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add model TSM

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authors:
- name: "MMAction2 Contributors"
title: "OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark"
date-released: 2020-07-21
url: "https://github.com/open-mmlab/mmaction2"
license: Apache-2.0
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include mmaction/.mim/model-index.yml
recursive-include mmaction/.mim/configs *.py *.yml
recursive-include mmaction/.mim/tools *.sh *.py
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# 训练方法
运行train.sh脚本进行训练
run_pretraining.sh脚本为FlagPerf使用
# 原README.md
<div align="center">
<img src="http://10.0.53.25:9090/cmcc-ailab/tsm/raw/master/resources/mmaction2_logo.png" width="600"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
[![Documentation](https://readthedocs.org/projects/mmaction2/badge/?version=latest)](https://mmaction2.readthedocs.io/en/latest/)
[![actions](https://github.com/open-mmlab/mmaction2/workflows/build/badge.svg)](https://github.com/open-mmlab/mmaction2/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmaction2/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmaction2)
[![PyPI](https://img.shields.io/pypi/v/mmaction2)](https://pypi.org/project/mmaction2/)
[![LICENSE](https://img.shields.io/github/license/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/blob/master/LICENSE)
[![Average time to resolve an issue](https://isitmaintained.com/badge/resolution/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/issues)
[![Percentage of issues still open](https://isitmaintained.com/badge/open/open-mmlab/mmaction2.svg)](https://github.com/open-mmlab/mmaction2/issues)
[📘Documentation](https://mmaction2.readthedocs.io/en/latest/) |
[🛠️Installation](https://mmaction2.readthedocs.io/en/latest/install.html) |
[👀Model Zoo](https://mmaction2.readthedocs.io/en/latest/modelzoo.html) |
[🆕Update News](https://mmaction2.readthedocs.io/en/latest/changelog.html) |
[🚀Ongoing Projects](https://github.com/open-mmlab/mmaction2/projects) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmaction2/issues/new/choose)
</div>
English | [简体中文](/README_zh-CN.md)
## Introduction
MMAction2 is an open-source toolbox for video understanding based on PyTorch.
It is a part of the [OpenMMLab](https://openmmlab.com/) project.
The master branch works with **PyTorch 1.5+**.
<div align="center">
<div style="float:left;margin-right:10px;">
<img src="http://10.0.53.25:9090/cmcc-ailab/tsm/raw/master/resources/mmaction2_overview.gif" width="380px"><br>
<p style="font-size:1.5vw;">Action Recognition Results on Kinetics-400</p>
</div>
<div style="float:right;margin-right:0px;">
<img src="https://user-images.githubusercontent.com/34324155/123989146-2ecae680-d9fb-11eb-916b-b9db5563a9e5.gif" width="380px"><br>
<p style="font-size:1.5vw;">Skeleton-based Action Recognition Results on NTU-RGB+D-120</p>
</div>
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/30782254/155710881-bb26863e-fcb4-458e-b0c4-33cd79f96901.gif" width="580px"/><br>
<p style="font-size:1.5vw;">Skeleton-based Spatio-Temporal Action Detection and Action Recognition Results on Kinetics-400</p>
</div>
<div align="center">
<img src="http://10.0.53.25:9090/cmcc-ailab/tsm/raw/master/resources/spatio-temporal-det.gif" width="800px"/><br>
<p style="font-size:1.5vw;">Spatio-Temporal Action Detection Results on AVA-2.1</p>
</div>
## Major Features
- **Modular design**: We decompose a video understanding framework into different components. One can easily construct a customized video understanding framework by combining different modules.
- **Support four major video understanding tasks**: MMAction2 implements various algorithms for multiple video understanding tasks, including action recognition, action localization, spatio-temporal action detection, and skeleton-based action detection. We support **27** different algorithms and **20** different datasets for the four major tasks.
- **Well tested and documented**: We provide detailed documentation and API reference, as well as unit tests.
## What's New
### 🌟 Preview of 1.x version
A brand new version of **MMAction2 v1.0.0rc0** was released in 01/09/2022:
- Unified interfaces of all components based on [MMEngine](https://github.com/open-mmlab/mmengine).
- Faster training and testing speed with complete support of mixed precision training.
- More flexible [architecture](https://mmaction2.readthedocs.io/en/1.x).
Find more new features in [1.x branch](https://github.com/open-mmlab/mmaction2/tree/1.x). Issues and PRs are welcome!
### 💎 Stable version
- (2022-03-04) We support **Multigrid** on Kinetics400, achieve 76.07% Top-1 accuracy and accelerate training speed.
- (2021-11-24) We support **2s-AGCN** on NTU60 XSub, achieve 86.06% Top-1 accuracy on joint stream and 86.89% Top-1 accuracy on bone stream respectively.
