insertions(+), 15 deletions(-) diff --git a/include/caffe/layers/inner_product_layer.hpp b/include/caffe/layers/inner_product_layer.hpp index 250576a4817..18d0d6192eb 100644 --- a/include/caffe/layers/inner_product_layer.hpp +++ b/include/caffe/layers/inner_product_layer.hpp @@ -44,6 +44,7 @@ class InnerProductLayer : public Layer { int N_; bool bias_term_; Blob bias_multiplier_; + bool transpose_; ///< if true, assume transposed weights }; } // namespace caffe diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp index d9088805501..e65349f0055 100644 --- a/src/caffe/layers/inner_product_layer.cpp +++ b/src/caffe/layers/inner_product_layer.cpp @@ -11,6 +11,7 @@ void InnerProductLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { const int num_output = this->layer_param_.inner_product_param().num_output(); bias_term_ = this->layer_param_.inner_product_param().bias_term(); + transpose_ = this->layer_param_.inner_product_param().transpose(); N_ = num_output; const int axis = bottom[0]->CanonicalAxisIndex( this->layer_param_.inner_product_param().axis()); @@ -27,10 +28,15 @@ void InnerProductLayer::LayerSetUp(const vector*>& bottom, } else { this->blobs_.resize(1); } - // Intialize the weight + // Initialize the weights vector weight_shape(2); - weight_shape[0] = N_; - weight_shape[1] = K_; + if (transpose_) { + weight_shape[0] = K_; + weight_shape[1] = N_; + } else { + weight_shape[0] = N_; + weight_shape[1] = K_; + } this->blobs_[0].reset(new Blob(weight_shape)); // fill the weights shared_ptr > weight_filler(GetFiller( @@ -80,7 +86,8 @@ void InnerProductLayer::Forward_cpu(const vector*>& bottom, const Dtype* bottom_data = bottom[0]->cpu_data(); Dtype* top_data = top[0]->mutable_cpu_data(); const Dtype* weight = this->blobs_[0]->cpu_data(); - caffe_cpu_gemm(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1., + caffe_cpu_gemm(CblasNoTrans, transpose_ ? CblasNoTrans : CblasTrans, + M_, N_, K_, (Dtype)1., bottom_data, weight, (Dtype)0., top_data); if (bias_term_) { caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1., @@ -97,8 +104,17 @@ void InnerProductLayer::Backward_cpu(const vector*>& top, const Dtype* top_diff = top[0]->cpu_diff(); const Dtype* bottom_data = bottom[0]->cpu_data(); // Gradient with respect to weight - caffe_cpu_gemm(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1., - top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_cpu_diff()); + if (transpose_) { + caffe_cpu_gemm(CblasTrans, CblasNoTrans, + K_, N_, M_, + (Dtype)1., bottom_data, top_diff, + (Dtype)1., this->blobs_[0]->mutable_cpu_diff()); + } else { + caffe_cpu_gemm(CblasTrans, CblasNoTrans, + N_, K_, M_, + (Dtype)1., top_diff, bottom_data, + (Dtype)1., this->blobs_[0]->mutable_cpu_diff()); + } } if (bias_term_ && this->param_propagate_down_[1]) { const Dtype* top_diff = top[0]->cpu_diff(); @@ -110,9 +126,17 @@ void InnerProductLayer::Backward_cpu(const vector*>& top, if (propagate_down[0]) { const Dtype* top_diff = top[0]->cpu_diff(); // Gradient with respect to bottom data - caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1., - top_diff, this->blobs_[0]->cpu_data(), (Dtype)0., - bottom[0]->mutable_cpu_diff()); + if (transpose_) { + caffe_cpu_gemm(CblasNoTrans, CblasTrans, + M_, K_, N_, + (Dtype)1., top_diff, this->blobs_[0]->cpu_data(), + (Dtype)0., bottom[0]->mutable_cpu_diff()); + } else { + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, + M_, K_, N_, + (Dtype)1., top_diff, this->blobs_[0]->cpu_data(), + (Dtype)0., bottom[0]->mutable_cpu_diff()); + } } } diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu index dc25aa33bd1..a58b56e3281 100644 --- a/src/caffe/layers/inner_product_layer.cu +++ b/src/caffe/layers/inner_product_layer.cu @@ -19,7 +19,9 @@ void InnerProductLayer::Forward_gpu(const vector*>& bottom, caffe_gpu_axpy(N_, bias_multiplier_.cpu_data()[0], this->blobs_[1]->gpu_data(), top_data); } else { - caffe_gpu_gemm(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1., + caffe_gpu_gemm(CblasNoTrans, + transpose_ ? CblasNoTrans : CblasTrans, + M_, N_, K_, (Dtype)1., bottom_data, weight, (Dtype)0., top_data); if (bias_term_) caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1., @@ -36,8 +38,17 @@ void InnerProductLayer::Backward_gpu(const vector*>& top, const Dtype* top_diff = top[0]->gpu_diff(); const Dtype* bottom_data = bottom[0]->gpu_data(); // Gradient with respect to weight - caffe_gpu_gemm(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1., - top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_gpu_diff()); + if (transpose_) { + caffe_gpu_gemm(CblasTrans, CblasNoTrans, + K_, N_, M_, + (Dtype)1., bottom_data, top_diff, + (Dtype)1., this->blobs_[0]->mutable_gpu_diff()); + } else { + caffe_gpu_gemm(CblasTrans, CblasNoTrans, + N_, K_, M_, + (Dtype)1., top_diff, bottom_data, + (Dtype)1., this->blobs_[0]->mutable_gpu_diff()); + } } if (bias_term_ && this->param_propagate_down_[1]) { const Dtype* top_diff = top[0]->gpu_diff(); @@ -49,9 +60,17 @@ void InnerProductLayer::Backward_gpu(const vector*>& top, if (propagate_down[0]) { const Dtype* top_diff = top[0]->gpu_diff(); // Gradient with respect to bottom data - caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1., - top_diff, this->blobs_[0]->gpu_data(), (Dtype)0., - bottom[0]->mutable_gpu_diff()); + if (transpose_) { + caffe_gpu_gemm(CblasNoTrans, CblasTrans, + M_, K_, N_, + (Dtype)1., top_diff, this->blobs_[0]->gpu_data(), + (Dtype)0., bottom[0]->mutable_gpu_diff()); + } else { + caffe_gpu_gemm(CblasNoTrans, CblasNoTrans, + M_, K_, N_, + (Dtype)1., top_diff, this->blobs_[0]->gpu_data(), + (Dtype)0., bottom[0]->mutable_gpu_diff()); + } } } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 6493a72d778..7edb6ae87e0 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -786,6 +786,11 @@ message InnerProductParameter { // all preceding axes are retained in the output. // May be negative to index from the end (e.g., -1 for the last axis). optional int32 axis = 5 [default = 1]; + // Specify whether to transpose the weight matrix or not. + // If transpose == true, any operations will be performed on the transpose + // of the weight matrix. The weight matrix itself is not going to be transposed + // but rather the transfer flag of operations will be toggled accordingly. + optional bool transpose = 6 [default = false]; } // Message that stores parameters used by LogLayer diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp index b888b510318..f1ec2333fae 100644 --- a/src/caffe/test/test_inner_product_layer.cpp +++ b/src/caffe/test/test_inner_product_layer.cpp @@ -60,6 +60,50 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) { EXPECT_EQ(this->blob_top_->channels(), 10); } +/** @brief TestSetUp while toggling tranpose flag + */ +TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeFalse) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_); + LayerParameter layer_param; + InnerProductParameter* inner_product_param = + layer_param.mutable_inner_product_param(); + inner_product_param->set_num_output(10); + inner_product_param->set_transpose(false); + shared_ptr > layer( + new InnerProductLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + EXPECT_EQ(2, this->blob_top_->num()); + EXPECT_EQ(1, this->blob_top_->height()); + EXPECT_EQ(1, this->blob_top_->width()); + EXPECT_EQ(10, this->blob_top_->channels()); + EXPECT_EQ(2, layer->blobs()[0]->num_axes()); + EXPECT_EQ(10, layer->blobs()[0]->shape(0)); + EXPECT_EQ(60, layer->blobs()[0]->shape(1)); +} + +/** @brief TestSetUp while toggling tranpose flag + */ +TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeTrue) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_); + LayerParameter layer_param; + InnerProductParameter* inner_product_param = + layer_param.mutable_inner_product_param(); + inner_product_param->set_num_output(10); + inner_product_param->set_transpose(true); + shared_ptr > layer( + new InnerProductLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + EXPECT_EQ(2, this->blob_top_->num()); + EXPECT_EQ(1, this->blob_top_->height()); + EXPECT_EQ(1, this->blob_top_->width()); + EXPECT_EQ(10, this->blob_top_->channels()); + EXPECT_EQ(2, layer->blobs()[0]->num_axes()); + EXPECT_EQ(60, layer->blobs()[0]->shape(0)); + EXPECT_EQ(10, layer->blobs()[0]->shape(1)); +} + TYPED_TEST(InnerProductLayerTest, TestForward) { typedef typename TypeParam::Dtype Dtype; this->blob_bottom_vec_.push_back(this->blob_bottom_); @@ -91,6 +135,79 @@ TYPED_TEST(InnerProductLayerTest, TestForward) { } } +/** + * @brief Init. an IP layer without transpose + random weights, + * run Forward, save the result. + * Init. another IP layer with transpose. + * manually copy and transpose the weights from the first IP layer, + * then run Forward on the same input and check that the result is the same + */ +TYPED_TEST(InnerProductLayerTest, TestForwardTranspose) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_); + bool IS_VALID_CUDA = false; +#ifndef CPU_ONLY + IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2; +#endif + if (Caffe::mode() == Caffe::CPU || + sizeof(Dtype) == 4 || IS_VALID_CUDA) { + LayerParameter layer_param; + InnerProductParameter* inner_product_param = + layer_param.mutable_inner_product_param(); + inner_product_param->set_num_output(10); + inner_product_param->mutable_weight_filler()->set_type("uniform"); + inner_product_param->mutable_bias_filler()->set_type("uniform"); + inner_product_param->mutable_bias_filler()->set_min(1); + inner_product_param->mutable_bias_filler()->set_max(2); + inner_product_param->set_transpose(false); + shared_ptr > layer( + new InnerProductLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const int count = this->blob_top_->count(); + Blob* const top = new Blob(); + top->ReshapeLike(*this->blob_top_); + caffe_copy(count, this->blob_top_->cpu_data(), top->mutable_cpu_data()); + this->blob_top_vec_.clear(); + this->blob_top_vec_.push_back(new Blob()); + inner_product_param->set_transpose(true); + shared_ptr > ip_t( + new InnerProductLayer(layer_param)); + ip_t->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + const int count_w = layer->blobs()[0]->count(); + EXPECT_EQ(count_w, ip_t->blobs()[0]->count()); + // manually copy and transpose the weights from 1st IP layer into 2nd + const Dtype* w = layer->blobs()[0]->cpu_data(); + Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data(); + const int width = layer->blobs()[0]->shape(1); + const int width_t = ip_t->blobs()[0]->shape(1); + for (int i = 0; i < count_w; ++i) { + int r = i / width; + int c = i % width; + w_t[c*width_t+r] = w[r*width+c]; // copy while transposing + } + // copy bias from 1st IP layer to 2nd IP layer + ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count()); + caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(), + ip_t->blobs()[1]->mutable_cpu_data()); + ip_t->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + EXPECT_EQ(count, this->blob_top_->count()) + << "Invalid count for top blob for IP with transpose."; + Blob* const top_t = new Blob();\ + top_t->ReshapeLike(*this->blob_top_vec_[0]); + caffe_copy(count, + this->blob_top_vec_[0]->cpu_data(), + top_t->mutable_cpu_data()); + const Dtype* data = top->cpu_data(); + const Dtype* data_t = top_t->cpu_data(); + for (int i = 0; i < count; ++i) { + EXPECT_FLOAT_EQ(data[i], data_t[i]); + } + } else { + LOG(ERROR) << "Skipping test due to old architecture."; + } +} + TYPED_TEST(InnerProductLayerTest, TestForwardNoBatch) { typedef typename TypeParam::Dtype Dtype; this->blob_bottom_vec_.push_back(this->blob_bottom_nobatch_); @@ -148,4 +265,127 @@ TYPED_TEST(InnerProductLayerTest, TestGradient) { } } +TYPED_TEST(InnerProductLayerTest, TestGradientTranspose) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_); + bool IS_VALID_CUDA = false; +#ifndef CPU_ONLY + IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2; +#endif + if (Caffe::mode() == Caffe::CPU || + sizeof(Dtype) == 4 || IS_VALID_CUDA) { + LayerParameter layer_param; + InnerProductParameter* inner_product_param = + layer_param.mutable_inner_product_param(); + inner_product_param->set_num_output(11); + inner_product_param->mutable_weight_filler()->set_type("gaussian"); + inner_product_param->mutable_bias_filler()->set_type("gaussian"); + inner_product_param->mutable_bias_filler()->set_min(1); + inner_product_param->mutable_bias_filler()->set_max(2); + inner_product_param->set_transpose(true); + InnerProductLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); + } else { + LOG(ERROR) << "Skipping test due to old architecture."; + } +} + +TYPED_TEST(InnerProductLayerTest, TestBackwardTranspose) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_); + bool IS_VALID_CUDA = false; +#ifndef CPU_ONLY + IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2; +#endif + if (Caffe::mode() == Caffe::CPU || + sizeof(Dtype) == 4 || IS_VALID_CUDA) { + LayerParameter layer_param; + InnerProductParameter* inner_product_param = + layer_param.mutable_inner_product_param(); + inner_product_param->set_num_output(10); + inner_product_param->mutable_weight_filler()->set_type("uniform"); + inner_product_param->mutable_bias_filler()->set_type("uniform"); + inner_product_param->mutable_bias_filler()->set_min(1); + inner_product_param->mutable_bias_filler()->set_max(2); + inner_product_param->set_transpose(false); + shared_ptr > layer( + new InnerProductLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // copy top blob + Blob* const top = new Blob(); + top->CopyFrom(*this->blob_top_, false, true); + // fake top diff + Blob* const diff = new Blob(); + diff->ReshapeLike(*this->blob_top_); + { + FillerParameter filler_param; + UniformFiller filler(filler_param); + filler.Fill(diff); + } + caffe_copy(this->blob_top_vec_[0]->count(), + diff->cpu_data(), + this->blob_top_vec_[0]->mutable_cpu_diff()); + vector propagate_down(1, true); + layer->Backward(this->blob_top_vec_, + propagate_down, + this->blob_bottom_vec_); + // copy first ip's weights and their diffs + Blob* const w = new Blob(); + w->CopyFrom(*layer->blobs()[0], false, true); + w->CopyFrom(*layer->blobs()[0], true, true); + // copy bottom diffs + Blob* const bottom_diff = new Blob(); + bottom_diff->CopyFrom(*this->blob_bottom_vec_[0], true, true); + // repeat original top with tranposed ip + this->blob_top_vec_.clear(); + this->blob_top_vec_.push_back(new Blob()); + inner_product_param->set_transpose(true); + shared_ptr > ip_t( + new InnerProductLayer(layer_param)); + ip_t->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + // manually copy and transpose the weights from 1st IP layer into 2nd + { + const Dtype* w_src = w->cpu_data(); + Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data(); + const int width = layer->blobs()[0]->shape(1); + const int width_t = ip_t->blobs()[0]->shape(1); + for (int i = 0; i < layer->blobs()[0]->count(); ++i) { + int r = i / width; + int c = i % width; + w_t[c*width_t+r] = w_src[r*width+c]; // copy while transposing + } + // copy bias from 1st IP layer to 2nd IP layer + ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count()); + caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(), + ip_t->blobs()[1]->mutable_cpu_data()); + } + ip_t->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + caffe_copy(this->blob_top_vec_[0]->count(), + diff->cpu_data(), + this->blob_top_vec_[0]->mutable_cpu_diff()); + ip_t->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_); + const Dtype* data = w->cpu_diff(); + const Dtype* data_t = ip_t->blobs()[0]->cpu_diff(); + const int WIDTH = layer->blobs()[0]->shape(1); + const int WIDTH_T = ip_t->blobs()[0]->shape(1); + for (int i = 0; i < layer->blobs()[0]->count(); ++i) { + int r = i / WIDTH; + int c = i % WIDTH; + EXPECT_NE(Dtype(0.), data[r*WIDTH+c]); + EXPECT_FLOAT_EQ(data[r*WIDTH+c], data_t[c*WIDTH_T+r]); + } + data = bottom_diff->cpu_diff(); + data_t = this->blob_bottom_vec_[0]->cpu_diff(); + for (int i = 0; i < this->blob_bottom_vec_[0]->count(); ++i) { + EXPECT_NE(Dtype(0.), data[i]); + EXPECT_FLOAT_EQ(data[i], data_t[i]); + } + } else { + LOG(ERROR) << "Skipping test due to old architecture."; + } +} + } // namespace caffe From b46133aff47d5ac9d2f0f1289c6d5a9c57b3c2c5 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Sat, 20 Feb 2016 15:45:31 -0800 Subject: [PATCH 400/446] fix library install name on OSX for relative path linking for linking of the caffe tools, tests, etc. the library install name needs to include the @rpath set for the executables and interfaces this was broken on OSX by #3311 --- Makefile | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 598d28d5beb..38635038ddf 100644 --- a/Makefile +++ b/Makefile @@ -283,9 +283,8 @@ ifeq ($(OSX), 1) # boost::thread is called boost_thread-mt to mark multithreading on OS X LIBRARIES += boost_thread-mt # we need to explicitly ask for the rpath to be obeyed - DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so ORIGIN := @loader_path - VERSIONFLAGS += -Wl,-install_name,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib + VERSIONFLAGS += -Wl,-install_name,@rpath/$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib else ORIGIN := \$$ORIGIN endif @@ -552,7 +551,7 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK) $(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR) @ echo LD -o $@ - $(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS) + $(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS) @ cd $(BUILD_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT); ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT) $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR) From dac4d0962dffb11f9fb670e70126aebe31ddae5a Mon Sep 17 00:00:00 2001 From: Mohamed Ezz Date: Fri, 5 Feb 2016 01:54:31 +0100 Subject: [PATCH 401/446] Fix OSX El Capitan CUDA incompatibility, by adding lib to rpath --- Makefile | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/Makefile b/Makefile index 598d28d5beb..9b5ffb960a3 100644 --- a/Makefile +++ b/Makefile @@ -248,6 +248,8 @@ ifeq ($(UNAME), Linux) LINUX := 1 else ifeq ($(UNAME), Darwin) OSX := 1 + OSX_MAJOR_VERSION := $(shell sw_vers -productVersion | cut -f 1 -d .) + OSX_MINOR_VERSION := $(shell sw_vers -productVersion | cut -f 2 -d .) endif # Linux @@ -277,6 +279,14 @@ ifeq ($(OSX), 1) endif # clang throws this warning for cuda headers WARNINGS += -Wno-unneeded-internal-declaration + # 10.11 strips DYLD_* env vars so link CUDA (rpath is available on 10.5+) + OSX_10_OR_LATER := $(shell [ $(OSX_MAJOR_VERSION) -ge 10 ] && echo true) + OSX_10_5_OR_LATER := $(shell [ $(OSX_MINOR_VERSION) -ge 5 ] && echo true) + ifeq ($(OSX_10_OR_LATER),true) + ifeq ($(OSX_10_5_OR_LATER),true) + LDFLAGS += -Wl,-rpath,$(CUDA_LIB_DIR) + endif + endif endif # gtest needs to use its own tuple to not conflict with clang COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1 From 29bb23fc92c5b71c0bf8af0b9e580015da9aedda Mon Sep 17 00:00:00 2001 From: shai Date: Tue, 23 Feb 2016 10:42:54 +0200 Subject: [PATCH 402/446] removing all references to Blob.num property (that assumes Blob is 4D). Replacing it with accessing Blob.shape[0] - for Blobs with num_axes() != 4 --- python/caffe/pycaffe.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py index 3054110771c..5020ecedb10 100644 --- a/python/caffe/pycaffe.py +++ b/python/caffe/pycaffe.py @@ -98,7 +98,7 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs): # Set input according to defined shapes and make arrays single and # C-contiguous as Caffe expects. for in_, blob in kwargs.iteritems(): - if blob.shape[0] != self.blobs[in_].num: + if blob.shape[0] != self.blobs[in_].shape[0]: raise Exception('Input is not batch sized') self.blobs[in_].data[...] = blob @@ -146,7 +146,7 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs): # Set top diffs according to defined shapes and make arrays single and # C-contiguous as Caffe expects. for top, diff in kwargs.iteritems(): - if diff.shape[0] != self.blobs[top].num: + if diff.shape[0] != self.blobs[top].shape[0]: raise Exception('Diff is not batch sized') self.blobs[top].diff[...] = diff @@ -257,7 +257,7 @@ def _Net_batch(self, blobs): batch: {blob name: list of blobs} dict for a single batch. """ num = len(blobs.itervalues().next()) - batch_size = self.blobs.itervalues().next().num + batch_size = self.blobs.itervalues().next().shape[0] remainder = num % batch_size num_batches = num / batch_size From bd6b03f15ee7d299b9106759aa3f01f18d79ced8 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Tue, 23 Feb 2016 23:34:46 -0800 Subject: [PATCH 403/446] [example] improve classification notebook - add subheadings and list steps - edit text, add comments, and try to make the code more understandable - add new section for summary and encouragement to try your own image --- examples/00-classification.ipynb | 13031 +---------------------------- 1 file changed, 312 insertions(+), 12719 deletions(-) diff --git a/examples/00-classification.ipynb b/examples/00-classification.ipynb index 89b7dd34f0e..1950f08f638 100644 --- a/examples/00-classification.ipynb +++ b/examples/00-classification.ipynb @@ -4,54 +4,72 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Instant Recognition with Caffe\n", + "# Classification: Instant Recognition with Caffe\n", "\n", - "In this example we'll classify an image with the bundled CaffeNet model based on the network architecture of Krizhevsky et al. for ImageNet. We'll compare CPU and GPU operation then reach into the model to inspect features and the output.\n", + "In this example we'll classify an image with the bundled CaffeNet model (which is based on the network architecture of Krizhevsky et al. for ImageNet).\n", "\n", - "(These feature visualizations follow the DeCAF visualizations originally by Yangqing Jia.)" + "We'll compare CPU and GPU modes and then dig into the model to inspect features and the output." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched." + "### 1. Setup\n", + "\n", + "* First, set up Python, `numpy`, and `matplotlib`." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ + "# set up Python environment: numpy for numerical routines, and matplotlib for plotting\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "# display plots in this notebook\n", "%matplotlib inline\n", "\n", - "# Make sure that caffe is on the python path:\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "# set display defaults\n", + "plt.rcParams['figure.figsize'] = (10, 10) # large images\n", + "plt.rcParams['image.interpolation'] = 'nearest' # don't interpolate: show square pixels\n", + "plt.rcParams['image.cmap'] = 'gray' # use grayscale output rather than a (potentially misleading) color heatmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Load `caffe`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# The caffe module needs to be on the Python path;\n", + "# we'll add it here explicitly.\n", "import sys\n", + "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", "sys.path.insert(0, caffe_root + 'python')\n", "\n", "import caffe\n", - "\n", - "plt.rcParams['figure.figsize'] = (10, 10)\n", - "plt.rcParams['image.interpolation'] = 'nearest'\n", - "plt.rcParams['image.cmap'] = 'gray'\n", - "\n", - "import os\n", - "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", - " print(\"Downloading pre-trained CaffeNet model...\")\n", - " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" + "# If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing." + "* If needed, download the reference model (\"CaffeNet\", a variant of AlexNet)." ] }, { @@ -60,50 +78,65 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CaffeNet found.\n" + ] + } + ], "source": [ - "caffe.set_mode_cpu()\n", - "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n", - " caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n", - " caffe.TEST)\n", - "\n", - "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n", - "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n", - "transformer.set_transpose('data', (2,0,1))\n", - "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n", - "transformer.set_raw_scale('data', 255) # the reference model operates on images in [0,255] range instead of [0,1]\n", - "transformer.set_channel_swap('data', (2,1,0)) # the reference model has channels in BGR order instead of RGB" + "import os\n", + "if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", + " print 'CaffeNet found.'\n", + "else:\n", + " print 'Downloading pre-trained CaffeNet model...'\n", + " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's start with a simple classification. We'll set a batch of 50 to demonstrate batch processing, even though we'll only be classifying one image. (Note that the batch size can also be changed on-the-fly.)" + "### 2. Load net and set up input preprocessing\n", + "\n", + "* Set Caffe to CPU mode and load the net from disk." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# set net to batch size of 50\n", - "net.blobs['data'].reshape(50,3,227,227)" + "caffe.set_mode_cpu()\n", + "\n", + "model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'\n", + "model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", + "\n", + "net = caffe.Net(model_def, # defines the structure of the model\n", + " model_weights, # contains the trained weights\n", + " caffe.TEST) # use test mode (e.g., don't perform dropout)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Feed in the image (with some preprocessing) and classify with a forward pass." + "* Set up input preprocessing. (We'll use Caffe's `caffe.io.Transformer` to do this, but this step is independent of other parts of Caffe, so any custom preprocessing code may be used).\n", + "\n", + " Our default CaffeNet is configured to take images in BGR format. Values are expected to start in the range [0, 255] and then have the mean ImageNet pixel value subtracted from them. In addition, the channel dimension is expected as the first (_outermost_) dimension.\n", + " \n", + " As matplotlib will load images with values in the range [0, 1] in RGB format with the channel as the _innermost_ dimension, we are arranging for the needed transformations here." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -112,21 +145,54 @@ "name": "stdout", "output_type": "stream", "text": [ - "Predicted class is #281.\n" + "mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]\n" ] } ], "source": [ - "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n", - "out = net.forward()\n", - "print(\"Predicted class is #{}.\".format(out['prob'][0].argmax()))" + "# load the mean ImageNet image (as distributed with Caffe) for subtraction\n", + "mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')\n", + "mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values\n", + "print 'mean-subtracted values:', zip('BGR', mu)\n", + "\n", + "# create transformer for the input called 'data'\n", + "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n", + "\n", + "transformer.set_transpose('data', (2,0,1)) # move image channels to outermost dimension\n", + "transformer.set_mean('data', mu) # subtract the dataset-mean value in each channel\n", + "transformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]\n", + "transformer.set_channel_swap('data', (2,1,0)) # swap channels from RGB to BGR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. CPU classification\n", + "\n", + "* Now we're ready to perform classification. Even though we'll only classify one image, we'll set a batch size of 50 to demonstrate batching." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set the size of the input (we can skip this if we're happy\n", + "# with the default; we can also change it later, e.g., for different batch sizes)\n", + "net.blobs['data'].reshape(50, # batch size\n", + " 3, # 3-channel (BGR) images\n", + " 227, 227) # image size is 227x227" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "What did the input look like?" + "* Load an image (that comes with Caffe) and perform the preprocessing we've set up." ] }, { @@ -139,7 +205,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -148,2843 +214,9 @@ }, { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - 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vAqVQbxvPBEJwHEd8Z1W22xNju7F/2Pn4crC/TAKKBefM4juKyeIIujtFkh+lMaFlclzN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w6H4LmXKSzzup33Bz5+8wlhecbNmlhypNnYsffcQww19/4aGX0xC9tmSOwIiMA6PXHcbHOgTy5Z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tOZ2GOkEfd/eMIXJ5/VRdu52rHkQSIIUR61t9XmNahA8ixFnJxmQfsrLSb3DM2bPJk4068z08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oxBRRmbs00dNMLX3vo6437HNKpj8/n9Gp9DwfQbQjhi24YwjEt763B9y7t/8SOeP7vGyj0C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h6kLU2FmzlEsB8fNYTiQhRjENYmNFVkwA1AxYSvG6UBvZmxdENBNq6mK5MI8hjHGUMmj3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\nmqAYfsg3sKhv50hcYl4QBQuCKPZGII9KmRWZQdvzQqRSHcb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P2RhGixNUQEtBnCfPplcM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6nVoYasNuXKPTAT83XmLl2g4sxfaMNRiD45o7Fyjrz942Zr4G1iVhJSqMmfOe6xnm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\nuVwu3OdlH5m9PP/E8/MLzjn+7d/+jZTrTjAOIfDy8sLnz59JqfD6+mdaH4e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caPfQS7zDQ01LG6qKyqDo+sa6G0xJdvfsKLu0wqAqIFQfAJ2qxso9ZoJj53D5hBHE4malsx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WLbkfnv6erUbMyx+5vW7kMlCo7Jd4zZUpZEaj3UxrLJPXQmPJEapFWiOnim35wB+kIogN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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2992,14 +224,16 @@ } ], "source": [ - "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))" + "image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')\n", + "transformed_image = transformer.preprocess('data', image)\n", + "plt.imshow(image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Adorable, but was our classification correct?" + "* Adorable! Let's classify it!" ] }, { @@ -3013,31 +247,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n", - " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n", - " 'n02127052 lynx, catamount']\n" + "predicted class is: 281\n" ] } ], "source": [ - "# load labels\n", - "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", - "try:\n", - " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", - "except:\n", - " !../data/ilsvrc12/get_ilsvrc_aux.sh\n", - " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", + "# copy the image data into the memory allocated for the net\n", + "net.blobs['data'].data[...] = transformed_image\n", + "\n", + "### perform classification\n", + "output = net.forward()\n", + "\n", + "output_prob = output['prob'][0] # the output probability vector for the first image in the batch\n", "\n", - "# sort top k predictions from softmax output\n", - "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n", - "print labels[top_k]" + "print 'predicted class is:', output_prob.argmax()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Indeed! But how long did it take?" + "* The net gives us a vector of probabilities; the most probable class was the 281st one. But is that correct? Let's check the ImageNet labels..." ] }, { @@ -3051,26 +281,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "1 loops, best of 3: 7.14 s per loop\n" + "output label: n02123045 tabby, tabby cat\n" ] } ], "source": [ - "# CPU mode\n", - "net.forward() # call once for allocation\n", - "%timeit net.forward()" + "# load ImageNet labels\n", + "labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", + "if not os.path.exists(labels_file):\n", + " !../data/ilsvrc12/get_ilsvrc_aux.sh\n", + " \n", + "labels = np.loadtxt(labels_file, str, delimiter='\\t')\n", + "\n", + "print 'output label:', labels[output_prob.argmax()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "That's a while, even for a batch size of 50 images. Let's switch to GPU mode." + "* \"Tabby cat\" is correct! But let's also look at other top (but less confident predictions)." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -3079,8706 +314,244 @@ "name": "stdout", "output_type": "stream", "text": [ - "10 loops, best of 3: 90.9 ms per loop\n" + "probabilities and labels:\n" ] - } - ], - "source": [ - "# GPU mode\n", - "caffe.set_device(0)\n", - "caffe.set_mode_gpu()\n", - "net.forward() # call once for allocation\n", - "%timeit net.forward()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Much better. Now let's look at the net in more detail.\n", - "\n", - "First, the layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('data', (50, 3, 227, 227)),\n", - " ('conv1', (50, 96, 55, 55)),\n", - " ('pool1', (50, 96, 27, 27)),\n", - " ('norm1', (50, 96, 27, 27)),\n", - " ('conv2', (50, 256, 27, 27)),\n", - " ('pool2', (50, 256, 13, 13)),\n", - " ('norm2', (50, 256, 13, 13)),\n", - " ('conv3', (50, 384, 13, 13)),\n", - " ('conv4', (50, 384, 13, 13)),\n", - " ('conv5', (50, 256, 13, 13)),\n", - " ('pool5', (50, 256, 6, 6)),\n", - " ('fc6', (50, 4096)),\n", - " ('fc7', (50, 4096)),\n", - " ('fc8', (50, 1000)),\n", - " ('prob', (50, 1000))]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(k, v.data.shape) for k, v in net.blobs.items()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ + }, { "data": { "text/plain": [ - "[('conv1', (96, 3, 11, 11)),\n", - " ('conv2', (256, 48, 5, 5)),\n", - " ('conv3', (384, 256, 3, 3)),\n", - " ('conv4', (384, 192, 3, 3)),\n", - " ('conv5', (256, 192, 3, 3)),\n", - " ('fc6', (4096, 9216)),\n", - " ('fc7', (4096, 4096)),\n", - " ('fc8', (1000, 4096))]" + "[(0.31243637, 'n02123045 tabby, tabby cat'),\n", + " (0.2379719, 'n02123159 tiger cat'),\n", + " (0.12387239, 'n02124075 Egyptian cat'),\n", + " (0.10075711, 'n02119022 red fox, Vulpes vulpes'),\n", + " (0.070957087, 'n02127052 lynx, catamount')]" ] }, - "execution_count": 26, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "[(k, v[0].data.shape) for k, v in net.params.items()]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Helper functions for visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# take an array of shape (n, height, width) or (n, height, width, channels)\n", - "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n", - "def vis_square(data, padsize=1, padval=0):\n", - " data -= data.min()\n", - " data /= data.max()\n", - " \n", - " # force the number of filters to be square\n", - " n = int(np.ceil(np.sqrt(data.shape[0])))\n", - " padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n", - " data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n", - " \n", - " # tile the filters into an image\n", - " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n", - " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n", - " \n", - " plt.imshow(data)" + "# sort top five predictions from softmax output\n", + "top_inds = output_prob.argsort()[::-1][:5] # reverse sort and take five largest items\n", + "\n", + "print 'probabilities and labels:'\n", + "zip(output_prob[top_inds], labels[top_inds])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The input image" + "* We see that less confident predictions are sensible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The first layer filters, `conv1`" + "### 4. Switching to GPU mode\n", + "\n", + "* Let's see how long classification took, and compare it to GPU mode." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlEAAAJNCAYAAAARaCA+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXm0Ldld3/er6Qz33PneN8/9ul/PaqEJi5aEwBIoBBOS\n", - "GDteduKV2Am2sY0hSN0tqVFLtFoSYBniZHklXthh4diEtczCEASWkDViqSWhFlLP4+s3D/fd8dwz\n", - "1amq/NGNrf37ft+6h1LjK8z380+/vXufOnWqdu1T99Rnf3dUVZUJIYQQQog/HvFu74AQQgghxJ9G\n", - "dBMlhBBCCFED3UQJIYQQQtRAN1FCCCGEEDXQTZQQQgghRA10EyWEEEIIUYNX/CYqiqJ3RFH0ZBRF\n", - "z0RRdM8rvX0hhBBCiG8HolcyJyqKosTMnjKzt5nZeTP7spn9laqqnnjF3kQIIYQQ4tuAV/qXqDeY\n", - "2bNVVZ2uqio3s181s//qFX4PIYQQQohdJ32Ft3fIzM5+U/mcmX3nNzeIokgR6UIIIYT4U0NVVRGr\n", - "f6Vvoia6QXr3ve80M7PPfu737S1vvtuiRiv4/1GS4YbjBOuSsC7Ph9BmPCR1/UFYUWAb9hNdmob7\n", - "+Q8/+g+hzfvuvxdf6I7KYLsHTTa7XdzPsgjKUYqnq9FoBuVmqwVt4m86dr//+d+3u990tzWycFuR\n", - 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Let's switch to GPU mode." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvVusXed5HTrmuu/L2jdubpISSVEUTd0S3a0okpKYseoo\n", - "TRs7TWvFaRPXSYAA7UFeCuPkoUBjFMhL0gYI0IeDUyNoXMM5QgPLRhJfEje2oxhSoosp2TIlUVdS\n", - "JCVyk/u+122vdR6Wx7fH/Oe35lp7ywrd4B8vm1xrrjn/+/z/8X3f+JJer4eIiIiIiIiIiIido3C1\n", - "CxARERERERER8X8q4kYqIiIiIiIiImKXiBupiIiIiIiIiIhdIm6kIiIiIiIiIiJ2ibiRioiIiIiI\n", - "iIjYJeJGKiIiIiIiIiJil3hPNlJJkjycJMmpJEleTpLk/34vnhERERERERERcbWR/KB1pJIkKQJ4\n", - "EcBDAN4C8PcAPtbr9b73A31QRERERERERMRVxnvBSN0L4HSv13u91+u1AfwJgA+/B8+JiIiIiIiI\n", - "iLiqeC82UtcCOCP/P/v9zyIiIiIiIiIi/lGh9B7cc6itMEmSmJcmIiIiIiIi4v8Y9Hq9xPv8vdhI\n", - "vQXgkPz/EPqs1K5Rr9exvr4OAOh2u/b52NgYAKBYLAIA2u02ms3mju599OhRAMCZM2fQbrcBAEmS\n", - "2F99HlEoFDJlIcrlsl3jlSX8bZIk8PzUKpVK6rpOp5P7XKJYLGJra8v+z3uzTh4KhULm3nnP4HMA\n", - 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}, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 70.2 ms per loop\n" + ] } ], "source": [ - "feat = net.blobs['conv1'].data[0, :36]\n", - "vis_square(feat, padval=1)" + "caffe.set_device(0) # if we have multiple GPUs, pick the first one\n", + "caffe.set_mode_gpu()\n", + "net.forward() # run once before timing to set up memory\n", + "%timeit net.forward()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The second layer filters, `conv2`\n", - "\n", - "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmsbldxv1nvOb7zgCc8GwMGMxkIMyKgRCgk/+RDuiMl\n", - "6UQBOg4eZQYj7GADRg42wthGcMEoHkCOlaB0R1GCWpGSNJmDiIAEOYAx4BHPxsb25frOZ+gPl2fv\n", - "9T57132dg92n8+/6fTnnPWe9a9eqVWvtVbVqmCwvL0ehUCgUCoVC4b+OudUmoFAoFAqFQuG/K+og\n", - "VSgUCoVCobBC1EGqUCgUCoVCYYWog1ShUCgUCoXCClEHqUKhUCgUCoUVog5ShUKhUCgUCivE03KQ\n", - "mkwm/2MymXx3MpncOplM3v90PKNQKBQKhUJhtTF5qvNITSaT+Yj4XkT8QkTcFxFfj4jfXl5evuUp\n", - "fVChUCgUCoXCKuPpsEi9NiJuW15evmt5eXl/RPwfEfG/PA3PKRQKhUKhUFhVPB0HqeMj4p7m870/\n", - "+VuhUCgUCoXC/1Q45Gnoc+Zd4WQyqbo0hUKhUCgU/ttgeXl5Mvb3p+MgdV9EnNh8PjEOWKWmcNxx\n", - "x8Wzn/3siIg44YQT4sQTT4w9e/ZERMTatWsjIuKTn/xkRESceeaZERGxtLQUERGLi4tx6KGHRkTE\n", - 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Examining intermediate output\n", + "\n", + "* A net is not just a black box; let's take a look at some of the parameters and intermediate activations.\n", + "\n", + "First we'll see how to read out the structure of the net in terms of activation and parameter shapes.\n", + "\n", + "* For each layer, let's look at the activation shapes, which typically have the form `(batch_size, channel_dim, height, width)`.\n", + "\n", + " The activations are exposed as an `OrderedDict`, `net.blobs`." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlEAAAJNCAYAAAARaCA+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3VuMHdd97/l/SSSbZLN5b7LJ5k0kRTISJdMWJcuyjizL\n", - "ythGjNgxjBMYuSGTGQxwkMyDHWSceZjIQBBMBnCekpMXjwPDmOOZIIkNx4EhObElHkqmJFIiJd7v\n", - "tya7m5fmXbyIrHkQe+lXS13F6rXrtnd/P4Dhf3HXrqpdu3Z1af3X+q8ojmMDAADA+NxX9wEAAAC0\n", - "Ix6iAAAAAvAQBQAAEICHKAAAgAA8RAEAAATgIQoAACBA4Q9RURR9IYqivVEUHYii6H8revsAAABN\n", - 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"feat = net.blobs['conv2'].data[0, :36]\n", - "vis_square(feat, padval=1)" + "# for each layer, show the output shape\n", + "for layer_name, blob in net.blobs.iteritems():\n", + " print layer_name + '\\t' + str(blob.data.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The third layer output, `conv3` (rectified, all 384 channels)" + "* Now look at the parameter shapes. The parameters are exposed as another `OrderedDict`, `net.params`. We need to index the resulting values with either `[0]` for weights or `[1]` for biases.\n", + "\n", + " The param shapes typically have the form `(output_channels, input_channels, filter_height, filter_width)` (for the weights) and the 1-dimensional shape `(output_channels,)` (for the biases)." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnWuwZVV1tt+tCGLQGENEuTaXbqDtbrq5NgEEFFGQaKRi\n", - "vlgVKxgvMWpiiKl8ookcYzTBsqiUGiSJxKh8CSZGUVNFQBAQL9BCS9PQdNMgGlBjLhoTNcZL9veD\n", - "fs46+z1n9Jxr7bXPPg3j+bPP3mfvdZlzzLnWeNcYYw6Gw6GSJEmSJEmS9jxq2geQJEmSJEmyq5I3\n", - "UkmSJEmSJB3JG6kkSZIkSZKO5I1UkiRJkiRJR/JGKkmSJEmSpCN5I5UkSZIkSdKRidxIDQaD5wwG\n", - "g62DwWD7YDD4v5PYR5IkSZIkybQZ9F1HajAYPFrSNklnSPqqpC9IetFwOLy71x0lSZIkSZJMmUko\n", - 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11, 11) (96,)\n", + "conv2\t(256, 48, 5, 5) (256,)\n", + "conv3\t(384, 256, 3, 3) (384,)\n", + "conv4\t(384, 192, 3, 3) (384,)\n", + "conv5\t(256, 192, 3, 3) (256,)\n", + "fc6\t(4096, 9216) (4096,)\n", + "fc7\t(4096, 4096) (4096,)\n", + "fc8\t(1000, 4096) (1000,)\n" + ] } ], "source": [ - "feat = net.blobs['conv3'].data[0]\n", - "vis_square(feat, padval=0.5)" + "for layer_name, param in net.params.iteritems():\n", + " print layer_name + '\\t' + str(param[0].data.shape), str(param[1].data.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The fourth layer output, `conv4` (rectified, all 384 channels)" + "* Since we're dealing with four-dimensional data here, we'll define a helper function for visualizing sets of rectangular heatmaps." ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 15, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": [ - 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- ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "feat = net.blobs['conv4'].data[0]\n", - "vis_square(feat, padval=0.5)" + "def vis_square(data):\n", + " \"\"\"Take an array of shape (n, height, width) or (n, height, width, 3)\n", + " and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n", + " \n", + " # normalize data for display\n", + " data = (data - data.min()) / (data.max() - data.min())\n", + " \n", + " # force the number of filters to be square\n", + " n = int(np.ceil(np.sqrt(data.shape[0])))\n", + " padding = (((0, n ** 2 - data.shape[0]),\n", + " (0, 1), (0, 1)) # add some space between filters\n", + " + ((0, 0),) * (data.ndim - 3)) # don't pad the last dimension (if there is one)\n", + " data = np.pad(data, padding, mode='constant', constant_values=1) # pad with ones (white)\n", + " \n", + " # tile the filters into an image\n", + " data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n", + " data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n", + " \n", + " plt.imshow(data); plt.axis('off')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The fifth layer output, `conv5` (rectified, all 256 channels)" + "* First we'll look at the first layer filters, `conv1`" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlIAAAJOCAYAAAB8y+mTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmMXdd17/nfEUWRLM7FKhZZHDUPLcu2HDg2XgBbL0Hw\n", - "AmR4iYOkAwSdP/JHA+5uJ51AkB14uIITDw3HaSCIjaT7OfFrdAYjhuP8Y9hOolY8QLFlJ9ZEDTQH\n", - "cagqVpHFSRQlUTr9h7j2XZd1eId9z3jv9wMYOt5VrLvrDqfOWWvttZM0TQUAAIDB3VD1BAAAAJqK\n", - "CykAAIBIXEgBAABE4kIKAAAgEhdSAAAAkbiQAgAAiFTIhVSSJP8lSZJnkyR5IUmSh4p4DAAAgKol\n", - 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hhBAN0EuUEEIIIUQDjt6JokGafol49vvy4e971FhiHpOvYm1oSOehuxD6Pfy9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\nAau7yDDmZ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -11786,334 +559,30 @@ } ], "source": [ - "feat = net.blobs['conv5'].data[0]\n", - "vis_square(feat, padval=0.5)" + "# the parameters are a list of [weights, biases]\n", + "filters = net.params['conv1'][0].data\n", + "vis_square(filters.transpose(0, 2, 3, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The fifth layer after pooling, `pool5`" + "* The first layer output, `conv1` (rectified responses of the filters above, first 36 only)" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlEAAAJMCAYAAADaNPObAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmMXfd14PlzWPvG2lhciquojSIVWZsj27GgcqA4GseQ\n", - "nX9sB4ghpNMBgo67Y4+nZSuDNKQ/0tM20OMMMsgf44kNJZioo0k7XgYtWEuz5FYUWZJlmSEliqTF\n", - "EllkVZFVxdr3qt/8wZJC1u+U9OPv3nfvfa++H8CweHiXU/fe997hrXPPU+ecAAAA4NpsyjsBAACA\n", 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Z7nrH+wULyFbd0NdYl1dXV2PnrbVGW1lAGC4Zk0qlPEE6SUT1oNoLCAgICAgI\nCNgkfmkYKcugmRkRK1Ipn2hE7OTAY2Nj+qx1wgGs35hl4OsceVYkSn+ifMNy3rnMRSqVUomX73fj\nZWzGOHgYS2IZ5yWNv+PWk/+N8rPbK04T1WrVU/Nwu4CKLxQK6rLMTJ978rZCU2zfvt2LuFytVvWU\ny263blsySzGKKXHr3el0PCN3C8Vi0VMVssE9J3BGf+BUls/nzRxvYDM4DgrHnsJvaCuwFdYJkN2L\noTKy3N+LxaLHkE1OTpqMFNqSx3bcCQ9tUSqVIvkDRfqn92HxowA3+j/PFawTKysrWi7X7d4FxiWz\n2UnBkbNFoiw1yrW0tOS9c2xsTNsfY/fUqVOmGgURstFP3C+Ih7R3716PkWJGMul434yxc5whMMq0\na9cubRf8tm/fPl2vcS2VSul1RO3ft29fJDq4+130X6lUijB+Iv2x4o7F9fV1ZalYZYzvQoX6/PPP\ne8wqq1LRtjCYFxmsXZxFgXNMuvlka7Waso/1et1UL74ViHPMGAa0L483rEUwvp+fn48kjcZzeBZt\nOj4+rn3NTLyrmq5UKl68Pt7jhuUAxHfdvS2fz3t9yKpdV/MUh8BIBQQEBAQEBARsEr80jBTA+fIs\nXTtLhxzRXCRqLwEJkzPG8+nTPRHkcjn9NyRqzr/E5XOZCw6cxidwN3o2fw+wDOT4G1ZUdL7fPXmn\n02llInCacWEZ5yU1MoyLHstwJX0+cQG7du1SxuWll14SkeEG8i4DUq/Xtb3QzsxGgc2oVCpqcMqw\n+iGOjWOjb5wYLdsChstITkxMeHZ1/BtOaPl8XvsVfyuVip7QcLLdu3evnnjB1HQ6Hdm1a5eI+DnD\nRGwmChHi0+m0ZzPiMhm4D7ZWaAsL7LLNp2zr5Mh2UCJRuzjcnyRIp+sezwA7wYFN3VxgLtCvBw4c\nEBGRJ554Qq+xrZIF15bqk5/8pHz1q1/17nNZ1GuuuUaeffZZERn0P9sRMUuI3HgIGGrZDlos2igj\newbaDeuK5SDCYKN0tpEUsW39lpaWNMwE6nb69GmPLeQ2eOyxx0RE5NChQ9qH6HOOlI15fu2110bs\nCEX67YNnbrrpJhHpOwmg3cBgMfvNrLDrJLR9+3aP9WLGkZ1sMOdQ30KhoH1nRdKuVquJbdSApFoG\nXseGBbBGuQH3nWwD+8Mf/lBE+nZs7n28zqKezDShb6w1Iolt4jBYAZJbrZbJjo8a3xa2XJCyPBXi\n0rygM0tL+WfKAAAgAElEQVSlkk7KOMPxdDqtjYW/lifP+Pi4TmYIUHNzc5Gov3gfqwhEhhsBcioC\nt74WXeiqDBmsFuCFyv2t1+vpgjFKDRKHUca4nKTZHYyFQsETmlqtljchz58/r0ajTJlbgPEtnr10\n6ZK2E3+LDRP5L4OTWnLkXSsJtuvV0W63tb9HqVpdFcHExISWFc+Wy2X9DeN3ZmZGn8EYq1arXl0q\nlYpuMthg2EgXqh2RgeEs7m+1WiqA3nrrrSLS7w8sZM8995xXH7TB+vq6eldBAHITwopEY1rh/vn5\nea07VAGdTseL/8YG/NiErXm7b9++yGKNceKmMBGx0xWNUpcgajbG6cLCggpQbuRtBs8BtNvnPvc5\nU5ACIKx96lOfki996UsiMhjbHC8JC/3U1JSq9r7yla9omVzngFwupwmZ+VCRxAsvn89rEuQ4lezs\n7Kx+11rbRkWxR3/de++9IiLyL//yL55wy0I92uOFF17QwwQEUvSVyOCQ8MMf/lAj/iPBMwOC6Gc/\n+1ntI4yXqakpFXi4TNgvIDwNiwmG+cjrOt4HIXV5edkTrnK5nPb11VdfrYfNpEgaE8oyQWGHmyQq\nv3q97sXLOnv2rJm02B13lkkAe0Lz+ok1kL0sk9ZzozHvcBgPqr2AgICAgICAgP8PsSWMFBuHW4bl\n7km0Vqt5zBXn9sFpx1Ixdbtdk1oFIO0uLi6qlHvHHXeISFS1x/n/cNJnFsiS2vE+jpHhqiP5FADW\nYGFhwZPam82mR63PzMxoWTgK7ygD2jhYRv9ALpeLRD7HX87Px2VhsIE3+mlqaioSw0jEZh04+eXN\nN98sIv2TNd4HdoGZDfd5/obVV9bY4Zgi1jOjTmq4jn4rFot6WkMb8djGWOTI1ujfubk5L5bWuXPn\ntF0wVyYmJpRFZUbSYjnvueceEZHIid5S5aF9b7zxRhHpszQoA8pksbKcHBwsATPJGONnz57VfoNa\nUsTvE465xLGlmIEB64A8c9xHPB8wji11CSJfMyuHfGqNRkPLGKdq4DmAMp88eTI2H90f/dEfiYjI\nnXfe6V2z1A1XXHGFsh1gmkqlkveNpaUljWuENrjqqquUwYG6ynLgaLVaWs+43G6Li4seo4J3ikTH\nB36z2uC///u/RaSvtnaN9Rloj7GxMWUEOasEygqG+/DhwxrlHIwUr2/XXnutiIjcd999+huPA4t9\ndpmUhYUFb43mOuJ7e/bskTNnzohIVJuCvrGi3p85c0bZRwu8P7LjiVsGvj/OyHzU2uYmtOe1jYH1\nhPcI7P+sJgXrifWO13KsA5ylJGn4Ei5nktAJfM9GVImBkQoICAgICAgI2CS2hJHiU7Sbs4ttCzho\nnWsPJSJeJHLLzoWfgVRcKBQ8piGfz6t9CJgotktit38r75L7GzMrnOsNJ2DrBM8na3yX2TScciBl\nX7582WOuer2enmysCNkcWsHKHxd3EhnmJu3m4rKQTqc9pspiPzhSNe7fuXOn2iGwsW9cfiYOcppE\nx59Kpcy25HLhvqTBNy2nCYxj9EGtVlNXd/wdHx/XsYjT6czMjLYvxollA9fr9fRZDvCHtkaZpqen\n9bQO9/GlpSXTMP+3f/u3RWTQpo8++qj2EeycGDiV79ixQ+uL8d5qtfQbYCb4JItxuLKy4hmO5/N5\nz4HDPTWiPCgDs1DsyIDycDuD0bAiGfNJFYyfZfweZ5D9p3/6p14UbgbK5xosi4jccsst8vjjj0d+\nW1lZ0b7j8CBgXrAWnT17Vh1okDNw3759HoPQarXMwMewXwLjZLHGHBWf34F2Bpty6tQp/c1iA8Eu\n1Wo1nY9oF4thZ6N5rJm1Wk0++tGPiojIN77xDRERefLJJ7V8+G6hUNA5BOP+u+66Sx566CEREXXd\n379/v7JsXG+X4SiXy5GI5igL24Lhu25gT94LLWav2WzGMnS8xiRZ7/h+tnd132Hl6RPxtQ48FzAH\nDx8+rLkE0XfNZjPCgIr05yIb9ov0x6KrhRqWBYODQ6M+Vn5V9APqxizVRvLSWthSY/NareZN3Eaj\n4anieMLzNddDL51Om1Syi2HX3GSqzWbTC11frVZ18rIxHBYt3oxdgSCTyeiAw6ZZq9U8Q0wWJnkD\nx0KBa5ahN+4VsZPuWlSzyMYT17J6Nm4QcjwvLBqYaO12W9sI6iXLaJkpdN44EUuGjTBRp7h24Xpz\nYkp3XORyuUTpb4Z5OAKs0nTbqlAoeLGlLl26pIIP3reyshKrqsXYXV5eNgVUF91u1zPsx+LOOHDg\ngI7Pp59+Wn+HwAA1suXNymkeIDytr6/r+ywDZGxsKysrKtDgL7+PDf5ZgIfRMPcl2t+qHzbLHTt2\n6AZmtR8ElWKx6HnZMTCPtm3bFknAC0DwsTz+/vqv/1pERN71rnd577WEu3q9rt5rWBOWlpZUTcVz\nCe2P96yvr3uCzKg1gJONW96arrqXD4aYo7fffrsKGbz5Yg5gTExPT+tYgBEzmzJAVQwVrsigj9bW\n1uSpp54SkajKxvXam5yc9ATfhx56SI4cOSIiA8/AdDodiXwuEu1TGIwvLi6q0wzq6NZTpD8GoCZH\nH/EeBwGKo6Jz/TYKFjasccTmIxbYIUfETrvGsbGwPyKpswusqdhb9+3bZ5ojWGY/7hoel1bNhXXw\ncdXVlne55UXpIqj2AgICAgICAgI2idRGXQLfCmSz2Z5IX5qMi3URF3uE1XhsnOeGFximhnHz+HAE\nbEjU7olApM+m4ATMEXddA8FhMTlcF9Fh6kgXhUJB62YxasPy4sWFVBjlvo8Tg3vScGGdVNzypFIp\nPf1xEk/rnTjR4rsLCwt6yoBR4jCGBn2Islh1KxaL3vVsNuuxT8yycL3YKFwk2i74jRlJsDZsNM/5\nvPBdNrTk74n0GSCXkRQZjN+kiaMRHuDgwYNKsbNRNfochrbnz5833ZOhPkJfuWon1HejTCfmfDqd\n3lCsI5H+WAOrxIas6AeEYLBUZ/l8XuuEZ5n9BOswPT2t+fficOONN6paw1q/Pv7xj4uIyP333+9d\nm56eVqN5mBlYhq979+7VfH6YC8ePH9d6Yi68+uqraiCP8VypVHR+wfB5WJwud36n02llkMGeZLPZ\nSEw+3O+u6+Pj49rOcSE29uzZ44WwqFQq3trLOQMBDgHCKkUwddAGPPzww/obR+B2tRqZTEbnMIcU\nAd7//veLiMj3v/99/S1OhSsiHttaKBS0fdGX27Zt0z7hOlrZJd6Magrv4TLE5YnlXLVx3y2VSqq5\n4HokyV03Ctx+KDMz4UmjtccZ5jObSePY9LgIjFRAQEBAQEBAwCaxJTZSnN/Mzd/E0h9nJ3clS2Y/\nrMCdHD3VMoK0Tl8uM7SysqK2DLifT8lsD+XqVfm0wGUHE8V6dYsZcn8bxkK40j23lSWNb0Rat5gv\nNgbEO9x2y+fzypAw8+OeqtmgEGzV6uqqF1CQgfbnd8HmyuqHbDbrGS3X63Uvcrh1Sup2u9rHbDhu\nBYC1XJbdLOJszMvMFcps9QfKvLq6arKKloE/6oR2WVpaUiYUJ2uOHA6k02m1D0H53FxZIv1xjzaN\niwK8UTZKxGZvLCSN2sz3wh7JyrvVbreVaRrmLi7Sb0urr637MaatOr3tbW8TETvo744dO+S9732v\niNjZFYBKpaJlhv3X0aNH9TfM3+npaWVCUZYLFy6o4TkYmmGMlNuPVvt0Oh0v5ymHU+D1zn3fzp07\nldnigKpg1pghxHX8VqvVPDunyclJXSdgg3TgwAEdy7/zO78jIv1QG+74PnDggBfVf319PdaRBkzU\nsWPHNEwG2xW5IWPYsBy/sf0k28+xLa2LjbBQbqgYtjdEf3D/W/PLzXMnMujXSqWiewPs8XhtQPnz\n+byyxng3s8euYT6XmdvIDbLNdRzWLhwmB3/dscg2ZBthzrZEtZdKpWI/6i5U2WzWM9y2ym2pyTKZ\njLepM7DZ1Ot1bVRX/TYMHMsGZWU62ErpYhnQYQBaiSDZo8JNQyMSXaDcZ1gojYsB4z6D8qMfeBKg\nnu6g5H8PixxvbbpYKECZl8tl/QYWlGaz+aaitLtgahqw+oZVo+69eI+IPSE5+SWrAN34RZtJR2CB\nF0r3G5cvX/bGdqfTMVVnMIKFeuHMmTPewjQxMaEbGtRCo+aKBW5H60CAdmNVC8aildaCEzbjvnK5\n7Anwu3fv1sWehU7UwepzjgUWJ+gDs7OzKlhYSXWR2uV73/uedy2fz8uXv/xlERH5n//5HxEZxFdi\nHD58WNVjUGFdf/31Wr6f/OQneg19zWpNqPswB5977jlzXbU2N6j2IASmUimN9I3fKpWKF4NqYmJC\nxxbWrkajEYlyLxJVoWHMrq6uavmwGXOGA57TUK1hv1hbW9PvWp6mlpnA0aNHRWQQ3Z6fteYOG8gD\nO3bs0H2Cjf/dtaFWq3mq0ampKXVssDzI0+m0Jzyw40vSg0ySgwGXK5fLeUI3q1MBToLupsFiTE9P\nR5K4i/T7DfOQU3LhNzaedw/8nBg5LrZhUtMDxwA9qPYCAgICAgICAt5KbGn4AzYU41Ovy3o0m82I\noSP+uiwVu9NbUrmlQoMUbcWYyWazesqCwW06nfZcda2YJywBM9vDsaxE+qcoV6K22CA+LVguvVai\nUGah8D4+tbFa1X2m0+lEVKt41qWpLYPsTqejbYR+GKY2wDdwchkbG9N7mfLlthHpn1LxXZwmOWm1\nRVOzGg/vQX/xaYrVwy6Tx7mdmCJG+a0TPZcFZd4ME2U5X/B8cK/xeHcTPFvfB0MgMjAiX11d9Qxs\nDxw4oPci/o77vSSIi6nGiXY5PhAw7NSNeqFf9+7dGykjnnGTYxcKBWVmsNZwvVlt5MI62V66dGlo\nAm6RQaR0C81mU0NTgHlhIFZRvV7XtoHaMp1Oqws+3PfX19dN13msHa7BLaNQKGiePmbWXEZ3ZmZG\nmSisRdu3b1dGCu8eGxvT+crsjRsWhuNmcYYA1BeqwIMHDyrLxmWCkTfCJKyurnrrxd13361sk8U0\n4VqhUNDrvNa443ZxcVHuuusuERGNRXXhwoVIUnCR/toFZobXddfwvdfrmWEeAM4CAgxTa+HbvL4D\nvN8OyxfI9/F6gr+cVJmTQrtsMM8VPLu8vGyqDYG4XJD1et1bd3iN5nyX1j7Fhva4z1UbJlHxBUYq\nICAgICAgIGCT+KWxkUqqpwXYziVOb53JZEx30TgjXY7qC4DpKhaLKuXiHWwLwHAN9iyp2NIts9Qe\nl5vLyovnnpQsGynLvRdgO6eN5jUaBdeNetgJaLOw2lckGtQU37eMKZOEEigWi3qf1eccdgOn683Y\nd7nzoVQqqc0YmLpWqxWJ6i/S7z+cDPGXjZktJgfv3b17t56e0QbPPfecRlSGXcx1112nv7HdB9ga\n2Br9315bmEGErdTtt99u2hehrJgDjUZDn4ERNoJ7MsbGxjwj3VHjGH0zPT0dG8yT8b73vU9EBkwY\nB3i89957RaRvX+VGiT927Ji8/e1vF5FBTrlXX301YpwdByuUCRguN4CryKCPr7vuOmVSONen9Wyc\nowDbp1rPWqwnAvMiXEImk9E1GvOIQ2PArk9kwIBxpHt37nGAZNjKra+vx66LYPHOnTun85DZG3aQ\nEem3N1gRdk5BW46NjWlfo91yuZyXPcFypLAYU372rVjfed/hwNbuPpvP5/V7PMbwDJhTjqjOBvnW\nfujmKmVGiqOeu6GRUqlUrBMBG7lT+5k2Ului2uPo5BxFXCQay4IFEPzGaiYAgggbm1sUJhvkWQsG\n3skRy/E87rNUIryhsqrFLbMVH6harXpJhjudjlc+a4Lwe3B/vV43ow4DPJABLlecoXoqlfJUgNxf\no+JhuZM5qRCVzWbNUP5YjHiyYDJbxsgAL4xot2w2a6qYLI8/y7gRbc7XrPe5fdNsNr1+Za9HoNFo\n6PjEwtztdr1UQhMTE150dzbS5G9h04IA0W63dcOF6oFjSOG3p59+2hQOXZo8LrvAZjBMYHE9f0UG\nY+3cuXNmtgOUlQ8RmMfYLK05V61WPQeJUePYXQeSAEIcjwOo9PAe7gPUjSPCI17T2tqaqRqKKyvD\nzT5gCYO8BrLhNoQbFk7QplZmAk4b5KqUc7mcbrTs3IDncc0yExEZGNojndf58+dNJwm3P1m1FBe/\nioGycyxCOB+gLiKD/l1bW9PfeI9BW1pzydoPrN8sVTvvn5iv1lpkwXLgajQa3lppjSXLRIVjJFpk\nCM8BN2l5t9uNCKP8fRf4Xtw+xYfxjRzyg2ovICAgICAgIGCT2BJGCtIwx9MALKm42+16SXXT6bRH\nwTI9Gmegxol7LYNcKw4TMzVxRrUsqVv34XTH9+HUwTGLLHd619jYojCtNuV3W/GoRAZ9wobW7gnD\nShDJJyWLdrXicnBZ4toSzABHCWe1G04vlqqAVVju9W6368We4VMKR0d3DSO5fdEfzWbTPDFa/WAl\nwXbrzkwtwKcyVkuBCWA3b7cNmNbmdsR78Fur1dI2hTqQT6749zBVpWvQPoyRQt3cXJl8rdfrecyp\nRcNXKhXTkBrvefXVV7UdeP6gv8HKsUMA6jc9Pe2xIul0WkM/WFGuASsUi+U8MQwIYWC56qN8VntU\nq1VVK2EscALoUbDMJPCeuFxvr7/+uhklHGMAbEyz2YwN/YFxd8MNN3hOAtu2bVM2jFlB18h5bGzM\n0ySIDNYlvGNubs7LJsCsJ9gxLicz3RYOHz4sIv0kyag/5hkY3W3btqlzANp7ZmYmEu4HZcdcKpfL\n3p7GxtJoAx7bQDqd9nKt8loZt1eKiGc+YK1rvBcx3HXM0hDVarUIO4Vr7rrNawKvwW5uXpGoFgXv\nc81l+N2oW5xpSBwCIxUQEBAQEBAQsElsafgDDhvAgTbZyAtwbXO63a5n3Mbv4bxA1ukEiAsiid/5\n77BcPG49rKCPXAZmOPBuy3iZvwEpnPXrbviIWq1mMjRsE8QZ4AH3GYt94vrwqckNdVCpVCJGvO4z\n/JvL+HCZ40IElMtlLT+79rrtazFrrVbL6xtm57h8bnTyWq3mhb9gjDr5u6e2QqHghYYYZueA9sA3\n3IjYbpmZlQObwIEAwcbwnHLZpGFhK1ywPWFcG4yNjenp2bK1scZIHMvcaDRMWwZ8Y3FxMWKwD2DO\nIcwD3OlFBm7+sAlilEqlWGYG4NAuwEaM78EqMesFI2krzxhw+fJlZTkOHDggIn3XebArzFZZgUVR\nN2akYOfE4TEsgImCsT7nyuNyoixg6DiyOfD666/re+DYMD8/74UtsYJDtlqtWDsY9MOZM2ciAZnx\nPoxjsF68DmEsceBgXkvY+QLltMoCu0Ss+el0WuuBiO7z8/P6jYmJCZOlRll573Cda5iN4Wfdf3Om\nETbcRttwPbEG4v5h2gVrT8V8ZvtotrWKg9sGvB5j7LbbbU/DwoGKLaY8zvksziAd2FJBSsQvJMdx\nwGLYbrf1N25IV4jIZrNKIWORGNUxLBBYBuiumo899IZ5u7n1st5rxTvCwrGwsKDPo/xstGi9e5RX\nH9Dr9VQAwLNu2bg+jFwuF6uOQVmtKMHDooRzueLgemNVq1VPFTuMgnWvD1OnueCxaHlIAsVi0VPZ\nWeD4YGjHsbExL0VMNps1k26Piuov0u8jVqeK9McGNgxedLCgYQxyGyT1LgN48cK8tbwod+3apWXh\nb6Bclvcu96urPh5mEIr5z44bLPhik0R0b/bQi4v1VSqVEsUASyqAMlDPt73tbWYZMP9/8YtfiEh/\n3LnfuXjxoqqNYGy+vr6u7fbHf/zHIiLyn//5n6YgFaeu5Jh11rqKhOIQoKampkyVM77BqkAkX4Y6\nb2lpSYVcjPdrrrlGPfjgwbZjxw5PZcrJ6zl+FYRDK44gZ4jAeIEANzMzo2VmQ+oka269XtcxhvFX\nrVZ1M+cMAZiPcPDg+l66dEmN5K3v8ZoQF8eNCQE3nRo/y2sB5jPAB1HeQ+K+yx7xmLNYy0cZdfM3\nMI45zqKr7rMw6hvsaWi1wSgE1V5AQEBAQEBAwCax5YwU58wRiZ4mcNpKpVKeoXUul/Mo/3a7racs\npk5dOnh9fd100YxjTDh2ELuLonxu5HURn4lKpVIe/VksFvXfOMlxrCp8d2VlxVRx4N04vVlJQUVs\n9QjXF+UCo9dqtfQEbLFsQDqdThQ2oNPpxMYKi2Ptcrmc9p3FdrF7e1zcLc5l5bJUU1NT+m5mOi16\n1x0nXH8+PbngfG64z3L3ZYcGPnG6+Q3ZPR/l7Ha7+m60RbVa9SJ+ZzIZrS+YobW1tQ3HlOH2ZmNf\nXHPfd+bMmYgbOMqOuqEec3NzWj68t1wua91G5faz4mQxcwG1KMrCxs34hsU+Xb58WfPavdXAHJ6d\nnZWnn37au+4mc3fbEdfQn4iyLTKYD1BX7t+/X9dKbhf0A9S+rD4Ga9NqtTxG6tChQ56qee/evfLU\nU0+JSD+KuIjID3/4Q72OsAW7du3Stsd437Nnjzc3huW9BGPCTkdoKw7Z4OZQnZqaiiQmBnAfWCiL\n6WBVNgOs3d/8zd+IiMiXvvSlSFYMlA//5phWyKKB355//vkIY+WqDS1mmtXOaMt6va79bzlQ4Zl6\nvR4JOYT749hV9Ekul/PMKqzwIRyOhvcXLoNbN95bLSYa74vL/JDL5UwWlTOM8N+NIjBSAQEBAQEB\nAQGbxJYwUhyF2Q0eKOJL2myr4hrpiUTDBkB6xXU2SmZ3eYu5cO2N2G0UkmoqlfIM2tlIl11YWVrH\nOyw2ww3IWa/X9VTEBvX4LtsL4Td2l7ZYDsuAmgMo4hnYV/B9HPTRjfptMRiWoT0bxlrsEwctdEM6\ndLtdbRs2gsRJxArBgPs4rx4bpbtl4Hoz3HpYUexTqZS2VZxNHrMoXHcwJhinVk45BtqlWCxGQmGg\nfGwAive6jE+z2dS6jWJ3ALbDskKEuOPKQqPR8E74bGjLbugu0zlsbLNNmBssU8Q+ZaIPX3zxRRER\n+djHPqasCO63TuLdblf7znW7f7NAv128eDHRO63QCCKDtQDr4q5du5QV+fM//3MREfnEJz4h73zn\nO0VE5IEHHtBnsRZZfXjVVVeJSN943WXqzp8/r+Py6NGjItLPVQe7HjBRhw8f1tAAaOe5uTktH8bx\nG2+8of1/0003iYjIiRMn1JYKufTm5+e94JZzc3PKjoFxEhmMc4Q+qdfrXriHQqGgYwfsba1W89aL\nZrNpjjXgy1/+soj0bdF+4zd+Q0QG83ZiYkJz6LFtHpioD33oQyIi8q1vfUt/S6fTnpNDNpv19jHO\ntcqwDMFx3zBWfCNotVqefRjbYY0qE9YEDmht7QNxsJhDZrCwTsTlueU9P2nOUJEtFqQ4FhQmJgsg\nroAhMlgAORYHb16u8d36+ropfKGB44Qcq+N4Y+NYVG6E6VarFYnPg2vuJsf1w6Lz6quveptbLpfT\nd+NZvoeNJd22cN/jqtasAbh7927tE05WabUJG+pxfUUkYlyNZ6GSuHz5ske3WxHVhzkCuGAq2aJ+\nLe8+BjZGtPPy8rK3ofAYYkHTEqDcxWt8fNzbgMbHx3Wx54nrqnaz2ayXvJfHIkd0dwV9vpc9jeIi\n4DPYwxRlwfe4nTFf0Y7DHAzcsc1tZ3kIcdR+S8Cw1MGc7DVOKMG3H3/88UhaDxF7gxEZCF+jHFni\nwNkT0Mfo/5dffjlWxeBG9HeB9oLq7F3vepfcf//9kWceeOAB+b3f+z0RiRp9Qy3IXtIYO1inLK9M\nnheWyQDG0JNPPqkR2iFEnDhxQu9nVRbUcmwUD2EXhvQwCBcZjJlisahjmzdNN35VoVDw5nej0fD2\nHSuifrVajTVuxje+9rWvyY033igiomrOpaUlfR/S1iwvL+v7vvWtb4mIyG233aZpfrrdrpcEm4kD\nhksIVCoVr+/YYYDHEfZKNrjGv9F+jUbDIwmazaa33/H3LHWfNX5HCUtu3dgMgtczVw3e6/XMJMRo\nU0tlaGU/GIag2gsICAgICAgI2CS2hJHCqTOfz+upjw2GIVkyE4VTGCRSVsWA1eAI0zg9NxqNiJpP\nJErFW278VqJDwMrdxgkW8T7LRbjb7Xou4r1eT38Dvb1//37PsNCiThlsmG+dGC22iPPlod1AV3Nu\nLstQnU/UbjyVbDar9DlT6/geqHDL+JrZJ+6HOFdjduO14qW4JyCOdg5WqVQqRcoqYkd+F7FzNrHK\nEeAxyN9iVKvV2JAJHDMoSYgFPiVzhHY3j+TY2FjseGJg7uH+RqMRy8AlzVEVpxqzjPZXV1dNd28e\nG2AvkPCWv2M5RaAeJ06cMI3IYQCO7168eNF0eHAxLIE2gHE6OzurLEBcxHJGXGRwZq45vMQ999wj\nIiLf/e53RaQ/Th5//HERGTDhJ0+e9MqcTqe9+DsccdsCzyMrrhuSKoORuuWWW7QsbHwNZgi/pVIp\nVe3h2WPHjsmPfvQjERkw3a+99prWCfn1JicnvX5j9pbVg7jPGp+odzabjR23qPd9992nISdQloWF\nBX0Wc6ZWq8ntt98uIoME1U888YS+d3x83DM/GLYOuGzR8vKyrkX43tramvYJa1GsWIYAx7FznVzY\nVIBhjSd3DvMazcbhLqysHI1GQ+fSZlTscflALfOFYQiMVEBAQEBAQEDAJpHaSLTdt+yjqdTQj87M\nzOjJDNL/7t27IxnCReyccpVKRX9jtgCB4nAisMAnSDbgc5kGzm8UJ6mylM0nmyS2PtlsVl1hYa9h\nwbK5cdvFtTcqlUqewXi73TZP+tDf4yTE91k2FHFgloyNpeNOBNYzDKvMSRH3rGWbx2Vyy2LlVePc\niFYd2Y7ItV9iRicuqjcHCmQ7NZQFJ3S2mwNTODc3p6dPN98YY3JyMmJvKDJ87MLGCPPj3Llz5jtR\nVtjA1Ot1r60zmYxX906n4xkWs8MKM42o+9jYmDIzFlOG9+VyOe0n7l/0E6KEP/PMM55d35vBwYMH\nlcPEANUAACAASURBVPWCDc2okzXWhmq16tlx5XI5+cQnPiEiIkeOHBERke985zva1rC/qVaraleJ\nQMAwAh8GRNyuVCrK/GFs3XDDDfLcc88NfRb9wc4Gn/rUp0Skz9rATgvv45AMcMZgRgbM+fr6us4z\nPFsul711qtlsemORtQbMHlvsvWtny3PPgrVH3HbbbSLSZ5rwbq4bynz11VeLyCDCvkifuUSbu2v6\nm4WVR5QjgidhZoZl8thoGbjf3CC9bJvFzJWrccjn87rOYX5Uq1VdTzgsBJcfz7pR1h1bT7PRt0S1\nx/Ee3AHHgxMVeemllzzjwlqtpgsBGotpSTT01NSUJ0AxNckdZ3nyuR3HYfnjQsj3ej0zASh7p/G9\n/N12u60CFEfyBlDmtbU1L6ZVvV7Xd1v06DADSUugcNM2sMBgeaxY6lm0DbcvGw8DvGm6iUEtQY/7\nkN/B33PB/eVSyaVSSZ9lw0w3NZEV08oydnbrB7gGmew5xBs9RwLG99361ut1fR+/lwULF1hgrPQi\nw4Cyjrof7YL7hi28aNO4JL69Xi8St0ikv9ngUIR3nDlzJtIu2OzR9pcvX/YEKB7H2OCvvPJKU5CA\nUMOqbrdfh6WXSgr0f9KNCPW1jOELhYIerthwH9HQYYT//PPPaxvApICBNeTaa69VA2/01x133BEx\nEBcRee655+TQoUMiMvCou/7661W4Qvvw+nPfffeJSF+1yImORURuv/12VXHxum4dimG8/uijj4pI\nNGUXZ12wPNzcZN4cKZvVYSxAcX2G4dixYyIi8oMf/ECFJRiO87rMcwr9inWXPRxdswORvnDlzjVe\nK1GnYrHoja21tTUvjQoL8Oz1bIHXepGo6QnPR7fN2fSE29CNss6mDJb5DWD1Ybvd1vbirCcsGLmw\n4khZcamGIaj2AgICAgICAgI2iS1hpCDhcbJKnJTX1taUOmdpG0wUpMRmsxlRWeAaJFo+kXJ0W5Eo\nW2EZrY4yaLXcPF2VHce8YdrSjTDL9DLewXGJcBLK5XJmzjC8DycwZoP4hMHsGNoQ5ec4XSyFu4wb\nn57ARPHpnk/qbswrpo0ttSCrrtzTicUMMcPFRtBxYINrqO/4pIw2YAoYz+C+RqNhMgdxajwGTnIo\n+8rKinniYSNOlMU9SXHIDiuyPlRPpVLJy1FVr9f1u3Gn63q9nogpSaVSpoPBRgEWip0wMG97vZ7O\nAawbLksHBheqogcffND7xsTEhI5fvLtQKJiu1QDXyc372el0vBN6UqyuriqDFJdBQGSwRlpMM8Ah\nZcCeXH/99fJf//Vf+j0XYJpYPcPrk1UOrNFW+TAXWNWH715zzTX6bqiu2DwBRuLHjx/33Nqnp6c9\n7cKVV14ZSY4s0u8DN/l2t9s1w+DgN14rXSYim81q/6Lso8wJuO5QSWIf4rGE933gAx+Q733veyIy\naA8262i3214cKStXooVRKmgO++Ky7Pl83kvS3u12tV0xZzjuG8YBr5W8P7rrTTqd9hgwiwljbRBr\nElDWpIwu3nHDDTeomhw5DTmsRVLGXiQwUgEBAQEBAQEBm8aWGptbASoto7Vh2cZxesUJgyVIXGu3\n23qd3cGTSMXDXJhdva9l+C4yYB9Q9mFtbdlSAZZhK4xTu92uZ/A4ytg8Kay6W4HpLAzrL8sGzWW9\nCoVCrDGglTMKyGazEZuIjWCY0TxsaGCQ+9RTT3ltOjY2pidaDtmAvsP427Fjh9YDdXNZVbduGEOp\nVMobY0n744orrojkqMT3kxhLJzU2nZubU4NsRJU+ffp0ItuhmZkZr/2ShlBgcLiSP/uzPxMRkb/7\nu7/z7uP2QPunUimPBRSxjYZh84LflpaWlNnY6Gl2YmJCmQowF8MYPaxpYBAs55mJiQm59957RWRg\n3Pzcc8/Jv//7v4vI4OQt4s+93bt3R2zBhuHAgQM6lxGclNcXDizMwT5FokbTbmBOxr59+zymSWTg\n0LBr1y4REXnsscf0PWgPnlNoMzZyZxsdl3HjEDWWkTue3bVrl9n+7ni59tprPfsvkYFBOZyocrmc\nRo7HHDh16pR88YtfFBGRf/zHf9Rn49Z0joDODKcVfHOjQFuNjY1pW+IvlwXls2wHM5lMJNCyC2bs\nrcDDAGsj0E+siUF98Y5CoeAFRk0aLqFSqfBa+ctjbA6wwGQJDEyXY0PDIpPJZHTCYEFgLzZcy2az\nKnhgAqdSKU991+12I+ldRKIdzQbQuM6dZVH7Ls3PmzUmeCqVUmEIbZBOp7WeHGcJg4cp3Y16rnGi\nTtSjUCh4cU247layYY5yyyl/8BvHPxKxE+Ky194o1YgrXFuCb7vdjt3A8I2pqSltL7QFC16oW7lc\n1rbGfTMzMzrGUB9Wp7Kw4wo+HLsFQjP3G6c6AuJURUmFjUwmo/XDd3kTYdUoRz4W6bezFa/IBadO\ncdVDw4A5vba2tinBKQ5xsZ7OnTunQh/WiUKhoOVhJwvMSd6csbFCSKjX67Hxd+LAJgocO8ua10lU\nGL1eTw4fPiwiA+++f/7nfza9f/EeeOeyEGUJEcDLL7/s/cZCPWLgZTIZb7Ni7ykWoK6//noRGajE\nTp8+rXG9UL6HHnpI6wHhid+Dww7PM7RtsVj0TAZEBvMe435mZkbnPNedDzQifQN9q4/e/e53i4ho\nbKuTJ0/qeoI6Pvroo14ftlotPYDg2uTkpApQV1xxhdeHxWJR2xzla7VaQyPyu2DPYZF+u7n7SDab\n9ZyDrEOqtf+wyhvgPYAP1tbhGXMP11ZXVz0Tj06n4yXLtrC+vq7OCxA0k3oaJpnbQbUXEBAQEBAQ\nELBJbCkjxaoYMC/M2nD+MDACzFy48X5arZaepCA1s5snwNKzRd1bTBTQ6XT0Xj6RuMzB+Pi4Ss84\nlfV6Pf2epdJhNs5lgVKplHdqZ3UEG01b7A6k/6WlJS8+j5U8mE9wTKOi/HzSdKlaNgpkVsZtaytp\nqBWTya0z7nPbY9u2bfoerjvaEuNgaWnJ6y+oV0SiEaHdd7AqlR0kUGZWFbj1GHVSTKqO5ATE6Bs+\nNaFN8b7FxUWPGchkMh5r2Ov1vNgt1ji11KClUknbNCllHmeUPioy+CiAvo97P6PdbusJneEyWzw+\nwVzceeedmpR3o6jX61507auvvlpVhRySASfpOKav3W5r5HDU58SJEzo+UG92hsE4FvFDWCSNE3fz\nzTfLI488IiIDBmlxcVGdhFjjcMcdd4iIyMMPPywifeYMTBTmYSaTUWN0/P3whz8s3/zmN0VE5Pvf\n/76IRFWAWGtarZaGwUD/saNGXI63XC5nakcwtvHeQqGgYSN4fwETBRVkq9XS9YRVfO644tyxwI03\n3hjJz8f9hDJhzHBmC5dp5rAwnB2B91eRvpYEY4uNzq3o9C44nhOve3GMumVqw7/FmR5wcnPIAfjb\narWUvYuLts75C0eZ34xCYKQCAgICAgICAjaJLWGk2GaJGRCRqK0SUCwW9bTBxmiQmjmgIaR6nOjK\n5bLex7Y8HC5gGEYF2ouT0NmuBN9lWxV+B04VnBPMLVer1VKWALYcb7zxhrYVrnU6Hc81VSQa9RVl\nwHuq1aqewjjTO8pjZXZn/bYV1dtqN7QXM1MuSzMsyjpg5bnD/ZY9RyaTMd3VXTALhfetr697UeAZ\n3MdWBPK4gK0WcOLcv3+/MkEYu/wt2NeJ2OPXzeDONmtsB+jaw7VaLc9Y3+oD/g11nJyc1GffiojL\nV111ldo+8Pizxoblog97HzZu5vtdNqzdbuvYQj6306dPe/Zh7XZbg0Kib5LapAyDG9l+7969yuow\nI4XvxTGXMzMzur7+9Kc/FRE7QwDnLWSWFad6jKFut5vIRoTtpmBjdujQIQ3OCePwlZUVZaKAN954\nQ/bs2SMiA5avVqvJhz/8YRERZaG++c1vesbrp0+f1iCjMP5mg2vMIw6XYq0hwPr6uo5ji9lHqIh0\nOq3hJawwBGBEtm/frnkfYejPdqrYuxYWFjxb3tdeey2idbEYU0vb4o6PpCE5eBzwnHKjv4tE7UPd\ncrD9rJX7FOC9xGUJU6mUtj87yLjBsAuFgo5P9PWoXJDcPsOclrh8SbAlghQPPDfeB8OKns0LIAa8\ntZChcXnD4Bgg7gaRyWQiHld8vws3/UAul4v1JuNYSegkqCAvXLigC+go41zchw1mdnZW28ba6K2k\nus1mUwcht6UlhKANMYGOHDmiCzurN11VLKdH4bpZA9MVNlh9yBQsxgnH5MJvTAG7k4BT+mDR4iTI\nqMcwodmKU+S2C6tnGHEClCXUYeFYWFiIjTYNrK6uKqXPghLawzLc5DgtVhT4jSb+xDsuXbqUSHBM\nGgWcI0wzXI8ljv/EsDIIALt27TIN6NGHMFoellLKjRm2tLSkmzMEkCSJjYdh27ZtnhqHyxeHd77z\nnVp+eO9dunRJBRmU74UXXtC25PUV7XLTTTeJSH88JEkKe/HiRc/UgtsPQkQmk5GPfexjIiJy//33\n63UkJkZfzszMeJHmK5WKegkC11xzjb4ba+rKyooavMM7bm1tTdsP656VYmt5eVnbHgLzmTNnIk4z\nIn2B0xWg9uzZE0m6jHbBWEXbszABg/oTJ05ou0EwPHv2rB6arCTJ2Ww2ogoD8BvWk7W1NY06f9dd\nd4lIXxD9wQ9+IMPAYw3j3ep/jkmIOYD7isWitpvlgYd/83rBMRXdeI35fF7/zWYplgc+1g60ea/X\n03ZhFSDGqJXNBLDWFxdBtRcQEBAQEBAQsElsSRypdDrdE+mfICDVW1Fn6X516X388cdFJHqyZddP\n/GblyQHl2G63vSjWTO3ziWEjLuYiA2Ygm83qiZXZG9cw24oFlE6nPYNhzmXE7BIbCov0JWtm1jim\nB67jGY674UZVLpfLymJY7IgFvLfdbntsAif5ZPbEVQPlcjkvblEul1O2gxPYAhYNbQHqkvn5ec/I\nkOMlWUwe/xanIkS/sQs2g1Www+ph1YGNw1n1CMSFj0ilUqrGtQw4k+aJw/wpFApeqJDdu3druaH+\n2EhIADdpKTssAKVSyWu/VCoVCemA59HXb7zxhjcWd+3apeW3mFgYPF+6dCnW+BR1P3jwoPYF/iaN\nOm3hIx/5iLYlIpInxb/927/JRz7yEREZqIrb7bb+G4beMNYeBsTcyWazemrHWspjiBlCa64gHMBP\nfvITEYm6nHPSYnf9Z/VXXLwpEfFy/PGzYOIajYZqOMAapdNpcy7FxQ5DOU+fPm3GHcNvUNPx2onv\nLy0tRdguvN8dizxva7WaFwsqqQp9mAOPa1RfLpe9cBDMFiFkyPLysjlvksKNJ8hOM2wMn+QdItG5\nLrIxlRz6k9W9loaLymM2emCkAgICAgICAgI2iS2xkcLJm09tOImUy2U9HXBmezBRwMTEhBqX8Snc\nzavHGcjZRRRgOxErA7WLbDbr5VviHHrMgFmSN77HjJnFnrmn+VKppN+1WDKcmKrVqn6D24WZEjef\nUaFQ0LrgRFWtVlUyR3/l83ktF4ctcE+T4+PjXrgAtsPg8ruMmcVm9Xo9L2Aow82ALhJlaFBWtltx\nM4E3Gg39N59I3Xxf+XzeDAkAtmYUg4myWvY/zEixwSbKYkXPx9iJc57I5/OenRjn7uKce3GMHrcV\n2henwUqlonXbaHDKQqEQybEn0jccdYOD7ty502PWOBK1SNRhA9cBsDLNZtPLCMDA2rF//361tbGA\n+l555ZXyi1/8QkSigXvdPh4V2gO2G0tLS5EI5BvBBz/4QTXO5jkF2x7MaQ5/YAFu9/l8Xm12wFKd\nPHnSY9y4rcBcvPzyy/LEE09E7stkMtpfDz30kIhEDYbvvvtuEZFIOAn0G7M299xzj4iIfPe731Um\nCtixY4cyUmCE8vm8jhkExuQQGVYuUqw5lUpFv4t3jI2NmXZ2WIMwvnjNx9rQ7XaViULbXn311V5e\nyF6vF2HW4uzuwHClUildn9C/POY4ZAvq4obQEbHtHNmhAO/hjASuRoffh72cmSaL9eK9GfMe/bGy\nsuI5DLRarUiuSJTFypvp5qrksEpoo6mpKb0OBjGJ1m5LBCle7LF4YAFfX1/31BTtdtvbCBYXFzVW\nBxoym83qIggjPk4eyWoBvI83PisxrrvZ8KDkgeN6E/DgYLWZ+76ZmRmtJ+hg9uTDe1qtlg4EbCac\n/oYNO3Ed7xOJbm4otxsZXCQ6aFzqt9FoaBwVtHkqlfI2zrW1Nb2P6+tS0ZlMxhSC0E/oD1ZrYeIy\ntcvPoq15jEHQgzfRNddc4xmtWml+0um0jks3MrhIdPFNaqQdl3KIVSaWAwL3u0i/TSE4MNWONuDF\nxBLC3IVqmHE14MYVExm0c7VajfUOgufS7OxsZFyK9PsH9cA3VldXtVxYDzgeFjaOubk5XUO4Lpbq\nARtfq9XSd6JdrMjRliDFzgb8XjyLMbF9+3adI8AolQM2ylOnTiVWpwNHjhwRkf7cgYCCyOZsAI31\n8aqrrtL0JBjb1157rQqE7EmIdkP59+zZ4yVO5sMFNtzbb79do0kDzWbT87z74he/qBG8IUDt3btX\nY1DhHZOTk/K5z31ORET+z//5PyISNRhHn1oCYqvV0jqxQf2JEydEJKraw1jEfdPT01p3vNuN2A3g\nQIDxVyqVdA23BC94l549e1b3LPQLm2ZYkel37tzpOSANc/RAf6EMV1xxhdYJQp91iJqYmNB9AMJz\nvV736sICIzteuOnbMpmMrk9sjgIwiYHfMS/S6bS2O9Y2juHHa6V1oE2SEosdsDB/La9gF0G1FxAQ\nEBAQEBCwSWyJsfmOHTt6In3pL04VAhaiVCp5tOaRI0fkscce855xDQX379+vTERczJ319XUv/IGI\nTS+6htkMsC4zMzN6KomLfTM2NharZnizwDdZTWq5vQNsiI7T16gI0zj54lQ0zMU9Lj6UZajKp163\nrTlPHxsou2UdllA6KaxEywDT5BgTSDz64osvvql4Sji1Y9yfP39eDWdxMmy1Wnp65hM11LzsPsz5\npUSiEe5RD5yEGblcTq+jT8fHx7W++Nb6+rqyHcxO3HnnnSIySGTLqnv87XQ6Wj60N0dtxhoxPz+v\nz3D8Odx3/PhxjUc0KvkuWCyMDVbfAO95z3vkxz/+ceQ3Lj/aoFwueyzVwYMHlVGJW+OOHTum/fDd\n7343tsxx+OQnPykifdXYP/3TP4nI4ES9tLTkJZe9/vrr9fSN9Wd6elrv4/GEuYf6spkBmIlSqaTM\nBec+A3PIbCfGLO6fn5/3DMoPHz6s4Q9ccw2RgcZhZWXFy7U2Pj6u38A1ax247bbblIFD/zGLgm+c\nOnVKQ0qgTdmxyUJcIvo3i7ikxbOzszqesGatrKwkKsf09LS2G8ZEp9PRdYQdpVz13TAmLI59Ajh8\nUFxCYwbWidnZ2UhsSXwTawKujY2N6RgE25vJZLRuuP/y5cteeCMuvwRj84CAgICAgICAtxZbwkil\nUqke/VtEBpJjp9NRqZj1lZDwOW8dAKOwbDarelCcONlOAb9Z+a2sXHZOmb3vMtx8bpxXj6VsN7gX\nS96wHanVauYJwjJKt8D3WacXnARhc/Paa6+ZzIabjyyTySj7hHYdHx+PBLgT6evak0bzdr/LpwSL\nOYsLEcB1x7OcQw3jZGVlxTMAnZiY0LaycsDx2AFQhpmZGWVh2GHBPTGOyh+HMb5jxw657rrrRETk\n6aefFpH+qR0hQHBaXFxc1NM6mIFyuaxjGnOh2WxGMgeI9PsP30ObgrkVGdh6lMtlz1ZgampK+5pz\nmsHol8cnGAlml1y7M44gjzJ1u13TpiQOvV5P88yBkVpaWjJzp7mYmJjQ63v37hWRvh2Ja+PDRtqY\nH8Vi0bORm5yc1Drx/e64PXbsmPzqr/6qiIj85V/+pf4+apy7+IM/+AMR6TM6jz766NAyjwLaCvNj\n2FqDunMYCmZoRaL2S7fccouIiDzxxBPemvTpT39avv71r4uIKKN45swZHVuYc9u3b9e8ewx+xgU0\nDo1GQ9cG1m4giv2zzz6r9XLZlrm5OY/1EhmwrLCj4xAv1n0Yk5zhAu04NzenfcQOPfgtlUp59ldW\nmw8D7uMsGwDGfdI128LOnTt1rKB9U6mUN054fefQM8xEi/TbxdL8xAX9jdsfC4VCZP0X6c9LrIu8\nV1ttOYqR2tKkxel0Wjc59hxCg/AgwYDCoOTKYvByjCQWoNBxltcDR1eNE5bi0pVwmhS+z41pxQkg\nGehE9oSBkINnz58/HzFQB7Axoj2GGRazAT8GEv5OT0/r80xJu5t+t9v1DJJZ6MA7OJEkvjE+Ph4R\ntET6/YYNCAs4RyC2PPSsDYb7wa27pUqysLKyov1lCZWoR6/Xi6QkQj3QXzwuMelRj6mpKW1T9OH4\n+LjnAZfJZLQ9IKAdOHBAVXtQpy0uLkb6XaSvykA9OAI7Fh4W+DBXXIGZ68HGnByXyF2QG42GObbj\nDNjRl7lczhOuhgkQrmdlt9uNCKyYQziULC0t6QbEHpOump+FLPQNG5rjfbOzs7qRYT2x1KqsJoSK\nSEQ8QeD1118315akAhQid8MTDYbmjGFClKW2dgWpRqNhCv+YAwx3TKytrWnbw+v69ttvV285tBuE\nKBHb2BdYXl42D6dxMY3Q55xUl2GlMHE3/+XlZZ3fXD6MbUt9yXuX67CQTqe136BO50MM+vLFF1/U\ng+v6+roppHGkdbwbZeT64j7UjYVc9Nv27dtV8OQYiPhunKDF5ec4gWhDblOORyfSn3soA49JjDus\nj/l8PpJgGdd4/8dvrpF7o9FQlR4OnxwtngU4t6/RJnEIqr2AgICAgICAgE1iS1V7HNUbkma5XNYT\nFKTOZrOp97FU7CZitcARq/n0MUpV5wKScq/X84xgOd8c3++e5Hbv3q1SOE4vzWZzpDE3EBdRG3Bj\n1bg0erFY1DKMygeGGCeo+5kzZyKJK0WicXxwSuXTPbM7Li2bz+f1tMHPWCdlK+9inCE4JwB1TxgT\nExOecWOtVtNv4HRSqVT032AIkqqber2e1g3j+LrrrtMxi98KhUJExSXSD9lh5XvCt8G6DDPIdMf2\nzp07dezwqRnsCdqRjXkZKDNO2el0Wplh4KmnnkqcBWCjwHcnJiYiqniR/lhCO8zPz0dy8In0+w0n\nSqwr+Xxe3xkXgZzVfcCVV16pp388y2ofjp4PHDt2TET6Yxfu9nw/Inz/x3/8h4iIGqlvBF/5yldE\nROTHP/6xfO1rX9vw8y44Xg+3tQuMMVZvjwIikWMdu3TpkrcWDYu55a6BzKy4qjbG5OSkN5dFBmwh\nGIwLFy5o3cHOcRJ5qNc5DyB+O3nypBrNg3XjGGmsLsVaxHn1LFhrPtp8bm7OnLNgXMBmiQxUl5uB\nGyOv3W7r2sHxstz5b+29SXNtiohnjpB0feFI6WircrlsmmzgG9jXlpaWlPnHupfJZJhJDsbmAQEB\nAQEBAQFvJbaEkSqVSj0ROxyBSDwjgWt8koR+tdPpeJGyM5mMKcm6hmmpVEolb5zALPZhIxGLkxiM\nWs+OjY3pyRsn3JMnT3qsjBUcUCRqD4X2cG2WRKKMBLu7xpUVJ3nOho4+4UB2SQNUxrkJc2BJDhAo\nErX7icuDx3ZWSU9CcbAyxpfLZR0TGIscUBKnTjbI5BxfOFWiL+v1eiK2lYF2TKVSkfyMIlG7HrTj\ntm3bPCNdy9akXC7rGETZJycn9VkEX3zppZfMfH9udno26ueM8Kgnxj3btHCQ1Ti3aMvAfxjAWHKE\nZtegOJ1Oa/lR1nQ6rUwFwIw5+pznJUJAPPLII1p+tvX49Kc/LSIDxvRf//VfE9VhenpaWaAvfelL\nItI3lP+Lv/gLEbEZZ7YxcvObbQboo7GxscRz3sWdd96pTCmzSVbYAxe8HsflmxQZhBQBu8BjicMv\nYO7hfcViUcfGXXfdJSIiDz/8sH6Xyxnn7s8suRuUNJvN6r1g0C9fvqzrNgeqxhqeSqV0LGJts9bR\n8fFxzwEllUrpWOX5mHSNxLwB27u8vBwJHu2Cg1e7xuYi4pWF+5DntBtMempqKmIHJ9JfY7CW4b6F\nhYVYZnUURhmbb6lqL5PJxMZu4vhArhHx5OSkp2JLErkU70tS7z179uhCxZsne4KIRI3mUZZhKiAM\nBKRRYONTFrzivBMYbvylsbExbatsNqtl5LZ0veLc6yLRBSCptyDDjb81Pj6u9DXH/bAW3zgVpmUI\nboHvc9MKcILquFha7O2GiT0+Pq5thPuWl5f1Pnz3woULqmpAVO9Lly55Auva2ppueKxuhJDL88Nd\npHkDT2qcDNx11106Zl555RUR6W/+brseOXJEqW5gcnJSn0VSXY6vZqWGQD1mZ2d1biTdeJN6zDYa\njcSCFNoXm+Dly5dNzyz0K+ZjvV6P1AXfR7/yRmapozE+jh49KiJ9FeDHP/5xERkYHt93332J6vDu\nd79bkwFjY/7gBz+o8aigImShjtvSit0WB+t+9MmePXt0fnOqDmt9t9rFOjhgjnJGCjemVbFYjKS9\nQVkgoFhI6ukGHDx4UCOLY8xOTk56gmq5XI6kGhGJtj3WEG4LLgubt4j4QpG7NvP+6d7jfgcYta9w\nHESUD2s4R1F32473FbyD9x9eEyD8oSyjEiAn9WDF+9LptOcF2Ov1dL6y4JWEQMDz/y+Cai8gICAg\nICAg4K3EloQ/gPHd4uKid8oZFYka0mk6nTYNRV2XeXaZhNTJcV8gHbNUjFPo+fPntVycmNdSp7l0\n8rDYIzhBgImanZ3V+uKUsGfPHi8mihWh3WorPsVYlH2v14s1GsVfNirHNzifElCpVPQ3dmHl/Hwi\nfbUFToxoc84nxqwX+hh9xE4J+K1cLnsqIjYiRjvs3btXVSYoE0dFByYmJiIJZ0X6bIXrcjzsFMvh\nAgDEwbFc4jl3l3uq3L59u6c+arfbOp42q0IRGcRIKpVKmhnATU7NKBQK3hy9fPmyqluYBbZOprfS\npgAAIABJREFUdXgGKgorEbhI1LFEpN9HmMus2rHYqY2qpsrlso5FjANrrqIceEakX0dXlcSncaBS\nqahKlMeQGxV9YmJCjdGt0AUWYJy+d+9eZaRYRYSTuZtMXGQQef/ll1/2+maU80mcU8zFixfNJLQA\nswpoDx5XaEsr96mlquPE8W6YkZMnT2psLoRdWF1dVWaYE0IjETOMwyuVisfystYAYQump6e1HlBv\n87zk8YQ16bbbbhORvooXawPWi0KhoPXFfBwfH4+o6d0xxomCMZ5brZbXXvyc1TeYkxxhHJoYiy1y\nyyES3VfAZi0uLpqsmKs54mTUQCqV8ozNR2GU9saSF8BIc8YHN/J+kvU2MFIBAQEBAQEBAZvEljBS\n7CaLUxGk4mFsFAf+w19Xr57NZj2DMut9jUZDJXk2SrMM3QDLiI/tbFz7kDfeeEPLBzfUs2fPevr5\nS5cuefY6Z86cUWkc9bVcei0G4MorrzSDIILxKRQKphsoB2UTETP7fCaT8YI41mq1WEN79E2lUolE\nlHXLxf2EEwBOWZZr7fr6uncy4uBxALuSs2s6noXenN3VAR6n6Dc23Gbja9hEcN3QzmAXX3rpJbPt\nAdQ3nU5reTYa3VskalOCv5yRXaSfTR5jK87OqlAoaPmZ2XCDJYoM7HTQ5la+tlqt5jEmBw4c0P6A\nvVav1/Oixbv/3ix6vZ7WxXLZZ/YE38PYbbfbep3zjDGLKTI6UwJYkZtuukntpZKGD0DeN9d2TaTf\nL1gDeaxxrk0XbAPDY1okylKh36xxUi6XvfIXCoVIYE+8A7+h/WZnZ5Ut4DnqBvDtdrv6DLNt+Dcz\nHVagSmaiAIxRsMbc52z3ijUOdmz79+/XcY55WywWdW1gJxqU5ZFHHhGR/n5gzR98D/176tQpueKK\nK0SkP6etPQB7Bs9NBAhF/3MEb4yNyclJL1+iFdA4n8+befCw34F9mp6e1jWL12Csi2g/y6CdDcFZ\nM4I5hT5kpx62XY6THbC+l0ol7ROU5dKlS8p285zHmIgLjeJiS4zNd+3a1ROxNy/LK45j7VheWKM8\nnFzPsAMHDuiCjWvT09NmioE4o+Q4lEolfYYXHjeCay6XM40KXSNnjkSNTl9dXY14Q4j4wpWVIgbP\nYyG+ePGiVwaO94HJDINLF5bx/TDPGReucXAul9N6wlgbiUxHIZfLaftiUllqvFHAgpbNZrUsbvTc\nUej1eipYoB2t9BYigzE96jDhgucKL5CucWg2m40k9MQ34ow8sWm+4x3v0Jg5aMdhxsmIp4Nxsry8\nrGMNbdrr9bxEp5lMxhSaANQtk8m8ZV57LsrlsmcMzHMOaDabSvmjbgsLC9pe2MRWV1e1b+La+fOf\n/7x86EMfEhGR3/3d3xWR0Ua18Bw7cOCAfPWrX41c27lzp24ElpE51hUWclGPXq+nZcZv1WpV68aJ\n2wG01a233qrrOSf2tdSLrsEzJ5nlueqqlId5RwO8zmOcf+YznxGRfqolqLItg+uPfvSjIiLyne98\nR/uXU524ewvvSQy3zFNTU97hyTJUtxyg9u/fr8JTNptVoQXtu337dm0vV2AVGZgWrK+va/tbDgMY\nG2zeEHfg473B2h8x1/fu3auCDO574okntCzoo3Q67ZmHTE1N6X0bdaSxxsmw+FWoLx+AUFbM5ZWV\nFRbCg7F5QEBAQEBAQMBbiS0Nf8DGbQDHfWLp2WWd2DgPYIod13bu3KknJVarWSclXL/11ltFpC/5\nv/jii1oGkf6pB0aLOBksLCwoi2C5avIJKElYA8uIPGnIBs4tJxKNgivSl65dCT+fz+tpDszA+Pi4\nx0AUCgUtf1KVE/qtUql4J3NmVNBurVbLZH04urVIf5zghAxDZVDnLjAmQDOvr69r3TjSOP4NupdP\nx6waBXBqm5mZ0TbinHw33XRTpOxPP/206aiQBKxWxSlqfHzcCy/Apzv0fbFY9JwICoWCGsSyMTdO\niXfffbeI9PsD7C1YS2sMFYtFVUnADZ5ZTrw3l8ttmCGMAzuT1Ov1TTNSHB/MMmjn38DqYDxx/CCO\nAu+qIXjOYxwfO3ZMx9GDDz64oTKzUwrKks1m9X18KnfZ7LW1NS9Ok7W+cE49XLfWl1QqpQw3zAJY\njYfycZgMzoiAscLrchx7wizZKCN5kf4cRRtg3PM6A/XRrl27InkaRfptBTUPG4djDlh5+NjBxDVf\nYEbKbTORqIbFirNnaRk46XeStXkUuweMj49HElOjHu5Y4ZAocQmyp6amtN2SMu9guMbHx3X/xzrK\nLBM0J+vr6x6jls/n1dEGfckaHfwtFAqeMfz27dvZeSAwUgEBAQEBAQEBbyW2hJESkS35aEBAQEBA\nQEDAJmEyUlvitWdRkqMEuiRG3xMTE0orbiYdyEYTGcdhfHxcPVBgMJg0grDIQK3JRucbeR5waWA2\nprOiZrMRoRtbistjRUVH+YYZ7rp1m5iYUCNuTl3BMWIANwkle3WwGhfUL947LKG0a5xvxc2ysHPn\nTk81YBkyWobPXBam762xDdWFazzP2LZtm6oIeGy4KpFhxrzD7kdZ3XqgnFxXqFVTqZSWJencG9Xm\n+C5S7FjOIPye9fX1Tav2NoKk68RbuZ4kRVITgLfiOyISUc27apJRUbb5XTz/8V53HHG8Lusb/BvG\nMhuJW55hbkaHdDrtxSLk63yfm7aq1Wp5620mk/EifrdaLc+0I5vNeh6VbPzP6zbUVtZY/7/V/xvF\nRudCJpPx0uxYdRtW3yTfs54dZpQ+Uj6JvRoQEBAQEBAQEDAUW8JIAalUSk/mbKQJYzSOR5HE7Xxl\nZcVz87bilgxDEmk5k8koKwOwcR2+f/fdd6uRHIxvN8Ioob54xno2n89H8owBcYbsVkwey5V3mKSP\nUxOYDQ4vwKc21y2fAUNMK5aK5VgwzDDSHRPFYtELqZHP5/VZ9D/H/eL74pgoPim7303KwHB78jvc\nmFucUwwGwe122+vXhYUF02nCKjsiMj/77LPedYxZ9xmUGeXm5OAATselUkkNdnGdjYAt1g19MGxe\nWGymBYtBSIo4w3KeA5aBbxx4zG40p91m4DImIjajmzR8x6gTfZK10lprUqmUV1b+DW3FbJE1vyxG\nylpzMDa63W6ivmPmh3NbumWwmCaLRRsWA81iuIaVxy1XHN4MG2WFHkryjEi/7paj1WZZ2VGskDWe\n+Td3vloaDB5jSdeaYdgSQYopTDcYYLPZjGSAF9lYtmZswljUa7XaW+IlxKollJknMzoAQsKrr76q\nAds2833UI+7Zubk5LQOEzlEeG6w6A6zJM4zidO9lLyyUmQVLS9CDp8qFCxdMockdzBxQkr/Pniry\n/7D3ZT1yXdfVu+au6olkcxIpyrJkG04sJw8O8pKnIK/5wQFiI4CBIAESA7EjxYoiKRopUSQlDj3V\n/D30t06v2nedoaqbajk464XNutO5Z7rnrL323qZNP6+//noIosfwz1gsFsl+hkki15Zsrsp5fwFY\nwCnvH7TrW2+9JQMKqo+1b7fZbBYWWojNxcH9SiZos/M24r7BnlwYc/gA8UIK9cJ9w8eiiYGTcKvF\nCOaJTZBqGzWplk60+/v7YSyuGwenFGqhFyvfuh/ITT4oatHpFy8pM4y/luMMpX7DNRi/PI45fYx/\ntlrkqA8zL6TUxxxjj/shl8/XgTIfqvPWOZ5CqbkvtihJAe+sArKa5ds7do46j5M0K3OfKmts8+rB\n77vJ4q+a9ioqKioqKioqNsSVMFLMQvhV33g8buzgbt++3RA5L5fLsLvFCvLFixeBMbisWDWKlo8x\nOGbnMVk4Ns8mKCn/aDQKdVXCRHko8TDT1F6A3mq1Qv0jNhKblBS1qwTUiJRt1mQbd3d3Gzub5XIZ\nWCeO9eOTpM7n8wbDhXLG3p1NlWoHgndSUeoVEIMm9kwWoaLMgGpz/Nbr9Rrphcy0SFuxZ2DrfvWr\nX5mZTjnEgIicY3+hftrtdmMXfnh4GMYjmDUGjt24cSOwbBx7LcU+c5ur80oZNSC2Q/csATOhPAZS\nO1XMU0+fPg1xa9AX2ex8ESF6iunMRX+/LJQyTSmTIzMJqXdigbmaxzwjwWlecH6/3w+/cVuq98Bx\nZr+UEBz1zE4nHM+N/+Vyqv4aM6+nmK1SlDI+yvyVg0ovpdIQsUPSun0e53O/9s5HseflnqWYXN9O\nJfVeGamKioqKioqKig1xpWLz0uiqFFV0ZYeD3ENgAabTaTLp4iY7tVIhccl577zzTljlgiGYTCbJ\nSLApnJycrNRNCXjlzUJMf3y5XAZmgyMZe/2a2XnkWT4PYOYALAfKrMSNKYE7o9PphL6gWCL0CWZU\neJfi9TmKCel0OtJN2YNZCu4HSpPhd1I8BlI6rG+//bZ4R+qZFXYm+Pd//3czM/vZz35mH3zwgZnp\ncaEYKYDrBNHMP/jggxARGGNQ6ex2d3cbYRLYRZ13gTjO7YtyqcTnuM4svRONHVP1kAproe7D/ZkT\nZput9nc1DpmlTM0n6vlq1/4qoTSGvm8zo6KYJr6XEqDHzvf3YycS/r/ZeX30+30pNvbsQ6fTSTJG\n/G6+Lfk9+LqUaD72TniG0oJdFlL9NxeOxvdB7tuoc3b0SiH2bqn3TTFmubrKzRFslSnFlSykmP6E\nuQIdZjabyQ8ywJ0Wod5hSrh//35oOFTG6elpMD+lJpmYuNp/3EoXfwqz2Sy874MHD8zs7CPizZHf\nfPNNUSOmkqGanQmUgXVFtcPhUC4E8CHjzNjcdin4BLCK0lfJMnd2dhq/K887Tq2DZ3HqBSyCeHAr\njy8sEufzeWgH3I/fEQLq09PTcO/UopgXUmpygqOCSnkxnU4b5tt+vy8TdXtnCNWXkKLEvxOAxe7t\n27eTi3W+t0+FYdYU0L948aLRh5SIdHt7OyyguHz4m5+VWrxuArR1bEGjPnj4jese74L+dPPmzZX+\nCOAZpZu2lChYfURy3lilZpCLePKlTHulz4uZX9QCCmCvZ7SD8pTj35SgHffB3LFYLBqx3mIek4Ba\nFKlFHf/rFzYXhWpD9pT081OsT+J6jBWeU9U8m0LpmFV9m38rFYznnH82mUOqaa+ioqKioqKiYkNc\nCSOFlWq73Q4rWl7de0p/OByG45xQEBGP4XatkqTGVsWeHVE7+16vF56L1e6tW7fC7rokWSbjk08+\nCWYwFuni2fitlFJk5g4M197eXlhlx4TWHmoFvrOz04jJZHa+gmdzS0oYz7FYVKyjXCwhVS4zLa6/\nfv16YEPA1sXiQ3kxKoOpXS9K536CPnRychLOU/2IkQo9sS5dHds5oc/D9M1mMJT597//fTB/MrsI\nYJyhv5qdjzPu94gFtrW1Jc2evl1fvnwp+yUYMoyto6OjINzme6APoVzb29sbZTHwUKxSKZRpitsS\n7fHVV1+FccqhOjxTUvp8Ni+h/l68eBHug/vm7pdyUS/dqSs2hpmBnHnO9/1Y3CQ/bpfLZajflGl2\nNps1mKMYW4H7cJnxG+aS+XweWFHcV4UFUe/OwnElCci58a8Lrkt+jjJ1rjuWUC9cvpQoPMbUlXzz\nlFk91of8eZuYD9dBZaQqKioqKioqKjbElTBSbH/FihC7J45EjdXswcFBOA+7j/39/ZDLDozIy5cv\nw+4AbM2zZ8/C6hS7wel0unJNDCon0qeffhqiTf/t3/6tmZ3pqP7hH/4h+96np6eNKOC8Il4n8KgH\ndrjtdjvoUlKu+Ix1VupKA6TO9bmzODQBIxX9GedzGzLr4XcxfH/cZ3t7O+wiuZyeBTDT4Rvwm9IA\nMDuqon4r+OOsuUNfVNqn8Xjc6DPT6TSpg0LZ+/1+eCceH3/+539uZma//e1vo+XlMB5golg3hf7Q\nbrdXxrDZGROi2BDPYHIQUY7UnhoPaNOdnZ0LjRuA+wZHmi8VrfodstJS3rp1K4xTDuNQUv5YPke0\nP/fFTZk1f++LXssaHw4H4Fkqrj8+Xwnz1bVeX6cwnU6lpkkxHL7+lstlGD88N6hcgN6BJxa2IMW2\n8f/5fdcNqspQZfWR4HPia6VL80wyo1SbF3OiUd+E0n6pnJhKA4FugitNWsxUIibXyWTSaHSkWDFb\njX305Zdfmtl5I06n0zAJqg9+SgjcbreTnkoMfFzgAZVajMVQ2ohYDD179qwxMbMwkkWQGOw502PM\npIfnAexNxBO22aopNlUus3PTEOpvd3c31J1agHAkbXxg/fPNzH7+85+bmdn777/fiDPEJkCuPyw2\neaHgo7C32+3QL5XYkwWX6NPrftQXi0UwU8EENxqNGh/Bk5MTaQb1EwZPXsqkyeVD/1ALSGA+nzcm\nNHVfLhP60Lffftt4D06jwfdFP7h//76ZndVtKjYae6ldtqdaaSTy3CLLxwp7/Pix/eIXvzAznarH\nx0BjqFhvZk0vK3Ve7gPJ71NqEsndC/dQJhhv7lEmGf6dF1cqPlMJ5vN5wwmDPfS4LP654/FYtol/\nNy4fI+WZzCJ3f+1FP/SlgutSbzw+H9/K1NiLLaJ8P+c+i348Go2Kv6ve63U6nUoT+7r1WeqEYVZN\nexUVFRUVFRUVG+NKGSn+m8MWYIfMuwQvMoxFDl93d4qV9c7OztrRyHMrZr/DiK3QwaJAvLizsxPK\nxe7jMKewWJ8TOwPY6fP7qNV1StQ4Ho/lDtm7mvKuE+C8VnzMM33MbPB9PZs1GAwkdcxMlNlqzLDU\nO/K9gZ2dnQZjuVgskuZIgN93Xfp9sVgEejyVNHkymTR242onr0TOzLDgWU+fPrV3333XzM5MdWar\noSIQSf7o6EgKxgEuC9g7Dq2g3hdlhJicTfxgHO/evWsffvhh9D7c/zYxeawbwbkUKoIz16VnoniO\n4HFWyiYoEbE6T6HEpH/R+EV+HlD1zeYvNa+wCciHJihNRszmOY5czrn4AJQR7ZEz8ar2SJ2nzGqM\nGLtXGkdOPY+dfszO6qDEoSD2jcBciTpiQbsyyeKb1O12pXOSv3YdK48K1aCciWJCd34uX7NOv6+M\nVEVFRUVFRUXFhrjSyOZqtdhut4PbNv7tdDpBz/Hw4cO1npETvLH2hoNumpnduXMn6FZSQUJjwE4U\nq96tra2g++A8gdiZ43zOoQfX9J2dHXvnnXfM7Hy1/tFHHzWe2e12wy4h9t5qte7DGnS7Xan3wQ7O\n55litFqtxrU/+clPGuVlpgPtwHqEVKRvs6ZeSu1Ob926JZlG1CGHLVDvgrZR2iz0k/F4vLZmgwFW\nEe2vysHi5ZSWhoG22traCnXIdY4dHJ7Pz+BnpZ7D/QHlRt/OXcv3wHlol9u3bydDXQCbMkrrXlfK\nYKldLGcBUPf1jB+3F5+nxMGpgJx8ntpl+2vVXBkb3yUMjXqusi7EBNn8PP439bwUvMPSdDpdYVTM\nzurAhySI3defx8w0EJsXvA5rUx1aKZSlZt1nsmOYvx/rnYF+v98IH6SYptFotBJA22w1z+VFWGP1\njspRIXdNDleykFIfcoCFpxAn7+zshEbCx0aZemAa8/dWi6A7d+6Y2fkkfXp6GoTduE+/3w8mNnSI\n5fI8dQoWd4vFQk6G+A0Lwr/5m78Jg/jf/u3fzOxsYYj3xQdoMBg00mfs7e3ZT3/605Vn8ESqPLT8\nuSVQpgl1n5RJYTqdNq599OhREBKz8wAWBagD/uhwqgH/Ttvb22FRzeYA3w4//vGP5UIK5YbQ++Tk\nRH5sVB/1wt5NzUsA2pgjDPtn3Lx5M8T1YhF+arJHf+b6xlhgup8Xz/43bo/Ux9jsfLzywhD9kheu\nqCuMBSXkR3yqHDaJIRXLYpDCJikp0J6Yq/g8FvijXrFoVyZeJZpVC5CYmUaVTy2aSswapXOJMvvz\n9d6cp8rkr0sJtzf5APr7rdMvvLmSP/6pOSRWLx5+UbypiTW2mCgx46prp9NpIxac2uwor93RaBTG\nfepbnvNSVA4yKbkJ46IifoVq2quoqKioqKio2BBXatpjRoV3QmAVYFZjswFHQvc7Wo4xhBVySmBm\nds4CjUajwERxOAXsEll0jOdhRX18fNwwu9y8eTPcG8wAvxu7gHtTnNqRvnjxomHWPD4+blwTEyzn\nhIU+X17MHIA6TgmjuV2BxWLREJvfvHmzEVWb25VFhN7Mk6JsGSwgVzFZUrFqckxTykU4B0WZgy0a\nDAYNxo+FsdzvUzto9F1+N25fnz9wNpuFvu2fxeA2Qv12Op3AvIKROjo6snv37pnZOSPFOcpwDxUR\n/fDwMPyuGIuUWDeHHOuQE6h68DkcYdyzowcHB2FO4520eobKW6iei/PYWcf3nRyboeI55c5LQTm0\nKPMcm25STI66H/eJV8EwrINU/+S4Wcq8GQv94H/bRGyeQomjQaxcrVarIRWIzUWeWWfrEDNRPsRK\nrCxIko55h2UVMTN07D2UQ4P67pXkOayMVEVFRUVFRUXFhrhSjZTZ6q7JbFXsBw3S6elpWBVyEC+f\ni6vUZXI4HDY0GOPxeCUnGYCVN8IQ9Hq9cC129N1uN5SZdVpeWP7P//zPKyybWVyX4LVPrLnyrqcl\nSO30eMeqWBbeVeCdle6Mdwb+2na73QgvwPdAW8aAukTZ+VowHLzLR3u99957IQI+GDFmCpT7LteF\nt/2zgJp3Ueu6zHI9+2vG43FDoPz8+fOg10MYDNX+LBjH+Nnb2ws6MTX2WLzODBj+9Tos3qGBCWHt\nIAPsFGsa0NYs+lW7Sp/hgB0gSgXGmyDHiij4aPeKBXr69GnjNxaWg33q9XrhbxVok4HnMYOlIqCn\nsG6fNdMsRUpfo3SxXm/Jx3OMcykDvC67uA68xism9FdaqhR7oti7q2Ld1Dvx/A5wOAUut+q3qbGu\ngPmp0+mErCKffvppOF6qhyqpQ2VdKNHNXalpj8EeEN7ccnp6GhqH42Fg8sIH5vj4eEWIa3Y2geNa\nfFhSEc5jUII4fLj7/X4jGfHJyUmY9DHZnZycJKl6YDQahYUAyv7y5Uu50NsEJSJDHgw8mTOVi98A\n5f3D5Qd8Gh8+7+TkRHqlQciMa9jkhL/Z5MTxt1LxwdQCPhUNN0bFq49CCWILaTbVmJ31tZIBrRL8\nxqhp9F88o9TLjh0zYJpV3mc41+zc4YK9MlUsoFTssslkIk2Ol2ny8CgZK2bND0Hso+pxenraMFtz\nZGa0OY8pZUpSDibKdOaviyHnMVVqWlfX+fpgEzo/19efWkjx/XJeipcJjoqeW/j4RX9MbuIXZrwI\nu8h7KPNWrF0xN+e8CVPpW7jdVD/C3/huT6fT8E3m5ylBObKJ8Pt4UzbP5etuEjbt19W0V1FRUVFR\nUVGxIX4wjBQjJYTjFTB2cPi32+02xH6xGDRqRY1I2W+//baZnUXMVrGaPJhtUbFvsBvf2tqyDz74\nwMy0aYx3JLgnzsuxEWBgDg4OQhmUiDfGPqmcbXgm1xEYBphnFLvHu19cu7e3F5g5vp9nNszOY0rB\nFNdut6VrONgJ705vZvb666+bmdknn3zSqLvBYLCST89MC8uZ4eLQBNgpKaFlqQmI60Dl0APQJ/b3\n9xvCfHWe2gnnGFgIpAeDQTAb8g5N7SoVU1caa42ZXLPVyNFszvPm4dlsVpTs9VVCmd/V7p5/4/Jz\nUmazszrHXMExuVIMMd8b40LFWlMsAN8vxZht4jyRYpD8uf55Hp1OR5YZYPZYsSelfWJT5kr1w1g8\nrBK2g6/l8xVLnoO6jy8Ln8fHUiY2vlYxsJ7t4rmDLUT4PmGuiZXds/KxNvLyIK43QLFUFwkB4VEZ\nqYqKioqKioqKDfGDZKQ2xTp59sBYQPvEkcOxEo5FUce1AAc8VAwYVuNPnjxJ7tqZQShZBbdarSAy\nPTg4MLMz0SmYC5W7iwFdF5cJjAmzO6x3Qh3jWl8eszOGAyJjdqdXuiolUMaOBYzUtWvXGqETzNKu\nsnxfznXG78PXql2MYhVUADiuq1y7+ZADZs1o8cyEMdv22muvmdlqTjxfFpVvissH4T2Ce/r3BDhY\nHuoS5Tw8PGwwIJ1OJxmBXO3gFcvKefjwDPS1Fy9eNPrxaDSKRr73SIl3c8LeFGvD7AmOcxuiXYfD\nYahLdt/GvVWoCYDrillUvPumzg6MWBgPf2/lIMHnKfE4/z/l7MLAXMSsjWILLjvydSlS2pmczkmx\nQUoDV/q8dc7hZ8bA+i8/TrkdWLOIORJzzMOHD1d0S4DXGytLkv879Q6qLrms/l6psbJpf/iTXkjh\ngwtzzzpRaWFmQqM+e/bMfv3rX6/8pnDr1i37+7//ezM7j748n8/lxw348ssvs/dlrDMZYhGUM0Gi\nw/PiCmYBXkilzEy7u7sNLyFOneI/Enwee4QB165dC3UDvP322w1x9HA4bCRn7nQ6YUAoMxnf19+P\n6xeT9enpaagjlHOxWDQWUOpDwFBJjteFqvvnz5/bm2++aWbnCykVc4uhxoVaSKFPcNl54sO7P3jw\nwMzMPvzww4a5cG9vr7GQUibUUvCClSOv+8n1+Pg4iFZzUO2uTLJsflXxjTxYGA3woohjryFBdMpM\na7Yaj0o9j++LspqVxzRTnroxkbY32SlwehSMmVhcJLWQUh9SnOcXVHyeSgulsEmst9h9UBb/keY5\nid9RCemBlHcfC9pLF4ybLARUTK6cSdEnljc774MgIPb39+0v//Ivzez8+/T06dMw3yDGnJk1HILU\nc19//fXgMAbTeM48pxbruQWtR8ncVU17FRUVFRUVFRUb4k+akUqZElJotVoNhsOsjDF65513wnn/\n9E//FK4D84IV7vHxcVi1lzJRrxJqVa1cppU5y8cWMjs3/Zids13KxMKxcTxU7KiPPvooCHKBO3fu\nNJgrNt0xg4N7vv/+++G5Pr4Y7yZ5R4V7cr+CWQn32N7eboiDzawRYVqh0+lItonz/Zmd7YqUm78X\nZ3Y6nWJzEICy7+7uhndCP9ja2mqwAAzVXjj/+vXrDfMr7/bAYB0cHITzVPmUswbH+lLDgu9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KfAolWVToe9N9VCm8XyZqv0slo0qQGED99oNGr0xfl8vjIBmJ2lTMH7slcUoExdwGg0arSrioNj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GFX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PwW3wODycw/2KBOpxOMWaFQ0PXI8Bguj5nGfoE102g0tC+xvuv1uvab1y82TlXkSFqGa5D+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\nQeA3mUxG9ybGqlgsmv4mP/rRj0RE5A9/+IN+B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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -12121,416 +590,29 @@ } ], "source": [ - "feat = net.blobs['pool5'].data[0]\n", - "vis_square(feat, padval=1)" + "feat = net.blobs['conv1'].data[0, :36]\n", + "vis_square(feat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The first fully connected layer, `fc6` (rectified)\n", - "\n", - "We show the output values and the histogram of the positive values" + "* The fifth layer after pooling, `pool5`" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlgAAAJPCAYAAACgtar/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xv8LEdd5//3OzdCEpIQAyeBBBKQICCQgITI9RAIBJQQ\n", - "RIEoGFkWXUVAVCTghYOiXBQVxXVXIRhZhPUHSww3ySHyVXA1LEsCIVwi/kBByQkriCDLLpjaP6Yn\n", - "ZzKne6YvVd3V3a/n43EeZ74zPVXV1bfPVFVXO4QgAAAAxHPQ0AUAAACYGgIsAACAyAiwAAAAIiPA\n", - 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d6fwfzz33XNa6H/3oR7UeN7r2jh49msQmJiaS2LVr12rNZXNzs3jbfjUZN+Hi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\nos+lqLE+V9S4/fTTT2dte+7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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -12538,361 +620,31 @@ } ], "source": [ - "feat = net.blobs['fc6'].data[0]\n", - "plt.subplot(2, 1, 1)\n", - "plt.plot(feat.flat)\n", - "plt.subplot(2, 1, 2)\n", - "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)" + "feat = net.blobs['pool5'].data[0]\n", + "vis_square(feat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The second fully connected layer, `fc7` (rectified)" + "* The first fully connected layer, `fc6` (rectified)\n", + "\n", + " We show the output values and the histogram of the positive values" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlcAAAJPCAYAAABRvvFyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xv0LGdd5/vPJ3dygRADOzGJJsgdkWRxNyAb5BJw5OaI\n", - "okBE5CAiIJ6jgs6Y7XgDR5DjcMRZQ4KRYXB00JyIoyYoP424JKIJCTcjZ5JlgskOs4gKXkGe80dX\n", - 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MViiLF298EU1brscek/78Z/9lSmv16s7+2YZ9tO5i2Nebhv3ev8rvIox1o8ZaLl98PEy0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\nAIBnBFgAAACeEWABAAB4RoAFAADgGQEWAACAZxX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -12900,7 +652,7 @@ } ], "source": [ - "feat = net.blobs['fc7'].data[0]\n", + "feat = net.blobs['fc6'].data[0]\n", "plt.subplot(2, 1, 1)\n", "plt.plot(feat.flat)\n", "plt.subplot(2, 1, 2)\n", @@ -12911,12 +663,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The final probability output, `prob`" + "* The final probability output, `prob`" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -12924,191 +676,18 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 38, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAmEAAAJPCAYAAAA0UwMNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X2Q7md93/fPV0fGMuLZphYWcnBAtoEB29iVabCdg00Y\n", - "hXEsppkxCD+kDkNoU9m0zXQI6YyR23/atJ0kDgmRXcVJXGJNkgKRW4jASc+YOg4gm4BjJCoFa6oH\n", - 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -13117,6 +696,7 @@ ], "source": [ "feat = net.blobs['prob'].data[0]\n", + "plt.figure(figsize=(15, 3))\n", "plt.plot(feat.flat)" ] }, @@ -13124,38 +704,51 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's see the top 5 predicted labels." + "Note the cluster of strong predictions; the labels are sorted semantically. The top peaks correspond to the top predicted labels, as shown above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Try your own image\n", + "\n", + "Now we'll grab an image from the web and classify it using the steps above.\n", + "\n", + "* Try setting `my_image_url` to any JPEG image URL." ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n", - " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n", - " 'n02127052 lynx, catamount']\n" - ] - } - ], + "outputs": [], "source": [ - "# load labels\n", - "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", - "try:\n", - " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", - "except:\n", - " !../data/ilsvrc12/get_ilsvrc_aux.sh\n", - " labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n", + "# download an image\n", + "my_image_url = \"...\" # paste your URL here\n", + "# for example:\n", + "# my_image_url = \"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG\"\n", + "!wget -O image.jpg $my_image_url\n", + "\n", + "# transform it and copy it into the net\n", + "image = caffe.io.load_image('image.jpg')\n", + "net.blobs['data'].data[...] = transformer.preprocess('data', image)\n", + "\n", + "# perform classification\n", + "net.forward()\n", + "\n", + "# obtain the output probabilities\n", + "output_prob = net.blobs['prob'].data[0]\n", + "\n", + "# sort top five predictions from softmax output\n", + "top_inds = output_prob.argsort()[::-1][:5]\n", + "\n", + "plt.imshow(image)\n", "\n", - "# sort top k predictions from softmax output\n", - "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n", - "print labels[top_k]" + "print 'probabilities and labels:'\n", + "zip(output_prob[top_inds], labels[top_inds])" ] } ], @@ -13178,7 +771,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.9" + "version": "2.7.10" }, "priority": 1 }, From f1d245c78c09b7121b0e05c5ead6328396812612 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Thu, 4 Feb 2016 19:41:55 -0800 Subject: [PATCH 404/446] [data] get_mnist.sh rewrite; prevents prompt in tutorial notebooks --- data/mnist/get_mnist.sh | 23 +++++++---------------- 1 file changed, 7 insertions(+), 16 deletions(-) diff --git a/data/mnist/get_mnist.sh b/data/mnist/get_mnist.sh index 8eb6aeedf9f..6d875219489 100755 --- a/data/mnist/get_mnist.sh +++ b/data/mnist/get_mnist.sh @@ -6,19 +6,10 @@ cd $DIR echo "Downloading..." -wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz -wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz -wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz -wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz - -echo "Unzipping..." - -gunzip train-images-idx3-ubyte.gz -gunzip train-labels-idx1-ubyte.gz -gunzip t10k-images-idx3-ubyte.gz -gunzip t10k-labels-idx1-ubyte.gz - -# Creation is split out because leveldb sometimes causes segfault -# and needs to be re-created. - -echo "Done." +for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte +do + if [ ! -e $fname ]; then + wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz + gunzip ${fname}.gz + fi +done From 5f50a1f773cb01bb8dd8c0bdb49aab0d586de083 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Tue, 23 Feb 2016 23:46:49 -0800 Subject: [PATCH 405/446] [example] improve learning LeNet notebook - add subheadings and list steps for structure - edit text and comments for clarity - switch paths and use chdir for idempotency of scripts [shelhamer] - title accuracy plots, rename ip -> fc to fit common naming, and rename output layer -> score [shelhamer] - add experimentation section [shelhamer] --- examples/01-learning-lenet.ipynb | 4680 ++------------------- examples/mnist/lenet_auto_solver.prototxt | 6 +- 2 files changed, 389 insertions(+), 4297 deletions(-) diff --git a/examples/01-learning-lenet.ipynb b/examples/01-learning-lenet.ipynb index 3562c7adaf2..1c328260dfa 100644 --- a/examples/01-learning-lenet.ipynb +++ b/examples/01-learning-lenet.ipynb @@ -4,11 +4,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Python solving with LeNet\n", + "# Solving in Python with LeNet\n", "\n", "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Set up the Python environment: we'll use the `pylab` import for numpy and plot inline." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -17,8 +31,15 @@ }, "outputs": [], "source": [ - "import os\n", - "os.chdir('..')" + "from pylab import *\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Import `caffe`, adding it to `sys.path` if needed. Make sure you've built pycaffe." ] }, { @@ -29,19 +50,18 @@ }, "outputs": [], "source": [ - "import sys\n", - "sys.path.insert(0, './python')\n", - "import caffe\n", + "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", "\n", - "from pylab import *\n", - "%matplotlib inline" + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "import caffe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll be running the provided LeNet example (make sure you've downloaded the data and created the databases, as below)." + "* We'll be using the provided LeNet example data and networks (make sure you've downloaded the data and created the databases, as below)." ] }, { @@ -56,72 +76,36 @@ "output_type": "stream", "text": [ "Downloading...\n", - "--2015-06-30 14:41:56-- http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", - "Resolving yann.lecun.com... 128.122.47.89\n", - "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 9912422 (9.5M) [application/x-gzip]\n", - "Saving to: 'train-images-idx3-ubyte.gz'\n", - "\n", - "train-images-idx3-u 100%[=====================>] 9.45M 146KB/s in 57s \n", - "\n", - "2015-06-30 14:42:53 (171 KB/s) - 'train-images-idx3-ubyte.gz' saved [9912422/9912422]\n", - "\n", - "--2015-06-30 14:42:53-- http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", - "Resolving yann.lecun.com... 128.122.47.89\n", - "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 28881 (28K) [application/x-gzip]\n", - "Saving to: 'train-labels-idx1-ubyte.gz'\n", - "\n", - "train-labels-idx1-u 100%[=====================>] 28.20K 107KB/s in 0.3s \n", - "\n", - "2015-06-30 14:42:53 (107 KB/s) - 'train-labels-idx1-ubyte.gz' saved [28881/28881]\n", - "\n", - "--2015-06-30 14:42:53-- http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", - "Resolving yann.lecun.com... 128.122.47.89\n", - "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 1648877 (1.6M) [application/x-gzip]\n", - "Saving to: 't10k-images-idx3-ubyte.gz'\n", - "\n", - "t10k-images-idx3-ub 100%[=====================>] 1.57M 205KB/s in 8.2s \n", - "\n", - "2015-06-30 14:43:02 (197 KB/s) - 't10k-images-idx3-ubyte.gz' saved [1648877/1648877]\n", - "\n", - "--2015-06-30 14:43:02-- http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", - "Resolving yann.lecun.com... 128.122.47.89\n", - "Connecting to yann.lecun.com|128.122.47.89|:80... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4542 (4.4K) [application/x-gzip]\n", - "Saving to: 't10k-labels-idx1-ubyte.gz'\n", - "\n", - "t10k-labels-idx1-ub 100%[=====================>] 4.44K 26.9KB/s in 0.2s \n", - "\n", - "2015-06-30 14:43:02 (26.9 KB/s) - 't10k-labels-idx1-ubyte.gz' saved [4542/4542]\n", - "\n", - "Unzipping...\n", - "Done.\n", "Creating lmdb...\n", "Done.\n" ] } ], "source": [ - "# Download and prepare data\n", + "# run scripts from caffe root\n", + "import os\n", + "os.chdir(caffe_root)\n", + "# Download data\n", "!data/mnist/get_mnist.sh\n", - "!examples/mnist/create_mnist.sh" + "# Prepare data\n", + "!examples/mnist/create_mnist.sh\n", + "# back to examples\n", + "os.chdir('examples')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We need two external files to help out:\n", - "* the net prototxt, defining the architecture and pointing to the train/test data\n", - "* the solver prototxt, defining the learning parameters\n", + "### 2. Creating the net \n", + "\n", + "Now let's make a variant of LeNet, the classic 1989 convnet architecture.\n", "\n", - "We start with the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n", + "We'll need two external files to help out:\n", + "* the net `prototxt`, defining the architecture and pointing to the train/test data\n", + "* the solver `prototxt`, defining the learning parameters\n", + "\n", + "We start by creating the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n", "\n", "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`." ] @@ -134,36 +118,38 @@ }, "outputs": [], "source": [ - "from caffe import layers as L\n", - "from caffe import params as P\n", + "from caffe import layers as L, params as P\n", "\n", "def lenet(lmdb, batch_size):\n", " # our version of LeNet: a series of linear and simple nonlinear transformations\n", " n = caffe.NetSpec()\n", + " \n", " n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n", " transform_param=dict(scale=1./255), ntop=2)\n", + " \n", " n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n", " n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", " n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n", " n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", - " n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n", - " n.relu1 = L.ReLU(n.ip1, in_place=True)\n", - " n.ip2 = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n", - " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n", + " n.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n", + " n.relu1 = L.ReLU(n.fc1, in_place=True)\n", + " n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n", + " n.loss = L.SoftmaxWithLoss(n.score, n.label)\n", + " \n", " return n.to_proto()\n", " \n", - "with open('examples/mnist/lenet_auto_train.prototxt', 'w') as f:\n", - " f.write(str(lenet('examples/mnist/mnist_train_lmdb', 64)))\n", + "with open('mnist/lenet_auto_train.prototxt', 'w') as f:\n", + " f.write(str(lenet('mnist/mnist_train_lmdb', 64)))\n", " \n", - "with open('examples/mnist/lenet_auto_test.prototxt', 'w') as f:\n", - " f.write(str(lenet('examples/mnist/mnist_test_lmdb', 100)))" + "with open('mnist/lenet_auto_test.prototxt', 'w') as f:\n", + " f.write(str(lenet('mnist/mnist_test_lmdb', 100)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The net has been written to disk in more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net." + "The net has been written to disk in a more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net." ] }, { @@ -186,7 +172,7 @@ " scale: 0.00392156862745\r\n", " }\r\n", " data_param {\r\n", - " source: \"examples/mnist/mnist_train_lmdb\"\r\n", + " source: \"mnist/mnist_train_lmdb\"\r\n", " batch_size: 64\r\n", " backend: LMDB\r\n", " }\r\n", @@ -240,10 +226,10 @@ " }\r\n", "}\r\n", "layer {\r\n", - " name: \"ip1\"\r\n", + " name: \"fc1\"\r\n", " type: \"InnerProduct\"\r\n", " bottom: \"pool2\"\r\n", - " top: \"ip1\"\r\n", + " top: \"fc1\"\r\n", " inner_product_param {\r\n", " num_output: 500\r\n", " weight_filler {\r\n", @@ -254,14 +240,14 @@ "layer {\r\n", " name: \"relu1\"\r\n", " type: \"ReLU\"\r\n", - " bottom: \"ip1\"\r\n", - " top: \"ip1\"\r\n", + " bottom: \"fc1\"\r\n", + " top: \"fc1\"\r\n", "}\r\n", "layer {\r\n", - " name: \"ip2\"\r\n", + " name: \"score\"\r\n", " type: \"InnerProduct\"\r\n", - " bottom: \"ip1\"\r\n", - " top: \"ip2\"\r\n", + " bottom: \"fc1\"\r\n", + " top: \"score\"\r\n", " inner_product_param {\r\n", " num_output: 10\r\n", " weight_filler {\r\n", @@ -272,7 +258,7 @@ "layer {\r\n", " name: \"loss\"\r\n", " type: \"SoftmaxWithLoss\"\r\n", - " bottom: \"ip2\"\r\n", + " bottom: \"score\"\r\n", " bottom: \"label\"\r\n", " top: \"loss\"\r\n", "}\r\n" @@ -280,14 +266,14 @@ } ], "source": [ - "!cat examples/mnist/lenet_auto_train.prototxt" + "!cat mnist/lenet_auto_train.prototxt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's see the learning parameters, which are also written as a `prototxt` file. We're using SGD with momentum, weight decay, and a specific learning rate schedule." + "Now let's see the learning parameters, which are also written as a `prototxt` file (already provided on disk). We're using SGD with momentum, weight decay, and a specific learning rate schedule." ] }, { @@ -302,8 +288,8 @@ "output_type": "stream", "text": [ "# The train/test net protocol buffer definition\r\n", - "train_net: \"examples/mnist/lenet_auto_train.prototxt\"\r\n", - "test_net: \"examples/mnist/lenet_auto_test.prototxt\"\r\n", + "train_net: \"mnist/lenet_auto_train.prototxt\"\r\n", + "test_net: \"mnist/lenet_auto_test.prototxt\"\r\n", "# test_iter specifies how many forward passes the test should carry out.\r\n", "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n", "# covering the full 10,000 testing images.\r\n", @@ -324,39 +310,44 @@ "max_iter: 10000\r\n", "# snapshot intermediate results\r\n", "snapshot: 5000\r\n", - "snapshot_prefix: \"examples/mnist/lenet\"\r\n" + "snapshot_prefix: \"mnist/lenet\"\r\n" ] } ], "source": [ - "!cat examples/mnist/lenet_auto_solver.prototxt" + "!cat mnist/lenet_auto_solver.prototxt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's pick a device and load the solver. We'll use SGD (with momentum), but Adagrad and Nesterov's accelerated gradient are also available." + "### 3. Loading and checking the solver\n", + "\n", + "* Let's pick a device and load the solver. We'll use SGD (with momentum), but other methods (such as Adagrad and Nesterov's accelerated gradient) are also available." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ "caffe.set_device(0)\n", "caffe.set_mode_gpu()\n", - "solver = caffe.SGDSolver('examples/mnist/lenet_auto_solver.prototxt')" + "\n", + "### load the solver and create train and test nets\n", + "solver = None # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n", + "solver = caffe.SGDSolver('mnist/lenet_auto_solver.prototxt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)." + "* To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)." ] }, { @@ -376,8 +367,8 @@ " ('pool1', (64, 20, 12, 12)),\n", " ('conv2', (64, 50, 8, 8)),\n", " ('pool2', (64, 50, 4, 4)),\n", - " ('ip1', (64, 500)),\n", - " ('ip2', (64, 10)),\n", + " ('fc1', (64, 500)),\n", + " ('score', (64, 10)),\n", " ('loss', ())]" ] }, @@ -403,8 +394,8 @@ "text/plain": [ "[('conv1', (20, 1, 5, 5)),\n", " ('conv2', (50, 20, 5, 5)),\n", - " ('ip1', (500, 800)),\n", - " ('ip2', (10, 500))]" + " ('fc1', (500, 800)),\n", + " ('score', (10, 500))]" ] }, "execution_count": 9, @@ -413,7 +404,7 @@ } ], "source": [ - "# just print the weight sizes (not biases)\n", + "# just print the weight sizes (we'll omit the biases)\n", "[(k, v[0].data.shape) for k, v in solver.net.params.items()]" ] }, @@ -421,7 +412,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data." + "* Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data." ] }, { @@ -434,7 +425,7 @@ { "data": { "text/plain": [ - "{'loss': array(2.301163673400879, dtype=float32)}" + "{'loss': array(2.365971088409424, dtype=float32)}" ] }, "execution_count": 10, @@ -458,216 +449,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 5. 0. 4. 1. 9. 2. 1. 3.]\n" + "train labels: [ 5. 0. 4. 1. 9. 2. 1. 3.]\n" ] }, { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAWwAAABKCAYAAACfHW4mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJztvXlQW1me5/s5EhJaECAJhEBgdrMbDNjgtNNOp7d02pk1\n", - "mVlZW1dWd0XH9ERMzxIzEzE1M3/M1HvzIt68iZjpF9HRPdFvpqeqZ6ajJyozy5VbpZ1e0k4n6R0w\n", - "JBizrwIJxCYJgQTc9wfcW+D0KiOwK+8ngkBcJN2jo3N/95zf+f5+PyFJEioqKioqzz6arW6AioqK\n", - "isrjoRpsFRUVlecE1WCrqKioPCeoBltFRUXlOUE12CoqKirPCarBVlFRUXlOiNpgCyFeEUJ0CCG6\n", - "hBA/28hGqaioqKh8ExGNDlsIoQXuAoeBEeAG8ENJku5sbPNUVFRUVGSinWHvBrolSeqXJCkC/G/g\n", - 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -676,8 +465,8 @@ ], "source": [ "# we use a little trick to tile the first eight images\n", - "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n", - "print solver.net.blobs['label'].data[:8]" + "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n", + "print 'train labels:', solver.net.blobs['label'].data[:8]" ] }, { @@ -691,204 +480,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 7. 2. 1. 0. 4. 1. 4. 9.]\n" + "test labels: [ 7. 2. 1. 0. 4. 1. 4. 9.]\n" ] }, { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAWwAAABKCAYAAACfHW4mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJztnWlwXNd153+3V3RjaaDR2Bs7sRMgQIKgKAokuIgUpcg2\n", - "q+QljstO4kpS9iQzlUlqMpkPSWZSlclM1SSTmg+umrI9ZWdGViS5ZMuyJVIUSJEUwA0QSew7QKCx\n", - "Aw2ggd4bbz4A7wncRABEo4Ho/apYbLzeTt9+fd695/7POUKSJFRUVFRUdj6aSBugoqKiorI+VIet\n", - 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -896,17 +495,19 @@ } ], "source": [ - "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray')\n", - "print solver.test_nets[0].blobs['label'].data[:8]" + "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n", + "print 'test labels:', solver.test_nets[0].blobs['label'].data[:8]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "### 4. Stepping the solver\n", + "\n", "Both train and test nets seem to be loading data, and to have correct labels.\n", "\n", - "Let's take one step of (minibatch) SGD and see what happens." + "* Let's take one step of (minibatch) SGD and see what happens." ] }, { @@ -937,7 +538,7 @@ { "data": { "text/plain": [ - "" + "(-0.5, 24.5, 19.5, -0.5)" ] }, "execution_count": 14, @@ -946,439 +547,9 @@ }, { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAATQAAAD7CAYAAADkSGhKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJztvV+obt113jfWOfvYcmSLEtvfJ/FZqnSR4siWsS8sG9Ii\n", - "XZSgEEiam8QCU18kJZg2LaUXcS6cpO1Fm4KMIYFQ6j84dew0UOy6hqRxikuNLxwLkkpuJepgCUup\n", - "8snQmqb6952zz+rFd8b5nv3s5xljzPfde7/7HL8DFnOuudaaa84xx/yNMdda797bvu9xlrOc5Swv\n", - "gzw4dQPOcpaznOWm5Ay0s5zlLC+NnIF2lrOc5aWRM9DOcpazvDRyBtpZznKWl0bOQDvLWc7y0sjF\n", - "bVW8bdv5e5CznOUstyL7vm+q/GCgbdv2kYj48Yh4GBE/se/73+BzfviHf/jadR//+Mfj+7//++Ph\n", - 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jFTwwn93j7J28IyBzl56sTpaurot9FallO3DApo6nS85qMkS9O7qfyl2WnLeCHYvQYu8o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\nDppK7vaW09lj2ulwVUYVfUUegxuDGe4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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1387,13 +558,15 @@ ], "source": [ "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n", - " .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray')" + " .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray'); axis('off')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "### 5. Writing a custom training loop\n", + "\n", "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n", "Note that this process will be the same as if training through the `caffe` binary. In particular:\n", "* logging will continue to happen as normal\n", @@ -1424,8 +597,8 @@ "Iteration 125 testing...\n", "Iteration 150 testing...\n", "Iteration 175 testing...\n", - "CPU times: user 12.3 s, sys: 3.96 s, total: 16.2 s\n", - "Wall time: 15.7 s\n" + "CPU times: user 12.6 s, sys: 2.4 s, total: 15 s\n", + "Wall time: 14.4 s\n" ] } ], @@ -1448,7 +621,7 @@ " # store the output on the first test batch\n", " # (start the forward pass at conv1 to avoid loading new data)\n", " solver.test_nets[0].forward(start='conv1')\n", - " output[it] = solver.test_nets[0].blobs['ip2'].data[:8]\n", + " output[it] = solver.test_nets[0].blobs['score'].data[:8]\n", " \n", " # run a full test every so often\n", " # (Caffe can also do this for us and write to a log, but we show here\n", @@ -1458,7 +631,7 @@ " correct = 0\n", " for test_it in range(100):\n", " solver.test_nets[0].forward()\n", - " correct += sum(solver.test_nets[0].blobs['ip2'].data.argmax(1)\n", + " correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n", " == solver.test_nets[0].blobs['label'].data)\n", " test_acc[it // test_interval] = correct / 1e4" ] @@ -1467,7 +640,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's plot the train loss and test accuracy." + "* Let's plot the train loss and test accuracy." ] }, { @@ -1480,7 +653,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -1489,331 +662,9 @@ }, { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAaAAAAEPCAYAAAAEfBBiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXm4HGWV/z9fwhK2JIRAgCTsYYkswsgiiwYBRVRwGxV1\n", - "dNRxcEGZUcdtVBhHZ3Abcf8xiruCjguigohIANmXQBISIAECYd9CSFgTOL8/zlvpun2r+1bf23V7\n", - "uefzPP10d9XbVe+t2/1+65z3vOfIzAiCIAiC0WadTncgCIIgGJuEAAVBEAQdIQQoCIIg6AghQEEQ\n", - "BEFHCAEKgiAIOkIIUBAEQdARKhMgSTMkXSjpRkkLJH2woM1sSSskzU2PT1XVnyAIgrGOpO9Lul/S\n", - "/CZtvi5psaQbJO1TZX/WrfDYq4F/NbPrJW0CXCvpfDNbVNfuIjM7psJ+BEEQBM4PgG8APy7aKelo\n", - 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tVTHRhi302f4ujBpV6KY4juMUE41VdXe4oKofBgVME5HEAnpNRIbWqmlFwmHv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\n2Plt1cru/ON88KEADR5c+0CEX/7Sbgy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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1827,7 +678,8 @@ "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n", "ax1.set_xlabel('iteration')\n", "ax1.set_ylabel('train loss')\n", - "ax2.set_ylabel('test accuracy')" + "ax2.set_ylabel('test accuracy')\n", + "ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))" ] }, { @@ -1836,7 +688,7 @@ "source": [ "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n", "\n", - "Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence." + "* Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence." ] }, { @@ -1849,109 +701,9 @@ "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAI0AAACPCAYAAADHlliuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAFZtJREFUeJztnVtsY8d5x//f4Z2H94skaiXvemUbsAsD9otbwA2ahyCw\n", - "USBpXxoYKFD0EvShN7QPddyHJo9pgAZF+1CgiB30hqRFCxfpQ1vbRQukD724sGOnaydZY8XVihJF\n", - "iXfykDwipw/kNzuHklbiRRRJzQ8Y8OgsdXYk/vXNN9988w0JIaDRjIJx1R3QLB5aNJqR0aLRjIwW\n", - "jWZktGg0I6NFoxmZsUVDRC8R0cdE9CMienWandLMNzROnIaIXAB+AOAzAHYB/A+AV4QQH023e5p5\n", - "ZFxL8wKAu0KIbSGEDeDbAD4/vW5p5hn3mN93A8CO8vUDAD+uvoGIdKh5wRFC0Gn3x7U0WhDXmHFF\n", - "swtgU/l6E31ro7kGjCuadwE8SUS3iMgL4AsAvjO9bmnmmbF8GiHEMRH9OoB/AeAC8LqeOV0fxppy\n", - "X+jB2hFeeKbtCGuuMVo0mpHRotGMjBaNZmS0aDQjo0WjGRktGs3IaNFoRkaLRjMyWjSakdGi0YzM\n", - "uElYAAAi2gZQBdAFYAshXphGpzTzzUSiQT8Z69NCiOI0OqNZDKYxPJ26EqpZXiYVjQDwDhG9S0Rf\n", - 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3544,7 +876,7 @@ "source": [ "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n", "\n", - "Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)." + "* Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)." ] }, { @@ -3557,109 +889,9 @@ "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAI0AAACPCAYAAADHlliuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAFZtJREFUeJztnVtsY8d5x//f4Z2H94skaiXvemUbsAsD9otbwA2ahyCw\n", - "USBpXxoYKFD0EvShN7QPddyHJo9pgAZF+1CgiB30hqRFCxfpQ1vbRQukD724sGOnaydZY8XVihJF\n", - "iXfykDwipw/kNzuHklbiRRRJzQ8Y8OgsdXYk/vXNN9988w0JIaDRjIJx1R3QLB5aNJqR0aLRjIwW\n", - "jWZktGg0I6NFoxmZsUVDRC8R0cdE9CMienWandLMNzROnIaIXAB+AOAzAHYB/A+AV4QQH023e5p5\n", - "ZFxL8wKAu0KIbSGEDeDbAD4/vW5p5hn3mN93A8CO8vUDAD+uvoGIdKh5wRFC0Gn3x7U0WhDXmHFF\n", - "swtgU/l6E31ro7kGjCuadwE8SUS3iMgL4AsAvjO9bmnmmbF8GiHEMRH9OoB/AeAC8LqeOV0fxppy\n", - "X+jB2hFeeKbtCGuuMVo0mpHRotGMjBaNZmS0aDQjo0WjGRktGs3IaNFoRkaLRjMyWjSakdGi0YzM\n", - "uElYAAAi2gZQBdAFYAshXphGpzTzzUSiQT8Z69NCiOI0OqNZDKYxPJ26EqpZXiYVjQDwDhG9S0Rf\n", - 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -5166,6 +1057,207 @@ " xlabel('iteration')\n", " ylabel('label')" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Experiment with architecture and optimization\n", + "\n", + "Now that we've defined, trained, and tested LeNet there are many possible next steps:\n", + "\n", + "- Define new architectures for comparison\n", + "- Tune optimization by setting `base_lr` and the like or simply training longer\n", + "- Switching the solver type from `SGD` to an adaptive method like `AdaDelta` or `Adam`\n", + "\n", + "Feel free to explore these directions by editing the all-in-one example that follows.