Learning Efficient GANs using Differentiable Masks and Co-Attention Distillation (Link).

Framework of our method. We first build a pre-trained model similar to a GAN network, upon which a differentiable mask is imposed to scale the convolutional outputs of the generator and derive a light-weight one. Then, the co-Attention of the pre-trained GAN and the outputs of the last-layer convolutions of the discriminator are distilled to stabilize the training of the light-weight model.

Tips

Any problem, free to contact the first authors (shaojieli@stu.xmu.edu.cn).

Getting Started

The code has been tested using Pytorch1.5.1 and CUDA10.2 on Ubuntu 18.04.

Please type the command

pip install -r requirements.txt

to install dependencies.

CycleGAN

• Download the Cyclcegan dataset (eg. horse2zebra)

  bash datasets/download_cyclegan_dataset.sh horse2zebra

• Download our pre-prepared real statistic information for computing FID, and then copy them to the root directionary of dataset.

  Task	Download
  horse2zebra	Link
  summer2winter	Link

• Train the model using our differentiable masks (eg. horse2zebra)

  bash scripts/cyclegan/horse2zebra/train.sh

• Finetune the searched light-weight models with co-Attention distillation

  bash scripts/cyclegan/horse2zebra/finetune.sh

Pix2Pix

• Download the Pix2Pix dataset (eg. edges2shoes)

  bash datasets/download_pix2pix_dataset.sh edges2shoes-r

• Download our pre-trained real statistic information for computing FID or DRN-D-105 model for computing mIOU, and then copy them to the root directionary of dataset.

  Task	Download
  edges2shoes	Link
  cityscapes	Link

• Train the model using our differentiable masks (eg. edges2shoes)

  bash scripts/pix2pix/edges2shoes/train.sh

• Finetune the searched light-weight models with co-Attention distillation

  bash scripts/pix2pix/edges2shoes/finetune.sh

Acknowledgements

Our code is developed based on pytorch-CycleGAN-and-pix2pix and GAN Compression.