The $G$-Triple Correlation Layer for Robust $G$-Invariance in $G$-Equivariant Networks

This repository is the official accompaniment to A General Framework for Robust G-Invariance in G-Equivariant Networks (submitted, NeurIPS 2023)

Installation

To install the requirements and package, run:

pip install -r requirements.txt
python install -e .

Datasets

To download the datasets, run:

wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate '[URL]' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=[ID]" -O datasets.zip 
rm -rf /tmp/cookies.txt
unzip datasets.zip
rm -r datasets.zip

If your machine doesn't have wget, follow these steps:

1. Download the zip file here.
2. Place the file in the top node of this directory, i.e. in tc-invariance/.
3. Run:

   unzip datasets.zip
   rm -r datasets.zip
   

Training

To train the models in the paper, run the following commands.

python train.py --config rotation_experiment
python train.py --config translation_experiment

To run on GPU, add the following argument, with the integer specifying the device number, i.e.:

--device 0

The full set of hyperparameters and training configurations are specified in the config files in the configs/ folder.

To view learning curves in Tensorboard, run:

tensorboard --logdir logs/

Pre-trained Models

The pre-trained models are included in the repo, in the following locations:

logs/gtc/
logs/max/

Results and Figures

All results and figures from the paper are generated in the Jupyter notebooks located at:

notebooks/file.ipynb

License

This repository is licensed under the MIT License.