Generating Pokémon with various GAN generator architectures, using pytorch. Part of the Seminar on Computer Vision, University of Helsinki, 2020.
I got Generative Adversarial Networks as the subject for my seminar report, and I decided to pit two advanced GAN generator architectures agains each other, using a tried-and-true DCNN as the baseline. The seminar report, with a bunch of images, explanation and results, is available here.
The code is still quite messy. I'll try to get around to cleaning it a bit soon!
- Make sure you have Python 3 installed
- Install requirements:
pip3 install -r requirements.txt - Check that everything works:
python3 ./discriminator.py- This should result in
Trainable parameters: 2765568
- This should result in
- You're good to go!
The raw data is expected to consist of
- .png -format images with
- one row of rectangular frames each
Personally, I used this dataset,
and the folders in data.py are set up so that this dataset will work if extracted under
3d/3D Battlers in the project directory.
Alternatively, you can change SPRITE_LOCATIONS to load data from elsewhere.
To generate a dataset for training, just make sure that you have valid sprites in SPRITE_LOCATIONS
and run data.py.
This will split the input sprites,
pick a given number of frames from them and save the frames under data/pokemon
(where PyTorch expects it to be).
To train the models,
just comment out the relevant rows under if __name__ == '__main__' in training.py
and run it.
(I know, it's a bit of a mess, sorry!)
This repo includes some nice interactive visualizations for SAGAN and StyleGAN.
To see those, run sagen_visualize.py or stylegen_visualize.py, respectively.