diff --git a/README.md b/README.md index 46a22bb17e..abf8fe59b1 100644 --- a/README.md +++ b/README.md @@ -122,7 +122,7 @@ Please refer to [install.md](https://mmaction2.readthedocs.io/en/latest/get_star ```shell conda create --name openmmlab python=3.8 -y -conda activate open-mmlab +conda activate openmmlab conda install pytorch torchvision -c pytorch # This command will automatically install the latest version PyTorch and cudatoolkit, please check whether they match your environment. pip install -U openmim mim install mmengine diff --git a/configs/_base_/models/tin_r50.py b/configs/_base_/models/tin_r50.py index 4a0dbbf6a0..cc27704f07 100644 --- a/configs/_base_/models/tin_r50.py +++ b/configs/_base_/models/tin_r50.py @@ -1,7 +1,9 @@ # model settings preprocess_cfg = dict( - mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], format_shape='NCHW') + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + format_shape='NCHW') model = dict( type='Recognizer2D', diff --git a/configs/recognition/tin/README.md b/configs/recognition/tin/README.md index 1e3db9145b..a250388fc3 100644 --- a/configs/recognition/tin/README.md +++ b/configs/recognition/tin/README.md @@ -34,7 +34,7 @@ For a long time, the vision community tries to learn the spatio-temporal represe | frame sampling strategy | resolution | gpus | backbone | pretrain | top1 acc | top5 acc | testing protocol | inference time(video/s) | gpu_mem(M) | config | ckpt | log | | :---------------------: | :------------: | :--: | :------: | :-------------: | :------: | :------: | :--------------: | :---------------------: | :--------: | :-----------------------: | :---------------------: | :---------------------: | -| 1x1x8 | short-side 256 | 8x4 | ResNet50 | TSM-Kinetics400 | 71.77 | 90.36 | 8 clips x 1 crop | x | 6185 | [config](/configs/recognition/tin/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv2-rgb.py) | [ckpt](https://download.openmmlab.com/mmaction/v1.0/recognition/tin/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb_20220913-7f10d0c0.pth) | [log](https://download.openmmlab.com/mmaction/v1.0/recognition/tin/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb.log) | +| 1x1x8 | short-side 256 | 8x4 | ResNet50 | TSM-Kinetics400 | 71.86 | 90.44 | 8 clips x 1 crop | x | 6185 | [config](/configs/recognition/tin/tin_imagenet-pretrained-r50_8xb6-1x1x8-40e_sthv2-rgb.py) | [ckpt](https://download.openmmlab.com/mmaction/v1.0/recognition/tin/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb_20220913-7f10d0c0.pth) | [log](https://download.openmmlab.com/mmaction/v1.0/recognition/tin/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb.log) | Here, we use `finetune` to indicate that we use [TSM model](https://download.openmmlab.com/mmaction/v1.0/v1.0/recognition/tsm/tsm_imagenet-pretrained-r50_8xb16-1x1x8-50e_kinetics400-rgb/tsm_imagenet-pretrained-r50_8xb16-1x1x8-50e_kinetics400-rgb_20220831-64d69186.pth) trained on Kinetics-400 to finetune the TIN model on Kinetics-400. diff --git a/configs/recognition/tin/metafile.yml b/configs/recognition/tin/metafile.yml index 6f69c73fda..7954bd90b3 100644 --- a/configs/recognition/tin/metafile.yml +++ b/configs/recognition/tin/metafile.yml @@ -66,8 +66,8 @@ Models: Results: - Dataset: Kinetics-400 Metrics: - Top 1 Accuracy: 71.77 - Top 5 Accuracy: 90.36 + Top 1 Accuracy: 71.86 + Top 5 Accuracy: 90.44 Task: Action Recognition Training Log: https://download.openmmlab.com/mmaction/v1.0/recognition/tin/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb.log