Add competing_methods

competing_methods/my_KPConv/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

competing_methods/my_KPConv/INSTALL.md

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+
+# 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://pytorch.org/get-started/locally/">PyTorch installation procedure</a>.
+
+* Install the other dependencies with pip:
+     - numpy
+     - scikit-learn
+     - PyYAML
+     - matplotlib (for visualization)
+     - mayavi (for visualization)
+     - PyQt5 (for visualization)
+
+* 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
+
+## 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) in a venv:
+     - torch
+     - torchvision
+     - 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
+