Exploring Content Relationships For Distilling Efficient GANs

Overview

Prerequisites

• Linux
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

• Clone this repo:

  git clone https://github.com/CRD-Official/CRD.git
  cd CRD

• Install dependencies.

  conda create -n CRD python=3.7
  conda activate CRD
  pip install torch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 
  pip install -r requirements.txt 

Data preparation

• edges2shoes

  • Download the dataset

  bash datasets/download_pix2pix_dataset.sh edges2shoes-r

  • Get the statistical information for the ground-truth images for your dataset to compute FID.

  bash datasets/download_real_stat.sh edges2shoes-r B

• horse2zebra

  • Download the dataset

  bash datasets/download_cyclegan_dataset.sh horse2zebra

  • Get the statistical information for the ground-truth images for your dataset to compute FID.

  bash datasets/download_real_stat.sh horse2zebra A
  bash datasets/download_real_stat.sh horse2zebra B

• summer2winter

  • Download the dataset

  bash datasets/download_cyclegan_dataset.sh summer2winter_yosemite

  • Get the statistical information for the ground-truth images for your dataset to compute FID from here

Pretrained Model

We provide a list of pre-trained models in link.

Training

• pretrained vgg16 we should prepare weights of a vgg16 to calculate the style loss

• train student model using CRD Run the following script to train a resnet-style student on horse2zebra dataset, all scripts for cyclegan and pix2pix on horse2zebra, summer2winter, and edges2shoes can be found in ./scripts

  bash scripts/cycle_gan/horse2zebra/distill.sh

Testing

• test student models, FID will be calculated, take resnet-style generator on horse2zebra dataset as an example

  bash scripts/cycle_gan/horse2zebra/test.sh

Acknowledgements

Our code is developed based on GAN Compression and OMGD