README.md

APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers

This repository contains the official PyTorch implementation for the CVPR 2025 paper "APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers". The code was modified based on AdaLog.

Getting Started

• Clone this repo.

git clone git@github.com:GoatWu/APHQ-ViT.git
cd APHQ-ViT

• Install pytorch and timm.
• Note: A higher version of pytorch may yield better results. The results reported in our paper were obtained using the following configurations:

pip install torch==1.10.0 torchvision --index-url https://download.pytorch.org/whl/cu113
pip install timm==0.9.2

All the pretrained models can be obtained using timm. You can also directly download the checkpoints we provide. For example:

wget https://github.com/GoatWu/AdaLog/releases/download/v1.0/deit_tiny_patch16_224.bin
mkdir -p ./checkpoint/vit_raw/
mv deit_tiny_patch16_224.bin ./checkpoint/vit_raw/

For more details on setting up and running the quantization of detection models, please refer to Object-Detection/README.md

Evaluation

You can quantize and evaluate a single model using the following command:

python test_quant.py --model <MODEL> --config <CONFIG_FILE> --dataset <DATA_DIR> [--reconstruct-mlp] [--load-reconstruct-checkpoint <RECON_CKPT>] [--calibrate] [--load-calibrate-checkpoint <CALIB_CKPT>] [--optimize]

• --model <MODEL>: Model architecture, which can be deit_tiny, deit_small, deit_base, vit_tiny, vit_small, vit_base, swin_tiny, swin_small and swin_base.

• --config <CONFIG_FILE>: Path to the model quantization configuration file.

• --dataset <DATA_DIR>: Path to ImageNet dataset.

• --reconstruct-mlp: Wether to use MLP reconstruction.

• --load-reconstruct-checkpoint <CALIB_CKPT>: When using --reconstruct-mlp, we can directly load a reconstructed checkpoint.

• --calibrate and --load-calibrate-checkpoint <CALIB_CKPT>: A mutually_exclusive_group to choose between quantizing an existing model or directly load a calibrated model. The default selection is --calibrate.

• --optimize: Whether to perform Adaround optimization after calibration.

Example: Optimize the model after reconstruction and calibration.

python test_quant.py --model vit_small --config ./configs/3bit/best.py --dataset ~/data/ILSVRC/Data/CLS-LOC --val-batchsize 500 --reconstruct-mlp --calibrate --optimize

Example: Load a reconstructed checkpoint, then run calibration and optimization.

python test_quant.py --model vit_small --config ./configs/3bit/best.py --dataset ~/data/ILSVRC/Data/CLS-LOC --val-batchsize 500 --reconstruct-mlp --load-reconstruct-checkpoint ./checkpoints/quant_result/deit_tiny_reconstructed.pth --calibrate --optimize

Example: Load a calibrated checkpoint, and then run optimization.

python test_quant.py --model vit_small --config ./configs/3bit/best.py --dataset ~/data/ILSVRC/Data/CLS-LOC --val-batchsize 500 --reconstruct-mlp --load-calibrate-checkpoint ./checkpoints/quant_result/deit_tiny_w3_a3_calibsize_128_mse.pth --optimize

Results

Below are the experimental results of our proposed APHQ-ViT that you should get on the ImageNet dataset. Checkpoints are available in Google Drive and Huggingface.

Model	Full Prec.	MLP Recon.	W4/A4	W3/A3
ViT-S	81.39	80.90	76.07	63.17
ViT-B	84.54	84.84	82.41	76.31
DeiT-T	72.21	71.07	66.66	55.42
DeiT-S	79.85	79.38	76.40	68.76
DeiT-B	81.80	81.43	80.21	76.30
Swin-S	83.23	83.12	81.81	76.10
Swin-B	85.27	84.97	83.42	78.14

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{wu2025aphqvit,
title={APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers},
author={Wu, Zhuguanyu and Zhang, Jiayi and Chen, Jiaxin and Guo, Jinyang and Huang, Di and Wang, Yunhong},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2025}
}