nhartman94 / cs233_gtda_hw4

Fourth Assignment of CS233, Geometric & Topological Data Analysis@Stanford (starter code).

Requirements

Please look at setup.py The code has been tested with Python 3.6 (any 3x version should work).

Installation Instructions:

If you have already an activated virtual-environment:

1. git clone https://github.com/optas/cs233_gtda_hw4.git

2. cd cs233_gtda_hw4

3. git submodule add https://github.com/ThibaultGROUEIX/ChamferDistancePytorch cs233_gtda_hw4/losses/ChamferDistancePytorch
    
4. pip install -e .  # to install the package as a environment-wide module

5. Go to main.ipynb or main.py                

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If not, please first make such an environment (see instructions below for Conda)

1. Install anaconda: https://docs.anaconda.com/anaconda/install/

2. Create an enviroment: conda create -n name_you_like python=3.6 cudatoolkit=10.1

3. conda activate name_you_like

4. conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

(step 4 is optional, but should be done to further increase the chances that you get pytorch that sees the GPUs of your system)

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Potential Hiccup:

The fast(er) implementation of Chamfer (the one from the submodule above) requires the ninja build system installed.

If you do not have it you can install it like this:

1. wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip
2. sudo unzip ninja-linux.zip -d /usr/local/bin/
3. sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force

If you cannot do it, you might have to resort to the ~10x slower provided implementation of Chamfer in losses/nn_distance/chamfer_loss (see notes inside the models/pointcloud_autoencoder.py).

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Best of luck! The CS233 Instructor/TAs