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". This algorithm is proposed by ByteDance, Intelligent Creation, CV AutoML Team (字节跳动-智能创作-CV AutoML团队).

Authors: 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

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 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.

@article{ren2021online,
title={Online Multi-Granularity Distillation for GAN Compression},
author={Ren, Yuxi and Wu, Jie and Xiao, Xuefeng and Yang, Jianchao},
journal={arXiv preprint arXiv:2108.06908},
year={2021}
}

Acknowledgements

Our code is developed based on GAN Compression

Charlottecuc / OMGD