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HuguesTHOMAS 2020-04-27 18:43:55 -04:00
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### Installation instructions for Ubuntu 16.04
# Installation instructions
## Ubuntu 18.04
* 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. Three configurations have been tested:
- TensorFlow 1.4.1, CUDA 8.0 and cuDNN 6.0
- TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.4
- ~~TensorFlow 1.13.0, CUDA 10.0 and cuDNN 7.5~~ (bug found only with this version).
* 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:
- PyTorch 1.4.0, CUDA 10.1 and cuDNN 7.6
* Ensure all python packages are installed :
sudo apt update
sudo apt install python3-dev python3-pip python3-tk
* Follow <a href="https://www.tensorflow.org/install/pip">Tensorflow installation procedure</a>.
* Follow <a href="https://pytorch.org/get-started/locally/">PyTorch installation procedure</a>.
* Install the other dependencies with pip:
- numpy
- scikit-learn
- psutil
- PyYAML
- matplotlib (for visualization)
- mayavi (for visualization)
- PyQt5 (for visualization)
* Compile the customized Tensorflow operators located in `tf_custom_ops`. Open a terminal in this folder, and run:
sh compile_op.sh
N.B. If you installed Tensorflow in a virtual environment, it needs to be activated when running these scripts
* Compile the C++ extension module for python located in `cpp_wrappers`. Open a terminal in this folder, and run:
* Compile the C++ extension modules for python located in `cpp_wrappers`. Open a terminal in this folder, and run:
sh compile_wrappers.sh
You should now be able to train Kernel-Point Convolution models
### Installation instructions for Ubuntu 18.04 (Thank to @noahtren)
## Windows 10
* 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:
- PyTorch 1.4.0, CUDA 10.1 and cuDNN 7.5
* Follow <a href="https://pytorch.org/get-started/locally/">PyTorch installation procedure</a>.
* We used the PyCharm IDE to pip install all python dependencies (including PyTorch):
- numpy
- scikit-learn
- PyYAML
- matplotlib (for visualization)
- mayavi (for visualization)
- PyQt5 (for visualization)
* Compile the C++ extension modules for python located in `cpp_wrappers`. You just have to execute two .bat files:
* Remove the `-D_GLIBCXX_USE_CXX11_ABI=0` flag for each line in `tf_custom_ops/compile_op.sh` (problem with the version of gcc). One configuration has been tested:
cpp_wrappers/cpp_neighbors/build.bat
and
cpp_wrappers/cpp_subsampling/build.bat
You should now be able to train Kernel-Point Convolution models
- TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.3.1

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@echo off
py setup.py build_ext --inplace
pause

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@echo off
py setup.py build_ext --inplace
pause