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Quantization Networks

Overview

This repository contains the training code of Quantization Networks introduced in our CVPR 2019 paper: Quantization Networks.

In this work, we provide a simple and uniform way for weights and activations quantization by formulating it as a differentiable non-linear function. The quantization function is represented as a linear combination of several Sigmoid functions with learnable biases and scales that could be learned in a lossless and end-to-end manner via continuous relaxation of the steepness of Sigmoid functions.

Extensive experiments on image classification and object detection tasks show that our quantization networks outperform state-of-the-art methods.

Run environment

  • Python 3.5
  • Python bindings for OpenCV
  • Pytorch 0.3.0

Usage

Download the ImageNet dataset and decompress into the structure like

dir/
  train/
    n01440764_10026.JPEG
    ...
  val/
    ILSVRC2012_val_00000001.JPEG
    ...

To train a weight quantization model of ResNet-18, simply run

sh quan-weight.sh

After the training, the result model will be stored in ./logs/quan-weight/resnet18-quan-w-1.

Other training processes can be found in the paper.

License

  • Apache License 2.0

Citation

If you use our code or models in your research, please cite with:

@inproceedings{yang2019quantization,
  title={Quantization Networks},
  author={Yang Jiwei, Shen Xu, Xing Jun, Tian Xinmei, Li Houqiang, Deng Bing, Huang Jianqiang and Hua Xian-sheng},
  booktitle={CVPR},
  year={2019}
}

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