上海交通大学学报

上海交通大学学报 >

2023 , Vol. 57 >Issue 9: 1203 - 1213

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.077

电子信息与电气工程

融合MobileNetV3特征的结构化剪枝方法

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  • 内蒙古大学 电子信息工程学院, 呼和浩特 010021
刘宇(1998-),硕士生,从事模型压缩研究.

收稿日期: 2022-03-21

  修回日期: 2022-05-30

  录用日期: 2022-06-06

  网络出版日期: 2023-04-18

基金资助

内蒙古自然科学基金(2016MS0617);国家自然科学基金(61640011)

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A Structured Pruning Method Integrating Characteristics of MobileNetV3

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  • School of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China

Received date: 2022-03-21

  Revised date: 2022-05-30

  Accepted date: 2022-06-06

  Online published: 2023-04-18

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摘要

传统的深度神经网络由于计算量和内存占用庞大,难以部署到嵌入式平台中发挥实用价值,所以轻量级的深度神经网络得到快速发展.其中,谷歌提出的轻量级架构MobileNet具有广泛的应用.为了进一步提高性能,MobileNet的模型由MobileNetV1发展到MobileNetV3,但模型变得更为复杂,导致其规模不断扩大,难以发挥轻量级模型的优势.为了在能保持MobileNetV3性能的前提下,降低部署于嵌入式平台的难度,提出一种融合MobileNetV3特征的结构化剪枝方法,对MobileNetV3-Large模型进行裁剪,得到一个更加紧凑的模型.首先对模型进行稀疏正则化训练,得到一个较为稀疏的网络模型;然后使用卷积层的稀疏值和批量归一化层的缩放系数的乘积判别冗余滤波器对其进行结构化剪枝,并在CIFAR-10和CIFAR-100数据集上进行实验.实验结果表明:提出的压缩方法可以有效压缩模型参数,并且压缩后模型仍然能保证良好性能;在准确率不变的前提下,CIFAR-10上模型的参数量减少44.5%,且计算量减少40%.

关键词： 深度神经网络; 轻量级模型; 结构化剪枝; MobileNetV3

本文引用格式

刘宇, 雷雪梅 . 融合MobileNetV3特征的结构化剪枝方法[J]. 上海交通大学学报, 2023 , 57(9) : 1203 -1213 . DOI: 10.16183/j.cnki.jsjtu.2022.077

Abstract

Due to its huge amount of calculation and memory occupation, the traditional deep neural network is difficult to be deployed to embedded platform. Therefore, lightweight models have been developing rapidly. Among them, the lightweight architecture MobileNet proposed by Google has been widely used. To improve the performance, the model of MobileNet has developed from MobileNetV1 to MobileNetV3. However, the model has become more complex and its scale continues to expand, which is difficult to give full play to the advantages of lightweight model. To reduce the difficulty of deploying MobileNetV3 on embedded platform while maintaining its performance, a structured pruning method integrating the characteristics of MobileNetV3 is proposed to prune the lightweight model MobileNetV3-Large to obtain a more compact lightweight model. First, the model is trained by sparse regularization to obtain a sparse network model. Then, the product of the sparse value of convolution layer and scale factor of batch normalization layer is used to identify the redundant filter, which is structurally pruned, and experiment is conducted on CIFAR-10 and CIFAR-100 datasets. The results show that the proposed compression method can effectively compress the model parameters, and the compressed model can still ensure a good performance. While the accuracy remains unchanged, the number of parameters on CIFAR-10 in the model is reduced by 44.5% and calculation amount is reduced by 40%.

Key words： deep neural network; lightweight model; structured pruning; MobileNetV3

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