Weights: https://download.openmmlab.com/mmaction/v1.0/recognition/tin/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb/tin_kinetics400-pretrained-tsm-r50_1x1x8-50e_kinetics400-rgb_20220913-7f10d0c0.pth diff --git a/dataset-index.yml b/dataset-index.yml index 57f2f4f62a..2637f88d4b 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -1,39 +1,40 @@ +openxlab: true kinetics400: - dataset: Kinetics-400 + dataset: OpenMMLab/Kinetics-400 download_root: data data_root: data/kinetics400 - script: tools/data/kinetics/k400_preprocess.sh + script: tools/data/kinetics/preprocess_k400.sh kinetics600: - dataset: Kinetics600 + dataset: OpenMMLab/Kinetics600 download_root: data data_root: data/kinetics600 - script: tools/data/kinetics/k600_preprocess.sh + script: tools/data/kinetics/preprocess_k600.sh kinetics700: - dataset: Kinetics_700 + dataset: OpenMMLab/Kinetics_700 download_root: data data_root: data/kinetics700 - script: tools/data/kinetics/k700_preprocess.sh + script: tools/data/kinetics/preprocess_k700.sh sthv2: - dataset: sthv2 + dataset: OpenDataLab/sthv2 download_root: data data_root: data/sthv2 script: tools/data/sthv2/preprocess.sh ucf-101: - dataset: UCF101 + dataset: OpenDataLab/UCF101 download_root: data data_root: data/ucf101 finegym: - dataset: FineGym + dataset: OpenDataLab/FineGym download_root: data data_root: data/gym diving48: - dataset: diving48 + dataset: OpenDataLab/diving48 download_root: data data_root: data/diving48 script: tools/data/diving48/preprocess.sh diff --git a/docs/en/get_started/quick_run.md b/docs/en/get_started/quick_run.md index ab7bd569c3..d16a2e53f8 100644 --- a/docs/en/get_started/quick_run.md +++ b/docs/en/get_started/quick_run.md @@ -19,7 +19,7 @@ Run the following command in the root directory of MMAction2: ```shell python demo/demo_inferencer.py demo/demo.mp4 \ - --rec tsn --print-result \ + --rec tsn --print-result --show \ --label-file tools/data/kinetics/label_map_k400.txt ``` diff --git a/docs/zh_cn/get_started/quick_run.md b/docs/zh_cn/get_started/quick_run.md index 00e984c4b2..f09c4a604f 100644 --- a/docs/zh_cn/get_started/quick_run.md +++ b/docs/zh_cn/get_started/quick_run.md @@ -19,7 +19,7 @@ ```shell python demo/demo_inferencer.py demo/demo.mp4 \ - --rec tsn --print-result \ + --rec tsn --print-result --show \ --label-file tools/data/kinetics/label_map_k400.txt ``` diff --git a/mmaction/models/backbones/resnet_tin.py b/mmaction/models/backbones/resnet_tin.py index b8ff3659f0..0958546926 100644 --- a/mmaction/models/backbones/resnet_tin.py +++ b/mmaction/models/backbones/resnet_tin.py @@ -325,6 +325,9 @@ def init_structure(self): if len(self.non_local_cfg) != 0: self.make_non_local() + def _get_wrap_prefix(self): + return ['.net2'] + def make_temporal_interlace(self): """Make temporal interlace for some layers.""" num_segment_list = [self.num_segments] * 4 @@ -365,6 +368,3 @@ def make_block_interlace(stage, num_segments, shift_div): self.shift_div) self.layer4 = make_block_interlace(self.layer4, num_segment_list[3], self.shift_div) - - def init_weights(self): - pass diff --git a/mmaction/models/backbones/resnet_tsm.py b/mmaction/models/backbones/resnet_tsm.py index 0079c96cb7..a2dcaf2939 100644 --- a/mmaction/models/backbones/resnet_tsm.py +++ b/mmaction/models/backbones/resnet_tsm.py @@ -305,6 +305,9 @@ def make_non_local(self): self.num_segments, self.non_local_cfg) + def _get_wrap_prefix(self): + return ['.net', '.block'] + def