PD-Quant

PD-Quant: Post-Training Quantization Based on Prediction Difference Metric

[arXiv]

Usage

1. Download pre-trained FP model.

The pre-trained FP models in our experiment comes from BRECQ, they can be downloaded in link. And modify the path of the pre-trained model in hubconf.py.

2. Installation.

python >= 3.7.13
numpy >= 1.21.6
torch >= 1.11.0
torchvision >= 0.12.0

3. Run experiments

You can run run_script.py for different models including ResNet18, ResNet50, RegNet600, RegNet3200, MobilenetV2, and MNasNet. It will experiment on 4 bit settings including W2A2, W4A2, W2A4, and W4A4.

Take ResNet18 as an example:

python run_script.py resnet18

Results

Methods	Bits (W/A)	Res18	Res50	MNV2	Reg600M	Reg3.2G	MNasx2
Full Prec.	32/32	71.01	76.63	72.62	73.52	78.46	76.52
PD-Quant	4/4	69.30	75.09	68.33	71.04	76.57	73.30
PD-Quant	2/4	65.07	70.92	55.27	64.00	72.43	63.33
PD-Quant	4/2	58.65	64.18	20.40	51.29	62.76	38.89
PD-Quant	2/2	53.08	56.98	14.17	40.92	55.13	28.03

Reference

@article{liu2022pd,
  title={PD-Quant: Post-Training Quantization based on Prediction Difference Metric},
  author={Liu, Jiawei and Niu, Lin and Yuan, Zhihang and Yang, Dawei and Wang, Xinggang and Liu, Wenyu},
  journal={arXiv preprint arXiv:2212.07048},
  year={2022}
}

Thanks

Our code is based on QDROP by @wimh966.