- (2021-10-29) We provide a demo for skeleton-based and rgb-based spatio-temporal detection and action recognition (demo/demo_video_structuralize.py).
- (2021-10-26) We train and test **ST-GCN** on NTU60 with 3D keypoint annotations, achieve 84.61% Top-1 accuracy (higher than 81.5% in the [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17135)).
- (2021-10-25) We provide a script(tools/data/skeleton/gen_ntu_rgbd_raw.py) to convert the NTU60 and NTU120 3D raw skeleton data to our format.
- (2021-10-25) We provide a [guide](https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/custom_dataset_training.md) on how to train PoseC3D with custom datasets, [bit-scientist](https://github.com/bit-scientist) authored this PR!
- (2021-10-16) We support **PoseC3D** on UCF101 and HMDB51, achieves 87.0% and 69.3% Top-1 accuracy with 2D skeletons only. Pre-extracted 2D skeletons are also available.
**Release**: v0.24.0 was released in 05/05/2022. Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
## Installation
MMAction2 depends on [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv), [MMDetection](https://github.com/open-mmlab/mmdetection) (optional), and [MMPose](https://github.com/open-mmlab/mmdetection)(optional).
Below are quick steps for installation.
Please refer to [install.md](docs/en/install.md) for more detailed instruction.
```shell
conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmcv-full
mim install mmdet # optional
mim install mmpose # optional
git clone https://github.com/open-mmlab/mmaction2.git
cd mmaction2
pip3 install -e .
```
## Get Started
Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMAction2.
There are also tutorials:
- [learn about configs](docs/en/tutorials/1_config.md)
- [finetuning models](docs/en/tutorials/2_finetune.md)
- [adding new dataset](docs/en/tutorials/3_new_dataset.md)
- [designing data pipeline](docs/en/tutorials/4_data_pipeline.md)
- [adding new modules](docs/en/tutorials/5_new_modules.md)
- [exporting model to onnx](docs/en/tutorials/6_export_model.md)
- [customizing runtime settings](docs/en/tutorials/7_customize_runtime.md)
A Colab tutorial is also provided. You may preview the notebook [here](demo/mmaction2_tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmaction2/blob/master/demo/mmaction2_tutorial.ipynb) on Colab.
## Supported Methods
<table style="margin-left:auto;margin-right:auto;font-size:1.3vw;padding:3px 5px;text-align:center;vertical-align:center;">
<tr>
<td colspan="5" style="font-weight:bold;">Action Recognition</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/c3d/README.md">C3D</a> (CVPR'2014)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsn/README.md">TSN</a> (ECCV'2016)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/i3d/README.md">I3D</a> (CVPR'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/i3d/README.md">I3D Non-Local</a> (CVPR'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/r2plus1d/README.md">R(2+1)D</a> (CVPR'2018)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/trn/README.md">TRN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsm/README.md">TSM</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsm/README.md">TSM Non-Local</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/slowonly/README.md">SlowOnly</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/slowfast/README.md">SlowFast</a> (ICCV'2019)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/csn/README.md">CSN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tin/README.md">TIN</a> (AAAI'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tpn/README.md">TPN</a> (CVPR'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/x3d/README.md">X3D</a> (CVPR'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/omnisource/README.md">OmniSource</a> (ECCV'2020)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition_audio/resnet/README.md">MultiModality: Audio</a> (ArXiv'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tanet/README.md">TANet</a> (ArXiv'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/timesformer/README.md">TimeSformer</a> (ICML'2021)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">Action Localization</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/ssn/README.md">SSN</a> (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/bsn/README.md">BSN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/bmn/README.md">BMN</a> (ICCV'2019)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">Spatio-Temporal Action Detection</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/acrn/README.md">ACRN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/ava/README.md">SlowOnly+Fast R-CNN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/ava/README.md">SlowFast+Fast R-CNN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/lfb/README.md">LFB</a> (CVPR'2019)</td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">Skeleton-based Action Recognition</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/stgcn/README.md">ST-GCN</a> (AAAI'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/2s-agcn/README.md">2s-AGCN</a> (CVPR'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/README.md">PoseC3D</a> (ArXiv'2021)</td>
<td></td>
<td></td>
</tr>
</table>
Results and models are available in the *README.md* of each method's config directory.
A summary can be found on the [**model zoo**](https://mmaction2.readthedocs.io/en/latest/recognition_models.html) page.
We will keep up with the latest progress of the community and support more popular algorithms and frameworks.
If you have any feature requests, please feel free to leave a comment in [Issues](https://github.com/open-mmlab/mmaction2/issues/19).
