The code accompanying our paper currently in preprint at arXiv for complex-valued shifted window (Swin) transformer-based spectral super-resolution.
If you appreciate our work, please cite our work as
@article{smith2023frequency,
title = {Frequency Estimation Using Complex-Valued Shifted Window Transformer},
author = {Smith, J. W. and Torlak, M.},
year = 2023,
month = sep,
journal = {arXiv preprint arXiv:2305.02017}
}
This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.
This is an example of how to list things you need to use the software and how to install them.
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PyTorch
PyTorch must be install using the CPU/GPU configuration desired prior to attempting to run the code.
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requirements.txt
pip install -r requirements.txt
Both SwinFreq and CVSwinFreq can be easily trained from scratch using the included tools.
-
Training SwinFreq
python train.py --model swinfreq
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Training CVSwinFreq
python train.py --model cvswinfreq
The experiments shown in the paper can be reproduced by calling paper_experiments
. The trained models are contained in the folder saved/models/
.
sh python paper_experiments.py
After running the experiments paper_results/make_figures.m
can be run in MATLAB to reproduce the figures used to create the figures and save them to both .fig and .png files.
Note: experiment 4, showing a rotating target and demonstrating the improved resolution capacity of the proposed methods, was not used in the paper due to space constraits.
Distributed under the GPL-3.0 License. See LICENSE.txt
for more information.
Josiah W. Smith - josiah.radar@gmail.com
Project Link: https://github.com/josiahwsmith10/spectral-super-resolution-swin