README.md

self-attention-GAN-pytorch

This is an almost exact replica in PyTorch of the Tensorflow version of SAGAN released by Google Brain [repo] in August 2018.

Code structure is inspired from this repo, but follows the details of Google Brain's repo.

Prerequisites

Check requirements.txt.

• Python 3.5+
• PyTorch 0.4.1

Training

1. Check parameters.py for all arguments and their default values

2. Train on custom images in folder a/b/c:

$ python train.py --data_path 'a/b/c' --save_path 'o/p/q' --batch_size 64 --name sagan

(Warning: Works only on 128x128 images, input images are resized to that. Tweak the Generator & Discriminator first if you would like to use some other image size. And then use the imsize option:

$ python train.py --data_path 'a/b/c' --save_path 'o/p/q' --batch_size 64 --imsize 32 --name sagan

)

Model training will be recorded in a new folder inside --save_path with the name <timestamp>_<name>_<basename of data_path>.

By default, model weights are saved in a subfolder called weights, and train & validation samples during training in a subfolder called samples (can be changed in parameters.py).

Testing/Evaluating

Check test.py.

Self-Attention GAN

Han Zhang, Ian Goodfellow, Dimitris Metaxas and Augustus Odena, "Self-Attention Generative Adversarial Networks." arXiv preprint arXiv:1805.08318 (2018).

@article{Zhang2018SelfAttentionGA,
    title={Self-Attention Generative Adversarial Networks},
    author={Han Zhang and Ian J. Goodfellow and Dimitris N. Metaxas and Augustus Odena},
    journal={CoRR},
    year={2018},
    volume={abs/1805.08318}
}