[paper] accepted as Oral by CVPR 2021.

Author: Yuang Liu, Wei Zhang, Jun Wang

East China Normal University (ECNU)

Figure 1. Overview. (a) previous methods; (b) ours.

To address the quantization issue without data, we propose a zero-shot adversarial quantization (ZAQ) framework, facilitating effective discrepancy estimation and knowledge transfer from a full-precision model to its quantized model. This is achieved by a novel two-level discrepancy modeling to drive a generator to synthesize informative and diverse data examples to optimize the quantized model in an adversarial learning fashion.

Figure 2. ZAQ framework

• python>=3.6
• torch>=1.2
• torchvision
• visdom
• numpy
• pillow
• scikit-learn

To obtain a full-precision model, please refer train.py.

QAT on original dataset:

python quantize.py --model resnet18 --ckpt 'path/' --data_root './data/' --weight_bit 6 --activation_bit 8

Zero-shot quantization without data:

python main.py --model resnet18 --ckpt 'path/' --data_root './data/' --weight_bit 6 --activation_bit 8 

• Segmentation networks
• Object detection networks
• Quantization supported by PyTorch >= 1.7
• Mixed-/Arbitrary- precision quantization

Note: This code is temporarily for reference and we will upload a improved version in the future.

@InProceedings{yuang2021zaq,
    tilte = {Zero-shot Adversarial Quantization},
    author = {Liu, Yuang and Zhang, Wei and Wang, Jun},
    booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2021}
}