Code for ICCV2025 paper 'Semantic Alignment and Reinforcement for Data-Free Quantization of Vision Transformers'

Environment Setup

conda create -n zsqvit python=3.8

pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html

pip install timm==0.4.12 IPython tqdm scipy matplotlib

Sample Synthesis

CUDA_VISIBLE_DEVICES=0 python -u generate.py [--model]
[--calib_batchsize] [--save_fake] [--softlabel] [--coe_sf] [--coe_attn]

optional arguments:
--model: Model architecture
--calib_batchsize: Total number of synthesized samples, e.g., 32 or 1024.
--save_fake: save fake data?
--coe_sf: weight of SL loss
--coe_attn: weight of the APA loss

Quantization

CUDA_VISIBLE_DEVICES=0 python -u test_quant.py [--model] 
[--dataset] [--w_bit] [--a_bit] [--calib_batchsize] 
[--iter] [--optim_batchsize] [--fake_path] [--rep] [--fake_path] [--box_path]
[--softtagets_path]

optional arguments:
--model: Model architecture
--dataset: Path to the ImageNet dataset
--w_bit: Bit-precision of weights
--a_bit: Bit-precision of activations
--optim_batchsize: Batch size per optimization iteration; varies with bit-width. Refer to PTQViT settings, see line 384 of test_quant.py
--rep: use reparameters or not (Please see RepQ-ViT and I\&s-vit)
--fake_path: Path to the synthesized samples
--box_path: Path to the boxes generated by MSR
--softtagets_path: Path to the softtarget generated by SL

Citation

We appreciate it if you would please cite our paper if you found the code/idea useful for your work:

@article{zhong2024semantics,
  title={Semantics Prompting Data-Free Quantization for Low-Bit Vision Transformers},
  author={Zhong, Yunshan and Zhou, Yuyao and Zhang, Yuxin and Li, Shen and Li, Yong and Chao, Fei and Zeng, Zhanpeng and Ji, Rongrong},
  booktitle={International Conference on Computer Vision},
  year={2025}
}

Acknowledgments

Our code is based on the opensource code listed in the following. We highly appreciate their contribution!

@inproceedings{li2022psaqvit,
  title={Patch Similarity Aware Data-Free Quantization for Vision Transformers},
  author={Li, Zhikai and Ma, Liping and Chen, Mengjuan and Xiao, Junrui and Gu, Qingyi},
  booktitle={European Conference on Computer Vision},
  pages={154--170},
  year={2022}
}

@inproceedings{li2023repq,
  title={Repq-vit: Scale reparameterization for post-training quantization of vision transformers},
  author={Li, Zhikai and Xiao, Junrui and Yang, Lianwei and Gu, Qingyi},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={17227--17236},
  year={2023}
}

@article{zhong2023s,
  title={I\&s-vit: An inclusive \& stable method for pushing the limit of post-training vits quantization},
  author={Zhong, Yunshan and Hu, Jiawei and Lin, Mingbao and Chen, Mengzhao and Ji, Rongrong},
  journal={arXiv preprint arXiv:2311.10126},
  year={2023}
}