This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

Last update: Dec 26, 2022

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Overview

Pruning Self-attentions into Convolutional Layers in Single Path

This is the official repository for our paper: Pruning Self-attentions into Convolutional Layers in Single Path by Haoyu He, Jing liu, Zizheng Pan, Jianfei Cai, Jing Zhang, Dacheng Tao and Bohan Zhuang.

Introduction:

To reduce the massive computational resource consumption for ViTs and add convolutional inductive bias, our SPViT prunes pre-trained ViT models into accurate and compact hybrid models by pruning self-attentions into convolutional layers. Thanks to the proposed weight-sharing scheme between self-attention and convolutional layers that cast the search problem as finding which subset of parameters to use, our SPViT has significantly reduced search cost.

Getting started:

In this repository, we provide code for pruning two representative ViT models.

SPViT-DeiT that prunes DeiT. Please see SPViT_DeiT/README.md for details.
SPViT-Swin that prunes Swin. Please see SPViT_Swin/README.md for details.

If you find our paper useful, please consider cite:

@article{he2021Pruning,
  title={Pruning Self-attentions into Convolutional Layersin Single Path},
  author={He, Haoyu and Liu, Jing and Pan, Zizheng and Cai, Jianfei and Zhang, Jing and Tao, Dacheng and Zhuang, Bohan},
  journal={arXiv preprint arXiv:2111.11802},
  year={2021}
}

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This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

Related tags

Overview

Pruning Self-attentions into Convolutional Layers in Single Path

Introduction:

Getting started:

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1.0(Dec 4, 2021)

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