This project hosts the code for implementing the algorithms as presented in our papers:
@article{zhuang2021effective,
title={Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations},
author={Zhuang, Bohan and Liu, Jing and Tan, Mingkui and Liu, Lingqiao and Reid, Ian and Shen, Chunhua},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2021}
}
@inproceedings{zhuang2018towards,
title={Towards effective low-bitwidth convolutional neural networks},
author={Zhuang, Bohan and Shen, Chunhua and Tan, Mingkui and Liu, Lingqiao and Reid, Ian},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={7920--7928},
year={2018}
}
ipytorch is a self-implemented package for running experiments on pytorch
pip install git+https://github.com/chenyaofo/torchlearning.git@master
pip install pydot termcolor
pip install tensorflow
pip instal pytorch
pip install torchvision
pip install pydot
For joint knowledge distillation on quantization, run python ./ipytorch/tasks/quantization/mutual_kl/trainer.py --conf_path imagenet_[2]_lambda1_T1.hocon --id 1
Copyright (c) Jing Liu. 2019
** This code is for non-commercial purposes only. For commerical purposes, please contact Jing Liu <seliujing@@mail.scut.edu.cn> **
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.