https://github.com/rose-stl-lab/liegan
https://github.com/rose-stl-lab/liegan
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/rose-stl-lab/liegan
- Owner: Rose-STL-Lab
- Created: 2023-05-28T22:04:21.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2025-04-16T21:54:13.000Z (about 1 year ago)
- Last Synced: 2025-04-17T09:32:03.585Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 17.6 MB
- Stars: 33
- Watchers: 0
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Generative Adversarial Symmetry Discovery (LieGAN)
Code for the ICML 2023 paper, [Generative Adversarial Symmetry Discovery](https://arxiv.org/abs/2302.00236).
## Experiments
### N-Body Trajectory
To discover symmetry from the task of 2-body trajectory prediction, run one of the following:
```
# LieGAN
python main_lagan.py --g_init 2*2_factorization --lamda 1 --sigma_init 1 --num_epochs 100
# Augerino*
python main_augerino.py --g_init 2*2_factorization --lamda 1 --num_epochs 100
# SymmetryGAN
python main_lagan.py --g_init 2*2_factorization --lamda 1 --sigma_init 1 --num_epochs 100 --model sgan
```
To discover symmetry in a larger search space (Figure 3c), run the following:
```
python main_lagan.py --g_init 4*4_factorization --lamda 1e1 --sigma_init 3 --num_epochs 100
```
To run the prediction experiments for EMLP and data augmentation, refer to `emlp.ipynb` and `aug_pred.ipynb`. We have provided the discovery results used for these experiments in `./saved_model/results/`.
As is stated in Section 5.2 in our paper, we need to slightly modify the EMLP implementation of SVD. It is located in line 307 of `emlp/reps/representation.py`, where we need to change the threshold from 1e-5 to larger values, like 5e-3.
To discover symmetry from the task of 3-body trajectory prediction:
```
python main_lagan.py --task traj_pred_3body --dataset_name 3body --g_init 2*2_factorization --lamda 1 --sigma_init 1
```
### Discrete Rotation Invariant Regression
```
python main_lagan.py --task discrete_rotation_synthetic --dataset_size 20000 --y_type scalar --coef_dist uniform_int_grid --uniform_max 10
```
### Top Tagging
To discover symmetry, first download the [top tagging dataset](https://zenodo.org/record/2603256) to `./data/top-tagging`. We only need `train.h5` for symmetry discovery purpose. Then run the following command:
```
python main_lagan.py --task top_tagging --lamda 1 --g_init random --n_channel 7 --y_type scalar --sigma_init 1 --eta 0.1 --n_component 2
```
To run the top tagging prediction task, first download the converted dataset from [this link](https://osf.io/7u3fk/?view_only=8c42f1b112ab4a43bcf208012f9db2df) to the data directory `./data/top-tagging-converted`. Then,
```
cd LorentzNet
```
The content of this directory is modified from the codebase of [LorentzNet](https://github.com/sdogsq/LorentzNet-release). We mainly made our modifications in the file `models.py`, where we implemented LieGNN as proposed in our paper. To run the experiments:
```
# LorentzNet (original)
python -m torch.distributed.launch --nproc_per_node=4 top_tagging.py \
--batch_size=32 --epochs=35 --warmup_epochs=5 \
--n_layers=6 --n_hidden=72 --lr=3e-4 \
--c_weight=0.005 --dropout=0.2 --weight_decay=0.01 \
--exp_name=lorentznet --datadir ../data/top-tagging-converted
# LieGNN (symmetry discovered by LieGAN)
python -m torch.distributed.launch --nproc_per_node=4 top_tagging.py \
--batch_size=32 --epochs=35 --warmup_epochs=5 \
--n_layers=6 --n_hidden=72 --lr=3e-4 \
--c_weight=0.005 --dropout=0.2 --weight_decay=0.01 \
--exp_name=liegnn --datadir ../data/top-tagging-converted --metric saved_metric/metric_liegan.pt
# Augerino+GNN (symmetry discovered by Augerino+)
python -m torch.distributed.launch --nproc_per_node=4 top_tagging.py \
--batch_size=32 --epochs=35 --warmup_epochs=5 \
--n_layers=6 --n_hidden=72 --lr=3e-4 \
--c_weight=0.005 --dropout=0.2 --weight_decay=0.01 \
--exp_name=augerino --datadir ../data/top-tagging-converted --metric saved_metric/metric_augerino.pt
```
### Rotated MNIST
```
python main_lagan.py --task MNIST --g_init affine --x_type grid --y_type scalar --lamda 0.1 --lr_g 2e-4 --reg_type fourier --num_epochs 15
```
## Cite
```
@article{yang2023generative,
title={Generative Adversarial Symmetry Discovery},
author={Yang, Jianke and Walters, Robin and Dehmamy, Nima and Yu, Rose},
booktitle={International Conference on Machine Learning},
organization={PMLR},
year={2023}
}
```