Online Multi-Granularity Distillation for GAN Compression (ICCV2021)

This repository contains the pytorch codes and trained models described in the ICCV2021 paper "Online Multi-Granularity Distillation for GAN Compression" By Yuxi Ren*, Jie Wu*, Xuefeng Xiao, Jianchao Yang.

Overview

Performance

Prerequisites

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

Getting Started

Installation

• Clone this repo:

  git clone https://github.com/bytedance/OMGD.git
  cd OMGD

• Install dependencies.

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

Data preparation

• edges2shoes
• cityscapes
• horse2zebra
• summer2winter

Training

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

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

  bash scripts/unet_pix2pix/cityscapes/distill.sh

Testing

• test student models, FID or mIoU will be calculated, take unet-style generator on cityscapes dataset as an example

  bash scripts/unet_pix2pix/cityscapes/test.sh

Citation

If you use this code for your research, please cite our paper.

Acknowledgements

Our code is developed based on GAN Compression