mmaction2 0.7.0

OpenMMLab Action Understanding Toolbox and Benchmark

Introduction

MMAction2 is an open-source toolbox for action understanding based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.3+.

Major Features

• Modular design

  We decompose the action understanding framework into different components and one can easily construct a customized action understanding framework by combining different modules.

• Support for various datasets

  The toolbox directly supports multiple datasets, UCF101, Kinetics-400, Something-Something V1&V2, Moments in Time, Multi-Moments in Time, THUMOS14, etc.

• Support for multiple action understanding frameworks

  MMAction2 implements popular frameworks for action understanding:

  • For action recognition, various algorithms are implemented, including TSN, TSM, TIN, R(2+1)D, I3D, SlowOnly, SlowFast, CSN, Non-local, etc.

  • For temporal action localization, we implement BSN, BMN, SSN.

• Well tested and documented

  We provide detailed documentation and API reference, as well as unittests.

License

This project is released under the Apache 2.0 license.

Changelog

v0.6.0 was released in 2/9/2020. Please refer to changelog.md for details and release history.

Benchmark

Model	input	io backend	batch size x gpus	MMAction2 (s/iter)	MMAction (s/iter)	Temporal-Shift-Module (s/iter)	PySlowFast (s/iter)
TSN	256p rawframes	Memcached	32x8	0.32	0.38	0.42	x
TSN	256p dense-encoded video	Disk	32x8	0.61	x	x	TODO
I3D heavy	256p videos	Disk	8x8	0.34	x	x	0.44
I3D	256p rawframes	Memcached	8x8	0.43	0.56	x	x
TSM	256p rawframes	Memcached	8x8	0.31	x	0.41	x
Slowonly	256p videos	Disk	8x8	0.32	TODO	x	0.34
Slowfast	256p videos	Disk	8x8	0.69	x	x	1.04
R(2+1)D	256p videos	Disk	8x8	0.45	x	x	x

Details can be found in benchmark.

ModelZoo

Supported methods for action recognition:

• TSN
• TSM
• R(2+1)D
• I3D
• SlowOnly
• SlowFast
• CSN
• TIN
• TPN

Supported methods for action localization:

• BMN
• BSN

Results and models are available in the README.md of each method's config directory. A summary can be found in the model zoo page.

Installation

Please refer to install.md for installation.

Data Preparation

Please refer to data_preparation.md for a general knowledge of data preparation.

Get Started

Please see getting_started.md for the basic usage of MMAction2. There are also tutorials for finetuning models, adding new dataset, designing data pipeline, exporting model to onnx and adding new modules.

A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.

Contributing

We appreciate all contributions to improve MMAction2. Please refer to CONTRIBUTING.md for 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, as well as users who give valuable feedbacks. 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 own new models.