Invariant and Equivariant Graph Networks (PyTorch)
A PyTorch implementation of The ICLR 2019 paper “Invariant and Equivariant Graph Networks” by Haggai Maron, Heli Ben-Hamu, Nadav Shamir and Yaron Lipman
https://openreview.net/forum?id=Syx72jC9tm. The official TensorFlow implementation is at https://github.com/Haggaim/InvariantGraphNetworks
Data should be downloaded from: https://www.dropbox.com/s/vjd6wy5nemg2gh6/benchmark_graphs.zip?dl=0.
Run the following commands in order to unzip the data and put its proper path.
mkdir data unzip benchmark_graphs.zip -d data
Additional modules: numpy, pandas, matplotlib
TensorFlow is not neccessary except if you want to run the tests (comparisons) between the PyTorch and TensorFlow versions.
Running the tests
Run the tests comparing between PyTorch and TensorFlow versions’ tensor contractions. All tensor contractions are implemented 1-to-1. The two versions have identical tensor contractions:
cd layers/ python3 test_tensorflow_pytorch_contractions.py
Run the (permutation) equivariance tests for the equivariant linear layers implemented in PyTorch (e.g. permute the input tensor and the output tensor must transform covariantly):
Running the experiment
The folder main_scripts contains scripts that run different experiments:
- To run 10-fold cross-validation with our hyper parameters run the main_10fold_experiment.py script. You can choose the datase in 10fold_config.json.
- To run hyper-parameter search, run the main_parameter_search.py script with the corresponding config file
- To run training and evaluation on one of the data sets run the main.py script
example to run 10-fold cross-validation experiment:
cd main_scripts/ python3 -m main_10fold_experiment --config=../configs/10fold_config.json
PyTorch implementation of tensor contractions and equivariant linear layers is in:
PyTorch implementation of invariant (basic) graph nets:
Covariant Compositional Networks For Learning Graphs https://arxiv.org/abs/1801.02144
Predicting molecular properties with covariant compositional networks https://aip.scitation.org/doi/10.1063/1.5024797
The general theory of permutation equivarant neural networks and higher order graph variational encoders https://arxiv.org/abs/2004.03990
Email: [email protected]