Compute Library v52.0.1

Author: Jenkins

CMakeLists.txt

@@ -24,7 +24,7 @@ cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
 
 project(
   ArmCompute
-  VERSION 52.0.0
+  VERSION 52.0.1
   DESCRIPTION
   "The Compute Library is a collection of low-level machine learning functions \
    optimized for Arm® Cortex®-A, Arm® Neoverse™ CPU and Arm® Mali™ GPU \

README.md

@@ -14,7 +14,7 @@
  <img src="https://raw.githubusercontent.com/ARM-software/ComputeLibrary/gh-pages/ACL_logo.png"/><br><br>
 </div>
 
-# Compute Library ![](https://img.shields.io/badge/latest_release-52.0.0-green)
+# Compute Library ![](https://img.shields.io/badge/latest_release-52.0.1-green)
 
 
 The Compute Library is a collection of low-level machine learning functions optimized for Arm® Cortex®-A, Arm® Neoverse™ and Arm® Mali™ GPUs architectures.<br>
@@ -42,7 +42,7 @@ Key Features:
 <br>
 
 ## Documentation
-[![Documentation](https://img.shields.io/badge/documentation-52.0.0-green)](https://artificial-intelligence.sites.arm.com/computelibrary/v52.0.0/index.xhtml)
+[![Documentation](https://img.shields.io/badge/documentation-52.0.1-green)](https://artificial-intelligence.sites.arm.com/computelibrary/v52.0.1/index.xhtml)
 
 > Note: The documentation includes the reference API, changelogs, build guide, contribution guide, errata, etc.
 
@@ -55,22 +55,22 @@ All the binaries can be downloaded from [here](https://github.com/ARM-software/C
 
 | Platform       | Operating System | Release archive (Download) |
 | -------------- | ---------------- | -------------------------- |
-| Raspberry Pi 4 | Linux® 32bit      | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-linux-armv7a-cpu-bin.tar.gz) |
-| Raspberry Pi 4 | Linux® 64bit      | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-linux-aarch64-cpu-bin.tar.gz) |
-| Odroid N2      | Linux® 64bit      | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-linux-aarch64-cpu-bin.tar.gz) [![](https://img.shields.io/badge/build-neon+cl-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-linux-aarch64-cpu-gpu-bin.tar.gz) |
-| HiKey960       | Linux® 64bit      | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-linux-aarch64-cpu-bin.tar.gz) [![](https://img.shields.io/badge/build-neon+cl-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-linux-aarch64-cpu-gpu-bin.tar.gz) |
+| Raspberry Pi 4 | Linux® 32bit      | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-linux-armv7a-cpu-bin.tar.gz) |
+| Raspberry Pi 4 | Linux® 64bit      | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-linux-aarch64-cpu-bin.tar.gz) |
+| Odroid N2      | Linux® 64bit      | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-linux-aarch64-cpu-bin.tar.gz) [![](https://img.shields.io/badge/build-neon+cl-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-linux-aarch64-cpu-gpu-bin.tar.gz) |
+| HiKey960       | Linux® 64bit      | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-linux-aarch64-cpu-bin.tar.gz) [![](https://img.shields.io/badge/build-neon+cl-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-linux-aarch64-cpu-gpu-bin.tar.gz) |
 
 <br>
 
 | Architecture | Operating System | Release archive (Download) |
 | ------------ | ---------------- | -------------------------- |
-| armv7        | Linux®            | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-linux-armv7a-cpu-bin.tar.gz) [![](https://img.shields.io/badge/build-neon+cl-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-linux-armv7a-cpu-gpu-bin.tar.gz) |
-| arm64-v8a    | Android™          | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-android-aarch64-cpu-bin.tar.gz) [![](https://img.shields.io/badge/build-neon+cl-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-android-aarch64-cpu-gpu-bin.tar.gz) |
-| arm64-v8a    | Linux®            | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-linux-aarch64-cpu-bin.tar.gz) [![](https://img.shields.io/badge/build-neon+cl-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.0/arm_compute-v52.0.0-linux-aarch64-cpu-gpu-bin.tar.gz) |
+| armv7        | Linux®            | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-linux-armv7a-cpu-bin.tar.gz) [![](https://img.shields.io/badge/build-neon+cl-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-linux-armv7a-cpu-gpu-bin.tar.gz) |
+| arm64-v8a    | Android™          | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-android-aarch64-cpu-bin.tar.gz) [![](https://img.shields.io/badge/build-neon+cl-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-android-aarch64-cpu-gpu-bin.tar.gz) |
+| arm64-v8a    | Linux®            | [![](https://img.shields.io/badge/build-neon-orange)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-linux-aarch64-cpu-bin.tar.gz) [![](https://img.shields.io/badge/build-neon+cl-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/download/v52.0.1/arm_compute-v52.0.1-linux-aarch64-cpu-gpu-bin.tar.gz) |
 
 <br>
 
-Please refer to the following link for more pre-built binaries: [![](https://img.shields.io/badge/v52.0.0-bins-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/tag/v52.0.0)
+Please refer to the following link for more pre-built binaries: [![](https://img.shields.io/badge/v52.0.1-bins-yellowgreen)](https://github.com/ARM-software/ComputeLibrary/releases/tag/v52.0.1)
 
 Pre-build binaries are generated with the following security / good coding practices related flags:
 > -Wall, -Wextra, -Wformat=2, -Winit-self, -Wstrict-overflow=2, -Wswitch-default, -Woverloaded-virtual, -Wformat-security, -Wctor-dtor-privacy, -Wsign-promo, -Weffc++, -pedantic, -fstack-protector-strong
@@ -113,13 +113,13 @@ Pre-build binaries are generated with the following security / good coding pract
 
 ## Experimental builds
 
-**⚠ Important** Bazel and CMake builds are experimental CPU only builds, please see the [documentation](https://artificial-intelligence.sites.arm.com/computelibrary/v52.0.0/how_to_build.xhtml) for more details.
+**⚠ Important** Bazel and CMake builds are experimental CPU only builds, please see the [documentation](https://artificial-intelligence.sites.arm.com/computelibrary/v52.0.1/how_to_build.xhtml) for more details.
 
