  Recently, vision transformer (ViT) and its variants have achieved promising
performances in various computer vision tasks. Yet the high computational costs
and training data requirements of ViTs limit their application in
resource-constrained settings. Model compression is an effective method to
speed up deep learning models, but the research of compressing ViTs has been
less explored. Many previous works concentrate on reducing the number of
tokens. However, this line of attack breaks down the spatial structure of ViTs
and is hard to be generalized into downstream tasks. In this paper, we design a
unified framework for structural pruning of both ViTs and its variants, namely
UP-ViTs. Our method focuses on pruning all ViTs components while maintaining
the consistency of the model structure. Abundant experimental results show that
our method can achieve high accuracy on compressed ViTs and variants, e.g.,
UP-DeiT-T achieves 75.79% accuracy on ImageNet, which outperforms the vanilla
DeiT-T by 3.59% with the same computational cost. UP-PVTv2-B0 improves the
accuracy of PVTv2-B0 by 4.83% for ImageNet classification. Meanwhile, UP-ViTs
maintains the consistency of the token representation and gains consistent
improvements on object detection tasks.