load_original_weights(self, logger): """Load weights from original checkpoint, which required converting keys.""" @@ -317,7 +320,7 @@ def load_original_weights(self, logger): for name, module in self.named_modules(): # convert torchvision keys ori_name = name - for wrap_prefix in ['.net', '.block']: + for wrap_prefix in self._get_wrap_prefix(): if wrap_prefix in ori_name: ori_name = ori_name.replace(wrap_prefix, '') wrapped_layers_map[ori_name] = name @@ -352,6 +355,7 @@ def load_original_weights(self, logger): if layer_name in wrapped_layers_map: wrapped_name = param_name.replace( layer_name, wrapped_layers_map[layer_name]) + print(f'wrapped_name {wrapped_name}') state_dict_torchvision[ wrapped_name] = state_dict_torchvision.pop(param_name) diff --git a/projects/gesture_recognition/README.md b/projects/gesture_recognition/README.md index 47ca04e472..f3d097c869 100644 --- a/projects/gesture_recognition/README.md +++ b/projects/gesture_recognition/README.md @@ -14,7 +14,7 @@ Hand detection results on OneHand10K validation dataset | Config | Input Size | bbox mAP | bbox mAP 50 | bbox mAP 75 | ckpt | log | | :------------------------------------------------------ | :--------: | :------: | :---------: | :---------: | :---------------------------------------------------: | :--------------------------------------------------: | -| [rtmpose_nano](/projects/gesture_recognition/configs/rtmdet-nano_8xb32-300e_multi-dataset-hand-320x320.py) | 320x320 | 0.8100 | 0.9870 | 0.9190 | [ckpt](https://download.openmmlab.com/mmaction/v1.0/projects/gesture_recognition/rtmdet-nano_8xb32-300e_multi-dataset-hand-320x320_20230524-f6ffed6a.pth) | [log](https://download.openmmlab.com/mmaction/v1.0/projects/gesture_recognition/rtmdet-nano_8xb32-300e_multi-dataset-hand-320x320.log) | +| [rtmdet_nano](/projects/gesture_recognition/configs/rtmdet-nano_8xb32-300e_multi-dataset-hand-320x320.py) | 320x320 | 0.8100 | 0.9870 | 0.9190 | [ckpt](https://download.openmmlab.com/mmaction/v1.0/projects/gesture_recognition/rtmdet-nano_8xb32-300e_multi-dataset-hand-320x320_20230524-f6ffed6a.pth) | [log](https://download.openmmlab.com/mmaction/v1.0/projects/gesture_recognition/rtmdet-nano_8xb32-300e_multi-dataset-hand-320x320.log) | ## Pose estimation stage diff --git a/tools/data/diving48/README.md b/tools/data/diving48/README.md index 02d19d5a62..0a2b68707f 100644 --- a/tools/data/diving48/README.md +++ b/tools/data/diving48/README.md @@ -21,10 +21,10 @@ For basic dataset information, you can refer to the official dataset [website](h ````{group-tab} Download by MIM MIM supports downloading from OpenDataLab and preprocessing Diving48 dataset with one command line. ```Bash -# install OpenDataLab CLI tools -pip install -U opendatalab -# log in OpenDataLab -odl login +# install OpenXlab CLI tools +pip install -U openxlab +# log in OpenXLab +openxlab login # download and preprocess by MIM mim download mmaction2 --dataset diving48 ``` diff --git a/tools/data/diving48/README_zh-CN.md b/tools/data/diving48/README_zh-CN.md index 825344039e..f6ebca9f2c 100644 --- a/tools/data/diving48/README_zh-CN.md +++ b/tools/data/diving48/README_zh-CN.md @@ -21,10 +21,10 @@ ````{group-tab} 使用 MIM 下载 # MIM 支持下载 Diving48 数据集。用户可以通过一行命令，从 OpenDataLab 进行下载，并进行预处理。 ```Bash -# 安装 OpenDataLab CLI 工具 -pip install -U opendatalab -# 登录 OpenDataLab -odl login +# 安装 OpenXLab CLI 工具 +pip install -U openxlab +# 登录 OpenXLab +openxlab login # 通过 MIM 进行数据集下载，预处理。