## Supported Datasets
<table style="margin-left:auto;margin-right:auto;font-size:1.3vw;padding:3px 5px;text-align:center;vertical-align:center;">
<tr>
<td colspan="4" style="font-weight:bold;">Action Recognition</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hmdb51/README.md">HMDB51</a> (<a href="https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/">Homepage</a>) (ICCV'2011)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ucf101/README.md">UCF101</a> (<a href="https://www.crcv.ucf.edu/research/data-sets/ucf101/">Homepage</a>) (CRCV-IR-12-01)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/activitynet/README.md">ActivityNet</a> (<a href="http://activity-net.org/">Homepage</a>) (CVPR'2015)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/kinetics/README.md">Kinetics-[400/600/700]</a> (<a href="https://deepmind.com/research/open-source/kinetics/">Homepage</a>) (CVPR'2017)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/sthv1/README.md">SthV1</a> (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/sthv2/README.md">SthV2</a> (<a href="https://developer.qualcomm.com/software/ai-datasets/something-something">Homepage</a>) (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/diving48/README.md">Diving48</a> (<a href="http://www.svcl.ucsd.edu/projects/resound/dataset.html">Homepage</a>) (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/jester/README.md">Jester</a> (<a href="https://developer.qualcomm.com/software/ai-datasets/jester">Homepage</a>) (ICCV'2019)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/mit/README.md">Moments in Time</a> (<a href="http://moments.csail.mit.edu/">Homepage</a>) (TPAMI'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/mmit/README.md">Multi-Moments in Time</a> (<a href="http://moments.csail.mit.edu/challenge_iccv_2019.html">Homepage</a>) (ArXiv'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hvu/README.md">HVU</a> (<a href="https://github.com/holistic-video-understanding/HVU-Dataset">Homepage</a>) (ECCV'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/omnisource/README.md">OmniSource</a> (<a href="https://kennymckormick.github.io/omnisource/">Homepage</a>) (ECCV'2020)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/gym/README.md">FineGYM</a> (<a href="https://sdolivia.github.io/FineGym/">Homepage</a>) (CVPR'2020)</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">Action Localization</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/thumos14/README.md">THUMOS14</a> (<a href="https://www.crcv.ucf.edu/THUMOS14/download.html">Homepage</a>) (THUMOS Challenge 2014)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/activitynet/README.md">ActivityNet</a> (<a href="http://activity-net.org/">Homepage</a>) (CVPR'2015)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">Spatio-Temporal Action Detection</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ucf101_24/README.md">UCF101-24*</a> (<a href="http://www.thumos.info/download.html">Homepage</a>) (CRCV-IR-12-01)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/jhmdb/README.md">JHMDB*</a> (<a href="http://jhmdb.is.tue.mpg.de/">Homepage</a>) (ICCV'2015)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ava/README.md">AVA</a> (<a href="https://research.google.com/ava/index.html">Homepage</a>) (CVPR'2018)</td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">Skeleton-based Action Recognition</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-FineGYM</a> (<a href="https://kennymckormick.github.io/posec3d/">Homepage</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-NTURGB+D</a> (<a href="https://kennymckormick.github.io/posec3d/">Homepage</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-UCF101</a> (<a href="https://kennymckormick.github.io/posec3d/">Homepage</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-HMDB51</a> (<a href="https://kennymckormick.github.io/posec3d/">Homepage</a>) (ArXiv'2021)</td>
</tr>
</table>
Datasets marked with * are not fully supported yet, but related dataset preparation steps are provided. A summary can be found on the [**Supported Datasets**](https://mmaction2.readthedocs.io/en/latest/supported_datasets.html) page.
## Benchmark
To demonstrate the efficacy and efficiency of our framework, we compare MMAction2 with some other popular frameworks and official releases in terms of speed. Details can be found in [benchmark](docs/en/benchmark.md).
## Data Preparation
Please refer to [data_preparation.md](docs/en/data_preparation.md) for a general knowledge of data preparation.
The supported datasets are listed in [supported_datasets.md](docs/en/supported_datasets.md)
## FAQ
Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.
## Projects built on MMAction2
Currently, there are many research works and projects built on MMAction2 by users from community, such as:
- Video Swin Transformer. [\[paper\]](https://arxiv.org/abs/2106.13230)[\[github\]](https://github.com/SwinTransformer/Video-Swin-Transformer)
- Evidential Deep Learning for Open Set Action Recognition, ICCV 2021 **Oral**. [\[paper\]](https://arxiv.org/abs/2107.10161)[\[github\]](https://github.com/Cogito2012/DEAR)
- Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 **Oral**. [\[paper\]](https://arxiv.org/abs/2103.17263)[\[github\]](https://github.com/xvjiarui/VFS)
etc., check [projects.md](docs/en/projects.md) to see all related projects.
