Awesome
PyTorch implementation of EWGS
This is the implementation of the paper "Network Quantization with Element-wise Gradient Scaling".
For more information, checkout the project site [website] and the paper [PDF].
Requirements
- Python >= 3.6
- PyTorch >= 1.3.0
Datasets
- CIFAR-10 (will be automatically downloaded when you run the code)
- ImageNet (ILSVRC-2012) available at http://www.image-net.org
Code
Please refer to the run.sh
files in the CIFAR10 and ImageNet folders.
Bibtex
@inproceedings{lee2021network,
title={Network Quantization with Element-wise Gradient Scaling},
author={Lee, Junghyup and Kim, Dohyung and Ham, Bumsub},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2021}
}
References
- ImageNet training code: [PyTorch official example code]
- ResNet-18/34 models: [PyTorch official code]
- ResNet-20 model: [ResNet on CIFAR10] [IRNet]
- Quantized modules: [DSQ]
- Estimating Hessian trace: [PyHessian]