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CIFAR10 image generation using fully-transformer-GAN with pytorch

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TrGAN

Generation of CIFAR10 images based on TrGAN (hands-on):

Dependencies

  • Python 3.8
  • Tensorfow 2.5

Usage

Train

  1. Use --dataset_path=<path> to specify the dataset path (default builds CIFAR-10 dataset), and --model_name=<name> to specify the checkpoint directory name.
python train.py --dataset_path=<path> --model_name=<name>

Hparams setting

Adjust hyperparameters in the hparams.py file.

Tensorboard

Run tensorboard --logdir ./.

Examples

  • CIFAR-10 training progress

References

Code:

  • This model depends on other files that may be licensed under different open source licenses.
  • TransGAN uses Differentiable Augmentation. Under BSD 2-Clause "Simplified" License.
  • Small-TransGAN models are instances of the original TransGAN architecture with a smaller number of layers and lower-dimensional embeddings.

Implementation notes:

  • Single layer per resolution Generator.
  • Orthogonal initializer and 4 heads in both Generator and Discriminator.
  • WGAN-GP loss.
  • Adam with β1 = 0.0 and β2 = 0.99.
  • Noise dimension = 64.
  • Batch size = 64

Licence

MIT

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CIFAR10 image generation using fully-transformer-GAN with pytorch

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