注意这将花费较长时间 mim download mmaction2 --dataset diving48 ``` diff --git a/tools/data/diving48/preprocess.sh b/tools/data/diving48/preprocess.sh index 10d9e42044..ddd9a1de48 100644 --- a/tools/data/diving48/preprocess.sh +++ b/tools/data/diving48/preprocess.sh @@ -3,6 +3,6 @@ DOWNLOAD_DIR=$1 DATA_ROOT=$2 -cat $DOWNLOAD_DIR/diving48/raw/*.tar.gz.* | tar -xvz -C $(dirname $DATA_ROOT) +cat $DOWNLOAD_DIR/OpenDataLab___diving48/raw/*.tar.gz.* | tar -xvz -C $(dirname $DATA_ROOT) tar -xvf $DATA_ROOT/diving48.tar -C $(dirname $DATA_ROOT) rm $DATA_ROOT/diving48.tar diff --git a/tools/data/kinetics/README.md b/tools/data/kinetics/README.md index 257c650eba..bd96a5f364 100644 --- a/tools/data/kinetics/README.md +++ b/tools/data/kinetics/README.md @@ -38,10 +38,10 @@ All experiments on Kinetics in MMAction2 are based on this version, we recommend MIM supports downloading from OpenDataLab and preprocessing Kinetics-400/600/700 dataset with one command line. ```Bash -# install OpenDataLab CLI tools -pip install -U opendatalab -# log in OpenDataLab -odl login +# install OpenXlab CLI tools +pip install -U openxlab +# log in OpenXLab +openxlab login # download and preprocess Kinetics-400 by MIM. Note that this might take a long time. mim download mmaction2 --dataset kinetics400 # download and preprocess Kinetics-600 by MIM. Note that this might take a long time. diff --git a/tools/data/kinetics/README_zh-CN.md b/tools/data/kinetics/README_zh-CN.md index a0d2e858e1..6047617985 100644 --- a/tools/data/kinetics/README_zh-CN.md +++ b/tools/data/kinetics/README_zh-CN.md @@ -36,10 +36,10 @@ MMAction2 代码仓库中提供的 Kinetics 实验性能，都是基于这个版 # MIM 支持下载 Kinetics-400/600/700 数据集。用户可以通过一行命令，从 OpenDataLab 进行下载，并进行预处理。 ```Bash -# 安装 OpenDataLab CLI 工具 -pip install -U opendatalab -# 登录 OpenDataLab -odl login +# 安装 OpenXLab CLI 工具 +pip install -U openxlab +# 登录 OpenXLab +openxlab login # 通过 MIM 进行 Kinetics-400 数据集下载，预处理。注意这将花费较长时间 mim download mmaction2 --dataset kinetics400 # 通过 MIM 进行 Kinetics-600 数据集下载，预处理。注意这将花费较长时间 diff --git a/tools/data/kinetics/preprocess_k400.sh b/tools/data/kinetics/preprocess_k400.sh index 9f07885095..d48c166375 100644 --- a/tools/data/kinetics/preprocess_k400.sh +++ b/tools/data/kinetics/preprocess_k400.sh @@ -5,5 +5,5 @@ set -x DOWNLOAD_DIR=$1 DATA_ROOT=$2 -cat $DOWNLOAD_DIR/Kinetics-400/raw/*.tar.gz* | tar -xvz -C $(dirname $DATA_ROOT) +cat $DOWNLOAD_DIR/OpenMMLab___Kinetics-400/raw/*.tar.gz* | tar -xvz -C $(dirname $DATA_ROOT) mv $(dirname $DATA_ROOT)/Kinetics-400 $DATA_ROOT diff --git a/tools/data/kinetics/preprocess_k600.sh b/tools/data/kinetics/preprocess_k600.sh index 438297a620..e6264909cb 100644 --- a/tools/data/kinetics/preprocess_k600.sh +++ b/tools/data/kinetics/preprocess_k600.sh @@ -5,5 +5,5 @@ set -x DOWNLOAD_DIR=$1 DATA_ROOT=$2 -cat $DOWNLOAD_DIR/Kinetics600/raw/*.tar.gz* | tar -xvz -C $(dirname $DATA_ROOT) +cat $DOWNLOAD_DIR/OpenMMLab___Kinetics600/raw/*.tar.gz* | tar -xvz -C $(dirname $DATA_ROOT) mv $(dirname $DATA_ROOT)/Kinetics600 $DATA_ROOT diff --git a/tools/data/kinetics/preprocess_k700.sh b/tools/data/kinetics/preprocess_k700.sh index 930bf8577a..8e48cdcd47 100644 --- a/tools/data/kinetics/preprocess_k700.sh +++ b/tools/data/kinetics/preprocess_k700.sh @@ -5,5 +5,5 @@ set -x DOWNLOAD_DIR=$1 DATA_ROOT=$2 -cat $DOWNLOAD_DIR/Kinetics_700/raw/*.tar.gz* | tar -xvz -C $(dirname $DATA_ROOT) +cat $DOWNLOAD_DIR/OpenMMLab___Kinetics_700/raw/*.tar.gz* | tar -xvz -C $(dirname $DATA_ROOT) mv $(dirname $DATA_ROOT)/Kinetics_700 $DATA_ROOT diff --git a/tools/data/kinetics710/README.md