We recommend using python3 and a virtual environment
virtualenv -p python3 .env
source .env/bin/activate
pip install -r requirements.txt
When you're done working on the project, deactivate the virtual environment with deactivate.
The training set is in Data folder. trainset.nc comprises of 500 high efficiency deflectors with size 64 x 256. They are only the half of a full device (128 x 256), because we enforce the reflection symmetry along y direction. You can see more details of the training set by loading them with python or matlab.
You can change the parameters by editing Params.json in Result folder.
If you want to train the network, simply run
python main.py
or
python main.py --output_dir Result --train_path Data/trainset.nc
to specify non-default output folder or training set
All results will store in output_dir folder.
-figures/ (figures of generated devices and loss function curve for every 250 iterations)
-model/ (all weights of the generator and discriminator)
-outputs/ (500 generated devices for every combination of wavelength and angle in `.mat` format)
If you use this code for your research, please cite:
Free-form diffractive metagrating design based on generative adversarial networks.
J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan