Awesome

Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective (CVPR 2023) (Link)

Overview

Prerequisites

Linux
Python 3
CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Requirements

MQBench
pyyaml
easydict

Data preparation

ImageNet

Add the imagenet path to the "data.path" in the config file.

Run MRECG

Run the following command to quantize the model using the MRECG algorithm, all yaml files can be found in ./config. You can change the bit width, batchsize, pre-trained model path and other quantization parameters in the yaml file.
```
python ptq_main.py --config configs/qdrop/mbv2_2_4.yaml
```

Results

Method\Model	W/A	Res18	Res50	MBV2×1.0	MBV2×0.75	MBV2×0.5	MBV2×0.35	Reg600M
Full Prec.	32/32	71.01	76.63	72.20	69.95	64.60	60.08	73.52
Pretrained	-	link	link	link	link	link	link	link
Ours+BRECQ	4/4	69.06	74.84	68.56	64.55	55.26	50.67	-
Ours+BRECQ	2/4	65.61	70.04	58.49	52.50	41.16	35.46	-
Ours+BRECQ	3/3	65.64	70.68	57.14	50.21	35.11	30.26	-
Ours+BRECQ	2/2	52.02	43.72	13.84	9.46	3.43	3.22	-
Ours+QDROP	4/4	69.46	75.35	68.84	64.39	55.64	50.94	71.22
Ours+QDROP	2/4	66.18	70.53	57.85	53.71	40.09	35.85	65.16
Ours+QDROP	3/3	66.30	71.92	58.40	51.78	38.43	32.96	66.08
Ours+QDROP	2/2	54.46	56.82	14.44	11.40	4.18	3.09	43.67

Due to the presence of random numbers in the experiment, the actual model accuracy may be slightly high or low.

Acknowledgements

Our code relies on the MQBench package.

Reference

@article{ma2023solving,
  title={Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective},
  author={Yuexiao Ma and Huixia Li and Xiawu Zheng and Xuefeng Xiao and Rui Wang and Shilei Wen and Xin Pan and Fei Chao and Rongrong Ji},
  journal={arXiv preprint arXiv:2303.11906},
  year={2023}
}