Ha2Mag

Prerequisites

Linux or macOS
Python 3
CPU or NVIDIA GPU + CUDA CuDNN

Installation

Install PyTorch and torchvision.

For pip users, please type the command pip install -r requirements.txt.

Dataset

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.

Train

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.

Test

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/.

Acknowledgments

Code borrows heavily from pytorch-CycleGAN-and-pix2pix