PF-GNN

This repository is the official implementation of PF-GNN: Differentiable particle filtering based approximation of universal graph representations.

Requirements

This code has been developed and tested on Nvidia-RTX-2080Ti GPU with:

1. Pytorch-1.9.0
2. Pytorch-geometric-1.9.0
3. Cuda 10.1/10.2

Other higher versions will probably work as well. Make relevant changes during installation for other versions

If you don't have Anaconda for python 3, install it from here

Make sure cuda and cuda-Toolkit is installed. Note down the CUDA version in your machine.

cat /usr/local/cuda/version.txt
nvcc --version

To install requirements for cuda version 10.1:

conda create --name torch_test python=3.6

conda activate torch_test

conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch

conda install pyg -c pyg -c conda-forge

If you encounter problems, refer to pytorch-geometric installation page here. The code should work with standard installation as well.

Training and Evaluation

To train and evaluate, run:

Regression task:
ZINC: export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7;python PF-GNNIR-zinc.py --num_particles=8 --depth=3 --batch_size=512 --dim=150 --parallel

Classification task: export CUDA_VISIBLE_DEVICES=0;python PF-GNNIR-triangles.py --depth=4 --num_particles=24 --dim=128 --batch_size=128

This code is based on pytorch-geometric