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Fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments

This package provides implementations of GAN and DDPM/DDIM models used in the "Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments" paper. The instructions below describe how to setup this package, train generative models, and synthesize new data.

Installation & Requirements

Environment

This code was developed and tested in the official pytorch container pytorch_1.12.1-cuda11.3-cudnn8-runtime. An environment similar to that container can be set up with conda, using the provided configuration:

conda env create -f contrib/conda_env.yaml

NOTE: this environment was tested only on Linux machines.

Requirements

calo-ddpm relies on the reference implementation of the iDDPM architecture by OpenAI. improved-diffusion package needs to be manually installed inside of the created environment. We used commit 783b6740edb79fdb7d063250db2c51cc9545dcd1 in our work.

Installation

Finally, to install the calo-ddpm package, run the following command

python3 setup.py develop --user

Environment Variables

By default, calo-ddpm will search for data in the ./data directory and store trained models in the ./outdir directory. If one wants to change this behavior, modify the following environment variables:

export JETGEN_DATA=PATH_TO_DATA
export JETGEN_OUTDIR=PATH_TO_OUTDIR

Training

NOTE: Due to the sPhenix collaboration policies, we are unable to share the training dataset outside the sPhenix collaboration.

In this section, we describe the following:

  1. How to obtain pre-trained models
  2. How to prepare your own dataset for training.
  3. How to train DDPM/GAN models using the official sPhenix dataset, or custom data.

1. Obtaining Pre-Trained Models

The pre-trained GAN and DDPM models have been uploaded to Zenodo. One can download them with the help of the provided convenience script ./scripts/download_model.sh .

2. Using your own dataset

To train the DDPM/GAN models on your own dataset, you can take one of the available training scripts as a starting point (e.g. scripts/train/sphenix/train_cent0_dcgan.py or scripts/train/sphenix/train_cent0_ddpm.py ). These scripts describe the training configuration, which should be straightforward to navigate.

Next, you would need to prepare your dataset to match the format that calo-ddpm expects or write an alternative pytorch dataset implementation. By default, calo-ddpm expects the dataset to be packed into hdf5 files and arranged in the following directory structure:

DATASET/
    train/
        DOMAIN.h5
    val/            # optional
        DOMAIN.h5
    test/           # optional
        DOMAIN.h5

where DATASET and DOMAIN are arbitrary names (make sure to change the path and domain fields of the training configuration to match).

DOMAIN.h5 is an HDF5 file containing the dataset. The dataset should be saved in an hdf5 dataset called data. The data should have a shape of (N, H, W, C) or (N, H, W), where N is the number of samples in the dataset, (H, W) spatial dimensions of the data samples, and C is the number of channels.

3. Trainining Models

To train the GAN/DDPM models, one can run one of the following scripts:

scripts/train/sphenix/train_cent0_dcgan.py
scripts/train/sphenix/train_cent0_ddpm.py
scripts/train/sphenix/train_cent4_dcgan.py
scripts/train/sphenix/train_cent4_ddpm.py

These scripts contain the default training configurations used in the paper. Once the models are trained, they will be saved in the ./outdir directory (or JETGEN_OUTDIR).

Note: by default, the training will attempt to use all the available GPUs. To bind the training to a single GPU -- set CUDA_VISIBLE_DEVICES environment variable to the index of the desired GPU.

Data Generation

calo-ddpm provides two scripts scripts/eval_dp.py and scripts/eval_gan.py to generate new data with Diffusion Models and with GANs respectively. For example, if one has a trained DDPM model, one can generate new data by running:

python3 scripts/eval_dp.py -n N_SAMPLES_TO_GENERATE --domain 0 PATH_TO_TRAINED_MODEL

Run eval_dp.py --help to see the additional generation options.

LICENSE

This package is distributed under BSD-2 license.

calo-ddpm repository contains some code (primarily in jetgen/base subdirectory) from pytorch-CycleGAN-and-pix2pix. This code is also licensed under BSD-2 (please refer to jetgen/base/LICENSE for details).

Each code snippet that was taken from pytorch-CycleGAN-and-pix2pix has a note about proper copyright attribution.