PyTorch implementation for the ICLR 2018 oral paper, training on CIFAR10. This is replicate from the Tensorflow repo by the paper's authors. We hope the PyTorch implementation could also help with low-precision training research.

• NVIDIA GPU + CUDA + CuDNN
• PyTorch
• TensorboardX
• Tabulate
• tqdm

Please follow the official instruction to install PyTorch and NVIDIA related prerequisites. Other things should be handled by

pip install -r requirements.txt

Start training using the following scripts:

./wage.sh

Averaging four seeds gives: 93.04% accuracy at 300 epochs.

If you find this paper or this repository helpful, please cite the original paper:

@inproceedings{
wu2018training,
title={Training and Inference with Integers in Deep Neural Networks},
author={Shuang Wu and Guoqi Li and Feng Chen and Luping Shi},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=HJGXzmspb},
}