\n", + "Look for \"`EDIT HERE`\" comments for suggested choice points.\n", + "\n", + "By default this defines a simple linear classifier as a baseline.\n", + "\n", + "In case your coffee hasn't kicked in and you'd like inspiration, try out\n", + "\n", + "1. Switch the nonlinearity from `ReLU` to `ELU` or a saturing nonlinearity like `Sigmoid`\n", + "2. Stack more fully connected and nonlinear layers\n", + "3. Search over learning rate 10x at a time (trying `0.1` and `0.001`)\n", + "4. Switch the solver type to `Adam` (this adaptive solver type should be less sensitive to hyperparameters, but no guarantees...)\n", + "5. Solve for longer by setting `niter` higher (to 500 or 1,000 for instance) to better show training differences" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 0 testing...\n", + "Iteration 25 testing...\n", + "Iteration 50 testing...\n", + "Iteration 75 testing...\n", + "Iteration 100 testing...\n", + "Iteration 125 testing...\n", + "Iteration 150 testing...\n", + "Iteration 175 testing...\n", + "Iteration 200 testing...\n", + "Iteration 225 testing...\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ObSIiqKpElpsBC4ETgFXAVOA8VZ0f2Se1Qs3fVXVgMdoXxwV3LzAXKzInWALC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\n8Zh0YhISptHOm5dbgKL06mVWSZs2iWrMlZXJAgR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_net_path = 'mnist/custom_auto_train.prototxt'\n", + "test_net_path = 'mnist/custom_auto_test.prototxt'\n", + "solver_config_path = 'mnist/custom_auto_solver.prototxt'\n", + "\n", + "### define net\n", + "def custom_net(lmdb, batch_size):\n", + " # define your own net!\n", + " n = caffe.NetSpec()\n", + " \n", + " # keep this data layer for all networks\n", + " n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n", + " transform_param=dict(scale=1./255), ntop=2)\n", + " \n", + " # EDIT HERE to try different networks\n", + " # this single layer defines a simple linear classifier\n", + " # (in particular this defines a multiway logistic regression)\n", + " n.score = L.InnerProduct(n.data, num_output=10, weight_filler=dict(type='xavier'))\n", + " \n", + " # EDIT HERE this is the LeNet variant we have already tried\n", + " # n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n", + " # n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", + " # n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n", + " # n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n", + " # n.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n", + " # EDIT HERE consider L.ELU or L.Sigmoid for the nonlinearity\n", + " # n.relu1 = L.ReLU(n.fc1, in_place=True)\n", + " # n.score = L.InnerProduct(n.fc1, num_output=10, weight_filler=dict(type='xavier'))\n", + " \n", + " # keep this loss layer for all networks\n", + " n.loss = L.SoftmaxWithLoss(n.score, n.label)\n", + " \n", + " return n.to_proto()\n", + "\n", + "with open(train_net_path, 'w') as f:\n", + " f.write(str(custom_net('mnist/mnist_train_lmdb', 64))) \n", + "with open(test_net_path, 'w') as f:\n", + " f.write(str(custom_net('mnist/mnist_test_lmdb', 100)))\n", + "\n", + "### define solver\n", + "from caffe.proto import caffe_pb2\n", + "s = caffe_pb2.SolverParameter()\n", + "\n", + "# Set a seed for reproducible experiments:\n", + "# this controls for randomization in training.\n", + "s.random_seed = 0xCAFFE\n", + "\n", + "# Specify locations of the train and (maybe) test networks.\n", + "s.train_net = train_net_path\n", + "s.test_net.append(test_net_path)\n", + "s.test_interval = 500 # Test after every 500 training iterations.\n", + "s.test_iter.append(100) # Test on 100 batches each time we test.\n", + "\n", + "s.max_iter = 10000 # no. of times to update the net (training iterations)\n", + " \n", + "# EDIT HERE to try different solvers\n", + "# solver types include \"SGD\", \"Adam\", and \"Nesterov\" among others.\n", + "s.type = \"SGD\"\n", + "\n", + "# Set the initial learning rate for SGD.\n", + "s.base_lr = 0.01 # EDIT HERE to try different learning rates\n", + "# Set momentum to accelerate learning by\n", + "# taking weighted average of current and previous updates.\n", + "s.momentum = 0.9\n", + "# Set weight decay to regularize and prevent overfitting\n", + "s.weight_decay = 5e-4\n", + "\n", + "# Set `lr_policy` to define how the learning rate changes during training.\n", + "# This is the same policy as our default LeNet.\n", + "s.lr_policy = 'inv'\n", + "s.gamma = 0.0001\n", + "s.power = 0.75\n", + "# EDIT HERE to try the fixed rate (and compare with adaptive solvers)\n", + "# `fixed` is the simplest policy that keeps the learning rate constant.\n", + "# s.lr_policy = 'fixed'\n", + "\n", + "# Display the current training loss and accuracy every 1000 iterations.\n", + "s.display = 1000\n", + "\n", + "# Snapshots are files used to store networks we've trained.\n", + "# We'll snapshot every 5K iterations -- twice during training.\n", + "s.snapshot = 5000\n", + "s.snapshot_prefix = 'mnist/custom_net'\n", + "\n", + "# Train on the GPU\n", + "s.solver_mode = caffe_pb2.SolverParameter.GPU\n", + "\n", + "# Write the solver to a temporary file and return its filename.\n", + "with open(solver_config_path, 'w') as f:\n", + " f.write(str(s))\n", + "\n", + "### load the solver and create train and test nets\n", + "solver = None # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n", + "solver = caffe.get_solver(solver_config_path)\n", + "\n", + "### solve\n", + "niter = 250 # EDIT HERE increase to train for longer\n", + "test_interval = niter / 10\n", + "# losses will also be stored in the log\n", + "train_loss = zeros(niter)\n", + "test_acc = zeros(int(np.ceil(niter / test_interval)))\n", + "\n", + "# the main solver loop\n", + "for it in range(niter):\n", + " solver.step(1) # SGD by Caffe\n", + " \n", + " # store the train loss\n", + " train_loss[it] = solver.net.blobs['loss'].data\n", + " \n", + " # run a full test every so often\n", + " # (Caffe can also do this for us and write to a log, but we show here\n", + " # how to do it directly in Python, where more complicated things are easier.)\n", + " if it % test_interval == 0:\n", + " print 'Iteration', it, 'testing...'\n", + " correct = 0\n", + " for test_it in range(100):\n", + " solver.test_nets[0].forward()\n", + " correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n", + " == solver.test_nets[0].blobs['label'].data)\n", + " test_acc[it // test_interval] = correct / 1e4\n", + "\n", + "_, ax1 = subplots()\n", + "ax2 = ax1.twinx()\n", + "ax1.plot(arange(niter), train_loss)\n", + "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n", + "ax1.set_xlabel('iteration')\n", + "ax1.set_ylabel('train loss')\n", + "ax2.set_ylabel('test accuracy')\n", + "ax2.set_title('Custom Test Accuracy: {:.2f}'.format(test_acc[-1]))" + ] } ], "metadata": { @@ -5187,7 +1279,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.9" + "version": "2.7.10" }, "priority": 2 }, diff --git a/examples/mnist/lenet_auto_solver.prototxt b/examples/mnist/lenet_auto_solver.prototxt index fa4bbf02710..481c84491f7 100644 --- a/examples/mnist/lenet_auto_solver.prototxt +++ b/examples/mnist/lenet_auto_solver.prototxt @@ -1,6 +1,6 @@ # The train/test net protocol buffer definition -train_net: "examples/mnist/lenet_auto_train.prototxt" -test_net: "examples/mnist/lenet_auto_test.prototxt" +train_net: "mnist/lenet_auto_train.prototxt" +test_net: "mnist/lenet_auto_test.prototxt" # test_iter specifies how many forward passes the test should carry out. # In the case of MNIST, we have test batch size 100 and 100 test iterations, # covering the full 10,000 testing images. @@ -21,4 +21,4 @@ display: 100 max_iter: 10000 # snapshot intermediate results snapshot: 5000 -snapshot_prefix: "examples/mnist/lenet" +snapshot_prefix: "mnist/lenet" From 9580577804b2c73d485afc3418d44b08641b6377 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Tue, 23 Feb 2016 23:43:31 -0800 Subject: [PATCH 406/446] [example] improve fine-tuning notebook - add headings and text detail - make nets by net spec - define solvers inline through python protobuf - do two-stage fine-tuning (first last layer alone, then end-to-end) - show sample results --- examples/02-fine-tuning.ipynb | 1175 +++++++++++++++++++++++++++++++++ examples/03-fine-tuning.ipynb | 947 -------------------------- 2 files changed, 1175 insertions(+), 947 deletions(-) create mode 100644 examples/02-fine-tuning.ipynb delete mode 100644 examples/03-fine-tuning.ipynb diff --git a/examples/02-fine-tuning.ipynb b/examples/02-fine-tuning.ipynb new file mode 100644 index 00000000000..07ca8df4d74 --- /dev/null +++ b/examples/02-fine-tuning.ipynb @@ -0,0 +1,1175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fine-tuning a Pretrained Network for Style Recognition\n", + "\n", + "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n", + "\n", + "The advantage of this approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful generic visual feature that you can treat as a black box. On top of that, only a relatively small amount of data is needed for good performance on the target task." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we will need to prepare the data. This involves the following parts:\n", + "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n", + "(2) Download a subset of the overall Flickr style dataset for this demo.\n", + "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)\n", + "\n", + "import sys\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "import caffe\n", + "\n", + "caffe.set_device(0)\n", + "caffe.set_mode_gpu()\n", + "\n", + "import numpy as np\n", + "from pylab import *\n", + "%matplotlib inline\n", + "import tempfile\n", + "\n", + "# Helper function for deprocessing preprocessed images, e.g., for display.\n", + "def deprocess_net_image(image):\n", + " image = image.copy() # don't modify destructively\n", + " image = image[::-1] # BGR -> RGB\n", + " image = image.transpose(1, 2, 0) # CHW -> HWC\n", + " image += [123, 117, 104] # (approximately) undo mean subtraction\n", + "\n", + " # clamp values in [0, 255]\n", + " image[image < 0], image[image > 255] = 0, 255\n", + "\n", + " # round and cast from float32 to uint8\n", + " image = np.round(image)\n", + " image = np.require(image, dtype=np.uint8)\n", + "\n", + " return image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Setup and dataset download\n", + "\n", + "Download data required for this exercise.\n", + "\n", + "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n", + "- `download_model_binary.py` to download the pretrained reference model\n", + "- `finetune_flickr_style/assemble_data.py` downloadsd the style training and testing data\n", + "\n", + "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories. (To download the full dataset, set `full_dataset = True` in the cell below.)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading...\n", + "--2016-02-24 00:28:36-- http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n", + "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n", + "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 17858008 (17M) [application/octet-stream]\n", + "Saving to: ‘caffe_ilsvrc12.tar.gz’\n", + "\n", + "100%[======================================>] 17,858,008 112MB/s in 0.2s \n", + "\n", + "2016-02-24 00:28:36 (112 MB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n", + "\n", + "Unzipping...\n", + "Done.\n", + "Model already exists.\n", + "Downloading 2000 images with 7 workers...\n", + "Writing train/val for 1996 successfully downloaded images.\n" + ] + } + ], + "source": [ + "# Download just a small subset of the data for this exercise.\n", + "# (2000 of 80K images, 5 of 20 labels.)\n", + "# To download the entire dataset, set `full_dataset = True`.\n", + "full_dataset = False\n", + "if full_dataset:\n", + " NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1\n", + "else:\n", + " NUM_STYLE_IMAGES = 2000\n", + " NUM_STYLE_LABELS = 5\n", + "\n", + "# This downloads the ilsvrc auxiliary data (mean file, etc),\n", + "# and a subset of 2000 images for the style recognition task.\n", + "import os\n", + "os.chdir(caffe_root) # run scripts from caffe root\n", + "!data/ilsvrc12/get_ilsvrc_aux.sh\n", + "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n", + "!python examples/finetune_flickr_style/assemble_data.py \\\n", + " --workers=-1 --seed=1701 \\\n", + " --images=$NUM_STYLE_IMAGES --label=$NUM_STYLE_LABELS\n", + "# back to examples\n", + "os.chdir('examples')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define `weights`, the path to the ImageNet pretrained weights we just downloaded, and make sure it exists." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n", + "assert os.path.exists(weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the 1000 ImageNet labels from `ilsvrc12/synset_words.txt`, and the 5 style labels from `finetune_flickr_style/style_names.txt`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded ImageNet labels:\n", + "n01440764 tench, Tinca tinca\n", + "n01443537 goldfish, Carassius auratus\n", + "n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\n", + "n01491361 tiger shark, Galeocerdo cuvieri\n", + "n01494475 hammerhead, hammerhead shark\n", + "n01496331 electric ray, crampfish, numbfish, torpedo\n", + "n01498041 stingray\n", + "n01514668 cock\n", + "n01514859 hen\n", + "n01518878 ostrich, Struthio camelus\n", + "...\n", + "\n", + "Loaded style labels:\n", + "Detailed, Pastel, Melancholy, Noir, HDR\n" + ] + } + ], + "source": [ + "# Load ImageNet labels to imagenet_labels\n", + "imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n", + "imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\\t'))\n", + "assert len(imagenet_labels) == 1000\n", + "print 'Loaded ImageNet labels:\\n', '\\n'.join(imagenet_labels[:10] + ['...'])\n", + "\n", + "# Load style labels to style_labels\n", + "style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'\n", + "style_labels = list(np.loadtxt(style_label_file, str, delimiter='\\n'))\n", + "if NUM_STYLE_LABELS > 0:\n", + " style_labels = style_labels[:NUM_STYLE_LABELS]\n", + "print '\\nLoaded style labels:\\n', ', '.join(style_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Defining and running the nets\n", + "\n", + "We'll start by defining `caffenet`, a function which initializes the *CaffeNet* architecture (a minor variant on *AlexNet*), taking arguments specifying the data and number of output classes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "from caffe import layers as L\n", + "from caffe import params as P\n", + "\n", + "weight_param = dict(lr_mult=1, decay_mult=1)\n", + "bias_param = dict(lr_mult=2, decay_mult=0)\n", + "learned_param = [weight_param, bias_param]\n", + "\n", + "frozen_param = [dict(lr_mult=0)] * 2\n", + "\n", + "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,\n", + " param=learned_param,\n", + " weight_filler=dict(type='gaussian', std=0.01),\n", + " bias_filler=dict(type='constant', value=0.1)):\n", + " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n", + " num_output=nout, pad=pad, group=group,\n", + " param=param, weight_filler=weight_filler,\n", + " bias_filler=bias_filler)\n", + " return conv, L.ReLU(conv, in_place=True)\n", + "\n", + "def fc_relu(bottom, nout, param=learned_param,\n", + " weight_filler=dict(type='gaussian', std=0.005),\n", + " bias_filler=dict(type='constant', value=0.1)):\n", + " fc = L.InnerProduct(bottom, num_output=nout, param=param,\n", + " weight_filler=weight_filler,\n", + " bias_filler=bias_filler)\n", + " return fc, L.ReLU(fc, in_place=True)\n", + "\n", + "def max_pool(bottom, ks, stride=1):\n", + " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n", + "\n", + "def caffenet(data, label=None, train=True, num_classes=1000,\n", + " classifier_name='fc8', learn_all=False):\n", + " \"\"\"Returns a NetSpec specifying CaffeNet, following the original proto text\n", + " specification (./models/bvlc_reference_caffenet/train_val.prototxt).\"\"\"\n", + " n = caffe.NetSpec()\n", + " n.data = data\n", + " param = learned_param if learn_all else frozen_param\n", + " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)\n", + " n.pool1 = max_pool(n.relu1, 3, stride=2)\n", + " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n", + " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)\n", + " n.pool2 = max_pool(n.relu2, 3, stride=2)\n", + " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n", + " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)\n", + " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)\n", + " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)\n", + " n.pool5 = max_pool(n.relu5, 3, stride=2)\n", + " n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)\n", + " if train:\n", + " n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)\n", + " else:\n", + " fc7input = n.relu6\n", + " n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)\n", + " if train:\n", + " n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)\n", + " else:\n", + " fc8input = n.relu7\n", + " # always learn fc8 (param=learned_param)\n", + " fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)\n", + " # give fc8 the name specified by argument `classifier_name`\n", + " n.__setattr__(classifier_name, fc8)\n", + " if not train:\n", + " n.probs = L.Softmax(fc8)\n", + " if label is not None:\n", + " n.label = label\n", + " n.loss = L.SoftmaxWithLoss(fc8, n.label)\n", + " n.acc = L.Accuracy(fc8, n.label)\n", + " # write the net to a temporary file and return its filename\n", + " with tempfile.NamedTemporaryFile(delete=False) as f:\n", + " f.write(str(n.to_proto()))\n", + " return f.name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's create a *CaffeNet* that takes unlabeled \"dummy data\" as input, allowing us to set its input images externally and see what ImageNet classes it predicts." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))\n", + "imagenet_net_filename = caffenet(data=dummy_data, train=False)\n", + "imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define a function `style_net` which calls `caffenet` on data from the Flickr style dataset.\n", + "\n", + "The new network will also have the *CaffeNet* architecture, with differences in the input and output:\n", + "\n", + "- the input is the Flickr style data we downloaded, provided by an `ImageData` layer\n", + "- the output is a distribution over 20 classes rather than the original 1000 ImageNet classes\n", + "- the classification layer is renamed from `fc8` to `fc8_flickr` to tell Caffe not to load the original classifier (`fc8`) weights from the ImageNet-pretrained model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def style_net(train=True, learn_all=False, subset=None):\n", + " if subset is None:\n", + " subset = 'train' if train else 'test'\n", + " source = caffe_root + 'data/flickr_style/%s.txt' % subset\n", + " transform_param = dict(mirror=train, crop_size=227,\n", + " mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')\n", + " style_data, style_label = L.ImageData(\n", + " transform_param=transform_param, source=source,\n", + " batch_size=50, new_height=256, new_width=256, ntop=2)\n", + " return caffenet(data=style_data, label=style_label, train=train,\n", + " num_classes=NUM_STYLE_LABELS,\n", + " classifier_name='fc8_flickr',\n", + " learn_all=learn_all)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the `style_net` function defined above to initialize `untrained_style_net`, a *CaffeNet* with input images from the style dataset and weights from the pretrained ImageNet model.\n", + "\n", + "\n", + "Call `forward` on `untrained_style_net` to get a batch of style training data." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "untrained_style_net = caffe.Net(style_net(train=False, subset='train'),\n", + " weights, caffe.TEST)\n", + "untrained_style_net.forward()\n", + "style_data_batch = untrained_style_net.blobs['data'].data.copy()\n", + "style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pick one of the style net training images from the batch of 50 (we'll arbitrarily choose #8 here). Display it, then run it through `imagenet_net`, the ImageNet-pretrained network to view its top 5 predicted classes from the 1000 ImageNet classes.\n", + "\n", + "Below we chose an image where the network's predictions happen to be reasonable, as the image is of a beach, and \"sandbar\" and \"seashore\" both happen to be ImageNet-1000 categories. For other images, the predictions won't be this good, sometimes due to the network actually failing to recognize the object(s) present in the image, but perhaps even more often due to the fact that not all images contain an object from the (somewhat arbitrarily chosen) 1000 ImageNet categories. Modify the `batch_index` variable by changing its default setting of 8 to another value from 0-49 (since the batch size is 50) to see predictions for other images in the batch. (To go beyond this batch of 50 images, first rerun the *above* cell to load a fresh batch of data into `style_net`.)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def disp_preds(net, image, labels, k=5, name='ImageNet'):\n", + " input_blob = net.blobs['data']\n", + " net.blobs['data'].data[0, ...] = image\n", + " probs = net.forward(start='conv1')['probs'][0]\n", + " top_k = (-probs).argsort()[:k]\n", + " print 'top %d predicted %s labels =' % (k, name)\n", + " print '\\n'.join('\\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])\n", + " for i, p in enumerate(top_k))\n", + "\n", + "def disp_imagenet_preds(net, image):\n", + " disp_preds(net, image, imagenet_labels, name='ImageNet')\n", + "\n", + "def disp_style_preds(net, image):\n", + " disp_preds(net, image, style_labels, name='style')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "actual label = Melancholy\n" + ] + }, + { + "data": { + "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\ncDPDu2eav33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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "batch_index = 8\n", + "image = style_data_batch[batch_index]\n", + "plt.imshow(deprocess_net_image(image))\n", + "print 'actual label =', style_labels[style_label_batch[batch_index]]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted ImageNet labels =\n", + "\t(1) 69.89% n09421951 sandbar, sand bar\n", + "\t(2) 21.76% n09428293 seashore, coast, seacoast, sea-coast\n", + "\t(3) 3.22% n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\n", + "\t(4) 1.89% n04592741 wing\n", + "\t(5) 1.23% n09332890 lakeside, lakeshore\n" + ] + } + ], + "source": [ + "disp_imagenet_preds(imagenet_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also look at `untrained_style_net`'s predictions, but we won't see anything interesting as its classifier hasn't been trained yet.\n", + "\n", + "In fact, since we zero-initialized the classifier (see `caffenet` definition -- no `weight_filler` is passed to the final `InnerProduct` layer), the softmax inputs should be all zero and we should therefore see a predicted probability of 1/N for each label (for N labels). Since we set N = 5, we get a predicted probability of 20% for each class." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted style labels =\n", + "\t(1) 20.00% Detailed\n", + "\t(2) 20.00% Pastel\n", + "\t(3) 20.00% Melancholy\n", + "\t(4) 20.00% Noir\n", + "\t(5) 20.00% HDR\n" + ] + } + ], + "source": [ + "disp_style_preds(untrained_style_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also verify that the activations in layer `fc7` immediately before the classification layer are the same as (or very close to) those in the ImageNet-pretrained model, since both models are using the same pretrained weights in the `conv1` through `fc7` layers." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]\n", + "error = (diff ** 2).sum()\n", + "assert error < 1e-8" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Delete `untrained_style_net` to save memory. (Hang on to `imagenet_net` as we'll use it again later.)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "del untrained_style_net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Training the style classifier\n", + "\n", + "Now, we'll define a function `solver` to create our Caffe solvers, which are used to train the network (learn its weights). In this function we'll set values for various parameters used for learning, display, and \"snapshotting\" -- see the inline comments for explanations of what they mean. You may want to play with some of the learning parameters to see if you can improve on the results here!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from caffe.proto import caffe_pb2\n", + "\n", + "def solver(train_net_path, test_net_path=None, base_lr=0.001):\n", + " s = caffe_pb2.SolverParameter()\n", + "\n", + " # Specify locations of the train and (maybe) test networks.\n", + " s.train_net = train_net_path\n", + " if test_net_path is not None:\n", + " s.test_net.append(test_net_path)\n", + " s.test_interval = 1000 # Test after every 1000 training iterations.\n", + " s.test_iter.append(100) # Test on 100 batches each time we test.\n", + "\n", + " # The number of iterations over which to average the gradient.\n", + " # Effectively boosts the training batch size by the given factor, without\n", + " # affecting memory utilization.\n", + " s.iter_size = 1\n", + " \n", + " s.max_iter = 100000 # # of times to update the net (training iterations)\n", + " \n", + " # Solve using the stochastic gradient descent (SGD) algorithm.\n", + " # Other choices include 'Adam' and 'RMSProp'.\n", + " s.type = 'SGD'\n", + "\n", + " # Set the initial learning rate for SGD.\n", + " s.base_lr = base_lr\n", + "\n", + " # Set `lr_policy` to define how the learning rate changes during training.\n", + " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n", + " # every `stepsize` iterations.\n", + " s.lr_policy = 'step'\n", + " s.gamma = 0.1\n", + " s.stepsize = 20000\n", + "\n", + " # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n", + " # weighted average of the current gradient and previous gradients to make\n", + " # learning more stable. L2 weight decay regularizes learning, to help prevent\n", + " # the model from overfitting.\n", + " s.momentum = 0.9\n", + " s.weight_decay = 5e-4\n", + "\n", + " # Display the current training loss and accuracy every 1000 iterations.\n", + " s.display = 1000\n", + "\n", + " # Snapshots are files used to store networks we've trained. Here, we'll\n", + " # snapshot every 10K iterations -- ten times during training.\n", + " s.snapshot = 10000\n", + " s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'\n", + " \n", + " # Train on the GPU. Using the CPU to train large networks is very slow.\n", + " s.solver_mode = caffe_pb2.SolverParameter.GPU\n", + " \n", + " # Write the solver to a temporary file and return its filename.\n", + " with tempfile.NamedTemporaryFile(delete=False) as f:\n", + " f.write(str(s))\n", + " return f.name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll invoke the solver to train the style net's classification layer.\n", + "\n", + "For the record, if you want to train the network using only the command line tool, this is the command:\n", + "\n", + "\n", + "build/tools/caffe train \\\n", + " -solver models/finetune_flickr_style/solver.prototxt \\\n", + " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n", + " -gpu 0\n", + "\n", + "\n", + "However, we will train using Python in this example.\n", + "\n", + "We'll first define `run_solvers`, a function that takes a list of solvers and steps each one in a round robin manner, recording the accuracy and loss values each iteration. At the end, the learned weights are saved to a file." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def run_solvers(niter, solvers, disp_interval=10):\n", + " \"\"\"Run solvers for niter iterations,\n", + " returning the loss and accuracy recorded each iteration.\n", + " `solvers` is a list of (name, solver) tuples.\"\"\"\n", + " blobs = ('loss', 'acc')\n", + " loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n", + " for _ in blobs)\n", + " for it in range(niter):\n", + " for name, s in solvers:\n", + " s.step(1) # run a single SGD step in Caffe\n", + " loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()\n", + " for b in blobs)\n", + " if it % disp_interval == 0 or it + 1 == niter:\n", + " loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %\n", + " (n, loss[n][it], np.round(100*acc[n][it]))\n", + " for n, _ in solvers)\n", + " print '%3d) %s' % (it, loss_disp) \n", + " # Save the learned weights from both nets.\n", + " weight_dir = tempfile.mkdtemp()\n", + " weights = {}\n", + " for name, s in solvers:\n", + " filename = 'weights.%s.caffemodel' % name\n", + " weights[name] = os.path.join(weight_dir, filename)\n", + " s.net.save(weights[name])\n", + " return loss, acc, weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create and run solvers to train nets for the style recognition task. We'll create two solvers -- one (`style_solver`) will have its train net initialized to the ImageNet-pretrained weights (this is done by the call to the `copy_from` method), and the other (`scratch_style_solver`) will start from a *randomly* initialized net.\n", + "\n", + "During training, we should see that the ImageNet pretrained net is learning faster and attaining better accuracies than the scratch net." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running solvers for 200 iterations...\n", + " 0) pretrained: loss=1.609, acc=28%; scratch: loss=1.609, acc=28%\n", + " 10) pretrained: loss=1.293, acc=52%; scratch: loss=1.626, acc=14%\n", + " 20) pretrained: loss=1.110, acc=56%; scratch: loss=1.646, acc=10%\n", + " 30) pretrained: loss=1.084, acc=60%; scratch: loss=1.616, acc=20%\n", + " 40) pretrained: loss=0.898, acc=64%; scratch: loss=1.588, acc=26%\n", + " 50) pretrained: loss=1.024, acc=54%; scratch: loss=1.607, acc=32%\n", + " 60) pretrained: loss=0.925, acc=66%; scratch: loss=1.616, acc=20%\n", + " 70) pretrained: loss=0.861, acc=74%; scratch: loss=1.598, acc=24%\n", + " 80) pretrained: loss=0.967, acc=60%; scratch: loss=1.588, acc=30%\n", + " 90) pretrained: loss=1.274, acc=52%; scratch: loss=1.608, acc=20%\n", + "100) pretrained: loss=1.113, acc=62%; scratch: loss=1.588, acc=30%\n", + "110) pretrained: loss=0.922, acc=62%; scratch: loss=1.578, acc=36%\n", + "120) pretrained: loss=0.918, acc=62%; scratch: loss=1.599, acc=20%\n", + "130) pretrained: loss=0.959, acc=58%; scratch: loss=1.594, acc=22%\n", + "140) pretrained: loss=1.228, acc=50%; scratch: loss=1.608, acc=14%\n", + "150) pretrained: loss=0.727, acc=76%; scratch: loss=1.623, acc=16%\n", + "160) pretrained: loss=1.074, acc=66%; scratch: loss=1.607, acc=20%\n", + "170) pretrained: loss=0.887, acc=60%; scratch: loss=1.614, acc=20%\n", + "180) pretrained: loss=0.961, acc=62%; scratch: loss=1.614, acc=18%\n", + "190) pretrained: loss=0.737, acc=76%; scratch: loss=1.613, acc=18%\n", + "199) pretrained: loss=0.836, acc=70%; scratch: loss=1.614, acc=16%\n", + "Done.\n" + ] + } + ], + "source": [ + "niter = 200 # number of iterations to train\n", + "\n", + "# Reset style_solver as before.\n", + "style_solver_filename = solver(style_net(train=True))\n", + "style_solver = caffe.get_solver(style_solver_filename)\n", + "style_solver.net.copy_from(weights)\n", + "\n", + "# For reference, we also create a solver that isn't initialized from\n", + "# the pretrained ImageNet weights.\n", + "scratch_style_solver_filename = solver(style_net(train=True))\n", + "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n", + "\n", + "print 'Running solvers for %d iterations...' % niter\n", + "solvers = [('pretrained', style_solver),\n", + " ('scratch', scratch_style_solver)]\n", + "loss, acc, weights = run_solvers(niter, solvers)\n", + "print 'Done.'\n", + "\n", + "train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']\n", + "train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']\n", + "style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']\n", + "\n", + "# Delete solvers to save memory.\n", + "del style_solver, scratch_style_solver, solvers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the training loss and accuracy produced by the two training procedures. Notice how quickly the ImageNet pretrained model's loss value (blue) drops, and that the randomly initialized model's loss value (green) barely (if at all) improves from training only the classifier layer." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ROQeFwjsdShwuuADYvx84etT1dX2eYOtWYP16632UlgI9e/LPRnFISFCzsnYE\nVFhJofBOhxKHmBhePjQ7W3utpQUYPhzYsoV//89/gHfesd5HRQXQvTv/bBSHbt3YORiT3uEMK/3t\nb8CCBaE7XkdAhZUUCu90KHEAuNev7xF+9RVw4ACwejX/vnGj50ahogJISeGfjeIQFwdERblP8BdO\ncdi0CcjLC93xOgJKHBQK73RIcdA/9G+8AVx4IYeSamqAXbv8F4fYWF7tzRhaCpc4EAE//aTWJvAV\nh4Nn8q2vV/khhcKKcC8TGnDS0rTG/9gx4OuvecnPCy8ENm/mRLOnWHN5ubk41Ndr4mBMSjc2ams/\nhFIcjh0DysqUOPiKw8HfU48e/Pn16RPuM1IoIo8O6Rxk4//++8C113LOITaWcw1XXNE25xATYy4O\nclbWUIrDTz/x/0ocfMPh4O9RVZYpFNZ0SHGQjf/evcAZZ/DPY8cCb78NXHklN6YtLebv95SQ9uQc\nwlHKuns3cOKJShx8gYi/v5gYVVmmUHiiw4mDPqyUn89zLgHAr3/NjfbYsUBSEoePzNA7h7g493EO\nkZRz+Oknvp6OKg47dwKzZwd2nw4Hf4dC8HdlVpqsUCg6oDjonYNeHM4/H+jdGxgwwL2iSY9VzsFb\nWEmJQ+DZuLHtgxqNyJASwP8r56BQmNMhxUE2/Hl5mjicdhqQlcU9RmNFkx471UqRIA6yUqkji8Ox\nYzwoMZDoxaEjOoejR7WybYWiLXRIcSgt5Qa7spLDTJIePfh/fejJiJU46KuVIiGsJAXwpJOA6mr3\nsRcdgWCLQ0d0Dl99Bbz0UrjPQtER6HDiEBvLDfWePRxG6tTJfRtvzqEtCelQicOePbzmRKdOfJ7V\n1YE/RmkpT4UeLo4d4+8pENOwSzq6cygtDc81lZUB06eH/riK4BFUcRBCLBBCFAshsiz+fpsQYocQ\nYqcQYr0QYmQgjtuzJ8+hJENKZn8/dowb8TffdO11G52DnLLbbs5B9kYD2aCZceQI508AIDk5OKGl\no0eBFSsCt4iSHZYs0aY/kd9RTU3g9t/RnUO4xGH/fuCtt0J/XEXwCLZzWAjAU98zG8D5RDQSwCQA\nrwXioGlpwLZtnsWhpIQTnnffDfz97/x6YyM3FvHx/LvZrKxmzkE/8V5UlHnoKdAcOcJrTgAsDhUV\ngT+GFMYVKwK/byteeglYuZJ/lqGzQIaWfgnOwWzm4GBTX9/xPstIgyj4nU49QRUHIvoWgGWzRUTf\nE1HVz7+C3BnvAAAgAElEQVRuBGDRnPuGN+cgcw6bNwO/+x2vILdwoeYahODt/Jk+A+Cfg90jNYpD\nMJxDOMTh8GG+NoDFoW/fwM6BpJxDcKirU+IQbGbPBp59NnTHi6Scw70AlgViRz17Atu3e3cOW7YA\nl18OTJgAfPaZa0gJ8K+UFQhN3iFU4nDGGcDataFpRBsbgYICvraWFi4rHjZMOQdfCKc4hMOxBIsf\nf+QZjyOJgoLQThYZEXMrCSEuBHAPgLFW20yYMKH158zMTGRmZlruLy2Nb1Zv4pCfD/zzn+wUdu1y\nTUYDvs2tFA5xOOEE/jmY4tC/P/+8YQPg4SMPCLm5bJuPHGFhSE4GevUKnjiE2jnU1vLx9B2QQFNa\nyoM3Q01HCytlZ3P0IZKoqrKe2UGyZs0arFmzJiDHC7s4/JyEng9gHBFZhqD04uANuViPJ3HIyWFR\nGDaME9L5+azM3pyDnbBSsMWBiMdwhMI5xMUBl1zCtfPBFofDh1mMjhzhkFJaGs+eGqywUqidw/z5\nwLJlXG4aLEpLuUov1HS0sFJDgxZWjRSOH+ecpieMHeeJEyf6fbywhpWEEP0AfATgdiIKWE2MFAfZ\nszaSmsqN/q9+xaWgMTE8R9GGDfbEIdzOobKSb5KkJP492OKQnh6agXaHD/NI9oICoKhIE4eO4hxK\nSjjZvm1bcPbvdHJJaXsIK1VXR/bgzYYG7dmPFKqq+LxCRbBLWRcD2ABgqBAiTwhxjxBivBBi/M+b\n/BtACoC5QohtQohNgThuWho3+r16mf89OprDR3JSPgAYMQL49ltXcZAzrTY1adVKdnMOwZx8T59v\nAOyJw/ff+26TpTjExobmpjx8mJ1c9+48r1JamjaoMVAYnUMoxaGiAhgyBJg2LTj7r6xkgQiHOPga\nVnrlleB9DoGgvj7ynENVFZ9XqAhqWImIbvXy9/sA3Bfo46alsTCYDYCT9OwJjBmj/T5iBPDeezyl\ntx7pHrw5BykkQPAbHTNx2LHD83vef5+vZdQo+8eR4hCqmWYPHwauv56vbcuW0DiHUDakFRXAX/8K\n/Otf7t9hICgt5Xs+HIlhX8NKlZWRPV16pIaV9O1MsImkaqWAMXIk8OGHnrd5/HHgssu030eM4F6X\nMVloJg7hLmXV5xsAe86hutp9JtqWFs8JLr04hMo5DBzI17Z5c2hyDqF2Dv378/154EDg919ayiHA\n9hBWksn5SKW+PjLDSqF0Dh1SHDp1As4+2/M299yjzbUEsDgArtVKgCYOVtVK8udonQcLhXPQ51Ps\niMPx4+7iMHUqMGWK9Xv0YSV/e3l1dfYbd704HDzYMZ1DSgqHJ4MhtjIZHa6wUkuL/UFaNTXKOfhK\nxImDEKKbEKLTzz8PFUJcK4SICf6phZYBA7gh9OYcZM6hqQn4zW/cXQMQnrCSHXEwjqIuKuKqLStq\na9vuHBYtAv7xD8/bELGzqa9nQZDX1hFzDikpwcvhSHEIV1gJsC9MkS4O9fX8OUZKBRYRP8MRJQ4A\n1gHoIoTIALACwB8AvBnMkwoHUVHA6NFARobr61ZhpepqHjhXWNg+xMEsrFRZCRQXW78nEM6hutrz\npIBNTSwCzz/PAi2ENrYiLY0bU08r9/lKJDiH2NjgPOThdA4dTRykeAcrtPTmm751EGpqWCAiTRwE\nEdUBuAHAq0R0E4ARwT2t8LB6tWsFEwAMHcpVPsaEtPxid+8Ojzj4E1YyOoeqKnvi0JaEdF2dZ3t+\n/Dj/ffFiDikBrs4hOtrzyn2+Ei7nQMTfUbCdQ69e4QsrAfZdS6SLg7yeYIWWHn3Us2s3UlXF922k\niQOEEOcAuA3AF768r71hVt109dXAF1+4l7J6E4dg3vgVFa75ksRE72s6WDmHo0et3xOIUlZvJYHV\n1Rw62rwZmDWLX9OLAxDY0FJTk1bxEUrnUFPDx+3cOfg5h/YSVorkhLT8foIhDvX1/Cz60kYcP87F\nBpEmDn8G8CSAj4noJyHEYAC/mLWmLrmExwjU1kaGcyDiG1bOHAuwqHXrxjeQFcePa3XwEukcrJKI\ngXIOnqx5dTWQkMACcOKJ/FpqKpd7JiZqvwdKHMLlHPTzdgXbOTQ3h3b2TsB3caitbR/OIRhhpcJC\n/t+X66+q0sQhVN+tV3EgorVEdC0RTRFCRAEoIaJHQnBuEUFCAvDrX3PMu3NnLecgH+49e0IrDg0N\n3LgZXU5KCo+ONUMmfGNj+SaTVFby/qxEJRClrHacQ0KC62tCAM88o82OG8hy1nDlHIziEKycQ8+e\nnD8LtXuQ33FHCSsF0zkUFPD/vopDjx783YbqnrVTrbRYCJEohIgHsAvAHiHE48E/tcjhqqv4gRbC\n1TkIYS4OwRznUFvr6hokqanW4tDQwDdVr16uoaWqKi7dtco7BKqU1VdxMNK9u5YvaW5um4sIpHNw\nOIB16+xtGyrnkJpqPoo/2Eix6ygJ6fp6ftYjSRwSEzkkGarQkp2w0ilEdBzAdQC+BDAAXLH0i+Hq\nq/mhA1xzDoMG8c0TSufgSRysGs3qar6xUlK0RtbpZMdw4onexSGYCWl5bp7QJ9yXLeMxKv4SyIn3\nli8H7rrL3rZ6cQhmzkGKQ6iT0jLUaee4Tmfkh5UaGrhTEoywkj/icPw4F2ZEmjhE/zyu4ToAnxOR\nA0CII5rhZdAgYN8+/lkfVho8mBuYSBEH6Ry2bdPOF+AbKyGBb3bpHGpqeD8ZGdZJ6VAlpL05h6Qk\nTRyOHfNcYeWNQE6899ln9h/UYDuHlhbuXaakhGYlQiN1dfw92XEsMm4eyeJQX89hnEhyDpEoDvMA\n5ADoBmCdEGIAgCoP23dI5Bz5+rBSfDyXXwZDHBoazGv77TiH//yHV7aTmDmHykq+2dLTPTuH+Pjg\nl7J6E4fkZC1XUlnZtrLWQDkHpxP4/HP7jUewcw7V1fxdyVmGQy0O9fV8j9k5ruyNR0K10g8/mCd4\nGxqCKw6dOnUAcSCil4kog4iuICIngFwAFwX/1CITGVZqbOSHfPDgwJWyPvig1qO8917g7bfdt6mt\n5cokI3pxOHqUF3yXmDmHqipudK3EQQ646dpVy6F4KpW1oq6O32vVo7TjHPRhpYoK69yKHQLlHDZt\n4u/BH+cQjLCSDDsAoc85OBzckbEbVqqp4c/Al2ckGBVYTidwzjnA3Lnuf5POIVhhpX79fA8rJSaG\nboZkwF5COlkIMUsI8aMQ4kcA0wGEYa2pyEDvHDyJg7HR2bMHWLDAer9NTcCrr3LZLBGwahUvQGTE\njnMwioN0DvrErnQOvXqZh5UaGvi6oqI48e6vG5KNp1Uj6o84tGXEdKCcw2efATfdxPeCnYY42GEl\n2XgAoQ8r1dezs7YrSjU1fC/60jheeSWPhQkkDgff308/DWRluf4tmM6hsJBD1b48TxHpHAAsAHAc\nwE0AbgZQDWChx3d0YPQ5h9hYnp/fjjhs3QosWWK9X9kb/vprYO9ejq2bJZhlrsCIXhyKinjiOtnT\nl84hJcW+c5D5Bom/5azy4bJ6yHwVh8pKbbSxPwTKOaxcyQ2W3Yc12OIgq1mA0IeVpMO0e9yaGm54\nfRGH3Fx7uaamJvsOo6mJ7/GnngKefdb1bzIhHWhxIOLnc+DADhBWAjCYiJ4momwiOkREEwAMDvJ5\nRSxG53DHHdzz0GNWylpf7zrGwEhpKffQv/4aWLuWb1qz2n5vzoGIH6KEBJ7aG/DPORjFwd9yVm8l\ngf44B8D/vEOgnENVFY/g9vawHjvG30mwcw7hDCvJe8WuY/HHOZSUeJ6jS3LLLfwM2UGOlh82zLWz\nIUOqwRCH0lIORyYmdoxqpXohxHnyFyHEuQAibDLb0KEvZY2N5YZrsEEqzZyDp8FmAN80Z50F7NrF\nic6rrjJ3Dt7EoaKCH9QRI7Q1A2TIwegcPCWkzZyDP+JQV+fZnvsjDlFR/ucdAuUc5AJPcXGeH9Zr\nr+XvMxQ5h3A5B3mv+OIckpP5Zzsi1tzMn58dcSgp0aqBvCHFIS7O9f50ODhhnJgY+JxDQQFXCNp9\nnqT7j9RxDv8PwBwhRK4QIhfAKz+/9otE7xyM4SSJmTjU13sXh4wMFohly4Abb/TNOfTowfs4epTd\nwEknaXkH2QDrE9KVla5hJaMVN3MO/oaVUlPbJg5JSa7VSiecEH7n0NTE33/Xrp57l0VFvPBUKMJK\n0jkEO+dQUgK8/LL2uz6sZKexl0UVdgeLyo6AHXGorbXfcbASB3k98fGBdw6+isP55wMbN0ZoWImI\nthPRSAAjAYwkotMBXBj0M4tQjDkHM7p25d6RHjthpdRU4OKLufE780zfnEOPHtxgFhWxOJx4orlz\nkGEZebPFxfEDIs/tk094DYZAOAenkx9AT/bcjjgkJvLn2dLC5z94cGDEoa3OQYqDp4e1tBRYupQb\n1FAlpIMdVtq2jee+kr1af8JKUhzs3FOyk2RXHOzeG/JeMIqDfLaNrweCggKgTx/7115cDLz7buSG\nlQAARFRFRLJ5ezRI5xPxGMNKZpx8MoeH9MiwklWyTIrDrbcCkyZZz0RqJQ6yB7R3r2/OAXANLW3b\nxpVSgXAOcvU8Tz0wO+IQFcXbVFWxOAwZEpiwUludg7ewUl0dN6AnnxyanEOowkolJXy83bv5d3/C\nSt262S/5ls+BHXGoqQmcc4iLC39YqaGBC1ki0jkoXDEmpM045RSurtDfzPX13FBYNZJSHAYMAO68\nkxvUlhb37a3EAeD379rlLg6y4dAnpPVhCH3j3dTEiWy5CpzEH+cgSxzlw+d0crmuHjviALCQFRVx\nLLhPn8hyDt6+0xtv1MaLAMHJOYQyrHTsGP+/YQP/72tYKVKcg5U4yGfbn7DSzTd7jhDIUey+iEND\nAz87dpxqIFHi4CPywZOD4MyIieGE8I4d2mvyC7W6cWRDIhHCfL4kO+LQuzfXUefl8bnKUta4OO3c\n9c5BH/ttbOTFhNpSylpaqgmh7IHJtaQfftjVPfkiDjk5/GB17x5e5+B0ciMYE+P5YS0pYQf4299y\nmFASqWGlI0eAOXO8b1dSAvTty2NyAP+cgy8j70tKtDVLPEEU2JyDr2Glhgbggw9cp64xIvdt1zXV\n1wO/+x0LvxARIg5CiBohRLXZPwB9QnN6kYcd5wDwkqNbt2q/y8bAKiltFAeAGxZjUtquc+jcmXMX\nBw5opaxCaO5B39PU36hNTdxIGJ2DL6WsN9zA5biy0ZAPWXk5N6wyHyMfZrMR30aSk4HDh/l/mV/x\nh0A4B9moCOE5rCS/0379gDVrtNeDJQ76UlZ/RG/dOo5ve+PYMV4/3SgOdh2LPiFtN6w0aJB3cWhs\nZLftq3OQDa7stOhzDr6EleTKbtnZ1tv4OpllQwNP7ihXfowIcSCibkSUYPHPZM00d4QQC4QQxUKI\nLA/bvCyEOCCE2CGE+JU/FxFK7OQcAGDUKODHH7Xf5RfqizhYOQerxjQ1lR1Br17aOWzd6jp/UUYG\nD5Dz5BwaGlgg/A0rHTrEDYgxrKQvo5XXEhtrvgKfkUA6B7kSnL/OQYaUAM9hJekcjJhNR/LII8D2\n7drveXk8G7D+NU/oB8H5G1Y6cMCeaJWUABdeyCN9y8pcnYOvYSU74lxSwoPGjEUeRmRD7qtziIpy\ndcb+OgcpCocPW2/jizjIz3LUKGDLFv45IsQhQCwEMM7qj0KIKwEMIaITAfwRgMksJ5GFv87B17AS\nYO4crEZIA9r7pTiccQbfVPppsS+7jKea9uQcALbG/iSkm5o4N1Be7hpW0seC5WdgN6QE8LkePqyJ\ng7fe4c6dHMIyO79AOQfA88Nq9p0C7DiMjcOqVcA33/DP27cDY8bwNezcae+cApGQPnjQ3nd87BiH\nLs88kyeu82eEtK/OYeBA786htpbvE1/FAXAVAn9zDtnZ/DkEyjno25ion1tqvcvRh62DQVDFgYi+\nBVDhYZNrAfz35203AkgWQqQH85zaip1SVgAYPpx70PobztNSnr44B1/FQe8crryS18SWI6QB1x6c\n/H/vXnvO4YYb3Hu8ckSwMaykT4YDvomDDCulpHBYyVsDkJWl9bb06MNKnTpxQ+3rPE1652AnrGSG\nUWwLCjSn+fbbwPjxnNz0tMa3Hr3Y+5tzsCsO0hGddRZPQBiKUlY7YaXaWr73Gxvt7ddKHOxWK61e\nDbz/vjYvU3Y2cN553sVBFid4O0d5Hnpkpdu+fTxQNpiEOyGdASBP93s+gL5hOhdb6MNKVoPgAP7b\nySdr6l5fzyWjZs6hrk6b1VKPPzmHTp20BmnUKG649Y3wOedwJVVzs9b4651DYyM/YPv323MOBQW8\nP4n8ubzc3Z7ry2gB/8QhOdmecygqMu/16cUB8M89GJ2Dr2ElwLWctaaGBVyKw/r1HLbp1cv+2hWB\ncA52w0rHjvHUISNHAj/9FPxSVrviIPdr5/4AvDsH+ZpV+fl11wFvvMFTdgAsCpdc4i4On32mibUM\ntfrqHCTSORQUeA+ztZXo4O7eFsLwu+lXMWHChNafMzMzkZmZGbwz8oDdsBLAg7Vyc7lBluJg5hzK\nyrhBF4ZPIjXVPebsTRzS0zULmpTEOYbcXO0hiI7m0NKqVdrxjM5hyBDgu+9cj2N1Mzc1uT6Iubks\nUGbOoS1hJTmFRkoKX1dNDX8P0RZ3cGGhea/PKA5yNLuxh+YJuzkHu86hsJCT1nl53PDu3Mkhm7w8\nHndih7bOylpezt9LlJfuYkMDX39iIlfkPf0033PBLGX1JawUH68VLPTu7Xl7b84hOpr/ydHweoj4\nOj7/XBuTlJ0N/POfPJGf/j67915O3g8Zoj0Tctp/T1iJQ0OD9f29Zs0arNFXP7SBcItDAYATdL/3\n/fk1N/TiEE7shpUAbvjkF9jQwA2AmThYNSK+OodevbjEUM8ZZ2jhHMmVV7qGXIw5BykOdkpZm5pc\n95+byxOZSecQF6fFbtsqDgCLQ1QU/15RYd0zLyqyJw7+9LKNYSWrEJcn56Af61BQAPTvz43Z/Pnc\n6MbFeV6I6fvvudMhr6mxUbsv/AkrHTzI37u3eYnkNQnBo/Bzc/l79bVaSZayenNtRPx89O+vLYBl\nVcAg99vSYi/v4M05yNdra93Fob6eX+vcmb+H775jcTjpJP4e8/LY7RDx/S7vRSkOdkJfZmElvXNo\nbna9BsC94zxx4kTvH4QF4Q4rfQbgDgAQQpwNoJKI2rAIZPCRzsHTOAdJfLxm/TyFlazEQeYc1q7l\nGLQs/bQSh7PP5p6MnjPOcG+Ar7uOezgSY7XSkCH8s51SVofD3TmcfrrmHIxhpd692yYO8n99Oeu3\n3wJ//KPr9nbFwZ91KuyGlew6BzlqdvRoHiR47rn8utWMuTU1wNixWrWT/BylE/RH8A4eBE491XtY\nSS94nTvzvbJ1q39hJTvO4fhxrdxU/zx52q/dUmd95ZrROejFwez7lccC+Pv66CN+T1ISi4IMLdXX\n83HkefuSc/AUVios1M4jWARVHIQQiwFsADBUCJEnhLhHCDFeCDEeAIhoGYBsIcRB8HKkDwTzfAKB\n3VJWgG8e2UB5Cit5E4cXXuA6+cZGzeqaIQTHgvWcc4577zUxEbj7bu13M+cA2EtIG51DTg6Lg6xW\nMoaVBg5su3MAXMtZP/yQ4/R6CgvN48WBdg7eqpXs5BwKC1kcRo3in8eO5detnEN5udZRAFzHOAD+\nhZUOHGDHB3h2HTLfIBkxgvNTwQor6T/DhATPoSXZcbJb6qyvXDM6B9ljt6pY0ovDeefxFBeDBvHv\nenGQ97q+kyhzDt46JXbEIRgr1UmCXa10KxH1IaLORHQCES0gonlENE+3zUNENISITiOirZ72Fwn4\nknPo1k27KRoafBeHnj254mnbNh534KmM1Yqzz+b8gieMzmHAAA7d2ElIm+UcfvUrFgyzcQ7+ioNs\n/KQ46HuHK1bw56MXgqIiDi8YH8BAOwdjtVJ1NfDEE9yrr6jghsoMK+cAaOIgx60YG3p53fLe0o9x\nANoWVvJWsmwMlY0Ywf/bCSvdfTeXvjY18XHsiENJifZs2BUHO9VsgG9hJSN6cTjzTL5uM3GQxRfG\nsJLdaiVPCWn9foNBuMNK7Q5fcg56cfAnrNS9O9/AjzzC+8rJ8V0c5Hl4wugc4uK4sbLjHPRhpZYW\nvmlPO819nIMUh0GDAuMcMjI4cZuTw/uNinKtgmpp4fcYH55gOAd9zzIvD5g6lavUEhKsXZ4x55CR\nwQ3t7Nl8nwAcW+/Rwz3vJD9v2VDqk9H+XpNdcTBzDoC9sNKKFcB99/H9KJeeDaRzkJ0ns7BSczMX\nYujLlr0lpI2vG48ln6uuXTl8qxcHORBO7xzkZxMTYz+sZJVzKCw0v78DiRIHH5EPgFkFgxEpDkR8\nI6SluTqHnBxgwgQOiZiJQ0wMz8szfjwn5H76yd5UE75irFbq3Bl48kktzCC38ZaQLirSxiE4HNxY\nBzqsJP//85+BWbN4uofLL+fPR5bRFhXx5Hz6kMCGDfw9BMo5WIWV5PEWLrQOKQHuzqFPHxaShx5y\n3c4s72AmDm0NKx06xNV1/joHb2ElmViOjdXuYbvOwZewkixlNTqHsjJe2vXIEe01u87BmzgA/Ixe\ndhn/rL8XZYelpkbrLNm9dquwUl0d3xNDhihxiCiio/kLkXPreEIm0OSqYcnJruJw//08F1JiIg8o\nMuODD1g4+vfnKZL9cQ7eMI5z6NKFz002xIB1QlofVsrN5fMUgkWioEBzDtXVfO39+/snDrJnLJ3D\nyScD11/P6wpcfjlXgskHv6iIE9/x8drDc9VVbPXNxMEf52AVVqqt5et/5x3rZDTgmnOQzsEMs7xD\noMNKRLzP1FTfncPAgVoHwJMoVVXxvTB3Loc6AXtxd72rTkjwnIA1lrLqke5LPymeHedgJ+cA8EzK\nskhIP92+fqoY/WSWbRGHykr+LHr0UOIQUURHc6PmzTUAWkJaxg4TE7Wb5csvube2aBFXOowZ43lf\nwRQHM+dgto1VWEk6h9xczlcAmjjIhuPoUW1NCX/EITqawxL67SdM4B73ZZe5ikNhoeYcamu58Tt+\nnP8eiEFwnsJKtbVcBFBTY885OJ382fSxmMpSDoTbvJndCKB93lbOwdewUl0df+cxMb47h6goYMEC\nDqV4Oq5835gxnLwF7DWQZWXcCAL2w0pmzsEXcfA152BEP7OBPqwkc3CA9sxZDbADrEdIA9yZ0Hd+\ngoESBx+RCWlv+QZACyvJLzkxUVvw57HHgOnTzRtiM0LlHKzCZWaNhtPJMdyqKv5ZOgfAXRyam/mh\n1S/5WV6uOQE7SNsu6dOHY7tpaebOQT7Yci2JnBz+X18n749z8JSQrqvjBj0z07NzkDmHkhL+TKw6\nG+npLB4LF3JVFhD4nIN+6g1fnQPAI4SluHgTBz1W4rBrl7afsjItqd+tm72wUiCdgz5vqMeTOCQl\n8bnI0GqnTlpYSYpDVBS/7ul7MnMOcpJAfecnWChx8BHZ6/RFHGRiSdriPXv4/2uusX/cfv24IQy2\nc9CHTIzbGB9kWSceH88NlF4cunfnhqRrV+1BM4pDTo62vb/IEb1WYaW6Ou14hw7x96cPBwbCORjD\nSvHxwP/7f1qYwQzZCHsKKQFaWOmrr/i6AC0BLxtKY1jJ15yD/v12xMHKEenDWfv2ufaKfRGHe+7h\nQWUAX6td5+CplLWkhGP0e/dqr9lxDvr7VY8ncRBCq5iqquLOguyk6J2AN+dkVfTStasmDu12nENH\nRFaf+Ooc5NTUcXE88d0FF3jPWejp358ftlA4BzNxkI0GkTalh6wTl3PZGJ0DwNfbqRM/CN27a4u2\ntLRwYy7DUG1FnwQ0hpVknkeKg/Ha2+IczMJKcXGcD7n9dut9yJyDN3Ho1YuT6YWFWmK6vJzfIxsG\ns7CSLzkH/fs9iUNjI5/DCSeY/10vSr/5jTYhHWAuDlbVSsePa6vN+RJW0ouDmXM491zfnYMncfD0\nLMrQUmWl9l35ui67WVgJcBUH5RwiCF/EQSq7/ktOTOSJuHydGko2usF0Dk6n66hR4zaNjTxYSs4G\nKR+ulBSOgxudA6A9DHFx/Fp0NH92Bw7w+3yZ08gTnsJKnsShrc7BLCFt5zvSOwerfAPAzmHTJh7V\nXlKiLWbTr1/ow0r797OYW4VC9cetqdGcDuCbc6iu1sJA/uQc5PQU+rLVkhIef1NZqe0jWM4B0MSh\nqspaHLyV8lo5h9hYlXOISPxxDvp65aQk7glecIFvx+3enW+GYDoHmaw1czSylDUvT2s8pJBIG2/m\nHPT14lIwkpLYfQwcGLhr6N2bGwC5noS+lPX4ce6BHzwYGOdgDCtJRwXw8ex8R/J92dlafbwZcvr1\nq67iz7SkhMWhf3+tkdNPvw74F1ayIw579nCVmBV6x1JXp/X+AWtxMBPm6mpX5yDvG7ulrGbLaZaU\nsNCeeKLmHjxNn2HHOdgRh8pKnu9MFqb44hzshJWUOEQQQnCYxK5z0FcrAdzDk2s8+3rc/v2D6xw8\njd2Qpaz5+a4hKBlWOniQHzTZg7VyDkBwxCE6mj/X777j3njfvq5hpeHD2d0Ewjnoe5xRUbwP2aAa\nl1e1QjbCcnyBFVIcLrlEG/MgxUGGlfS9a3lNnsJKy5e75gP0zsOTOOzeDZxyivV+9aJUV+dagmvX\nOcjZTktKtBJbX8NKgPv4BHn8oUM1cfA0fYbeOcixCnrsiENJibtz8CXnYBVWOvVUHoOkn54nGChx\n8AMZGvFGTAxvW1HhGlbKzPQt3yAJljhI52CVjAa0Gzkvzz0/kZLCU3zok8uhdg4Ah1puvhmYOJE/\nZ31YKSODz8l4fWbOITcXuOMO6+PonQPgGlryJaxUX+9dHFJTeWLB9HQWP7nKnj6sZCYOVs7B4eBZ\nefWzrwbSOTgcHM5pbPRPHGTp8bFj3JhGRWn3kN2wEuDeq5aJ9GHDXMUhFM7B35yDlXN4912e/VU5\nh0fe6kMAAB5GSURBVAjErjgAfAPJkaEAlwFefLF/x/31r7VJ8QKJ3jlYiYNsNPLzuVeqz090786N\nvZk4hMo5AOzGxo7l0dOAa1gpKYkbVDvOIT+f5wCywvg56UMYdsNKdsUB0GZp7d2bK7yamvhnK3Hw\nFFYqLOTGV78gTaDDSvL9dsJKxsZRXtOxY64hJcB+WAmwdg5DhrDLBdzFQT+9fqBzDsZBcGbXP3eu\na/WRtyl6lDhEIHJuFDvEx/ONKXsi8+Z57pV64qmnODEZaKRz8BRWkjdyfj7/LrePiWEhyMpyFQdj\nWEmO6AT4gSsuDrw4vPgiL9soXZk+rJSYaC4OZs6hvt56OVfA3TnoK5bshpW6dmUX1qWL60h0T/Tu\nzaGd7t21smi5JKu+EfUUVpLfn6/i0NzsOnOrGVKU5GfhzTmYJWRravj7KylxFz1PI6SdTteYvr7h\ndDq18FRSkveEdLCcg7ecw5Qp3FmQWIWVJEocIhB/nIN+OL7VYiXhQjoHT2El2Wjk/byoqxQH6Rwa\nG92dQ6dOWmP82mvaIDbZEAVaHFJS3MM93sTBzDl4EwejiPobVvrpJ++uQU+vXpo4yAFhVVXapHf6\na5KC99vfugqF/P70jZA+52A1Bfnhw3x8T8Inj2sUByLX2VUlVs6hb192Dvp8A+DZOdTV8Wcqx73o\nG/vycn5vTIxrg2omDvX1rkvoJif7Lw7FxXys3r3thZWOH3d1O3acgxrnEGG0JawUifjiHPLyuNGX\n1U0y5wC4ikOPHq6NZP/+2oOYnMz7sKqXDxRG59C/vz3n0NCgNRJmGEXU37BSZaVv4tC7NwuKdA7V\n1e69a0DrwTscPFWFvkHNz+eGy1fn4C0ZDWiOpa6O73spDnK+KePnYlatVF2t5VOKi12vTc4wYIZR\nlPVhIr1r0YuGmTgcPszlurIDp5/VQI8dccjO5u8pPp7vmepq64S0nOLFV3GQ12hnuVVfUeLgB3IO\nGjt06+YaVopE7DgHWcJbV8fJUZmjkNVKgKs4pKdzItWMpCQWBqvprAOFPufgq3MArHup3sJKdktZ\nAd/FobjYNaxkjMsDWiMte5X63mVeHnD++b6Lg7d8A6CJUn093wulpRzSsVou1co5JCWxKOzd63pt\nKSksqPrxCxJ9vgFwnTDPKA6enIMxByRDyMbwjR1xOHKEr0Um1UtL3Z2DvPfq6/m6/BWHyy4DNm60\n3tYflDj4ga/OIdLFQe8cPM31FBvLll+WteoT0oD7VBgjR5rvJykp8CElM4xhpdGj3ceXWOUcAK2X\n+uKLrqEF4+dkDCvZLWUFfA8rAa5hJTPnIMM7UhT0DVt+Pn8GenGwM0Lazmh2fVgpOZnPsaLCd3FI\nSODt9+51T7QnJZkvAWocsax3CPrj60VD/z127syN89697t+JWd7BjjjINUUArR2wCivJjoheHHzJ\nORw65Dr6OxAocfADX8QhPj7yw0p2xjnI7fr21W5qfc6ha1fPs5DqOfVUYNy4wJy7J4xhpSFDgMmT\nXbfx5BykOEybxnMbScycgz9hJcB35wBo4lBTw/eWlTjIBsfoHMaM4b+ZTfltJQ6Vld4nSdSLQ1wc\nV+YVF/smDjU1LA5paexWjNeWluZaBSUxOjZ9w2knrCQE/y0ry70i0CgOcjZVTx2p+Hi+Pim63sRB\n3mu+OAc5zqGlhce/5ORYb+sPShz8oC0J6UjEzjgHQHMOenGIieHXVq+2P3bjgguAxx8PzLl7whhW\nMsMq5wBojWtFBfDNN9rfjSLqT1jJH3Ho1o3/paTwPdilCzsBq5yDVVjphBPcVyvz5hwqK71XVckZ\ni+VgLzlpoJU4mFUrVVfzNfbsydN1mImDcWU8wD2s5Mk5mIWV5Ht27jR3DpWVwNatwIMPaq7B0/0u\nBLsHvXOQE1FK2ioO8lrktCry+wwUShz8wNecQ1NTZIuDv85BhpWEsF6sKJwYnYMZ3pxDYyP/rhcH\no4j6E1aSU5lLN2CXXr1cp7DOzTV3Dvqcg74xLCvTRujLiiU74lBV5V0cpHOQJZvp6dwgenIOZglp\n6RyamtzzKT17mjsHY1jJm3MgMheHPXusncPWrTy63FtISZKaqn2usqTdF+fgLawkz106BuUcIgBf\nnQMQ2WEl+VA3NHh3Diec4B5WilSMOQczvOUcKiq4YSkrcx3jYRZWInKvZbciPZ0H2vk6Ur53b9e5\nhnJy7IeVCgv5uJ06sThkZ/N32NysNUJtcQ5WYaX1683zT95yDoD/YSUr5xATwwnipib3SSbj4vg1\nY25FlrMeOcKfd1mZfXHQOweHI7BhJYCvef9+Hn8ixSEvj8ektBUlDn4gLb0d9IuQRypysfeaGs+N\nvXQO+gS2sfonkpC9x6oq6xXnvDkHOcDswgs5dAaYj5CWNfJdutgfx3Lqqb5dDwD86U/aiGkrcbAK\nK+Xna+XDUhykcEqRaqs4GMNKBw+y6/rNb9y3l8KsLxPVOwfAflipuNh1HIVxnIN+P/Jvxvs3Lo4r\n2ozPtnQOublcfbVzpz1x6NnTNecgjyHxJA5EvonDWWex+Dc388p8CxZ4Pz9vKHHwA1/DSkBkOweA\nH1Rvy59ecglw+unuYaVIJS6OG0a5noQZVjkHue51RQXH+C+8UAstmc2tVFdnP6TUFm68kRswwH5Y\nSf4v8w0Ax9UPHHANKQGexUG/nRn6EdIyrPTOO1w6ayYsQriLszfnYBVW2raN702JPqxkTKbLXJRZ\nWMksB6QXh4QEDi/ZEYdevTTBkq7GU85BCE0cHA6+b711NOLj+XscMIA/7/x8nvX5nHO8n583gioO\nQohxQoi9QogDQognTP6eKoRYLoTYLoTYJYS4K5jnEyh8rVYCIts5AHyjVld7buynT+dyVTtzMUUC\n0dGuM8WaYeUc0tI055CSwvNabdrEfzfmZuTiMnYrlQJFQgI35N7CSrKRzM9n5wdwQ7p1qz1xkGNg\nvF2bWVipvJwnQ7TCGFrSVysB7hVSVmGlbduAUaO03/XOweh6ZLjRzDmYzV2mF4fMTD6WHXF45hlg\n/Hj+2Y5zSE01nxnWE1Ic+vRhgcjO5vEOES0OQohOAF4BMA7AKQBuFUIYh9E8BGAbEZ0OIBPADCFE\nkIdGtR1/cg6RLg7SOdhp7I3VSpFMfLxncbDKOaSn8wMr17lOT9eWnjQmpOVsqXYrlQKFDJUZk7b6\nsFJMjLlzyMjgv+3c6V0cZDLaW34kKor/1dRoziEmBrj2Wuv3GCuWZLVSWhqfl3GgpJk41NVxozh8\nuPaa0TkYxaGqivcdFeX6upVzqKjgsM3FF9sXh4QE17Wo5TEkRnHo1ct/ccjIYHFYtozdld2yck8E\n0zmcCeAgEeUQkQPAuwCMkcciAPLRTQRQRkQ+LHAYHh57zH51TnsJK3Xpwg+1nVxKewkrAd7Fwco5\npKe7hpVSUlgoZJWL/nPq3ZsbjlCLg7y3rMJK1dV8HVIciov5d8mYMcCqVa6fj5k42Mk3SKKjueHt\n2hU47TTgv//1/F5jxZIMKw0aBLzxhvv2ZjmHrCxOyBpDRLIqyXj+8fH8vRrv3auuAi691P2YSUna\nmItTT7VfraRHv86ERC+MZuJgp0Mpx7tI5/Duu+xyA0EwxSEDQJ7u9/yfX9MzH8BwIUQhgB0A/hTE\n8wkY48a5TyJmhXIO4SUuzrtzMIpDQ4N7WCk2lhu+ujp359CnDzsH48RqwSYhgc/JeH36EdK9emni\nYJy9VYqDXedgh5gY/txkqe6tt3re3hhWkuIQHc35FSNmOYdt23gJUD0ydNTQwI5H3zmLi2PBMN7r\n99zjvh+AP5+dOzmketJJ/Jqv4iDHReiP6ck56BcI84QUnT59eNaBgoLAhJQAIJghHPK+Cf4BYDsR\nZQohBgNYKYQ4jYjcZrWZMGFC68+ZmZnI9HUR5jDRXsRB5hx+ac7BKqyUlsYPWkWF67rY5eXuCWkZ\nVvK26HygSUjgczKGe4ziIMMrUugkY8awm/AmDnaS0fpjy5li7WAlDlakpPA2cklbgHMnxkZdJp3N\nXI+Vc7BCTtnRrx83wnFx/olDXJzrd2UUh6FDtbEnvoSVoqNZNLkEdw127FgDXXPpN8EUhwIA+nk3\nTwC7Bz2/BvAcABDRISHEYQBDAWwx7mxCIK42DMjGItLDSr44B30pq68PSaixE1YqLgbeew847zx+\n+I1hJVkFk5KiTSanj4XHxvJx8vJCH1YyhpQAbaRydTULl1z1zSgOZ5zB/9sRB1/CStI52MFXcYiK\n0tZKkAMIt20D7rrLdTsZVjJes/ybmXOwQl57//58/CFD/AsrGT+TQOUcevfm8xo8GEhOzsQrr2S2\nVjlNnDjRtxPVEcyw0hYAJwohBgghOgO4BcBnhm32ArgEAIQQ6WBhyEYHor05h19aWGnAAO4RPv00\n8Oab/Jo+Ia1vXLp35zls5KhwPX36cGIw1GElM3Ho1IkFTDY4+rCSvqHs0YNj+2Y5B/3YA1/EQToH\nu/d7aqoWJnI67VV86UNLDgdPY24cZCcT0lbOwRdxkOIpHeRJJ/nvHPR4CyvZ+Qzj4/neA/g+PnAg\ncOvFBE0cfk4sPwRgBYDdAN4joj1CiPFCiJ8LvPA8gDOEEDsArALwOBGZzLnYfmkv4mBnnIOkI4WV\n+vblKRHuuksbiKTPOchqJUATB7PPqHdvHvAV6rCSmTjI8QMVFZo4OJ3mDeU557hWtsja+uZmYOZM\n/p59FQdfnIN+Gg+5YI+3xk1fsVRUpE1EqEc6B7Nz99U5GMVh0iTgppvsvVfSrZt7GxAo5yDFAbCf\nC7VDUMtGiehLAF8aXpun+7kUwDXBPIdwExcHXHNN5PewfXUOVVWRP84B8C4OksRErmMHXMc5xMVp\n4pCSojkHI336cOz7xBMDd+7euOYa8wQqwOEdKQ5yChEZn9bzn/+4X09sLN8Ljz/OAx99DStVVNgX\nh8GDtenDvYWUJPqKpcJCLuM0Ip1DRYW5c8jJsX/vyvtHDj70tFSqFSkp7nmbQIhDcnLwFs2K+DEF\n7Z2oKOAzYzAtAvHVOegX+4lkevSwV/OtX2VMn3Po0sW+czh0KLRhJVlia4bROZjF3gHz8EhsLK9r\n0NLC11RZqVXpeCMmhj83X5yDXKTGrjjow0qFha49Z/15yLWozZxDRYX9ezc6mq+/LWuQjBrl3g7o\ny3j9DSuNH+9eUBEolDgoAPiXc2gPYaXJk+2tOJeU5CoOSUncuBw75ioOO3ZYi0NDQ2jDSp6IieFB\ne97EwQy5vjXAvXpfS1kB+2FUf52DN3EAWAQKCtqecwDavpCOENqob/35yZHazc38/TQ2cgjQrnMI\nZmdEza2kAKCthOVrQjrSxUGOT/CGdA5y3eiYGH4tKkp7AFNSOMZtFVYCIkcc5DXLsJI/4hATozkH\nX8JKgO85ByL7g8t69WJRAPh/q2nP4+P5722tVgoWQ4bwtZeXa5Mfyhl+7YpDMFHioACg9YZ9SUi3\nh7CSXaQ46AcfJSa6jiPwFlYCIkcc5NrHsrb+6FHfxGHXLmDsWN/FQd4PdsUhOZkb6dJS+85BzigL\neHcOhYXWYaVwi0N8POcLNm/W8hoykW43rBRMlDgoALiupWtn2/YSVrKLXhzkQ5mQ4NqgekpIS3EI\nZc7BEzExWi+8Wzceg+Grc7jsMv/FwZeGTboHu+Ige9yAZ3GIj7cOK0WCOAA8hmbdOu26pTgo56CI\nGPx1DpHwgAUCM3FITHRtULt3t07aR5pziI7WGpxu3XhGVuMEfVbExrIIXnwxv6+01Ddx6NLFdUI7\nb8i8g11xyMjQZsH1J+cgF/WJhHv39NOBb791dw4VFfZHpQcLJQ4KAL45B31CuqOFlfQTnpmJA2D+\nGcmS2UgRB71zkKO3fXEOAHDyyVy1dfSo/YYqOtp39ySdw6FD9gQsKkqbnrqoyLNzqKszdw5A5IjD\nli2u4lBby5+5r0vIBhpVraQAoPWGfRGH5ubIeMACgbz+ykrXnIO+BywbVyt3lZERWeIgG2kZVho3\nzt57Y2O5skbOjpqXZ69HbzyuXQYPBhYt4kqwLW4T51i/56efOIltNhAQ0M7DzDkAkXHvnn46F4IY\nncPRo5x4DyfKOSgAaA+Kr+McIuEBCxSJifxQWuUcZHmr1Wf0wQfA6NHBP087tDXnIBe9GTyYr9tu\nmMgfcRg0iFfZe+QR9/WbrRg8GPjuO+5dW60zoa8y0xNJzqFXLxZiozgUFYXfOShxUADwzzl0pLAS\nwA9ocbEmDklJrmGOqChuaKw+o+HDAzevTVuJjnYVh9JS38RBLnrDk7n5dlxfq2xGjACuvJJHZNtl\n8GCO1XtqQKUIGENikeQchGD3EInOQYWVFAB8dw6NjVybHgkPWKBISuLBVbJxe/hh9zESKSn2PqNw\nExOjhYJkI+mLOMgpKQYN8k0c/HEOPXsCX3zh23uGDOE1Fq6/3nobuaaE8R6NJOcAAGef7VqtVFnJ\n+S+rcFmoUOKgAOCfc7C7fXtBOgeZczCbs6Z79/ZxzcawEmBfHO69V2uYxo4Fbr/dt+OGopx38GDu\nnFglowEWATNhiyTnAPCMwDI0FhfH8z717OlbxVcwUOKgAODfOIeoqI4dVjKjvTgHY1gJsC8O+iVw\n+/YFHn3Ut+OGQhwGDOAG1ZM4xMWZi4MU/0i5d43rWB86FP58A6ByDoqf+aWPcwDsiUP37u1DHPRh\nJSkOvoSH2nLcUIzs7dKFnZ0/4iCnRInEezcujkt0w51vAJQ4KH7Gn3EOv1RxaA/XbBznINdlDsVx\nQzVKfMwYz7PFWoWVACUOdlBhJQUArTdsx2rrE9KRYs0DgTHnYEZ6evhjwXYwhpXshpQCcdxQicOH\nH3r+e1qa+VoPAAtHpIrDsWNKHBQRROfO3NDbafg6d+Yy1o5WrZSYyJUinpzDY4+F7nzawkUXaQsB\nhVIcYmIip8Nwww3W1UyR7ByAyMg5KHFQAGA3YPdhkYnojjjOAfAsDpEysZ437r9f+zk+/pcpDkJY\nD5CLZOcAKOegiCA6d/Yt0dqlCzsHq4evPWJHHNojZ56plR4HmwsuiBxx8ESkOwclDoqIwRfnILd3\nOoN3PuFAjqQN91TJgWbAAPvTUrSVK64IzXHainIO3lHioADAD4ovD0vnztqqaR2FjuocFO6kpoam\ntNdX5OhtJQ6KiKFbN3tLNEq6dFHioGi/vPFGaEp7fSUujsuOI2F23wj8eBThYMgQnhnTLl26cEK6\nI6HE4ZdDpOZFkpPNp20JB0Gt2BZCjBNC7BVCHBBCPGGxTaY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(np.vstack([train_loss, scratch_train_loss]).T)\n", + "xlabel('Iteration #')\n", + "ylabel('Loss')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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rQuOqcvDiVuL2qW1z41YqRg4clLQiB7U2kAoeIaZShjGxcyu5UQ1WKEdAmg1O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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(np.vstack([train_acc, scratch_train_acc]).T)\n", + "xlabel('Iteration #')\n", + "ylabel('Accuracy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the testing accuracy after running 200 iterations of training. Note that we're classifying among 5 classes, giving chance accuracy of 20%. We expect both results to be better than chance accuracy (20%), and we further expect the result from training using the ImageNet pretraining initialization to be much better than the one from training from scratch. Let's see." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def eval_style_net(weights, test_iters=10):\n", + " test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)\n", + " accuracy = 0\n", + " for it in xrange(test_iters):\n", + " accuracy += test_net.forward()['acc']\n", + " accuracy /= test_iters\n", + " return test_net, accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy, trained from ImageNet initialization: 50.0%\n", + "Accuracy, trained from random initialization: 23.6%\n" + ] + } + ], + "source": [ + "test_net, accuracy = eval_style_net(style_weights)\n", + "print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n", + "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)\n", + "print 'Accuracy, trained from random initialization: %3.1f%%' % (100*scratch_accuracy, )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. End-to-end finetuning for style\n", + "\n", + "Finally, we'll train both nets again, starting from the weights we just learned. The only difference this time is that we'll be learning the weights \"end-to-end\" by turning on learning in *all* layers of the network, starting from the RGB `conv1` filters directly applied to the input image. We pass the argument `learn_all=True` to the `style_net` function defined earlier in this notebook, which tells the function to apply a positive (non-zero) `lr_mult` value for all parameters. Under the default, `learn_all=False`, all parameters in the pretrained layers (`conv1` through `fc7`) are frozen (`lr_mult = 0`), and we learn only the classifier layer `fc8_flickr`.\n", + "\n", + "Note that both networks start at roughly the accuracy achieved at the end of the previous training session, and improve significantly with end-to-end training. To be more scientific, we'd also want to follow the same additional training procedure *without* the end-to-end training, to ensure that our results aren't better simply because we trained for twice as long. Feel free to try this yourself!" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running solvers for 200 iterations...\n", + " 0) pretrained, end-to-end: loss=0.781, acc=64%; scratch, end-to-end: loss=1.585, acc=28%\n", + " 10) pretrained, end-to-end: loss=1.178, acc=62%; scratch, end-to-end: loss=1.638, acc=14%\n", + " 20) pretrained, end-to-end: loss=1.084, acc=60%; scratch, end-to-end: loss=1.637, acc= 8%\n", + " 30) pretrained, end-to-end: loss=0.902, acc=76%; scratch, end-to-end: loss=1.600, acc=20%\n", + " 40) pretrained, end-to-end: loss=0.865, acc=64%; scratch, end-to-end: loss=1.574, acc=26%\n", + " 50) pretrained, end-to-end: loss=0.888, acc=60%; scratch, end-to-end: loss=1.604, acc=26%\n", + " 60) pretrained, end-to-end: loss=0.538, acc=78%; scratch, end-to-end: loss=1.555, acc=34%\n", + " 70) pretrained, end-to-end: loss=0.717, acc=72%; scratch, end-to-end: loss=1.563, acc=30%\n", + " 80) pretrained, end-to-end: loss=0.695, acc=74%; scratch, end-to-end: loss=1.502, acc=42%\n", + " 90) pretrained, end-to-end: loss=0.708, acc=68%; scratch, end-to-end: loss=1.523, acc=26%\n", + "100) pretrained, end-to-end: loss=0.432, acc=78%; scratch, end-to-end: loss=1.500, acc=38%\n", + "110) pretrained, end-to-end: loss=0.611, acc=78%; scratch, end-to-end: loss=1.618, acc=18%\n", + "120) pretrained, end-to-end: loss=0.610, acc=76%; scratch, end-to-end: loss=1.473, acc=30%\n", + "130) pretrained, end-to-end: loss=0.471, acc=78%; scratch, end-to-end: loss=1.488, acc=26%\n", + "140) pretrained, end-to-end: loss=0.500, acc=76%; scratch, end-to-end: loss=1.514, acc=38%\n", + "150) pretrained, end-to-end: loss=0.476, acc=80%; scratch, end-to-end: loss=1.452, acc=46%\n", + "160) pretrained, end-to-end: loss=0.368, acc=82%; scratch, end-to-end: loss=1.419, acc=34%\n", + "170) pretrained, end-to-end: loss=0.556, acc=76%; scratch, end-to-end: loss=1.583, acc=36%\n", + "180) pretrained, end-to-end: loss=0.574, acc=72%; scratch, end-to-end: loss=1.556, acc=22%\n", + "190) pretrained, end-to-end: loss=0.360, acc=88%; scratch, end-to-end: loss=1.429, acc=44%\n", + "199) pretrained, end-to-end: loss=0.458, acc=78%; scratch, end-to-end: loss=1.370, acc=44%\n", + "Done.\n" + ] + } + ], + "source": [ + "end_to_end_net = style_net(train=True, learn_all=True)\n", + "\n", + "# Set base_lr to 1e-3, the same as last time when learning only the classifier.\n", + "# You may want to play around with different values of this or other\n", + "# optimization parameters when fine-tuning. For example, if learning diverges\n", + "# (e.g., the loss gets very large or goes to infinity/NaN), you should try\n", + "# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value\n", + "# for which learning does not diverge).\n", + "base_lr = 0.001\n", + "\n", + "style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n", + "style_solver = caffe.get_solver(style_solver_filename)\n", + "style_solver.net.copy_from(style_weights)\n", + "\n", + "scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n", + "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n", + "scratch_style_solver.net.copy_from(scratch_style_weights)\n", + "\n", + "print 'Running solvers for %d iterations...' % niter\n", + "solvers = [('pretrained, end-to-end', style_solver),\n", + " ('scratch, end-to-end', scratch_style_solver)]\n", + "_, _, finetuned_weights = run_solvers(niter, solvers)\n", + "print 'Done.'\n", + "\n", + "style_weights_ft = finetuned_weights['pretrained, end-to-end']\n", + "scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']\n", + "\n", + "# Delete solvers to save memory.\n", + "del style_solver, scratch_style_solver, solvers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now test the end-to-end finetuned models. Since all layers have been optimized for the style recognition task at hand, we expect both nets to get better results than the ones above, which were achieved by nets with only their classifier layers trained for the style task (on top of either ImageNet pretrained or randomly initialized weights)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy, finetuned from ImageNet initialization: 53.6%\n", + "Accuracy, finetuned from random initialization: 39.2%\n" + ] + } + ], + "source": [ + "test_net, accuracy = eval_style_net(style_weights_ft)\n", + "print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n", + "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)\n", + "print 'Accuracy, finetuned from random initialization: %3.1f%%' % (100*scratch_accuracy, )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll first look back at the image we started with and check our end-to-end trained model's predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted style labels =\n", + "\t(1) 55.67% Melancholy\n", + "\t(2) 27.21% HDR\n", + "\t(3) 16.46% Pastel\n", + "\t(4) 0.63% Detailed\n", + "\t(5) 0.03% Noir\n" + ] + }, + { + "data": { + "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\ncDPDu2eav33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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(deprocess_net_image(image))\n", + "disp_style_preds(test_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whew, that looks a lot better than before! But note that this image was from the training set, so the net got to see its label at training time.\n", + "\n", + "Finally, we'll pick an image from the test set (an image the model hasn't seen) and look at our end-to-end finetuned style model's predictions for it." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "actual label = Pastel\n" + ] + }, + { + "data": { + "image/png": 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WKs9hFZYitTN2cR87zMqTyhlYiOUvdRVeXdJREPOPBUQLuaLGrEgo3mWYO8JX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9p9Ybt4Of19W7OXfbsAgKNmoNTwI0vwBGcQKvGY5rN9HPF6L//O5aJjNCQtQWBMX/OJhfm4G1Ge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\nKJVy+/ZPkPUZu9dfpDdYJ8gc51qO7ryOOHqPk7d+SLY8oaEk6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EIa92mO1lg/t8jkyCSB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\nv+3j8qpvhwWF6TsmyabsE1+C94x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FzzNvLUUeNPY7UYZSIpOA1VfGkiSSHlslWq/o8cor\nj1iVK1SaUhlL1qkY1C56B5imPW1WpLYPQWlm0GaJWc+Zzr7FbPYMhyXJUorimKPRPfr9CWkyJhiT\ndp8db5pYeaaEwEuJJZhUsyyjWp2xmJ4zOr5PkZ+QpZLlesGinAEwmdxBNLn+ugvbRsdiZGeafsVt\nKhvOqruB27GBIDrE1aW6m6/LBbbPaGsohkhXSVb0GErFejFnvZwzv7rEOsfo+DgoSFVC3iC5NnFJ\nvP26adhj2Ky5zslGZ9hwZc1nhBS6okLLsRoEMiu4e/8Ri/mUF8HN5TOwhjTNSIteqGmvDYPhiF7R\nQ4gEoRyOmnq9DumgkoS0yFFa4Koa0izEqQvZKH08kixUIrLBPbjXJJqIByywfJLhYARCMru6YL6a\n4hIYjo6DmMH+6L+YwiMEiZI4J3bZ1WaCnPMs5wus0QwGQywSlYREIq3XYjyB1rNJ0y29RDvBynqK\ndLsi8mxAkmaoNMVYi7c+WF3YUpw2+rDNhBQWowZRUVaXPDv7Ms8uPs9i9VVmy7dYlmdUegEEP/zh\n8Jjh4D6JvMeo95288egvcjr6KL6zVLqbrgXVmD3rJuVZMTwlK0YhM7RU9IZDHj56jdViyuz8Aqd9\nKBbaxG3E1DvmFLpstI3OSUAlispa8ijZSnsu3thJ9LANUmtk7xhxxPMjRXB42hwTkiLLkcNwbrmc\ncTU9xwrH8eSkyai9Fae8D9R8nwjTHVMR/+hcE1mzN9cErtJvzeCdZ25KsvX6qCTlRXBjYsI7b7+F\nkimj8QkyEVxcXlDkOaPxGOc80/kC6RxKhsnqDUdBnrcGnAtmsywLPu+02NRT25ClWODJUhUKTHiL\nRyKF2lkc1jnm8yumV+c45+kPxownJ8gmzr/LsrYTGOcmFEDk7xQoCWHygydYyCFoETvZc1rfhNAW\nxze/9ockqeL+ow/hhWpKdbfhyR7lPNOLM/I8ZTieoI1B12UTuJIHN1rvMdqGMmHeMl085cnbX6Aq\nv8LC/F8uF3/E1eLr1PYcr2q8sqyM4Xy2pjaOLJUM+gnHk4fcPfkOhFb0/Ct89ys/ySt3P4GS/ecU\nbV1laDsu2vtNRF4bdt2es97hjOHNN7+EdJ6j8Zi7D18hKfLN5ozZeaJ3bMQqthRWRu9sg5ra4CTY\n5Yyuk8FjLjD+3eWCYt8R7z1luWI2n6J1jTOWXq/P8ckpSZLt5Z72IdB9v+N2ddsTn/QiVAkTIkSU\n7vMjoRmbtHEYu3ViQppmWOuodUlKyrA3oKxKVuuSotcPtty6osgTdKWpqor+YEDSuNcqa6mdx1eG\nRECWhXp1QimUlLiq5uLiDLyhn+f0B2NkE7DTDrCSktEw5EiYnp+xml4igdHkBNnIei32baHVVrds\nmG++a+OwiKYEWcDibQqsbVaebQRjkLEDIlkt5qRScjQaIAiKqLb0lwW0FxjhQi6/xmyHlMwXwQNz\n5eeMjo7RSIR3zGZTLi7fZrZ8k8p8icv687x1/kUups+o1mv6veByjezhkoLZMpQlG/YURQbT2ZRE\nXjLKTtD1l/jiH01xBl5/9AkEfRxBgapEcBUm6lP7mXZiBeLFnAgJacajV17lG1//Y6YXFzhnOf3A\nI4qiH4qdtjLvNc+IdSXtXyYEBjapybtIO25f3OYuIoiv6W7mHYQhQuWvgfUsFjO8MKxWS2pdc+fO\nvaa24n79yovk/bi/8edzIFpOV2y5hGv6mVyn2Iofd1OcwWy1AufRZYVKBMNeDyccVVnRGx6FTbJe\ngbMUWY7zYcNlRYbAI5o4Aact5bqiX+Rk+VZOq42hLNeAI0sSirwAuS2sGWN4ay3z6SWzizO8h8Hx\nHUZHx40YQHA8kg3b3ZCXtq5gO8QhEYbFI8hSuUOhYkzdVkkGNlFqtdFgDf0sxZHgZceMR0id5oXY\ncBPCe8pqxeXFMxKZkqQZ/eEYJFTlivXynKdPP8es/CJL9TZX7imzes7jJ39MtXqGUhYpUmqTcPZs\njdEwHEqOjx39vmBYZAzygkwVpNzhKP9zfPT1v8mr934YR8htaGxoUyqfL6UOL1ZOBvbWUZdrptMp\nF88eMxj2mNy9z3g4CQ5ibDdM10oSbybj2OSRbBGV8J5UqZ1r2/MttPqGriK2S727SsVu37xzVGXJ\ncjnDmpCtS6qUyeSUotd/DqHFn/usKNfBPmTSPb8PecVwKzmDvCjQxtJLFavZJVdVidYVw6OjsNmF\nIE0SqsqCUqRSIVQIKbbOhrJkeU6aKoTPqOoKJxxZljUlsGQQI5RqzI0C4aAlZPFoSKUYHh2D98yv\nrljNrxDA8GiCk2rD0rcsWKwA2ughhCBLVQjyQeywzKLzfUNLG8yepSkuSYNjVXOqu/izRuG4UXIJ\nQS/vI0/vs1jMKKs1HkNvMEaplCw7ol/cZ73+FoW9YiTHGNVEg5oCbQ269Hg0o5Oc1dqgFcxNQj0D\nqz3O1WSJJs/X2FJwPvsh7p18HyoZhuAl2RYWbb3st/3sjnGswNzoAYQk7w84zXJEIjl//BhpnqHu\nC4rRUUgtz26uhXhsNu8SoC2kqtGXtNWR2J/sZR8nYH3gxLpIbQeR+O2cxc9CSopeDyE8s+kVCId3\njrPzp4zGE4bDcajSdc0zJVxrkYrh3S6Jkdm+AKl3g5tTIGpLmipqa5EiAQW9tKAohsynM8qypj/o\nhyAYZ5FJghIqpDr3ASnoRoOcZ8HZSGuNpiRLE6RwKGewzqKyIsSei2iwGs1smytPKcVwcoJ2ntX8\nivUyOGv0xxOEUjgbvBtTSZNrIUxNHIDTBt+4hn3wkY9/bJrciClsqZ4SndgFdie1fUbsZWgQpHmP\niUqYL2csF5cgBGnWQ6icLDuhlz7Ary4xlIyoeTh4jYweF8u30W5OnnhS5UmUC5aUVHA0yOhnGUbX\nrFeaXpFANmNRvkVlLhkkw40HoPCO1XLJarkMjjj9IRCos3OOTKkdT8iY1fYi/M6ShLsnd9Fry/nZ\nY2qhuWNrRpM7+KbMWKuAc2ytCTTPUSJkbdrZ/JEjkLOhgnSbmyDmNto22WZNxB6IXf1Ia2EiOtZ+\nRwjyos9EKmazK+q6QkoafZTlaDyBxswd398mUd0nNjyHVCPR6UWIYYfgfBtwc1WY07BIjAeZpCQy\nOKys1yEFmhDB3j/s9UMsO8F11AmCE40P7Jlv5cNEYaymWq/QlcB7R5v+1yqJUMHjrR0kAzuFMwQh\nzPj45AQpPevFlHp+ibCGwdExUmUI4cFbtK5DGnaV0lbOaScooUlv5ncRQMzydk1Lm2xG0fh0Kxh1\nKVy7CTwgkpThYIw1lnK1wNiaXu+Ifv+YevUIX5W4dYIzDu/XpOo+x6MhpZ1R6wW1W5PnjqopZ14u\nDU6XOBEUr4lIMarmyez/8Pj8z/DGwyOUDPkC8ZIs6yGEbES7KpQyS2Rg06PIwy5FloSQaO8cWZrw\n4OF98lHB9Owx08sLQDCanKKavAvdzRYCNXIAABK+SURBVKs6Y+F4flMpwEoR/C5o4vojhNBCN5tx\nC6Lzfd81m7kUgizPOTo6YT6fUtcViQpFUQQwGk02kY6xQvO9vBuCZ2FbxOe9QFc8ejcEcWM6g3aD\na13jmsIT9WKNQFAM+xhr0XVJrzcI2Wgac5zxIUGqN5YskU19xa0Mqq0FH4pmWmup6gqEJM1zsjR9\nTuZzziE9G8UkhNj2p+98k+X0gjzv0RscMT4+QaUJ6/UCgaNXDBBJRqUtsvF3iNlY02h6N5ps50ia\nkuBxG+JJasUOTwhZjnUODpqFsB3HrghijWY+P2e5mJJlQ5KsR21WLOZPqVZnLNfvsDRTal3j/Zq1\nvmS5foJ2U7wweCXw0oIwwV4uMpQakqQD8qIgJYXliGH6Yb7z9R/mAw8+FKI/fdOC1jgu2ElEsg+8\n3+YsOLu6YjgaUaiEcrWgrNesVwvK5YLJ0R0md+8FhMAWacbcVTwesUgRy+dtUJIUvuEAdkW5LhK5\njvru0x90kZAgZLa6vDxnuZwjlcQaR78/4OTklDTNnnvOe31vF4ldB/v0NR5emOnoxpBB+96wKDyr\nukLPVyipSIs82NFNqH40HI+2CjwA59BVjbe2qaSUbRZCa9YLI9dkTtKm8WtIybNga93xanMOJcQm\nVBQ8lS6pywpTG5z3TTKWflPV1jXBUSFhq5QiCifeTmfrigygHXgcRQchtHdsqiPz/KS3LG1cNDX2\nimyf4QihtRcXZywuLugP+wwnR1jW1KVBKInMs1ARSZesl1ecnb1FWV/iZYm2S4zXWJGQpmOORg+Y\njB8x6N8lzwoUHmFr8JI8PSJPBzuUf98KcwRqHNxxd69wbVlzH1KICSGoyopVuUQlktnlJWaxZjQ5\n4uT+w5AclF0uyjZItoU4WCjerPFnGLCtGXIjtkT37dN7wO4mizmdriekICimZ7MrLi8vg1lZSZIk\nYTI5odfvbzQt172ju0ZaYvF+4EUKxBtHBmEJSypTs54tQp2CPEd4H2osqoT+MKTNajeEdw6sw+qm\nck+WhPzyRIoq50OVHCVRPugTvCc4XqgQ778z6NZhtcHoCuM0KstQadHMgsYZjTWevOiFIiGIbflx\nAOeQeGbTKUopBoPhJpklgGm4H+990D4vllTrNccnp2R5FkyIBL/2TISya/HMbLwHCQ5VqZLP2eJ9\n8wxrNOvLpzz++pskKRT9E4rRXfqTY/I8p9IVdVmircE1BdpC1anG81IKkqxHmvTJZEYiVERJwyi3\nScsE+yljVzlnrQlm0U7E4nK1RkpJlmeNSTHULFwuligBn/xP/5EPf/CDfOgj38347l1SFYrLx1Q9\n5hZiy0DMGbTzHHtQis736yIk4/HdR7ljZNC2bRM/4Rx1XfP0yWPW5YokUfR7fU5O75LnBdfBu+kF\nroN94kD8rFuLDOq6Yj6fcTQJbpzVek2paxyQSYW1NQJJrzcICU9po+VMo3VJKMsVtS7J0wFFniMb\nG5ODkDPRONJ065bsnG8q+YaqRxAtBucw2iJl8GjzjenB6ppqvWC1mJOkOUeTE1Sa7cYeeB+cjAie\niVJKvNWcPXtKmiYcTY7xCGprMdZQL9es5wuOTk4ZjUc4IdBs6yK0s9UqxWJFovaNMssGTX6qtprq\nVvtu5pf84ad/icuv/TaTh9/BnTf+PEevfASR92i9U5JEIdtMxGLLcTiC8jMOZ34Re+qbNjlCfoF9\n7rZthF33OdZ7am3RLnB5RZMk1DmHdYKnT9/mU//tv/IdH3yV7/7Yn+X49C5pltMmCSV6V+wuTecY\nPO9E1o2I3Ncv9pyLj3c5BU9Yo5Jd277RhsvLM8r1EmsMWdHj9PQeeXE9Qvh2YR8yi88Fq8UtRAbO\nO/BgTM26rOkN+5iqDotQyrAYag0+VJspikARnK1YTc/QxjGa3EETcvdlaR9vLGmebRQy1jrqWgcz\nZRYi97wLiTzwBLOk3J1Qa30oCCK3i805y2x6znJ6jvOO4dEpo/EpMkl3gk689xjPxtNQEoqwyiRB\nqgRjgj+hxSO9QNcVaRrKx0m2eQ5j3UGXasUl0lpR3XlP0ijJhGjqDXpHOX3CN7/066wunjC+/xGO\nX/texnfvIxFh8wqaTRWcoloKF0Oc2+BFC61u2pQ2f46Qh3G9WJIqSa9X4BpOqTXTtveGUhhNPorm\neN28M3WO+XTKv/u5f8N3vvEG3/dD38/47gn93uhaNpvOMb/neGzm3EdJ9z2vi+D2yeXtcd/muIyU\nPLqumU4v0HWFNqGI6mRyQr8/QIiukPn/B24lMih1HbLRek9VVyF5ZpoESpUkBH8CB9birGny8jms\nLlnNp1gnGI6OsAikzEhVhkqTUDq9oUDGOqqqxltHkoXz0HIHod9JKmmLQQsaW3KjqFPRkFXliotn\n76CtRqU5RTFgOJ5sOASIFrjfKg+F31bhaReTbTeiD4gn7Uz/RjfCbs5/wbawqGUbmdhC4LYsvbxJ\ncuoden7O46/8b+ZLw9HDD3Hy8DX6gx6VCanpA9fuSZN0Uz1qsyD989ru9lxXPGn1I7HSszaWs2dP\n8c5weucuSV5s2OeqDIgwiTIFdRVpG6ToPc+mU37x5/89J0XOJ/7yX2J0eofeeLy5r8sVdMWE9ni3\nHF33vfs2+nvZoN1d1G6rHU9VwOia6fSKuioxtQahOD45DZWe5XadvIgT2/fe94pAbiUyqGqNx21S\nT69WS6SQFP3BdtMAAo90Dl1rrNUkqSJNUvA1Ri8pK4uSI/ApeZEgUrW1F3tPVVbUdU3e66GyNGx2\nxyYMFAFS7k6ascGPIci44YRzFu8t1hjWVYV3liRRJGmPJMsgytDTUnLXprNid6G2lN0BwjbyP7sT\n2l4TPzNeqJt3sEu9jfU44yhy1VggPLpc8uzJW8xnU0bjU07uPSDJctalIW1MvFLIYLVhy3XEG0RG\n74wVZW17Wq4i3mgGMFqHsZIh6jRpUpEZ51muVkgh6ffy4B7NLpJx0XOdh3q54rf+56/x+O1v8cM/\n8nE+8OprDMfHm3Z1nYtaiBV9+zZarPPoihJ0fu/TicTPeRGXskFOzrGczyiXS6zReGAwGjMcjVFN\nrM17hW+Xk7iVyMA3noRamyAW1DXeW2SakuW9TS89DZb1jrJaY61hkBeY+pKr2RPy/hF5fkqWDRt5\nzeGMJWk0t+uqolqvKfIeqsgDNWjyJAYXBYlSYqOM84SIO20MqVKkSUAutTZ4b0mThFprnK3Q1RoQ\nDIdj0rQIqdPF826gXSrVfrcOvK0Dm54oBCqyaOyKA90sx/Ez280AYfPXxqBkSNXmRQiScqZidvE2\nb7/9NqPJPR48eESa5RitwblN3kmVJA3X4cHviksxxH3sIgOi4+213sFyucYJT79XBI7Ae+raYK0n\nydKgq5G7MQUeWKxL1lcLBsdjvBLYuuSP3/x9Mpnw6utvMJwch1TmnfZ1WfrumMVItaXcMfjO8fh3\nfH98rotEY4hFMOE96+WcxXyKNwbrHP3hmPHRBJV0ecX3DkEdf/3dL0IG79dS8b5ASBWoKgTZ1zmc\ntSxXC+q6QrjIVVNK8rxAqYTaGdJixN27r5GnA0xdoU3VEGeHtwbvHNpZEJ40S/De4EwdJHNBKEzR\nRHlh2fECS1VwmDHWBr8FAVma0MtyEqXIs4zf/PXfwFlLXa6YXZ1TlWuCW+5+GXVDdRrlknfgtGY1\nu6JezKgWC6qq3EEAGy7Duh0teFxbMbant553SRJiHGyTYzsBVJJz9+5rfPR7P87JvQeUdY0CPv2b\nv9X4eXho8iNIGo6iqlmv1pvahmF0dxf+znxG/VQ0NRvb4xJUnlFWhuW6DvUrhaCXpwx6GdI7lvM5\npt7m/G+fNeoVFIOCq/MzpNb005zv+Z7vQ6D4yh/8Af/9v3wS5+1zrH3c3m4b4+taXUz7O+6b7Pz2\nvglT7zyry7nFn+13Gf0hBMVgxPHJfWTa59O//b9YLuacnz+jrta8iEi/mHz/yTUON4oMIGCqLElC\nHUORkuV9cJ7p5TnL1WIzUcHnXNHLBySqQGswNkHKPkr1UCIl6L8lIYbcN8VLkiahZYIMZTwb/YNs\n4ggsQtiQ9pvt4kkSRaokRmuM1kiCfC8J2Y0+89u/Qy8fBKRRlyymV9R1yXVTtVlcbeJLCUkiyYsM\ni8VJj2qcUWKWVtKkMrdB4x7YeP8cNQ46Ak+pQ9h0KoM505hgAJWAFZIkSRkkGa5ac3n+lE/96qfw\nXpAkOe1CEgT//kSC0RWr9bIpdPPugTXXIQoIUaKDQZ8iz3cTfQpQqWI4HqGyJIRvG7eDFMajIQ8f\nPGA+nfH4nXeQScb3fOxjfPDD38Uv//L/YP70HGl2cyLFVLzbvuvmZ9Omzn1t3yWt2drvnGuhRfiu\ns5n3ISCBIM0yjk9P+PRnPoNHYLTm7Oyc1XIZTOjfJryfDX2jyCBQkKBgS7IUpTKsC9aDPM+pV0vK\nZUi80U6OksFbUCpJVVUh13+iNiZFJWXjB+CxtslrKCW1D0o7wdYBxkvQXuMxJHI7GC1ly5KEPFUY\nXVGuVw3VCpMshAwmLilJBOh6xXx6gWs2TRBZ9iuiBEF5aOuK1XzO9OoK0eg5rDabm9oFlIiQjk0g\nMM4iXeAsfOe5Ugh6abJJu52oBF1rTMRZhBoTKePRCGsNVbnGmFCmPnbWUUCRZQx6fdImwaxpFK/O\nbTmpWNaO7etd9hkgSQS9IiFLxY4n5WY9tNYECQ6H9WbbPwFKSe4/uB/MtDLBS8nkzh2SLOMLn/sc\nF4+foI3ZERH2bf4XUVbR+WuPbfoqGtdn75qK3Fuk3F7bVkKK3x0/r11nQgS9VZKmDEcj0rygqg3O\nai7On3J1dYG2egfxW17c/vcj9N8YMrj6ypu49Wy78L2g6Afzn0oSsqIH3qONRtc1rqGGFjBOs1jM\nKct1MNPUK5yu8SasUtGUsTbGBEtCmiPTBFuv0POn2PljMr0mQ4JXoWRWXYNz4BzO2U1mG6VS0jRF\nG01Vl1gbPBIdkBUFaZ6yXC3AGRLhKcsZ3prAmovdhdUmQZFAVdc8fvKUr/zR10Ak9PuhtLYxjrIs\nN+MUUzYpJUIqpN8u0Jgz2CCa1nXbeUpjNn75gvDPC4koBgxP7qKSlOViynJ2ia6rzbscgBAIlSLT\nIiSkbfQHptZYY/dSz/beLpsem02v25yxIjBLEpaLJYv1GtdwbRZY15rBeIRKBdaG1Or90YiP/OAP\noqXk8vycKqLaXXaeznc6x9+TElEGb9XWjyW+d9/mjx3GROdz+3LJvQcPuXvvAUqGsV4uZsynM7Te\ncjwC8L7dDfv78CeFG1MgvvSXHuAABwC4XdaEAxzgALcPblyBeIADHOB2wAEZHOAABwBuABkIIX5M\nCPGmEOIrQoifftnv/5OCEOLrQojPCyF+TwjxO82xEyHEp4QQXxZC/IoQYnLT7YxBCPFzQognQogv\nRMeubbMQ4meaeXlTCPGjN9PqXbimD/9UCPHNZi5+Twjx49G529iHV4UQvyaE+H0hxBeFEH+/OX67\n5iKk8Ho5fwSl9leB1wnxLJ8FPvIy2/A+2v414KRz7J8D/6j5/tPAP7vpdnba9wngB4AvvFubgY82\n85E28/NVQN7SPvwT4B/uufa29uEB8P3N9yHwh8BHbttcvGzO4OPAV733X/fea+A/AD/xktvwfqCr\nhf2rhJL1NJ9/7eU258Xgvf8N4LJz+Lo2/wTwC9577b3/OmEBfvxltPNFcE0fYL8V7bb24bH3/rPN\n9wXwB8AjbtlcvGxk8Aj4RvT7m82xPw3ggV8VQvyuEOLvNMfue++fNN+fAPdvpmnfFlzX5g8Q5qOF\n2z43f08I8TkhxM9G7PWt74MQ4nUCp/MZbtlcvGxk8KfZjvkj3vsfAH4c+LtCiE/EJ33g7/5U9e89\ntPm29udfAW8A3w+8A/yLF1x7a/oghBgCvwj8A+/9PD53G+biZSODbwGvRr9fZRcD3lrw3r/TfD4D\n/jOBbXsihHgAIIR4CDy9uRa+Z7iuzd25eaU5duvAe//UNwD8a7Ys9K3tgwgVbH8R+Lfe+082h2/V\nXLxsZPC7wIeFEK8LITLgrwO/9JLb8G2DEKIvhBg13wfAjwJfILT9p5rLfgr45P4n3Cq4rs2/BPwN\nIUQmhHgD+DDwOzfQvneFZuO08JOEuYBb2gcRorJ+FviS9/5fRqdu11zcgGb1xwna1K8CP3PTmt73\n2OY3CNrdzwJfbNsNnAC/CnwZ+BVgctNt7bT7F4C3CVnEvgH8rRe1GfjHzby8CfyVm27/NX3428DP\nA58HPkfYQPdveR/+AiE84bPA7zV/P3bb5uLgjnyAAxwAOHggHuAAB2jggAwOcIADAAdkcIADHKCB\nAzI4wAEOAByQwQEOcIAGDsjgAAc4AHBABgc4wAEaOCCDAxzgAAD8P7tWdgG4qV/gAAAAAElFTkSu\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "batch_index = 1\n", + "image = test_net.blobs['data'].data[batch_index]\n", + "plt.imshow(deprocess_net_image(image))\n", + "print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted style labels =\n", + "\t(1) 99.76% Pastel\n", + "\t(2) 0.13% HDR\n", + "\t(3) 0.11% Detailed\n", + "\t(4) 0.00% Melancholy\n", + "\t(5) 0.00% Noir\n" + ] + } + ], + "source": [ + "disp_style_preds(test_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also look at the predictions of the network trained from scratch. We see that in this case, the scratch network also predicts the correct label for the image (*Pastel*), but is much less confident in its prediction than the pretrained net." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted style labels =\n", + "\t(1) 49.81% Pastel\n", + "\t(2) 19.76% Detailed\n", + "\t(3) 17.06% Melancholy\n", + "\t(4) 11.66% HDR\n", + "\t(5) 1.72% Noir\n" + ] + } + ], + "source": [ + "disp_style_preds(scratch_test_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course, we can again look at the ImageNet model's predictions for the above image:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "top 5 predicted ImageNet labels =\n", + "\t(1) 34.90% n07579787 plate\n", + "\t(2) 21.63% n04263257 soup bowl\n", + "\t(3) 17.75% n07875152 potpie\n", + "\t(4) 5.72% n07711569 mashed potato\n", + "\t(5) 5.27% n07584110 consomme\n" + ] + } + ], + "source": [ + "disp_imagenet_preds(imagenet_net, image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n", + "\n", + "http://demo.vislab.berkeleyvision.org/" + ] + } + ], + "metadata": { + "description": "Fine-tune the ImageNet-trained CaffeNet on new data.", + "example_name": "Fine-tuning for Style Recognition", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + }, + "priority": 3 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/03-fine-tuning.ipynb b/examples/03-fine-tuning.ipynb deleted file mode 100644 index cc90b16bbfa..00000000000 --- a/examples/03-fine-tuning.ipynb +++ /dev/null @@ -1,947 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fine-tuning a Pretrained Network for Style Recognition\n", - "\n", - "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n", - "\n", - "The upside of such approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful feature that you can treat as a black box. On top of that, only a few layers will be needed to obtain a very good performance of the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we will need to prepare the data. This involves the following parts:\n", - "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n", - "(2) Download a subset of the overall Flickr style dataset for this demo.\n", - "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n", - "os.chdir('..')\n", - "import sys\n", - "sys.path.insert(0, './python')\n", - "\n", - "import caffe\n", - "import numpy as np\n", - "from pylab import *\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# This downloads the ilsvrc auxiliary data (mean file, etc),\n", - "# and a subset of 2000 images for the style recognition task.\n", - "!data/ilsvrc12/get_ilsvrc_aux.sh\n", - "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n", - "!python examples/finetune_flickr_style/assemble_data.py \\\n", - " --workers=-1 --images=2000 --seed=1701 --label=5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's show what is the difference between the fine-tuning network and the original caffe model." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1c1\r\n", - "< name: \"CaffeNet\"\r\n", - "---\r\n", - "> name: \"FlickrStyleCaffeNet\"\r\n", - "4c4\r\n", - "< type: \"Data\"\r\n", - "---\r\n", - "> type: \"ImageData\"\r\n", - "15,26c15,19\r\n", - "< # mean pixel / channel-wise mean instead of mean image\r\n", - "< # transform_param {\r\n", - "< # crop_size: 227\r\n", - "< # mean_value: 104\r\n", - "< # mean_value: 117\r\n", - "< # mean_value: 123\r\n", - "< # mirror: true\r\n", - "< # }\r\n", - "< data_param {\r\n", - "< source: \"examples/imagenet/ilsvrc12_train_lmdb\"\r\n", - "< batch_size: 256\r\n", - "< backend: LMDB\r\n", - "---\r\n", - "> image_data_param {\r\n", - "> source: \"data/flickr_style/train.txt\"\r\n", - "> batch_size: 50\r\n", - "> new_height: 256\r\n", - "> new_width: 256\r\n", - "31c24\r\n", - "< type: \"Data\"\r\n", - "---\r\n", - "> type: \"ImageData\"\r\n", - "42,51c35,36\r\n", - "< # mean pixel / channel-wise mean instead of mean image\r\n", - "< # transform_param {\r\n", - "< # crop_size: 227\r\n", - "< # mean_value: 104\r\n", - "< # mean_value: 117\r\n", - "< # mean_value: 123\r\n", - "< # mirror: true\r\n", - "< # }\r\n", - "< data_param {\r\n", - "< source: \"examples/imagenet/ilsvrc12_val_lmdb\"\r\n", - "---\r\n", - "> image_data_param {\r\n", - "> source: \"data/flickr_style/test.txt\"\r\n", - "53c38,39\r\n", - "< backend: LMDB\r\n", - "---\r\n", - "> new_height: 256\r\n", - "> new_width: 256\r\n", - "323a310\r\n", - "> # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer\r\n", - "360c347\r\n", - "< name: \"fc8\"\r\n", - "---\r\n", - "> name: \"fc8_flickr\"\r\n", - "363c350,351\r\n", - "< top: \"fc8\"\r\n", - "---\r\n", - "> top: \"fc8_flickr\"\r\n", - "> # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained\r\n", - "365c353\r\n", - "< lr_mult: 1\r\n", - "---\r\n", - "> lr_mult: 10\r\n", - "369c357\r\n", - "< lr_mult: 2\r\n", - "---\r\n", - "> lr_mult: 20\r\n", - "373c361\r\n", - "< num_output: 1000\r\n", - "---\r\n", - "> num_output: 20\r\n", - "384a373,379\r\n", - "> name: \"loss\"\r\n", - "> type: \"SoftmaxWithLoss\"\r\n", - "> bottom: \"fc8_flickr\"\r\n", - "> bottom: \"label\"\r\n", - "> top: \"loss\"\r\n", - "> }\r\n", - "> layer {\r\n", - "387c382\r\n", - "< bottom: \"fc8\"\r\n", - "---\r\n", - "> bottom: \"fc8_flickr\"\r\n", - "393,399d387\r\n", - "< }\r\n", - "< layer {\r\n", - "< name: \"loss\"\r\n", - "< type: \"SoftmaxWithLoss\"\r\n", - "< bottom: \"fc8\"\r\n", - "< bottom: \"label\"\r\n", - "< top: \"loss\"\r\n" - ] - } - ], - "source": [ - "!diff models/bvlc_reference_caffenet/train_val.prototxt models/finetune_flickr_style/train_val.prototxt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For your record, if you want to train the network in pure C++ tools, here is the command:\n", - "\n", - "\n", - "build/tools/caffe train \\\n", - " -solver models/finetune_flickr_style/solver.prototxt \\\n", - " -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n", - " -gpu 0\n", - "\n", - "\n", - "However, we will train using Python in this example." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "iter 0, finetune_loss=3.360094, scratch_loss=3.136188\n", - "iter 10, finetune_loss=2.672608, scratch_loss=9.736364\n", - "iter 20, finetune_loss=2.071996, scratch_loss=2.250404\n", - "iter 30, finetune_loss=1.758295, scratch_loss=2.049553\n", - "iter 40, finetune_loss=1.533391, scratch_loss=1.941318\n", - "iter 50, finetune_loss=1.561658, scratch_loss=1.839706\n", - "iter 60, finetune_loss=1.461696, scratch_loss=1.880035\n", - "iter 70, finetune_loss=1.267941, scratch_loss=1.719161\n", - "iter 80, finetune_loss=1.192778, scratch_loss=1.627453\n", - "iter 90, finetune_loss=1.541176, scratch_loss=1.822061\n", - "iter 100, finetune_loss=1.029039, scratch_loss=1.654087\n", - "iter 110, finetune_loss=1.138547, scratch_loss=1.735837\n", - "iter 120, finetune_loss=0.917412, scratch_loss=1.851918\n", - "iter 130, finetune_loss=0.971519, scratch_loss=1.801927\n", - "iter 140, finetune_loss=0.868252, scratch_loss=1.745545\n", - "iter 150, finetune_loss=0.790020, scratch_loss=1.844925\n", - "iter 160, finetune_loss=1.092668, scratch_loss=1.695591\n", - "iter 170, finetune_loss=1.055344, scratch_loss=1.661715\n", - "iter 180, finetune_loss=0.969769, scratch_loss=1.823639\n", - "iter 190, finetune_loss=0.780566, scratch_loss=1.820862\n", - "done\n" - ] - } - ], - "source": [ - "niter = 200\n", - "# losses will also be stored in the log\n", - "train_loss = np.zeros(niter)\n", - "scratch_train_loss = np.zeros(niter)\n", - "\n", - "caffe.set_device(0)\n", - "caffe.set_mode_gpu()\n", - "# We create a solver that fine-tunes from a previously trained network.\n", - "solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n", - "solver.net.copy_from('models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", - "# For reference, we also create a solver that does no finetuning.\n", - "scratch_solver = caffe.SGDSolver('models/finetune_flickr_style/solver.prototxt')\n", - "\n", - "# We run the solver for niter times, and record the training loss.\n", - "for it in range(niter):\n", - " solver.step(1) # SGD by Caffe\n", - " scratch_solver.step(1)\n", - " # store the train loss\n", - " train_loss[it] = solver.net.blobs['loss'].data\n", - " scratch_train_loss[it] = scratch_solver.net.blobs['loss'].data\n", - " if it % 10 == 0:\n", - " print 'iter %d, finetune_loss=%f, scratch_loss=%f' % (it, train_loss[it], scratch_train_loss[it])\n", - "print 'done'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the training loss produced by the two training procedures respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcXFWd9/HPtzt7AlkkJCGAgbCIqCSyuIDaRECEYZvB\n", - "EQRFB5iMo8CjzuMwOlpdioo4IM4iM6wTgdHhgRFBRAhLM6gQtgQCIQQkYc8CJIEQQpb+PX+c01hp\n", - "eqmqrl5SfN+vV7266tZdzr11+3tPnXvuLUUEZmZWHxr6uwBmZlY7DnUzszriUDczqyMOdTOzOuJQ\n", - "NzOrIw51M7M6UlaoS2qUNFfS9fn1OEmzJS2SdLOkMb1bTDMzK0e5NfUzgAVAW6f2M4HZEbEbcGt+\n", - 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"UlvRigAAAABJRU5ErkJggg==\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot(np.vstack([train_loss, scratch_train_loss]).T)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the fine-tuning procedure produces a more smooth loss function change, and ends up at a better loss. A closer look at small values, clipping to avoid showing too large loss during training:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXeYHNWVt98jgXIY5ZyQMNlIJJMMwhhssI0Dxsbr8Dms\n", - "zTpne9e73qa9tnFYrzMYe53WOeyuFzA4YBAYTEYiCQQCCSRAaZQTEtL5/jj3TlXXVHdX9/SMZsR5\n", - "n2ee6a6uqq5Ov3vu7557rqgqjuM4zv5Hv319AY7jOE734ALvOI6zn+IC7ziOs5/iAu84jrOf4gLv\n", - "OI6zn+IC7ziOs59SSOBFpL+ILBSRK6s8/g0ReURE7hGRea29RMdxHKcZikbwHwQWA52S5kXkXGCO\n", - "qh4MvAu4rHWX5ziO4zRLXYEXkanAucB/ApKzy3nAjwFU9TagTUQmtPIiHcdxnMYpEsF/Ffg4sLfK\n", - "41OAFan7K4GpXbwux3Ecp4vUFHgReTmwRlUXkh+9d+yaue/1DxzHcfYxB9R5/GTgvOCzDwJGiMh/\n", - "qepbUvs8CUxL3Z8atlUgIi76juM4TaCqtQLsqkjRYmMicjrwMVV9RWb7ucD7VPVcETkR+Jqqnphz\n", - 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Note that we are running a classification task of 5 classes, thus a chance accuracy is 20%. As we will reasonably expect, the finetuning result will be much better than the one from training from scratch. Let's see." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for fine-tuning: 0.570000001788\n", - "Accuracy for training from scratch: 0.224000000954\n" - ] - } - ], - "source": [ - "test_iters = 10\n", - "accuracy = 0\n", - "scratch_accuracy = 0\n", - "for it in arange(test_iters):\n", - " solver.test_nets[0].forward()\n", - " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", - " scratch_solver.test_nets[0].forward()\n", - " scratch_accuracy += scratch_solver.test_nets[0].blobs['accuracy'].data\n", - "accuracy /= test_iters\n", - "scratch_accuracy /= test_iters\n", - "print 'Accuracy for fine-tuning:', accuracy\n", - "print 'Accuracy for training from scratch:', scratch_accuracy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Huzzah! So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n", - "\n", - "http://demo.vislab.berkeleyvision.org/" - ] - } - ], - "metadata": { - "description": "Fine-tune the ImageNet-trained CaffeNet on new data.", - "example_name": "Fine-tuning for Style Recognition", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" - }, - "priority": 4 - }, - "nbformat": 4, - "nbformat_minor": 0 -} From 08edfddbbae6abc66aa51ef655f782eee5109f97 Mon Sep 17 00:00:00 2001 From: Jeff Donahue Date: Tue, 23 Feb 2016 23:42:11 -0800 Subject: [PATCH 407/446] [example] improve brewing logreg notebook - create solvers inline through python protobuf - drop manually written solver prototxt - remove ordering prefix, since there is no real sequencing constraint for this example --- examples/02-brewing-logreg.ipynb | 5771 ----------------- examples/brewing-logreg.ipynb | 1164 ++++ .../nonlinear_solver.prototxt | 15 - examples/hdf5_classification/solver.prototxt | 15 - 4 files changed, 1164 insertions(+), 5801 deletions(-) delete mode 100644 examples/02-brewing-logreg.ipynb create mode 100644 examples/brewing-logreg.ipynb delete mode 100644 examples/hdf5_classification/nonlinear_solver.prototxt delete mode 100644 examples/hdf5_classification/solver.prototxt diff --git a/examples/02-brewing-logreg.ipynb b/examples/02-brewing-logreg.ipynb deleted file mode 100644 index d36871fcdfd..00000000000 --- a/examples/02-brewing-logreg.ipynb +++ /dev/null @@ -1,5771 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Brewing Logistic Regression then Going Deeper\n", - "\n", - "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "import os\n", - "os.chdir('..')\n", - "\n", - "import sys\n", - "sys.path.insert(0, './python')\n", - "import caffe\n", - "\n", - "\n", - "import os\n", - "import h5py\n", - "import shutil\n", - "import tempfile\n", - "\n", - "import sklearn\n", - "import sklearn.datasets\n", - "import sklearn.linear_model\n", - "\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAiMAAAImCAYAAACB54oCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - 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"X, y = sklearn.datasets.make_classification(\n", - " n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n", - " n_clusters_per_class=2, hypercube=False, random_state=0\n", - ")\n", - "\n", - "# Split into train and test\n", - "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n", - "\n", - "# Visualize sample of the data\n", - "ind = np.random.permutation(X.shape[0])[:1000]\n", - "df = pd.DataFrame(X[ind])\n", - "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.783\n", - "Accuracy: 0.783\n", - "Accuracy: 0.783\n", - "Accuracy: 0.783\n", - "1 loops, best of 3: 508 ms per loop\n" - ] - } - ], - "source": [ - "%%timeit\n", - "# Train and test the scikit-learn SGD logistic regression.\n", - "clf = sklearn.linear_model.SGDClassifier(\n", - " loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n", - "\n", - "clf.fit(X, y)\n", - "yt_pred = clf.predict(Xt)\n", - "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save the dataset to HDF5 for loading in Caffe." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Write out the data to HDF5 files in a temp directory.\n", - "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n", - "dirname = os.path.abspath('./examples/hdf5_classification/data')\n", - "if not os.path.exists(dirname):\n", - " os.makedirs(dirname)\n", - "\n", - "train_filename = os.path.join(dirname, 'train.h5')\n", - "test_filename = os.path.join(dirname, 'test.h5')\n", - "\n", - "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n", - "# To show this off, we'll list the same data file twice.\n", - "with h5py.File(train_filename, 'w') as f:\n", - " f['data'] = X\n", - " f['label'] = y.astype(np.float32)\n", - "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n", - " f.write(train_filename + '\\n')\n", - " f.write(train_filename + '\\n')\n", - " \n", - "# HDF5 is pretty efficient, but can be further compressed.\n", - "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n", - "with h5py.File(test_filename, 'w') as f:\n", - " f.create_dataset('data', data=Xt, **comp_kwargs)\n", - " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n", - "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n", - " f.write(test_filename + '\\n')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from caffe import layers as L\n", - "from caffe import params as P\n", - "\n", - "def logreg(hdf5, batch_size):\n", - " # logistic regression: data, matrix multiplication, and 2-class softmax loss\n", - " n = caffe.NetSpec()\n", - " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n", - " n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n", - " n.accuracy = L.Accuracy(n.ip1, n.label)\n", - " n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n", - " return n.to_proto()\n", - " \n", - "with open('examples/hdf5_classification/logreg_auto_train.prototxt', 'w') as f:\n", - " f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n", - " \n", - "with open('examples/hdf5_classification/logreg_auto_test.prototxt', 'w') as f:\n", - " f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Time to learn and evaluate our Caffeinated logistic regression in Python." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.782\n", - "Accuracy: 0.782\n", - "Accuracy: 0.782\n", - "Accuracy: 0.782\n", - "1 loops, best of 3: 287 ms per loop\n" - ] - } - ], - "source": [ - "%%timeit\n", - "caffe.set_mode_cpu()\n", - "solver = caffe.get_solver('examples/hdf5_classification/solver.prototxt')\n", - "solver.solve()\n", - "\n", - "accuracy = 0\n", - "batch_size = solver.test_nets[0].blobs['data'].num\n", - "test_iters = int(len(Xt) / batch_size)\n", - "for i in range(test_iters):\n", - " solver.test_nets[0].forward()\n", - " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", - "accuracy /= test_iters\n", - "\n", - "print(\"Accuracy: {:.3f}\".format(accuracy))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do the same through the command line interface for detailed output on the model and solving." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I0318 00:58:32.322571 2013098752 caffe.cpp:117] Use CPU.\n", - "I0318 00:58:32.643163 2013098752 caffe.cpp:121] Starting Optimization\n", - "I0318 00:58:32.643229 2013098752 solver.cpp:32] Initializing solver from parameters: \n", - "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n", - "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n", - "test_iter: 250\n", - "test_interval: 1000\n", - "base_lr: 0.01\n", - "display: 1000\n", - "max_iter: 10000\n", - "lr_policy: \"step\"\n", - "gamma: 0.1\n", - "momentum: 0.9\n", - "weight_decay: 0.0005\n", - "stepsize: 5000\n", - "snapshot: 10000\n", - "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n", - "solver_mode: CPU\n", - "I0318 00:58:32.643333 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n", - "I0318 00:58:32.643465 2013098752 net.cpp:42] Initializing net from parameters: \n", - "state {\n", - " phase: TRAIN\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " hdf5_data_param {\n", - " source: \"examples/hdf5_classification/data/train.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"ip1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"ip1\"\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"accuracy\"\n", - " type: \"Accuracy\"\n", - " bottom: \"ip1\"\n", - " bottom: \"label\"\n", - " top: \"accuracy\"\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"ip1\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "I0318 00:58:32.644197 2013098752 layer_factory.hpp:74] Creating layer data\n", - "I0318 00:58:32.644219 2013098752 net.cpp:84] Creating Layer data\n", - "I0318 00:58:32.644230 2013098752 net.cpp:338] data -> data\n", - "I0318 00:58:32.644256 2013098752 net.cpp:338] data -> label\n", - "I0318 00:58:32.644269 2013098752 net.cpp:113] Setting up data\n", - "I0318 00:58:32.644278 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n", - "I0318 00:58:32.644327 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n", - "I0318 00:58:32.646458 2013098752 net.cpp:120] Top shape: 10 4 (40)\n", - "I0318 00:58:32.646502 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:32.646518 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n", - "I0318 00:58:32.646538 2013098752 net.cpp:84] Creating Layer label_data_1_split\n", - "I0318 00:58:32.646546 2013098752 net.cpp:380] label_data_1_split <- label\n", - "I0318 00:58:32.646556 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", - "I0318 00:58:32.646569 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", - "I0318 00:58:32.646579 2013098752 net.cpp:113] Setting up label_data_1_split\n", - "I0318 00:58:32.646586 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:32.646595 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:32.646601 2013098752 layer_factory.hpp:74] Creating layer ip1\n", - "I0318 00:58:32.646615 2013098752 net.cpp:84] Creating Layer ip1\n", - "I0318 00:58:32.646622 2013098752 net.cpp:380] ip1 <- data\n", - "I0318 00:58:32.646664 2013098752 net.cpp:338] ip1 -> ip1\n", - "I0318 00:58:32.646689 2013098752 net.cpp:113] Setting up ip1\n", - "I0318 00:58:32.652330 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:32.652371 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n", - "I0318 00:58:32.652393 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n", - "I0318 00:58:32.652407 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n", - "I0318 00:58:32.652421 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n", - "I0318 00:58:32.652467 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n", - "I0318 00:58:32.652480 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n", - "I0318 00:58:32.652489 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:32.652498 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:32.652505 2013098752 layer_factory.hpp:74] Creating layer accuracy\n", - "I0318 00:58:32.652521 2013098752 net.cpp:84] Creating Layer accuracy\n", - "I0318 00:58:32.652534 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n", - "I0318 00:58:32.652545 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n", - "I0318 00:58:32.652562 2013098752 net.cpp:338] accuracy -> accuracy\n", - "I0318 00:58:32.652577 2013098752 net.cpp:113] Setting up accuracy\n", - "I0318 00:58:32.652590 2013098752 net.cpp:120] Top shape: (1)\n", - "I0318 00:58:32.652642 2013098752 layer_factory.hpp:74] Creating layer loss\n", - "I0318 00:58:32.652655 2013098752 net.cpp:84] Creating Layer loss\n", - "I0318 00:58:32.652663 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n", - "I0318 00:58:32.652672 2013098752 net.cpp:380] loss <- label_data_1_split_1\n", - "I0318 00:58:32.652679 2013098752 net.cpp:338] loss -> loss\n", - "I0318 00:58:32.652689 2013098752 net.cpp:113] Setting up loss\n", - "I0318 00:58:32.652701 2013098752 layer_factory.hpp:74] Creating layer loss\n", - "I0318 00:58:32.652716 2013098752 net.cpp:120] Top shape: (1)\n", - "I0318 00:58:32.652724 2013098752 net.cpp:122] with loss weight 1\n", - "I0318 00:58:32.652740 2013098752 net.cpp:167] loss needs backward computation.\n", - "I0318 00:58:32.652746 2013098752 net.cpp:169] accuracy does not need backward computation.\n", - "I0318 00:58:32.652753 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n", - "I0318 00:58:32.652760 2013098752 net.cpp:167] ip1 needs backward computation.\n", - "I0318 00:58:32.652786 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n", - "I0318 00:58:32.652801 2013098752 net.cpp:169] data does not need backward computation.\n", - "I0318 00:58:32.652808 2013098752 net.cpp:205] This network produces output accuracy\n", - "I0318 00:58:32.652815 2013098752 net.cpp:205] This network produces output loss\n", - "I0318 00:58:32.652825 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", - "I0318 00:58:32.652833 2013098752 net.cpp:217] Network initialization done.\n", - "I0318 00:58:32.652839 2013098752 net.cpp:218] Memory required for data: 528\n", - "I0318 00:58:32.652964 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n", - "I0318 00:58:32.652986 2013098752 net.cpp:42] Initializing net from parameters: \n", - "state {\n", - " phase: TEST\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " hdf5_data_param {\n", - " source: \"examples/hdf5_classification/data/test.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"ip1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"ip1\"\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"accuracy\"\n", - " type: \"Accuracy\"\n", - " bottom: \"ip1\"\n", - " bottom: \"label\"\n", - " top: \"accuracy\"\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"ip1\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "I0318 00:58:32.653069 2013098752 layer_factory.hpp:74] Creating layer data\n", - "I0318 00:58:32.653080 2013098752 net.cpp:84] Creating Layer data\n", - "I0318 00:58:32.653090 2013098752 net.cpp:338] data -> data\n", - "I0318 00:58:32.653128 2013098752 net.cpp:338] data -> label\n", - "I0318 00:58:32.653146 2013098752 net.cpp:113] Setting up data\n", - "I0318 00:58:32.653154 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n", - "I0318 00:58:32.653192 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n", - "I0318 00:58:32.654850 2013098752 net.cpp:120] Top shape: 10 4 (40)\n", - "I0318 00:58:32.654897 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:32.654914 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n", - "I0318 00:58:32.654933 2013098752 net.cpp:84] Creating Layer label_data_1_split\n", - "I0318 00:58:32.654943 2013098752 net.cpp:380] label_data_1_split <- label\n", - "I0318 00:58:32.654953 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", - "I0318 00:58:32.654966 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", - "I0318 00:58:32.654976 2013098752 net.cpp:113] Setting up label_data_1_split\n", - "I0318 00:58:32.654985 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:32.654992 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:32.655000 2013098752 layer_factory.hpp:74] Creating layer ip1\n", - "I0318 00:58:32.655010 2013098752 net.cpp:84] Creating Layer ip1\n", - "I0318 00:58:32.655017 2013098752 net.cpp:380] ip1 <- data\n", - "I0318 00:58:32.655030 2013098752 net.cpp:338] ip1 -> ip1\n", - "I0318 00:58:32.655041 2013098752 net.cpp:113] Setting up ip1\n", - "I0318 00:58:32.655061 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:32.655072 2013098752 layer_factory.hpp:74] Creating layer ip1_ip1_0_split\n", - "I0318 00:58:32.655148 2013098752 net.cpp:84] Creating Layer ip1_ip1_0_split\n", - "I0318 00:58:32.655159 2013098752 net.cpp:380] ip1_ip1_0_split <- ip1\n", - "I0318 00:58:32.655170 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_0\n", - "I0318 00:58:32.655180 2013098752 net.cpp:338] ip1_ip1_0_split -> ip1_ip1_0_split_1\n", - "I0318 00:58:32.655190 2013098752 net.cpp:113] Setting up ip1_ip1_0_split\n", - "I0318 00:58:32.655199 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:32.655206 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:32.655213 2013098752 layer_factory.hpp:74] Creating layer accuracy\n", - "I0318 00:58:32.655223 2013098752 net.cpp:84] Creating Layer accuracy\n", - "I0318 00:58:32.655230 2013098752 net.cpp:380] accuracy <- ip1_ip1_0_split_0\n", - "I0318 00:58:32.655237 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n", - "I0318 00:58:32.655251 2013098752 net.cpp:338] accuracy -> accuracy\n", - "I0318 00:58:32.655259 2013098752 net.cpp:113] Setting up accuracy\n", - "I0318 00:58:32.655267 2013098752 net.cpp:120] Top shape: (1)\n", - "I0318 00:58:32.655340 2013098752 layer_factory.hpp:74] Creating layer loss\n", - "I0318 00:58:32.655354 2013098752 net.cpp:84] Creating Layer loss\n", - "I0318 00:58:32.655361 2013098752 net.cpp:380] loss <- ip1_ip1_0_split_1\n", - "I0318 00:58:32.655369 2013098752 net.cpp:380] loss <- label_data_1_split_1\n", - "I0318 00:58:32.655378 2013098752 net.cpp:338] loss -> loss\n", - "I0318 00:58:32.655388 2013098752 net.cpp:113] Setting up loss\n", - "I0318 00:58:32.655397 2013098752 layer_factory.hpp:74] Creating layer loss\n", - "I0318 00:58:32.655414 2013098752 net.cpp:120] Top shape: (1)\n", - "I0318 00:58:32.655422 2013098752 net.cpp:122] with loss weight 1\n", - "I0318 00:58:32.655438 2013098752 net.cpp:167] loss needs backward computation.\n", - "I0318 00:58:32.655446 2013098752 net.cpp:169] accuracy does not need backward computation.\n", - "I0318 00:58:32.655455 2013098752 net.cpp:167] ip1_ip1_0_split needs backward computation.\n", - "I0318 00:58:32.655462 2013098752 net.cpp:167] ip1 needs backward computation.\n", - "I0318 00:58:32.655469 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n", - "I0318 00:58:32.655477 2013098752 net.cpp:169] data does not need backward computation.\n", - "I0318 00:58:32.655483 2013098752 net.cpp:205] This network produces output accuracy\n", - "I0318 00:58:32.655489 2013098752 net.cpp:205] This network produces output loss\n", - "I0318 00:58:32.655503 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", - "I0318 00:58:32.655511 2013098752 net.cpp:217] Network initialization done.\n", - "I0318 00:58:32.655517 2013098752 net.cpp:218] Memory required for data: 528\n", - "I0318 00:58:32.655547 2013098752 solver.cpp:42] Solver scaffolding done.\n", - "I0318 00:58:32.655567 2013098752 solver.cpp:222] Solving \n", - "I0318 00:58:32.655575 2013098752 solver.cpp:223] Learning Rate Policy: step\n", - "I0318 00:58:32.655583 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n", - "I0318 00:58:32.683643 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.3736\n", - "I0318 00:58:32.683686 2013098752 solver.cpp:315] Test net output #1: loss = 1.00555 (* 1 = 1.00555 loss)\n", - "I0318 00:58:32.683846 2013098752 solver.cpp:189] Iteration 0, loss = 0.869394\n", - "I0318 00:58:32.683861 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.3\n", - "I0318 00:58:32.683871 2013098752 solver.cpp:204] Train net output #1: loss = 0.869394 (* 1 = 0.869394 loss)\n", - "I0318 00:58:32.683883 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n", - "I0318 00:58:32.698721 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n", - "I0318 00:58:32.701917 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n", - "I0318 00:58:32.701961 2013098752 solver.cpp:315] Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n", - "I0318 00:58:32.702014 2013098752 solver.cpp:189] Iteration 1000, loss = 0.54742\n", - "I0318 00:58:32.702029 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", - "I0318 00:58:32.702041 2013098752 solver.cpp:204] Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n", - "I0318 00:58:32.702051 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n", - "I0318 00:58:32.718360 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n", - "I0318 00:58:32.721529 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7696\n", - "I0318 00:58:32.721562 2013098752 solver.cpp:315] Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n", - "I0318 00:58:32.721593 2013098752 solver.cpp:189] Iteration 2000, loss = 0.729569\n", - "I0318 00:58:32.721603 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n", - "I0318 00:58:32.721613 2013098752 solver.cpp:204] Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n", - "I0318 00:58:32.721622 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n", - "I0318 00:58:32.740182 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n", - "I0318 00:58:32.743494 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.77\n", - "I0318 00:58:32.743544 2013098752 solver.cpp:315] Test net output #1: loss = 0.591229 (* 1 = 0.591229 loss)\n", - "I0318 00:58:32.744209 2013098752 solver.cpp:189] Iteration 3000, loss = 0.406097\n", - "I0318 00:58:32.744231 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.8\n", - "I0318 00:58:32.744249 2013098752 solver.cpp:204] Train net output #1: loss = 0.406096 (* 1 = 0.406096 loss)\n", - "I0318 00:58:32.744266 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n", - "I0318 00:58:32.764135 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n", - "I0318 00:58:32.769110 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n", - "I0318 00:58:32.769170 2013098752 solver.cpp:315] Test net output #1: loss = 0.590972 (* 1 = 0.590972 loss)\n", - "I0318 00:58:32.769223 2013098752 solver.cpp:189] Iteration 4000, loss = 0.54742\n", - "I0318 00:58:32.769242 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", - "I0318 00:58:32.769255 2013098752 solver.cpp:204] Train net output #1: loss = 0.54742 (* 1 = 0.54742 loss)\n", - "I0318 00:58:32.769265 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n", - "I0318 00:58:32.785846 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n", - "I0318 00:58:32.788722 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7696\n", - "I0318 00:58:32.788751 2013098752 solver.cpp:315] Test net output #1: loss = 0.593946 (* 1 = 0.593946 loss)\n", - "I0318 00:58:32.788811 2013098752 solver.cpp:189] Iteration 5000, loss = 0.72957\n", - "I0318 00:58:32.788833 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n", - "I0318 00:58:32.788846 2013098752 solver.cpp:204] Train net output #1: loss = 0.729569 (* 1 = 0.729569 loss)\n", - "I0318 00:58:32.788856 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n", - "I0318 00:58:32.804762 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n", - "I0318 00:58:32.808061 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7856\n", - "I0318 00:58:32.808112 2013098752 solver.cpp:315] Test net output #1: loss = 0.59028 (* 1 = 0.59028 loss)\n", - "I0318 00:58:32.808732 2013098752 solver.cpp:189] Iteration 6000, loss = 0.415444\n", - "I0318 00:58:32.808753 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", - "I0318 00:58:32.808773 2013098752 solver.cpp:204] Train net output #1: loss = 0.415444 (* 1 = 0.415444 loss)\n", - "I0318 00:58:32.808786 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n", - "I0318 00:58:32.827118 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n", - "I0318 00:58:32.831614 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7848\n", - "I0318 00:58:32.831657 2013098752 solver.cpp:315] Test net output #1: loss = 0.589454 (* 1 = 0.589454 loss)\n", - "I0318 00:58:32.831707 2013098752 solver.cpp:189] Iteration 7000, loss = 0.538038\n", - "I0318 00:58:32.831728 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.8\n", - "I0318 00:58:32.831745 2013098752 solver.cpp:204] Train net output #1: loss = 0.538037 (* 1 = 0.538037 loss)\n", - "I0318 00:58:32.831759 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n", - "I0318 00:58:32.849634 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n", - "I0318 00:58:32.852712 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7796\n", - "I0318 00:58:32.852748 2013098752 solver.cpp:315] Test net output #1: loss = 0.589365 (* 1 = 0.589365 loss)\n", - "I0318 00:58:32.852792 2013098752 solver.cpp:189] Iteration 8000, loss = 0.684219\n", - "I0318 00:58:32.852840 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n", - "I0318 00:58:32.852852 2013098752 solver.cpp:204] Train net output #1: loss = 0.684219 (* 1 = 0.684219 loss)\n", - "I0318 00:58:32.852861 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n", - "I0318 00:58:32.868440 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n", - "I0318 00:58:32.871438 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.7816\n", - "I0318 00:58:32.871461 2013098752 solver.cpp:315] Test net output #1: loss = 0.589656 (* 1 = 0.589656 loss)\n", - "I0318 00:58:32.872109 2013098752 solver.cpp:189] Iteration 9000, loss = 0.421879\n", - "I0318 00:58:32.872131 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", - "I0318 00:58:32.872143 2013098752 solver.cpp:204] Train net output #1: loss = 0.421879 (* 1 = 0.421879 loss)\n", - "I0318 00:58:32.872153 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n", - "I0318 00:58:32.889981 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n", - "I0318 00:58:32.890224 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n", - "I0318 00:58:32.890362 2013098752 solver.cpp:248] Iteration 10000, loss = 0.538933\n", - "I0318 00:58:32.890380 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n", - "I0318 00:58:32.893728 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.782\n", - "I0318 00:58:32.893757 2013098752 solver.cpp:315] Test net output #1: loss = 0.589366 (* 1 = 0.589366 loss)\n", - "I0318 00:58:32.893775 2013098752 solver.cpp:253] Optimization Done.