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4 changes: 1 addition & 3 deletions .github/scripts/setup-env.sh
Original file line number Diff line number Diff line change
Expand Up @@ -23,15 +23,13 @@ case $(uname) in
esac

echo '::group::Create build environment'
# See https://github.com/pytorch/vision/issues/7296 for ffmpeg
conda create \
--name ci \
--quiet --yes \
python="${PYTHON_VERSION}" pip \
ninja cmake \
libpng \
libwebp \
'ffmpeg<4.3'
libwebp
conda activate ci
conda install --quiet --yes libjpeg-turbo -c pytorch
pip install --progress-bar=off --upgrade setuptools==72.1.0
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3 changes: 1 addition & 2 deletions packaging/pre_build_script.sh
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,6 @@ if [[ "$(uname)" == Darwin || "$OSTYPE" == "msys" ]]; then
# Installing webp also installs a non-turbo jpeg, so we uninstall jpeg stuff
# before re-installing them
conda uninstall libjpeg-turbo libjpeg -y
conda install -y ffmpeg=4.2 -c pytorch
conda install -y libjpeg-turbo -c pytorch

# Copy binaries to be included in the wheel distribution
Expand All @@ -30,7 +29,7 @@ else

if [[ "$ARCH" == "aarch64" ]]; then
conda install libpng -y
conda install -y ffmpeg=4.2 libjpeg-turbo -c pytorch-nightly
conda install -y libjpeg-turbo -c pytorch-nightly
fi

conda install libwebp -y
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4 changes: 2 additions & 2 deletions setup.py
Original file line number Diff line number Diff line change
Expand Up @@ -28,9 +28,9 @@
# video decoding backends in torchvision. I'm renaming this to "gpu video
# decoder" where possible, keeping user facing names (like the env var below) to
# the old scheme for BC.
USE_GPU_VIDEO_DECODER = os.getenv("TORCHVISION_USE_VIDEO_CODEC", "1") == "1"
USE_GPU_VIDEO_DECODER = os.getenv("TORCHVISION_USE_VIDEO_CODEC", "0") == "1"
# Same here: "use ffmpeg" was used to denote "use cpu video decoder".
USE_CPU_VIDEO_DECODER = os.getenv("TORCHVISION_USE_FFMPEG", "1") == "1"
USE_CPU_VIDEO_DECODER = os.getenv("TORCHVISION_USE_FFMPEG", "0") == "1"

TORCHVISION_INCLUDE = os.environ.get("TORCHVISION_INCLUDE", "")
TORCHVISION_LIBRARY = os.environ.get("TORCHVISION_LIBRARY", "")
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