Generative-Low-bitwidth-Data-Free-Quantization

We provide PyTorch implementation for "Generative Low bitwidth Data Free Quantization".

Paper

• Generative Low bitwidth Data Free Quantization
• Shoukai Xu^*, Haokun Li^*, Bohan Zhuang^*, Jing Liu, Jiezhang Cao, Chuangrun Liang, Mingkui Tan^*
• The European Conference on Computer Vision(ECCV), 2020

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Dependencies

• Python 3.6
• PyTorch 1.2.0
• dependencies in requirements.txt

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Getting Started

Installation

1. Clone this repo:

    git clone https://github.com/xushoukai/Generative-Low-bitwidth-Data-Free-Quantization.git
    cd Generative-Low-bitwidth-Data-Free-Quantization
   

2. Install pytorch and other dependencies.

    pip install -r requirements.txt
   

Set the paths of datasets for testing

1. Set the "dataPath" in "cifar100_resnet20.hocon" as the path root of your CIFAR-100 dataset. For example:

    dataPath = "/home/datasets/Datasets/cifar"
   

2. Set the "dataPath" in "imagenet_resnet18.hocon" as the path root of your ImageNet dataset. For example:

    dataPath = "/home/datasets/Datasets/imagenet"
   

Training

To quantize the pretrained ResNet-20 on CIFAR-100 to 4-bit:

python main.py --conf_path=./cifar100_resnet20.hocon --id=01

To quantize the pretrained ResNet-18 on ImageNet to 4-bit:

python main.py --conf_path=./imagenet_resnet18.hocon --id=01

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Results

| Dataset | Model | Pretrain Top1 Acc(%) | W4A4(Ours) Top1 Acc(%) | | :-: | :-: | :-: | :-: | | CIFAR-100 | ResNet-20| 70.33 | 63.58 ± 0.23 | | ImageNet | ResNet-18 | 71.47 | 60.60 ± 0.15 |

Note that we use the pretrained models from pytorchcv.

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Citation

If this work is useful for your research, please cite our paper:

@InProceedings{xu2020generative,
title = {Generative Low-bitwidth Data Free Quantization},
author = {Shoukai, Xu and Haokun, Li and Bohan, Zhuang and Jing, Liu and Jiezhang, Cao and Chuangrun, Liang and Mingkui, Tan},
booktitle = {The European Conference on Computer Vision},
year = {2020}
}

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Acknowledgments

This work was partially supported by the Key-Area Research and Development Program of Guangdong Province 2018B010107001, Program for Guangdong Introducing Innovative and Entrepreneurial Teams 2017ZT07X183, Fundamental Research Funds for the Central Universities D2191240.