WaveSFNet: A Wavelet-Based Codec and Spatial--Frequency Dual-Domain Gating Network for Spatiotemporal Prediction

1. Environment setup

This repository directly inherits the codebase and dependencies of OpenSTL. We recommend first making sure you can successfully run OpenSTL.

conda env create -f environment.yml
conda activate OpenSTL
pip install -r requirements.txt
python setup.py develop

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2. Data preparation

Data preparation scripts are provided under:

tools/prepare_data/

Please ensure that processed datasets are placed under:

yourprojectname/data/

(for example, yourprojectname/data/mmnist/ for Moving MNIST).

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3. Core code

├── data/moving_mnist
├── configs/
│   └── mmnist/WaveSF.py      # Moving MNIST config
├── openstl/
│   ├── modules/
│   │   ├── wavesf_modules.py                                 
│   └── models/
│       └── WaveSFNet.py    
├── tools/
│   ├── train.py                       
│   └── test.py                        
└── README.md

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4. Usage (Moving MNIST example)

4.1 Configuration

We provide an independent configuration for WaveSFNet, for example:

configs/mmnist/WaveSF.py

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4.2 Training

From the repository root, run:

python tools/train.py -d mmnist -c configs/mmnist/WaveSF.py --ex_name mmnist_wavesfnet --test

If multi-GPU training is needed, you can write your own launcher script as in OpenSTL, still using tools/train.py as the entry point.

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4.2 Testing

From the repository root, run:

python tools/test.py -d mmnist -c configs/mmnist/WaveSF.py --ex_name mmnist_wavesfnet --test

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5. Acknowledgements

This project is built on top of the OpenSTL framework in terms of code structure, training pipeline, and data preparation. We sincerely thank the authors and contributors of OpenSTL for their great work.

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