## Contributing
We appreciate all contributions to improve MMAction2. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline.
## Acknowledgement
MMAction2 is an open-source project that is contributed by researchers and engineers from various colleges and companies.
We appreciate all the contributors who implement their methods or add new features and users who give valuable feedback.
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their new models.
## Citation
If you find this project useful in your research, please consider cite:
```BibTeX
@misc{2020mmaction2,
title={OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark},
author={MMAction2 Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmaction2}},
year={2020}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Projects in OpenMMLab
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
README_zh-CN.md 0 → 100644
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[👀模型库](https://mmaction2.readthedocs.io/zh_CN/latest/modelzoo.html) |
[🆕更新](https://mmaction2.readthedocs.io/en/latest/changelog.html) |
[🚀进行中项目](https://github.com/open-mmlab/mmaction2/projects) |
[🤔问题反馈](https://github.com/open-mmlab/mmaction2/issues/new/choose)
</div>
[English](/README.md) | 简体中文
## 简介
MMAction2 是一款基于 PyTorch 的视频理解开源工具箱，是 [OpenMMLab](https://openmmlab.com/) 项目的成员之一
主分支代码目前支持 **PyTorch 1.5 以上**的版本
<div align="center">
<div style="float:left;margin-right:10px;">
<img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/mmaction2_overview.gif" width="380px"><br>
<p style="font-size:1.5vw;">Kinetics-400 上的动作识别</p>
</div>
<div style="float:right;margin-right:0px;">
<img src="https://user-images.githubusercontent.com/34324155/123989146-2ecae680-d9fb-11eb-916b-b9db5563a9e5.gif" width="380px"><br>
<p style="font-size:1.5vw;">NTURGB+D-120 上的基于人体姿态的动作识别</p>
</div>
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/30782254/155710881-bb26863e-fcb4-458e-b0c4-33cd79f96901.gif" width="580px"/><br>
<p style="font-size:1.5vw;">Kinetics-400 上的基于 skeleton 的时空动作检测和动作识别</p>
</div>
<div align="center">
<img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/spatio-temporal-det.gif" width="800px"/><br>
<p style="font-size:1.5vw;">AVA-2.1 上的时空动作检测</p>
</div>
## 主要特性
- **模块化设计**：MMAction2 将统一的视频理解框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的视频理解模型
- **支持多种任务和数据集**：MMAction2 支持多种视频理解任务，包括动作识别，时序动作检测，时空动作检测以及基于人体姿态的动作识别，总共支持 **27** 种算法和 **20** 种数据集
- **详尽的单元测试和文档**：MMAction2 提供了详尽的说明文档，API 接口说明，全面的单元测试，以供社区参考
## 更新记录
### 🌟 1.x 预览版本
全新的 **MMAction2 v1.0.0rc0** 版本已经在 2022.09.01 发布：
- 基于 [MMEngine](https://github.com/open-mmlab/mmengine) 统一了各组件接口。
- 全面支持混合精度，训练测试速度更快。
- 更加灵活的[架构](https://mmaction2.readthedocs.io/en/1.x)。
欢迎在 [1.x branch](https://github.com/open-mmlab/mmaction2/tree/1.x) 发现更多的新特性。欢迎 issue 和 PR。
### 💎 稳定版本
- (2022-03-04) 在 K400 上支持 **Multigrid** 训练，达到 76.07% 的识别准确率并加快了训练速度。
- (2021-11-24) 在 NTU60 XSub 上支持 **2s-AGCN**， 在 joint stream 和 bone stream 上分别达到 86.06% 和 86.89% 的识别准确率。
- (2021-10-29) 支持基于 skeleton 模态和 rgb 模态的时空动作检测和行为识别 demo (demo/demo_video_structuralize.py)。
- (2021-10-26) 在 NTU60 3d 关键点标注数据集上训练测试 **STGCN**, 可达到 84.61% (高于 [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17135) 中的 81.5%) 的识别准确率。
- (2021-10-25) 提供将 NTU60 和 NTU120 的 3d 骨骼点数据转换成我们项目的格式的脚本(tools/data/skeleton/gen_ntu_rgbd_raw.py)。
- (2021-10-25) 提供使用自定义数据集训练 PoseC3D 的 [教程](https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/custom_dataset_training.md)，此 PR 由用户 [bit-scientist](https://github.com/bit-scientist) 完成！
- (2021-10-16) 在 UCF101, HMDB51 上支持 **PoseC3D**，仅用 2D 关键点就可分别达到 87.0% 和 69.3% 的识别准确率。两数据集的预提取骨架特征可以公开下载。
v0.24.0 版本已于 2022 年 5 月 5 日发布，可通过查阅[更新日志](/docs/en/changelog.md) 了解更多细节以及发布历史
## 安装
MMAction2 依赖 [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv), [MMDetection](https://github.com/open-mmlab/mmdetection)(可选), [MMPose](https://github.com/open-mmlab/mmpose)(可选)，以下是安装的简要步骤。
更详细的安装指南请参考 [install.md](docs/zh_cn/install.md)。
```shell
conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate open-mmlab
pip3 install openmim
mim install mmcv-full
mim install mmdet # 可选
mim install mmpose # 可选
git clone https://github.com/open-mmlab/mmaction2.git
cd mmaction2
pip3 install -e .
