Corrections

INSTALL.md

@@ -1,39 +1,54 @@
-### Installation instructions for Ubuntu 16.04
 
-* 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).
+# 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. 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
      
-* 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:
+* 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:
+
+        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

cpp_wrappers/cpp_neighbors/build.bat

@@ -0,0 +1,5 @@
+@echo off
+py setup.py build_ext --inplace
+
+
+pause

cpp_wrappers/cpp_subsampling/build.bat

@@ -0,0 +1,5 @@
+@echo off
+py setup.py build_ext --inplace
+
+
+pause