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, AutoML Team (字节跳动-智能创作-AutoML团队).

Authors: Yuxi Ren*, Jie Wu*, Xuefeng Xiao, Jianchao Yang.

Overview

overview

Performance

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
- 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
```
cityscapes
- Download the dataset Download the dataset (gtFine_trainvaltest.zip and leftImg8bit_trainvaltest.zip) from here, and preprocess it. ```shell python datasets/get_trainIds.py database/cityscapes-origin/gtFine/ python datasets/prepare_cityscapes_dataset.py \
- -gtFine_dir database/cityscapes-origin/gtFine \
- -leftImg8bit_dir database/cityscapes-origin/leftImg8bit \
- -output_dir database/cityscapes \
- -train_table_path datasets/train_table.txt \
- -val_table_path datasets/val_table.txt ```
- Get the statistical information for the ground-truth images for your dataset to compute FID.
```
bash datasets/download_real_stat.sh cityscapes A
```

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. DRN model can used to compute mIoU 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