\n", - "I0318 00:58:32.893786 2013098752 caffe.cpp:134] Optimization Done.\n" - ] - } - ], - "source": [ - "!./build/tools/caffe train -solver examples/hdf5_classification/solver.prototxt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n", - "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n", - "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_solver.prototxt` which we will now use.\n", - "\n", - "The final accuracy of the new network should be higher than logistic regression!" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from caffe import layers as L\n", - "from caffe import params as P\n", - "\n", - "def nonlinear_net(hdf5, batch_size):\n", - " # one small nonlinearity, one leap for model kind\n", - " n = caffe.NetSpec()\n", - " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n", - " # define a hidden layer of dimension 40\n", - " n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n", - " # transform the output through the ReLU (rectified linear) non-linearity\n", - " n.relu1 = L.ReLU(n.ip1, in_place=True)\n", - " # score the (now non-linear) features\n", - " n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n", - " # same accuracy and loss as before\n", - " n.accuracy = L.Accuracy(n.ip2, n.label)\n", - " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n", - " return n.to_proto()\n", - " \n", - "with open('examples/hdf5_classification/nonlinear_auto_train.prototxt', 'w') as f:\n", - " f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n", - " \n", - "with open('examples/hdf5_classification/nonlinear_auto_test.prototxt', 'w') as f:\n", - " f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.832\n", - "Accuracy: 0.832\n", - "Accuracy: 0.832\n", - "Accuracy: 0.831\n", - "1 loops, best of 3: 386 ms per loop\n" - ] - } - ], - "source": [ - "%%timeit\n", - "caffe.set_mode_cpu()\n", - "solver = caffe.get_solver('examples/hdf5_classification/nonlinear_solver.prototxt')\n", - "solver.solve()\n", - "\n", - "accuracy = 0\n", - "batch_size = solver.test_nets[0].blobs['data'].num\n", - "test_iters = int(len(Xt) / batch_size)\n", - "for i in range(test_iters):\n", - " solver.test_nets[0].forward()\n", - " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", - "accuracy /= test_iters\n", - "\n", - "print(\"Accuracy: {:.3f}\".format(accuracy))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Do the same through the command line interface for detailed output on the model and solving." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I0318 00:58:43.336922 2013098752 caffe.cpp:117] Use CPU.\n", - "I0318 00:58:43.654698 2013098752 caffe.cpp:121] Starting Optimization\n", - "I0318 00:58:43.654747 2013098752 solver.cpp:32] Initializing solver from parameters: \n", - "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n", - "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n", - "test_iter: 250\n", - "test_interval: 1000\n", - "base_lr: 0.01\n", - "display: 1000\n", - "max_iter: 10000\n", - "lr_policy: \"step\"\n", - "gamma: 0.1\n", - "momentum: 0.9\n", - "weight_decay: 0.0005\n", - "stepsize: 5000\n", - "snapshot: 10000\n", - "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n", - "solver_mode: CPU\n", - "I0318 00:58:43.654855 2013098752 solver.cpp:61] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n", - "I0318 00:58:43.655004 2013098752 net.cpp:42] Initializing net from parameters: \n", - "state {\n", - " phase: TRAIN\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " hdf5_data_param {\n", - " source: \"examples/hdf5_classification/data/train.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"ip1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"ip1\"\n", - " inner_product_param {\n", - " num_output: 40\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"relu1\"\n", - " type: \"ReLU\"\n", - " bottom: \"ip1\"\n", - " top: \"ip1\"\n", - "}\n", - "layer {\n", - " name: \"ip2\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"ip1\"\n", - " top: \"ip2\"\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"accuracy\"\n", - " type: \"Accuracy\"\n", - " bottom: \"ip2\"\n", - " bottom: \"label\"\n", - " top: \"accuracy\"\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"ip2\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "I0318 00:58:43.655120 2013098752 layer_factory.hpp:74] Creating layer data\n", - "I0318 00:58:43.655139 2013098752 net.cpp:84] Creating Layer data\n", - "I0318 00:58:43.655264 2013098752 net.cpp:338] data -> data\n", - "I0318 00:58:43.655297 2013098752 net.cpp:338] data -> label\n", - "I0318 00:58:43.655310 2013098752 net.cpp:113] Setting up data\n", - "I0318 00:58:43.655318 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n", - "I0318 00:58:43.655365 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\n", - "I0318 00:58:43.657317 2013098752 net.cpp:120] Top shape: 10 4 (40)\n", - "I0318 00:58:43.657342 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:43.657356 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n", - "I0318 00:58:43.657373 2013098752 net.cpp:84] Creating Layer label_data_1_split\n", - "I0318 00:58:43.657384 2013098752 net.cpp:380] label_data_1_split <- label\n", - "I0318 00:58:43.657395 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", - "I0318 00:58:43.657407 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", - "I0318 00:58:43.657418 2013098752 net.cpp:113] Setting up label_data_1_split\n", - "I0318 00:58:43.657426 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:43.657433 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:43.657441 2013098752 layer_factory.hpp:74] Creating layer ip1\n", - "I0318 00:58:43.657451 2013098752 net.cpp:84] Creating Layer ip1\n", - "I0318 00:58:43.657459 2013098752 net.cpp:380] ip1 <- data\n", - "I0318 00:58:43.657467 2013098752 net.cpp:338] ip1 -> ip1\n", - "I0318 00:58:43.657479 2013098752 net.cpp:113] Setting up ip1\n", - "I0318 00:58:43.662454 2013098752 net.cpp:120] Top shape: 10 40 (400)\n", - "I0318 00:58:43.662477 2013098752 layer_factory.hpp:74] Creating layer relu1\n", - "I0318 00:58:43.662497 2013098752 net.cpp:84] Creating Layer relu1\n", - "I0318 00:58:43.662508 2013098752 net.cpp:380] relu1 <- ip1\n", - "I0318 00:58:43.662520 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n", - "I0318 00:58:43.662530 2013098752 net.cpp:113] Setting up relu1\n", - "I0318 00:58:43.662539 2013098752 net.cpp:120] Top shape: 10 40 (400)\n", - "I0318 00:58:43.662546 2013098752 layer_factory.hpp:74] Creating layer ip2\n", - "I0318 00:58:43.662555 2013098752 net.cpp:84] Creating Layer ip2\n", - "I0318 00:58:43.662562 2013098752 net.cpp:380] ip2 <- ip1\n", - "I0318 00:58:43.662571 2013098752 net.cpp:338] ip2 -> ip2\n", - "I0318 00:58:43.662580 2013098752 net.cpp:113] Setting up ip2\n", - "I0318 00:58:43.662595 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:43.662606 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n", - "I0318 00:58:43.662654 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n", - "I0318 00:58:43.662665 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n", - "I0318 00:58:43.662678 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n", - "I0318 00:58:43.662689 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n", - "I0318 00:58:43.662698 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n", - "I0318 00:58:43.662706 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:43.662714 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:43.662722 2013098752 layer_factory.hpp:74] Creating layer accuracy\n", - "I0318 00:58:43.662734 2013098752 net.cpp:84] Creating Layer accuracy\n", - "I0318 00:58:43.662740 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n", - "I0318 00:58:43.662749 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n", - "I0318 00:58:43.662756 2013098752 net.cpp:338] accuracy -> accuracy\n", - "I0318 00:58:43.662766 2013098752 net.cpp:113] Setting up accuracy\n", - "I0318 00:58:43.662818 2013098752 net.cpp:120] Top shape: (1)\n", - "I0318 00:58:43.662827 2013098752 layer_factory.hpp:74] Creating layer loss\n", - "I0318 00:58:43.662839 2013098752 net.cpp:84] Creating Layer loss\n", - "I0318 00:58:43.662847 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n", - "I0318 00:58:43.662854 2013098752 net.cpp:380] loss <- label_data_1_split_1\n", - "I0318 00:58:43.662863 2013098752 net.cpp:338] loss -> loss\n", - "I0318 00:58:43.662873 2013098752 net.cpp:113] Setting up loss\n", - "I0318 00:58:43.662883 2013098752 layer_factory.hpp:74] Creating layer loss\n", - "I0318 00:58:43.662901 2013098752 net.cpp:120] Top shape: (1)\n", - "I0318 00:58:43.662909 2013098752 net.cpp:122] with loss weight 1\n", - "I0318 00:58:43.662922 2013098752 net.cpp:167] loss needs backward computation.\n", - "I0318 00:58:43.662930 2013098752 net.cpp:169] accuracy does not need backward computation.\n", - "I0318 00:58:43.662936 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n", - "I0318 00:58:43.662942 2013098752 net.cpp:167] ip2 needs backward computation.\n", - "I0318 00:58:43.662976 2013098752 net.cpp:167] relu1 needs backward computation.\n", - "I0318 00:58:43.662988 2013098752 net.cpp:167] ip1 needs backward computation.\n", - "I0318 00:58:43.662997 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n", - "I0318 00:58:43.663003 2013098752 net.cpp:169] data does not need backward computation.\n", - "I0318 00:58:43.663009 2013098752 net.cpp:205] This network produces output accuracy\n", - "I0318 00:58:43.663017 2013098752 net.cpp:205] This network produces output loss\n", - "I0318 00:58:43.663028 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", - "I0318 00:58:43.663035 2013098752 net.cpp:217] Network initialization done.\n", - "I0318 00:58:43.663041 2013098752 net.cpp:218] Memory required for data: 3728\n", - "I0318 00:58:43.663158 2013098752 solver.cpp:154] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n", - "I0318 00:58:43.663179 2013098752 net.cpp:42] Initializing net from parameters: \n", - "state {\n", - " phase: TEST\n", - "}\n", - "layer {\n", - " name: \"data\"\n", - " type: \"HDF5Data\"\n", - " top: \"data\"\n", - " top: \"label\"\n", - " hdf5_data_param {\n", - " source: \"examples/hdf5_classification/data/test.txt\"\n", - " batch_size: 10\n", - " }\n", - "}\n", - "layer {\n", - " name: \"ip1\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"data\"\n", - " top: \"ip1\"\n", - " inner_product_param {\n", - " num_output: 40\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"relu1\"\n", - " type: \"ReLU\"\n", - " bottom: \"ip1\"\n", - " top: \"ip1\"\n", - "}\n", - "layer {\n", - " name: \"ip2\"\n", - " type: \"InnerProduct\"\n", - " bottom: \"ip1\"\n", - " top: \"ip2\"\n", - " inner_product_param {\n", - " num_output: 2\n", - " weight_filler {\n", - " type: \"xavier\"\n", - " }\n", - " }\n", - "}\n", - "layer {\n", - " name: \"accuracy\"\n", - " type: \"Accuracy\"\n", - " bottom: \"ip2\"\n", - " bottom: \"label\"\n", - " top: \"accuracy\"\n", - "}\n", - "layer {\n", - " name: \"loss\"\n", - " type: \"SoftmaxWithLoss\"\n", - " bottom: \"ip2\"\n", - " bottom: \"label\"\n", - " top: \"loss\"\n", - "}\n", - "I0318 00:58:43.663349 2013098752 layer_factory.hpp:74] Creating layer data\n", - "I0318 00:58:43.663365 2013098752 net.cpp:84] Creating Layer data\n", - "I0318 00:58:43.663373 2013098752 net.cpp:338] data -> data\n", - "I0318 00:58:43.663385 2013098752 net.cpp:338] data -> label\n", - "I0318 00:58:43.663396 2013098752 net.cpp:113] Setting up data\n", - "I0318 00:58:43.663422 2013098752 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n", - "I0318 00:58:43.663457 2013098752 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\n", - "I0318 00:58:43.664719 2013098752 net.cpp:120] Top shape: 10 4 (40)\n", - "I0318 00:58:43.664739 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:43.664754 2013098752 layer_factory.hpp:74] Creating layer label_data_1_split\n", - "I0318 00:58:43.664772 2013098752 net.cpp:84] Creating Layer label_data_1_split\n", - "I0318 00:58:43.664783 2013098752 net.cpp:380] label_data_1_split <- label\n", - "I0318 00:58:43.664791 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_0\n", - "I0318 00:58:43.664803 2013098752 net.cpp:338] label_data_1_split -> label_data_1_split_1\n", - "I0318 00:58:43.664813 2013098752 net.cpp:113] Setting up label_data_1_split\n", - "I0318 00:58:43.664822 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:43.664829 2013098752 net.cpp:120] Top shape: 10 (10)\n", - "I0318 00:58:43.664837 2013098752 layer_factory.hpp:74] Creating layer ip1\n", - "I0318 00:58:43.664846 2013098752 net.cpp:84] Creating Layer ip1\n", - "I0318 00:58:43.664854 2013098752 net.cpp:380] ip1 <- data\n", - "I0318 00:58:43.664862 2013098752 net.cpp:338] ip1 -> ip1\n", - "I0318 00:58:43.664875 2013098752 net.cpp:113] Setting up ip1\n", - "I0318 00:58:43.664901 2013098752 net.cpp:120] Top shape: 10 40 (400)\n", - "I0318 00:58:43.664924 2013098752 layer_factory.hpp:74] Creating layer relu1\n", - "I0318 00:58:43.664945 2013098752 net.cpp:84] Creating Layer relu1\n", - "I0318 00:58:43.664958 2013098752 net.cpp:380] relu1 <- ip1\n", - "I0318 00:58:43.664966 2013098752 net.cpp:327] relu1 -> ip1 (in-place)\n", - "I0318 00:58:43.664975 2013098752 net.cpp:113] Setting up relu1\n", - "I0318 00:58:43.664983 2013098752 net.cpp:120] Top shape: 10 40 (400)\n", - "I0318 00:58:43.664990 2013098752 layer_factory.hpp:74] Creating layer ip2\n", - "I0318 00:58:43.665000 2013098752 net.cpp:84] Creating Layer ip2\n", - "I0318 00:58:43.665006 2013098752 net.cpp:380] ip2 <- ip1\n", - "I0318 00:58:43.665015 2013098752 net.cpp:338] ip2 -> ip2\n", - "I0318 00:58:43.665030 2013098752 net.cpp:113] Setting up ip2\n", - "I0318 00:58:43.665052 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:43.665066 2013098752 layer_factory.hpp:74] Creating layer ip2_ip2_0_split\n", - "I0318 00:58:43.665077 2013098752 net.cpp:84] Creating Layer ip2_ip2_0_split\n", - "I0318 00:58:43.665086 2013098752 net.cpp:380] ip2_ip2_0_split <- ip2\n", - "I0318 00:58:43.665093 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_0\n", - "I0318 00:58:43.665103 2013098752 net.cpp:338] ip2_ip2_0_split -> ip2_ip2_0_split_1\n", - "I0318 00:58:43.665113 2013098752 net.cpp:113] Setting up ip2_ip2_0_split\n", - "I0318 00:58:43.665122 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:43.665128 2013098752 net.cpp:120] Top shape: 10 2 (20)\n", - "I0318 00:58:43.665137 2013098752 layer_factory.hpp:74] Creating layer accuracy\n", - "I0318 00:58:43.665144 2013098752 net.cpp:84] Creating Layer accuracy\n", - "I0318 00:58:43.665153 2013098752 net.cpp:380] accuracy <- ip2_ip2_0_split_0\n", - "I0318 00:58:43.665168 2013098752 net.cpp:380] accuracy <- label_data_1_split_0\n", - "I0318 00:58:43.665180 2013098752 net.cpp:338] accuracy -> accuracy\n", - "I0318 00:58:43.665192 2013098752 net.cpp:113] Setting up accuracy\n", - "I0318 00:58:43.665200 2013098752 net.cpp:120] Top shape: (1)\n", - "I0318 00:58:43.665207 2013098752 layer_factory.hpp:74] Creating layer loss\n", - "I0318 00:58:43.665216 2013098752 net.cpp:84] Creating Layer loss\n", - "I0318 00:58:43.665223 2013098752 net.cpp:380] loss <- ip2_ip2_0_split_1\n", - "I0318 00:58:43.665230 2013098752 net.cpp:380] loss <- label_data_1_split_1\n", - "I0318 00:58:43.665241 2013098752 net.cpp:338] loss -> loss\n", - "I0318 00:58:43.665251 2013098752 net.cpp:113] Setting up loss\n", - "I0318 00:58:43.665259 2013098752 layer_factory.hpp:74] Creating layer loss\n", - "I0318 00:58:43.665273 2013098752 net.cpp:120] Top shape: (1)\n", - "I0318 00:58:43.665282 2013098752 net.cpp:122] with loss weight 1\n", - "I0318 00:58:43.665290 2013098752 net.cpp:167] loss needs backward computation.\n", - "I0318 00:58:43.665338 2013098752 net.cpp:169] accuracy does not need backward computation.\n", - "I0318 00:58:43.665351 2013098752 net.cpp:167] ip2_ip2_0_split needs backward computation.\n", - "I0318 00:58:43.665380 2013098752 net.cpp:167] ip2 needs backward computation.\n", - "I0318 00:58:43.665387 2013098752 net.cpp:167] relu1 needs backward computation.\n", - "I0318 00:58:43.665393 2013098752 net.cpp:167] ip1 needs backward computation.\n", - "I0318 00:58:43.665400 2013098752 net.cpp:169] label_data_1_split does not need backward computation.\n", - "I0318 00:58:43.665407 2013098752 net.cpp:169] data does not need backward computation.\n", - "I0318 00:58:43.665415 2013098752 net.cpp:205] This network produces output accuracy\n", - "I0318 00:58:43.665421 2013098752 net.cpp:205] This network produces output loss\n", - "I0318 00:58:43.665431 2013098752 net.cpp:447] Collecting Learning Rate and Weight Decay.\n", - "I0318 00:58:43.665441 2013098752 net.cpp:217] Network initialization done.\n", - "I0318 00:58:43.665446 2013098752 net.cpp:218] Memory required for data: 3728\n", - "I0318 00:58:43.665534 2013098752 solver.cpp:42] Solver scaffolding done.\n", - "I0318 00:58:43.665568 2013098752 solver.cpp:222] Solving \n", - "I0318 00:58:43.665577 2013098752 solver.cpp:223] Learning Rate Policy: step\n", - "I0318 00:58:43.665586 2013098752 solver.cpp:266] Iteration 0, Testing net (#0)\n", - "I0318 00:58:43.683938 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.5184\n", - "I0318 00:58:43.683981 2013098752 solver.cpp:315] Test net output #1: loss = 0.716141 (* 1 = 0.716141 loss)\n", - "I0318 00:58:43.684236 2013098752 solver.cpp:189] Iteration 0, loss = 0.764954\n", - "I0318 00:58:43.684267 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.5\n", - "I0318 00:58:43.684285 2013098752 solver.cpp:204] Train net output #1: loss = 0.764954 (* 1 = 0.764954 loss)\n", - "I0318 00:58:43.684305 2013098752 solver.cpp:464] Iteration 0, lr = 0.01\n", - "I0318 00:58:43.714700 2013098752 solver.cpp:266] Iteration 1000, Testing net (#0)\n", - "I0318 00:58:43.721762 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8168\n", - "I0318 00:58:43.721818 2013098752 solver.cpp:315] Test net output #1: loss = 0.434918 (* 1 = 0.434918 loss)\n", - "I0318 00:58:43.721899 2013098752 solver.cpp:189] Iteration 1000, loss = 0.282425\n", - "I0318 00:58:43.721917 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", - "I0318 00:58:43.721932 2013098752 solver.cpp:204] Train net output #1: loss = 0.282426 (* 1 = 0.282426 loss)\n", - "I0318 00:58:43.721942 2013098752 solver.cpp:464] Iteration 1000, lr = 0.01\n", - "I0318 00:58:43.750509 2013098752 solver.cpp:266] Iteration 2000, Testing net (#0)\n", - "I0318 00:58:43.754590 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8224\n", - "I0318 00:58:43.754621 2013098752 solver.cpp:315] Test net output #1: loss = 0.416874 (* 1 = 0.416874 loss)\n", - "I0318 00:58:43.754660 2013098752 solver.cpp:189] Iteration 2000, loss = 0.51988\n", - "I0318 00:58:43.754672 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", - "I0318 00:58:43.754683 2013098752 solver.cpp:204] Train net output #1: loss = 0.51988 (* 1 = 0.51988 loss)\n", - "I0318 00:58:43.754690 2013098752 solver.cpp:464] Iteration 2000, lr = 0.01\n", - "I0318 00:58:43.782609 2013098752 solver.cpp:266] Iteration 3000, Testing net (#0)\n", - "I0318 00:58:43.789728 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8176\n", - "I0318 00:58:43.789777 2013098752 solver.cpp:315] Test net output #1: loss = 0.415907 (* 1 = 0.415907 loss)\n", - "I0318 00:58:43.790487 2013098752 solver.cpp:189] Iteration 3000, loss = 0.5093\n", - "I0318 00:58:43.790510 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", - "I0318 00:58:43.790530 2013098752 solver.cpp:204] Train net output #1: loss = 0.509301 (* 1 = 0.509301 loss)\n", - "I0318 00:58:43.790544 2013098752 solver.cpp:464] Iteration 3000, lr = 0.01\n", - "I0318 00:58:43.817451 2013098752 solver.cpp:266] Iteration 4000, Testing net (#0)\n", - "I0318 00:58:43.821740 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8252\n", - "I0318 00:58:43.821770 2013098752 solver.cpp:315] Test net output #1: loss = 0.409124 (* 1 = 0.409124 loss)\n", - "I0318 00:58:43.821822 2013098752 solver.cpp:189] Iteration 4000, loss = 0.284815\n", - "I0318 00:58:43.821835 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", - "I0318 00:58:43.821846 2013098752 solver.cpp:204] Train net output #1: loss = 0.284815 (* 1 = 0.284815 loss)\n", - "I0318 00:58:43.821890 2013098752 solver.cpp:464] Iteration 4000, lr = 0.01\n", - "I0318 00:58:43.847015 2013098752 solver.cpp:266] Iteration 5000, Testing net (#0)\n", - "I0318 00:58:43.852102 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8256\n", - "I0318 00:58:43.852145 2013098752 solver.cpp:315] Test net output #1: loss = 0.404445 (* 1 = 0.404445 loss)\n", - "I0318 00:58:43.852188 2013098752 solver.cpp:189] Iteration 5000, loss = 0.511566\n", - "I0318 00:58:43.852200 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", - "I0318 00:58:43.852210 2013098752 solver.cpp:204] Train net output #1: loss = 0.511566 (* 1 = 0.511566 loss)\n", - "I0318 00:58:43.852219 2013098752 solver.cpp:464] Iteration 5000, lr = 0.001\n", - "I0318 00:58:43.876060 2013098752 solver.cpp:266] Iteration 6000, Testing net (#0)\n", - "I0318 00:58:43.880080 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8328\n", - "I0318 00:58:43.880105 2013098752 solver.cpp:315] Test net output #1: loss = 0.396847 (* 1 = 0.396847 loss)\n", - "I0318 00:58:43.880700 2013098752 solver.cpp:189] Iteration 6000, loss = 0.397858\n", - "I0318 00:58:43.880718 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", - "I0318 00:58:43.880729 2013098752 solver.cpp:204] Train net output #1: loss = 0.397858 (* 1 = 0.397858 loss)\n", - "I0318 00:58:43.880738 2013098752 solver.cpp:464] Iteration 6000, lr = 0.001\n", - "I0318 00:58:43.913795 2013098752 solver.cpp:266] Iteration 7000, Testing net (#0)\n", - "I0318 00:58:43.917851 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8316\n", - "I0318 00:58:43.917876 2013098752 solver.cpp:315] Test net output #1: loss = 0.398135 (* 1 = 0.398135 loss)\n", - "I0318 00:58:43.917956 2013098752 solver.cpp:189] Iteration 7000, loss = 0.243849\n", - "I0318 00:58:43.917971 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", - "I0318 00:58:43.917989 2013098752 solver.cpp:204] Train net output #1: loss = 0.243849 (* 1 = 0.243849 loss)\n", - "I0318 00:58:43.918002 2013098752 solver.cpp:464] Iteration 7000, lr = 0.001\n", - "I0318 00:58:43.943681 2013098752 solver.cpp:266] Iteration 8000, Testing net (#0)\n", - "I0318 00:58:43.947589 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8312\n", - "I0318 00:58:43.947615 2013098752 solver.cpp:315] Test net output #1: loss = 0.394763 (* 1 = 0.394763 loss)\n", - "I0318 00:58:43.947651 2013098752 solver.cpp:189] Iteration 8000, loss = 0.513399\n", - "I0318 00:58:43.947664 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.7\n", - "I0318 00:58:43.947674 2013098752 solver.cpp:204] Train net output #1: loss = 0.513399 (* 1 = 0.513399 loss)\n", - "I0318 00:58:43.947682 2013098752 solver.cpp:464] Iteration 8000, lr = 0.001\n", - "I0318 00:58:43.973080 2013098752 solver.cpp:266] Iteration 9000, Testing net (#0)\n", - "I0318 00:58:43.977033 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.834\n", - "I0318 00:58:43.977056 2013098752 solver.cpp:315] Test net output #1: loss = 0.395663 (* 1 = 0.395663 loss)\n", - "I0318 00:58:43.977710 2013098752 solver.cpp:189] Iteration 9000, loss = 0.399341\n", - "I0318 00:58:43.977735 2013098752 solver.cpp:204] Train net output #0: accuracy = 0.9\n", - "I0318 00:58:43.977746 2013098752 solver.cpp:204] Train net output #1: loss = 0.399342 (* 1 = 0.399342 loss)\n", - "I0318 00:58:43.977756 2013098752 solver.cpp:464] Iteration 9000, lr = 0.001\n", - "I0318 00:58:44.003437 2013098752 solver.cpp:334] Snapshotting to examples/hdf5_classification/data/train_iter_10000.caffemodel\n", - "I0318 00:58:44.003702 2013098752 solver.cpp:342] Snapshotting solver state to examples/hdf5_classification/data/train_iter_10000.solverstate\n", - "I0318 00:58:44.003850 2013098752 solver.cpp:248] Iteration 10000, loss = 0.244639\n", - "I0318 00:58:44.003871 2013098752 solver.cpp:266] Iteration 10000, Testing net (#0)\n", - "I0318 00:58:44.008216 2013098752 solver.cpp:315] Test net output #0: accuracy = 0.8308\n", - "I0318 00:58:44.008252 2013098752 solver.cpp:315] Test net output #1: loss = 0.397291 (* 1 = 0.397291 loss)\n", - "I0318 00:58:44.008262 2013098752 solver.cpp:253] Optimization Done.\n", - "I0318 00:58:44.008270 2013098752 caffe.cpp:134] Optimization Done.\n" - ] - } - ], - "source": [ - "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_solver.prototxt" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n", - "shutil.rmtree(dirname)" - ] - } - ], - "metadata": { - "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.", - "example_name": "Off-the-shelf SGD for classification", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" - }, - "priority": 3 - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/brewing-logreg.ipynb b/examples/brewing-logreg.ipynb new file mode 100644 index 00000000000..c053b73b39f --- /dev/null +++ b/examples/brewing-logreg.ipynb @@ -0,0 +1,1164 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Brewing Logistic Regression then Going Deeper\n", + "\n", + "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import os\n", + "os.chdir('..')\n", + "\n", + "import sys\n", + "sys.path.insert(0, './python')\n", + "import caffe\n", + "\n", + "\n", + "import os\n", + "import h5py\n", + "import shutil\n", + "import tempfile\n", + "\n", + "import sklearn\n", + "import sklearn.datasets\n", + "import sklearn.linear_model\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZsmQhkpQhEGjir//6Ty6pBHu1mI5x9/v9yLKMy+W67OvV7Xbz29/uJBBQOHLk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\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luroFURRRlDwTE+1cdlkFb7xxknRKxGi0YTIbaGx0cPfdX/pY1lH5pP9RUhSFn/zk/5BO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BaDQabrr9dqp7euizWvEFArT399PgdGLKyOCDrVs5sH8/DodjzHaSJNHW1sbJkyfp7e4e9yJQ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S5dN9iyZT8PP/wEx47d8IYo2UgJ/+2/we///pXek++HEIk6cvw4vOMdV3pvXjtc0UrZK43z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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X, y = sklearn.datasets.make_classification(\n", + " n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n", + " n_clusters_per_class=2, hypercube=False, random_state=0\n", + ")\n", + "\n", + "# Split into train and test\n", + "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n", + "\n", + "# Visualize sample of the data\n", + "ind = np.random.permutation(X.shape[0])[:1000]\n", + "df = pd.DataFrame(X[ind])\n", + "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.781\n", + "Accuracy: 0.781\n", + "Accuracy: 0.781\n", + "Accuracy: 0.781\n", + "1 loop, best of 3: 372 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "# Train and test the scikit-learn SGD logistic regression.\n", + "clf = sklearn.linear_model.SGDClassifier(\n", + " loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='auto')\n", + "\n", + "clf.fit(X, y)\n", + "yt_pred = clf.predict(Xt)\n", + "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save the dataset to HDF5 for loading in Caffe." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Write out the data to HDF5 files in a temp directory.\n", + "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n", + "dirname = os.path.abspath('./examples/hdf5_classification/data')\n", + "if not os.path.exists(dirname):\n", + " os.makedirs(dirname)\n", + "\n", + "train_filename = os.path.join(dirname, 'train.h5')\n", + "test_filename = os.path.join(dirname, 'test.h5')\n", + "\n", + "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n", + "# To show this off, we'll list the same data file twice.\n", + "with h5py.File(train_filename, 'w') as f:\n", + " f['data'] = X\n", + " f['label'] = y.astype(np.float32)\n", + "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n", + " f.write(train_filename + '\\n')\n", + " f.write(train_filename + '\\n')\n", + " \n", + "# HDF5 is pretty efficient, but can be further compressed.\n", + "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n", + "with h5py.File(test_filename, 'w') as f:\n", + " f.create_dataset('data', data=Xt, **comp_kwargs)\n", + " f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n", + "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n", + " f.write(test_filename + '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from caffe import layers as L\n", + "from caffe import params as P\n", + "\n", + "def logreg(hdf5, batch_size):\n", + " # logistic regression: data, matrix multiplication, and 2-class softmax loss\n", + " n = caffe.NetSpec()\n", + " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n", + " n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n", + " n.accuracy = L.Accuracy(n.ip1, n.label)\n", + " n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n", + " return n.to_proto()\n", + "\n", + "train_net_path = 'examples/hdf5_classification/logreg_auto_train.prototxt'\n", + "with open(train_net_path, 'w') as f:\n", + " f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n", + "\n", + "test_net_path = 'examples/hdf5_classification/logreg_auto_test.prototxt'\n", + "with open(test_net_path, 'w') as f:\n", + " f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we'll define our \"solver\" which trains the network by specifying the locations of the train and test nets we defined above, as well as setting values for various parameters used for learning, display, and \"snapshotting\"." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from caffe.proto import caffe_pb2\n", + "\n", + "def solver(train_net_path, test_net_path):\n", + " s = caffe_pb2.SolverParameter()\n", + "\n", + " # Specify locations of the train and test networks.\n", + " s.train_net = train_net_path\n", + " s.test_net.append(test_net_path)\n", + "\n", + " s.test_interval = 1000 # Test after every 1000 training iterations.\n", + " s.test_iter.append(250) # Test 250 \"batches\" each time we test.\n", + "\n", + " s.max_iter = 10000 # # of times to update the net (training iterations)\n", + "\n", + " # Set the initial learning rate for stochastic gradient descent (SGD).\n", + " s.base_lr = 0.01 \n", + "\n", + " # Set `lr_policy` to define how the learning rate changes during training.\n", + " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n", + " # every `stepsize` iterations.\n", + " s.lr_policy = 'step'\n", + " s.gamma = 0.1\n", + " s.stepsize = 5000\n", + "\n", + " # Set other optimization parameters. Setting a non-zero `momentum` takes a\n", + " # weighted average of the current gradient and previous gradients to make\n", + " # learning more stable. L2 weight decay regularizes learning, to help prevent\n", + " # the model from overfitting.\n", + " s.momentum = 0.9\n", + " s.weight_decay = 5e-4\n", + "\n", + " # Display the current training loss and accuracy every 1000 iterations.\n", + " s.display = 1000\n", + "\n", + " # Snapshots are files used to store networks we've trained. Here, we'll\n", + " # snapshot every 10K iterations -- just once at the end of training.\n", + " # For larger networks that take longer to train, you may want to set\n", + " # snapshot < max_iter to save the network and training state to disk during\n", + " # optimization, preventing disaster in case of machine crashes, etc.\n", + " s.snapshot = 10000\n", + " s.snapshot_prefix = 'examples/hdf5_classification/data/train'\n", + "\n", + " # We'll train on the CPU for fair benchmarking against scikit-learn.\n", + " # Changing to GPU should result in much faster training!\n", + " s.solver_mode = caffe_pb2.SolverParameter.CPU\n", + " \n", + " return s\n", + "\n", + "solver_path = 'examples/hdf5_classification/logreg_solver.prototxt'\n", + "with open(solver_path, 'w') as f:\n", + " f.write(str(solver(train_net_path, test_net_path)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time to learn and evaluate our Caffeinated logistic regression in Python." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.770\n", + "Accuracy: 0.770\n", + "Accuracy: 0.770\n", + "Accuracy: 0.770\n", + "1 loop, best of 3: 195 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "caffe.set_mode_cpu()\n", + "solver = caffe.get_solver(solver_path)\n", + "solver.solve()\n", + "\n", + "accuracy = 0\n", + "batch_size = solver.test_nets[0].blobs['data'].num\n", + "test_iters = int(len(Xt) / batch_size)\n", + "for i in range(test_iters):\n", + " solver.test_nets[0].forward()\n", + " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", + "accuracy /= test_iters\n", + "\n", + "print(\"Accuracy: {:.3f}\".format(accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do the same through the command line interface for detailed output on the model and solving." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I0224 00:32:03.232779 655 caffe.cpp:178] Use CPU.\n", + "I0224 00:32:03.391911 655 solver.cpp:48] Initializing solver from parameters: \n", + "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n", + "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n", + "test_iter: 250\n", + "test_interval: 1000\n", + "base_lr: 0.01\n", + "display: 1000\n", + "max_iter: 10000\n", + "lr_policy: \"step\"\n", + "gamma: 0.1\n", + "momentum: 0.9\n", + "weight_decay: 0.0005\n", + "stepsize: 5000\n", + "snapshot: 10000\n", + "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n", + "solver_mode: CPU\n", + "I0224 00:32:03.392065 655 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n", + "I0224 00:32:03.392215 655 net.cpp:49] Initializing net from parameters: \n", + "state {\n", + " phase: TRAIN\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/train.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0224 00:32:03.392365 655 layer_factory.hpp:77] Creating layer data\n", + "I0224 00:32:03.392382 655 net.cpp:106] Creating Layer data\n", + "I0224 00:32:03.392395 655 net.cpp:411] data -> data\n", + "I0224 00:32:03.392423 655 net.cpp:411] data -> label\n", + "I0224 00:32:03.392442 655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n", + "I0224 00:32:03.392473 655 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n", + "I0224 00:32:03.393473 655 hdf5.cpp:32] Datatype class: H5T_FLOAT\n", + "I0224 00:32:03.393862 655 net.cpp:150] Setting up data\n", + "I0224 00:32:03.393884 655 net.cpp:157] Top shape: 10 4 (40)\n", + "I0224 00:32:03.393894 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.393901 655 net.cpp:165] Memory required for data: 200\n", + "I0224 00:32:03.393911 655 layer_factory.hpp:77] Creating layer label_data_1_split\n", + "I0224 00:32:03.393924 655 net.cpp:106] Creating Layer label_data_1_split\n", + "I0224 00:32:03.393934 655 net.cpp:454] label_data_1_split <- label\n", + "I0224 00:32:03.393945 655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", + "I0224 00:32:03.393956 655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", + "I0224 00:32:03.393970 655 net.cpp:150] Setting up label_data_1_split\n", + "I0224 00:32:03.393978 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.393986 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.393995 655 net.cpp:165] Memory required for data: 280\n", + "I0224 00:32:03.394001 655 layer_factory.hpp:77] Creating layer ip1\n", + "I0224 00:32:03.394012 655 net.cpp:106] Creating Layer ip1\n", + "I0224 00:32:03.394021 655 net.cpp:454] ip1 <- data\n", + "I0224 00:32:03.394029 655 net.cpp:411] ip1 -> ip1\n", + "I0224 00:32:03.394311 655 net.cpp:150] Setting up ip1\n", + "I0224 00:32:03.394323 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.394331 655 net.cpp:165] Memory required for data: 360\n", + "I0224 00:32:03.394348 655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n", + "I0224 00:32:03.394358 655 net.cpp:106] Creating Layer ip1_ip1_0_split\n", + "I0224 00:32:03.394366 655 net.cpp:454] ip1_ip1_0_split <- ip1\n", + "I0224 00:32:03.394374 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n", + "I0224 00:32:03.394386 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n", + "I0224 00:32:03.394395 655 net.cpp:150] Setting up ip1_ip1_0_split\n", + "I0224 00:32:03.394404 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.394424 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.394443 655 net.cpp:165] Memory required for data: 520\n", + "I0224 00:32:03.394450 655 layer_factory.hpp:77] Creating layer accuracy\n", + "I0224 00:32:03.394462 655 net.cpp:106] Creating Layer accuracy\n", + "I0224 00:32:03.394479 655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n", + "I0224 00:32:03.394489 655 net.cpp:454] accuracy <- label_data_1_split_0\n", + "I0224 00:32:03.394497 655 net.cpp:411] accuracy -> accuracy\n", + "I0224 00:32:03.394510 655 net.cpp:150] Setting up accuracy\n", + "I0224 00:32:03.394536 655 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:03.394543 655 net.cpp:165] Memory required for data: 524\n", + "I0224 00:32:03.394551 655 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:03.394562 655 net.cpp:106] Creating Layer loss\n", + "I0224 00:32:03.394569 655 net.cpp:454] loss <- ip1_ip1_0_split_1\n", + "I0224 00:32:03.394577 655 net.cpp:454] loss <- label_data_1_split_1\n", + "I0224 00:32:03.394587 655 net.cpp:411] loss -> loss\n", + "I0224 00:32:03.394603 655 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:03.394624 655 net.cpp:150] Setting up loss\n", + "I0224 00:32:03.394634 655 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:03.394641 655 net.cpp:160] with loss weight 1\n", + "I0224 00:32:03.394659 655 net.cpp:165] Memory required for data: 528\n", + "I0224 00:32:03.394665 655 net.cpp:226] loss needs backward computation.\n", + "I0224 00:32:03.394673 655 net.cpp:228] accuracy does not need backward computation.\n", + "I0224 00:32:03.394682 655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n", + "I0224 00:32:03.394690 655 net.cpp:226] ip1 needs backward computation.\n", + "I0224 00:32:03.394697 655 net.cpp:228] label_data_1_split does not need backward computation.\n", + "I0224 00:32:03.394706 655 net.cpp:228] data does not need backward computation.\n", + "I0224 00:32:03.394712 655 net.cpp:270] This network produces output accuracy\n", + "I0224 00:32:03.394721 655 net.cpp:270] This network produces output loss\n", + "I0224 00:32:03.394731 655 net.cpp:283] Network initialization done.\n", + "I0224 00:32:03.394804 655 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n", + "I0224 00:32:03.394836 655 net.cpp:49] Initializing net from parameters: \n", + "state {\n", + " phase: TEST\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/test.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip1\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0224 00:32:03.394953 655 layer_factory.hpp:77] Creating layer data\n", + "I0224 00:32:03.394964 655 net.cpp:106] Creating Layer data\n", + "I0224 00:32:03.394973 655 net.cpp:411] data -> data\n", + "I0224 00:32:03.394984 655 net.cpp:411] data -> label\n", + "I0224 00:32:03.394994 655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n", + "I0224 00:32:03.395009 655 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n", + "I0224 00:32:03.395937 655 net.cpp:150] Setting up data\n", + "I0224 00:32:03.395953 655 net.cpp:157] Top shape: 10 4 (40)\n", + "I0224 00:32:03.395963 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.395970 655 net.cpp:165] Memory required for data: 200\n", + "I0224 00:32:03.395978 655 layer_factory.hpp:77] Creating layer label_data_1_split\n", + "I0224 00:32:03.395989 655 net.cpp:106] Creating Layer label_data_1_split\n", + "I0224 00:32:03.395997 655 net.cpp:454] label_data_1_split <- label\n", + "I0224 00:32:03.396005 655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", + "I0224 00:32:03.396016 655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", + "I0224 00:32:03.396028 655 net.cpp:150] Setting up label_data_1_split\n", + "I0224 00:32:03.396036 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.396044 655 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:03.396051 655 net.cpp:165] Memory required for data: 280\n", + "I0224 00:32:03.396059 655 layer_factory.hpp:77] Creating layer ip1\n", + "I0224 00:32:03.396069 655 net.cpp:106] Creating Layer ip1\n", + "I0224 00:32:03.396075 655 net.cpp:454] ip1 <- data\n", + "I0224 00:32:03.396085 655 net.cpp:411] ip1 -> ip1\n", + "I0224 00:32:03.396100 655 net.cpp:150] Setting up ip1\n", + "I0224 00:32:03.396109 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.396116 655 net.cpp:165] Memory required for data: 360\n", + "I0224 00:32:03.396138 655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n", + "I0224 00:32:03.396148 655 net.cpp:106] Creating Layer ip1_ip1_0_split\n", + "I0224 00:32:03.396157 655 net.cpp:454] ip1_ip1_0_split <- ip1\n", + "I0224 00:32:03.396164 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n", + "I0224 00:32:03.396174 655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n", + "I0224 00:32:03.396185 655 net.cpp:150] Setting up ip1_ip1_0_split\n", + "I0224 00:32:03.396194 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.396203 655 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:03.396209 655 net.cpp:165] Memory required for data: 520\n", + "I0224 00:32:03.396216 655 layer_factory.hpp:77] Creating layer accuracy\n", + "I0224 00:32:03.396225 655 net.cpp:106] Creating Layer accuracy\n", + "I0224 00:32:03.396234 655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n", + "I0224 00:32:03.396241 655 net.cpp:454] accuracy <- label_data_1_split_0\n", + "I0224 00:32:03.396250 655 net.cpp:411] accuracy -> accuracy\n", + "I0224 00:32:03.396260 655 net.cpp:150] Setting up accuracy\n", + "I0224 00:32:03.396270 655 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:03.396276 655 net.cpp:165] Memory required for data: 524\n", + "I0224 00:32:03.396283 655 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:03.396291 655 net.cpp:106] Creating Layer loss\n", + "I0224 00:32:03.396299 655 net.cpp:454] loss <- ip1_ip1_0_split_1\n", + "I0224 00:32:03.396307 655 net.cpp:454] loss <- label_data_1_split_1\n", + "I0224 00:32:03.396317 655 net.cpp:411] loss -> loss\n", + "I0224 00:32:03.396327 655 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:03.396339 655 net.cpp:150] Setting up loss\n", + "I0224 00:32:03.396349 655 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:03.396356 655 net.cpp:160] with loss weight 1\n", + "I0224 00:32:03.396365 655 net.cpp:165] Memory required for data: 528\n", + "I0224 00:32:03.396373 655 net.cpp:226] loss needs backward computation.\n", + "I0224 00:32:03.396381 655 net.cpp:228] accuracy does not need backward computation.\n", + "I0224 00:32:03.396389 655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n", + "I0224 00:32:03.396396 655 net.cpp:226] ip1 needs backward computation.\n", + "I0224 00:32:03.396404 655 net.cpp:228] label_data_1_split does not need backward computation.\n", + "I0224 00:32:03.396412 655 net.cpp:228] data does not need backward computation.\n", + "I0224 00:32:03.396420 655 net.cpp:270] This network produces output accuracy\n", + "I0224 00:32:03.396427 655 net.cpp:270] This network produces output loss\n", + "I0224 00:32:03.396437 655 net.cpp:283] Network initialization done.\n", + "I0224 00:32:03.396455 655 solver.cpp:60] Solver scaffolding done.\n", + "I0224 00:32:03.396473 655 caffe.cpp:219] Starting Optimization\n", + "I0224 00:32:03.396482 655 solver.cpp:280] Solving \n", + "I0224 00:32:03.396489 655 solver.cpp:281] Learning Rate Policy: step\n", + "I0224 00:32:03.396499 655 solver.cpp:338] Iteration 0, Testing net (#0)\n", + "I0224 00:32:03.932615 655 solver.cpp:406] Test net output #0: accuracy = 0.4268\n", + "I0224 00:32:03.932656 655 solver.cpp:406] Test net output #1: loss = 1.33093 (* 1 = 1.33093 loss)\n", + "I0224 00:32:03.932723 655 solver.cpp:229] Iteration 0, loss = 1.06081\n", + "I0224 00:32:03.932737 655 solver.cpp:245] Train net output #0: accuracy = 0.4\n", + "I0224 00:32:03.932749 655 solver.cpp:245] Train net output #1: loss = 1.06081 (* 1 = 1.06081 loss)\n", + "I0224 00:32:03.932765 655 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n", + "I0224 00:32:03.945551 655 solver.cpp:338] Iteration 1000, Testing net (#0)\n", + "I0224 00:32:03.948048 655 solver.cpp:406] Test net output #0: accuracy = 0.694\n", + "I0224 00:32:03.948065 655 solver.cpp:406] Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n", + "I0224 00:32:03.948091 655 solver.cpp:229] Iteration 1000, loss = 0.505853\n", + "I0224 00:32:03.948102 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:03.948113 655 solver.cpp:245] Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n", + "I0224 00:32:03.948122 655 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n", + "I0224 00:32:03.960741 655 solver.cpp:338] Iteration 2000, Testing net (#0)\n", + "I0224 00:32:03.963214 655 solver.cpp:406] Test net output #0: accuracy = 0.7372\n", + "I0224 00:32:03.963249 655 solver.cpp:406] Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n", + "I0224 00:32:03.963276 655 solver.cpp:229] Iteration 2000, loss = 0.549211\n", + "I0224 00:32:03.963289 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:03.963299 655 solver.cpp:245] Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n", + "I0224 00:32:03.963309 655 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n", + "I0224 00:32:03.975945 655 solver.cpp:338] Iteration 3000, Testing net (#0)\n", + "I0224 00:32:03.978435 655 solver.cpp:406] Test net output #0: accuracy = 0.7732\n", + "I0224 00:32:03.978451 655 solver.cpp:406] Test net output #1: loss = 0.594998 (* 1 = 0.594998 loss)\n", + "I0224 00:32:03.978884 655 solver.cpp:229] Iteration 3000, loss = 0.66133\n", + "I0224 00:32:03.978911 655 solver.cpp:245] Train net output #0: accuracy = 0.8\n", + "I0224 00:32:03.978932 655 solver.cpp:245] Train net output #1: loss = 0.66133 (* 1 = 0.66133 loss)\n", + "I0224 00:32:03.978950 655 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n", + "I0224 00:32:03.992017 655 solver.cpp:338] Iteration 4000, Testing net (#0)\n", + "I0224 00:32:03.994509 655 solver.cpp:406] Test net output #0: accuracy = 0.694\n", + "I0224 00:32:03.994525 655 solver.cpp:406] Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n", + "I0224 00:32:03.994551 655 solver.cpp:229] Iteration 4000, loss = 0.505853\n", + "I0224 00:32:03.994562 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:03.994573 655 solver.cpp:245] Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n", + "I0224 00:32:03.994583 655 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n", + "I0224 00:32:04.007200 655 solver.cpp:338] Iteration 5000, Testing net (#0)\n", + "I0224 00:32:04.009686 655 solver.cpp:406] Test net output #0: accuracy = 0.7372\n", + "I0224 00:32:04.009702 655 solver.cpp:406] Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n", + "I0224 00:32:04.009727 655 solver.cpp:229] Iteration 5000, loss = 0.549211\n", + "I0224 00:32:04.009738 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:04.009749 655 solver.cpp:245] Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n", + "I0224 00:32:04.009758 655 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n", + "I0224 00:32:04.022734 655 solver.cpp:338] Iteration 6000, Testing net (#0)\n", + "I0224 00:32:04.025177 655 solver.cpp:406] Test net output #0: accuracy = 0.7824\n", + "I0224 00:32:04.025193 655 solver.cpp:406] Test net output #1: loss = 0.593367 (* 1 = 0.593367 loss)\n", + "I0224 00:32:04.025545 655 solver.cpp:229] Iteration 6000, loss = 0.654873\n", + "I0224 00:32:04.025562 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:04.025573 655 solver.cpp:245] Train net output #1: loss = 0.654873 (* 1 = 0.654873 loss)\n", + "I0224 00:32:04.025583 655 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n", + "I0224 00:32:04.038586 655 solver.cpp:338] Iteration 7000, Testing net (#0)\n", + "I0224 00:32:04.041016 655 solver.cpp:406] Test net output #0: accuracy = 0.7704\n", + "I0224 00:32:04.041033 655 solver.cpp:406] Test net output #1: loss = 0.593842 (* 1 = 0.593842 loss)\n", + "I0224 00:32:04.041059 655 solver.cpp:229] Iteration 7000, loss = 0.46611\n", + "I0224 00:32:04.041071 655 solver.cpp:245] Train net output #0: accuracy = 0.6\n", + "I0224 00:32:04.041082 655 solver.cpp:245] Train net output #1: loss = 0.46611 (* 1 = 0.46611 loss)\n", + "I0224 00:32:04.041091 655 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n", + "I0224 00:32:04.053722 655 solver.cpp:338] Iteration 8000, Testing net (#0)\n", + "I0224 00:32:04.056171 655 solver.cpp:406] Test net output #0: accuracy = 0.7788\n", + "I0224 00:32:04.056187 655 solver.cpp:406] Test net output #1: loss = 0.592847 (* 1 = 0.592847 loss)\n", + "I0224 00:32:04.056213 655 solver.cpp:229] Iteration 8000, loss = 0.615126\n", + "I0224 00:32:04.056224 655 solver.cpp:245] Train net output #0: accuracy = 0.8\n", + "I0224 00:32:04.056236 655 solver.cpp:245] Train net output #1: loss = 0.615126 (* 1 = 0.615126 loss)\n", + "I0224 00:32:04.056244 655 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n", + "I0224 00:32:04.068853 655 solver.cpp:338] Iteration 9000, Testing net (#0)\n", + "I0224 00:32:04.071291 655 solver.cpp:406] Test net output #0: accuracy = 0.7808\n", + "I0224 00:32:04.071307 655 solver.cpp:406] Test net output #1: loss = 0.593293 (* 1 = 0.593293 loss)\n", + "I0224 00:32:04.071650 655 solver.cpp:229] Iteration 9000, loss = 0.654997\n", + "I0224 00:32:04.071666 655 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:04.071677 655 solver.cpp:245] Train net output #1: loss = 0.654998 (* 1 = 0.654998 loss)\n", + "I0224 00:32:04.071687 655 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n", + "I0224 00:32:04.084717 655 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n", + "I0224 00:32:04.084885 655 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n", + "I0224 00:32:04.084960 655 solver.cpp:318] Iteration 10000, loss = 0.466505\n", + "I0224 00:32:04.084977 655 solver.cpp:338] Iteration 10000, Testing net (#0)\n", + "I0224 00:32:04.087514 655 solver.cpp:406] Test net output #0: accuracy = 0.77\n", + "I0224 00:32:04.087532 655 solver.cpp:406] Test net output #1: loss = 0.593815 (* 1 = 0.593815 loss)\n", + "I0224 00:32:04.087541 655 solver.cpp:323] Optimization Done.\n", + "I0224 00:32:04.087548 655 caffe.cpp:222] Optimization Done.\n" + ] + } + ], + "source": [ + "!./build/tools/caffe train -solver examples/hdf5_classification/logreg_solver.prototxt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n", + "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n", + "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_logreg_solver.prototxt` which we will now use.\n", + "\n", + "The final accuracy of the new network should be higher than logistic regression!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from caffe import layers as L\n", + "from caffe import params as P\n", + "\n", + "def nonlinear_net(hdf5, batch_size):\n", + " # one small nonlinearity, one leap for model kind\n", + " n = caffe.NetSpec()\n", + " n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n", + " # define a hidden layer of dimension 40\n", + " n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n", + " # transform the output through the ReLU (rectified linear) non-linearity\n", + " n.relu1 = L.ReLU(n.ip1, in_place=True)\n", + " # score the (now non-linear) features\n", + " n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n", + " # same accuracy and loss as before\n", + " n.accuracy = L.Accuracy(n.ip2, n.label)\n", + " n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n", + " return n.to_proto()\n", + "\n", + "train_net_path = 'examples/hdf5_classification/nonlinear_auto_train.prototxt'\n", + "with open(train_net_path, 'w') as f:\n", + " f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n", + "\n", + "test_net_path = 'examples/hdf5_classification/nonlinear_auto_test.prototxt'\n", + "with open(test_net_path, 'w') as f:\n", + " f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))\n", + "\n", + "solver_path = 'examples/hdf5_classification/nonlinear_logreg_solver.prototxt'\n", + "with open(solver_path, 'w') as f:\n", + " f.write(str(solver(train_net_path, test_net_path)))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.838\n", + "Accuracy: 0.837\n", + "Accuracy: 0.838\n", + "Accuracy: 0.834\n", + "1 loop, best of 3: 277 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "caffe.set_mode_cpu()\n", + "solver = caffe.get_solver(solver_path)\n", + "solver.solve()\n", + "\n", + "accuracy = 0\n", + "batch_size = solver.test_nets[0].blobs['data'].num\n", + "test_iters = int(len(Xt) / batch_size)\n", + "for i in range(test_iters):\n", + " solver.test_nets[0].forward()\n", + " accuracy += solver.test_nets[0].blobs['accuracy'].data\n", + "accuracy /= test_iters\n", + "\n", + "print(\"Accuracy: {:.3f}\".format(accuracy))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do the same through the command line interface for detailed output on the model and solving." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I0224 00:32:05.654265 658 caffe.cpp:178] Use CPU.\n", + "I0224 00:32:05.810444 658 solver.cpp:48] Initializing solver from parameters: \n", + "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n", + "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n", + "test_iter: 250\n", + "test_interval: 1000\n", + "base_lr: 0.01\n", + "display: 1000\n", + "max_iter: 10000\n", + "lr_policy: \"step\"\n", + "gamma: 0.1\n", + "momentum: 0.9\n", + "weight_decay: 0.0005\n", + "stepsize: 5000\n", + "snapshot: 10000\n", + "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n", + "solver_mode: CPU\n", + "I0224 00:32:05.810634 658 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n", + "I0224 00:32:05.810835 658 net.cpp:49] Initializing net from parameters: \n", + "state {\n", + " phase: TRAIN\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/train.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 40\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu1\"\n", + " type: \"ReLU\"\n", + " bottom: \"ip1\"\n", + " top: \"ip1\"\n", + "}\n", + "layer {\n", + " name: \"ip2\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"ip1\"\n", + " top: \"ip2\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0224 00:32:05.811061 658 layer_factory.hpp:77] Creating layer data\n", + "I0224 00:32:05.811079 658 net.cpp:106] Creating Layer data\n", + "I0224 00:32:05.811092 658 net.cpp:411] data -> data\n", + "I0224 00:32:05.811121 658 net.cpp:411] data -> label\n", + "I0224 00:32:05.811143 658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n", + "I0224 00:32:05.811189 658 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n", + "I0224 00:32:05.812254 658 hdf5.cpp:32] Datatype class: H5T_FLOAT\n", + "I0224 00:32:05.812677 658 net.cpp:150] Setting up data\n", + "I0224 00:32:05.812705 658 net.cpp:157] Top shape: 10 4 (40)\n", + "I0224 00:32:05.812721 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.812729 658 net.cpp:165] Memory required for data: 200\n", + "I0224 00:32:05.812739 658 layer_factory.hpp:77] Creating layer label_data_1_split\n", + "I0224 00:32:05.812752 658 net.cpp:106] Creating Layer label_data_1_split\n", + "I0224 00:32:05.812762 658 net.cpp:454] label_data_1_split <- label\n", + "I0224 00:32:05.812774 658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", + "I0224 00:32:05.812785 658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", + "I0224 00:32:05.812798 658 net.cpp:150] Setting up label_data_1_split\n", + "I0224 00:32:05.812808 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.812816 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.812824 658 net.cpp:165] Memory required for data: 280\n", + "I0224 00:32:05.812831 658 layer_factory.hpp:77] Creating layer ip1\n", + "I0224 00:32:05.812841 658 net.cpp:106] Creating Layer ip1\n", + "I0224 00:32:05.812849 658 net.cpp:454] ip1 <- data\n", + "I0224 00:32:05.812860 658 net.cpp:411] ip1 -> ip1\n", + "I0224 00:32:05.813179 658 net.cpp:150] Setting up ip1\n", + "I0224 00:32:05.813196 658 net.cpp:157] Top shape: 10 40 (400)\n", + "I0224 00:32:05.813210 658 net.cpp:165] Memory required for data: 1880\n", + "I0224 00:32:05.813230 658 layer_factory.hpp:77] Creating layer relu1\n", + "I0224 00:32:05.813241 658 net.cpp:106] Creating Layer relu1\n", + "I0224 00:32:05.813251 658 net.cpp:454] relu1 <- ip1\n", + "I0224 00:32:05.813258 658 net.cpp:397] relu1 -> ip1 (in-place)\n", + "I0224 00:32:05.813271 658 net.cpp:150] Setting up relu1\n", + "I0224 00:32:05.813279 658 net.cpp:157] Top shape: 10 40 (400)\n", + "I0224 00:32:05.813287 658 net.cpp:165] Memory required for data: 3480\n", + "I0224 00:32:05.813294 658 layer_factory.hpp:77] Creating layer ip2\n", + "I0224 00:32:05.813304 658 net.cpp:106] Creating Layer ip2\n", + "I0224 00:32:05.813313 658 net.cpp:454] ip2 <- ip1\n", + "I0224 00:32:05.813321 658 net.cpp:411] ip2 -> ip2\n", + "I0224 00:32:05.813336 658 net.cpp:150] Setting up ip2\n", + "I0224 00:32:05.813345 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.813379 658 net.cpp:165] Memory required for data: 3560\n", + "I0224 00:32:05.813401 658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n", + "I0224 00:32:05.813417 658 net.cpp:106] Creating Layer ip2_ip2_0_split\n", + "I0224 00:32:05.813426 658 net.cpp:454] ip2_ip2_0_split <- ip2\n", + "I0224 00:32:05.813434 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n", + "I0224 00:32:05.813446 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n", + "I0224 00:32:05.813457 658 net.cpp:150] Setting up ip2_ip2_0_split\n", + "I0224 00:32:05.813465 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.813473 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.813480 658 net.cpp:165] Memory required for data: 3720\n", + "I0224 00:32:05.813488 658 layer_factory.hpp:77] Creating layer accuracy\n", + "I0224 00:32:05.813499 658 net.cpp:106] Creating Layer accuracy\n", + "I0224 00:32:05.813508 658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n", + "I0224 00:32:05.813515 658 net.cpp:454] accuracy <- label_data_1_split_0\n", + "I0224 00:32:05.813524 658 net.cpp:411] accuracy -> accuracy\n", + "I0224 00:32:05.813539 658 net.cpp:150] Setting up accuracy\n", + "I0224 00:32:05.813547 658 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:05.813555 658 net.cpp:165] Memory required for data: 3724\n", + "I0224 00:32:05.813565 658 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:05.813585 658 net.cpp:106] Creating Layer loss\n", + "I0224 00:32:05.813599 658 net.cpp:454] loss <- ip2_ip2_0_split_1\n", + "I0224 00:32:05.813616 658 net.cpp:454] loss <- label_data_1_split_1\n", + "I0224 00:32:05.813627 658 net.cpp:411] loss -> loss\n", + "I0224 00:32:05.813642 658 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:05.813663 658 net.cpp:150] Setting up loss\n", + "I0224 00:32:05.813671 658 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:05.813679 658 net.cpp:160] with loss weight 1\n", + "I0224 00:32:05.813695 658 net.cpp:165] Memory required for data: 3728\n", + "I0224 00:32:05.813704 658 net.cpp:226] loss needs backward computation.\n", + "I0224 00:32:05.813712 658 net.cpp:228] accuracy does not need backward computation.\n", + "I0224 00:32:05.813720 658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n", + "I0224 00:32:05.813729 658 net.cpp:226] ip2 needs backward computation.\n", + "I0224 00:32:05.813735 658 net.cpp:226] relu1 needs backward computation.\n", + "I0224 00:32:05.813743 658 net.cpp:226] ip1 needs backward computation.\n", + "I0224 00:32:05.813751 658 net.cpp:228] label_data_1_split does not need backward computation.\n", + "I0224 00:32:05.813760 658 net.cpp:228] data does not need backward computation.\n", + "I0224 00:32:05.813772 658 net.cpp:270] This network produces output accuracy\n", + "I0224 00:32:05.813787 658 net.cpp:270] This network produces output loss\n", + "I0224 00:32:05.813809 658 net.cpp:283] Network initialization done.\n", + "I0224 00:32:05.813905 658 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n", + "I0224 00:32:05.813944 658 net.cpp:49] Initializing net from parameters: \n", + "state {\n", + " phase: TEST\n", + "}\n", + "layer {\n", + " name: \"data\"\n", + " type: \"HDF5Data\"\n", + " top: \"data\"\n", + " top: \"label\"\n", + " hdf5_data_param {\n", + " source: \"examples/hdf5_classification/data/test.txt\"\n", + " batch_size: 10\n", + " }\n", + "}\n", + "layer {\n", + " name: \"ip1\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"data\"\n", + " top: \"ip1\"\n", + " inner_product_param {\n", + " num_output: 40\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"relu1\"\n", + " type: \"ReLU\"\n", + " bottom: \"ip1\"\n", + " top: \"ip1\"\n", + "}\n", + "layer {\n", + " name: \"ip2\"\n", + " type: \"InnerProduct\"\n", + " bottom: \"ip1\"\n", + " top: \"ip2\"\n", + " inner_product_param {\n", + " num_output: 2\n", + " weight_filler {\n", + " type: \"xavier\"\n", + " }\n", + " }\n", + "}\n", + "layer {\n", + " name: \"accuracy\"\n", + " type: \"Accuracy\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"accuracy\"\n", + "}\n", + "layer {\n", + " name: \"loss\"\n", + " type: \"SoftmaxWithLoss\"\n", + " bottom: \"ip2\"\n", + " bottom: \"label\"\n", + " top: \"loss\"\n", + "}\n", + "I0224 00:32:05.814131 658 layer_factory.hpp:77] Creating layer data\n", + "I0224 00:32:05.814142 658 net.cpp:106] Creating Layer data\n", + "I0224 00:32:05.814152 658 net.cpp:411] data -> data\n", + "I0224 00:32:05.814162 658 net.cpp:411] data -> label\n", + "I0224 00:32:05.814180 658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n", + "I0224 00:32:05.814220 658 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n", + "I0224 00:32:05.815207 658 net.cpp:150] Setting up data\n", + "I0224 00:32:05.815227 658 net.cpp:157] Top shape: 10 4 (40)\n", + "I0224 00:32:05.815243 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.815253 658 net.cpp:165] Memory required for data: 200\n", + "I0224 00:32:05.815260 658 layer_factory.hpp:77] Creating layer label_data_1_split\n", + "I0224 00:32:05.815270 658 net.cpp:106] Creating Layer label_data_1_split\n", + "I0224 00:32:05.815279 658 net.cpp:454] label_data_1_split <- label\n", + "I0224 00:32:05.815287 658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n", + "I0224 00:32:05.815299 658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n", + "I0224 00:32:05.815310 658 net.cpp:150] Setting up label_data_1_split\n", + "I0224 00:32:05.815318 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.815326 658 net.cpp:157] Top shape: 10 (10)\n", + "I0224 00:32:05.815335 658 net.cpp:165] Memory required for data: 280\n", + "I0224 00:32:05.815341 658 layer_factory.hpp:77] Creating layer ip1\n", + "I0224 00:32:05.815351 658 net.cpp:106] Creating Layer ip1\n", + "I0224 00:32:05.815358 658 net.cpp:454] ip1 <- data\n", + "I0224 00:32:05.815367 658 net.cpp:411] ip1 -> ip1\n", + "I0224 00:32:05.815383 658 net.cpp:150] Setting up ip1\n", + "I0224 00:32:05.815398 658 net.cpp:157] Top shape: 10 40 (400)\n", + "I0224 00:32:05.815413 658 net.cpp:165] Memory required for data: 1880\n", + "I0224 00:32:05.815435 658 layer_factory.hpp:77] Creating layer relu1\n", + "I0224 00:32:05.815450 658 net.cpp:106] Creating Layer relu1\n", + "I0224 00:32:05.815459 658 net.cpp:454] relu1 <- ip1\n", + "I0224 00:32:05.815469 658 net.cpp:397] relu1 -> ip1 (in-place)\n", + "I0224 00:32:05.815479 658 net.cpp:150] Setting up relu1\n", + "I0224 00:32:05.815486 658 net.cpp:157] Top shape: 10 40 (400)\n", + "I0224 00:32:05.815495 658 net.cpp:165] Memory required for data: 3480\n", + "I0224 00:32:05.815501 658 layer_factory.hpp:77] Creating layer ip2\n", + "I0224 00:32:05.815510 658 net.cpp:106] Creating Layer ip2\n", + "I0224 00:32:05.815518 658 net.cpp:454] ip2 <- ip1\n", + "I0224 00:32:05.815527 658 net.cpp:411] ip2 -> ip2\n", + "I0224 00:32:05.815542 658 net.cpp:150] Setting up ip2\n", + "I0224 00:32:05.815551 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.815559 658 net.cpp:165] Memory required for data: 3560\n", + "I0224 00:32:05.815570 658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n", + "I0224 00:32:05.815579 658 net.cpp:106] Creating Layer ip2_ip2_0_split\n", + "I0224 00:32:05.815587 658 net.cpp:454] ip2_ip2_0_split <- ip2\n", + "I0224 00:32:05.815600 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n", + "I0224 00:32:05.815619 658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n", + "I0224 00:32:05.815640 658 net.cpp:150] Setting up ip2_ip2_0_split\n", + "I0224 00:32:05.815654 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.815662 658 net.cpp:157] Top shape: 10 2 (20)\n", + "I0224 00:32:05.815670 658 net.cpp:165] Memory required for data: 3720\n", + "I0224 00:32:05.815677 658 layer_factory.hpp:77] Creating layer accuracy\n", + "I0224 00:32:05.815685 658 net.cpp:106] Creating Layer accuracy\n", + "I0224 00:32:05.815693 658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n", + "I0224 00:32:05.815702 658 net.cpp:454] accuracy <- label_data_1_split_0\n", + "I0224 00:32:05.815711 658 net.cpp:411] accuracy -> accuracy\n", + "I0224 00:32:05.815722 658 net.cpp:150] Setting up accuracy\n", + "I0224 00:32:05.815732 658 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:05.815738 658 net.cpp:165] Memory required for data: 3724\n", + "I0224 00:32:05.815747 658 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:05.815754 658 net.cpp:106] Creating Layer loss\n", + "I0224 00:32:05.815762 658 net.cpp:454] loss <- ip2_ip2_0_split_1\n", + "I0224 00:32:05.815770 658 net.cpp:454] loss <- label_data_1_split_1\n", + "I0224 00:32:05.815779 658 net.cpp:411] loss -> loss\n", + "I0224 00:32:05.815790 658 layer_factory.hpp:77] Creating layer loss\n", + "I0224 00:32:05.815811 658 net.cpp:150] Setting up loss\n", + "I0224 00:32:05.815829 658 net.cpp:157] Top shape: (1)\n", + "I0224 00:32:05.815843 658 net.cpp:160] with loss weight 1\n", + "I0224 00:32:05.815867 658 net.cpp:165] Memory required for data: 3728\n", + "I0224 00:32:05.815876 658 net.cpp:226] loss needs backward computation.\n", + "I0224 00:32:05.815884 658 net.cpp:228] accuracy does not need backward computation.\n", + "I0224 00:32:05.815892 658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n", + "I0224 00:32:05.815901 658 net.cpp:226] ip2 needs backward computation.\n", + "I0224 00:32:05.815908 658 net.cpp:226] relu1 needs backward computation.\n", + "I0224 00:32:05.815915 658 net.cpp:226] ip1 needs backward computation.\n", + "I0224 00:32:05.815923 658 net.cpp:228] label_data_1_split does not need backward computation.\n", + "I0224 00:32:05.815932 658 net.cpp:228] data does not need backward computation.\n", + "I0224 00:32:05.815938 658 net.cpp:270] This network produces output accuracy\n", + "I0224 00:32:05.815946 658 net.cpp:270] This network produces output loss\n", + "I0224 00:32:05.815958 658 net.cpp:283] Network initialization done.\n", + "I0224 00:32:05.815978 658 solver.cpp:60] Solver scaffolding done.\n", + "I0224 00:32:05.816000 658 caffe.cpp:219] Starting Optimization\n", + "I0224 00:32:05.816016 658 solver.cpp:280] Solving \n", + "I0224 00:32:05.816030 658 solver.cpp:281] Learning Rate Policy: step\n", + "I0224 00:32:05.816048 658 solver.cpp:338] Iteration 0, Testing net (#0)\n", + "I0224 00:32:05.831967 658 solver.cpp:406] Test net output #0: accuracy = 0.4464\n", + "I0224 00:32:05.832033 658 solver.cpp:406] Test net output #1: loss = 0.909841 (* 1 = 0.909841 loss)\n", + "I0224 00:32:05.832186 658 solver.cpp:229] Iteration 0, loss = 0.798509\n", + "I0224 00:32:05.832218 658 solver.cpp:245] Train net output #0: accuracy = 0.6\n", + "I0224 00:32:05.832247 658 solver.cpp:245] Train net output #1: loss = 0.798509 (* 1 = 0.798509 loss)\n", + "I0224 00:32:05.832281 658 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n", + "I0224 00:32:05.859506 658 solver.cpp:338] Iteration 1000, Testing net (#0)\n", + "I0224 00:32:05.862799 658 solver.cpp:406] Test net output #0: accuracy = 0.8156\n", + "I0224 00:32:05.862818 658 solver.cpp:406] Test net output #1: loss = 0.44259 (* 1 = 0.44259 loss)\n", + "I0224 00:32:05.862853 658 solver.cpp:229] Iteration 1000, loss = 0.537015\n", + "I0224 00:32:05.862864 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:05.862875 658 solver.cpp:245] Train net output #1: loss = 0.537015 (* 1 = 0.537015 loss)\n", + "I0224 00:32:05.862885 658 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n", + "I0224 00:32:05.883155 658 solver.cpp:338] Iteration 2000, Testing net (#0)\n", + "I0224 00:32:05.886435 658 solver.cpp:406] Test net output #0: accuracy = 0.8116\n", + "I0224 00:32:05.886451 658 solver.cpp:406] Test net output #1: loss = 0.434079 (* 1 = 0.434079 loss)\n", + "I0224 00:32:05.886484 658 solver.cpp:229] Iteration 2000, loss = 0.43109\n", + "I0224 00:32:05.886497 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n", + "I0224 00:32:05.886508 658 solver.cpp:245] Train net output #1: loss = 0.43109 (* 1 = 0.43109 loss)\n", + "I0224 00:32:05.886518 658 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n", + "I0224 00:32:05.907243 658 solver.cpp:338] Iteration 3000, Testing net (#0)\n", + "I0224 00:32:05.910521 658 solver.cpp:406] Test net output #0: accuracy = 0.8168\n", + "I0224 00:32:05.910537 658 solver.cpp:406] Test net output #1: loss = 0.425661 (* 1 = 0.425661 loss)\n", + "I0224 00:32:05.910905 658 solver.cpp:229] Iteration 3000, loss = 0.430245\n", + "I0224 00:32:05.910922 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:05.910933 658 solver.cpp:245] Train net output #1: loss = 0.430245 (* 1 = 0.430245 loss)\n", + "I0224 00:32:05.910943 658 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n", + "I0224 00:32:05.931205 658 solver.cpp:338] Iteration 4000, Testing net (#0)\n", + "I0224 00:32:05.934479 658 solver.cpp:406] Test net output #0: accuracy = 0.8324\n", + "I0224 00:32:05.934496 658 solver.cpp:406] Test net output #1: loss = 0.404891 (* 1 = 0.404891 loss)\n", + "I0224 00:32:05.934530 658 solver.cpp:229] Iteration 4000, loss = 0.628955\n", + "I0224 00:32:05.934542 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:05.934553 658 solver.cpp:245] Train net output #1: loss = 0.628955 (* 1 = 0.628955 loss)\n", + "I0224 00:32:05.934583 658 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n", + "I0224 00:32:05.955108 658 solver.cpp:338] Iteration 5000, Testing net (#0)\n", + "I0224 00:32:05.958377 658 solver.cpp:406] Test net output #0: accuracy = 0.8364\n", + "I0224 00:32:05.958395 658 solver.cpp:406] Test net output #1: loss = 0.404235 (* 1 = 0.404235 loss)\n", + "I0224 00:32:05.958432 658 solver.cpp:229] Iteration 5000, loss = 0.394939\n", + "I0224 00:32:05.958444 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n", + "I0224 00:32:05.958456 658 solver.cpp:245] Train net output #1: loss = 0.39494 (* 1 = 0.39494 loss)\n", + "I0224 00:32:05.958466 658 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n", + "I0224 00:32:05.978703 658 solver.cpp:338] Iteration 6000, Testing net (#0)\n", + "I0224 00:32:05.981973 658 solver.cpp:406] Test net output #0: accuracy = 0.838\n", + "I0224 00:32:05.981991 658 solver.cpp:406] Test net output #1: loss = 0.385743 (* 1 = 0.385743 loss)\n", + "I0224 00:32:05.982347 658 solver.cpp:229] Iteration 6000, loss = 0.411537\n", + "I0224 00:32:05.982362 658 solver.cpp:245] Train net output #0: accuracy = 0.8\n", + "I0224 00:32:05.982373 658 solver.cpp:245] Train net output #1: loss = 0.411537 (* 1 = 0.411537 loss)\n", + "I0224 00:32:05.982383 658 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n", + "I0224 00:32:06.003015 658 solver.cpp:338] Iteration 7000, Testing net (#0)\n", + "I0224 00:32:06.006283 658 solver.cpp:406] Test net output #0: accuracy = 0.8388\n", + "I0224 00:32:06.006301 658 solver.cpp:406] Test net output #1: loss = 0.384648 (* 1 = 0.384648 loss)\n", + "I0224 00:32:06.006335 658 solver.cpp:229] Iteration 7000, loss = 0.521072\n", + "I0224 00:32:06.006347 658 solver.cpp:245] Train net output #0: accuracy = 0.7\n", + "I0224 00:32:06.006358 658 solver.cpp:245] Train net output #1: loss = 0.521073 (* 1 = 0.521073 loss)\n", + "I0224 00:32:06.006368 658 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n", + "I0224 00:32:06.026715 658 solver.cpp:338] Iteration 8000, Testing net (#0)\n", + "I0224 00:32:06.029965 658 solver.cpp:406] Test net output #0: accuracy = 0.8404\n", + "I0224 00:32:06.029983 658 solver.cpp:406] Test net output #1: loss = 0.380889 (* 1 = 0.380889 loss)\n", + "I0224 00:32:06.030015 658 solver.cpp:229] Iteration 8000, loss = 0.329477\n", + "I0224 00:32:06.030028 658 solver.cpp:245] Train net output #0: accuracy = 0.9\n", + "I0224 00:32:06.030040 658 solver.cpp:245] Train net output #1: loss = 0.329477 (* 1 = 0.329477 loss)\n", + "I0224 00:32:06.030048 658 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n", + "I0224 00:32:06.050626 658 solver.cpp:338] Iteration 9000, Testing net (#0)\n", + "I0224 00:32:06.053889 658 solver.cpp:406] Test net output #0: accuracy = 0.8376\n", + "I0224 00:32:06.053906 658 solver.cpp:406] Test net output #1: loss = 0.382756 (* 1 = 0.382756 loss)\n", + "I0224 00:32:06.054271 658 solver.cpp:229] Iteration 9000, loss = 0.412227\n", + "I0224 00:32:06.054291 658 solver.cpp:245] Train net output #0: accuracy = 0.8\n", + "I0224 00:32:06.054314 658 solver.cpp:245] Train net output #1: loss = 0.412228 (* 1 = 0.412228 loss)\n", + "I0224 00:32:06.054337 658 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n", + "I0224 00:32:06.074646 658 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n", + "I0224 00:32:06.074808 658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n", + "I0224 00:32:06.074889 658 solver.cpp:318] Iteration 10000, loss = 0.532798\n", + "I0224 00:32:06.074906 658 solver.cpp:338] Iteration 10000, Testing net (#0)\n", + "I0224 00:32:06.078208 658 solver.cpp:406] Test net output #0: accuracy = 0.8388\n", + "I0224 00:32:06.078225 658 solver.cpp:406] Test net output #1: loss = 0.382042 (* 1 = 0.382042 loss)\n", + "I0224 00:32:06.078234 658 solver.cpp:323] Optimization Done.\n", + "I0224 00:32:06.078241 658 caffe.cpp:222] Optimization Done.\n" + ] + } + ], + "source": [ + "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_logreg_solver.prototxt" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n", + "shutil.rmtree(dirname)" + ] + } + ], + "metadata": { + "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.", + "example_name": "Off-the-shelf SGD for classification", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + }, + "priority": 4 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/hdf5_classification/nonlinear_solver.prototxt b/examples/hdf5_classification/nonlinear_solver.prototxt deleted file mode 100644 index b4aacf6e423..00000000000 --- a/examples/hdf5_classification/nonlinear_solver.prototxt +++ /dev/null @@ -1,15 +0,0 @@ -train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt" -test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt" -test_iter: 250 -test_interval: 1000 -base_lr: 0.01 -lr_policy: "step" -gamma: 0.1 -stepsize: 5000 -display: 1000 -max_iter: 10000 -momentum: 0.9 -weight_decay: 0.0005 -snapshot: 10000 -snapshot_prefix: "examples/hdf5_classification/data/train" -solver_mode: CPU diff --git a/examples/hdf5_classification/solver.prototxt b/examples/hdf5_classification/solver.prototxt deleted file mode 100644 index 8587b5a1e5a..00000000000 --- a/examples/hdf5_classification/solver.prototxt +++ /dev/null @@ -1,15 +0,0 @@ -train_net: "examples/hdf5_classification/logreg_auto_train.prototxt" -test_net: "examples/hdf5_classification/logreg_auto_test.prototxt" -test_iter: 250 -test_interval: 1000 -base_lr: 0.01 -lr_policy: "step" -gamma: 0.1 -stepsize: 5000 -display: 1000 -max_iter: 10000 -momentum: 0.9 -weight_decay: 0.0005 -snapshot: 10000 -snapshot_prefix: "examples/hdf5_classification/data/train" -solver_mode: CPU From ca22227089e9be0391c0d5532cab4b7ec64ab4ff Mon Sep 17 00:00:00 2001 From: Niels Ole Salscheider Date: Wed, 24 Feb 2016 17:00:58 +0100 Subject: [PATCH 408/446] CMake: Do not include "${PROJECT_BINARY_DIR}/include" with SYSTEM option This is important for the include order. Without this patch, a previously installed caffe.pb.h might be included instead of the one that is generated during the build. --- cmake/ProtoBuf.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake index fc799bd3906..73f647f5fae 100644 --- a/cmake/ProtoBuf.cmake +++ b/cmake/ProtoBuf.cmake @@ -23,7 +23,7 @@ endif() # place where to generate protobuf sources set(proto_gen_folder "${PROJECT_BINARY_DIR}/include/caffe/proto") -include_directories(SYSTEM "${PROJECT_BINARY_DIR}/include") +include_directories("${PROJECT_BINARY_DIR}/include") set(PROTOBUF_GENERATE_CPP_APPEND_PATH TRUE) From 00598ca84e2611cf3bbd363d4920796ce1517ff2 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 16 Oct 2015 16:44:23 -0700 Subject: [PATCH 409/446] add InputLayer for Net input Create an input layer to replace oddball Net `input` fields. --- include/caffe/layers/input_layer.hpp | 44 ++++++++++++++++++++++++++++ src/caffe/layers/input_layer.cpp | 27 +++++++++++++++++ src/caffe/proto/caffe.proto | 13 ++++++-- 3 files changed, 82 insertions(+), 2 deletions(-) create mode 100644 include/caffe/layers/input_layer.hpp create mode 100644 src/caffe/layers/input_layer.cpp diff --git a/include/caffe/layers/input_layer.hpp b/include/caffe/layers/input_layer.hpp new file mode 100644 index 00000000000..f4472678c69 --- /dev/null +++ b/include/caffe/layers/input_layer.hpp @@ -0,0 +1,44 @@ +#ifndef CAFFE_INPUT_LAYER_HPP_ +#define CAFFE_INPUT_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Provides data to the Net by assigning tops directly. + * + * This data layer is a container that merely holds the data assigned to it; + * forward, backward, and reshape are all no-ops. + */ +template +class InputLayer : public Layer { + public: + explicit InputLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + // Data layers should be shared by multiple solvers in parallel + virtual inline bool ShareInParallel() const { return true; } + // Data layers have no bottoms, so reshaping is trivial. + virtual void Reshape(const vector*>& bottom, + const vector*>& top) {} + + virtual inline const char* type() const { return "Input"; } + virtual inline int ExactNumBottomBlobs() const { return 0; } + virtual inline int MinTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top) {} + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) {} +}; + +} // namespace caffe + +#endif // CAFFE_INPUT_LAYER_HPP_ diff --git a/src/caffe/layers/input_layer.cpp b/src/caffe/layers/input_layer.cpp new file mode 100644 index 00000000000..667d8ad67ef --- /dev/null +++ b/src/caffe/layers/input_layer.cpp @@ -0,0 +1,27 @@ +#include + +#include "caffe/layers/input_layer.hpp" + +namespace caffe { + +template +void InputLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + const int num_top = top.size(); + const InputParameter& param = this->layer_param_.input_param(); + const int num_shape = param.shape_size(); + CHECK(num_shape == 0 || num_shape == 1 || num_shape == num_top) + << "Must specify 'shape' once, once per top blob, or not at all: " + << num_top << " tops vs. " << num_shape << " shapes."; + if (num_shape > 0) { + for (int i = 0; i < num_top; ++i) { + const int shape_index = (param.shape_size() == 1) ? 0 : i; + top[i]->Reshape(param.shape(shape_index)); + } + } +} + +INSTANTIATE_CLASS(InputLayer); +REGISTER_LAYER_CLASS(Input); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 7edb6ae87e0..702ce6be112 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -306,7 +306,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 143 (last added: scale_param) +// LayerParameter next available layer-specific ID: 144 (last added: input_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -374,6 +374,7 @@ message LayerParameter { optional ImageDataParameter image_data_param = 115; optional InfogainLossParameter infogain_loss_param = 116; optional InnerProductParameter inner_product_param = 117; + optional InputParameter input_param = 143; optional LogParameter log_param = 134; optional LRNParameter lrn_param = 118; optional MemoryDataParameter memory_data_param = 119; @@ -431,7 +432,7 @@ message LossParameter { // Outputs that receive the ignore label will NOT be ignored in computing // the normalization factor. FULL = 0; - // Divide by the total number of output locations that do not take the + // Divide by the total number of output locations that do not take the // ignore_label. If ignore_label is not set, this behaves like FULL. VALID = 1; // Divide by the batch size. @@ -793,6 +794,14 @@ message InnerProductParameter { optional bool transpose = 6 [default = false]; } +message InputParameter { + // This layer produces N >= 1 top blob(s) to be assigned manually. + // Define N shapes to set a shape for each top. + // Define 1 shape to set the same shape for every top. + // Define no shape to defer to reshaping manually. + repeated BlobShape shape = 1; +} + // Message that stores parameters used by LogLayer message LogParameter { // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0. From bddd04b32c297035b38abbcb41a452bf7167ba17 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 16 Oct 2015 16:44:45 -0700 Subject: [PATCH 410/446] deprecate input fields and upgrade automagically --- include/caffe/util/upgrade_proto.hpp | 6 ++++ src/caffe/proto/caffe.proto | 6 ++-- src/caffe/util/upgrade_proto.cpp | 45 ++++++++++++++++++++++++++++ 3 files changed, 54 insertions(+), 3 deletions(-) diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp index c94bb3caaa3..14e1936a8c2 100644 --- a/include/caffe/util/upgrade_proto.hpp +++ b/include/caffe/util/upgrade_proto.hpp @@ -59,6 +59,12 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param, const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type); +// Return true iff the Net contains input fields. +bool NetNeedsInputUpgrade(const NetParameter& net_param); + +// Perform all necessary transformations to upgrade input fields into layers. +void UpgradeNetInput(NetParameter* net_param); + // Return true iff the solver contains any old solver_type specified as enums bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param); diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 702ce6be112..3b27bbd94d2 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -63,12 +63,12 @@ message FillerParameter { message NetParameter { optional string name = 1; // consider giving the network a name - // The input blobs to the network. + // DEPRECATED. See InputParameter. The input blobs to the network. repeated string input = 3; - // The shape of the input blobs. + // DEPRECATED. See InputParameter. The shape of the input blobs. repeated BlobShape input_shape = 8; - // 4D input dimensions -- deprecated. Use "shape" instead. + // 4D input dimensions -- deprecated. Use "input_shape" instead. // If specified, for each input blob there should be four // values specifying the num, channels, height and width of the input blob. // Thus, there should be a total of (4 * #input) numbers. diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp index ff3f8ffc4f0..449975bd733 100644 --- a/src/caffe/util/upgrade_proto.cpp +++ b/src/caffe/util/upgrade_proto.cpp @@ -60,6 +60,16 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) { << "V1LayerParameter"; } } + // NetParameter uses old style input fields; try to upgrade it. + if (NetNeedsInputUpgrade(*param)) { + LOG(INFO) << "Attempting to upgrade input file specified using deprecated " + << "input fields: " << param_file; + UpgradeNetInput(param); + LOG(INFO) << "Successfully upgraded file specified using deprecated " + << "input fields."; + LOG(WARNING) << "Note that future Caffe releases will only support " + << "input layers and not input fields."; + } return success; } @@ -937,6 +947,41 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) { } } +bool NetNeedsInputUpgrade(const NetParameter& net_param) { + return net_param.input_size() > 0; +} + +void UpgradeNetInput(NetParameter* net_param) { + LayerParameter* layer_param = net_param->add_layer(); + layer_param->set_name("input"); + layer_param->set_type("Input"); + InputParameter* input_param = layer_param->mutable_input_param(); + bool has_shape = net_param->input_shape_size() > 0; + // Convert input fields into a layer. + for (int i = 0; i < net_param->input_size(); ++i) { + layer_param->add_top(net_param->input(i)); + if (has_shape) { + input_param->add_shape()->CopyFrom(net_param->input_shape(i)); + } else { + // Turn legacy input dimensions into shape. + BlobShape* shape = input_param->add_shape(); + int first_dim = i*4; + int last_dim = first_dim + 4; + for (int j = first_dim; j < last_dim; j++) { + shape->add_dim(net_param->input_dim(j)); + } + } + } + // Swap input layer to beginning of net to satisfy layer dependencies. + for (int i = net_param->layer_size() - 1; i > 0; --i) { + net_param->mutable_layer(i-1)->Swap(net_param->mutable_layer(i)); + } + // Clear inputs. + net_param->clear_input(); + net_param->clear_input_shape(); + net_param->clear_input_dim(); +} + // Return true iff the solver contains any old solver_type specified as enums bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param) { if (solver_param.has_solver_type()) { From 51f79a837ddea002746d86d69e342a44f099654f Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 16 Oct 2015 21:11:32 -0700 Subject: [PATCH 411/446] drop Net inputs + Forward with bottoms Drop special cases for `input` fields, the `Net` input members, and the `Net` interface for Forward with bottoms along with Forward() / ForwardPrefilled() distinction. --- include/caffe/net.hpp | 34 ++---- src/caffe/net.cpp | 104 ++---------------- src/caffe/solver.cpp | 8 +- src/caffe/test/test_gradient_based_solver.cpp | 6 +- src/caffe/test/test_net.cpp | 78 ++++++------- src/caffe/test/test_split_layer.cpp | 61 ---------- src/caffe/util/insert_splits.cpp | 21 +--- tools/caffe.cpp | 5 +- tools/extract_features.cpp | 3 +- 9 files changed, 61 insertions(+), 259 deletions(-) diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index 543133e2517..43e0a845133 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -32,11 +32,10 @@ class Net { void Init(const NetParameter& param); /** - * @brief Run Forward with the input Blob%s already fed separately. + * @brief Run Forward and return the result. * - * You can get the input blobs using input_blobs(). */ - const vector*>& ForwardPrefilled(Dtype* loss = NULL); + const vector*>& Forward(Dtype* loss = NULL); /** * The From and To variants of Forward and Backward operate on the @@ -49,14 +48,6 @@ class Net { Dtype ForwardFromTo(int start, int end); Dtype ForwardFrom(int start); Dtype ForwardTo(int end); - /// @brief Run forward using a set of bottom blobs, and return the result. - const vector*>& Forward(const vector* > & bottom, - Dtype* loss = NULL); - /** - * @brief Run forward using a serialized BlobProtoVector and return the - * result as a serialized BlobProtoVector - */ - string Forward(const string& input_blob_protos, Dtype* loss = NULL); /** * @brief Zeroes out the diffs of all net parameters. @@ -82,9 +73,9 @@ class Net { */ void Reshape(); - Dtype ForwardBackward(const vector* > & bottom) { + Dtype ForwardBackward() { Dtype loss; - Forward(bottom, &loss); + Forward(&loss); Backward(); return loss; } @@ -194,18 +185,11 @@ class Net { inline const vector& param_display_names() const { return param_display_names_; } - /// @brief Input and output blob numbers - inline int num_inputs() const { return net_input_blobs_.size(); } + /// @brief output blob number inline int num_outputs() const { return net_output_blobs_.size(); } - inline const vector*>& input_blobs() const { - return net_input_blobs_; - } inline const vector*>& output_blobs() const { return net_output_blobs_; } - inline const vector& input_blob_indices() const { - return net_input_blob_indices_; - } inline const vector& output_blob_indices() const { return net_output_blob_indices_; } @@ -229,7 +213,7 @@ class Net { protected: // Helpers for Init. - /// @brief Append a new input or top blob to the net. + /// @brief Append a new top blob to the net. void AppendTop(const NetParameter& param, const int layer_id, const int top_id, set* available_blobs, map* blob_name_to_idx); @@ -241,8 +225,6 @@ class Net { void AppendParam(const NetParameter& param, const int layer_id, const int param_id); - /// @brief Helper for displaying debug info in Forward about input Blobs. - void InputDebugInfo(const int layer_id); /// @brief Helper for displaying debug info in Forward. void ForwardDebugInfo(const int layer_id); /// @brief Helper for displaying debug info in Backward. @@ -281,10 +263,8 @@ class Net { vector param_display_names_; vector > param_layer_indices_; map param_names_index_; - /// blob indices for the input and the output of the net - vector net_input_blob_indices_; + /// blob indices for the output of the net vector net_output_blob_indices_; - vector*> net_input_blobs_; vector*> net_output_blobs_; /// The parameters in the network. vector > > params_; diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 05bee7987da..b7320e95a86 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -56,22 +56,7 @@ void Net::Init(const NetParameter& in_param) { name_ = param.name(); map blob_name_to_idx; set available_blobs; - CHECK(param.input_dim_size() == 0 || param.input_shape_size() == 0) - << "Must specify either input_shape OR deprecated input_dim, not both."; - if (param.input_dim_size() > 0) { - // Deprecated 4D dimensions. - CHECK_EQ(param.input_size() * 4, param.input_dim_size()) - << "Incorrect input blob dimension specifications."; - } else { - CHECK_EQ(param.input_size(), param.input_shape_size()) - << "Exactly one input_shape must be specified per input."; - } memory_used_ = 0; - // set the input blobs - for (int input_id = 0; input_id < param.input_size(); ++input_id) { - const int layer_id = -1; // inputs have fake layer ID -1 - AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx); - } // For each layer, set up its input and output bottom_vecs_.resize(param.layer_size()); top_vecs_.resize(param.layer_size()); @@ -379,19 +364,17 @@ bool Net::StateMeetsRule(const NetState& state, return true; } -// Helper for Net::Init: add a new input or top blob to the net. (Inputs have -// layer_id == -1, tops have layer_id >= 0.) +// Helper for Net::Init: add a new top blob to the net. template void Net::AppendTop(const NetParameter& param, const int layer_id, const int top_id, set* available_blobs, map* blob_name_to_idx) { - shared_ptr layer_param((layer_id >= 0) ? - (new LayerParameter(param.layer(layer_id))) : NULL); - const string& blob_name = layer_param ? - (layer_param->top_size() > top_id ? - layer_param->top(top_id) : "(automatic)") : param.input(top_id); + shared_ptr layer_param( + new LayerParameter(param.layer(layer_id))); + const string& blob_name = (layer_param->top_size() > top_id) ? + layer_param->top(top_id) : "(automatic)"; // Check if we are doing in-place computation - if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id && + if (blob_name_to_idx && layer_param->bottom_size() > top_id && blob_name == layer_param->bottom(top_id)) { // In-place computation LOG_IF(INFO, Caffe::root_solver()) @@ -407,11 +390,7 @@ void Net::AppendTop(const NetParameter& param, const int layer_id, } else { // Normal output. if (Caffe::root_solver()) { - if (layer_param) { - LOG(INFO) << layer_param->name() << " -> " << blob_name; - } else { - LOG(INFO) << "Input " << top_id << " -> " << blob_name; - } + LOG(INFO) << layer_param->name() << " -> " << blob_name; } shared_ptr > blob_pointer(new Blob()); const int blob_id = blobs_.size(); @@ -419,22 +398,8 @@ void Net::AppendTop(const NetParameter& param, const int layer_id, blob_names_.push_back(blob_name); blob_need_backward_.push_back(false); if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; } - if (layer_id == -1) { - // Set the (explicitly specified) dimensions of the input blob. - if (param.input_dim_size() > 0) { - blob_pointer->Reshape(param.input_dim(top_id * 4), - param.input_dim(top_id * 4 + 1), - param.input_dim(top_id * 4 + 2), - param.input_dim(top_id * 4 + 3)); - } else { - blob_pointer->Reshape(param.input_shape(top_id)); - } - net_input_blob_indices_.push_back(blob_id); - net_input_blobs_.push_back(blob_pointer.get()); - } else { - top_id_vecs_[layer_id].push_back(blob_id); - top_vecs_[layer_id].push_back(blob_pointer.get()); - } + top_id_vecs_[layer_id].push_back(blob_id); + top_vecs_[layer_id].push_back(blob_pointer.get()); } if (available_blobs) { available_blobs->insert(blob_name); } } @@ -566,11 +531,6 @@ Dtype Net::ForwardFromTo(int start, int end) { CHECK_GE(start, 0); CHECK_LT(end, layers_.size()); Dtype loss = 0; - if (debug_info_) { - for (int i = 0; i < net_input_blobs_.size(); ++i) { - InputDebugInfo(i); - } - } for (int i = start; i <= end; ++i) { // LOG(ERROR) << "Forwarding " << layer_names_[i]; Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]); @@ -591,7 +551,7 @@ Dtype Net::ForwardTo(int end) { } template -const vector*>& Net::ForwardPrefilled(Dtype* loss) { +const vector*>& Net::Forward(Dtype* loss) { if (loss != NULL) { *loss = ForwardFromTo(0, layers_.size() - 1); } else { @@ -600,37 +560,6 @@ const vector*>& Net::ForwardPrefilled(Dtype* loss) { return net_output_blobs_; } -template -const vector*>& Net::Forward( - const vector*> & bottom, Dtype* loss) { - // Copy bottom to internal bottom - for (int i = 0; i < bottom.size(); ++i) { - net_input_blobs_[i]->CopyFrom(*bottom[i]); - } - return ForwardPrefilled(loss); -} - -template -string Net::Forward(const string& input_blob_protos, Dtype* loss) { - BlobProtoVector blob_proto_vec; - if (net_input_blobs_.size()) { - blob_proto_vec.ParseFromString(input_blob_protos); - CHECK_EQ(blob_proto_vec.blobs_size(), net_input_blobs_.size()) - << "Incorrect input size."; - for (int i = 0; i < blob_proto_vec.blobs_size(); ++i) { - net_input_blobs_[i]->FromProto(blob_proto_vec.blobs(i)); - } - } - ForwardPrefilled(loss); - blob_proto_vec.Clear(); - for (int i = 0; i < net_output_blobs_.size(); ++i) { - net_output_blobs_[i]->ToProto(blob_proto_vec.add_blobs()); - } - string output; - blob_proto_vec.SerializeToString(&output); - return output; -} - template void Net::BackwardFromTo(int start, int end) { CHECK_GE(end, 0); @@ -644,16 +573,6 @@ void Net::BackwardFromTo(int start, int end) { } } -template -void Net::InputDebugInfo(const int input_id) { - const Blob& blob = *net_input_blobs_[input_id]; - const string& blob_name = blob_names_[net_input_blob_indices_[input_id]]; - const Dtype data_abs_val_mean = blob.asum_data() / blob.count(); - LOG_IF(INFO, Caffe::root_solver()) - << " [Forward] " - << "Input " << blob_name << " data: " << data_abs_val_mean; -} - template void Net::ForwardDebugInfo(const int layer_id) { for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) { @@ -912,9 +831,6 @@ void Net::ToProto(NetParameter* param, bool write_diff) const { param->Clear(); param->set_name(name_); // Add bottom and top - for (int i = 0; i < net_input_blob_indices_.size(); ++i) { - param->add_input(blob_names_[net_input_blob_indices_[i]]); - } DLOG(INFO) << "Serializing " << layers_.size() << " layers"; for (int i = 0; i < layers_.size(); ++i) { LayerParameter* layer_param = param->add_layer(); diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index a5ccf9c71b1..ece3913e88a 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -192,7 +192,6 @@ void Solver::InitTestNets() { template void Solver::Step(int iters) { - vector*> bottom_vec; const int start_iter = iter_; const int stop_iter = iter_ + iters; int average_loss = this->param_.average_loss(); @@ -220,7 +219,7 @@ void Solver::Step(int iters) { // accumulate the loss and gradient Dtype loss = 0; for (int i = 0; i < param_.iter_size(); ++i) { - loss += net_->ForwardBackward(bottom_vec); + loss += net_->ForwardBackward(); } loss /= param_.iter_size(); // average the loss across iterations for smoothed reporting @@ -311,7 +310,7 @@ void Solver::Solve(const char* resume_file) { if (param_.display() && iter_ % param_.display() == 0) { int average_loss = this->param_.average_loss(); Dtype loss; - net_->ForwardPrefilled(&loss); + net_->Forward(&loss); UpdateSmoothedLoss(loss, start_iter, average_loss); @@ -341,7 +340,6 @@ void Solver::Test(const int test_net_id) { ShareTrainedLayersWith(net_.get()); vector test_score; vector test_score_output_id; - vector*> bottom_vec; const shared_ptr >& test_net = test_nets_[test_net_id]; Dtype loss = 0; for (int i = 0; i < param_.test_iter(test_net_id); ++i) { @@ -362,7 +360,7 @@ void Solver::Test(const int test_net_id) { Dtype iter_loss; const vector*>& result = - test_net->Forward(bottom_vec, &iter_loss); + test_net->Forward(&iter_loss); if (param_.test_compute_loss()) { loss += iter_loss; } diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 84c6747f61a..09ec3a7e918 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -185,9 +185,8 @@ class GradientBasedSolverTest : public MultiDeviceTest { this->InitSolverFromProtoString(proto.str()); if (from_snapshot != NULL) { this->solver_->Restore(from_snapshot); - vector*> empty_bottom_vec; for (int i = 0; i < this->solver_->iter(); ++i) { - this->solver_->net()->Forward(empty_bottom_vec); + this->solver_->net()->Forward(); } } if (devices == 1) { @@ -231,8 +230,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { // Run a forward pass, and manually compute the update values from the // result. Net& net = *this->solver_->net(); - vector*> empty_bottom_vec; - net.Forward(empty_bottom_vec); + net.Forward(); ASSERT_TRUE(net.has_blob("data")); const Blob& data = *net.blob_by_name("data"); ASSERT_TRUE(net.has_blob("targets")); diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp index ab4afba1a93..1e0788ec127 100644 --- a/src/caffe/test/test_net.cpp +++ b/src/caffe/test/test_net.cpp @@ -555,11 +555,14 @@ class NetTest : public MultiDeviceTest { virtual void InitReshapableNet() { const string& proto = "name: 'ReshapableNetwork' " - "input: 'data' " - "input_dim: 1 " - "input_dim: 3 " - "input_dim: 100 " - "input_dim: 100 " + "layer { " + " name: 'data' " + " type: 'Input' " + " top: 'data' " + " input_param { " + " shape: { dim: 1 dim: 3 dim: 100 dim: 100 } " + " } " + "} " "layer { " " name: 'conv1' " " type: 'Convolution' " @@ -821,7 +824,7 @@ TYPED_TEST(NetTest, TestLossWeight) { Caffe::set_random_seed(this->seed_); const bool kForceBackward = true; this->InitUnsharedWeightsNet(NULL, NULL, kForceBackward); - const Dtype loss = this->net_->ForwardBackward(bottom); + const Dtype loss = this->net_->ForwardBackward(); const bool kCopyDiff = true; vector > > blob_grads; this->CopyNetBlobs(kCopyDiff, &blob_grads); @@ -836,7 +839,7 @@ TYPED_TEST(NetTest, TestLossWeight) { for (int i = 0; i < kNumLossWeights; ++i) { Caffe::set_random_seed(this->seed_); this->InitUnsharedWeightsNet(&kLossWeights[i], NULL, kForceBackward); - const Dtype weighted_loss = this->net_->ForwardBackward(bottom); + const Dtype weighted_loss = this->net_->ForwardBackward(); const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]); EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin) << "loss weight = " << kLossWeights[i]; @@ -865,14 +868,13 @@ TYPED_TEST(NetTest, TestLossWeight) { TYPED_TEST(NetTest, TestLossWeightMidNet) { typedef typename TypeParam::Dtype Dtype; - vector*> bottom; Caffe::set_random_seed(this->seed_); const bool kForceBackward = true; Dtype loss_weight = 0; Dtype midnet_loss_weight = 1; this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight, kForceBackward); - const Dtype loss = this->net_->ForwardBackward(bottom); + const Dtype loss = this->net_->ForwardBackward(); const bool kCopyDiff = true; const bool kReshape = true; Blob data_grad; @@ -887,7 +889,7 @@ TYPED_TEST(NetTest, TestLossWeightMidNet) { Caffe::set_random_seed(this->seed_); this->InitUnsharedWeightsNet(&loss_weight, &kLossWeights[i], kForceBackward); - const Dtype weighted_loss = this->net_->ForwardBackward(bottom); + const Dtype weighted_loss = this->net_->ForwardBackward(); const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]); EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin) << "loss weight = " << kLossWeights[i]; @@ -903,7 +905,6 @@ TYPED_TEST(NetTest, TestLossWeightMidNet) { TYPED_TEST(NetTest, TestComboLossWeight) { typedef typename TypeParam::Dtype Dtype; - vector*> bottom; Dtype loss_weight; Dtype midnet_loss_weight; const bool kForceBackward = true; @@ -916,7 +917,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) { Caffe::set_random_seed(this->seed_); this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight, kForceBackward); - const Dtype loss = this->net_->ForwardBackward(bottom); + const Dtype loss = this->net_->ForwardBackward(); const bool kCopyDiff = true; vector > > blob_grads; this->CopyNetBlobs(kCopyDiff, &blob_grads); @@ -928,7 +929,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) { Caffe::set_random_seed(this->seed_); this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight, kForceBackward); - const Dtype loss_main_2 = this->net_->ForwardBackward(bottom); + const Dtype loss_main_2 = this->net_->ForwardBackward(); vector > > blob_grads_loss_2; this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2); vector > > param_grads_loss_2; @@ -939,7 +940,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) { Caffe::set_random_seed(this->seed_); this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight, kForceBackward); - const Dtype loss_main_3 = this->net_->ForwardBackward(bottom); + const Dtype loss_main_3 = this->net_->ForwardBackward(); const vector > >& blob_grads_loss_3 = this->net_->blobs(); ASSERT_EQ(blob_grads.size(), blob_grads_loss_3.size()); @@ -974,7 +975,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) { Caffe::set_random_seed(this->seed_); this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight, kForceBackward); - const Dtype loss_midnet_2 = this->net_->ForwardBackward(bottom); + const Dtype loss_midnet_2 = this->net_->ForwardBackward(); this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2); this->CopyNetParams(kCopyDiff, ¶m_grads_loss_2); @@ -983,7 +984,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) { Caffe::set_random_seed(this->seed_); this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight, kForceBackward); - const Dtype loss_midnet_3 = this->net_->ForwardBackward(bottom); + const Dtype loss_midnet_3 = this->net_->ForwardBackward(); const vector > >& blob_grads_midnet_loss_3 = this->net_->blobs(); ASSERT_EQ(blob_grads.size(), blob_grads_midnet_loss_3.size()); @@ -1032,40 +1033,35 @@ TYPED_TEST(NetTest, TestComboLossWeight) { } TYPED_TEST(NetTest, TestBackwardWithAccuracyLayer) { - typedef typename TypeParam::Dtype Dtype; const bool kForceBackward = false; const bool kAccuracyLayer = true; this->InitTinyNet(kForceBackward, kAccuracyLayer); EXPECT_TRUE(this->net_->has_blob("accuracy")); - vector*> bottom; // Test that we can do Backward even though we have an 'Accuracy' layer. - this->net_->ForwardBackward(bottom); + this->net_->ForwardBackward(); } TYPED_TEST(NetTest, TestUnsharedWeightsDataNet) { typedef typename TypeParam::Dtype Dtype; this->InitUnsharedWeightsNet(); - vector*> bottom; Dtype loss; - this->net_->Forward(bottom, &loss); + this->net_->Forward(&loss); EXPECT_GT(loss, 0); } TYPED_TEST(NetTest, TestSharedWeightsDataNet) { typedef typename TypeParam::Dtype Dtype; this->InitSharedWeightsNet(); - vector*> bottom; Dtype loss; - this->net_->Forward(bottom, &loss); + this->net_->Forward(&loss); EXPECT_FLOAT_EQ(loss, 0); } TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) { typedef typename TypeParam::Dtype Dtype; this->InitUnsharedWeightsNet(); - vector*> bottom; Net* net = this->net_.get(); - net->Forward(bottom); + net->Forward(); net->Backward(); Layer* ip1_layer = net->layer_by_name("innerproduct1").get(); Layer* ip2_layer = net->layer_by_name("innerproduct2").get(); @@ -1081,10 +1077,9 @@ TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) { TYPED_TEST(NetTest, TestSharedWeightsDiffNet) { typedef typename TypeParam::Dtype Dtype; this->InitSharedWeightsNet(); - vector*> bottom; Net* net = this->net_.get(); Dtype loss; - net->Forward(bottom, &loss); + net->Forward(&loss); net->Backward(); EXPECT_FLOAT_EQ(loss, 0); Layer* ip1_layer = net->layer_by_name("innerproduct1").get(); @@ -1102,7 +1097,6 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) { typedef typename TypeParam::Dtype Dtype; Caffe::set_random_seed(this->seed_); this->InitDiffDataSharedWeightsNet(); - vector*> bottom; EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1"); EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2"); Blob* ip1_weights = this->net_->layers()[1]->blobs()[0].get(); @@ -1111,7 +1105,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) { // locations. EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data()); EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff()); - this->net_->Forward(bottom); + this->net_->Forward(); this->net_->Backward(); // Compute the expected update as the data minus the two diffs. Blob shared_params; @@ -1146,7 +1140,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) { // locations in memory. EXPECT_NE(ip1_weights->cpu_data(), ip2_weights->cpu_data()); EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff()); - this->net_->Forward(bottom); + this->net_->Forward(); this->net_->Backward(); // Compute the expected update. Blob unshared_params1; @@ -1186,7 +1180,6 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) { // Create a net with weight sharing; Update it once. Caffe::set_random_seed(this->seed_); this->InitDiffDataSharedWeightsNet(); - vector*> bottom; EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1"); EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2"); Blob* ip1_weights = this->net_->layers()[1]->blobs()[0].get(); @@ -1195,7 +1188,7 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) { // locations. EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data()); EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff()); - this->net_->ForwardBackward(bottom); + this->net_->ForwardBackward(); this->net_->Update(); Blob shared_params; const bool kReshape = true; @@ -1228,7 +1221,6 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) { TYPED_TEST(NetTest, TestParamPropagateDown) { typedef typename TypeParam::Dtype Dtype; - vector*> bottom; const bool kBiasTerm = true, kForceBackward = false; const Dtype* kLossWeight1 = NULL; const Dtype* kLossWeight2 = NULL; @@ -1238,7 +1230,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) { Dtype blobs_lr_w1 = 1, blobs_lr_w2 = 1, blobs_lr_b1 = 2, blobs_lr_b2 = 2; this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward, kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2); - this->net_->Forward(bottom); + this->net_->Forward(); this->net_->Backward(); const vector > >& params = this->net_->params(); const int num_params = params.size(); @@ -1258,7 +1250,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) { blobs_lr_w1 *= 2, blobs_lr_w2 *= 2, blobs_lr_b1 *= 2, blobs_lr_b2 *= 2; this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward, kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2); - this->net_->Forward(bottom); + this->net_->Forward(); this->net_->Backward(); const vector > >& params2 = this->net_->params(); ASSERT_EQ(num_params, params2.size()); @@ -1274,7 +1266,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) { blobs_lr_w1 = 1, blobs_lr_w2 = 0, blobs_lr_b1 = 0, blobs_lr_b2 = 1; this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward, kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2); - this->net_->Forward(bottom); + this->net_->Forward(); this->net_->Backward(); const vector > >& params3 = this->net_->params(); ASSERT_EQ(num_params, params3.size()); @@ -1293,7 +1285,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) { blobs_lr_w1 = 0, blobs_lr_w2 = 1, blobs_lr_b1 = 1, blobs_lr_b2 = 0; this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward, kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2); - this->net_->Forward(bottom); + this->net_->Forward(); this->net_->Backward(); const vector > >& params4 = this->net_->params(); ASSERT_EQ(num_params, params4.size()); @@ -1315,7 +1307,7 @@ TYPED_TEST(NetTest, TestFromTo) { // Run Forward and Backward, recording the data diff and loss. Blob data; data.ReshapeLike(*this->net_->blob_by_name("data")); - this->net_->ForwardPrefilled(); + this->net_->Forward(); this->net_->Backward(); data.CopyFrom(*this->net_->blob_by_name("data"), true, true); const Dtype *loss_ptr = this->net_->output_blobs()[0]->cpu_data(); @@ -2277,12 +2269,12 @@ TYPED_TEST(NetTest, TestReshape) { filler.Fill(&blob2); this->InitReshapableNet(); - Blob* input_blob = this->net_->input_blobs()[0]; + shared_ptr > input_blob = this->net_->blob_by_name("data"); Blob* output_blob = this->net_->output_blobs()[0]; input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(), blob1.width()); caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data()); - this->net_->ForwardPrefilled(); + this->net_->Forward(); // call backward just to make sure it runs this->net_->Backward(); Blob output1(output_blob->num(), output_blob->channels(), @@ -2293,7 +2285,7 @@ TYPED_TEST(NetTest, TestReshape) { input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(), blob2.width()); caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data()); - this->net_->ForwardPrefilled(); + this->net_->Forward(); this->net_->Backward(); Blob output2(output_blob->num(), output_blob->channels(), output_blob->height(), output_blob->width()); @@ -2303,7 +2295,7 @@ TYPED_TEST(NetTest, TestReshape) { input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(), blob1.width()); caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data()); - this->net_->ForwardPrefilled(); + this->net_->Forward(); this->net_->Backward(); for (int i = 0; i < output1.count(); ++i) { EXPECT_FLOAT_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i)); @@ -2312,7 +2304,7 @@ TYPED_TEST(NetTest, TestReshape) { input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(), blob2.width()); caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data()); - this->net_->ForwardPrefilled(); + this->net_->Forward(); this->net_->Backward(); for (int i = 0; i < output2.count(); ++i) { EXPECT_FLOAT_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i)); diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp index ba2ccbb2b18..007142126ea 100644 --- a/src/caffe/test/test_split_layer.cpp +++ b/src/caffe/test/test_split_layer.cpp @@ -886,67 +886,6 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) { this->RunInsertionTest(input_proto, expected_output_proto); } -TEST_F(SplitLayerInsertionTest, TestInputInsertion) { - const string& input_proto = - "name: 'TestNetwork' " - "input: 'data' " - "input_dim: 10 " - "input_dim: 3 " - "input_dim: 227 " - "input_dim: 227 " - "layer { " - " name: 'innerprod1' " - " type: 'InnerProduct' " - " bottom: 'data' " - " top: 'innerprod1' " - "} " - "layer { " - " name: 'innerprod2' " - " type: 'InnerProduct' " - " bottom: 'data' " - " top: 'innerprod2' " - "} " - "layer { " - " name: 'loss' " - " type: 'EuclideanLoss' " - " bottom: 'innerprod1' " - " bottom: 'innerprod2' " - "} "; - const string& expected_output_proto = - "name: 'TestNetwork' " - "input: 'data' " - "input_dim: 10 " - "input_dim: 3 " - "input_dim: 227 " - "input_dim: 227 " - "layer { " - " name: 'data_input_0_split' " - " type: 'Split' " - " bottom: 'data' " - " top: 'data_input_0_split_0' " - " top: 'data_input_0_split_1' " - "} " - "layer { " - " name: 'innerprod1' " - " type: 'InnerProduct' " - " bottom: 'data_input_0_split_0' " - " top: 'innerprod1' " - "} " - "layer { " - " name: 'innerprod2' " - " type: 'InnerProduct' " - " bottom: 'data_input_0_split_1' " - " top: 'innerprod2' " - "} " - "layer { " - " name: 'loss' " - " type: 'EuclideanLoss' " - " bottom: 'innerprod1' " - " bottom: 'innerprod2' " - "} "; - this->RunInsertionTest(input_proto, expected_output_proto); -} - TEST_F(SplitLayerInsertionTest, TestWithInPlace) { const string& input_proto = "name: 'TestNetwork' " diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp index 475a2a9f618..7a899c69787 100644 --- a/src/caffe/util/insert_splits.cpp +++ b/src/caffe/util/insert_splits.cpp @@ -19,12 +19,6 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) { map, float> top_idx_to_loss_weight; map, int> top_idx_to_bottom_split_idx; map layer_idx_to_layer_name; - layer_idx_to_layer_name[-1] = "input"; - // Determine the number of times each blob is used as an input (bottom) blob. - for (int i = 0; i < param.input_size(); ++i) { - const string& blob_name = param.input(i); - blob_name_to_last_top_idx[blob_name] = make_pair(-1, i); - } for (int i = 0; i < param.layer_size(); ++i) { const LayerParameter& layer_param = param.layer(i); layer_idx_to_layer_name[i] = layer_param.name(); @@ -45,7 +39,7 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) { blob_name_to_last_top_idx[blob_name] = make_pair(i, j); } // A use of a top blob as a loss should be handled similarly to the use of - // a top blob as an input (bottom) blob to another layer. + // a top blob as a bottom blob to another layer. const int last_loss = std::min(layer_param.loss_weight_size(), layer_param.top_size()); for (int j = 0; j < last_loss; ++j) { @@ -57,19 +51,6 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) { } } } - // Create split layer for any input blobs used by other layer as bottom - // blobs more than once. - for (int i = 0; i < param.input_size(); ++i) { - const int split_count = top_idx_to_bottom_count[make_pair(-1, i)]; - if (split_count > 1) { - const string& layer_name = layer_idx_to_layer_name[-1]; - const string& blob_name = param.input(i); - LayerParameter* split_layer_param = param_split->add_layer(); - const float kZeroLossWeight = 0; - ConfigureSplitLayer(layer_name, blob_name, i, split_count, - kZeroLossWeight, split_layer_param); - } - } for (int i = 0; i < param.layer_size(); ++i) { LayerParameter* layer_param = param_split->add_layer(); layer_param->CopyFrom(param.layer(i)); diff --git a/tools/caffe.cpp b/tools/caffe.cpp index ebe95d61ef1..95b2f82c4be 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -251,14 +251,13 @@ int test() { caffe_net.CopyTrainedLayersFrom(FLAGS_weights); LOG(INFO) << "Running for " << FLAGS_iterations << " iterations."; - vector* > bottom_vec; vector test_score_output_id; vector test_score; float loss = 0; for (int i = 0; i < FLAGS_iterations; ++i) { float iter_loss; const vector*>& result = - caffe_net.Forward(bottom_vec, &iter_loss); + caffe_net.Forward(&iter_loss); loss += iter_loss; int idx = 0; for (int j = 0; j < result.size(); ++j) { @@ -322,7 +321,7 @@ int time() { // Note that for the speed benchmark, we will assume that the network does // not take any input blobs. float initial_loss; - caffe_net.Forward(vector*>(), &initial_loss); + caffe_net.Forward(&initial_loss); LOG(INFO) << "Initial loss: " << initial_loss; LOG(INFO) << "Performing Backward"; caffe_net.Backward(); diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp index d6562f98059..704467250a6 100644 --- a/tools/extract_features.cpp +++ b/tools/extract_features.cpp @@ -133,10 +133,9 @@ int feature_extraction_pipeline(int argc, char** argv) { LOG(ERROR)<< "Extacting Features"; Datum datum; - std::vector*> input_vec; std::vector image_indices(num_features, 0); for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) { - feature_extraction_net->Forward(input_vec); + feature_extraction_net->Forward(); for (int i = 0; i < num_features; ++i) { const boost::shared_ptr > feature_blob = feature_extraction_net->blob_by_name(blob_names[i]); From 0d9a78f5a083db859f01d45da91c5a0ca1389de8 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Wed, 2 Dec 2015 18:31:26 -0800 Subject: [PATCH 412/446] collect Net inputs from Input layers Restore the list of net inputs for compatibility with the pycaffe and matcaffe interfaces and downstream C++. --- include/caffe/net.hpp | 13 +++++++++++-- src/caffe/net.cpp | 6 ++++++ 2 files changed, 17 insertions(+), 2 deletions(-) diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index 43e0a845133..1c2a19126d5 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -185,11 +185,18 @@ class Net { inline const vector& param_display_names() const { return param_display_names_; } - /// @brief output blob number + /// @brief Input and output blob numbers + inline int num_inputs() const { return net_input_blobs_.size(); } inline int num_outputs() const { return net_output_blobs_.size(); } + inline const vector*>& input_blobs() const { + return net_input_blobs_; + } inline const vector*>& output_blobs() const { return net_output_blobs_; } + inline const vector& input_blob_indices() const { + return net_input_blob_indices_; + } inline const vector& output_blob_indices() const { return net_output_blob_indices_; } @@ -263,8 +270,10 @@ class Net { vector param_display_names_; vector > param_layer_indices_; map param_names_index_; - /// blob indices for the output of the net + /// blob indices for the input and the output of the net + vector net_input_blob_indices_; vector net_output_blob_indices_; + vector*> net_input_blobs_; vector*> net_output_blobs_; /// The parameters in the network. vector > > params_; diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index b7320e95a86..c1760ea1970 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -103,6 +103,12 @@ void Net::Init(const NetParameter& in_param) { int num_top = layer_param.top_size(); for (int top_id = 0; top_id < num_top; ++top_id) { AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx); + // Collect Input layer tops as Net inputs. + if (layer_param.type() == "Input") { + const int blob_id = blobs_.size() - 1; + net_input_blob_indices_.push_back(blob_id); + net_input_blobs_.push_back(blobs_[blob_id].get()); + } } // If the layer specifies that AutoTopBlobs() -> true and the LayerParameter // specified fewer than the required number (as specified by From 2cc3844cb2a4a72de10d321781dc8f994bef95c7 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 3 Dec 2015 19:39:19 -0800 Subject: [PATCH 413/446] [examples] switch examples + models to Input layers --- examples/cifar10/cifar10_full.prototxt | 11 +++-- examples/cifar10/cifar10_quick.prototxt | 11 +++-- .../cpp_classification/classification.cpp | 2 +- examples/mnist/lenet.prototxt | 11 +++-- examples/net_surgery.ipynb | 43 +++++++++---------- .../bvlc_caffenet_full_conv.prototxt | 15 ++++--- examples/net_surgery/conv.prototxt | 11 +++-- examples/siamese/mnist_siamese.prototxt | 13 +++--- models/bvlc_alexnet/deploy.prototxt | 11 +++-- models/bvlc_googlenet/deploy.prototxt | 11 +++-- .../bvlc_reference_caffenet/deploy.prototxt | 11 +++-- .../deploy.prototxt | 11 +++-- models/finetune_flickr_style/deploy.prototxt | 11 +++-- 13 files changed, 83 insertions(+), 89 deletions(-) diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt index 446479da961..83cf0d86b35 100644 --- a/examples/cifar10/cifar10_full.prototxt +++ b/examples/cifar10/cifar10_full.prototxt @@ -1,12 +1,11 @@ name: "CIFAR10_full_deploy" # N.B. input image must be in CIFAR-10 format # as described at http://www.cs.toronto.edu/~kriz/cifar.html -input: "data" -input_shape { - dim: 1 - dim: 3 - dim: 32 - dim: 32 +layer { + name: "data" + type: "Input" + top: "data" + input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } } } layer { name: "conv1" diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt index 9352fbf65df..cf3b2a358be 100644 --- a/examples/cifar10/cifar10_quick.prototxt +++ b/examples/cifar10/cifar10_quick.prototxt @@ -1,10 +1,9 @@ name: "CIFAR10_quick_test" -input: "data" -input_shape { - dim: 1 - dim: 3 - dim: 32 - dim: 32 +layer { + name: "data" + type: "Input" + top: "data" + input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } } } layer { name: "conv1" diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp index 974662e59da..6b67c537a47 100644 --- a/examples/cpp_classification/classification.cpp +++ b/examples/cpp_classification/classification.cpp @@ -159,7 +159,7 @@ std::vector Classifier::Predict(const cv::Mat& img) { Preprocess(img, &input_channels); - net_->ForwardPrefilled(); + net_->Forward(); /* Copy the output layer to a std::vector */ Blob* output_layer = net_->output_blobs()[0]; diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt index dff7123bf73..8cf78e62c89 100644 --- a/examples/mnist/lenet.prototxt +++ b/examples/mnist/lenet.prototxt @@ -1,10 +1,9 @@ name: "LeNet" -input: "data" -input_shape { - dim: 64 - dim: 1 - dim: 28 - dim: 28 +layer { + name: "data" + type: "Input" + top: "data" + input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } } } layer { name: "conv1" diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb index ff780fbb9f7..a6092db0c40 100644 --- a/examples/net_surgery.ipynb +++ b/examples/net_surgery.ipynb @@ -5494,48 +5494,47 @@ "name": "stdout", "output_type": "stream", "text": [ - "1,2c1\r\n", + "1,2c1,2\r\n", "< # Fully convolutional network version of CaffeNet.\r\n", "< name: \"CaffeNetConv\"\r\n", "---\r\n", "> name: \"CaffeNet\"\r\n", - "4c3\r\n", - "< input_dim: 1\r\n", + "> input: \"data\"\r\n", + "7,11c7\r\n", + "< input_param {\r\n", + "< # initial shape for a fully convolutional network:\r\n", + "< # the shape can be set for each input by reshape.\r\n", + "< shape: { dim: 1 dim: 3 dim: 451 dim: 451 }\r\n", + "< }\r\n", "---\r\n", - "> input_dim: 10\r\n", - "6,7c5,6\r\n", - "< input_dim: 451\r\n", - "< input_dim: 451\r\n", - "---\r\n", - "> input_dim: 227\r\n", - "> input_dim: 227\r\n", - "152,153c151,152\r\n", + "> input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }\r\n", + "157,158c153,154\r\n", "< name: \"fc6-conv\"\r\n", "< type: \"Convolution\"\r\n", "---\r\n", "> name: \"fc6\"\r\n", "> type: \"InnerProduct\"\r\n", - "155,156c154,155\r\n", + "160,161c156,157\r\n", "< top: \"fc6-conv\"\r\n", "< convolution_param {\r\n", "---\r\n", "> top: \"fc6\"\r\n", "> inner_product_param {\r\n", - "158d156\r\n", + "163d158\r\n", "< kernel_size: 6\r\n", - "164,165c162,163\r\n", + "169,170c164,165\r\n", "< bottom: \"fc6-conv\"\r\n", "< top: \"fc6-conv\"\r\n", "---\r\n", "> bottom: \"fc6\"\r\n", "> top: \"fc6\"\r\n", - "170,171c168,169\r\n", + "175,176c170,171\r\n", "< bottom: \"fc6-conv\"\r\n", "< top: \"fc6-conv\"\r\n", "---\r\n", "> bottom: \"fc6\"\r\n", "> top: \"fc6\"\r\n", - "177,181c175,179\r\n", + "182,186c177,181\r\n", "< name: \"fc7-conv\"\r\n", "< type: \"Convolution\"\r\n", "< bottom: \"fc6-conv\"\r\n", @@ -5547,21 +5546,21 @@ "> bottom: \"fc6\"\r\n", "> top: \"fc7\"\r\n", "> inner_product_param {\r\n", - "183d180\r\n", + "188d182\r\n", "< kernel_size: 1\r\n", - "189,190c186,187\r\n", + "194,195c188,189\r\n", "< bottom: \"fc7-conv\"\r\n", "< top: \"fc7-conv\"\r\n", "---\r\n", "> bottom: \"fc7\"\r\n", "> top: \"fc7\"\r\n", - "195,196c192,193\r\n", + "200,201c194,195\r\n", "< bottom: \"fc7-conv\"\r\n", "< top: \"fc7-conv\"\r\n", "---\r\n", "> bottom: \"fc7\"\r\n", "> top: \"fc7\"\r\n", - "202,206c199,203\r\n", + "207,211c201,205\r\n", "< name: \"fc8-conv\"\r\n", "< type: \"Convolution\"\r\n", "< bottom: \"fc7-conv\"\r\n", @@ -5573,9 +5572,9 @@ "> bottom: \"fc7\"\r\n", "> top: \"fc8\"\r\n", "> inner_product_param {\r\n", - "208d204\r\n", + "213d206\r\n", "< kernel_size: 1\r\n", - "214c210\r\n", + "219c212\r\n", "< bottom: \"fc8-conv\"\r\n", "---\r\n", "> bottom: \"fc8\"\r\n" diff --git a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt index 0cadde9b58b..f8f5c3c325a 100644 --- a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt +++ b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt @@ -1,11 +1,14 @@ # Fully convolutional network version of CaffeNet. name: "CaffeNetConv" -input: "data" -input_shape { - dim: 1 - dim: 3 - dim: 451 - dim: 451 +layer { + name: "data" + type: "Input" + top: "data" + input_param { + # initial shape for a fully convolutional network: + # the shape can be set for each input by reshape. + shape: { dim: 1 dim: 3 dim: 451 dim: 451 } + } } layer { name: "conv1" diff --git a/examples/net_surgery/conv.prototxt b/examples/net_surgery/conv.prototxt index 6b3e5c768d5..8671bb5bf0a 100644 --- a/examples/net_surgery/conv.prototxt +++ b/examples/net_surgery/conv.prototxt @@ -1,11 +1,10 @@ # Simple single-layer network to showcase editing model parameters. name: "convolution" -input: "data" -input_shape { - dim: 1 - dim: 1 - dim: 100 - dim: 100 +layer { + name: "data" + type: "Input" + top: "data" + input_param { shape: { dim: 1 dim: 1 dim: 100 dim: 100 } } } layer { name: "conv" diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt index 332731bd75f..5d783ba02ca 100644 --- a/examples/siamese/mnist_siamese.prototxt +++ b/examples/siamese/mnist_siamese.prototxt @@ -1,10 +1,11 @@ name: "mnist_siamese" -input: "data" -input_shape { - dim: 10000 - dim: 1 - dim: 28 - dim: 28 +layer { + name: "data" + type: "Input" + top: "data" + input_param { + shape: { dim: 10000 dim: 1 dim: 28 dim: 28 } + } } layer { name: "conv1" diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt index ff10daa9399..45b2b0e361a 100644 --- a/models/bvlc_alexnet/deploy.prototxt +++ b/models/bvlc_alexnet/deploy.prototxt @@ -1,10 +1,9 @@ name: "AlexNet" -input: "data" -input_shape { - dim: 10 - dim: 3 - dim: 227 - dim: 227 +layer { + name: "data" + type: "Input" + top: "data" + input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } } } layer { name: "conv1" diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt index 1f90ee21630..50b54a9f3c1 100644 --- a/models/bvlc_googlenet/deploy.prototxt +++ b/models/bvlc_googlenet/deploy.prototxt @@ -1,10 +1,9 @@ name: "GoogleNet" -input: "data" -input_shape { - dim: 10 - dim: 3 - dim: 224 - dim: 224 +layer { + name: "data" + type: "Input" + top: "data" + input_param { shape: { dim: 10 dim: 3 dim: 224 dim: 224 } } } layer { name: "conv1/7x7_s2" diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt index 127f1e265fd..907116ef91c 100644 --- a/models/bvlc_reference_caffenet/deploy.prototxt +++ b/models/bvlc_reference_caffenet/deploy.prototxt @@ -1,10 +1,9 @@ name: "CaffeNet" -input: "data" -input_shape { - dim: 10 - dim: 3 - dim: 227 - dim: 227 +layer { + name: "data" + type: "Input" + top: "data" + input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } } } layer { name: "conv1" diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt index ae1df967742..e330a770676 100644 --- a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt +++ b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt @@ -1,10 +1,9 @@ name: "R-CNN-ilsvrc13" -input: "data" -input_shape { - dim: 10 - dim: 3 - dim: 227 - dim: 227 +layer { + name: "data" + type: "Input" + top: "data" + input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } } } layer { name: "conv1" diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt index 0f07e47acab..b8f99c74453 100644 --- a/models/finetune_flickr_style/deploy.prototxt +++ b/models/finetune_flickr_style/deploy.prototxt @@ -1,10 +1,9 @@ name: "FlickrStyleCaffeNet" -input: "data" -input_shape { - dim: 10 - dim: 3 - dim: 227 - dim: 227 +layer { + name: "data" + type: "Input" + top: "data" + input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } } } layer { name: "conv1" From f88073aad81d8119dc9d62f882b8cb6d20c3b7ee Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 26 Feb 2016 22:20:50 -0800 Subject: [PATCH 414/446] Deprecate ForwardPrefilled(), Forward(bottom, loss) in lieu of dropping Relax removal of `Forward()` variations by deprecating instead. --- include/caffe/net.hpp | 9 +++++++++ src/caffe/net.cpp | 12 ++++++++++++ 2 files changed, 21 insertions(+) diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index 1c2a19126d5..0addb3c2a6d 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -36,6 +36,12 @@ class Net { * */ const vector*>& Forward(Dtype* loss = NULL); + /// @brief DEPRECATED; use Forward() instead. + const vector*>& ForwardPrefilled(Dtype* loss = NULL) { + LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() " + << "will be removed in a future version. Use Forward()."; + return Forward(loss); + } /** * The From and To variants of Forward and Backward operate on the @@ -48,6 +54,9 @@ class Net { Dtype ForwardFromTo(int start, int end); Dtype ForwardFrom(int start); Dtype ForwardTo(int end); + /// @brief DEPRECATED; set input blobs then use Forward() instead. + const vector*>& Forward(const vector* > & bottom, + Dtype* loss = NULL); /** * @brief Zeroes out the diffs of all net parameters. diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index c1760ea1970..23d94c97c07 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -566,6 +566,18 @@ const vector*>& Net::Forward(Dtype* loss) { return net_output_blobs_; } +template +const vector*>& Net::Forward( + const vector*> & bottom, Dtype* loss) { + LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: Forward(bottom, loss) " + << "will be removed in a future version. Use Forward(loss)."; + // Copy bottom to net bottoms + for (int i = 0; i < bottom.size(); ++i) { + net_input_blobs_[i]->CopyFrom(*bottom[i]); + } + return Forward(loss); +} + template void Net::BackwardFromTo(int start, int end) { CHECK_GE(end, 0); From 6cba462401c7a8596afa957eb57c618fdfc63292 Mon Sep 17 00:00:00 2001 From: Evan Lezar Date: Tue, 5 Jan 2016 16:27:28 +0100 Subject: [PATCH 415/446] Add Dockerfiles for creating Caffe executable images. These can be used as direct replacements for the Caffe executable. --- docker/Makefile | 50 +++++++++++ docker/README.md | 52 ++++++++++++ docker/standalone/cpu/Dockerfile | 43 ++++++++++ docker/standalone/gpu/Dockerfile | 43 ++++++++++ docker/templates/Dockerfile.template | 42 ++++++++++ examples/mnist/train_lenet_docker.sh | 119 +++++++++++++++++++++++++++ 6 files changed, 349 insertions(+) create mode 100644 docker/Makefile create mode 100644 docker/README.md create mode 100644 docker/standalone/cpu/Dockerfile create mode 100644 docker/standalone/gpu/Dockerfile create mode 100644 docker/templates/Dockerfile.template create mode 100755 examples/mnist/train_lenet_docker.sh diff --git a/docker/Makefile b/docker/Makefile new file mode 100644 index 00000000000..725208c6b2b --- /dev/null +++ b/docker/Makefile @@ -0,0 +1,50 @@ +# A makefile to build the docker images for caffe. +# Two caffe images will be built: +# caffe:cpu --> A CPU-only build of caffe. +# caffe:gpu --> A GPU-enabled build using the latest CUDA and CUDNN versions. + +DOCKER ?= docker + +all: docker_files standalone + +.PHONY: standalone devel + +standalone: cpu_standalone gpu_standalone + + +cpu_standalone: standalone/cpu/Dockerfile + $(DOCKER) build -t caffe:cpu standalone/cpu + +gpu_standalone: standalone/gpu/Dockerfile + $(DOCKER) build -t caffe:gpu standalone/gpu + +docker_files: standalone_files + +standalone_files: standalone/cpu/Dockerfile standalone/gpu/Dockerfile + +FROM_GPU = "nvidia/cuda:cudnn" +FROM_CPU = "ubuntu:14.04" +GPU_CMAKE_ARGS = -DUSE_CUDNN=1 +CPU_CMAKE_ARGS = -DCPU_ONLY=1 + +# A make macro to select the CPU or GPU base image. +define from_image +$(if $(strip $(findstring gpu,$@)),$(FROM_GPU),$(FROM_CPU)) +endef + +# A make macro to select the CPU or GPU build args. +define build_args +$(if $(strip $(findstring gpu,$@)),$(GPU_CMAKE_ARGS),$(CPU_CMAKE_ARGS)) +endef + +# A make macro to construct the CPU or GPU Dockerfile from the template +define create_docker_file + @echo creating $@ + @echo "FROM "$(from_image) > $@ + @cat $^ | sed 's/$${CMAKE_ARGS}/$(build_args)/' >> $@ +endef + + +standalone/%/Dockerfile: templates/Dockerfile.template + $(create_docker_file) + diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 00000000000..0eb8c8639b6 --- /dev/null +++ b/docker/README.md @@ -0,0 +1,52 @@ +# Caffe standalone Dockerfiles. + +The `standalone` subfolder contains docker files for generating both CPU and GPU executable images for Caffe. The images can be built using make, or by running: + +``` +docker build -t caffe:cpu standalone/cpu +``` +for example. (Here `gpu` can be substituted for `cpu`, but to keep the readme simple, only the `cpu` case will be discussed in detail). + +Note that the GPU standalone requires a CUDA 7.5 capable driver to be installed on the system and [nvidia-docker] for running the Docker containers. Here it is generally sufficient to use `nvidia-docker` instead of `docker` in any of the commands mentioned. + +# Running Caffe using the docker image + +In order to test the Caffe image, run: +``` +docker run -ti caffe:cpu caffe --version +``` +which should show a message like: +``` +libdc1394 error: Failed to initialize libdc1394 +caffe version 1.0.0-rc3 +``` + +One can also build and run the Caffe tests in the image using: +``` +docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest" +``` + +In order to get the most out of the caffe image, some more advanced `docker run` options could be used. For example, running: +``` +docker run -ti --volume $(pwd):/workspace caffe:cpu caffe train --solver=example_solver.prototxt +``` +will train a network defined in the `example_solver.prototxt` file in the current directory (`$(pwd)` is maped to the container volume `/workspace` using the `--volume` Docker flag). + +Note that docker runs all commands as root by default, and thus any output files (e.g. snapshots) generated will be owned by the root user. In order to ensure that the current user is used instead, the following command can be used: +``` +docker run -ti --volume $(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=example_solver.prototxt +``` +where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir` Docker command line option. + +# Other use-cases + +Although running the `caffe` command in the docker containers as described above serves many purposes, the container can also be used for more interactive use cases. For example, specifying `bash` as the command instead of `caffe` yields a shell that can be used for interactive tasks. (Since the caffe build requirements are included in the container, this can also be used to build and run local versions of caffe). + +Another use case is to run python scripts that depend on `caffe`'s Python modules. Using the `python` command instead of `bash` or `caffe` will allow this, and an interactive interpreter can be started by running: +``` +docker run -ti caffe:cpu python +``` +(`ipython` is also available in the container). + +Since the `caffe/python` folder is also added to the path, the utility executable scripts defined there can also be used as executables. This includes `draw_net.py`, `classify.py`, and `detect.py` + diff --git a/docker/standalone/cpu/Dockerfile b/docker/standalone/cpu/Dockerfile new file mode 100644 index 00000000000..4fef25aa6a1 --- /dev/null +++ b/docker/standalone/cpu/Dockerfile @@ -0,0 +1,43 @@ +FROM ubuntu:14.04 +MAINTAINER caffe-maint@googlegroups.com + +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + cmake \ + git \ + wget \ + libatlas-base-dev \ + libboost-all-dev \ + libgflags-dev \ + libgoogle-glog-dev \ + libhdf5-serial-dev \ + libleveldb-dev \ + liblmdb-dev \ + libopencv-dev \ + libprotobuf-dev \ + libsnappy-dev \ + protobuf-compiler \ + python-dev \ + python-numpy \ + python-pip \ + python-scipy && \ + rm -rf /var/lib/apt/lists/* + +ENV CAFFE_ROOT=/opt/caffe +WORKDIR $CAFFE_ROOT + +# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this. +ENV CLONE_TAG=master + +RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \ + for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \ + mkdir build && cd build && \ + cmake -DCPU_ONLY=1 .. && \ + make -j"$(nproc)" + +ENV PYCAFFE_ROOT $CAFFE_ROOT/python +ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH +ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH +RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig + +WORKDIR /workspace diff --git a/docker/standalone/gpu/Dockerfile b/docker/standalone/gpu/Dockerfile new file mode 100644 index 00000000000..1ddc6560d16 --- /dev/null +++ b/docker/standalone/gpu/Dockerfile @@ -0,0 +1,43 @@ +FROM nvidia/cuda:cudnn +MAINTAINER caffe-maint@googlegroups.com + +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + cmake \ + git \ + wget \ + libatlas-base-dev \ + libboost-all-dev \ + libgflags-dev \ + libgoogle-glog-dev \ + libhdf5-serial-dev \ + libleveldb-dev \ + liblmdb-dev \ + libopencv-dev \ + libprotobuf-dev \ + libsnappy-dev \ + protobuf-compiler \ + python-dev \ + python-numpy \ + python-pip \ + python-scipy && \ + rm -rf /var/lib/apt/lists/* + +ENV CAFFE_ROOT=/opt/caffe +WORKDIR $CAFFE_ROOT + +# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this. +ENV CLONE_TAG=master + +RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \ + for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \ + mkdir build && cd build && \ + cmake -DUSE_CUDNN=1 .. && \ + make -j"$(nproc)" + +ENV PYCAFFE_ROOT $CAFFE_ROOT/python +ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH +ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH +RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig + +WORKDIR /workspace diff --git a/docker/templates/Dockerfile.template b/docker/templates/Dockerfile.template new file mode 100644 index 00000000000..8834f057968 --- /dev/null +++ b/docker/templates/Dockerfile.template @@ -0,0 +1,42 @@ +MAINTAINER caffe-maint@googlegroups.com + +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + cmake \ + git \ + wget \ + libatlas-base-dev \ + libboost-all-dev \ + libgflags-dev \ + libgoogle-glog-dev \ + libhdf5-serial-dev \ + libleveldb-dev \ + liblmdb-dev \ + libopencv-dev \ + libprotobuf-dev \ + libsnappy-dev \ + protobuf-compiler \ + python-dev \ + python-numpy \ + python-pip \ + python-scipy && \ + rm -rf /var/lib/apt/lists/* + +ENV CAFFE_ROOT=/opt/caffe +WORKDIR $CAFFE_ROOT + +# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this. +ENV CLONE_TAG=master + +RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \ + for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \ + mkdir build && cd build && \ + cmake ${CMAKE_ARGS} .. && \ + make -j"$(nproc)" + +ENV PYCAFFE_ROOT $CAFFE_ROOT/python +ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH +ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH +RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig + +WORKDIR /workspace diff --git a/examples/mnist/train_lenet_docker.sh b/examples/mnist/train_lenet_docker.sh new file mode 100755 index 00000000000..049f013841f --- /dev/null +++ b/examples/mnist/train_lenet_docker.sh @@ -0,0 +1,119 @@ +#!/usr/bin/env sh +set -e +# The following example allows for the MNIST example (using LeNet) to be +# trained using the caffe docker image instead of building from source. +# +# The GPU-enabled version of Caffe can be used, assuming that nvidia-docker +# is installed, and the GPU-enabled Caffe image has been built. +# Setting the GPU environment variable to 1 will enable the use of nvidia-docker. +# e.g. +# GPU=1 ./examples/mnist/train_lenet_docker.sh [ADDITIONAL_CAFFE_ARGS] +# +# With any arguments following the script being passed directly to caffe +# when training the network. +# +# The steps that are performed by the script are as follows: +# 1. The MNIST data set is downloaded +# (see data/mnist/get_mnist.sh) +# 2. An LMDB database is created from the downloaded data +# (see examples/mnist/create_mnist.sh. +# 3. A caffe network based on the LeNet solver is trained. +# (see examples/mnist/lenet_solver.prototxt) +# +# For each of these, a step is executed to ensure that certain prerequisites +# are available, after which a command that actually performs the work is +# executed. +# +# In order to provide additional flexibility, the following shell (environment) +# variables can be used to controll the execution of each of the phases: +# +# DOWNLOAD_DATA: Enable (1) or disable (0) the downloading of the MNIST dataset +# CREATE_LMDB: Enable (1) or disable (0) the creation of the LMDB database +# TRAIN: Enable (1) or disable (0) the training of the LeNet networkd. +# +# As an example, assuming that the data set has been downloaded, and an LMDB +# database created, the following command can be used to train the LeNet +# network with GPU computing enabled. +# +# DOWNLOAD_DATA=0 CREATE_LMDB=0 GPU=1 ./examples/mnist/train_lenet_docker.sh +# + + +if [ x"$(uname -s)" != x"Linux" ] +then +echo "" +echo "This script is designed to run on Linux." +echo "There may be problems with the way Docker mounts host volumes on other" +echo "systems which will cause the docker commands to fail." +echo "" +read -p "Press [ENTER] to continue..." key +echo "" +fi + + +# Check if GPU mode has been enabled and set the docker executable accordingly +if [ ${GPU:-0} -eq 1 ] +then +DOCKER_CMD=nvidia-docker +IMAGE=caffe:gpu +else +DOCKER_CMD=docker +IMAGE=caffe:cpu +fi +echo "Using $DOCKER_CMD to launch $IMAGE" + +# On non-Linux systems, the Docker host is typically a virtual machine. +# This means that the user and group id's may be different. +# On OS X, for example, the user and group are 1000 and 50, respectively. +if [ x"$(uname -s)" != x"Linux" ] +then +CUID=1000 +CGID=50 +else +CUID=$(id -u) +CGID=$(id -g) +fi + +# Define some helper variables to make the running of the actual docker +# commands less verbose. +# Note: +# -u $CUID:$CGID runs the docker image as the current user to ensure +# that the file permissions are compatible with the +# host system. The variables CUID and CGID have been +# set above depending on the host operating system. +# --volume $(pwd):/workspace mounts the current directory as the docker volume +# /workspace +# --workdir /workspace Ensures that the docker container starts in the right +# working directory +DOCKER_OPTIONS="--rm -ti -u $CUID:$CGID --volume $(pwd):/workspace --workdir /workspace" +DOCKER_RUN="$DOCKER_CMD run $DOCKER_OPTIONS $IMAGE" + +# Download the data +if [ ${DOWNLOAD_DATA:-1} -eq 1 ] +then +$DOCKER_RUN bash -c "mkdir -p ./data/mnist; + cp -ru \$CAFFE_ROOT/data/mnist/get_mnist.sh ./data/mnist/" +$DOCKER_RUN ./data/mnist/get_mnist.sh +fi + +# Create the LMDB database +if [ ${CREATE_LMDB:-1} -eq 1 ] +then +$DOCKER_RUN bash -c "mkdir -p ./examples/mnist; + cp -ru \$CAFFE_ROOT/examples/mnist/create_mnist.sh ./examples/mnist/; + sed -i s#BUILD=build#BUILD=\$CAFFE_ROOT/build## ./examples/mnist/create_mnist.sh" +$DOCKER_RUN ./examples/mnist/create_mnist.sh +fi + +# Train the network +if [ ${TRAIN:-1} -eq 1 ] +then +$DOCKER_RUN bash -c "cp \$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt ./examples/mnist/; + cp \$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt ./examples/mnist/" + # Ensure that the solver_mode is compatible with the desired GPU mode. + if [ ${GPU:-0} -eq 0 ] + then + $DOCKER_RUN sed -i 's#solver_mode: GPU#solver_mode: CPU##' ./examples/mnist/lenet_solver.prototxt + fi +$DOCKER_RUN caffe train --solver=examples/mnist/lenet_solver.prototxt $* +fi From cfa2c0cf596a4e1157e651389601d05779f5d27d Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Sat, 27 Feb 2016 12:10:16 -0800 Subject: [PATCH 416/446] fix flags in #3518 for nvidia-docker nvidia-docker requires long args with equal sign as of docker 1.10: see https://github.com/BVLC/caffe/pull/3518#issuecomment-189576419 --- docker/README.md | 8 ++++---- examples/mnist/train_lenet_docker.sh | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docker/README.md b/docker/README.md index 0eb8c8639b6..fdab641bdca 100644 --- a/docker/README.md +++ b/docker/README.md @@ -28,15 +28,15 @@ docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest" In order to get the most out of the caffe image, some more advanced `docker run` options could be used. For example, running: ``` -docker run -ti --volume $(pwd):/workspace caffe:cpu caffe train --solver=example_solver.prototxt +docker run -ti --volume=$(pwd):/workspace caffe:cpu caffe train --solver=example_solver.prototxt ``` -will train a network defined in the `example_solver.prototxt` file in the current directory (`$(pwd)` is maped to the container volume `/workspace` using the `--volume` Docker flag). +will train a network defined in the `example_solver.prototxt` file in the current directory (`$(pwd)` is maped to the container volume `/workspace` using the `--volume=` Docker flag). Note that docker runs all commands as root by default, and thus any output files (e.g. snapshots) generated will be owned by the root user. In order to ensure that the current user is used instead, the following command can be used: ``` -docker run -ti --volume $(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=example_solver.prototxt +docker run -ti --volume=$(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=example_solver.prototxt ``` -where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir` Docker command line option. +where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir=` Docker command line option. # Other use-cases diff --git a/examples/mnist/train_lenet_docker.sh b/examples/mnist/train_lenet_docker.sh index 049f013841f..32cf1c8e4a3 100755 --- a/examples/mnist/train_lenet_docker.sh +++ b/examples/mnist/train_lenet_docker.sh @@ -85,7 +85,7 @@ fi # /workspace # --workdir /workspace Ensures that the docker container starts in the right # working directory -DOCKER_OPTIONS="--rm -ti -u $CUID:$CGID --volume $(pwd):/workspace --workdir /workspace" +DOCKER_OPTIONS="--rm -ti -u $CUID:$CGID --volume=$(pwd):/workspace --workdir=/workspace" DOCKER_RUN="$DOCKER_CMD run $DOCKER_OPTIONS $IMAGE" # Download the data From bef2c05d612a1d25d1e0c506d15e0817f9f03bac Mon Sep 17 00:00:00 2001 From: shai Date: Thu, 25 Feb 2016 12:09:45 +0200 Subject: [PATCH 417/446] supporting N-D Blobs in Dropout layer Reshape fixing lint errors --- src/caffe/layers/dropout_layer.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp index 9cb64d9735f..533ab26c04d 100644 --- a/src/caffe/layers/dropout_layer.cpp +++ b/src/caffe/layers/dropout_layer.cpp @@ -23,8 +23,8 @@ void DropoutLayer::Reshape(const vector*>& bottom, const vector*>& top) { NeuronLayer::Reshape(bottom, top); // Set up the cache for random number generation - rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(), - bottom[0]->height(), bottom[0]->width()); + // ReshapeLike does not work because rand_vec_ is of Dtype uint + rand_vec_.Reshape(bottom[0]->shape()); } template From 666da79ad2f4d72c804ddadc7b10157e4d04bdd0 Mon Sep 17 00:00:00 2001 From: Thibault Deregnaucourt Date: Mon, 29 Feb 2016 10:21:07 +0100 Subject: [PATCH 418/446] Use 'six' library to ensure python3 compliance. Use '//' instead of '/' for entire division. --- python/caffe/pycaffe.py | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py index 5020ecedb10..c5c0b824a77 100644 --- a/python/caffe/pycaffe.py +++ b/python/caffe/pycaffe.py @@ -14,6 +14,8 @@ RMSPropSolver, AdaDeltaSolver, AdamSolver import caffe.io +import six + # We directly update methods from Net here (rather than using composition or # inheritance) so that nets created by caffe (e.g., by SGDSolver) will # automatically have the improved interface. @@ -97,7 +99,7 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs): raise Exception('Input blob arguments do not match net inputs.') # Set input according to defined shapes and make arrays single and # C-contiguous as Caffe expects. - for in_, blob in kwargs.iteritems(): + for in_, blob in six.iteritems(kwargs): if blob.shape[0] != self.blobs[in_].shape[0]: raise Exception('Input is not batch sized') self.blobs[in_].data[...] = blob @@ -145,7 +147,7 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs): raise Exception('Top diff arguments do not match net outputs.') # Set top diffs according to defined shapes and make arrays single and # C-contiguous as Caffe expects. - for top, diff in kwargs.iteritems(): + for top, diff in six.iteritems(kwargs): if diff.shape[0] != self.blobs[top].shape[0]: raise Exception('Diff is not batch sized') self.blobs[top].diff[...] = diff @@ -174,13 +176,13 @@ def _Net_forward_all(self, blobs=None, **kwargs): all_outs = {out: [] for out in set(self.outputs + (blobs or []))} for batch in self._batch(kwargs): outs = self.forward(blobs=blobs, **batch) - for out, out_blob in outs.iteritems(): + for out, out_blob in six.iteritems(outs): all_outs[out].extend(out_blob.copy()) # Package in ndarray. for out in all_outs: all_outs[out] = np.asarray(all_outs[out]) # Discard padding. - pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next()) + pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs))) if pad: for out in all_outs: all_outs[out] = all_outs[out][:-pad] @@ -215,16 +217,16 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs): for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}): batch_blobs = self.forward(blobs=blobs, **fb) batch_diffs = self.backward(diffs=diffs, **bb) - for out, out_blobs in batch_blobs.iteritems(): + for out, out_blobs in six.iteritems(batch_blobs): all_outs[out].extend(out_blobs.copy()) - for diff, out_diffs in batch_diffs.iteritems(): + for diff, out_diffs in six.iteritems(batch_diffs): all_diffs[diff].extend(out_diffs.copy()) # Package in ndarray. for out, diff in zip(all_outs, all_diffs): all_outs[out] = np.asarray(all_outs[out]) all_diffs[diff] = np.asarray(all_diffs[diff]) # Discard padding at the end and package in ndarray. - pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next()) + pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs))) if pad: for out, diff in zip(all_outs, all_diffs): all_outs[out] = all_outs[out][:-pad] @@ -256,10 +258,10 @@ def _Net_batch(self, blobs): ------ batch: {blob name: list of blobs} dict for a single batch. """ - num = len(blobs.itervalues().next()) - batch_size = self.blobs.itervalues().next().shape[0] + num = len(six.next(six.itervalues(blobs))) + batch_size = six.next(six.itervalues(self.blobs)).shape[0] remainder = num % batch_size - num_batches = num / batch_size + num_batches = num // batch_size # Yield full batches. for b in range(num_batches): From cb277769a4c36aa70af2bb860cf40c71af2d3033 Mon Sep 17 00:00:00 2001 From: Anatoly Baksheev Date: Mon, 29 Feb 2016 18:01:07 +0300 Subject: [PATCH 419/446] NetSpec: allow setting blob names by string --- python/caffe/net_spec.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py index 93fc01927db..63de4cce4b2 100644 --- a/python/caffe/net_spec.py +++ b/python/caffe/net_spec.py @@ -175,6 +175,12 @@ def __setattr__(self, name, value): def __getattr__(self, name): return self.tops[name] + def __setitem__(self, key, value): + self.__setattr__(key, value) + + def __getitem__(self, item): + return self.__getattr__(item) + def to_proto(self): names = {v: k for k, v in six.iteritems(self.tops)} autonames = Counter() From 15a979df454339682d18a2760a9b06896f23d62b Mon Sep 17 00:00:00 2001 From: Oscar Beijbom Date: Sun, 20 Dec 2015 23:02:42 -0800 Subject: [PATCH 420/446] Added tutorial on how to use python datalayers and multilabel classification. --- .../04-pascal_multilabel_with_datalayer.ipynb | 4152 +++++++++++++++++ .../layers/pascal_multilabel_datalayers.py | 262 ++ examples/pycaffe/tools.py | 111 + 3 files changed, 4525 insertions(+) create mode 100644 examples/04-pascal_multilabel_with_datalayer.ipynb create mode 100644 examples/pycaffe/layers/pascal_multilabel_datalayers.py create mode 100644 examples/pycaffe/tools.py diff --git a/examples/04-pascal_multilabel_with_datalayer.ipynb b/examples/04-pascal_multilabel_with_datalayer.ipynb new file mode 100644 index 00000000000..6839841a5ea --- /dev/null +++ b/examples/04-pascal_multilabel_with_datalayer.ipynb @@ -0,0 +1,4152 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multilabel classification on PASCAL using python data-layers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we will do multilabel classification on PASCAL VOC 2012. \n", + "\n", + "Caffe supports multi-label classification through the SigmoidCrossEntropyLoss, and we will load data using a python data-layer. Data could also be provided through a HDF5 data-layer, but the python data-layer provide endless flexibility, so that's what we will use." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preliminaries\n", + "\n", + "First, make sure you compile caffe using \n", + "WITH_PYTHON_LAYER ;= 1\n", + "\n", + "Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n", + "\n", + "Third, set paths and import modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# set root directory, e.g:\n", + "pascal_root = '/data/pascal/VOC2012'\n", + "\n", + "# import some modules\n", + "import sys, os, caffe\n", + "import numpy as np\n", + "import os.path as osp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n", + "sys.path.append(\"pycaffe\") # the tools file is in this folder\n", + "\n", + "import tools #this contains some tools that we need\n", + "\n", + "# make sure we have the caffenet weight downloaded.\n", + "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", + " print(\"Downloading pre-trained CaffeNet model...\")\n", + " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\n", + "\n", + "# initialize caffe for gpu mode\n", + "caffe.set_mode_gpu()\n", + "caffe.set_device(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from caffe import layers as L, params as P, to_proto\n", + "from caffe.proto import caffe_pb2\n", + "\n", + "# helper function for common structures\n", + "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n", + " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n", + " num_output=nout, pad=pad, group=group)\n", + " return conv, L.ReLU(conv, in_place=True)\n", + "\n", + "# another helper function\n", + "def fc_relu(bottom, nout):\n", + " fc = L.InnerProduct(bottom, num_output=nout)\n", + " return fc, L.ReLU(fc, in_place=True)\n", + "\n", + "# yet another helper function\n", + "def max_pool(bottom, ks, stride=1):\n", + " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n", + "\n", + "# main netspec wrapper\n", + "def caffenet_multilabel(data_layer_params, datalayer):\n", + " # setup the python data layer \n", + " n = caffe.NetSpec()\n", + " n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \n", + " ntop = 2, param_str=str(data_layer_params))\n", + "\n", + " # the net itself\n", + " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\n", + " n.pool1 = max_pool(n.relu1, 3, stride=2)\n", + " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n", + " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\n", + " n.pool2 = max_pool(n.relu2, 3, stride=2)\n", + " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n", + " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\n", + " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\n", + " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\n", + " n.pool5 = max_pool(n.relu5, 3, stride=2)\n", + " n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\n", + " n.drop6 = L.Dropout(n.relu6, in_place=True)\n", + " n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\n", + " n.drop7 = L.Dropout(n.relu7, in_place=True)\n", + " n.score = L.InnerProduct(n.drop7, num_output=20)\n", + " n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\n", + " \n", + " return str(n.to_proto())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \"tools\" module" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "workdir = './pascal_multilabel_with_datalayer'\n", + "os.makedirs(workdir)\n", + "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n", + "solverprototxt.sp['display'] = \"1\"\n", + "solverprototxt.sp['base_lr'] = \"0.0001\"\n", + "solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\n", + "\n", + "# write train and val nets.\n", + "with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\n", + " # provide parameters to the data layer as a python dictionary. Easy as pie!\n", + " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n", + " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\n", + "\n", + "with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\n", + " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n", + " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This net uses a python datalayer: PascalMultilabelDataLayerSync, which is defined in ./pycaffe/layers/pascal_multilabel_datalayers.py. Take a look at the codel. It's quite straight-forward, and gives you full control over data and labels.\n", + "\n", + "\n", + "Now we can load the caffe solver as usual." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PascalMultilabelDataLayerSync initialized for split: train, with bs:128, im_shape:[227, 227], and 5717 images.\n", + "PascalMultilabelDataLayerSync initialized for split: val, with bs:128, im_shape:[227, 227], and 5823 images.\n" + ] + } + ], + "source": [ + "solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\n", + "solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", + "solver.test_nets[0].share_with(solver.net)\n", + "solver.step(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check the data we have loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ground truth: horse, person,\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAQMAAAEACAYAAAC3RRNlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmwZdd13/db+5w7vKnf6/d6QmNqgCQIEiQIkBRJgaRI\n", + "ypSc2FFkW4qHiqtSKftjUvmSlFUVx5GjfImTfEil8sGVciUp2Y6TckJlEBWREgnOBGcSQKMbaKDR\n", + "89xvfvfde8/ZKx/2eM6973VDJNyQq3eh8e6955w9rL2G/1p77X1EVblf7pf75X4x97oD98v9cr+8\n", + "M8p9ZXC/3C/3C3BfGdwv98v94st9ZXC/3C/3C3BfGdwv98v94st9ZXC/3C/3C/A2KQMR+TdE5JSI\n", + "vCYif+/taON+uV/ul19skV90noGIFMBp4PPAJeD7wN9S1Vd+oQ3dL/fL/fILLW8HMvgYcEZV31TV\n", + "MfAvgN98G9q5X+6X++UXWN4OZfAgcCH7ftH/dr/cL/fLO7i8Hcrgfn7z/XK//Dks5dtQ5yXg4ez7\n", + "wzh0EIuI3FcY98v9co+Kqsq0398OZfAD4D0icgK4DPwN4G9N6dDEgwpYBVSxCiqS3Q8iDsqY8LO4\n", + "egQQEaaOMKtbPWgJqkh8/QpYq1irqFpXMcbVLYKIa1/F16PK7/3D3+U//Qf/eeyYZPUZDbUK4mqP\n", + 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('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')\n", + "print 'Ground truth: ',\n", + "for idx, val in enumerate(gtlist):\n", + " if val:\n", + " print classes[idx] + ',',\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alright. So far so good. We now have a working python datalayer that we can customize to our needs, e.g. by adding more data-augmentation or modify for other data-sets or tasks. Next, we will look at how to make it more efficient. The PascalMultilabelDataLayerSync loads the data syncronously, meaning that the GPU sits idle while the CPU loads the data. Fortunately, some simple multi-threading solves this problem. Let's do that next. First, though, lets measure the step time of this syncronous layer. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 13.9 s, sys: 363 ms, total: 14.2 s\n", + "Wall time: 14.2 s\n" + ] + } + ], + "source": [ + "%%time\n", + "solver.step(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Now, let's setup solvers and nets with the PascalMultilabelDataLayerAsync layer. Take a look at the code in ./pycaffe/layers/pascal_multilabel_datalayers.py, it's not hard." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BatchAdvancer initialized with 5717 images\n", + "PascalMultilabelDataLayerAsync initialized for split: train, with bs:128, im_shape:[227, 227].\n", + "BatchAdvancer initialized with 5823 images\n", + "PascalMultilabelDataLayerAsync initialized for split: val, with bs:128, im_shape:[227, 227].\n" + ] + } + ], + "source": [ + "workdir = './pascal_multilabel_with_datalayer'\n", + "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet_async.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet_async.prototxt\"))\n", + "solverprototxt.sp['display'] = \"1\"\n", + "solverprototxt.sp['base_lr'] = \"0.0001\"\n", + "solverprototxt.write(osp.join(workdir, 'solver_async.prototxt'))\n", + "\n", + "# write train and val nets.\n", + "with open(osp.join(workdir, 'trainnet_async.prototxt'), 'w') as f:\n", + " # provide parameters to the data layer as a python dictionary. Easy as pie!\n", + " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n", + " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerAsync'))\n", + "\n", + "with open(osp.join(workdir, 'valnet_async.prototxt'), 'w') as f:\n", + " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n", + " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerAsync'))\n", + "\n", + "solver_async = caffe.SGDSolver(osp.join(workdir, 'solver_async.prototxt'))\n", + "solver_async.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", + "solver_async.test_nets[0].share_with(solver_async.net)\n", + "solver_async.step(1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check runtime ..." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 15.7 s, sys: 476 ms, total: 16.1 s\n", + "Wall time: 16 s\n" + ] + } + ], + "source": [ + "%%time\n", + "solver_async.step(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alright, that is a modest runtime gain. However, as you data pre-processing becomes more complicated, this difference will increase." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's train the net for a while. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def hamming_distance(gt, est):\n", + " return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n", + "\n", + "def check_accuracy(net, num_batches, batch_size = 128):\n", + " acc = 0.0\n", + " for t in range(num_batches):\n", + " net.forward()\n", + " gts = net.blobs['label'].data\n", + " ests = net.blobs['score'].data > 0\n", + " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n", + " acc += hamming_distance(gt, est)\n", + " return acc / (num_batches * batch_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alright, let's train." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "itt:0 accuracy:0.9430\n", + "itt:100 accuracy:0.9511\n", + "itt:200 accuracy:0.9573\n", + "itt:300 accuracy:0.9600\n", + "itt:400 accuracy:0.9583\n" + ] + } + ], + "source": [ + "for itt in range(500):\n", + " solver_async.step(1)\n", + " if itt % 100 == 0:\n", + " print 'itt:{}'.format(itt), 'accuracy:{0:.4f}'.format(check_accuracy(solver_async.test_nets[0], 10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great, accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline accuracy:0.9243\n" + ] + } + ], + "source": [ + "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n", + " acc = 0.0\n", + " for t in range(num_batches):\n", + " net.forward()\n", + " gts = net.blobs['label'].data\n", + " ests = np.zeros((batch_size, 20))\n", + " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n", + " acc += hamming_distance(gt, est)\n", + " return acc / (num_batches * batch_size)\n", + "\n", + "print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver_async.test_nets[0], 5823/128))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Let's wrap this up by looking at some qualitative results" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ground truth: bird, \n", + "Estimated: bird,\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAQMAAAEACAYAAAC3RRNlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvMmvLVma5fXbvTWnud3rvIu+IpU1qJKQUkwYgQQSA5ih\n", + "+h+Y89cwZ4pKooRUk5qAIEFCRUFGVkZFeHjz/L13m9NZt3sG9kiVUOJZg3A8kO6anSO7do6da3vt\n", + "b31rfSZqrTzjGc94hvyxv8AznvGMPw08k8EznvEM4JkMnvGMZ3zEMxk84xnPAJ7J4BnPeMZHPJPB\n", + "M57xDOAHIgMhxH8ihPiNEOJvhBD/1Q/xGc94xjP+uBB/7JyBEEIBfw38R8C3wF8C/6TW+ld/1A96\n", + "xjOe8UfFD1EZ/AXw21rrl7XWCPw3wH/2A3zOM57xjD8ifggy+BT4+t96/c3H957xjGf8CeOHIIPn\n", + "fPMznvH/Q+gf4JzfAp//W68/Z60O/hZCiGfCeMYzfiTUWsXf9f4PQQb/C/ArIcRPgbfAfwH8k//n\n", + "Qf/pf/Brmu4aVwNNObO7veE8npiTotOFXCPjUqkYlN3y4bDgY2W33dA6R8kZqSQ5nAmXDwjdcvf6\n", + "C9qmI4eENIpGJQ6HR85e0beONN9Ti0cJgXMtxnYIBMI0lJw5H9/jS+Xzn/waqySPTydSFWy3O/ZX\n", + 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('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')\n", + "print 'Ground truth: ',\n", + "for idx, val in enumerate(gtlist):\n", + " if val:\n", + " print classes[idx] + ',',\n", + "\n", + "print '' \n", + "print 'Estimated: ',\n", + "for idx, val in enumerate(estlist):\n", + " if val == 1:\n", + " print classes[idx] + ','," + ] + } + ], + "metadata": { + "description": "Multilabel classification on PASCAL using python data-layers.", + "example_name": "PASCAL Multilabel with python datalayer", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/pycaffe/layers/pascal_multilabel_datalayers.py b/examples/pycaffe/layers/pascal_multilabel_datalayers.py new file mode 100644 index 00000000000..036d16787c3 --- /dev/null +++ b/examples/pycaffe/layers/pascal_multilabel_datalayers.py @@ -0,0 +1,262 @@ +# imports +import json, time, pickle, scipy.misc, skimage.io, caffe + +import numpy as np +import os.path as osp + +from xml.dom import minidom +from random import shuffle +from threading import Thread +from PIL import Image + +from pascal_multilabel_with_datalayer_tools import SimpleTransformer + + +class PascalMultilabelDataLayerSync(caffe.Layer): + """ + This is a simple syncronous datalayer for training a multilabel model on PASCAL. + """ + + def setup(self, bottom, top): + + self.top_names = ['data', 'label'] + + # === Read input parameters === + + # params is a python dictionary with layer parameters. + params = eval(self.param_str) + + # do some simple checks that we have the parameters we need. + assert 'batch_size' in params.keys(), 'Params must include batch size.' + assert 'split' in params.keys(), 'Params must include split (train, val, or test).' + assert 'pascal_root' in params.keys(), 'Params must include pascal_root.' + assert 'im_shape' in params.keys(), 'Params must include im_shape.' + + # store input as class variables + self.batch_size = params['batch_size'] + self.im_shape = params['im_shape'] + self.pascal_root = params['pascal_root'] + self.im_shape = params['im_shape'] + self.indexlist = [line.rstrip('\n') for line in open(osp.join(self.pascal_root, 'ImageSets/Main', params['split'] + '.txt'))] #get list of image indexes. + self._cur = 0 # current image + self.transformer = SimpleTransformer() #this class does some simple data-manipulations + + # === reshape tops === + top[0].reshape(self.batch_size, 3, self.im_shape[0], self.im_shape[1]) # since we use a fixed input image size, we can shape the data layer once. Else, we'd have to do it in the reshape call. + top[1].reshape(self.batch_size, 20) + + print "PascalMultilabelDataLayerSync initialized for split: {}, with bs:{}, im_shape:{}, and {} images.".format(params['split'], params['batch_size'], params['im_shape'], len(self.indexlist)) + + + def reshape(self, bottom, top): + """ no need to reshape each time sine the input is fixed size (rows and columns) """ + pass + + def forward(self, bottom, top): + """ + Load data. + """ + for itt in range(self.batch_size): + + # Did we finish an epoch? + if self._cur == len(self.indexlist): + self._cur = 0 + shuffle(self.indexlist) + + # Load an image + index = self.indexlist[self._cur] # Get the image index + im = np.asarray(Image.open(osp.join(self.pascal_root, 'JPEGImages', index + '.jpg'))) # load image + im = scipy.misc.imresize(im, self.im_shape) # resize + + # do a simple horizontal flip as data augmentation + flip = np.random.choice(2)*2-1 + im = im[:, ::flip, :] + + # Load and prepare ground truth + multilabel = np.zeros(20).astype(np.float32) + anns = load_pascal_annotation(index, self.pascal_root) + for label in anns['gt_classes']: + # in the multilabel problem we don't care how MANY instances there are of each class. Only if they are present. + multilabel[label - 1] = 1 # The "-1" is b/c we are not interested in the background class. + + # Add directly to the caffe data layer + top[0].data[itt, ...] = self.transformer.preprocess(im) + top[1].data[itt, ...] = multilabel + self._cur += 1 + + def backward(self, top, propagate_down, bottom): + """ this layer does not back propagate """ + pass + + + + +class PascalMultilabelDataLayerAsync(caffe.Layer): + """ + This is a simple asyncronous datalayer for training a multilabel model on PASCAL. + """ + + def setup(self, bottom, top): + + self.top_names = ['data', 'label'] + + # === Read input parameters === + + # params is a python dictionary with layer parameters. + params = eval(self.param_str) + + # do some simple checks that we have the parameters we need. + assert 'batch_size' in params.keys(), 'Params must include batch size.' + assert 'split' in params.keys(), 'Params must include split (train, val, or test).' + assert 'pascal_root' in params.keys(), 'Params must include pascal_root.' + assert 'im_shape' in params.keys(), 'Params must include im_shape.' + + self.batch_size = params['batch_size'] # we need to store this as a local variable. + + # === We are going to do the actual data processing in a seperate, helperclass, called BatchAdvancer. So let's forward the parame to that class === + self.thread_result = {} + self.thread = None + self.batch_advancer = BatchAdvancer(self.thread_result, params) + self.dispatch_worker() # Let it start fetching data right away. + + # === reshape tops === + top[0].reshape(self.batch_size, 3, params['im_shape'][0], params['im_shape'][1]) # since we use a fixed input image size, we can shape the data layer once. Else, we'd have to do it in the reshape call. + top[1].reshape(self.batch_size, 20) # Note the 20 channels (because PASCAL has 20 classes.) + + print "PascalMultilabelDataLayerAsync initialized for split: {}, with bs:{}, im_shape:{}.".format(params['split'], params['batch_size'], params['im_shape']) + + + + def reshape(self, bottom, top): + """ no need to reshape each time sine the input is fixed size (rows and columns) """ + pass + + def forward(self, bottom, top): + """ this is the forward pass, where we load the data into the blobs. Since we run the BatchAdvance asynchronously, we just wait for it, and then copy """ + + if self.thread is not None: + self.join_worker() # wait until it is done. + + for top_index, name in zip(range(len(top)), self.top_names): + for i in range(self.batch_size): + top[top_index].data[i, ...] = self.thread_result[name][i] #Copy the already-prepared data to caffe. + + self.dispatch_worker() # let's go again while the GPU process this batch. + + def dispatch_worker(self): + assert self.thread is None + self.thread = Thread(target=self.batch_advancer) + self.thread.start() + + def join_worker(self): + assert self.thread is not None + self.thread.join() + self.thread = None + + def backward(self, top, propagate_down, bottom): + """ this layer does not back propagate """ + pass + + +class BatchAdvancer(): + """ + This is the class that is run asynchronously and actually does the work. + """ + def __init__(self, result, params): + self.result = result + self.batch_size = params['batch_size'] + self.im_shape = params['im_shape'] + self.pascal_root = params['pascal_root'] + self.im_shape = params['im_shape'] + self.indexlist = [line.rstrip('\n') for line in open(osp.join(self.pascal_root, 'ImageSets/Main', params['split'] + '.txt'))] #get list of image indexes. + self._cur = 0 # current image + self.transformer = SimpleTransformer() #this class does some simple data-manipulations + + print "BatchAdvancer initialized with {} images".format(len(self.indexlist)) + + def __call__(self): + """ + This does the same stuff as the forward layer of the synchronous layer. Exept that we store the data and labels in the result dictionary (as lists of length batchsize). + """ + self.result['data'] = [] + self.result['label'] = [] + for itt in range(self.batch_size): + + # Did we finish an epoch? + if self._cur == len(self.indexlist): + self._cur = 0 + shuffle(self.indexlist) + + # Load an image + index = self.indexlist[self._cur] # Get the image index + im = np.asarray(Image.open(osp.join(self.pascal_root, 'JPEGImages', index + '.jpg'))) # load image + im = scipy.misc.imresize(im, self.im_shape) # resize + + # do a simple horizontal flip as data augmentation + flip = np.random.choice(2)*2-1 + im = im[:, ::flip, :] + + # Load and prepare ground truth + multilabel = np.zeros(20).astype(np.float32) + anns = load_pascal_annotation(index, self.pascal_root) + for label in anns['gt_classes']: + # in the multilabel problem we don't care how MANY instances there are of each class. Only if they are present. + multilabel[label - 1] = 1 # The "-1" is b/c we are not interested in the background class. + + # Store in a result list. + self.result['data'].append(self.transformer.preprocess(im)) + self.result['label'].append(multilabel) + self._cur += 1 + + +def load_pascal_annotation(index, pascal_root): + """ + This code is borrowed from Ross Girshick's FAST-RCNN code (https://github.com/rbgirshick/fast-rcnn). It parses the PASCAL .xml metadata files. See publication for further details: (http://arxiv.org/abs/1504.08083). + + Thanks Ross! + + """ + classes = ('__background__', # always index 0 + 'aeroplane', 'bicycle', 'bird', 'boat', + 'bottle', 'bus', 'car', 'cat', 'chair', + 'cow', 'diningtable', 'dog', 'horse', + 'motorbike', 'person', 'pottedplant', + 'sheep', 'sofa', 'train', 'tvmonitor') + class_to_ind = dict(zip(classes, xrange(21))) + + filename = osp.join(pascal_root, 'Annotations', index + '.xml') + # print 'Loading: {}'.format(filename) + def get_data_from_tag(node, tag): + return node.getElementsByTagName(tag)[0].childNodes[0].data + + with open(filename) as f: + data = minidom.parseString(f.read()) + + objs = data.getElementsByTagName('object') + num_objs = len(objs) + + boxes = np.zeros((num_objs, 4), dtype=np.uint16) + gt_classes = np.zeros((num_objs), dtype=np.int32) + overlaps = np.zeros((num_objs, 21), dtype=np.float32) + + # Load object bounding boxes into a data frame. + for ix, obj in enumerate(objs): + # Make pixel indexes 0-based + x1 = float(get_data_from_tag(obj, 'xmin')) - 1 + y1 = float(get_data_from_tag(obj, 'ymin')) - 1 + x2 = float(get_data_from_tag(obj, 'xmax')) - 1 + y2 = float(get_data_from_tag(obj, 'ymax')) - 1 + cls = class_to_ind[ + str(get_data_from_tag(obj, "name")).lower().strip()] + boxes[ix, :] = [x1, y1, x2, y2] + gt_classes[ix] = cls + overlaps[ix, cls] = 1.0 + + overlaps = scipy.sparse.csr_matrix(overlaps) + + return {'boxes' : boxes, + 'gt_classes': gt_classes, + 'gt_overlaps' : overlaps, + 'flipped' : False, + 'index': index} + diff --git a/examples/pycaffe/tools.py b/examples/pycaffe/tools.py new file mode 100644 index 00000000000..8e658b29a82 --- /dev/null +++ b/examples/pycaffe/tools.py @@ -0,0 +1,111 @@ +import numpy as np + +class SimpleTransformer: + """ + SimpleTransformer is a simple class for preprocessing and deprocessing images for caffe. + """ + + def __init__(self, mean = [128, 128, 128]): + self.mean = np.array(mean, dtype=np.float32) + self.scale = 1.0 + + def set_mean(self, mean): + """ + Set the mean to subtract for centering the data. + """ + self.mean = mean + + def set_scale(self, scale): + """ + Set the data scaling. + """ + self.scale = scale + + def preprocess(self, im): + """ + preprocess() emulate the pre-processing occuring in the vgg16 caffe prototxt. + """ + + im = np.float32(im) + im = im[:, :, ::-1] #change to BGR + im -= self.mean + im *= self.scale + im = im.transpose((2, 0, 1)) + + return im + + def deprocess(self, im): + """ + inverse of preprocess() + """ + im = im.transpose(1, 2, 0) + im /= self.scale + im += self.mean + im = im[:, :, ::-1] #change to RGB + + return np.uint8(im) + +class CaffeSolver: + """ + Caffesolver is a class for creating a solver.prototxt file. It sets default values and can export a solver parameter file. + Note that all parameters are stored as strings. Strings variables are stored as strings in strings. + """ + + def __init__(self, testnet_prototxt_path = "testnet.prototxt", trainnet_prototxt_path = "trainnet.prototxt", debug = False): + + self.sp = {} + + # critical: + self.sp['base_lr'] = '0.001' + self.sp['momentum'] = '0.9' + + # speed: + self.sp['test_iter'] = '100' + self.sp['test_interval'] = '250' + + # looks: + self.sp['display'] = '25' + self.sp['snapshot'] = '2500' + self.sp['snapshot_prefix'] = '"snapshot"' # string withing a string! + + # learning rate policy + self.sp['lr_policy'] = '"fixed"' + + # important, but rare: + self.sp['gamma'] = '0.1' + self.sp['weight_decay'] = '0.0005' + self.sp['train_net'] = '"' + trainnet_prototxt_path + '"' + self.sp['test_net'] = '"' + testnet_prototxt_path + '"' + + # pretty much never change these. + self.sp['max_iter'] = '100000' + self.sp['test_initialization'] = 'false' + self.sp['average_loss'] = '25' # this has to do with the display. + self.sp['iter_size'] = '1' #this is for accumulating gradients + + if (debug): + self.sp['max_iter'] = '12' + self.sp['test_iter'] = '1' + self.sp['test_interval'] = '4' + self.sp['display'] = '1' + + def add_from_file(self, filepath): + """ + Reads a caffe solver prototxt file and updates the Caffesolver instance parameters. + """ + with open(filepath, 'r') as f: + for line in f: + if line[0] == '#': + continue + splitLine = line.split(':') + self.sp[splitLine[0].strip()] = splitLine[1].strip() + + def write(self, filepath): + """ + Export solver parameters to INPUT "filepath". Sorted alphabetically. + """ + f = open(filepath, 'w') + for key, value in sorted(self.sp.items()): + if not(type(value) is str): + raise TypeError('All solver parameters must be strings') + f.write('%s: %s\n' % (key, value)) From cf765b983a7a3dd30f4c339f2bb3d296e6990c4b Mon Sep 17 00:00:00 2001 From: Evan Lezar Date: Thu, 18 Feb 2016 11:50:15 +0100 Subject: [PATCH 421/446] Refactor and improve code style. Fix some typos. Correct imports. Refactor data layers. Apply PEP8 formatting. --- .../04-pascal_multilabel_with_datalayer.ipynb | 3766 +---------------- .../layers/pascal_multilabel_datalayers.py | 299 +- examples/pycaffe/tools.py | 48 +- 3 files changed, 254 insertions(+), 3859 deletions(-) diff --git a/examples/04-pascal_multilabel_with_datalayer.ipynb b/examples/04-pascal_multilabel_with_datalayer.ipynb index 6839841a5ea..43aa539d594 100644 --- a/examples/04-pascal_multilabel_with_datalayer.ipynb +++ b/examples/04-pascal_multilabel_with_datalayer.ipynb @@ -11,9 +11,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial we will do multilabel classification on PASCAL VOC 2012. \n", + "In this tutorial we will do multi-label classification on PASCAL VOC 2012.\n", "\n", - "Caffe supports multi-label classification through the SigmoidCrossEntropyLoss, and we will load data using a python data-layer. Data could also be provided through a HDF5 data-layer, but the python data-layer provide endless flexibility, so that's what we will use." + "Caffe supports multi-label classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use." ] }, { @@ -23,7 +23,7 @@ "### Preliminaries\n", "\n", "First, make sure you compile caffe using \n", - "WITH_PYTHON_LAYER ;= 1\n", + "WITH_PYTHON_LAYER := 1\n", "\n", "Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n", "\n", @@ -34,13 +34,10 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ - "# set root directory, e.g:\n", - "pascal_root = '/data/pascal/VOC2012'\n", - "\n", "# import some modules\n", "import sys, os, caffe\n", "import numpy as np\n", @@ -48,6 +45,9 @@ "import matplotlib.pyplot as plt\n", "\n", "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "# set root directory, e.g:\n", + "pascal_root = os.path.join(caffe_root, 'data/pascal/VOC2012')\n", + "\n", "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n", "sys.path.append(\"pycaffe\") # the tools file is in this folder\n", "\n", @@ -133,14 +133,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "workdir = './pascal_multilabel_with_datalayer'\n", - "os.makedirs(workdir)\n", + "if not os.path.isdir(workdir):\n", + " os.makedirs(workdir)\n", + "\n", "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n", "solverprototxt.sp['display'] = \"1\"\n", "solverprototxt.sp['base_lr'] = \"0.0001\"\n", @@ -161,7 +163,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This net uses a python datalayer: PascalMultilabelDataLayerSync, which is defined in ./pycaffe/layers/pascal_multilabel_datalayers.py. Take a look at the codel. It's quite straight-forward, and gives you full control over data and labels.\n", + "This net uses a python datalayer: PascalMultilabelDataLayerSync, which is defined in ./pycaffe/layers/pascal_multilabel_datalayers.py. Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\n", "\n", "\n", "Now we can load the caffe solver as usual." @@ -169,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -178,8 +180,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "PascalMultilabelDataLayerSync initialized for split: train, with bs:128, im_shape:[227, 227], and 5717 images.\n", - "PascalMultilabelDataLayerSync initialized for split: val, with bs:128, im_shape:[227, 227], and 5823 images.\n" + "BatchLoader initialized with 5717 images\n", + "PascalMultilabelDataLayerSync initialized for split: train, with bs: 128, im_shape: [227, 227].\n", + "BatchLoader initialized with 5823 images\n", + "PascalMultilabelDataLayerSync initialized for split: val, with bs: 128, im_shape: [227, 227].\n" ] } ], @@ -199,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { 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yfEqfTqydq8iO0PglMmba3EMqWugs9GmbkWACPEsTjNiCLcqQyIhm99d9lYrh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\nECQkaYxl6fKhNlFVLIMQx3bxSgLDBCk1FVtZgGWhpIE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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1812,7 +228,7 @@ ], "source": [ "% matplotlib inline\n", - "from pascal_multilabel_with_datalayer_tools import SimpleTransformer\n", + "from tools import SimpleTransformer\n", "from copy import copy\n", "transformer = SimpleTransformer() # this is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n", "\n", @@ -1824,7 +240,7 @@ "for idx, val in enumerate(gtlist):\n", " if val:\n", " print classes[idx] + ',',\n", - "\n" + "print ''" ] }, { @@ -1836,20 +252,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 13.9 s, sys: 363 ms, total: 14.2 s\n", - "Wall time: 14.2 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "solver.step(10)" @@ -1866,22 +273,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BatchAdvancer initialized with 5717 images\n", - "PascalMultilabelDataLayerAsync initialized for split: train, with bs:128, im_shape:[227, 227].\n", - "BatchAdvancer initialized with 5823 images\n", - "PascalMultilabelDataLayerAsync initialized for split: val, with bs:128, im_shape:[227, 227].\n" - ] - } - ], + "outputs": [], "source": [ "workdir = './pascal_multilabel_with_datalayer'\n", "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet_async.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet_async.prototxt\"))\n", @@ -1914,20 +310,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.7 s, sys: 476 ms, total: 16.1 s\n", - "Wall time: 16 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "solver_async.step(10)" @@ -1949,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": false }, @@ -1958,6 +345,7 @@ "def hamming_distance(gt, est):\n", " return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n", "\n", + "\n", "def check_accuracy(net, num_batches, batch_size = 128):\n", " acc = 0.0\n", " for t in range(num_batches):\n", @@ -1978,23 +366,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "itt:0 accuracy:0.9430\n", - "itt:100 accuracy:0.9511\n", - "itt:200 accuracy:0.9573\n", - "itt:300 accuracy:0.9600\n", - "itt:400 accuracy:0.9583\n" - ] - } - ], + "outputs": [], "source": [ "for itt in range(500):\n", " solver_async.step(1)\n", @@ -2011,26 +387,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Baseline accuracy:0.9243\n" - ] - } - ], + "outputs": [], "source": [ "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n", " acc = 0.0\n", " for t in range(num_batches):\n", " net.forward()\n", " gts = net.blobs['label'].data\n", - " ests = np.zeros((batch_size, 20))\n", + " ests = np.zeros((batch_size, len(gts)))\n", " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n", " acc += hamming_distance(gt, est)\n", " return acc / (num_batches * batch_size)\n", @@ -2049,2059 +417,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ground truth: bird, \n", - "Estimated: bird,\n" - ] - }, - { - "data": { - "image/png": [ - 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}, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "% matplotlib inline\n", - "from pascal_multilabel_with_datalayer_tutorial_tools import SimpleTransformer\n", + "from tools import SimpleTransformer\n", "from copy import copy\n", "transformer = SimpleTransformer() # this is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n", "\n", @@ -4123,6 +446,15 @@ " if val == 1:\n", " print classes[idx] + ','," ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -4144,7 +476,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.6" } }, "nbformat": 4, diff --git a/examples/pycaffe/layers/pascal_multilabel_datalayers.py b/examples/pycaffe/layers/pascal_multilabel_datalayers.py index 036d16787c3..f0039eff4b6 100644 --- a/examples/pycaffe/layers/pascal_multilabel_datalayers.py +++ b/examples/pycaffe/layers/pascal_multilabel_datalayers.py @@ -1,5 +1,10 @@ # imports -import json, time, pickle, scipy.misc, skimage.io, caffe +import json +import time +import pickle +import scipy.misc +import skimage.io +import caffe import numpy as np import os.path as osp @@ -9,12 +14,14 @@ from threading import Thread from PIL import Image -from pascal_multilabel_with_datalayer_tools import SimpleTransformer +from tools import SimpleTransformer class PascalMultilabelDataLayerSync(caffe.Layer): + """ - This is a simple syncronous datalayer for training a multilabel model on PASCAL. + This is a simple syncronous datalayer for training a multilabel model on + PASCAL. """ def setup(self, bottom, top): @@ -22,78 +29,60 @@ def setup(self, bottom, top): self.top_names = ['data', 'label'] # === Read input parameters === - + # params is a python dictionary with layer parameters. - params = eval(self.param_str) - - # do some simple checks that we have the parameters we need. - assert 'batch_size' in params.keys(), 'Params must include batch size.' - assert 'split' in params.keys(), 'Params must include split (train, val, or test).' - assert 'pascal_root' in params.keys(), 'Params must include pascal_root.' - assert 'im_shape' in params.keys(), 'Params must include im_shape.' - + params = eval(self.param_str) + + # Check the paramameters for validity. + check_params(params) + # store input as class variables - self.batch_size = params['batch_size'] - self.im_shape = params['im_shape'] - self.pascal_root = params['pascal_root'] - self.im_shape = params['im_shape'] - self.indexlist = [line.rstrip('\n') for line in open(osp.join(self.pascal_root, 'ImageSets/Main', params['split'] + '.txt'))] #get list of image indexes. - self._cur = 0 # current image - self.transformer = SimpleTransformer() #this class does some simple data-manipulations + self.batch_size = params['batch_size'] + + # Create a batch loader to load the images. + self.batch_loader = BatchLoader(params, None) # === reshape tops === - top[0].reshape(self.batch_size, 3, self.im_shape[0], self.im_shape[1]) # since we use a fixed input image size, we can shape the data layer once. Else, we'd have to do it in the reshape call. + # since we use a fixed input image size, we can shape the data layer + # once. Else, we'd have to do it in the reshape call. + top[0].reshape( + self.batch_size, 3, params['im_shape'][0], params['im_shape'][1]) + # Note the 20 channels (because PASCAL has 20 classes.) top[1].reshape(self.batch_size, 20) - print "PascalMultilabelDataLayerSync initialized for split: {}, with bs:{}, im_shape:{}, and {} images.".format(params['split'], params['batch_size'], params['im_shape'], len(self.indexlist)) - - - def reshape(self, bottom, top): - """ no need to reshape each time sine the input is fixed size (rows and columns) """ - pass + print_info("PascalMultilabelDataLayerSync", params) def forward(self, bottom, top): """ - Load data. + Load data. """ for itt in range(self.batch_size): - - # Did we finish an epoch? - if self._cur == len(self.indexlist): - self._cur = 0 - shuffle(self.indexlist) - - # Load an image - index = self.indexlist[self._cur] # Get the image index - im = np.asarray(Image.open(osp.join(self.pascal_root, 'JPEGImages', index + '.jpg'))) # load image - im = scipy.misc.imresize(im, self.im_shape) # resize - - # do a simple horizontal flip as data augmentation - flip = np.random.choice(2)*2-1 - im = im[:, ::flip, :] - - # Load and prepare ground truth - multilabel = np.zeros(20).astype(np.float32) - anns = load_pascal_annotation(index, self.pascal_root) - for label in anns['gt_classes']: - # in the multilabel problem we don't care how MANY instances there are of each class. Only if they are present. - multilabel[label - 1] = 1 # The "-1" is b/c we are not interested in the background class. + # Use the batch loader to load the next image. + im, multilabel = self.batch_loader.load_next_image() # Add directly to the caffe data layer - top[0].data[itt, ...] = self.transformer.preprocess(im) + top[0].data[itt, ...] = im top[1].data[itt, ...] = multilabel - self._cur += 1 - def backward(self, top, propagate_down, bottom): - """ this layer does not back propagate """ + def reshape(self, bottom, top): + """ + There is no need to reshape the data, since the input is of fixed size + (rows and columns) + """ pass - + def backward(self, top, propagate_down, bottom): + """ + These layers does not back propagate + """ + pass class PascalMultilabelDataLayerAsync(caffe.Layer): + """ - This is a simple asyncronous datalayer for training a multilabel model on PASCAL. + This is a simple asyncronous datalayer for training a multilabel model on + PASCAL. """ def setup(self, bottom, top): @@ -101,51 +90,68 @@ def setup(self, bottom, top): self.top_names = ['data', 'label'] # === Read input parameters === - + # params is a python dictionary with layer parameters. - params = eval(self.param_str) + params = eval(self.param_str) - # do some simple checks that we have the parameters we need. - assert 'batch_size' in params.keys(), 'Params must include batch size.' - assert 'split' in params.keys(), 'Params must include split (train, val, or test).' - assert 'pascal_root' in params.keys(), 'Params must include pascal_root.' - assert 'im_shape' in params.keys(), 'Params must include im_shape.' + # Check the paramameters for validity. + check_params(params) - self.batch_size = params['batch_size'] # we need to store this as a local variable. + # we need to store this as a local variable. + self.batch_size = params['batch_size'] - # === We are going to do the actual data processing in a seperate, helperclass, called BatchAdvancer. So let's forward the parame to that class === + # === We are going to do the actual data processing in a seperate, + # helperclass, called BatchLoader. So let's forward the parameters + # to that class === self.thread_result = {} self.thread = None - self.batch_advancer = BatchAdvancer(self.thread_result, params) - self.dispatch_worker() # Let it start fetching data right away. + self.batch_loader = BatchLoader(params, self.thread_result) + self.dispatch_worker() # Let it start fetching data right away. # === reshape tops === - top[0].reshape(self.batch_size, 3, params['im_shape'][0], params['im_shape'][1]) # since we use a fixed input image size, we can shape the data layer once. Else, we'd have to do it in the reshape call. - top[1].reshape(self.batch_size, 20) # Note the 20 channels (because PASCAL has 20 classes.) - - print "PascalMultilabelDataLayerAsync initialized for split: {}, with bs:{}, im_shape:{}.".format(params['split'], params['batch_size'], params['im_shape']) - - + # since we use a fixed input image size, we can shape the data layer + # once. Else, we'd have to do it in the reshape call. + top[0].reshape( + self.batch_size, 3, params['im_shape'][0], params['im_shape'][1]) + # Note the 20 channels (because PASCAL has 20 classes.) + top[1].reshape(self.batch_size, 20) - def reshape(self, bottom, top): - """ no need to reshape each time sine the input is fixed size (rows and columns) """ - pass + print_info("PascalMultilabelDataLayerAsync", params) def forward(self, bottom, top): - """ this is the forward pass, where we load the data into the blobs. Since we run the BatchAdvance asynchronously, we just wait for it, and then copy """ + """ + This is the forward pass, where we load the data into the blobs. + Since we run the BatchLoader asynchronously, we just wait for it, + and then copy + """ if self.thread is not None: - self.join_worker() # wait until it is done. + self.join_worker() # wait until it is done. for top_index, name in zip(range(len(top)), self.top_names): for i in range(self.batch_size): - top[top_index].data[i, ...] = self.thread_result[name][i] #Copy the already-prepared data to caffe. - - self.dispatch_worker() # let's go again while the GPU process this batch. + # Copy the already-prepared data to caffe. + top[top_index].data[i, ...] = self.thread_result[name][i] + + # let's go again while the GPU process this batch. + self.dispatch_worker() + + def reshape(self, bottom, top): + """ + There is no need to reshape the data, since the input is of fixed size + (rows and columns) + """ + pass + + def backward(self, top, propagate_down, bottom): + """ + These layers does not back propagate + """ + pass def dispatch_worker(self): assert self.thread is None - self.thread = Thread(target=self.batch_advancer) + self.thread = Thread(target=self.batch_loader) self.thread.start() def join_worker(self): @@ -153,72 +159,96 @@ def join_worker(self): self.thread.join() self.thread = None - def backward(self, top, propagate_down, bottom): - """ this layer does not back propagate """ - pass +class BatchLoader(object): -class BatchAdvancer(): """ - This is the class that is run asynchronously and actually does the work. + This class abstracts away the loading of images. + Images can either be loaded singly, or in a batch. The latter is used for + the asyncronous data layer to preload batches while other processing is + performed. """ - def __init__(self, result, params): + + def __init__(self, params, result): self.result = result - self.batch_size = params['batch_size'] - self.im_shape = params['im_shape'] + self.batch_size = params['batch_size'] self.pascal_root = params['pascal_root'] self.im_shape = params['im_shape'] - self.indexlist = [line.rstrip('\n') for line in open(osp.join(self.pascal_root, 'ImageSets/Main', params['split'] + '.txt'))] #get list of image indexes. - self._cur = 0 # current image - self.transformer = SimpleTransformer() #this class does some simple data-manipulations + # get list of image indexes. + list_file = params['split'] + '.txt' + self.indexlist = [line.rstrip('\n') for line in open( + osp.join(self.pascal_root, 'ImageSets/Main', list_file))] + self._cur = 0 # current image + # this class does some simple data-manipulations + self.transformer = SimpleTransformer() - print "BatchAdvancer initialized with {} images".format(len(self.indexlist)) + print "BatchLoader initialized with {} images".format( + len(self.indexlist)) def __call__(self): """ - This does the same stuff as the forward layer of the synchronous layer. Exept that we store the data and labels in the result dictionary (as lists of length batchsize). + This does the same stuff as the forward layer of the synchronous layer. + Exept that we store the data and labels in the result dictionary + (as lists of length batchsize). """ self.result['data'] = [] self.result['label'] = [] for itt in range(self.batch_size): - # Did we finish an epoch? - if self._cur == len(self.indexlist): - self._cur = 0 - shuffle(self.indexlist) - - # Load an image - index = self.indexlist[self._cur] # Get the image index - im = np.asarray(Image.open(osp.join(self.pascal_root, 'JPEGImages', index + '.jpg'))) # load image - im = scipy.misc.imresize(im, self.im_shape) # resize - - # do a simple horizontal flip as data augmentation - flip = np.random.choice(2)*2-1 - im = im[:, ::flip, :] - - # Load and prepare ground truth - multilabel = np.zeros(20).astype(np.float32) - anns = load_pascal_annotation(index, self.pascal_root) - for label in anns['gt_classes']: - # in the multilabel problem we don't care how MANY instances there are of each class. Only if they are present. - multilabel[label - 1] = 1 # The "-1" is b/c we are not interested in the background class. + # Get the next image in the batch + im, multilabel = self.load_next_image() # Store in a result list. - self.result['data'].append(self.transformer.preprocess(im)) + self.result['data'].append(im) self.result['label'].append(multilabel) - self._cur += 1 + + def load_next_image(self): + """ + Load the next image in a batch. + """ + # Did we finish an epoch? + if self._cur == len(self.indexlist): + self._cur = 0 + shuffle(self.indexlist) + + # Load an image + index = self.indexlist[self._cur] # Get the image index + image_file_name = index + '.jpg' + im = np.asarray(Image.open( + osp.join(self.pascal_root, 'JPEGImages', image_file_name))) + im = scipy.misc.imresize(im, self.im_shape) # resize + + # do a simple horizontal flip as data augmentation + flip = np.random.choice(2)*2-1 + im = im[:, ::flip, :] + + # Load and prepare ground truth + multilabel = np.zeros(20).astype(np.float32) + anns = load_pascal_annotation(index, self.pascal_root) + for label in anns['gt_classes']: + # in the multilabel problem we don't care how MANY instances + # there are of each class. Only if they are present. + # The "-1" is b/c we are not interested in the background + # class. + multilabel[label - 1] = 1 + + self._cur += 1 + return self.transformer.preprocess(im), multilabel def load_pascal_annotation(index, pascal_root): """ - This code is borrowed from Ross Girshick's FAST-RCNN code (https://github.com/rbgirshick/fast-rcnn). It parses the PASCAL .xml metadata files. See publication for further details: (http://arxiv.org/abs/1504.08083). + This code is borrowed from Ross Girshick's FAST-RCNN code + (https://github.com/rbgirshick/fast-rcnn). + It parses the PASCAL .xml metadata files. + See publication for further details: (http://arxiv.org/abs/1504.08083). Thanks Ross! """ - classes = ('__background__', # always index 0 - 'aeroplane', 'bicycle', 'bird', 'boat', - 'bottle', 'bus', 'car', 'cat', 'chair', + classes = ('__background__', # always index 0 + 'aeroplane', 'bicycle', 'bird', 'boat', + 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor') @@ -226,6 +256,7 @@ def load_pascal_annotation(index, pascal_root): filename = osp.join(pascal_root, 'Annotations', index + '.xml') # print 'Loading: {}'.format(filename) + def get_data_from_tag(node, tag): return node.getElementsByTagName(tag)[0].childNodes[0].data @@ -247,16 +278,38 @@ def get_data_from_tag(node, tag): x2 = float(get_data_from_tag(obj, 'xmax')) - 1 y2 = float(get_data_from_tag(obj, 'ymax')) - 1 cls = class_to_ind[ - str(get_data_from_tag(obj, "name")).lower().strip()] + str(get_data_from_tag(obj, "name")).lower().strip()] boxes[ix, :] = [x1, y1, x2, y2] gt_classes[ix] = cls overlaps[ix, cls] = 1.0 overlaps = scipy.sparse.csr_matrix(overlaps) - return {'boxes' : boxes, + return {'boxes': boxes, 'gt_classes': gt_classes, - 'gt_overlaps' : overlaps, - 'flipped' : False, + 'gt_overlaps': overlaps, + 'flipped': False, 'index': index} + +def check_params(params): + """ + A utility function to check the parameters for the data layers. + """ + assert 'split' in params.keys( + ), 'Params must include split (train, val, or test).' + + required = ['batch_size', 'pascal_root', 'im_shape'] + for r in required: + assert r in params.keys(), 'Params must include {}'.format(r) + + +def print_info(name, params): + """ + Ouput some info regarding the class + """ + print "{} initialized for split: {}, with bs: {}, im_shape: {}.".format( + name, + params['split'], + params['batch_size'], + params['im_shape']) diff --git a/examples/pycaffe/tools.py b/examples/pycaffe/tools.py index 8e658b29a82..88b1834af1e 100644 --- a/examples/pycaffe/tools.py +++ b/examples/pycaffe/tools.py @@ -1,11 +1,14 @@ import numpy as np + class SimpleTransformer: + """ - SimpleTransformer is a simple class for preprocessing and deprocessing images for caffe. + SimpleTransformer is a simple class for preprocessing and deprocessing + images for caffe. """ - def __init__(self, mean = [128, 128, 128]): + def __init__(self, mean=[128, 128, 128]): self.mean = np.array(mean, dtype=np.float32) self.scale = 1.0 @@ -23,15 +26,16 @@ def set_scale(self, scale): def preprocess(self, im): """ - preprocess() emulate the pre-processing occuring in the vgg16 caffe prototxt. + preprocess() emulate the pre-processing occuring in the vgg16 caffe + prototxt. """ - + im = np.float32(im) - im = im[:, :, ::-1] #change to BGR + im = im[:, :, ::-1] # change to BGR im -= self.mean im *= self.scale im = im.transpose((2, 0, 1)) - + return im def deprocess(self, im): @@ -41,33 +45,38 @@ def deprocess(self, im): im = im.transpose(1, 2, 0) im /= self.scale im += self.mean - im = im[:, :, ::-1] #change to RGB - + im = im[:, :, ::-1] # change to RGB + return np.uint8(im) + class CaffeSolver: + """ - Caffesolver is a class for creating a solver.prototxt file. It sets default values and can export a solver parameter file. - Note that all parameters are stored as strings. Strings variables are stored as strings in strings. + Caffesolver is a class for creating a solver.prototxt file. It sets default + values and can export a solver parameter file. + Note that all parameters are stored as strings. Strings variables are + stored as strings in strings. """ - def __init__(self, testnet_prototxt_path = "testnet.prototxt", trainnet_prototxt_path = "trainnet.prototxt", debug = False): - + def __init__(self, testnet_prototxt_path="testnet.prototxt", + trainnet_prototxt_path="trainnet.prototxt", debug=False): + self.sp = {} # critical: self.sp['base_lr'] = '0.001' self.sp['momentum'] = '0.9' - + # speed: self.sp['test_iter'] = '100' self.sp['test_interval'] = '250' - + # looks: self.sp['display'] = '25' self.sp['snapshot'] = '2500' - self.sp['snapshot_prefix'] = '"snapshot"' # string withing a string! - + self.sp['snapshot_prefix'] = '"snapshot"' # string withing a string! + # learning rate policy self.sp['lr_policy'] = '"fixed"' @@ -80,8 +89,8 @@ def __init__(self, testnet_prototxt_path = "testnet.prototxt", trainnet_prototxt # pretty much never change these. self.sp['max_iter'] = '100000' self.sp['test_initialization'] = 'false' - self.sp['average_loss'] = '25' # this has to do with the display. - self.sp['iter_size'] = '1' #this is for accumulating gradients + self.sp['average_loss'] = '25' # this has to do with the display. + self.sp['iter_size'] = '1' # this is for accumulating gradients if (debug): self.sp['max_iter'] = '12' @@ -91,7 +100,8 @@ def __init__(self, testnet_prototxt_path = "testnet.prototxt", trainnet_prototxt def add_from_file(self, filepath): """ - Reads a caffe solver prototxt file and updates the Caffesolver instance parameters. + Reads a caffe solver prototxt file and updates the Caffesolver + instance parameters. """ with open(filepath, 'r') as f: for line in f: From 9f8f7775a890b693964049c5154ad95bfe944029 Mon Sep 17 00:00:00 2001 From: Oscar Beijbom Date: Fri, 26 Feb 2016 19:17:21 -0800 Subject: [PATCH 422/446] Finalized tutorial. Removed asyncronous layer. --- .../04-pascal_multilabel_with_datalayer.ipynb | 484 ------------------ .../pascal-multilabel-with-datalayer.ipynb | 478 +++++++++++++++++ .../layers/pascal_multilabel_datalayers.py | 99 ---- 3 files changed, 478 insertions(+), 583 deletions(-) delete mode 100644 examples/04-pascal_multilabel_with_datalayer.ipynb create mode 100644 examples/pascal-multilabel-with-datalayer.ipynb diff --git a/examples/04-pascal_multilabel_with_datalayer.ipynb b/examples/04-pascal_multilabel_with_datalayer.ipynb deleted file mode 100644 index 43aa539d594..00000000000 --- a/examples/04-pascal_multilabel_with_datalayer.ipynb +++ /dev/null @@ -1,484 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multilabel classification on PASCAL using python data-layers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial we will do multi-label classification on PASCAL VOC 2012.\n", - "\n", - "Caffe supports multi-label classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Preliminaries\n", - "\n", - "First, make sure you compile caffe using \n", - "WITH_PYTHON_LAYER := 1\n", - "\n", - "Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n", - "\n", - "Third, set paths and import modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# import some modules\n", - "import sys, os, caffe\n", - "import numpy as np\n", - "import os.path as osp\n", - "import matplotlib.pyplot as plt\n", - "\n", - "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", - "# set root directory, e.g:\n", - "pascal_root = os.path.join(caffe_root, 'data/pascal/VOC2012')\n", - "\n", - "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n", - "sys.path.append(\"pycaffe\") # the tools file is in this folder\n", - "\n", - "import tools #this contains some tools that we need\n", - "\n", - "# make sure we have the caffenet weight downloaded.\n", - "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", - " print(\"Downloading pre-trained CaffeNet model...\")\n", - " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\n", - "\n", - "# initialize caffe for gpu mode\n", - "caffe.set_mode_gpu()\n", - "caffe.set_device(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from caffe import layers as L, params as P, to_proto\n", - "from caffe.proto import caffe_pb2\n", - "\n", - "# helper function for common structures\n", - "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n", - " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n", - " num_output=nout, pad=pad, group=group)\n", - " return conv, L.ReLU(conv, in_place=True)\n", - "\n", - "# another helper function\n", - "def fc_relu(bottom, nout):\n", - " fc = L.InnerProduct(bottom, num_output=nout)\n", - " return fc, L.ReLU(fc, in_place=True)\n", - "\n", - "# yet another helper function\n", - "def max_pool(bottom, ks, stride=1):\n", - " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n", - "\n", - "# main netspec wrapper\n", - "def caffenet_multilabel(data_layer_params, datalayer):\n", - " # setup the python data layer \n", - " n = caffe.NetSpec()\n", - " n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \n", - " ntop = 2, param_str=str(data_layer_params))\n", - "\n", - " # the net itself\n", - " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\n", - " n.pool1 = max_pool(n.relu1, 3, stride=2)\n", - " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n", - " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\n", - " n.pool2 = max_pool(n.relu2, 3, stride=2)\n", - " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n", - " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\n", - " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\n", - " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\n", - " n.pool5 = max_pool(n.relu5, 3, stride=2)\n", - " n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\n", - " n.drop6 = L.Dropout(n.relu6, in_place=True)\n", - " n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\n", - " n.drop7 = L.Dropout(n.relu7, in_place=True)\n", - " n.score = L.InnerProduct(n.drop7, num_output=20)\n", - " n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\n", - " \n", - " return str(n.to_proto())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \"tools\" module" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "workdir = './pascal_multilabel_with_datalayer'\n", - "if not os.path.isdir(workdir):\n", - " os.makedirs(workdir)\n", - "\n", - "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n", - "solverprototxt.sp['display'] = \"1\"\n", - "solverprototxt.sp['base_lr'] = \"0.0001\"\n", - "solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\n", - "\n", - "# write train and val nets.\n", - "with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\n", - " # provide parameters to the data layer as a python dictionary. Easy as pie!\n", - " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n", - " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\n", - "\n", - "with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\n", - " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n", - " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This net uses a python datalayer: PascalMultilabelDataLayerSync, which is defined in ./pycaffe/layers/pascal_multilabel_datalayers.py. Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\n", - "\n", - "\n", - "Now we can load the caffe solver as usual." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BatchLoader initialized with 5717 images\n", - "PascalMultilabelDataLayerSync initialized for split: train, with bs: 128, im_shape: [227, 227].\n", - "BatchLoader initialized with 5823 images\n", - "PascalMultilabelDataLayerSync initialized for split: val, with bs: 128, im_shape: [227, 227].\n" - ] - } - ], - "source": [ - "solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\n", - "solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", - "solver.test_nets[0].share_with(solver.net)\n", - "solver.step(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check the data we have loaded." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ground truth: horse, person, \n" - ] - }, - { - "data": { - "image/png": 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yfEqfTqydq8iO0PglMmba3EMqWugs9GmbkWACPEsTjNiCLcqQyIhm99d9lYrh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\nECQkaYxl6fKhNlFVLIMQx3bxSgLDBCk1FVtZgGWhpIE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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "% matplotlib inline\n", - "from tools import SimpleTransformer\n", - "from copy import copy\n", - "transformer = SimpleTransformer() # this is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n", - "\n", - "image_index = 0 #Lets look at the first image in the batch.