b/tools/data/kinetics710/README.md index 76a239fbf5..4dd52b393f 100644 --- a/tools/data/kinetics710/README.md +++ b/tools/data/kinetics710/README.md @@ -25,10 +25,10 @@ Before we start, please make sure that the directory is located at `$MMACTION2`. Kinetics-710 is a video benchmark based on Kinetics-400/600/700, which merges the training set of these Kinetics datasets, and deletes the repeated videos according to Youtube IDs. MMAction2 provides an annotation file based on the Kinetics-400/600/700 on [OpenDataLab](https://opendatalab.com/). So we suggest you download Kinetics-400/600/700 first from OpenDataLab by [MIM](https://github.com/open-mmlab/mim). ```shell -# install OpenDataLab CLI tools -pip install -U opendatalab -# log in OpenDataLab -odl login +# install OpenXlab CLI tools +pip install -U openxlab +# log in OpenXLab +openxlab login # download Kinetics-400/600/700, note that this might take a long time. mim download mmaction2 --dataset kinetics400 mim download mmaction2 --dataset kinetics600 diff --git a/tools/data/kinetics710/README_zh-CN.md b/tools/data/kinetics710/README_zh-CN.md index b428be43df..650a05c7d5 100644 --- a/tools/data/kinetics710/README_zh-CN.md +++ b/tools/data/kinetics710/README_zh-CN.md @@ -23,10 +23,10 @@ Kinetics-710 是基于 Kinetics-400/600/700 的视频数据集，它合并了这些 Kinetics 数据集的训练集，并根据 Youtube ID 删除了重复的视频。MMAction2 提供了一个基于 Kinetics-400/600/700 的 OpenDataLab 版本的标注文件，你可以通过 [MIM](https://github.com/open-mmlab/mim) 从 OpenDataLab 下载。 ```shell -# 安装 OpenDataLab CLI 工具 -pip install -U opendatalab -# 登录 OpenDataLab -odl login +# 安装 OpenXLab CLI 工具 +pip install -U openxlab +# 登录 OpenXLab +openxlab login # 下载 Kinetics-400/600/700，注意这可能需要很长时间。 mim download mmaction2 --dataset kinetics400 mim download mmaction2 --dataset kinetics600 diff --git a/tools/data/sthv2/README.md b/tools/data/sthv2/README.md index 5e05e6ff90..5c6ee953b4 100644 --- a/tools/data/sthv2/README.md +++ b/tools/data/sthv2/README.md @@ -22,10 +22,10 @@ For basic dataset information, you can refer to the dataset [website](https://de ````{group-tab} Download by MIM MIM supports downloading from OpenDataLab and preprocessing Something-Something V2 dataset with one command line. ```Bash -# install OpenDataLab CLI tools -pip install -U opendatalab -# log in OpenDataLab -odl login +# install OpenXlab CLI tools +pip install -U openxlab +# log in OpenXLab +openxlab login # download and preprocess by MIM mim download mmaction2 --dataset sthv2 ``` diff --git a/tools/data/sthv2/README_zh-CN.md b/tools/data/sthv2/README_zh-CN.md index 0bc5baf3fc..83647eb735 100644 --- a/tools/data/sthv2/README_zh-CN.md +++ b/tools/data/sthv2/README_zh-CN.md @@ -22,10 +22,10 @@ ````{group-tab} 使用 MIM 下载 # MIM 支持下载 Something-Something V2 数据集。用户可以通过一行命令，从 OpenDataLab 进行下载，并进行预处理。 ```Bash -# 安装 OpenDataLab CLI 工具 -pip install -U opendatalab -# 登录 OpenDataLab -odl login +# 安装 OpenXLab CLI 工具 +pip install -U openxlab +# 登录 OpenXLab +openxlab login # 通过 MIM 进行数据集下载，预处理。注意这将花费较长时间 mim download mmaction2 --dataset sthv2 ``` diff --git a/tools/data/sthv2/preprocss.sh b/tools/data/sthv2/preprocss.sh index 440a3d42ba..39b574d317 100644 --- a/tools/data/sthv2/preprocss.sh +++ b/tools/data/sthv2/preprocss.sh @@ -3,6 +3,6 @@ DOWNLOAD_DIR=$1 DATA_ROOT=$2 -cat $DOWNLOAD_DIR/sthv2/raw/*.tar.gz | tar -xvz -C $(dirname $DATA_ROOT) +cat $DOWNLOAD_DIR/OpenDataLab___sthv2/raw/*.tar.gz | tar -xvz -C $(dirname $DATA_ROOT) tar -xvf $DATA_ROOT/sthv2.tar -C $(dirname $DATA_ROOT) rm $DATA_ROOT/sthv2.tar