Awesome

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

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.

  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