brecq's Introduction

BRECQ

Pytorch implementation of BRECQ, ICLR 2021

@article{li2021brecq,
  title={BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction},
  author={Li, Yuhang and Gong, Ruihao and Tan, Xu and Yang, Yang and Hu, Peng and Zhang, Qi and Yu, Fengwei and Wang, Wei and Gu, Shi},
  journal={arXiv preprint arXiv:2102.05426},
  year={2021}
}

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

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