torchvideo
A PyTorch library for video-based computer vision tasks. torchvideo provides
dataset loaders specialised for video, video frame samplers, and transformations specifically for video.
Get started
Set up an accelerated environment in conda
$ conda env create -f environment.yml -n torchvideo
$ conda activate torchvideo
# The following steps are taken from
# https://docs.fast.ai/performance.html#installation
$ conda uninstall -y --force pillow pil jpeg libtiff
$ pip uninstall -y pillow pil jpeg libtiff
$ conda install -y -c 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 libtiffNOTE: If the installation of pillow-simd fails, you can try installing GCC from
conda-forge and trying the install again:
$ conda install -y gxx_linux-64
$ export CXX=x86_64-conda_cos6-linux-gnu-g++
$ export CC=x86_64-conda_cos6-linux-gnu-gcc
$ CFLAGS="${CFLAGS} -mavx2" pip install --upgrade --no-cache-dir --force-reinstall --no-binary :all: --compile pillow-simd
$ conda install -y jpeg libtiffIf you install any new packages, check that pillow-simd hasn't be overwritten
by an alternate pillow install by running:
$ python -c "from PIL import Image; print(Image.PILLOW_VERSION)"You should see something like
6.0.0.post0
Pillow doesn't release with post suffixes, so if you have post in the version
name, it's likely you have pillow-simd installed.
Install torchvideo
$ pip install git+https://github.com/willprice/torchvideo.git@masterLearn how to use torchvideo
Check out the example notebooks, you can launch these on binder without having to install anything locally!
Acknowledgements
Thanks to the following people and projects
- yjxiong for his work on TSN and publicly available pytorch implementation from which many of the transforms in this project started from.
- dukebw for his excellent lintel FFmpeg video loading library.
- hypothesis and the team behind it. This has been used heavily in testing the project.