```
## 教程
请参考 [基础教程](/docs/zh_cn/getting_started.md) 了解 MMAction2 的基本使用。MMAction2也提供了其他更详细的教程:
- [如何编写配置文件](/docs/zh_cn/tutorials/1_config.md)
- [如何微调模型](/docs/zh_cn/tutorials/2_finetune.md)
- [如何增加新数据集](/docs/zh_cn/tutorials/3_new_dataset.md)
- [如何设计数据处理流程](/docs/zh_cn/tutorials/4_data_pipeline.md)
- [如何增加新模块](/docs/zh_cn/tutorials/5_new_modules.md)
- [如何导出模型为 onnx 格式](/docs/zh_cn/tutorials/6_export_model.md)
- [如何自定义模型运行参数](/docs/zh_cn/tutorials/7_customize_runtime.md)
MMAction2 也提供了相应的中文 Colab 教程，可以点击 [这里](https://colab.research.google.com/github/open-mmlab/mmaction2/blob/master/demo/mmaction2_tutorial_zh-CN.ipynb) 进行体验！
## 模型库
<table style="margin-left:auto;margin-right:auto;font-size:1.3vw;padding:3px 5px;text-align:center;vertical-align:center;">
<tr>
<td colspan="5" style="font-weight:bold;">行为识别方法</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/c3d/README_zh-CN.md">C3D</a> (CVPR'2014)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsn/README_zh-CN.md">TSN</a> (ECCV'2016)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/i3d/README_zh-CN.md">I3D</a> (CVPR'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/i3d/README_zh-CN.md">I3D Non-Local</a> (CVPR'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/r2plus1d/README_zh-CN.md">R(2+1)D</a> (CVPR'2018)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/trn/README_zh-CN.md">TRN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsm/README_zh-CN.md">TSM</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsm/README_zh-CN.md">TSM Non-Local</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/slowonly/README_zh-CN.md">SlowOnly</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/slowfast/README_zh-CN.md">SlowFast</a> (ICCV'2019)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/csn/README_zh-CN.md">CSN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tin/README_zh-CN.md">TIN</a> (AAAI'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tpn/README_zh-CN.md">TPN</a> (CVPR'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/x3d/README_zh-CN.md">X3D</a> (CVPR'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/omnisource/README_zh-CN.md">OmniSource</a> (ECCV'2020)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition_audio/resnet/README_zh-CN.md">MultiModality: Audio</a> (ArXiv'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tanet/README_zh-CN.md">TANet</a> (ArXiv'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/timesformer/README_zh-CN.md">TimeSformer</a> (ICML'2021)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">时序动作检测方法</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/ssn/README_zh-CN.md">SSN</a> (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/bsn/README_zh-CN.md">BSN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/localization/bmn/README_zh-CN.md">BMN</a> (ICCV'2019)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">时空动作检测方法</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/acrn/README_zh-CN.md">ACRN</a> (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/ava/README_zh-CN.md">SlowOnly+Fast R-CNN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/ava/README_zh-CN.md">SlowFast+Fast R-CNN</a> (ICCV'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/detection/lfb/README_zh-CN.md">LFB</a> (CVPR'2019)</td>
<td></td>
</tr>
<tr>
<td colspan="5" style="font-weight:bold;">基于骨骼点的动作识别方法</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/stgcn/README.md">ST-GCN</a> (AAAI'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/2s-agcn/README.md">2s-AGCN</a> (CVPR'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/configs/skeleton/posec3d/README.md">PoseC3D</a> (ArXiv'2021)</td>
<td></td>
<td></td>
</tr>
</table>
各个模型的结果和设置都可以在对应的 config 目录下的 *README_zh-CN.md* 中查看。整体的概况也可也在 [**模型库**](https://mmaction2.readthedocs.io/zh_CN/latest/recognition_models.html) 页面中查看
MMAction2 将跟进学界的最新进展，并支持更多算法和框架。如果您对 MMAction2 有任何功能需求，请随时在 [问题](https://github.com/open-mmlab/mmaction2/issues/19) 中留言。
## 数据集
<table style="margin-left:auto;margin-right:auto;font-size:1.3vw;padding:3px 5px;text-align:center;vertical-align:center;">
<tr>
