Code for models in:

[1] Kansal et. al., Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics, ML4PS @ NeurIPS 2020 arXiv:2012.00173
[2] Kansal et. al., Particle Cloud Generation with Message Passing Generative Adversarial Networks, NeurIPS 2021 arXiv:2106.11535
[3] Kansal et. al., On the Evaluation of Generative Models in High Energy Physics, arXiv:2211.10295

This repository contains PyTorch code for the message-passing GAN (MPGAN) and generative adversarial particle transformer (GAPT) models, as well as scripts for training the models from scratch, generating and plotting the particle clouds. We include also weights of fully trained models discussed in [2].

Additionally, we release the standalone JetNet library, which provides a PyTorch Dataset class for our JetNet dataset, implementations of the evaluation metrics discussed in the paper, and some more useful utilities for development in machine learning + jets.

For the exact code and scripts used for [2], please see the neurips21 branch.

A complete list of talks can be found here.

• torch >= 1.8.0

• torch >= 1.8.0
• jetnet >= 0.2.1
• numpy >= 1.21.0
• matplotlib
• mplhep

Can be installed via pip install -r requirements.txt.

• torch
• torch_geometric

A Docker image containing all necessary libraries can be found here (Dockerfile).

Start training with:

python train.py --name test_model --model [model] --jets [jets] [args]  

where model can be specified as mpgan or gapt, and jets can be any out of ['g', 't', 'q', 'w', 'z'].

By default, model parameters, figures of particle and jet features, and plots of the training losses and evaluation metrics over time will be saved every five epochs in an automatically created outputs/[name] directory.

Some notes:

• Will run on a GPU by default if available.
• The default arguments correspond to the final model architecture and training configuration used in the paper.
• Run python train.py --help or look at setup_training.py for a full list of arguments.
• For protoyping purposes, models can also be trained on a 'sparsified' MNIST point cloud dataset as in [1] using train_mnist.py. Normal MNIST training and testing .csvs need to be downloaded and pointed to with the --datasets-path arg.

Pre-trained generators with saved state dictionaries and arguments can be used to generate samples with, for example:

python gen.py --G-state-dict trained_models/mp_g/G_best_epoch.pt --G-args trained_models/mp_g/args.txt --num-samples 50,000 --output-file trained_models/mp_g/gen_jets.npy