/eman

Equivariant Mesh Attention Networks

Primary LanguagePythonMIT LicenseMIT

Equivariant Mesh Attention Networks

This repository contains the code to reproduce the experiments of Equivariant Mesh Attention Networks published in Transactions on Machine Learning Research (TMLR - 08/2022).

Running experiments

The instructions provided below assume that the python command is triggered from ./eman:

FAUST experiments

python experiments/faust_direct.py --model RelTanEMAN --seed 1 --epochs 1 -equiv_bias

TOSCA experiments

python experiments/tosca_direct.py --model RelTanEMAN --seed 1 --epochs 1 -equiv_bias -null_isolated

Installation instructions

Follow the commands below to create a new conda environment and install all dependencies:

conda create --name eman python=3.7
conda activate eman

# GPU installation
# conda install pytorch=1.11 cudatoolkit=11.3 -c pytorch

# CPU installation
# conda install pytorch=1.11 cpuonly -c pytorch

conda install pyg=2.0.3 -c pyg
pip install wandb pytorch-ignite openmesh opt_einsum trimesh

Project structure

eman
│   README.md
│   LICENSE    
│
└───data
│   │   FAUST/raw/MPI-FAUST.zip  # Download from http://faust.is.tue.mpg.de/
│   │   TOSCA               # Automatically downloaded on first experiment
|
└───eman                    # Implementation of Equivariant Mesh Attention Networks
│   └───nn
│   └───tests
│   └───transform
│   └───utils
|
└───experiments
|   |   faust_direct.py 
|   |   tosca_direct.py 
|   |   paths.json          # Specify dataset locations (default: "./eman/data") 
|   |   ...
|
└───gem_cnn                 # Implementation of Gauge Equivariant CNNs
│   └───nn
│   └───tests
│   └───transform
│   └───utils
│   
└───spiralnet               # Implementation of SpiralNet++
|   |   spiralconv.py
│   └───spiralnet.utils

Citation

Please use the following snippet to cite this work:

@article{basu2022equivariant,
      title={{Equivariant Mesh Attention Networks}}, 
      author={Basu, Sourya and Gallego-Posada, Jose and Vigan\`o, Francesco and Rowbottom, James and Cohen, Taco},
      year={2022},
      month={08},
      journal={Transactions on Machine Learning Research}
}

Acknowledgements