\n", - "plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))\n", - "gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)\n", - "classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')\n", - "print 'Ground truth: ',\n", - "for idx, val in enumerate(gtlist):\n", - " if val:\n", - " print classes[idx] + ',',\n", - "print ''" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alright. So far so good. We now have a working python datalayer that we can customize to our needs, e.g. by adding more data-augmentation or modify for other data-sets or tasks. Next, we will look at how to make it more efficient. The PascalMultilabelDataLayerSync loads the data syncronously, meaning that the GPU sits idle while the CPU loads the data. Fortunately, some simple multi-threading solves this problem. Let's do that next. First, though, lets measure the step time of this syncronous layer. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%%time\n", - "solver.step(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "Now, let's setup solvers and nets with the PascalMultilabelDataLayerAsync layer. Take a look at the code in ./pycaffe/layers/pascal_multilabel_datalayers.py, it's not hard." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "workdir = './pascal_multilabel_with_datalayer'\n", - "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet_async.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet_async.prototxt\"))\n", - "solverprototxt.sp['display'] = \"1\"\n", - "solverprototxt.sp['base_lr'] = \"0.0001\"\n", - "solverprototxt.write(osp.join(workdir, 'solver_async.prototxt'))\n", - "\n", - "# write train and val nets.\n", - "with open(osp.join(workdir, 'trainnet_async.prototxt'), 'w') as f:\n", - " # provide parameters to the data layer as a python dictionary. Easy as pie!\n", - " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n", - " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerAsync'))\n", - "\n", - "with open(osp.join(workdir, 'valnet_async.prototxt'), 'w') as f:\n", - " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n", - " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerAsync'))\n", - "\n", - "solver_async = caffe.SGDSolver(osp.join(workdir, 'solver_async.prototxt'))\n", - "solver_async.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", - "solver_async.test_nets[0].share_with(solver_async.net)\n", - "solver_async.step(1)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check runtime ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%%time\n", - "solver_async.step(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alright, that is a modest runtime gain. However, as you data pre-processing becomes more complicated, this difference will increase." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's train the net for a while. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def hamming_distance(gt, est):\n", - " return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n", - "\n", - "\n", - "def check_accuracy(net, num_batches, batch_size = 128):\n", - " acc = 0.0\n", - " for t in range(num_batches):\n", - " net.forward()\n", - " gts = net.blobs['label'].data\n", - " ests = net.blobs['score'].data > 0\n", - " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n", - " acc += hamming_distance(gt, est)\n", - " return acc / (num_batches * batch_size)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alright, let's train." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for itt in range(500):\n", - " solver_async.step(1)\n", - " if itt % 100 == 0:\n", - " print 'itt:{}'.format(itt), 'accuracy:{0:.4f}'.format(check_accuracy(solver_async.test_nets[0], 10))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great, accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n", - " acc = 0.0\n", - " for t in range(num_batches):\n", - " net.forward()\n", - " gts = net.blobs['label'].data\n", - " ests = np.zeros((batch_size, len(gts)))\n", - " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n", - " acc += hamming_distance(gt, est)\n", - " return acc / (num_batches * batch_size)\n", - "\n", - "print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver_async.test_nets[0], 5823/128))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "Let's wrap this up by looking at some qualitative results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "% matplotlib inline\n", - "from tools import SimpleTransformer\n", - "from copy import copy\n", - "transformer = SimpleTransformer() # this is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n", - "\n", - "image_index = 0 #Lets look at the first image in the batch.\n", - "test_net = solver_async.test_nets[0]\n", - "test_net.forward()\n", - "plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))\n", - "gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)\n", - "estlist = test_net.blobs['score'].data[image_index, ...] > 0\n", - "classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')\n", - "print 'Ground truth: ',\n", - "for idx, val in enumerate(gtlist):\n", - " if val:\n", - " print classes[idx] + ',',\n", - "\n", - "print '' \n", - "print 'Estimated: ',\n", - "for idx, val in enumerate(estlist):\n", - " if val == 1:\n", - " print classes[idx] + ','," - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "description": "Multilabel classification on PASCAL using python data-layers.", - "example_name": "PASCAL Multilabel with python datalayer", - "include_in_docs": true, - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/pascal-multilabel-with-datalayer.ipynb b/examples/pascal-multilabel-with-datalayer.ipynb new file mode 100644 index 00000000000..fd66114d8e9 --- /dev/null +++ b/examples/pascal-multilabel-with-datalayer.ipynb @@ -0,0 +1,478 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multilabel classification on PASCAL using python data-layers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we will do multilabel classification on PASCAL VOC 2012.\n", + "\n", + "Multilabel classification is a generalization of multiclass classification, where each instance (image) can belong to many classes. For example, an image may both belong to a \"beach\" category and a \"vacation pictures\" category. In multiclass classification, on the other hand, each image belongs to a single class.\n", + "\n", + "Caffe supports multilabel classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Preliminaries\n", + "\n", + "* First, make sure you compile caffe using\n", + "WITH_PYTHON_LAYER := 1\n", + "\n", + "* Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n", + "\n", + "* Third, import modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import sys \n", + "import os\n", + "\n", + "import numpy as np\n", + "import os.path as osp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from copy import copy\n", + "\n", + "% matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (6, 6)\n", + "\n", + "caffe_root = '../' # this file is expected to be in {caffe_root}/examples\n", + "sys.path.append(caffe_root + 'python')\n", + "import caffe # If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path.\n", + "\n", + "from caffe import layers as L, params as P # Shortcuts to define the net prototxt.\n", + "\n", + "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n", + "sys.path.append(\"pycaffe\") # the tools file is in this folder\n", + "\n", + "import tools #this contains some tools that we need" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Fourth, set data directories and initialize caffe" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set data root directory, e.g:\n", + "pascal_root = osp.join(caffe_root, 'data/pascal/VOC2012')\n", + "\n", + "# these are the PASCAL classes, we'll need them later.\n", + "classes = np.asarray(['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'])\n", + "\n", + "# make sure we have the caffenet weight downloaded.\n", + "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n", + " print(\"Downloading pre-trained CaffeNet model...\")\n", + " !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\n", + "\n", + "# initialize caffe for gpu mode\n", + "caffe.set_mode_gpu()\n", + "caffe.set_device(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Define network prototxts\n", + "\n", + "* Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# helper function for common structures\n", + "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n", + " conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n", + " num_output=nout, pad=pad, group=group)\n", + " return conv, L.ReLU(conv, in_place=True)\n", + "\n", + "# another helper function\n", + "def fc_relu(bottom, nout):\n", + " fc = L.InnerProduct(bottom, num_output=nout)\n", + " return fc, L.ReLU(fc, in_place=True)\n", + "\n", + "# yet another helper function\n", + "def max_pool(bottom, ks, stride=1):\n", + " return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n", + "\n", + "# main netspec wrapper\n", + "def caffenet_multilabel(data_layer_params, datalayer):\n", + " # setup the python data layer \n", + " n = caffe.NetSpec()\n", + " n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \n", + " ntop = 2, param_str=str(data_layer_params))\n", + "\n", + " # the net itself\n", + " n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\n", + " n.pool1 = max_pool(n.relu1, 3, stride=2)\n", + " n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n", + " n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\n", + " n.pool2 = max_pool(n.relu2, 3, stride=2)\n", + " n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n", + " n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\n", + " n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\n", + " n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\n", + " n.pool5 = max_pool(n.relu5, 3, stride=2)\n", + " n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\n", + " n.drop6 = L.Dropout(n.relu6, in_place=True)\n", + " n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\n", + " n.drop7 = L.Dropout(n.relu7, in_place=True)\n", + " n.score = L.InnerProduct(n.drop7, num_output=20)\n", + " n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\n", + " \n", + " return str(n.to_proto())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Write nets and solver files\n", + "\n", + "* Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \"tools\" module" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "workdir = './pascal_multilabel_with_datalayer'\n", + "if not os.path.isdir(workdir):\n", + " os.makedirs(workdir)\n", + "\n", + "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n", + "solverprototxt.sp['display'] = \"1\"\n", + "solverprototxt.sp['base_lr'] = \"0.0001\"\n", + "solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\n", + "\n", + "# write train net.\n", + "with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\n", + " # provide parameters to the data layer as a python dictionary. Easy as pie!\n", + " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n", + " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\n", + "\n", + "# write validation net.\n", + "with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\n", + " data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n", + " f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* This net uses a python datalayer: 'PascalMultilabelDataLayerSync', which is defined in './pycaffe/layers/pascal_multilabel_datalayers.py'. \n", + "\n", + "* Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\n", + "\n", + "* Now we can load the caffe solver as usual." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BatchLoader initialized with 5717 images\n", + "PascalMultilabelDataLayerSync initialized for split: train, with bs: 128, im_shape: [227, 227].\n", + "BatchLoader initialized with 5823 images\n", + "PascalMultilabelDataLayerSync initialized for split: val, with bs: 128, im_shape: [227, 227].\n" + ] + } + ], + "source": [ + "solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\n", + "solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n", + "solver.test_nets[0].share_with(solver.net)\n", + "solver.step(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Let's check the data we have loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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QFN44TWaiBFYSW67ATBkn5xuRuVUkhi6l+hUo07CNwKdlJCYkCypcljbHvoibNJJFCsBL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\nYb2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9ZrNesbnbo40lp0iYJsaL3LuYtFBPqcjKkfKAJzDEnsuU8UmhjEWlwMIcUjfP4v/H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ms0IlK897IZMylGQxwVP0Mx2XSjDQxEDb7/jHP/8jP/74HVkNx1YjNFM1dm/7R56fQIVe\nD+6kz209WF4Pt7oeypo6ZUBN6qSAlqLJP+U1iFoCLF5ff9EwCUMgeEMIjf53wlDKumBOueDdgreW\n5JyAL4Ir6mS3qJWEvI9dEGqmMHrEP79Wg6kWykIMlvv7IzlVhmHg6fkZbyvZVWYylkTXH+i7vbDv\njGD4wq3PWO/Z+06xd8OcK/OUJSXqdx5/SCGfauV6nUgnCZM9nU9crwPjOLPMmcGPm/MhekIPw5WX\nFy8XRikaPJDINW0XpU7KG65pgCUlTpezGtBLITe66PDOYyfHOI8ApLyn71uMEyFJNUYc+GKk63qm\nado+eKfLO+ucOL05Sd5Zx24QHu6yzNRFRtF14Yq1LKXgtJt+fnnh6eWZ6zCw33X0fUvb+C11aJ5H\npbs6qrGkXDmfB7GM7eT7Q3CUvJCK2I1aIzzbWrKm1h/ZdS2ny4Wnk+R3Ahx2stw8HA5EZ4ne4Z2a\nVi1CB5XX9spykNex+klojJli4etnsZr/W+cE37Myfs7LwnW48vxyogL3797JzmMneZ7BiqLWGEOb\nOt5/+CgCsOjxseE6DpwuIm+2mM0zfO2+YoxywztZfALClFHGz36/V2aFRjwoW6hkoBhVJsqklRYJ\n9BjHyjyNm/hpxXqtKn/3O0cTW9q2p+93XIcZ6wTPX5YF4yqFvHlPr46HKETxamoFkqajmY/azeUs\niUVpnul3HfvDTgy/Vh50latfxDZrsYb1lJAv/b268xWa0YNAf5+UeSUOO/JdqVRwlmQNU0kEa+j6\nlo8/fuTjdx/4+PEDeVmYBlHUppyZZqXWbacVAjVap8ZSXp05VXinIdpQRBHprU4M6vlo1L3TrIlG\na5FdO3t9sdpQyCFYlNFkMSreMwoBWmMJCNOoVo1XVCbb8/ML5/NZUqg0rERovJKMVHWhXU1Wef+a\nm2toosW7DuioFcaxI0ZP1/eE2GDsauKXyflKLWJZIfed7AAsciBZpA6VCsUa6puG6O3jDynkL9cr\n07QwDDPjPJHSRFoWlrkwlZl1ky5+12JOzypowahhfEYkHYKlrUG7Kz3QqM8zs57KFZXpFpyRjrhr\nhc8ZykKIHucNxkFoIqR1CZVZbVabpmHJZTv1U8nUtIhJvtK3JNlDgnuNsYzjQNbsTaNxad5LsVrT\nVZ6eXyQIYpnBtHRdS/A90zjx9PLC5XplSTNMAylXxmnBVMEOu67j7u6Wfie8+Y1WV2ZqWjQrMrPr\npVAuaaE+i0GVj9JFtrEhBhHPoNSpZZLxs+SiY7Nwt6ULf0sDVKxYO+L1EFu0c2jaVnImtV08n048\nPj/x+PTEje47Pn74+FpkVK4dTGRnLP3+oB7piesgEMYwXKlV4uRkORo2T3BrLdELr1vsClD7gRna\nVvi6ufByOnE+n7nO0/Y6Skp0bVROd1H0efW9KCxmVsxa4Je4qnyNl0QaF2hiy/6QSdkQmygsHvWI\nf8t1B3SKfFvIpXd0FpXHW0oxpCr2EcM0YYLDdQ02Bkjqua33wLq4fPUYXztds2H0r127Fnuzdqwb\n2i5FiUpWSGSDf5wnG5hrZbIGv+/4/h9+4PbuyM1+Lxmwve6NponrMGJ1+c+bPYGkSHmFUpQiW+Fy\nHRiHM1A4GEPTWoVj1gPJbFOiqagTokAeTkOeNy7OtifRjt1YiZr71XSkVgxOU60UKjQYvn79xpev\n35hLUdpySxscjRIanBPVtUEEOyv1uFRoo+DiIkKy9H2m61pilASuUsE4iSKc00jJBmsbvPHb8Gb0\nCLUGqpVQnWIt5fch8j+mkP/06WdyqiILz2qapBjUWnhkKy8LIKl3OmMaMDVrUsZKa5GdF7VoLqDB\n6A1EfQ1IrZp5KJQ3iymZfYjs9j27vsN7oTCleSYvC845dn3HUiQCbr/fM86z2iUKkyblrNFjqHfI\nzDhemWbhLo+DWLN6H5imeRODdF3Hfr+n6zumecFYEYDkXPDOc3d7x67reHx64tPXLzyfXpjnYYMQ\nxBPAcj6dSTlxm4/s9jtmtQkNQXIwr5cLnz592ixfp3kCZ7m7u2V/c8NwOTNcL1BEVi+ysCvshgAA\nIABJREFUeChqfrTictZUxWLrVqxrrRtWvBbx9c8k+Flsh1faWSlFFmGXK945jocdh12PLVk9SsRe\n1umEg/VM88Lz+ZnHpweGywBGlr37/Z7Dfi8CHWtlp1CKcG6blrYR++JahGee9bCZ1czr4fGJh6cn\nHp6fGKcJYwx91/Hdh/d0fdRQ3nabPFaRUEp57XMpsH2my5KkGckixcZGmu5A0zZULEt67X5RGKSo\nYlMIIK+dpbdW2C/WSgoOIq6ZrOGpZi6u4nY96XylJKXDrXDKrzDU1w79Vfn5Wsj1rpKFr3kFQEop\nJFspVq1gV9jHQI2e2niWnKWQ//inbRFvvExfsY30u55+nFiyU/fBtZ7LcnA10JJIQHkfH56e+Pz5\nZ5Zl5s9//jMf3ksqlR5FMqHoQSTwyrpTkEbpFat6ZfAUXXBWW7HFsnLJVxuH9X2HoH5Csh/KKfP4\n+Mx/+ctfyBX6tue477g7Hrm9PXKzP2hxDlhngIKxKhZSjFvESFYtPTQukRXuyVRT8c7obqJiaiZo\nWlapGqdn3hzUBoz9N4SRD5ezFm35BWx1el1UrYqvXBLLPArnso3c3OwxWJGuDxPzopQoI9t97x2u\nGKhiSdr4gFe8Vy5kPYW93fzPu6bh9njDfr/Hh0BaFnVfmxiHKyEEdrsdd3f3DNPEnBYt+kLeP5+l\nmM6zyM3VZuPNL1kCioOd1e5kXcKsuYG32u0J7XDXdbR6+vf7HafzmW8Pjzw/n5nnEbHhlUzPagrz\nMtNfr1jraFp5PaGJ7J1YqE7DIApFL6HOTdNye7wlWMvT4zc+/+UXFC7GGLFx7duG/V4j4tqWGP2W\nTlRrVVMuKdxrwgnErcNZ7V5XrLyUwt3xli7K4qbrGjoVPBhjSbq5vzw/byHUPojx0c3hyO3NnaaL\nC2zhFa4Rr3qnSy4RIqVl5nKedbGVN778mlLe73qMs3T7HddhoNRM2zTs9v0WyGzN6okihcQ5j7We\nYRwVPkiUlJhHcT+8nM9M80K1lpvb93S7G6mByPX51gt8w3XVwGtJi0xsXplCvCYQQeWyJMZvD3y6\nnDkbcMc9aV6oqUioxRtcXIrm2yL+Cou9ZbTU9ZslsQEo4m+lyU/FiFcM1lKdkTBwbzFR3vt4PNDt\n95Krebm8Fq8qTVNOaiC7vgcrAF7XGmq21KRaC5frwLeHJ8Zx4P7+HbfHO1q9VtbsyxAiToZ1qRcp\nsdgVYtO/ZS3gOv2skXqrrbFZP1OFKVZv8E1B7Bx3t3f88P2P/PT5gYfHFy7nJx6fXvjp84NcJ13L\n7c2B2+OBw0GCa2KUwHHvDdYUjKq+VxkSdRUMyTMVOBBylsOopIlarLznyES2fUjFiI3v79fxP6aQ\np2UUbLJAzlUoOuuFpcQmMbSvrDabMQqm1bYN3su41TYt81LUpMjRarBCymIf38ZA37U0TVDMvWJt\n0HGx4i3su8DxsON4c0O/2+G9Z55nTdGpnC8zbdvS9z03NwfCFLlOk8rT1TfkemFZZnHAW2aaRoqN\nLMEajCa829XvQbFRH0TgsOt3qmz0WGvwxhKsmAn1XU/bdRwON1grLo2Pj89QE7Ua5mWmnitzSpzV\nuvZw2LPb9TRtJDSRm+MtZyOiiphbprQQfEPbSjc7TSNfv37h5XxhuIpyttfDzXlJTFrVlcJUke52\nGEbFzJ0WObvx00E+L4EtJbhX7BNu/n/m3qxHkiTL0vtk0d3MfAuPzKysZWrpwYDAoBtDgP8fBAiQ\nzwOQmKV7smvLyIj0zczUVFVWPlxRNa/qfM+2gqMqKsI3NVWRK/ee8x1udhJ5FVPAGC2wIJPJoWBL\nl5l5lnBuU4lapBp6Gltvs4kYxGXpnLTi1sxDr5QYQkLgeDpLz1VR7O6rCqWmaVsONwcSMLtlk0DK\nIlFs5vlKOoxpbffpQlgsrae1reQ93sl/6+I9kErtOlhcWwtSxAjgal4WYc7MM/vDYfMhwFqFShbl\n6Dynpwufp5mL0ZjbHWqcwEdy9Ftr5P0w+qde/2YxXz8RyuzomoW6DeCMAMSSUdKntRbVNuihh7rG\nhYhJc2m9lRDtVNpFSvM32sPye4kK5XpNUCVFaglMs8OHNVk+bzAz4fDUmFXUEEtCkGeblbxPi1r/\n9/ohC7kphdbVILXNtMoKq5Tm5vaWX0X466cfmafA56cXTlHkpmRhoXy4u+Hx4ZaHuxu6rqWphSfe\ntaKj7/oebUTltrV3WX9f2SRlXpNL6HiUE0NR8qxSUpkDqPeX8N+8fpaFXKdALkGuiSTDs6raelQp\nKmKSI37bNuyGXeGk1DR1S981HA4HPtw/UNXCoV6ZF6fziU+fvmfXtez7jqFrickVjkagaddBEVit\naKymKfK69Rje9z1933Nze8uyzKJr7rpNi6pQaKuxGEiJqdJUphZ3qK1X7zg5J7p+V25uRd/3aCPJ\n4PMyyWZSgjR8WFjcZTtF5KoBC8lFYpYp9v3tHTkr3t5OzPOFnBPGVmTixtUGCMEzDMMmCQxOqr1+\n2GOM4XQZsbYhJcXhcMd+v+fj11/x3Xf/yp//9Bd+/PJFqtuqoq5b+t1OWgQ58/b2xjiOW6xc3/fs\ndgPW1htQH9jwBevCvqoiKlvLw6IVRldbtUUIpJgJMdIPA7e3d7LRKCNDtGViulxYYUjzPLEs87aY\nS1tCZiR1CTpeZXB103B/L9iFpm2xBSiWsqTLKHNb+tGJZV4Ii7TW1oc/hIJyzZmQBQAmlMYKVVUM\nXYe6vUV9I8RK2zS03Y5sxOtQTsbXxTWv6AQtgcOfP3M8HvnDH/6BvhPdecqZWNQUs4HX6Pm0nBlJ\nxKZGW4s5z3J/LFclw/vK8u/VK3+/uEvPWZE2AQBYnbF6XWQ0i9Jko8lW2izSI084rViUYk6ZoDRJ\nKZHw6pUrX4aRSm05o/I9ufa5YRunKiXzlL7v5cTUdRt3R5uVBigUTE0ugdviGVgHlOupe5V4vn+t\ni/u6aL/3Aryv1HW575u24eHhjn/4/W+5XCZeTydigEBBA6TE7DynUZ7DEEKBfMHNYc/XX3/kD7//\nDZ2t0SrjixBgLeLQRk5gpeo2xbdAuSax+FZW5tCG1f33VJEPQy8DMzQpStVlrMCsVlJfypKXt7ZD\nTHEWVpU457yD83mhbuUizYtUZjF4hqHjw/0NXVORY+RynEUWFSI5Zobdnt1uR1NbtBJHZQyx9Kmr\nbXBpK7sNJOdpkkqsMEBWYFSMgbZtC1vEXB1mWcjNOSVO05lxurDb7WmbRoaHrLwRs50WVOEYA6UN\ncL3ZVBnQvb68kHOUTEBdlYGqIzhPVTegNMs08/Tjk7Rz6pp5vEgIRN1w2B8YBsH/nk4nYnC0nSCB\nf/f7P/Dw4SOvzy8Etwhrum1k0DaOTJeRsei667oqtm69LXLLMpOScEE21yCQoyyY2hjmomgARd1Y\nqiKjtFpT5YwNfkP35iwBxMEtOCd9bGMNxMRlmvjh82eeX56IKdK1Lfvdjrubm5Iypelshy35nkMv\nx1+tFG6Zcc6LhryqyNHgfOB8vvDy9Mp0mSAm+q4pWa/miqAtape1L55SJMeVv2HE8VkLO2XtCOf3\n0rpNtSKL1zRNHI9HLpdJqvyUBcyUBFUxO8+rCjwTOKqELwoUqwz2sIM5kCZH9qFQrOR76VXFUV5/\nbxRSXM1AqihetMqSdmOkEkdpYiFGKq03rEEui+fL8ch//+5fiY93fHtz4KHvaUrrLaVMSJklitNa\nr01e1nbjOtzOoKSgefzwATIsy8zd/T22qkSxVNWlUKjk53jHonmvznmvxrluWgpRxbM9kyssZqWD\nLsVfsQ5Vq8LXV0pxexh4eDhw99ST3s6QFFmLPHfoWomoVFroh7NjXgKLjzRdR9aG/eFA19S4xZV5\nksM5jy/h4Anxprzvq4NGZ6nUVSpAsRy2ltRPvX6WhfzDhwfWo1XwK8RIcg1TmUKnLQm9HEm0MEjk\nzVM4n3g7jlTzsvUVYwxUlWG360BlnF+4jCMvz8+4xaFQNE4gUZWxEC0xepybcM5jjICJuj5IuG9l\n5bheuBAhylBOzCYSR2e0pt/ttiTwHAv8qFSk0zIzTiPPb6+4GNj1PV3TUhmDqgoxUaIhy7E+k2Ig\nlLR4aWFMTG7h9e2Nt9MRXXrcVS0LuTEL8+xJOZCTxi0Lx9c32qalaRumeSKFQE6JYdhRtw0pZ+Zl\nIkQDWQZ7Xb/ncLjhw8MD83ghOkcKktByOh45nY54L9V+VQ+FmVJvGnE56kbZDAtvO8WIC7EcJQ3n\n6SJHcWPpUkMVri46H+TkpIIi6IBXDj8veCeLf1PA/EpLS8CXoGVlNIM1m7N2DTqurBU3Zt1S1XU5\nvsssw/sgC6xWjOeR4+nMy+uRpy/PXMYZlWE3dOyGjq5vQZWczqJECSEwzZOgCpK0IwYaieQqYr6/\nGb1ti+pqDV99BeKUFUlevR2lcxYX4yV4jnhOOjLZ0lsFUVf1DdW+I40NaczkFKQjqTbV9ibbk9f7\nVWA9HSgpIMhUZBqjabWSloBSLFbhtYQvr8d9EGv+2zjyL3/9KzeN5eN+T9f34tpGQjNcTAQy2f/b\nb8t6lcqPZLTh5uYGay3eO3a7QZbgBNZWW0ttHTKjpI0jrcqCIigs9VwG8+QSZKF1WRDzdgnezyvW\nxdx7MfbVTUUInqquqBvDh4cbvv36kZwz02zJGZqqou8auroS1ZoSI1WIiTgvXJaFBLSt3EO+dtv3\nmaaJOE74UAKey0VRZdiPNoAp70spCFcm/DvO/PvXz7KQ/+Y3vyZFYVOcTyNwdey5UBQAQVxgIUTh\nMRdJlc1FiK81PmXC4jaIUIxBqi6t+Rw+4/0s4anjhFaarm0IWfqbL88vKMC5mXmZJQJNKeGWfLjn\n4+MH7m5vqN/lQmaQ4WKCvm83tspagXovxiBrbdEme2Gfi8BEZhZZrOTROfkIYsAxJTN0UzMojbWa\neZl4en7mz9//lZhTaXc09ENH13U0tUjsLuOFT59+KETDgM/w+vRM1TZkI7mCIScmv2CbIs9ra3a7\nTuSOlWVeRAaqgYeHe1KITONJ2ihIuEHbiRmqKy0KcUVWRbctN1nXdQLxKjCt6TIxzY7ZB47jRAL6\nYUfXtmXDnbHGyJHSy/xB5JkVrgwWY04M+x1N7CTxaLfjm2++Yb/fU7UVt4cDt/s9tbEkElllKmPY\n9QNd3ZEyXC4XFueLy65BG03MipfnT3z6/JnTOHK5LLglkBOcxrNQ+dqatqnpupZ+6ETJFAKXaWJZ\nZPA99B3t0NPverqho2oaXDL4cF3E11bGdSFXPHy4p+06QLM/3Eivt6irQs5cUuKiE7NKRHUddiUU\nqjaYXYO6HVii4CzEsWK24Wouw7Irn7w8hBvTXLTKFZlWQ280rUFOHkazGEUw4LUq6F8Z0GVg8oEv\n55FLTFA3DPsbGqT/Pzsvi2hKqFC+HzLgJK99bFhPJiBpXX3fEaLQJaWllcvAXHTzRcmI0opaWWnd\n5JV1H1nNVu93jFW2mFfJC+9aPKsbubRdcsrEaOQ5L9fq8eGeyhrqynI6X6SFmVMZhlM+rgNZlGwu\n3otRLacapQQeZ2yFrWoWV04Chd+/ftRNgzaV8FnezS4MqwLv35Fq5fe//pVwSwp/1/tYSH2asWiF\nj+eREDUmCvdBFRlOSpGYysOQC5ehSKRWCts4zngnOYL73YGh35dQAtFpLvPCOE6smZDeeUJheizL\nwuwmxsuZ4/GOu8NBwD5FgTHNE25x1FVV3vRVEXFFul4uF1xwJKBuau5ubtnt9tQrAEtJj1Bcau/t\n1dJ3Na3dSH4xKvaHwDdkkXY1ctxv6nrTT8eYmPqJylreXk+czxfJOF0qqV6sEYdYkNNODNIOEhlm\nTVNnklGs8XZRRcbLJID8quGbb37B/rDnfD4CSdg01pYJ/TvkrrqGE6cYCSnhY+A8XXh6fuPL0wun\ni+QQVnVpMSlFVpHd0NPUkksZnKMqGnkFhWkSrmqVEroxNC3x/p6QI421VErj5pmAxGupWljkIQah\nZCrBQ1BVsil7z/E8Fo69pmlaliWitCw0AamOCZGkPC5FxmVivDR0bcvd4VYkaOt70jQy6LYVCYtL\n75EO8v6+1zprLfFmTd2S0VKNATFnphQ458Crjlx0JpRFXOoBVaq/QGcVh7uBbtczvZx4+fxSBAB/\n105dK9H159C6LAoCmxus5mAMO5NLWo4oZoKS1tCoMt7I4p5L7mXIMMXIX1+P/PHLMx+GPXkchb0d\nAqqp0FWPrnaspxTF2sNORbGithPsdg+VhCtdBAeQSTkWZorQCzWQC8ZavuaaDhXetZDEWGWVJpdq\nXdQjeRvGru/DOszPOm/P+jrbaZqGYRi4ub1hXhzTvHA+nTi+vXI+j7jFb4NMazSm0lS1IviJ8+kZ\nkxfh92eRsbrFi79kHbCX50drLSx7H3B+kYALdTVybZKXn3j9LAv5YRhYwfKHoSOmFXQE4zTzejpJ\n9ZoSIQZMXnfRlS2cNnxpztdJsyn9y8WJokNrgU315UGrawtkxouE8oYCwAoh4hZHKHZ/HwLnccQa\nAWltHPDqmobjvb/ah/O1R6e1sM59CKiCQW1WI0BJ6VasIbbye4UQgQTGYBpLXTcl/Ug078NuoGrq\n69dr6tJzlK8TdUJ3EkfWVg1tfeTl5UhWSfguQcnQMMrgNMQgk/W+l6xNLVwRuRlFgeILXqBqWrq+\npela6q7Zqj5FxpbWgAylrsqMnGVusTjHtCyM88x5unAcz5zHC4sPrA+qsRpbGXIMxK6hsprgHL4M\nnzc4ViqnspTIMUpAspG4PT875tnjUpbcThK60uhhwFceqywxSZWmlWjOF+e4TBPn05kYU1EUFXJi\n6VdqI1PKNS0HpYo5ScKQ724O3N3d0rad4FWNlfcjJd6OF1yQhkVJuS73CFv7EERNY0sRnko7JZA4\nuolnd+GsAosWZo2Cv6HjqhjZNRW/frjhq+HA9Dry5933fP/pR07jhPeJzfquyjdfv46CujLU1shm\ngGIg0yA+B7S8L6aryyxJcTYaZ3SJqzEkpfAJPr0e+a77kft+oF1mTBRXtVWZSjfodWSgKKds/y4U\n41oMxRKosaqe1h9b1q4yN0Oq7phFW6+VDAfF/BPkFJyK1UOvi3n5OkWEsCqJVszEilJelS+rcGJN\nflr/9wf1gRDj5vz88csXfnx64uX5jdkFtPPorNntO25uBvquIrgLl7On6VpSlKQz5wI5hS3m732b\n5zyOLMvMvARBb5TBsCn981Uy+fevn2UhH89nUQUgg8/d0GGrmpQyNyGw3+0wq7rDLcWiW3YmnUtv\nsyzc74dHWuQ8ctAyxKhYFs/tsGff72kLbOvu9p6MJBOkpPA+cj6dGc8n5uXCyjVuKunNNW1DP/TS\n79rtAVU4GLlM1K/xZCklkpIe4opu1VmRYyIu0utdHWdiV9bFBUoJD5AqcNU9p5wlE1NrqSy98CCm\nRY5tot2WjaypKtoPD9wc9twc9nx5eeXtfGFykbZqUKph8ZnFLUUDntHasvGYkQTxum6KjlmqE5QS\nGWDXy+aRZVHNMZYj5RWluj6U0zxzvlzEiTkvZAWH24Mczy/zpsUX/EEjCUGVBDybpikUOU/Owq5p\n25quqdBk3GXCqYnFey7TxNv5BDlRFR15RCz9tbX42lGZCmNqYoy4AuI6j2cu84wPYTteXy4XIUWm\nhDUVRkuog9KZYWjo+o66rhjaln3Xcxh27FqRh9qqgqKGGS8XPn/+EUxHv7+XYz2RNQxhXbRkIbtS\n/XJWxJzxOfHl/Man8wsTnoiwOFTpK6gMFkUVEo83B/7xH37L73/xS7LL/PlPn/k//6//m+/+9S8c\no6TNKLW2EtT2rOSsGLqG+5s990ODnWfUZZKwYQCjqdqK+4c7Qt9RLY7v3UJImaxkHqSUJgI/ni98\n9+WJfdvwH+/v+er2jr6V6xFVVZjkQBbZ4PvQamN0IXFOHI9n9vs9Nzc327O0RviVUgml5BrmGCGL\nfHUt8FIMwsFJCbXOE0wuvXKpbnOR/8n1MNsJ0hbDHLD92VoJPhHvhC73d6CtG/bDntubG25v7/jO\n/hEXEpPzhBy4/3DDt7/8im+//cj8+oyfxwIDEwxvCJmubYriTtQ5y7JwPB45nc7yvKDI2W73Zixt\nG/PTBfnPs5DntAYEZ7wLZGasD6iiD9Y5cXfY4f091mqO5xG/wuGVRq+6bCXKhnW3t7ZYevPVhOAj\n/Ph25mWcyEYyNvu25rBr2e97aXMYQ3U4cNj3kBOmMqzIyrZpyuCzRmnFUExDKyVOr2aErfcmA4vg\nIss883K54OaFFAK7YZB4sIL6XKflwzDIsSolXl9fSltFhigi6ZPq1E2TnAS8qGzIEomnyuCnqetN\nFldXtmBZLU/PLygiKczEqEvFrTmfT0INLA9VVZlynMtolbBGvtZutxezUlVLvzqBUYqqrlZvHSkm\ngpcq93Q68fJ25O14FCv85YJzgg9oG+k3Oy9BBVVl6fqOoetoqqooOS6kJJubMoa6bRiGvnCnpZd+\nHkdm53AxUK05iVpjtRXAv4LxPOIXj9GvxKQ2lYL3EopBwbNapQvxsCoERelfa0RFZKwRFkxTY6yh\na2qGtmPXDXRNLy29DMEFzpcLzy+vvJ1HuqFi0GrrR+f1IyFmOMklIyUIMZOUYkqB1/nCl3nkLSwE\nU3rs+Xr6zCGSXGDQmjZn3PHMk/6BxjYchpr/43//z/zy26/46/efi8FmLYak39x3Hfd3t/yH3/yS\nX3/7NYe+hmnGn0fmcRSHbVXR7Xf0twcmBf/85Zn/53/+Ty5PLwThvpKQxTHkzMkvfDqf+E+/+iX7\n+3t2VuNzZvbCNRd1ysoEL7sRAivTRtF2rQzK60qaMOna/khBWDPRG3Rr0VkMVM55UduURS7FwhfK\nMvhdgy9ypuA5FEqZ7T0wZi1CrtygDQNQPlY3ckqJZXElBN7jfWBeZiY30w0djx8/0O8HfAp8eLzn\n5nCDKfLCECPj8YjWlpjBhcjucCdJZF0Lhdkjz3ArGvqMyKTXk7cWPgz/nhbyL09PpbelCutApH5N\nAR6BPJRD2+B3PcZopsWz+LAZM7QSqZ+kmqdyY0iFs7nnUPio8NNcKh2JghraGu8GcsriWqwbuq7G\n2LXHVY7zWUiIpmBTQ/BkJawIWxadjGxMKWdWhrU1lmBkaOu9Z5pnovc0dSVD0tqyLDDNF07nEWWk\nD6egaEfXAZBljdeanUy7Vyu4VrIgx6KcSTkgtpAyFLaW26ICsEqREjgfuMwLMXmCF7R+CK4gRSN1\nZVBKQGVNpWhrQ+wEoWurBkrUVc75nZwslYcncrmceHl55eXtjdM4cioV+eIkU7FtGoFz1VU5Wss1\nq+qKtl4X6SwqIVdwozGKA7PrSnUpD/jinOjHVcY2FZU2IpesW8lPDyL1uvgLKUslNE2r5jzLSatp\n6LTIP7XSMrBsKnIuAQi1uE6roiu21qCMoq4tTdXQVB0pacbzxOk8MjnP2+nE89srr+eRx7oXg97K\n1lalZ16yI1dQVozyOzkFZ7fw+fTKq5uYcpAhOWyr+Rpn1uTM47Djtm6I08xrfKZrOrqu55e/eOTh\n/sC3Xz/y/HoqGAFpTTRNw36/48P9Pb/69mu+/vhAWxmS8/hpErY/Cl1Z2q6j7jvmmOjvXvj+eOT5\nMvPifFGOQFKZqBIX7/l8OvHmHEEr2q5DhYBPAcWarIW0RaIvkDUJz2hbOZF1ff/OUPQuJSlLoZBS\nKi02SXeO0RV6Yi6JU9fKW57MVD6Kez9KV2gVtie1UjlVkbv+rXno72df0yxAMFdQD2sGbFVb7h5u\nuEkHQkrs9gNd228bhneB8XxGW0tMMDuPrVv6YUddV+SCLFBK0RUk92oiCvGqklvnPT/1+lkW8v/6\n3/6/kjtpSz6lpmkq9n3P0ArcSVtDWCYsmYfDnrfThRwunNyM0iWhvfQsc6G1+QJ5ojyIMrwQuaLA\n7WRLGyePn1+5XBa++vhA97GnaiQNSIwMibhN+2152ALTJCqYEIJI8Eo7JcSARezbprJYMg1sk/K6\nqgne/Y0sLhfq418//YCPgbu7u8Jv6KjsanE3uGVhHEfe3o7bQMgvC3XJSZxntZmZgpdjuzGGYbdj\nX7AD97d3pJQ4nc98+vwDz69HkVdVwpZxTnE6CpwsJQ9xZqg0+6GC0JLCRN3sqOpeeseqbGw+oHNA\nIRjPt7cvfP/D95zGC8pW2EpwAW0nban72zt2u7Uvv3Iy8ja5j+WhmeuZcbzw+nbEO8dutyNFuL+V\na7eZdJBBt1ZCqOvqhrvbW3JGWjvnI77c+EZbXt9OvL6+EZNkwIYo8rZ1aF4ZaaVYY2gqw2G/o+8E\nZ7wer1EZW+mCKVC8nSb+/JdP/PGPf+Y0XhjnC7Nf0HXFcLiDddSprhZ9VSZ0mXVIl0lZscTAcbrw\n/eszI04s8ll0JddPiNQK7ruW3zw+8lhb7DIxX2QIvzjHV7Xhm8d7/uPvfkNVNRLYoDU+ycC4q2sJ\n8c0yb0gpooyh7Vp2d3dr6pmY37RmqGt+/+0v+MOnT3w+nXj99L20PlSRN6KYvOPL8cy/fP+Jj7uB\nX9we0Cluqo6YhS8Sk2dxM9M8MU3T5g+oy7B447TnvEHFNFIgKShsmSQhK1bj/TUvVgbHTQHUqTLX\ngLUnnsvmIJRIeb5TAcXBVVf+vvW1tlvWwJrFyzUOBdu8zs92u16Q1gUrYbUlxsTiI9PsOJ/OGFvh\nYmScFzCWruv58PCBkFcnKtvpXxvNPC9FGeWKiXAWVtJPvH6Whfwf/+mfyk1ctqzCI7BKSTxX8GS/\nUBlFe9jR9Xse7h+YXeA8zRxP5yIT8+QYNsBWTkjbxawJ7pJoEkJJgylhvAlwwGXxfH56ZXaOu7sD\nldEiY0yJru3Y9T1a1awEubqp2as9KaZtMLIqTmQwG8sDK33mqmrou53cdFqJzhcOZ+T3AAAgAElE\nQVQAxX5/w+NjJCsx/RyPR+ZxklZOAc5Lj65mt9tjbcU4jizzslWITSP5fXLTGplBaRmIdn0vgKp5\nkqi3puH+7pa2rbm7O/J2PHM8n/E+C4jLe4xVxDAR/YlsE9kpwlxR9zvqdk/d7WnagarqSs85k6Ij\nR4cxidvbPU1Xc55m0BaUJUW5Hrr0VJum5HcqjXNOePKZshDLz3l7d8uHDw+8vL4xXRa0EjzttIgj\n2FYVPkp7xQdPN/So3Z6uanF+7dsb6qZDBcfsHKeTtJEqW9PVLfPi+PL0gnOL8DAUVFZxe3Pg8f6e\nfdlYV4LiqhRRWuYVLkTG08i//vEzn7+8MC6egAJj0UWV4b1jmReEpquK3nxt++VtEQ8xMefM0+XM\n5/GNU3Y4FeV8uYYloETKtyzcdC3/8PVHfv3hnvu6QgcHSnTobdPycHcjKqByf6TibWi0ptZaHvoY\nSCGWk1EsKTkyTJPnUtpBVgvSuVGG//TLb3m9jPzp+TNjBqfWaaOcSF2MfPflR766ueG3X3+kt4aq\nBr0sEK/V7qrsOp3P7HeCp9BKetRaXa+VFFLCSkk5EqMSRnxZe9foQMjldCc//0brLJtmCOmdLLEk\naq19ir/ZYKVCX+XE67MFMqD2MUhGbwlyWU/PK35i3ZjlxG5kzlC3KG2ZpoWQJlwxFaasqJuO27sP\ntMNA27W0nZZNF7XhBYyRLFOAxjXM8/STa+rPk9lZN9JrSmKmSFHGk2uf0lTVpg21VU3fyWAg5sww\nXlA5kIMj+dKGyEJGS2pV6KwXlY0xnBRSgSgBLaENPmXGecHHsOle14V5NwzM+8i+j1SVxlqFtarI\n14rKJonMy1ZqS47XxhC8xKhdLhOqaNMPw7A5/kKI1HXLzc0N2phSmThyLLgCU2NNXfrSohtfj37G\nWDEttR2mVKfTNInKBAnR1QXotRSqX9u0soAYy77gDpqmoa4t58vCNHmWaQIdiWEi+JGkPcllojfU\n/kK9nKncic7d0LZ7mnpA6YocgzwkMWJtxb5uaXcHVFHCxIKwXRVIqrwxUskJR0PVEou3qgPmZS5t\nKWmlhRTwQeRqKWdsENZ1jPKwS7dV2lKXyyQtqiR+gZjCVvGvrmBT1ZwvI+fLiWmaMMXBOjQ9N/sD\n97d3Avfqexm6r7yVGInBEy6ecbzw8nzkT3/5gdfjiAtJPAM5o40VSl0WlYJZDVElhUeq3YxPgRAz\nkw+coufH6cjTPHIh4RFVltzbCM0zeO6bmt9+uOc//+bXfOg6eq1QKWBtiy25sbv+Hf0xi9VflXaS\nRsn1WFkmORWduyIr0benEmUo9nGh8GkSv7i74fdff+SXd7f8+SyyYRnCyrMVUuTz25HvvvzIP//w\nhd99fEAiGso7lFfJsCyOOefCGKrkz0laNZT3c13UpaKWPE4fIsZcF19rxNAWy0lcKuirHFa+TyoS\nR9lI0Xq7ayRAYi02yvd7N++Sn1X+dV3EB6uscxvG5uvJqkjSCndGoboBW7U4F5gWJ5sBENPI6+uR\n55dn7pQEXsj9rEuguejM1155VdrO7xU9718/y0L+p+++k7ivciRKSRyZjx8e+frxkZubG4xWWxWV\nc8Jo2fmczdQ60ZhMbjQuKjIGoyt82e1cSXPf+l2lys6F6VuVdo628qYuPnH8/Cz9Ni3yo+O48Hac\nuBl6dkPDMLSCui3GHaUQiZYypTW0Ik/h+fmFp6cn/vynPzPNC0Pf8/XXX/N4f0/fdoUQp2jajqZE\nw7lFrkdXi8nHWkMmbBrTEDx9P7DfH+j7YXtDl2Xh9eWF17c3mkYYNCkljqdj2URamqYm+EhyaXt4\nur7n4+MjP/74xqcffuSH8TPzMuHDQk6BgEeCiRI+eZYwUfkTyzzS97cM/Q1dfygySUMImcVLoHHX\n77BVUzT95xI4IZWymxe8C6QIVVOJwam1mx7+dDrxv777jk+ffuDl5bW8t4amaXh8fBBnbQhi/mqF\n3a4q0WC7IC7YGPyGrNVl5iGwNYm4W3zEx5mQFmyl2O92fHx85Ne/+pZvHj8WY5ERHXwK4qvLSSr7\nceT15Une4+c3nl9PzEuQ6DMrC+nmO7DVppsWznfJ6kzClMk+MPvIaVn4cbnweTrxGia8lrbCJr1T\nGUJALwu/+/Uv+C+/+y3/5Q9/wI0XgnfkHOmavrwXUr2LmWa1fss9WtW2DLcdwQlbffVXoGURzUk2\n5RwzyhS1lSzv7CrDr+5u+N9+9S3nf/0Tp+NJCqSyIJIyp2Xhu88/sv8f/8zQWO66gVDYSusSXVcS\niOLcItW4NtucYB2EqlVaItpi+cxiEMxJoYza2qfylyVLN0tBZgxF0SJ9+ZSC/L2AzFE6orOkGFGu\nc/lVpDjU6+fmTb6ouW4OWmuyWVVE17AQbfS7xVwGvk3bEWOWNlCUUzgqMY4jXz5/LsWnYTxPhJjw\nTnJgU8rbPG63220CiJ96/SwL+ePDB9FTKyVDwwL8qiqBS72dTgByMylDcIEFOQLPlwmjNfvdwH6n\nqfueqmpAGz798JnntyOu3MTbmFeVqkYLntXo8q6lcsQtu2zKAqlKcSb6SHCBeZp4fjXC0agrmtrS\n9y23t4diYmkKKF/Y14tbWOYZ7xZ88Exu4TRNfHl95dD3PN4/8PHxI9oqxmnk9fWF0+mEWxwpJmrT\nXAOGa8PhsOfm5lAIjMOWerPeUNZadvs9a1oRILFzUZKNvPccj0eqShyva1qKlVRZ7u9uaeqG28OO\nHz5/5uX1hdN5JGWYF0+Mjmrx2CpT14a+dzLcjJ7LdKLrBtpuwJgGWzVkVZNzRYgymBx2vQxUvScF\nj2ltMVMZztPI2+nIspTrHRMhKdyS6Lo9GcuKyTVaMS8XOiVgpWEYBCWcI6fLyNs4kkKgNiJVbNsK\npaWK9m5iHN9E+RAzKM3N0HN/eyNAtKaTVhbw8vTE0+fPLPMswSFk0KYMuLwgBMricjjsWUIkqYnJ\nBVJMLNETXKJqJP3GKNEp55TRXk4U4zTxej4z3NxxWhxPl5Evy4mTX/ClJ6uR+WYyolLpNTzuB/7x\nd7/jD19/Q14WopP4MaMLRz+I1C4qVXgz8Qqa0op5njZZa12wq3Vl0UpInilHlLHUxkIuhhyliEnS\nibz3mLjw2w+3/K8vn3kaR45KUmuKqpEEvE4T//zpM7/+6hF3EzEBaTVkTS44WeETPWyxiKs0NAT5\nnbQVJntljaiZKKAsohhX8zUtR/reamt1xCj+iqxEMZQLbTKEACqjE+iUUCqU3rvaZH7rmHQtAlfG\nznvjjmAx1nahbJwpiP8kxIitbaGfVlhT0fY9u5s9HvDTjC9sHoF9TZATbddT122Jnyunv7KQV1VD\n23eCwf73tJCnGCXjsOvY7/dF7pfx3nE6ynFXKb3xu93iWeaFTGZxM1qpYiuv6Hc7TFURU2JeekLy\nhBSY57Adt/QqQyrDm/dWaQk5lp5XiuuEOhO8hCpPRoaJ1hhRMNSG/tKwOM9lt9A2DSA8bKnWIQV5\nuJq6ZokJP02cpxGdE11d03cdSWVO5yNPTz/y8vJC8EH6YXVf0m0qoNk07UoLS2aaJVZNElZk4V5d\nmlqvfWf/dxpYU1xqJdQ6RdIi7Q2ttbSu7B1Gy3V9fXvj9PqMnz1xccTosC4QFshBrOBumTC2JoYb\nYgpo1WGqfWG/JyqjSgK5VHYWTdaWZDRuCYzLxDSVlJ7xgl8CziVcgagprejalpQjWq/S0kzTVAx9\nx93tLVUtLbimbWTz8r4ES9RYazZ+hoCKRB+O0oIUqGpMVZcwAY1K4iidy5F2nmep+oxBW8vzy6vo\ne7MMpLqupRsGbpIiZM3sjoRy1A4qipojXHMm0ZlQfA7neebL2xujNoze8bJcePMzSwokEqunXyrE\njIqBXVXzm/t7HoeBGsV4OsnXVwptxR0pRayRDSBnSJGwCO4XCt659ICrSlNXVjJiy2apUmkjbD18\nUUyE4FguF9yyEN3MfVPx2NV8qi3jUuY8iF09objEwJfzmX/54TMk+Gp3g6XY2LnOkCTqLWy8+Gma\neH15YZovMqjf7wWVm1f2UsAS5ZrotUXCtonI5ZKMg2y2K4iEV6yxcAF0RsUkjpMsszpT0oN0acDn\nvGK12UxDWttNe27MKoHWrNAFnSW8fa3gpXg01HXDbr9jcp4lBoITtHKMUZAa3qOVEgZS2YxkA5VW\nalXX4lUo5rufev0sC/nz85NUVJWlqsXdlGIiuICbHdMoXG2NIhKZLpfrlQEZJJaQA6MyOcoU+bAX\nWFbMiRyPwp8AjKbY6SURXClbcvpEjRLD1Vm27oYATsnxrDJSRaYMvkyvX48nurqRNJGUaOuaw37g\n4f4OqxRd03F7c4vtZuqp5jJX3PU7uq5h8RPjJCRBqUICSksbZL8/SPLNrqNrGw6HgX7oSFkW/nG8\nSOVeN6Vt0hRTTUddAoZDwdmuPefVwaaU9JHHsRhfitlGPiq++uoj9w93HE9H/vt/m3nzR6IL5BCI\nyYt+OSx4N1O3Z+p2ABLBR5yraDvPsE9Y3VG1NcYo5lkCj41SVE1FDBG3zLwdP3O5CIY2xlDCFSLn\nyywY0bqiaaQCaVtB0BqtqG1N23b0fUc/9NRNzfv+awlOFDhWmfanFIvPwGKNbGw+JqZ55u14LPmc\nHl9wuCCbY9sLbCuh+PHphXnxZKXJKmAbRVe13Ny2zC7x+jpCyY9dh15x7ZsinHNbFoHRzXx/fMZk\nj9eKmciUowCmkJ7uhnmNmSp47vue33/1EWbHi38ieidtnDLso0Dl6qreWhJbItM04ZaFutbsdzu6\nVp4bRSx67avUbw3fkAV2IUS5Lstl3O6tCs2H2vKhtvx4ccxGE7XeYuGCyswk/tcPn+mrlo+3H0Xt\nlOU+FEa/IsSMc1cC4TiO/OnPf+Lp6Ylf/4dfY+uKfujx3qNjxNiAJZENAgdbpQPqGgACVwmvvKQp\nAoaclLBPVBT+fQlvjjGgVRKlWKFxai1OXWEIVVtguDXV31jqKe+1pCNZbF2J63jtAiD33TAMnKcL\nS3AiQMqZnEsYzTzTe1/yPEWwIUWaeFKqYprbws5/4vWzLOS73Y6bmxvu7u5K8vjMNM1Yo/nw8MCH\nh4dSXYiYfmpb3o5SvU3zhFTAFcPQs9/LA932HRmoTEXf9rzuDpxOZ8bzRVJ9PIQo02C3CIhJUYw8\nee3eXQ0LucgPcxbcbiy9FwmmlSpLbgDpd80+MC4zT29H9qWffnt/j72cJU+QSNtY0bHmwP6wY7ff\nkR5lWFvXtYRXDDtBw1IkVlWFMgo/z2StMXVFWjxLcbxW7xZqYBscLcvC+XxmnmdCCKVvuyJzefc5\nchpKSXS9s5uZ3cSw64l+z3ReZAGPnkQskjDB1UYfBLnae6r6Bq1OpBi5jJqcWuq6RelKBkxJzB9K\nKfrO8utffcXxPPJ2HHl7PXO5eBHqFVNXW1cMfYu1WgKHd9JWWkOOm7YVCeM2EKP0P1dCnMJaMZ5J\nxeeZ54WcZnlvS+5oW1fopkZROOBFcrYiIHxcK/SF8TLhk8Jax3nyPL9dMMbinKfvd3JzF7660hpb\nV/gYhCdfToJGC9d7ITFNJ2JTEa3GF0mcyko+lIIUUYvjFzcHfvvhgQ9NizudcWWjblfmTlNTeXmU\n66qRAX9xAa/Rgk1dsd91cvoyipxXq/gKmyo00iABByE6lnkihAXvFpZpwi0TwTlSiOjLyBAj+yz9\n86VUsposMtDhwE2/p7YdwUMuah9UEvMORphJOW2wuXEcGccLl2liniTgJCeZu6yqNDllv3tGS/Wq\nlClyQ0OMwpL33pZQ8kQsMD5XNPDaRMAg0vQki7sSpYnRWlojBXJlbCmCVuZ4Gd5v7RiltvtmLRqk\n565oqgpUxtiV2ZKo20bmcVl+/6enF8CgTcVu11+LL20wVgQglTXltBF/ck39WRbyjx8f6Qofep5n\nLuOId46q76WhX4whqRhjYgk/DcVg47aMyCC7F4bkE84t0lcCKpWpNXidyVZULYrIEmIxRwhWVetr\nIPJqNFoVKaiCypQ03KJ8kY+1glQqonUxHxQp5fnScHez5/HDXVEPHKgbi0DlJC3H1nVpj1Tsd3sJ\nd20a2rpGIT3LWE4Ui/eczpIDSs5UdYVRmrqSxb9t281Kv2Jvj8cj8zzji97Ve1fYDpaVCWPtNTkJ\nhMwozs1E1/dY/YG4b1Fx5jK+ch5f8P5C9pGUFuGuZGHmdH0g54UYW2xtCKGlaXrqWoBlEsumCcWY\nonRmt2uorKFrWqbJ43wmZr09AF1To42haSq6rsWUk4O1wqfIxSiyxnnl1Qn4zsSxLuJiBIplkKXK\nwyFtmPUlx+/ywC+OECLeR7yT5JacjTBboiItER8WrF2j7mQDXcsw6WyIgWOlYGdYDw8ySM3IIB5I\n5ALFUqVBnrA50xrFbx8f+e2HRw5VzeIjycgwTr/TSWsj180Hz+vb23ba07oweJoaa4aSfJVKNm7a\npHTyTfU7S7/c7G5ZmC8jyzQRg5N5h3PY6NlrxX1tt98jKF0ULPJLGlNhdFVaRVIQCfJAZjUrLmF1\nOMt1tNvmJD8rRUYc8cpjkicVk9UGvsoKpUQLr3QqBWBAe41ZpJ3onBjzfBCmrirBDqsM0ehcPCSi\neSdGovOoEFlNRiWAD5C2yFoMrZX5Km/UpU0DiqghJk9KkaqyDDvxYkh70m8a86puOdzdkXInjZqU\ntg0oaM88i9oohvdM4OvrZ1nIv/r6Y7m4jrfXN7xz0kdEzCERqThSlsRzlTO7QRb5upHMRKUkcefm\ncIM1hvN55Hh6K20KzTRNhMWhc6C25emKEFIsi0nhGtuSxWgkeFUs/vJ9c0kvWh9OVZrtOUr0V0pC\nPZP3cZU4RcZ5ZloWYk58/eGO3bCj7zvGcSwMc0NM8vAqLeqVruuprEFrGUZK/1nhUyIuC2/HI84L\ndfH2cOAw7Nn1A33Xb8YjH0uf8fWVp6enrbWyQvIlnKNmXubiiFMsi8KY4hbT5VivNV3Xc7sf6Opv\nMCrx45e/8pe/al5fZQETypxIumThm6inI03X0/Y93tV4P9E0gabom+tuYHaOaRHFyG53w+3tnrtb\niw+JnA2oGqNMyd4sieNFziWBHXmTfa1/XuFlUcIPhQkdry0C2fwdRpdZhpX7qKkbqqou/yaWayIP\n6XgRuJdglDPWNjRNIvtYFoH1WF1u6rKZrBz6GCM3zonHQRWidL4O3iQdygrnm3enQgXKKPCRViu+\n7nf84auv+c3dA3Zy1DtL1kWRUZRA1hpJUzKWaZr4y1/+wvPrC4tbqKua/TBwf3fLw92hmF5EZhij\n3LN5Xbw1Wx/WJEWOkWk8E5y0VzZeTBZC4s5qHtuacXHMKROLJz74wPk8EodbYBOdQOmPU05Jl2nm\neDyxhk63rUhybWXktN4WHTwIOjYtGBvK7OXKbV+/di4Df1MGqCsqVyrxUo0XebEu7+HKhM9WzGAZ\nUWnlxZd7S1Q0IhAoBkZtNgXSFaOx2v0zTb2yaCBEh3cLIXiMNQxNK9jiHDmfRpb5hfM800ySibC4\nRU5IZWYnUtBcThgC9vup18/TI3/6UnrRYt21laHScvFXSeIqrzNG09SdTLeB/X7Al+y9tm0llipJ\n/FrfteUCU3gIXpJfLqNMlEPm7XXkeL5wnheSuvKV3wlcZChj1sTt6414Dc19x4xA3mzRlJdKJ60Z\njgvTeGa366jqCu9CCctIJSFEbpAvT0ceH+74cHdH39WiJNCGnLX8Gyx3h3tB6PqF5+cX6V2nAtAH\nFucYLyMvLy8sy8Jut9sehvfxVtYYmkUekKqqOR7fSCnT9y0+OEGr9kPxEsqQabqMLMGizR70SPQy\nW8gYdNKEKG0TFzUuGtySaXtH9IHoIbdy4yoyVWVo+gFdHSAbGbBlRGOvhPk+TRPLLAlRSteiy1ai\ndlhVvO+jvFYWfPDh+j69+3tJNJKevWBxpJW0OHDuPcBKJGSr5v35+Zlp8ShTkZUYjJL2pQ2Ry0nH\nX9sxObEmXK3KB6OkVYJSJAUhi95cGwvmqpTAaFLRjOcEdVTc7/f80+9/z7e3H7hpe7St8TlI3FxK\nkMAaOZnFGCUg43jkMl3ke5dnRpXTV4ylMEILakIX9oiSof8KnMqrdDcmurpF7W/QKE7jkWkp2bQh\nolKiy5keRQMsCDDO58ScAqObmfxCKIoYk7Xo+E0ulayh67rynBuGoS/3YdhmaDklQk7Y8nyFECCU\n3VN0e+VaF3ZlYR+Zkm8AMjSMKZO5WvLhmvK0LLIGrBrErMpgtCi81t65NaIAskWhxrZ+XAeggjE2\nVChSiMwlSEJp8a/UTcthd4AUiS6h1Cs5CT777XgkpEjbNtSVtIirTiSluXgY1mLj718/j2olpU03\nrbTalANZI8eXJH0vs3JYVk1uARgtzkl1k6UapJgMJPpMKrgqWlIrWmZjJW3e+0j28m+brkFXVhaP\nwi2Bq+xofShXoX9MqTjh3lcxRU++mgRWGVRIuOiZJ7Ehd524NbWWFKRc3HwhypBpcpHLtHA8Xzj0\nrcSSdS0qI+HO88z5NOK8K/3ECwqLNnU5xQrQ53wZcSFgq4ph2DEMA23bbFZiqZ49K3UvpcT5fGZF\ngDovRESlLV3XYo3I2o6nmdMYWIIh0aGsVIOxVDwxZwieoBVB5dKbdQS34JdAdD3RzUQ/0fQtje6w\nzUAOSSodI+2slD0pBs7nF8ZxJgQwtkfpClUSU9be4/rgmDXP0WgUIl9dF9E1izHnXHgV0voSTfHK\nPCmnr1IAGCPSz6ZpyqaysIQJbRoSuvSRw3YfXMOZY6mcig1cqw0Op1YptJLFJmsNVvARqOscf11A\nVIg02XJX7/j2/iOHfi9tplxTIT13IQAGDHLSSjFvg7HD4UDb91IkGctuGEpyUqkUtQbWcIe4hT87\n52AtUtY2TypBKPJLoUxVZjqaQSdUnTnbwOxEE++Ulgo6J+YQcEWJk8l/k5KkjaFuarrYA8UYVtRo\nMRUDVdksN0VKAhe8KE9WfXmp8BOFp6IVRkeMtduGgSrqo7QWY2uK19peKqENrCiB9YBUjD3alA3T\n0lRVCUnXRU1zdXhaY+i6BpUCixWioeQXLMTgsVUtcsq6IrosbcWuxQfRqp/OJya3UNctbdty2CMw\nuVrmTLauqah+ck39WRbyumm2yLQUyoVUihrpgyolyE5xORppacC2YC8FIJVyptKSe7kuxALgksGA\nVhpbG4aux6gZrRy7oaFpKzCapu9lkJnEZxijDDNCYZJ771m8x7ko8WvOE0KpvpA3bht45Cu/Ieci\nX/QCobenC3Vb0/c70XMrTUKRsizqx8vMZVl4fntj37YcbiRTVCvFMpUh0PkMyAIRU0LZBV1NpTec\ncN4zzZOAoDoxGq0Pwyo9XEmH0zSRkihWjscja/xZCDJ/iCFxd1+SaxScLjPj7FmCQlcDdbPDaElL\nEqJdwC0jKVRErUkqEv2CnyXBKSwTrm1YlprWd7R+R4gJoyqaSpQhzgdSdPgw83r6xMvLmdllrB3Q\nusVqoSausi9jNFVVF4mlwdT11u6QFqcw49cpv/y59HiLxl2CgtfVRW0pL3Vds9tp+v7Ey9uF1+Mb\nxjp0YZYszm9ApdU0kuIKhloNJOtpbkW4it44q0w2ZSEvP68CdGlZqJSofGRne3amo8LKyUwZtJWT\na6Uy5AKKKYsQlbj/ur7j1t3K80WirmvauqGtauoVCmfEWRqjuH9PpyNvb29M01QQAhJdZxBkhWyy\ngLFUjUbZTFVFmpjpM/hlYTlPnNwojHIUKYNLAZ8jWVMGlmz9c0EoNFBOF9spuKCQffACyIPCktfE\nBKF4G1KBjistQ85M2RSVliSl4LE2IyKeYhzKquQIhGJSK3CsotmOKW0LOQUpIfuv4HJjORVEEm3b\nogqMbRUUKMC5muAmtEqM51E6DEBdV+yqWmiaKhNywBrNzWGHsRWzT8yLw18WlJmp6pZxDhx2Pfu+\no61r2qbaEsv+/vWzLOQATdOg9RV6s8wLRpdhVmVl6LdNha9gm5wzx9ORt7cjZOjbVrIjjQz76rrC\n2LZsAPL5dVWVfEkrk3ByUYDURb1wtfKvMKbn52e+PD0zTiIPWuZALIYLKIPBUvGZYokPhV0RSnBC\nXbeCYa2rwlIQCdE6xJCqLmONcJJDzIyLx7+ceDtPYjCaF6apVEsU3bAxhKzRpmIYBkylsArSdB2I\nrryG0DR0XUPOsvjk8p91UW+ahpyrsiFBbS2msXRtg600IUf6fUvVGmIYUCpJFmgtrBVJq5/54x//\nhct8YZocUSeCFtRuipHgZ5yzOFezuJ7L5KjOgboyDF1LinuapqWuZaGLYeI0vnA8OYzdsRtuub25\np2ktdSX3jUgs19MTZUh3zWtfYUYrPKnOYjhz3uKcLic4GRDWdWFrlM0/K0XdGPY3B4bzBfX8Kmx0\nH0tm7BpKoLeNIBuKmoLtPVqhautAUv5SF++5QglJ96o5TokqJ/bWsNeG5Xzhv/6//4O/HnbcH/bc\n7/fcHAb2Q0tbN1i74rSurZ3cyuK0hmFUtsIohc6gckArSduZp5l5mVgWAVfFGAhRFjej5foIc28N\npgO0EZYRYtTTNlOheKwqlqx4uzgBkWXJDPDBl9ZpuQ7FNSmnkFz060U9oMrpRYtSTGZTsfwMkRgi\n2YsENvhYzEsKZTLiEKKoV2STVhSqZBns69IPr+tqe89Wme4mvcxlX1x/643PIj+zcI6kol4WV4JZ\nJIRiWWaCF0PTeHEoBC2BlnzgumkZ+oGuaSBFtFa0fYNtGoZDZloC58nzNs4sPhGWQMpnvHPMl4m2\n+Ce6tvnJ9fRnWchFldDQVA3L4iS9Jkkgr0JR4umkUkeodCvUffZOKvG6LsqGViR4yIMlErUaYQ+X\nKqCi6EFrghU8asxJrPrWymJeduQQA4vz5BSZp5Hz6cg8ObxPpFTUL0ptQ6IGahAAAAySSURBVDcU\naKM2M5FYjZMYiBoZplV1XRLcKZV6Ud0U/S5Jo6nI2uB8wvmFzCyTbedLtp9Ul8YabAXnaUK/rn3G\nStjhBYV7zdIsA1yli02c4uarcMvC5TJyuUiQhih3RNuTMnIiKpVMvau34U1OgaZpaeqWlAQXO55P\n7Hc35IxEWIWZTCJpOck4n6mcxvmGxWWqWWMbTdskkq8gTYS+x9iaVBYSoww+JELyKD2iVCb6C7th\nT9/vhD+jq4IyFpUPbMslKwBJslAlJWYlStYl6Umq5bUvXZRKyP+nc2bYDQy7nrqtiLO49oKP2z38\nN+2dEiJSWxmeGWM47MV9Kqc0SrWeMRl0ypCC9KkRNkyjMoe64pv/v71z220kObboyktdWCxS09Mz\nDb+d//+s82DYMNwtiSLrljc/RFS1zvG8GDAwIJDrAwSJYkVlRuzYexwZ3UBnOlIx3KaNGD+YHoH3\n+8J4PnEeOs59r9vGToMwJES5lAC67OWdO0K9RXb1uY20+2DLsouzTr3cjUoFHUeh3eW4QDHpuE04\nazgZyy9dz7dx5H6fRRkG/LQq1ud6f2kdNyeZSezfrb13ba3F62eWip7GUyZugbyKBW4miaFYyiIb\nNF5bLbvJmHiU5CISXdkhkNu69w7fWJZlk783ZRmoF7HUKwBZB9NFb1pkYi6EYAmb+ADtyVWN93Rt\nQ9/646a3NxHkpue01eN1Vrfp8NsQk2GNhS1Kq3X/PArozkXAag99Xbf/o7L6zJ9SyL2Roro/sI0m\nq1jnjyGWKTrcstJikfX3jft0x3nPyy89l+FMq0naOZfjYToCJoDdpEceZkfJQf4RMciXFyuJ4ToB\nN7mwLTPLPLPME9P9Lq2VLBIqg/TkCqo/FSmMLn+gUig5pUuGY4dvpMd13D7W9djYxBhMcWRryc4f\nygeRh8VjiBdjlAKhV8gtRt4+7syLtIuulxMvl7NejR3icewOhYVBFh5a50kxsq4LuWTujzvrutB4\nx+Uy4JCk6BwzDidRZGoZYIwhhFU3T0+UDPf7B6YUrpcr3jom/+Bxv7GFxBoDOQeMCfggLaEtNDRb\nS7N5clgoCUgP1vWEb04Y29G4hr4bcDaSsczzxDq/M7UNy/UXvuRvfPnyTRQWWHnAdcCYtThYZ2l1\nG69ou8s7T9N0lKY95K1ZC3hGlEySISolpe9aTkPP6dwTWaWYx0xJ+TjN71t/bSuxguMgRdZ7x3A+\n0+ip/bg1ZHC54LJuAVpZFnI5cW4cv/cd//Pbrwx+xNqOWBwpyC3v/WPmPq80tzunU8d1PHMZToxD\nz9B39I3DAdsiiiLvjGxRe7GlMOxyPeQ76axGHmbaTVoZRU2rxFdfzayKFLFSshS9cohsKMbgbeHS\n9fzl6vjnmpjiwsKnFC0+bSPqXEnaXzpPYJcH22OY7b34r8iQXOdOa2CbN0oOFFM0Y3rXkKuBl9nD\noQ0uiU5cXrqG4mTBxlmx3CiIim2LUZaV9heWDjopn0QM2gY2QHCREEXOmkLg3PeM48BZ5dMS0LJ7\ntRjxeMqFsM3Mq7QwYymEWFhDYV6TnMLVBM6geb4AGWIs5CB1a56XP66p/53S/J+xR7ntJ+amaX72\n4fTaKteVVU2z5EONMRyxaFa1nfswJOcsfTXN0BwG0cwmlQp6L4U6JjkFdV0nFgA6DFvWSW1fJV8S\nEuM4MC8Li5O+MYgXt7Fi0nT4HR99RBmg7S6Ibtf2hk1aOnqKt0aMjAoaAuGcRJutm5wQd40w7ijq\n8jMlp7L1zZEesq4byzLx48cPSQVSU7C+6xjHgS8vL/z+21dab8X9zzqG4Sz67FOP9463t1fR8q+B\naCzeiuPicBq0HbFye3tnmh9AYRxHxvEq12sj0tDTqaNkQ9git/uNv/39f3l7/SshrHgng664JUqJ\nlLwSUyHFOylkcujpwhnnFkppSUW259rOs4UiyS+sbNvE29vCND94THeG05Xen35e20E/Vy1EmrMq\nUj049bL9ajDyAAcJwQ2rBFiQC23X0fZy5d0Dwduup88O51WVkvbcVfl/eO/Ev4fM6dTy9esL3iFW\nviTIiVzsIZczIENKZJPSOsPYt1ybhi9tz8W3DH0vs4j2dMjQck46W5Gf87EEHmvAvd0lWKPxtN5J\nHqQtdI3jcu4ZT60so/GzVWmsIadCymBsw2m40HaD3Nys3mCbXk+HEnCQi7QE1y2wrdKGxMiSinEZ\n4zr+8ZiZNFg9ZQngzqoFR+W2P4Mb5HZsjZh3GbMP8nblj2SOWu1Pz4u0GXIWzxQch/oF7GFUJf1w\nsLbQ6i1a7xVHS4xcMEWCxBvvdSdEbtelGDK6i5DVujZlchRlk40QklFrDxVbODm8iXeMvpqMkZog\nfxHFeLLNbBT+8f2V948H07IRghR8o8+uV//7tmnxtsE4j0fkkbtH+//nzzmRazr1bsE6zTNbCOxK\nij25elcEOOdIGrG0hJW27+j04T0GJDkxTzPzsrCGjWGYSSkxTbIJOpxOXC8XTr2YBXnXaG+7sG0r\nj/uDNYiGs+9arL9KYHPTSIgtFu/3JR6P846Us4QMh5VtC2xbJAQ5ZeT9i7tfHWk+TczVWjeLnega\n47EoUnSi73WT67NGOhfxud6vqTmX47QuDpHqC6PeKqe+5cfbO2+3Gy+Xketl5Dqe8d6oVKplvJxJ\nacPo1J0sv28uP6V16zYzLxPbtjCcT2InmwMxiabVWLA4YoZcHNZ1DMMLYZu43xYtkhlSIKeV5A02\nbqRwI4dMChsxZtou4z0UY7EUvIVgIsYGXNmg7Kb/8tk92ht9M9C1Pd53ONeQUfMkkBabytGkry9e\nFuu6crvfeUx35nkhaa/Ue08qBdd0DOeRj2ni8XhgjKNtrW7lIhI9HcRJDzyTi6ZX2UzXWbRWiBwW\nGbZuYVOtu0TlifFSobGGzlrGtmFsW1rnZGnMQt84bNfIQTFJdm3WU15S9UzKhS1DWAvTGrU5VPA2\nMW0Ljzkx9A2tt3Sto2ssDo910LQGjOc06G2kJB0mO7xrCTGyhYCNcqCKLgIeYyJtLlhnmKeJlDda\nMr+0Lddm5X1fRNPeO/7nwe1zEs+u+NoPOZ9dD432zvenSd8w2rVPKkdMGESHjnXiy6OyYVckb9Wm\njEviA4PZ4yA1QNwZusZ/+pnweSck5/0lKsokShEfnax2BjntoxHVmsuhtMnSGfCuo2k8jc5eHlvg\n+/uDf749+LjPYn9c9pZtZjOZJmY6CU7FmKg3xV1M8ceV/M/pkX9SEtzvd368vjLN82HqtC/77FtT\n3vsjhGDLUc3k5X4mfT35sOdl4fbxwbQsPKaZeV54fX3DOcfLy5WUI233O41ajWZdG1+WlXmeiDni\nGs84jhQD57DR9x0Ug/cdp/6sqece4wzLtvKYZz7uHzweM4/HInFiQcJhcym63mtp2n0in/XWkElJ\nTPbj7vFRNH7K7W92fZeXIm9+MimZY5sxqaPbPkvYPSecFa+Hjzu83268vb/z7etXvv32FWcsfd9o\nKG2h68TNscSEs40OYKU/t+jiVQgylbfOHNmVkIlxYw/hjSEzz5F5kWLbdQPn8YXH/TtpWyglYZL4\n4pgEJhtSmsUXOyQwDmtbWr9LzrI+oAlrIpaEMSoJTfC4RxY7MTcnzsOV83ClbWFL+Vg935eGnJf2\nx67imaaZ79+/8/r2qp7touzpTz3vHw+K8by8/Mpjmpi3AMctS/qoRReSJIdSpal5n3lItNnefTdi\nsXmoaHZNv3iPyKnSOxlWd07kbdaIWqTkhC2ZVm9/KWUdNjoMYji1xUzImaBS3BDECKrozWFeAnOX\nGPrEqbNcxw5rWzUDM7Te45t8zFQMYvhmjSzH3aeJIP4Uh5rFmEzjRZrXNA3bGiCvmJA4W8fZOVwR\nz6OYxOQNJ/Ob/WQMHHMsw956yvpyM3obkpqekB629eIljzW63q+e9EUHnMVIazDp4NQYknSDxGLD\nON02LTozKseSWMhR5OlGNo/3Fufecv0sS9bBFllzYMmaUuU8XdernNdRise7XtuTjfyP0o3v7x/c\nHguLGo6J4m6Pdksafu3URiAQU9ZCbnbNx7/X1M+LFZVKpVJ5Pv7YE7FSqVQqT0Mt5JVKpfLk1EJe\nqVQqT04t5JVKpfLk1EJeqVQqT04t5JVKpfLk1EJeqVQqT04t5JVKpfLk1EJeqVQqT04t5JVKpfLk\n1EJeqVQqT04t5JVKpfLk1EJeqVQqT04t5JVKpfLk1EJeqVQqT04t5JVKpfLk1EJeqVQqT04t5JVK\npfLk1EJeqVQqT04t5JVKpfLk/AsNeRHC6zkzzAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "transformer = tools.SimpleTransformer() # This is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n", + "image_index = 0 # First image in the batch.\n", + "plt.figure()\n", + "plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))\n", + "gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)\n", + "plt.title('GT: {}'.format(classes[np.where(gtlist)]))\n", + "plt.axis('off');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* NOTE: we are readin the image from the data layer, so the resolution is lower than the original PASCAL image." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Train a net.\n", + "\n", + "* Let's train the net. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def hamming_distance(gt, est):\n", + " return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n", + "\n", + "def check_accuracy(net, num_batches, batch_size = 128):\n", + " acc = 0.0\n", + " for t in range(num_batches):\n", + " net.forward()\n", + " gts = net.blobs['label'].data\n", + " ests = net.blobs['score'].data > 0\n", + " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n", + " acc += hamming_distance(gt, est)\n", + " return acc / (num_batches * batch_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Alright, now let's train for a while" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "itt:100 accuracy:0.9526\n", + "itt:200 accuracy:0.9563\n", + "itt:300 accuracy:0.9582\n", + "itt:400 accuracy:0.9586\n", + "itt:500 accuracy:0.9597\n", + "itt:600 accuracy:0.9591\n" + ] + } + ], + "source": [ + "for itt in range(6):\n", + " solver.step(100)\n", + " print 'itt:{:3d}'.format((itt + 1) * 100), 'accuracy:{0:.4f}'.format(check_accuracy(solver.test_nets[0], 50))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Great, the accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline accuracy:0.9238\n" + ] + } + ], + "source": [ + "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n", + " acc = 0.0\n", + " for t in range(num_batches):\n", + " net.forward()\n", + " gts = net.blobs['label'].data\n", + " ests = np.zeros((batch_size, len(gts)))\n", + " for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n", + " acc += hamming_distance(gt, est)\n", + " return acc / (num_batches * batch_size)\n", + "\n", + "print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver.test_nets[0], 5823/128))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 6. Look at some prediction results" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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oaWdkMB8GUkqEcSRME+Nmk/2wAgHT5AIwnJ4wbDdIENP29nt0VhgHZLJ3ZRoN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OO8yWu6zq7RYpL+o9HK2ny/UivVbRNDELHNYhGiNK2V2hdVFcPD7aY6722SeDWM2XeD2Xnt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\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KJteAqh6jvRi8GYUhYJhlDTdYUinxIQopk6pI+XWbVqVU0ztoTWcdO+OEQ84aJ+qtqXmH\nEukWmLdmdXut6yPr/ZQ1s1+nHLU3pQq2s8WUX16/zczO1lyYl4V5XoR1kcXE3VmL1p4lO1LS4j/R\n0txaKguaw+NbAMZ5EuZALaiat+k1WSHDk4MINeYlEkIUS1frMJ1gpNckJetcpOnkteHgunbCqo32\ntp6oajUg4nbSSlNQPggN6AJKrSShut1vDJG04o9BDhynFckkdoMnpsJ1ylSMUPGspdMadMLERKiF\nag1qGFD55gOSUkYZQzJaeHSIejI6gz/0Qv1Tlss0MU8TcV6gFnSQDnhqsu6qxI+lVinlpyhClZwS\n1qomJJH7ySUyLxPGgFGCPbu+a835ilWw5MQSA+O8oM0ztXaMY6TvnYiMlDTFnHMcDwcOuz1ud+A0\nZYyZ8UYz7Pdo56nOUp0D29SlVDQT0zTy01/+xJ//47/w4cMHxunK+3d7nvYHdkNe8S9iSeSiqUqG\nLCxR+iSuOLwfCCERpjOvn/6Gmv+BngVjKsYocinMU+CyVObkSHVHbiwenwtv+qH5t2v6bqCWSAoz\n1lpSSijr6J3HDTsKwoD59NO/8PnLJ378+TPP54XrHBFHWwnGVNU43mw+QEVpKBVtK4dDx7fvD/zx\n+0eeHga8MtiN/aBYsphu1Srsp5AiSwyg1sO/UQuVWAqjKxah9V6Dx2iZA1tKpu8HUIrL1WE7K0SF\nklEO7IPneOjkvfSavu+Yp54chfi/2jwUCsZa4mlh+elCDiMllV/klvUOG76BxIp1+s/6Lbese3Um\nhDuYZYXhVME6xcPRYfZHsH1r9FqGw3vefPMDh4cn5tMHTj/+X4zjn4lzFLGZamtFGVROt39TN42F\n0qAdg3HstGmHSIsBbcqRghZ4Kyq357UKJLMmhqrBuHIAlBtyu1Vgm/VKcyf9FaIO8BsF8hITuoK3\nDtUrTDCYHNsQA0PBERE3MbFsCMQcoZ3cNKk2zYDJKBH1aKtFHGE0OmqhiLVMOZVGuwqxQSzNa6MK\nPqiUotOWg5Vmnli3rAvmBp3cHbLtWpswdwux/TUj1pen04z5yxcqteGAFWMkoHlreNh5lPVEZYlZ\nkY2wTrQzdA2uSONIpJK1RmuLc0KnC/Pcsn9ZKMLOMChvxVCr+TEbLab6RinSslBVarCIHHbkKjRE\nJHiUIswa67xs6lopSsQ8nZOJP8oY0jqsoLLWMKJYLILzbtxnLQKo2nDTKlEKiiblyOn0gp0XxlBJ\ny4w2mqX2MOwp7MhavF3E80YGK4QQmMYLLy+fOJ0+A4X97j3Hg2booigqfSKkBFnKZjEz00IBjGKx\n64879g9HHo9vePz9P+Ie3lKqZRqFsjleR6ZxZByvLNOFsAh91Psrx+MR33viPDOezlxPr5zPJ+Zl\nZlkW4hIYvIfmdZ3izE8fnvnTj898eL4whyQq2nX5tICs65o0sDEcrNUcj55v3x/44bsn3j70PAyd\nHBRGDMkosORIKuIlpI2MyFtis2bIhRCTeBVlYWxIpimQWr/ZEIuZmzZSxfWDwXqxe73OgZQSFZo9\nc4MbasKa2vpZzSu9atGDNBm8dpZRqU3C/vVWUl89tsasG3zcgqRaab+3r69PXx9VGvqd5fh+R3Wa\nqqWavk4JqydsvrIzRwqZtMzEKYgnkdbU2oaUOIVR7cDR0u8yLSuvWDyWXhlUUc3+WRhgtGqgBYYW\n9G63thX1dZXg393+evPrY9v9/zrdcr1+k0CeU+veOy/sAxspOWG1JmVNyAaqR2tHKYXpmlGqlYvN\nhrQUoVJ1RtNZTWfF+VAhizcpvZ3YJsqbGrOo7Yw2m/lTj+XBOpwWPuhOO1zzXcm08U1tJQlB/66l\nsZ682/srTJDVa6FUxRIyry9XliiY7erZoY04xVlnOQ2e3eOR7nikWmHBJkrjZUvwmq5X4Z8qBcah\nvfhq5yhloamgi0JZgU686ygpsSyREBYZYlClf5BMlEOl8ZmXMTBdFjovpXzIkbhEFGJhW7JqODko\no9rwCSdmQsvSrIDXAbuVpWQyrXGqHAqR4+cUmGJoBl8Zp2XAQVaZLz/OaOskC5pnMIbx5WQJZAAA\nIABJREFUOhB3B+Z+j+v29Psd1llQME/TVo0op+h3FmfE8bDrlDgOZvDasnMdqhPG0Ip5LrFs9gi7\n3Z7vvvsjh4e3HN+8J7s918tCmF+Yp5HrdeR0fmWZLuRlZBlHSspY5zl0Gj/0zMvM8+dnPn/6yMvr\nMyjTDrhK6ntU1YQQOZ2e+fNff+ZvH1+5xsDma1/FkkLwW9XcZ9Wm9LPW0HeWf/j2kR++e+L790cM\nBacESrTrsOyqsFpTceLfoaQpF6xBG0XMmdkEppSZ8yL+KhusLOwgGWsn/ZyYYrOjlTQkNR56bgpi\n1QaHkAoxLcjAHVHq5pTEJVMbbPOFadrtXwRhtWWsZY1mK6h8913rdY+X36HiW0VCe/+GwfHtNwdi\nliqylEpZFpZp5pxGfBkZzy8sly/kGEVVa6T5DG0AimsH2y/oMiWLsKxrE68a4C8WtmssUGzsFWrz\n/1/JE/XudW9Bu34dzNu/q9Z72zD4v79+k0Ae2niqNdONJZKbKc24ZEIW68mYI8sSWKal0X0avqsM\nJRe8FQc5rStzjuLxLE1nUsuGUlOTDc5Rh56oDSlE4jxTcuZtf+Cf+gODEtxa13UprKzddlVAle0R\n4XKz8Uf1VtKtk4ZkYdZSCFMgLLF1v1s8p9n3aph8x1PSPPV7HvYHfCeUyZzaTEMri8obyQi8dzjv\ngYILDmtqGwgNqWRSLDgsFrHZzDpRVGHJiRCl2YnzhDny5WWiVHh5HZtdq1QNuXX4K2qrYLTRQpns\nHNZZUhGPipTFXdJqLQpLq8nN1bJ3lun1lenlyucvJ67zwrxEllD47tDzj9+84b/+4XuqNtB5fN9x\nfX5GGc3++MC1wFwNQQ+8/eO/oXs4SiXQnBHfffc7tDPM1xNpvpKiDFiwOhByJKQItYonutEsKUlD\nuQhjwSiN0bY14D2X1xdefv6Z+Xzm+vKZ1y+f+fT5C+fzK4NVfPM4UJIIgZJxvHImVvh8vjAtiXGa\nmZaFqjTWS+VSM8RUuZxHfv7pJ86XiaIszjZctYgR14anqpVOKKW0szL1/R++e8N/8cd3fPO0p3OW\ncZmYloXzPKGVxlvP4Do6K/0L42ToR3GivK2qEmJsTpYBUyTrvmZx3DRVBFM0T6BIo89pqRBys7K1\nTuO8GN1Z7bher6QUULk9V4OuRqrKKmPQVE6UJZLmdEd1vF2ruI62h9YIrbbdKBtzVUxuQVCtkMbt\n5xVV0FrhvGW336GrIobEdZwpWcbzTeGFl5cPGBLaTAyswipRA8cUhN67Qqtt36pGSSwFjNP4OlBq\nhioSfa3dNpatIOIzVWRPGdXqyY0LflN23sCG1WV1q0FYzW4Vkqz92vXb+JGjcFbUZlprUleIMbOE\nTKiJOVUgURAeca3CS9ZtgcaYqCXhtMY5g1CwAqkN2KUg01NCYFmEZ10V9N6Tg9C6SsocteXfDkf+\nu4d39MYQcxO1rNapW0ddbWZZaw389VJsGP66/urq09KifCnUVn7etKByUBilUFqEElo17w8jzI6a\nC6U1pjSNhdAadhrBsql187Ney9mYMzGEdpBIY5EkNrLr82LOhFz4/Czjyawz22aojS72lfpMSRBX\nSprLWqvNPrdWKc9l8tNqXwv7zvOHN4rd9wvKLpyXj5ATpiRUiJipw1wz/bVHdz1OJTwRVwOmGnya\nydPMPEeyctjfv8ckTZoL0zSzjDOkmb7v0OyZSuL1+RPkK1ZHasuwjBLnu5gKJUBMAs3IDEVDjpHL\nl0+cfvyATRM2L5gUsWHGX07Y8ZldWPhm98B/+f6B8XIhRvHvfkoXni8j8afPLEvk4+uFn09XihWx\n0NB7DvsdfddRqiKlRTBmpL9Qaia3NbQO6rBKRFUiDFO8eez5/psH/vi7Nzw+9C2zFtfHMSxcl0W8\nhKrYx4Zs6KqnV9Bh0cqgrRLaZhF74zlEQhb14gpXWGPYe09JcpD3RrVGaSEUEbKFGImzJCXalDad\nSyxfU8lQFNWAUaLJ8NbQWytNWK2bY2jbMm1ttb/c9seWrt7vsFUZeQehr19r+MRG0GtV9GVc+NOP\nXyipChVxTlznxLxkYs4oEsZWvK6iSK2QCsTafHNahrxW0aXSZhRI1eQBqyCWjNXtkGk+K6t30TY3\nQK8xRd1ubYspDQ6qoMpKXZaTrH71vV/bhdxfv42ys5WCznnZ/IBWWXjeubDEDHFBm/pVkNBa6D8h\nJyi5TYIRO9Pchh+IkVFliZE5yIzKGrOY8ztHTTK53KH5p+7Af3t4wz8dnvDGUGtpHGkJ5mFFxLfT\n/u/xu68ebc2M9YPegn1zLdsKv7omLsJh74yMx9JaExaBKYxtbmlKtRO8BdP2c0uT3tfSDK2qapOS\nSnNxi5s/OUqh2rQerRRLCM3vunCeg3ytlfTb4mk3dC9YuEF+svkKm/r4lj2tCkMU73Y7vp09/fsH\n9kdLOkjZnZPhckl0KrFTE3U6Y1Wl15WhZrzWMh6vQI6BbpmoVdGPJzSZOE8slwvX68R5CmRrBX9O\nideXE8t4QpWI9YbOd1hryTkyL5EpBuYiPineQekqy+XC6Tpz/fkTjyZyMAJHeK0xZSHaiDoY/vDu\ngf/m+2/4+BHGeQEUA4nL9UT8/JF5Wji9nPhyvmB6sYjNXYdLe4bHRxEQlQylTYNCNrtqfGo5CAUi\nMVrjnWHoHT98e+T33z3w7dsd1CpQWXNUDEn8XpQ2qCLujhPQlUgsieJkVqYxmpwLIUXmGLmGIKyM\npkpd8z/dsGARPFlyzXIAKBmMkRaB4mg9qWTWXot4rIj4S6wpvHMM3rPzHqUqwYatMQi3RuUa0NTd\nGtt6Lk1Tobb/1tW4cqtu11e5VqmcLzOf//yREAsxVsTAUIRlSotJX6cVHoXWlSXBkmTWQW42AUoB\nRbc4dFehKpme5ZQ4fqrGLTT3GV57X0VF3l7/FlJuAqc1NlBp/kxrEL97U1aPgFL5tes3m9kZYmAO\ni5gOVUgZLovmumRCyKQUZFGpKv2YXKhRUZmxRqOtQaNIQTLv6zhvXiIpy+CIOQTmZYFcyEmTZgns\nDvh9v+N/e/sD//3hvTA7qHhtebCeB+vx2jDmLJj09n6qGx7WgqzwxdswgdpoRvWOxcK6wG4Zvnyu\n8rM0MkvSeEdRmnGJ1CVitWHn/TYQthQ53XOthHnZSvIVUsmlkKtAJyHKWLbVdKcqJeVto0fFGMkx\nomtlaMVqqbndz9c43JqZQ7vlNUVogfy2GNfKRb63orhMM//Ph498F7/l3f4t335bGFxF1ch8vRJD\npsbCj+ef6a4n3nQPuOEJ1XmMt+hSYIq4kNFZkf6P/5tsNVedmNTCl+dXPrxesQ+P9A+PxFx5+fTK\nx7/9zPV8Ec5uE7nk2ixe6y04HA57fN7R57+iesuDCvzueOSgDOl5ooxXVI70Bobe8aANZo7Eq3DG\ntTKMl8Cnn5/500+feS0JtzP8u+/f8f5pYD/07Pqe49MerR2fXiP/8h8/kscZ3TQMq5LPVIVTGtsq\nwt4ZHh8Gvv/myB9+98Tjg0c1JlAupcEXmU6BMkYcKmOhKMGylzhznSxzt2ff9TJGsVZKRhwlqU1g\nJe6NMSauqXDVE9aIt9G+74Quq5WMKsyRqAIpBoyz1FzFSbGZSjltcZ1j33sehgFjDLuu5zAMqJKZ\n3CjeJnUdynwHkXDXf9pKW9kz+m4xFthwZtUq5fv0Y12PpYp75OUSqMpQq1STuYp4ylBxpg2KTjJ5\nLNfMnMSiODf4wxiJM0IH1duGVmic0ri25sua9NXcOkLrVCCpootS2x4uqGZx23QmCkrDZjeZfgve\nq/OhmLYBv2618hs1O6tgq/MSSCnjnKMqx7xowpIIoZC3oIl8mE1ebdppqIDarETnEJlCIixBPKJz\naX7RgpvrKnziXAuP1vFvuwf+h4dv+HfH7/im3wvVCymN3KrWWnm87SWIkVMre6ocMKw5wRqgWbON\ne7l+e75as9t6t/jkt93Djm7oSCVzvo5UFN55RBhmQSmyNhRkAvdq45qzZHcp3zyUUxaBjVIbE755\nZYt3eQqJuEQelOGHYU9ZpAF5SivYs3JZ15d+y3y23+8SKTnAWimpbu+FRkFMjJeRL58vvP/mkePj\nI50t6BJwRhGXSIrib0HJXMqFdF5Qk2+Zk+b59YQtlUfXE8tMpDCqyNVmzq8vjNcrPzw5aoLTKbJM\nE/M4M11GspJ1L0xgeY0G2uFX0VVxejnzu7c9RlWu4ysXC9YNOKtJqnCZZz7MM0/HB/b6xKdQ+fT5\nC9cYqUpznUf+8vrK7CvfPh54OnoeHxzH/cD+cGD/sGe/H/j46crrpy+8fn5mmhMZGte5tAEkGnRE\nW8HVv3M7fq8dP+ie97OhrzSGkAHVvN6zIZeOkAtzuXGec0nC3LKWLmt8rHgjtq5dsnTBM0QYo2YK\ngSXCEiRjlUxaM/SWXbLCsqpFJjlF2GdLMoNYwCpFMaCcaWZr4pQ49J6971C6TaivWlSlMVNTuSU+\ncAvSd+tNtQi/GWbdFt923dae/I/+6lvqpp5c9wNtnVYlVhDOKoyqkiwUqFqCf67S4K/NspZKY6s0\nFlGTZjqtNsMsBcRaqEUIG6ZqaH5Faq021qlBSl5dVXprMkvgrtvfJd5Azc1qWCvp/bU5AL92/UbQ\nimykksXIxyhFVavFZiKnSm0KwK0JQGssrp7VRYYmxJBYglDMlhgFWmlZRskZjeDeDsW+Kv7Q7fmf\njt/y7x+/5/vuAd9GZKGU+INrhdd304JuBdv2Otak/L6Kur9uz+D2txa95Y92U+0P33mZUqIRQ6uq\nQBmUStLlbzaw6wcKUGtmtWWIpRBWGuFKX9NC2yi5CMySxVAsLRFf4Q/dnn//8A3TPPHjeOZfLmfG\nFgzWTKfCDff/lbvbFiktcN+14nVtVVRJvH448fr+iTfvvkc7MDiM1Tgvk3XEuzsT5sBlvKKTRRVF\njoWPL1c8muAHdsUScuS1BF5s5bpcsTrzZlA8T1dePr0wXS7kEFA5b2dOUSt9dO1jyIYoITJfJ/EJ\ncZZrzXw6nRj1zGA6xvnKx8uZv75emQqoXKnXkZ/OZ6acKUZzmiYuRPZvBv743RNPD46+s/R9z3B4\nwO8G8VH5NPKXv3zherqQYqaqG5C1aRSUBhPRJeONYTf1HMfEe13ZLYpYDUrZmzq5CCc51sqUZRhJ\nKbL2sUoYQ9qhMeiisdWQsyEWS6ieCc/MwlwTC4m8Ngm1pcPjqxfr4yw9qV01HDAcncGYpizWqjFm\nmlDGgK+GLjuppItGhUKKmS4ojrZj8JqoLTFFaspboSQqi9qET9z2f1t3K5Fgy8jr7Zvu6YgZYbdF\nrfHqNj9XMmZwWjN4S1elUq1ZqIm1KDQy5MQ2Cm5hlfTL00vDvTsUXePhGwUJod0KH7zRMVVjHbX7\nKY1JIWMeW2W49j3v4vO219Ym6Ppgab9+5fpNAvljv2fvBtI+b69rXBJaTVt5UUrePBGUNnKCapp6\nSrrwMll8IYQgwaD5Ra/YsVEyY3KwlnfV8Y/0/K9vf+C/2j/x3g9NPyPj0NYTUSqZG2al1L2DQnvw\n7tdX308b9bb+nPa1rVnKPU6+/huK5bowvKs8PAxcp4kliumOZJEFXXPDTmWSOzWTrGqTaSrM0uQq\nNVNSapiHYm7vTUlZqIvNzP/f+AP/y9Pv+N+/+ydSTfyfX36Gv/2//IfxTExiiL/upVIVZWXr1Jtk\neH1/4G5DbRvs1nn3WhO+XDj/+ML0h2/RDx7Tezp/gDATl5F5PFNzRXuDt/KexJQZp8CyS3w5B/78\n6QXfDvspJq458uap4/e/e4P3HacPz/zlLx/48vGZNC3IaEZ5tXbrVEsGZ9vLdaWic4Wicd3Au+/e\n8fNfP/AfPn/kfI5Mi9D0IjB3itgVpmHPq48UwDlNd9zxOyPDM469p+89/b7jcDyirWeeEn/6l5/4\n53/+K3/6T59QpeJbInN7z9aMsVBz5vr/MfdmzbIkuZ3fD+4ekctZ7lq3qou9sdmkZoZDyUwyk43p\nUZ9ZT/oQkplspKFGJLuLXXvd7Sy5xOIOPQDuEXmqqNerKDt1z8mMjIzwBfgD+AMYBv5pHLg7HSnT\nxO9+s+dX/bXVUXFhIKqUADlADsIzXBipkPNsyisKYbshbjtCn2wNzwWdMmWY0U1tKGx1WLTo6l7c\nxRFsb1gHImWOhTFlBKEEq+2i2a75tOBTFss9AOuidF2u+NWrjnkuTM4oyt7EoSamZSzQmGt8ByyX\nBFNaswcecWZYKbm5Yqp3OaOMArtkDUaKLrx1VaWL1p5tB5DNHatOkdyKsIlQotFT5zwz10Y06qWC\nQ2CPeNXDQBSlWyVw1ecPweZ4metq5ctSXTSoJRFqzf6kBYRrwS6KWplp6ms/Pz6JIK8Rf0Eoo2XZ\naS7sd4HzYDXKg3OYQxTczQRYEsM4j4zjyDCMThWywB+opeKTSEG4CpEXkvhNt+d36Zrfpxt+v3vG\ns7ixAl2+btXNuFqqdSzWtqoKZ/v/8pd9bHGmND+6a1/xDMKmSv076t9Wb7kZjRweDuzvj9y8uOHV\n7TWP55HjaWKeRiiRqJZGJH1nDTLUShtkUUpQtETIkXm0JsB5zs5gsR6R201Hn+FWEm/ilv/++jX/\ncP2aF/0ODcrfvMjW9uu7r/jm+MiolTFfzdewGodLv2ZzJFWPi/8vqJCAXoTfb2/4m3hN9+7MMWeG\nm44b7diEDWkT2caOuTuba2wa0aL0WkjbDukiyCPvppGT2oKOm8TrzYZXr264efmc93cnvv3xIz+9\nvzf+e1FLLntiG1Epoy7YNRfOD0fu7088f77nZrPjxauXxL5nc33ifLb65jEGnl1veXl7xe2zKzbT\nzkvLmgWXkgUm933PZtsTu47Hx4l3Hz7y3Q8f+cu/vuWnH+4ZztbJKdb7kVUIT2kp2eZugYmRtx8f\nGF8M9L2yj50J6Box8+5Pi57yNaq1iQcg0SDq4C6NosgsMMWlgJMoosndFEJNNdeirQ2ZZWmay2J2\nmFiw2IwWW58l2PVrXGXWQqnuBSmUTWYOliNhnDTroVuyNw9nhcprgB/Lci2yvEf2ZCW12EfGcjcq\nMAKYXJiP0Z04BSjeXcldQNEV55wLOQQGhNEtNmOpWOZ3dV0Wr9aaYuAqJX5/dcvNdk8XTHZM88x5\nGOmxRuIaxeoKh0As4jKGRmNsNoRU2bFYFa1An1RX7L9l/9vxaQR5Me2fc2Y4j/ZiEG62iWFnrhGd\nLd3VqDxWeQ1VNGfyNDJXtDllQikk4Comq9CX4Cb1vI49X8iGP3TX/La/4Yvuyuqt1GSDFV8VxSlU\nLsh1SSGup7RkhYa6LwdXnvy7PvOps0Wh1cA4H89MHx9Jt7d8+eqGx23i3Xzgfh6gWBas/Zjpp5qp\nnU6DqFVBjMKALb7gtbyvJLHrIzddxzMSv4pb/rq/5t9fv+aLzZVVbQvCm/0N//GV8qe7DzwOIz/N\n54tnqt72snKdCHZP9e+yesKqpOrnXqQNX+iG/GHmXSmc5pk8zuy3vTWBiFfEbUeXRhgnKxNLIUkx\nvnwIFATT/yY4r/c9u6s9c+j4+vt7vnt7z+NxIGVd5QKsXGFttpbNUnJhOJz4+P6B65sd6dWWzf6a\nl5sN+5sT8zAiWuhiYLvtuNpvubrao2CZpdOMl5BHApQQOU7KeBj46e09X3/7nm++fc/H94/MQ2lF\n3qqwafeja1iAV1kQmAp3jwceHk5Mu5nr3Qa8uYRxlk1KepHC5fDlrQWjrw6KMlPr7IciyBxaBygV\nadYvuiS+oRCKxz9csZd67WDXry33rD6IePau/czUpBgX5p1SUmF0AFLE3RpuLdZ+Ac2l4h+UuAT9\nRIC5EHyMMmqWiqzWnQqz51Bnd8WIYqSHVYcqHLBlUWa3bHLAgrHqUrdg9ebnbFaH30+XIs83G677\nzjLD58I0DDyMmY1GIymsGECRYIPn1F5xY6yyeCpMrGuhgcXVnnMTn186Pg1rJQUeD5YxVwpsu45N\n6tn1kfBsT5c63r8/GIfcg3pm2uE9Ji0NePYdu42R267jhsRzTbzSnl+nK77o9nzW7bxCWbRmCFUW\nN9lqi8gxmjUI0NJ6+9V+egsKBwuLLuO7YAc/lHa96s8LyKWwW81HUWX6eADe8rebW+b9nu+uIl/L\nIydmNAqpj4gUyjRQslkftgis72dP5iYK+01H7HquQ8cLTXwWen7V7fj19pbPuz0v4sb6Y0aLpFOU\nTiKvN9f88fo5b09H3t6PaKj4CCojIEhotT8qZepSeNfzbDxmAo+58MPjI7+9O/B3uyvifeHbYeDb\nuwObTWS/67nZ79hsE31/zWYXKXlCi/tlux1pe8WzF8+ZRiucVkTQDPeHkR8/3PNPX7/nw/2JJMES\nPHwOgtRaFYrlWNNQuXHlC3mcePfDBzQETvNzbvY9+02k7/bsNzuSWAyn660naOh6AoHUQdzYBrfm\nvRP3D0d+evfA9z/d8+1PdxwPA+U8082+vsRQ5HqdNAWjtraX9HNjOTyOIz8cDrw7nXi1vzaedltj\noS3Atv4E9yO7iysvQbsQKvqX5mZQQMNCi8PLGFywtZTFKqvoUFx4l8ByG9rOR712SBXQYSn1OpWa\nOaoNoRYtnDUTJbKpjaUxYyIXbYomEZqrIoi0VoS5CkaxMcyO+DPFE6xsCczFLJOgQoj27DlCiYJ2\nAZIV2iuloFMhjYWQLbFJpmLWkAfsJC6VW1NSphwJYUTVa7UEWnlsq2Vex9LlmFYLjEV5aZUP0ij2\n7hXyfbayMFfHp2kskQK7TYeWrU9SaJS8q20EUaYBZmvPQdhYYu82w/Mhsp0VKT1TtyOkwg2JN3HH\ntXRckbiSxFXouAodu5C8ySrNr0bVdNV35S8JFkTaBCtCBG7iYBQoV+YAi0JYB2b8dVbnrUF73Whm\nQi8niAjDNHE4nLh+zLwOV/w+9bzvOg46M2hmHs2SMR9huEDp1aUTNkrqAxsi+9D5OFgJgqvYsQ2R\nDe6nCgIJ800XoZfI6+0VLzY7gnwEoKw5BbJ+HGmbuR4tBuDjoU4dU5R/fbwjqPL2dOTz1y/44vmO\n26ued+PM4Xji/uFkdS2SFZ+y/kLqyKh4OQZlmgrjbHkGx9PI6Tjy8DhwOgzIeaIbZ3AEWee4dVLy\nOQp1wyyPxOH+kZwzx8cDV7d79ldbdttElwLJXXtdDHRpIKbYmgtP08w4TpyHmeN55O7+yOPDmdPj\nwHAarLVZWb4PlqDZ5WguN6MVXOAWkBb+dP+B237LVUo863fsYmesqpUiqJdYGZj2e6nV9cSEcZMM\ni/BXXe0HxYKWykI7VafJVdy4KgwVfPKrgbusF2mZ2ybcZZkXgYw01F+51EFnRAMJ+8FpfdkpvRHL\nu6A+2+r72je5oiwEipjbpfqcBWk+7Cr0ccvFxlvAa64bGaMQSu3B6YJfKkpTpHj11iIEVYacOZTI\njHjnJ5xpF4xx4rsKj80sQVtdmemr51F+Vrn2l8X4p3Kt5Mm0dApIsUzDUjI6z9bHMilXO2FOgAb6\nlOhT5MUU+O37yPVktTTmXsixcB17Pg9XXE2BXp0qVJFNk7y6WuTqE+6lM507F8QKM+1jopOwGr91\nRtXPh3J9Xnlyhj455wK5r94Zc+ZxHMiHgecbeBH3nGPHWTPnYlmYoxozQakNjo1KFyWQknGQNxLo\nJdGH2BJLlvrntlokWKXEEqByoGII3G53XHW95x7Y65WKuPB0l7uvdEV1kFeToepnK83yp/OJ8zTz\n4+HI/xQif9dv+WK7ZZsHftKR++PEGAoTpRXmQhZ3TVWY1tS3MI2Zcp7ZjpDOkQ/HzHCeKZMh9jay\n6olXlT3gz3ExAwLT2Uo5nA8nHu52bPZbttue1EdCCsQAz/sNN5uebdeR50KZZsqYLcYzzEzHM28/\nfOBwHmFWohYSdY2t/JxLivDPV5CyWkzuDxb49vRA/yGSUP747DVf7p/xrN/62r207ioNVJ6kAK5r\nfNSkkirIngqSZmnqpWKoc3p53Xa7TxSUI3b/W1frJ4hYNUdq5qPt11DstXqOuee0SdqAu5OaWryQ\nce0bTBlZq8W00mpm7cQV7K1zoujsVtrM4mLywmXVTSjVDBeFbKg+luDKrJCLUQ/XVO/aiLwpkYbK\nLy3any0J1WYVLcvj3xLjn0iQ/3R/Z41si6Wgay2rGUc2ThXcdYHcmRDapQ3P9zu+HHv+7hy59RT6\nww7uNjOy6UjdNd27gXR2KqGjDEN2NdGgTqZrXdYI05ZQFwPP+p6tp8kLy+KVmvu71vJiASdz4Rtz\nwE5eoXYqepHmG2uvmqnAjHLIMz+cHvmr+ZbP4jV77dhLhy31pRkEtc2ab0rxpgBmylqgLDjK0Uq/\nEv98sHIHlf4Z1RIXigQ2m56+7zz5QZvwM0FaEOdK1Ucs9TnBxrr6erVhdgRhRPmYJx7nmb85PvK7\n8zN+W57xXBND2XPQzAcmPpaR+zxy1sJAYVIrgaqloFnZIWyzsM891+GK511Hnkf+l/M75lHJ7oNc\nKlnY/5e/HYGxZKxWPrOUQhkmTuPE8OGegwSkS0g0n/yrl2/48vmOv7q5QYuSROg6oU+B3Gfeh0f+\n1w+PfJNHhmJskmaZLYPY9oCyrAP19YSj2oX5alL6Pk/84/07/vLwgf/5VzOb15EX/WZBtbIstDW3\nellfNCpcRd6hvieeOezvhXWThNqyrN7jyod+UYmvNglRahCgCVnFfOFztJsKvmDsdUP/NYU9IMaX\nbuaE1t4jVI0lT6Iei3d5DdP0AnGXsrh8QpA2L/WzJgt8b5VFRoSaEi91/xoAQrCyso40glrtFVM2\nYiU3gv0e8XiH1qyO5b7qRFc2TA16t9W6crtoWO21Xzg+iSA/nyz5fVYYh5k+WV/FvrMWYl0IpLTl\nOA+M88w4T4xjooyJTelJEslkxjzwMJwpJSHZ/FHmZ8RMl0qhqmisUb38WJHra1AqcqYIAAAgAElE\nQVSml8hnuyuuunuQQxvstjDVWqNVIWUTpKtrrVSD1kUiq00rLSOyZnW5LOdcMt8c7vn98ILf2Ypi\nvVxDdXX49wfB0HXdQ1U6yLLca3U9M699v3nvv5hXSE2sOUE1aytzpSKkhnZWgqEeTTnWgXwisNaJ\nRlEinSa6yQNsarzjqxx5ox2j7swv6sGq6p7R4PcXhJjMX79R4WE+cS2JhDTU2+Rg/dFl09Y5gfW5\ndqYWPEsPkIwUSyaZA+R05nYr/N3zZ7ZJ1YKGASGHwnWJfNFdcRcGzmXwNaduFVzaMXXMLgRSTQlf\nj54s9XtmVU5kjtlcbdVSqht+OVbP5JKjWks2nKtznUNd125dhy27cn1Vd5WxEpBQGTPaFKSshRPY\nfowCSZmC9brsRiDbvBZ6990vuufiu/37alXIqiTsvWXPqY+tKWl/nmp1VwW5doPq6tpruCV1y4tT\nj1c3oopkqaYBNRkOMdphJUhECZQo5C5YM/MCtVCYQHNtLu5ITIHUXJOqVHSxZKjnrMTX+vgkgnzf\nbdriDBn2/Yar3c6q6nnabAxCPlnWIlpaZlitblhQzmVmzhPMM3lKDDkR1GqMpyKEslqgLCyKX9Jq\ndfF0EnmzueZZvzEONJUbuwhvWV1zrSWX1xbxsDaN6i9NK7e/LBA0qfLD6cC74ci5zGxCt9Q/CbL4\nGT0Sr6H6OHEO8LLJmhCvmzQsW1zzko1ZS52qWq/NJJY9K08GqW6I1WP774tQbK87mrIvW565YK34\n8lwgm9KNCF0RdkVA0yIkXICvpXKQOn9K9q/IMbONieiUzxqL0gWWrlX3xfNczOMKCbWtVKDW2Hg8\nnDgfB7YTXHUbuhCbrzhb6jBfbK74tnvgbR5aUGtZAKv5lvVdcPFzoYUu7teVYLLORm58XB5tIS5C\nHFkQeiNA+O8V7rVaJiv3S7Ve7XK/PIJUIOKZ0VIRyup+RAISQZJJodozFEfJRBfNa/0il8MUnX+9\nrKQFmPgT+OeqQqExbUQNPf9sTO0BV+Olq4mo3+SB4vVHXDNKkWUPCV7xtGbpCoTKJCskLfQs9yCB\nxSIUGjC7HNtFmDflpRdq7OL4JIL8d5+9YVK1Kn15ZlO7qFjLGQtwTFZvpA+J0AVPhnEuafFkiBS5\nPie6UbgSYZRMUWXvWWxWjUwoq0FcOoosC2C9oKMKbzZXfNZtuY6Ru7wm9FdkUE3TxT1Tjyc4pv3/\nAjOp11nAFn+VXUWUD+PA2/OJu3Hg9dYaQQjBm0RgvrmyIOIcBVf5vgZtJWd/rd2FYPpCxQro+7nF\nuciUWtJ1ldXahNFa+fzsaeq6W/+5ir47ynU0+HE48WE4WyC1blaneZkwCdSgdGuo62wO615uTakD\nFgvpQ/TiaYZeGndcZamz7U+gLEKilh1uz0kVDtoUgikFq9j3w3DkTw8f+MvH9/z1izd0fXL+tbkH\ntl3i8901t4cNcn64HCddKdGVVbh2DawXkazcWlWIJhH2KfHZ1TXPd/ulzEOTZoswbAHni2UoKw+M\ngNYyxf6ep9yv0XBFwlDnYrln1cpusZuo1mG9jbYvgkAMSBA6FboSCVqsdkiwFPTaRYcqnJ9YK3Vu\nm9JZASVt7+BuTm2gwdx9hSksayu4AMbdGRebV6zmiYTFqmnuKJ+7UtfGnNv4Bj+nst+qksmiPGZr\nJ7nDqrQ2oLPeV/WGFbwQ/Xra2vrR9sLPj08iyA9e+EcV5xGb/zar1UseZuvkM87WoXvO1gKtoi3E\nBjVKtKQShVg81V4LpWCddFCjMdVBc635hHLbxsbAV+A69Xy+veKL7TWPxzuyrBbZCgfUK1TKmOqy\nkGqwVWX5XHOR+ApakoZMYBXgVDJ3w5mPpyMvtlfWIAErexrUfOFVoyug2bglzR+IUqllC8oTR5dm\nzCKgrUiPZdeWoBbYixaXqAteXWE1aV3RwxNhsZivVWhdnoff49145v1wYsqFGM0FVC2DRaPRFq+0\n8XXWhC4sAhEhxsib7TXPuzt+Gk9tY1/6cVcmckWnq7/bEqkmOcsc1qD4WDJvhxP/dPeBN9fPuer6\n+lAAJAKvNnuedRs6FTLBsxr1cryacv25AG/C74KiWOfVqHOVimtra6H+Sc3KXJvjIvUR2neLLEi2\nCWbxkg51f1QQ4DEWwNPKFxehDZfTB1cPUvvbQvWpC6FAykLBrBhJy31KqX7x5cJ1DBZQIBfzWsep\nMsrqDFfFZADFZ71kxgQikTBD59MsoYIwH61K/QyrwKw6xREHkLK8Ti4k9e9ysJhRVKxyZ7TC7px0\nQhVm2bamGtE3r7aHlcvaH0+erSm2XwyU2/FpfOSzpezaYklMqxohY5k5z9btHbD7nzObLMxTt0y4\nD6b1ODeBkL11VSGab+qJi2AJzi+Bz3aIEzhU2ITI59trfru/5ZvzoyUE4OnIzdSpCMuuWa230pYZ\njc3xVOs/+aVtmAKMJXPIE/fTaPzWhFdN0yaU2xCwKhEQDNGzcietn2+hehlPVv384pljKtB1iW3X\nsY2RoczM1I1bMe2id56aeMtT2/mX23JZkB+ngbenA4/nM9ebvpUVrenL7VQAQpPtdQKFheUAkCTw\n2+vnfHX4yNfHe84s9TiMu68X92p7YuWP/iVB2m7ElIuawcBxnnk3nKyrTlHceEAw8//V7opX/Y6r\nkDiUBSmC+211/cr6Wy7HUtb/2kQ2dBxlYWUtCsm06Np6ak+x2i7rX3SZEmdpWHAz59kBUV0/0gRz\nJDY3yuKn0QsUia7f8/03Y5mNoSJetc3mBavCMiyXI+H3sFYUJsCXE2tUYd3XU4AS8blXSljFjJYR\nczaRNIzSsMpqEOs6KShjKFSXWxDrNGUWA42zXuqg+v3MWPmBRtlsw7bsqTbRcgE5FrT+ZEx+6fg0\nPTsxdDHOEw/jyf1L9qAFvIP1DI7AkwqpZM5TgHwDrmn1dCJITyQRxQpOabawtxK8NKSjDZZJV8yn\nVX3m1jU7YwsChMDnuyv+5uYF/3j/jikrJy0u8DyZoH62XdE26lLO8hL5VEFt/ts2z03SKzVrTjlT\nOFIYe0hRYBbi5ADR+2eKeqanBN9cipa5Cb5mKrMEriQIIUVyKM6x9QXmSnTbbbjuNtymjsM8MaKI\nc/hq4EdXG6qa1fXvprP0UjwtgsUE+fenAz893rPhueUTxJUJX8+uCMn99zV5JXgbPlVTcJHAH28/\n418fP/Jf794y5qk2qWkbtSpbWVspbW5WAmCtjWTZdE2ZuSwurd5HdcaYe+XV/oo3+2tedBtO49BQ\nLW4JqZQW0GrjshYY/lZQZ9X4vQe//jYl9v2GTdcZi6GNs6wAxvIMlgi0JOKEUr/XUuDrvkC11Sia\ny9j2hIg1ZK6gJ1Tueg1ursenIn3Vy3lXkNk2coguFhXf6OKsq+r2WVsievmvP2krb/BEmIfVpNaG\nDiUIc7JWibGYZSAWclsQGGpuHgdBa8aPal1vlv15CjNTsTjdxpML8XuatViFUbVS0xmL45gL0BOK\nFKqFd7kIWPz7K8ld+e2Nf//z1duOTyLIf3j3ttGbFv+1l490wROxwvR9jFakJkd66ZGzPUhUYa+W\n4BIVSpm9FrjxQrTYxtGaCeZItGWtuTCwVNziwrGiFWHfbfjy+ob/+OwV//n+PV8PR6pXTNC2yepk\nOjEPWflmA44E1NCvMYhWftGK+NrrIBIYcuEwjsisdAiUVRlNL15kEXkX1m1TeSTdL11WO83qIruy\nKMv9NSQMJA282ez54+0LPs4jx5JdwNSmxRXFrjHSIqib37a+d4FqDJ1NWrgfB765v+Oz3U3zqzZB\nWj9RpVpDa0KMPvqKM2+gQ5C+5+Xuis/3N9w/3pPVZ6py2cSy6Kr0WVtTC+NjdfPwhEJpx6yF4zww\nq6V2h6rO/f62seez7Q2/uXrO2/knplwF98IsapZRva600WkD1tLU61hWa00zo2ZmSmOL1Loy1dys\nirleR9tEVIlh14xOWUU9i7E0roSd1qwHo6eGFTCoz9Jc2fWzFW3W8VWg1R5f5lkLrSRrQ/0slmYN\nGLZ41mqdLVbHU4G2SHZBSDMWyA8BKSbIU+3SFaiCwD6WlwkRhCU2amF1xQCUpI4uR0IpdLOdW4kC\n1iUprzj6gR6IRPaarHCbLvNblV5lvIkaeeEiyKB1+a4Rxi8fn0SQa1ELqnXG1S6+EUK0xJWixd0k\n6vxJ812FsIjAiLBT60spLlGjsz9CbdXGU/Tl4yNWajL4l6uuB8qWcxciLzY7/sOz1zzmzGOZeT+P\nTNj9XCA5X1brBJYl1bqm/9e1Iqt//Te58CozztkSS6biPRRDi8KvhYDtZW0BGWnRpwXB4pTMehON\n3YJ4YLHetFkaLzdX/PH5a/50uOc4z5ycILuImacCXC6g7CIcK8pa3at/7nGe+Orxjr958YbXpSz3\nUcdg9QXVRx/AnkO9J2rddBLoAry5uuH3Ny/57nxinpRZ1AKqKwVq97f4Rp8qoqoBl/GVRVgJjFp4\nmEdOOZNVm5JuazJYduzvr1/w/zx+ZMjZ6upjyMxomKUJjKoYLxR7G1P7K7Sbowm6i03ugrR+TO3L\nFhdBveL6D7hwRbTxDIEgsQlqE+Jh5UMHXSu+qjPK6rWVO6/xoFfuQLQK8WUv2Mvannu9WKpb53In\nu9prz6QXz1f3XSoBjSwASlfPvfJD2dlt+lcKqX43hAKbSazUbRZSqZaSCdpaL6ZeJ2pg46p+i9dk\nX0fnVtqpfW/xFxSXaZdzpk9fWB2fRJC/un5G77zx4o0R5pxRMZP/nCfuDyeO00CYJjZdoqOjzF0z\nTyKRvfSWvosFPhHQUFyQR1qN53aY9jOzp9AVT3HHfNSCbwDf7LvY87fPPud+nrmfBx4fPxoaoi6M\nRUhUgVPEqihVJOXiw3t4hoZA1kivvlI37TzPnM4DOmbzJyJNALfNhQvxslgSdYM3d4rvKyF40pV7\nDaMh9BCjZdT6xtIiPNvs+eOrN/zXu594GAfO5/PF2DwFBo3m98RH2Z5QFgSHc6VPeebPpzveD0e+\nnG/Zha6Nec2nre6Uak2EEFqwWqnVp/xbAnx+dcu/e/GGf7p7y5BnHr3GfKi+aWf6LPuj3qE2iVBd\nCBdCvO57EaZSuJtGczuVQhftgVu8RYUXmyv+cPuSl++/5XGeODqyrTkAReQCZFz475tgW73mboeI\nlVHoCVa7YxUgbFxuP7/Iwp2uFNxF6bKILV2Eg7UjiySJrWRrXWutf4Cj66dsFguihra+FS/ehitf\ntwYVDP22WhdVYq+0jqwCrCvEugzJau9IVSxKLVnsM9LGMuVg9EPxPIaLc5YLNZfPaj2ghsQphTQp\n+8HL+arSqTe6CebWER8z9XWZVOg0EYlWabVZy9qAk22JFaCq+7bOVwVldqKt1pWraX18GkSOdQ0v\nxSh1JVst49QFznnmfB64PxzoJbHrrN1ZraNcuaqKF+mRincC0Xt8CpUZYt/WXCi+cCtADYR2rXVS\niwkQy8uKUfjrZy/JQRlRvjo88HG0io3RTd7ZTbVQaOnllfEcN8Gi9ECQWLHESusvDpd6vwSxcrq5\nMPqGMMMkuD9foThn1R3uIo62V+iM+ixzbune+KKD0rivbVOJsUBu+g3/8PILTnPhbvqBM6VZnzZY\nCzZolny7vCLrTbLag1U4TihvxzP/+njHm901f9i8pFCpa4sXvgmMsDQHUBbhrnh1O4XrbsNvbl7w\nP7z5NeH9d/zzw8dmlSjS3EpNEKyQUEt2aTOxQoassxS91rU3W5DYLcjShd02Jd7sbvgPt2+YCnx1\nerAMTLErBdbWB8svuv6jDpz/rtZD9M3mimfdlq0ka3LRnmXl8lhZsSq07EN9ivBkmRjxWkc2xMaI\nqTEWceHRBLtgY6+L0FkLcZp7c3lAARfe6jkMT9UVC+deF3fQcrsryLN6r6F1vbxWO+qa9ESvX/q+\nn8UWVusZxMv42ietUTqgpcXJBByRFyuvW+9Lg3PN/VZqsJNVPECX71zmYnmuJyrYjzUwXY5PIshT\n1xG9nKyI9cWzrtyCZhe27pcL0RBBVCGqCQoVQ9RFCqbzbCNFIq0AkB81/bUpdrHghFL5qfAzK6au\nErFzXvY7/ubmJefagPXhjiEvrIQa7CFYYKWuoChw+9kN2/0GBPq+tzK6SPPxigghpoYIVAtvxsTn\nureAzZwRKZ5NavdUXJBXd0gtZ6qlLBs3LEtBVJogl2B0zkwhC8Q+InHJGDUNF/jy+Qv+ehr4cT7z\nzemR02wlW8svIoLF8vilRKL1sIrQyhG8PR/5OJ0burZzVlJuDYnbbjWhvJ5mQehS4uX+ir9//StG\nDzz95XjgrNkBoFIrUK6FjLqgFKkW0aVIWLvHWiVLrYHmqhmX50sh8myz498//4yP08CH8cxDmchK\nxWMXglsW7di+DViEBMYhf9b1/OHmBc+6jWWxlipxfE/ga1apLNGmkBrAXOQgq38Wb8hqQbdgpytP\nhRW/+skc+z6uiHKl5v37tAlyqe4DxADBWqHiDJklBeJyJi4UXr3nXxThLGqOp4Dev09W62d5/iUW\n7f+vY+JHjSvVXJJFAVbLxOdRFkCgQVtnoaf33x5J68StJ+vy5H8DjAOfKrNzu7PF4n/HmMgUZp0h\nCqlLXF/trSiN1/3oJbKR6GntyizWUaRzQe5SytdiZRUs+qx5pC+oUg2vOVpb/M1ULS5CHxJvttfs\nd1tmJnKZ+O44ci7WSDaqsyMIVo/Z/aApCV/85g3P3tyCwM31FdvtlpSS9S7EEHzXb7wGhEIuvPlp\n5jc/FbaI1ecOxhcsOte92sReUXX0hAt2QL2GslSfXWjNlaMGHscjj3niFOBmd03aduaymBeEtU07\nfhNe8ZBmju8ynAdyNtOymt6/pPzagq7BxbWUUJqJrVk4MnNkZk5L4Nn0pwtWYZlH/2y9XvaFH1aI\neBs3/OHFGwMHAsfvv+btODBooZBbnXJT3ob8ltKr61IE+KpZi1dbPxFrFdZ5nWlivW9jiIgI267n\nj88/4+1w5PvTHcMpM7hoqXu5zsySOOXK2IeqBspFhF0MfL694u+fv+G231wIIdpd+xVVkezrwznR\nLYh48XxrsLOY7PV5QdpzKZVDbmCjlMXX+zMhXq9TOeeucZfM4/qdK0u5xpNqUEWrIP63jnqXvyAV\nV4/WhHmVA/V9WfnY22e0WYQXCL+6bqjU1/WFDCSplOXZg7N76tpfPa8Xf28ypt79ArjW8TqllSn2\nfaDrh3tyfKIytj0VnyjegWOaOUwnxjyStTDpzDTO6JytBZNeMecNFGmCfJLMRpW0EiCL+dJmzRfP\nshkX00Xb77WYjn1k0cZN2UjgWhP/zdUrrkLPu3nmT3cf+frwwLvZKZQ4lQrLkNx2PS+f3fL69WtC\nCvRdR+oSMQZfFIv/V7xWdFLh5Xni+cPMdvA2ZaUiR1ogsaJfQdyd4ijd342eMWfNA2bmebIqid2G\n7x8+8s/DPV93E3/3h7/l9uXWkivzsoiTKsObPTe/+yv+/vyc0zQxz97JpQndBdqVXJimiXEaGaap\nNdYORGNWuNumFljSDN3VNcdngZ82g9XeyAUmr1fhjXubEFVBolEOS7H6ciFF63KeMfdRNjrl86s9\n/4HPUSn8l4/v+erxgYfJ3CImxLWNX6qaseGwFdKtUl+qYIOpKIMWJixgvgjAYF2sxJTEdtfzh+cv\neZwHHn74C++mgXEBolzUqlkLBlw4ujDvRfjb56/5h1e/4te3z9mmzu+tSohyUXGzVczLaiyK1bqv\nx1p+LeKwSnsXQBeuk+o6ogkjO60i/tVakOV8oL1eqY4t4QgDDBZM9fhBdcvIxeA3IdrcPGKv/oxF\n8+QJ5elDswjNttXbuK1GoSYjVcm9FuyLeG2jqhVouMsvBctjGTRbOzgsVqeiKwNCmjCv4OViHvg5\n4Ky39kvHpyljO8/eQFYukIg6XcsKEplJWYJ14Ga14dTJ9+dY2JZCKhDbQlowBVw+eJ10+LmGW7ia\n6ohH1x80wSvCbez5q+0VnwXhJiSeb3r+fLrjx+M9h8m7HWFuoa5LXO323F5fE5K7iiq3XaRxov3i\nBBF6FbZJ2QQlepLH2jRUQGqpNWr2nGvzimKaC8AWS1GjRhW/2GEc+HF45C868bov6JWQC2iR9l0W\no+rpZMPrvGXKlgQDTzepLt2ehoFhHJnyxOk8ME/ea1S8nG6K1t8xmzC47TaM/Zavt6aw8zQzj5M1\nEIjRFF+wOioBwSG6+SPFGpTEhKV3T4UyO4u/JKbTnq3c0JczkgeGYCheV1u5sT58Va1kOLCw0gJe\nejUGShc4xsxDnEhxNI+an1d7ldqeVm6v9vxR3/Axj/zzw0d+OB05FutL+XO7vgIQ+30TIy+6Db++\nuuG/ffUr/vb5a64326ZupKHZlfCmwZNl6Tr6rLx6WS3ztieeCL02r3V91f3SttgqEFzvo95XQ+hy\noQjs7xqXqcHRupZWlnB7vsV1yUpGUNF/26urjbE+VriMtWVQUbff588SburFah7Iymq/vPwyAKqX\nox7a/SoLUWU5pz7qxSM3RfrzZ2mf+bdNlE+U2TkMdF2Czpo+ICbkNrEn5hlRyDqTekuDHsaBTe5I\nRCPvY3UMxlCYsbFKtdcTK7QCNE22Rg3QFkT9dVGUK73uqraWAM0omjNdKdx0G14+f8mX11c8O3b8\n7z8YEs0+gRICISX6vmPTJUuICOb6sYQS56DWsp+e66yKISk1b+7iMxZDQqqod+u2/RFWiSuBtqdX\nsgLhYhHlUpiLkkXI4p3YawMCr8pWO7WnEEhRCcXqd6aUWhq/LWJDWPM8M40dU55AlWmaKEXpYsdm\ns6FLyYK4OYPi5qcyK3xdCnMujNPEeTibEoyRPhW6Tq2uupurtexpEQgRQigEDzSVIhQtTGPh8FD4\n4Si8f0wczj2HuNSfaWi0CWAwt9SKHubuo+DfmbpIv4mEq4673cSP3ZGhgz6Ya89jYNYEIxdkVmKK\n3N5e89+FvyKkwFktkD+XWr3wcmcu7hS46np+e/uc//HNr/n97UtebPfkJoVdcauXmbWuD47wrKZ7\nzfat66flObByD1QL9omAqIJJfL2iXBRlC3HtClsr9yXo22rfO3PM6uQsaDx4gpCqLo2NkSbgzQpd\nUIWKrDj/dlO6Ho/VHq55Im0P1xOq8hKsnLNviCWm4KcpzWqr5Ilq2dTPG/7yRD5PBFoDrlpLqSYi\nIdoSfJ66JZfiZItyXjCnrp51mc+nxycR5M+ur40T7kKsdtHYxo6cM/M0sQtWgwWBOW15NnZsS6Jq\n4aiWJJScqxlCcHJdFWR1GJ5m8+myOFdKsP62Srxbzl6jJgWZCpMOhBjZBfj9/oZ/6Xp+CNJaxAUX\nNEIxn2+wBgUARVe8VS1ewlnRbA1zNRd301tHe0s6qxrd3AcLoanCSm8n5ZGiqsEzVuvcmuCqcegD\npkB8o659hdVkXkUVEImEUK9prhWy+4VZu7RoXP7Yx2VsSyHPEyGmljyy9P8UiEKXOmKf6LfbhjYV\nKCEyiXVcCSFYb8ZGlbHa61EKREuTPg8zP71/4JvvP/Cnb97x4XDmWDJTsJINzW8qq4BUbSBcsvs7\nSwu4hQhdJ3z2as/tyx3PXmz4Osy8Dx/ZxgPXmx19jIQgaIHxPHN8GHh3d2A4zWYlFOU+D3zcdNwz\nc85LbKNOgGCWaMT69X5xtSPut7yVmfF8x3Y6WBnfaIojqgn9GIQYYvtbBOvMLhAy7rYLDUGbgBFS\nsXo60dfqUutEV26GBQ2EhqDrmvF4RliEeAt0+hoydpN6dmht1eguFrykRvWRh9pL19b4es+tVO8T\nCC0XFuiCXZe/YL24uTxEWWoh+WdW0rjVK1Ifi9XXaH2jui8RdEW/nDVzZqYvPbEYEcFYmE+skPX9\n+u3r6gVLkVme/5ctiE/lI++6ZbAwp34URZIVuc/JGprWh82S2ZHopkTBqD8dgmCdfCK12Gu95upY\nzWPTylVJ+4TUU4pU94R/t7AS7CawUohsYufmU4Sg3CQLiIYWRKk+YfOLFacr5bws7CpIlkxrn8GV\nw7MKPKt5HBezmAoJqolbmpVSF4LJ2uojX2t0r0Hhmy27b3vOMyFadm2IsdWwwp8/qzLlmXEabVFq\noJPQ+n8abTt4sSCaizNnL0WcldiokYKIGh0zVCvCxiO65aH1vte518HvXYsnrihoRoMwz5nDceSH\nt/d8/cNHvv3xnh8fjlbXx11CFcGJS7RWjMuzcUsQt5a81kiAzSawu+l4+fkVL55v6bdwP43c60gi\nccVMJ7GN7TlP3A0n/vJ4z+PjSJ6VJMn862QOMTBLagk4rrHb+q1JJj+IMs9nvj+8t16kPjYxRBP2\nxYRLdBddEu8a5fGWKMbOCh50VNTzHixrOnlLtY5AkujuL8uiNrefuQiMhGvXDRUxuwBaf3+tOlnd\nTCgQ3KIpkU00Vyp+TfH7t/UQWop9nd8LZFEtpZWvuKJjm0Ntp1Z32SVy1cvF7Ii8BL+PbEKguZ7q\nZ/TiCk2p+Y01N81cM9TXhd/QBiwNaymi5n2ovv4WSFWai7S6up5IsdVN/PLxSQR55QA3bmqsaSCW\nbSnR0rFytgCWSKTXROqSpUar0Bn2IxHNF0w1iy6fdl0Bbz1A9a/K5bRJKWTx9PcqlKtf1oVqFxNp\nYxtKg3AKBZGh+fpNMAAixGTDWxtHl2J+5qJqG9JNRg3r+V+iK4aozRXRSWo3K61okjM73ITOa4Gt\ntv5rSn+DzBhKzy5IcjFBPk0jKXVIhyeBePDOxyvnzGk48+F8xzRNhBK4jjuutlu2fUJr4Cos5YmK\n2iIfp9lqe8TZ3TImMGJ06iQw5+zUxrrQjSpoWfnqj2usmVwyIdSsYGux9XgY+fHtPf/1qx/49t0j\nHx7P5JLJJTdXlrh7RnyO61JpSqNaCljwu++Fm6vEZy/3fP5qz34Tmcah3S1SLzAAACAASURBVOsc\nFMniFpN9x+E88vZ44uvjIw+niTxDkLRaH4IkS1aTsHJD+JoRR+V/ngf+nAcrSdEEmLsfFKTkBS2D\nC3or5RBjtNrlDhSKWhNzKdblKWDxp06sBHAvHZtkDdC3qafzhsJRhD4Eb+gR3IXkMSyx70xiDZGt\nsp8Qqcwpk6g9gWvpebPdsEvJXKiSXOCbog7VvUJhzDNDnlsGdWXu1E0bROjFAooSHMRJGz7fQ8rc\nSg64oNeatLeANw2rDj65kUMRV3pUAUv9nRVF1WNDWDPp7OCI1Tw3pVbt25p82GZTG+hrSmQVV1hA\npe/n/w9J/okEuY2mrCLF1ZAvKBogps7MuKBIB122Aa/aXrCFmDw4WoVtU/I12OCHnVIu3C/VPRFq\nYMM1euXwNi0svtkxGlYKHZt+QxGYyoTmobooV8FCIYXoGYkwTaMh3zmjBTabnq5LpBgIK4uiqR1h\ntay0NRJQf0+CtM7kRaD2A6QuUhcuVr+9NGRQTUEEQgp0faLfJMwFZArCBFpBsyN+LUzjmcPhkbvD\nHUOegcCjjuzPR3ZdQiRxvd2y7fu2QOeSOY8jx7MJv9hHerdo9t0GYUZKaDVsqiKc5olhnBjHkf1u\nS9+nFugUTImKBLJmzsPEh7szX333nq++fc+7+wOH08B5GsnzxKzmCjI6YzKLI4ihMLyyZK0lEiKp\nF/pNYL/vefGs59n1hpt9BxGGXNCQiL1ZI31M3Gy3iCrjNJJLZghzQ2mmSGzNqNhqq4kpohkp4r54\nmtCQZnovR8kzpcxknVduLFmlnq+yfQXIhpIjHstwl0Wlt9XqhkGynRdmkk6kMpHms1mWPhfVFVNf\nq6i2FiALLtxFtdUbUVeeULgOiV+lLf/p5jVf9Dt2MfGq37MPiaDKPM9UC3PWwk8PH/jq/j1/Hh4Z\nPQaQ3P3RSeQmbfjD7Ut+dfWMXd83t0NWq6c/a+ExT3wzHjnkyXJGfF+WInRRrKl2CoRkSqj3/gXW\n/1ZI6oX6WKyN4HtahGbRWOntwuQlG0o9r4AUoVdzXzmKs8DAWi//TDCu7YEKNRd517JCf+H4NNUP\nczM6WgZmFa9aCiVn5nFmmmc7V5WrszBOiaJiTSMcvTQBvj5k0V71/83FUMVjXZQr8Vn7cF62XV5+\nq9pTvOawLeDZEFrdkEANqBmlsZDzxOg11ksxNF43Vi1yX406rQX//WLr7MulTgQtyLU0Rl9pdP9Z\n+h+qmQmiLT1aWConGgUy0iL59TpSL2X+niCgWTmfR8a5sJGZPG0om55NtyF3nQmvnM2aKMUCjMEp\nWJjAicmSkCqlMjhjR4L3Hi0ZRCmSmcpEcUoiBQKRIJEYlcfjmbcfHvj2x0e+fXfPTx8PnIaBaR6Z\nZ3MXZS+IFiSgoaDFXEdRLHhbfGxsBpSrfeLmJnFz03G9j2x6U+nnaXLTXQgpEKIQPLlrkzr2/das\ngynwPg2GGDEFO5eZGiGrWYsVNFRU0cZHgrl9qGZ6sLhRnshlXlmcznzCfdeluiMUY2dWhsqSdVkL\nr/kG8PkPvpeUkGckz1S/WJ17W8vLZ4sWWnPkVdkJQ67Fg3+2x5+p0km0FnV5triOm4virrEKk+d5\n5uPxwF/u3/N/Hd9zrrEDUVSFPkRepA37EHmeNuw9kdCKNpjr4pxn3k4H/o/Hd/wwH4kBhhyYszGz\nYoAk5r4K0V1H7mZKYmOaFHM5OY04huiuK9jkzKYUOglcp459TCSXRSlED7QGokQvmuWRJnHgqLCu\ncCgXwvvnUmctzxsC/YXjkwjyPHvNYzzttWp7NSE+jROn05lxMGE+l8x26jgPW0pOtpGp5saSqr8O\n2q2PSnlaIulVYC0BvfrhoFUxVqMIKlyvArV2eTFFqwzTTPYApR3aJm/OM8M4Mswjec6+MJKXCViE\nbpO3lbDSGIYulLFYglW0s5vUKEz98rkyzWYiFu8RW4TSCvfbfV0IgtCwhm344EqqPbcJuMo0SSHS\nhYRkIQ+ZnGZySI503FWEkkt233ewtn3JWvtpKWz7DX3XGar2MQoxtDE3/72iQdEIx2lgPit5yuRR\nCRqJMRGC8Pb9I3/5/iPfvjtwGLKxQdpcg5e1cipbtjmytu2s695Y3MVaGd/se17eJvZ7V9JjIc+R\nmCw4Hwh0IYHa/c9zZtdtuN5fISjnc2bXH+hrpyW1etRtGbkQV6pStWsGsWBfEUv+qmnvuEIsxQrJ\nXdLyjHohLe3frlW0uDtlbms1SDT2T/Tx82qjoZLffQ/kkpsgz8WAFdC61xtLKRt/X2JTBvXH4h7W\nDUcRYkh0qTfrVCpgsjXtrbWNRaPKMA18HE/8MBz4YTwyeNeg4vm4nQQOYeD31885zyORK9sbvl4o\npvg/jEf+79N7/jQ+EBI8TIkhB7IaddkyoOtYmrWbHL4Fp/xGnLElNmYpCF2B23Hgdp7Zp8ibzZ7P\nt1d8tr3iWdpyHbd0Bt1t7YtYwJlqEVa3Yd2B7j6RxWd+EditcqQpP3jqOq7HJxHkwzhSvNNJEBMC\nyf3kwzhxOp85HI6UXMjFWsJJsVKQ7TG1Bgsrkl1w+drFYufav4Y8FvRtv9QPLfBIqx+ioibXuJqL\n87iim7bmPrg7nzjl2eqNh0DJxQVt5DCMlNOROc/Eull1ZpwN2aSY6KW6MAygSHbKUlFbYB7IqoI1\nqDJcd4y3kXlj0z2cMh9/eGB/VG5K4nq7MWFTIHihHwM+1S9nPOyUAl2qhQW0IfJczOGRS+Z0OnMe\nB87jSCDxfH/Ls51ViNxutmw3G3ZdIiXji9M5WhXQktAqoHP28gzJF7bFSsY8M84jp2ngOA1Mc+Y8\nDRyGM8M0c3gcebwbGU4ZihCIzHPhcB55PE3MJbQlr2o+6RSX2ju5WEJUcWZK1EgNoFVRvttEXj7b\n8rvPn3F9lTjPoylGLUgQdmlDn3pSsufsgj+rwjBN6OlACIHzPDFZnjKqc4tRLNmfNPeXdyGw9RqM\nqhfESgZbdquzT4Kgan5l9R62FiS3Gjh4jcUQIuplLSiFWaVR44pmsgS6rjPgk+cmWKpSr/EKkWhK\nQrx8hCzbBEJTpNT95Iu3+JpZisWZdbuRYAjYhRnu9sNGiViUPGfePt7xzeGO74cTY7F63m2YXCGf\nNHPME6c8kbF4mVmjBVTIJdvcMaOhUAjMKsyIZxW70qmC0fd5YeVOFbHG8FoVvhI9gztQuEmRV1c3\nvN5ecdNtmLF7VdyqjJbxay0VLaBdxAP5VPfwKqy5AnP1qHuxuhtpwv4XkCqfqtXbcGaeZ9vYIdKn\nRJdMJ06zmcPiDZilKBqFbgx03uqsoWZZUpovjmau6OKSWAvntpMWx0ppwn+1anX5tfoXzZ2CmeOi\n5FI4z5MVlae6ZSyIaT6xQIodKSb3mxubJYbaVg1DCZ7BWQpQZEmMco+IuI++oJSNcNrBeS+UCOOU\neX965J+//4b9OfCr/pp/133WBOVaiasHIIuo+efNEjTh4un3MzAOA4hlY4aY2PSBLvXs94vZExAT\n3jF5/Ro3JEJk9oGrmzdZGLtRJOeizmfPjPPE4+nIYThzmgbGeeY8jZyGgWGY+PjxzPt3A8M5Y7lA\ngZKV2emaIXXEaCg/EDy5KmK1PAqhmNWx7nJerYEUhGe317x5dc1vvnjOy5ueLiqncbQEtSDm2/fk\nJCGg6pml7pKqNUhKzojAtk/sNx3HPlplz7kut1qkzYVfWLk3QmyskHUjFJHgTBQL7qkV32exrqpA\ncgHqFeEqog+BmpLgqD86BNFVX1dP6xdTBiGa26FaLRVRar2rKrt9rzQGSd1UWml9hrZ7Z91UBozt\nJz81Zx6HkY+nA/949yN/Pn7k/TzY+qCWUah70ai0o6+bWl4BbJ8EzFV2nidwLv2U1dlbC5O+AcDV\nM1jtLAeFNct78ZVSgE6EV5sdv00bPtvuuO625k4ppfU5kCDkaA25p1yc3ilVnPi/2pS7q5IqZVj/\nWqtcLB9e4opPj08iyMeSzX85Z7pQBYNNclGIKbFLxhlXVWYt7B4S3Vg9iXbY4OtK4DpKqP4QvRiF\nNu01INlQ8M8GR1YfW7ikRkexO1AX5EWV0RfVynj2+7C0/P1mSwzBEZkigUbbM7QFtVu3CW1BtBY+\nXbtDlDkUztvEcSuco2nyj4cjf3n7nn/8+htiidzfvOTL61uuOllyRXybFYVJzZyMwdCGZdyVpkSL\nwuF0IsXIbrtj02+dfsZFB/BSWn6dsV/mJbiX1WrhkAu9U9vMGsnkKTPmidM4MuSJKRfuHx45DZYs\nM5aZcbR2f6fjwMPdmY8fz4yjM3PAg1BmykfJ5hoSQRpDxGfSmwskqbx2Jfh8dylytdvw2y9f8rsv\nX/HbX70k6IyW2YLSqoQYSJtoQlyFkgvn4Wx+4hDpup7gtMVpmtl2Pc+urvj85Wjp2vdHHg8TeXaF\n6vdWlZpx9IP72+0OmylNRSws1NbqqtayZHfqiuq2qvJkgfZFYMXoyihY7R0DEE63zLb+UqcQAqKx\n1f+vcaFlezTsuvLxLu4ZrbeB+aJ7qQHTasu6K7VAniY+HO758/17/svdT/xlPPBQZmYNtDihXz3X\nHy2NoaXtlirSV4Yyt304Zqs71PZYRS04H74KyzXYuZAClR4I+xj5cnPN7/sr+mgMoWoJFpz+G4Qc\n1Kulmi89NVdstXuXTmI/L4NQhVnD602B2vT+/wiRv7y9YZgmxnGizIWUkiWEiLBxHmyNENdFvy8z\ncVD0tEDphsbFtWEzFJeOPb627NW10F7P1sWeWQbUXDSL/72VCMXQq0a8q/uyeUpZTN8uRbZed92u\nZ3S/PM/UgGh01CzB2DMxutYtxbjZ1SVCQaMwbxIfrxKTQB6sGe+337/lq6+/4/544j5nzii/vrvl\nj8/f8KzfOgKbHSlZGdnZvxMxM3ecZw7jmexlTMfZAmt9zoROES1kIC6xKdoKF8sItSzRzC4kIsJ5\nVu7PR3o1tkpKiUkzp2ng/vjIeRoZ84wC0zi1hiP7bsNGesYyMZWCzBNBIymYW0nd5KUF62p99kJh\nZnG4wRIMV0KAlAKbbSKFwPOrPb/98jW//vwZL5/t6bvkQdDeTPTzgGpGirLpekKIjW8PWPmB1GEy\nyWIJu+2O26trXj9/yfu7R757e8efvn7H3XFinD3oWhegXgrJZnKrsSFy9mCxF4Ermj2GUZWAP2JF\n+X7lnL1mO9oSlajIcJUgUAOrxmqq6Fzaf82arVeu36fVsl3twSe4Uh25JxEvMLZmudhZWQsPpyPf\nPn7kXx7f8/145lCyo1tzR1RhXr+rsDBvMtX9YEluqXjGd60/o8JQvaH4mqmKMixjvhaXrdxBkymG\n9pMI+xjZxkQfK7trma9qLc9iYjrjTDKpY7jQTBfrf60IaWSKOgstCLga21/G45+qjG2IkCxSX2Jx\nV4OlYVdu7ZRrD00XFB7EC0Qzt9SCMcY9kNXAwsLcqC+wsloWZsZqDB1FVPRSTR+biNag2AX7+muK\nOo9UK/PB3o0psN9t6DpDNkZzs+fLLgRti1ZtvSxYW+weqKMuNmGIwkOChzyjY2CaZz7c3/HN92/5\n4cMdx5x5yBP59Mj/9u579mlDf23FW4sHhSz4lskBJEVwKl+ssQo1ulXaGgrvgqDzxKyeYCQ0posF\nXu2nYDXmc555nCYIwlQywzRzmkYoHrCVwJQz98fB6F+hJ4RgyjwGrrYbisLdw4m748jjQRknIYSO\ntF7bVdjIglbNF2lKp45biObXnKaJECJ9L1zvzcW13yl9ygzjicNRCXpF2nYkz9SktzVgbrDUEphS\n17kwdT95U7QdfSpsusy2K+RR+ZjO9HFDF2sJYF8/K2vvl44mrIr72bVY8NLXemWL1A4+y+rWJlyq\nAhNW5n1tgehW2II4q383NjfOavfwZNSXV1qyzvJqBU6IAZFejDJaaws1aiOBPnVsU88mdHbfvk46\n58RnEW+irs3NuISzChqiAZwEYRZKgByNuTMjzCtq8SJ4DUOvffmyfn+RFM1KCgR2IbIN0XI6go+l\nZ1rX8SiotRpUk2kULyGt6g6t6vNu/1sdwuWrl1mnPHl3fXwaHjnW0UdSsDtwP2rtApPzzDBNTSAX\nlHkMkJ1bjm2cXBTq4uBnFtLPv7f5Ulgk+VrerxCHVVlzMx6WIGFYFIBiyGnM86pOt30mxchu1xNT\ndCXjvmIxBDnPs7ULU72gYBoKF090UERKy0A9RuUuFM6TUiZ4PJ741+9+4Lu37/l4OFpVPlU+TGf+\n88ef+HJ7w03oeLnbNnZARXvF71HcbxoksAnJfMoIm01sbqwamK4ZoqFWbHTFW8TQsOZMnicezyMh\nGT/8PI2czxPTNKMlkyRRsnIcRvb7LV3f0YeOEjPbLvFsu+XhOHA+HHn3fuDjw8xxVGNz+Byul/uy\nrGt/1tI2YAwQEyCFYSz0XeR6F3l+bQybbQ/oyPGYCZrZpR7ZdOZGKhD7ts3NFy5GW6xp6U1AOkND\nxKwSLcJ5PnM6TRwOE9MMaCCSm7+3/fjMy8XLDZNRLcSKIYr752s9kaCmZNDVWNTgWQ2OS+UvV1eK\nfUsu2dF1BVLSvq+VL6AC9RWKvHBDVNqr/6VLTEa1QOyMveFrpnFk1JTRtttw3e+4ShtL/nLA0Ytx\n73MMDfgELYS8IOhaK6cm1bVevMEszlyERaX7KFdfuRaq83LxFDmgUm2grgl1gR2BjSdHicDkAKk9\nPq581Z6vw6p3NgUh6++qK+vyuLBrFJam51zO8ZPjkwjyGiCqaNr6cdp7pRQsXhWYp5lpGhmHkZwT\nRaOZ/mq6z1zW9uhxnYO+qNS2JVpK7JPocPvMaoRq2m/B6XSiXkOFtshVXMFoYSh5CRxVF4+IJR20\nDSHMuTREUgDNhVxGdl3n2a3irdcMr1jesunxcxLuQ+ZejH1wPJ/58e4DX33/PR8PB8ZiQjwDY1He\nTmf+z48/sQuR/7T5svHuS/Hgj60SVIVcjH2T89wa8ZqQMsZEipEUU7OEwF2quRaGdXOxWKenuShl\nnhnzxN3hnmkQRCL9NnH/eOJ8Gshz5jyNbLqefW/dDQeZeLg/8y9fv+W7d/d8eDwxz9mSa9S/q2bd\nOofbrCvjEElQLP/Q3UYRYuelbgm8vN3x+sWe57cbUgxsUmK/3bLbdP5vIgUbJwnBgs5rpMpSRMpv\nZVk67m4oOXM6nPnqLz/xL9++4+u39xyH2carmnEOXC6D9RXxgjFDnHPf2XtaTEnmbAgdrWVf7TvN\nVWfjUTncq9KOKIXZa+BT3SKavcOSZ8xKIKk2iyt69ixmyLkAtYB4VeKLe6U05VEpoGAJQkG1JQ61\nZGGp1gLMZeY8j4zVXQJ0GP1UYkTVeAMRpTOz3ECPYO7IWQhFrWyBK6+p2A8a2xq17eR+7UXr0WqA\nt5eXuFhdcyD0LMFa/6RndIrVNHKwU7ISVemKkzXU7rNITbBiUXZ17bRvrkBxLe217cl/6/gkgjxG\n/1q/r+DRXgELTGlEOyWn5EHByP4c6GMgizJJtkQLlI5I78gEuAgGNIxc0Ukpy0DJLwxKNZEuEIdt\nuOJVApsvTW2jqCrTXHwfX2gDRCzJYZ4md20s1wtAFrvOnIulCossC14sdVkFpii838JjJ0xiFKt3\n9/d88/Yt745HhnlmBmaK9yO1+/zX8wPXDx1vtnu+2F9z028NyVSh4kHJUvnMwTZNwfyzIQY6iRSc\nddDKFdhYzWX2lPlifPl55jzPjT54HE48HB4pczT/cgmcTiPTmIn/b3vv8SNJkqV5/kSUGXHuwSOy\nKrNquma6e4DZ02D/f+xpscBiLzPdPd1VWRkZxMOZcTNVFbKH90RUPTJr9xiVgEki0pmZmqqQR7/3\nvQixCFgijTW0B8fdtuN+ueX2cctqd+DgemXNG1AHcnwkXARGy56jdpOPBBuGjlGFlKvPJhVnl3Oe\nn59wdTblZF5TFbK3qrpU+GsJhcnrI1BAgZFaqyaHGUJfqJJPsMIQPK53LBY7Pt+u+POHez4vNmxb\nKWrLvRiSsDDDPht8Cw3PjeK0KUUbLEQ9NtGInTkUuI0Fasx8JanZrwhWowyGJm/9IaEqx8FohatK\nypzMTorKZ2EtgtWmsneSFzFCYyRvgEgJonwK9Q70s0MIdK5n27esXUufIaFKCBZFcHol2GvKguum\nYT6ZqOGTiurARqHA9i6wd57WRVwge9bpeA/cLCbzu0CiShif+6eC1Bo0rKJhIZPqv8meRJqBDo+J\nkVlMfPrC6zKufRk+KI4iA0m2DPlBvRlGt/qr4xsJ8qfCNoPlh0AVxliCCVRFSV3XnGwCdSEHtTUy\nWc5EGsG7UaELylOBGtUKysIcctJjCMakf18rgeGARJNItQbNKa5uoMs8ITryiVcSMC/+YC6xjxDV\n8osIVptkRYbBcjEY+iKyrWDZQFsYvIft/sDN4pFPj49slS42EDXBMiR37vuWP29XXFZfqIqCWVVJ\nJWCykOxwqOT2NdGssVlJJmpOQkuVYwwKCdMm1lrJ52OkdY591+Gc43DYczgc8K3P82u8QOwqU1Ir\na1+JhRDZblu+3G/4+W5F54QrI8XgDTHTKOTKSN34if4Uo1jiAKYUyGRdW+aTiqvTGW+enXJxMuN0\nNmHSlFSFlOtjJUnZB8eh9TgfaXxFXVZiBdkUbiDDBaXyUg9diDgX6Lqe5XLDx5slP3165MPdmm3n\nkVTPSEijwi+K9znmEYl55cgW7nhfpiQndlAq5L8kojEvDUIYutbIlJksyFIIAaN0FOp1PGnyEJS1\nU28ucYmj5ycSCHHgORnOzdiylXNdYLXLPAJj0T3nvSj8VXdg2R/oo8xQEpEVIqBaI4VSZVlxeXHG\nrJhIi8LseWvRVwAXPLve0QZwOZaRT2xeyyHvK38ft7vNDoPOkUFgqrOioDYSkEmFjAlSmXsEG0On\nCPieIPtbQ1tJig+29VhmjH7Mc2og75lfxBGejG8iyBOwNU1xinvmG1ZpVxghnpICjJ4yerCWUFq8\nsfS+15CHZRoFKZFcu7SAmYoSlK9j+MwheRmfJIxyBxN54bAHCoMpbE4EBSJdiOw12Ul+qkFQlmVJ\nURbSek0rV70PpIIYo1agjwHjwPUQfAkUBAO7MrJooCvl6r53fLy75/PjgsX+IJYWEi7pMWSxqWGW\nu67l/3y84Xoy46JuuJrKkhfWUFcFdSn2Za/EVkW652Ig5pKQilpRTjlEovBpmBipy4KTegIWWt+x\n33cUwEnTMK0bCgomVcl8NmW5a+nanipEem9oW8+nxYYPd2sWmxYXpKBCugMFTfiJMgzaWi/GKLGd\nxGNdCIo9We31tOT0rOHkpOL1xTmvLs54djalKCpsUYApiFic9/T7luV+y7Zt6QM0Rcl8MuFifirh\nsUIahJR5LUtsNLm0PWLoOsfD45Y/v7/l/c0jXxZbWmcJqpRJFMApnqoexrhlmmxFBYqaAaoag9eY\nQFYdUtCZ4YAp9mzVM1DEjovqzfhs0VnNz4gUkzOQ4IADmiUQQp89k6jFZIGgNQFF5iNPNmQmmDNJ\npZgc27UKYghBQw+FAmHVWNi1Bxbtjsf+gGMEwTXK8x6hAQ4xCGS2LLGzCbaekNSpQCzFQu9jZO09\nHYZoSl0jjQ0N+BV5dqvNbax6LVrH4YlZh6Znk5L8miZTcwzY9cIkkjmFwCYyPFUkRp9jpOHI0joH\nzcXjTVa95FLGinR4y6+Nb8S1IkQ52eLyCpZSZIe1Iw2GfFsE+VdFQwwWZw1dETFFpPSR0KWstjpO\no+RjnsCYtPEg5JMbOPZYBO+cHR1Z0BiFDKcgKwmPtP3aBaeCXDdUDNpYImVGh42dQi4RjXEGsDZg\ngnnSoNebyN5EVjawJRJ9ZH9ouV0u+en2jsftToqQTMxWeBLiaeGDkUo41wf+dfXAaVlzUk/wKPeM\n9wTvhM60LHCK9RJuErU4CvvEHY6FHopgGLI3QtFrsEyrhvq0oCoF/ZF2njVSRVqYmod+x91izXrf\nszv0tJ1j3XqcsZgyiYIon8EgNENIlYqyJsGELGAigmiqq4LL0xlXFydUlWE+qZlUpXg6yq3h9bmj\nDzjX0Tuhguicx5ReoHImUmCZTibUTUVEGBr7EJTqVQD1213Ll/s1P39+5KebBx7XO/adI1KQ7IRB\ngCtLYkoca1x9cDJSCCBx2Atc8mQ+ZTqtpUFJOuoBep8OuKyFeH9C25uKrYJ3uN7RdT6DA5I5moSE\nUa6ZiFRnWg2zFNYKwoUohnQEa+TerE3G0Mj/1crrwSG1NLZgWpRK8PbklOkZCPgYshBPB35Ae4sg\nN0XBrK6YT6fUJzNC2bBzhpmHMpINGhcj2yANS2RS9WZMCtjkm83hrnTP1hoKI6GSZKEbRKYURipU\ny0TtMCI7k3oGFeAJex/TSR48Ovn9SK6ZNA8Dag2SVy0/WVWoJivaXx/fhmvFKx42Wba6ub3zlIVY\nPYVS3UoMTIR9EQQraqKlKwyhkn2ZrIPBPxk98BM/afhdssYHXmDzZJ6SM5yvGWPmLDGIjPEh0EdP\nFyQ2rYHGfAhtOQjA5Lfl5Fm6x7yYWS+DMfQmsrSetY1CHnRwLBYrPt7d8Xm5ZN91utghOTAj5TM8\nekrC/MdmyVnV8Hp+xt57fBQucuccNZVgqKOEeYITdEWRrIkMRcuqTeYrpu4pkiewWKbVlKIxTJqa\nuiwzDt75wKHr8X3LZuv4/HhgtWs5dE5CQwYGXmpdVD3QknYusiUaI8KXYQwmSjioLAqmTcnV2YxX\nL865vJzho+dsOqOpaiRsJFDL3vfK0hcUFlpQlzUGR6MUAr0T4q8YDdaW4iuqgCRIgvFw6Li5W/Pz\nlwU/3yy5X29pXSosd1mQ57BbVGjaiGAtHeYxGscYKIqCSVMwn1W8fHbO5dmM2aSW+9C8RueCEtCB\nd8L77rzHGPEA+17a7603e5brA/vey+dqxXGGKhoxA4z+aVqXTCclTd5KLwAAIABJREFUk0k5nAIj\n1moMhkhBjCXOQ9s59dayoY8GWATSaguxYnMln0lfwBiqsmRaVsyLilWMOMWZWGKuCq4xlNYyK0tK\nW9BjWBH4HDpehJJTrO4LSXBufRAjJwKEkUgYwiwxiuEj2ApDodiCxN+e4I5EMCFSGqnsLGwqCEzX\nN8O5NsJ/hBnkxCBPRs+txmF6+1iIp9kbhc7l3XZ8tV+Ob2ORx6HsGKSIxgXZiD4EyhBoqkoRH1E6\nzvuICZKdNiFSB0NVWBpvmDqhnTQ6MyYOwHr5vBFEaizv42D4P5kmk46UbONsVaU4viG7xql/pIzk\nwkU1eiSm7J2ELVJncWNRrLJU9XklprcRql4SawcTeKwDBysFHu1qx6fbe97f37HrRx5AHFyxdKBE\nHYwsA2P44vb86+aRy/sJi74lmAnRSOPrGCJ1VUNR4Jyn5UCnfDipSMFqY4GiqijKEl9KQjQodWmI\njqqsqYzciOscrnOUhaGqSlzvebjf8i9/ueHn2xWbJMBDIPjI6Exkzg9B1YzWJQ7WTSwkuVVEqe68\nOJvx+sU5/+X3L3h2fsKkKTk4J/h0W6rgF0ik9B8Vg6CwFdO6YVrLdeuqxmDo+55J3dA0NSaW0vik\nkCpF72G13fLh8z3/8eGO28WOzcHRuZCNCpk7zSWEOJCWobStWInnayIvccCnuonZtOT6cs7vXl/y\n9vkFF6dT6rJg2NWSnxC20EDXdnSdFFBNJxNc51mvd9w9rPl537LoxTKPqpjNIFmTzUhRGmbTitcv\nLnn17Jyr0wmF0TMAdM7Ru0DvIm0Li2XLp9sVrZcaA2PE2EnqPlVDSP2A/Ms9ahGKiIv5KW/bCx7a\nPZvdo3hLkGPP6YmnxjIJkc1mS9f2PJiCj9HwT/UJVTllXtRK2xDYO0evoRiTLFq12GMqHgtIzoMU\nf5bUrAcNxyavLObuXlWljVOCyXUjMHhcKQzpEUWUUDSp+XjKrkZDKrfWCvG0CIoGiinkxch6/zsU\n5NkIHrvdtiDaIfnjvKdILnNkFJOSxFsdLFdtSYWlidJcYhR1z4nN7NaihvnYWM8W7PC+4fcpQUqG\nOGbkS0hCNPnOT54uP5O1hbLHajOJOBzDrBRgoHENsv23RcTWEKsSnGO/P/DX+1s+rBYsDwd84hfX\nDeijtnPLrjmDy27EovXRct+3/D+rO3YxMD0vOS2Uv0PDDTaKB5Ti5GnunHPSYky72qem2caIhRaC\nPIeEawLehYxm2fnAbr/hYbnj/c0jN48b1ruOhIRLSdN034LLHgrBiqIk9QU1I8Fu1Upp6pJX12e8\ne3nJ25fnPDufMW0ayrLgTAWHwAbleiFGTlydwzPZ88qWsVh2zpUq8OT5+ygGR9877h7WfPyy4MOX\nBferHfveiyuuIRMUxeJj4iQfJS5NZCj5spho837FRKrSMp1WvHlxztuXF7x9cc7ZbMKkrp5QyZIt\nc69UF4amkvoEGwN937LfbrlfrFjs9hyCk7L8qF5G1OYWyqA4aUrOTie8fnHG2xdXvLg45WQqnboM\nCpeN4nkdWsfnLytWi4NUkYbklw3GBUa5vwtlAVSb1ETpLpVCD5O64e35NdFYylXN+92Kx+5AU5TU\nRYmxBXskMbrZ7XnoepwtpJFJjGzm57TzK/7bybWGVgKtEm6lIjgT7YhrSr1vtXqbacnV+YRXz86Y\n1JXef8h7rm871ss90z1MS6l5KIwwuCb5m1YlqNdWYwVxE0WJRRu1h4CKiyS8RzLHjObuiSX/lQD/\nu2I/zJnzUSJSMvLpJxHkMSVjoskkUnoBylhwps0mhEQ/bSYVpV897yCqR1px/LsnCz1cZ8yqmBVF\nEjQjV+3r54uI2xbCUCM8ICxkwYIWr6RGxjZEQufYq3cevaE/dCzWG35cPHCz27D3iZpUrYg4xNSS\nnspGj95eUOti43t+3K/AFLzwJxL/Vq/Ie8n0pGsU1g6UsCEIv57xKlDlWRLmPAQV4CHges/+0LFv\nO3aHju2+43G5426x5eZhRacuqYlaHo640ylPIf1bJdwxcF1bTfyrkMdgy5L5rOHZ+Zw/vL3i7ctL\nnl3MKQsR3MaKEMmhC31OgBgqXbWk7Effgya9qhyPjyHQ9YHdrmW53vLTx3s+3i75stjS+wRpTW3i\nFDmiCWGvAl1Ca+rVIXwvVslrJD4LTW05PWl4fnXC92+uePP8gqvzWW6/ZuwA90sr673Ne6gsLa63\nuIPUXqw2e+5WO1aHDhdhoAJQ4aEEbk1dcH0549Xzc75/c8Wzi1POZlOqQpt4KB+QNdD1jtX6QPQr\nuk7pmxVfn3DXxsj+OZ3VnDSVMEViMwmcJHFFkdZVxdXslKooacqS82rCp/2aXikhuijcTL0PtN6z\nbls2GFqMgG1doI4l3zdnVEYMmh4S7f7T82nMKHcmNzyfVbx9dcE//O4l80kzhFpjwDnPbrvnpngk\nPByYlRW1LSl8HAE0no4YpPCuHBuWBmJ2gGK2AdMFBuKAfKpHq5Qu/NQw/Xp8G/hhOTpUCb7mFc+s\nrnkIQRMHUphhFQGQizCMle5AkLX9k6XTAyPzNprQkViOT99COs5JlOcCGP1Qo9adTYGLmIT1aNfo\nNX0I7LsW6wSBAYAVPHZZFCIQ1QJMRRKxC4RFh9tC3wvL3nK75vPikU/rJdveaQgiZjZGafyKlDGT\nhPjTsuSIFCP04ssB8r21EuKyxuBNyMnScZgm6sELwUvCzPf5eaVjvLizXuPNXed4XG75eLfk5n7N\n/XrHoe9xijJJsSWheUkNsz3OdcSo92RKSquMkZorgSg8NIgHM503/PDmmj/97jkvz+dM6loQFUaS\nfsHFJDeVffFrxasi0cgaGIzyKamREcgNmX3wHPY7lqstP3164OP9hsW+J1hxgKOiamJ0BGQ/O6fI\nHo3RphRu4ohLkNtCk4pFabk4m/Dm5SV/ePeCZxdzTqcTQe/o61NoYiAFEw4TSUZHQqwpKocPFmd2\n7F1k3wecA2sqTGWzIWGAojRMmpLrizk/vH3G799c8/rZuShANW6EjliNnyDrv9m2PG72rPadMBSm\n+kkV5NYapnXJy2enPCvmTA7CXGq1uC2oNR6NVHjXheWsMczrhhfzU77s1nzaL7k97LlvW0qDBqQM\nEwNtNHSakP+4PzAzK/7QLPndRCqYffLc4uBtpdBWwvwGZE5PTxrevb7m9dUp07rONAoGhCCtaYib\nnsPOMK0qSgpMHDhUEo49ifUQDT1xoMk16V8UpFAUeZXI30ReGCXLYzCSNMCU5Za2iHyCdBqNbxRa\nSdakWHJDBj+hEmTzxyhPbpFkZ/TiFgYjlo9oeLXnTT6aQ9IyKWK1XMdhDSCjHcZezmB1S0gkvSYf\nxZTUMBrS0LZWeXpjVIRGST2tKWsVRklw20LLvdOHCBTQ7AN25ai2Hg6Rg4ts+z0/Lx/4afnAznVS\n+pu4PfIuGKoDhaJ0cOOzQB9mflBVJhUADVA4Y23OByQoVFJi3nu6vmffHnA+iIu976irgqoocX3g\ncbXnNhX0bA9s9z2H3hOiOqA235p4CYmrIiUAQyT6QCwDwUhCNvGzGGNpmoLptOR0XvPs4pRXl6ec\nNpXmIkRJxZAStMM+EF6ekJtd6GnJ4ZWktJNJnrAYvevY7VpW6z3vPz/w6W7Fl8ct20PHoXc4H7KR\nJVvE5lqGwkqSfmjtllxoQGPUpYbfJk3N1eWc37++4t2LC15cnzGtS+rSUNqnVlqiT0Y9FGsNxgrj\nY1Kmhzaw2nqW+0g0FWVtFTIp+8UaqTq+PJ/y/OqEN88veXV9xvX5nElTZyNlDMM2iOfVtj3L1Zb1\nZi8J91ERDwaqquDibMrrZ+f88Oycl21F0wViH1l2Wx66jglgEc+uqZosoApraL3j0Pc8HPYs2lYS\n82jxHEJpUQO9gT3QxcDn9sD/sbzjv8cLFkHCYANtku54YzIxZMqZNWXFaVNyVhWEvqMLCtXUWHbw\nEaKntFDboVAvC+expa+KOVqy55FpqNM86hJGa7RHqEYnkkeoUNugFpThKY8OGP5WrPybCHL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wInswnXFye8fnnBD2+ueXY+Y9YMjZSz68nIKMmTrglLXWC5jQFamsI5ciuB6GHfdqzWO+4eNrz/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\nndiSd5M5/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SvKcDPnh0NgrylEdRhKqwVGW/KrVSGq02wCtOTKq59VCGgTs5MJfCVDJD9Gyc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\nEBi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EhlEArk0dqVxB4TTGASbTLmteWketE9MxovNAVTiev3jF88++ZLFa8u2vf8vXv/41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RRY1llCiUUucAYVRI6pFEQVhIuCQJTODTBai46fyuf7iqvNIrfkrImpNACdp+RzKMTyE/I0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\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sqYRGZJ4mcs5Y55jOI8pqEhnnGlpjcArGZWFcpLdPq0Y8JUYRo6hzUpTaudJJiewSAomC\ntgrXOPb7LbaxrDYtTeuwjagRbGsJvnA+DoQYQBcaJw7VXCNKLaK6yFkmNe8X/CIVVtpqcc4p0dUS\nCliH0hlKYpkD59ORaRrZ7Pas1i1aZQ7PA+fzwHiepPBYxjSCTwzTiKGwXzeMU2CZFuZpYd1YnGvp\n2hVtvyZRmBdP1zVstxs2m54UNUuFUJZFGlTCEjhPEde0dKtAGEfm85H5dCCGheQcteqXtttxc3OD\n1obN3rPebXCt4/HnR15fXjmNY5W+eZYskINWBd00qDoey1R1ydvO12Ow6HzNdbKTNDuZ4sShJwta\nTgX5B1ncSv6ipECZ67RqLwFKIZF1lfgpOdIXZdDKcXFvlmrSUVpyv7NWhFIgVHepEvIx1wwPkVDG\n+mmU92oq/IkrU04fWUntWy65Qi2ymMT45eeAFmgAi9MGe3VBahTmSrRywamrmgUAVapOvWq6jSWj\nmJdC8CO5LEIWBnFi5xjBVNVXlEyUpu8pVuNPA8/Pr5wOJ0r6ItPLVS8qC7nIMb+Yo75oTmoDa31k\nsqqv/YXArfJBZUDXHlelcp2WMyYnTJZgLTKkKG5tPy9oYL3uMbbFWie8XScuVNd3PL4eOZ1Hgl9E\n/VMJTumGFchOJnP+7Cr/9PGLLOSruy3b7ZbtdkWrbrh//4Z5WshFsi7maeL9uzseng6EGFnv1sx+\nYfzpI8s8Y4rCjwuH5wO5tWzvt3z1zRvSHJiPAx5LrqoQ2zhyTPR9y93dnv/+3/+WtWt5/PTK73/6\nxHGUHX6J0O9W7DYrwhIY5weej2dO54HNZs12syIYzc+fn7EF8iQus6ZtaFdr+psVphU9qnbCbjdG\nM1asUxktBauzRJa264JpDZbM+Hrg4eXEeQ68vX/DeisL4svDE9McUFje3N7iSySaSKoOP62k+mq/\n37LerWhaUVdEv7CkmXE8cT68cnh5oG0dzmisLURVZVExknMQ8s1YYkqczyOHlyMpBJrOst727HY9\nuog93mpFYxU5Bh4fXvnjj5+YFs/f3G6xKuPnmcPDK58+PjJNM3frNWGRQC6VFT/99IRfFu5uOkoq\npCjH+zTP6MbQ7zas19LJ2VrDZr/i5m7Nbt8zTXAeF8bzyHiUotwYA4+PL7Rthy7w07//wPH4ysPn\nB15ezijbcdOueP/2Hd9+84H9fsuwzDRpYrVpeff2lh/++fd8/+8/svz0EdICUez6y3iCEtF6KxOX\nsmhlQFcosJJcXBdhS8aIRjgXfEjkDG1bF5Wqerm6IWv6oMpVhXHRNyuqkiiRKtciRRQyNwr52IKS\nU0Ciui0rbBJzIS8SBQDVGaukZjDXlihK5KJ5pi4U8iNEJqeLhmJqfgyAkkRKBcQv3xdjJoaMDpm2\ndzitpHLNaRoj/49yFl2hGF0UthIERRd0zHUjUEDDsqQKaR7xPpGC8A4IxYRqCs4qmkaKF/rdmkDh\n86dPvDy+MA3jNWvmWhhBJVrrIlgqxKMqqVy4SECFiIV0xfe1qdk46oscVaNATKuEUog+oAhUZhen\nJD9Ga8Oqb1G6F4lj1fG7pmW1dtAYpsx1aldchIfqcpV1U5X312Uz/0uPX2Qhv11vcdqQ54VjHFnm\nRSbgObLZbrFtR8mKMEcmv3AazzgTsNrRGC362c6QdmtK52jblmWJxGUh60J/v+E3v/sVv1aF3W7F\ndJplV9w0LOMA1qMobLYd7bohAq+HmZwLp+MkwTaxYNE4DIeXE6+vZ/q+47/8H1/zqw93tNHU+jRw\njeN239I1mWE6cn6SWianNbvdmq5fo02HIkni3zBRcmTxiZQLw3nBT4ESI3EeOM5nYko0fc/9/oZu\ntWa731yxPgqkuBBDkFJcHzi8nnh5ObLd9jjnWJaMUWK4cX2LojBNM6fDmckHyInWFEpMNOsVqpGs\nk3GYCD6y2TRQCsPrmeEwoQBrDOt9pLEGYiYuica2aGVxJRPGmfE4kAbP6TBwmmc2q45d69juN2xv\n97yeBia/MPnIyln6tcY2MjUOy8jj78/81Xdfs99t2KwcJgXGlyPzeSKpBts4/uo333KzpH061wAA\nIABJREFU7Xj6/JFPnz5yHGbAkFJiOA98/vzKH3585mmY+e63v+U//93v+O7Dd5hciN5X6zdSIrBp\n+PDrr2jXLXdvbvnjz594fH7hfBayLPqF4aRYrdeySRtbXYtO9OhZIIqCQpWmfhTTVfqXSiE7c5Xk\nab6oTC7SQEkIlNjbUi4LZ9WkNzUDXBViVa5YpeWeN1JITIZYROGUk3gVxH4v1npjKoTjJLcm5iIN\n8MjvkcYddSXsSilkJZi+UgZTap2esVVBYdE51NgA0c0rlEzqSkosUiqgIskaVOV9jBFXamlbrGsp\n2rDM4xceaJkIcxTPRZL7qm1BG0CLLNMYTbdq6Hct623P8TTw8PmZP/7hJ8bzSSZtY64xM+qCa1/g\nnopvay3KFyqMpJS5TsHlcmKCesKqm9xFYENNukyxihiEvFSVFI9G1ejgjNESaYzW4rLXiZA9L8eJ\nJUTGeSHNC52z0FpMfS+gFe6K0+uqfdc1jvh/f/wy6Ydx4njy+MWjHMQYmKaF55czISX6riXEakKI\nWcLhnUK5gjOGYZhYlkAKhbZ3pBB4fnyVCbMUNvd7vjIQU8A5xaZr0WSsKYzHM2NWhADaaMgFPwfi\nIrVauRRaZyklY5WicY6mkQhcZQ3rvmHdtxivCEUUBNoahvPMeJ4Yh8jrMBOiNIy7ztL0nWhorZZM\nbG2qoSDjc2YOBYHFFcXHKya63W1ZbbZ0/Yp209eFRJNDInrp0VRI23zwkXn2qBhw1jCnTNc10gVf\n+01jiJJ5XI+FKEWYvZAqLjANkirXGmgbydzIqdR6N0VQhSXMQIPV0nW6vdlQcoUYsqJxLbd3N9yc\nBkKBJQmhJZVcDdvdhpwLnTOsG40hEf1cixUKIUpXZtGi8InDyDIkorKobkW3WgvM5Zy06RRN6zrB\nkrNM0rbt6DdbynkBNF3b8e6rd/hh4nw8kXxhGgeWZcFaiyriZLzZbbCtY3+749PnJ87HE4sPpGVh\n0YpcMq7p5MNlzdX6zuUIXJzAgLkupDnJaxO0xChXF6dSF+OOfB7KVcpXFy8uZ/4i0MwFjlDSMJ+1\nJhmDMQ0JTchZGulrmXIRqfOVhLyoUJSRU4Cuemc5OdRmoJKkT/TPVsB6jRdYoV6DOGEv5Fu+blK5\n2s+VkgLqJUd0TFhrsbbULkotRS5LIETPeJLPSs6ZJQQRAxQlfQL2ohMpYpnXGtM12F6idV+PJ54e\nnnh+eGIez0g0RcWh9QWK+BO1ibpoQOS5XUyol1hieTEKmerYLBdG4X9R3JQ/m+0lrbRi6FImWDth\ncxbeo8jCH2NABbn33ie89yzLQo6xjgKaS8ywQpzLF1euyiKBLuU/0EJ+eH3g5eHI+TRx+/YWKAzD\nwOPHJ2LybPdbCTdyliZbVJDQ/6QlYfDx+YVxWGisY98aJqVYTjPOOla7Ndu7PbkEXp5feH45c3u7\nwTmNjonz68I8B3wurG42HA4Tj49HUhDbs7IFvemvMqC+b3n39ob9rmeYPdvWkaaF00kkk8oadKP4\n9OmEnxZyiJxjwkdxnvXrjpyha75Um612W+JJ/7/MvdeSJNuVpvdt7SJUiqo6AkBjKMaseUGj8f1f\ngUbyjkZOi8EBDk6JFKFcbcWL5ZFZ3YO5RruViqyQHhFrr/2vX7AskbEkirYoa1kdoLHO4fuWh0+P\n2NCA0mjvMK0XXwkWeeuUQxnFMskW3lpHjZkYI1FlrF8/buNCzomKou17fNECGQBlLizXhVwmxjGK\ny+HWY3WRfELjsGhSlW1krpmiC7rxeDy+9aJSNAqLo/EdDw87ZkA7x2mYxPckF8YxiqVBcLTeYXVh\nOl8YXk4sWYH1bPtOfJ+rCMOWZWYaIwsGj6PkgUrl+fnI8+vIdYIQGlwWCXZVlbvHO3TbcjwPzMPM\n8fUirBgtvGE9Ol6eXrkcB5SuTJeBeZioOfEPf/yZn+tP7P70F/70z7/w/PQqborjVcLBa8U0Hdpa\n0CLeqEpa/FqAHKlVcYu9KVW+sK5q7BoqfCMvCDMhUmqSc8g6DFRCkcvrl9qsg3Gl7FshT8aC0qSo\nGOeK8QLJKC289xtVrZRIiUJ6U7pgjcMai9WaXJAgpaQpOVJqlK7VSAetVrM3GVCqN+aHgPVWhpdk\nlF4fL69DubUDnVOUgGUEZgFNKZppqUzLyHCVXN68uiEqVXHW4p3DGr/S/2R5M1phg8FsA0teOL6+\n8Of/8guX04m4TFiLQF9GC4yzDikrEu+Y65qdWdcGMd9EU3WFm777v7pGy60F+x2C4nbp3cfcgDUS\nbn0rpzdcvtRCVgWVEmpRK6RV1+ckC1+pac1iFf/72+ItUYPr4qD1G07+tiX4d8ffpZBfnp6Zx5my\nGhOpUqgxMQ8TL19fiDGx2Wz53e8faLRiGRJfjzIQKFWy7UIT6DYtuRTGYZLtZq/QtuIczOPM9Thw\nPQ303tJsAloZlmVgXCJDKhIWcRw4nwZaZ+QNUJWcE9fLRIyZhw9bPn3a8HjYsiyVMkcuLwvnMXL/\nwwcefvjA48c9v/7LXzh+exU7Xi2MleE88O3zkfESeTzsoMqg9+HjHU1n0K6Sa2K37fDeQM1sO0to\nPKHraLqGaiypVOZp4bc/f2Y4X9i0Hs0agGsl5teHlp9/+sRwPHM5nlguE32naIKHLQzDSFoWljSv\nXHpHKQrbwC2Ewfkg2z9TKek9rCBsHDoW4rBwvcxEN7E0F+m0XcD5gMGTqRSVKKkQ2sBuv2UphTTB\n5SVS5oHzcEWpQnvQpFKo2uK3B5oQQBtyrDx9ORGnjP/xA67pmJcr42kidJI+Pk0T1ml2hz3WB7qm\n0vhMWkb++Z+/cl3OjEtm0wWuxxN/+dOfuY4Xtpsdd6FlX2COkZgiry9fcQ6aQ4O2Hb5RmFr59NMe\n6oIPht9+fabkKAEGZwkGb5oO37aymK7GYe8Gf4Zqq9gflwxFy9C65FuNQykwQsRe7WDhjSe8NsVC\nyRbqobitJBmoFtA5SeBvNuS4ktKN2Opqbd465IIYV1FEJh6zhDEbtw7QAG1Ed1mzkd3XjXZpBP8X\naCEjQRv6zccbFEWtK5gSHxNhxkhHadeEIeUsubEkFDmtuoqSwVR8Z1fmjnC3rXNY79DGrrRPwc/n\neeY8Dhz/9JmXp2dOL0eB8VIUJpAqb532bVdwu5zXQnk7p+8UvhsO/T2VdO241XsRNTffFavXRU4W\nuLxaFsisQ3ZgtYoBWll90m9SfRno3hYsvT5eEfdPZN5ltH57nkoJxfLNwVKvQ2v+A3XkzsgEu2Qk\n3WWScIE4TStnW7PtW+GuGk3bOcJsidVhtAhqUqrUohiniZorThsSmcjC8Xrk5esr0zCia2EZJoZV\ncjwuiSUlYsx8ez7z9HphHBcOm4bdpmXTd4RNQ9+31FrZbD1dczOrglQlpzLVRYQ1qtC0nhACPgRq\ncDgDOSUarbhMkg1ZcuFyHYm14DoHbUBXobR1rSN4ccKzpmKdfFCG44W5KOYifN4vv37m9emZu0OP\n1VVoUN6zPdzR9y2HQ08aBmqSQWZNieLErWJKEigxxxkvnyxSVvR9g/cWbWV7GFNmGRNRZRFQWMUw\nJozWBO+IOpGmmXmM5KrQ1hOalu1mh2801gvf2RhF4y193wiVLC6YAdI8oWwlZkucI8ucmRM4L5+F\nFBPDdcQouGw7as1c54UxZe68wRlNXjnK2nq0MSzTUSiZU+T59cLpKlCN0Yplnnh+euLXv/zKfjfR\nNj2b3Q6spWjNNM6iEG0CoW/EqniJ5JTZ7/u1a1J8fX7leh1JcRHopyRiiXjXYKxg58KXrjIENRZW\nb2mlVwVnvhm11DcGiroFa65f3n8LgUpRLOvWvuYVjb/Z0JIEGtFCdy1F3VCe9dZyf6sPosBkpUCV\n7xhq9RO/kQCVedspSBda1+4SZPVYn1PV3y0873CQUYqMwHF55U1XpcgarpcLyxxX/HsVw6yfkxtu\nDYhfi9ZvxVgokjBNM+MwcDpfuJwuTMMkLJZbpfzehETxbwr5m4hGIelN6jYPWCmFb7d7oyXJAqfX\nzvsNo77d3/v1Ffo72GUdVaoby+gdkpH7XGcNSnYntd4CovUbbv/9IXP0dUExesXx/wMV8qZtsGNG\nzaBLZRpnLpcrlUheCnGc0Tny/DSgNfz0wz2h01jf0XvL+TpxPM8cjzPDPFJKxhvHlCbiU+I6Tdiq\n6YNl13vSvHDOGe0s47iQksjgT8cTl/NASoXSWrZ94Mcf7gl9JwZDZKEPJsVwzRStCMHRWI2bEuM0\n8fryyrYPLPMib6x1eAPaGYKzNHMExIgnl8I4ife4zgVvRArchPCG6938YmJOXF+fOc+ZKUPbWo4v\nr7y+vFLLjDUKawyhbTncP9B3DU5XdEqQJCIuLjNKVXKpDHNkGGem8cp1SRLoW8B8epQtv9XS/WVI\ncyUTURZMNYxLpe9bui7QxcLpdWa4RGJBMhDtjPpo2RqN9je2QMUZxW7TSJRZLZQ0o6qE+85xZhoW\npqkQk8bULAO9WIjLwjgqXl9PjGkWnxwtU34XPMporHErDXHkT789E+eRFCPPryOXQST4m7YBUzmf\nzvy//88/sd18Zbfb8+mnn3g9nbiMo7CCtMF4hcUwjaPMO64jm23g8eMB13jSWojHYaDkyDRG6jLR\nNb0YozkH2onyVgkPGmegWiC/qTEFQxfc1KxDwlsKkVkLB/VmZFtBSZi0cP0NN291ZbR0+8quiltW\nqECLHwiAkq5aG+kFdYWyhkWIEfg62lPpjTdtlYSL3Mg4UvyFovomalkL+Due+518vK7ujFl87atR\nkODLlycuZzGbEnx+VTjeEIuKWA/k8gY1SNcqPxPn0LQmNd0KZObG0Zfetb6hD99L2mtVCDd8hSvW\n7le/4SPr6dK329zc3etK95QCXUC67CIL122AKjdeH0shw9bvHv/NKpeyQjL63y2Gche3R31fhFjD\njNTqY/MdDeffHX+XQv6XL18xpuFwv2XTNwzXC84bPv60F78B7cg54r1HacXluoaclsJ8SSuXX7Zp\nXRvwXtM4y+dvR748n3g5X9lvWnbbe3748Y7Ty8DpOnIargwXwYKHKXI+DzTesr/v+LjfsAlBuoYy\nklJinhfO14lNF9huWkLY4Juerm14OFiWErm+XPi/fz1irMY4+W27QNM4mr4hvlyZrjPLMHLY9oS+\nIQSL0ZZcHVU59ocdOc2M1wtNI3g+unB+PYvfcYkwXnEqE7xg63MWLN1uHMVW5nTl+fNAnAe0SUSS\nvL5Y2Rw69n1DoHI1hss4Y5pM6zXbh462aySSLBf6XcDoDcfXkeNl5DJFPn7a44JnjhKgUYqibTu2\nvef19SJ0sdMTl+sR13j2D1u0NWx3G5o5U5R0aGlJqKnKAHDShG5H0xtSruQ4CNyhYFpmcoyQK0Nc\nsN6z2wbmy8j4emE4DzR9Q6EwDmdenl9IOUvYgDeEGshZTKCstYzjyP/5f/xfPD4+cjjs+Zd/+Scu\n1yspRlrveXp+4fPnL2gjHthdCDxse5w3FAOhbdBYWu/55Ze/EuPMkhM5RoZ0Yh4GtLXY0GJdEJHM\nTQewLKvYxkjRNeoNlhCoRDBlobQJC8Ssoc8SQ2ZYFTpSkJCtf01gnXqDaRQyfM23Ln713L0NopXS\nGGslkLpkVDHrYlHW2xZKjqvB13ddq9Hrc16NoZRC67qmGClUETOwUsvKVJGOMZcqw9e4kJbC8fXE\n8fXIPI9rFfh33Sc3Fs2tKH/XzH4HOb3R/wBWlaVg1rfBLCuP/T267W3BWG0WBPeX85SLDGZLKZDe\nn8NNBWuMqEm9c1i3qjfrSkxAxFppVQTnnN5k9zfF7w1nZ4Wc5L5vA2WkUq/ukbf33FjxkWmsIQSH\nd57gnHDa/yPZ2B5fL2w2iiYExmnGWsNm2zEvE77ROOcI3oAyGCu+KqTMdL7y7a9PVOdZqoxB+r6h\n6xwWyDEzjwtpSXhvaFpxb8u1MM4Lx+PA+TyKx4TW3N1t2W0b7vYtvXFopSgxMs6JcYyknGm6wP5u\ny6ZviZOSzgBwVkO11FqJZLo2EFoHWrPEyBJHlFWUKKno1lq8Mzgtg6yUIsoaTAgcHh+IcVzxZbGH\nbVvBCLttyzROPP/2BasqbePxXjipvmnp9j3WKtISyZN0C0JZsqRYSTqiS8FqRXCO2sAUZajW71v6\n3Z6m9WhgmmeWZWYcZHcRGo+u6wfoZrnbd6Qiryu0ji4FUs7iW1IW/JJx3gm1s7VYpUlUlhhlgOnl\n/rxz+K5Ha02KCzlW8jkzHC/YYAjG4C1o77Ah4J3h62/fGK8jyzSxu+vxwZJjFI9qVala07SBJYtF\nqG8CKMU8SchzWiKvLy/r8KtijIRdB6NRtbJcIh8fP7LZ7thunFilagjeYj45SqkM08zL8wt5HMlZ\nQgByyrAsqDmK4MNZjDOkJRLnSCXLAFNbWfCNE+qbBpR5Y7GUdXNe1PpVV2snebO2VStcUsEUoQ0K\nayS9GW3d8iiVVu+WuEq9da5vwhwjpbLCm1fLjd9xAxtKuXXmsvu7wRFKV0qVrhq9UimLUA/NiusW\nJfj8OE4iDrteicssA2N5ItJD1++wIOpbYf4elXiHPuBGEHyDVNafv7NPVsx7XUje4KEVyvruJm9U\nzxsHX2tRlRoj8K01N48EwahjzlKsc5E0oLWQlzUp6DY0XV8JotBcYRr1Dp/dHktrhbEKoxzOutVv\nSc61c4Y+eLrgaUIg+LB64vwHKuQ1ZkgLOY7MuRCcZbfZ8NvniPaGEDyNd9SqsN6x225hWogvV779\n+gxdi24DNmhcMFirqTGT16iuXdew7QJaK87XgfMwcLoMHE8jl2Gi7QJ39x37fsNhH+hbS7yKfapg\n7xJxZVzgx9994IcPB7xxfPvrkbxElrygjazSTRs47Lf0XcBYy1IqT19euFxGYo1svKdvPW3rqOkm\nutCSx6kiRktupXIeN3lOrwO2GDrrxNfFapa54fL0jbCmwux6R9d1hLZFh4CrkMYFEwE01jZ0XhOR\nJG+zhk0jlGO6paCdZ7Pf03Y93hsZIk+J03nmfDzRt70MlJ2lLFnUqN4T+g3jLJbBIWecczRtw+v5\ngq4GkyvzdaHvHCZYilXUtHp55yIdRtvQti0mdLIQqkIyHnXVzHNivw9sgqO3BtVoqnIsc+SXXz5z\nPV/RqlLqTL9p0Uaz3bYMU2RKGe+cNDnAdrdhnGaWq0TYDacTl9OJ03XAeumc69fKH3//A9u+J0bF\n3YdPPD7u8WVmug6UWrHaYbeemAvDvLDMMzFFxFFElIYSbL2sRVJyNXNKYpZ0gwCUOF46H3DOY5xw\nspVafUvesGn9PuRCfD+0ucEbK8dbFUBSprSuksepb+yUdeqqq/wfEktX680aVb8V8hW9ee+wgdsJ\nLAmUkSbAGI3RIpqpKqOqEO1KFYplWTn1q2MBRYl//fl05vnpWaBHhIGzTgXkeXF7XAXkN0hDVFHr\nG7liz7dqXViL761QKgk/pr4X/7cLK4wjXbj8+M0+YC3uxoCx0kA67/HOr8wZTSqJmMXBdBxnSU6K\naZ1XrBDW6okiv1aHy1vDfevqVw64MQarhVVjnSF4Q/AtTdPQtH51VASnNV0TpJA7L0lWRv+7Gcr7\n8Xcp5P/4+y0FR1KO2Xc4oyhEQmglDikqYqosc8ZMlc4NlCkyjwuUymWcaBvLHx7veX458jTJF3WK\nkcPdhofDlufXV37501f+6jTX68jlMsnQzmo+fNzyxz9+wuqGtrE4U3mdrrStoe1afrQ70B7bOA4f\nAsyZ8TQLjl0TRldwsL3bst1u6IJmGTPjnJhiYnvY49uecY44U7FeYZ0hW4tvWtpty+n5xHSdGca/\ncnl+woeAtobL64Wnvy78qit3dwe2dy3ewcPDFhs0Q4xoI1L1JU50nSKdE0YZmtbRbjyhC/imxbUB\nbaCkAYVnnhPL6cTPf9jR9i2+a0hLYbjOnE8DL68nUspY2zPNhVIWfJAtc50SVQ2oWrmcr1zOV8ZB\nClcuhZQ1TXDY4CSBJ8r5uJynG9qL8Z5EQReBQUpaiFGofZfzSJwVD4+fyNOZeSroYLi+nrmsBmG/\nfX6m5EzfWZqNItWI1hZtLIpIHiau1wlKYdM3fPiw5eVFsSxCC/VWrBtKqRJ2Pc1M48LdoaftA5tD\nx6c/fOLhsOf67RsmZVSeoURqtbRN4Icf7rm8nGRoVwfxz0EhniaroKfUN157vXmZrArJkjMpRuYb\nvc/Y1aNDumhhSLh3+p8W+buu+V19qDRFyxDUIpHGudxsA0RReMOK11EaGkXRea2NahXBgKor1bDo\nlf1x6/RXZvPazcdYKAZkUShoXd9EN+M8Mo2zpPAoRa2FlBPXYWAcJFP1pioVBslaCL4bREp3voqr\nbl36G9XuVr2+68LfLq8rx9uf6z/WBeB7BEeCoMXPxTmLcxbvpWgbKzTSXCrLEplmSRtKa0JSqeug\nuCDB2bqieMfCxRVSwjvMWqhlPmGkw9ZmFX5WDGtakzOEYNjtDrT9Bt8ESo4YClavC4tSOCXUVRcc\n9mYI9e+Ov9Owc8M0VWpS+E2g33RCNfKeVIT61Dt4/nZiuEx8jonGWkzw/PjHH1mCpdtv+PnTB9qu\n5+nplaeXIz/9fKANAW8MT88njqeZXDIxR0qu+OD48Lhl07VMYyY0BV8F55ui4rpEpmr5+Cnw+PGR\nvm+ZhjPPz2dOzxci4my3AHVJNL1Yn5ZF8fpy4TpHVAg0bctm59HWUcvCMg6M5yvGK3AWbTxLqgzj\nzJJnxrP4aRvrpEsoCWdA20SqDUYrhuOJl9OF8zhh0ISmoe97tFeUCKlkYgW1KHQu5PiOly7ziLGF\nGDPXKaHUxLJE9OsZEzp80/Hx5wO266UDNYbxMlDijKoZo4U6l1KixCS5qt6B9QTvoSpiuuCtxweP\n1nUVqlSK0Uj2mHQwyxKpRGLKzJN40UzDhVgVTddwt91y+qaYrlfOl4k1PhNyoQ0O7xs2m8BlXEhF\n0TXSNY3jxDTPOG9wTtM0jr4x5K2jpIZpNlgXyAW60YuIw1jcYce261mmxNPnv7K/+yeWn39k3wYS\nsMQCSwIr8IEzmq5v6LqGeZ5JAnHKdl/Vt+DiW1iCwAW3gZZ0kSKyunXpkffkGtmtGX1zIrwl1Uix\nN1pgGK0NqhoqK2d77QTrWnyNLm+2rFq7FR8ub0PMN+OldZsvneOqPOT9uQvXWpp9ZW/e3fKaKpll\nXjhfBo6vJ8ZxkqxJwSzIRfjzKaWVhlfffq9n4ha19H4Z3iGe79ge3JxQ/k2llr/fWSC8M10Ub/j4\n2+K4UgjNamOt1qGjNusg8ztK4g2rBoup5m3hEfXubU5Rv3tPhHpILRi9PvYahu5Wh0NnrJATjKFx\nVkR+baDpvJjFab0qfoUAYW4w1u1xtVmbFvc3a+rfx8Z284BKI6SMs46m6wjBUpRCG4s3hkBiuCyc\nXq88XUfu7rZsNx0/3G9RrcE3DZt2KwG6bc+cDX/4h0+0wXN9vdB3zxxPI8MUscHTdo7NpuGPPz+w\nLJnnbwPttpJSwSnD6RoZl0iIirufFM2moW0DT79+4/XLifPphN040lLJEao2hHYkaIMyDZfzyJQy\n7bot2x22bO921BR5/vzE69cTrZeBS6nCMx+nmXEZhBM7TORS6LYNrTd0XqNfJ64XB1lxuY48vQjL\nRlXF4+Mdxhna2r5P1VNhzuLUVJJ0NbmIU5wPUWhcs/CtVc6QE5uHRz5uD3z43Y+4vqNUcYoczwPT\n5cIyjGKmNY3MV3EFLEZTvac4y2bTE1yD1mLY5RtDIsnCMiey0pBXeCUlcixoXUgpM88L4zAzXGZ0\nG2iDZb8LqKWjxEXMuFq/ijs0jbfisNhY/ulPX8jVEIJkwI7TLJz8zQavLcEajKp0XlO3zWrWb4mx\nEKyjawO7bc/hsEG5hqfXM3/6lz8TY2S8nPnf/tf/haVkxiWR5xnXriFeKdIES9M4nJEZSaGuw0wE\nM68I/PDWLN40e+qtUAkwIkU/33CB9biJcqwx4izo7CrkkcvaOLT2EoCsQK3JQW/1r9zAYk02FnTB\ncFOOsuLoes0elcg51gJe1l1FTkm83KvGWkdo6ipoElinpMjlfOHz52+cjhfJz+X71/FdQf4e1lXf\nX+2/xXv1urjU+l6gb+fknZJ4K6Y3Tv5NoKO4BWHfumLB94XPbp3AG5W6zjYKS0xUkuxaVvhDbH71\nmwL2lvGptAX0Wsw1wQeCC2iryWmmpgVdxINGOPEGbwzeWBE6BU/XNmy7nn67odl02NaTl4V5GJnH\nEWX8++vM6zumNcZ4jHZY/R+okP/0n/+R09cXjt9emSsMLxeOOfJ8PHP34Z7u8UDftGz3V4YxYmLE\nbTponIT1Ph2Z4zOFwI+fHmjbjh9/+sTHTx/ousDycAdGExrPf/3lN3746Z77xy27Xc+u83z9/Mrr\n88T5OHJ6GqixMsdK2Df0hw2H+y3TOHL++sLL8wvKFrqtI+XIeBkZx4xynq7zbNuOu8eWf9j/TK5Z\nFgPnsFRqnNjuetLYEayhCx6ntGCpKYvJ1zCijcI7zTxFXr69cDWKNmienyrBOZwTK9Q4zaR54Tos\nxHHh8jrw9HSmaVq8bwiNp2ktmsoyzmwOO6x3xATa2TX81UlQg1XompiWzOn8SvmL4vRywgXH4X7H\n/r5jf9exzCJXPj09keOM0wJhna8j1/EVPsDv/3Dgf//Hf8QGxRQHnp5e+PN/+a+8fn3B+oZlWsTT\nxSi2Dzu6bYvxmq5IyHLKmcu0sHw7kpaZxsJ+7whhL/BEhrZpuFwuXMaRb18vLFPGmMqcMiihZ/aN\nog2OYD1GG8ahMK+zE28sZMUyJ4Zx5vF+x/1hg1nj0UKFRitOT698/e0rxz9eKCVRy8L1cmHjWhlM\nkfBOwg60VWiMwLlZEUAUsAaW1YNF3UDNcuvQ5VBKAIP6fcDBWw0Uj5acEyzLmzet0PrNAAAgAElE\nQVS2W8OWnfOE0IFKbxg71d4a7BUall6u5LiKVFZl48pEcYg/yzRPDK/HVfm7puHkQlqdO7XSOOdl\nKBxEWYyqXK8j13WImWLinUx3e4HrH+rfFmTWTvh25VthvV1+r/Pv3bb6bjiodF19bKS7vwmJNIJF\nr/XvtvmhlMKUlrf7lxlkWUNDboi8LGy3ODWUeqNAai1Ehds5t85jbUApT9N37PYb9vstzipUidRp\nIOeMBry1ArdojddisOfbFr/ZyAxQK5ZSSWWmKvFsslpgNm2AcltsoTEeb6Wr/1vH36WQT0NhieIV\nPF4GjLfYJnD34ZEffnrk/n4LS8Q2AR0cqiSyqiLyyIXraWRJif7OUuuCVoq2cRxPF1LJ7Hc9h7st\nP3y8I8ZJCpwPbLsOQ8UbJ26GWhJ0dK1cr4mwbdg0gXQZ+Ta8cDmdicskjm+mMI2JOSVSLXhdEbvL\nTA3rB65qfNCUPDOMC+NsGYcr19MoNDNjKKqQc6TZBn53+AFrf+Db5288f3vm9SWxLGL3WqN0FcmB\nMYWlFqZRzMVyKczjRJkjn19O3D8+8OHDHaHRaOsEtx4q18tAKYppKGxypts07PY9oQ0YrSBqLpcj\n5+vIt6cTTWPodcc0Cn6ntUjCq1H4vmVzf2A6vlL1jDaG+8MOZx3juBCXiWlJnC9nvn155XoaReHZ\nO8hZ0oac0KdqTFyejjJzMIrdoae8CMdZ5SRqwlRRSZEyWO9pNg2hb3DHo3TAxtJvOva7ljJN5MVI\nEPeay1iUQllLWRTzUqkxctjt2fQbsoJNF/BW0QZLUYbYBz4+HpiXSF1mXl6eKDlzOZ759vWJ/hIw\nRlFK4jLMzHPEWSePpzRaZdLKutBaYWohJyRgBIF76zp8u1Wsd7jg33euqwv4WuFTEhl3TmIbsdjE\nsqR1OCdDd2PCCsnAe4akETFRZoV9RE6vlaIoxzyOXC5nrsdX0s3vY91GlLLCYBWMmZnnUeidq5hv\nWdIa5p1Rqwjqe45zVatVLIiY5vaabgX/u3Ohbpdr/Q5tqavC8vZv5PKb/PK9nigElhKTMrGD+B49\nEoWsMHxuu6LbAPT9ivpdmHQT86hbHNy7CMgYjfMiTETBsg5BTR/o245mt32DRG6pT+IYanHBoVdI\nMlXxkSoxUWKhpEpNsKhCKhptwVlpFqzRWOuxVnYWf+v4+9APv56Y4kysCmvlxIS+Jex2fPj4yLZv\nOD0dxbBdC30w5SRshVhIi4gN9ltHShPLMjMvladfr/Tbjj/+4QeWacJ7zYf7DUtBhgco0hSxWrM/\nbKnWEILFW800JKz3hMYzvV44ns6cLydhBRgxFhrHhLKaLjjapiE0GlRhXEZ5I0pF6cI0T8Qlk6MM\nhkqqa1yX+Klfh5Fa4OPjHT/+cIerihojaZmEYz1HSGKtWQvEAssatlvQYle7RMZx4Ms1op3jcNfh\nXY+1lVwUqcB0vjJPies1k1KHNpXNvqXUSE1VbBGmmdfjlesU+en3B5yDy0l239o6YRlbjbIK3zec\nT5qiFE2w3B86YtIM14HPv/6FuCxig3seIGe2m5b9vmd0MEzyGlJKzNdCnCPGG3wb6PqW1HhySlhV\nUWJ3TpVYHZSx2LbBd14KUi30udD1gW0foDEsg2YcFOdJzM9qLnirUMZgjYeS2d9v6Tc9yipSHElx\nxjaeYsQZ8+ffPzKcBmwwDJcjy1J4eTnx5ekF/yrxZrkWCYNWBmec5HXmsvKVxSjJKr0qKHlnWMB7\n0X4n0a0DR7UmB62lRbHaqb7Xq1KkS4cq+Y5TFC8iZ8Wi2MYVgrnJyB3GWJRKAvtUqDWhzLqw5Mw0\nDEyXK8s8rYuFFDezeq0E77hVzVozyxxRCjEaQ+GdEdHT9+W33NSjq+pz7ar1yk8Xb3PeRFHWqHcU\n5gZTIYthXu/r5oFyMyl7dzF8V2gqvqNBZsHUbywRKuJP/rYDUG9D3xvEcoN01uVVdgDmnd/NbVdT\nMtSEUhCXTEriqZ+WDlW3hIc7QiMMEwlqThjAG4v2BowW98klUWNCpSL1fg13SWvKE0nRNBm/4vyZ\nQqR8P1b4N8ffpZCP00yxhv7DA3/4+QPDZeD4ema5ToznAVMK1+OVuhQMRjyroxg2zUuk2XSEYGi0\n4a+fX/j2fOZ0mfn2dKJtG16+PDGdL1hV2W4aPny4Z7sNtKZwHhLWG7q2x6xdgnWGf/jxA00TSDHz\n9ZdveOdpQsef//kvvJ6uJFU5PG7440+PfHjYoV3AWUvJmb/+62ecteRVhj8vkRwzKlWGZaFpPR8e\n71Bx5nqd+PMvX3l+vfLDDw+k//w7pmvG2Z7tvtJ0wkDRwDicGYeJlDN3XUsukZwXlC6cXq/MpdJk\nRfAKZwsuQJwnhiFzOs3oUBnLxJeXF+bSE9VMUpFcVoqTqUxLIadInkeGoyVNEaWu1A8SqJApbFpP\nKZHrcOHldCKR8I1mzhcUhpIMn/90oiaxDThsNdsf7gihoSiL0pl5nrg8vZJCoO86+k3PkoRpQk7Y\nFUUeh8h219D0Ht8ItzqrQlquxFXZ1zY96TSTBpiV8MiTMixFzL2mKTKnBNeJh/2OTz8faDrP5v4g\nLpPPhsvrRImSieqbhnbT8If/6ZH5NJNSRblCnCIqZ0zV1FSYUmZcFsZpITSB3c6LYZIqokjMgv9n\nCtUUMb5SSFdexBSpvFHqgFpvVuCIE0ddU4Tevyv/lk994z1nMpmcM3FeGAYw5iLUNmvWTj3g3UpZ\nuxlZcRsGVlIeRUVMxXkZ/ubVhVApJZ/ZDweCd5ibKnmU5J22ade5AKC0RBmWgqqyw47TQkkJYySJ\nK9eCcaLErSiy0sRYSLnSN0F2vOvOJRdAa5q2YRhHrsNAWg2ubrsWu/K9rZEhoOREZ7lsZbG1ykhW\nQNsiNM0IJdJ1DcY4qrIU7bheJ86nK85JTOOyiLnc6iNJqVn0GCWyLIlljIx2kEXTeYxxoBTj9cz5\ndOZ4vnB3v2N/2LLd7NDVUVNmGmeYxftfW0NZxChNAcpb2QH7vLpCChw4jIVlqlgFimkd2v7tmvp3\nKeS7x0ds6+n2PYePB4w7EpNinibKUlmIVAx3j4+ErqVtHTEu5BTRtRJ6j3cGA9SsAYPxgR9/9xOb\nvmW/bbExUZaFshTymHDbls0mcHwauFwnriWT54Q28qHxOpA3In4IQTjhRike7u8lhcQVHj426Fx4\n+XpB2ZnD/Z62DXSdXj3AI+U84UJDE8CXQr1KZFzTOLzV0AY+PtyzP+z5+OnApx/vWA6F4brlOoyk\nJNu+UjPT0PPtyzOn40C/2dJtLM4DMfPZvRBCz+8bR98HrFL88q9fsdaSC1wukXRNXIeJ4+lMLqu8\nuVSWUQZaxhZMFXFK3zjmKYpqs3OQI2mKDMvCdKzMi4hqTpcr87wQl5l5vNCFhl2/oWs8KUdxYVQ9\ntVQZdqaZOE1oVbm72+CtJTSOptOoWQMV6yrGVWwxuGrZ7TuoyCxgWhjGkXEaaBpP07ZsD1tC24pz\nXlxYtHjgxFI5DzMU2DQdd49bdpuOrm1wTcN5WPjt2yv/5V//wnS5ylB516JiQo+R86uhRumWprP4\n40tqSxWhhgs0fcu+SqevjCFHsUQoNaG17NyqEly0GoGmZJp587i+CXR4M7O6sUTeiS31HaZYmSiA\nhHu89WTqnQVSxD4g6USMmnlZsNO80hhXqMcaWh/wTn5GjlASqgr322glASNFE4KT3dSmYdO38v3o\nPAYtQzvfoL0MBJW2a3qwdMQvn594/fbC5XjCGkhxkd1xuwpenEM3gdMQOV0iXdfyeH/g8WFP0zSA\nJebKFCd++dNf+Pz5qxhToUg5cx1G6ayBJjju9lv6psHVQtO1lFI5nq80PnB4OPDp5w88fXnh6ctX\nXp6eMMC2b9jt98TqmMeFuNvQtB60kANyrsQUiTmRgOE0cD2PJElJpsZCLGKfrZUCo5kGzfVy5nw6\ncnzp2O427Hdb+k1H1zQyt7EyTyEv0rasyUsCvSmKMlSzWjBk4favprjC889FYLK/cfzdCnnTi3uh\nCwHfJrptxlqPopIS4intHe2mx1K5XM+M48iyLPRNgzWGJSZ2uz3KNrQxs3+8Y7/p6K3mRWtOL0em\naWG+zJRDTwiSqjJNCy/nK+N1IHjPbr+j9T0KQ9d59oct8zijSuX+/sBObzGh0PeVL3868vrtjLKO\n0LVsDxuxffWGy2XCPF+wjaf1ls5oVBCVX79paZpA31l2uwOu8+z2LftdyzxltruOZc7krFhiZFpG\nrhfDNCViUvi2YX+/YbcVXrvWDdv9xG7vKTlzPF7465+fBHbRiuu4MMTIMM2MF8HVV6if6SKqVW3h\nbrfj7rBhu2s5XmeU0TQb6cBjTEzDyHAeuFwGhmFgyZlhmRmGieF85X7X45ThsPWkaWGaI25w5KTR\nOqFLJscFZxVd10vnpiolz+/yci3MC+ctjQ2EYNegkYHreeJ0PHO9njnc7zDO4xtoNw7ILBPEKROX\nTMyFecm0PrDf9Pz48X7lucOyFJ6ezvz5z1/5l19+w9TCw74jpcxCJCW4DInWGWouDONCygkFbDYi\nXApNoN9sMN4zzpnTZSSy5kPm/FZ8hT2h32lkK8Z6E7G8geF1ZRuVcvPSAlXfjZ3WjMi8Try0UmtS\n2bs/x61fL7VCFl8SIiwqotT0JlAK3rIJhq4JeOu4jgVVMrpIwlBC5Dhaw6ZrOOx69n3L4bCl3/bY\nNrBtA20IKCe7JeuM2Bxrh6qKnAtP+x3Phx2vzy9oCuPlwuXllaYxeC+Yvu0CGxfZ6Eiz6fnd7z/x\n+3/4ib7rsK4lZ/j2/A2VI+TIkpL46iT53BIz3hj6JnC37bnfdXROE7oNBcPuMrLpOz788MDP/+kT\n//X/+5WyRC6vR7SyBB/Y7zagHGwKpuxoGov1YoObcmWJiTlGlpJ4/vLCqzkyJ9kx5QpTEh8baxTa\nWaYlkdLEfF1YpivHlxe+BrHf2B927Hd7vPNSyEuUWDytVzMsJzsbWVElhUqBKWvcnCqrAAt0/dvg\nyt+lkOvQoIwRYcaYqMoSug3WN8S0QMk01jBNkWnMFN3Qbg22aRmuAx4LKKor/A//80dyTnz+coQQ\n2O97Ph561JoyEsuZlKLIg1Om6xpC8NTTiZgW2SJZQ9d7dtue/aZnuw98/fWZ0/OVWDNd5whOMT5d\nuJ4WrtOMajLFFUxvCE3AephSJUZRdGrVse137K3Be8tm19O3HW3f0GxEgp/myOll4DpHfPD0hx6n\nLPO0cHytvHx5IfiWhw9ePsBFY/D4reHHZsM0R8blKsZeegKtOV0mhnHi9TwyLXE17QfjpOO+cmIe\nIhWF7wL3Dzs+ftzTtIZkwG8bto8dly8X5mlhiZGX1wsvX8Q6NCslKe1a0bYb2qbBGo1RMI6Rl9eB\n86TY7hObbUvfBVoXxHI1Fc6vF+ZppmqFbz3eW7wV+1IXKspqLmfxEP/y2ytLgst1YBhGlHPMqXA8\nnmlaQ8mKJRauw0hJYtDfupbNpqHpPPOSuF5mLpeZmApDyWIVkJLQMufEMi4Uk6lqJmKwdzuhUVaF\nmif6NrB9eOT1+UhoHB8+3YNxvL5cmaZMmiWUWLjYawEvRQZmN96xYlXkKenokBmEBbLRZCXYr5Rz\n9UZdc87ivGOcIrlUrLUsy0yM6S1BR2aE9b/5jomm5h1P9trw4W7Lxw8PhKbh65cXhvNAdJGkCpdh\nZhwXqJWub7m/2/F4t+Xjpw843/Dl6cIljkx+Bufx54yzCuMC2/0eYz0xZpptz6eu5cMffybOM98+\nf2PRVkIWUKSkGZ4jnVZ82naE/YY+OMoceb68sD1kur6l85aff3gkWM3rywsqGMacKUWRhwmvFNtu\nI7bSxzOl9bxeIk274+PHH9nttnQbx3gd0EbRdqtDZ9fgQsuywP3O0e0ki1dVyeb1oUXjqcoQS+E6\nndhozV0TyFoCxHOpPH07sdu37PY9zlu+vZw4XQaoldfzzPE88vz8wpe/PgkM5CWgxjtH4x27bcf9\n/Y7HxwPbLuCcg7IOO+NqBKwFYtWrItaoglX/gQp5CHb1LlZr1zeL+X6wBBtW6SwEbdFr9ibIwLPd\njpRFMLOgLbv7njTPXM+Fqcp+RClF6Dv2Dw+EzZZcJA/yeFyIKDaHnp+C4vVbIM2FeVh4fj5itBT1\nqoRGWAxYL/afc6mMFdpDj920VGU4H2f+8q/fuL/r6Lcd8RpRKbPb9nRdAOBw2BKCx3pL0/eE1uOM\nlqFtAmUM3imohmkuRFtlYdvs+f3/2CLimZnT63E17rcY37LZWDaqkGLD85dXdIH73YbrOPP0fOaX\nX7/y/HJkHGfQ4C2iSNWaftuw2fQ8PD6wO2wwXlN1IqbE8bdnvv72TB4zoQu0u4Z+F0hLB2SUNygr\n74u3gdZVmk7jWyeGYBFCCFyuE6fLILJjU0VijqKaKpFjc2a+JkpW2F0jrz3OxJWSOV4nVFkI3pOS\nZZoMl/Mggc7W0G48VE1cxOI05YRzhp9/91H83Z1mzpmqjETkacU8ZXSpBGugKHKtvJwH7rcb+r5h\n1zT0fUdoPObOsFyv4jNdC/PqQ2ONwjQN4xRXCGR1DdQGayxKZarSZFYYpELWdWVM3EZpIrfXKIyW\n1PVbsMGtkDsrHWwbAt4n5uWWYMXbdf97RVyOFZ5R0Daeh/sdP376wOPjI13X8+OPP/Hy9YWXpxfG\nNKGfTyilabxj00nGZM4S+MI4cbkOnI4LKWfGKE6W1mi6puP+4wGU4rTGJPabjru7g4iEamF/vxXq\nelVYrECBOcIkjpXT6cxSMk/nhcMw8Pi4IzjLYd+Sc8/59cxwmhiXBV80Dz995H6/ofENl+NReP4V\nur7DNa0sdFWxRJhfRqxR3O07zA+P3H16pNvtcKEhXs4CMVEpEYyzND6gjDhZenl7aX+34eNPlWEU\nqK3kxIfHB9reE1qDUpWHjzvJwc3wdJz47duRv/zlM8sUqblgNfR9QCnDPC28lsgwDHz9+sru0HN3\nv+fh4Y627ei2vShxM2/isVISVhXxsP8bx99HEGQ0OYsbntaSOC1KKY21Fq0kespri/UBlxuZ2udE\naDtiFFqUM0JLi35ie0jYZcIHTTWOZrtdvTxgSQPzdeBymShaVFUmOGy2DJeZZcmMl4lLO+CDZ7yO\nTPNMqkKxGgeR6UZVadpAYwxpgdPryHCcUHFhuQrf2nvHftfT9h1ZKTa7nqaR0ITQdVhroWTGKVJU\nxTYB32hirCypklPBGEuzbXnYNFAXpuGM1YXraaIUgVnEKyNjtcei6Vzg8ZMM855ezljn6BrP6Xwh\nl8y2D7SNx1rPdtPweH/g44cPxJqZlkiZZq7ngdfXgWmYMNqxe9igO70GADj62uM6v0rijWCwRJQt\nYBShC2yyFKD5eOZyFQaL0QVrK86KItUbh7eOrCzWBmzTksaFkiLzMq1y/Qi1Yi2ExtIvjWytYyYt\niZRlp5ETIlRRGuu8GJw1gVoqc14wrtAoqCVRjWZOiQ8Pe6jgjEFbS2hbdvsN221PRlwC2y5gKFyv\nM+fjmcvpTC0ihPFdR6oSsqxWZoSqFlPLKnUHQ6Ss3iZK3QIFBFd+N6hau3hE3v3GmzYap7U8v1Xp\nqZQoa3O+Jd28U/n+1vE9/7ptPIddz3bb0Xcdm92Ow8OevuvQxvByeuJ8HVliYrPpaILg6LkWLpcr\nqRTOw8w8zEzjzDgm5jRjlGbTdjLbKZnnbye0UuwPO4ZPE/O8YHWla8R2INhAFxo+ftiTlonT8wvn\nrydSiuTLhS/fzlwuF5brhcfHPTpIyIr3geGyoBfFvu14OBx4eNzReIeqmTho9sGy+/EjRQcJJsmZ\nOlfm65VUF/Ky0AbP3f2W/f0dxjW8pMgyCrOkJFFyWuepqw2tLmC1o+kajPf468g4XKk1s9tsQa+x\ncCR2q0shGPy3iyzm84wEuEmhvf9wT62Kl+ezKLqnhdNpIOaFSsF5hzKWzpg1zlCER7VCWcVG+r/z\nnv9dCnnMhcvxwnSd2B162i7gw0Zkqqm+iRNutploSMtCTlmGk30n/hRKjJls0/CxbSFmyUlUhV24\nhQIUlsvEkVem6QUL4l09jPz/zL3XkmRJkp75GT3EWbDMrOrqbvQMBuz9X2SxewWRBTBoWpUkiJND\njOJCzT1ydntwWxMiKZUZFeHs2FFT+/UnDs/T4w5jDafjGYMWFsAlkqrQ+778+TOneSEphd9uudv1\neGuYp5VwDnTWUubKt9MrprMcPtzRH3b0WxG9uLHDdgPODRjbU6sixyi8XKfZ7Af6sadEiVyb5hU/\naIa9RxuYXgLnrxPr60oKCdM7NruO08uZ129HlnXl9LcjpMJms+XwcKD/ccBV+HAYeXl74+00cThs\n0Eozz5H7D1uGbc+SAyEmptPM8eXC5XhBabjbDqRqWWPm8y/PhGmWLEUtyyVdZsKU0F2P7yBXxWYN\nYq0wGtCGp4eRzsNfPx+Z32bKGjEK7Og53O/57acP9NstfhgwncWMK+t84XRKuGEgZC3dt044o3l6\n2OK6jmkOvL6JMGhZAkrBpx8+MnTCo17OM8vxgkau19B7UoZpTnz48YEPPz1xdz9ILBiWWgrdYNiO\nnsOu5/MvJ86XhXgJzClyOp55+fLCZZ6w9hpZZim2R2mLtolaNQqDVk4oh1mgEq25ueGBYNzOOgnQ\nSJmYMyWKP7ux4rZnTbOFLZVliRxPC2vMrDG1BqZwzZ/8P9RxQIq5c0IjNFZzvkz0/UQ39oS0ojuH\n2wxcfm6BLkqi6ZSRlC3nDaUo1pCZLyvTvFBSZvCGYRhliIphuUTWZSWcJ4wxPIfM8+sZoxTbwXJ3\n6Dhs92wetnx62rM/eFIWy2Fle3JOzGFhmSdevrzw5S+feXzY8vTbHxjvDjx8fGLoRsmT7T2ny8Tn\nz688PI0oq7l7uOP3n+65+8PvOV0SX/7yilaRVCrfjmf+9M9/JC0rd9stjJ45rHS+w1XN0HfU4lii\nkkGst8ICzYmaE3kJhCWItYbVbA4bsIaQMpeLUJ+NFrZV5yO+H/ny5ZXXr290RvH7P/yOh/s7TIVP\nv/mAc5bLaWIOCy+vR3755Y1ShHb48nzk29cjQ9ez327ZHjbsDnu22w2GgRIiOcS/e61/pfBl8Yke\nN6KUUsqQM8QQuPqgqatnr1YYLE47SinM88KwHSWGynBTg9mWYViLQdUiUtbmAhdtwPUbNg+PYoSz\nrsRlJWdxiqu54PtnrkF8pRQu55nTMRCiRiuPtwZvPbvtnv22p8REihnvLI+PW8KSSKVSqiZmhcsK\nkw01QlKFlCOVilEaQyUpIw58/UaeM0dyhr73OGdRuRCXxDoHcs0Mu44eS6Hw+vkrJWnGcctmv+fD\n4xPOKcaDOBHmlLG2MIxgbSWsiaEf0GjKCmVWXGKglkCqCapiOw4MfYfrDX5wLG1TmZaAim0AVyrr\n2wSImxslYqrGaCf0u1rEKW6RpPquc/zudx/4+U9feF4Cl3VlMIoxZaKG3lRqDYRpwhnpbq3vMWlt\nbAtL53sRvCjDEgPbhy0ff//EX/74ldfnIykGqkJgHd/z+jrx9u2NGCIff1vZ7Qa8c9RYcVXWmVFG\nBpSm0HcW6z1FaU5zJJRCSJF5DYScWEO42pmgUVhlW5aiQTuHMZWkFdFoarakVIhF4rskKUYS2q0R\nI6S+s5RSiSHLcxRxUGxjUpTSknZVqgyW10VS5nN5L+L/h6+ritJojXeGw34jxeOy8lm9yvNeJr5+\n/YqynvO08PXlxNvbhZAS0xKIKVFzxSqJXAux8HpeOZ/OqCqnu20/Mg4dgxPpuFZb6m/uxFv+6obY\npP+pFN7OF0KMvJ6OPOw6McrLhbRmlCp4Mj89bVh3A6V9hr/88pX69Y0Ui7CQDnuefvzAx6qIKRDX\nM71Z2HjPuL+nJCgpszt0nF5eWJeJ9XSiUxXjLbFWnj+/kdfM/jCy3chp2VkvHuFacT5dOL6d0TU3\nSuBA1/co65jnC/NxEjLCUvj87YXjWXQHZQ3sxoH/+I+/Z993bH76SOcNH3/4yG6/xyjL5rBBW8Ow\n2THPZ/resR09KIUyHnRHCBmrFUPfsTkcKFXqYkXU2dr8/ZL9qxRyFGKu1IutJ1pk2jFEOUq2IyXN\nc0C3LL+SKyGkW6cuZjXt2NrwxdqOqtcQVnGX8/hxZNssR0uW/MJairjRrYFh3AjdKBW8HVCuR5ke\n0w3k3Dg/znD/+MjTwx5vNTFEoND3Bl3FsfESE9pYYiiUNRDXgusLthexkFGiJkUb0BbxQlMoUzBN\nnKRUIa+B9RKJ80LOAWrGdRqq4uXtzLA7sLm7w/Ud49jRdQpYoCrCshJ2nrT2rJeB02Zgv9viXMd+\n3OO6jlIr67rwdjqKGZQ3+KHHdBbtFH2q5BCIRXy+S3lvArURbFepwmAVXouIKYQooR1zpO88m00v\n4q6x49RZprCSSyWmzBQDXXZUEmFe6TphDBhnKSyNBiYJ8MpYtDHksKKdYdgN2N4zbkdU7bGuR9se\n7TuqWphDYrosjJcJYzRj12GMx5oO1eCPZVlYqdB78b5JmpgiyySBwClmGRSnjLFabBe0k+KG5Dbi\npZArJfQxTKbWRKLc1qe6KgaNwZmKd3Jsd1aw8au51dB7SZKpsmGUImyNEN+j0dTfu5euNxTcoBmt\nFJ2zbIeOh7sNWhtCzDy/noghsc4Lzgv8GCusayLl0lSTirBmpvNKpxfc0ImX/2nifLqgakbpgu8d\nIx5rFL03dL3D9YbjSdTHKca2sSemNVHyLAPaWnna9OwHR98ZdGnZoEbhFLjRQoUlBKZ1YZ0XUlFU\n78VZ0BruH+4ptfLzn/+CqgXnJGbvfLxwOs9kMi9fn5nOR0pc0UoCGox31FzJa6KsiewjtbNSI7xl\nWRYurxe+fXkRTHs3snvy5BRJa+Lt5ZV1mQjryrQUvr2eOE4zGM10vHA3DmNY4S0AACAASURBVPzm\n8YH94cAwiNmdtxqrCrvdAFYTSmFuVridM3RPW/FC6nds948sa6SkhNWwezgwz4G3l0StWUI+/L+h\nQu69/RcL/Wr1aeo1QkqBM++KX1WxnROp9maEq7n6lX9bK6o2Gn+VAkDDcRVVzHNaOK513EJMteZW\nzO+fnm5hqrkUapIs0ZfXFy6nC9N5Yr7MPHy84/HTPXd3Wy4vR86vR+bpwu5hy3438NF53p5PHJ9P\nPL8cSaqyOWz44YcP9F2PrpocItYYEQq8zfinHcPBM9JT1pVwnomXmXBaKPFCXc58+3piu9+wO2zY\nbXfsf3xg9+EARTo4XRNpSqQE67RyPk/kNTA6w08fH9k93LN7uOfw8MBmuyHnzNvLK//Pf/1v/PK3\nX/h2eaEbR0pVxFR42DqMBuuFHZJTFvzzaSe+7Uuicz1DVzEqczleJMj6shIzPNyNOFVYLguWymE3\nEJMUuCUEXs8nnFf01hPWQiTivcY7TUiR8zRzOp2JwEFVDoeB7c5xOV/4/PWV529HdrsdH54+4Yw4\nMM5zZbvbEZ9Evp5WeP5y4dJH7j7e8XB3h0bx/MsLz6c3ztOFV2vY7Qa0NUwhsi4BVTKdNaRVsGhl\nFbthh1EO8GhlsFZhnGJVDl0qqrQtWUlkndGiSlRknGnrVYvM3HtP1ymUMnRZ+NVPT3esU2A6L5yn\nmRgSMQg75d19719+XeX033uaaKXwRjz5n+42PN1vSFVxPK+8HS/yfoxh3zlcs2Xd9iOd1jir2e0P\nxHkhhoWX45EujcwhcjydmaZFQigUgKKERBpXNtsRly11hr/+5YXX5zPLvOK0Bpkry5AviODmdex5\nuhu423lxlkT8xM8xM/aWh0PHjw8btO1JxbImRyyK5XziT//vxB/+s2LYbYixQMiQI+YosYgvbxc+\nf35menljnSdSFuXzfr/lcT8Kt7vr8cpQ18KqVlGbroq3lxNfvn7m/Ham6wbQjtovvLw98+XrG2Ga\n8V7CIJY1Yr3n7rDlcplxWcFaOR0D20dLMpqv396of/0r9/uBf/oP/46YPN+OM3/+4xc6XTnseg73\nW7788kY3ZtywZVpXwrygc8Z0Gq09m6EnLGeyzlT397fzXydYohXf2mS6IopohvjNN0FM1KWY51Ju\nvNxylRhfbTGb6qu2NBOaNPf757kqwiR15WqOI9Ji8UBp368JXQsOha6KMoqnd3gIt46zHz392KGt\nw/bgRsWaNCFZTLBsuoFleuPl24XnX96YU2DcTZSlst1OOO/RVjFstuyGDr/phD+KhB8XVahloq4r\n+Xzm/PqN8+WM0p5lXkgl43yHPZ9ZYuT4ZeLuYctmdOTlQuc7aoYwZWosWGPZ3XkOj3s2d3vcuKHf\nb8XhrTf8If4D1Wv+13//Ixg5DkvCkXCeS0FSkXJGWY1VGtNbcBpdMzmsrGtgiZnabBTE9FnhRs92\ns2O6LBSt6ZfAEiLH88zzeeb565HtZmDsesZNh7MKcuLt7UIlcbiTtJ/zZSbnSE6JaY2c10gMmXEL\nxSiUKlh7TU8xfPzxA08fP5BTYZ1mfO/4h3/6R/aHPTkE5t/+wFIi9RuUmIlrJJ4nXo4T3on7Zk2K\nPDXfm1roDx3OW0oBnZX8QXjEycq/S2me4qbFsyUJfqA2L+5cKTkwNw+TFFMzfdIscxQ3yHVhXmaW\nRjP8Xgj6r34p8NYw9p79pmdwHYf9gfvHA34QP35nMp8ed/TjQD9u8M4yzyvTZSGVgDHSWJ2niTCv\nUDObfqCqQszv9N1aKufzSopJcPPNiD8tWCte6efzwrImYi5i7dBk+ZoqTBxr6ccOnGGqcsKuIUHJ\n5KpYimVyCnU/YlRrvvIqHHon/jEmXVBRs+kN0yIWxqFG5hUul4U0B8KaUFpzt99gnGfoB4w24pPi\nrRhtVUCVJvryDLuOD+6Bw25HyYBSXF4upFWUx1MuEAoWjXJGgt8L3A0jH37ccH934B/+6fcMdzum\naeHl25Hp7YXPP1d+eT5yWRPTHMUDfzuSPj1gnBcSQ1LMxyNvp1kiHi8LOaz04watPc5UwrIwva1/\ndwn8OoW81Oa5cJUuv0/0r/tNjBFr680d7dacX02H2kH/3dymwSrqGmrboBkQ0u61Y6Gll2tunY5G\nU02FarnmRZkWwGqspYwyfCql3oKFqeB7KNWA7SQN2xq06bHdSL/ZMe4zrIuIl9bCrBa5ea1mDSIu\nMFaTciF7hzeadVpY3xbW00JeAzkkSsroEQoiVChFkb68kqvi9CrBB2nbsZ7O3D8exKRIWTAeZSq+\n63Cdb/iaIVWpM7bz7O43dGMnLoHTRE6ZuBZmq8QkqkLNYkGbqqLmLEIQZUjzQphm8QLP4qOiqBJ1\n5kUwdXi6w3YLuSqmZaWeK9Nx4pdvJ15ej4xjz2G34bAbsVqTY6TqQuc03jlI4tM8XQI5JqZVCqFC\nsywrx+ORaAxjL0Zf85zoxg39OEIqeK8ZtxuePn7Eas0lRpSBza4jl5E0JcKyErPg/YPp8cqiQsXm\nCjmTU6IzkhyTspgbpaVQTKQYaSZKC06o9arYyy0KLmEQD5CYMqGWllkvakqnHCUVpsvCvC5MyyKp\nRt934/86piLr3Ugo+P3dhk+PeyyOh6ePPP34iWk+sywJoxSHXU839CjnKLmwLoFpWqg1UxEZ/hpm\nKAXvDL5zJF0b5JhalFmRgPSUyKmgqqKLRRg2RYq29w7fe4yikTAFshGvdfHuCaWyrollDtiS8S1X\nNMXCfAkczyubnZY1WwTO0VWJi+VyJqEgRS6XicvlDJ0hZ0Nc5QS+JlHT7rQT+2NnZM1r3eioGnJu\namexdRpGTz9ocg/LHOQ6rBlvDYfdiOlkLkQtrAXyJUAu7O977nd7Hh7v2e7GFrOXQYsYbZpXjuEr\nyxKhQt958VVp77VkxPPo2zdej0KxrSHx8lXTDytdP/DwsMMqjf23xFq5poWUhm8b0zBtLUewlDKn\ntxO2CSJkot9wyXp1aBNYxGjBm4u+ekmom0PblWNbytWhjaaUKCKnrtc/VzmsaYyB0hSHqh2XQRcF\nVUt0lsqUmjF2xG9GdkBOSU4VSvHD73/H029+IIWVZVmISyAukVKEBpUuK7+8vvLt8zPHD3fsxi27\ncaB3huX1RDieycuC6RX93R3mbsMUL1jn0coxva28fD2Ta2H7sCHnlbfnhedfXtDOcbjbsd9uSd5S\nSkIZWJdCqZGRxFs5YywYlTm+vPL67ZWXbyfWNKOAznqscgyDw1o5qoclE5ZMWtaGmVfWV5HOX8LM\nFDI1F7S1+H4j0VWbDf1+h3EDShliiFitmObANAWOU0a9nPmbfuGwHcWEScFvf/eAc45lLtztN+Si\nuEyx+WxkrJLB8tvLG+fjK+PguduNdM7x178e8ZuBw8OOu82G+8d7Do93VKWZTwsvPz/zl//1z7hR\n8fg4oEbF6/MFrzvud090pqMslcvLRN87VIckDm0HrNfElDieE2tYqcuK6jUxJ0IQkVNOkZQCcVka\n5zhinUPVQgmBJQgN0nWSReub6OMyT2JFMMvPlPx9jmZb7/8fzrhWciJwzjKOnvvHDZ9+c8flGLn7\ncOCnP/yWP/3zn1H1GyoHjJb1kKaJdU3EkDEaijEyN8pCr9ztejbbAe8tKQRSjm1TKdzOB1XdTKo6\nZ+itx2TItuA2Pf12y+XlTE0CLa2lkIvMpZY1kuZCzIW4LNztOnb7EZU0aclMp8yf//bGJwV3dxuw\nwiYjJEx1pNOJOGVeT5G//vzM2+mI7yX30vuefjcw/1KZLzMqR54eZQPQfcUoJUQI45iXc2P2NK93\nXUR34IXXnwE9SAHQ1vLTfiQvgfPrmb9+fqPTAaMr49Bx93THuNvw/O0Z62Rm8vG3j2x7y3SZmHPi\n8bFjHDp2ux7nHApNWAL92LGGlb/89ReW84Vu7Ng9bbmsopQeh8DhYc/ucODu4f7v1tRfpyOvjZfS\nBpr/4nta45xmu9si/mTqtmhyyd8t5jbgaUfqKx/9+w1LIBa5EUoWs/xrpLZqA1TxRtbvj3jbCFq3\nc/VBllFUU+gZOVE0yXVFUYwR5zUqxVisdSTjcP0g3Vou0lmUgiqFp1XsWr13WJTgiQrsMMpNbDS6\nN9hBoWvg9a+i1qwsbLqeD795xPeOYiSstaTC7rGgvZMQ4k3H27oSk6LvOwyefujYP4xUrVmXmePz\nG1//8kw4z9xvB46nRMzC7d8dthz2G5SufP7rFxKGYmGOCWJCocRLPlbOc+Z0CRgFzitivdANPa47\nE0JBa8N0nvn27Y2aMrt+4B9++xv63QAK1mlmu+lQwLJGctLUohg3vikxZRP+4TcHjDfMKfH1y4nj\n65l5mpmS+OYoNM9vZ8rxyOdv3+id5+Hhnp9+N2PcjjQtPH954fnPz+zutuiNxUSFCQ5fLMa1gaOt\njNueaVqISSLcfHaU4iRV0ilUghQzawjEHIgpUDWEHFnjervmMRVSXNBUvDf4XrxESkUMyowUx3WN\nLCGKc2PzLr9i0dev7329tVKiinUW6xxWG8KUOX6b6WxHmhe+ff6ZlFcymSlE8rdT8xHyMowvV8aX\nQplr+HBHKQJRHN9mlpyZzuFdrNReh7USP6YNOKexTpFDQRvN0Pc8PBzolcFqxWbTgzViSjWvXM4T\np8vC6bKw1EC1FrcZ6GxHniIqFu6ftljnOZ8Lr89HrI50ppJDJEZF1RPLAr3V1M2GWisfPzzw8YcP\nPP7wAdv3fP7rZ9Q6CU1UGQbvMRpqjZBkML3GyHm58BoyY+cZ+541RJaYWLP4E/WdaB9UTKxrZFoS\nIWQ2m5GHw8i/+8NPbHdbvO9k7mcR5tIc6R8td4c7ii6ARPhZq+i9RleIsXDMF6rSWNdxuJOAbopB\n5SynyccHvO+JUQwH/97XrwStvBv/XOmG8k99O34p71oy9dVInpsvhXTeMrQsVRSDWr13LaWWm1/F\n1T2uXhVxrdNRWjB4Y5sQoxXkGw2M91tIoqHECAkQxV7Rt/RxEfgZCrpNl6+hsBr3vRKrypzWGsWu\n2W7UWqkptwStinYe3XlMGKhWYzyovOKHwCWcCDkxuo5+v2O3H0nUG87rNhWMpyqN67RwgFcxU9pu\nB1xncF704jEqYY+cJ3KIbEaHKh1rMihv2e4Hxr1w8VXj12oUIWbUmjDaEIoiJCXqyiCno0LGETm+\nnAmx0PkzfbOozTHR955xt+P+h55hJ+Ku5y9f6Z18tiFVUs4oxFQ/hUTOFWMN47bD9JYyrWJElUSp\nqaoiLsI4SjGxxEg6FYwyrGtEYRj7vXjZv54gGXTqUKsnL5m6KmEzlESx0mDY3hLPkTWtaK+ZQkCX\nItaqFKoG0OQg+aQpJUrNhBylqNdrRqc0IJ1RGNdc7lIhRjHtEKMxcb1LKd9i0b7vSK4NBq2RuMaW\neStKZGs0VstcpyaF7wxxXfj6+TNLyEzzwrIEYoQQMs5GYpJTaK2QkNmGM4q+N6xrZV2zBJErRS3S\nFF058RLCI6HGIWVCyehqKLrizftrGsYOZzT90IkhVdLYqui8px8j3TjjOs9mtBjbkdEUpTFOY7qe\njCGsK+clospKZypj59Be8O8YZa7mnKXUSt95UY1bxX4/sE4b4inTWUno0cbcHA2pcs3WNbLGSL4s\nqHGk055UxLGxfDeHy2ukhkiYAzkldmPHfjvw9CheKl0nbqjGGtCKNSbmUHAbhzMG21vCKjF/UBg7\ng9aVFAvVKNRkmacVlQVWC1PEKRHXbQ+yqV3mmdPp8ndr6q8GrXzPWrni16pBI/Du7HbzGboeL1Fo\nZW4+xLVKbBNatfDv2mhroqlSrSuXfERDVdI1K6UpVRb/DXf/zgNaNYz8tim0+KdS5GbLLdLp+mul\nqPcLL3rkhvlfO5n2fFqhzbWzEQlw0/gJ1Y5CLTtyToRUSHHFhMDjjyNmc+I8XVC6EnRP1AOb0bNO\nMyFNkkaPoyiHNlk81C+R89vEH/5xgzaVuJ5RXYcyhW5wbZMqWF94uutJQLSGcedRTpHWgh8dVfeU\nFMVaYE1Cm0salQ1kg76qdVOl9/DyfCR9O7HrezajY+gdd2PH+Lhn+3DP9v6BXBSvz6/MxxdUTljn\n2O43pDWxziun10XYIUphKoSSWd5WfvnbK5///FlyPMeObhwoKRFqpHaeWoVRARIxd3p+4b/9X/+V\n3bBl9D1Pn35k2+3RWXNZX8lrIawLSwx0uwE/dBinWXNkLRnvHJclkuJKjJF+Z7Fjh+57rK4kJBc2\npUCOkZJjM1ept4ZDta63ojDKoUyFUpqXfuNuV9CV5skN11X/fq9IF+6cFCah3Iq81RvLdhBVse8M\nMa5c3k6clsDb65F1jXS9Zl7FVyW2axVTYQkiU3eN565a5Jm1RhwJr0Km5sqplNgbLCGTUsF0gVgU\nzio6J/Oi+TShrRW63fHEep6oqeB9x8effuDug+NjziKsmxfmy8y31zdKyvTe4KdZ8mAB03e8fZs5\npsinT56nvUjxX5Yza0ikIoZrl2km/Oln/sc//42YExCx3rD1HZ23BFWw7USstWJeEmsotxO/NFoa\nP3aQDGpNFKSZCNMZ1Tauzhh++t0j+/2BYRjF1KxcEYYC1aAK2CIDXz84drsdp+OZFBJWO8bBYy1A\nYf904Pnlwl/++JnXl1fejkfmEPnwcABVsE7yAJY18PJ6+rs19ddRdsaE9bJzX7tcxTtmDe/G8zIE\nFTOiWz1vASD61m4LNCMsFW4sFpCLxvX3QFxEW79dciHWilKNlYFuqTXNPLIqStFtQ8nUq4ekokEy\nTaDRsEOFnAZqG+5Ak1E3gYRsPIpc2mZFpaYiG1ObEcDVSrM0r2tF0RbnBtxmw33OsqlohWsZjm4c\nGM0W00es0VSjWEmYbWZDT18r42YriT8JQpqZl8g8rXjroVpeXwJatVxKFEq90I0DWmtyzFAUFcOS\nC8VVTK8Zxj3dtqM/d9iXV85LZI2Fb+eFfhh43G95etgxePmsT6eAbyciXeH52yuvX16IcxJKpodi\nEyFIUayIyrNWWELifBQZeVrXxniqpJRRMeCcYbPrGJLYKIQiwp/Bd4zdyHa749CN9NZJMPC0kEKm\nuiw3r+8wxZIQab8yBjorYc5jfxOugUJJjCprKqxlZY4X5nVGpxbxpS0pZxy6sZSM7PY5t3zOFjpR\nJSUrN38gqxRFVaRHaAP6Bvs5b9hsOrabsdF35d7IWU4h2miO88zy82e8NbdueV0TyxpENxAFlw4h\n3wIMSjthXIMxCgWDiOKWJct9qBR9J8HPIbUussoKpsLlMhNixGjF+TzTuxO995gWdGEUxCVABesc\n5zUyjBI8PIwj437LuNuyezqQlkiJ4i2+zivTNHM6n4ipAoa/Pa98vfwN4yxVWZbm312V4c0vDJ2l\n6yxxSTKY15VFRWzxdMYSGo/cqIp2YLUhF03nd2SleVsmdLIysE6JdUnENVJzoh8tm97R9R3D6Kg1\nEKNis9minUUZQ+80ymrcYNGjIidFTXA8TszLKgZ9+wGTC7Vmis0or9GdxY+OJ//A/cc7UgZVM65z\nMjgPC0oXxs2/oczOZQ10GrQzN6WazCDFhlMpWeTAzS/ivfxKXFqrprxX91ZcVaVxh240Ra5El9vX\nd0W2SOkS0U9F16a+VBXQlIbr5CI3otZt6HpNXblSKAX4kWm1ArgOXBvc07qAHAspZ2yLZAOoWl/L\nuoTxJgnktY0qqZ1Fe40f2vM09Sm1Sqai9ijTY7vcOosiEMdWs+23KBRuEOlxTJUlBZa5EFZw/YZu\n3KPUhZBXciyQMxe3oK1n3HiMsqwpMq+RyxyIpWC85f7DgdRZqIlD2mB85m1a+fLtBes93hse7zeo\nJEKhqmWxa63QNVPCSphn5mkRb1BlcK3Qy8ddmVYRTyxzYI0BRSWGiHOGkkWoNE8BtenoRxnQ9VVT\ntWXseywWrz2bzRYnOWCgM2tYWZaVqqNcOQWJwhwCRYNRHtVZrDaYzoExEkGHYs2RZV2Z55VlnVjX\nhZQiXhu65gG+rIEkwBcGOcnlZkF7PQHmIpqFXEvr3BVKbGskN16pRgvUOGew9rrZXwVCSjyAUpZY\ntrI0DyJJYq+1itKynSBVhtACh6/D1Ob7BYpGg5UepJTCGiIx5xY+/H26/PvgNRfBbUOMN/jRmUVY\nLEb8y53R5PQdseE8M44d+/3IoRS6fsBaL3g/Soob3IRZISSyhqIU8xJJ5xmlFf1GZPshZVKGi57Z\nDh2P9yO9E5phrTKAr1qTC6yLwF5alTbDqJJpqzULBadg3GxwncN0hrIkjDO4wTHsfbs+un0Oco2s\n8xjvMd6JyV6tYsZVIa6JZYrMUwAyu/2A7yzz20SMC9kkTNakFKGREoyGYXCkrJiXlW+fX8QagILr\n/g1lds7LKtJ7a1HfvQKtjDA/rm02qqk49Y0qeD2q1jYEvdZzDTcYpQgt+wZ1ALybuNcb5qhvwqIr\nC6YNVIkoZaUbLbmltlRKSZLCYpxkI5bankPdGDU1iQJL6fcb9pacnivrunKZZoZhwHuPbUfWXErj\nFkcZjFbIlFvwrtbmth9VVbEGQN08UJQpbZeX15pSot91wtYRrSCxZGpJrEkREqRa8dt7to+Fu6ly\nPr2xnM6kGKBa+n7D3f0dZYW3t4Xn54kQVpbJ4rRl9+96zjmRgc1ug98qcBM///yVZV5JMTB0hnma\nyUvGb0Tu7K2BEumdwhqYlhlvPB6FLbSs0MIaA8fLhWmeWKYZrxTD4Bg3HZvtQEmwzonpMmG0Z9t3\ndMOW0Vp873l8uMckUBGcdbx9+cblcqRsNEtJTHFhmk5Y5ynoliwUwRp6Ba7vMM6BNsxRvLBTzpwu\nJ6bLhXVaZUhIk8T3nsE5nNIoY5jnWZSJQShrKMlhFbos5JqasVYD11r2pc7XBkbhnLrxscMaWVpa\nvdwX0pELnp0AyeM0bc6kFdQWO3fVYpQqfHZ5TnmMkgo07L0qoZzGLJCLUkkeq8GKt5OCeoc9YxIV\nLEj8WylK6InXz8VYiuJGNTbGEKKoP+d5EXqs84ze4azBeSvhMVG6XxssqWZSKZQWslAzTEukJqGH\nrqkwZ4GrHvcDH3+7p/Oe89si94+xhFA4vkykuKJ1Iq+FdSksq4BZnVfsth47DPhhxPYenTLbznDY\nj5hNx9uXI9NpoY9iJNd1I8Z12L7HdE5Ow5eV+RQ5Xy5cTotYKV8WdqNj24tt7vmycD6dKSXgxih+\n8Dnxp798xZB4fBjohpHL28zX5YV//59+Qg+e+q9U7F+lkA+dx2ktF6E0TBkwWorXVbFp9FWqzw1H\nN/pdnn91itMKVFUNnpDhZQypFcfmp1KlM4kpimtdFdN/Kt8t6pYPmCtLWoVudXtOkcfnVCg5YnTB\naCsFVrKqUKpinbttQxIdFRq22Um35GAcJVDV3NJb5D3WCjR7AoVqHaCknet8pX3JZ6O1dAXXE0fr\nD+QUYIQvq1SWTU5becMyfcN0hW4obPaS2bm5e+Lpx98S4yo5jpcJrTXd6PDeULLlbYqo1zMqV/Z3\ne374zScOD3fkVDF2ouTAGiNTClxSosyV4+uJ58/PxKVSrefuac+4GVFYpimicOz2d/zm97pRQxWl\nQDWFfu8Z7npOxxn9oikxY2oBLEYPfPrxJ3o3UCPCLGlp79vdjq4XHNl3nXDyzxPz8cJ0PrKEmaI9\n8yUyz4E1JUKRLNRUoTqLdpqqKzGtrHEhxkJYonS1qtD3Fj/2RGWpumCM2M523kOtsvZSJEcRMaUs\n1EmtjTjm5UTMmZCum3ZthV2yJq/8fenaoSbhO6drFw1cj5kyn3k/wVaqBHwX4ambxs82pukVGnFA\nVKCNCaPkZBxz4nQRuKSUSmpK3KQUWjdG1v9PodSyNL9rjK4/cy36tcqpgdqIA0azxkQ9zcxLwNgJ\n6wwb54UGaAx2cKgq8XP52v1qEQSlKmlLvTXY3r3ndirFtu853B0oxfB2Wvn67cQ4djx+fOLDj594\nfvtvvB6PlBQxypOrJpZKWQMpQdUJfTqz1krXSw5BLoWXlzPx+cI6LVil8N7R9x3ey/OL5mMlKGEj\n1ZTYeEv/uGF/N7IslXHwbPcjWM/u8YD1nvW8YHwhlMxqDZ1zcoJ2jnEcScuZKc4yjEZLoPff+fqV\nJPpdo/Q16KHJ5GNKkvJtkNBlJQ6I1wWiFGSlb2o4rQVD5nrke59XCsWwTRN1K4hyzOXW8crPXV/V\ne8tfq3gB51KpNVGaWk8r0+CaQq03J6VWJBtKrq58l3pjLlwfXboRjcOJH7W+qkzfESJJ8X5/bVdY\nRqZg9XbTKypF6fcHb09wsy9V33FwrpsfQLVYoHq5KbthYNhu2d+LlWYMEs2VgigSK5muP6Bcz7jb\nE8PC4bDlw8c7xv2WeVaMu8w6nwnLkXmtwiW3HotjviTWqDCDYu8MtSpSFLteqsF3A7sDnOeL5JMu\nCWPFxsF7J0fkw559c4j01rPd7Xl8/IA3nhIqNSdhfCDDwM45YTJIlKQkvswyqCwlk1JmjdJ9Z6XI\nufl8I1JxkiLMsa3LQkml2clKsvnQG4xylN6Ta8ZYcRkETQiRmCU/tWLkxFUMMQnMUUoh1SqsiPJu\nglVa8ML7sV02bFGDSmFPTW0I4lJ4W7ltPnT7rVpvw39d6411VXNtYjgp4u/U35ZSlOX5rmtKpiJt\nNV8hwu8ICKrNir7nt5da2jylbU7UW6ZprRVV2uxJZZJS2GwwRmOiIZl4Ix9Uq+UkgiLk0O7FK0lB\nbpHaWG0igDMSLxgjl3mSOUjInKaVFDOH+8xuu6HvekrVXObMbmPY7jb4vmM9XchpRRux+1jnmWWe\n0Fa43mlNWOvonWW/HcQbKkV0XBuDRzWefWmUZRpJQqN0gSpDy5wLl9NEBVznoSiUWqHCZuz59PGB\ndV1JOfN2uhDSiu/lM16WleMU/m5N/VUKuXM96AJKVFUlV9ISuRxne3cybgAAIABJREFUrJcpr+k8\nhYxSErTaaiW1Jq6p3NZm6WjbLv6+zltRb8935eNqpXHWUbTg0DcnOdW6B654um5QiWYJgUTBGc04\nCLUPZaWwWwVKcG2B22WYZIxpR1/p0EFESbph+FpXjLHSKdPStm9FWMvIqdbbQO99k6kNepICJd32\nVaLaoKgrNf+6KVTINYvRk/qexVPRqoofeKNNKaVgI4PdsIhlZskZ99Txw0+/FbFLjtI9I53neOe4\nS5bTywscE7VcuD888GEcedyMoByxFlLWhLVQY8AoI6ct0dSitObl5cTb2wlSYdf3FGtIeqHvBx6e\n7tnfb4lLwduecdhgOyf4YxBr1ZKDWBZPGZU8xThiFtZNaf45V6pQohJKJNYM3pDWQMyJQhU2xypF\ntqSCrcJV3h62DBuJoet6JxxqhMKpjEI7zbIkUpWlPYyWIWtU6rHKcLkE3s4Ll2UWDpMygvk0hDCX\niiqArjf9Qyky+NTX1VlFM1CRe+Z7ttUNdqu35X9rXuTQptBUTON/q+scop1Ur/Tca6dudLPdNRqN\nItUqTo2F7wq3QIrvDDOxdBBWWFNbAzXJjKk0AZHWhaREkdobEedQFFNOoiIumVjzbaBbqOQkEGHf\ndVgrzK8YAst6VWIL1VSryvFo2I53GOPJpXI+LizHGRMT97sDp93CvMC42/LT7z7y6YcPHL+9cXp9\nYZpPDL1niYnjeeLr25m345kSEz88HvjxwwPKjEzzImHw60pvZ2hxbZrEZjegrSUulRzkVL4sE6vv\n0EaG5uNmwDiL7jQ5yIa4GR3j7z/x/HrhLz9/5fPPv7DpDb95uiPlxHGe+PJ2/rs19deBVjaegngy\nhzUTl8R8PPHtL58xTjMcRrZPD9iuEzm4Vi0pA6CljZdEKVmOjCld4XIpDg1rr40bblrwgJztVEsr\neU9aKaXK1Ea9D5iuAbgdniJRHUznCWWEuK97CTkG6fBruUI3Ce8F5qmt+7lRDa8AiBIIQY6a8p6o\nlVry7ZRyvTmvd3rNRbps1V53sz7V9WpFIF7W12JfC8J1Vm3sq1qH9E7hF1vbKoXUOffdhqDRg6N4\n8Xe/MnSollKH2+zBdQXrB7aHe5bLxMff/QPTPJPigqO5DOcieYa1YFwhrwmKCEes1qxh4vjtxOnr\nmRIjjw8b/vF3P7LbjFAV/XZE95ZqDZ0yeG0wyhByxhiFVo5TiIQ1EtYFqyMTMyiNagPlkiJLXpnC\nzHmZeHvNxFIkJLkaphhYQ0SpStc5nJfcRqxm8I7DdsN2t6HrvBSfTYezGl1lcBxSJcbrYFH8yGOC\nGgsqV6yvKK/Y7DzGybqIuZBPInuvGVD6vfiqd9dEYbS1zrOte5D1Ueo1jKJKslUVOORa0IVxct3w\nVcsPFV40tBPk9eeuzBmrsc0qxiow9X2+ZJCB43dL6LsiLo/4XXOOUu0dXeE/JfOdWlteM5W6JFIW\nu2VjZMvKpRDiOyxqnSWVDDlJQ+cteCdNyfW9OYWtVrryqki14CyM1pNU5jJN/PmPf+Lf/6d/zx/+\nwz/wy89HUpy4fzrw9MMjw7ih23a8HTtOl1XyBzrPZjO0ximzud9TjGZeF/a7nvNxQQH3d3v6YSfh\n0mhUC2hWCskeOM8cX84cHgx+dGiruHvcE1Lir3/6mfPbmXmeyCXy+Hhg93DHf376A/H/Drx++cof\n//QL3XaL7Tz397u/W1N/JdMsbi2DqnL01068OdBVLF65LmZRXgr9r1Kr/B1EKCRGRXIsplY04qlQ\nyTdoQ+d33jkNclBKC6f8us4aridd0ZXKWEFJp1qUHGdryKS0UooW8x0jzAmKdCOtbN7e6HX4CG2j\nABk+KlGkXtkK75z5715TK/LX0N7aPGMq8phViZe1eG4JvCRFtjTxhpwKpCtTrQOTm1crJSeV9phG\nv7dxlcZ51ppaGs+fqxjqOyZOrfiuZ9hsyLs99znLQKwKe6bmTIqpQQqJnAJxDZSUUEoS5G1/ZgqZ\nsELJifu7no8//sBmGMmx0o0erKaoSmc0uhZKjKzrigJcB34wxGTJ1YrRUoFMbqcORdaJVUWWHFmi\nFN5QMqkWaoDLNMkxWSkqGec01NLsZ8WedbPrGYcOpzS6c1KUYmoU1cZ4Mpq+d1inmKMhoalk9HVD\nvdr0FlhDZEydcOS1MC9ykSKcrwG7V+z3uqquu/8VWrgJ1SRRvgC6GW39i0V4tYZGvFBq8xMSmi43\n3P07tOV2k7534FeR27UpEWGQVnLvldbhy8v+XpvR1tht3cjjyn8U5EJFuOBXdlqtlZgFUzffvY1c\nKmuIpFKwKYtvvxJ6sk7iL6SNYN6XdaFQ2fSdxMgdj4T/ETh8fOLjxx/4h3/6Se4VBSEXcoVqNNUa\n1lJvPjJD36HbUFlbS6hiQ3tZZtIaMSjSZiDaVd6XLtTUTipaE+NKykGuV5bZCUqxLivzGjgdL/z8\n8xfCujB0ml2vqBvH4C2Ph5Fw7pmOZ16ejwy9519Jevt1CnkKqS2i5orWWbTd48aBkqW7dt4Lq0WZ\nhgG+43SysFqxqrX5ZGphlgBFCS5Vq2DvuuabiCg3eqBuu/l1TGiuv19rGwJdu3Xp8ysW5bT4gzef\ncT9IFieNiaC0lkTxa7UFUQzmfNt8jDYCZTQankAtum1Q9Sb9rxVR6l0Xfqlkmvd187AWrwuR1Osr\nj56rGq1+B9kIxbJWceirVVEQ+Eib0jxu1Pu8oF6PxpWipFO6fk7XwRZt87kxgzovf0c+02tByKXR\n1xBYLCWxSig5EZaFzelMv7vj048zKQaMEoGENZYwN/zZgDYVlLgtpjUQ5kUKo3cMW0c1IzY4Omeb\ng2NiSVFoZrGQdSaSKQo671jnyLwsLGskhEUGmVpOcp1T2MbVD1axxBVtK0Nv6a0nK0OM4hK5TIlc\nE8aA7zRmMCgsbkkEayTRpYo2oNSC7wwhREpRbIceb22TfUdCirJWeC+KurmA6lLRReiJt+ukTfPq\nV/RDJ2cpI3CaNDmyOSplUNrirKzF3AK563dr7dqA5NadC9r7HYwis/7bWlO1tk1ICXOU9gvQHCDf\nKYeo61qt7/DNFbcHCc1Izcpai6FVubqZKi1Yv1ZkpcTCIKbbY5hGcEApNjsJ0aAU1vXCsiykOIjf\nfEj8/OUZ2/f8l/8S+Q//8Z+4//QbXl4v/PF//pnlfOJ0mThPq3iCLwFTCrZz4B0xJUlpolJSQr9U\nnNKMzpNCptSJ1Sw4rdDIcNk4xxJmMol+66gqEdJMLZnwty+EEFmmhZfXVzSFw3igzDPz56/k08Tj\ntqc83fNLrLx9eWWiYv8tZXZ2ThZZLnJxatboIgOum6BGAUUmxpTvJvVVYARaUSfLQK5cubhib9hu\nCFlUUqnaSiQ37DHJ/2sdT32fXIoEu4jHhla2DZza+KZlipZcWM6TDISsEaqS7xonV4uE/3oCaDde\nrVp2/lLQDft493tsxfT6Pmm4aTs6og215lsO5PVnUi6oBv+WutwsDExT512Lrr6GcbTuXGnT3BBL\nYz00muL7i5XPT8lnd/3+9TRxvRHlR7+DqGr7GIuwdqTja5tCUWhjMdahFXjf47uRzf5BfHCQwZyw\nPyBHYYmUUsgxEpYzNQu85X3X9AQCStvOYMcNT58OHF9e+frzV5awcn/YcNhK0O7ZCAMj1Yz3lp3p\n6Z0hrHJNjPXia19Bp0LIidO6sM6FsBZ++HTPj58ecP1A1ZKE460RiqqK1ALzLPOFeY2tKVBYpaGJ\nvELIzdYVeif+PMkKzbDve5RW5FyZl0hMBY1iM3YYo4k5k0IU/B4p1s5ZhtGjlGJdI2FNeOdutr61\nCuRSEFqu9QbnDSlViIkSJZn+fQ+X616qrIkrjKLb/EVwcYVqRnS62Yhqo7FWk+u7rTQ0CnB+Lz5G\nK6wzMmPQmhhlcy+lvrOwrmExSloorVtGALXBTkpgplqxbR3GnDkdL8J4sYZaMosSy9jNfhCVtVb8\nz//+v5l7019JsiS773c3d4/tbZlVWWv3dE83RyIEUBABARL/Cn3Rny2BgDYS5JAz3V1dVbm8LRZ3\nv5s+mF33yGER0AcBNdHo6urM9yI8rl+3e+zYsWN/4eXxzA8/fOS//x//Fbe3d3zz9QNPT47X04kP\nPz/JsJWU8YBxnqwF8pTk2oIznDYdQwjsho45zhxu9gzbDdHLwBWbDO4yc54vlFrofUcXNoyniQ/v\nf6brB7re4zv43W/eMJ5HzscL8TUx3UQegmcfdjzsO/JNz/PLhZyq1Oh+4fXrDF/2jlrBK1Lx2ZG9\npHm5tno3iiwquWaWVnejAVcLeUVIxaUgBNAcDGtVhMLyFyx8dNUqk1IRErREr01tNIlM9VHxC4Lc\nPThHSVlGWuUEKZIvBRMLrsv44NWtURJDaQxS9YyC9UKVQapmTWdbERfdpLIGEiSbdr4dO6Wu1rqS\nXbT/X5aqV60swbwYKCUS55GcIYSOzW638JhV0/VWJC21PUzNVKwdelpJZfV9B6MIqiwUUEX4e7d0\nyQrt1Rq8nLWYTobyDpuVCzBGgr1ZKAtRjqSU6aaBvJvIU2SYo9jEFhmQHVOmmIrtesImMxwS5wg+\nDDgnI9p22x1u2GE6T4oT8zQxXkbGi4daRdGgfi1xjtQkXPY4Zj59PGGNI3jP4VbmbM7TTIliNDaV\nyBiz2A7P0tDi1dXTIPfBW0ONWceKAaUSndYglLbwztJ3ni7I+DwL9EMH1hDnxFjFyrgFNucsfd+J\nd7lmqMaIPbJzTibKZwmUpULwluAdvjPgHS5k7fAUrjzn9Jklb2tMunJE0sfI6D4GnKptnIO09kSI\nw6g+RxgZimUNoRNxAgZKFqmnYCSV+uqIRotkkynrqDv9vt4JjZjzqtQxGLX+kH4U6f2SQd14aaaq\nqTJdIjEmaS7zlrdfvmUzbJhi5OV04nIZOR3P0hVqGl0pmYu1ooyZC5xVQvl6ufDx+ZWHhxtu7w4M\nm15MuhQMjHGiUmR2aozkVEl5po6Vkh19tULbbXsum8j4emaMlQ8fjlwmkdbOc+Q8zmJ77f4ZBXLr\nwsK1oel5zlIsSlkE/85aKJIKziDwo64IsQWTnI3SFqLOWFQrkhdKkS9XqqtXbnLK9WGVT4aKUAxS\n41SZYZGNJK5wIjGyaEHRO3JwlBTJ88w8ReqcCTlTsqeEoN2bOiKlWpUiSRAXtFOwuAUFSQbhZDiD\nIu/m2V7VfRGjg0qrZBYYq7+Ddo9e00/6fasUemNMnI4Xcq70Q6UbNur3oL9j0M9ohSwN2u1/aeG2\nLgXZlpLLuhkJ4qqCMEadGSvYUimqdjGl1T+cuFcqndoqZc5rIasCaD2kGqrZqCSwKpJLYrN6mYiz\n6Ngv84gfbrl7u6Gagc4DaabvD7zZ3uE2A93thvPLC6fnZ07Hk/hZ18p+tyPPhXGcOB5P2OgYY2Kc\nK9OYefx0whoY5wvBO8pcqKkypsQxRk5jxJbMxhsZBh2kozDVpIHMEBRNUit5TtK5qwqPlORw3/Ve\nWryt/GwGcirYAtkmoskLrWiMwVvPnCPUKtpxJ4oQ5yzzXNUDp2CkzkxFOkW7zmH6gLZuSMYwzSKh\nRDh7fVo+UzvVRvEhwMtZq/0caixXIZu6/Hst0nDknSF4i++EiiylEpxSJ6YQgsg4vXNU40CnfY1z\nVlmkofOOvnM4a5ijaN9rLeJYWiXQd51nVuo2l8r5NIETw7HBdcw58vj4xL/93/8vdoct+5st/XZg\nmmZikgEyJWYS4JReNN5w2MpYwFwgWcN5mgUInC58cb7w9nLh5rCV+FYNtsg+LTVTbWW7OTP0HaH3\nMmx9SqQ50+08h5sDt/d3PD2eePz4wodPrzw+XTBOQNhZ/an+KwOCfqVip/LPBtlwHi8KhJJxSZBD\n09JSHX3fglfjyfV9KuRshfcrGbIg+JIlGC/ypSxccnWmxZ7GCLIUcWQOl3iXO5bOTNEKCCKxlpWi\nsCIzLN5RgsOHQJoTaRbu1Ton/h2h04YcoY8a2ogpk60hubw8KNeWulRzhYq12cK0a5Q+bmPVp0OD\ndzucpICp/KE+cdYZNrYj+HtqlYEZIcgAjqI1h1YQbd4xTU5m26Gr1TB5T7tog4uiLzlQnFoF6/3C\nYgooGSBSeBq3LkysXczNWpHb6L1u66LUEX6hz4z1+FAp3UD0I6FkBio3Vb9HLnzx7ddUCnmeuX/3\nnSBECzFnnvwHhrDh3ReGjx8+Mk4Xhk1PiZXhNBFq4PX0Qi0Xok9SoIwzP71PfHw5MgTPJsgDG1Ph\nPM8cp4ngDXXwYBwdBuMK0ywueylnQhfEiU/TPGlpdwza4Rucw2EZfCdURc6cp0SOFV9g04Vl74sf\nuSXlLJYAKRIsBCccv3Nwe7fjdBx5fb5Qa2GeMtM0C8XhnKDz4Akh0PUdoQ/0MZNivqIUtHO01qW+\ntLg7qtyQWvAOAjKP1LiV604xSjdysITOa/ZcwVZcEPdCg1FELhl3avffGPpq8MZBlXqA06ek7y0m\nOnKWzu+SMzkVxnFWebHYgORcKWJewnY30HXSZ2CDZ0qJ+PRMeXpWzvoik7ii0ILOGHIt4t+vNZmM\n5fxy5HIeiVOEVHg5T5ggaNoZkXCKtXYlRSn63+yjZFpFei0MFUtiDoXj8UyoHt979jeB/e0DVCO2\nBd7x5ZQZk3QW/9Lr13E/jI3PNiIHbHyuIpDGlzVfiVYI1Bgs/9RgLkG8iNVTo2IypBRVQXFlZKUv\np8jAGCnIJO0wRQPKwscvlLVVsy3dtEgwa4WW6li6NK01xFmohJgiBXC+4lzQBKEh2yrZRFabKpWc\nGWtlOpFpTSJFA6l0/YlELCi1UrTjTusF1WuThNAR0vnazMXkz1PK+NDhQyCEIAZNqkmsVaWZ7bur\nVLPRNHrhFGugyeGqFPFKMdgqDVzkll8or1mX6hyrOKaIk17jPbk6LNqdNiwj/kCKbEvZ3qHKF/n/\n3nuZaq/UVM6FvN0p5VS4uX+QgjYylfzm5o44jjhrePP0zOVyltpIhnieOT+98OHDT4SnJ8x5FIqo\nVGJKhMFzf3/g3ds7nIHX45n3nx45/jgDBhc6pZPAYelCh/dipHW5REqMGMQ/v9bK4AO7YUMuFe8s\nN7uB25stwXvGmPjhxyfimAgW9rstd04m4CwoOibxhPEdnTPUIvsKK4Xb0nti50hFMtHgHfvdBorU\nWApV6ZWkqhYBHH0I9H23fO+SRNaKUoJFDb+GvmO/6bnZDrKftbZzc3fA94FSC8fThdP5zOUykrNO\nktJO12bLC0g9rKCmEvJdrF6PWBY4HQBjwVVCULfLIgHcWNjte6ZZDqOUmq2ADsDuA/1uEGOtpLGh\nGo7HM5fLhXmcJINRUzFp9ikycNxdGLYV47wcbln2fgiGUhKXcSTGzBCkXlCtJY2JmisOyzQlkacC\nvTYSplxIH1547UY2oRNfJZ2B4Lxlt+nZbQd8MHSIqO+XXr9KIC+xYEUWIGm/il2tdTKVnIoxVVC6\nFYlhVu7X2ua/0vhtq5l/axwSpDiOIzHOlCIFwrbpSoauM/S9FWXEnBnHiEky7gpN6Vr0aCZHzQCr\ncS5VDxqrSFIk7hbrA9YLgklqTG+1iWcRhhgNnEX1tMr/NVdDnKhamqytGIMxopl3zkuhT3XrYsak\ngds2eaHoiaVzFHBysKSUGMeJwThCJ0GS9mBqwDasihM5WO3qkV01myrNIZFlfakVk8Uhz1S1O6B5\na688uxQ9E3GeKSUJn+sCIXQ4T2sRQjIlu8rhSqUVGCqlnapyLVmK0t4KshQ1lBwS1om/dVN/mAo1\nZd68faseJJkvp5lpnDhfLhgj7dbj85HDX+/Yvf/I9vHI0HtyzkzjzHAY+PqbN3z//Tu8yTw+PvEP\nf/qB57MMbt4MGynCWela7IKn2wRKKfz1z4/M0yT3tlaGoWe323JzuyNGcbY8bHvu77aE4DlNmcdP\nZ+bjhcFZ3jwc2Oy3GOuIc+H59cSHjy+YjezH4AzPLyM5ZrEPMNAHy2bTkXOhD4Gb/ZavvnpDipnj\n8czL6cLpMjGOM3OaRX3lPc4GmT7vvahZYsUbx2bTYazRTCByu9/y9v7AFw93pFhIWjv45jfv2N5u\nqbby/ucn/vyXn/jLDz9xulyW+lWtCR8sXXDEWbyOSi4Y5xF7Wemydg68t8KvVw9V5MN+GHDOCyiw\nFusqt3cbxkvhdJpI81n2kpXRgb4T1OuGwOX5hDEWZ4IM9rgoRYrEilxWeabJwHEiV8swGLyVbIhq\nCV5qdGWOjGSccVTjicmRx4mAZbvbgbNkjQHOVqo1pGJ5/XSm9xOH/ZYpSVHc1kroLYfdwO1hS9j2\nUohvSqB/8vp1WvR7J0HCtYKZpNlWPSFs62BUemUJIqDFPaUR7NWg5SI0gbGG4DyYgA9iGCQxv2rw\nk6KccwVrPaETzl1kcZqyWu2eK3IyL2n+Iv1RvXZhoWVqNWAKxmZcDyYYXNFiJxZskWKqs6s+XItB\npZl3NcrJyPtKh6hRPTMMm4HNdsPQ9RhrSSlzOo5yaKm7okJpjGnKAEEr1lh86NhsRa2Sc+FynvQg\nbeutEkjNEq4LT8KZS9NSvaJ8UF1yyhHnxBComQO0wqY47QkySiny+vzCz3/5QbIF7/Bdz/39PbvD\nXmZK6nxH0/yKayvGgaiWqxYHrUx52kmThNHvKg+fVUCgB4hh2UetqGysxeFwzhP6nu3hsAAEvsm8\n/f4dvzufOV2Etpguo5h3DYGuD/jgcBTefrVje/uG4wWePn7Ekbi/u1sCvy1gqtBvX765l0BeC93Q\n8eW7tzy8vWe73fD6+srp9ch0uVCSuDFaC0NvyZvArgu8fbjh5uEW7zrGY2RjekKymA7CtsN1nn/3\n//yJ0+ksncymA1foewnyN/s9bx/e8JvfvMMFy+Uy8vPPH/nxp098+PhCGQWhp5yYXkZ2uw193wOw\nC1v+5rff8z/9m3+NcZ7T64mffviRzlfu7nZ89dVbzGYgT5Hp+cR2t6XUwnm8iJGXL4RQ+M//8DPn\ncRJAozWvUqRYKYmxxTkBNAXDPEcCBm8dIXQYbYevtTKNE9hE6Czey3P5/HSm77cC1HLWmAElJS6X\nSZqLXiFeolAwitCCd3g3iO1vaQILJYJqFdVKNQSVfUaXiBGZiRoMYXAMG0fOiN1ySgQqvhelULcb\nyLkyHs+MZaZaR8SJN8+cqKmIEmrT020Hpjjz8enEy+vI7f2OTecJC7L5/PWrBPJr2d/aPCA8qnDR\nTa+sBTPvpGNN8/uWenw2fsquzQogQU3UMa3Kqwl9O2FpBwE4V5fOzJYWVkT+l1Nei39a5GtITqhl\nea9mXNSuCXOlWkGLtLapE1QMrHTRGn/lXxYe3rglaIEG/SxSLMu6RqKnFZ9zb71+b7dw3CUVLuOF\nSiX0TSJZ1XOmLNIxMJScmWNehl/Umpeip7Aemo6qHlgoG01fa+NLM59TU3V58HLOzOPE66ejDslA\nBtamhKFIcLTCmVakKaMpd+Tj6/LgtbtqVBnSCt2LWNWatszUKFRPy4rkf6/oLO9X+khvh+sC25sD\ntzEJCEhSAxH1q/DCRgt5m5T5l/9d5eXxkTxPdH3P5Xzm/HrC5ES3CYQ+YA5IvYZCtZbNdou3AzmC\nsx3WdoyXI2XKhN7hNoG3X90JEncd3/7mK24e7vB+IM+F48uZp4/PjHkkUUi18M0XZ+rbB7b7LdM4\nc7pc6HxgOwSGYcNmM7A/bOmHjmE7MOeZMWWmXLm8TxgvtMx8KfSDpwuWyynR33a8/eqBv/2732JN\nx+n1zN1+jyHTBcd2O2CHDrO1mJsHck5cTmfMecKFjpvbA6Umnl5n8qdnxknksnMtJApx1m5l2yY2\nyeALqdPIfFJnWeaLplyYY6YQiVEy8lpl3oEtM7UUht4zTYlW7UoxElMUS4ZUoUiBeLsb2GwGfHC8\nvpxJUbpuY5bmrZgku45JVEpD8BgjwCanTPVe4kMpeC/mX96JxHQ79Gz3A9UHcpnJamksUldpGHPK\nKPTBs9lv6Lc95SVxGWfmy0zXeUiZ9Msutr9WZ+eSPC/IGtDW4SUa0wpfkoI1NL0OVy5FChLtV8zC\nr65NB4CickPzVG7Sw4aMgYWTXo2MtFstt6ALtUoVPGeZCNNa8xf6fRmMoXIttQUw+gFVXeakZlRX\nbls/X7S6njYfFFjUIdWI/KxWSFHQfa0sk+utk9LJWmtwaombKSnz+vhCqYXDm1s2fjXrylnHZXXa\n3lyqNFFZpwZIQl81mqNt1qIHWC1ZAm4rShrpgJVis1Jgy/2VlXHGEULPfHkh50hnLePxyLTbkA8H\ncR9sb2bsErLbfxbOvnUMolYIRu4rNHqO9d4VORjQPoNlb1mnxd4r+wM9kJwLyAzZTrxxdA1ijEvd\npeSs8rjMH/9uR5wm4iRNJafjK5fXV+o8E3pF8Visg0LmPEamS2QeIyXOuN4RwoBzPZDJxVBS5fbN\nDUPXsbEd33z/DTcPD7hui8Ewn0eOn1748OkTr0eRz/3NN19xuD1w/+YNf/rze56en4nzhaF3YCzd\n0NMNnm7oqdYzbDfc3BembHh5nQhDIXSVsXfstwMWR7wUtvsN+7sNfnB4G3DugHci18068CNPFd97\n/G5gfHliipkUJUMK/cDN3S0Pby5cpiSNN7VSU1nuaXCiWsml8ddtypIqSHIixkqMhRhFzZJrpUyV\n7TCIUiZXyjzjvFATlAs5yyjCmsVy9zLPUvR3Fuc6+q5jf9jQDZ2KJmQw9VwNr8cLr8ezIOdapDFP\nR0RafXadCHHISQ6Pvh/AWoauY+gD/eAZY13tNawwD74UghfKNHSefujpuyAiECsoJM6J8TJRc+S/\noj78lRB5C7gqL7ItmFVoDSrXTmeoa5yg9cabFm1+gYUwRbyOjTVLIUkeTkubAH4ty7vOBoxpSFp8\nUJrul+ufr1ZP00xFOEdBB1mplkIp+ucq6Vr8pqu0jheFgyuKabpUAAAgAElEQVTnLsUUoxQwOcum\nUJuANgDAaVU+xcjIRNGZi86LP4bzFu9EFZJilg5ZPYhyjFzOZ2KawSPSs+CFStHvbpDmDescfS+w\nVR6krBmEXYpKi9rEGTJymDl1pGwZgrWyOZ1z5JyW9bbGcvfFA5v9lq9/8xXjeCYVaajY7vdCgZS6\nDA8x+qRIcTgtB4hzXmkTfTUKBjlYqqlLN69ph5tZXflaELfWSqdpyWrjYBcaTjoXvdQujF+dA61m\nO5qddJollM2gHYzaTFOTWBPEpIqgqp8VSfNMf5IO1TROECObw4DvLb//w2+JU+Tjxyf+/u//kct8\nZn9IbB4Cw23H4e0eN9wwjZFqDbscGeOAM4V912PePPDw7h1vvvmG7/8Yef70iacPPzO+fgQKYejZ\n7w+4rse4TGDgfu8IpmN+nfFDwXeZaT5DBJMcu4c7vry/wZTKf/x3/8Dt3Z79Yc/uzZ6+Gygpc3k9\nkqYsKp7ThU8fHknThAuOLveUsZAulsNhx83thfP5wjwLpee9KHiGYaALHXESm99pjhzPFpOT+MWP\nUYJ4ElBlnZUWfVPJccJ7z27w7LYDzjtSLZQkWaaxVu5Frpgi5nTSASsZdozSVT7OE5u+4/Z2z/bm\nwOPTiZ9//sSnjy9st1tu7/ZseidDWEplMJkueJxOG/JOZm36vkfIN8N5LuQUl5pACPJzxVrKKIj/\nZczk1yPOi4snxjDmzFgz5Xgk2AUT/RevXyWQO+dBkWlLldegahZOe0HqtS5/B4ZmdiUxdpVTNIUK\nWR4kGvVihDduvHtDy43SELpE3/Pq/Rqgs1a4alMNplRskSAnX0AoEGHVpUBTSllollK0HXqhewu0\nkXFVT2e9Oa0rc/nO7du1AFRZ5jQaa3BtrSjaBWrXDKGKX4ixDt8Hbh9uSTnRbbulIaspX4zR4N8o\nC/VnKerZ0oqmjeoyrUFIs5QmQTStcErVQu16f2uVAI0REyTntvjg2M4bUpnp+x7fdctwA10eMYa6\nOoBtOwjtOq1mlStWyK0JZW0DFxpNgzcqdDFri0vbWVUp2yb1bCoka3ROrDZXObtaKWT1P5Ht6flM\nc6+KnpbBZLVrriWTc2Z7m0mTukymSDfIaDnjKqfjiRo2XBJgI6YmLpfMX3985hwNm/0F5gJjpJwn\nbDF45yFkXD9wuL/jq++/xdqO8/HEp59/4j//3/8n4+VIGAL721tyNkyXE6Fa9jd7vnx7x5u7HbZ3\nZCKfnn5mfJnIp4qJjkO3wcyFTz995NOPH+j6ntvbO3a3ezabnt45sUYYRy6XM+PLkRiTeGvvB4zZ\nkObM7U3m6flVMxytZaCBvO8Yup7XeMbUIh2kOVO0sQkkM89qIyBqW9mfLdly3mK9EwVIrYS+I5VZ\nRuIZ8KGp0KRpynnPNCfi05EKTOeZsq1shoG9sWyGjpuDZEDGwnSZsVkaloZerG2nuXCZEgZHnzJd\nyTidb2owZCPgxNhK3wmNF5PQyXGWpp8ck6jpfKAYK5lHyiLeiJlqoPvnxJF7768QrCo2dPMvbeWw\nBlnlSZveujXJLDI5WIKwqChWhUNr6NEEQAp37QE3jdpoh0BdPlfedL3mVni1n9EEqvpWhUJTebTA\nVaiqa2/IGlXQKN+tvfXCRTejq0prUjJX6F3+ThCjU/7fWiNa7lYUVErJWckeCgVjRSF0193LIWKF\n2miBXHj3Kmb9C14twqG31n3R0dF83U2TKZq6FIAl22g0UdGHLl8ZIZmlUG2bUVcI2ODo6AVZGVG9\nXC/9chAUaYlqJ8rVFlG3SrR/QIfgtr2kP2TMSrXY5f1Vy65GahrfJXhjdSiD0EENjbdDwy4KmvX3\nXCuSV9TGQVQXznsqBS+LLp9sxBOoJqXqkliwWiucf7Ydt8bTbfbUMnM+nnh6/5Gnx8jp8ol+c2TA\n0WWDTaJ+ygmmmHEmUbFsdjsON7fkt/f0254f/vQPXOYRnGXYbRlPkZph03Xc7HfcPOz57ts3EAKX\neWL7U+C8v5DPhZB7ur6Dajk/n/j4/pGSKjeHA7u7Hfdvbnj79o7zmGSi0+mF6XXC2kDoevpNJw6b\nBXyGp8dXtsMGY6STtqZK1T4N6ywxRuZpJo0zdZZJQFUWVux1vcoT5eGRLmKlNEuFqJYIGbN40piS\nCb2nU5GBA/HXMY7jODGfxUbXYJlc5Hga2V5kwIyYoXnOxzPzZYSIquwc2VTOc2IaM0PHors30eKC\nfKeMyJFJmc7o/cqSLdRSZSB0kgHkTUw2R+m69cbIujnHxv9yyP51Oju1hbIolSIccL0q8l35HCMp\ncguyi8rFiFStzem8VrnIqS18++IsqHIi6UIzVz97/dLDRD9f94gG4rwE/lrRIqwHp5x/U3cYoXVy\nKfKwW6seeCo5hIVHX9znEKSWS/rMja7q4VNLVv9ph3UO41vjjsy1bMOprVGvaWO1E0602rkkivVL\n1rGoW6p4cQgaFUhdqnqBGNHqtn4+FI07Y8SHXRGvzU51+GuNoaGmRo+tyywppUxUEnReiyFXuyBv\na8oyp9Igha2m5lkU5aoNRwGAnIeKlp1TDzP5no2ass29sX5+XUtm1QK4tn/LvpKbUEoW1VFph6o6\nVOr7oYdiRpB2yYXQdUqxFFC6zxij9QPdPxL9RRXdsrJaKbES3IbDwXM43FNLIsWZL77+mhQnXl9e\n+PjpI7kfKD7QBc/pPHI8Jo4vJ4KPHO4eeXz/ge1mAD0ci7eYTvZQSRJEvLO8+fKBLgzYHHA1Sw0m\nW+6399x0N+y2e77/5nc8fnjix7/+zF9++JHxeGGeZnKKvDx94vn9wKeHGx6PF06nC3Gc2PUdv/n9\nb/j+b39LmifyPOEMbPCkWWaIPr5+4sPPL7w8XqhWDLjmWng5TeTzCDFCkYHQVq9fpghZjHeMl4k4\nZ2k6q5WkxmipSHqVk3gneQObw4ALVr1eLDbBFAunUVpee2fpNx1d76nVEqMMd0DvefCW3cYzuMA8\nSw1jjoXzOJKmiDfS3CXhrRBLwuEgF8Yp8vp4hDlx6ETL3uyfbu82WAbiaebx5cx0PovswzmGYWCz\n6/E1cXfYcXvY/2JM/XWKnSWRUxPri57UCG8AVTr9rs3qm9JAgmpTr5glBadqQbNRNChSLVeUzdKR\nqdTK8iAqUm9vxGrYX/XU/Oxgqe070GLhEqiage3ahWkWS1WjwX7p3mzvg6ENp8glqEOcDkIAzVSM\ntum3Q641+Shqr0h2Y2SKjNE6A6Y5QFZQS1tQfaxy94s9bePzDRicrO3VoVWKBCrlemjeLtD8bMpV\ngUrSiWYd3AZkLKP0NNtYmry0Tf3zAu9KT7VGKCmAVijiVCMJSxEe19S17mBaURSwVtG20b3T1rUo\nvXJN0VWVPrZFbf4j7QC0Ur/Q6zSoJYHSLKZdlXFiU6DvUa8oMxCHxZYtyo83KwPJfiQr6KF2anwl\n9sC7Q6KkzO5wx/7mQQtiDm8cuzhzM16YLyPBee7fPlCr4fh6AuNIMbM73DBPE3GeeHociZeZNEZ8\n77AVhr7nzdf3Ops04frK8eWVrhv47o+/4e7dl4TdgdfXiXTJzJezKEmc0YanKB5EMZNjYvvuht3d\nhtAZ5nMEoN8G0uuF/Tbw/W/e8nAaePfwhtfnkefXI2OceXm8MM0JV6Fzns4p+vUW1zuMUpW5FFxp\nt3kdx5iiOBUWoBZD6MRCIBbDPBVsLDLpSYepOC+FxeA8u77DBBmsbjDUaaJgSaVyVndO18KOkcNj\nmgXJe6+eM9qOYhzMRTp7n59eidNEZy3JdsRJePHpMvF8tvTe0VuL845tF7DeyzVYj3Gw7TvuHra8\nfXPzizH112kI0gJcimkp99pmG6vo/FpiBtoKXFb6wxgpclSNxuLYqhy4+rSIlFCinSj/PMupgAZx\n/e811bLw07UhfbdwtMvv/ZPvJIi20saqOU3v8xX/3/hW07IEGu9sqdXhNUsRXl0CjjVtUMYaMNpF\nSHB0n62VWJBqANFuzpyb5YHRwN4CWlWq24ivjaKY1Wis6XdkLUpuJ4cuQltvUCUGkjPWdbyYnjca\nzLW4RGvFvqYt1gyoZWJyqOnPWKf3Uqgk01ptDSSyUjbS1VmXk7ksElDxyJADsxoJ2ItHfQvkdv1s\ndF80Y7IWeKXLmGW9F0qm0VqKyFozlyxVobZOEMSWWb/YchhbXcdqDa5ND9d9Zoy4VKKDtPcHeHiT\nydqIZXV0Wi1iD+ysI/hOtN6nCYwhpcz+cEuKmdPrkTg7UjTUYinFYaxj2A28/eYryShzpN97Pv70\nnloN3W7D2/0tuJ6XT2cO2wPj8YU6TxhbKbYQbWEzyFxbUxLdEMhFiq1pivSbnjD0zD/NkDO7bUfn\nDry5vSN/a/j5pyf++tMn4vzIzc2OrhZ6wBXx33Gdk3FpU1lklU4trp0S5LlCiZm5iCYbDM55knEy\n8CMK4Ase2AQx+kKajXrv6HygGhURWAEoSYHiNI54L81L6wi8qveyah2mZa/y7I5z5PU48vJ8xttK\n2ASyrZynmfN54nI6U3Jm6Dx3uw2H/cBuP9BtBtKUmKLc52qszB+ovzy081cK5M1/20hXni2iBV5i\nVCXpBS9xo8iDnXLGOod18jC0gpe1dikqUYo0p3gJNq1QKDROhuY21zoPDVI8qzr9pK4lT6fa1Ov5\nmw2ht+k/wtmvE+xbE4+xBlzB4IQPre1hbwXRVbcuEVWLis5Ih2tD3Q3ra4u6UCeScawOcw3latBV\neqm0AMq6trUVaJ3+bGkKjBbAxaFPuGKzfOdGCSz8emmukS2A6WG82KJWUmpTjwopTjKzNHis7ZZA\nJY9EufoOLXCKARR6LbWhZq7P47q4LbbgL79cF9Smv6HUj1oeI1YIztmFWhGVVBWetB187fKM8PxV\nG1fa4bnUnkzT/0tnqrVX61ratCrNINTcWzxqYP0IPVicrFejaRp15qwUXXEG764VUYhfO0mCud5f\n9Fqk96Cy24rCJL8pXM4z4+mV+XyGCuGwIexvqKanksgFzufKPHvinPj5zz/z8OWXPHzxwL/6n/8H\nHn/4mdOnR+bjKy/Pj5zPJxk0vg+arc28/+ETn358ZrfZ8v3vv2J78w2+P3B8gdeXC8WIt/zd2wP3\n7+65vf89b999yadPT0zlAjlS50g6XfChw4VAxZBmsaz98a8/EWMk5aSDqxu+qALkFKWlLKqwVCrz\nLCMXDYHzWYqPVesbUgOSDLALjs0QsEFcFudY6IYljSZPSVQrWf38EWCZaqTWHorBzJXT85HLacRT\n6L3FmUqKE3EuxCioPKVC0mdpvwt4D12vYKBk0gSfHs+8PF0I7sMvxtRfJ5AbpHsrOEFHxoh9qHqE\nGw1OS9dmLdTl4WkqglXNsCLSqgF6RdOYRpXIwsRaVm+HpWgmPOg1ddIQ8aKf1mDeHraWfS/DaK/S\nbaEC2tQd1ZLbqqqIKw+ZYvXfFVqadqJf5W56CBkA65YE3WpTj0jVZZ3E9lq+gDeO4rP4spRmCSAt\n0Dlqbmoa6pfDw2ijSzVlCdrWtO5YsxaNkQMSU4WbLNL+/0+zlkZTye8ZjOm0Vnk1sLfRJ61QqsRF\n853WZVE0XK447StJo9Zcqipllg7cugbkUg21JKhqXcCq+GnIOMZIM7Ly3i97qf2cHGCfC3lzLotF\nQdXssi7o/UpJJXmLeNtU9fK2akdgjLRut/uuEantK0Hw6h+jqHzZh3a9RtHsO5nqo3RSew9jHd0w\nEHS9u83I9hDI8VYQdx8I255prlCdBPRScH5PrYmSPfNY8TvL7cMDh/2ONH1NnM+8fnzi57/8lb/8\np39kfnoiTgmL4zJNpDhxPl4gVIr3vPvGc/PFLdZVxtMrDBs2my296em3HUN/y81Nz4fHZ3764WdO\nTyO3/cCmH9gcNuzu96RsePzwzPl4ZDrLQJIkqZ4+J7KLXJFnyGSlyCpsgmc/DNzutnKPQJG7+Nw4\n75a1H+ck3jtzZp4SMUWxGQ6O7aaj85WgevB5TjhTCYrYvZPnZjN4jOmJk+45xLG1FBmisdvumJMM\n+k6l8HrSxr3zKGZrc2aaCmlOsoMW5PD569cb9aZa45alliycuXVW51AiyBGzPgxVgsASgNdcnDbf\nb9nQtdKkYDLFRFUyKcsIsyr+yWuQb4hbcV6RgGVto0YaBbM+NI2daAFFE+EVWWmwEKRq1FKmPXxi\nWVpbGm3Xh3/NQq7WTJF5O3iane5CjWA0OMj1WGuxVZC9/H7RgOtwRnTmRYNydQ0JG81fMtdNScsF\n6PKYq2OrGkE/MhgCLbrqWlQ+CzjOh1W18fluAFY3lqtFbh9Jm3JklmypoehmAyBoe7Up0H3RKBhE\nUglVnena3pKieKliKGaMBR3mfQW2V7qHvL6f0i5NW9++iSBt066CNhWnZTO1ZKpplsPqpF8r4gTW\n1uSqRlLNUjuhKYWMWVQ1GLN8plXXz5btXIsEjO8WzOA66POgz5hd7tsU25Qe8RwK/R7npfs3zpXZ\nZzY7y/b2ButuyDWxvbmlGsfT05FwmvAuQ++YI+R8FtfIHx8xvseHDTeHA8FZLl0gu0wwljpVXC+t\n9s52nM4DJltKrGzvNuy2G/aHAw9fv2GaKmWu7LuBS9+TY2LMiWosqYrMzzpB2K5KYC0GbC3c9IE3\nhx1v7g5Y3w47QyGRgZgr43kkpsQYC5lCTpUcC3HO4AsBsL6j87J2WMtoZ6yp7DY9XefxzpAReaJk\n+y2myPPqHRr0A1OcGceRaRw5nifmKeKcIxXRy0+zKHvaffyl169U7GyJ8Dqf0BjRf8pGq4sM0aAF\nBBMUEcl/TRUT/oXrLSuakd8VMZKg1orRaTU5K7LJRV0JvShZlF8tqr4wKHq+Nm5q+mka6lo7F9tB\n0H50fYhaYL5G8xp4FY3ZRdUgro1rcBPO2hhBZO06rieX618tKbzV91l5dh3VpsHLe9nUxco8xutg\n24JKNWtan3XIQFFeP7RGIlXbtGYMjKeFWGvNgrRTyYokhB7JVdzmXCtoI5mEUz0xbe2NAWNlkhPN\nU0e+h7hF2pXjLkbVPVWD2oruRSW4Bv129rffb06RTcXhvKTJtQol1gLrkrlVKfgWlZXKQSLyQqoO\nRqlrkXjJqJZTQbcRCHJUgNAa3uQHJGCD1g5qK/BLXwCIGqYkbZoyMgjEGDFysprJXB8i1ogbnxwC\nFVv7xVrY6AEgw0uygivxQAl9YDnocuV0moixyEBzJ8qmFMFvb/n6d3+AsOPx4yOn05lhe+Tl+MrL\ny5HL8cyHH44E3tP9oefm9sDuZs/79++lkSgd8abTGZqVwXi++fKed3dbHh429GFLv92z39+Rz0+Y\nS6TPcBh2GAJjjORaOM0zpyni+oE+9GxCx2HfU21ljDP7znG373m427Db76QBqQvYUHidEh8eL/z4\njz8yjjMxJ1JJdN3A0A9cXidcjZQp8zpdcF3ABunG3PSB4B3b3QaqSEm7fpDRkKnq2qvqy8F2u6Hr\nxJRsiomnxxfm88glJmazTj5KGVKqxNyA7BW6u3r9SoH889R9CcaqxljUHYoqqG0Dio2jQR7clApG\n5YvC2VZMkcBq1ZCrFfmoTTKmcKWh3lokFS1rEFon+6zp7qo3RzjgBeUB+apw2VCzXhNFUFytrfBW\nl4BnzNVhoA/zytfKGjS0jUN5+CIPWW1HihZRWa9Bvoe+N0an9DR9s5QKizHYuqLOWlsaLiyhNZZi\nhXKSkW0VsNLqvlBQmgE5I+3Taie8SEmrtFW3wLccFkV4yxZE1Gp6Cf61VD2oZT9UINWimnndB2XN\nJOR9JADTuBha4fiqDgLLWtdS1S9MDgzrDB5z5c8ik6lYDmFWOq1U1QoXgncyVk+7kG1tap5G+bX1\nlWtqh3TF0Kb2SBFflU1G9nprWq01s1JsguzUW3gBBLW9fxWZa10CuVn2STVS/LMSn9XwzansUe9R\n84wvqvW3bR/qfVBpbNLpWDbL1ByqIWwGbr98ixt6vvj2K/H2nkeOxxPPz6+cX444a9nttvT9gRQN\naZ6xMTD4LcZFnj+OXKYLxVZuv/qC+y/uIWeCg+mcKEw8dJ6Huxs2f2N5d3/gpw+P/PjTJ3748QM1\nR3ZxZjP0/M2//ANf//Z7Hu6/4PH9J37+8a/89Ne/MnQyOq8ftgTX4a2MHvQ9bK3jPgHfvqV7fOHT\n4ysvR5E29i6wve8INlFy5Pk1c4mZmjOd9wydGuIZKajWkqkB0hzJUXTlwQgwqLngQk/fdwy7juOH\niXmO655VMNA7I0K+Wkm1Kga4ypKvXr9ai76YFYGpVf3s2ki1hihUZFlbuNREfwmuVSV5LYVefZBl\n9xZtc2/okIUeWOgNRWi04ppd1SVrZm+WB3PtLmW5rkpDxtdBRP++BRE0hWgfVa8c/OQHF0QNq38D\njfttx4+iN8x1V2NbGbMGiwV9tgzA6vlQqbYuwf/6cJJr07FWehhKB6PD2aTF1Xb9LElDI1msdeLj\nHMW+N4QgHuF25cNbHbIdBuY6uOp3a4jWqh2vBHK1FdULsFaQdrNysCYv61JX5Ttt8pTUStbPcsZR\nG92lWZGxja4qes+vPez1YNKEUbKdNg7NLJ+93ImFIrvaLzQ1TVs4HYR9pcYyzXveGjEL+6w/QmkA\n3eMryNB9wGotUC2L7/1C5yxAAUyRrKW5W9b2Xetyg9Q6Wag20za0rkgqGVO1ucsKOLDOM+wd3abX\nw1gktONl4nS6MJ5PlCXwQ5xmZnOhv+uwrpJL5OmvH5lPlUzCukF9TwpR+eKYIq9PZ26Hgfvvv2Dz\nd99x/5cP9H//F+ZccHUm58Q4Jv74++/57o9/4OHdd3z8+T03d3ux8p1mgqkE76Urc9Phhx4bwDjp\nzAyDpxtkAAhKbTkMuz5wuNmAqUz5yHS8iP9REdWM9yLznKakRXVpDqrKh8vQ64I1omsPXoL/OM5M\nc9T413orZG95KzOO57bP/is9+r9OZ2fwV0hXuKtWyGzWtLUhjyr+1cvfG+GCG1JoGt8Vxa6bW6R4\nrZotG7Eh1dZ8JKeoXYcr66tN6BaFSCbnzxH6gvSWB7YsD8w19UGjAZSPb9SNVYq7DVNuHKttB0LV\ns0wfCvS7hRAWzax8L1WmlOtDakXjkrsbloPIyBg9kZ0VKlkLfyqXzFZQXa1KeTjxUS5t+roGwOp1\nqEddUvocJ07PZ54enznc7jncHRi2gwSZUmUwgXRyrTp4dW40Rgb3Lk1bRrIGoVJg7cxE94EcHDnJ\nBHv0wGrZlMgl0aYc2UsybMNighObBUVAVteolKzmY+j3zBrc7FLfwCi9QpUBx6ZKI1fWxrV2sFu5\nxpaFlEXvDKYoTYRIyooBVNaYK5hql4Nf7lvbY6L0MqbirB6iCn5M0SIdmqWiVY4rmsWijXil0X5C\n3dUm09X1kKHKGapbgn/bW3JFRa9JFGdWu42tM6v6zDo659hsdtzeFVKKmik4sSlQNIu2rs9TZHf3\nke6H9xxfXqlsiAlynDkfZ4rpKKny7//tn/jjf/OON+++5qvvvqR0PWOqHI/P9J3o8OdThlj46c8/\n83IsfPObNzx8+Xf89m+/4U//8Ude3j/CdOb+fsf+dovvB2IqzHEmhJGQJva7LV883LE/7Pjw8wuX\n1wu5Zg7ffsX+bs/p/IOY0Z0Kc8qMk6GWSIlF5scCyWRSzdjOsRk2PL1/Ik4zoZNRe8EaYkyM00jK\nM04594yMk5tS5bCx3AyO9JoZ04JF/8uY+v8p8v7//LKmcY/QuOPWwt3mCy6omBZ4m+ytgBpmre6H\n9ip4NlWIW1Gw/tMYloAnm7uh4KKpZnNVlJ+XQlj7LG1yQXJT4dQFYS3vU+WhSkkGJgj6aIXb1tgi\ngV5oa0FZ+Urxcc2jG4xKzkQWd51JYNY8pdFOn2UEemXms4fwquhmzaI4MZiFezWayaxnWkOuwqtL\nH/y6nmrtJZV/U3DeELwjpUSMMzfdHhRJtqG+BnTAMnqDBfU56+hC9xmaLZpplTYHtJVEmya8aCDU\nLt/GJYteXv8cHUyia5MaMrJWvNkLEgSrpbkfllJlQDCqaNLlpdE4KjVcUbn5zHfFYNWyoPm3y/fJ\nOj3JtJqG1nC8sTo8paxqLYDadDyavel3nFPG1Ew7EKwpTVVLm8hU1AZYktEGZCTA26o1qIree1mz\nVstoeGJ5WK5ftXn/tCcriwKrrkO7G6XZ6hgybNlirQfnIVwfHpW+zwTfcXi4ZZ4mjDNk7WiNly+I\nsTBdIufHV1y/5zR6fv7pxOmU8WHD/RfvmI4nTuczr6fE+fjI+I8fiek/8PVvHnj37RvefvmW7/7w\nHenbL5menxnczLDrCLstcY7YS6JceuL5iDMzbmv54s0tve9JMfNmv2V3u8cEx/ffwaHf8fx05GUa\nBWAZiOcLMes+iQZipdhEdDNd5xi6Lf2uw3jPnDLTJCqyFFsGL1lp1iY6Xyudhf2hg0vhPP4zGvW2\ncMXX1UGgPTSCFtxqkIRRSREiBa9qWlQSMkBANm9KCZAHUPw3Gg3SuIC6BGRB/426QFFr+3HVN2gW\n0NC3sYJ3jP5529B66fLzKvFrXHtr/sm56HiyVQ3SukxzSyJqC+R2oXjkespndQWj67J88LKILVvR\nwF3b+rUmlvXVFC5tqPNSoBTIuPLgVZBgC1ymld81u1ksBqrQR84Zui5oClgXy+FaKnjxOwfNPOp1\nwq4Hd5CDsPUNGKM/tK6aBs4rSqjon7ej0TQKaA2CLPSXasmL7EOZBbkeoo2uKUUsThfqqwVShLYw\nVqwQSl1+XfXpsq9TTHJbmumWk0M2F/R99KCxrSfOtNKj3EP9PoX2WcsXlZRdtdGgVJyr6t2uB2Bt\njVOa1ZSqk5T4rHbU7quyi2sW1/Z/XTPnda+ttJzcTymQV/zSkSzSWwEMWGkClC2mIwtto6qq+vY4\n/G1ge9jK85bFmySlKNazc2a6zIy3Z4aNpxjL6zmTCKWh/DgAACAASURBVHT7Gx7ewbN7ZC4v2HQk\nH48cX458+vDI89NHPn184nd/m/nuN9+y3205bHpcPsl+No5kJJ7EqRLPRbtxC53ruD04nHG8e3NL\nLpYxJR7ubzn0A28fbnmeRzCWkgrj64nH44lpigTnKOlCKmKU1gXP0Pfsb/cUbe+PUxLb3Apzkkwu\nV0PSTHIuhmIch32gUpjnf0bUysqbFkU1DbUIQmlouOC0uFNEmdAeYnlCRC2wbLTCNI3UWun6Tj1H\n2s+qBlm7F2utZFPouk7QNdAGxTa3Pwnw+mAsxaaVx16DQysgCQeQc9LU2wnKd3I4zXNU1z/RAQsn\nKkW2lQ5qCgKBQy3wNESsX5Sl4chAThq1loeyqlZbDxljcdar3rk1Lq065qX4V9f6gqmsgzOWQNWo\ni8ZNZ5w31IzOkZAAYKzFdR4fHH3Xtcdeis/OAk7uQ6oyVxJxwxTUiB6S0IZXtIPAWada8Lrw11Xv\nWztwAXHMM0JrVP0+8udWMyz5jFoFyTeaQP5i7bi1rdXaWApBkbHQCe2wtdbJQG/9j9VbkVPi9fGE\ntcIZh75r1WeRwFY5HGoR7r2W1qCmAKdooNOMx7mmKqo68LplsYJ8nW2dy6icVfsFasJ1spdKreJp\nrT7bXd8RfMDhl3qJAKRMUZ16s1UATcQWILEquVq/RjVS56rq819RJVWWUXwlt5mz6gho7VUtyrTH\naQEPznlx7gwdtWb6bWF3WzHv7pcaS6kRWys+JrrdHd3NPdvjK3fHVx4/PtH/8J6c4PHxhfPLz7z8\nZebjdx/5mz98y+/++D039w/MpzNP758458Tz0wufPjwzHSdKTVQndKe3MhrS1sI4JuYp028cb759\nQ7/rGW3lfImkKeGo/Kf/9BOfPr5gqiGbZ+p0wVsxv9puNtzf3vH0fGEaR0rUvpZmSKcWDnlB545i\nA4dtoKTK5fzLkyV+HdWK8sLNpXDVQSt9ojRFbR2GbvUDoVr1HFeOsa4pXtdJocUaq9x5XhGsBk4Z\ngnzNt8rLGJU/ghZRWekF2/CgFtJaAeqKyWiFLB9kOs+CqBUhtU2LIp5atWWcvBwgi7+MKRpkS4vR\nNIvaqijI2n9KgawgvRVnrb2qBVgrg3Gv1mtpR9e1rTpsliVwVg08qpOu+bOh1mInKjabAlyrZvp1\nyXqSTuaBgnWrNJFcKSmqP4tQHOZKIyudom4J5A29t+DQCqfQ9hG0wnbLloxpDUJmoblE9VMorCZo\na/PXmoUJOi9XyFH3QBUf66XJKlfmeWYeJ5lO3wVC3xGGHucMLigNVrLYArDMkBe7VXH4ksLsAmbs\n0jdRqaRs1n2MyB0dhmoFdYvaRE3O9AAvtTKNieP7E5fjhfkyQUn0247d7R7XBfHbt3J9S1bSqDvW\nQSEoJVdp/kWtWH/9XGhxWu99rahxnaiY0G9tVCIq67C6U66xQda/NXfJvXD6v9qYpoZulU5AQUgE\n39Hteg4Pt6Q08+V54rvfn/jD4zPPT6/kuRB8x82h583bW4bdDa7z9G7D3vQc//wekzv2uxse3vak\nlDifLjx9fAQDvu8IfU9vKrnO5GkihI7d/kCZMu/f/8TLhye6YHmzPfD12y/Z3ez5y5/fc3x5wbso\n39c6jPXcHRwPN1tCJwDy8eXED++fOJ4uzPMMJTN4z/3DlvuHLS5O3N90/M3vBn7p9SsF8qugtfg3\nr6mePipQ68LzycssCLh1xTU/bJCqcZO5XX/O0sZeVVBvVv5d+YwWX+VPDGvjSdusuuPEd0OKROsX\nWpUSBtYhC/qdZBapWz+PttkFYQm18XmBVA4M5Vs+WzuWVN/S0LReKKwa/doC04qaVi5DH/bSVDes\ngZgrxFWbXlqGaaSUlnmT6AM+RzFAa2xPqYKS5fA1y4BoKNgs126oKtOTiUe1aEOKEBMsQz5qEd8O\nYxcOHwPGW3xd90vLlqTAqQM8al3WR4LfGshFNdTqFTIib/GwYQ2albZuLFnTdWZUs9ZPdApTiVkO\nJCMueqb1MJiyrHuz6cXIOrVLrNf/VJpqObyS0mW2dXXqWpnWPyAo2pr19xrSzamS5kKeM961ifVO\nab+GrOuyv40egi1DslbzX4seukLhGK5UOcbo3teh3O251oPItD2mZmamGh11tnwsclis1yDFf3O1\n9itax7UhJnIINaDiascgG5lyL01qcZ45Hy8kna4VvGUYeobtoOMNK7XbsjsVbLeh1kzY9ZSYOR8v\n+LDBlsymd7hhg0kjxhQZwxcGrO8ZSmXbbSibyHbjuXm4Y3d7IAwdfeiYpgdCnzm9zhxfR44vI34I\n7PcDb768I/Qdx8vMl++f+dOff+L0/IrNia+/vuPuzZ7Ntmd6PbI/7Ll5uOOXXr9aIF/MiLjidI3R\nkVqOlOPyEF2rNARp6k1XDfDy4JOX97se87bMr6wFpx7CztoFWcprnerTLEybRLD5WVwjB0OTP7YT\nQGiQFAshCF5oG9CqgiHlBDXTDKWqFtacszjTDpu6BtUV7nPtKFiLAim0MaTB9nKtlGmySQngq+/K\nyi1LCl7WB0RPhVobWhVVUFITpRgjl8tF11f8zs/niXmaCd6Kzah1eN/jfcCodW6bt+qdYxpnao70\nnTQ9eCu2vJ061OVSSAliErP9aUqgo+GMtRhvsMHSWZ1ApB4ZKArNsazqGhoNpEHfgkXnuGoHpPeG\nSF5cLqUorRzvcthmWt2iohTRcsgVnLNsdgPd0GOc02YZC1VkaFJzEJsAC4uLY0Uv0IAoQFRnX1tR\nWu6H+PIo2HAVa1XFRLtlVbKPWlRdIxN3tvsO5265f7iBWuk6kf9Z67Eu6B6SIu9iG9xoi+YPYyvW\nSSHaaGaVc+tdkM/HarCmCt+rz4Q05YExqiAygC2qnmnjAhvyr4s9hzFNsy4UmlPVU86aLfuCdRVj\ndZ6sFipsVfmpcTgPXegYNht2u8PSfeuDl67PRSABZkh8/S82YhaWMuN5BApvTOWr331PPE/Ey8g4\nz1w+/UzMM+++/RrbOVKEw75j999+jzOWw7YnW8f5MvP08RNfvN0zHO7Z3nf847/7kTx+5BQv2FDZ\n32z59rff4XfSWPT7VPk//rd/z0//+CPMF/71v/kXbG+2TJOM0bu/v+PtF29/Mab+SoEcDQYaHzXD\na50hxQqyaEZV1x1yckPsEhyNaV1w0Lo5a6mqopDPa8ZRuSZyybhScN7L4Fy9HvlXi8EujRHypmqy\nY1sTTqOCkPdvLIKm9kZRkqllcbRr/J9Tp7V2KAhqr59lAy34L8i9fZByPQrI1R+iLA896OfqB7Y1\nbuu2ZgJaNahaGW+HUdVDQU8zcZbUqTZNIqkIeJ4nckmIbFNUKNM8YZNQBcbKMGYZwxXoNKCXUrhc\nxMO6Wsd+6GXYsgFbJ8iqpJkKNEP9EEADT9srporDXi2ZYts6yo2UDkeUV27ce8tuaDdKaxSylmJK\nJXuvHaaVrMOp6xJoMMJJiyueEcqgsnitSHDRIG4MIDwvRrT5Nbeic1Pe/FPtf3s+zFVH5toUJZmi\n1IuEwnJY5wk2UBEfmRb8Ktq275o2Hoz1LEZaJKk71fXwkitpckO510IjSVbRlCoy59VS164lpYIa\n8JJgPM0jxoiKyXkZqC1eNY2KYdmPy3qwPk+t0CpDSuSAKgiaxxScFRpVOqtbCtrWdH1EvXOgVGGz\num0Uo0HqIV3XQ5Bnpe+HBRCVUih3WvwslYcv3nI+n3Cdp84RWwvO9fR9EDBGZrwkXl8mHj+8ymc4\nsUSI5wuWzP6mZ//wBXdv7zDGM18K6XhinC5s+sBu6DmeTvz01xPzn1/5+PGFeJk47Dfc3uz4X/5X\n/ovXr2Oa1QjOxgED1ayVdJkEI8WtljaDhjFzRQe0xgsNUtYY6iIJ0w2hT0eOmWmaGC8R3wc2hw2d\nnsyghwssKWVLdQ1QnURio2m31ULNclJU2aC2VjF7YqVFhGduiobPntbl86ioeRhwReOsPH7j2xtn\nbpY1kP+rvGZD5rSPKAsaXZH61Wfqe5kliOcFuZSKSkFlqEWpRTlPWZucZDZmeyAv50luqbUY79lU\nmUzU5YDXxhNpT7akYsnFgPP4LhAclPlCmjMxQ5oB43Gdx3jhFI1r/sz6FdUtrpSKqXm9N8auliVX\nAKDqfWovW1uTlDQ+1drey2kwzOt+KNIxaa0VW4j/l7k325Ikx5FEBSRVbXP32HOtzFp6ps/pM///\nQz3Tc7sq1whfzExVScwDICAtKu9zlHVHRYa7ubkqlQQEAgFAVy7wPhpW2k7ULolUm8RniMtWe4td\n+PPkARge3RCdaeQNGC2pB1/Jbbk1gDMEu9l+g6lvqtdGhHMHG0L5vQkdSgM5cYj12QF6noAXR8EA\nVT22n72KUQkqbO9LEqzrYuuBOaq1tYGozY1orwmBnxX2xOlOrtdA2KW5PWADM3c88cjjTPiPOLUq\nDkbi7GiNBnw59b5LJZfhPYGIkMUmKy3LBVutqNcVUht2By9+g0K3K7a0YL8p7h5e43xe8fz4gk//\n9yOyrKh1xbI1vJ4PmPd3aDLh6fEZz0+fcH76hOW8IVVFRsGn3xdcriuefrsaEFrOuD5d8UevL2TI\nLXGmQ/8Re5Ef83+lvqFJv/T3K1TXGwMFYcUiDyOLPRTbdcPLry/4+R+fsHvY410GyvGEVLoaw/ha\n/zvAMKvpjM9IriopZQq977ausHrvgTJxB7RtHqa25r/HnUBz3O/SOUYopJESM2ywr0OsEhANkJzi\nvttA1QAIA00ETBmdlaLTOWiUcbO7IdsIcODEtq1YrhuWZaBkYOF/ThmtTFiWxVssKF6eF6yrI/FZ\n0FJDnjPkpWA3TZh94Ozxbo+yFJOt5YK0mzHPgsvlguW84rw0tDwj7zLKtLOJK8X/aDdqRIa19qgk\ne3IUjkkjiQk6SlIIXhrviiQVk4dFZbAYUs05RbXqWq2RUdpnL2TpdI34hglHrAkqNh2794dXoMHK\nPZxWaSB6MAvVfAJRzk47NjFFi3EwYIGSDmckJUXK1oOlNXPK25act9+g2PyeGZ00NLWv2b5Tz2FY\nFJGzdyBFgnhbaIVdR89HsdbCDLElu1tELariY2nNGNbWINvqvzNDkN14wkf8Ef36n0DjjIIa2Gcn\nFZap+57miEI6QukRQThzcSPOGhICxFatgjUlp3sQjs/IUVdPEQCIYHfY43B3QE6mi2d0azTthowV\nu7Xi7u0bfPj6a3z6/Yz/+58/4e//dcG6WWOs83XBqw8btpawyg6/f/oVv/33Rzz9+jv0ukBUsd8d\nUS8Ns0z49u1bzIcDWltR1/Mf2tQvRK2wP7NvcJf+Ae2z9yEMUIT/jQ/JDxEHGbtRQjaDQZmhOfWG\nMmfs7mY8bCfIZKOu6rpZIrLYxpQAzNRs23T6LtGzrL2Vh1s1owRZTS59mL3pljhJghSHkkz4gIU4\no3Py0DLkhr2QRJJl+hs0yrQBM162WIwm1JtdjU6yU0iswrGKUcVSK6ii2Fr14cDWt3lbe4+YrVaj\nMzJ6K1mFr49poR8fz1iWFfu7CeenBFTgUlbs5hm7OWOaPZEKQcmC5XzB7+dnfFyv2NaKqoKaZ+z2\ne8z7A+bdzke35VgfellLYieUou4kTc1QSu9nY0M1LBkJ8USfI6+qPSdi+8ebf7FxmKMxhSAVxc4b\nm3HtBOh1CEqKwh6EQJGKUxpJrSGYWh9xm77kfW8S9fpG/SVN0TytZFIGEgg0Qazq0wEPHQKjP+Ot\nTW/c6YUEgUU17AeDaoojVozm0iMVScWxMtB0c2PV8zkm+00RobFtRFQUu1KrVQF8iiZpEzgqb6oQ\nV12ZKsloreq94lmkZolR5qEQ0bA2BLjIHn2xnbSI5Tcq818sEBwNuxvrPJVA5APBA6rTTAqbojin\ntepyURsfF86cxl8SVIs30wLmvSId7zC9usfr7z9guTxjvVyxXhe8eveAPE24LisO8xHn/T3O0wrI\n3mxATrh7OOLV3R73xx1yMXDa6r/QYAllL+sIh3poBTb9se8gYs7oHQHQO8MXGtIXciyYiWINTSjz\nhP3pAEgysRbldWxCFbEywza70s4fimvZeUho5Czcl5ztkGk11Q0RgF9+V0L0EJWbPyJIv10Nw6ux\nPoYStfPgjFpEhkPLn5fh5xWtje/R+CxDtBaOG5VSIwEc6Kj5YAMzGVjXDRyabQfRei8fDjMuLwlo\nwG4qKEjWJ6Mt1r2vZqyLoaVpKjjs95DqPPemQJmR84wy7bA/HDDNM0opVg+QaLjccHrEY8oX7wsC\nhLa+2+axgraGbt6AQC98En9vyaSgJGg9MzjjzwlaYy90K7FXolBtN/SNXbdL50Cn6hOxFN7jpIWk\nUtAHkiTpjsH2UAKSl9MHG2NJwboZrZhSjy5TNjWOJxH8HtUVYBkUBgQAEqJcUlF2L3ReljsZQEeU\nUzi9Bl+DluLffQZv8hNkz6lqBbsfKNh/JXmtA2GFgyEF2iC/zJ4A5juMG6cz9MSpIowvwLYDDqpS\nP9ekVZuffYd9hE4eJft5Ja3lv5gUkECApNEfyvVQFq1nwXHeYXc64uHNK9R1RV02aysxWYuL88sF\n+90ed3cPePP+vY3K2+z3nU473J9m3B0s+s85+bzbf359MUQeCUT0oo+bUDjMJxN33ahRXUAk1oc3\n+EHWCjKUVBqUMiGVjLKbrOdH42ZwyiBUMTbdpVaX520Nxbl0cspwVLxxKG9WzPPeEI8NzQz5YpxF\nIRol9eFUCiQ2bwIPTg8xaZgU1EAbsqSHIOKJtRKqdjQqGIN1CY9hBnCrNQz6uq3OgXdNNcQ1/Uz0\nJeByWdHaZga5OPUhgodXJ6S24XqZsTseME87SLLy9yQKbavRTLWi7SYcDzMmySjTAfn+AWmeIWVC\nkuKVjHZvWXo+QnL2iMipCY8maLkVpjDpK29Uio0/TOEI2XBMYWF/gg5FGRp8qnVEVJRZ0XFwMuqi\naaw79/TYb6cBQaOxF709I4OQCfBkpxWaVBGfF0rtP0A6JdpH5AkqYQGhagh7Wy1qyk79eUUYWHxl\na+lFO2romFW69pxTOBybfmT7QLysHgPa5QZOLvuUAD4uKRyoUjrOaDcMA0KcSbuqIqUNOXlrZKjh\nstC2E90zegUEzZQvYmeqqRUcqTJPJNgI4IbzR/VWSh0k2XQyz2kkun+NBLMplYX4kpjMloB0owNI\n+PPjWgEwBZfvoUkaZN4BJ08Muzb+zTuNQr3WKrZ1xXJesJ43B4ANkhqmnLE7ztZI7A9eX6j7oTc2\ncokdN4mL9nyTdu9oSUwgvHQYG0MRt4bKQzCVvvNco520QooAxQ6ChZfcfIPumwAFXlod16I9vIZi\nWxsuzxc8PT8CtWK7LDg/Wp/i4/0Jr9+9xrw/YJqsuTx12qWYtzYnrshE0WoGQYnKlBydGwi2CqDt\nuumoSCrA105HeWdyvpxct32/B4aWMNyWBeu6omnFNJXgHas3LWvVkV+ZUKaC0+mE3TwjJcF8mnE8\nHW2SiWuUoUBbN7TVKgqRC/JuwjTPyJLRJKPlCdO8RyomC8ve45rdMVManm/dhkhBQU0484G2h3pl\nLGV0t+PuxA4HrGIvzZN18hOTQjJRrhAr34YaV6xcS5Ow+QPwHe1JZEYyHnFZzsN16kPBl/+EgxRL\nHFrbFIb/Xl0MUwE9Pj5h2zbMhxn7/cEqYZvTBuqqoqaoarNfkwgyZiArmqzomm8DRCkbNRj2HgrV\n2wlPUAmdvF1V7qAgKjJTfGZEiXRCYi0ImCviOql/tiB533sMQ0nsggyINN+dCSWxr9FgVP1oa7Pf\nWbJNX2I+qkfpGvsXAqQ8G3hCL8ITVSBsAdA8HwZSXUmQJxYldfbAlFNACZaAlG/P4w34Hr3HvO9x\n/w5gLaNVJ+zmPY5HDUcR/Zy0yzP/6PVFDDngtpFFD6qOTqQbJG9URF4sGkNJ/DB6eb55wWzxoyGi\n6AeSA9HZQx8XorcqHfcwqQwz6p1P7mGoNSRKjuISEtZ1wboYqtUtmfa69fBVRKJCtBcL6WgL7FrC\nsfi1wNn0G8Mh/H9fsxZ0Q0cgfs9hzJpzkBXNaTaqIWu1cK9uG7Z1xVZ53X3yvHVwTNjtdoBIJCAl\nM2lm5dTJJW7TNGEuBZM0PP76G14en7CuC8rpiGmeMM87SLZe0FKKl2wncCxcd1Kt7wnth4/ywZwF\nfWMwQuEe07j/iNCcykr+LE2XTwURP9tXnoffG4zx2XCARYpnlQKQpGZSw5Y0AIFIQpWuzeZzFUmQ\n5jhfugEwkGz3k4tingtyFkxzwbwryLmAYhLLhwzAJzmSFTpByxlQSZWg6P1yYrOArXm9pVacIwAd\ndfYY03caqaPaaQghCh72durRoFFZ2SNuOlxzRim+Bqgj5yTq5f/+YU3oscEjdPsPDSPu2Cj2D7l+\n61BpBUehhR/60tieEd+GnU4ajqpFhE6JASY9JUVlkbbfb+t7sa+JU5Vj9GKxhk198n0QSXK1WcVs\nqvVHry9GrdAXmREStOR7y3lrDhGIGx1CHQCd/0Y/6Cn3RE3l4AIZmldJ8oIFe5nTNLpCvSoNyp4g\nAN8p8WbcFHSWKSMlqxC7nHdYj0fom4aGZImUQhmT8fB0AsKwEd3YjHw4keWoxlDYhubFd8lhPyRD\nSIIk2WkT9Xaz1fpH6IbVeosF77etFctivZytNWzDmryQpdkotGnaYZ5n1yybC9yqO1sVXC9X6+II\nRZ4y9vs9jncHPJwm1MszXn6vOD89oRx2kHnC7nhELrMrk9rAp0rkF6IVQkQb4y7W4OiJ3Oi4bpDv\n8N8pktOu93Zj1Q24Bt0EVy0kpysiqQ04Rw5ILsHLByRNgmQF9OjtljMk2f2YsZQwjMatJy9jxxBJ\n2U4uJeHhzb0DAa8QphaaVbe1NyMDnG4wFOTr4kVH7jRUmKD1wyCclFSRszrwSagthY0UAeXog1FS\npy79Pex7Q3phXJvP9N38Wk4p+syHbVDbX2yyVbUbMItWcz8fjGz82iRRjuznq8lNgpi931kciMG+\nMFXAlg9QsuBDwzq2WHaAwDqL3Oy5pCRonqug0KBHPXQK9rkpJetCCul5J7XzhJQtBnGv0bY23NU/\nv76IId+2DTmnKBO2q+uFDIFUx0hP1duSUlEAC9G4yTykM1RnxtKGBrvZcSVILl7a3zSGC8CRWmXJ\ntYfaVhTTr0NEwkHYK0FyQZkF+5Qx7fp4tegJ4VwY74yPIgIDlWj9CcBDaiEgDooB8ApL5gWAKCsP\n6sHDegCeFOnRxrZWtLZBsn2/tor1YpK7bduwrCugwFYrrssV6wbsdgW7/QStCVkyEhJKyaFaWNbN\nStNrxXa9YrtcIVpRDgVP1wtePhV8Osy4PF6Qd0d8/ae3mO/vsDseIblEEpMcI5y/RdyXxN8dEdqr\nOcqM9raiUbTF8XTcN1zX7FWX0aPGkb+6vrup0Q46JKLjOQkjRSa2ugExQzYorkStMtSNDXMhxoum\n7nSorHKj2Jy+YlJNRSGMFiSBuM2i64xtYwGXI3hVqCcxaYy3zUGDKqJhFa2zerGSWuQqyH4uxREn\nqQVnGjyCttF2dq3LejXjWQV17c42oh/xa3LduyRuWDpGz1KpUTF06EYlpH4e7K2ea4Lz5QPQAm0B\nP5sRiqCGfl87HaR0/IizlCDWrVFTjz2iHYVfQiVF585KvfK3iHdchY/g84lgbp9sy6nbLMsHVKfE\ncmZvJqCiQRqMznP7YLYgjZfxT68vNiEI8Iwzw43xxETIOoTJnuQCeM57q9euVAFMB90NANG9DAgr\nZFI5mTrBD1IS4+1Ajtr52R7aN5BvTJkDDAwxp5zRpj7l3YqALPS0BBNQpUsCbx6KV9fZ9fdELmDX\nKYFmegjZm195mMYD4QU9NtGnoVWrzry8nLHVBU7ID8hVsCx9qsnmrUNzEmCXMBXBBitln6aMMplT\namvD+eWMrW4Q+MSTbIZn0oaXy4rqAWaZ9tgfZ+xPR5R5Z9GKI+IbpgudCqJTNnUTD3csGFgcdfP+\n2DsIY80eNqRtuPIxNLnVQI0KfzaZXLU4qu4RFMNhFrjEHvbnFwiRURIwIHM+P3NabXBUluxWaOp6\nbHNyGVHyVqsbmxYH39ZMfKIQ7B68nQP3u3rVcjjC+EuBxsimACaM8bU2nTeS3z+viajUDiVSssHM\nAuPdicY1zrb9Uc/uF1eZ0Y5b3gmQyuSpR9AApql4/ySjKmPwdbTkgP8+ve10yVyD/waro3DDwajB\nz7sMC0JpZnTDhNtx0d5Ww88Mxt8XRYUae8u9qzvQYfkpAfX7bm64wc/259KHjPOADPK2P3h9EUOe\nczbP1ay9pd8fOOaNYeTIeCq6caP3Td6ciVxkDApuiEXn9Bxxb9zQ1SEpZ0hUMvqGpqqAB1+YcDQV\niPU6LyilgE2nRASpSX+feguN6oerKZoYfRMGKgyz35/ZJlitkMkCIYKsXhTir5RYXtyLM5i8NJTQ\nsG1bDE+wvt7A+eUZ1+UK5oCtIrAAKt7recW2LVDdXHI3Gf+fM6RtNgtgzkgZWDdD7U+Pj1iWBSUD\n79+eDO1WG4uVtMLUQnscT3fY7w8oszdrQjdqMhxM0inBybshD/PYz25I8DwV5IbdueZs6K9VHdaN\nPLY7t2rVqbWu/qypV8eAWrnXhghMEM9RkhlXPoc0GGqbRqQeTbUeartT/rzvTSTjRMCIXxw1h4Nu\n1flia388Fat+rlV9jzCBncgMAAk+OBtu0GkgWkhNcyq9fQSq37ufq2SJQ1IlLHxLCdZbRyZDovDz\n3Oze6SjhRtCu3Q66JBZt2HPkNC463uyNx6Z5wjRl1FWg7YqtVvRcmUI0oU8P6wY2KZPFvhpJw7GL\nK5N6dALw+PCZ9NIMW6vEVXNKC/HJGCLvOMn+x505ow6PAI0OctrNr7eGXaOjQZznbiLMTnYbePv6\nYv3IbfMh0DPci0e1GxqS+GDbbBtL9famicRsSAco1QAAIABJREFUMX0qOob+1ArbeO7lU069n0jj\nUdXPrk3iQNG4CJOamvygedk4WhTQBMriZHUAEE6Edy+LvnEisiQucQMWczuTN5Bi4QYQNJSthg8i\nULmZvMOy+uuyGGftFaVbtT4z6u+xqMI6Gm5LxbZ48jZPKJPgsN9jvz8gpR2aXrA1wVIrSjMt+bZc\ngeWK+vICLQCwt4pTCK6bYj6dMO0P2N8/YO/KneSac48pgkLweNtPjKC1jnAEnCNKugDBw0a/DKFx\n7AaTlFv2viddWuhTPRN6gU4PpB1xuxSRFI8nqVtraFvFtlwjOjufz5CcMO33KGXG2OhKkim2++dS\nvcKooE+fqpUgwYqa2MXSDETPh9S6+fpkzDkZzaLWlpeDNLiHjZ5g9CWoa/Iqzi4ksEtjI7rU0TSq\n72lGcO5AhUnyTuuoEm76QBVJ0ATvq2I/04SoVyE+u9PUTQ05A8mTspReqlqLZGshI5DMIqnhHAHx\n35KIdFkAhBunGc44IlG1EyS3a5X5+3mt4o6GiFwQKLsrWBit8TnTpjCp4JESv+qtARSwRl/0CrD8\nimtT+88DIUWWyN/dvr5QslOGMMGONc9SEyf8kyVpzDmlvqHRkysQOKoQaPTA5ss9vtCQdlH/6PGY\nyOqtcv0a+5V5CGZIiOiEzYmI8njQ7P76IeShEozfxxA++9UKPPHXN4lFLjpsvp44oXFT7c2uWm1Y\n1xXLukTy0hKJYv23o5LOtMfX5QoRq1CdjhMgNodyt8s4HPbIpZiRWTa0dUNdlohgtFUcDxPamrEs\nV3z65RNOd3fYH48oux2m3QHzbo9pt8c0Tch5it9tGfscz8LWOVYbgMYaRjrL14pJJtJPlGX5pxIL\nxdMz40YVky0+MT51zjEshAAg+qv2vSRxwM1p1m0FWkXdNpQ0o7hmnPuTiS6T17EXe3ZHb4exNYbm\nHRFKashePWkIDBH1lalg2wStAutiAxytp7l0Y+rIORB9sjW0dTOHZmvA4c8tZG12y5Z4FSA4357f\nSXEWAAc7vme53xnNaFL0omsJaaGdyYCqoBVjEjD1A+vGvJ9TpIQYysFHJAmQBo0eMeifTcmii8AZ\npSOeD+IahD82ULrJz3fqOk2LBELuak7jRg2niLWk8+t7SSK6MqeZbJ1uNpvGvbOFSezN4do+f30Z\n+WGEJwkiQ1jBsxyIWKFaUeuQYJL+4FPy5KPTJ7kUR61ehQZxtYB9joXaPTyKEN4bGpmBVDBdLHQO\nRIye2SKqjbAp0Jy9NQyDX/OIMm4UKkB8nUagsdOb9zSxJG+NyTDKCC96qXQ1xLasuC5mxNdltWQK\nqkUOWpFyxjRPyEmsQOfSsDtYb+Z5N2PZFiQB5jnbxPFasbxcsF0u7hhW6FIxTYL9Ycbp7gSte+hW\n8fLxit18h+n1CYeHV5jmGVMpkdAmUqOCIg2I1yIShHPVNNbOWySS/GSoVxhqa7EmWbLvj+E8adfR\nayT7/DnE4xFIylBY5MIh1wrmSvzamnpBpkSyjgcy54J5mjFNNm1K4X5ArLS7tWqVl41FWuT8zRpE\n0Qy3mHuj5LLKGjkAM+SQjFUrluuKphtmFMy7GZzrOpbnC8SduBvm4qfO0W+rKahN8OhJNsmfahSr\nsXoyaTfkQgoAfZqUgQ87R7Wqn0MAIE1mf+hyhXw6VSXixoqIVOjE1PIWkhATlqAAetuEqj6zFNnP\nd+1I3dtqiC+wVc6yZkBu7JD6uXUr4wV2Es5W6OjpzrjvApX1s/xHZx0OyKAhUAQBqv2r3UQN/P3c\nV58hjHh9MWpFpKGJOkfWO4/1Qg97WOQyp2lCKcN0oAgLJRIrTDTdJh+4QLHmsRYtDDpAJGj+26rn\nWlOkbD0OgDSI8RVAHx1XN0vrUV2RWKWmVn2IpCh0Mv77UuJ1DchcNcKuJBGUh+6clWNwhGYb0/tQ\nt4atGs+9bos1vdo2p1RWZJFo9FWXBSUJDm/v8fDqAce7I8pc8Pz0hPPzM5aXM9AuqOuGy/OC5bLg\n119/w+8//4r2tOB42uHh3QPWr95jnvd489UH7E/3uHt4wP50Qi7FBxj0XEJrNkm9lGycYjwfDBOg\nbPOWgDz9IGhs4gbkXhzS1Gm13HMato/s/WaEmofMRMKuv5SewGLSkoocaY6sPJAKY5YTpt3sBSLO\ny6ds0RoBvYMN9s0vxZQqBiY3sGlc8iSjzbAdk300khwj6KoGSUhJUYoAmm18nvce6bUHKaK/oJI8\nEtPIl3nrBaghav8/RinagN5gqjs0aswt0sxxjazZiJDz5uxZtECHpf49tp82etWcrUW57rRBOtFH\nF4Ln3QBggD01Ckcx9epbPya8f3rIhuqUjuVEmJM0CnRUlvHgaf8s5TXxjAv6mzVkiaRduGcZJYzi\ngqk4wEvWElml16oQXNjerbfXwLzDH7y+kGqlF/P0pCL5NM/fh8HSgFBEAMkNJrGwOrc6RGWBkkPZ\nEQeM608emyGTxINIaobazyPsVLTw0PSYgCslcu6ORFxpQerF0Qs7OWrjFQxhkta4XkqR+nr4hmE1\nozcWqrVi26yk18atVazrgk0tVJ6mjGXbcL2ueH5+wTwX7GbvD9MUp9MBbz68wuF4QkoZtVYUUeBy\nxfnn33Et9nmX84Kn8xX/+Mcv+PXvv2KnCdoesD8dAcnYne5wvLvH/njCvN8jT4b4hYaWoMwPFZuZ\nBcIRhmGIzUxoLY7SO/4hxWGO0dQInlATCUfLs2tKA39W0veApjQgV05oEttDPJCxd+x5aThLo6tM\n1cTCHO8fHvSEG9bB6AkSrBNlC6QazpzrARof9P0a0SmvxDdlgBLfzcn6yjCq4CI0v9bskJ+BTxJF\nk2b9atxYM/nX26jZ4er4VOgJELSYWBFLo0baLsZkjKJRuMSFkdjTROYuAPBIyNTCA30KWGvqIer1\n5iiQzNoPWOJTvcwo0SQP1y1DtSc3SaBl6XmQwRAjdmZffZA64qr4/6TBkMe7wwF1+snygL4kCUiT\n9cppPsDFBs+YyMCiWEYoJk3UentFfH0xQ85DaW0siYwQmmm+TLfbK+KIzDonrX0TCuxh+YLxWckI\nw2/QORcZNxs50Af3An93/HznueiIlHpex/SQ5JWBPdFatxrFBlYkYrRODQsvhrZ8Q/iX4rfWVlHX\nFa0ZF76uqyHuWrHVinVZoR6mT1NBuqyoq+Ll+Wra1tYg0jBLtsTOLKiouJyvuDy/ILcVy/MTzr/8\njrWesdYNS93w+HTGx7//ik8/f8Kr+6PJyPZHHO4ecPfqFQ6nO0PhbEUXJ1cdVSD+tNoGJR+HObv2\npFnbBFOH2IGy71Fm2vcPu+YpNDrCUcZH/pTtfhmO0zix/4lnYFzGN6hpiJA8MqTIyJynqSTMUKWo\nthPxAhNHduyqR17Z7gHo+R6NnAirDBnFKRvKCY2971/tjgKBH3VwWuIFJu4UmikjKLe1LoP20y0Z\nVWJrw9yFF4m16gWUhsEdUvlR4NkaE37WmTMMnfhg6iGZK1S+RDTp65DRo9hm98T3KQBUQYu7tslA\nWv3fyRF9s83FpKZhA3GFliNt2hh4LsKjbtoNVnCbqdAw1mGKaCNoM5pFM6DNoJ+R4WcGhxBzD1ze\nzPGHTY112FZreV2bRWrTVDBNk9kREQMrvUT2n15fiFrxTYUCUmdQNmgC3dxNqDoWdozyMGp/MThD\nhlfc5JEwBL/v71UzGraDXQEDE/gnHiJVsKCCB5Q/z9mP7KkB/5oZam/4730zcs5efWfVYEbl2wOy\n1qrOW7JSFQA10k2bd3O7NSjVpW+SBLoqnh+vaLAE2HE/QcT6upQEFFHTeqcEtA0///Qz/uu//45S\ndhaZ1IrjUVBfzljbC37+6Rekfcbh1RGHRXBIghcVpFxw//4dvvnbn/H+u29wOJwwuyKFYWRrxsdb\nRSrQm8M4ZaLohxuk06rLAgU3eRPfL3zObivBfjvwA5dYVdcapOO3iJ6sstX3jEmaw0iaQWJkaJQB\nS6KNQusbx8rjTVppHLjvwjSACTjacp1yGxLpKRkyH2fNGupObhQY76uvE0BlTvL8T5IW128gqKPL\nRI03zCFsGznXDlIYGaVkrRa0Vt+7joyz9hwNT5mUjirVnpftT5637kT70UrW5dHRe/OCu5SMeksp\nGW2UzJBbpapGboGGtLFlhAMD9SihrkBtrBMY1CpwABV2oQYAbDYvDoDtT6H9GW2Do2buC0qYIQL1\nDK4NSjHgYi0gXDqZKEWUGzELHKSKg5RWK5bzhu3xCm2bgb1p9jwfr7t5Wo5OnQWD//z6Qjpyb44V\nL/p8boKOi0WMbhgnnZu3pnxNA1F3Yy/9U+Ow+CHzM9K8P4lxdU5jJAl+TuEFPS2CKPvMNoSd9DRE\n/f7gh4g8UAwrEHkPdpisHWqSroWnFh20fX5wWu29wkna+LGyLn3ZKi9fns+QpDYhLQG7ueCwt37g\n+/2M3X5Gu17w/PEZ//1fP+OyrACAaco4vbLvvfz6EZdLRX0W6McXlNqQ5wO++9tbvP/hG3zz1x/w\n9qsPOBxPmMsUMxDDqHqC2WaUEg32jYxhHaA6yLySr58GOu3rXi2h1b8UPajjwyT570CM8SNKDDmp\n8hmaf06ftQUlX05JpyGh/p6xylRvfq5HFhS5m/QxeTUg+W/xSIKfYXtIxBAsn2yKzTP8ErFIzroy\n9sZidl0p1rmjx664YDGrAZLmCU1etw473OWDvpd7d8N+rgz52vXbvrVohg3D+OzMuPufaoq01irY\nkjX7po8ckJ06N1o5opJxTJ9q7m1iQDCUrOcOqzmlK4PMudDMuZTVvmGwaWjxTJqEa1ib6c21MXHu\n8YkA0ixxb8xvi3PO+3YvwODanDELiTzavjwv+PTz7wCu2N/vcffuNWYvlmM7Dqt7cIpsoA8/f30Z\n1UqQUL5i6sdiCEu48fuP9F2t4NQONjiCoxcPx+HGdwh3wtD6w+zl0YAkT1rBeh8kRjA6nIghMvDC\nOcAdzZiISd7nHGN46IhQW08oGbpwdJNs2kkYcr8faytgh6NumzkDNt8f/hSxCrhpytbPWDe0Zi1m\nd3PB6bjHNAnmudiosiJIdUN9fMKn3z5i2TZMu4KnTztczy94+v0Tyv4Ol1VxXRrevX3A99++xw8/\n/ohv//Y97t+8xv54xJQnsFoylsufW/NhzwJA1GoBcsqel+jPnkkgthO1Stgeichg8NjcqjfUUt/g\nORQQpGWSeM+WQPWjg9VAjxj2iT3z1hUsHmqbTUgYL1375op7t/YNzsX6vjO+tluelPOw34lWbYM1\nMPeiVhAnRtMR8QaNYRutgxrfp7bnByuXcCOrHQuujD5q0dcGgi4RjB0ddseMVWbExfavbDYQwu2g\nGBGO2ROZQg09VTrp5pmoO0CBMxaeV2KPdT6f5teiBADu0HimCAhJo1SnQO0DTAUkTcFWBqZSs3u2\nYjKe817TEUM1lHUrAJlu82ekc+C0noMK5st8r9KOi9utbal4eTyjrs9YtxVpnoGDYt7tkKap7w2F\nDTD//0l0Al/IkK/bdrMx7eFlQDQawkdhB1g44Q9JWHLcsygRdidxL9vAsu7s3Q/t4A7d5xLVLgDA\n9/VDS54tpUE9AkMpyF0fDvjDc3RXihc7OSUTMxr9uu1gOgfnv4PGhOhMXQ2z1eaTbDaT/lU7KOt6\nxfW6WHm8b3YAmHeC3aGgtoScJpuukxKOOCBjA+qG5dMFZVbcv97jx//5AXd/z3h5esK2XLFezzh/\numD5uODp4ydI2eH+1Sv8+3/8B/78tx/x4ev32B8PmHezD3zIN6gDQ+gNWEQgcFlcg83WpvHGoAAI\nowr0YoqOEYngarOByNV5V/pLEXMaObGiz39eDeEpmtdW9IQ61CfTN09YQ1w2aEamFLjR6XI1RgtR\nyKMtJuTY7Td+kht4u9fs04WYpGflI/cOeXJ6lN7H2xE917U53+/Id2TMrVBIXMnSc062Dw0l1wqs\nqzVGy3xf0EsyLJtZJAM5fu4SwC6bNFjs0c0IjO0h+vPr/dmNSvE15bcB/yyN39mGSIpnOAAUz5qD\nq5uCpgxLBCqj6R45qz9Xq6Ng5aY163LRV0e6irg/y1E5EwCj3Qzt+x6s4u0zSOuoRybm0BkxRf4B\nQ/5GgN39jNffv8bvPyk+/n7GT3//P9jtCh7e3uH1h9c47maU3QzkjO264fx0wfXlX2hmJwDCNwAI\njsyQGashZVhUZpLlZsMBXV+p4+e5e1TAk239YJgEyzWyNQ19pgNE9zYBUQRCA0XnMyob4Jvef1/i\nqe6cZBu+z/mMoGcX4wXjl0OjuGfbKtZtxbqahhtqQyAuL2dcLpcw5GUq5ghbw+l+D8BL/X1N5nmC\nrg3n8xUff/odKgvqdsX1fMbH337Hcr5AUXF5sRa396fXOL5+hYcP7/D+u6/x/b/9iHcf3uHu/oRS\nJi/Wchyu6I40Iqg2BlvWz0P6c+EpJvjEYJCC2iC9xG2glGaZ4YcC45G3X0OD7d9vPlBa6djtvTYO\nLjgJX/8WSJMnjxI87seuJ3bn2aim6Eia+2HbNpRcXP8scb3JjYvIMI6sb2hQb39jWGM9FGwnYQar\ng6E4I0rAQOqEhrc7V0ZO3HJ8NkSi/iTCAEEEVHSoo1jdKmrdrEVDmVxFVIbf48VWtWHdNrTUnBMf\nDObw3OP3yPCsBcO+Mdyfingfkh6NJ0koOaOh9cptRkyxQ6QjZ37VShK8NqAN+4hgz2pOgi0AnY7n\nPwIzeA4A8KlVXD97ThbV9eZ4dPaSM/anA+5bw3w4YF2qRyMZz7+e8bI+Ync3Y/fqCGg2qfN4W8Pr\ny6hWPv83N5HAB+w6MtXezY/v6393b2wbcjgQEXJa6IjWNxaAm+IiuUERHgYCIOdoz1CHze9Owjci\nEYeFqr1EnwnZEWl09MXqMLvmyn4zsINUvVdKbQ3bsmK5XrFuKwBLxJ6fL3h5ecF1uaKposwF87zD\nYbfD3d0B01SwLhs21+UWmXBdLrg8X/Drf/+M8+UJy3rBtm04//6EZV28jXDG3ek13r/7Gt/8+Xt8\n9cO3ePPNB8ynI/a7HeZ5QpIpkE7YGsEQApPttPeM5dF9kAhpJcTz4EEbK1b5in45wj4ZbtB1eA6+\nB5iMgg4Vr17dCrimOiVHmK431961UrVr0jE8PyUEBf/2favkv/3X8hlviurj5boTVwDch0Shre9F\n+rm+G0EqkPsH3IF8P282YO6wJ+lolajUokYW89w4VwaGdF6py3PHX2NrZknHZVnw8vKC3W4H1Z1r\nyzWeJUFMrZbIzjlFdBKA3J/f7dlG5/vBPeZFZaRQtBtycVoEOVmr3gBfvl6et7Lf1WK/2CPV7tP9\nflXhxb0pFDpE0s64oUzJo5UWgDP2C/R2/f2z42u+LgJBmSbcvXrA6aH52lYsLyuun65YXq4mEd0X\n5HmHNCUU/WOT/WVK9Fs3dr38vVcwwT1ek3r7c595e6Abk3ECC0Nae2/38rX1/iw2NDi5BFy68fBV\nZ3LCEIQVMFkXRXtfbZ3+MaTBIgEdNKWIB8udYx0GNzAz3WCThjo1pLiuC5brYk2t3EjU1UY/KYBU\nCuqmeP50wcvlGYfTEQ+vEu6OJ5Rpwv6wx8N9xvPFHECSDedPGy5Pj3j85Rd8/PgR1+sVEEFSwdN5\nxS8vL/jw3Tf44c/f49//13/gmx9/wN39Pco0QxTIxRpeWV91QzEc6hBrQKwhToM4DbCuRn3UbIaQ\nZfZs1qQpgcoWo6V4EK33hg1Q3iyOCupFY92DO/ev28ZAPExVwVar9fQglUC1kk+1ZySUXW1jstjs\niH6D1s2NgTtdj0CCgxdWhJr5VXCMXu3RJTx/4wY0sxBI+kE3hRIpM8blGveTmOSk4SV6BIJma0ME\nRMPLNTWnm/rZoEOrQKv2bKbi/V5o/Ogwfewc+6WXMuF4PHqxXnEpKSJZzPERpUwxELy1hlKKn2eE\nYSb9waIuqmAsSuIYuxzXzLPfmkWp1oXTnNSUZkAoRZRwMKyaprOruYX6izJoCKJAhwBCm0U5tQVZ\n4wbf9fdeXcr9ZMNK5GYAOh2k2RqYSi6Zwmyeip8La/c7TQWHhz3Q7qOzqgLIc8N8+Bcy5EBHpvzv\nMObkHACIZDcWf/wiF8j/ZmLQNhvf1Y1N6NcpFSMM8aPHJFhzNYsqD45/EkPQMAK3oW0lahPYmLY2\n9sZ2g+5GMBKEPHQwdLZuG67XBeeXBXU9Y7fLmOaEMmdD2WtF9epAUZsiPk08eM0HQ1SsSbCtG5bl\niloXKBpyERRRXF4ueLlcbFLPtMOrt2/x1V//gh/+57/hT3/+E7757hs8vHmDedrZPaqFxUm6Ttw2\nOpNIipaYiIQ7zhbyvHUzbhuaMU0F7Ptj6JDd6TCgGi44KycTkhRwIDILcjq91amNnKUbOncqqPCJ\nToOKgLtD4KP3bL4lC7I672B/kdMednAcah56/2dcDw3qtlEv3iVqRPwdyfdCqZHGSUmg2RQSrdLY\n0cj3SKcbwQTV2hU92o0ZB2IAPSK0ysoUa54D9QoYeTDK4EAGRo8pZZQC76XDHAIg0kJKGB4HPZJm\ntDXeiwircHnGhiiYzkqYs+pgTSDOv1svGRYGkh6xKMCTtCn1fu8gmCsxM5h/mqoNu6A+zOsORL2m\noDXvUGnUi0UyLQqSAJhyLOoEuGI9nmOfdzpW89cCeDK61Ya2KvKcjM4UCUv1R68vWNnZN3w/N+KO\nraNtJpjGn+XfvVjCF1WA5H2eU2JjJV/EAT3Dy377pvIH5GFyGzZPxFT8nTIaEH7dh1Iwq13ti4wS\n7E3oxgE8GPx4OzRsX7tt1SoyP11wuptwn2fklLDC9dcJmOaC/WGHSYHj3RGH4w6pmFrkuqxY64br\n9Yrr5YLrcoEuZ2hdbb6gAoKMebfHw9u3+PDtN/j2Lz/i+7/9Fa/evsZuv8M87wFXkDAJaOs4rs1g\nROMQ8vk2D0MlwlNKqFiSnwYjzvNuAq/uyNlnx2gVfq7EM4EG2cWdEAfSELj9ni0iJ9I4CGNgBzkh\ns8dvPNxuQFQ0pKdsXtZVVXY8O2XEfc19QsOqKChDu1vuwe6IgKGSczB4o2qLC23yPQx0v+3x2qqj\naPK5tua9Tz/AAdwsU2fnxFtKoztnntOUrbUyw0PJySOrrlyxe24Yz00kVUHglaLFQI7K7rGxna+J\noOvDZeifExGbfz8l75TYHU9fY+4L+wzLOdi+MACUhvcI0KyCkg7fkqmCIhkte/GfNIyOqWJDcodq\na6URHY2Oy8G8rbX3czJA76o533ttVWxLDcFCSZkI8A9fX8iQp+G8eFEBO6LZCblBWwAibDRUSGUv\nwxE7SOati3tnT3Sh9qoq6LDhukftNtsTXoHGNLylURwmj7ON2RU25LaM+3bDlZiYQ7xng0kIt9Uq\nMeHe2JoTsT809dgNy3VBrVesW8HhYHKkXDIOB2sLezztobphPh4w73eW4NwaluvFq0it3/ZyvuDp\nl1/w/NOvqNcVp90Rx9OE+/fv8Jf/9e/4/q8/4qtvv8LheMI0TwOy85A7nlMNA8YCHIDhrfUvsbOX\nohTf2rJ6P5AEUDqaPKanLr5P7VGAmnQNr9MLVDR5V8cgNcxgK+VgRFeASInqyZRMs88ojOvd2wbb\n/rOJ7v4e3wuKDAXbIKyodQtQUEr2Xt7FKm23LSZgdYRqLRySGEK0EYEp9kyPRHyXejWrONILxO/O\nh/QgUvEClA52FExo0uF05UTy6T8iwFZXNDUqi5WxppLJvl4OsLxBi6BhLgWqGTVv9nPk0yMqJo8N\nEy0ERVEHp2cRbS62L8Lxwq6xbRXrukVew2S1xSKTpli3JZxKLsknYcH7wo/VmHyGjCzc4NYWNR3W\nd8V+t4ktZIi8wsOBDmOaCjCl3vPEb6k1xPUBBqZYLJh9ShBkyDVYt/w4L4Gyhfu/AakhTxK90RvU\n8jGfB4b++iKGvOnmnl4g0bGZ8xcRhwSAbypH29Rmwpe5dZH8mGBEHGSinCFUduUJZKz8su81Zanx\nEDj797r8qKPx2JrCZkOsoruljehUWFhQq7U/DfST7KDXZka+1hWCimm26zek3mIDnV/OUK3QXNG2\nDefHR1wfn33yDkxrvl5QlysuL1c8fXpB2iru9ifsvpnw+k977F6/wqtvPuDDt1/jzds3ON3dRwOn\nURuO1Fxi1akB/uHkFXMwNExOH7QUVFmiQUtdRx3GCO7YY2/0Z2Wcbt+5t2HqsPkBhH48D4oPDCGv\npPgZdaOKJGDlpDmEHH2iG9EgQUdT0/sXMypGLaSb30fuN6qPIzIQiBQ3cv3+aawjSnDUPQ5pjr32\nWWtdVfjwEAX12DwLSbKVvjvCC0N508BJIJJRikBKSFk+O0vZxLK+FnQ6PJt2XCWMoTrKzVkGHlzN\n+HtdAR09r1+Gc7hVow0VnVsGbHAG+1WM9BOgMUDFSt6N/rOBKUx2qzctG2cZwH+WDlksVSEpksE5\nFbQs2GK4cgp6UTKiUZjC20rQ4QnlhwOog2J8kUa2s9Nu9mVL3bmZTSwdnKRRGXb7+jLJTmb9g3+W\nm43fH6K//EDxfeqeMo609BJ5ce+Xhx7mgfqi93FCjKtCp3ZY5EmejLIvC087lWI/Njwe34xtoFMq\n+6Rr9Sy/XbMls2wYcoIlG5s6QqwVdVlQ1xWqG6ZJfFCENa/SrWFbTY2Six3ubV2xPl+dT5swzQlJ\nNuD6jMvjCy5PF5yfNhyOOxyOJ7x6/xUOb97i9O4N7t69xfFwxG6erTOi+BzVlPyw9VCR9xWOM3Et\nbY3zZygwFV/diLbAmHKgHtJgOPr6BjId1CFj3mJMlg+PICK54DqpuW5t0JejO3I1JM4JQTQ+jVQR\n8bs/35yzUxRlAAxmUAg4csphiLlDhD/LIp+mvi4dhCTJcY1VttCq88RwReCHPwBG3K+jYSXqtqEN\nnR/w/yQ10XhuElgrwfMA8ffxzCUgsZHJMItPAAAgAElEQVSV/84spCudf1YrVgMG3js+0daGyU71\nHi/VnTlH3lVHsvD3C8zh1a2aeuPGyGkg7Or9hpJkoCQU6cCDz0ahdn2kJ1R9ipOtlQbl4fmDlMyG\npBrPkFGonQ67hup8triRZyM02x4JMaWLe2EwagR54xlLarmcXBLgnSxtnyCc/h+9viC1MuplyTXd\noq/mJcvJi3DM4I6Ilz+L8IYENvbdhJKTzcqMuhzf9H7Qu1qEVVy2WKkB1R+4qlpHOIZjMiIq8cw/\nOUeNhIoVr2xutHqfZ9tcmxn6Jl7g4oqW9YptXezaClDXBcvlGbpdcH28YL2skF3GvLNBDXVpuF6u\nWFc7RLtZMWGBvDzh8acnXJ83QDPa23vMrx7w9i8/4uHDe+xPJ5Q0YSpT0E02waf0eaTNEkgMtYP/\nbQ1VFZs3xCL1tK6rJWmyWrva3I0tKwg7f2k0g21MVup2ox/5heQIR40yMMdq3CErCsOIkw5JPWqK\n2ZzaE4I93E1uvDtNogqnhYYIQBskZRRGhvH5twg6oshmA8atXwgpCjbXyn5NTneQsssCuLySZ2Gs\nLmQkpw3oJesIdFpKgaoZjlw4T7YbCqMZuP9a/H6oGc+cxSs3xQxnUwgyymSKEThHa/t6A2jOPBKV\ncDDWkbJWDefFcy5itRHaNM5Wl1X6IQZzJ+zN0h1xFIb58CKJPi+mKjP6zYqiFAmZNgEDQmbkIYwg\nDH3TABO1k7IiFdVZAo1IUxVoG3A9r5jmgjT1ilXWWrQqVlvi9xefowSLLNbyEYdJIMoIvz/j5vTQ\nZ+A+Xl/EkNfqCynqI6CaB/Ic5zVs1CSWNa4ckotAyjZ1m6hmLCKQ8GQg7RGNmwB66ZEm0eFv8d+b\n/XNqZXFJL2+W/k5HBbcojLKoZVkBMane1ppragU5T3av7rVbq44sjGNd1w3barrv3/7+M0TP2F4u\nWK8LWhIcX93hcDpAteD55YLz5YK6Npz2BQdsSE+f8PRpRZr2eP3dV/jqbz/g3fff4tX799gdjih5\nckNok0rsXLEpUUO03UXf9BiaM0G7UR4TjSn3kXpaOxJNSiWBOLVQCaNh9BrlZhWUhTLZGvI7hY/c\nyoFi7Fo7IJCa4lnwsnprYAFgA7cdOgTCUW1YV/u8batYljWSo8mraOwzFSg5DiyRoWq9MbojAuvJ\nOwBIRiFsJnVs4TDE0Z8M1IqDHd/fjGVvIkPpFaPw30WjQA4+hRHiuRLnp+k4XXW1ebK/muOk9Nbu\nyVU/MGcUKFONe7fBExKGmWj9pobAz3clLaHU8ffoxGgo+7cmb00rcnPP6vvPUHjyM54jYrJjPdQj\nSE9O98/SWC8rJHPqwiOq6iMWUy6255IgJoE5UtRmzcumkmxWQmENQon9CSdSzGYAKhhaTyBsUE4J\naWKkq7hpqgYr0d+2hjUklLevL2TIrXqziYC9CABAUG9DZklOPXQvGsa3me5VJfkUanKqEhuXh2B8\ntWEhbpKu0NhsDeM13PApvQ0t7HCRYyUaZwhba8W2blg3S7IpBI0DkVVRptyvjT/fqrW5RDMU2BrW\nlysef/6E8/OvWM7P2NYVTTLun19wenWHPB1wXRZczhdcXhZcS8EhAfttg+wOOH34gA//9hd889cf\n8fDuLaZ5hyyld9sbuGNSP1BA2qAeCYTIzS+xHkSNPHChffbnNZaLswGZOYeOLlR6RMaIRYbPCAMk\nbB1MjXMd3u90kDRfPnfWoeigMUuorP4EQ/p2gwxbJMQUmVNohuZR/HMLjzrFwt7loxLFjF6z6sNK\nTlejj3atLfh230a+8p6MSwJrtdsrB2NSjki/kpvt3n83nSgNd8o9L6GOpmlcGdmmLIE8GYnx2Qay\nDZRpe0l186I4Bdv/5WgnjNtnDE9ou5Mr5KFJTyTAFNQEOxq2oN+bExCpOzMCLtKjlPtybSMqHKix\nptbWlyuu0h0Pc3QR9UO8O6K1U97tCiRna22cRpbB/goK2QEg8Uzi3ndKN56LcJ36c7Y9ulo9yR+8\nvogh37xKEQAExTeCZ7pBb2fSNNWEtrHfNKzHh3NHLMBhJr+qZ7PRNbXwsF/EQhb2k+6b7zZMVhoF\nAXBTPm+fye6EhiZsw1QvqW9xAhXbavI/9p9e1wryuwZk3Hw2hVYaQyBPgsn1sJr2uL874ffdjP/6\nz094fPyIWjfM+x3WVnG5XjHdH7GXjF2tWK5nPH1suJYJ797f40//46/47m9/xoc//4D98YRpmn29\nvdEQ+21wl/qhFTEEwIhDldPLvQhmBOZe/FEZwnvC0KYDJZQyctWklDyEppXVrswwOqJCmkQrYUNN\nLqNL7hRbC9UQ9wYlYikZXUV6w77f7y14RyEC3zzkhw+JLphT6i0QPMHZ6T7v9LeZMbYEXwYbUhl7\nYW1wGxDOTdU09UzYRe+SJtigyK1XlIpPUaKhtqX1Xh9eqLIuCxTmbNSNQOQ5YLK+Hh10RyQeBZMb\nt8IVhCHkf6chVwK1vj8YHJnZQKsPgAqSFEBrtFcmxd+FC82Tk9Xv0aQFlZFu6ZQrk+Hikc+2WQ/v\n6glS8TVPzHeJVVVvrQFCQGi+JKUcsmGjO43eSbmEkocRdEPDNDmlkvoeYYQndKKVXHbBNBWPXv8Z\n7Se3QaRgR4dg/kyxqRWbSeoFW5bj8QZ/mwOLpuhV6LevL2LIl2VF75SxhhcWekAfumC9jbqhppfL\nQ5tK0xfDhzPQSPocQdgCZedqBcAYttqYsI7+B5zpn6ORbYd29QyACLNbaz7Uwf5WbD1s9QxpgoQx\nr9Xnr8iK7bLg5bcn/PLT76ipYTpNmPc2aT6JoCbF/lXBVz88oG1f45d/ZDw9PUGToK1XPP12BZ4e\n8fbuDqfdDqd5xldfv8Xbr7/C13/6Gm++/oCHt29wvLtDmSbkNEEkx/VIZlhujmzsIWNOtMWBInrh\nczGDZqElExhEveIN8GutLi/3ggogHKE9PnLjvepPgWh0xnyGKIbuhs4rsm/FkDDl0xNxjXXOUBZ7\n8LpgjZMg7hgkA1NCkuqGXyFakZLROFDBulYvTOtFPmYb3XBbdszbLRvHvLxcrK1CEuyOB6RsUsis\nAk0Z0Ox0HmlCViL6JHkag9bQQurZo5i4d2hPwglg3Vz9GpMgC3ldhANhlNO7dBo9EoUzpAkbW2SY\nY8nBKnS+vVM5/awEn6/mXMoQreXcjZwdQ9NSC88LjIKLAr9BwcNcWWjN3YeEw/P7tGt0wytMdnJ/\nZrBtArgW/v5ITKpJRDOk/7x6fOp7LZfuHBznxXVI/0dEMD38BCzCGNAQHMjmFC2h7fIak0uQpChT\nQsoz/uj1ZRB5XW0juFxv1LdaOa5NW1Efd2QifkNCtSrgaJtIkuiYa8bmPr1PQi8eGqJdsFm8JRg0\nFl3doDBh2bzKIBJRbkz4Xo5dW2tFawtUXQaow0P1kNs48BXb2vDy2yf89r9/wn/95z+wpIbp1Q6H\nuz3u7/Y47CZsraGtZ0xzxeu7Aj3vMMuGNCecn684nxdsq2LKgtP9CbvDHd7/6Xu8//47vPvmKxzu\nTqYvzyVUEcmljl0x4YdImASmuoNUQ5eCBcfoBlwhUMmOnKhEYgir1o2uaSC/Ecl3rrLLr/j5RJYR\nLfn/iHRKg0k8YZVkhMjeCQk9OeVWohstt2bJ0U9MZRLfR/AiE6945P5CUCedIlCGyrx7GXhtz++M\n7Vh7eN8RPveGqk/w8Ta8gCmhRrYk9q90uW5w1zT0rMmAnR2BxNrRkJPSaNB4P3+UBnzszmn0UlxF\nOAOuTfNzYOuboBlBNfjSmCMj514VzROUvFeCKhr1XtYO0NhBBKg02dwdna7gPuLN9PXyZz+ol1jY\nNb64R5vTHSnsiwYtklLyqtDBuQqvO4HdJkXQqVj5PC/XD4MQsYtgfNrk7+06rWqZxXSfv76Qjrxa\nUcxSvdxV/CIzUmpISZFTRZksPE8xoUqhrWJTO8BZJpfq2UpGsjMMfIvQsrK82dETUVX0CE5uomPx\neLW+E8S05NvmyNuH55pRqVDntbWp9Q43+GmTz2GbPSdgbRXX5xc0XfHpHz/hl//83/j4f37Gx2XB\ndU7YnWa8fX3Em7s96lqRyga0C5ZPH5Hbgjd3E16/O+LpccLj04oVe7x+/xYfvvsGX/3wI15/+xVO\nr15hLjvMs00cgYjzdOYwlYoJT+KR5+xTcrh+6SZROEr64KG7qkJzj3SCRx+WL3IPRCGu2VVliG1R\nCJ2LeuiZUup92FXDcH2uQmHZdq0W8QjI13bknhLnMUpMYRnzMSklzN4DGnAnrLe0TDfiLRQVxmnS\nSXtoXDKOD/eoPos1hiSA4b16wyffb07DdEks5WziiTO4w6DzsGuapsmutQ0IWtwJ8ay1rUe5Xj2p\nRLwiEPWiINKDjkoBhaQKYfteiLdRtvcyiQ045eFrn1LCtJuwS9YzxJxGp3dE3NC7jCxnAYvgbpxg\nUD00hN0p2bqNtGinAYmSOd0ret0I4KEdogiQz7Wx1D8HndedQbcBjDxSUlr52BsCi/pKztHGWmEJ\n5H6fTv1mq2PoVMwQEdhdgHNgGX32yOBfiFp5fnpG3RrqaqWwkoEy2QSblBMkGRpozUY5QZLPtdtw\nvSxozZo4HU479HSQuGTIvSRLptWy6ezmNpoZ0i0AN1s/bFAmfgB4I6CqiykOvH8KS6Rb28wDp2Yl\n5t4fu9WGTe1htHVFU8Xycsb10ycslyertnz6iKfLJzw+veClVuwPO5TrHeblhEkKlm3Fdj0D16tV\nNArw868vABLmwwn702t8/be/4psf/4SHN2+xPx0xldmNXt9khkAt1G7UYg5I0uwKHZgNJ463qBnu\nIV4EYHwnEW804Ve4gQZiAji6ow3kXKn+6UlOCbqjh8BSBCmxmMRD7NTRevPnQeilgLdf4PPrySPe\ncKB5seETzHWoV6iyKRTD3TDkTcAZnYaIR6rNqyhDslaA5uXyttnApFeXugG5WD6hTQmi5JK55J0v\ntpxA9eipV6/aggja5lJFARLzFYwEG9CkQqQNBrUbBG2IPiEp5yFB3BPXHDtm7SOumOfJpZuuGslO\nhzpAyJ7847Pl806em+nRLbliiedkA4aJUFOcWa6LNqfqUor7gYMRAgdrfCVQzYF+W9PBofl7XZrI\nqMC+NebQehQkcf2kVYCu5DLgsG0t6MMeuTgwGJR1QaoM/1a1CBZJIzKFFBMeDE78j15fRrUSCUfX\nIjeX5HgoCmhsdt0SWjPkuK2rFbg8b8hThuSGkicv9wY0a6AE69tgi1MbN5dt7jGr3FrPVHPjejSE\n4MrHULr5pA5HYICi6WatZ6slY2wqjCf3YJnm9XK1z1muyNsVeH7C9vyIy/kZT+dHPL4847pWFD2h\nHgrqccI8A7qs2F4WL0E2Z3NV4PTqHg/v3uHuq2/wzZ9/xLtvvsJuf4jufdSxsjTegczNiw27dJAV\nkrMGgK7Y6VK/UKk4DwoAkmg0JdaLkVEPXd04+tozeaTotAUjAck5Lpa8JnvR8Gtd0tevfURk/Q/i\nMEb4za/BeVZy3cBwACX2gl2e9H1DgBc3dxsO+0V2hOkGiffdYi09J8SRcLatAnH3zyV9wOvqTkvg\nCeDceWsqj/wy/HHS8PDr/dn0RlLiNQQ03mbomNBu2pz77ueB18Vl6J/b+W0iWaJWtr3gJE7bMzIY\nRb9e8Bl0Ix5JcyByAbye5DJVITAheIDfB9cGY46Nv67vFz5f258jddbfNyDA2DvizmjsM3/bjgS+\nj28FA/ZxTtPxjULQYhFrlyL+sSn/Ioa8h1w2/WbbbIJ0O1+swnE3YSoFuWTzbouX2bYKLBtefjlD\npozD6wlpb9kOtoy0F0tgreze5gRWaEvQQeI0NtQyuSJDfh4UvdnkNArGSIiHpBWqG64vz7heLrhc\nF+x2xUrWM6CbYr2suL5cIALsRHGcAOiGvC1Y1jMeL894Wl4ACHbTCbspoUwJ097kl7oIXiogmiEy\nQ7DH6d33+Op//BXf/uUH3L++w36/Q/EkMUS8mIBGE35YPi//pvG2zZijUrtvPvsUQU9gtRuECKgp\nTKi2AHMdEiEyD6LEQbcPrz7guFaKiRGqAMB5yQiVu06ahry3Y+3IL8rjMTTyD2qCNEt3Op0qsq9N\n82RnVMQTsLY3p4n5g4Hi8GsZjXqLUNj+TRUJhjWsdYtCEKMuHL06ACD6oxHkuhEhgvei3TCmVOK5\n8J54fU3HKkYWrJjh1GYDmulUKM+NJKz3mNmu1VssJKPshq6kjFBFgKkUKBJq27ANKDr6sOSEnCe0\nai2RWaln7xvK1T13oopY87GWg+ebeuxO3/RIK3nUxBoSyh+bqz9IG8ae8VwG9wlVR31HDmtv7LkD\nxRZoO9oue1SUiwNEJdVLvp2OvY+PY+I0RAZhc9y7BBL559eXSXa+vKDBoog+UNiN45qxSbGKN3Rt\nbd0UioTDqyPelwmSBIfdDvNkE2syEyMs607kvDKaTjDNcfXy+C08bsrWm0EbsHmCz8JAXm03dtT/\n2sixiq2q9amuCx7/v3/g8R8/4/z4hLybMB132J92wOYJoJyxO00o0lC3K1re0KTa9JRaMZeM3X6H\n48Me+9OEKQukKu4OB9zv9/j0eIbmHXav3+D9n/+EV19/hVfv3uLu4d5Gr00FxasdVW0S0gglxO+B\nnCHDOSYn2QOkOv8vgiiGIZqxv21tjKpyuiBUIz1ZI4MemN/r3QN1+L10Nu4AcjdWIrgxSqFukm68\n+yEYiGG10ukuF6TxJjq3U03jwWhivB5AUIrJGE2/3uFTGDkWuYjELFJDnab37hDYWgqv6+pVvNam\nbzQcjSXzDJ/MmyAXRKLfDEWH1aODHg2SrQ1RuaDGWeI6iRsWc55j8yqrnGyOvhvqtuL8+IJf/+/v\nmPcZp1dH3L97bVSKSyR7Ob4ZbhuU0qOnJFYxXCYr2ol7ySn2hPiZBSkPpdMRR7U19gdljaQQWbHZ\n18Pvkx/HiC8JsmSnbNsQcXbkzBoAS1J64j0iXO7HimWpCKfehnxN7uDCnDeGYqg05C8knE/Uu4hF\nGVTlRLdQAdTnio4Aa3x9EUOek1ihD+CjsJovLDeouucaQg414zAfd5gPs3nbufQDnCTkYwrT3M4p\nmUdUoFZ3BpSueYIkcwMEZ+YeMKLTHj6a1tiQ6LZuoYuWuuHl0yM+/eMXvPz2G8qcsTtMaKc9chKU\necJ0mAHZYZOGdbliWS9Y2oq1mdqlNkUqCXev97h72GFXCvQqkGnCfr9HuXuNcv8apw8f8P6Hb3F6\nuMd+f7CpPd58vmQrN+fA1kAWIw3B+5MhAHUjFkUU6kUWA01C28JQOCIZ/ix8DTGE/v7moDhy6knR\nCG99cwfq7BFCa8MBBblxhslGncWcTVpp+244HPdgYEm0fY41WKJB5PV1RUPwD6BunXmTUalgdlB9\nbqfEj4oKJFMHLugRTK/gba0il4zsHC6jPjrDkdOPB2GbsxeSWVXKcM09HGdVrKoggxTOQJn49wTW\nRRDC5KrPWK1w3faGbVmxXRckKdh8wAkLs6hycmLqJiqOtY3orA9cbr62bJJGB08uXlVNHjsofmgk\nre84e43TyDrSduqlz9Q1g+1L15OpzfYpoynxfdIRe3d03aFIrFsgaRBFEyAMCHrcS+hRFb/mgiV3\nQDq8k+etA6RYm38lauV4vLOwFsDWGlYvTW8Yus35Prak4QaW4OZSMM89Y75V6++BZoZ82QwpT1MB\nRJEngc0x3LyYAJF5B2wRzWlYKJOS9TFRRRTqGJeq0YOiebGJIUtDa2utOHP01aVCXoD0WLA/HVBO\neyhmXK8FqyrObUOrKy7rilWBy1axbBv2dcXpoeD+1Q5ZZ7xcK9Y6YTfd4933H/Dqu29w9+4dyjxj\nKjOmqWAqVJVkJCk3hrdvgFtKhdQDk3gWosMUPMLsehqMhKNZEz3YS8dCD7n5vSLSqYNkjiWljMLO\nh+hFMWz1anIu++htq964auucarLWp1SM2HANIKx1cnlacict7qizwGSEQN2cGko9YWqI0g95utVH\nD2M9/d4sCmEy1J49IrqBH3JD6JYQa8qmSdTYV6yr7R9J8NEFffSXQE0KS8NG40hD4b/ZLpxr6HSC\nq08snBcwAdeROKMYBfuyAAjUmVxKWm/olYY0Ce5eHyFFUHaT52FK9CHvTkdCLoxBWiByY6LsnInG\n3HFCaTbI6pN1XPHjCUZSQ63mXpjn66XuWfu19EQjIxRGXilZD6Zercsz4vZgAD0WwdrgiqD4BDAA\n4hTeTWFaws05Y/RLsAiJrqHwKLc1BViB6vsyh7zaY1w1ifNW/4UqO/d3d/Hf1Y1o9SG5dthMTw5f\nhFLmaCNZt4qreic04YIIoA3Xy4LHTxc8fbpCSsLxfodXb4847XfIklCmEr2N4Qjb+hMPvBkU2ro8\nDaAutMvYWPzBHtySM+ZX99DjHr88vyDVBYeS8Pp4wKqCBYI5JUhWnM8bHp+umO6yVXtuK7Qa39k2\nwa8/X7CXO7y+22G+K3j48AHvvvsaD+/f4vBwj93xiJyLN/phlaZtmkbj6GsbjJpwrBi9vt1rrZS8\nCbQLP8JQ0AHQyHEjs3WogBbMw2KRqE6LHMXWon/EBngyz3twEAlHwQtRijmGSIw6wWl9WLpUUkIV\nY3iwIUErC1IUTaurYLrO3G5fvdzcroHrpC5dtF/nST70/AGUSdrO2ddaLRHts0KTSV2w4XYdmLSC\nIOi963VBzhOmQr6f8CxF4zKTJnYEvdWK1JpLdXtSV32PKtiQSwIxMjoirVTdGNDgsCeLRvtPn0oD\nRSmC3W7C/f0dkFyxkRhZrWhqSp3gzIVGjM6yd2M0uat9L0a0baya9AhBvGdKktChN4UrWYCUO53R\ntPcWb9p8j0s8F95Obx/gCNudIyPukVZTj97DOLthr21DSsU/v585EYtejQ3gAO8u47Xfo7HH+vwD\nA08ZEpFnh+fCgwgmfK3eoURDtM9fX8SQl90u/juNqgFlMkgj1HNTCmbrw3OSWxsQScz/rEBtG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+lyPjMACKM57a1lPgxBS9QiH2RH5hoZusZHWM14Y8FsMpxkzMuQyEKU4Wt5nI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AppPEqMVhBOIr0noiQRGzL2CKWx+oxGprSxpM8kzkYoJNo6/NCRm5aq1yg9I05mSB3T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Rqq5o10uu1plFbVnWDt+fxMeaIux5fOT5acdi6bDOCBSQIiF6Ygrs9s+8f/839vsH8WTPiW++\n/prFcoNza64u3rFpr0hJ48eJyQtXPeRIIDIlzzANwowKxV1THEoJjJgMlepZGI+ZMt6LUk/HQC5K\n44gU+LtPO/6v//NfQFtefzlirWLqT5x2W3aPj5wOB/rtge5pz+HuGX3syVbzcLOhWdTUi5pZFyBZ\nsJblsubKixWFqQ2N9vjTM7uPGpQWCmj0kCPaZ8I4MQ0Dp1PPw92Wn35+5F9/+Mjd9sRpDPg0cyL4\n7DoXXDsSyHFEpQntHCZYolcFeimYbS5FZZ4Wke6aHNFZ1J5GZQzyS5ViaeuK9vKS4XjAT4HQQ0yW\nkB1ZO4FhlMahUFFJkSVL0ckQciAgC8EKK7vF+XUnKf8+BkbvhWGUMsTCeDESfKyTOu9AKD2tOG0I\n5TfpxPF05JdffuFf//IXrq9qlpuWXMHFzS3eBx4eHuj7ge32iLYnVBAOeSzvqiKTlRwTkUjMEass\nwUeyD2BkKS4qaFuU2Oms/lZZfvKZQ66LBUHMn0FgZ2GhKq9dnMpjiByOB379cM9qseTi1dXv1tQ/\npJAvncEri1OOYDRV3WKcZRy70kENzFaaVa7RyuGspW1lWZERdVVlhPWiTaE3zaZUaX4zJGlH699i\nf/Jeic+vDyOURaKfhNpY1TXeByY/SRGYCzKUJJ2I94qmoZhtFSpVWZTGspn3pduWyC1Tuv/P2x75\nfT8MxOAxGlJMhWc/0fdHmrqibRtSyiyWS6wzcrDMN2FKBD8fODBNQrerK4PKGaMyOeriKpk4nSZW\n7YK6NjR1ZD/uRUrdGkYvjpD7w54bJ6ZhzrUsrePdzRu+fvsVv+Y7bpoLjMnYFRyHHfvDXqK1gtj/\n+hhAZ5pFhbu94Ol5z2HXEytF359IUXxumramXYjjZfRe+PFOo4holaitw1WOnCvqqma1fIU1S5zd\nEMdcAohHuWamkWnqCNPENIg98eRHUhR+b/SZ7jQRY6CtNTZnlibSxID3iKI2hlLA5FdIkaeHA3/5\nl7/TLGr605bLmxV+6On2ew7bLd2xo3vYcfr0TP/x6004GwAAIABJREFUUdSLNxt2U+LKZzZJnbnE\nUsgNzaLhIsukqZ2mXlhSODHsig5h6lFhIgWh3039wOl44nm35+8/3vO3H+/45dOe/eAZk4QUzz4d\nM0KSsio+HomUAiGJj40uAjUpn/NoL3hu4XxAnguYsFmM1tgiglJIpGFGmpV+muj9yOCT5HKq2aFF\nOu9aaxSGnCLTC0mPGdY8+3aXZSqfNTlKlwDvLGZp2ikISe4lHCpM2CwOg6n4NSlVWDIz3l4m6GHo\neXx84O2XfyYkzfH5ifD0RI4Z309Ya3C1pJMxGUgKldJ5ukHFQiGMoME6K/dcSIVeKZx5p42El6gC\n7ebIC6FzLkOpHFiiddGfQatnSEYWdyKe7Aee0x5lDHbxnyizs7Hil9A6hXYNtl6QlWa796ASIUzk\nFNAqkLPH6BpdulJb1E1z6G0h+EBOYsSOlVElw6xkM8YyG7qDjITWWrQSeCOnQE6BYRgYp4ll3hRF\n4kvQrlKiJA1RhCMxzUHR8rVmFsmZHVOEDLlwXbMxLx9mWXzORT4Gz+GwI4wj1lr6vqM7Hglx5Prq\nEucUfgrUdQUgmJ0S/FI6EIM1NavlFd1J8jTXqwWDGfGjIYVMSJFhHHje7qh1w7JtqBca/RzITGiT\nsE4zDRPd6cirqw1NVbFabbh0Fe9uvuC7t/9A2GfWFwuublfcH574+Ok9cQzomMiMZREM6IyrDdeL\nFR8//cqnT78SK8s4DlSV4+rqks1VTdM6wDCNPYu6YtE4lm3NzeU133z5LUpr+qHjeDzSXl4Rg6Xv\nPd14ou87/NiTfGTsO8bhJAvOYcRPUrRz8ZIPo+ewPTKOE/GiQuFoNSzyJAG7AXQSSI4UIHpihv0U\n8YcdRnv64yN/+v5LYhgZ+57heGLoR7rtntPDM9PzHnW9Jq0ajiiOEYaYMVZuSmU0yjqcViy1CFa0\n1WinSXkk9FOBtiNp8vhhYurEOvXpeccvH+/5b3+746dfdzwdeiYkNi2pIkbLM2r7eXeHLNPihNYL\ntJGcW4qJ1bkLL1Cuyi84OQjF0JZCPm965KCAcZwY4xOHqcNnjTIr2f3I0YBSUGmD1Q4fEiErESdp\nhclioGXJRYyT0UZ9llUr9y3FRK5tG5wydNuD2EQbg/FOXmoK+DxnZ5bwCvRZ42GdJavEsTvi2pax\nj/z66YHd9gesMrRVLYZZRqMqhXai7sxKdnhKZ9CZGMWLXhktEXMxEHOBXYw+0zFDucfPgW4ZyBmj\nKIehvD+oLB41lP2fghh9OZQo77N8Fqfc44YKM32uYn95/CGFfH21EAwIhTY11jXkXJaaajbGMmJ/\n6Uem1IMS35H1+oKx79Da0CzWGFuRYmToDlTVQrocY89YslZzOsdMPSw4tLI4Jx9gUuJ4WDeyjRaV\nqRTZqqrPOyCtNOPpyDCNKJ3xYcB6g1IiYjIFVMxKbgDxiph9mIHPYJYZrpFDKEIMBD8yDB19d2Qc\nBpbLBSlF+r4TkYSxqGxBGaZJxm2ddUkpaWnaWxbLTuCaaeJ6s0alzO5wIumMsYa6cmAmwFHbShym\nYyTHkZRGnIXG1Sg8lam4XC64qBbkBM/bPX0MrOsFi8sv+HJ5w0W75u3FFc/bjm3XMaZIVTmGrmOr\nnmBxwRgGfJgYponjqSdrxc2rS169uUIbxfb5IPbB1lA5xc3lFevFihA8TbuiaTYSfpEsfhzpDkdO\nuy3d6cjYixlTnLx40oRAnEaSn0ghlHFcHPuqSkzU4iTpRPSRtQoco0wjMcsegxhROZFSCRXJgfc/\n3kOS77FaW1ASQhBDJjmDvWqxTtEsxcP80J147GqW3shkURaolMg+4yJVRkQ6KZFH8cKfjdzGYWDq\nBXp8ej7w4/sH/utf3/PrpxPb04hP4bOCKRdoygXrFnSXs/AnRUi+OBdalKkl6SjPODplSc9nBf28\nxishCJ85rWhNQoqyTwGPJymLM4qcIirF4tGiUcZinCV6j0fS7sniVSL884RGUonQRmCVnOW9QIKj\nQw5klc5fiwwqKyrjyDHi89zUlS64mN3llElTZLffoy2MDKz/65p2UdO0Dbunnfz0ypF8D2HCZUXl\nxF46RkUcI6aSibY/DkLIzzD2vQRa5FyYTi9ZvjNVstKOoLRwzHVJPtIKpWH0IyHPa2k5fFIW8EaE\nQ0VhjhIHzyQTWvrP5Ecu8WQSbOu9ZPDVdYNWF4xjBzmybBdQxCJD1/H8fE/X9WiVMFphWmG1SMch\n3a91SQ6Bz1WVZ6bffFa/nPjWWCpXk7MECVtbUbkGrQ2hdDnOVefnKAzjOKH8SIyeYeiYKYq62GuK\nBDqXkVTGWF0Mxn/rbFhWakpTO0daLKisoZ9GjNEsF0sWyyUxCo1u2TYlGkrYAiEE+m5gHEZA46oK\nToaqXrBaRWqfeHN1gzWWxXbH9vTI5PsSCTfQD6BUYBomcgxYlWkrg9GOtm5AK+q2ZePWmKh4Lpv/\n07jnwm/ICdaLDUsLm7phUR24DuKZMYQTOUzs9k8MxxOjH4QzPEZySLi64XJzycX6mrapuF3f0HUj\nishq4Xh1+xWXm1uBdqwoS60xjMNAfzxx3G457bb03Ylx6BmHThocNKQoXfkoXfnMyogpSmelEsPQ\n46dI8pnWQk2gixofbZnQ4jkgIWcPeWK3yyj1jE+Rt++WXFw1NK1FO4V2FW61oeGSuqqom5q2tYSF\n5ykd2LBmWfSOUjULfFPslGdMNWfOqVkzJr57PvH394/87acHfvplz7EPjCGeXRizepk0z9DhfI3N\nHTeJHD0peLQyVLbB+16mj1mVyYvrylzQZ57Y7Ff+wsAClBZ7hyyBzLlklOYcySmd7VrF40h0DzkK\n3RhVDq2scEpLilGRq0t4SyAGSfUJOUigdopiGdtU+CESgwS1ey0LxjTj40qVybwY5inFNHl2+yOB\nwF9/+JHrmwtMYdOI86kjjmCyERW0GhknMenSxtI0Na7S+NGjkiLHRAi+3PeadBZXyfcr/LTfTOBC\nf54/l3L/l4lBABt13mskhNl3DpfJsg+bxon+2P9uSf1jBEEl3GHyHj8NOOtYtkts3RCmgZQCTdOC\nsTCNpNPE/d0DD/d3GA2vbq+pnCWEgRCqwggRepDV8uHkWIJ9obypuiwfypuXJZnHuQqtKirbQFlO\npJzQWpacRs/qUINWjqaZ8GGkCz3DKN7LRlsq14gNXJ6hmLIQKnFwn99gZwpiudCrqsYoyIuWNnih\nq2mDtZbt8wOn/fPZAXEOy8gpMXnPdn9EtRltFZMfsNaxWV9hTcO723dUrmKxfMb/PDFNwu7wZmJ/\niuz2R47HTsZKDYu6wpoKV9XEpLCuZX1xw2l75O5py3g8sFwnhuGasT+ydJa2WVIbB2mJdo5A4HH3\niYen92x3T0wnT4xQVY4UFWGyOOdY1gsat+B6c8GrzZrtoWMYR1CZi8vXrBcXxOCxuibFLAW6HxmO\nR7rDlv50KEV84LDbo9CyfFYwjSNjLxREXcyO5hsmkxjGnhASOSkqa6h1xGWNRD9KYRW8eSTjyQSm\nZNgdR8KnB9wqsLjSXF+20pRgQLWslwsaU1EbQ9tqunjiMexZR4vSjpWuyvQTJSw4iJgsl8MjxYwP\ngbEf6ceJ5+2BH9/f869/vePnD3uedr4AiTPIUaAgVcb3eWRnZksJyCDWqwHvJypbU1ctYdzL9UT+\njJKbJcGHQvgolScVGq44EwrzJBtZ2IWS3CWjRPgszb5AjbzEuyk1v95IyuLylwuzxSj5uQS6RIyz\nUiJmD0kO4kSkWdT4qSPGiHFVMbp7wcSN0jgtGHdU8jpjgmHwxBR5//4j3TiwWiyZfMTqmpwM06jQ\nOBbLhkMUC+eswFpHUzfUjaWvT+InkxQQRDmuZFkPopzNCFkCJTuKuWhr9QKTzNMGBSlI5bObmW7z\nH+k5ILq4JU6D55D+E3mtpFyyKVGsFhWVzeQ0YZXFaU3MWmhUxpKSGGhplTEq0u1P9E3F0FYMjbjr\naeNkFCxja2NsKdYFE1GKjFDshNZYboPPcGqR98qpqVE4J2PS7M9CwcObpiUmT98fkNDjdDbMEpOc\n2fKWMoWWws68gC1TREpnj5a+7+mOR1IKrK8uJMZLix960/d0hwMffvnE5EHZBlM11HXNctmyO+7Y\nHe/Y9Q/4aU/O0NRLri/fslq9hgxGd1xf3WKdputOKJ04Hk/cf3oGnYghE3yP0Q6lPUqPkmwTWlp1\nwXDo6U8ngh94u9qwaA0p9Hy6O3CxXrNoWqpa5PZWweWy4f7Bczhu6U9SjBf1klfXV9zdPTCFiJ+O\naH1FJrM9dvRlEmud5fD8C/gTTbOSzi8pFIEUA6QonthGMymZ6kLKTONIjoMEZfhRFp7DUOAMBUoT\nY2QO75DbJqJzpNGRVsEQhMin5LQvHhmgjKJdJy5eV9x+teT66xWrL1Y0t4sSUJJxTlFXmnXbsqwW\nnIae0zZzHAZ0/4heZeqlwG06p+K2J2HNMUb8FJgmfxYq/Xq354ef7/jLX3/m7rljfwr4rJnpgsIb\nL7yKsgvScGafyDWXz1TBnBN+HOQeszOby5NzkJ1TCWVJ6gUnV+hSfIrfSvEohyxRgaizv7lSSRwP\nkzRQKC3LVpE2YnKJgUuelCMqW9ntaPFblNeuCCEIJbdMqtooalOdD7n1alUUzmVq0DL56qyLJ44U\nc10gmhiK/YJW2KS4/7hnfxipmxYdoK1OLNoDkchIoPMjvgvoaGiMK0wbRHRkDZOWaUhbS1PXGKXp\n+kEOucIY0kagtxxfHFZDUX7zWRKQQlhKqqQHUSih508vU1hvQnBIxYrj9x5/SCF31kHKhOgJ00hw\nPblq0FWLyhE/jRwPe+rFCj+NnPaPWDXROMXRizOgUYraaXL2jF1P3/UFA66gauUiLnjfvCFOOX+2\nRJClqjUGp6106OU0/Y9ZmfPkqlTGWkNTN6zWl7KQ1foMv+QsSxEhBahzEZfnlmO2jFepjErH45Hj\n4cTpeCCnxOpig9UW6ypAY20DyrE/9NSLjotxZBE91misg6xGcp5wVvHq6oacDUY3WNUSozqzAZq6\nJcQlMcLoB/oxszsNOKdRORMnyHZe7EQWtpJFVc7kaWRRO5qLG64uLxmGgR9+/DeGceJyveH28obN\nekNta3HOMzIh+SB0LaPk62qTWa4q6Ef2+0eqh4ppHEr35TEqM1nDohKsXCTOI1lVohFQYJyjWSzE\nbEtbPIomQz72DKeBY9eTw0T0k7jSzZ95WdaJfqBcF0mCJSqdaa2i0yNjKIUJRbPIVLXF1Y6L17C8\n1dQ3ARaBQfU899K1rlcVq1VDzolT7Oi6wLH33D0e2X565ukU4Gaifqu4uVpjgJwixEiaAn6cGPuB\nYzexO3Y8PO3595+f+Puvj/x8d6D3iZDEC+S3s516wbaZl5z686Eeyn0g8OMkE4DR4uevDTnORlrS\n8GgjJlcqzdySoo6m3DhwtnbNpTES9gkUgxJ5VjHqikWr4ZSmKTYUIQvE6VBUSBHSzJ1/YYWFgNMa\now2VrRhjjx89rARv1kbIB9ZYbDLYKCoPg8JqQyjTAikWwZc8yQ+RGEfGLkGCzgwcmw7VaEIqUGPI\nrBYty+WaYRoLc2TEz3CLFRVpzsKeyzGQjSlTRoHRy+EBan4jhUGTMy8O8Foo0sqdv9aZhjgLswR+\nJ6dMTrFkwf73jz+kkNfOoXMmhYFjd8IaRdu0qCqQUmAcerq+Z4OoNMOwpTaRZWPYanDG0FQ1q+WC\nafKM/Uh33OIvL0jxQi4oNS9vFLEoM8PM7aRgcV4of0oXQcgsOy7SHxnXZue2QolSClfVXF7MfM4i\nJda6FM3i5XL+PF4ohzOXIBWfhXEaORyOHA5H+q5DK/CTJzVR0kuKXDzjmEJmGMQYa5omVGVITIR4\nwprMptqwXF0ADj/B4STmWTl7MlmWr65m0Sh8UKR0EE+OGDFZICRVjIyMsWzWK1aLWtz+VOTicsXt\nqxuWyxV3n37hlw8/M/rAxXJN92rP9//wZ2wtxSSmCGi0qTBO8M8QI4fTAe00yiuePj0whshysaVp\nG1ylMRqsUlxeLGhDQ0pahFimxdoGZTSuaWgA+/8x9yZPciXZet/PxzvElAMSQFVXN1+/1xRJLWjS\nUguZSf++TCtKNCONlPp1NapQAHKIjOGOPmhx/EZk9eunbXfA0gDkEBmDX/dzvvMNzYrsKqK1qKrG\nuA448NL3hEnw4LlUd6mwmHQ5qAPFWClDmANWaWqXqW0sIRjgvOPjb1ZsbhzWw/o90EyMZiIwcOwy\n3dBLspTZsVrXzPPMue849Ylx0jz/vOflT0+kxyP6w0w7WxonHtmkSJhmpmGkP/ccjx3Px57Pj6/8\n8dMv/D8/PfHL85nzmEC7YuP85gJHYDxVKnCVFwrbUoGX3UMvm7yIa2KYiLowV4oSOavlsJNh/yKa\nz8v6RWGXa4BMVPr6Owpb5i/ZGBlNWIaQKeOVotaGlGzpjQ1WGWrAlWvtLVtGii9ZR0ZrmIs7ZRTo\nRmkRAdosvilOGcgJU6yrtQpFlSqHScyGmMXDPKdInIrFhYZx0uTZEFMmzwmNZrtd8fD+hpfXE/vn\nA30/MowleN0ZVMqEMJFmseYQQZKVgGQtkCnFrzwXmmEu9MtcDmCtxfxOa1uG3OU9zBfO0TIfJ6kl\nq/ev76l/k41cqlhDio5OKeZpZOhPVH5VxD6RU9/jakdTG96/3zEPHZrEet1Q1zVV3dA0G7QeUZSN\nfb3BW1fUmZTqN5cB38QcooiFygLVhV2itRaBwfL5kr8pw6JCuVJIS55k2Ohdw69wrYLRLUHSl9e7\nHMoLpHJpewseP40jp9OJGGaa2tOfu8I5T2hTCTaewVcNU4jsX1/xTc3oYRgPDOMZpaHxK3arB5xr\nmAMY0xHjSDcMjFPPHCecM7TNHXW7Zg4zXx8/E/MAOYgPdGvR1qCNZXuzAZU5Hp9ZrT3vHm65v79n\nHjNaG5wTCMw6oYSSM9M0lA4o0zRrdptbzv2ReQyc+o7Hl5HNdsswTjy9vNANgXE9cLtbkdc1xjlm\nbXk+d9TtDR9u1ji3I0bLOAWM9dgq47VBhUiNJiiDrmqqekVTr4DM89ev9J3gqHKIi0eGQqO0Wcx3\nSFkxR5mjGA2tF/We8Zb1fc3/+r/9e27fe55fvzCqnnPQmNFSmxU6KeIcCBr2L2emmHBWMYTMcUic\nDz3751cO344MvxwwrwnTZTa15/6mxVsI80R/Hng9dHz99sofPz/xz58f+dOXb+zPE/2cSdrJAZTF\npiGX9PrEUtmpMkRTlz+ywMq474KdZySZaCrfn5aF+Wu6Wwwl3V1Cq0HAnEVEJht5LsatZTBcDhYZ\n+0nwBihi5mIlIV4zmlpLJqdWGqcMjZYrLZYh8GIDm5GNdp5nQoglJMITo6icpTiR9B6DodKOSBLc\n2lp5uWIqzzkx58g0zTJY1QpltagsjSYmOWBSYdJrl6nWhnbn2Z+CcMeBxcLDWrDGM3SBOMbl1b0M\nKVMuNmRGE0MkI2HtKQxv5hLLvmMllKMMvGU2IXCLNZJiprRCmQKdpcWC+Ne3v8lGrhQXpaKzDnJi\nHDqO6pkQFb72bApfPKZIu1ozW0vKmodZc3//js1mhzE1vvJoU2N9Q91uMa6WCbo86zJYnAt+F0l5\nweESVdWyqKjgzTSZ64T5QuECYaRQFsKloS3tZtm5FxnycoeX/6vFbB4WQUVKyCILiXmcSqbhxDx1\nTNst3q0gZTbrlvTh/nLITPPMOHd0wzNjOsvQTEeaYUddrfFtA8oyTT3aBMa54tgVjDhldqsN+f1H\n5v7E0+Ezw9TJQMZqnLc0dcvD7QNpGDmOe969u+G7jx+4ufnI8XDi3K3Z9GtWCW63N9zf3dK2KzKz\n+FVMAVSmriW3Ua0t59PA6+kz08uBEAJ17bHOkFVkmDpiN+ObmqZtOPVHTt2R281ITGO5SmRgZozB\nJMF0nfPUleCxtviH34QHxmmiH4TRM4/SjcVIgWqEtqeNVJUxK3KUw9i5TLv1bN41vPvdlt/+QaK1\n1kd4Oe+xxzNaT1jjqb3HO8PYTxjlcVR4qzmdjuy/7jkfO/rTmRAj45z48nhAT4n1quIff7jn3a4h\nx5mnlwM/f9nzx5+e+dO3PZ+fD7yceuakiFlsIPIVkbvsyXId5aVWKVXwdd1JzaDLaPRKLSQF4czr\nwqC5gDJKZPjxamkbiFfBClK5J5VJSheF46LLlLGqSapsebpg5NIFp5ykI1T+MljVpcp3i4tnype8\nTbL8W2mFtgaKPYbK0rHGS2D0AlUs+H353SwbayIgKtFrroAcfDkqLBJU4pynbTeElGXgnjL9aeL5\n8ZXX/SvTOGIMbJpa4BqVqLwjzpHZyIwMbSV7FAnaEAYSKOPk1dNa8kkBXQ6q5RBdSAxGO6yVlDRR\nihuMt/KBKGLtZSH8+va3iXqLUhlb67CugiSxW+fzCetaqrpl7SzTPIjRUdWijKVVlne64vbmnvV6\nC8pjnUaZQFYa62p0SdSWmyqtTURYq1FsMKeJEDKVr1kMrsjXJV0KFaBs0GppdK7t63L/by4xwfdi\nuvy8UuLAljPFAGtJOJJWKcZImOVQGYaRPg70xyA0QRKVm6jrlrat0HZLiNKSKhT9NNANJ0LuGeeO\nOfZUrqWuV6xdTdt6KqeAiXPvOfVFNapgs1uzrhsaa/lvf5rZn55JSjHPAWsc62bNtt0yxiOjgdub\nNXe3t6zXd8SQaNqW9XqD1Vp8nu8/0DYrxulIPwTO3Zk5CGOksRWb1Q2reuTLlycOr2e0hg/v3xGz\nJqZAN/boMLFWgqHHKAZGw3xGJycZjHiWPkkrjdHiJR2dlza1TPfXt7f0w0jX9RxfXpmmRJgKSyIl\n8XsPM8ZZjJUhdwoyoLMebr9r+PD7LT/8D7es763Y8rotE5P4jBtPjJq2rlmvWp7HV1HOztJK9/ue\nl5++iY9QL5qCmDOH80joJpzRxGFg+LhDq8SnX574f3965L99euLraeQ4zsXfQxgRS/GgVBmELarF\nTFmk+ZIeLxBLGdCXSj0XQ4/LJZGjONlS+NgIVU8saRXmEgKhCgQiPuOqPI+oMkkbsUMQ1zrZ7PM1\nuFkVC+eYZCMPKVBZj0cGk7koaDXLY10eXzHryjJD0EbjKgchSZcQEkFlUlwGhgKbpDKYXgaxwnCR\nUXAsmLRKxStGKZISX3RyJqmEyoZV0zLPM3EYibPh/DIx9nsOhzM5Zaz21G3NMI3MccYaJ66SOsjj\n1xZjHVZb4jgKpJeT7G9KIEKULjkKJZ9Tie1tTgGVxShvu93giy33GBLKWVzjaZ2lsYbqzdzt7e1v\nkxB07qjrBmscSlVoayX/Lhu0FkqRNRaoyNnirMjfvYfUgK88xgqrA6UlTWYY8NZJm1+gkZQEv9ZG\nPLdRE6+HEzEmnKtwlQUlydemuK2JmEAqEaWFHpQvlcu/vC1wjEA4sYRLWFloKdJ3HWTwviZbcU0U\njDwyDz19d2bqBrpTxzQe0XnC1w0KTd915CSZjeeho65rVivxcMcMTNGTzgMwkxX04xPPey85nc0a\npx1pnjnsXxjOZ6z13G4fuNk+4J1ns3rgl8efmGPAVxUvr3ucqfCu4fVwIk0jznlyTMzjSK97zt3A\nNAdBUbWmWd+xvflImgZSUsxT5rDvOE5nQhqxSuF3Drvy3LUbqmRpVw3/8E8/8NPXL/zy+MjLocM7\nj3M1Jntu1nes2zUpR1atR2fLPKSLY6VWCmsM2QneqBGvixQTvvJsb2Ug++3zN0LqGYbANMyYkhq1\nmI2hwTlLDhllNLqF5q7i5uOW99+9Zwoz4+szU+zphgOVt9zv7jkcT4R54vQ68vWXPUMXCndZ8fLy\nwsvzKyEmHA41KIZuRoXMBPz58xMxjPzy2FJ5y5+/fOPHby88djNj1MRCk9VINa7VNTnzQk1jWadw\n4StfPn9dmMsfKLXIEofI4vFhCqtFVJamKCMFHoOAWDywQIw5CwU+K+YUiGXYr4vIbqnMF4OskBNT\nyoxhwimDM7Z8vQw2yYSsCtNL4Y1lzpQtGPEKcoq6bpj7mTAnwZJjIqSMSoExBcY0EdNi7xoJU5D3\nNyN5mJTBojKYBavOIiQ6x0AcO+LLC6TEPMyoVCNolsKbGpUjJmuc1swZ5ikyxYF5EAsPY4R3o/PS\n+8j9xyQ+9BCYwvkyL9MymIMsQ12VKJYVa7777W8xOnN63TOfeuZ5IibpVqd56Xj+5e1vI9H3Lc5W\naKNp261UV9aQspF2UMkwxhWlp1LgnFRPstCyVHzJ4HyNs562aTBGXhyJXBMPb8q0WuWENpaqagDh\nbnvnL7i20aYsfjk5327cVxjlL2/58pFyIsaZaR7xqhLuchK1Zk5J0tezA2QTyTlx7l44vH5lnE50\np1dOrwfa1pOiLIgUIylGcFcurjESphFzReUqnKuFkZMi5/4ZsjRv1jbENHE8PPH09Sem2HFz+46b\n3Y71ao1CM40TbdOS8o521UJSNM2a9+/eo6bEkAIxW7xvmMaBrvvEy+GFw+mZYRxZuy3KVGhToWxg\ntdqCMpwGiKdMN0SYkzgONhUfHt4xbAJJZQ6nI6+nI6ehkyGUFuinO55Y/W7L3c1H2mZL47fESTGP\ng8AEKgvEnRXZaMhWNvGCM87R4uqaZr2m2e44vByZpkgqNqT58p6BVpE4Z5RBpNlWMw8z55ee/ddX\nUApfK+qVwXupTL1NvLtZ051Hvn098OXnrzx9ORFG4aXP08QcJoyT8Is8SkegE8w5cxoiP33b83R8\nxVjNy6njpRvpYokhZHHAXJTJ8lzfQioU+O96K+4mapnpIDBIEQMtq3gBwrOSijdfexyWedDCrFhg\nCb2ImJb7UMthKFx7iwiHDCWwOedLQEICQr6mAqEk4V4V9XN+83wUSqL0tBalZi6wl9JY55gHgUgX\nWm7KMsyOWTrgrPLloIlZqmFpWAocqjRaGeH0E0jnAAAgAElEQVSrFzxebG5hSIutrszBmkahffFC\nQaPkTgnjTJqTODiEQAoIj93Ka5RCvAibZAtJgGTCpjjJTqJloiBzPFUwcRExSsh2RVVpyIFzNxKR\nxzVPI3OK5Ph3RD9sVxu0lgqmXYE1otzL2SAmZ4JwGWtKiEIk5RlQYoKUxaDJmIhHYb3H2g0Lnp1T\nRGknbZ42qGxROmOsYrWS+DNbhqIxSZu2TJXl1JTHprJ0Aij1ZjFfbxdmAJmcAyFOjGNXJuq22OkG\nUpyZp4BOTqqQMJNSpOsOnLo90zQw9Ce685m6qkiRYss6Q66xRlNV1cUfxhiNNQZrJFEnRphizzif\nqP0WciDHxNC/0ncvkEZqX7Fd7bjd3tI0jcSwxYm2rjBmTduuIGlWK6ETHp6embRCeYf3YhVwPu0Z\nhlem8SxdjHVyURVzobpeYV3DaYwcpyNdfxJ6mHa0TcPHjw8MY+Tl8Mqff/mR58Oefhwvw+FpnHh9\nfUVlQ1NtWbe3GFUzxrkc6CXMtvBKbYFTUsriRpkcNoj5VtU07O5uefz5G3NpzWNcmBC5MC2kyq1b\nT7V1+BtPDImXL0dUCOhGcfOu5bt2J6EOMTN2PU7XDMeJly+vfPnzN778/MrURUjiS+KcoarLQT5M\npDmiUkmwiYnn80g+T/KcU2ZOqgRmLOWC+os/XEgdy7z8Mssp61C9OaIWjcSVI0H5WxUtRSxDRdmG\ndZa1H0tSVirbeOTqqAhX2CQv6sWcZD0uGzmUj6tdQAaRol+6Kbk/o94+DzkcLn7nIH4lxohjo7No\nG1AhipFWgRdCKFX3crgsBVV+04mUD5ETLCVZKb7KoUWSDd1qjas0q50FrQkxo00FGHIOTENgnqPY\nMoBAJVY2Z4LAPBrE49wYpJRMMMcCY+XLDAAKR1yJfYdRMqOY5xnnPNbZN5u8ZppG5jAR/p42cutr\nYQ+QUTphtLBHUtYY50CJ54K4xclSnoN4iMzTjPOVDL2cEyOiIoXPxMtwUTQgphwYUU5n47B1dVGD\n5ZzQSexmF4/glMWfw5Y3aPEcRy0b969vsg6KuCeMnLujRNb5SvjgRhbENJxxvgUlWO35PDJOCWM8\n03ggp0BdaXydmeeO/cszGlgv4cu+wjox6IohioVmyljrcBZyEveK25sHbrfvSCkyjE/4KvI//cf/\nmc3mgfXqHW17R1aReX4hxIlVW2OnGa3g3f0tVlcMXceXzz+hVOT+3Q3G1XhT/GecISc4dT1N3WBN\nJoaOoduj1vegHVOYeX584fVw4Hff/YC1nrqu+Pjxgdf9mePpyOl0JkaFUpYUEse+pzeKuG54fN5z\nd7vHe4/ViRSV8J4JaJXk4i9Oe8qAShqdDS45/GwJztE0De/eP/DL+mcyiilO13WWIhqJE6wqz7vf\n3HLz2y2rDysev7xyfD7x/F+P2Nrw8Yctq9bTNoZ5jDx/O/HzP/+Rz5+e+eXnJ54fB4Y+QlDXTiEb\nueDjTIwjuoTvinxH1lvIquhMRYbtC/dj2SBlGqL5VdK6gktYSS6ydko1WpruRSy0bGrLYFHzdt6z\nbGMJcGQlociZSMiRpAK5uP3JdEjJtYgmqWIoVaAWVa41jcLkxSGxxLyV6yYhQ1JlkLlEkAPClq4D\nJcNOUr5QLLNS1Oua9d0alSfqLPTYaZbQGIOWChhhkmnKUFjJhpmT2BFrJVj0ZQ9VyCGWF6+ZTJwh\njxHfODa1Z7teoa0iZY13LXFMdIeO/usz05iYc8Z4Jz3RckgVB1TlrBSmzrKpLd1hoA9gtEcRLrMA\nymtnlUbnTJ4DY9fx/PjEuTKoODOPo4ibtGaME0FFcv13ZJoltq9L66hQKqCIqFSUCMqUFJ58qQbE\nZdCJIksXYyxjS3Ugp7TCllOvoHVKKm1rHSkVbwXtWUx1lBznUMRB4hWRpKLViqwFpljYLyD3d6V4\nwVLpCJvF4H0tLWUMJeGk+GKgsLYCbQjTSFIzISrGSSblq03D9vv7MuARZWoRREs1EEemlFGz/O6Q\nRqa5I4WZcTgzjhPGOGKA0/nA/rDHMFBXjrZpWTdbVs0G72piGtBEiD2tdzR+h/M1IUB3PnN4fSFM\nvQiOSvisrxpq39CudoQgs4eHu3fs1jsqXxFTw9Nhz+vhxJevv3B8PTIcBx7VE3EIHLZrVk3NOAW0\nUdze3XH++sg09iWaL+Odl25h7Did96xXDcQBTY1RFYuzZOmYyapsIkZjksFoiQz0NlBXnvVuw/Z+\ny2rb8vLlLHimNTjnsMUcrW7Ez957z/r2hjmDdoa+7nj9duTP/+2R82MHOjCNgeNp4uVrx/F1oDvN\nTKNGxcLryBmS4PVRFc7zwrDIIo5aqkFdvDdMvtbdi/x+IfMtfhxvdSVLBf4XVxRSB8dSiS9rLrMM\n84tM51Ily09lUIlc5D4JRVJLRZ4uEI1s/pGsPBHNVBSycnUsDkbyfUsFvEj/M4mQE3NOQjEsuD+6\nSJcW3QXlcCk+NCqL4+HN3Q2ZnpPKYnQWFEoZqegVJa+2CI+Qw81pmN7ac2QuB5PKZR9QBctHKKcO\nResNm41ls/E0TYX3Hl+t6fvIvvIMwyTPYxSmUk5iS0COUpgqI6llQLOu+fj9LV95Zp4iwzRfXniV\n4uXfugiAlM5oG/AOSJGhG0RLYhTZgDY1daNxq7+jjTyXqsJoDdoLvpSKCyARMGhthb+ZFw6nxhpP\n5RtiUki7I1X0EmV1McVSXC96pS64mgyLrpWPPJbi1xwmFjWoHAaCZ1Pw58U/Qi+Kz8tefq1UnPU0\nzYoxCAYe40xIM4mEcRZXVWQMKibQlpgooh1YbRref3zHNM0o7cV5UXtRPeZANxxQFpTOjFMPJEKa\nCPNImHvxS1EbTueOw+nEl8efuN+tseZGXABL22cK5cmQaJxBtxtRxNYt52PPcDoxjUeUkrmBAfFr\naTY4U2GMZRgnUgo0VX3B6W2eePr8mR9//DP7lz1j16MTDN2ZpzBxPh3ZrNZEMkOY8FUlMvgQwSia\ntmbTtmxWaypnZTCJEnhJWaytC5K7gA+CgWoNJmuiErWvKRmn3gWadc32dsv2dsv+6yMhyFpyJRDE\nOot1FhHWVKzWdyhXUa9WnDcnjq8T+88Hnj+dOZ+PjNPMHGEei3gji6eHsCFA2gS48L1Jl2p6ocpp\nlpZaX9dolmGcrP6Fh70YJhU0vGw86g1ssIQSLMjfMvx8+wclToWLF8sVcFhgjQVT50LlWyrxZcNf\nNtikRFo+ZWGrGKUw+RLlQPEzpbwSGCUsm5gyIRZ1ai7ZuRpQi0sipRK/+uEsD8BaS7vZMXUjik6S\ns8oBbnQix1h+7uqfrtAErs/tch2rhGx5IuNfTklnDY0z1F7jveDbtTesVxW2ssSYUAZ847GdQc2g\nZLpODkGsKbSwjNI0Y3TGG027rvGVw1iDdYo0a+EcL4WhWoAoYdIkI8ZuKUSmaUK3hqwSc5T53mZb\nsf1Q/dU99W/jtVIMaZRZTH3K8SpfLRepkhcqz2XIaVCIVDvGSI4Jk8MFSlli0i6Y9lI3F6xOXdgn\n+XryE0lJgizCPEDOWGPwXip7lcuyKqZGlBYza/WrZaK0xip79SbvJ8apZxw7chLKWV3XGCsGPsZq\nsoqyEceJ1XaF9Y5xDlT1CqUsRjtud3es1i2oyDD2tK7B157z+MI095cYM+dk88rZ8Pz6yKk78vz6\nja5rGfsB996wXd+R8yTV/TRhjePDw3dM006qEm0w2TFNJ4ahZeiESbJd3XGzfc9284A1DcYIG0dr\neHn+itM1zrVMc2B/eOLr408cXztaV3Nzs+bhfs25mxmHxOF45tgdOfZnTkNPHAcab9De892H71hX\nDSbDbz78lt9+/3tudu/ou4F5TMQ5S4eTrlixUldMViOf1EZjjRhz2RxYbVZsdhuauqE/J2KIjP0k\nzBdrmKdAxGGqHbu7H9ikifPmxGP1xO4bxL5mmF45jx3hPDMnfeVKUzzmlWLx0Fj43DFHliFbSkHW\nk+JiEGXesFGk+NCXzUsujkLpoxAEc6mu1cJMyRfF6rKqrxj59UO9ucs3O/MFMlzuaxHiUBSil7We\nZUiccqHMkpjTiCZismzkwrKR36jKL0rlmSVk849R+NwxLxx2YaBcrkhVbGjL40gZ9s8vrB9b3n/3\nO16MJwRxNtQmo4yS5r0UbVbpi99RLolgesH0S+rTZfir9EUJ6yovEZKrCnTm3I/MWRGmzNBFjD3x\n5duer18PkAwxDJAGUhpE9xHB6YbaGrRS9EMnAR5p5PnpK935DCrTblb0h5kwyjWYCyS08MljEjbM\ny+GAStJptM4zzQP9cKaynptby2//zd9RsIQ2rmDki3mRTJJDLFWWERP2pWUEkRJP08x+/0pVeawX\nwYG5QChLvVEgiXxtQRdFmixaqfJTmqVingcJskD45WPKaLVG64jSCRlls8B9XB3nloOh3BbqVgaj\nM5XTeOshm0uYstbFL8QYjNE0bc3t3Y55OENOjGNkt2lp2hXWeZyRKbavDLfpgaqpsc4wTwPHIrf2\nzknYcgzENHEaDrye95zHI3EeMCmxdp7d9h3r9UTOQr0UeueG3h7o+1e68yvn05GuO9MPAyHOrNcf\n+e7j7zHGE5PIor1t2aw/iJhpHun7Vz5/PnPsDzw/feN4fKXrR9raUzUOaytS6LEGPn73kee9x58q\n1mFm2nWM08gwRe43DTfbO9pqzcO779ms3+H9Go1n1BN9Gi8QxJK+ssi4tJZN/jI8KkZozlra9Yr1\nboO2VoZnGXGO7CdySMTZcetbNjfvub35HnKkrXuM3mH/cMuu/spj/YnjsSOdQ9kMC1cafdm4QUmC\nizHU1mC8YQoD09DJhlsS4XXWIiNXcuGrvIzf9IULLfWGugzolpWdL793UWzqN0rP5buWHnHZTi81\nKQvU8vYmPztd9njB3QXaE3m0HJNSjQcigZgHdLnO1IWJshRkyyOQayapYlmdRamZUyqqlqW7UgXb\nFoGQVTIbQGe0Uxh3VYemFEkhEcNMjJM4LyoKW03WRcyJKcwsYRmmXJOZN69iqQS0FgZYu2owlWEK\ngeE8knXkWQ94rVEqMowzYz/LeglzYbFYDJV08EHBJCE0la344eN7PvzmltW958/2Ky8vJxKKx/mV\nLmRS0Be8HrUclkCIjEPAKoPKhvOhJ4SRMCW0zbx+m7Dm78j90BiHKLyWRSX29dJ2qQvdVaoGsbmc\nQ2SaRrrujLUapzISFKIl2WOpxt8s1LdBx5eqI8uGF8IosEQUu1qrtXgwhMXRcEkQf8PTXaoXIuqt\njdACtecS6hylYnHeokQGgVKuwDrie2KUpqk9NzcbjvvIPE6QxYBrs9lQNy0xJKqmpmlq2SRchTaK\nnGdSnMhpJgHdcKIbOrLyDHPHUPJQQxwxKdMax+3NB9pmB8rjXYt1TXldh0swx5JOErPI8DfrG+7v\nv2OOE/14pkJRs6KuNrDJTNORx8dPPO4/cR46xu5MjsImCGkiEkAr6qbC+4qPHx5kOOs857EHKkKY\n6fvAzaZlt27YrG9Yr7fU1QqjKwKzSO2Xab280OVglQ1EJ64bOfJvYw0uW1brlRiRVR7dGWKW7moO\nsdjIZuYxk4JBzY6hC4xDRs81Tjf4qsWvV5hmhbI9pEmG5dpc23MArTC22OJ6T7NqGIaTpBOFDDqV\nkuTKzVDLMFN6ULQuAz8WVoRUwde0dS6bvOwB0sm+xb/lkFlw1Fw2jHT52aVK53KP0j1culglh90C\nygubS5fKutD98nyBLZfO6HoxcIGDlkMpKmGuxBSLGNAIlz+V3Uxx2dQXH/OsELsIq5lTZIqBOc7E\nErIdYiCm4l6KQaUkc60S9nBxfiyPaYGLbIFlVMHpvbfUtScrGEOg62ZCjhilJfQ6TkJdRBGGgZwT\nxhnaVUNOmjgm4hDQIaJtpnae794/8LvfvcevFWGYcU4zhshw7oiTYYjlQFEyL4wFbiFpcshgxD5g\n6Edxb0xCmNh/7elO41/dU/9GFblFvUVTjCs0nFxMYoRVYoxMyEERQs88S/it1pIi5L3HuQqt3dW7\nF3XZwN9Gu6UUiWEkzGfm2DGHgXkSbqcxIvOvvScZK4q6cj9LCIAqCfZvD4bl4FAFX4zFcL7vj2gC\nWq2wdoXWkiAkLV4osKkEZFSVYyiJRJWr0DrhvWG725JRZTBnUcpgnXDlnYOcR3KeGcaO0/HA0/6Z\narUrG16SmLkI567jc/zKevOJjGK3PXN395HV6g5tWqa5ZxxPpDRzd3eL9tCHExrLerOjqitO+2+k\nKZPySFu3eNNQ+YaHd7/ncHykH54ZzgNtVXF/847TceJ4PNFUhvfvdvz2H35g3e6odM26DZz7nm/7\nb6zXntvtDd83Qh1VKjPNPegZbQA0zy/PnI6vpBDxdiPvQVqw4lL9KcFjjVbManGMUzhjWa1WbG9v\nabcbzocj8yz4vtWSD5mnyLdPX2k3/8zDzXt+/PFHTucjVeX5859+4nQ4CivEVbhVKxxxIz4Yzlpx\ne1RSjeti/GWswa8aUBDGQD/JIN8ogQOgQKWUAWFRQhqVUCZjMswIBS8pYaFAMacqCTUAV+pewV4F\n4Smd4yJ8SVyjmRcsO1+N4N78uYakibWs0hqThYobC6Ml5nTB9FVWMiwtg0UJExaIaLGIysjUa8qJ\nKc6EMGKVI6bIVOYfWUuKTkqZKXNh9IQEU4SRRBcD52mCKRDnuQj9DGWhiIPnpQATRsxF+UkuEqNS\n+RuLsSKUt8ZgrKGfJuZCE3QVrJqKxlecTwOS/heFpBADVeX44R/viFPkvO85fc2oFMVgzil224ZN\n6+nHA+vWkmg5DSPjfUueIvMgmgi0bOR5nlFZOOjeKFlXWZOpmZORJKI4cTr2pMPfkfuheAkvu3hR\nhGlJpVcqXaqUKzUJrK1YrbY4a/BVhfO1VJVlkPlmdi6DiEv7KbcYZsb+RNc9MYUjIU0opfG2RduK\nFAXVs1bwVW0F/lka05yvcVILE2DBSOV3ysHjnGfV7oCIddJ55OXxFNbCYtZV1xVK7WgqD0m49EZH\nvDfUdV0UqlL1eOcxxqKMxqsW71qcqejTWTD4SjaWbjgTphmLETaHcRjnGKaep5dfeH19Zpx63r2b\n2W4feHr+if3+MylMtKsdqdgLuyI3JmtW7Q1KWZxriPPAeRyZ50A/HhnHQF3tuN19IETF7ngiqUB/\nPmK1pvIVc4i8vB4I52dCmukHwcqnVHPoJ+BEd+5RGTbrNevNHca03O1+YBg6zt0rOST8ZoXWhvjG\n/kYp8aROpTO7pigpnNbUVcVqs+L2/pb916/EOIlyMEtKjcUydD2Pn7/yX/7v/8wcZlbblu9/9x3n\n1zPDa8fr4wvjuSfMAsGltCTCR4xKZG2K9XBN1XicdyUQWbpPZyuIZfx2YXbIwk6AyiUIWYtdbFYZ\nWwJK5rS4ClIGluqyHslcWFsL+0otWPOl0MiXsnQpnH4FruSlLgdtDI2vZVJZ5kJGWTGFyBRKH2hl\nZaNn8QxKhQJZlIsIIkkud4UiEBlToJsnYhaL2SEGkopEJa9niIk5JUKWg+j1NGAe95hbx5QSpvL0\n54kcihKybq45AMYQJ6lWvXOYrAgpEuMVWloWjCrX33rVslqvqJxlGAeIWaT82YpOwyuMywxxZg4z\nARnnhpTpp0GglhQLrKvJ2mL9iv3zmcobbK3R2VP5RFAlR7cNvKpJDiAtB2xACwRlLKq2NGtHUzv6\nc+Z0iMxzORSTQkgY//L2t4l6W8QP6tr6iDevYzGXeoNlAFlgBW0kEk4btBFhD+oaU3Ud7Sw/XxY7\nwtSYwsgwnRgnqUCd8+BatLHEkMlJAlgv+XuF6ZLJRQkXpPVU9rJ5L79TlGkGnJdqj2LneXmCsqoV\nsj97Z8lNjXeKylqcdbLpzR1Ns8J7j/DpMzmJ05suVaQylST5WE/tau629zhXE5XlcDoSQy6bsDy8\nlDPdcBZsMURCloT4ECe+fP2R8+mZuqoIURR1OUPl6yJqgtrvMMZJGso8MAwDp+7E/viNOc7stg88\n3L/ncN4T8sT7hx3HChrn2bRrunHmdDoyHAaUyZy7E+fzmTEkdEloObyeSTFwPHV89/EXNus7yJZj\n98w4n7FKFj5F3Sx4Qi7tv4T2ymtU4BatUAjMsV6tuH+455d/rhArpVg8OhTaesI4cnp65pPOfPzd\nb3j/8T3/+O/+QJrAYCFk5hgZeySoG1krMYFBDKESFZVyVK2naVqO+3Op8BTWeDFyKy6DS4Sayqn4\nTJchXHm/FII755QJ+eo8uOB4uTz1BfO7whvXgf5yDeRfbdtXcdGvr0fZxH3lWK9XaCBOM2M3gLIX\nCIWwQBYKVSpvqcQLh70MqzRgs3QUufC7owqMOUKcGRAse0yBqMQ2eJHtp3K96izQwuHlQP3VUTnD\n5n5Ftz8RQSxw64YpjMQYBF6ZZ+nMtcJljU6aKUdM0iJuugx2BXLylb8I7aTWElroqqnZblvq2jBO\nHd04MxVfGZQmR8X5PKFTJs7ia46WvNbv/+Ejt++2tI3AoNMcUNlQV565mqmdFfGPFkZKSIHFtA9l\nMV5Tbx3bXYMy4oOOymjtirjx74h+eOF8XIuF8oUizy/0q+smCMoYoSsWvRRKXcztlwW8cHSVejvB\nl69HpclGQYn+IomdvTYGYy0xTgyhgznj2y0qX830xTdhJMUZZ9coK4o4WCb812dmtBVxxTIhz8LA\nUUpwv1iqnaauMCYzDHA+nqnrhrv7d5A11lYYXclFkQFdBjTLYVU8I2rnaHYP3G7u6caex8Oeb/s9\n2hwxRtP3pYoMM1aDdxUxwfjlE/1wpjt94+ef/0zKE3f3t8Rs0KrG2ZqVW+GsI4aIsS05K0IKgGWe\nZ06nJ74+/pHNZsf9ww/c3/6Gp5cnXl4e8ZXm4d0N22bNw+6OT7/8xDjsCSrLoLY70Z9HGjxt41k3\n91hmzv2BYdzTDxNfHn/i2/NnwtxTVZ71+hZsYVYsr0eGXNp7RSopUoX2ppUktQDrtub9xzt+3K5w\n1kgQcE5EFXBE0jwyn450KnD3H/8Dv//DH/jHf/8f2G7veP/D99z/7gP/9T/9X4T/cubwIhHClxWm\nIlopXHY00VP7d2zXG/ZfXxn6kTQFvHWQjTj6qcWgSrzXUxA4bp6ny4botCuzmYRS8bKS86V6VtcZ\nAZSKKJDU1QJWBEGKXNwG5YpSVziHayepNeJ6uWrY3W/xVjH3I0+fI1PMJA3ZV+gxX64xUpY1kbMQ\nJ5VGFXdDQwmjy5C1ISlHZmLImT6DCSWnsnDetcpYpajUm7mTyrgwo84D3dcDD//+A/a+5unnJ+YQ\nSCbha0McxQI3xcII0oass3SkaGqdmZJYM6QihgpZ0nbGOdGk8lpMmZzAVIYPH254+LDDesV5OHM6\n96Aky0AnhY6G/hCplEFPBmLGVoqH7zb8L//7f+APv/tIpeCnHz/zn//riaGb2d57RnPCLF15YdGk\nJGKiBSK0JlPVjnq9pjvOoDtQmqrZ4DDof8Vs5W/EI//XvqL+4u/y/Zcvy4aYLidAEUDEEl4bi5xC\niQnSwtPUyMbn7Arvt3i/EpeyOOP9CmdbjG5QyhDjjFKuqDrL4CVladHK8EdrIYVxuSh+/XgXubB4\nlwtEpJUmG3NRm2oj3greVZAUVS3hzworrB5lluu1tKyykecidqr8CrV+jzW1sEzyK95HmmpN5feE\n6Yy4L0ZOw4zGolTPue/YbtekPJDpUV6GLC+nM+c//hemceTc7TmbV+IoXt7N6oYQJ4bxRJyWxxJp\nmxsqVzPPA09Pn+j6F6zNtJsdbdWwbW9o1x/4/vuK9eaWw/HEP//4z3SnCYUjThAJqDzzUG/4sL2B\n6jegK/706We+fvlMziMfPzzwT7//J1J01NUN1rSXKla9gRlAMEdTxGbee0alqJuG24d7Nrc7XNPQ\nhUH8b2KAeaKyjtZmvIbudc/h+Zk4z7Rtw4fvP6CcJgw93X7Pt0+fJBUm54tfSCITQuT1Zc88Brz7\nzOnYESZRCTutsVp82ysr8WAaJfMfIyk2EemSZP0stFw5qJYKPGcu2PbCdrgwsy6VZr4GRVyOmyvE\novTys4XiqyRRp6k866bGGVsGiopq0xLHAaU1q90W03n0uWPo+3LviayiYPhZl2FzQpMkLd5onDfg\nLWF0hLk4L+ZU4KSEU9AaS2sclXZyRiTxQtfK4LVjZRwP726o144/7X5iHiZSSsxBKnCjDf0gxnGB\nTD/1oCtU1jK4V2Ugmq+YnJhXKVbrmncPO4auI+aIrSv+8G9/T0gjP3/+mfOpv3RWimUWI2EXCkXW\nmeTEA2noZ/703z9Tobi7rVGNyGTMoDC54nxOdHPGrWrCPEs+qU4oq1ExQ5wJ08TpeCajCUE0D6tV\n5ubdLcyRuev+6s75t4FW/n9vpbou6/UNUMKlhyx0JEWm78503Znz6USaSzOnldiUGvloViuc8+L3\naxqstVRVzTQNWFNhbVs2XEuIE8ZUKGVlMy9BEkaXCl6/aTWXx/tXnsM1Km5R6S24vxF4RluSFSWi\nLoeDMQ6FvVgOXJ54Xp72MlgF51r5GV0xxSNKDxL4YCUHcYqxHDBakmu6gZgTp+6IttA0jnGuySYz\nzoHj64kU91SuYVWtMDqADqTcMU6Wp/03vj7+TJrgZrvl5mZH7Vdk5D6P6YU+nMkqi9f7pmG9vsU1\nt7QaYgr0/UCYItMYpHMpzKDKajZWgkHqm4ZjGNj3rxxeT+Q0sGlbpmkgtiPKiDozhVxeC4k/UGo5\nZLWwmVBUzgtMVFdsbrasb3fU6xX74x6TFV4rKgW1zdQWvIHheOC4f2EaBqzRbLdrMIqX7z/y6e4W\nbx1zEKqdHGfyBqUEYzcyncWFL+dlHUBQFO8NhzIKp11RLisZ9GldLFfLDCaJYGhJmr9AKW+uA/nc\nlT2+/Pvi1J2XbfztLS8KfZaflIxLixmm0CcAACAASURBVDcebx3eGIZZ7DFW2waGTIpCPRXGUcU8\nzmKVUCCKINlmXKTxSGfkvMOvG+yq4mU/kZJ4fdtiYasRP/KVcaxdhdcOkrA4pihMGoemsY5tW1Pv\nKpp1hXuxhEEsoK2zaG0IaUAVrH0Is2y2aDkc1WJzvDxruSbrpmK3W3F7t+bbZ8fJyBUTQuRwOPPt\nyzNDN0OSw06lXOC9fOHYZ5NRdRmepsxhf+LTp194PThwidfTiaGfqX3D8TzTzxJcMoeRQCSrjLEG\npRJpDIQpMPUzzgW5FluB93bbNXmemMzf07DzV3DEX//a22pDLZ//FcAnft7PL4/8/NMnfv70iXmY\nyCScM3hf4byn8hXf/+Z77u4fWK13KGVBOZT2OKexpsaaVjAuXUmlok2xECjVnatIxbBKFR/nCy/9\nLSx/fRYItezaYSw+0lAuUnKx1bTYtbt8jcv3lco+53/xeuUMxtRkZQgxElQiqpmYR0gTKs2EMKOV\nwRlPstAPM6E43sWYUdrRrnbsT8+8HF759nigruEffvMH/t0//I9YPbPdNGw3G/pB8efPf+JPP/5Y\nhE+RzbbBuorzONBPJ4yLDET6nPn2yxfqquX+3qB9zfl14PH5G49fP9N1Z3JOWCtmaL7S3LzbYU5Q\nzXCrGpQxpO17qn9jmaYTN7sV3nrataNuZKIfxgApCde/iDuM0dhkUUWO4qwjpiTroG1Y3exodxvy\nZ0WlLSvr2Vaexmm8zXidiUPPdD4zjQO1r4Qd5SztqqVtW3HMLLxoQXViCRNOZQCYyzjk6sonvtwj\nY1BMYaJ2LbVrcLZGKwpHWwaf5MycZpkXFfWyWq6CYhy2XAJZc4n+ym9BlbdV+rJ15SukKZVSKfGV\nwmiPlpgFvJf8WmMd7cpSj9CdOk77ZzRS5Trj0SoWkV0mTOJLInCXiJ+ytqzaht27G1a3a/qxF5oe\nkQpJC7Ja4bShNh6vnRRLGnTUJfosolMqBF6hDfra4r0jTzDHhHUSdr5cNhlxXBzSjLA5pW9DyaFV\nEkZRGna7Dbd3G9arGqVFtNQNHf/n//GfmKaBfujQhTVmyMQ4k3UimUzWUrCgFHZd47XoQnbbiuen\nR378dOI0zZz3A055crAczyPjHNBZnE1zFM91byxZZaYQSYFiGrfiZn3LYHpOSlhUtrao5u9o2Pl2\nY/pL34i/bkz1dvL8tkZXDN3I8XDmcDgzjEIPUjpDsYh0zhWuaE3V1EDBurQtULvhwqEtkInwcBff\nB0CVWCi1XAy/Avb/ykab/+rnFx56+Slkcgfka1WvSsn0L886Rc5iJTDOA1MY6ccT+9Mzz6/fOJ/3\n5DygCKyaFus8cU4M/UQKMM49MSWstTzcv+eHj/+Gj+9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3ixQXIuegiOFpNfSg6XvIowRf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CzKWYN5VyeC4pUdigk+rM1HkDUY6evohkolcTztOZxUEJHygZxnWhF82NBqZJ4L//A//pH/\n+X//I3/89z+xHE/UXHSD5ErNFsR9W6XxSOu5KAC77cSrFzte3e7YBE/t2WgxDxV0Y6gzYVgrkipC\nLjq2qxbdeMMQV3WsiFCppKKv7das0ducx6ZNYlu4K/UZPSibaFWgAb5xPBz48PYtVz/+yIvf/mcb\nDOI6GY7T/sC7Nz/w9qe3nFLi9Tdf4aaJuVRSPRnGe8aS++HcRCX2OWfmpHM3a07kdJ7BWYpqBmJw\nbKaRUmfzWnFK3TS8u+bKab+wf9zTUmJ+/MyfvnvHj7//jqdPD/gYzENFiFGHK4fgrR9wph/qGuEZ\nuql/6SW4SIdLBNfa6lvtLFj2DF/FQQLeM8RRmRfoCL+zJ4vBTa3RWsERzfNFA0NzfRQYxBjwQ0Ca\nDvoYd5Htywtubq/AN57uH8g2ri6VRJFKpZGOsz6nEBnjpNnvkhQKcN4si2GcRhXDUJG5GuwptATH\nfeE0z/gJLq8n6t4hkvXZlcqS1PwqBMWTj4eF6XAieK8TiJKw2Y1MVeHB+ZARH3Tvzgt4he9qqtRT\noi7FjMUEPKSSlP9tFUoWEwUCIUajoqqVQ0PX++l4Wp9pbeaRMg2Muy3iPW1xxDSoyKdUTQwM6qhN\nYSdcM3TM7G4tSXLBM2wGnRFbG614nOtjKlX1rInYuSegbVJt2mJ9nb64HOf48HPXLxLISymIU/61\nGgwFvNcMNpekkIr4FWd1RpvQQ0usaRFwYcCHAe+jus+JZbZ2032IalfaBN9gsOZFEZhLXUURvcmw\nJt0rzGL/hrcv6fi4Juqmtz8+cjw+0KQw+IFSMp8/veE4C5/uT/zwwyP/9k9/5s2f3pGOyzqsOM+F\nbBaY3qFQiVnGIsrjjT4wOXhxveGbV1dsx4iUQs6FXIpOwekJrDVItE/Qm7xWmeAYQjT+qjZfxCqS\n7tymZfsZPul9dO9sKJPGDHsN6wyILv5iGLhrwv3bdwz/9M9cXL7g5a9+w/bmFqKnVGH/8Mif//07\nSq3c3t3y62++5nK7YXCiPP/n0J+9TmlCy5UlJU7zzHJK5HmhzDO1ZFopWkI3EzIFLWfFGnFebACB\nj0zTQAwj211k3DiWw2fefbfwT//nv/H48ZHolRMcB1VsatalC6IjRM5EGt5pgI3B8Mv1jnV/H11P\n+tdzBQOagOCV1eTFmETizo00o61J689AgSVQaoW0oodGCNa0g1ohOsfF3RXX377i6cMTadZMchgi\nc0q8+/BISE4rOadmTWEKbF9OMAolQ6ueUirNC37Q5jVFsX4XPHLSZKjkZT1gShCCFFyLhKLNYaZG\nXSpLKuusSxkHunXBNA6Ijxz2lThZRZUbQ5hIVJYoyOVkuacjtEgtakO8lIxoM0ghihjA2X58POKd\nYtBFoDqdkOTiRBgcgytIW3CoPmBJaUUDaA3xQquF0/5Jp245pxz8EC2G6qg7J2ok1+FehRatKqYZ\nmy4gFds8Zx56jycdbuzAoVtZcn6F0/T1HK49V7Cck8/n1y8SyLU5oAOoggtK9QomAGmCtPOHFZxy\nLm1CUA9a2lyIqlx0Osi0B5pSVRXpYmW02lXw+v1BaC6QijYCEVYs94xKGizDs9vmVIWlzABjYOTE\nkhNDVO+KUoTHpyfe/HjPd3/6yL///gMff9pzeDhqhiPNAnmipgLSdIPYAAexE9eLeqPsNpEXNzte\n3l0yxMCc1ASr1mY0MF0MvimroyCrvLe180EUg7emnfYfqmiGJ0U51k5k/Zxu/dPgAG/3xQLb2oBs\n7fw+RAgYF/sP3xHjhsPnB+6++Zq421Kr58O7D/z0p++pNbOZImX/yFOaOYSgsnF7VbVr1edbnVL0\ncq7MuVJyo6REOp1oVTH/VnRzBaWXI6hXR2vKjnBOxSsxBm5vttzeXXL3+gJP5unTgXc/vKEm9ekY\npmDNQFY+rwbnDo10QVAX7/SN3CN9h1XOINFf2SJz9vBwoSu+tJJ8RvdZ/1QYu7+HXrU046/rPqnV\nBl5vN7x4eYubG8fiSEWpdcdj5pgrOz9q2HFCSxW384TRq7OiqINozola1NsoxgGqNWCdwT6nwjJn\n4hjxU8BFFX81AZc9YQA/QrwU3OxpRXFsL9Hk6EKYRggDqXlqFmoWJFdyK6RayVJpzhGjHiaxeaq9\ntzktBLEALWpbQNQE8Hg84VolJ7UQENTqwI0DfgyMPrCcVHOAiFlTWK9McV5qqSynI8M0EcZRA3Dw\n+BZW8kLfJ947xAWtwAy69B6GQQU/LatiusMiKw7+bL3oJu02uL2/Zc9/bfr3nszPB3H4hQJ5agkR\npYpps8Gyb8uYO0St//8lbl2qmDMaOKfMCXGKHBXEsnLFVl1QHDoOmrX7YSRGpxNK5FzK90lEf9nw\n1L9Xw57Ng8JwRRc8LkR8mNS7YRhpCxzTyB//+Il/+5c3vP3xgXJq5HkhzSe8j5RcSEmbnMEgkdX5\nsfVpIJ7oA7c3E3d3O66vdtQslmm3Ve6vAqCGdLikda8ZMVtVg11CsODSA/GZR94HWD9XLnr+IpN8\n1j1XzLPz45v5jBjlsxQePnzg6fGRH/7tX7l58ZKrly9x4wX7Y+Ljjz8yzwc+v/uB48efTF3bV66a\n7+uQCk8wwcl2s2OcdvhpB5srSkocTied7FKrslVaVjhkgFwWcs2Ig3G7xVFVQ+Aar7+64m//7hte\nfnVLDI6PSyKODrdT/DzY3NHuZLeG6N6cMln+8/6THW/P2FI9aJ9zqOdXh156BuacwmPV1Ll6hGoP\nRp9JXOmT3fI0OB1qIcFDhSiCLImUE6fDCY9jiBO1VpZjVfWi9yy1sNuOjNPIUzrAohPjj0ti2WfK\nviBLISV1bmHjOS1JR55NOqDCjUFVisET4sC43RhTA3J21Kkx7DyXt5GwBOIpczoWXI60U6UulVI8\n4WJkuJxos0KNLTdNrhahzY3j/MTF7VZ9d5pnXhpFMqVmRALORaKPjOOIHwM+GdaftRnuZFy1D34I\nOhkIgdlpRWuMJkQbzs7pOVNzQwIEX/G+rmI6FwNSTGnpdPiL94oIBBeRrCsh+MhmGHGi0M3KroaV\nmKEHcd+fJulvup/CsyrPeX0+Dd3zPbn6uesXCeT7wxPOiXl5bJ+tecMEnwUdAB+MHRA0K64i5KrZ\nmveBzW7DbjNycXXFtNkRh0GHMATPMI0Mw8B2u2FzsSUEz7jZEYbxfPLxZeDq+7DfuPPEd7PCrFUD\nWPAkaTx8fuTy4pbHp8IPPzzy0497Hj4ulJOQZy0bvdcmVPcB14cn56arZQSgzbTdNPLrb+94cXdJ\nDEEZLu2c0dG8sVU03PTSfOWn9saXHVA6Ek+ZLgo99caL/lwP4OYuYB5Rz3nzrM6Rq91orXogotJ7\nLwY/1Mzy+IkPpyfev3uDny6Zi+PDh3uW455A4/DTO8YYbOFaUMOD00PHBQcRhjASpwuGmxe8/t/+\nG/O8sCxpdTyUolSuaQpsd5794yPSMmN07DaBaTNxcTHw+vUlX39zxdX1yGajNNTdxcjtq0sO90+U\nk1aJ6juuzfaVGeIMwRSteHTQ4/MD/0v4pPv/PHere27R3JugfRpM/1GFDt3qDyTWVO8yfxXNKBYe\nB5Xe4x2uVJz3HA862egq7hhjxIdA3AbaBG5wjCEQxoAbPGOY1KclF4PhlFVUU9Y1GANuEKbtgCCE\nQRgAFyM+7jThCh6qMGwGaqss+cTNZsvt1zuuX1zw6WHP8DgzPCXqUW0jchZ1L2QghJGUEmnOlFNi\nTnXtHdFgOSxIrmz9hnYSfFV2Tl8rDs80TYybgeAFmZUt5ix79d7hB+WGt1qYl6MlL5Y09agjXUzk\n1j6D89Casshap38O0SZkCZJty0rFOx1+3q1C0jLbqdDWRqnzbkUSNHP31jjXHF284Cpr1aU2JOZZ\n5L+s6H7u+kUCec1FTyL70J6AHzqU0i+PiHKSnYPtxZbb22tqrez3R2rdc9gvxGFgGDy3d1e8/Oob\ndpfXhDCQcwbABb1hwzCwmQZ8CGx3GzabnTahnmXmf3kprtWoTTNoa60otCKajc4lcf/0QKmO+49H\nfvj+I58+HDjuEzU1bcq0ZjRIDHZVaiLWMAzBr5OOvHNshsjN9ZZvvrrj6nILon2FUtV/Wixr6yV9\n7yXIynTQplKMwTJEpUqWkg0br2cjKsP29I4bLu66LJwzRrD+D6zTmgwWQMQMukwU5KGVhSWdWEqj\nDU+k6jg+niinGS8NfzwxDTqzEN+DmWOtzpw2sTwet7lgOCYufvVbUpJnDaSq3HEvTJuBzSbw6eOC\n1MrgPF4yU4xcX2345ts7rq83imsbvW/cDNy9vqEuC3vjHmt6FtYAvv6pH/yMfjy7L71a+et1ZJxz\nCxJthQWf/QrvzAHCrVBYiFohSG0WyPUG9R5HjN5EStZ0k0ZFpyyxF64uJ6LBRH7ylChIcMQpIl7I\nreCClv45dRMojwueiqxMDp1PocM/FP+VlQwQBg0drTb7jACNm6tLXt1dc3k3ckqJZS60CdKS8dEx\nToG4Vbw8n06kw4wkhRNP86xU0ia44MhLpSVRvDm3FSvuYpuuFQk29CQvHVX3BLTp6ccINJ3jepqV\nvbWasPXSSg/KMHiGbUSiW5lk2sDUXgutN8AdYQosc6YU24leX9dHr7Tgor4pYupN5x212kPvis1u\ntNbXiSnDV5X3ihg8q/r+Iyk7N+OkGHmrPD4+IFdCiOo/rJmvYl+dgYITXr684+b6EhA+fXoghPc8\nfD4wbSbGIXD38gXf/u53XN++xIdBHdUcuBAoRcUzXoQQI7vNyOXVBTHqx//LDehsirdmRIViUuEY\nddBBcJElLyw1cUwn9mnP6bTw/s0jP/35PfuHA2lOtKTDDmggMWoQbqgTo7U5aFU3KcoQ8B4uLye+\nfn3Nq1fXbIaR+ZRJKa2BuLa2BoQeIDTINPNLCQyDiaFaM3GMDqgQiyZSZcXuekB3Ng7OP2NCmMTU\nAo6+P7pvuYPQ3duagHHHw+B0LmPV97fUBEslloUhwBAHLnaTctQ79qt3ns75x7Jyj0fGET+OCFBa\n1RK6dhWsNsunaWQaAylnFYOIY3nYMwXBy46rKxWBlNw47TPTVo3YXry64fHDAyJ79bkxBFNwiO8u\ndai9AILgTYfgjHBip9AzWO7/LzFY/52+ic36Fu1zdD3REALB6VDgktW7H1RsEmIwDnhD1Y+NnBJL\nS3gfuRy3ROfxrqk1ufWWqlO+cypqTzFsRpYlkU5Zx7cNA7IVlppoWT1YaB6xXpJzjeIcrjTKbCKZ\n4HR/tIzgGOOG1zdf8/JiRy176r7SjhDKgCyFgcDF5cSrr17w8WnPn9584HSoXF1csru8IJ0SKVdK\nVytLgBZIreGbSvD6esTM6Eop+CyqVDY82eEYfFC2yhQpTQeO+CXbszPltvTUQeemDruB8XpkmRNt\nEVz1jJsJhkh1sMwnXPSM08A0DTw+HDjsF0qrxOCIQ2CcNkpkaMVsQTShDMY1V63HmU4tVYidzup1\nbTnbl7V0y2Qs/cfEYX99/SKB/OriBpFGLolD2xsVR02teoGvmyQwTRtCuKNd9hpFGIaJeUm8ffee\nWkdojXmeSctMSQtx1LKoZzvSCo62coCHcTQjrrBmUt1yFgeYV4SI+qGnsmjQAKpAblkXkIvEMOHd\nQPAbpB45Ph7JszJS1KmxC0acDjlI2WxV9SGJ8XJ1+IS69t3dXPLNN3dsNxuo2LTvQq5Kmyy1Qx3O\nxqcBTu/XMKrfifcKp+SUmE9H5fE2NdKS1lZ2C421UnDRGW/cP2vJPPvPBCyCaJOz2cHEswaePcEz\nCUPQubsCU7fIdYRgxmgd0lo/Rqd4AU4bqiE6pt3G7I0LVay6KBmRxjBEmjROi/ZElMXioVXKXDg9\nzXy+33Nzd8VuOyjD3wnjJvLi9RWf3lxy/HxkbmBM8PXw0jitlZmzLEmqMTmcZoN9U1aeB3I9Djp4\n3pMpcee7Kp2q4xw+sjY8xaFsDImU0vq3aGU5eeII3gt0ypqxdqZhYDdN64BsJ448V4oPMA408ZTU\nOB0XjsfEUgtN4GKzwY/eDuFAPmYkCb5FSm245pnctPZThikSYkA81CLMqRCGSNwF/vTdn/jxDVQK\n7z/tSUlwbmA5LLhSWYJnGjcsh0Q7NFz2lCQs3gJeDIA2cluuCp15oUiitGQwXyU3pWiONRBRLyYX\nHDFObB1oH9npXE2a+ues6lvWfe84Q1utFDXwKqJYeamImwl+g58mNpcXOBoZoS2aVDmDvwhBIUZL\nLtRLhXWQC/UZecD1A51n0FuHVm35IYZGmAGHQSz/oQL5Ztwo08FHWmsqrX2WxPTmovdebWeHwGrp\n6NTw5vXpyOHwDU8POw5Pe+bTkcdPn8hLZhhGBM3UhiFSW2EcI+PFls3mku12w7jZqlTfXqsZjKKH\nSiZ49UXoKtDaKm2ZKSKaMXjPZtiym67YTdc8fUo83J84PB5VsGCinRXbqo2SCzUrW8VE62CNrY6b\n7bYbXtxd8frFNTFEFbdk5eKWqg5vTdakWDMzp6ZXMfoVF280lVwvC/OyKNWQvojaGoS1QlD50TlD\n0cXSA7gHuz8anVdoQEFd/d5Ob3QdbenUOm0UDsHhJz04e5OPfjhwZoMgmGmWNVRFy/thM5kWoFBb\npeZEq3ovQ4yUUlmWkwZ+c6l0TdkQZc4cj4mLazlzwr1nHCK77YbLmwu2u4k8L+aJ8eUG7wMgvN0C\n13oFYxCUW2/NOWL79QatQFiz+9URqW5s5pxY9aFNL+etElkNXAAn+ABhYKVH9kaaiFJML7Ybrm8u\n1b7BSvEyF9oQCG5Qt70CZa4stVK94EZlnXjvdaJWCPjmlfmTFE70XnTepCmIve+iJkccIs05xm3k\n+sUV9WnP4XHmcEzc3+8pRWnAJak1RPaB6D+TUqUtBScDNTeSU3MztaY2zNg3fDCRV8Mqog45NDJZ\nhUzeE8aBMOiaHoiqtaiZtmSF6ZoytnUugfSTzu5t72tASY2W7bAWbUJ78/AJw6DxoPWxcHRw04gP\nUQ/ipsG8WqIkDoirtckAACAASURBVIUyu+Jz3UtOITTpyZRl61ItwZIvLBp+PoTr9csIgrz6KQ/j\nyNVw++W/OQ/ilLLk1WukZzgaPCrTZuLrb77i+vaKw9OBn77/gX/9p3/i7Z+/p5SmU12cYxwjm82E\nC3D36oar7bdcXU5cX1+w2W5xxl3HWApVbMpHOjLELTGO4BwhDNQqnE4nilR8DGwvL5F2Rc1CScJ3\n//LPfP/dO/aPB9KSlGaYjXkg0FrWrLg2O21lhUOg4tBGy93NBa9eXnNzc4kDUi6ktBic0GymobPG\np0mQzYpAZejm51Aqy6xBPOesrSHnzcK2nRulhtutgdoWVc8YOh7Jc1zcnpP6uejy8qZU1NFidWXP\naKFjuKK5UvbrTHpkDVj9mLB8FkHZQWEcKaWSSlkHj9SipbLzjpQW5tNR4Rz0sBITftBETZScI4xm\nAuUHQpzYXoxc3mzZXo48farP2CRuLXFB1kxobQiLYprQBy/3qqJvOM2unjNYxCCo1X+masavlb1o\nT8Crxw8F+hGANKtS1KNFxXKeYkHDCWyHkZvrS158dcfhcNAG4qJNRL8ZGcNAcIIXhytem5pbtVvI\ntTJFE8LVoglWgFRODFE9T5Y0n8stcQSJjGFgO20RX7h5ecmv//43uIcjn99+5s9//oC0J+bjgrQF\nkYJ3geIix+N7PI4Q1Dah1UbW0bI6iq45Sq0MIaoLZ4y0lllyYnVcRBuNLnjiODBuRsqc8SIMceSx\nJFoqSK6rHbBWx94iSbPfoypWZSwFWnWUXNBehTbFXW1qUhfVNS2EwOiF6jPFZcQ7hmlkHCdYFrKo\neEt5vlqNN0BJR7rnnw+HF5S9FkPA49VK12xLMNjRrSzGn4ftfplmp83b7Fv23LywG27fJ3Rp96Ni\ngyFYlj0RQmTa7Li6uERK5uPbHzk+7jnu98wm291MI1zuGKZIyxPOF3a7yMXFpLQldw4XempqNj4v\nT5S6MLQJkUr0I95HUprZH/dUJ1yWE615vDhudy9ZDoWHz3tSqUaX1c+j5VwfKttRVs7NRnMzicFz\ncTHyzTe3vLi7ZBgG5tOJZUksSbvnq7SXjo9bttqtRs2HvZTMsizqrpiKZm292WbBTQfOainoO6VQ\nTOLgO3PCrf0CtVJQbLpPYkKsMeqdLkIn9vqFPoyjqxr79dx/pI+2+surHyS9OYjzuBDIWT02fHO6\nSYs1o5xjSZXjaVH/DOcZvFe+sc7XI88ZncASlaZJQ8z4/eJmx+Xtjvc/YF75bs2sKiqvltrHbwnO\nDwTsffWg8OzokS9WcD8fz418dd7TYF5NrBZjh7S0SpLSVqxfGUnVsje3UtG8a0yD52o7UYLn8nLH\n7es7fvVf/46HT5/54d+/J5aFFoRcFo7HE2mZcb4xjoqLhxDpgw3EsSJL3jum3bAutDBuSSkhrak7\npHNov1UoqFZht41cX94RvfD+/jMXt5cUAvN+wTe9n6klpUealYBzkPNCSZUxQnCRKEEPzBAhRkrw\nFB+oBJpgh5Kn1ExOlfmYiE2gVJxruFAQl/EexmGjPjJoj8yLJYpStA/gVFw19jmztdHKWTLfqmiG\nTMGFivOazBQjEBADwzASNzoftczVCAOOKmexnahT4DpZKnr9jN3/CFErjeBHnPPEOGp/ELcmS709\n8HPXLzSzs5ecdkJawDhzcE1UIX0CugpXxInhlioEcj7SQmC73bHdTDpguBbSnMEaabWOBJMm6mQT\nExqEHhD7RtRTMKWZOR2Ibaa0gdYq2+kaiCs2l2tCDpVp2NFq5Pi48Pjxkf3Dk7JCeiYqrJlsqXUN\nYudErxdmEAfP5eXEV69vuLragqjv9rIkUsqU0syUydkhgDn6hTWIg6OUwjwnlnk2vnq1Kt9CiQPf\nqwGzi5T+9WcYnj4g47I6a0AKK1xy5laryi0GZ2KknrV/eTg/v+TZ89eK6Mt/7yGx+43gNZB3g6xa\nKnnJ1Ko4tQ+RKkLKlegCg/NEOtSj7zvPWYUn4iA4gwbUsMwPER+HcwNT32RH3bRHkZtOU0KDRQjA\nEA0rt5XUIRY75PT3db8V/bJm4+fMXB0PLcv33l6vQMqIDeCWVmn4tRzXZdUIXkfayWYkO/DSSDnx\n1YtbwmbicJr5XGZOvrG0hdqyiVpEsf0mSG7qRhoUntH5pOoIKN6p0lOUeledZpYBWyfm+tQEgh+5\n2l6xiTDtjky7idtXjoYnLRVyn5Xb1qrb28CKUqtVHcre8mKOkna/suHNmMYkeK24a1WF9Mk5Ylbi\nQAjnA9M7zxQHiuhQCSd28Dez4W0aGbvLaV+bIZqcGQ0NTUTJErXircnepOiQaHfWvkgzR81ObVwX\nsu6zXrmp0Zn2t3q867a6OnD6mVnWOSdYI+bPXb+Q+6FfA/dzIc75crqQmmZMm81ItY313P62VLOf\nbAJ4hmEgxsjsMs05WvAwRNww4EK0BfQM4+p+0jRwjlIyc5pJeVFZf4VSFgQhhp0KBcZATnBc9gzj\nxLJU/vD773n35h2Hh701P2SlOBWbiVnNJFz3vFGWXJ8jCcMYuLre8vrlNbtpVCbCvLDM6vJXa1On\nN8vQNFFSjDJExRVLaSxLZp5nlnlRkyVkPSDPgoKuSHxGYnSod7I/f/3cMncr7awiq2ObcyoNj17r\nimKHVvfFOUMKz+CUZ6yOzqv+60huBbAOUDTb3EiZGyl386xs/jiBMA5wVG/0IQxEEXyr6hljTdi8\nZErSwb46WBfwjVQS81yZF/VxD83YOEb3Wy14m+HzxQ5H55mGUT0MnEVpwaAyWZ+rBmIzWhNrltpB\n3wMVZpSG1808n07KDRdVCtZWjfgq60FbWyWifRG/US75fNjz9s0b7n77ay5urvn2P/2WH58+sZ8f\nKVIYxogvlZIy3nv1qZkLYRoZNpHdMEEbOORFXTOzUj0LOoi79ErMYCC10df75hi52LxgPjywFM/2\n4oLxakfD8Xh/pLQC1a2Oi955e3aRSTyhOoagLBlXhNF7ltrIeaE53e/BB1wwe1dbNWlRlo0fYRoC\nflSmCxJxrmqCIx5x1Z59QJruldqUcBAI6oEkWgJuhg0EO2y9GSR4wVFpxWAWKXYQBPV4WrTxXlM2\n61/FGtSMssOW5noZ/Pnw7ywVO/C1X9YZCM3qd/QQcD8XK/X6RQL5ad4zDCPjMHHe8OZtbZcKg/RI\nU5vbLhZxq2Q4RvVSGacdm4tL/DCq2Y1za6BPuTJtAiKemtHA0M+1NUPWutJ77ajj7miSWPKBx6dH\npI1sNp6lJh6PT3x+/MTj4YHry0eOD5V//Mc/8unDB2qacbUYdNHW01lsfFunGIodHBpk1UFvs524\nurrgYrfFe8fplNTxrWPjXQ7fueLGn9UmjWLSKVVS0vFmKz6NlXS9Qw/0Y75bxPYmU1gX2NmGwCNn\n87X1zmmmPXivVDnvbLizMSxss/am5/PrC5vg9a3oofFFkO90PJQ2F2KkSOK0LBxOM805NU1r6sWS\nckZEZ7LGUnC1WEauWW5eEinNtJYZpy3HQ+LtT4/c3+/58OaBh7efOe4ru8kTB8OjrQxmzUMHpAh5\nycx+YYyDBpfoqDaY+HkK5da+Tj/AWAUifTBDT9VLLqRS1ekxZ7VPkKa8d4TooVJxQYM5VahNfWak\nCeMU1fZ0KTy8+YBkgUHnro5twAWhzom0JFKqxI3S+yKei80F2zgxop7wc11ocyXtjSWCcJof1Rt/\nUisDPDp+bRoJpbA/7Pnn//WvKlSi8eLVNX6EnDM//nnAO5TUUBqyFKKPxGGgBW3aeiu+rP1PaA0p\nghStmujQiHfoTACdZq82yoVGsXutk4nE9srSkh7EDvCmiO5Vsa2zVivtpBBk0JREn70DiY4wjbgY\nEZsS5L3TIRTNUJdFPdE9Qmg6nara5+gHvArszjzx2myegPReyrM9Yiyp8+XW/fdXSY9dv0wgX57A\nXzAMEZFAZ+6ep9LrRhfaM6oRUIsOZTZ6YpOGEMhNu9f9w/oeLLvXcoOUG8fjwjwv7EoxaKUhUqii\nNKzSMo1KjIMGjOOJj/efycVxWRupwmE+8vHxnp/e/cgmfGb/sfDHf/+ep4e9ntZNVql8z8INuaAb\nMSns61cZ+DAOXFyoFH8zRZBGMgVjKWXN5vrD9t4ThqBezVb+5pQNhjn/TGepYJWMQ7OZVaZvb8x5\ntWvtiXQT5fH2qy8+ZUApQ8ZhCkOv5XC1N+hAS0agR7GedHfY4S/Lzr8K+NKzWzQ7j4E4Djivdran\n45G8qJVtSzOFmWVZcC4wbTb4+UTLvYLDxoZV5v2Jzx+fKAt8/rTn08cn7j+pF07az7gkTKNmmipS\n0vsX41ma7wSWRShFmE+JaRwJzgKF0QmlVz2a4Cnb4hmsJtaPUHWtls9NlNWk0vFiHjINEXvtpt79\nKRVi8NSkDCYVf0WmQRlYVRqP7z7oYInJM+eZMHj8EHjaz6RUybnRXMWjY+wu4ghz41RmIg6yMOBp\ncdSpNy1zPJ1UWeoUemit6iSq5hDfOC4HfnjzZ4ZxYrOJ7DYenxr5sEDu3uC2pqwxH8YBt1GaYWsN\nQtBGbgBfCkEqXoRSlYXjnWLL1YRxPkS85NWFUtCh7a0kzYi7mKfPNfCW9LRuUKeHaKtuFTYRYIha\nlebakBDUgmOKlEOvqlXnUsV+Vym06lBGetT5m72YNeis9aSqoYI8cZrciTvvlX68yFkI5qzak+5d\n/2xfPr9+kUC+5CPDGGmywbtpXdit9SETYo27Sm0avFPNijM3xxD0Z1I+In7g4fiZ/elJ1ZyCLQYt\nRZpAqo3TnHjaH3k6PLK7uiUOVzhXEcnUpr/7uBxY8swwRI7zgYfHBz4/PFBaJbWCCxfs5yOf90/8\n9O499fCBp3eJn374wHJa6Go8qSBFJ/74ENaY1R+BUriCYnY0pu2O66tL7q4vGKOnLJo55ZQMo8SC\nsS4GHx3DoKIfgFqKDiOeZ8vGNfCuM0gd6n+t6Bu1VQueGghcsODsAZplltGqFpS6ZQpbHxzBBQKB\nYTRqYtHDy63rsK0Zz5o/2BdWgy7phzVr2dnhojXqN2AAF2zjR4+UyrI/sjwdOO335HSEg05Mn+LI\ntN3QaqadetPRDtIiHO5PpPyJXD/z6cMTT48nWgFas6lJajzW7HDrB2AMDoKqZcMQcIdAWQrLXMjb\nonYCwRqVuFXYcx5icn4v63llhxXVfDbErf4xrJCXNjYRRylCo+CPi3qyJO0fDUNguxuULhsdUjNP\n9584vX/PLIX9lbB9ccVmu+Fz+UzJjZqFnBPDFJjGyAbH08OJ4+OJaVSvm00cGC4j1Xt8TuzzyaoB\nPfxLqZRWcFTc5Cg1sTwdFd6Kkd12oh0WHt4fKE8LLetndg4Gm9YUp4jbeepxIS2FGDdMcWJonraf\nwReqU28mJ2qMNgXH4qzh69VPJYg+O/VWL6QlEYiMcWTYTJDV26k5VTU3yTR9Q1pxWLWMB4IwXUTS\nUihLUZdV10e2GV7eHINEsAEvToRWPeICcRhxkpSFZIFXmWKqkREBSRUXgo6XsxilzppYBSy2h82a\nxBs5QMd0/GxM/UUC+cXmhjFOemJSKbUonawVvPlqq8eBWHMvsMwHjsuRtCRe3L5mt90xjhe8efee\nd+9+5P7+AznN9NOrT8pptVJSQtoGH4Q57TnOD8RxMtZKJdeF/fGBN2//xOeHD9zdvsD7kRgHvv32\nb2lSOJ5O/PTxOx72B+4/7ZkfhcPHhaf3J8VfjW5YUlGcrDVi7D7qIN7KaHF4CXiBOEQ2u8jViyte\nfn3Ly5e3eOcpWdWLz+l+OLdOcPfBqx2tQM6FlLUcrzaYYs3+/BpGz4Ida/4ZzLlOnQ9BrT2D/e71\nJ+0A8Q79IQv6+k/VJht1HxGFcL6kGH4Z2KVXBx1a6i/Q4S5nFhVWhTgR6unI8f0H0vsn8ruP1E+P\nyGGhLplcCtJmNmNkEzfWrNODC3vPysdtPH56IH8+UBg5nQo5aVbaitoHB48NrtY12Jk73j6/anA8\nUk2Ik22CuwRiiBTBbBJYvXm0apQ1WemMHZ2hasHcOa2UYjQevk3A6qwHsTvlB06nohldrkTt6DI2\nNUGLIWjDPy+0pOPNJDnKDI7GaW60psEjnRaaCGNQnn0+zJRT4nK3JS0Lp9OJJSU2l5eE0bObRgpJ\nJ8svRRuPPuDbAK2Z57vjdJyZc6ZkRz0W9nOhtGIGVmI9HlbxEUEZHa1gPSN9ZiF4GAebsYlqBqzJ\n3yu4nIvSKXE2aatXO2cxW/QBFyM1ZU6nI80pJNUb/OdU4/z/NQtSNFsefGQk4LOQjwuSKq05Tvmc\nnXuj1Yrip7iqRIA4RNsbKBnA8gqFEbVSO9szmMDO9Z5VT/vOOPm6gX7m+kUC+Xa6VOK/lQ5apmkg\nCja/zhvOqrLxkdo0C+gskFbF+MOJZT6xpIUm9RyhQAOLQQqdYaGj2e4RL2zGDSLCkmf2hweOhweW\n5Yh3r9htLglxQwMe9/ecHu/59PCB4zGznDJt8ZweM4eHmZIKOalqs48dc6IZHK43D3swVcvQ4BxX\ntxvuvrnk5sU1X33zgrvbS2jqOV5KNYvYL/Ez3yl9okrAnOsaxL8I/KCL3nVYp8PyTSlazlgnaEOt\n48ne9UaUPwdb+94+XKHTILuh0zlI2ctaUIczgvKX2PgXBmUdAupv2wRLzd5/zon94wP7/YF5Pllp\nrRhts880BMd2DAwBZZLY8++VR2tOZ31Konpt0Kn61AYQWLaYi5zZMNbw1LVjB413VnU1tVsVVcn2\njIpnwaQn3bIGMTkH9dZZCpZ0BFTIhI6jE9yKqeJ0OEIcJl0bzZpi4sgNTrniU2G09V4qFDwlBppz\nlAJyqmoVPQhC0bUpUHNlOS6afTYYY6QsiZYq9ZRpMRHcwBg8QdSXuy6NOpia1Al3NxdMm0CrhZJm\nUtIst4o6Nk5DwBsLRFCWSNx44hZyUJjRuwEvOsU+OIcYq8M149WLIs6l92HotExtKNamfSjQ7Dz4\nqBWWCfCooj78q797B3Q1qxGzBZAKy7FoNuyjcsnFQWmY+b7i8FWIPhDNQA2nsakbZ4k0peraulf5\nPWvlWTthQTqz3a2V6RcbmA5trhjpz8bUX0jZueMsiHmudDOzGl+VVWBv3PtB8SIcm82ENKXmKZfW\nhuqaYg+vC6NP84ghMsRoUmoh5YWnw0fm9MT11R1NYFkWUlqIYeLu+jVfv/wtFxd3CJHP+3tyec/+\neOA4z+QkSEalxadCOiUNpqlYdqiVxAprIKw+49GGswpMU+Tumxt+8/dfc319weubay63W46fHjXD\nXgN53/ysyYNDg0WtTTFxC+T9cq4Hzi8BDssV9f1wDj6+B2qnJWZnbPTOOnQpseJ6GvfOUcoBfRbh\n8+t50HY9Y3dnjvrKWvkLeJwe6JTXxiKV5XTgcTlyapkcoA0B1wa8G/EhsZkmLjYjY1A7YzEwS+9T\nXf0qXNNS1fugbo5ZBUbO1mIpkAu6cfr7dRq0+mkYo9BG5f83p5LxvtnOhkfnPdkPFdCNizXJxHx2\nXK8gvdo9VbO5xSs9V2dXDsRhBBcRF7SZFh0ZR02NNleGpgfwUjx5DNSLETY675Zc2U5bsnMUJ0xt\n0PVVG8uc14lKUxzIPhJdYJSITw18Jo4Q/UAVVT8mCnGoXIyeb7++4upy4nDYc3p6pJWC855hjEyX\njh2emnQoC9GDE4bLgbBzZFH/ozGCb+Zp6D3Fo7bR1QgD6CCH2lRVHdyZvaX2CGoFoUZngSEOOJya\n8jmlxioVta4BVJ+NZdIYd7w0lkMiDANhM9qouYZk7bn18XYqcgumTlflp6CDKcQ0F6Ua1dJsL7x7\nlnmjiUovHH/+MohNAudG53+gQO40pq4lkvPOuNBeMa6cWOZHVQ6ip9ecjqQ862YUjxsd280Fr158\nRUuN/fsH0lPGu0yMWmrFqGKZYRhIqfDx4wOMgj9WhMzT/snUnZ7d5pJXL37N5e6GVzffEuLE/rjn\n/bsP/OGPf+D7n77j4bAQ3YZ80jJ9Oc7UrNm4VOWJtib2gJ1iuu2ZgAaVNofouflqy1e/u+Wbv3nJ\nJkR2ccCZl0jKlWR2sXqPzrBDx5WrjXHLOa+jzuAcPFczp56RclYJ9pLeoTaawXuG4FeZvqAUPR91\noAc2EcehXs5Kp2xnNk1tK1wiYLRHvgjsqzDoLzIKPcAbf/lFj6M5T/WRpTmWpXD/eOTh/onD50fK\nUhmi4+Likuubl1wEYWO9lfOklX6Q6IEVUT+UORcy2gSvWW1bveGupWojszadW6oUWdADUKsRH2CY\nAj6q/4igEJd4GzLQM5Nn5ZBCNM4wVuygaqvXhlYlznKz89BwHOtIwyUXQowMccJNXicM4al45jZy\nmlGYsjX8tGG6fcHr33xFPs08vfvIvP9MWRq1KryCaK8kxpHkTSXbnVPxBBe0U9JQl8RhxOGUUTNU\npsFzdzdytXPc7Dy7sOFD9DyeEstS+S+/+x2vb64ZPfz09hMlQLyI3N8/kKXBGJRWN2oPos6ZUy5G\n+2zmV6Rre23498DoVZDUn7Pzqs4MgJPGEAatyFwfjGH4mDFkvA2EqBSyqJJzJQeAKt2CjpBrSZvl\nYgc+Yl4+9L2m/kFUMcIDdIql2H6qUjXD94HgWdXVamDXKatuhT5tI8DaDLW14P5ir9j1y7BW0kHp\nPVael6rTc2Lc4COkZeGHt39myUcEIY4jx+OBvBxwLNzdfMMQN4zDhjhOXF3NXF/f8il8Qk8/TO7s\nrXPtyaWxPJ7ILrG5hO2lZ5wWRJL6mqOuhmXYUWsmxolxmLi6uGa3uWQIEy0nUqnM+8xpn0mnQslV\nH4SAw5sUHbqVsEjTQQBDxA+OaTtwebPlb//+V/zu73/Ft3/zFTs3MB0SfHpafVV6Nt5s4bhueelY\n1ZM553Xw8Gpj0DNl0GDyLMCq6MB44F3w4JpVNh271tdap7NHj1mfaL+hGCWyQ5btywOkp6FfZOeu\nH0Tn5mbPXFeYYf3+M37YxNGaYz5k7u8fuH/3wOHphIhjvJi4e/2CV1+/ZDtG8sM95fGeYRwYxlGd\n7+x1alUWAc1D89Ra9K/N2agvvbGtwxIddnE2Ps65jsPYfQmEFqhNtP8iSnX1RD3IWs/IZYW31s/b\n9BmohYG+tjgzW1oz+e4qst6SFSbyURko0UdKqjpwpKKWyU0nzKeadULNw8zVVwHaRGsTJQ2kOZNy\noRa1cYgIe5dIWacCpVpWUZstYLw4pjCSRS1aU04QhZaE+VT44Y+f2G837MYN6VFoR6UOXm83fPvN\nHbd3E7tXkX1W/Pyw95SjjperOVMPibosFJvrqQEYXG0EEbPI0DrLrV7wGOxkMJ9oX0WHbsRVRV0N\n5ijK0dLfgV/VqUUEke5po2u4uUIMAz46csmUpVBzXXF3j35/s7kEymoKlrTp7+kMny8YS00Q3827\neLZP9aVtJxnjxdvMUXfGxZXD+LMx9RcJ5IfTE+M4MsRAKQs5L/RNggukMvPD2+/5cP+WIpnb2xe0\nXCnpRE2PjHHDi+vXDHGk0BjiwLTZ4l1Yb06XbjfD1qTBkjOP8z23dWR3eUdwnmSeJMswWBMpczFt\niGFgM2751Te/5bQcmJfE4emPPOxPHB4Ty7GR5kZN3XbS4AfndQqRk5XiFIJaX7oIV7c7vvntK/7r\nf//P/OY/fcuLV7dcyoblzXs+vv2sAoxabBLRc5Uka1XVqmbjKRVj8uhCcWvAeBYU3TnItuZ0OHTP\nVeU5TGJNFq9ZuLcGqI8qeMhWeaRloRbFnCH03k3XVp0hIPnrYE6HiDi/T83Iz+pHHfSsw0OKNGpu\nzMvM4+eZ/ecDOQvDdsvlyyt+9V/+ht/87teU/YmPKZEePjNuJqbLHWleKMdZcVGbb+8M45UCEhQa\nWd+HHXjViUq6DfZx/pytO7D7otlc6bir8fa798z6eeif1YK7DXCota6DPpxZlLUVamTNQDsmT7WM\nPqgSMgwBoqdW5ZtLM/65DRhf8oykQi6O6eIFbhjIS6DmgeUUOM6QF/254CqUPVIzUwzsjydSymSj\nAremVsWbYatBrWaDISPp1Lj/NPP202euhi3fvn7N4b7hSmAXBzbjwPXtxNe/vcS/KLz9+JEf//xI\nPSXyQyNlT15OzMtMXhLVVKTN1m00Ln8MntwsCHttAktT2MWZCyUilKKy/jFE7RuJzh+VVtX0zu5r\nAFX/OmWN1+Y518xCcxVCIwQhnzKyZCjNhNjBrIGhSgWvgbyTKywSoFYOfUO01RYCw/HVDMv2ncE1\nZyFQV7z7FRtftS/u2Z56dv0igVyZXbrjfRjZ+FF9GJZEoXFaDuSaOcxHcl3YXlzRzFYyOthETwyQ\nS2af9tw/feT+4RNLmvWB9SZID2pNS8Zh1KzGAXmpHA+POOfZjRNf3X3FdnPJOGyZtpfrUOfduONX\nr35NTYVlFv7l4U/MT4+quCxKawpeVYhnua7CKc3YHD4GNhcj40Xkq9+84G//27f89ndf86tvv+X2\n5iXxWPngHki528OaqrXb2zk9IBA0wFX7PruH55Bh/QbLClZxoTsPXG614Xw429HiqE0Dp/qPAEMw\nNWzACRbA1S5Ajf97si9rQ9RSXsV2V1MIv0I1nU7HswC3YujeQa0dfDLuv0Ifh8OJY/Y0B5d3N0xF\nYZtf/eZXXF9csDwduH/7gYf7Rx3gux25m24Zh8D7735EcoHm0O2r5XA0fFI9ONAbJqhc3wm5CUUK\nzfVgD9hG7QyWLpbqe1cHULc1ED/PFGsVaoFS0BmVJvFuUm34tceZ2x5iPR6MN9w8VG82C56yqN9N\nDTrHsrbzCMJam062b4AXakkcHu5xcaTMmlXWVCgnHQQuUmk0nmrBSWP2nvLTPa5BqI3JNWJztOpY\nbEp9dwxrpuIZd5F2GsB7aqhMFwOX1ztubm65utlycbvjxdcveP8vn3l6e+Tjd0/MDzPHp8JpaStj\natxEatIZtySjogAAIABJREFUn9VGNYIG4IyjtmQWA+rHgkBuCWFRfjmeLKrc9s5pkJWm82xFAZjo\nAslU5cEmdmHQp+4HrczCMChMOhdIhdCazpUV2yft/2Puvb4kSa40v58Jdw+ZsmRXd6OhMbOjwJld\nLvnAw8Mn/vXk8pwluRzMYIAW1SWyUoVwYYoP95pHNoB97gmcalR1Z2VGuJtfu/bdT0QMQnMu2Wig\nhXbZVoNbinbnRsCybMQipFDItuCyPCtFCR31vFDfK0WvMWKoJe/WkvO/Ix65d2JCBSJmEVl7YN8/\nEHNkGHcYA+fn5xhrON9c8Hi3IxPYbrZYV5jSkcP4yOPxkcfDI/14FOFA3eHKCX8Vb5AqlzWQLTla\npiGxWa+4PHvG9cVnLJdbvGvx7ULf2zQX5TglxuNEvx8YjoNMw5GJei5Z/aGFgpXnLMyMby3d2rM8\nb3n9+RWfffWMz7644vrZlouzLWerDcNxRwyZaaxQSVaTqnzyM9ZuIuZMiJmoSs+nxzYxrdLuHNQx\nzVDqv6+E9LkzPg1RZ66sdzivpvymht7q+6qCp1KHgPXIJ0Mm6dJrh2Hmn2WebjYVZoH5PfAEDqqF\nsBZznGG5XdF1G7Jt6PuRGCOvPntNCBPff/uOu+8/Md7d4cOB+xu4vLxge77lfnFDLFKkajCjGBoV\nTBZNAuXk75OyISJOfFNMVHZgrkdcbQ5SqnbHMsgWJ8MERnByY4xutLJHhJCVv51Ik3TP+Ylx3CxQ\nQbBrq9dUYPYCGqmndBRhzjirJIky21TM+gHrwHqca1ksRCY/ppFh6JmmnpykAakOkSXKzzE5Ew9H\nnDE0xpCVOJCTIcdArsIoJx0pSiFcn695cX7BL3/2GQ9393Tdmpcv3rBcZc4vV2zOJPR6uV5SnAPv\nMV5OsrZtMWRcMIQkHv7OGU3aEXpnKBm8JB6lEBQcMTjfEMtEJgm0UQSLjgqlWeGhzE2E1S5XzPc8\nRqEtq7hGNlCswbctYAhj0ACWupY95ulC1cKfUvkBlCpCRnl2rfPMViQYmZE5SwkKnxnkHpQTqeFk\nqV3nOzLqlfXy7whasbaZTd5jDJQciXHgcLxnyhP9OOBbz/PtSxrfQnLs8wGD4+JiC7ZwGHdMpWF/\nPNKPvTiNoUdhzAwZVF8LdIds2gWLbs2yO8f7ls3qGddXbzjbPmex2GKtp5AYhiNTGPGN59gfub/f\ncfP+lsf7HUM/qmugBedEUKDmSeJFJLxkawvdumV1sWB7veQnv3zFmy+uuXy2ZbtdqtGXJwwjYz8w\naiEXsUUmPsHcMmL8E1IixjxL99HPSBaHtx/I3Gd+alYMtnCilstiqdDBjP2qy2Q1cBIsPmqgQ11c\nZsbR/7S7xkANftb/MC96GeaZE0SU8+k+1bdE1Q/IhtGtFpxfP2Nx/YKQCo8Pe/ph5PrFM95+8z3v\nvv3A7v099A+05cCHuGPRNlxcXtGuO0iBEqobo0BAzhRIkZwEM58/hDazMWXGKUmWpXZsYuhUr60M\ntNJs1VvmYm6NRKblKP8tZYSeGsTwqwQ1gtMB+ImKmH5wTWVvqwPqKH4es3mUFCNtIwWj1xPGfC+s\nxVhP1y6YUiTGiUO/Y5wOlDLJxTYyL8laQAqSSWsMBC1q1nu5RwFsHSBaKydQwBTL5fNLfvbTz/mH\nv/0ZH99/pFue8frNT+n371lvIBNplw3dpsOvW8wi4gK0TcYvl1hVIhfEbRJr6BYdUzAMk5hm+cWC\n1lvSsccEYbj4phPv8CKS+rrBllLmoaLRDbeuv0rftd5DiHLNjaeeG2t8XIpZIikzyDHEYn2DQymI\naT50ceLUVLV5OcFjVuEhIzCkOIVaYbhVwGQ+iqH3u8xzEuWEzY3QnzLD6utHsrHtAYe1jtZ3hGhI\neSRFGMeJECPr5YZUMrv7Pd9/8444Hbm8XLJevyYWw/4YoT+QLfimYbFY0LUtkx2ZgggW5mGnYpTW\nNXz+2RvefPEZL16+BCzdYslqtca3jdyUkkkpcP9wx/74yPnFBdY3tO2Kqc+SI5jSfAMrPbBpTtmA\n5CgFo/NsL1dcvthy/fqMV29ecPXsnK7zrBZnWFr648Bht6M/HATCCJEpZkKSgGm5f3L0n5JQHGuR\nqB1ZRWDmDrv6Y1fWytMu3VrgJLn3XvIOG1VOylDN6M/IjMOkdMgn/uJop6AzojJ3tfrnnH/YeXOC\n9sRHgnmDqVVUlqueoJSeZY3l7OKcizcv2bx6zWF3xHvD4SA+8943GCQwIWdhN4194PF+j/MNzaYj\nThMx5oowYih4V7AhC5xTvF4XpUUiePg46jDOi3IQezr9yPuvn1kLQ2NmuMton5ezYPw5y3GblChZ\nsGmJJjtJ9Cul7U+5+pUqWs24pGUrep11IFwglrr5qzAmjZQ4MvzuSCpZGoVhLxGIpYAe1WtiUd1s\ns96xjBRBVyR6kZwwoZBMEU9+LCkVxkPgl7/9Gb/+xU9YLhZ8/mbN9vKKZ5+95MPbPQ+PH3n3/37g\n//vd13z39oHDLpLGhEkJX6C1GnRsHLFYshHriClOBGXgpCJhNM2yo2AJe1F6ds2CRCKEnikE6cC9\nmOfVYYzYG8j0upQyd98xJZkVGalDMq+QE1R/6KU5SuCNAxLWZxabhhwLccjC9dfGJlEgZZJuhLUp\nMVRfKDklpeqJpKHOFBEdVchODo3VWK8CgbXI183h31FHPoWDWkd6ShaOZNOu2Gyucb6lTQHjOm7u\nPnE33RPCyNXlBZeXGyngppWC5MSsKVFoulawLSMRZ0L9qUdfCW6wTrCxzXLDs6sXYBvkUmWO/QFr\nR7xrZ1tLay3Hvufm5o7vv//E7ccH+n0/T7D1TkioupECFqNM/5vOs9wuWGwazq5WfPb5S168fMHF\nxRZLYdmdsezWYvczBcZxYJokDehpnJvCt5Qi3i0pRomhysxH86eb9Hz6AKn/xUgVrQu7Ns4KhzTe\n0XiHr0Uc5g4+xsSksEot5PI9ZHHZJ45scqRXxac5QRHUfxooOtR52lXMdq5P1scsonCW7XbD65fP\nef7lGz68v4EYlWuMnChMwXUeU5YYdcvrx0g3Bly7wPiRVCacqe8NGmdprDA2pko9K1aP3vJ+pqkw\nTYnUaNDxPIiEyiKQzy4DOIuciIReKF4aRdkpp9NwYb5hRWXbT6yMtcacnBJn06U8H7+llmu3X9Bf\nUlxDTsQisvaSDSUNHO96KQApyXBU8BqBDOdN9IlVAlCR/0hhzJIN2rROOvYqUiqWHDLjfmLYHxj7\nnrxZ8fzlazYXG6xLPOx3fPf2ho8fH/j4YeDhdmJ3e2R6HMQQrkApjtZYbLE03lekGBqLs57WGAiZ\nMiWSCRgMvvXzILExAiNltY6tm2GO0kx479VQ7dS8lCKeLB4P2pFX9W/RzThrwxKLzE2sBb80hAHK\nVOGaiq/nJxh3bRpOcYkUedaKUVplyVToUWAi7cbnQWaFUBS+4Ymb5r8nr5UQByL6IJQG65d433F+\ntmC5WAvW7VvuH3ZQ4Oxyw+dffslqseDd+/dier9oWW08YxykeKq8HCOUMnKVQ0vn56wMq+IUKMXQ\ntku8XxJyZBiPHI47rHV07ZLV4ozlcknME3ePO7755nv+7fffcHvzQH8QWAWQa1pqnRR+dsngW0+3\n6thcrFltWy6vt3zxxWuePX/BarEgTRPedjSuw7fikjgNoxTyGBX/ziJV16N9zkWm+jGRQ9YOQ4rj\nDwIaSpkLjcxK9Ig2f0k5YeJWpMPVFErqi+J7KZGSQCohJvUBUQGR1S4jq6+yQjOlMDsG5nIq5E9f\nFQM/QUC1Oj6BOJDv49uWi/MzXj6/5uWL5wyPB46rFaUYHT4lnC90246ysBA64n7HWBxDLKwXC3AD\n2Q6A+mgjFGHvDM4mTMpgHNUHmiLFagpayBcO05ycHCvjzTzZGKSpLfOJLkfISY76MvAWCGw+yehD\nX5TSWB/o+ifBeutxuu7mFUeVzTBmKbSxSLFJORGJhCIcHQCyIYwyy5E9JJ82IsMcYQfzVjKXDqO/\nD6VgizAz0M7W6o3MITPGiQ/fveN6u+JsteLVZw3FwO3dDd98944//OE9tx8PhGiZjpn+sWfaDQxT\nENvjsUDX0XlRUVpdD8ZbaTCcw+RIHiR83HZifYsr5H6SRCDriN7rSaWu/QzWzgHrlRUk96gQoqQL\nSUyiV2OuJ8uRyiNRTQSWBCRksFx1oXKL8rzpzhYYnODGCufMMv6shbl+7byp6rpAGlDM07txWm9/\n6fXj+JEj0VEpBrGNjQnnEtvNOZv1OTFGHvZHcswsVwtev7nm5fPPScHw/sOem083LJZHLq+27Cfx\np57GkQLzUKEKLgriQVyVigXBQGMUwZCzBu/FV6VCB06xxeNw4NOnT3z77Xe8/e4dQz/pgyz915wA\nZAtpkkFr27Wszzo2lyvOrzc8e7Hh889f8cUXn3F9fUUKifvHHfv9DlMsLsNw2DMOgxRxxb6zFvGg\nvisxKsMhZ/EG0b2kGEO195XOV3YXW8UllRmiXXTOipPrcNZoF1CKIcWMdQZfivrf6KaSJBgjRSm+\n3lmM8Vgrcn+5tqdu1jkHqYZMQC3oZV6kJ1OgWvyl8CsEZCxtu2B7dsGbV6+5ODsnToH720dMMbx8\n8ZxjzDReePnL7YqSDWmKDA9Likn0xeFLQ25XsEyEaSTnhC9ZBCFo9qQpiApGHyjFrEMUKp94njek\neBoYW1sfc+ma03zklY4rxEiaDCnk2TNHBpqVhXR6EmpnVj3cTycqTbLXylLDsqW46OAbLea5UEoi\nkkhGxF8ztIajenUUPX0+rUGmlNMaUNgLKt3NkktiShH6gcZZHOI/UjCUmJniyOF+4NPNJxabxMQO\n7zseH3t+97u3vHv7if39iPVOHCptIeTEmCIhJ8iDrOuuxblW1LYpkUKia1oa12C8ZAWUAo1vKc4S\nQ6CXHRNMoe0a0kyckGFyRjI3ayMjCFFWCEMKaa0X8xcYQynqTU6FDx0xGO4/DZQUMVk2DydttMyq\nTBWPOT1Fa5i1eRpagUImJ0hrLuIzTp5nfB1OStVS3A9OrX/6+pHcDyNTCOQUaJsljW9ovKjGrHVY\nJ4vde4t3hpwk7i1FSybRtBbnC8P0SH94pD8cCf2kvE5VraE4YxZOedM2LNYtL1694vzqUvyFTWHo\nj+z29wzjgcViTdMstFAbQszc3j5we3PP7n4vKTFPrmbV4ZUkB6nlZsHVs0tefP6MxaYhpp6Xr57x\n4sULzs4uaJslxRbOzwrL5RZy4fj4yHg4MI3ChJFCLhzjXItoyISQpfiUKsTRSm4sNs9jsfnIlykn\nA6m6BKQKnABrJwsm5UxMYubvnaNt3Fw0areYMgL35GqBW7FAi2/sPOwVpqQRKfzTQ8LTDrTokXQu\n8k+oooCxBd9Y2mXLvj/y/Ydbpvue97d7zrZrri7PSbcPtK1ntd1gvGSqTsOEyxtyCgRT2EfAt7Dd\n4qLY2zINwqVXNauzim0W8fY2CA0x5cw4BcbJkZKEg8vGGhV60z1Tr3yFwXIsxDGRk1G6p8xUqm3Y\nrH4tkBWnNlb44aYYZdKcTi7Cfjj9f6H8oJDX32NAYu68Fu76vkSiP6/beiqjnujmr+R0x5WqqX8n\nx8J0DOC9hLXo3zEOvJ5YjJOmZr/fMfT3fPyw49OHR467UXFjWRyucSQjSUtON/+QJvIQaJ3AQylH\nNWYTSCnVoOMEeQhifKXMlHrASBaR1WdEYVky4i1aP7MRloypSauJKU80NHjrcVZhnVKDHjLZVEdP\nPZGN8mSJU6IMk+WE556sZXtqquRCURuU6sGjBMP58+WSdROtfj16zMdKk6ERd/Wz/qXXj1LI98eB\nEHoMhe3SiwoKVUqprNV5Q9s12AM8PtwRxknM3F1ktW2xrrAfHglBdknqUVQZFaXUrlzZKr5hs91y\n/fI5q+2GVCIxT4xTz7E/EOLEYrmlaTsSmWEK7PY9Nx8eeLzbM/ajYMQVf0autc3i1rfoWl68uuY3\nf/MrXv3kJdjEx4/f8+rVK549e8F6dY63MqBbd2f4dsG4e2TY75n6njgFguLSKWqqUE1AShIeXDu5\nrPj4fCzPJ7xTtnz92uqhDTyBW58c4KizM6EeK+3QOie+zk8XsQ4Thbcs11VEC2nuJquf+VzMFVeu\nb6u+55lVQy3j5gcYJgbaZcP6Ys2u77n/+ImjXXJ3GDm7PGezXfPp9k5ocotGvEdyoJiMadxs+zml\nROMtTbPAl47iChkNATASitE45XmTZmRTzluFfoz0YyDEVplQ0qlna7DKRhCPa/keOUMKMsso2aoM\nW4p5MQgTyJyuvXTiDucaXONm3cF8awuz6jUXocfVe6UjULmGRrrnYhwWkRWXkkUWXrt8ynxSL3Mx\nl3uStaDod5t9SOrNKBniULAt0IBtpKX3jaVbN7iuzp8a4ph5vNvz7ruP7O56wqAugUUk/9bLQNMY\naKyoK0MKjCGR3All80bi1uqcxirdNR5HUkkyYJythi1Y8TKXWMcExtbcY05sf4NDgi8kaD1gNRqw\n8Y3EoqYJWzQYoj7rmlAuBgAV3rAyKDUGpyfRWfaBUnN1jpMV8snICVoGrpXdciricIK05PdVumfI\np1bgL9bUH6WQf7q/ZxyONNby4uJzjsc903TH5cUzmnaJsY62a1ksO6wzPO7u2e92LJYrzi/OGKIh\npMIQEmfnZ5xvDNPjxPf3k4YUoxJdoc9N00RhRdt1pBR53N9yGB65OLvGtw0XF88YxoG2XZCLDFXu\nHh/47u0Nb7/9xO7xKPQwvZT2KXsBoRhdvzjjN3/zc/63//1/IZvEsd+zPet48fwZz65fcnF+DdnR\nNgtWyxVgKcMElQecarhxEv74jIkXxdQKJanisJSZx3166E+7ueDYEjwsvl2ZTBUp1KMbYiHayvGy\naaBberCW45CEfheTCjOYwbmcpRtLBWIsOFfUX6aWZXlZpcCVunPo96iKxdl7pWLp+Qmu6Axnl2d8\n8fMvecwNOxpSkqFj11raxhLGgbE/MB0OpGyYhoFxGCghYik0zrBoPZ23tNbgTSYkyzB5RjNhjKUp\nhU6P+qkoE1FxaG884xQ49hP90NB1jao/LTEVTbnPs6I1ZTPPEuZhKPIsp1zULK1uYmYOFnHOize3\nd4gGVVknmNn5z3BiR4lSWTaa+tDL7xwFp78XPPfkhS4vZ50W8ie8Zb3muZy4/kVZUkLZdBjjBUpz\nHt8Z2pWoH/3C0W07UokcD0d2dweWiwXjbmT3SbyPJPw702ykyzQlKTwpZIeUkpgKYjSr09N1Ldvz\nM8Z+5DAcGWOYTegKELLkZRoDrfN0vqVt1wI5moxtJZy5jo7MPMioHXH1QamKWUtbGiiZlIQqmOdj\nUcWodQnr9RW5PWCcWjxDsTIPmYVj1s5GetX4+WT4JRTGpxMJg1xzip0PTlJmTuyu0///8PXjSPQP\ne1FjtS3TGDgej6QU8V4MrkJK3D/c8+7dO95+/z0P9/d46zlLhYuLDUY9hfsQWHQLVosFa7Pio/sI\nVMxVCldK4ndeJdPH4Ugwo4QpOMt2dcliuaJpW6EIlcRhv+ebb7/jX/75D7x7e8Pu8TgbUxkdPNXB\nj7OFRef56S8/5+e/+ZLNeUdMI+1iyWb7SsUZii1bh/cNzjXKO86EMcivKQgenvKM4ddfORZR/lVJ\nJYUfqEayKMvKk4Vr1Xkx5yJm+lmsV9WUAotI9o3mJtqupY+Z2I9SxItIiGX4xCxkqAv8yUx+PjbW\nSf+fvuahc6m4L1R6YvWYEi9qha6y4fDY8/brd4TFirw+p2k2pOnA1B8IY48zhdbC0hVs4xiLpU3C\n4HAUGgetSzQkfM7Ykkk54MhqTSuBuV0sDEmAolIkA9QiM4eYAsOYOBxHpQVatVM+DbarpQHUbtmo\nLzZz4S2UeTidsqgp581Yu8qcq3VvmTu3Ys0PbvfTR1nGHPURFwZ21ri5glG4qDJpZGGYJ0UpG4W2\najNgkP9uoHLlZXYg8xBDy2LRsr1wbK7gcX/AesfZ2QZnPYfdwLf9B5xxPNzvebjZc3FxDUtHfxyZ\njpExjPTTCFk2SmdbUhqlGFqLcxqq7iRQG8THxlpVVCaZNMcsbB1KwZaEc4EmBdWkSHCzUSvjqeof\nnsIbPOmagZjTqTnT/+aezBXml6nTA00oyxkjV1hmIKomxRSyMdhi57lbJgoMaovAMuaJqM48eZbr\nD6q/SoVZdJP9y3X8xynkQ39ks17TNi37w0G8G7yT4aOz9NPAx08fufn0id3uCHiMacnZMhwndmPP\nIQTGBK1xtCvPZXeOd2IV6Zybu9rKSxY6XeRwPNIZh28t++ODYvKepllSgHHoub2955s/fMu//csf\nuLu5YzgOclwuRShjohTBUlitOl68vuBXv/mKz3/yklRGnCt0iwVtd8bh0GMM5BSx3UoFR0b46jFJ\nhuI4zS6GQYt4COIwmGMSo6uMVopMsYjplX2CsJR5bDYLfORYl2eDJioVThWeKUExlmQsY4bjYaTv\nI+OUcY2l9ZbWG1otfI21mBosq8W9UjxL+SHr5IcPTpk3VWB+IOpD9bQ7lCmvYXe3px+/ZvHiGV02\nuBVMh0eG44pp7Fl2DZfbBf35AoOht4FDMoSC+r1nGiT70aREjpEyBYryh6XDs7TO0liEt1+SiNWs\nE3FQMUwxcxwibZfn4Imo96ScECwxc1IxVSnIvStlniUUFWzFHCl1HGlEjZhVWBTrkLswmz0V3Uhr\nezZfVyoOXNRJpsz+NEX9SjLMEYimWKxt8I3HeE8giDJYFDhqbVE7ynrUr32kwRpP4zxd61gt4HiU\nArtZdnQ4whg43B7oD4HjfmAcAs+uGrp2gc2OtD9SEuQgYcdYof5lpbA6Z/HezYWt+usDWOvUf6ie\ngo383SwDXhsNbhxEGVpETt81LRSEeaTK2FOhtvPpTwzPEq6UuehSUJ9wNejS9Uw9KclCpRTZOKWQ\nRynkgocJVGwk+Bk0j9hk/cxe7r9VQzp0qlXKDyu1AcxMEXgCyf3560cp5DEM5NQSw8jdIbBarFlu\ntnIMMT19v+PT7Sec73j9+ies1yuMsYz9kffvP/L24/f0cWJ1sWV3c8twdmDz+RJDEXmvM9gkN1pS\n3aVw9v1AN3YstlsWiwX74yP9MLDb77m6+gxrPYfdnu+//sB3f3jLx7fvmPpeHrSCyJRjhCQMkrZ1\nfPb6Gf/5f/1Hfv5XX7K9WHE4HjjfnLFcrGkWC4xb4PQY553AA8VADZEN06iFPOnQMYsgSOPTRJiQ\nRS1ZF3EpcuLTzrj+qso1p7aauViV6ddiW6hgaymFEguRxG6cuN2PPD5OTEEq9PKsYaX0rLZ1NM7I\noDXqAK92pUVsUb1/Qv16AuGcAiTyfC+MQixZxR5Cr1QMsMgmkdJASInu6oJpf6R/HDg+7JieXUDJ\nnJ+vMJ8/Z90mHm4/cRsiwU6kMsp8AaFHGozwy4eJ0I+kMVOSU0xYHvoGEf2EIowc7wSvrj40IRpi\nNBSnRlmIhNxag288xQlH2DiP8S0YR5gC/e5ADEENlhQ6K5JDCyoCyQZjpACkUmcQCANFsyZtxV2R\nolJPNlEhlkQh68/IRX6PAoGppFMh9gu2Z2cst0sGesZxEnFO8Rz6I8PQz8/oaVAt5cM6mIbA46eR\nMESO48T6zOBzYLnyFO+JU2T/4chxP2HwvHv/ie2mY7Nuefl6TT867u4Mn+KecRRWUEyZxlta7zCI\nT4olMw29RCXqPmap20rVO4gFrTUSz3ccxdCrFAmsbp0MqEfN7yw6LXSuVQhDsLxcRBwkxbYeWAU+\nqT+7yuIrXbbO4vS8qoytuumdarEpSjs0BfQkJjx1hVisw/lWVNPKR5/PBJaTP1apm3ie2Sx/+vpR\nCvnrl69YLdeslxu8XeJsRy6Fb99+x2a7xjrH1cU1IWZ2hz3D8EjTOob+yLv3N4SUaLuORbekibBa\nrFlvtkgART3KqxBIi8Y4BQ7Hnm1czxzl41HyOdtFSz8e+PTxgW/+8D2/+39+zx//9Rv2Dzt5ELUT\nzinNLJi2hS++eM4vfv0Fb948F5O6ENmut1ycP2Ox2mCco2vlPbS+wfkWUEpUDIz9gX73wDSNSvWT\n7kE6XmHdmCJtd866mI3RKDtA6Y8SnCwbmPMWq+GxdSMTSMZg8FiFeZKRwJOHQyDsBnb7gRAMpYi7\n31QiMbSYTYv3Fucd3hva1qvjXyJOWZVrwugw0l7MjBcpOsqMKSe1IKoXqpFu9fRkQGEKXbRkinMs\nz87ZLM4YjxP3H2/5r//Hf2HZWRY+s+0C3WVDw4rGJh4aS9+PhCDQ19QH4nES/UBKkBTn1MGqt4bW\nQmMKYxI5fXFAVpsHKyW0qlCjeq87Y8RUzMnAM5VMmCZKiMJmqClLXqiYtdUzSe5FKRKAPCtBjQp8\nqCKfOAuMHBansJlcviK0w1rE63UGTcCp4AtgBE0vxpKKKIPbWOgWKxbNEjbSobY7z35nGIZBbH4r\nvEKiEMklCmtsMpi2oc2Z88UZX756w/l5x3AY+fbwCYt4swhUIlS8oQ8stx1nXYdzZxweBqZhgJyl\ngJtCigGMwdlGZjvZ0FgLjaOkrJFuMgfo2kbK6RBEnFaAbHG2lW64VB+dcvLGRyETHbCWnCU0QgfV\n4n+ffzAIltCaVoy7UtCrDdqaM+t4TR1KnsIjpMBXbP7EKZN2X6DQ+n1mn7n6vbXrNzOAqfCLdXNj\n9KevH6WQX2wvWC5XbNbnrFcXxFh4eHzkfvdIKonNZsvlxRW3949Mwx0pBYapcDzs2T3u2Jyv2J6f\nc3Zxzca0PNte0nYLMCIbjmoVmotiwUUSyMcpzENAAGu9ynMzj7tbfv/7r/lv//X3/PF33/Jw+8DQ\nD8I40K3bZMGNO+84O1vyi19/xa/+6mecnW+YkkAkq/VWfq3OZICWZfjhnHoWY6RjDBNTf6A/PEgi\nfFTvlkbnAAAgAElEQVQ/Ey3kQnqVYl7JhZUPOw9xtLs1ylJyXn4ZK7O42umShTUg3TokI1LuMRWm\nfeA4DDzsDxg83nraRoZQOSiEpN7uzjU0Xj3XFVOt3tViLapv7gmsIh3OaVxT01FAURQE1y5qTFUd\nBefxjmtol2sW23Msjg9v3/LuD3uuLztePlvy7GrJsmkxZx3eFtrW8bDz7Pcj4TAxDZHxIF26M1Vv\nV0n44KyhsYbGyMOUQxQ66UwpQ9+rqDVTzsKTt5X7rZBShjiOpCIwi287QJLes9O1k2UTkGlAjSpL\ndQ73hI2iLIVS9YKVPVF/lHbipii74ocslpNukHoVKQjFchxHcI4FC7qFp2m8ZrSu8QYeS6EfIagB\n3amQB3JqIDX43GANrP2K89U5z89X7M2B9/YBZzzOZazzCnEWpsNEd3Ss1i2LrsE7R+OcFEBriWki\npSRJX6q+pij04OXZK9q4YKwYuiGqzlxEyDNrOoxVwZ4yoRRGnDvd+evKXBjtTIvV4qytsLGGtuuI\nMQgJQjfzPy+lc3kXeqNuLqYGI+gAvX7lDJBofSDnJ4XbzndOtcbUDt01ThqDv/D6UQr5bn+kaZc0\nbUvXtRiTaNuGs+050zSy2+25vHqOt7L7np1fcPd4wy5NNM5xttny4uoFz19+zovzS1rjOe4PajZV\nCEmP7EUNfpQXbophu9zQWkcaRpZdyziO3N09cne74/f/8ge+/v03PNztGYeRGKKEvhrZIV2ROrVe\ntbz58iX/4e//mp/98iv2/RGmxJQjj8c7npXXEuuWlcaEFDRbRQcUSoqkMBLGnhirDD6evEaqYdMT\n1gHaLYgRj9D9iuJosobNKXS4kkUUqqjRVI0zmJyZQmKYInEXmEKCbAkpgC14BBvOkySSl5x0IGYp\nZJZiYIeLVOiWysio2N/sJaLH1aJH1piq2RdUXzf5VChMI8frgjAsrHFMYaK//cT7dx94/8e3TIdH\nppdb6FeUYcXZ+ZpuueTqfE3XLWjbHsOe230gx6KFuQb76k+bL6rw7b2z+FiYMdA6a0IGlCmIZUAt\nBvU4LbNb/YCyc4NRc6ts8EqNwyYJw67NWK5H9pqNOk9I52YawxNNRMXEzUwWnLWfPzzP66cqp6P5\n/G8kn3Z8DDzuHlksWparlqZr2CzXXJ5dYiPk/CBKRyN4LyRKiZTiBS4oQvXLU+T2wy1nywVpdITR\n4FxDtzS41hFSYBgicZoY3x5YbxYsuiVkw3q5xvmGMQwcx0QxjsVyhXceiiNHlCKYT8piRD8y+/wj\n17OYDEa83YtCWLW7zvO9lmZI+OfiFJpyxhmP916LdTUmi0otFS1Lzk4LbJlvjJhtuScbfsGoirog\nehYJZTbkVCG0kyixDr5NUk2vrnVj1AxNG6W6OeAcruvwi+4v1tQfpZD/6x9+z3E4yqWNEWsbnC1s\nt2cMoxzt9ocdl5dblkvHFA+MQ4O7vOaL6y/57PVLrq6vWa7O6ZqOMA4cdzJFt95JRBiSsGGBccoU\nm7BN4HAccJ3Ft7BcrRj7npt3N3z3zS3ff/OB+9sHhqOklaQoMTi1u3EFrq42fPnVa/7uH/+K7dma\nKUy0XYPrNsQSCfk0lJEpvGT+pTp0LZmYImEamMaeOE1MUyCmdJpw5xPL40ROLaf/n3FnWTzOnoZF\nAllk4qRDuShwgnOC6GVjGUPgMAQOfVDLXQmebRW3k8IBY0iMQZLqj8PEoR95drnCbDs2rce20rUJ\nciA4ZDZZaWKnIlMP6RkZsErA8UkQZHWGMXus6IZRYuH2+3d8uttxnBL3Hz8w9SNxKHx6f2DaD9x9\neOTsrGO1WdAuGlIx7I8Tu/uB4+OROAVVL6qqMZ9Uc9KdSbH0FhqbCLmKvorMMFLmOEqsWNso115n\nENZIR5XiKTlJKTjEMOqR20laj9XeSt05izEYgWaRwe+TI7huclUE8AMhELXz1s3jtDvpmmAu5rXD\n1/9Cqpa+YpDB0EdCmHDeM3WJzjWEKc+c71Si/hwdqdZ7VApt22GK5+6mp+QPApFZh1s0khfQeKZh\nAt3YU44SoefluQghMQ0jxYgjqTetrA8VRMWUGONEiKN2wlYphYUcJ+p8qBgvnbNviJqTmbJQSg3l\n1B1zcj6sXuRjmsh6sk0mydfKysBYR9O1rFYrSjlw6NN8D0wRnNsZ7Y7LybGSp4zvIkVaYukspVhd\ng2a+ZdnUr4OTO+hJvFRnXykX0jSJGvYvvH4kQdCRx90jj4/3rLqF8LeRJPFUBBe/v7/jbLuhbSzj\nMHG2WtGePeN8+4LryzO2my1dtyHmwuOUCCHhmobFZk12C8IkykhjDN1qRdM2dKuWVDwpO2yxTFPi\n/vaB777+nj/+6w0f337iuDuSpkhJWaTD2tl4Z1h1DV/97A1//Xe/4td//WuG8cjj4z3bsw2L5QLj\n1sRS8Fb8jI0eBU1h9qcoit2nOEkRH8fZg/w0tJYlU6lMtXcFOMknmSEVq1RKEUJJ9FwKwkGv0W54\nweYSheMUOPQT/SAblTXglTngrFjY5lJmGuQYBsYpMk5ROecNXdvQNBXDFwuBrNeqpjPV91l7R4EA\nVCUafyhDrl7agPScBXKJTO8+MBlHP0am/YCJUpiHw0TsM8d7eFx6uqWj6STZaAoSQTYeIxIto9iD\ndlx1CGuNnNiMsTOdMeU8C54KiSlJTmOrdr+ttTNkJRROcbyLUyTF00MWY9JTkMe5BmPK3CFWymUt\nsnWIWecIyn3QOl2LuHxlvY7175l5vTz5X6VeUIGAyt9PSGqp/ruYCDFiTEMaYXSNQFvF4q0XOfuT\n/j+TMLbQdI7lusUYx93HIyEXulVHt17QHnuYAt55huNAAZq2pVnIRmtdg/WBbCJTjFhvaLoG560M\nX7NAglMKjEEcEJ16pjjNj61OkNZ7jM5o5PliPmzVbILTtVJOfrWTwMiw3kQiEHTTMkZ4+845mqah\nbRuOPZQ50lvKtPDus+6jRu2AdY3pm0g5qUmd4na6odRTWb1DokGttlin42D9q/OwfEqUMP3Fmvqj\nFPLP33xB1zTkbGh8RzHQT0ce9ns+3Hzi7Xdv+ePv/42X19dcX13Q+MKXX3zF1dVLcllw6A+knHn1\nYo3FkkLmsO+xvuHq2XNedls5ymCkW+1abOPFVvZiw2qzwHnD7c17vv7DDb/7v//I91/f0h8FqxMv\nDjPHSllbWC4bXry85Lf/6R/429/+He2y4Y9/+GcOj3fYPNK615xfXXF+8RzbNHrMK5o1iJrmKGvD\nChZcYiQohFPUM7kuRkM5Ta6L0WQfhGNr3ZwLOg84FZuWlBhdVUkKrTEaSuwMMReOU+Q4JkKUXnke\npAFGzYvqCF8m7YZ+SOQy0LQDXbdguVxwtlrgXcG4wHQ4Ujm/ouq0+t6fFjDd2IxF6TSUXIgz9Usd\nFgsqbc8QItl5LJauRKyNTC5KR1wMJcJwyPT7iVLUolVlzWj3BNKlnTrXomwCo54X4k3TGsNkEqEw\n+6dkpMueQqLRlKCUCs5lonP6wGbN4SxzfmqMUQqMi3SdMkqAULniWQyvtISLerLeNn2PlRcxK2GV\nrli79npSPJXw/KTUqL8IJ7ZLxdxnlNY4LM0sLIspU3LEOUvjOmKO6vNfv2vEtZnNZcdy2TINiU/v\nd6yuVqzP16w3K/ZH0VykkDkcj3hrOT+/4PL6Qor1MLHzPdZbPF5OCR5854jZyUkyR6Y0itrWGazz\nLLqOxjtyCnowldI35USIgXEaKcja8s4TwqRDfrDF4K2hse6ESxeBLnLJTGUkFGEXOfVKaVyDd56c\nArmcBp1Z/qJ+D7nGzlhSUUuBOmspEgBjjZx+CkVcFnUXL+gzZ9CTt/mBj79w+u3sF59ynk8Xf+n1\noxTyV89e0jUdXdtxOE4cpj0Px3se9g/cPT6yH3bio7Fc4XDcfPjA1flzCne8/XDPMD5ytlkRU2C/\nC9x8uOW7b7+hFMNyfcbl9TMppMofb5cLcEIVsqUhDoZQMsf7wt3HkZv3e6Y+gIYw1/Fina+sVwve\nfPGS//g//T2//pu/5vnrzygU3nw+MQ3PWS46Fqtzlpst7XKphH+Q43n1Ds9zOEPOib4/Mow9KacZ\nw6tDmfpHY8zccRkdOFonvHfrCs6JlYFz2mHkE29cNSeUUlPgJanmMATGqehQTmCfbCplTZz0KOlJ\ndqebMfAYMo8PR7rWYR2EsGTZOolOc5aig0+j3hj1AtY8w9nD3EkOJEmFHVnwQelL0rzILQg0FPXB\nywUXM03JuBk/1OtMpFq0itmgqhdNnVKcHqC5F5qfC52BmGpSJAWwYvsGYS1MAcUyC9ZmrI1P3Cnl\n/la+8kwzTBORRC6iCJ1STY9Rf5YnUm1NFqUYgwccwnGPVXmqHeZ/n1MsJdtwgrROn1b+/kmAomuT\nCEUyMRMS7TczN54Ye8zqUmPxi46L51viFDnsB5abjqvrK15/9pLbm1tyGPG2IaQNMUaKyXTLFigc\njgFTaeSl0PqGpnXyZy/7uy2Gbtmy8kuMMYxDYIoiVCs5i5jIyLA0FpHbZ21WvHVgioja9PTROEdr\nvczGtDESlriIqJLG7lncyYslJaZh4DEGUQzPA/taF8oMJxaTlQ8uS9XoOirUuUpNk5IFZzGnDbmO\n9f90gqpD2ZrneTqh/eXXj1LIX1y9wPuGEBJ//Pprbu4/cpgewWcOQ08ukavrC54/v2bdLTXIwfGw\n2/H1u2/o+we26yW+sdzfDNx9eOD25oazswvWm3O6RYfV6XWKibaTZJOUCjlmxhiJIbJ/nNg9jBx2\nEzlKmvxsQ2nEJdA7z8vXz/jNf/g5//BPv+X155+z2mzJKfH8xWtymoTO2Czw7VKmgMrzLaUQUiBF\n8S1JardrjCWEUR3dylxMqPiYedJt6T+MJs9Yr3RDZ7SIm5nylGtnqKwXGTwKBBNzZpgiu8PIqOZf\nJ66qdqaqJEyp4B1zmok0tRmjdL7H+x5rDTEk1suGrrGYknCAN4DafBYBSeQzWXWNyBIiLMU2oexK\nCsIeEJyyclbK00mpDtqKoESlKL4tLVABqerZilGUdtRQKPWhq4XZyPHdGPn2Rr3rDTIEs0Y588hD\nHUthTNJdlVBl8+LNImwW+f6xBvyiW0uWQiG2VY5cDKH8UJySSFrYkxZxmWALHqzD0rnTzic4xmhB\nrwXBVMoaP3ja66ZU6/Jc/hWikKN+BhMEOlERixQR+Zxz6o12hiFknOLgq4tG4T3YbBesNi3j2Mns\nKif6XoLVU5HA8H4YpDt1FlcKrhGLAqsNCb5omo5VRllhJBCC2Ng6NfTJFooVbx2Lk4GkEXWo95YY\ne0mAMmJT27iGxrWaqyrwhi4dTltjnUQjARDTRJgQEoBetur5YtVltJQ4n4Dq3MIgz45rHIvFkpIL\n++NhLsRKxH1COazv4SnYVv+t/NPMBl5/+fWjFPJnV8/JJfP+/Xv+z//yf/Hu/VtsAz/79ReQCovW\n8/nLV3z52WuuL6751a/+ik+Pd3zz7jvGcuQwHpjCEfddZvdu5HA3MBxHSc8eDsTQY3D64BZiHAGR\ny0blB0/TxG5/YBgmUqqMkjoekqFK4yyr1Ypf/9VP+cf/8e/56ue/FFqZyTgHq/UGkILhmwXGNkJ9\nLEFw8ARTODIF9Uwvha5dsmzXlV6qjBqhPBk1YardaC3wxYiy0jqDcQbjmQu6qz7HuYbvQk7SfRqn\ntCUPh3HgsR956CeyhDHijAGrTBNE+eewGP37QldUZoauepNgPEzc5czUB5aLhrbz+AY264bN0gvM\nk+XvxXQK9UhWwihclqeoGMBnihNTsKyYo6giE6mE+YERschJ2iz4umxw1dnP2EINSa54Qt0SBPLQ\nr8POxlBSvLVQFtnMHYUGo5AQxGLoc5HZg4m0iEgHVUJWi9lYqrUsFMuMLpsUtZibirDqJmKIJhJN\nUm8g+ayuuLngOiVkZpWCF5xcJaOQzCy1t/NnFvdOKQyuDsb1msm+XTc5ue9yUkm6OWUaOpxp9OsS\nxRSSyRiTmKbA3c2BizPHcuNZnnUchz139x952K9wnWV9tqZ1LQnwbcM4BB72e4bjyGHXY9HsAGsp\nOKxtabwjukiDpC1lCofjwOHQyzMaMxaHd50IxihEX1ifbXBYjmGn7JBC44VWXIx6z1iPdS2NX+AQ\nS+YpTsypWcZiVCSnbjeIPS1ioJYKFDvDKLLJWn0fUSA9FSlVIkLjHNvViuur56SUCZNkieaSyCXK\n99B1mFVXkAgY62ZmUDLSsGCK3scZlf+z149SyI1tSXFgzIGhTCQHXbdgvb6gW3TKwLA87vcY23B1\n+RIaR7aFkCcZtjUN1jdcPltxsTXEIdLvD9x8/47Dw272Dffe03QNBUMIid1hZH8YORx6DrsDnz7c\nYEqcd7uSwZpI1zZcXV/w67/+OX/z27/li69+ivMe8VM4cdArjlW7ulQiUzxy7Hfsdg9M4aCpKo5x\nfOR8e8mrZz+haZf4ZoWxjRbv2olqP5dPXaQ+nTKA8UYYKFYzQw2UnDRVKKvk2aBUXDKGKRmOY6Ef\nJWBYTJCkQ3Z4bF2gFJxBunyNEjKlsk9Kte0WHjmGEgvTkIhTwbhCHCNh2bBZL3jz5edsLq/Y9yPH\n3T3Dfkc+HLC24LwUveIcJSvvVyaHFIUeUtRwigzyIe0MLVDFNpVqWZt30HtQNCJMBrBZKWlyglXM\nWIfIpaBdno7zSiYVI1BGLYdaoKecMWpi5vS0MLucFM3NxOrRPIsdrkGKAE7EWzbTGY8tEFKSYTFZ\n8e/67gwZSzKGoRT9vefEYK7rws+6gtoVVtM0+eVwVBjCEEvSa5JP+HkxVZ9IXWp1rXnbkuMkcx7t\n/KcYeNgduLt35LIgKWNj9zDy9tsbUrDEaNg/PAobKwgsloKYRDnjxa45SAqWc+J66IzHYYgpM0wT\n/TjSjxPjFMgh460MYL2RWDaKfL/UKzauzaw1kgpkndMN/8T6wRi881KsQxS/cBpM8XraUdxb52PW\n2bmhSkXDaZDvR0kKBTKf+CpkIzOZxBgDtw/3s/+N3DqF/2SLnyU/pa63FOUZK7phoMI+85Sq+uev\nHyezs2QOw5H7/SPRJNrlgs3ZGdvNBdY7ocPZwjBOmMOBxWbiOA70YSSWSNd1rBcrlss1m/MzmtKS\nhsg3//JvPNzes7u9I8aEc47lYkG3WIA1jCHw4WbP7d2e3cOBFANhmjA5UoN1hS5UuLjc8tNffMFv\n/9Pf8dNf/oztxeXsGlcHmaV2NBhyToQ00U89IQ3sj/fc3H1LTIN4WpmOkkcWywWpBIx3ON9gnMd5\np97WspBO3iMqAFLVZuPl1+z9rd2dSPuFKge6AK1g4CEXxiEyJUMuTsU9Xif3Ql+0VlgrFoFqvCYt\nAbLwqqeLwg7eGbwTChdJIB2TC1OR0NvV2vP8y5/wxa9+wX6M3H38jt3NB8b7HePhSL/b8/jwiI1G\neLTGiGBGf6SxDmwFKMqfLf6iD1rRzU+ky+rXnWSgKwlLiv8X2Tik0wEQCCdrkXalPpCZKWcNMq4u\nhBXWgJg13q7ojGH+PplYMrEovqpQVzGWbA1Yh29avGtobKFJGRMSORtsSTqQrg+70WGwZkGWpCVa\npVImz3RKMfeqJ7es7IeiNORTKW9si3eeUiKhRKYcSFnpmOa0F9YjvTFyf72zRJyaVMlVCClx6Ef2\n+4mmcbjGk4tlHBKH/cRyuaYkw+72cfb4SakQg1BdRcUo3OocCo0V13BTRBSTY1IjvZEpBtEVIFQ/\n79S9sT4bWCnIWLzVDOC2wVs7zzoqZBGz0H4bK0rSYnX4aJxqHaIoWnWTk82yzHCUnmsE6avgSIX2\n9OQ/z7OQAfgwjYwhMWdTSfuvqm39HPX4aOr31xMbCWMabVJOm7T979TUHyfqLY98ur/h+49vKbaw\nOdtwdXXNdnPO3d09fX/k5bMLSikMY+T24ZFP93c87B9JObFYLTk7O+fsfMvV2RWda4nHwM3btzx8\nSgzHI8M44b3HkCXKrHEUIuPY0+8PHHcHSg6C51YZO+qf4T2fffGcv/sffsM//NM/0q2WIoWOo+CG\nAo6iem+KgZgG+nHP4/4OTMvh2HP/+BHMqCwFz+XFc5q2I6RJjrJOfpZrpLucU2QUt67eMb4xNI2j\nbUUmXzuygkiQY0jqnKicfG9wjaVpPWVMDLueghMjLJugqKLNGqzNOKOduLXCBrGWbCp4XbRr1IVk\nrRZyKyZaepp0XuT/zjkWmzVvfvVT/uZ//o8k33Lz9t+4//CO8aHn49t3fP/Hr9n96wBHKUoO8Z0x\nMJue4SBao54tSk2sCfNU3q7ACylHdZOEFMrcTadSFOKAhLBc6oMo7oCCf1uESmiMbEahCNBQOA2l\ninb8kSI4v96frJTOZCTtPVMoRaLjjPO4zkFjWW/XrJdLGgxpd2TaHXFRYBRLns2TDFaFIfLTxLnP\nUc8SBUnEcVrwi+L8CoPPRafiuBZH6xoWTYe3MKaJYxgkoat+9cyGkKJiVQizWnpCdozBzDh5KuLz\nPoyZcSwsbJJTYJH0nlcvn7Ff7Xm8vSPGQhkmhtgzDaP4rWdZZ956iol0raNrxCwvZNE+hJBmOq58\nrcMb+fSRTERmK9a7+ZTVtg2LxVKj3arl9Am7DilwTJklDcVIw4H1WOOxzpBzwBQZLBeT5fQSArY2\nCDD7AcmmV0kRynCrTQgCS6acCFNAoKOG1nVyB2eBl2688180YKyKnbK+R0v19D/Bgn/59eN05GGC\nlFg1HT978xOG40CDYTz0eDxX2ys+e/kZXbdgSombx3vuHh55eNyTgW7RsVqtWCwWDOOR5ALLrmOx\nWdAuOnFMdBbTiD2r0c6h9WKda72ICGpijV5DMAXfes4vN3z1iy/4+a9/RrdcU0ikPNJYr9FQmqBN\nmjvfGCeOw577h1vGMHAY7pjinrPtJc4tiNHStWtKzhz29xxuPnF/f8swTkzKg48hkUJRH3vFyp08\nVG0nHZLUV+3Fc5FEmiiued43NIuW8+sLvvjqS2we2d0/Ytv33D9MHPpAiAKo1MxOqyILq11YdYST\nrkFW0OwBZ8o8XK3q9Dp09R6MNazPVnzxi6+4evmcxWZJNpbLZ9csVi0xZS6/eMWzLz/j+s1LPn7/\ngftP9xx3R8bjwNhPTMMkizULo6WkBBGx4DV2drQE5iFnypDEy0zYAchxNxcVUiBQi3Tkin1rl54q\n1o5QFSPqdaIDKa2RM6sHhC42O8LkE4SVtblyjeHs7JzLF9ecP7+g27RkEjFMTIcjoUl4k9inIuCH\nfp5ci7kBi8eURs4gxs2QkNGCRlGqpxzZoDWY1mIXnouzDYuFqCBjMLx89hmfvXpNCiMPjzvevv/I\nf/vnf2Z/PM5FShhSFnCyGa9anj/fkjkyjkeFB7UvzYkQAevZnK/o2iW+8fTHPdYkWm08ukVHt1qy\nOFtByOxud9zd3FNnQr6TFChjjIQxD9K1o+utImdmNgOLTFkGvtY1tIsFLhZ8EghwHAaiZtCip2tR\nekbh9Ks/uCwv6auNlXBpbxwyfGKGWEQABfOZRU+lpz9XSKqefyr3W/9U5Jlw1tGoepRcT/GcBtao\nZUN1u5RVPW8Uc1wj1bfxz18/SiFvXcfF5gLzCj57YegPB2IMrNZrrGlYdkueX17j25bd8cB3Hz9w\nPAz0/ah5loJBhXGk5MRkDJNriES1lEOwaydm+Kb6HWMk1slUXJD5uGONZb1dcX55xuWzM16+ec35\n9TWpJGLsKSXSLTqqLLuUNPOipVN1NH7BYrnlMOyYwiDf13aAJ4SBnAJx6jn0A3efPvFw90h/FBmz\ndCFl5iMDYIUj3rTSjWPEnyOj/sy5yn81RLZ1nD+74Ce//Ip/+M//xPRww4dvviFORxrf0+5GDsMk\nHaMx6tmsi1an+5KslLFKlaIwd5+G/IOwZWMtxlthMHiD9YbNxRk/+fUvuXz+grZdCDNic45vPPvh\ngY3b0HWOi8s1775/y+PdI2HIDPuB/d2Oh093HPdHjvuBw2FkOPaEcYIQxctFXRdhPjCQkiVlLaoo\n2q1vXyxhlRY2r8ByogBShTkCXkTQJJ4/9dSQQp1BYSDZ2LL5/5l7zy67rjS/77fTSTdVQAGFQBJk\nN8nu0fQESR5L9it/fa9layzJ9ow8M+pmk0SqcOOJO/jFs88tzKj1mrpcXABJVBE4Ye9n/6NCW8mZ\nL5ykW9ZNwRdf3vLyq1dc3FwypsA4jQxDz9iXxE3DtO45Lnr6vqefBsYQsGWJdTYfIeT0U2QFllai\ngx5HUVAU1kjgW2HFaFNqgo1Em9gsGqpKo21kGDRv3rzlqy++YvvwiR/+8I7dcYfJHMj8Z58n2/mv\nwhnWm4a2rTkeC/opnJ8DUmAYJ3yEZlVjtZPvEiLj2DNNUtxRVgVXFysW6wWnxyN/9D9y/+ER4zSl\nsyirUYUl+oCfIiGXctgMifrJS9ooGTiKoqBR2mC1prAFTmUqePLEMOWmJs0cJfsEkWRJaN6RBbby\neSM22bSXXbdpPu3x9NDMBMIsPfn8uqn8C58wqvyl6gznfc55GYzg6p8t2xk9PEMssySV/PsBcRL/\nD7WQbxbXrBcbvriNcowPnhAmxugpbCXFC0HIgugjYztKc3xQpCkxDSPt6UQYe1xhsqX/SHdss1Ro\n3rs089laRWkidGhsAh3kKJ+UBq1wBbx884KvfvWGxbri8tkNiZLjaU8MHc5Z7LLIk58HAjqZTFCA\nsw2bTc1iecXkR4apz+5IRT8c2O0+UTgPoSEMid3Dgf2243gc6bqRcZKpOsTswktPEiZXGmxh8GHO\nrM71YlEeEKUs1ihcbbj98pbf/pt/xd/8b/8rj3/8BxZF4PDpJzF5lAZz0JJDnh/AmDIsEWVSnwO7\nZmnW2T7MnL5Ilj9qVNa0y48aVxnWV1e8/e57Lq5eYFUJcUTZhn7o2D++R8VIU9Xc/vYrLt+sSO4q\niXgAACAASURBVEmzqJ8znQYe3t/x0z/9ng8/v+fu4wPb+x2fPnzieDgyDhNxTMTB51jSWZ8PMc1G\nGZmAzhnp/+yArc5k6BPKml8UNcM16fw15wuQzvO6/J0dmudSCKNxdU2zbGiakrq2XFzW/NlfvuXV\nV7e4puL3P7xHTwWLVY0rNpg+kI6B8dFzOrUc25ZT33H1/BmLxRI/jvRDi60sz15co0gU1rAoSx7v\ntqiY2KyWLJqa1WrB6qImWc9pPLFtdyTvZ5CEYXK8en3L89sLOv+Jdtxy9/CeyQ9nLTvMxqknB7LW\n0DSW1aJmX9ccT2MWXMq02A8Do/cUTcHYDig0dVVzPJwYeikpN87y+ouXfPtnv+Kf/u6fuP/wSExQ\nO0dRl+jSMAQvnETMUlAti3zjlvTHlq5tiSoyBS+RvElh0RTKUCpDYQ0pedp+yJLfLMFNkTl98NyT\nGhPJCvQRQzzLRFHzJpWX/nmIUXL6DJ/Ba/AkADg/JvMGN39mwjlDTgSplQtJojDO2TrzxvC5Xl+J\nSm0+eSqyQksJJ2DUn0bJfxnVipqPJIILayNdkSbmTIyULc4oKldwvdyw/qZm9F/RDi1NXeOcuAd2\nxx0f79/z84efKXp5oa2zwpInYcF9StIAguRIGxFJizbZJKrG8fKrZ/z2d7/i2+9+RVXXPHt2i9WG\n/f6BonC4qgJtME5s13PKmfeBfuqZ/EQIWYurJfe47yIPjz8yDB0xBu4fH9myY9j3tB9ado9buq47\nt7X7kKNbdSazCo12oh+PmcCT+553dB1JVlMvSy6uV7z85jlv//w7XvzqFYdxhyocZbPEGotRPYVO\nLAvLFOciZTGazCFQKS9a8gCL9Eqrp3Q/os4Ji0qmKSRN0WpwRnF5dcGL17esLmQC9z4wjS3d8MDh\n9IFx+kRh1ii9JEZYL19gbE3hlqRGsVjdcP3yNbvdI117pD0c+I//4f/k/c/vOB5a9vd7+tPI1AfG\nzjP2nmmYEK5CGtUT5AhTjdHCkZioMEFITJEI+kwy2UykqUwQioXbWoN1DmsSfvK0pxEVcwaNlv7X\nsiyoFiW2NNTriuaixllDaRWX65pnzy9oGk1ILYXydKeeUxewJvHmxS03X18SXnREnWj9wMeHB168\nfEldVzx8uqftLNoZrm7WrJdr1osFy7Jie/8JpxXPr64p6iWulDrEn3/6Pf1+oNv3nPqBMHlUUhhb\n8rj4CG7k4bBjeziyP7RMYc5En5ewTHbn6V8jeTLLRcVm2XD/cJTyBBIpWfw0ycScDJvrFevVgs3q\ngmka6NsBo8Xgddg/8E//NPHj+4+0vqNa1RKENkz4fpDSaRLGapRVJC/vgcoSQKUUU5zwOTDsXAwd\nIY2eoETpkZhD2uQ9wcjokZI4KxUqm31kI59TUfNkInBp9KLCSU/odzpvXk+E5KzfJ5PvAs7IQh7n\nTPEkC7lVBoPJV3eWJ4gB6Uwxq1m9bjiHPCgtmfogYWtZ+aSU+5Nr6i+ykM8BPPMWp7WI/2fJWwhB\nbOhasaiX3F7fEJJE7SWVNckx0A2t2GC5o207dGwkQ9hapuBJSSRePgbMGU4Q/BSdKCrHalPz7MUF\nb799zdtv3nD78hnaGBaLksIZqqKirGqca4RMzIlnxjiCn0SPftrSjyfGqWPyE/t2x/64o21b2v6E\ns4bLixtWiyV+nBjiSHfq6U4dwyBhSz7L7vjM/WgLg3UmB29lTPwspUqCTVeG2zc3fPHrN7z97Ws2\nL55Trwra4UDhDMVyRVUvsabHoihMjghQOVs7CSyhMu4+vwB6DuOSY0uuNhNFjzaiYQexPhfOsFgt\n+PLrr/nVb37D6vICYx2THzkctzzsfuDUfiAxURQVZblE6YK6vMC6GpRBl5ayXrC82LC+uaIfDpwO\nj4zqyPWbDfvdnh9+/xPdcSB5Q38Y2T+cOGyPTN0kKZU64Zxh6Eb6fkK8BIkUA9oHdMwbj00YZzDa\nYJXOud/iiNXOUNQli/USZxNd23P3cc80yGCgtZHnoimoVzVF4yjXBeVS1BJNYbjYLFgsa7SGvh2Z\n+oGp7/BDwBYFy8WCF7c3mDBxHI/Y7kQqIs9uNywWDc3ScGprINEsG5ZNw2ax4nK1oS5Bp8C6abD1\nAm0dUwwMPnA89jxujxw7sacXRtPUib7fczwmhmHieBo4HFq8j+dT2Ywni6ZeNuWyMCybApsWnA4d\nVlumGGRqRkj2cZg4HXpubq64uGoorWXoZWgpSk3VrPAEfvzxJ/a7jkSkWpSE3jMNkvNiMyyYcnmL\nD3ONnj47NmUal9OTlacTIvh+zBLWkPtuc3JolpSkpM4ntRmRCJnf0FpDyP1JeUKetfaflzeciU01\nj54zNv6kbsnyA2YRwkwaw9OJYHZ6ngPPmIf+OUJhloKqDGcK0cnMg8QZhf8faCKPcfpsEZdMDJht\nreLwKkrRChdFiXOO3f6eGAObzSUAbd8RHyOrZs2iWlOoCqdLSTKzE3oSSdg0eYJ3JCc6aFQiqYCy\nifVlw1e/esU3337Bmy9ecX11iTWGcewIvsfWS66f3aK1JZLo+hPSvi6t6t57uqHLZc4PnLo9p67n\nYf9A13UkL8H5N1cv+PrLb2mqJd2h5cP0I49xJyTn3MsZ5RbPJydjhDCy1kp63OTPDSaJgDGKonas\nNgt+/btv+O6vvuf262e03cA09UxjoqjWuPWaenOF+3hCqw6tvLgFFUDWwBPxMcspFeeCWGPACNxI\n1CnnPMczcQQK6zTNouLZ7XO++4vf8du/+msWmw0paYa+Z7t/4Od3PzD6Hbe3tywX1yyba4y1OLfA\nmIKQRoR0Fo17VZX4cCKokbfffsmrr67Z77ekBbSnEacqum3Phx/vUD9p+sGTUsQVhsuLmsdPBx4+\n7Ek+DwYxkgYvM5GzuFVJ1ZQUZYFThoDHewkwc0XBcrXg8tkFRaU47I/Y6hOHbUeaxFJeOENRO4ql\no1xWmFqjC7DGsGgqVpsFyiqmKdG1nuO+Z5omisKw2Sy52Ky42KwpdGL3w57T4URdVJTG0tQVm82C\noVvhR8Gagw8kHyhMybJZMnUn9rs9xZhQrmAMgeNpYLvv+PRwpB8DhTWYxoH2hDDQd44wJvp24rBv\nxeRC5npSks0UhSJQWMWyKXh2vaIrLcd9hzU2Ry/ISTnFRN8NPH7a8evvrnAu0Z72HPZHhkGkiW9e\nXLE7SP+tSg5nHUVVcBymcwWdn7zo8UMiThID4UPARwUpZKLa5wXYPEllIwz9gLOyfszxyFJ0nPOd\n53WXOa4hEgOSbKgNY5hduXOhRHyCV84LNqjZPDdDIGpOtM8Q7iw3nTNTVFYCqBmuS2dMnDTLbDMb\noT4nTp9wepPmZE6E5P9s4/hTn18MWplzgyc/yrFOW6wVra3KmlClOKfixfCkDTbaUNiC9XKN0prn\nV8/54vYr1JAYdh2+24u0LptJUsyxqQqM02yultTLgi++esnrL5/z6osXvHrzBgLsHnb8/NMfuXk+\n8PoLx8XVM4bxxLHb8nB4j8ZQFwuWzSVaW3wYQHu2+0/c7z7Rj55jeyQlaIoFF6sNm2aDTQWb5gY3\nHrn3d/g+Mg7+HLsZEXxQayk7KKymKmy2yCdGn6NwIZOKC1598ZK/+Nd/zhe/+Q3V1Zp39+8JfUdT\nL7i9/RLjCqajZ3W9on5nKfaKKejcwJIfvIy3q0waSYEEWK2z+UiLmiYfIJVSIjU04j5cbZZ88fYr\n/vrf/Xt+/Rd/zvrZFZHE5E/0w47T6UBVbLjYXPPm1Zc05UtKt8ZajVJlDstSxNgDEzH2HA53HA4P\ntO0h50UbloslzWrN3f4D93fvaB9OHPctnZ4org2utKwWBV+9eU7xB8sQAv1R0hoTwOjFUr6uuXix\nwRSiJKhcwfX1NcYZ9rs9+/2ecRzADDSrBWW1JiXNY3NgbEd0AK0TtjLY2uBKhassrrJ50Uh0Q8f2\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fOvaPHbcvr3jzxVd88eU3fHp3x/F44Nju+XD3HlJEBUncDCmcQ7XkVTYoowmDZwqJqirl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RUhR5HkR/N1fDvdeyzMZoKG9Y6eFXIzU2a2zgwvU+0lf4lgm8cS2uHLk5clpT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sKGrNP7NRNngryQnFUskJBgBmP45HY+8EfH700hDkNjgCee04PIamev1NkpPjFv+DTNf4Ne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3D8/iD/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\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ykiV5fQ0B0VZy3cEVyfWOV9tKJvxvE1nzQi9BKZPiN+d58DgPBVqbsXDuKcdyFQvhmwf0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0MjzjTa98IED2+/DLYg+hCwZXJmnfS2Pvj3MXQBOKSSlAgBwPhoqKL0O0fh8oHLO95fNOq5xkDE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\nn8B48mPxaJEvvqCmUEVomZiDcIOt6kMTm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Vk2s0SKoMiG0thBSMglRkgtjkxqA8yIrwJk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\ni0aW/LvL0gAYM0MXlDinifrIi3LRCQkHsPwuS3Ci88EGOYVlp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ovFuqR0uKWI4aVShFKRqzYvP3jt1+alWWGmYXN7OkYSFzsCWPZlIRafnqRYTt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\nGErFecga8zl95UV6xmPX7tRDrFt08eJ51TB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5dNPxE08+yrwZEsjzcOoR55z+78Pw/+Vi\nopuHv5xBdRQJxtoHKJSX2k+/9I4UsHm6jvsoE9hmjNKIiBnIRGh0HFTfOahiNKpXvPXzsWFspMqB\nQA6U+suD3cPFesu8ZoFBSJqBctgRuiouAOElEJU2NxYZED7TAczc4sk+cBxso/Sxk31n0ryXpTcK\n8N7fEIuuzeBnWuKUlVD+FcN3tUjyZQYQQsLTBr4FAN8/Jm3123gBjsX6yrOPwy7GYoYvBfI76fI7\n4ilrBnZKWH0CAFUzpZYptuOnOK7SoB6ftq6Wi2ZWK/b5bqph6oQaqIkVMi+oGQrzxEWwodcTECZZ\naT1LCwyvjQe0dSXExcnp5O4AkZjMyz0XZK3TL932sp7AV6VLeTKI0v0mk0RXJ5kdkp3S/NOB1IN7\nX4hKkMphmSI/By/me1xIs2KTI9VXI9Y1QKHDwcC4KPRik0noalG7OOGOcRz4Odh+tlgMwdI0Lo8u\nI5jZdBwDNRvkAdGqGvphhr+GwX+EKuh/LDaI1OJ82cWzc2T7o7BybRAyvFIDkJ4Ke906HevcAUC2\n/lfVmKju3HIG1SoI1AETEWH1WYTPdiRwkypb1rUXiSoNs6gLo13Z/tUdkCanDu5KKAKDrLc4Yzf+\n5siNQ8AC5vMgiUoW3PQi1TXU73PWBYS31gDVaYaZggU5KwHI+6hvnzl7sFpX+elHg/sMoXDMOC+1\n380ypYQJ89gUDJDd5xfhaxY7b8DxD6SEmssYYuBrpWVnOdI1gNpu7nkF1uv7M1S7GJB2rzx4ta0/\nMkc1RXPppZZAyLhtNFDTaiY2CaQlTTLbGOdx7zCqXazKzYEyEKaHPmiTKlO+pNZlO29etqtIHWLF\nJWqnZkDBiWsjEHWcvtVfCRWvRWHubSQHrZNQsn7LprniHVVfscBlecZnxP4wC4+aGHGgtcfGDG4i\ny8lQHE/nj/RkcOSAd8Ct9ZRsp3mAretZ39zME+uSUXEPG6FOeazrGLS2GVUHbJPRfS43YtXJTIY8\nqacFGp3kls4aaoUhLJNd34OgWKXa8jF+pmWPp07Yww+4mYOnInnG85+fj81f0CwgHpmh0JGHRBuP\nkczca8YSdthWu1SjHmb2raz77PultqryqG8kIWQkD2PmTCdUjKUOmUetAzmA+QMFvpjNqLn7lSlS\nKIYPnFmMeVqqCQEMn3g4CRbbPPJ6OPAfD9cYoVa0BuQZqqBjGn4eP4pwRrqj9lCEKjAXhNFluApf\noyO/0Nh/FNTvAJb3Sl9VjLCZZ5kR8aYjF02xgAif0edP78p37iyl3qx4E4Go31g6DO/z1Db6iYkY\n6N8hvUPOER74gJqOAvR1Hfbe3JRTfR7c3p4bpzLNcjGbkTVjjHrrlYTooWbBzKaUQBmjw8HzFWYB\nw6IxjPUHz8VVxuEXNuIUalinow6krlgcZ+XAB1IfG5gvEEEXtcl5huGHx4KpOYAfoe74H2+3pLRQ\niDr4kSXQwRRCmkyTO/moNmG7DaROXNqqZ0txBNz/PCb+9zHxI+uIJ/Hkul/N4BYGzcy5xMvrRBQD\nkGaT8F78BoK0t1WSKrlSpGb7HQ4cR6ihkGPnMVI14p59zVJwoFh1LFx7tdtRquBYGwDJRgp7Curu\n0xyHXS7KTEiOQUJjXfc1wx4GeB9i3YeGR9rTZ1lrTZcYCdregp9AMquP9+yAfbBNhgMDfswkH2JT\nGqzf2vxyPorY5QCrzwDyWBOYCfB34UuAfDxGNSBwz9BP0H4hAPjeFdiuz1znRS1n6iNBqS6VWV9/\n9+X9zH8Cbi+KtDniqSjIKaw3mHMwuNVw40QiBMQMxs31kdD7ejJyT+s5nmOYcWS8j0EW0ddLDxjS\no1j4KOHQINVadcuMolVVeYkDMNxUWcftOfDM6zlLJsMyhiFEOgSDzFayrs1Q/sIdoUumAKOz38qT\nd36YwiwgQ7JbB3BkXc1QBWCk/5C0XDGUcGFrlEDJdg/QotUHNz95MfBwuJTCjEwxGzqbMA96PnL3\nqQrPKsrSgXM5poS9V92j3jF9P/tjm2Z2vK1iYJ/1aIVUJxxHuJn9P0eANnerjnzPmLXMwGDeeA+A\njxSwOwgbTXCpqvG2hqEAAwVwizGrX52GeQN01y3XfHJWO8J7Kmfm0f+9XEY0FGyWLlnJXPCm/3bP\nfQTFyFkoM5h7bb/n5jDiyvSJOZLsTJaTaxMGG1zYRFvUwOEXuMbwJUD+48cjpz5otUSe7qF42kGA\nKUtYPhSeMnlbPp/UgwDVLlg4IDjITDoViim5c5Gq3ydbsertmZZRyVAJ8kZFqgKC1WIWCybNMLzq\nxCymfQcZCRqAgADBYtwZaQGt1BHH088UGtH/El4mKk/BooEaPzloIpLVlS+RxmGpysg44KBPaEWl\n1peHbprOPCHtYVzFl75BwRjgGgX1bJ8J1GBzXkmh1qflTAxvCKSZ6Si77mRbtJ9OgRDCz2v36o/R\nFiTGRJiupwti9hFQXZKqm5rJdPPDtX+2kCsYkzETTLFRntYWBXiZj0lGnmDjzl3U7K8Gn6PHJ44k\nuKkasQQyzaPF8W69+StvDsvNU8xfC9jqVlIbGrjwXOdWdkvAgdq/MS0cyGFSX44SikNVZoNEJo0U\nMIVslaTBIJAneZgW6pHhDpAhI7Z/pYSWMrQJ5QNqosi1FfZ9h43ZQG6rehOIBfDDYtfrM6T7EiD/\nn7/CYRHddM7pmCO9oVEtofox9lcZqAyv1DRqyhiXpeeBHbdGmlQWu70tAwXYvtc7jnmsq4JWACcd\ntGhKP6TsiZgvmN4DNC/YnpkcOFNsk2G6GBqfY45y11mKCpbfvZge0AcMcABiaxMCEAGdvljIgguo\nitGQjaSukvpjcJYQswHu8BxkJwQ6T8dNmccCC+OATBWMyQEBTmVBDkLWf0nGFm7IPNOnBSs4Fth0\nfUPuJuuixzo9Yo8+0ntWIKTFAbVTdlDHJWQiTTPD1LJnF9VTpP2typAwR14wWKa05rHUiQPp2Cvr\nklg0tZnD78zDDPaINQXq7FP9jWEz676Nyh/ONmf/AsxnCWw13y21oYeaoQu0iIFIS8YLkg2TYBDe\n54HSqFgJ5NyfMONowho/1l46l/Sc5CP7jVdJTmM2fODTlyZn1jKek4gURymKEpjCft8CuUsUwG7V\nRpvMPoUvAfK//nrkQgQayH2E1MlFQud0wptVadNeMfGzrrzubE8+k23X77SVCk7X+ekX8e6nHi0D\nMChl5duWdBJpjHHv70tErJ/Urc9qeRSIx1+ccD4MBTK1Fb+mhqGiAcg2VaIQyBudWq/e+u2yUClQ\n7IFgkI6ZH/T1wpPZLQGMhwNoffXUvRVcNSXlgClhIvUMVmerqyBCqIYZT3SKRoVK3WCSnJt5CwKE\nw6zerJSCpISNCDS2cxEEISzalkC5VOBCfKt4Om8K4rUpiGokz7rMZxrIZ9eHebO/kYDKzBrKuugv\nAZWYefIAcJIXKyG39JNquxb/9I0kpQmrKleuLYHtyPeld2lnmpk21TjVdWa0yzGrQ4ioRpWLWVer\nMgpgKUgJrodRaqnqpwVLLcFL3FUjLmSo+hSWf6vUkr8dyRi+BsgfueU79b1UrTxor5mnBtGqIlxY\nelWySydwV6A6F/Ne/aKAeRYCd3FcWbQ8U+/MOW/z9izfvH+v879Pszcx1RVwYSsAIZkeLA9WyEOG\niwHHwuiDC24y+aV8yY/ehFIA1gyxY5MyTdZZ5ieuJth4LQw+DDisFwphEF8avZEoSxeflr4/kIu6\n3sNCPcjlw60WwQRtlY+Mu9tc9dZZBjJNznoWxhUQ4dbTaCQD59uUg8hbOlCLuU8K58g9sApAFaIA\nvWsmtbYSBagdvwnpIcRGqitm9Y0QAEA4v4gMtnoi1UsUhKlTpxAwzNQ5O9x7E5AiZQl/AbkmP3nN\n1I96v78qFVIQlLCU8ZiUtVUweT3JYVkAZScugSdjcMcLgm8rQ5t0sH6inXrzTu1SdVT9w1b1Epx1\nCYHxPUj/pT7qJnzRzk4O+qhc9zbPgccodu+NF/OY6aqzFxxoX0mdWE23bll5XbnM0ztWM58xl7x7\nR4XIcRxLR3onXIL/QjMqAwv7I+NJZ6sxiIcX46OlQJxQnkAxVjhmJwUSyEebRpKZP5ZuvzIRLiZq\nxjnN/uHIo66onsAyKPndaKrGYeYAlTXkfjNnKEOokGUeijcZLS+8Zn4945aZDNZxZFnequ9E7VKd\nyIY9pXeE1QZzL/VXq/6inehZkM/Ggq2ADNthhK0yZwx8PjGqyvtz9qzymB4zl5zi1HpnxAA9nJxj\ntYDT2gqHByUDHkeE5l93P8JglKd8o+wES+yxlyoVfu4w1FGIRvDvPqX9iXExD+fZdCowst1aGHdP\ns8oWb269kG1Da5i8N4R0LLvUTeO1nDEJiGt9lyky6/8JiuOrrFa4sJKSGRYSbm4leiQbnxZMnSqG\nw3MH3mzfDgBOG1d0sXK7U/efMfE1nv5+Z2f+zvt3966AXNn6OzOHVjYUXCSIazffmDoMmN7AlOBz\nWINzsZZ8S1fyrVhz2E8T2HkGorEcmaOexZgM1PhWLNxTiACi62zwoF7WeI0gQMaXHZ8bkLQXFGc2\n/kI5YGoVnlVdh+qkcl+CzqzNNFH1HuWah9cisOVaQNk+E6wXUFjbREPp5M2BGjfxeoFe6ra5P6A2\niVXs0X+GByFyD5M2pMCka2I4Si3B7NmWpV5bSXBx6ZOji5kah+ov3EhFxj3lfheWYqyrhgJa71fN\nDV7zZVzqXodSSzrkzS6UVZtIkP7aAldJgF7rNDV+y77YGNSf9WRd9oqtK1zq2GSN4CZ8DSN3ToxL\n9peU1zBzJ5VLKRxp20qVi3weR/o89gb5TPGUh1XnfQ/o+1RL1R2qWnn2/jthV9Nc2cbzc4yB62Db\nJzvUOf6K9zR64nvpzuVwZgIyBySSIXJbOg/PfowhYN7+x8k6Pd8liHLqPMzyJBTLhSDvE5MoMDis\nOaDMQC8iwRC9dO5AeayBZzrxjteAmfAoo7eAcu0PRmHS1KM2wjjAGUAUKd2nJtMi46VDN2R51lkK\nQah1qX3X5FfUVt3zPFGJ7zv6iLCRh2CkOsEtFq+NpzPldvSw+BjpMM0SFCMN7oYdoO450TnL8Ric\nsVnlvSxeSkRbtXf7LUnVlcg07a81YlWtWEIWUX9Zrw8nUG/s3hpdHCjhsdAZdwy0OgQ6lqmhIpBK\nFqPr0F9RXCzzwiVk63bnaMDmTG8rcyWAVtLB2Mef48jXAPnoTqqLYSyvgucC7in1HpabUqY0osf2\n2+NIJ/XHrNOGyFg6/oqwKrQbeZWse1hmg5zX4bwIKpmWZ2+e4Px3See+4V4Jh33GsD9PtiAx5rN6\nn4OnK4xTa90RakgtwkxQP9Jp1aA3yJkgz8W6dI2gGzC4WcKBn4Z0l7u2CgwYdM4k2U6jxprmxwEY\nSLZOa410/ATVk/sKIlufIBS5d/7qarblQWFTz3JKGL1tIsAUCbQBXCZw3Gy9zw5NbXZma+bir7Fh\nCDAFcGTFmfdhvStY6SToXgDlPIomee0l0qvhjxRs3FVsCXb87zgAO/RsyVyYTtvoozaybAeeWM8b\nsyMU2BG+WP81KmhK6fk8hXCdTtRtQgusUBdRAqjqrJvoYJ5YH5Ul75kFGYN0EQq9svunGq2rup6m\nWtMZDWdKsqa0/Du73UwA/xUf/KKdnS0JT8xzdqOtFcOKADBCf4r0H8LF4/lwHMfAf+bEzyOsYGY6\nNWoDf1rE8MTtXS76mui13FyEwX7vosSnKztovhcP371+7tmu06vnNH8xRjh8NDFweDVkLQMkRVn+\nEycFhd491DMJ4o88qLZAutPVdMpcsDDFa2AUQ0Fa0zSlCyBxa+sRtCXNI9n8D1B37zW4ugasBqMK\ni9p7BQqRFDD57qi8ufRT1WV3HVHfoj2taIMlQBQEe3rPZLvkQ6P7jFGKoi19avCzf7GOLNO3bmNu\nallVbVy0ZJ4I1AlIoBBjmi51RkFPp1Um5oat146mq1qpvHDyFTisbKJBOLKR55zui/AIFj8q/iZw\nnA1Ul6nyYOkL0Y9Qw9HhpbpbfJ3E+ngUbWgbsX1ciCMFUzdKqQkFgar1eb2eWarqMnyN0ywBEnqV\nMyB13D0w7ZR574Ialm2xoUJ0zOF4TMPPBwrIF3/F1K0fnY+yhBFdVTEgGUR+aqz69emq6E77Hog/\nj+t1HLsur65LDnRhhQNY07gtreeAzLab5mm3nuqSYXIYdgNWvEpmRXbV16/oyIN1JhhnnudmZr65\ns/LHAB4evldqk45MyTloBrfWw4MgkKu6gDjVTADIDgvoTfrnkDTQjDHqkHl2+ZBazWRiw5U3GlW9\nRSLDPfyuU3gNlqETSnIaKpIUrGFmeFS+IfUN9zxBq2dDEffc+oFVHiknRiwKwD0OBOmTr6zAiE6u\ntFAcfz5R/rIMJAxpokxoLpUmlQ9W71pW/rIq4un1EKtFTOSH4z8ebH7R479nCyWuiVANwNq29VQS\nE5arnonycwbTopugTlcWvuBCqUBvwrdwY8vsxgK6ePmyLfNkP7LxpKVYDiRuIR7A9Af8R9gI0Yzx\nOCZ+Hobj54itsmnWqCZDywLJAt6fB+xnocygfkM8zwLZ2itG/+z7O4uufI7C2ckGEXr0xyN06Plg\neB48hL1o/St4CROOo/a2tXwPnx4Gz81ABw4z/Bzhn4ZnYu4qEoCLtCh/520bH0fVcaANUDDF3r6I\nqTeaBOBj6TfTT10pUnVZHSygIti12WUPZjQrToQJNUCaseWpPGLZnf5AvOoxTcmX2dty8o1Zuf/l\nrkYz4MGpSbV9CPiZbczZRqmEFhnbP8qu2gCa7HHM1fmsIgAIxL7UQ9d6MV8gaj5B26RtuaZTliRZ\nkJHkzWqjEAU2V1kalE8g6pynrnbx0Ubx7kTY5c/ZtuXJdmpXtJqHcv3H0ea1pd6EfT9fK1fBRJfy\nwAbm/ZRMPbSPUw9MkEl9qKF3Vlp0luOwUL88HHMeuSEpThcvc8fZevfXHPf3BDKn8/X3LGuex/1x\n6xyGZxY67yzw9vQ9eq4h/F1w+/+czWiKwSwgYAXoTlMNtHUFn8rUcpxYscRg6khAu3gn0XMkWD3I\nbM3SZ4oh/HUQ2OPZHwD+yuyYDrrS75KJ08lSasR9Y2EEExnq1be3Dlj1iHO/DBVU+L1m+ageogDg\nAp2ZC5Cjgdw6bieDBWKNqczhUkViLUR7JhVH1mn9sl6U9Vttk2+hxDgiDS91hHvb9cM0VhXEtfKS\n+TbsqLc6siPjlro2YdIlW424Cy6WTj7FfRA6Y5X4szfngnTN8yrtWnj3EkHlRsLhS9u5d/Xdgfn3\nAPKSlNnBDGkfTp6DvN8dyWDVCXvV2uXJbrJeXTf4MBwP4Efq031OHB6ng/xnzjjO62cz9VX98vvY\n+UfiehdAP5LmR+7vFjp3ebjNl7OtGqL0Ua/74sSrn2wmWiMsF+IyogIlGGqfeb3D0Z9qnEp3HRXl\nZgDAmMjzRCEql4jhkbby/5OWDeUOABM8tq1JRi4MOgSYRoM103MCeSkL0rOgKYmLO2qjnfVDnWzO\n4StHsHCt2/Uc8MKFNqo05vSKg+tXFE5l8ZFjIXy+JzGyXuwub5f0jEM1kxPMs65Nr1317fhcLFyi\nhZr1ekVV/SNKrZuRFglYFdaWNSZ9gQamJAmGPKFbgDzLyVZyLgCjTp9inob0xwJxkx3DJcH5FPNv\nvfAf06aab9Cy624Ifw8gz2D1TwyeyREMKv65kGPp4zieremk6vpM6isZhRk3hxjwMPwYD8zcSToe\nDpuOn4djjNStT3bQNmf8zKagV+F3qVZep3GvVtHnPnvtOj7UiTVzGo6DuxBbL3ocYXG0OPGSqTaA\nk64eyY5GwZ8Lw+u298pEUlJQax1vsjvQypJbO8YU749hZ5gsfYQnycfA4eIYSwCO7meRBKNmGMwG\nWr8cdurZt+HQzUcB9g6x09nqnaXvI98oQCwZZe0wzdlMkEwlPauFmM0jF4RzWy+4kSeftgQxnzCb\nwCDTzX0epjMUr7ppqMq2yyzIrvnFUmnmmZ/LUqpRIESe2z16PwMA5kcTDukvbVtvNStg2/TGJYNz\nQ4sJI4f1rl+grIvMAHrMZL/j7IjxTmidLxr8dr+QecnIuy0rH1HuO5j40sOXATa2FIz/OEpyFhPH\nruN1YWqATuoE0jFn66RKUCTbGWmrjuHpWMdjkB6zXEiGu8k4+JXe5NQqVbPzbngFqn9KH/8sfNza\n5fz+qorJzxy4xzHxMzvlMVEnpZNIekpfhy/1jAQHlfQN36WEz/QEMC7UY14LbbOFA038CNwCivwd\nQBCmjI8Rexl+lgMygEADoHbKZuEbNrOMZO0PBfMSJAO6mYelReVDbSesZgFhF+2Ljhr5OMuWlcJs\ngTNZanup46b6ZQwvs51g5gGUh6WFihu4BkpnaDTupCpLh0bbPQlAejdrbejyPM+z5Lha4nQ6PuVl\npouOl0lpPep/HMOcTRSDVniRNPuUnpD8arHDfEdP8CornW6127XsS7kOwA1v01udEpXVM813kOCL\nzA9VJ0b/2QRsdjSXjiB6xRwVZMctUWW4SskZGw8MaIHRU9eBEQffPixOCHfgeEwc88BP9wScYJNz\nJsDnLrt1lxpRo5uXmz00U8tmB+bzDeC829Rz9/yr68/Cu6qf95h5WjJMB35OHHPAfqLaj/2bagyf\nDeRH2XlpxzZw6j6z75znnALuFBIy/+XieBQiY5WBY32jWm4kuD2iGHmARN9vtYXOsGS9J9OhaeQP\nyzhT715eE02FCvutQz0+EjINqOPNYA1z8D7kwxKcx8Cpz1KAhjAYffrU8NR3W4EZNyFxm6HZAzFz\nSmC1Xqge+TtmRf1f9QcRxYaYKVNzNuF1VNok8+0KFD11S+kSwOayq5Tt0x4gS6Kl4JkpMKaPOoVH\nJX+obgXkQeac6jRH7TBlL6j9X6BuPhqh+DT7slvUlPP0qlX6aH/v88euw9ds0R9xAK4ypu6gyW9S\nP1RsKN+1ZGwoNoGFlattOFmcsrkkYBv4R15iu7nMAGxgwGs33GMkoE/HmFZMsx17YQFyDiKGYqg8\n3VzKBKxl4O8r2/C/M2iengmf16qbbHOEY/2E2bNu0b1t/d3DsVNJcLZLDDaCnVSsQIVV/KU6Rscx\nBhawrV7oBEsCv5d/jzGSgT4sWOnRr9bU2ZpdV91grRNDLq6OkaqZAPSHCZBnHlqIZLzJ2AsAaQI5\nsG5MoR9yBw5zjEfs1g0Ni4f6gKcYA/AxQr0wgWkFaYtKszaCpQsCOuxit4/WijinoXy7hwVRPMtZ\n8aICA5CO6ute6KNpvZHj2ZsRR3NyhaLryhhP1joXeMulMC3ejHkOYXEwTWeMiU1uel5FfjLvYdJq\ntW4RMY4U6LDQdTOvRDFHyMLa3ZlZHjVtYtrsMP70UAngC496G9mP2P/RHy2JTK+imS1LzwJfpVHP\nYf2k4Kix75J2DvQxolJH+/Pw4aHHHYYx46y+Y6I69zTnnm3oinv7eBahUeXRHAuoLeD/61YrvyMo\nUN8B9l1e9Vl6g9Tr/M1Tz3u26xastwAAIABJREFUJXpyW4UJB++yiq9ALrO+YuWVXj7TjsghjKDy\n5P1i/MYIdxHez59mSdYMuIkECUkvTNbGmXx+JBN+ABjLHLvZZu+ipJoxnJtxgcxrAxGAApsWmHTC\n5Z6Lplxnsu6rbeOM8iXDLfWTsk2YIYcvAZZWGpbv0V+OqsmcJI1tVfFYVW3sCm0gN+eGvgZyXZht\nHMmYZXGVJqft6UPUMJJ/ddGAfhRtweNQP/IC+csvV9+cJQSsnuPMIyaadRQ1lIZ06wvGPYGBr2Hk\nOQDNqMvj9LGnHzSGX8HcUn+Yn0aGvXOeDncYGG3eetV1T0SDBV16Ugc5feaMYuSxax6bJ2q1AuAB\nMO6Ow5De4DjgUeXi4Nbr/Z35fw6Mz577bPisdcvdvd3q5dZsMR5oQPYYFLWQZL1GQp1p16HM0ND9\nR7B4yye2w4RRDy6zDjLb0YO29J6+LYBnXyaLBZo1j7R2qRRzbaaZdkz/fwLLLsGqQ4Tu9TGaJQ8Y\nfozse5mvdh/cG048wX74QBy0jJwVJu91EbDGbfej9e2Q9acSSNlvbTUA4FItdeWJWVVmd6DXEr0A\nzREqDqBVHmqdTwHCA5GpDipGjgbhXtDtutDFQ+ql41Qmr/QaxBuElShSPka/ZL4UgMn0R9cA69BW\n4hnFWVPkGlHKC0mHwunULSp8DZCXhE9bbw4MZDk8dWJmp62v6lNhhL/IkPznbaAoFiZsjq2xeko0\n8KirZcS7kH8AGI5HLh6NgTzQOHbYVXpAHpgRA8tmmzLSlrV2LhaDIOtSjiLZ8HWw/Mlwp4d/V7Xy\nKs5nG40a5IqLNPnde3Fep6D3pS4THQrgrwTMeWCwDeIwkGSRqbMvT6UJEu5eu4b7fQKcSZoNFLVb\nr1j5rDFAw704EcjqvYg3QckdP138yVsuwFnsxBzuZcoYQEyXwo7HdDyM25kMbhN4HOtmFu+xF4dG\nB2FyII93m6FGKFbaQrfGb7J2Mt4saaYpBgJsUY51jg0ydgHQUmt7nmGQLy8aDSiIt8CkVUjN4qSt\nQ/UTT9JF9moMGIk7JM6GkC4fiYUxLcWseGpQj2463h30NTPBekBNJrquZAH2JnwJkNciF39DxpQM\nyNBXrgO/BBsb2kyarXF4MelzHRYVG3pyhfpOU0M1d+Mat8HhFtNft+jsE2iH8SybxaKpe+jSw0lR\nn38YaeQgYAeSSvgMZj+z7/5oHM/UJu+aMb5K57SQe5HuAu7bu9NzyF3K7xyIRnC6F05rDgAOT11o\nqnUXW9uZCO1osFAnb2zbCYOJCWwBS7GugEGHYebmo7XPZodPf+JxqEHOTpDnVebiW5APK117q2y8\nBQDzWwsFJDm6ZmF4TORaQgCOASHQuJMTht0eI971KkNln/WWpfKNeBGsJkYTnWwO98hDCAJpg8t2\nZ615xas5y4ap9zlzWt6jIC3wb9T2qp1CjNTD952egVAENOFg2iWcGKdgGs04uzHsVNY9fAmQHzNM\nxnxpDSdUVkX2PbEOSAc1+qrJAIvyc+Dm1aVfZLq2xl0RsDnzoVotr6208c8wTu+AsBXthdc4pDWu\nh7tNL+ZGQaELenTqRQFn3d+0giq41FkFy7JhFX5/InwUxO+EjAqF+1nHhQCuTs9BsgqWd4TZZVrC\nsrrTJIDLbCnaDOVDw8Bdn+n6l2VDCyO2q1luMDJSCa8GbdNIma6T1ecOZEtOPZKZT4S1RamYkvy0\n0zBRsxhPRGprmTgdiuMOqEOpnT5WcnPUoDOyVlZEkuo50Zouu0CotQpiAdnqsxxDqeIgicomsDRD\nWcDfehwsoIfm1PytH6xT1zgMyzvepcvfXvbz1U/I5r3T5B8dG7sQ0YLitKqpLFkUgKKEuvjdnYD2\nzKvwRUBuxRz2LafFoLRWG7Jlm3UU1mj7I41h1nGswqLlLiADnrrETQUQbUd9eQ8WjhkOOHduzYgO\nSVMqd+TxdUBvrY4FU9736ZhH6Ft/yqAvVUHqYWmGtjpfcoT5ZuSbfqW96qP4xFMw3cMrc8NejP4k\nYGKt62fvtswtUQ41JaSa48q65y6N/Xq1NU3SZgBas9MRg20ajgQVunFw1oONxVwlQOuQ9ZFAcqpW\nYsMN5Hmrspro/WxYqOmA5YBiLpSZz7Llhs/e8m3c0cwzWrucAeycHdD2nP17UEqGQMhZ5QNxMv2D\nMw9fsg9wJoHMAwVFttvhaQuQbJPYu8w6DAAegFka1GT7uFqc0HVtq2IMJivE1/2JQrPIUc6cARmv\nsLK6CbBGP+cdT2W14m4gbxPR7q3xsQFygTn7rXHSlaSwx/BCzG6Gy5cA+ZwliNi/UTDcAr0HYI8o\n+ERsVEB3hCs4qYVQMApV56xgXtYJN/GgBp90XsbvHLQ5GFmM5U+le9vaVrIDOHzgEcZ2pVMPnG79\n/Zzd2MHw5hJ3Z5pp+cnv2GfDR8B7f++X9PuuwrmF9M7ArwD87SRqitbWujYV6DttoHXuJRCoR6Xw\nzTYuS4eIBcpS11oU8N9Agmvo7r1tvR6yENxWvoG8dgIZCNzyV9dioS8+OUNonfrybBKTgRAYbfPO\nhdy1T9BHd1h6NRP12h3a8LbUTZ8AIuOph/7wFkIkUIWqW03yrwVBRQ0mTsuWPlLNoMfSFdnOvDcw\nS2uVJQrnFaYJSHrXrb4SPOtH+G52fFsuXocv05FzdNSGh8yrPnNiTnmv9aN5H93AQLMErhr3wF51\n84xb9WQLYLFHQgdGppL5FzipZxp4vCStNHG3a7IePKJMD9DEysvyJajJCHOsIffgeY6pRrbp0qpD\nbJ39E2DMz79r0XUP0mW26/cmjx8p53k2JsJXBAifUTbf6x6z+lKriyqWJgXkhwJsCmsad9toI98R\nIZbMt/KUG6m0kqKvDnDWiQJyWtMgTnYyz5OdkKf/GGVFcEJvC5tyY5DfSYZbXxzM/SBQOtWmUTe1\nqlU4znrZiA7LBfIdtZZBCkbG2fFtcAls6dECpNbBCjCGtAtaqOQz9EVP98XeKYO7YtvKhOldgzib\nb0E2Y76qWWtGc+r4W/g2vlZYqIZmXu+p82LmZYCpQ6BJIA4pdz+IG/bXBbxzRemShl4jI9f8Qa7t\ni4I1gPiIkbfltdxx97CRFi+yWzQ/j8P7rNIEiHKRKR2iFgi14V/3g8r3nwi/wuaf/d4XYD8bnrH7\nZ89fqXTuVDv7wqono7uOe2U2FWXhHdtcBYZXX5EOEe9b+tqTPhiWLcgDs3PH6pgYNvEYo051Atq9\nahzQEUf5DVqgDMCp7pgxQ4w9Ink8GdeDpBy1tsF/a2BIOUDzwx6vE4Yx1UnKLGKlZG/uzVdYa8s1\n1WDH7IMbjER1k2TMDPTUm0KDM68AX+e+hSlOs1zb66KtpU442yswhzShdx3cdfMvtFq5GXwLi7mL\nAIBI+Hwtg60s/xSuwKArvUGn86OvVMfSAXSVioC42/ImyMRrQKU8is0daPMr78Y+huNnbUQKdcuc\nI4CcR96h67ZP2HlRl2+GOzD+O8F/T+t3ODK7AuCrxdM71c09WOMyb8HcA6bcOMsTVl5s3NMFs/Rv\n4TEF4hv7r+eqs1kjgjt9Z2W/TNWb9e7kkbO+4YBNFFAGkMc5mXEKUy5+OlLFhwJhM+Axk9XnZqAC\n5VzDKabPmQLIlIWNs08jz2G1SIvgz30EBGP+WxyY9VqCUPrS0io93vRx1p9V28Qnd6lOb/Y+sh65\nxGCgoNjyoH2hEiHrh9RFCwzeeDbUvly1AiDzuTLfYpaXYWmuAn8COIAFnK/f1/y05Ix3+74D6jmg\nARwBpsuDW9RmYTu7oql0u+wYYbZmPeboFtOblVDIHB5TWfeRTr1QJnAOMvk4r5RCQDckfSbcqVb+\nNIjvaesfgfM4DvwKkAN4+f7dYundc6/iCwGEBcj3tZqFUFizbTLuENZcDAcIBmSIkRHt16tkj2gs\nBQZgPmtbfex/CCscdSj2SOAOXXkukuZ9zhCosmhf7ijw7oU9iAqG+evsVV+uXNPSJscjTSFB8hXv\n1jW0cFTViwkgGJk32h69MccTk5LkTZc6NjltCNVmDpTKlJuyAII5a3wNvrQRK4Zl6bHm0OMRr8OX\nqlZ8+b7w674hEnuhBwjdHiW4FvPMmLJ5O6ILUFptfHtBkjblsmkIbLr8RrRfmBDjFb2eDnBdACEL\n8y5cWPSkO3qR9jZDbT7T/Kwc9tAnhPNwjBjgrYZZZxD7Ai8F4Z8Il+V/8736G2neR/vpnHLolv8/\nkWfgOXBrPzqt6VzMKuacdW0azQJpNQNom3Tfab+exRqnn3eWXsw28y1gA5Los7J+k+Ns0ncvtP4B\ns1FqlYeYT2reOP2MmegswIUjPcyynN6bjiz93hDuTMwQkRZboHqH4kDyyHFtUSiD9VmneT1cBZcD\n2ixTg6PxnGCk5VctgGbR0i1D+VzPtSn1e16bnejvJtkf63ar/co70ALccnpkpyc7rHc7fA0jJ/Vc\nSXmBuK5a103ec/E3UXa33D7fzyhjrxmW4ugTUFGQtpS6PNFjod7KJrzfPse3pruvcU9vQSPQWhK7\nfDaDG06MYwYm8bW1hJdfjLKeGJzSi+4P67S84vElB6c6Y+fU8Ey3/FEA1wXoFaLaLBQC8nu4A/ar\nPN6pip7leV8Duc77em2Ps9VuDZZRQilvomBZftTLqDaLPnJfXr4nCoKKqPIQP8SnP9+PTJgBNg7M\nMXAYgbyZLuS5EAhW7JJAvggpD+ES3h5nWaTErViYnVK+JP5SljVvTKfUlce6CWfIO8XmB93Qtl03\nizJGmxwTmRxsb4Ivuv6lfvNu7/auCi7ajVVf7kJW+TZT7E842rT4InwRkAPdrgQiZbjacc+dNN6f\nKOvt2qwjx7TB0IuhO7u60M9rb0E30DIV3R5nr2qrl+0hrGNMp0q1cp+x0ZEecqDVs5PggNIrAlar\n5tFBkxt51OYA4I9RjLwGxQAWh14F4tRx8l5uSKiOozm1ZXvy7w4rQLJncErLQTcqn8AK0M9AfH92\nv/eR8Exvfleu0zWw22WZL59o0NKxEXHy+ntBhfSaggobuSiCxAbgY2KM0fbsJvCTAmnQlv5EbLYZ\nMIIpH8MbyEnqQeucynXmK2fflsZkBHD5Tr09VT6dvxhDA8jdqQRr9Db+zBNyRysXiAtdXMlSjqFk\n4tzlTanlTNNTgJC8CnudyHx6p2MaP/pkIPiKGHv4GjtyYSgLCKKvR+C9C4bDgfTCUPqjY1QZS7Ph\nbeCjAZrJ16LNbv1SKppVDOwMDATP+uklPZydjn9yv+cd4RYVxWSi7qaHX2efOacoeUP1kWPOkfHF\ndHGm397uRM3aagPGXf29ANSPBOe835HHkZEJ58k1m7ronxq0f1yVZR0jn6vjcx+Ue8tUdXuJopy7\nsaenAyhmrvNYQE5KsgG3juNwnBcM1weB2aAungnkK9lgWsi0UGQqZqyWencsQB9H6gE+rPTz8Fb9\n8JnCWgRIE3dJbg6PQ1EOmgAXVOUsZSF2dvqv8SwG95wySxDW154e17Lcha9b7Lz43tfInoDb7CeY\nh76s1SkmHUsee5oHAKjj4PQZkYH74yZfQt93k01wKDxPn2oOfucrdB5Wd1aZV9NjmNWuPp1SA8AP\nQ1nC5MM1LYyB1BYPM+3VbYYrBZ5qTxDvYu/51xQ/HxamW/jlBeTx1y4e/o7F1z8Z3hFC7y6i/mr6\n19EnR7Yhqpx1nHBR3yoOF0IhDJVwXqBLM0dsAkDylxe0rfnkPFzUOdE/phmOmikI67bcxMSNRdab\nm2oyXor/8PcyzVB+V3JuSOODMiyont+HXcPD/JIsPKLVOhOiyry69F9v74+FNKU7v+7jX7bYuU6L\n7556f2DWFOuNd24HxEb+ubSiujD90syUZTmnvhCcmoGc80M9fPyWONlBikwHcz6VgOxVgiGdHoF6\neKp0UmHBTsrdZdMwh8cJPsNhc4TFi6/Mt/PRHblj/43BVcCpysy0QiL9fyCIA+e++Kx//Gq4092v\n38/p1ywwHqzxULhDsmSAH84nUz0nazD5EBcCh6GOixuj0y45bj2+ZEgEicixapJ2uzXuohAXCKSW\nnizjYA/xJAmSrhhbEwZMa0wh8YEHKRpIaxZl0YEX5cnQOr9m2+gorOirdTdJFfNS6wE37QN8ow1B\nETqT+3Ty/ChZZy+4mChv2fnvBvh5YHBFfZWbU763/+puJW2woZ0182h5fIjm5QT2pX5R5h1PxeYK\nX6rCMguLkADglVt9sNkIv3NK6FVX/XwxFo+TaHj4R4OJV4flwm2XA6DVwu9ijws7c9r8O+Bj0Xv/\nE8Np8fNCf3/1HPC5Mj8jEq8DxyPB1gVwGQ+wbKxzvrdajPG9w0OHHKfskNEv0vkyv704nIy7BDw4\nEJZ8gSAOT6d7ITgmRlq00O0v4mBtQ8bpyfa1QFbFsrSHLFWQNQnbeZXJ+CM2VJyiMrUGsEzHNm3t\ndVt92QlB+2+vRq+mX5ifEjCVnqViiIiqVqtyLwpeuqy+ku9IL2B8mY4neBX9cLUcyVV2U7MnNdGC\nNByWwijLdb1f7FzNK4UZXYTdNWiVTDpV2cuUrl0GIllSFsyKeaCntjlz0LUDFT60uSWTr7zd5PlZ\nqDUQlr0YTi/K/lNBXMNHBN87ZpFP3n7jup+eU3Dqzz0/JoJon2Wgxrcyf52Nx2tCmxKgadWzq9CK\n6NHMj77eEUYOcbdHS9l1xZlqeSyjwy3PM0AeGLNZAHEMNwAnHqSpIFWPDmDnUV1Son0aLRT4NLiX\nvT7aiiaqrLcVPevpX87I1XC/p19xBQCgnUcAh24vWUiJUdhCA41yYVdw4PVEtJ391NnWhSktLBgj\nOypNhVQvp0scBclkwwulFkXNxnThXGTMslwOJIX/PZC5yjvn8bbo1o3nO07ITlMy8nbEL5lMF6Th\nydHMy+8H65/g+zKsVXwqB7/uTOnvDHfleCVYnr13VzXvC6vrCNZ4ZTR8oMoWwL19RqnWnqfrmUCD\nPNUvktBCzPz0bgWqLSx0+XX4tXfPs3yO4zh2UOehzBbjfGb6a45TSKAtZQrVnQQnx3ZhTo+jqoMC\ncmKDF5+j1RLzudSek9g+HzffZLEzC6G+U9yrCaqigGLD03MKZC2tYvNMMwbucqypS0o+opilk5xa\nrQ4EE4YkbGOXok35S9qTZTwM6UJUBUis9le0U80AVcxcdXnTXlFMlH813fZdqLHKtilqJkyG3c+Q\nHchi6EDULNP39PlSEXXoQ6gN0+Ic09b/UzA+B/SanlYnvwAAAFYHMPqKSCdGdc1gPwvESz5kRnBn\n087nrhYsr1VDZ/b5fri2a5dfBZq1zvDBsNvZ7yqhc35XerHeX/OqOvG9DE9VQsoUbOKsLF9jVJWL\nGzdmATaVfrewo5VLzwgZfxOVyA9T2vJVee0xO+S+iNY1xxX365nn1zDyS9erHxhAVQNbg/PTG0DO\nqhUKBG1/7Qn7k3cDvgUGgYdSO3TSFC7RE8q+W4FqY0qq5lCdIsvE5xZTROMz9xL7mV51YfR2PdC6\nrPH58BSaW1xxGjlq6/kwmlQ2i59iRqb9PWPvWYmU6gQNH2KSz5nMrz7/0Xfv9OB599NpfzT86hrG\nr6l43o/77vvvUKsd7rDaaTtrPFGdUwQQff7pGhJscww2a85vBOKceQRIWJleavZjfe9C6C4zj/uy\nfJFq5SJHFyOWlVDTK/n3KixPeUu0bbvRqrvWhGUqB6ofLtJcV6C9HjdLkya01KV6xQ1tl823WvAv\n08pmanpPAXkhDUu2PxtUOKzBYUYH8vcCIdhz3Jviw2OkxAoQB45aDOUhwFg6vQ6F0nFqX75hbf+k\n8BzM/77wJ9YY/tS6xfW62h2jv8+HyaBy93SZQLac94cAOcJM8gwFbYx44tPSRwtOAMC48cvK1URB\nz4JDimEXTP8ifJFq5a6ht4xmLaz6WMux3aBCJtyHN6/AX4uUkPfqiXMHqbQN2HmnJTgNTq1E32im\n5yQ2Ozf0JMTze2p8grrPgiyJT1fmz+xd87vqGu/DVcff1TRXQE6IDeETcwvaAmuOagrqPFgg2yqZ\n+JGF9/QHSlZOdVibRUY9xEDD1eTr7fBMTXEFpn8ChP6udD4T3jF/fPX+Xse/Wl4F4XcFwxWIvyob\nZ+zxGcfowQ0DI30YeexkVUB1KbMlkG9jocF5ne3yIAuzfJXgbLh8v4H8SrOwhm/CyG8ymei0NMCt\nHlIZqjLaFZwCMJWhSwT5ezET3JIzNDjvOk5OwYY47aHACJCjcBCgTlmwi5SFndP8cJ4FC8H1WTur\namUfGG+Z8C1+BsBKPGnIbAAPZsesjBDccyOGA8NT356fqxqMXTtnUw7MQSsYYSnK1j8QTgvZv5kV\n3+r9N136q+e/MvyqKuqj759Vedft8gzQ7wT0sz7NGXHGkGN9Y8ljxAHV5cyO6z0J4KLurDxvM3lb\nrnmN+55Re2GSIHjURLqnVhXNXfhyq5VnoSthQWksA/iibATwAfELcp6ll9qlf28AZ2l/fQXmUHXE\ndh0riGtGXdor9HFS1qsK2D36LBOT3mL8zvB5tcB33fHP185r6/KkyVbn/OIWZzzOfNcd8NlAPtN9\nQE8ee9E6Hc8loLuA/3FR5lfMTdc1vh+I/pPC71h7uCIW5/ZZZ5wrp9v0zDcztysh0KQhiVLty2yU\n7Rmq5M96PLtEVMJjT1sLBSxxmgX5ESO6qoQA8tkE50V9f5HTrG3Kw+sXUyECJoXi0ujyb7/TDezA\n4uCJJkKhs163zF51knpgm/UsYC15qiZvAVxStxkkwajNzUz+Wapgb+CLEAS1hcSrBc13w7njnHe/\nnZd/LiDeWN8Gk85Pk8bwNdEgXsOkXAPUnEbOMp1yUpIMeADt6AtL1yCz+Uj5n1mhVPGe1u2FEPxG\n8uOpJciL8HnLmjWOVQXC9tSOf/2d46qui176ChlOxcoxWA8+KUNhuacZIrsS++aNylIt6Na8NwFU\nq6w9C1P2b3xvIBd2WoIR546hU5GyMxXgWwvZUxfXtOoVS3Ap3tj36ocXKofVyT2WssvQDh5IR1vG\nbbq9MOoQRzhbWX395yl79gWTOkK/rb/XgHT3+9JsLqWerQ/3h/upjBR1LehyMjniNBqOC1+iMxkw\nhpkbOdzjyDH32b6r2eHBuqD+U+uo4+asbS/b/v2VXv3eNG5jcpfNed0uH9UnPwvPFlY/wqp/VzzP\nQ6+PXOch6rW1F9nwBhH4F0Lb1ay504r/GZn0554WZF/WMdBkTAGsdfp81eqZtXrUXl5MfqWMigH6\n9yx8va8VAcqT7paNBDrgF+nIeEAe6NXIzdzP/g34sUyZHPAhlZcVyR1id3rN4tkELJ6uonO88m5G\nu3FVIUi+FqB5BuIr2/xdg6hX8M/xXk6LsQ63wsuLZ2tBVX/nW5ZTG4+pUnnE685sxZ7qOK3cDMZN\nYeUpbq626zUA3OvQjXWaS1C3Fj4fCO8B7ta4vzG82/bfxUrmKnxsBmCb+qQJg5kLDmxkqfqSXFxC\n9jHemw4MOQSkeusOrJ2ZRW4IE98/K9/O/qdEwAXUz2D+LHwTHTmlmlyRgU/2tEsvyH1K8kUjcqVm\n4J8ySstpTP50yQynQVcVehogxQoFbOvkoVd10PpfLZeqeHQqSjXRO1PcS5VVMYhr1v3qfX4+Y7P6\n/DP2v1jLUI1WzzXT6VV8eqJP9Qz/hhyj5p6ue/NdcXkbnhRJEnLUXzDvZ0B9beHzb/iTYR1rvrik\nkAnWjprIfflvp2F5PCNnv4YNA5jEmp3n+b24/ryPvR6XGr4JkH883LLkEtFoiSzAAxDM191tixDZ\n0torVAFVVSNldyHEjws16t2tnjwJgb1xPwYUCi6fYWFXQuFO5/7UIuCDU3YF8T2EBaPUW02ievfp\nnOgNR6BvdcBt1KCbo8F9Dgd4TJo1i0PNsc55ZLmu1Fbfle3+N4a1rvm9mM7NO1RlvJVCt7MhZ9j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jhWge/XVZv3I4163ajnDrCeQNF2AqwBeJiVjp2eGXVqWcA+gcNDnz7dCsSPdP41h+PwgWN4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\nH3UUUI++wiIfWUWtO/xaXbM02ec8H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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_net = solver.test_nets[0]\n", + "for image_index in range(5):\n", + " plt.figure()\n", + " plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))\n", + " gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)\n", + " estlist = test_net.blobs['score'].data[image_index, ...] > 0\n", + " plt.title('GT: {} \\n EST: {}'.format(classes[np.where(gtlist)], classes[np.where(estlist)]))\n", + " plt.axis('off')" + ] + } + ], + "metadata": { + "description": "Multilabel classification on PASCAL using python data-layers.", + "example_name": "PASCAL Multilabel with python datalayer", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/pycaffe/layers/pascal_multilabel_datalayers.py b/examples/pycaffe/layers/pascal_multilabel_datalayers.py index f0039eff4b6..68e4fa7960a 100644 --- a/examples/pycaffe/layers/pascal_multilabel_datalayers.py +++ b/examples/pycaffe/layers/pascal_multilabel_datalayers.py @@ -78,88 +78,6 @@ def backward(self, top, propagate_down, bottom): pass -class PascalMultilabelDataLayerAsync(caffe.Layer): - - """ - This is a simple asyncronous datalayer for training a multilabel model on - PASCAL. - """ - - def setup(self, bottom, top): - - self.top_names = ['data', 'label'] - - # === Read input parameters === - - # params is a python dictionary with layer parameters. - params = eval(self.param_str) - - # Check the paramameters for validity. - check_params(params) - - # we need to store this as a local variable. - self.batch_size = params['batch_size'] - - # === We are going to do the actual data processing in a seperate, - # helperclass, called BatchLoader. So let's forward the parameters - # to that class === - self.thread_result = {} - self.thread = None - self.batch_loader = BatchLoader(params, self.thread_result) - self.dispatch_worker() # Let it start fetching data right away. - - # === reshape tops === - # since we use a fixed input image size, we can shape the data layer - # once. Else, we'd have to do it in the reshape call. - top[0].reshape( - self.batch_size, 3, params['im_shape'][0], params['im_shape'][1]) - # Note the 20 channels (because PASCAL has 20 classes.) - top[1].reshape(self.batch_size, 20) - - print_info("PascalMultilabelDataLayerAsync", params) - - def forward(self, bottom, top): - """ - This is the forward pass, where we load the data into the blobs. - Since we run the BatchLoader asynchronously, we just wait for it, - and then copy - """ - - if self.thread is not None: - self.join_worker() # wait until it is done. - - for top_index, name in zip(range(len(top)), self.top_names): - for i in range(self.batch_size): - # Copy the already-prepared data to caffe. - top[top_index].data[i, ...] = self.thread_result[name][i] - - # let's go again while the GPU process this batch. - self.dispatch_worker() - - def reshape(self, bottom, top): - """ - There is no need to reshape the data, since the input is of fixed size - (rows and columns) - """ - pass - - def backward(self, top, propagate_down, bottom): - """ - These layers does not back propagate - """ - pass - - def dispatch_worker(self): - assert self.thread is None - self.thread = Thread(target=self.batch_loader) - self.thread.start() - - def join_worker(self): - assert self.thread is not None - self.thread.join() - self.thread = None - - class BatchLoader(object): """ @@ -185,23 +103,6 @@ def __init__(self, params, result): print "BatchLoader initialized with {} images".format( len(self.indexlist)) - def __call__(self): - """ - This does the same stuff as the forward layer of the synchronous layer. - Exept that we store the data and labels in the result dictionary - (as lists of length batchsize). - """ - self.result['data'] = [] - self.result['label'] = [] - for itt in range(self.batch_size): - - # Get the next image in the batch - im, multilabel = self.load_next_image() - - # Store in a result list. - self.result['data'].append(im) - self.result['label'].append(multilabel) - def load_next_image(self): """ Load the next image in a batch. From 01d5a9e0afdbdf1e93f343bc5656ad1ec53c1673 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Mon, 29 Feb 2016 21:13:27 -0800 Subject: [PATCH 423/446] output all logging from upgrade net tools output info, warnings, and errors for fuller description of the upgrade --- tools/upgrade_net_proto_binary.cpp | 5 +++-- tools/upgrade_net_proto_text.cpp | 3 ++- tools/upgrade_solver_proto_text.cpp | 3 ++- 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/tools/upgrade_net_proto_binary.cpp b/tools/upgrade_net_proto_binary.cpp index 8a0dd7af743..ede07ecc2ff 100644 --- a/tools/upgrade_net_proto_binary.cpp +++ b/tools/upgrade_net_proto_binary.cpp @@ -16,6 +16,7 @@ using std::ofstream; using namespace caffe; // NOLINT(build/namespaces) int main(int argc, char** argv) { + FLAGS_alsologtostderr = 1; // Print output to stderr (while still logging) ::google::InitGoogleLogging(argv[0]); if (argc != 3) { LOG(ERROR) << "Usage: " @@ -39,11 +40,11 @@ int main(int argc, char** argv) { << "see details above."; } } else { - LOG(ERROR) << "File already in V1 proto format: " << argv[1]; + LOG(ERROR) << "File already in latest proto format: " << input_filename; } WriteProtoToBinaryFile(net_param, argv[2]); - LOG(ERROR) << "Wrote upgraded NetParameter binary proto to " << argv[2]; + LOG(INFO) << "Wrote upgraded NetParameter binary proto to " << argv[2]; return !success; } diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp index 9200431bc27..d8e84d6d91b 100644 --- a/tools/upgrade_net_proto_text.cpp +++ b/tools/upgrade_net_proto_text.cpp @@ -16,6 +16,7 @@ using std::ofstream; using namespace caffe; // NOLINT(build/namespaces) int main(int argc, char** argv) { + FLAGS_alsologtostderr = 1; // Print output to stderr (while still logging) ::google::InitGoogleLogging(argv[0]); if (argc != 3) { LOG(ERROR) << "Usage: " @@ -50,6 +51,6 @@ int main(int argc, char** argv) { // Save new format prototxt. WriteProtoToTextFile(net_param, argv[2]); - LOG(ERROR) << "Wrote upgraded NetParameter text proto to " << argv[2]; + LOG(INFO) << "Wrote upgraded NetParameter text proto to " << argv[2]; return !success; } diff --git a/tools/upgrade_solver_proto_text.cpp b/tools/upgrade_solver_proto_text.cpp index 7130232aed7..ddff1ce6bad 100644 --- a/tools/upgrade_solver_proto_text.cpp +++ b/tools/upgrade_solver_proto_text.cpp @@ -16,6 +16,7 @@ using std::ofstream; using namespace caffe; // NOLINT(build/namespaces) int main(int argc, char** argv) { + FLAGS_alsologtostderr = 1; // Print output to stderr (while still logging) ::google::InitGoogleLogging(argv[0]); if (argc != 3) { LOG(ERROR) << "Usage: upgrade_solver_proto_text " @@ -45,6 +46,6 @@ int main(int argc, char** argv) { // Save new format prototxt. WriteProtoToTextFile(solver_param, argv[2]); - LOG(ERROR) << "Wrote upgraded SolverParameter text proto to " << argv[2]; + LOG(INFO) << "Wrote upgraded SolverParameter text proto to " << argv[2]; return !success; } From effa9411ca270f32730400861c08dd2aa3f03ffa Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Mon, 29 Feb 2016 21:15:56 -0800 Subject: [PATCH 424/446] check all net upgrade conditions check all conditions all the time; V0 -> V1 and V1 -> V2 do not suffice. --- src/caffe/util/upgrade_proto.cpp | 3 ++- tools/upgrade_net_proto_text.cpp | 5 ----- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp index 449975bd733..775285f14ed 100644 --- a/src/caffe/util/upgrade_proto.cpp +++ b/src/caffe/util/upgrade_proto.cpp @@ -13,7 +13,8 @@ namespace caffe { bool NetNeedsUpgrade(const NetParameter& net_param) { - return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param); + return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param) + || NetNeedsDataUpgrade(net_param) || NetNeedsInputUpgrade(net_param); } bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) { diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp index d8e84d6d91b..617b48dc918 100644 --- a/tools/upgrade_net_proto_text.cpp +++ b/tools/upgrade_net_proto_text.cpp @@ -32,7 +32,6 @@ int main(int argc, char** argv) { return 2; } bool need_upgrade = NetNeedsUpgrade(net_param); - bool need_data_upgrade = NetNeedsDataUpgrade(net_param); bool success = true; if (need_upgrade) { success = UpgradeNetAsNeeded(input_filename, &net_param); @@ -44,10 +43,6 @@ int main(int argc, char** argv) { LOG(ERROR) << "File already in latest proto format: " << input_filename; } - if (need_data_upgrade) { - UpgradeNetDataTransformation(&net_param); - } - // Save new format prototxt. WriteProtoToTextFile(net_param, argv[2]); From ff6c6e487534e4e738b301da2169bd369344b7f0 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Mon, 29 Feb 2016 21:17:21 -0800 Subject: [PATCH 425/446] fix input field -> input layer net upgrade: only convert full defs convert inputs in legacy definitions (prototxt), but simply strip inputs from legacy weights (caffemodel). fix #3750 --- src/caffe/util/upgrade_proto.cpp | 46 ++++++++++++++++++-------------- 1 file changed, 26 insertions(+), 20 deletions(-) diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp index 775285f14ed..511a2dea5a9 100644 --- a/src/caffe/util/upgrade_proto.cpp +++ b/src/caffe/util/upgrade_proto.cpp @@ -953,29 +953,35 @@ bool NetNeedsInputUpgrade(const NetParameter& net_param) { } void UpgradeNetInput(NetParameter* net_param) { - LayerParameter* layer_param = net_param->add_layer(); - layer_param->set_name("input"); - layer_param->set_type("Input"); - InputParameter* input_param = layer_param->mutable_input_param(); + // Collect inputs and convert to Input layer definitions. + // If the NetParameter holds an input alone, without shape/dim, then + // it's a legacy caffemodel and simply stripping the input field is enough. bool has_shape = net_param->input_shape_size() > 0; - // Convert input fields into a layer. - for (int i = 0; i < net_param->input_size(); ++i) { - layer_param->add_top(net_param->input(i)); - if (has_shape) { - input_param->add_shape()->CopyFrom(net_param->input_shape(i)); - } else { - // Turn legacy input dimensions into shape. - BlobShape* shape = input_param->add_shape(); - int first_dim = i*4; - int last_dim = first_dim + 4; - for (int j = first_dim; j < last_dim; j++) { - shape->add_dim(net_param->input_dim(j)); + bool has_dim = net_param->input_dim_size() > 0; + if (has_shape || has_dim) { + LayerParameter* layer_param = net_param->add_layer(); + layer_param->set_name("input"); + layer_param->set_type("Input"); + InputParameter* input_param = layer_param->mutable_input_param(); + // Convert input fields into a layer. + for (int i = 0; i < net_param->input_size(); ++i) { + layer_param->add_top(net_param->input(i)); + if (has_shape) { + input_param->add_shape()->CopyFrom(net_param->input_shape(i)); + } else { + // Turn legacy input dimensions into shape. + BlobShape* shape = input_param->add_shape(); + int first_dim = i*4; + int last_dim = first_dim + 4; + for (int j = first_dim; j < last_dim; j++) { + shape->add_dim(net_param->input_dim(j)); + } } } - } - // Swap input layer to beginning of net to satisfy layer dependencies. - for (int i = net_param->layer_size() - 1; i > 0; --i) { - net_param->mutable_layer(i-1)->Swap(net_param->mutable_layer(i)); + // Swap input layer to beginning of net to satisfy layer dependencies. + for (int i = net_param->layer_size() - 1; i > 0; --i) { + net_param->mutable_layer(i-1)->Swap(net_param->mutable_layer(i)); + } } // Clear inputs. net_param->clear_input(); From 7eaeb3aeaa23b6a3ba4be5f09bad34f00e10958d Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Mon, 29 Feb 2016 21:18:50 -0800 Subject: [PATCH 426/446] refuse to upgrade net with layer/layers inconsistency die loudly if a net definition (prototxt) mixes proto formats by defining both `layer` and `layers` fields instead of complaining but discarding and continuing. fix #3381 --- src/caffe/util/upgrade_proto.cpp | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp index 511a2dea5a9..9e186915b43 100644 --- a/src/caffe/util/upgrade_proto.cpp +++ b/src/caffe/util/upgrade_proto.cpp @@ -656,12 +656,14 @@ void UpgradeNetDataTransformation(NetParameter* net_param) { } bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) { - bool is_fully_compatible = true; if (v1_net_param.layer_size() > 0) { - LOG(ERROR) << "Input NetParameter to be upgraded already specifies 'layer' " - << "fields; these will be ignored for the upgrade."; - is_fully_compatible = false; + LOG(FATAL) << "Refusing to upgrade inconsistent NetParameter input; " + << "the definition includes both 'layer' and 'layers' fields. " + << "The current format defines 'layer' fields with string type like " + << "layer { type: 'Layer' ... } and not layers { type: LAYER ... }. " + << "Manually switch the definition to 'layer' format to continue."; } + bool is_fully_compatible = true; net_param->CopyFrom(v1_net_param); net_param->clear_layers(); net_param->clear_layer(); From 326a486d7e15a1e7a23a3a5b81ae591766062a06 Mon Sep 17 00:00:00 2001 From: Jacek Czaja Date: Tue, 1 Mar 2016 11:32:21 +0100 Subject: [PATCH 427/446] - doc and cmake update MKL related - cosmetic change to mkl related doc --- cmake/Modules/FindMKL.cmake | 2 +- docs/installation.md | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/cmake/Modules/FindMKL.cmake b/cmake/Modules/FindMKL.cmake index d2012db579a..5ab93b2d6b6 100644 --- a/cmake/Modules/FindMKL.cmake +++ b/cmake/Modules/FindMKL.cmake @@ -20,7 +20,7 @@ caffe_option(MKL_MULTI_THREADED "Use multi-threading" ON IF NOT MKL_USE_SINGL # ---[ Root folders set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs") -find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKL_ROOT} ${INTEL_ROOT}/mkl +find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKLROOT} ${INTEL_ROOT}/mkl DOC "Folder contains MKL") # ---[ Find include dir diff --git a/docs/installation.md b/docs/installation.md index ef781e8d638..893164584d9 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -54,7 +54,8 @@ There are several implementations of this library. The choice is yours: * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe. * [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses. 1. Install MKL. - 2. Set `BLAS := mkl` in `Makefile.config` + 2. Set up MKL environment (Details: [Linux](https://software.intel.com/en-us/node/528499), [OS X](https://software.intel.com/en-us/node/528659)). Example: *source /opt/intel/mkl/bin/mklvars.sh intel64* + 3. Set `BLAS := mkl` in `Makefile.config` * [OpenBLAS](http://www.openblas.net/): free and open source; this optimized and parallel BLAS could require more effort to install, although it might offer a speedup. 1. Install OpenBLAS 2. Set `BLAS := open` in `Makefile.config` From 93af70e464e973e79d8b9a12c5431c7c209d4ec9 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Tue, 1 Mar 2016 15:41:33 -0800 Subject: [PATCH 428/446] [example] groom multilabel notebook title, order --- examples/pascal-multilabel-with-datalayer.ipynb | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/examples/pascal-multilabel-with-datalayer.ipynb b/examples/pascal-multilabel-with-datalayer.ipynb index fd66114d8e9..94b9b4fed8a 100644 --- a/examples/pascal-multilabel-with-datalayer.ipynb +++ b/examples/pascal-multilabel-with-datalayer.ipynb @@ -452,8 +452,8 @@ } ], "metadata": { - "description": "Multilabel classification on PASCAL using python data-layers.", - "example_name": "PASCAL Multilabel with python datalayer", + "description": "Multilabel classification on PASCAL VOC using a Python data layer.", + "example_name": "Multilabel Classification with Python Data Layer", "include_in_docs": true, "kernelspec": { "display_name": "Python 2", @@ -471,7 +471,8 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" - } + }, + "priority": 5 }, "nbformat": 4, "nbformat_minor": 0 From 4f4d0209495aa858de4ea7d71b3aea9196b14a05 Mon Sep 17 00:00:00 2001 From: Viveka Kulharia Date: Wed, 2 Mar 2016 10:37:56 +0530 Subject: [PATCH 429/446] minor mistakes removed --- examples/finetune_flickr_style/readme.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md index 4e9d41f13cc..9ba4c9217ff 100644 --- a/examples/finetune_flickr_style/readme.md +++ b/examples/finetune_flickr_style/readme.md @@ -14,9 +14,9 @@ Let's fine-tune the BVLC-distributed CaffeNet model on a different dataset, [Fli ## Explanation The Flickr-sourced images of the Style dataset are visually very similar to the ImageNet dataset, on which the `bvlc_reference_caffenet` was trained. -Since that model works well for object category classification, we'd like to use it architecture for our style classifier. +Since that model works well for object category classification, we'd like to use this architecture for our style classifier. We also only have 80,000 images to train on, so we'd like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed. -If we give provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name. +If we provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name. Because we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model. Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt. From db7a26162c9a1636b83b7d8f788ff669e694a150 Mon Sep 17 00:00:00 2001 From: max argus Date: Fri, 4 Mar 2016 01:59:48 +0000 Subject: [PATCH 430/446] Removed lint script reference to non-existant caffe_memcpy function. --- scripts/cpp_lint.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py index f750489f4f9..14c76ecd6bf 100755 --- a/scripts/cpp_lint.py +++ b/scripts/cpp_lint.py @@ -1564,7 +1564,7 @@ def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error): caffe_alt_function_list = ( ('memset', ['caffe_set', 'caffe_memset']), ('cudaMemset', ['caffe_gpu_set', 'caffe_gpu_memset']), - ('memcpy', ['caffe_copy', 'caffe_memcpy']), + ('memcpy', ['caffe_copy']), ('cudaMemcpy', ['caffe_copy', 'caffe_gpu_memcpy']), ) From ebfa2af1ca80cd7e0f23d92fca1a491b86605686 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Fri, 4 Mar 2016 12:53:49 -0800 Subject: [PATCH 431/446] [travis] force protobuf 3.0.0b2 for Python 3 This is temporary measure to avoid an apparent upstream issue with protobuf 3.0.0b2.post1. --- scripts/travis/travis_install.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh index d18dc223a06..ca8c410cfbe 100755 --- a/scripts/travis/travis_install.sh +++ b/scripts/travis/travis_install.sh @@ -93,7 +93,7 @@ if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then fi if [ "$PYTHON_VERSION" -eq "3" ]; then - pip install --pre protobuf + pip install --pre protobuf==3.0.0b2 else pip install protobuf fi From 7a8b19f957820af928aab3cbfae28e51a87b0d67 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Fri, 29 Jan 2016 18:41:12 -0800 Subject: [PATCH 432/446] [pycaffe] add coord_map.py for computing induced coordinate transform This provides a framework for automatically aligning different layers of a net despite up/downsampling, padding, and output size rounding. --- python/caffe/coord_map.py | 95 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 python/caffe/coord_map.py diff --git a/python/caffe/coord_map.py b/python/caffe/coord_map.py new file mode 100644 index 00000000000..dd41f293b3b --- /dev/null +++ b/python/caffe/coord_map.py @@ -0,0 +1,95 @@ +from __future__ import division +import numpy as np +from caffe import layers as L + +PASS_THROUGH_LAYERS = ['AbsVal', 'ReLU', 'PReLU', 'Dropout', 'LRN', 'Eltwise', + 'BatchNorm', 'BNLL', 'Log', 'Exp', 'MVN', 'Power', 'Sigmoid', 'Split', + 'TanH', 'Threshold'] + +def conv_params(fn): + params = fn.params.get('convolution_param', fn.params) + axis = params.get('axis', 1) + ks = np.array(params['kernel_size'], ndmin=1) + dilation = np.array(params.get('dilation', 1), ndmin=1) + assert len({'pad_h', 'pad_w', 'kernel_h', 'kernel_w', 'stride_h', + 'stride_w'} & set(fn.params)) == 0, \ + 'cropping does not support legacy _h/_w params' + return (axis, np.array(params.get('stride', 1), ndmin=1), + (ks - 1) * dilation + 1, + np.array(params.get('pad', 0), ndmin=1)) + +class UndefinedMapException(Exception): + pass + +def coord_map(fn): + if fn.type_name in ['Convolution', 'Pooling', 'Im2col']: + axis, stride, ks, pad = conv_params(fn) + return axis, 1 / stride, (pad - (ks - 1) / 2) / stride + elif fn.type_name == 'Deconvolution': + axis, stride, ks, pad = conv_params(fn) + return axis, stride, (ks - 1) / 2 - pad + elif fn.type_name in PASS_THROUGH_LAYERS: + return None, 1, 0 + elif fn.type_name == 'Crop': + axis = fn.params.get('axis') + return axis, 1, - fn.params['crop'] + else: + raise UndefinedMapException + +class AxisMismatchException(Exception): + pass + +def compose((ax1, a1, b1), (ax2, a2, b2)): + if ax1 is None: + ax = ax2 + elif ax2 is None or ax1 == ax2: + ax = ax1 + else: + raise AxisMismatchException + return ax, a1 * a2, a1 * b2 + b1 + +def inverse((ax, a, b)): + return ax, 1 / a, -b / a + +def coord_map_from_to(top_from, top_to): + # We need to find a common ancestor of top_from and top_to. + # We'll assume that all ancestors are equivalent here (otherwise the graph + # is an inconsistent state (which we could improve this to check for)). + # For now use a brute-force algorithm. + + # walk back from top_from, keeping the coord map as we go + from_maps = {top_from: (None, 1, 0)} + frontier = {top_from} + while frontier: + top = frontier.pop() + try: + for bottom in top.fn.inputs: + from_maps[bottom] = compose(from_maps[top], coord_map(top.fn)) + frontier.add(bottom) + except UndefinedMapException: + pass + + # now walk back from top_to until we hit a common blob + to_maps = {top_to: (None, 1, 0)} + frontier = {top_to} + while frontier: + top = frontier.pop() + if top in from_maps: + return compose(to_maps[top], inverse(from_maps[top])) + try: + for bottom in top.fn.inputs: + to_maps[bottom] = compose(to_maps[top], coord_map(top.fn)) + frontier.add(bottom) + except UndefinedMapException: + continue + + # if we got here, we did not find a blob in common + raise RuntimeError, 'Could not compute map between tops; are they connected ' \ + 'by spatial layers?' + +def crop(top_from, top_to): + ax, a, b = coord_map_from_to(top_from, top_to) + assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a) + assert (b <= 0).all(), 'cannot crop negative width (b = {})'.format(b) + assert (np.round(b) == b).all(), 'cannot crop noninteger width (b = {})'.format(b) + return L.Crop(top_from, top_to, crop_param=dict(axis=ax, crop=list(-np.round(b).astype(int)))) From 6149e7383e84264f315a23bd5b924ab49d4f5804 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Sat, 27 Feb 2016 23:57:42 -0800 Subject: [PATCH 433/446] [pycaffe] document, style, and complete coord_map - document by docstring and comment - pep8 - add latest layers and alphabetize - respect default crop params - handle graphs with compositions of crops by walking only the first, cropped bottom of Crop layers - make python3 happy by replacing arg tuple unpacking --- python/caffe/coord_map.py | 119 +++++++++++++++++++++++++++++++++----- 1 file changed, 104 insertions(+), 15 deletions(-) diff --git a/python/caffe/coord_map.py b/python/caffe/coord_map.py index dd41f293b3b..884d9070576 100644 --- a/python/caffe/coord_map.py +++ b/python/caffe/coord_map.py @@ -1,27 +1,68 @@ +""" +Determine spatial relationships between layers to relate their coordinates. +Coordinates are mapped from input-to-output (forward), but can +be mapped output-to-input (backward) by the inverse mapping too. +This helps crop and align feature maps among other uses. +""" + from __future__ import division import numpy as np from caffe import layers as L -PASS_THROUGH_LAYERS = ['AbsVal', 'ReLU', 'PReLU', 'Dropout', 'LRN', 'Eltwise', - 'BatchNorm', 'BNLL', 'Log', 'Exp', 'MVN', 'Power', 'Sigmoid', 'Split', - 'TanH', 'Threshold'] +PASS_THROUGH_LAYERS = ['AbsVal', 'BatchNorm', 'Bias', 'BNLL', 'Dropout', + 'Eltwise', 'ELU', 'Log', 'LRN', 'Exp', 'MVN', 'Power', + 'ReLU', 'PReLU', 'Scale', 'Sigmoid', 'Split', 'TanH', + 'Threshold'] + def conv_params(fn): + """ + Extract the spatial parameters that determine the coordinate mapping: + kernel size, stride, padding, and dilation. + + Implementation detail: Convolution, Deconvolution, and Im2col layers + define these in the convolution_param message, while Pooling has its + own fields in pooling_param. This method deals with these details to + extract canonical parameters. + """ params = fn.params.get('convolution_param', fn.params) axis = params.get('axis', 1) ks = np.array(params['kernel_size'], ndmin=1) dilation = np.array(params.get('dilation', 1), ndmin=1) assert len({'pad_h', 'pad_w', 'kernel_h', 'kernel_w', 'stride_h', - 'stride_w'} & set(fn.params)) == 0, \ - 'cropping does not support legacy _h/_w params' + 'stride_w'} & set(fn.params)) == 0, \ + 'cropping does not support legacy _h/_w params' return (axis, np.array(params.get('stride', 1), ndmin=1), (ks - 1) * dilation + 1, np.array(params.get('pad', 0), ndmin=1)) + +def crop_params(fn): + """ + Extract the crop layer parameters with defaults. + """ + params = fn.params.get('crop_param', fn.params) + axis = params.get('axis', 2) # default to spatial crop for N, C, H, W + offset = np.array(params.get('offset', 0), ndmin=1) + return (axis, offset) + + class UndefinedMapException(Exception): + """ + Exception raised for layers that do not have a defined coordinate mapping. + """ pass + def coord_map(fn): + """ + Define the coordinate mapping by its + - axis + - scale: output coord[i * scale] <- input_coord[i] + - shift: output coord[i] <- output_coord[i + shift] + s.t. the identity mapping, as for pointwise layers like ReLu, is defined by + (None, 1, 0) since it is independent of axis and does not transform coords. + """ if fn.type_name in ['Convolution', 'Pooling', 'Im2col']: axis, stride, ks, pad = conv_params(fn) return axis, 1 / stride, (pad - (ks - 1) / 2) / stride @@ -31,15 +72,27 @@ def coord_map(fn): elif fn.type_name in PASS_THROUGH_LAYERS: return None, 1, 0 elif fn.type_name == 'Crop': - axis = fn.params.get('axis') - return axis, 1, - fn.params['crop'] + axis, offset = crop_params(fn) + return axis, 1, - offset else: raise UndefinedMapException + class AxisMismatchException(Exception): + """ + Exception raised for mappings with incompatible axes. + """ pass -def compose((ax1, a1, b1), (ax2, a2, b2)): + +def compose(base_map, next_map): + """ + Compose a base coord map with scale a1, shift b1 with a further coord map + with scale a2, shift b2. The scales multiply and the further shift, b2, + is scaled by base coord scale a1. + """ + ax1, a1, b1 = base_map + ax2, a2, b2 = next_map if ax1 is None: ax = ax2 elif ax2 is None or ax1 == ax2: @@ -48,22 +101,48 @@ def compose((ax1, a1, b1), (ax2, a2, b2)): raise AxisMismatchException return ax, a1 * a2, a1 * b2 + b1 -def inverse((ax, a, b)): + +def inverse(coord_map): + """ + Invert a coord map by de-scaling and un-shifting; + this gives the backward mapping for the gradient. + """ + ax, a, b = coord_map return ax, 1 / a, -b / a + def coord_map_from_to(top_from, top_to): + """ + Determine the coordinate mapping betweeen a top (from) and a top (to). + Walk the graph to find a common ancestor while composing the coord maps for + from and to until they meet. As a last step the from map is inverted. + """ # We need to find a common ancestor of top_from and top_to. # We'll assume that all ancestors are equivalent here (otherwise the graph # is an inconsistent state (which we could improve this to check for)). # For now use a brute-force algorithm. + def collect_bottoms(top): + """ + Collect the bottoms to walk for the coordinate mapping. + The general rule is that all the bottoms of a layer can be mapped, as + most layers have the same coordinate mapping for each bottom. + Crop layer is a notable exception. Only the first/cropped bottom is + mappable; the second/dimensions bottom is excluded from the walk. + """ + bottoms = top.fn.inputs + if top.fn.type_name == 'Crop': + bottoms = bottoms[:1] + return bottoms + # walk back from top_from, keeping the coord map as we go from_maps = {top_from: (None, 1, 0)} frontier = {top_from} while frontier: top = frontier.pop() try: - for bottom in top.fn.inputs: + bottoms = collect_bottoms(top) + for bottom in bottoms: from_maps[bottom] = compose(from_maps[top], coord_map(top.fn)) frontier.add(bottom) except UndefinedMapException: @@ -77,19 +156,29 @@ def coord_map_from_to(top_from, top_to): if top in from_maps: return compose(to_maps[top], inverse(from_maps[top])) try: - for bottom in top.fn.inputs: + bottoms = collect_bottoms(top) + for bottom in bottoms: to_maps[bottom] = compose(to_maps[top], coord_map(top.fn)) frontier.add(bottom) except UndefinedMapException: continue # if we got here, we did not find a blob in common - raise RuntimeError, 'Could not compute map between tops; are they connected ' \ - 'by spatial layers?' + raise RuntimeError('Could not compute map between tops; are they ' + 'connected by spatial layers?') + def crop(top_from, top_to): + """ + Define a Crop layer to crop a top (from) to another top (to) by + determining the coordinate mapping between the two and net spec'ing + the axis and shift parameters of the crop. + """ ax, a, b = coord_map_from_to(top_from, top_to) assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a) assert (b <= 0).all(), 'cannot crop negative width (b = {})'.format(b) - assert (np.round(b) == b).all(), 'cannot crop noninteger width (b = {})'.format(b) - return L.Crop(top_from, top_to, crop_param=dict(axis=ax, crop=list(-np.round(b).astype(int)))) + assert (np.round(b) == b).all(), 'cannot crop noninteger width ' \ + '(b = {})'.format(b) + return L.Crop(top_from, top_to, + crop_param=dict(axis=ax, + crop=list(-np.round(b).astype(int)))) From 25b9ef95f35a4de766500c9c70a18d839a5f7c70 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Sun, 28 Feb 2016 11:51:52 -0800 Subject: [PATCH 434/446] [pycaffe] align coord_map and #3570 Crop layer - crop -> offset - adjust crop axis by 1 --- python/caffe/coord_map.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/python/caffe/coord_map.py b/python/caffe/coord_map.py index 884d9070576..a3413cfa855 100644 --- a/python/caffe/coord_map.py +++ b/python/caffe/coord_map.py @@ -73,6 +73,7 @@ def coord_map(fn): return None, 1, 0 elif fn.type_name == 'Crop': axis, offset = crop_params(fn) + axis -= 1 # -1 for last non-coordinate dim. return axis, 1, - offset else: raise UndefinedMapException @@ -176,9 +177,9 @@ def crop(top_from, top_to): """ ax, a, b = coord_map_from_to(top_from, top_to) assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a) - assert (b <= 0).all(), 'cannot crop negative width (b = {})'.format(b) - assert (np.round(b) == b).all(), 'cannot crop noninteger width ' \ + assert (b <= 0).all(), 'cannot crop negative offset (b = {})'.format(b) + assert (np.round(b) == b).all(), 'cannot crop noninteger offset ' \ '(b = {})'.format(b) return L.Crop(top_from, top_to, - crop_param=dict(axis=ax, - crop=list(-np.round(b).astype(int)))) + crop_param=dict(axis=ax + 1, # +1 for first cropping dim. + offset=list(-np.round(b).astype(int)))) From 880e1474270523e3e5585a14a370fda39bb743c1 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Thu, 3 Mar 2016 16:39:18 -0800 Subject: [PATCH 435/446] [pycaffe] test coord_map - test known mappings: conv-pool-deconv stack, ReLU and 1x1 conv - test effects of padding - test rectangular/anisotropic coordinate mapping, test N-D - catch error cases: negative crop, scale mismatch, tops that are not spatially connected --- python/caffe/test/test_coord_map.py | 192 ++++++++++++++++++++++++++++ 1 file changed, 192 insertions(+) create mode 100644 python/caffe/test/test_coord_map.py diff --git a/python/caffe/test/test_coord_map.py b/python/caffe/test/test_coord_map.py new file mode 100644 index 00000000000..613260e25df --- /dev/null +++ b/python/caffe/test/test_coord_map.py @@ -0,0 +1,192 @@ +import unittest + +import numpy as np +import random + +import caffe +from caffe import layers as L +from caffe import params as P +from caffe.coord_map import coord_map_from_to, crop + + +def coord_net_spec(ks=3, stride=1, pad=0, pool=2, dstride=2, dpad=0): + """ + Define net spec for simple conv-pool-deconv pattern common to all + coordinate mapping tests. + """ + n = caffe.NetSpec() + n.data = L.Input(shape=dict(dim=[2, 1, 100, 100])) + n.aux = L.Input(shape=dict(dim=[2, 1, 20, 20])) + n.conv = L.Convolution( + n.data, num_output=10, kernel_size=ks, stride=stride, pad=pad) + n.pool = L.Pooling( + n.conv, pool=P.Pooling.MAX, kernel_size=pool, stride=pool, pad=0) + # for upsampling kernel size is 2x stride + try: + deconv_ks = [s*2 for s in dstride] + except: + deconv_ks = dstride*2 + n.deconv = L.Deconvolution( + n.pool, num_output=10, kernel_size=deconv_ks, stride=dstride, pad=dpad) + return n + + +class TestCoordMap(unittest.TestCase): + def setUp(self): + pass + + def test_conv_pool_deconv(self): + """ + Map through conv, pool, and deconv. + """ + n = coord_net_spec() + # identity for 2x pool, 2x deconv + ax, a, b = coord_map_from_to(n.deconv, n.data) + self.assertEquals(ax, 1) + self.assertEquals(a, 1) + self.assertEquals(b, 0) + # shift-by-one for 4x pool, 4x deconv + n = coord_net_spec(pool=4, dstride=4) + ax, a, b = coord_map_from_to(n.deconv, n.data) + self.assertEquals(ax, 1) + self.assertEquals(a, 1) + self.assertEquals(b, -1) + + def test_pass(self): + """ + A pass-through layer (ReLU) and conv (1x1, stride 1, pad 0) + both do identity mapping. + """ + n = coord_net_spec() + ax, a, b = coord_map_from_to(n.deconv, n.data) + n.relu = L.ReLU(n.deconv) + n.conv1x1 = L.Convolution( + n.relu, num_output=10, kernel_size=1, stride=1, pad=0) + for top in [n.relu, n.conv1x1]: + ax_pass, a_pass, b_pass = coord_map_from_to(top, n.data) + self.assertEquals(ax, ax_pass) + self.assertEquals(a, a_pass) + self.assertEquals(b, b_pass) + + def test_padding(self): + """ + Padding conv adds offset while padding deconv subtracts offset. + """ + n = coord_net_spec() + ax, a, b = coord_map_from_to(n.deconv, n.data) + pad = random.randint(0, 10) + # conv padding + n = coord_net_spec(pad=pad) + _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data) + self.assertEquals(a, a_pad) + self.assertEquals(b - pad, b_pad) + # deconv padding + n = coord_net_spec(dpad=pad) + _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data) + self.assertEquals(a, a_pad) + self.assertEquals(b + pad, b_pad) + # pad both to cancel out + n = coord_net_spec(pad=pad, dpad=pad) + _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data) + self.assertEquals(a, a_pad) + self.assertEquals(b, b_pad) + + def test_multi_conv(self): + """ + Multiple bottoms/tops of a layer are identically mapped. + """ + n = coord_net_spec() + # multi bottom/top + n.conv_data, n.conv_aux = L.Convolution( + n.data, n.aux, ntop=2, num_output=10, kernel_size=5, stride=2, + pad=0) + ax1, a1, b1 = coord_map_from_to(n.conv_data, n.data) + ax2, a2, b2 = coord_map_from_to(n.conv_aux, n.aux) + self.assertEquals(ax1, ax2) + self.assertEquals(a1, a2) + self.assertEquals(b1, b2) + + def test_rect(self): + """ + Anisotropic mapping is equivalent to its isotropic parts. + """ + n3x3 = coord_net_spec(ks=3, stride=1, pad=0) + n5x5 = coord_net_spec(ks=5, stride=2, pad=10) + n3x5 = coord_net_spec(ks=[3, 5], stride=[1, 2], pad=[0, 10]) + ax_3x3, a_3x3, b_3x3 = coord_map_from_to(n3x3.deconv, n3x3.data) + ax_5x5, a_5x5, b_5x5 = coord_map_from_to(n5x5.deconv, n5x5.data) + ax_3x5, a_3x5, b_3x5 = coord_map_from_to(n3x5.deconv, n3x5.data) + self.assertTrue(ax_3x3 == ax_5x5 == ax_3x5) + self.assertEquals(a_3x3, a_3x5[0]) + self.assertEquals(b_3x3, b_3x5[0]) + self.assertEquals(a_5x5, a_3x5[1]) + self.assertEquals(b_5x5, b_3x5[1]) + + def test_nd_conv(self): + """ + ND conv maps the same way in more dimensions. + """ + n = caffe.NetSpec() + # define data with 3 spatial dimensions, otherwise the same net + n.data = L.Input(shape=dict(dim=[2, 3, 100, 100, 100])) + n.conv = L.Convolution( + n.data, num_output=10, kernel_size=[3, 3, 3], stride=[1, 1, 1], + pad=[0, 1, 2]) + n.pool = L.Pooling( + n.conv, pool=P.Pooling.MAX, kernel_size=2, stride=2, pad=0) + n.deconv = L.Deconvolution( + n.pool, num_output=10, kernel_size=4, stride=2, pad=0) + ax, a, b = coord_map_from_to(n.deconv, n.data) + self.assertEquals(ax, 1) + self.assertTrue(len(a) == len(b)) + self.assertTrue(np.all(a == 1)) + self.assertEquals(b[0] - 1, b[1]) + self.assertEquals(b[1] - 1, b[2]) + + def test_crop_of_crop(self): + """ + Map coordinates through Crop layer: + crop an already-cropped output to the input and check change in offset. + """ + n = coord_net_spec() + offset = random.randint(0, 10) + ax, a, b = coord_map_from_to(n.deconv, n.data) + n.crop = L.Crop(n.deconv, n.data, axis=2, offset=offset) + ax_crop, a_crop, b_crop = coord_map_from_to(n.crop, n.data) + self.assertEquals(ax, ax_crop) + self.assertEquals(a, a_crop) + self.assertEquals(b + offset, b_crop) + + def test_crop_helper(self): + """ + Define Crop layer by crop(). + """ + n = coord_net_spec() + crop(n.deconv, n.data) + + def test_catch_unconnected(self): + """ + Catch mapping spatially unconnected tops. + """ + n = coord_net_spec() + n.ip = L.InnerProduct(n.deconv, num_output=10) + with self.assertRaises(RuntimeError): + coord_map_from_to(n.ip, n.data) + + def test_catch_scale_mismatch(self): + """ + Catch incompatible scales, such as when the top to be cropped + is mapped to a differently strided reference top. + """ + n = coord_net_spec(pool=3, dstride=2) # pool 3x but deconv 2x + with self.assertRaises(AssertionError): + crop(n.deconv, n.data) + + def test_catch_negative_crop(self): + """ + Catch impossible offsets, such as when the top to be cropped + is mapped to a larger reference top. + """ + n = coord_net_spec(dpad=10) # make output smaller than input + with self.assertRaises(AssertionError): + crop(n.deconv, n.data) From b9ea0267851ccc7f782327408fe7953ba0f13c53 Mon Sep 17 00:00:00 2001 From: Jun Shi Date: Fri, 22 Jan 2016 09:50:31 -0800 Subject: [PATCH 436/446] add check and find GPU device utilities --- include/caffe/common.hpp | 5 +++++ src/caffe/common.cpp | 42 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 47 insertions(+) diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp index 6b902a42e2d..3c6a076ec2f 100644 --- a/include/caffe/common.hpp +++ b/include/caffe/common.hpp @@ -153,6 +153,11 @@ class Caffe { static void SetDevice(const int device_id); // Prints the current GPU status. static void DeviceQuery(); + // Check if specified device is available + static bool CheckDevice(const int device_id); + // Search from start_id to the highest possible device ordinal, + // return the ordinal of the first available device. + static int FindDevice(const int start_id = 0); // Parallel training info inline static int solver_count() { return Get().solver_count_; } inline static void set_solver_count(int val) { Get().solver_count_ = val; } diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp index 299d67d4bec..dee681654aa 100644 --- a/src/caffe/common.cpp +++ b/src/caffe/common.cpp @@ -70,6 +70,15 @@ void Caffe::DeviceQuery() { NO_GPU; } +bool Caffe::CheckDevice(const int device_id) { + NO_GPU; + return false; +} + +int Caffe::FindDevice(const int start_id) { + NO_GPU; + return -1; +} class Caffe::RNG::Generator { public: @@ -192,6 +201,39 @@ void Caffe::DeviceQuery() { return; } +bool Caffe::CheckDevice(const int device_id) { + // This function checks the availability of GPU #device_id. + // It attempts to create a context on the device by calling cudaFree(0). + // cudaSetDevice() alone is not sufficient to check the availability. + // It lazily records device_id, however, does not initialize a + // context. So it does not know if the host thread has the permission to use + // the device or not. + // + // In a shared environment where the devices are set to EXCLUSIVE_PROCESS + // or EXCLUSIVE_THREAD mode, cudaSetDevice() returns cudaSuccess + // even if the device is exclusively occupied by another process or thread. + // Cuda operations that initialize the context are needed to check + // the permission. cudaFree(0) is one of those with no side effect, + // except the context initialization. + bool r = ((cudaSuccess == cudaSetDevice(device_id)) && + (cudaSuccess == cudaFree(0))); + // reset any error that may have occurred. + cudaGetLastError(); + return r; +} + +int Caffe::FindDevice(const int start_id) { + // This function finds the first available device by checking devices with + // ordinal from start_id to the highest available value. In the + // EXCLUSIVE_PROCESS or EXCLUSIVE_THREAD mode, if it succeeds, it also + // claims the device due to the initialization of the context. + int count = 0; + CUDA_CHECK(cudaGetDeviceCount(&count)); + for (int i = start_id; i < count; i++) { + if (CheckDevice(i)) return i; + } + return -1; +} class Caffe::RNG::Generator { public: From 64e78bdc76b8cabdf4282506438ab2d7f321adf3 Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Sat, 27 Dec 2014 01:44:36 -0800 Subject: [PATCH 437/446] add CropLayer: crop blob to another blob's dimensions with offsets configure offset(s) through proto definition. --- include/caffe/layers/crop_layer.hpp | 49 ++++++++++++++++++ src/caffe/layers/crop_layer.cpp | 78 +++++++++++++++++++++++++++++ src/caffe/layers/crop_layer.cu | 60 ++++++++++++++++++++++ src/caffe/proto/caffe.proto | 10 +++- 4 files changed, 196 insertions(+), 1 deletion(-) create mode 100644 include/caffe/layers/crop_layer.hpp create mode 100644 src/caffe/layers/crop_layer.cpp create mode 100644 src/caffe/layers/crop_layer.cu diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp new file mode 100644 index 00000000000..bab290718e0 --- /dev/null +++ b/include/caffe/layers/crop_layer.hpp @@ -0,0 +1,49 @@ +#ifndef CAFFE_CROP_LAYER_HPP_ +#define CAFFE_CROP_LAYER_HPP_ + +#include +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Takes a Blob and crop it along either the width or height dimension, + * outputting a cropped Blob. