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.
- Python 3.5
- Python bindings for OpenCV
- Pytorch 0.3.0
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.
- Apache License 2.0
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}
}