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Patch Similarity Aware Data-Free Quantization for Vision Transformers

This repository contains the official PyTorch implementation for the ECCV 2022 paper "Patch Similarity Aware Data-Free Quantization for Vision Transformers". To the best of our knowledge, this is the first work on data-free quantization for vision transformers. Below are instructions for reproducing the results.

Installation

• To install PSAQ-ViT and develop locally:

git clone https://github.com/zkkli/PSAQ-ViT.git
cd PSAQ-ViT

Quantization

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

python test_quant.py [--model] [--dataset] [--w_bit] [--a_bit] [--mode]

optional arguments:
--model: Model architecture, the choises can be: 
         deit_tiny, deit_small, deit_base, swin_tiny, and swin_small.
--dataset: Path to ImageNet dataset.
--w_bit: Bit-precision of weights, default=8.
--a_bit: Bit-precision of activation, default=8.
--mode: Mode of calibration data,
        0: Generated fake data (PSAQ-ViT)
        1: Gaussian noise
        2: Real data

• Example: Quantize DeiT-B with generated fake data (PSAQ-ViT).

python test_quant.py --model deit_base --dataset <YOUR_DATA_DIR> --mode 0

• Example: Quantize DeiT-B with Gaussian noise.

python test_quant.py --model deit_base --dataset <YOUR_DATA_DIR> --mode 1

• Example: Quantize DeiT-B with Real data.

python test_quant.py --model deit_base --dataset <YOUR_DATA_DIR> --mode 2

Results

Below are the experimental results of our proposed PSAQ-ViT that you should get on ImageNet dataset using an RTX 3090 GPU.

Citation

We appreciate it if you would please cite the following paper if you found the implementation useful for your work:

@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}
}