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Installation

Requirements

  • Linux (Windows is not officially supported)
  • Python 3.6+
  • PyTorch 1.3+
  • CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
  • GCC 5+
  • mmcv 0.5.7+
  • Numpy
  • ffmpeg (4.2 is preferred)
  • decord (optional): Install CPU version by pip install decord and install GPU version from source
  • PyAV (optional): conda install av -c conda-forge -y
  • PyTurboJPEG (optional): pip install PyTurboJPEG
  • denseflow (optional): See here for simple install scripts.
  • moviepy (optional): pip install moviepy. See here for official installation. Note(according to this issue) that:
    1. For Windows users, ImageMagick will not be automatically detected by MoviePy, there is a need to modify moviepy/config_defaults.py file by providing the path to the ImageMagick binary called magick, like IMAGEMAGICK_BINARY = "C:\\Program Files\\ImageMagick_VERSION\\magick.exe"
    2. For Linux users, there is a need to modify the /etc/ImageMagick-6/policy.xml file by commenting out <policy domain="path" rights="none" pattern="@*" /> to <!-- <policy domain="path" rights="none" pattern="@*" /> -->, if ImageMagick is not detected by moviepy.
  • Pillow-SIMD (optional): Install it by the following scripts.
conda uninstall -y --force pillow pil jpeg libtiff libjpeg-turbo
pip   uninstall -y         pillow pil jpeg libtiff libjpeg-turbo
conda install -yc conda-forge libjpeg-turbo
CFLAGS="${CFLAGS} -mavx2" pip install --upgrade --no-cache-dir --force-reinstall --no-binary :all: --compile pillow-simd
conda install -y jpeg libtiff

Install MMAction2

a. Create a conda virtual environment and activate it.

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

b. Install PyTorch and torchvision following the official instructions, e.g.,

conda install pytorch torchvision -c pytorch

Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.

E.g.1 If you have CUDA 10.1 installed under /usr/local/cuda and would like to install PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.

conda install pytorch cudatoolkit=10.1 torchvision -c pytorch

E.g.2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.

conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch

If you build PyTorch from source instead of installing the prebuilt package, you can use more CUDA versions such as 9.0.

c. Clone the mmaction2 repository

git clone https://github.com/open-mmlab/mmaction2.git
cd mmaction2

d. Install build requirements and then install mmaction2

pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"

If you build mmaction2 on macOS, replace the last command with

CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .

Note:

  1. The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models. It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.

  2. Following the above instructions, mmaction2 is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).

  3. If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.

  4. If you would like to use PyAV, you can install it with conda install av -c conda-forge -y.

  5. Some dependencies are optional. Running python setup.py develop will only install the minimum runtime requirements. To use optional dependencies like decord, either install them with pip install -r requirements/optional.txt or specify desired extras when calling pip (e.g. pip install -v -e .[optional], valid keys for the [optional] field are all, tests, build, and optional) like pip install -v -e .[tests,build].

Install with CPU only

The code can be built for CPU only environment (where CUDA isn't available).

In CPU mode you can run the demo/demo.py for example.

Another option: Docker Image

We provide a Dockerfile to build an image.

# build an image with PyTorch 1.5, CUDA 10.1
docker build -t mmaction docker/

Run it with

docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmaction/data mmaction

A from-scratch setup script

Here is a full script for setting up mmaction2 with conda and link the dataset path (supposing that your Kinetics-400 dataset path is $KINETICS400_ROOT).

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

conda install -c pytorch pytorch torchvision -y
git clone https://github.com/open-mmlab/mmaction.git
cd mmaction
pip install -r requirements/build.txt
python setup.py develop

mkdir data
ln -s $KINETICS400_ROOT data

Using multiple MMAction2 versions

The train and test scripts already modify the PYTHONPATH to ensure the script use the MMAction2 in the current directory.

To use the default MMAction2 installed in the environment rather than that you are working with, you can remove the following line in those scripts.

PYTHONPATH="$(dirname $0)/..":$PYTHONPATH