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INSTALL.md

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Installation

Requirements

  • Python >= 3.8
  • Numpy
  • PyTorch >= 1.3
  • fvcore: pip install 'git+https://github.com/facebookresearch/fvcore'
  • torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this.
  • simplejson: pip install simplejson
  • GCC >= 4.9
  • PyAV: conda install av -c conda-forge
  • ffmpeg (4.0 is prefereed, will be installed along with PyAV)
  • PyYaml: (will be installed along with fvcore)
  • tqdm: (will be installed along with fvcore)
  • iopath: pip install -U iopath or conda install -c iopath iopath
  • psutil: pip install psutil
  • OpenCV: pip install opencv-python
  • torchvision: pip install torchvision or conda install torchvision -c pytorch
  • tensorboard: pip install tensorboard
  • moviepy: (optional, for visualizing video on tensorboard) conda install -c conda-forge moviepy or pip install moviepy
  • PyTorchVideo: pip install pytorchvideo
  • Detectron2:
  • FairScale: pip install 'git+https://github.com/facebookresearch/fairscale'
    pip install -U torch torchvision cython
    pip install -U 'git+https://github.com/facebookresearch/fvcore.git' 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
    git clone https://github.com/facebookresearch/detectron2 detectron2_repo
    pip install -e detectron2_repo
    # You can find more details at https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md

Pytorch

Please follow PyTorch official instructions to install from source:

git clone --recursive https://github.com/pytorch/pytorch

PySlowFast

Clone the PySlowFast Video Understanding repository.

git clone https://github.com/facebookresearch/slowfast

Add this repository to $PYTHONPATH.

export PYTHONPATH=/path/to/SlowFast/slowfast:$PYTHONPATH

Build PySlowFast

After having the above dependencies, run:

git clone https://github.com/facebookresearch/slowfast
cd SlowFast
python setup.py build develop

Now the installation is finished, run the pipeline with:

python tools/run_net.py --cfg configs/Kinetics/C2D_8x8_R50.yaml NUM_GPUS 1 TRAIN.BATCH_SIZE 8 SOLVER.BASE_LR 0.0125 DATA.PATH_TO_DATA_DIR path_to_your_data_folder