Zxinze 2a336920a4 | 1 year ago | |
---|---|---|
data | 1 year ago | |
dataset | 1 year ago | |
models | 1 year ago | |
options | 1 year ago | |
util | 1 year ago | |
README.md | 1 year ago | |
metrics.py | 1 year ago | |
requirements.txt | 1 year ago | |
test.py | 1 year ago | |
train.py | 1 year ago |
Install PyTorch and torchvision.
pip install -r requirements.txt
.The data we used in the paper is in ./dataset
.
The 2048 x 1024 images in our dataset are pairs of $H\alpha$ images and the corresponding SDO/HMI magnetograms. The dataset mode is aligned.
You can train a model as the following instruction:
python train.py --dataroot ./dataset --name Ha2Mag_pix2pix --model pix2pix
Models are saved to ./checkpoints/
.
See opt
in files(base_options.py and train_options.py) for additional training options.
You can test the model as the following instruction:
python test.py --dataroot ./dataset --name Ha2Mag_pix2pix --model pix2pix
See opt
in files(base_options.py and test_options.py) for additional testing options.
Testing results are saved in ./results/
.
Code borrows heavily from pytorch-CycleGAN-and-pix2pix
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》