<td colspan="4" style="font-weight:bold;">动作识别数据集</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hmdb51/README_zh-CN.md">HMDB51</a> (<a href="https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/">主页</a>) (ICCV'2011)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ucf101/README_zh-CN.md">UCF101</a> (<a href="https://www.crcv.ucf.edu/research/data-sets/ucf101/">主页</a>) (CRCV-IR-12-01)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/activitynet/README_zh-CN.md">ActivityNet</a> (<a href="http://activity-net.org/">主页</a>) (CVPR'2015)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/kinetics/README_zh-CN.md">Kinetics-[400/600/700]</a> (<a href="https://deepmind.com/research/open-source/kinetics/">主页</a>) (CVPR'2017)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/sthv1/README_zh-CN.md">SthV1</a> (<a href="https://20bn.com/datasets/something-something/v1/">主页</a>) (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/sthv2/README_zh-CN.md">SthV2</a> (<a href="https://20bn.com/datasets/something-something/">主页</a>) (ICCV'2017)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/diving48/README_zh-CN.md">Diving48</a> (<a href="http://www.svcl.ucsd.edu/projects/resound/dataset.html">主页</a>) (ECCV'2018)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/jester/README_zh-CN.md">Jester</a> (<a href="https://developer.qualcomm.com/software/ai-datasets/jester">主页</a>) (ICCV'2019)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/mit/README_zh-CN.md">Moments in Time</a> (<a href="http://moments.csail.mit.edu/">主页</a>) (TPAMI'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/mmit/README_zh-CN.md">Multi-Moments in Time</a> (<a href="http://moments.csail.mit.edu/challenge_iccv_2019.html">主页</a>) (ArXiv'2019)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hvu/README_zh-CN.md">HVU</a> (<a href="https://github.com/holistic-video-understanding/HVU-Dataset">主页</a>) (ECCV'2020)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/omnisource/README_zh-CN.md">OmniSource</a> (<a href="https://kennymckormick.github.io/omnisource/">主页</a>) (ECCV'2020)</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/gym/README_zh-CN.md">FineGYM</a> (<a href="https://sdolivia.github.io/FineGym/">主页</a>) (CVPR'2020)</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">时序动作检测数据集</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/thumos14/README_zh-CN.md">THUMOS14</a> (<a href="https://www.crcv.ucf.edu/THUMOS14/download.html">主页</a>) (THUMOS Challenge 2014)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/activitynet/README_zh-CN.md">ActivityNet</a> (<a href="http://activity-net.org/">主页</a>) (CVPR'2015)</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">时空动作检测数据集</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ucf101_24/README_zh-CN.md">UCF101-24*</a> (<a href="http://www.thumos.info/download.html">主页</a>) (CRCV-IR-12-01)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/jhmdb/README_zh-CN.md">JHMDB*</a> (<a href="http://jhmdb.is.tue.mpg.de/">主页</a>) (ICCV'2015)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ava/README_zh-CN.md">AVA</a> (<a href="https://research.google.com/ava/index.html">主页</a>) (CVPR'2018)</td>
<td></td>
</tr>
<tr>
<td colspan="4" style="font-weight:bold;">基于骨骼点的动作识别数据集</td>
</tr>
<tr>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-FineGYM</a> (<a href="https://kennymckormick.github.io/posec3d/">主页</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-NTURGB+D</a> (<a href="https://kennymckormick.github.io/posec3d/">主页</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-UCF101</a> (<a href="https://kennymckormick.github.io/posec3d/">主页</a>) (ArXiv'2021)</td>
<td><a href="https://github.com/open-mmlab/mmaction2/blob/master/tools/data/skeleton/README.md">PoseC3D-HMDB51</a> (<a href="https://kennymckormick.github.io/posec3d/">主页</a>) (ArXiv'2021)</td>
</tr>
</table>
标记 * 代表对应数据集并未被完全支持，但提供相应的数据准备步骤。整体的概况也可也在 [**数据集**](https://mmaction2.readthedocs.io/en/latest/supported_datasets.html) 页面中查看
## 基准测试
为了验证 MMAction2 框架的高精度和高效率，开发成员将其与当前其他主流框架进行速度对比。更多详情可见 [基准测试](/docs/zh_cn/benchmark.md)
## 数据集准备
请参考 [数据准备](/docs/zh_cn/data_preparation.md) 了解数据集准备概况。所有支持的数据集都列于 [数据集清单](/docs/zh_cn/supported_datasets.md) 中
## 常见问题
请参考 [FAQ](/docs/zh_cn/faq.md) 了解其他用户的常见问题
## 相关工作
目前有许多研究工作或工程项目基于 MMAction2 搭建，例如：
- Evidential Deep Learning for Open Set Action Recognition, ICCV 2021 **Oral**. [\[论文\]](https://arxiv.org/abs/2107.10161)[\[代码\]](https://github.com/Cogito2012/DEAR)
- Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective, ICCV 2021 **Oral**. [\[论文\]](https://arxiv.org/abs/2103.17263)[\[代码\]](https://github.com/xvjiarui/VFS)
- Video Swin Transformer. [\[论文\]](https://arxiv.org/abs/2106.13230)[\[代码\]](https://github.com/SwinTransformer/Video-Swin-Transformer)
更多详情可见 [相关工作](docs/projects.md)
## 参与贡献
我们非常欢迎用户对于 MMAction2 做出的任何贡献，可以参考 [贡献指南](/.github/CONTRIBUTING.md) 文件了解更多细节
## 致谢
MMAction2 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。
我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
## 引用
如果你觉得 MMAction2 对你的研究有所帮助，可以考虑引用它：
```BibTeX
@misc{2020mmaction2,
title={OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark},
author={MMAction2 Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmaction2}},
year={2020}
}
```
## 许可
该项目开源自 [Apache 2.0 license](/LICENSE)
## OpenMMLab 的其他项目
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱和基准测试
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具箱
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
## 欢迎加入 OpenMMLab 社区
扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3)
<div align="center">
<img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/zhihu_qrcode.jpg" height="400" /> <img src="https://github.com/open-mmlab/mmaction2/raw/master/resources/qq_group_qrcode.png" height="400" />
</div>
我们会在 OpenMMLab 社区为大家
- 📢 分享 AI 框架的前沿核心技术
- 💻 解读 PyTorch 常用模块源码
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- 🚀 介绍 OpenMMLab 开发的前沿算法
- 🏃 获取更高效的问题答疑和意见反馈
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干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
configs/_base_/default_runtime.py 0 → 100644
View file @ 5b3e36dc
checkpoint_config = dict(interval=1)
log_config = dict(
interval=20,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook'),
])
# runtime settings
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
# disable opencv multithreading to avoid system being overloaded
opencv_num_threads = 0
# set multi-process start method as `fork` to speed up the training
mp_start_method = 'fork'
configs/_base_/models/audioonly_r50.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='AudioRecognizer',
backbone=dict(
type='ResNetAudio',
depth=50,
pretrained=None,
in_channels=1,
norm_eval=False),
cls_head=dict(
type='AudioTSNHead',
num_classes=400,
in_channels=1024,
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
configs/_base_/models/bmn_400x100.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='BMN',
temporal_dim=100,
boundary_ratio=0.5,
num_samples=32,
num_samples_per_bin=3,
feat_dim=400,
soft_nms_alpha=0.4,
soft_nms_low_threshold=0.5,
soft_nms_high_threshold=0.9,
post_process_top_k=100)
configs/_base_/models/bsn_pem.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='PEM',
pem_feat_dim=32,
pem_hidden_dim=256,
pem_u_ratio_m=1,
pem_u_ratio_l=2,
pem_high_temporal_iou_threshold=0.6,
pem_low_temporal_iou_threshold=0.2,
soft_nms_alpha=0.75,
soft_nms_low_threshold=0.65,
soft_nms_high_threshold=0.9,
post_process_top_k=100)
configs/_base_/models/bsn_tem.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='TEM',
temporal_dim=100,
boundary_ratio=0.1,
tem_feat_dim=400,
tem_hidden_dim=512,
tem_match_threshold=0.5)
configs/_base_/models/c3d_sports1m_pretrained.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='C3D',
pretrained= # noqa: E251
'https://download.openmmlab.com/mmaction/recognition/c3d/c3d_sports1m_pretrain_20201016-dcc47ddc.pth', # noqa: E501
style='pytorch',
conv_cfg=dict(type='Conv3d'),
norm_cfg=None,
act_cfg=dict(type='ReLU'),
dropout_ratio=0.5,
init_std=0.005),
cls_head=dict(
type='I3DHead',
num_classes=101,
in_channels=4096,
spatial_type=None,
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='score'))
configs/_base_/models/i3d_r50.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3d',
pretrained2d=True,
pretrained='torchvision://resnet50',
depth=50,
conv1_kernel=(5, 7, 7),
conv1_stride_t=2,
pool1_stride_t=2,
conv_cfg=dict(type='Conv3d'),
norm_eval=False,
inflate=((1, 1, 1), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 1, 0)),
zero_init_residual=False),
cls_head=dict(
type='I3DHead',
num_classes=400,
in_channels=2048,
spatial_type='avg',
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
# This setting refers to https://github.com/open-mmlab/mmaction/blob/master/mmaction/models/tenons/backbones/resnet_i3d.py#L329-L332 # noqa: E501
configs/_base_/models/ircsn_r152.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dCSN',
