BRECQ
Pytorch implementation of BRECQ, ICLR 2021
@inproceedings{
li&gong2021brecq,
title={BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction},
author={Yuhang Li and Ruihao Gong and Xu Tan and Yang Yang and Peng Hu and Qi Zhang and Fengwei Yu and Wei Wang and Shi Gu},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=POWv6hDd9XH}
}
Pretrained models
We provide all the pretrained models and they can be accessed via torch.hub
For example: use res18 = torch.hub.load('yhhhli/BRECQ', model='resnet18', pretrained=True) to get the pretrained ResNet-18 model.
If you encounter URLError when downloading the pretrained network, it’s probably a network failure. An alternative way is to use wget to manually download the file, then move it to ~/.cache/torch/checkpoints, where the load_state_dict_from_url function will check before downloading it.
For example:
wget https://github.com/yhhhli/BRECQ/releases/download/v1.0/resnet50_imagenet.pth.tar
mv resnet50_imagenet.pth.tar ~/.cache/torch/checkpoints
Usage
python main_imagenet.py --data_path PATN/TO/DATA --arch resnet18 --n_bits_w 2 --channel_wise --n_bits_a 4 --act_quant --test_before_calibration
You can get the following output:
Quantized accuracy before brecq: 0.13599999248981476
Weight quantization accuracy: 66.32799530029297
Full quantization (W2A4) accuracy: 65.21199798583984
