python 3.6

TensorFlow 1.5

Data

Download Imagenet dataset and preprocess the images into tfrecord files as instructed in improved gan. Put the tfrecord files into ./data

Different Dataset Size

To use different data size NUM_DATA for training, modify utils_ori.py:

dataset = dataset.take(NUM_DATA)

Training

The current batch size is 64x4=256. Larger batch size seems to give better performance. But it might need to find new hyperparameters for G&D learning rate. Note: It usually takes several weeks to train one million steps.

CUDA_VISIBLE_DEVICES=0,1,2,3 python train_imagenet.py --generator_type test --discriminator_type test --data_dir ./data --train_size TRAIN_SIZE

Evaluation

CUDA_VISIBLE_DEVICES=4 python eval_imagenet.py --generator_type test --data_dir ./data --train_size TRAIN_SIZE
tensorboard --logdir=./checkpoint/eval/LOGDIR
ssh -fgNL 6006:localhost:6006 [email protected]

Then, open tensorboard to check FID/IS score and samples.

If you find Self-attention GAN is useful in your research, please consider citing:

@article{Han18,
  author    = {Han Zhang and
               Ian J. Goodfellow and
               Dimitris N. Metaxas and
               Augustus Odena},
  title     = {Self-Attention Generative Adversarial Networks},
  year      = {2018},
  journal = {arXiv:1805.08318},
}

References

• Spectral Normalization for Generative Adversarial Networks Paper
• cGANs with Projection Discriminator Paper
• Non-local Neural Networks Paper