pretrained2d=False,
pretrained=None,
depth=152,
with_pool2=False,
bottleneck_mode='ir',
norm_eval=False,
zero_init_residual=False),
cls_head=dict(
type='I3DHead',
num_classes=400,
in_channels=2048,
spatial_type='avg',
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob', max_testing_views=10))
configs/_base_/models/r2plus1d_r34.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet2Plus1d',
depth=34,
pretrained=None,
pretrained2d=False,
norm_eval=False,
conv_cfg=dict(type='Conv2plus1d'),
norm_cfg=dict(type='SyncBN', requires_grad=True, eps=1e-3),
conv1_kernel=(3, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(1, 1, 1, 1),
spatial_strides=(1, 2, 2, 2),
temporal_strides=(1, 2, 2, 2),
zero_init_residual=False),
cls_head=dict(
type='I3DHead',
num_classes=400,
in_channels=512,
spatial_type='avg',
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
configs/_base_/models/slowfast_r50.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowFast',
pretrained=None,
resample_rate=8, # tau
speed_ratio=8, # alpha
channel_ratio=8, # beta_inv
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
norm_eval=False)),
cls_head=dict(
type='SlowFastHead',
in_channels=2304, # 2048+256
num_classes=400,
spatial_type='avg',
dropout_ratio=0.5),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
configs/_base_/models/slowonly_r50.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
type='I3DHead',
in_channels=2048,
num_classes=400,
spatial_type='avg',
dropout_ratio=0.5),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
configs/_base_/models/tanet_r50.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='Recognizer2D',
backbone=dict(
type='TANet',
pretrained='torchvision://resnet50',
depth=50,
num_segments=8,
tam_cfg=dict()),
cls_head=dict(
type='TSMHead',
num_classes=400,
in_channels=2048,
spatial_type='avg',
consensus=dict(type='AvgConsensus', dim=1),
dropout_ratio=0.5,
init_std=0.001),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
configs/_base_/models/tin_r50.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNetTIN',
pretrained='torchvision://resnet50',
depth=50,
norm_eval=False,
shift_div=4),
cls_head=dict(
type='TSMHead',
num_classes=400,
in_channels=2048,
spatial_type='avg',
consensus=dict(type='AvgConsensus', dim=1),
dropout_ratio=0.5,
init_std=0.001,
is_shift=False),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips=None))
configs/_base_/models/tpn_slowonly_r50.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
out_indices=(2, 3),
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
neck=dict(
type='TPN',
in_channels=(1024, 2048),
out_channels=1024,
spatial_modulation_cfg=dict(
in_channels=(1024, 2048), out_channels=2048),
temporal_modulation_cfg=dict(downsample_scales=(8, 8)),
upsample_cfg=dict(scale_factor=(1, 1, 1)),
downsample_cfg=dict(downsample_scale=(1, 1, 1)),
level_fusion_cfg=dict(
in_channels=(1024, 1024),
mid_channels=(1024, 1024),
out_channels=2048,
downsample_scales=((1, 1, 1), (1, 1, 1))),
aux_head_cfg=dict(out_channels=400, loss_weight=0.5)),
cls_head=dict(
type='TPNHead',
num_classes=400,
in_channels=2048,
spatial_type='avg',
consensus=dict(type='AvgConsensus', dim=1),
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob'))
configs/_base_/models/tpn_tsm_r50.py 0 → 100644
View file @ 5b3e36dc
# model settings
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNetTSM',
pretrained='torchvision://resnet50',
depth=50,
out_indices=(2, 3),
norm_eval=False,
shift_div=8),
neck=dict(
type='TPN',
in_channels=(1024, 2048),
out_channels=1024,
spatial_modulation_cfg=dict(
in_channels=(1024, 2048), out_channels=2048),
temporal_modulation_cfg=dict(downsample_scales=(8, 8)),
upsample_cfg=dict(scale_factor=(1, 1, 1)),
downsample_cfg=dict(downsample_scale=(1, 1, 1)),
level_fusion_cfg=dict(
in_channels=(1024, 1024),
mid_channels=(1024, 1024),
out_channels=2048,
downsample_scales=((1, 1, 1), (1, 1, 1))),
aux_head_cfg=dict(out_channels=174, loss_weight=0.5)),
cls_head=dict(
type='TPNHead',
num_classes=174,
in_channels=2048,
spatial_type='avg',
consensus=dict(type='AvgConsensus', dim=1),
dropout_ratio=0.5,
init_std=0.01),
# model training and testing settings
train_cfg=None,
test_cfg=dict(average_clips='prob', fcn_test=True))
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