IAE-Net: Integral Autoencoders for Discretization-Invariant Learning

By Yong Zheng Ong, Zuowei Shen and Haizhao Yang

This repository contains the code used for the implementation of the paper:

• IAE-Net: Integral Autoencoders for Discretization-Invariant Learning

Requirements

• Python 3.7.11, PyTorch 1.6.0

Dataset Generation and Training Procedure

The prepared dataset is placed in the corresponding data folder in the datasets directory. For e.g., darcy dataset is placed in datasets/darcy/data. The next few sections describe how to generate the data and run the training for each of the experiments.

Burgers Dataset

The burgers dataset is generated via the data generation code found in https://github.com/zongyi-li/fourier_neural_operator/tree/master/data_generation. A copy is found in datasets/burgers/data_generation/gen_burgers.m. Move the generated file to datasets/burgers/data. Please refer to https://arxiv.org/abs/2203.05142 for the settings used to generate the dataset.

Training is done using the command

python train.py iae_net burgers multi

Darcy Dataset

The burgers dataset is generated via the data generation code found in https://github.com/zongyi-li/fourier_neural_operator/tree/master/data_generation. A copy is found in datasets/darcy/data_generation/gen_darcy.m. Move the generated file to datasets/darcy/data. Please refer to https://arxiv.org/abs/2203.05142 for the settings used to generate the dataset.

Training is done using the command

python train.py iae_net darcy multi

Scattering Dataset

The code to generate the scattering dataset is obtained from the authors of

• https://arxiv.org/abs/1810.09675

A sample dataset is found in the following link. Move the downloaded file to datasets/scattering/data. Training is done using the command

Inverse Problem:

python train.py iae_net inverse_scattering multi

Forward Problem:

python train.py iae_net scattering multi

Ellipses Dataset

The code to generate the ellipses dataset is obtained from

• https://github.com/adler-j/learned_gradient_tomography
• https://github.com/odlgroup/odl

The data generation code is provided in datasets/ellipses/data_generation/odl_data.py. Move the generated files to datasets/ellipses/data.

Training is done using the command

python train.py iae_net ellipses multi

Fecgsyndb Dataset

The dataset is obtained from

• https://physionet.org/content/fecgsyndb/1.0.0/

The data generation code is provided in datasets/fecgsyndb/build_dataset.py. To run the code, place the downloaded fecgsyndb dataset from https://physionet.org/content/fecgsyndb/1.0.0/ in the fecgsyndb folder, then run python build_dataset.py. The end result should give a file fecgsyndb.mat in datasets/fecgsyndb/data folder.

Training is done using the command

python train.py iae_net fecgsyndb multi

Training

The code can be run using train.py, following the below command

python train.py iae_net DATASETNAME LOADTYPE

• DATASETNAME denotes the problem, following the folder name on datasets folder
• LOADTYPE denotes the training style, default refers to no DA, multi refers to IAE-Net, and multi3 applies to the scattering problem for multiple datasets training.

Credits

The following sources have contributed greatly to the development of this repository:

• FNO implementation, burgers and darcy dataset: https://github.com/zongyi-li/fourier_neural_operator
• Scattering Dataset: https://arxiv.org/abs/1810.09675
• Ellipses Dataset: https://github.com/adler-j/learned_gradient_tomography, https://github.com/odlgroup/odl
• Fecgsyndb Dataset: https://physionet.org/content/fecgsyndb/1.0.0/

Citations

@misc{ong2022iaenet,
      title={IAE-Net: Integral Autoencoders for Discretization-Invariant Learning}, 
      author={Yong Zheng Ong and Zuowei Shen and Haizhao Yang},
      year={2022},
      eprint={2203.05142},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}