https://github.com/bytedance/OMGD

Online Multi-Granularity Distillation for GAN Compression (ICCV2021)
https://github.com/bytedance/OMGD

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Online Multi-Granularity Distillation for GAN Compression (ICCV2021)

• Host: GitHub
• URL: https://github.com/bytedance/OMGD
• Owner: bytedance
• Created: 2021-08-16T03:04:34.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-10-08T10:30:56.000Z (about 2 years ago)
• Last Synced: 2024-11-25T04:05:10.903Z (27 days ago)
• Topics: research
• Language: Python
• Homepage:
• Size: 1.03 MB
• Stars: 331
• Watchers: 12
• Forks: 41
• Open Issues: 15
• Metadata Files:
  • Readme: README.md

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README

        
# 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](https://arxiv.org/pdf/2108.06908.pdf)".  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](imgs/OMGD.png)

## Performance

![performance](imgs/performance.png)

## Prerequisites

* Linux

* Python 3

* CPU or NVIDIA GPU + CUDA CuDNN

## Getting Started

### Installation

- Clone this repo:

  ```shell

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

  cd OMGD

  ```

- Install dependencies.

  ```shell

  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

    ```shell

    bash datasets/download_pix2pix_dataset.sh edges2shoes-r

    ```

  

    - Get the statistical information for the ground-truth images for your dataset to compute FID. 

    ```shell

    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](https://cityscapes-dataset.com), 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. 

    ```shell

    bash datasets/download_real_stat.sh cityscapes A

    ```

- horse2zebra

    - Download the dataset

    ```shell

    bash datasets/download_cyclegan_dataset.sh horse2zebra

    ```

    - Get the statistical information for the ground-truth images for your dataset to compute FID. 

    ```shell

    bash datasets/download_real_stat.sh horse2zebra A

    bash datasets/download_real_stat.sh horse2zebra B

    ```

    

- summer2winter

    - Download the dataset

    ```shell

    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](https://drive.google.com/drive/folders/1JKJlpUDdD4TdXdwPwfdWUiF4PsXLAbto)

### Pretrained Model

We provide a list of pre-trained models in [link](https://drive.google.com/drive/folders/1lDSguCuRDKl2bKQzAuc8hR-UE7eTqWvW?usp=sharing). DRN model can used to compute mIoU [link](https://drive.google.com/drive/folders/0B_4LoEXGO1TwcmhzLXpWUVFEMXM?resourcekey=0-PMTQHtlWMtSBYozjajFLXA).

### 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

  ```shell

  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

  ```shell

  bash scripts/unet_pix2pix/cityscapes/test.sh

  ```

## Citation

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

  ```shell

@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](https://github.com/mit-han-lab/gan-compression)