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ + +template +class CropLayer : public Layer { + public: + explicit CropLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Crop"; } + virtual inline int ExactNumBottomBlobs() const { return 2; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + int crop_h_, crop_w_; +}; + +} // namespace caffe + +#endif // CAFFE_CROP_LAYER_HPP_ diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp new file mode 100644 index 00000000000..76409bd7408 --- /dev/null +++ b/src/caffe/layers/crop_layer.cpp @@ -0,0 +1,78 @@ +#include +#include +#include +#include + +#include "caffe/layer.hpp" +#include "caffe/layers/crop_layer.hpp" +#include "caffe/net.hpp" + + +namespace caffe { + +template +void CropLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + const CropParameter& param = this->layer_param_.crop_param(); + CHECK_EQ(bottom.size(), 2) << "Wrong number of bottom blobs."; + CHECK_EQ(bottom[0]->num_axes(), 4) << "Only works with 4D blobs."; + CHECK_EQ(bottom[1]->num_axes(), 4) << "Only works with 4D blobs."; + crop_h_ = param.offset_height(); + crop_w_ = param.offset_width(); +} + +template +void CropLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + // Check that the image we are cropping minus the margin is bigger than the + // destination image. + CHECK_GT(bottom[0]->height()-crop_h_, bottom[1]->height()) + << "invalid offset"; + CHECK_GT(bottom[0]->width()-crop_w_, bottom[1]->width()) << "invalid offset"; + top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), bottom[1]->height(), + bottom[1]->width()); +} + +template +void CropLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + for (int n = 0; n < top[0]->num(); ++n) { + for (int c = 0; c < top[0]->channels(); ++c) { + for (int h = 0; h < top[0]->height(); ++h) { + caffe_copy(top[0]->width(), + bottom_data + bottom[0]->offset(n, c, crop_h_ + h, crop_w_), + top_data + top[0]->offset(n, c, h)); + } + } + } +} + +template +void CropLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + if (propagate_down[0]) { + caffe_set(bottom[0]->count(), static_cast(0), bottom_diff); + for (int n = 0; n < top[0]->num(); ++n) { + for (int c = 0; c < top[0]->channels(); ++c) { + for (int h = 0; h < top[0]->height(); ++h) { + caffe_copy(top[0]->width(), + top_diff + top[0]->offset(n, c, h), + bottom_diff + bottom[0]->offset(n, c, crop_h_ + h, crop_w_)); + } + } + } + } +} + +#ifdef CPU_ONLY +STUB_GPU(CropLayer); +#endif + +INSTANTIATE_CLASS(CropLayer); +REGISTER_LAYER_CLASS(Crop); + +} // namespace caffe diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu new file mode 100644 index 00000000000..262f5fa8483 --- /dev/null +++ b/src/caffe/layers/crop_layer.cu @@ -0,0 +1,60 @@ +#include + +#include "caffe/layers/crop_layer.hpp" + +namespace caffe { + +// Copy (one line per thread) from one array to another, with arbitrary +// strides in the last two dimensions. +template +__global__ void copy_kernel(const int n, const int height, const int width, + const int src_outer_stride, const int src_inner_stride, + const int dest_outer_stride, const int dest_inner_stride, + const Dtype* src, Dtype* dest) { + CUDA_KERNEL_LOOP(index, n) { + int src_start = index / height * src_outer_stride + + index % height * src_inner_stride; + int dest_start = index / height * dest_outer_stride + + index % height * dest_inner_stride; + for (int i = 0; i < width; ++i) { + dest[dest_start + i] = src[src_start + i]; + } + } +} + +template +void CropLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + const int lines = top[0]->count() / top[0]->width(); + + // NOLINT_NEXT_LINE(whitespace/operators) + copy_kernel<<>>( + lines, top[0]->height(), top[0]->width(), + bottom[0]->height() * bottom[0]->width(), bottom[0]->width(), + top[0]->height() * top[0]->width(), top[0]->width(), + bottom_data + bottom[0]->offset(0, 0, crop_h_, crop_w_), top_data); +} + +template +void CropLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + const Dtype* top_diff = top[0]->gpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + const int lines = top[0]->count() / top[0]->width(); + + if (propagate_down[0]) { + caffe_gpu_set(bottom[0]->count(), static_cast(0), bottom_diff); + // NOLINT_NEXT_LINE(whitespace/operators) + copy_kernel<<>>( + lines, top[0]->height(), top[0]->width(), + top[0]->height() * top[0]->width(), top[0]->width(), + bottom[0]->height() * bottom[0]->width(), bottom[0]->width(), + top_diff, bottom_diff + bottom[0]->offset(0, 0, crop_h_, crop_w_)); + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(CropLayer); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 3b27bbd94d2..ace1a418592 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -306,7 +306,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 144 (last added: input_param) +// LayerParameter next available layer-specific ID: 145 (last added: crop_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -360,6 +360,7 @@ message LayerParameter { optional ConcatParameter concat_param = 104; optional ContrastiveLossParameter contrastive_loss_param = 105; optional ConvolutionParameter convolution_param = 106; + optional CropParameter crop_param = 144; optional DataParameter data_param = 107; optional DropoutParameter dropout_param = 108; optional DummyDataParameter dummy_data_param = 109; @@ -598,6 +599,13 @@ message ConvolutionParameter { optional bool force_nd_im2col = 17 [default = false]; } +message CropParameter { + // Assumes standard dimensions: ( N,C,H,W ) + // This could possibly be extended to use "optional BlobShape offsets" + optional uint32 offset_height = 1[default = 0]; + optional uint32 offset_width = 2[default = 0]; +} + message DataParameter { enum DB { LEVELDB = 0; From 952fd17e52b24736f2644c32c249538241b34474 Mon Sep 17 00:00:00 2001 From: max argus Date: Tue, 19 Jan 2016 18:35:04 +0000 Subject: [PATCH 438/446] Extend Crop to N-D, changed CropParameter. --- include/caffe/layers/crop_layer.hpp | 22 ++++- src/caffe/layers/crop_layer.cpp | 124 +++++++++++++++++++++------- src/caffe/layers/crop_layer.cu | 97 ++++++++++++++++++---- src/caffe/proto/caffe.proto | 19 +++-- 4 files changed, 210 insertions(+), 52 deletions(-) diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp index bab290718e0..f84bab1ec49 100644 --- a/include/caffe/layers/crop_layer.hpp +++ b/include/caffe/layers/crop_layer.hpp @@ -41,9 +41,27 @@ class CropLayer : public Layer { virtual void Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom); - int crop_h_, crop_w_; -}; + vector offsets; + + private: + void crop_copy(const vector*>& bottom, + const vector*>& top, + const vector& offsets, + vector indices, + int cur_dim, + const Dtype* src_data, + Dtype* dest_data, + bool is_forward); + void crop_copy_gpu(const vector*>& bottom, + const vector*>& top, + const vector& offsets, + vector indices, + int cur_dim, + const Dtype* src_data, + Dtype* dest_data, + bool is_forward); +}; } // namespace caffe #endif // CAFFE_CROP_LAYER_HPP_ diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp index 76409bd7408..82729f17c9f 100644 --- a/src/caffe/layers/crop_layer.cpp +++ b/src/caffe/layers/crop_layer.cpp @@ -1,8 +1,10 @@ #include +#include #include #include #include + #include "caffe/layer.hpp" #include "caffe/layers/crop_layer.hpp" #include "caffe/net.hpp" @@ -13,40 +15,108 @@ namespace caffe { template void CropLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { - const CropParameter& param = this->layer_param_.crop_param(); CHECK_EQ(bottom.size(), 2) << "Wrong number of bottom blobs."; - CHECK_EQ(bottom[0]->num_axes(), 4) << "Only works with 4D blobs."; - CHECK_EQ(bottom[1]->num_axes(), 4) << "Only works with 4D blobs."; - crop_h_ = param.offset_height(); - crop_w_ = param.offset_width(); + // parameter setup moved to Reshape because it depends on size. } template void CropLayer::Reshape(const vector*>& bottom, const vector*>& top) { - // Check that the image we are cropping minus the margin is bigger than the - // destination image. - CHECK_GT(bottom[0]->height()-crop_h_, bottom[1]->height()) - << "invalid offset"; - CHECK_GT(bottom[0]->width()-crop_w_, bottom[1]->width()) << "invalid offset"; - top[0]->Reshape(bottom[0]->num(), bottom[0]->channels(), bottom[1]->height(), - bottom[1]->width()); + const CropParameter& param = this->layer_param_.crop_param(); + // bottom[0] supplies the data + // bottom[1] supplies the size + int input_dim = bottom[0]->num_axes(); + CHECK_LT(param.axis(), input_dim) << "crop axis bigger than input dim"; + // initialize all offsets to 0 + offsets = vector(input_dim, 0); + // initialize new shape to bottom[0] + vector new_shape(bottom[0]->shape()); + + if (param.offset_size() > 1) { + // the number of crop values specified must be equal to the number + // of dimensions following axis + CHECK_EQ(param.axis() + param.offset_size(), input_dim) + << "number of crop values specified must be equal to the number of " + << "dimensions following axis."; + } + // apply crops + for (int i = 0; i < input_dim; ++i) { + int crop_offset = 0; + int new_size = bottom[0]->shape(i); + if (i >= param.axis() && param.offset_size() == 1) { + // if only one crop value is supplied, crop all dimensions after axis + // by this crop value + crop_offset = param.offset(0); + new_size = bottom[1]->shape(i); + } else if (i >= param.axis() && param.offset_size() > 1) { + // crop values specified must be equal to the number of dimensions + // following axis + crop_offset = param.offset(i - param.axis()); + new_size = bottom[1]->shape(i); + } + // Check that the image we are cropping minus the margin is bigger + // than the destination image. + CHECK_GE(bottom[0]->shape(i) - crop_offset, + bottom[1]->shape(i)) + << "invalid crop parameters in dimension: " << i; + // Now set new size and offsets + new_shape[i] = new_size; + offsets[i] = crop_offset; + } + top[0]->Reshape(new_shape); +} + +// recursive copy function +template +void CropLayer::crop_copy(const vector*>& bottom, + const vector*>& top, + const vector& offsets, + vector indices, + int cur_dim, + const Dtype* src_data, + Dtype* dest_data, + bool is_forward) { + if (cur_dim + 1 < top[0]->num_axes()) { + // We are not yet at the final dimension, call copy recursivley + for (int i = 0; i < top[0]->shape(cur_dim); ++i) { + indices[cur_dim] = i; + crop_copy(bottom, top, offsets, indices, cur_dim+1, + src_data, dest_data, is_forward); + } + } else { + // We are at the last dimensions, which is stored continously in memory + for (int i = 0; i < top[0]->shape(cur_dim); ++i) { + // prepare index vector reduced(red) and with offsets(off) + std::vector ind_red(cur_dim, 0); + std::vector ind_off(cur_dim+1, 0); + for (int j = 0; j < cur_dim; ++j) { + ind_red[j] = indices[j]; + ind_off[j] = indices[j] + offsets[j]; + } + ind_off[cur_dim] = offsets[cur_dim]; + // do the copy + if (is_forward) { + caffe_copy(top[0]->shape(cur_dim), + src_data + bottom[0]->offset(ind_off), + dest_data + top[0]->offset(ind_red)); + } else { + // in the backwards pass the src_data is top_diff + // and the dest_data is bottom_diff + caffe_copy(top[0]->shape(cur_dim), + src_data + top[0]->offset(ind_red), + dest_data + bottom[0]->offset(ind_off)); + } + } + } } template void CropLayer::Forward_cpu(const vector*>& bottom, const vector*>& top) { + std::vector indices(top[0]->num_axes(), 0); const Dtype* bottom_data = bottom[0]->cpu_data(); Dtype* top_data = top[0]->mutable_cpu_data(); - for (int n = 0; n < top[0]->num(); ++n) { - for (int c = 0; c < top[0]->channels(); ++c) { - for (int h = 0; h < top[0]->height(); ++h) { - caffe_copy(top[0]->width(), - bottom_data + bottom[0]->offset(n, c, crop_h_ + h, crop_w_), - top_data + top[0]->offset(n, c, h)); - } - } - } + crop_copy(bottom, top, offsets, indices, 0, bottom_data, top_data, true); } template @@ -54,17 +124,11 @@ void CropLayer::Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { const Dtype* top_diff = top[0]->cpu_diff(); Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + if (propagate_down[0]) { caffe_set(bottom[0]->count(), static_cast(0), bottom_diff); - for (int n = 0; n < top[0]->num(); ++n) { - for (int c = 0; c < top[0]->channels(); ++c) { - for (int h = 0; h < top[0]->height(); ++h) { - caffe_copy(top[0]->width(), - top_diff + top[0]->offset(n, c, h), - bottom_diff + bottom[0]->offset(n, c, crop_h_ + h, crop_w_)); - } - } - } + std::vector indices(top[0]->num_axes(), 0); + crop_copy(bottom, top, offsets, indices, 0, top_diff, bottom_diff, false); } } diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu index 262f5fa8483..7b832c0a0dc 100644 --- a/src/caffe/layers/crop_layer.cu +++ b/src/caffe/layers/crop_layer.cu @@ -22,19 +22,90 @@ __global__ void copy_kernel(const int n, const int height, const int width, } } +// recursive copy function, this function is similar to crop_copy but loops +// over all but the last two dimensions. It is implemented this way to allow +// for ND cropping while still relying on a CUDA kernel for the innermost +// two dimensions for performance reasons. +// An alternative way to implement ND cropping relying more on the kernel +// would require passing offsets to the kernel, which is a bit problematic +// because it is of variable length. Since in the standard (N,C,W,H) case +// N,C are usually not cropped a speedup could be achieved by not looping +// the application of the copy_kernel around these dimensions. +template +void CropLayer::crop_copy_gpu(const vector*>& bottom, + const vector*>& top, + const vector& offsets, + vector indices, + int cur_dim, + const Dtype* src_data, + Dtype* dest_data, + bool is_forward) { + if (cur_dim + 2 < top[0]->num_axes()) { + // We are not yet at the final dimension, call copy recursivley + for (int i = 0; i < top[0]->shape(cur_dim); ++i) { + indices[cur_dim] = i; + crop_copy_gpu(bottom, top, offsets, indices, cur_dim+1, + src_data, dest_data, is_forward); + } + } else { + // We are at the last two dimensions, which are stored continously in memory + // With (N,C,H,W) + // (0,1,2,3) cur_dim -> H + // cur_dim+1 -> W + const int lines = top[0]->shape(cur_dim); + const int height = top[0]->shape(cur_dim); + const int width = top[0]->shape(cur_dim+1); + std::vector ind_off(cur_dim+2, 0); + for (int j = 0; j < cur_dim; ++j) { + ind_off[j] = indices[j] + offsets[j]; + } + ind_off[cur_dim] = offsets[cur_dim]; + ind_off[cur_dim+1] = offsets[cur_dim+1]; + // Compute copy strides + const int src_outer_stride = + bottom[0]->shape(cur_dim)*bottom[0]->shape(cur_dim+1); + const int src_inner_stride = bottom[0]->shape(cur_dim+1); + const int dest_outer_stride = + top[0]->shape(cur_dim)*top[0]->shape(cur_dim+1); + const int dest_inner_stride = top[0]->shape(cur_dim+1); + + if (is_forward) { + const Dtype* bottom_data = bottom[0]->gpu_data() + + bottom[0]->offset(ind_off); + Dtype* top_data = top[0]->mutable_gpu_data() + + top[0]->offset(indices); + // NOLINT_NEXT_LINE(whitespace/operators) + copy_kernel<<>>( + lines, height, width, + src_outer_stride, src_inner_stride, + dest_outer_stride, dest_inner_stride, + bottom_data, top_data); + + } else { + const Dtype* top_diff = top[0]->gpu_diff() + + top[0]->offset(indices); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff() + + bottom[0]->offset(ind_off); + // NOLINT_NEXT_LINE(whitespace/operators) + copy_kernel<<>>( + lines, height, width, + dest_outer_stride, dest_inner_stride, + src_outer_stride, src_inner_stride, + top_diff, bottom_diff); + } + } +} + template void CropLayer::Forward_gpu(const vector*>& bottom, const vector*>& top) { + std::vector indices(top[0]->num_axes(), 0); + // This works because crop_copy uses caffe_copy which calls cudaMemcpy. + // My intuition is that calling this thousands of times is probably less + // efficient than writing a custom kernel. const Dtype* bottom_data = bottom[0]->gpu_data(); Dtype* top_data = top[0]->mutable_gpu_data(); - const int lines = top[0]->count() / top[0]->width(); - - // NOLINT_NEXT_LINE(whitespace/operators) - copy_kernel<<>>( - lines, top[0]->height(), top[0]->width(), - bottom[0]->height() * bottom[0]->width(), bottom[0]->width(), - top[0]->height() * top[0]->width(), top[0]->width(), - bottom_data + bottom[0]->offset(0, 0, crop_h_, crop_w_), top_data); + crop_copy_gpu(bottom, top, offsets, indices, 0, bottom_data, top_data, true); } template @@ -42,16 +113,12 @@ void CropLayer::Backward_gpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { const Dtype* top_diff = top[0]->gpu_diff(); Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); - const int lines = top[0]->count() / top[0]->width(); if (propagate_down[0]) { caffe_gpu_set(bottom[0]->count(), static_cast(0), bottom_diff); - // NOLINT_NEXT_LINE(whitespace/operators) - copy_kernel<<>>( - lines, top[0]->height(), top[0]->width(), - top[0]->height() * top[0]->width(), top[0]->width(), - bottom[0]->height() * bottom[0]->width(), bottom[0]->width(), - top_diff, bottom_diff + bottom[0]->offset(0, 0, crop_h_, crop_w_)); + std::vector indices(top[0]->num_axes(), 0); + crop_copy_gpu(bottom, top, offsets, indices, 0, top_diff, bottom_diff, + false); } } diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index ace1a418592..60d387a7de1 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -600,10 +600,19 @@ message ConvolutionParameter { } message CropParameter { - // Assumes standard dimensions: ( N,C,H,W ) - // This could possibly be extended to use "optional BlobShape offsets" - optional uint32 offset_height = 1[default = 0]; - optional uint32 offset_width = 2[default = 0]; + // To crop, elements of the first bottom are selected to fit the dimensions + // of the second, reference bottom. The crop is configured by + // - the crop `axis` to pick the dimensions for cropping + // - the crop `offset` to set the shift for all/each dimension + // to align the cropped bottom with the reference bottom. + // All dimensions up to but excluding `axis` are preserved, while + // the dimensions including and trailing `axis` are cropped. + // If only one `offset` is set, then all dimensions are offset by this amount. + // Otherwise, the number of offsets must equal the number of cropped axes to + // shift the crop in each dimension accordingly. + // Note: standard dimensions are N,C,H,W so the default is a spatial crop. + optional uint32 axis = 1 [default = 2]; + repeated uint32 offset = 2; } message DataParameter { @@ -680,7 +689,7 @@ message EltwiseParameter { // Message that stores parameters used by ELULayer message ELUParameter { // Described in: - // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate + // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate // Deep Network Learning by Exponential Linear Units (ELUs). arXiv optional float alpha = 1 [default = 1]; } From ca9fa498fb76a83befe6cb299a71f7f779aeb5d9 Mon Sep 17 00:00:00 2001 From: max argus Date: Mon, 29 Feb 2016 11:24:25 +0000 Subject: [PATCH 439/446] Crop: fixes, tests and negative axis indexing. --- include/caffe/layers/crop_layer.hpp | 4 +- src/caffe/layers/crop_layer.cpp | 50 +++--- src/caffe/layers/crop_layer.cu | 3 - src/caffe/proto/caffe.proto | 6 +- src/caffe/test/test_crop_layer.cpp | 228 ++++++++++++++++++++++++++++ 5 files changed, 263 insertions(+), 28 deletions(-) create mode 100644 src/caffe/test/test_crop_layer.cpp diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp index f84bab1ec49..5c605b2ae9e 100644 --- a/include/caffe/layers/crop_layer.hpp +++ b/include/caffe/layers/crop_layer.hpp @@ -11,8 +11,8 @@ namespace caffe { /** - * @brief Takes a Blob and crop it along either the width or height dimension, - * outputting a cropped Blob. + * @brief Takes a Blob and crop it, to the shape specified by the second input + * Blob, across all dimensions after the specified axis. * * TODO(dox): thorough documentation for Forward, Backward, and proto params. */ diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp index 82729f17c9f..e81bdd732f3 100644 --- a/src/caffe/layers/crop_layer.cpp +++ b/src/caffe/layers/crop_layer.cpp @@ -15,44 +15,52 @@ namespace caffe { template void CropLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { + // All logic that depends only on the number of dimensions is here, + // the rest is in Reshape because it depends on Blob size. + // bottom[0] supplies the data + // bottom[1] supplies the size + const CropParameter& param = this->layer_param_.crop_param(); CHECK_EQ(bottom.size(), 2) << "Wrong number of bottom blobs."; - // parameter setup moved to Reshape because it depends on size. + int input_dim = bottom[0]->num_axes(); + const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis()); + CHECK_LT(start_axis, input_dim) << "crop axis bigger than input dim"; + if (param.offset_size() > 1) { + // the number of crop values specified must be equal to the number + // of dimensions following axis + CHECK_EQ(start_axis + param.offset_size(), input_dim) + << "number of offset values specified must be equal to the number of " + << "dimensions following axis."; + } } template void CropLayer::Reshape(const vector*>& bottom, const vector*>& top) { const CropParameter& param = this->layer_param_.crop_param(); - // bottom[0] supplies the data - // bottom[1] supplies the size int input_dim = bottom[0]->num_axes(); - CHECK_LT(param.axis(), input_dim) << "crop axis bigger than input dim"; + const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis()); + // initialize all offsets to 0 offsets = vector(input_dim, 0); // initialize new shape to bottom[0] vector new_shape(bottom[0]->shape()); - if (param.offset_size() > 1) { - // the number of crop values specified must be equal to the number - // of dimensions following axis - CHECK_EQ(param.axis() + param.offset_size(), input_dim) - << "number of crop values specified must be equal to the number of " - << "dimensions following axis."; - } // apply crops for (int i = 0; i < input_dim; ++i) { int crop_offset = 0; int new_size = bottom[0]->shape(i); - if (i >= param.axis() && param.offset_size() == 1) { - // if only one crop value is supplied, crop all dimensions after axis - // by this crop value - crop_offset = param.offset(0); - new_size = bottom[1]->shape(i); - } else if (i >= param.axis() && param.offset_size() > 1) { - // crop values specified must be equal to the number of dimensions - // following axis - crop_offset = param.offset(i - param.axis()); + if (i >= start_axis) { new_size = bottom[1]->shape(i); + + if (param.offset_size() == 1) { + // if only one crop value is supplied, crop all dimensions after axis + // by this crop value + crop_offset = param.offset(0); + } else if (param.offset_size() > 1) { + // crop values specified must be equal to the number of dimensions + // following axis + crop_offset = param.offset(i - start_axis); + } } // Check that the image we are cropping minus the margin is bigger // than the destination image. @@ -77,7 +85,7 @@ void CropLayer::crop_copy(const vector*>& bottom, Dtype* dest_data, bool is_forward) { if (cur_dim + 1 < top[0]->num_axes()) { - // We are not yet at the final dimension, call copy recursivley + // We are not yet at the final dimension, call copy recursively for (int i = 0; i < top[0]->shape(cur_dim); ++i) { indices[cur_dim] = i; crop_copy(bottom, top, offsets, indices, cur_dim+1, diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu index 7b832c0a0dc..9ed8f7cce57 100644 --- a/src/caffe/layers/crop_layer.cu +++ b/src/caffe/layers/crop_layer.cu @@ -100,9 +100,6 @@ template void CropLayer::Forward_gpu(const vector*>& bottom, const vector*>& top) { std::vector indices(top[0]->num_axes(), 0); - // This works because crop_copy uses caffe_copy which calls cudaMemcpy. - // My intuition is that calling this thousands of times is probably less - // efficient than writing a custom kernel. const Dtype* bottom_data = bottom[0]->gpu_data(); Dtype* top_data = top[0]->mutable_gpu_data(); crop_copy_gpu(bottom, top, offsets, indices, 0, bottom_data, top_data, true); diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 60d387a7de1..6900bb71482 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -610,8 +610,10 @@ message CropParameter { // If only one `offset` is set, then all dimensions are offset by this amount. // Otherwise, the number of offsets must equal the number of cropped axes to // shift the crop in each dimension accordingly. - // Note: standard dimensions are N,C,H,W so the default is a spatial crop. - optional uint32 axis = 1 [default = 2]; + // Note: standard dimensions are N,C,H,W so the default is a spatial crop, + // and `axis` may be negative to index from the end (e.g., -1 for the last + // axis). + optional int32 axis = 1 [default = 2]; repeated uint32 offset = 2; } diff --git a/src/caffe/test/test_crop_layer.cpp b/src/caffe/test/test_crop_layer.cpp new file mode 100644 index 00000000000..ba962e4c6f1 --- /dev/null +++ b/src/caffe/test/test_crop_layer.cpp @@ -0,0 +1,228 @@ +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/layers/crop_layer.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class CropLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + + protected: + CropLayerTest() + : blob_bottom_0_(new Blob(2, 5, 6, 5)), + blob_bottom_1_(new Blob(2, 4, 5, 3)), + blob_top_(new Blob()) {} + virtual void SetUp() { + // fill the values + for (int i = 0; i < this->blob_bottom_0_->count(); ++i) { + this->blob_bottom_0_->mutable_cpu_data()[i] = i; + } + + + blob_bottom_vec_0_.push_back(blob_bottom_0_); + blob_bottom_vec_0_.push_back(blob_bottom_1_); + blob_top_vec_.push_back(blob_top_); + } + + virtual ~CropLayerTest() { + delete blob_bottom_0_; delete blob_bottom_1_; + delete blob_top_; + } + + Blob* const blob_bottom_0_; + Blob* const blob_bottom_1_; + Blob* const blob_top_; + vector*> blob_bottom_vec_0_; + vector*> blob_top_vec_; +}; + + +TYPED_TEST_CASE(CropLayerTest, TestDtypesAndDevices); + +TYPED_TEST(CropLayerTest, TestSetupShapeAll) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + + // Crop all dimensions + layer_param.mutable_crop_param()->set_axis(0); + CropLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + for (int i = 0; i < this->blob_top_->num_axes(); ++i) { + EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i)); + } +} + +TYPED_TEST(CropLayerTest, TestSetupShapeDefault) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + // Crop last two dimensions, axis is 2 by default + CropLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + for (int i = 0; i < this->blob_top_->num_axes(); ++i) { + if (i < 2) { + EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i)); + } else { + EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i)); + } + } +} + +TYPED_TEST(CropLayerTest, TestSetupShapeNegativeIndexing) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + // Crop last dimension by negative indexing + layer_param.mutable_crop_param()->set_axis(-1); + CropLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + for (int i = 0; i < this->blob_top_->num_axes(); ++i) { + if (i < 3) { + EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i)); + } else { + EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i)); + } + } +} + + +TYPED_TEST(CropLayerTest, TestForwardNum) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_crop_param()->set_axis(0); + + CropLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_0_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_0_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_0_->width(); ++w) { + if ( n < this->blob_top_->shape(0) && + c < this->blob_top_->shape(1) && + h < this->blob_top_->shape(2) && + w < this->blob_top_->shape(3) ) { + EXPECT_EQ(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_0_->data_at(n, c, h, w)); + } + } + } + } + } +} + +TYPED_TEST(CropLayerTest, TestForwardNumOffsets) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_crop_param()->set_axis(0); + layer_param.mutable_crop_param()->add_offset(0); + layer_param.mutable_crop_param()->add_offset(1); + layer_param.mutable_crop_param()->add_offset(1); + layer_param.mutable_crop_param()->add_offset(2); + CropLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_); + for (int n = 0; n < this->blob_bottom_0_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_0_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_0_->width(); ++w) { + if ( n < this->blob_top_->shape(0) && + c < this->blob_top_->shape(1) && + h < this->blob_top_->shape(2) && + w < this->blob_top_->shape(3) ) { + EXPECT_EQ(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_0_->data_at(n+0, c+1, h+1, w+2)); + } + } + } + } + } +} + +TYPED_TEST(CropLayerTest, TestGradientNum) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + CropLayer layer(layer_param); + + layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_); + + // Copy top data into diff + caffe_copy(this->blob_top_->count(), this->blob_top_->cpu_data(), + this->blob_top_->mutable_cpu_diff()); + + // Do backward pass + vector propagate_down(2, true); + layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_0_); + + + // Check results + for (int n = 0; n < this->blob_bottom_0_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_0_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_0_->width(); ++w) { + if ( n < this->blob_top_->shape(0) && + c < this->blob_top_->shape(1) && + h < this->blob_top_->shape(2) && + w < this->blob_top_->shape(3) ) { + EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), + this->blob_bottom_0_->data_at(n, c, h, w)); + } else { + EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), 0); + } + } + } + } + } +} + +TYPED_TEST(CropLayerTest, TestGradientNumOffset) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_crop_param()->set_axis(0); + layer_param.mutable_crop_param()->add_offset(0); + layer_param.mutable_crop_param()->add_offset(1); + layer_param.mutable_crop_param()->add_offset(1); + layer_param.mutable_crop_param()->add_offset(2); + CropLayer layer(layer_param); + + layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_); + + // Copy top data into diff + caffe_copy(this->blob_top_->count(), this->blob_top_->cpu_data(), + this->blob_top_->mutable_cpu_diff()); + + // Do backward pass + vector propagate_down(2, true); + layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_0_); + + + // Check results + for (int n = 0; n < this->blob_bottom_0_->num(); ++n) { + for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) { + for (int h = 0; h < this->blob_bottom_0_->height(); ++h) { + for (int w = 0; w < this->blob_bottom_0_->width(); ++w) { + if ( 0 <= n && n < 0 + this->blob_top_->shape(0) && + 1 <= c && c < 1 + this->blob_top_->shape(1) && + 1 <= h && h < 1 + this->blob_top_->shape(2) && + 2 <= w && w < 2 + this->blob_top_->shape(3) ) { + EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), + this->blob_bottom_0_->data_at(n, c, h, w)); + } else { + EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), 0); + } + } + } + } + } +} + +} // namespace caffe From e03a2873a0fdfd871b11a86d7feed45fb71013b0 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 4 Mar 2016 02:20:13 -0800 Subject: [PATCH 440/446] Crop: more tests and test tuning. Changes are: reduce test blob dims for speed use standard Gaussian filler, polish formatting and rename tests, test HW crop and 5D crop, standard gradient checks --- src/caffe/test/test_crop_layer.cpp | 183 +++++++++++++++++------------ 1 file changed, 110 insertions(+), 73 deletions(-) diff --git a/src/caffe/test/test_crop_layer.cpp b/src/caffe/test/test_crop_layer.cpp index ba962e4c6f1..45f24e2ee8d 100644 --- a/src/caffe/test/test_crop_layer.cpp +++ b/src/caffe/test/test_crop_layer.cpp @@ -18,18 +18,18 @@ class CropLayerTest : public MultiDeviceTest { protected: CropLayerTest() - : blob_bottom_0_(new Blob(2, 5, 6, 5)), - blob_bottom_1_(new Blob(2, 4, 5, 3)), + : blob_bottom_0_(new Blob(2, 4, 5, 4)), + blob_bottom_1_(new Blob(2, 3, 4, 2)), blob_top_(new Blob()) {} virtual void SetUp() { // fill the values - for (int i = 0; i < this->blob_bottom_0_->count(); ++i) { - this->blob_bottom_0_->mutable_cpu_data()[i] = i; - } - + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(this->blob_bottom_0_); + filler.Fill(this->blob_bottom_1_); - blob_bottom_vec_0_.push_back(blob_bottom_0_); - blob_bottom_vec_0_.push_back(blob_bottom_1_); + blob_bottom_vec_.push_back(blob_bottom_0_); + blob_bottom_vec_.push_back(blob_bottom_1_); blob_top_vec_.push_back(blob_top_); } @@ -41,7 +41,7 @@ class CropLayerTest : public MultiDeviceTest { Blob* const blob_bottom_0_; Blob* const blob_bottom_1_; Blob* const blob_top_; - vector*> blob_bottom_vec_0_; + vector*> blob_bottom_vec_; vector*> blob_top_vec_; }; @@ -51,11 +51,10 @@ TYPED_TEST_CASE(CropLayerTest, TestDtypesAndDevices); TYPED_TEST(CropLayerTest, TestSetupShapeAll) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; - // Crop all dimensions layer_param.mutable_crop_param()->set_axis(0); CropLayer layer(layer_param); - layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); for (int i = 0; i < this->blob_top_->num_axes(); ++i) { EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i)); } @@ -66,7 +65,7 @@ TYPED_TEST(CropLayerTest, TestSetupShapeDefault) { LayerParameter layer_param; // Crop last two dimensions, axis is 2 by default CropLayer layer(layer_param); - layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); for (int i = 0; i < this->blob_top_->num_axes(); ++i) { if (i < 2) { EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i)); @@ -82,7 +81,7 @@ TYPED_TEST(CropLayerTest, TestSetupShapeNegativeIndexing) { // Crop last dimension by negative indexing layer_param.mutable_crop_param()->set_axis(-1); CropLayer layer(layer_param); - layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); for (int i = 0; i < this->blob_top_->num_axes(); ++i) { if (i < 3) { EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i)); @@ -92,15 +91,13 @@ TYPED_TEST(CropLayerTest, TestSetupShapeNegativeIndexing) { } } - -TYPED_TEST(CropLayerTest, TestForwardNum) { +TYPED_TEST(CropLayerTest, TestCropAll) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; layer_param.mutable_crop_param()->set_axis(0); - CropLayer layer(layer_param); - layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); - layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); for (int n = 0; n < this->blob_bottom_0_->num(); ++n) { for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) { for (int h = 0; h < this->blob_bottom_0_->height(); ++h) { @@ -118,7 +115,7 @@ TYPED_TEST(CropLayerTest, TestForwardNum) { } } -TYPED_TEST(CropLayerTest, TestForwardNumOffsets) { +TYPED_TEST(CropLayerTest, TestCropAllOffset) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; layer_param.mutable_crop_param()->set_axis(0); @@ -127,8 +124,8 @@ TYPED_TEST(CropLayerTest, TestForwardNumOffsets) { layer_param.mutable_crop_param()->add_offset(1); layer_param.mutable_crop_param()->add_offset(2); CropLayer layer(layer_param); - layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); - layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); for (int n = 0; n < this->blob_bottom_0_->num(); ++n) { for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) { for (int h = 0; h < this->blob_bottom_0_->height(); ++h) { @@ -138,7 +135,7 @@ TYPED_TEST(CropLayerTest, TestForwardNumOffsets) { h < this->blob_top_->shape(2) && w < this->blob_top_->shape(3) ) { EXPECT_EQ(this->blob_top_->data_at(n, c, h, w), - this->blob_bottom_0_->data_at(n+0, c+1, h+1, w+2)); + this->blob_bottom_0_->data_at(n, c+1, h+1, w+2)); } } } @@ -146,36 +143,25 @@ TYPED_TEST(CropLayerTest, TestForwardNumOffsets) { } } -TYPED_TEST(CropLayerTest, TestGradientNum) { +TYPED_TEST(CropLayerTest, TestCropHW) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; + layer_param.mutable_crop_param()->set_axis(2); + layer_param.mutable_crop_param()->add_offset(1); + layer_param.mutable_crop_param()->add_offset(2); CropLayer layer(layer_param); - - layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); - layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_); - - // Copy top data into diff - caffe_copy(this->blob_top_->count(), this->blob_top_->cpu_data(), - this->blob_top_->mutable_cpu_diff()); - - // Do backward pass - vector propagate_down(2, true); - layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_0_); - - - // Check results + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); for (int n = 0; n < this->blob_bottom_0_->num(); ++n) { for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) { for (int h = 0; h < this->blob_bottom_0_->height(); ++h) { for (int w = 0; w < this->blob_bottom_0_->width(); ++w) { - if ( n < this->blob_top_->shape(0) && + if (n < this->blob_top_->shape(0) && c < this->blob_top_->shape(1) && h < this->blob_top_->shape(2) && - w < this->blob_top_->shape(3) ) { - EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), - this->blob_bottom_0_->data_at(n, c, h, w)); - } else { - EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), 0); + w < this->blob_top_->shape(3)) { + EXPECT_EQ(this->blob_top_->data_at(n, c, h, w), + this->blob_bottom_0_->data_at(n, c, h+1, w+2)); } } } @@ -183,41 +169,50 @@ TYPED_TEST(CropLayerTest, TestGradientNum) { } } -TYPED_TEST(CropLayerTest, TestGradientNumOffset) { +TYPED_TEST(CropLayerTest, TestCrop5D) { typedef typename TypeParam::Dtype Dtype; + // Add dimension to each bottom for >4D check + vector bottom_0_shape = this->blob_bottom_0_->shape(); + vector bottom_1_shape = this->blob_bottom_1_->shape(); + bottom_0_shape.push_back(2); + bottom_1_shape.push_back(1); + this->blob_bottom_0_->Reshape(bottom_0_shape); + this->blob_bottom_1_->Reshape(bottom_1_shape); + FillerParameter filler_param; + GaussianFiller filler(filler_param); + filler.Fill(this->blob_bottom_0_); + filler.Fill(this->blob_bottom_1_); + // Make layer LayerParameter layer_param; - layer_param.mutable_crop_param()->set_axis(0); - layer_param.mutable_crop_param()->add_offset(0); - layer_param.mutable_crop_param()->add_offset(1); + layer_param.mutable_crop_param()->set_axis(2); layer_param.mutable_crop_param()->add_offset(1); layer_param.mutable_crop_param()->add_offset(2); + layer_param.mutable_crop_param()->add_offset(0); CropLayer layer(layer_param); - - layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_); - layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_); - - // Copy top data into diff - caffe_copy(this->blob_top_->count(), this->blob_top_->cpu_data(), - this->blob_top_->mutable_cpu_diff()); - - // Do backward pass - vector propagate_down(2, true); - layer.Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_0_); - - - // Check results - for (int n = 0; n < this->blob_bottom_0_->num(); ++n) { - for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) { - for (int h = 0; h < this->blob_bottom_0_->height(); ++h) { - for (int w = 0; w < this->blob_bottom_0_->width(); ++w) { - if ( 0 <= n && n < 0 + this->blob_top_->shape(0) && - 1 <= c && c < 1 + this->blob_top_->shape(1) && - 1 <= h && h < 1 + this->blob_top_->shape(2) && - 2 <= w && w < 2 + this->blob_top_->shape(3) ) { - EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), - this->blob_bottom_0_->data_at(n, c, h, w)); - } else { - EXPECT_EQ(this->blob_bottom_0_->diff_at(n, c, h, w), 0); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + vector bottom_idx = vector(5, 0); + vector top_idx = vector(5, 0); + for (int n = 0; n < this->blob_bottom_0_->shape(0); ++n) { + for (int c = 0; c < this->blob_bottom_0_->shape(1); ++c) { + for (int z = 0; z < this->blob_bottom_0_->shape(2); ++z) { + for (int h = 0; h < this->blob_bottom_0_->shape(3); ++h) { + for (int w = 0; w < this->blob_bottom_0_->shape(4); ++w) { + if (n < this->blob_top_->shape(0) && + c < this->blob_top_->shape(1) && + z < this->blob_top_->shape(2) && + h < this->blob_top_->shape(3) && + w < this->blob_top_->shape(4)) { + bottom_idx[0] = top_idx[0] = n; + bottom_idx[1] = top_idx[1] = c; + bottom_idx[2] = z; + bottom_idx[3] = h; + bottom_idx[4] = top_idx[4] = w; + top_idx[2] = z+1; + top_idx[3] = h+2; + EXPECT_EQ(this->blob_top_->data_at(bottom_idx), + this->blob_bottom_0_->data_at(top_idx)); + } } } } @@ -225,4 +220,46 @@ TYPED_TEST(CropLayerTest, TestGradientNumOffset) { } } +TYPED_TEST(CropLayerTest, TestCropAllGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_crop_param()->set_axis(0); + CropLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(CropLayerTest, TestCropHWGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_crop_param()->set_axis(2); + layer_param.mutable_crop_param()->add_offset(1); + layer_param.mutable_crop_param()->add_offset(2); + CropLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(CropLayerTest, TestCrop5DGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + layer_param.mutable_crop_param()->set_axis(2); + layer_param.mutable_crop_param()->add_offset(1); + layer_param.mutable_crop_param()->add_offset(2); + layer_param.mutable_crop_param()->add_offset(0); + CropLayer layer(layer_param); + // Add dimension to each bottom for >4D check + vector bottom_0_shape = this->blob_bottom_0_->shape(); + vector bottom_1_shape = this->blob_bottom_1_->shape(); + bottom_0_shape.push_back(2); + bottom_1_shape.push_back(1); + this->blob_bottom_0_->Reshape(bottom_0_shape); + this->blob_bottom_1_->Reshape(bottom_1_shape); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + } // namespace caffe From 01528918c707df82e5910bea0270d7987db5abd8 Mon Sep 17 00:00:00 2001 From: Jun Shi Date: Fri, 22 Jan 2016 09:58:37 -0800 Subject: [PATCH 441/446] split p2psync::run() --- include/caffe/parallel.hpp | 5 ++++- src/caffe/parallel.cpp | 20 ++++++++++++------- src/caffe/test/test_gradient_based_solver.cpp | 2 +- tools/caffe.cpp | 2 +- 4 files changed, 19 insertions(+), 10 deletions(-) diff --git a/include/caffe/parallel.hpp b/include/caffe/parallel.hpp index 85fc2b55984..6c496c884e3 100644 --- a/include/caffe/parallel.hpp +++ b/include/caffe/parallel.hpp @@ -93,7 +93,10 @@ class P2PSync : public GPUParams, public Solver::Callback, return solver_; } - void run(const vector& gpus); + void Run(const vector& gpus); + void Prepare(const vector& gpus, + vector > >* syncs); + inline const int initial_iter() const { return initial_iter_; } protected: void on_start(); diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp index 62f5d738593..5bc41c6a6e5 100644 --- a/src/caffe/parallel.cpp +++ b/src/caffe/parallel.cpp @@ -380,7 +380,8 @@ void P2PSync::on_gradients_ready() { } template -void P2PSync::run(const vector& gpus) { +void P2PSync::Prepare(const vector& gpus, + vector > >* syncs) { // Pair devices for map-reduce synchronization vector pairs; DevicePair::compute(gpus, &pairs); @@ -391,15 +392,14 @@ void P2PSync::run(const vector& gpus) { LOG(INFO)<< "GPUs pairs " << s.str(); SolverParameter param(solver_->param()); - vector > > syncs(gpus.size()); // Build the GPU tree by finding the parent for each solver for (int attempts = 0; attempts < pairs.size(); ++attempts) { for (int i = 1; i < pairs.size(); ++i) { - if (!syncs[i].get()) { + if (!syncs->at(i).get()) { P2PSync* parent = NULL; - for (int j = 0; j < syncs.size(); ++j) { - P2PSync* sync = j == 0 ? this : syncs[j].get(); + for (int j = 0; j < syncs->size(); ++j) { + P2PSync* sync = j == 0 ? this : syncs->at(j).get(); if (sync) { const SolverParameter& p = sync->solver()->param(); if (p.device_id() == pairs[i].parent()) { @@ -409,12 +409,18 @@ void P2PSync::run(const vector& gpus) { } if (parent) { param.set_device_id(pairs[i].device()); - syncs[i].reset(new P2PSync(solver_, parent, param)); - parent->children_.push_back((P2PSync*) syncs[i].get()); + syncs->at(i).reset(new P2PSync(solver_, parent, param)); + parent->children_.push_back((P2PSync*) syncs->at(i).get()); } } } } +} + +template +void P2PSync::Run(const vector& gpus) { + vector > > syncs(gpus.size()); + Prepare(gpus, &syncs); LOG(INFO)<< "Starting Optimization"; diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp index 09ec3a7e918..975a8f0f88a 100644 --- a/src/caffe/test/test_gradient_based_solver.cpp +++ b/src/caffe/test/test_gradient_based_solver.cpp @@ -204,7 +204,7 @@ class GradientBasedSolverTest : public MultiDeviceTest { Caffe::set_solver_count(gpus.size()); this->sync_.reset(new P2PSync( this->solver_, NULL, this->solver_->param())); - this->sync_->run(gpus); + this->sync_->Run(gpus); Caffe::set_solver_count(1); } if (snapshot) { diff --git a/tools/caffe.cpp b/tools/caffe.cpp index 95b2f82c4be..5d9331f0c22 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -214,7 +214,7 @@ int train() { if (gpus.size() > 1) { caffe::P2PSync sync(solver, NULL, solver->param()); - sync.run(gpus); + sync.Run(gpus); } else { LOG(INFO) << "Starting Optimization"; solver->Solve(); From 21197f7a4cc558976aea38cf9f355e3a9f80a7f9 Mon Sep 17 00:00:00 2001 From: max argus Date: Fri, 19 Feb 2016 12:45:31 +0000 Subject: [PATCH 442/446] Added Normalization Layer. --- include/caffe/layers/normalize_layer.hpp | 50 ++++ src/caffe/layers/normalize_layer.cpp | 234 ++++++++++++++++++ src/caffe/layers/normalize_layer.cu | 220 +++++++++++++++++ src/caffe/proto/caffe.proto | 14 +- src/caffe/test/test_normalize_layer.cpp | 300 +++++++++++++++++++++++ 5 files changed, 817 insertions(+), 1 deletion(-) create mode 100644 include/caffe/layers/normalize_layer.hpp create mode 100644 src/caffe/layers/normalize_layer.cpp create mode 100644 src/caffe/layers/normalize_layer.cu create mode 100644 src/caffe/test/test_normalize_layer.cpp diff --git a/include/caffe/layers/normalize_layer.hpp b/include/caffe/layers/normalize_layer.hpp new file mode 100644 index 00000000000..c211fc475ca --- /dev/null +++ b/include/caffe/layers/normalize_layer.hpp @@ -0,0 +1,50 @@ +#ifndef CAFFE_NORMALIZE_LAYER_HPP_ +#define CAFFE_NORMALIZE_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Normalizes input. + * https://github.com/kuprel/caffe + */ +template +class NormalizeLayer : public Layer { + public: + explicit NormalizeLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Normalize"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + Blob norm_; + Blob sum_channel_multiplier_, sum_spatial_multiplier_; + Blob buffer_, buffer_channel_, buffer_spatial_; + bool across_spatial_; + bool channel_shared_; + Dtype eps_; +}; + +} // namespace caffe + +#endif // CAFFE_NORMALIZE_LAYER_HPP_ diff --git a/src/caffe/layers/normalize_layer.cpp b/src/caffe/layers/normalize_layer.cpp new file mode 100644 index 00000000000..6d0b7bc36f8 --- /dev/null +++ b/src/caffe/layers/normalize_layer.cpp @@ -0,0 +1,234 @@ +#include + +#include "caffe/filler.hpp" +#include "caffe/layers/normalize_layer.hpp" + +namespace caffe { + +template +void NormalizeLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + CHECK_GE(bottom[0]->num_axes(), 2) + << "Number of axes of bottom blob must be >=2."; + buffer_.Reshape(1, bottom[0]->channels(), + bottom[0]->height(), bottom[0]->width()); + buffer_channel_.Reshape(1, bottom[0]->channels(), 1, 1); + buffer_spatial_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width()); + NormalizeParameter norm_param = this->layer_param().norm_param(); + across_spatial_ = norm_param.across_spatial(); + if (across_spatial_) { + norm_.Reshape(bottom[0]->num(), 1, 1, 1); + } else { + norm_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width()); + } + eps_ = norm_param.eps(); + int channels = bottom[0]->channels(); + int spatial_dim = bottom[0]->width() * bottom[0]->height(); + sum_channel_multiplier_.Reshape(1, channels, 1, 1); + caffe_set(channels, Dtype(1), sum_channel_multiplier_.mutable_cpu_data()); + sum_spatial_multiplier_.Reshape( + 1, 1, bottom[0]->height(), bottom[0]->width()); + caffe_set(spatial_dim, Dtype(1), sum_spatial_multiplier_.mutable_cpu_data()); + channel_shared_ = norm_param.channel_shared(); + if (this->blobs_.size() > 0) { + LOG(INFO) << "Skipping parameter initialization"; + } else { + this->blobs_.resize(1); + if (channel_shared_) { + this->blobs_[0].reset(new Blob(vector(0))); + } else { + this->blobs_[0].reset(new Blob(vector(1, channels))); + } + shared_ptr > scale_filler; + if (norm_param.has_scale_filler()) { + scale_filler.reset(GetFiller(norm_param.scale_filler())); + } else { + FillerParameter filler_param; + filler_param.set_type("constant"); + filler_param.set_value(1.0); + scale_filler.reset(GetFiller(filler_param)); + } + scale_filler->Fill(this->blobs_[0].get()); + } + if (channel_shared_) { + CHECK_EQ(this->blobs_[0]->count(), 1) + << "Scale size is inconsistent with prototxt config"; + } else { + CHECK_EQ(this->blobs_[0]->count(), channels) + << "Scale size is inconsistent with prototxt config"; + } + this->param_propagate_down_.resize(this->blobs_.size(), true); +} + +template +void NormalizeLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + CHECK_GE(bottom[0]->num_axes(), 2) + << "Number of axes of bottom blob must be >=2."; + top[0]->ReshapeLike(*bottom[0]); + buffer_.Reshape(1, bottom[0]->channels(), + bottom[0]->height(), bottom[0]->width()); + if (!across_spatial_) { + norm_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width()); + } + int spatial_dim = bottom[0]->height() * bottom[0]->width(); + if (spatial_dim != sum_spatial_multiplier_.count()) { + sum_spatial_multiplier_.Reshape( + 1, 1, bottom[0]->height(), bottom[0]->width()); + caffe_set(spatial_dim, Dtype(1), + sum_spatial_multiplier_.mutable_cpu_data()); + buffer_spatial_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width()); + } +} + +template +void NormalizeLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + const Dtype* scale = this->blobs_[0]->cpu_data(); + Dtype* buffer_data = buffer_.mutable_cpu_data(); + Dtype* norm_data = norm_.mutable_cpu_data(); + // add eps to avoid overflow + caffe_set(norm_.count(), Dtype(eps_), norm_data); + const Dtype* sum_channel_multiplier = sum_channel_multiplier_.cpu_data(); + const Dtype* sum_spatial_multiplier = sum_spatial_multiplier_.cpu_data(); + int num = bottom[0]->num(); + int dim = bottom[0]->count() / num; + int spatial_dim = bottom[0]->height() * bottom[0]->width(); + int channels = bottom[0]->channels(); + for (int n = 0; n < num; ++n) { + caffe_sqr(dim, bottom_data, buffer_data); + if (across_spatial_) { + // add eps to avoid overflow + norm_data[n] = pow(caffe_cpu_asum(dim, buffer_data)+eps_, + Dtype(0.5)); + caffe_cpu_scale(dim, Dtype(1.0 / norm_data[n]), bottom_data, + top_data); + } else { + caffe_cpu_gemv(CblasTrans, channels, spatial_dim, Dtype(1), + buffer_data, sum_channel_multiplier, Dtype(1), + norm_data); + // compute norm + caffe_powx(spatial_dim, norm_data, Dtype(0.5), norm_data); + // scale the layer + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels, spatial_dim, + 1, Dtype(1), sum_channel_multiplier, norm_data, + Dtype(0), buffer_data); + caffe_div(dim, bottom_data, buffer_data, top_data); + norm_data += spatial_dim; + } + // scale the output + if (channel_shared_) { + caffe_scal(dim, scale[0], top_data); + } else { + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels, spatial_dim, + 1, Dtype(1), scale, sum_spatial_multiplier, + Dtype(0), + buffer_data); + caffe_mul(dim, top_data, buffer_data, top_data); + } + bottom_data += dim; + top_data += dim; + } +} + +template +void NormalizeLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + const Dtype* top_diff = top[0]->cpu_diff(); + const Dtype* top_data = top[0]->cpu_data(); + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + const Dtype* scale = this->blobs_[0]->cpu_data(); + const Dtype* norm_data = norm_.cpu_data(); + Dtype* buffer_data = buffer_.mutable_cpu_data(); + Dtype* buffer_channel = buffer_channel_.mutable_cpu_data(); + Dtype* buffer_spatial = buffer_spatial_.mutable_cpu_data(); + const Dtype* sum_channel_multiplier = sum_channel_multiplier_.cpu_data(); + const Dtype* sum_spatial_multiplier = sum_spatial_multiplier_.cpu_data(); + int count = top[0]->count(); + int num = top[0]->num(); + int dim = count / num; + int spatial_dim = top[0]->height() * top[0]->width(); + int channels = top[0]->channels(); + + // Propagate to param + if (this->param_propagate_down_[0]) { + Dtype* scale_diff = this->blobs_[0]->mutable_cpu_diff(); + if (channel_shared_) { + scale_diff[0] += + caffe_cpu_dot(count, top_data, top_diff) / scale[0]; + } else { + for (int n = 0; n < num; ++n) { + caffe_mul(dim, top_data+n*dim, top_diff+n*dim, buffer_data); + caffe_cpu_gemv(CblasNoTrans, channels, spatial_dim, Dtype(1), + buffer_data, sum_spatial_multiplier, Dtype(0), + buffer_channel); + // store a / scale[i] in buffer_data temporary + caffe_div(channels, buffer_channel, scale, buffer_channel); + caffe_add(channels, buffer_channel, scale_diff, scale_diff); + } + } + } + + // Propagate to bottom + if (propagate_down[0]) { + for (int n = 0; n < num; ++n) { + if (across_spatial_) { + Dtype a = caffe_cpu_dot(dim, bottom_data, top_diff); + caffe_cpu_scale(dim, a / norm_data[n] / norm_data[n], + bottom_data, bottom_diff); + caffe_sub(dim, top_diff, bottom_diff, bottom_diff); + caffe_scal(dim, Dtype(1.0 / norm_data[n]), bottom_diff); + } else { + // dot product between bottom_data and top_diff + caffe_mul(dim, bottom_data, top_diff, buffer_data); + caffe_cpu_gemv(CblasTrans, channels, spatial_dim, Dtype(1), + buffer_data, sum_channel_multiplier, Dtype(0), + buffer_spatial); + // scale bottom_diff + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels, spatial_dim, + 1, Dtype(1), sum_channel_multiplier, + buffer_spatial, Dtype(0), buffer_data); + caffe_mul(dim, bottom_data, buffer_data, bottom_diff); + // divide by square of norm + caffe_powx(spatial_dim, norm_data, Dtype(2), buffer_spatial); + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels, spatial_dim, + 1, Dtype(1), sum_channel_multiplier, + buffer_spatial, Dtype(0), buffer_data); + caffe_div(dim, bottom_diff, buffer_data, bottom_diff); + // subtract + caffe_sub(dim, top_diff, bottom_diff, bottom_diff); + // divide by norm + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels, spatial_dim, + 1, Dtype(1), sum_channel_multiplier, norm_data, + Dtype(0), buffer_data); + caffe_div(dim, bottom_diff, buffer_data, bottom_diff); + norm_data += spatial_dim; + } + // scale the diff + if (channel_shared_) { + caffe_scal(dim, scale[0], bottom_diff); + } else { + caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, channels, spatial_dim, + 1, Dtype(1), scale, sum_spatial_multiplier, + Dtype(0), buffer_data); + caffe_mul(dim, bottom_diff, buffer_data, bottom_diff); + } + bottom_data += dim; + top_diff += dim; + bottom_diff += dim; + } + } +} + + +#ifdef CPU_ONLY +STUB_GPU(NormalizeLayer); +#endif + +INSTANTIATE_CLASS(NormalizeLayer); +REGISTER_LAYER_CLASS(Normalize); + +} // namespace caffe diff --git a/src/caffe/layers/normalize_layer.cu b/src/caffe/layers/normalize_layer.cu new file mode 100644 index 00000000000..659f3eb4faa --- /dev/null +++ b/src/caffe/layers/normalize_layer.cu @@ -0,0 +1,220 @@ +#include +#include +#include + +#include "thrust/device_vector.h" + +#include "caffe/filler.hpp" +#include "caffe/layers/normalize_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +// divid a matrix with vector +template +__global__ void DivBsx(const int nthreads, const Dtype* A, + const Dtype* v, const int rows, const int cols, const CBLAS_TRANSPOSE trans, + Dtype* B) { + CUDA_KERNEL_LOOP(index, nthreads) { + int c = index % cols; + int r = (index / cols) % rows; + if (trans == CblasNoTrans) { + B[index] = A[index] / v[c]; + } else { + B[index] = A[index] / v[r]; + } + } +} + +template +__global__ void MulBsx(const int nthreads, const Dtype* A, + const Dtype* v, const int rows, const int cols, const CBLAS_TRANSPOSE trans, + Dtype* B) { + CUDA_KERNEL_LOOP(index, nthreads) { + int c = index % cols; + int r = (index / cols) % rows; + if (trans == CblasNoTrans) { + B[index] = A[index] * v[c]; + } else { + B[index] = A[index] * v[r]; + } + } +} + +template +void NormalizeLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + Dtype* buffer_data = buffer_.mutable_gpu_data(); + Dtype* norm_data; + if (across_spatial_) { + // need to index it + norm_data = norm_.mutable_cpu_data(); + } else { + norm_data = norm_.mutable_gpu_data(); + // add eps to avoid overflow + caffe_gpu_set(norm_.count(), Dtype(eps_), norm_data); + } + const Dtype* scale; + if (channel_shared_) { + scale = this->blobs_[0]->cpu_data(); + } else { + scale = this->blobs_[0]->gpu_data(); + } + const Dtype* sum_channel_multiplier = sum_channel_multiplier_.gpu_data(); + int num = bottom[0]->num(); + int dim = bottom[0]->count() / num; + int spatial_dim = bottom[0]->height() * bottom[0]->width(); + int channels = bottom[0]->channels(); + for (int n = 0; n < num; ++n) { + caffe_gpu_powx(dim, bottom_data, Dtype(2), buffer_data); + if (across_spatial_) { + Dtype normsqr; + caffe_gpu_asum(dim, buffer_data, &normsqr); + // add eps to avoid overflow + norm_data[n] = pow(normsqr+eps_, Dtype(0.5)); + caffe_gpu_scale(dim, Dtype(1.0 / norm_data[n]), bottom_data, + top_data); + } else { + // compute norm + caffe_gpu_gemv(CblasTrans, channels, spatial_dim, Dtype(1), + buffer_data, sum_channel_multiplier, Dtype(1), + norm_data); + caffe_gpu_powx(spatial_dim, norm_data, Dtype(0.5), norm_data); + // scale the layer + // NOLINT_NEXT_LINE(whitespace/operators) + DivBsx <<>>( + dim, bottom_data, norm_data, channels, spatial_dim, CblasNoTrans, + top_data); + CUDA_POST_KERNEL_CHECK; + norm_data += spatial_dim; + } + // scale the output + if (channel_shared_) { + caffe_gpu_scal(dim, scale[0], top_data); + } else { + // NOLINT_NEXT_LINE(whitespace/operators) + MulBsx <<>>( + dim, top_data, scale, channels, spatial_dim, CblasTrans, + top_data); + CUDA_POST_KERNEL_CHECK; + } + bottom_data += dim; + top_data += dim; + } +} + +template +void NormalizeLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + const Dtype* top_diff = top[0]->gpu_diff(); + const Dtype* top_data = top[0]->gpu_data(); + const Dtype* bottom_data = bottom[0]->mutable_gpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + const Dtype* norm_data; + if (across_spatial_) { + // need to index it + norm_data = norm_.cpu_data(); + } else { + norm_data = norm_.gpu_data(); + } + const Dtype* scale; + if (channel_shared_) { + scale = this->blobs_[0]->cpu_data(); + } else { + scale = this->blobs_[0]->gpu_data(); + } + Dtype* buffer_data = buffer_.mutable_gpu_data(); + Dtype* buffer_channel = buffer_channel_.mutable_gpu_data(); + Dtype* buffer_spatial = buffer_spatial_.mutable_gpu_data(); + const Dtype* sum_channel_multiplier = sum_channel_multiplier_.gpu_data(); + const Dtype* sum_spatial_multiplier = sum_spatial_multiplier_.gpu_data(); + int count = top[0]->count(); + int num = top[0]->num(); + int dim = count / num; + int spatial_dim = top[0]->height() * top[0]->width(); + int channels = top[0]->channels(); + + // Propagate to param + if (this->param_propagate_down_[0]) { + if (channel_shared_) { + Dtype* scale_diff = this->blobs_[0]->mutable_cpu_diff(); + Dtype a; + caffe_gpu_dot(count, top_data, top_diff, &a); + scale_diff[0] += a / scale[0]; + } else { + Dtype* scale_diff = this->blobs_[0]->mutable_gpu_diff(); + for (int n = 0; n < num; ++n) { + // compute a + caffe_gpu_mul(dim, top_data+n*dim, top_diff+n*dim, buffer_data); + caffe_gpu_gemv(CblasNoTrans, channels, spatial_dim, Dtype(1), + buffer_data, sum_spatial_multiplier, Dtype(0), + buffer_channel); + // store a / scale[i] in buffer_data temporary + caffe_gpu_div(channels, buffer_channel, scale, buffer_channel); + caffe_gpu_add(channels, buffer_channel, scale_diff, scale_diff); + } + } + } + + // Propagate to bottom + if (propagate_down[0]) { + for (int n = 0; n < num; ++n) { + if (across_spatial_) { + Dtype a; + caffe_gpu_dot(dim, bottom_data, top_diff, &a); + caffe_gpu_scale(dim, a / norm_data[n] / norm_data[n], + bottom_data, bottom_diff); + caffe_gpu_sub(dim, top_diff, bottom_diff, bottom_diff); + caffe_gpu_scale(dim, Dtype(1.0 / norm_data[n]), bottom_diff, + bottom_diff); + } else { + // dot product between bottom_data and top_diff + caffe_gpu_mul(dim, bottom_data, top_diff, buffer_data); + caffe_gpu_gemv(CblasTrans, channels, spatial_dim, Dtype(1), + buffer_data, sum_channel_multiplier, Dtype(0), + buffer_spatial); + // scale botom_diff + // NOLINT_NEXT_LINE(whitespace/operators) + MulBsx <<>>( + dim, bottom_data, buffer_spatial, channels, spatial_dim, + CblasNoTrans, bottom_diff); + CUDA_POST_KERNEL_CHECK; + // divide by square of norm + caffe_gpu_powx(spatial_dim, norm_data, Dtype(2), buffer_spatial); + // NOLINT_NEXT_LINE(whitespace/operators) + DivBsx <<>>( + dim, bottom_diff, buffer_spatial, channels, spatial_dim, + CblasNoTrans, bottom_diff); + CUDA_POST_KERNEL_CHECK; + caffe_gpu_sub(dim, top_diff, bottom_diff, bottom_diff); + // divide by norm + // NOLINT_NEXT_LINE(whitespace/operators) + DivBsx <<>>( + dim, bottom_diff, norm_data, channels, spatial_dim, CblasNoTrans, + bottom_diff); + CUDA_POST_KERNEL_CHECK; + norm_data += spatial_dim; + } + // scale the diff + if (channel_shared_) { + caffe_gpu_scal(dim, scale[0], bottom_diff); + } else { + // NOLINT_NEXT_LINE(whitespace/operators) + MulBsx <<>>( + dim, bottom_diff, scale, channels, spatial_dim, CblasTrans, + bottom_diff); + CUDA_POST_KERNEL_CHECK; + } + bottom_data += dim; + top_diff += dim; + bottom_diff += dim; + } + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(NormalizeLayer); + + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 6900bb71482..9bfe2582476 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -306,7 +306,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 145 (last added: crop_param) +// LayerParameter next available layer-specific ID: 147 (last added: norm_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -380,6 +380,7 @@ message LayerParameter { optional LRNParameter lrn_param = 118; optional MemoryDataParameter memory_data_param = 119; optional MVNParameter mvn_param = 120; + optional NormalizeParameter norm_param = 145; optional PoolingParameter pooling_param = 121; optional PowerParameter power_param = 122; optional PReLUParameter prelu_param = 131; @@ -868,6 +869,17 @@ message MVNParameter { optional float eps = 3 [default = 1e-9]; } +// Message that stores parameters used by NormalizeLayer +message NormalizeParameter { + optional bool across_spatial = 1 [default = true]; + // Initial value of scale. Default is 1.0 for all + optional FillerParameter scale_filler = 2; + // Whether or not scale parameters are shared across channels. + optional bool channel_shared = 3 [default = true]; + // Epsilon for not dividing by zero while normalizing variance + optional float eps = 4 [default = 1e-10]; +} + message PoolingParameter { enum PoolMethod { MAX = 0; diff --git a/src/caffe/test/test_normalize_layer.cpp b/src/caffe/test/test_normalize_layer.cpp new file mode 100644 index 00000000000..96b9338966a --- /dev/null +++ b/src/caffe/test/test_normalize_layer.cpp @@ -0,0 +1,300 @@ +#include +#include +#include + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/layers/normalize_layer.hpp" +#include "google/protobuf/text_format.h" +#include "gtest/gtest.h" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +template +class NormalizeLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + protected: + NormalizeLayerTest() + : blob_bottom_(new Blob(2, 3, 2, 3)), + blob_top_(new Blob()) { + // fill the values + FillerParameter filler_param; + // GaussianFiller filler(filler_param); + filler_param.set_value(1); + ConstantFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + blob_bottom_vec_.push_back(blob_bottom_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~NormalizeLayerTest() { delete blob_bottom_; delete blob_top_; } + Blob* const blob_bottom_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(NormalizeLayerTest, TestDtypesAndDevices); + +TYPED_TEST(NormalizeLayerTest, TestForward) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Test norm + int num = this->blob_bottom_->num(); + int channels = this->blob_bottom_->channels(); + int height = this->blob_bottom_->height(); + int width = this->blob_bottom_->width(); + + for (int i = 0; i < num; ++i) { + Dtype norm = 0; + for (int j = 0; j < channels; ++j) { + for (int k = 0; k < height; ++k) { + for (int l = 0; l < width; ++l) { + Dtype data = this->blob_top_->data_at(i, j, k, l); + norm += data * data; + } + } + } + const Dtype kErrorBound = 1e-5; + // expect unit norm + EXPECT_NEAR(1, sqrt(norm), kErrorBound); + } +} + +TYPED_TEST(NormalizeLayerTest, TestForwardScale) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeParameter* norm_param = layer_param.mutable_norm_param(); + norm_param->mutable_scale_filler()->set_type("constant"); + norm_param->mutable_scale_filler()->set_value(10); + NormalizeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Test norm + int num = this->blob_bottom_->num(); + int channels = this->blob_bottom_->channels(); + int height = this->blob_bottom_->height(); + int width = this->blob_bottom_->width(); + + for (int i = 0; i < num; ++i) { + Dtype norm = 0; + for (int j = 0; j < channels; ++j) { + for (int k = 0; k < height; ++k) { + for (int l = 0; l < width; ++l) { + Dtype data = this->blob_top_->data_at(i, j, k, l); + norm += data * data; + } + } + } + const Dtype kErrorBound = 1e-5; + // expect unit norm + EXPECT_NEAR(10, sqrt(norm), kErrorBound); + } +} + +TYPED_TEST(NormalizeLayerTest, TestForwardScaleChannels) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeParameter* norm_param = layer_param.mutable_norm_param(); + norm_param->set_channel_shared(false); + norm_param->mutable_scale_filler()->set_type("constant"); + norm_param->mutable_scale_filler()->set_value(10); + NormalizeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Test norm + int num = this->blob_bottom_->num(); + int channels = this->blob_bottom_->channels(); + int height = this->blob_bottom_->height(); + int width = this->blob_bottom_->width(); + + for (int i = 0; i < num; ++i) { + Dtype norm = 0; + for (int j = 0; j < channels; ++j) { + for (int k = 0; k < height; ++k) { + for (int l = 0; l < width; ++l) { + Dtype data = this->blob_top_->data_at(i, j, k, l); + norm += data * data; + } + } + } + const Dtype kErrorBound = 1e-5; + // expect unit norm + EXPECT_NEAR(10, sqrt(norm), kErrorBound); + } +} + +TYPED_TEST(NormalizeLayerTest, TestForwardEltWise) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeParameter* norm_param = layer_param.mutable_norm_param(); + norm_param->set_across_spatial(false); + NormalizeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Test norm + int num = this->blob_bottom_->num(); + int channels = this->blob_bottom_->channels(); + int height = this->blob_bottom_->height(); + int width = this->blob_bottom_->width(); + + for (int i = 0; i < num; ++i) { + for (int k = 0; k < height; ++k) { + for (int l = 0; l < width; ++l) { + Dtype norm = 0; + for (int j = 0; j < channels; ++j) { + Dtype data = this->blob_top_->data_at(i, j, k, l); + norm += data * data; + } + const Dtype kErrorBound = 1e-5; + // expect unit norm + EXPECT_NEAR(1, sqrt(norm), kErrorBound); + } + } + } +} + +TYPED_TEST(NormalizeLayerTest, TestForwardEltWiseScale) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeParameter* norm_param = layer_param.mutable_norm_param(); + norm_param->set_across_spatial(false); + norm_param->mutable_scale_filler()->set_type("constant"); + norm_param->mutable_scale_filler()->set_value(10); + NormalizeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Test norm + int num = this->blob_bottom_->num(); + int channels = this->blob_bottom_->channels(); + int height = this->blob_bottom_->height(); + int width = this->blob_bottom_->width(); + + for (int i = 0; i < num; ++i) { + for (int k = 0; k < height; ++k) { + for (int l = 0; l < width; ++l) { + Dtype norm = 0; + for (int j = 0; j < channels; ++j) { + Dtype data = this->blob_top_->data_at(i, j, k, l); + norm += data * data; + } + const Dtype kErrorBound = 1e-5; + // expect unit norm + EXPECT_NEAR(10, sqrt(norm), kErrorBound); + } + } + } +} + +TYPED_TEST(NormalizeLayerTest, TestForwardEltWiseScaleChannel) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeParameter* norm_param = layer_param.mutable_norm_param(); + norm_param->set_across_spatial(false); + norm_param->set_channel_shared(false); + norm_param->mutable_scale_filler()->set_type("constant"); + norm_param->mutable_scale_filler()->set_value(10); + NormalizeLayer layer(layer_param); + layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); + // Test norm + int num = this->blob_bottom_->num(); + int channels = this->blob_bottom_->channels(); + int height = this->blob_bottom_->height(); + int width = this->blob_bottom_->width(); + + for (int i = 0; i < num; ++i) { + for (int k = 0; k < height; ++k) { + for (int l = 0; l < width; ++l) { + Dtype norm = 0; + for (int j = 0; j < channels; ++j) { + Dtype data = this->blob_top_->data_at(i, j, k, l); + norm += data * data; + } + const Dtype kErrorBound = 1e-5; + // expect unit norm + EXPECT_NEAR(10, sqrt(norm), kErrorBound); + } + } + } +} + +TYPED_TEST(NormalizeLayerTest, TestGradient) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_, 0); +} + +TYPED_TEST(NormalizeLayerTest, TestGradientScale) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeParameter* norm_param = layer_param.mutable_norm_param(); + norm_param->mutable_scale_filler()->set_type("constant"); + norm_param->mutable_scale_filler()->set_value(3); + NormalizeLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(NormalizeLayerTest, TestGradientScaleChannel) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeParameter* norm_param = layer_param.mutable_norm_param(); + norm_param->set_channel_shared(false); + norm_param->mutable_scale_filler()->set_type("constant"); + norm_param->mutable_scale_filler()->set_value(3); + NormalizeLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(NormalizeLayerTest, TestGradientEltWise) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeParameter* norm_param = layer_param.mutable_norm_param(); + norm_param->set_across_spatial(false); + NormalizeLayer layer(layer_param); + GradientChecker checker(1e-3, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(NormalizeLayerTest, TestGradientEltWiseScale) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeParameter* norm_param = layer_param.mutable_norm_param(); + norm_param->set_across_spatial(false); + norm_param->mutable_scale_filler()->set_type("constant"); + norm_param->mutable_scale_filler()->set_value(3); + NormalizeLayer layer(layer_param); + GradientChecker checker(1e-3, 2e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +TYPED_TEST(NormalizeLayerTest, TestGradientEltWiseScaleChannel) { + typedef typename TypeParam::Dtype Dtype; + LayerParameter layer_param; + NormalizeParameter* norm_param = layer_param.mutable_norm_param(); + norm_param->set_across_spatial(false); + norm_param->set_channel_shared(false); + norm_param->mutable_scale_filler()->set_type("constant"); + norm_param->mutable_scale_filler()->set_value(3); + NormalizeLayer layer(layer_param); + GradientChecker checker(1e-3, 2e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); +} + +} // namespace caffe From 1d74defc64b9d280362e8c452eceb32a98ba11c5 Mon Sep 17 00:00:00 2001 From: max argus Date: Fri, 19 Feb 2016 13:10:24 +0000 Subject: [PATCH 443/446] Added UnPooling Layer. --- include/caffe/layers/unpooling_layer.hpp | 57 ++++ src/caffe/layers/unpooling_layer.cpp | 378 +++++++++++++++++++++++ src/caffe/layers/unpooling_layer.cu | 307 ++++++++++++++++++ src/caffe/proto/caffe.proto | 24 +- 4 files changed, 765 insertions(+), 1 deletion(-) create mode 100644 include/caffe/layers/unpooling_layer.hpp create mode 100644 src/caffe/layers/unpooling_layer.cpp create mode 100644 src/caffe/layers/unpooling_layer.cu diff --git a/include/caffe/layers/unpooling_layer.hpp b/include/caffe/layers/unpooling_layer.hpp new file mode 100644 index 00000000000..b69c49f5d39 --- /dev/null +++ b/include/caffe/layers/unpooling_layer.hpp @@ -0,0 +1,57 @@ +#ifndef CAFFE_UNPOOLING_LAYER_HPP_ +#define CAFFE_UNPOOLING_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief UnPools the input image by assigning fixed, bilinear interpolation, + * etc. within regions. + * + * TODO(dox): thorough documentation for Forward, Backward, and proto params. + */ +template +class UnPoolingLayer : public Layer { + public: + explicit UnPoolingLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "UnPooling"; } + virtual inline int MinBottomBlobs() const { return 1; } + virtual inline int MaxBottomBlobs() const { return 2; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + // fill mask for different unpool type + void FillMask(); + + int out_kernel_h_, out_kernel_w_; + int out_stride_h_, out_stride_w_; + int out_pad_h_, out_pad_w_; + int num_, channels_; + int height_, width_; + int unpooled_height_, unpooled_width_; + Blob mask_; +}; + +} // namespace caffe + +#endif // CAFFE_UNPOOLING_LAYER_HPP_ diff --git a/src/caffe/layers/unpooling_layer.cpp b/src/caffe/layers/unpooling_layer.cpp new file mode 100644 index 00000000000..df081e9244a --- /dev/null +++ b/src/caffe/layers/unpooling_layer.cpp @@ -0,0 +1,378 @@ +#include +#include +#include + +#include "caffe/common.hpp" +#include "caffe/layers/unpooling_layer.hpp" +#include "caffe/syncedmem.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +using std::min; +using std::max; + +template +void UnPoolingLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + UnPoolingParameter unpool_param = this->layer_param_.unpooling_param(); + CHECK(!unpool_param.has_out_kernel_size() != + !(unpool_param.has_out_kernel_h() && unpool_param.has_out_kernel_w())) + << "Out filter size is out_kernel_size OR out_kernel_h and out_kernel_w; " + << "not both"; + CHECK(unpool_param.has_out_kernel_size() || + (unpool_param.has_out_kernel_h() && unpool_param.has_out_kernel_w())) + << "For non-square filters both out_kernel_h and out_kernel_w are " + << "required."; + CHECK((!unpool_param.has_out_pad() && unpool_param.has_out_pad_h() + && unpool_param.has_out_pad_w()) + || (!unpool_param.has_out_pad_h() && !unpool_param.has_out_pad_w())) + << "Out pad is out_pad OR out_pad_h and out_pad_w are required."; + CHECK((!unpool_param.has_out_stride() && unpool_param.has_out_stride_h() + && unpool_param.has_out_stride_w()) + || (!unpool_param.has_out_stride_h() && !unpool_param.has_out_stride_w())) + << "Out stride is out_stride OR out_stride_h and out_stride_w are " + << "required."; + if (bottom.size() == 1) { + if (unpool_param.has_out_kernel_size()) { + out_kernel_h_ = out_kernel_w_ = unpool_param.out_kernel_size(); + } else { + out_kernel_h_ = unpool_param.out_kernel_h(); + out_kernel_w_ = unpool_param.out_kernel_w(); + } + CHECK_GT(out_kernel_h_, 0) << "Out filter dimensions cannot be zero."; + CHECK_GT(out_kernel_w_, 0) << "Out filter dimensions cannot be zero."; + if (!unpool_param.has_out_stride_h()) { + out_stride_h_ = out_stride_w_ = unpool_param.out_stride(); + } else { + out_stride_h_ = unpool_param.out_stride_h(); + out_stride_w_ = unpool_param.out_stride_w(); + } + if (!unpool_param.has_out_pad_h()) { + out_pad_h_ = out_pad_w_ = unpool_param.out_pad(); + } else { + out_pad_h_ = unpool_param.out_pad_h(); + out_pad_w_ = unpool_param.out_pad_w(); + } + } else { + // Compute out_kernel and out_stride automatically + out_kernel_h_ = static_cast(ceil(static_cast( + bottom[1]->height()) / bottom[0]->height())); + out_kernel_w_ = static_cast(ceil(static_cast( + bottom[1]->width()) / bottom[0]->width())); + + out_stride_h_ = static_cast(ceil(static_cast( + bottom[1]->height()) / bottom[0]->height())); + out_stride_w_ = static_cast(ceil(static_cast( + bottom[1]->width()) / bottom[0]->width())); + + // In case either width or height of bottom[0] is 1, we set stride to 1 + if (out_stride_h_ == bottom[1]->height()) { + out_stride_h_ = 1; + } + if (out_stride_w_ == bottom[1]->width()) { + out_stride_w_ = 1; + } + + out_pad_h_ = static_cast(floor(static_cast( + (bottom[0]->height()-1)*out_stride_h_+out_kernel_h_ + -bottom[1]->height())/2)); + out_pad_w_ = static_cast(floor(static_cast( + (bottom[0]->width()-1)*out_stride_w_+out_kernel_w_ + -bottom[1]->width())/2)); + } + if (out_pad_h_ != 0 || out_pad_w_ != 0) { + CHECK_LT(out_pad_h_, out_kernel_h_); + CHECK_LT(out_pad_w_, out_kernel_w_); + } +} + +template +void UnPoolingLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + // reset the out_kernel_size and out_stride, etc. + this->LayerSetUp(bottom, top); + + num_ = bottom[0]->num(); + channels_ = bottom[0]->channels(); + height_ = bottom[0]->height(); + width_ = bottom[0]->width(); + unpooled_height_ = (height_ - 1) * out_stride_h_ - 2 * out_pad_h_ + + out_kernel_h_; + unpooled_width_ = (width_ - 1) * out_stride_w_ - 2 * out_pad_w_ + + out_kernel_w_; + top[0]->Reshape(num_, channels_, unpooled_height_, unpooled_width_); + + // fill the mask + this->FillMask(); +} + +template +void UnPoolingLayer::FillMask() { + // Different unpool method needs different mask, but they are same across + // channels and samples + mask_.Reshape(1, 1, unpooled_height_, unpooled_width_); + int* mask = mask_.mutable_cpu_data(); + switch (this->layer_param_.unpooling_param().unpool()) { + case UnPoolingParameter_UnPoolMethod_FIXED: + // mask_ records map of positions from bottom to top + caffe_set(mask_.count(), -1, mask); + for (int h = 0; h < height_; ++h) { + for (int w = 0; w < width_; ++w) { + int uhstart = h * out_stride_h_ - out_pad_h_; + int uwstart = w * out_stride_w_ - out_pad_w_; + int uhend = uhstart + out_kernel_h_; + int uwend = uwstart + out_kernel_w_; + int uhmid = floor((uhstart + uhend - 1) / 2); + int uwmid = floor((uwstart + uwend - 1) / 2); + uhmid = min(max(uhmid, 0), unpooled_height_-1); + uwmid = min(max(uwmid, 0), unpooled_width_-1); + const int unpool_index = uhmid * unpooled_width_ + uwmid; + const int index = h * width_ + w; + mask[unpool_index] = index; + } + } + break; + case UnPoolingParameter_UnPoolMethod_DIV: + case UnPoolingParameter_UnPoolMethod_REP: + // mask_ records counts of contributions to each unpooled position + // same for DIV and REP unpool operation + caffe_set(mask_.count(), 0, mask); + for (int h = 0; h < height_; ++h) { + for (int w = 0; w < width_; ++w) { + int uhstart = h * out_stride_h_ - out_pad_h_; + int uwstart = w * out_stride_w_ - out_pad_w_; + int uhend = min(uhstart + out_kernel_h_, unpooled_height_); + int uwend = min(uwstart + out_kernel_w_, unpooled_width_); + uhstart = max(uhstart, 0); + uwstart = max(uwstart, 0); + for (int uh = uhstart; uh < uhend; ++uh) { + for (int uw = uwstart; uw < uwend; ++uw) { + const int unpool_index = uh * unpooled_width_ + uw; + mask[unpool_index] += 1; + } + } + } + } + break; + default: + LOG(FATAL) << "Unknown unpooling method."; + } +} + +template +void UnPoolingLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + const int top_count = top[0]->count(); + caffe_set(top_count, Dtype(0), top_data); + const int* mask = mask_.cpu_data(); + // Different pooling methods. We explicitly do the switch outside the for + // loop to save time, although this results in more code. + switch (this->layer_param_.unpooling_param().unpool()) { + case UnPoolingParameter_UnPoolMethod_FIXED: + // The main loop + for (int n = 0; n < num_; ++n) { + for (int c = 0; c < channels_; ++c) { + for (int h = 0; h < height_; ++h) { + for (int w = 0; w < width_; ++w) { + int uhstart = h * out_stride_h_ - out_pad_h_; + int uwstart = w * out_stride_w_ - out_pad_w_; + int uhend = uhstart + out_kernel_h_; + int uwend = uwstart + out_kernel_w_; + int uhmid = floor((uhstart + uhend - 1) / 2); + int uwmid = floor((uwstart + uwend - 1) / 2); + uhmid = min(max(uhmid, 0), unpooled_height_-1); + uwmid = min(max(uwmid, 0), unpooled_width_-1); + const int unpool_index = uhmid * unpooled_width_ + uwmid; + const int index = h * width_ + w; + top_data[unpool_index] = bottom_data[index]; + } + } + // compute offset + bottom_data += bottom[0]->offset(0, 1); + top_data += top[0]->offset(0, 1); + } + } + break; + case UnPoolingParameter_UnPoolMethod_DIV: + // The main loop + for (int n = 0; n < num_; ++n) { + for (int c = 0; c < channels_; ++c) { + for (int h = 0; h < height_; ++h) { + for (int w = 0; w < width_; ++w) { + int uhstart = h * out_stride_h_ - out_pad_h_; + int uwstart = w * out_stride_w_ - out_pad_w_; + int uhend = min(uhstart + out_kernel_h_, + unpooled_height_ + out_pad_h_); + int uwend = min(uwstart + out_kernel_w_, + unpooled_width_ + out_pad_w_); + int unpool_size = (uhend - uhstart) * (uwend - uwstart); + uhstart = max(uhstart, 0); + uwstart = max(uwstart, 0); + uhend = min(uhend, unpooled_height_); + uwend = min(uwend, unpooled_width_); + Dtype div_data = bottom_data[h * width_ + w] / unpool_size; + for (int uh = uhstart; uh < uhend; ++uh) { + for (int uw = uwstart; uw < uwend; ++uw) { + int unpool_index = uh * unpooled_width_ + uw; + CHECK_GT(mask[unpool_index], 0); + top_data[unpool_index] += div_data / mask[unpool_index]; + } + } + } + } + // compute offset + bottom_data += bottom[0]->offset(0, 1); + top_data += top[0]->offset(0, 1); + } + } + break; + case UnPoolingParameter_UnPoolMethod_REP: + // The main loop + for (int n = 0; n < num_; ++n) { + for (int c = 0; c < channels_; ++c) { + for (int h = 0; h < height_; ++h) { + for (int w = 0; w < width_; ++w) { + int uhstart = h * out_stride_h_ - out_pad_h_; + int uwstart = w * out_stride_w_ - out_pad_w_; + int uhend = min(uhstart + out_kernel_h_, + unpooled_height_ + out_pad_h_); + int uwend = min(uwstart + out_kernel_w_, + unpooled_width_ + out_pad_w_); + uhstart = max(uhstart, 0); + uwstart = max(uwstart, 0); + uhend = min(uhend, unpooled_height_); + uwend = min(uwend, unpooled_width_); + Dtype data = bottom_data[h * width_ + w]; + for (int uh = uhstart; uh < uhend; ++uh) { + for (int uw = uwstart; uw < uwend; ++uw) { + int unpool_index = uh * unpooled_width_ + uw; + CHECK_GT(mask[unpool_index], 0); + top_data[unpool_index] += data / mask[unpool_index]; + } + } + } + } + // compute offset + bottom_data += bottom[0]->offset(0, 1); + top_data += top[0]->offset(0, 1); + } + } + break; + default: + LOG(FATAL) << "Unknown unpooling method."; + } +} + +template +void UnPoolingLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (!propagate_down[0]) { + return; + } + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + caffe_set(bottom[0]->count(), Dtype(0), bottom_diff); + // Different unpooling methods. We explicitly do the switch outside the for + // loop to save time, although this results in more codes. + const int* mask = mask_.cpu_data(); + switch (this->layer_param_.unpooling_param().unpool()) { + case UnPoolingParameter_UnPoolMethod_FIXED: + // The main loop + for (int n = 0; n < num_; ++n) { + for (int c = 0; c < channels_; ++c) { + for (int uh = 0; uh < unpooled_height_; ++uh) { + for (int uw = 0; uw < unpooled_width_; ++uw) { + const int unpool_index = uh * unpooled_width_ + uw; + const int index = mask[unpool_index]; + if (index != -1) { + bottom_diff[index] = top_diff[unpool_index]; + } + } + } + bottom_diff += bottom[0]->offset(0, 1); + top_diff += top[0]->offset(0, 1); + } + } + break; + case UnPoolingParameter_UnPoolMethod_DIV: + // The main loop + for (int n = 0; n < num_; ++n) { + for (int c = 0; c < channels_; ++c) { + for (int h = 0; h < height_; ++h) { + for (int w = 0; w < width_; ++w) { + int uhstart = h * out_stride_h_ - out_pad_h_; + int uwstart = w * out_stride_w_ - out_pad_w_; + int uhend = min(uhstart + out_kernel_h_, + unpooled_height_ + out_pad_h_); + int uwend = min(uwstart + out_kernel_w_, + unpooled_width_ + out_pad_w_); + int unpool_size = (uhend - uhstart) * (uwend - uwstart); + uhstart = max(uhstart, 0); + uwstart = max(uwstart, 0); + uhend = min(uhend, unpooled_height_); + uwend = min(uwend, unpooled_width_); + for (int uh = uhstart; uh < uhend; ++uh) { + for (int uw = uwstart; uw < uwend; ++uw) { + const int unpool_index = uh * unpooled_width_ + uw; + CHECK_GT(mask[unpool_index], 0); + bottom_diff[h * width_ + w] += + top_diff[unpool_index] / unpool_size / mask[unpool_index]; + } + } + } + } + // offset + bottom_diff += bottom[0]->offset(0, 1); + top_diff += top[0]->offset(0, 1); + } + } + break; + case UnPoolingParameter_UnPoolMethod_REP: + // The main loop + for (int n = 0; n < num_; ++n) { + for (int c = 0; c < channels_; ++c) { + for (int h = 0; h < height_; ++h) { + for (int w = 0; w < width_; ++w) { + int uhstart = h * out_stride_h_ - out_pad_h_; + int uwstart = w * out_stride_w_ - out_pad_w_; + int uhend = min(uhstart + out_kernel_h_, + unpooled_height_ + out_pad_h_); + int uwend = min(uwstart + out_kernel_w_, + unpooled_width_ + out_pad_w_); + uhstart = max(uhstart, 0); + uwstart = max(uwstart, 0); + uhend = min(uhend, unpooled_height_); + uwend = min(uwend, unpooled_width_); + for (int uh = uhstart; uh < uhend; ++uh) { + for (int uw = uwstart; uw < uwend; ++uw) { + const int unpool_index = uh * unpooled_width_ + uw; + CHECK_GT(mask[unpool_index], 0); + bottom_diff[h * width_ + w] += + top_diff[unpool_index] / mask[unpool_index]; + } + } + } + } + // offset + bottom_diff += bottom[0]->offset(0, 1); + top_diff += top[0]->offset(0, 1); + } + } + break; + default: + LOG(FATAL) << "Unknown unpooling method."; + } +} + + +#ifdef CPU_ONLY +STUB_GPU(UnPoolingLayer); +#endif + +INSTANTIATE_CLASS(UnPoolingLayer); +REGISTER_LAYER_CLASS(UnPooling); + +} // namespace caffe diff --git a/src/caffe/layers/unpooling_layer.cu b/src/caffe/layers/unpooling_layer.cu new file mode 100644 index 00000000000..d8c2a31cc89 --- /dev/null +++ b/src/caffe/layers/unpooling_layer.cu @@ -0,0 +1,307 @@ +#include +#include +#include + +#include "caffe/layers/unpooling_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +__global__ void FixedUnPoolForward(const int nthreads, const Dtype* bottom_data, + const int num, const int channels, const int height, const int width, + const int unpooled_height, const int unpooled_width, const int out_kernel_h, + const int out_kernel_w, const int out_stride_h, const int out_stride_w, + const int out_pad_h, const int out_pad_w, Dtype* top_data) { + CUDA_KERNEL_LOOP(unpool_index, nthreads) { + int uw = unpool_index % unpooled_width; + int uh = (unpool_index / unpooled_width) % unpooled_height; + int c = (unpool_index / unpooled_width / unpooled_height) % channels; + int n = unpool_index / unpooled_width / unpooled_height / channels; + int hstart = (uh + out_pad_h < out_kernel_h) ? 0 : + (uh + out_pad_h - out_kernel_h) / out_stride_h + 1; + int hend = min((uh + out_pad_h) / out_stride_h + 1, height); + int wstart = (uw + out_pad_w < out_kernel_w) ? 0 : + (uw + out_pad_w - out_kernel_w) / out_stride_w + 1; + int wend = min((uw + out_pad_w) / out_stride_w + 1, width); + int offset = (n * channels + c) * height * width; + int unpool_offset = (n * channels + c) * unpooled_height * unpooled_width; + bottom_data += offset; + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + int uhstart = h * out_stride_h - out_pad_h; + int uwstart = w * out_stride_w - out_pad_w; + int uhend = uhstart + out_kernel_h; + int uwend = uwstart + out_kernel_w; + int uhmid = (uhstart + uhend - 1) / 2; + int uwmid = (uwstart + uwend - 1) / 2; + uhmid = min(max(uhmid, 0), unpooled_height); + uwmid = min(max(uwmid, 0), unpooled_width); + if (unpool_offset + uhmid * unpooled_width + uwmid == unpool_index) { + // find the mapping, assign & return + int index = h * width + w; + top_data[unpool_index] = bottom_data[index]; + return; + } + } + } + } +} + +template +__global__ void DivUnPoolForward(const int nthreads, const Dtype* bottom_data, + const int* mask, const int num, const int channels, const int height, + const int width, const int unpooled_height, const int unpooled_width, + const int out_kernel_h, const int out_kernel_w, const int out_stride_h, + const int out_stride_w, const int out_pad_h, const int out_pad_w, + Dtype* top_data) { + CUDA_KERNEL_LOOP(unpool_index, nthreads) { + int uw = unpool_index % unpooled_width + out_pad_w; + int uh = (unpool_index / unpooled_width) % unpooled_height + out_pad_h; + int c = (unpool_index / unpooled_width / unpooled_height) % channels; + int n = unpool_index / unpooled_width / unpooled_height / channels; + int spatial_dim = unpooled_height * unpooled_width; + int hstart = (uh < out_kernel_h) ? 0 : + (uh - out_kernel_h) / out_stride_h + 1; + int hend = min(uh / out_stride_h + 1, height); + int wstart = (uw < out_kernel_w) ? 0 : + (uw - out_kernel_w) / out_stride_w + 1; + int wend = min(uw / out_stride_w + 1, width); + Dtype divval = 0; + bottom_data += (n * channels + c) * height * width; + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + int uhstart = h * out_stride_h - out_pad_h; + int uwstart = w * out_stride_w - out_pad_w; + int uhend = min(uhstart + out_kernel_h, unpooled_height + out_pad_h); + int uwend = min(uwstart + out_kernel_w, unpooled_width + out_pad_w); + int unpool_size = (uhend - uhstart) * (uwend - uwstart); + divval += bottom_data[h * width + w] / unpool_size; + } + } + top_data[unpool_index] = divval / mask[unpool_index % spatial_dim]; + } +} + +template +__global__ void RepUnPoolForward(const int nthreads, const Dtype* bottom_data, + const int* mask, const int num, const int channels, const int height, + const int width, const int unpooled_height, const int unpooled_width, + const int out_kernel_h, const int out_kernel_w, const int out_stride_h, + const int out_stride_w, const int out_pad_h, const int out_pad_w, + Dtype* top_data) { + CUDA_KERNEL_LOOP(unpool_index, nthreads) { + int uw = unpool_index % unpooled_width + out_pad_w; + int uh = (unpool_index / unpooled_width) % unpooled_height + out_pad_h; + int c = (unpool_index / unpooled_width / unpooled_height) % channels; + int n = unpool_index / unpooled_width / unpooled_height / channels; + int spatial_dim = unpooled_height * unpooled_width; + int hstart = (uh < out_kernel_h) ? 0 : + (uh - out_kernel_h) / out_stride_h + 1; + int hend = min(uh / out_stride_h + 1, height); + int wstart = (uw < out_kernel_w) ? 0 : + (uw - out_kernel_w) / out_stride_w + 1; + int wend = min(uw / out_stride_w + 1, width); + Dtype val = 0; + bottom_data += (n * channels + c) * height * width; + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + int uhstart = h * out_stride_h - out_pad_h; + int uwstart = w * out_stride_w - out_pad_w; + int uhend = min(uhstart + out_kernel_h, unpooled_height + out_pad_h); + int uwend = min(uwstart + out_kernel_w, unpooled_width + out_pad_w); + val += bottom_data[h * width + w]; + } + } + top_data[unpool_index] = val / mask[unpool_index % spatial_dim]; + } +} + +template +void UnPoolingLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + const int count = top[0]->count(); + caffe_gpu_set(count, Dtype(0), top_data); + const int* mask = mask_.gpu_data(); + switch (this->layer_param_.unpooling_param().unpool()) { + case UnPoolingParameter_UnPoolMethod_FIXED: + FixedUnPoolForward + // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, bottom_data, num_, channels_, height_, width_, + unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_, + out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, top_data); + break; + case UnPoolingParameter_UnPoolMethod_DIV: + DivUnPoolForward + // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, bottom_data, mask, num_, channels_, height_, width_, + unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_, + out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, top_data); + break; + case UnPoolingParameter_UnPoolMethod_REP: + RepUnPoolForward + // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, bottom_data, mask, num_, channels_, height_, width_, + unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_, + out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, top_data); + break; + default: + LOG(FATAL) << "Unknown unpooling method."; + } + CUDA_POST_KERNEL_CHECK; +} + + +template +__global__ void FixedUnPoolBackward(const int nthreads, const Dtype* top_diff, + const int* mask, const int num, const int channels, const int height, + const int width, const int unpooled_height, const int unpooled_width, + const int out_kernel_h, const int out_kernel_w, const int out_stride_h, + const int out_stride_w, const int out_pad_h, const int out_pad_w, + Dtype* bottom_diff) { + CUDA_KERNEL_LOOP(index, nthreads) { + // find out the local index + // find out the local offset + int w = index % width; + int h = (index / width) % height; + int c = (index / width / height) % channels; + int n = index / width / height / channels; + int uhstart = h * out_stride_h - out_pad_h; + int uwstart = w * out_stride_w - out_pad_w; + int uhend = uhstart + out_kernel_h; + int uwend = uwstart + out_kernel_w; + int uhmid = (uhstart + uhend - 1) / 2; + int uwmid = (uwstart + uwend - 1) / 2; + uhmid = min(max(uhmid, 0), unpooled_height-1); + uwmid = min(max(uwmid, 0), unpooled_width-1); + int offset = (n * channels + c) * unpooled_height * unpooled_width; + int unpool_index = uhmid * unpooled_width + uwmid; + Dtype gradient = 0; + if (mask[unpool_index] == h * width + w) { + gradient += top_diff[unpool_index + offset]; + } + bottom_diff[index] = gradient; + } +} + +template +__global__ void DivUnPoolBackward(const int nthreads, const Dtype* top_diff, + const int* mask, const int num, const int channels, const int height, + const int width, const int unpooled_height, const int unpooled_width, + const int out_kernel_h, const int out_kernel_w, const int out_stride_h, + const int out_stride_w, const int out_pad_h, const int out_pad_w, + Dtype* bottom_diff) { + CUDA_KERNEL_LOOP(index, nthreads) { + // find out the local index + // find out the local offset + int w = index % width; + int h = (index / width) % height; + int c = (index / width / height) % channels; + int n = index / width / height / channels; + int uhstart = h * out_stride_h - out_pad_h; + int uwstart = w * out_stride_w - out_pad_w; + int uhend = min(uhstart + out_kernel_h, unpooled_height + out_pad_h); + int uwend = min(uwstart + out_kernel_w, unpooled_width + out_pad_w); + int unpool_size = (uhend - uhstart) * (uwend - uwstart); + uhstart = max(uhstart, 0); + uwstart = max(uwstart, 0); + uhend = min(uhend, unpooled_height); + uwend = min(uwend, unpooled_width); + Dtype gradient = 0; + int offset = (n * channels + c) * unpooled_height * unpooled_width; + for (int uh = uhstart; uh < uhend; ++uh) { + for (int uw = uwstart; uw < uwend; ++uw) { + int unpool_index = uh * unpooled_width + uw; + gradient += top_diff[unpool_index + offset] / mask[unpool_index]; + } + } + bottom_diff[index] = gradient / unpool_size; + } +} + +template +__global__ void RepUnPoolBackward(const int nthreads, const Dtype* top_diff, + const int* mask, const int num, const int channels, const int height, + const int width, const int unpooled_height, const int unpooled_width, + const int out_kernel_h, const int out_kernel_w, const int out_stride_h, + const int out_stride_w, const int out_pad_h, const int out_pad_w, + Dtype* bottom_diff) { + CUDA_KERNEL_LOOP(index, nthreads) { + // find out the local index + // find out the local offset + int w = index % width; + int h = (index / width) % height; + int c = (index / width / height) % channels; + int n = index / width / height / channels; + int uhstart = h * out_stride_h - out_pad_h; + int uwstart = w * out_stride_w - out_pad_w; + int uhend = min(uhstart + out_kernel_h, unpooled_height + out_pad_h); + int uwend = min(uwstart + out_kernel_w, unpooled_width + out_pad_w); + uhstart = max(uhstart, 0); + uwstart = max(uwstart, 0); + uhend = min(uhend, unpooled_height); + uwend = min(uwend, unpooled_width); + Dtype gradient = 0; + int offset = (n * channels + c) * unpooled_height * unpooled_width; + for (int uh = uhstart; uh < uhend; ++uh) { + for (int uw = uwstart; uw < uwend; ++uw) { + int unpool_index = uh * unpooled_width + uw; + gradient += top_diff[unpool_index + offset] / mask[unpool_index]; + } + } + bottom_diff[index] = gradient; + } +} + +template +void UnPoolingLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (!propagate_down[0]) { + return; + } + const Dtype* top_diff = top[0]->gpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + const int count = bottom[0]->count(); + caffe_gpu_set(count, Dtype(0.), bottom_diff); + const int* mask = mask_.gpu_data(); + switch (this->layer_param_.unpooling_param().unpool()) { + case UnPoolingParameter_UnPoolMethod_FIXED: + FixedUnPoolBackward + // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, top_diff, mask, num_, channels_, height_, width_, + unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_, + out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, bottom_diff); + break; + case UnPoolingParameter_UnPoolMethod_DIV: + DivUnPoolBackward + // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, top_diff, mask, num_, channels_, height_, width_, + unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_, + out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, bottom_diff); + break; + case UnPoolingParameter_UnPoolMethod_REP: + RepUnPoolBackward + // NOLINT_NEXT_LINE(whitespace/operators) + <<>>( + count, top_diff, mask, num_, channels_, height_, width_, + unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_, + out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, bottom_diff); + break; + default: + LOG(FATAL) << "Unknown unpooling method."; + } + CUDA_POST_KERNEL_CHECK; +} + + +INSTANTIATE_LAYER_GPU_FUNCS(UnPoolingLayer); + + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 9bfe2582476..7e086cbbd1e 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -306,7 +306,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 147 (last added: norm_param) +// LayerParameter next available layer-specific ID: 147 (last added: unpooling_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -396,6 +396,7 @@ message LayerParameter { optional TanHParameter tanh_param = 127; optional ThresholdParameter threshold_param = 128; optional TileParameter tile_param = 138; + optional UnPoolingParameter unpooling_param = 146; optional WindowDataParameter window_data_param = 129; } @@ -1134,6 +1135,27 @@ message ThresholdParameter { optional float threshold = 1 [default = 0]; // Strictly positive values } +// Message that stores parameters used by UnPoolingLayer +message UnPoolingParameter { + enum UnPoolMethod { + FIXED = 0; // Put in the middle of a kernel + DIV = 1; // Divide equally through a kernel + REP = 2; // Repeat through a kernel + } + optional UnPoolMethod unpool = 1 [default = FIXED]; // The unpooling method + // Pad, kernel size, and stride are all given as a single value for equal + // dimensions in height and width or as Y, X pairs. + optional uint32 out_pad = 4 [default = 0]; // The padding size (equal in Y, X) + optional uint32 out_pad_h = 9 [default = 0]; // The padding height + optional uint32 out_pad_w = 10 [default = 0]; // The padding width + optional uint32 out_kernel_size = 2; // The kernel size (square) + optional uint32 out_kernel_h = 5; // The kernel height + optional uint32 out_kernel_w = 6; // The kernel width + optional uint32 out_stride = 3 [default = 1]; // The stride (equal in Y, X) + optional uint32 out_stride_h = 7; // The stride height + optional uint32 out_stride_w = 8; // The stride width +} + message WindowDataParameter { // Specify the data source. optional string source = 1; From a9b66552b00db7c704fdd7db8c3de925dce89ffa Mon Sep 17 00:00:00 2001 From: max argus Date: Fri, 19 Feb 2016 13:22:15 +0000 Subject: [PATCH 444/446] Added ParseOutput Layer. --- include/caffe/layers/parse_output_layer.hpp | 71 +++++++++++++++++++++ src/caffe/layers/parse_output_layer.cpp | 67 +++++++++++++++++++ 2 files changed, 138 insertions(+) create mode 100644 include/caffe/layers/parse_output_layer.hpp create mode 100644 src/caffe/layers/parse_output_layer.cpp diff --git a/include/caffe/layers/parse_output_layer.hpp b/include/caffe/layers/parse_output_layer.hpp new file mode 100644 index 00000000000..3d87d174cfb --- /dev/null +++ b/include/caffe/layers/parse_output_layer.hpp @@ -0,0 +1,71 @@ +#ifndef CAFFE_PARSE_OUTPUT_LAYER_HPP_ +#define CAFFE_PARSE_OUTPUT_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Compute the segmentation label of the @f$ H \times W @f$ for each datum across + * all channels @f$ C @f$. + * + * Intended for use after a classification layer to produce a prediction of + * segmentation label. + * If parameter out_max_val is set to true, also output the predicted value for + * the corresponding label for each image. + * + * NOTE: does not implement Backwards operation. + */ +template +class ParseOutputLayer : public Layer { + public: + /** + * @param param provides ParseOutputParameter parse_output_param, + * with ParseOutputLayer options: + * - out_max_val (\b optional bool, default false). + * if set, output the predicted value for the corresponding label for each + * image. + */ + explicit ParseOutputLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "ParseOutput"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int MaxTopBlobs() const { return 2; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times 1 \times H \times W) @f$ or, if out_max_val + * @f$ (N \times 2 \times H \times W) @f$ + * the computed outputs @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + /// @brief Not implemented (non-differentiable function) + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + NOT_IMPLEMENTED; + } + + bool out_max_val_; + + // max_prob_ is used to store the maximum probability value + Blob max_prob_; +}; + + +} // namespace caffe + +#endif // CAFFE_PARSE_OUTPUT_LAYER_HPP_ diff --git a/src/caffe/layers/parse_output_layer.cpp b/src/caffe/layers/parse_output_layer.cpp new file mode 100644 index 00000000000..0f66e74e5b7 --- /dev/null +++ b/src/caffe/layers/parse_output_layer.cpp @@ -0,0 +1,67 @@ +#include +#include +#include +#include + +#include "caffe/layer.hpp" +#include "caffe/layers/parse_output_layer.hpp" + +namespace caffe { + +template +void ParseOutputLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + out_max_val_ = top.size() > 1; +} + +template +void ParseOutputLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + // Produces max_ind and max_val + top[0]->Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width()); + if (out_max_val_) { + top[1]->ReshapeLike(*top[0]); + } + max_prob_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width()); +} + +template +void ParseOutputLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_label_data = top[0]->mutable_cpu_data(); + Dtype* top_prob_data = NULL; + if (out_max_val_) { + top_prob_data = top[1]->mutable_cpu_data(); + } + Dtype* max_prob_data = max_prob_.mutable_cpu_data(); + int num = bottom[0]->num(); + int channels = bottom[0]->channels(); + int spatial_dim = bottom[0]->height() * bottom[0]->width(); + for (int i = 0; i < num; ++i) { + caffe_set(spatial_dim, Dtype(0), top_label_data); + // initialize max value from first plane + caffe_copy(spatial_dim, bottom_data, max_prob_data); + bottom_data += bottom[0]->offset(0, 1); + for (int j = 1; j < channels; ++j) { + for (int k = 0; k < spatial_dim; ++k) { + Dtype prob = bottom_data[k]; + if (prob > max_prob_data[k]) { + max_prob_data[k] = prob; + top_label_data[k] = j; + } + } + bottom_data += bottom[0]->offset(0, 1); + } + top_label_data += top[0]->offset(1); + if (out_max_val_) { + caffe_copy(spatial_dim, max_prob_data, top_prob_data); + top_prob_data += top[1]->offset(1); + } + } +} + +INSTANTIATE_CLASS(ParseOutputLayer); +REGISTER_LAYER_CLASS(ParseOutput); + +} // namespace caffe From 0aef2f0da63501c43531034e1bc3d016aac58b07 Mon Sep 17 00:00:00 2001 From: max argus Date: Fri, 19 Feb 2016 13:34:47 +0000 Subject: [PATCH 445/446] Added ParseEvaluate Layer. --- include/caffe/layers/parse_evaluate_layer.hpp | 68 +++++++++++++++++ src/caffe/layers/parse_evaluate_layer.cpp | 75 +++++++++++++++++++ src/caffe/proto/caffe.proto | 11 ++- 3 files changed, 153 insertions(+), 1 deletion(-) create mode 100644 include/caffe/layers/parse_evaluate_layer.hpp create mode 100644 src/caffe/layers/parse_evaluate_layer.cpp diff --git a/include/caffe/layers/parse_evaluate_layer.hpp b/include/caffe/layers/parse_evaluate_layer.hpp new file mode 100644 index 00000000000..eddff06c249 --- /dev/null +++ b/include/caffe/layers/parse_evaluate_layer.hpp @@ -0,0 +1,68 @@ +#ifndef CAFFE_PARSE_EVALUATE_LAYER_HPP_ +#define CAFFE_PARSE_EVALUATE_LAYER_HPP_ + +#include +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/** + * @brief Count the prediction and ground truth statistics for each datum. + * + * NOTE: This does not implement Backwards operation. + */ +template +class ParseEvaluateLayer : public Layer { + public: + /** + * @param param provides ParseEvaluateParameter parse_evaluate_param, + * with ParseEvaluateLayer options: + * - num_labels (\b optional int32.). + * number of labels. must provide!! + * - ignore_label (\b repeated int32). + * If any, ignore evaluating the corresponding label for each + * image. + */ + explicit ParseEvaluateLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "ParseEvaluate"; } + virtual inline int ExactNumBottomBlobs() const { return 2; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + /** + * @param bottom input Blob vector (length 2) + * -# @f$ (N \times 1 \times H \times W) @f$ + * the prediction label @f$ x @f$ + * -# @f$ (N \times 1 \times H \times W) @f$ + * the ground truth label @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times 1 \times 3) @f$ + * the counts for different class @f$ + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + /// @brief Not implemented (non-differentiable function) + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + NOT_IMPLEMENTED; + } + + // number of total labels + int num_labels_; + // store ignored labels + std::set ignore_labels_; +}; + +} // namespace caffe + +#endif // CAFFE_PARSE_EVALUATE_LAYER_HPP_ diff --git a/src/caffe/layers/parse_evaluate_layer.cpp b/src/caffe/layers/parse_evaluate_layer.cpp new file mode 100644 index 00000000000..3693e52c3f7 --- /dev/null +++ b/src/caffe/layers/parse_evaluate_layer.cpp @@ -0,0 +1,75 @@ +#include +#include +#include +#include + +#include "caffe/layer.hpp" +#include "caffe/layers/parse_evaluate_layer.hpp" +#include "caffe/util/io.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void ParseEvaluateLayer::LayerSetUp( + const vector*>& bottom, const vector*>& top) { + const ParseEvaluateParameter& parse_evaluate_param = + this->layer_param_.parse_evaluate_param(); + CHECK(parse_evaluate_param.has_num_labels()) << "Must have num_labels!!"; + num_labels_ = parse_evaluate_param.num_labels(); + ignore_labels_.clear(); + int num_ignore_label = parse_evaluate_param.ignore_label().size(); + for (int i = 0; i < num_ignore_label; ++i) { + ignore_labels_.insert(parse_evaluate_param.ignore_label(i)); + } +} + +template +void ParseEvaluateLayer::Reshape( + const vector*>& bottom, const vector*>& top) { + CHECK_EQ(bottom[0]->num(), bottom[1]->num()) + << "The data and label should have the same number."; + CHECK_EQ(bottom[0]->channels(), bottom[1]->channels()); + CHECK_EQ(bottom[0]->channels(), 1); + CHECK_EQ(bottom[0]->height(), bottom[1]->height()); + CHECK_GE(bottom[0]->width(), bottom[1]->width()); + top[0]->Reshape(1, num_labels_, 1, 3); +} + +template +void ParseEvaluateLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + CHECK_EQ(bottom[0]->num(), bottom[1]->num()); + CHECK_EQ(bottom[0]->count(), bottom[1]->count()); + const Dtype* bottom_pred = bottom[0]->cpu_data(); + const Dtype* bottom_gt = bottom[1]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + caffe_set(top[0]->count(), Dtype(0), top_data); + int num = bottom[0]->num(); + int spatial_dim = bottom[0]->height() * bottom[0]->width(); + for (int i = 0; i < num; ++i) { + // count the number of ground truth labels, the predicted labels, and + // predicted labels happens to be ground truth labels + for (int j = 0; j < spatial_dim; ++j) { + int gt_label = bottom_gt[j]; + int pred_label = bottom_pred[j]; + CHECK_LT(pred_label, num_labels_); + if (ignore_labels_.find(gt_label) != ignore_labels_.end()) { + continue; + } + if (gt_label == pred_label) { + top_data[gt_label * 3]++; + } + top_data[gt_label * 3 + 1]++; + top_data[pred_label * 3 + 2]++; + } + bottom_pred += bottom[0]->offset(1); + bottom_gt += bottom[1]->offset(1); + } + // ParseEvaluate layer should not be used as a loss function. +} + +INSTANTIATE_CLASS(ParseEvaluateLayer); +REGISTER_LAYER_CLASS(ParseEvaluate); + +} // namespace caffe diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 7e086cbbd1e..0597b48aa1b 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -306,7 +306,7 @@ message ParamSpec { // NOTE // Update the next available ID when you add a new LayerParameter field. // -// LayerParameter next available layer-specific ID: 147 (last added: unpooling_param) +// LayerParameter next available layer-specific ID: 148 (last added: parse_evaluate_param) message LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the layer type @@ -381,6 +381,7 @@ message LayerParameter { optional MemoryDataParameter memory_data_param = 119; optional MVNParameter mvn_param = 120; optional NormalizeParameter norm_param = 145; + optional ParseEvaluateParameter parse_evaluate_param = 147; optional PoolingParameter pooling_param = 121; optional PowerParameter power_param = 122; optional PReLUParameter prelu_param = 131; @@ -881,6 +882,14 @@ message NormalizeParameter { optional float eps = 4 [default = 1e-10]; } +// Message that stores parameters used by ParseEvaluateLayer +message ParseEvaluateParameter { + // Number of total labels. Must provide. + optional int32 num_labels = 1; + // Ignore evaluating following labels. + repeated int32 ignore_label = 2; +} + message PoolingParameter { enum PoolMethod { MAX = 0; From 12ba1cd36a36e5c89fc1c7234c436602187cd7b4 Mon Sep 17 00:00:00 2001 From: max argus Date: Fri, 19 Feb 2016 13:55:14 +0000 Subject: [PATCH 446/446] Added eval_type to solver. --- include/caffe/solver.hpp | 1 + src/caffe/proto/caffe.proto | 5 +- src/caffe/solver.cpp | 100 ++++++++++++++++++++++++++++++++++-- 3 files changed, 102 insertions(+), 4 deletions(-) diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp index 38259edad9f..9acba0abc24 100644 --- a/include/caffe/solver.hpp +++ b/include/caffe/solver.hpp @@ -103,6 +103,7 @@ class Solver { // The test routine void TestAll(); void Test(const int test_net_id = 0); + void TestSegmentation(const int test_net_id = 0); virtual void SnapshotSolverState(const string& model_filename) = 0; virtual void RestoreSolverStateFromHDF5(const string& state_file) = 0; virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0; diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index 0597b48aa1b..2daf238f1ad 100644 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -98,7 +98,7 @@ message NetParameter { // NOTE // Update the next available ID when you add a new SolverParameter field. // -// SolverParameter next available ID: 41 (last added: type) +// SolverParameter next available ID: 42 (last added: eval_type) message SolverParameter { ////////////////////////////////////////////////////////////////////////////// // Specifying the train and test networks @@ -228,6 +228,9 @@ message SolverParameter { // If false, don't save a snapshot after training finishes. optional bool snapshot_after_train = 28 [default = true]; +// Evaluation type + optional string eval_type = 41 [default = "classification"]; + // DEPRECATED: old solver enum types, use string instead enum SolverType { SGD = 0; diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp index ece3913e88a..b3d8afcc047 100644 --- a/src/caffe/solver.cpp +++ b/src/caffe/solver.cpp @@ -325,9 +325,15 @@ void Solver::Solve(const char* resume_file) { template void Solver::TestAll() { for (int test_net_id = 0; - test_net_id < test_nets_.size() && !requested_early_exit_; - ++test_net_id) { - Test(test_net_id); + test_net_id < test_nets_.size() && !requested_early_exit_; + ++test_net_id) { + if (param_.eval_type() == "classification") { + Test(test_net_id); + } else if (param_.eval_type() == "segmentation") { + TestSegmentation(test_net_id); + } else { + LOG(FATAL) << "Unknown evaluation type: " << param_.eval_type(); + } } } @@ -406,6 +412,94 @@ void Solver::Test(const int test_net_id) { } } +template +void Solver::TestSegmentation(const int test_net_id) { + LOG(INFO) << "Iteration " << iter_ + << ", Testing net (#" << test_net_id << ")"; + CHECK_NOTNULL(test_nets_[test_net_id].get())-> + ShareTrainedLayersWith(net_.get()); + vector > > label_stats; + vector*> bottom_vec; + const shared_ptr >& test_net = test_nets_[test_net_id]; + Dtype loss = 0; + for (int i = 0; i < param_.test_iter(test_net_id); ++i) { + Dtype iter_loss; + const vector*>& result = + test_net->Forward(bottom_vec, &iter_loss); + if (param_.test_compute_loss()) { + loss += iter_loss; + } + if (result.size() == 0) { + continue; + } + if (i == 0) { + for (int j = 0; j < result.size(); ++j) { + shared_ptr > label_stat(new Blob()); + label_stats.push_back(label_stat); + label_stat->Reshape(1, result[j]->channels(), + result[j]->height(), result[j]->width()); + // copy the result + caffe_copy(result[j]->count(), result[j]->cpu_data(), + label_stat->mutable_cpu_data()); + } + } else { + // add the result + for (int j = 0; j < result.size(); ++j) { + caffe_axpy(result[j]->count(), Dtype(1), result[j]->cpu_data(), + label_stats[j]->mutable_cpu_data()); + } + } + } + if (param_.test_compute_loss()) { + loss /= param_.test_iter(test_net_id); + LOG(INFO) << "Test loss: " << loss; + } + for (int i = 0; i < label_stats.size(); ++i) { + const int output_blob_index = test_net->output_blob_indices()[i]; + const string& output_name = test_net->blob_names()[output_blob_index]; + const Dtype* label_stat_data = label_stats[i]->cpu_data(); + const int channels = label_stats[i]->channels(); + // get sum infomation + Dtype sum_gtpred = 0; + Dtype sum_gt = 0; + for (int c = 0; c < channels; ++c) { + sum_gtpred += label_stat_data[c*3]; + sum_gt += label_stat_data[c*3+1]; + } + if (sum_gt > 0) { + // compute accuracy for segmentation + Dtype per_pixel_acc = sum_gtpred / sum_gt; + Dtype per_label_acc = 0; + Dtype iou, iou_acc = 0, weighted_iou_acc = 0; + int num_valid_labels = 0; + for (int c = 0; c < channels; ++c) { + if (label_stat_data[1] != 0) { + per_label_acc += label_stat_data[0] / label_stat_data[1]; + ++num_valid_labels; + } + if (label_stat_data[1] + label_stat_data[2] != 0) { + iou = label_stat_data[0] / (label_stat_data[1] + label_stat_data[2] + - label_stat_data[0]); + iou_acc += iou; + weighted_iou_acc += iou * label_stat_data[1] / sum_gt; + } + label_stat_data += label_stats[i]->offset(0, 1); + } + LOG(INFO) << " Test net output #" << i << " " << output_name + << ": per_pixel_acc = " << per_pixel_acc; + LOG(INFO) << " Test net output #" << i << " " << output_name + << ": per_label_acc = " << per_label_acc / num_valid_labels; + LOG(INFO) << " Test net output #" << i << " " << output_name + << ": iou_acc = " << iou_acc / num_valid_labels; + LOG(INFO) << " Test net output #" << i << " " << output_name + << ": weighted_iou_acc = " << weighted_iou_acc; + } else { + LOG(INFO) << " Test net output #" << i << " " << output_name + << ": no valid labels!"; + } + } +} + template void Solver::Snapshot() { CHECK(Caffe::root_solver());

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