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iopath dependency
Summary: Add ioPath as a dependency of PyTorch3D in preparation for using the new PathManager. Reviewed By: nikhilaravi Differential Revision: D25372971 fbshipit-source-id: d8aa661d2de975e747dd494edc42bf843990cf68
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@ -75,6 +75,7 @@ jobs:
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# - run: conda install -c pytorch pytorch torchvision
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- run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/fvcore'
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- run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/iopath'
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- run:
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name: get cub
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command: |
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@ -75,6 +75,7 @@ jobs:
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# - run: conda install -c pytorch pytorch torchvision
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- run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/fvcore'
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- run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/iopath'
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- run:
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name: get cub
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command: |
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@ -13,6 +13,7 @@ The core library is written in PyTorch. Several components have underlying imple
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- torchvision that matches the PyTorch installation. You can install them together as explained at pytorch.org to make sure of this.
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- gcc & g++ ≥ 4.9
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- [fvcore](https://github.com/facebookresearch/fvcore)
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- [ioPath](https://github.com/facebookresearch/iopath)
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- If CUDA is to be used, use a version which is supported by the corresponding pytorch version and at least version 9.2.
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- If CUDA is to be used and you are building from source, the CUB library must be available. We recommend version 1.10.0.
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@ -21,7 +22,7 @@ The runtime dependencies can be installed by running:
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conda create -n pytorch3d python=3.8
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conda activate pytorch3d
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conda install -c pytorch pytorch=1.7.0 torchvision cudatoolkit=10.2
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conda install -c conda-forge -c fvcore fvcore
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conda install -c conda-forge fvcore iopath
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```
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For the CUB build time dependency, if you are using conda, you can continue with
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@ -19,7 +19,7 @@ conda init bash
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source ~/.bashrc
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conda create -y -n myenv python=3.8 matplotlib ipython ipywidgets nbconvert
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conda activate myenv
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conda install -y -c conda-forge fvcore
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conda install -y -c conda-forge fvcore iopath
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conda install -y -c pytorch pytorch=1.6.0 cudatoolkit=10.1 torchvision
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conda install -y -c pytorch3d-nightly pytorch3d
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pip install plotly scikit-image
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@ -6,5 +6,6 @@ sphinx_markdown_tables
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mock
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numpy
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git+git://github.com/facebookresearch/fvcore.git
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git+git://github.com/facebookresearch/iopath.git
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https://download.pytorch.org/whl/cpu/torchvision-0.8.2%2Bcpu-cp37-cp37m-linux_x86_64.whl
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https://download.pytorch.org/whl/cpu/torch-1.7.1%2Bcpu-cp37-cp37m-linux_x86_64.whl
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@ -17,4 +17,4 @@ setup_conda_pytorch_constraint
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setup_conda_cudatoolkit_constraint
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setup_visual_studio_constraint
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# shellcheck disable=SC2086
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conda build $CONDA_CHANNEL_FLAGS ${TEST_FLAG:-} -c bottler -c defaults -c conda-forge --no-anaconda-upload -c fvcore --python "$PYTHON_VERSION" packaging/pytorch3d
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conda build $CONDA_CHANNEL_FLAGS ${TEST_FLAG:-} -c bottler -c defaults -c conda-forge --no-anaconda-upload --python "$PYTHON_VERSION" packaging/pytorch3d
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@ -22,6 +22,7 @@ requirements:
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- numpy >=1.11
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- torchvision >=0.5
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- fvcore
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- iopath
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{{ environ.get('CONDA_PYTORCH_CONSTRAINT') }}
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{{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT') }}
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2
setup.py
2
setup.py
@ -109,7 +109,7 @@ setup(
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description="PyTorch3D is FAIR's library of reusable components "
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"for deep Learning with 3D data.",
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packages=find_packages(exclude=("configs", "tests", "tests.*")),
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install_requires=["torchvision>=0.4", "fvcore"],
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install_requires=["torchvision>=0.4", "fvcore", "iopath"],
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extras_require={
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"all": ["matplotlib", "tqdm>4.29.0", "imageio", "ipywidgets"],
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"dev": ["flake8", "isort", "black==19.3b0"],
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