 <br>
 
 ## How to contribute
 
-Contributions to the Compute Library are more than welcome. If you are interested on contributing, please have a look at our [how to contribute guidelines](https://artificial-intelligence.sites.arm.com/computelibrary/v52.0.0/contribution_guidelines.xhtml).
+Contributions to the Compute Library are more than welcome. If you are interested on contributing, please have a look at our [how to contribute guidelines](https://artificial-intelligence.sites.arm.com/computelibrary/v52.0.1/contribution_guidelines.xhtml).
 
 ### Developer Certificate of Origin (DCO)
 Before the Compute Library accepts your contribution, you need to certify its origin and give us your permission. To manage this process we use the Developer Certificate of Origin (DCO) V1.1 (https://developercertificate.org/)

SConscript

@@ -33,10 +33,10 @@ import codecs
 import platform
 import SCons
 
-VERSION = "v52.0.0"
+VERSION = "v52.0.1"
 LIBRARY_VERSION_MAJOR = 52
 LIBRARY_VERSION_MINOR = 0
-LIBRARY_VERSION_PATCH = 0
+LIBRARY_VERSION_PATCH = 1
 SONAME_VERSION = str(LIBRARY_VERSION_MAJOR) + "." + str(LIBRARY_VERSION_MINOR) + "." + str(LIBRARY_VERSION_PATCH)
 
 Import('env')

arm_compute/core/utils/misc/ShapeCalculator.h

@@ -575,14 +575,14 @@ inline TensorShape compute_deconvolution_output_shape(const std::pair<unsigned i
 
 /** Calculate the im2col output shape of a tensor
  *
- * @param[in] input           Input tensor info
- * @param[in] kernel_dims     The kernel dimensions (width and height).
- * @param[in] conv_info       Contains padding and stride information
- * @param[in] has_bias        In case biases are provided expands the matrix with 1
- * @param[in] dilation        Dilation, in elements, across x and y
- * @param[in] batch_size_on_z True if batch size is on z axis
- * @param[in] num_groups      (Optional)  Number of groups when performing a grouped convolution
- * @param[in] input_pad_right (Optional) When fast-math is selected, per element padding for the im2col matrix may be necessary
+ * @param[in] input             Input tensor info
+ * @param[in] kernel_dims       The kernel dimensions (width and height).
+ * @param[in] conv_info         Contains padding and stride information
+ * @param[in] has_bias          In case biases are provided expands the matrix with 1
+ * @param[in] dilation          Dilation, in elements, across x and y
+ * @param[in] batch_size_on_z   True if batch size is on z axis
+ * @param[in] num_groups        (Optional) Number of groups when performing a grouped convolution
+ * @param[in] channel_pad_right (Optional) Amount of padding applied to the channel dimension
  *
  * @return the calculated shape
  */
@@ -592,8 +592,8 @@ inline TensorShape compute_im2col_conv_shape(const ITensorInfo   *input,
                                              bool                 has_bias,
                                              const Size2D        &dilation,
                                              bool                 batch_size_on_z,
-                                             unsigned int         num_groups      = 1,
-                                             unsigned int         input_pad_right = 0)
+                                             unsigned int         num_groups        = 1,
+                                             unsigned int         channel_pad_right = 0)
 {
     // The output shape will be the 3D shape [ out_channels * kernel_area, num_elems_per_out_channel, batches ]                           if batch_size_on_z == true
     //                       or the 4D shape [ out_channels * kernel_area / num_groups, num_elems_per_out_channel, num_groups, batches ]  if batch_size_on_z == false
@@ -611,7 +611,7 @@ inline TensorShape compute_im2col_conv_shape(const ITensorInfo   *input,
 
     std::pair<unsigned int, unsigned int> out_dims = scaled_dimensions(
         output_shape[width_idx], output_shape[height_idx], kernel_dims.width, kernel_dims.height, conv_info, dilation);
-    output_shape.set(0, ((output_shape[channel_idx] + input_pad_right) / num_groups * kernel_dims.area() +
+    output_shape.set(0, ((output_shape[channel_idx] + channel_pad_right) / num_groups * kernel_dims.area() +
                          (has_bias ? 1 : 0))); // NOLINT
     output_shape.set(1, (out_dims.first * out_dims.second));
     if (batch_size_on_z && output_shape.num_dimensions() >= 3)

docs/Doxyfile

@@ -60,7 +60,7 @@ PROJECT_NAME           = "Compute Library"
 # could be handy for archiving the generated documentation or if some version
 # control system is used.
 
-PROJECT_NUMBER         = 52.0.0
+PROJECT_NUMBER         = 52.0.1
 
 # Using the PROJECT_BRIEF tag one can provide an optional one line description
 # for a project that appears at the top of each page and should give viewer a