57 lines
2 KiB
Markdown
57 lines
2 KiB
Markdown
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# Installation instructions
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## Ubuntu 18.04
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* Make sure <a href="https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html">CUDA</a> and <a href="https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html">cuDNN</a> are installed. One configuration has been tested:
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- PyTorch 1.4.0, CUDA 10.1 and cuDNN 7.6
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* Ensure all python packages are installed :
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sudo apt update
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sudo apt install python3-dev python3-pip python3-tk
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* Follow <a href="https://pytorch.org/get-started/locally/">PyTorch installation procedure</a>.
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* Install the other dependencies with pip:
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- numpy
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- scikit-learn
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- PyYAML
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- matplotlib (for visualization)
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- mayavi (for visualization)
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- PyQt5 (for visualization)
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* Compile the C++ extension modules for python located in `cpp_wrappers`. Open a terminal in this folder, and run:
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sh compile_wrappers.sh
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You should now be able to train Kernel-Point Convolution models
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## Windows 10
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* Make sure <a href="https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html">CUDA</a> and <a href="https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html">cuDNN</a> are installed. One configuration has been tested:
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- PyTorch 1.4.0, CUDA 10.1 and cuDNN 7.5
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* Follow <a href="https://pytorch.org/get-started/locally/">PyTorch installation procedure</a>.
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* We used the PyCharm IDE to pip install all python dependencies (including PyTorch) in a venv:
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- torch
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- torchvision
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- numpy
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- scikit-learn
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- PyYAML
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- matplotlib (for visualization)
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- mayavi (for visualization)
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- PyQt5 (for visualization)
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* Compile the C++ extension modules for python located in `cpp_wrappers`. You just have to execute two .bat files:
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cpp_wrappers/cpp_neighbors/build.bat
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and
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cpp_wrappers/cpp_subsampling/build.bat
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You should now be able to train Kernel-Point Convolution models
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