Online Multi-Granularity Distillation for GAN Compression (ICCV2021)

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Deep LearningOMGD
Overview

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

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
  • 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

Owner
Bytedance Inc.
Bytedance Inc.
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