  Data-free quantization can potentially address data privacy and security
concerns in model compression, and thus has been widely investigated. Recently,
PSAQ-ViT designs a relative value metric, patch similarity, to generate data
from pre-trained vision transformers (ViTs), achieving the first attempt at
data-free quantization for ViTs. In this paper, we propose PSAQ-ViT V2, a more
accurate and general data-free quantization framework for ViTs, built on top of
PSAQ-ViT. More specifically, following the patch similarity metric in PSAQ-ViT,
we introduce an adaptive teacher-student strategy, which facilitates the
constant cyclic evolution of the generated samples and the quantized model
(student) in a competitive and interactive fashion under the supervision of the
full-precision model (teacher), thus significantly improving the accuracy of
the quantized model. Moreover, without the auxiliary category guidance, we
employ the task- and model-independent prior information, making the
general-purpose scheme compatible with a broad range of vision tasks and
models. Extensive experiments are conducted on various models on image
classification, object detection, and semantic segmentation tasks, and PSAQ-ViT
V2, with the naive quantization strategy and without access to real-world data,
consistently achieves competitive results, showing potential as a powerful
baseline on data-free quantization for ViTs. For instance, with Swin-S as the
(backbone) model, 8-bit quantization reaches 82.13 top-1 accuracy on ImageNet,
50.9 box AP and 44.1 mask AP on COCO, and 47.2 mIoU on ADE20K. We hope that
accurate and general PSAQ-ViT V2 can serve as a potential and practice solution
in real-world applications involving sensitive data. Code is released and
merged at: https://github.com/zkkli/PSAQ-ViT.
