基于自适应层信息熵的卷积神经网络压缩

doi:10.12263/DZXB.20201372

摘要/Abstract

摘要：

网络剪枝是一种有效的卷积神经网络压缩方法.多数现有压缩方法因迭代剪枝了“不重要”的网络结构，一方面破坏了网络结构的信息整体性，另一方面其迭代操作耗费了大量的计算资源与时间.为了解决上述问题，论文从网络结构全局考虑，提出基于自适应层信息熵的卷积神经网络压缩方法.首先，在获取压缩网络结构的过程中，本文设计了一种端到端的结构化网络剪枝方案，将卷积层看作一个整体，利用层信息熵之间的关联性直接确定各卷积层过滤器的保留率，避免迭代剪枝训练造成的信息损失.其次，对剪裁后的网络进行重训练时，综合考虑压缩过程中使用的层信息熵指标，通过对卷积层与批归一化（Batch Normalization，BN）层进行自适应联合嫁接，让网络学习到更多的信息，提升网络性能.针对3种主流网络在不同的数据集上进行了实验，验证了所提方法的有效性与优越性.例如在CIFAR-10上，针对ResNet-56，相比于基线网络，在计算量压缩36.2%的情况下，本文方法准确率提升了1%；针对ResNet-110，在计算量压缩52.4%的情况下，本文方法准确率提升了1.42%；针对轻量型网络MobileNetV2，在计算量压缩55.2%的情况下，本文方法准确率提升了1.29%.

关键词: 卷积神经网络, 网络剪枝, 信息熵, 嫁接, 模型压缩

Abstract:

Network pruning has proven to be an effective approach to compress convolutional neural network (CNN). However, most existing CNN compression methods iteratively prune the "least important" filters, which not only destroys the information integrity of network structures, but also results in significant computation cost due to the iterative operation. To solve the problems, a convolutional neural network compression method based on adaptive layer entropy(ALE) is proposed, considering a global network structure. Firstly, an end-to-end network pruning strategy is designed, in which the retention rate of each convolutional layer filter is directly determined based on the entropy correlation between layers. The pruning strategy takes the convolutional layer as a whole, which decreases the information loss and computation cost of iterative pruning. Then, an adaptive joint grafting method, in which both convolutional and batch normalization(BN) layers are considered, is presented for the pruned network retraining to learn more information from the network. The layer entropies used in the compression are also utilized for the grafting. Experiments are conducted on different benchmarks and three popular networks, which demonstrate the efficiency and superiority of the proposed ALE over other methods. For the experiments on CIFAR-10, ALE achieves 36.2%, 52.4% and 55.2% pruned rate in FLOPs for ResNet-56, ResNet-110 and MobileNetV2 while with increase of 1%, 1.42%, 1.29% accuracy respectively.

Key words: convolutional neural network, network pruning, entropy, grafting, model compression

中图分类号:

TP391.41

魏钰轩, 陈莹. 基于自适应层信息熵的卷积神经网络压缩[J]. 电子学报, 2022, 50(10): 2398-2408.

WEI Yu-xuan, CHEN Ying. Convolutional Neural Network Compression Based on Adaptive Layer Entropy[J]. Acta Electronica Sinica, 2022, 50(10): 2398-2408.

图/表 26

图1 逐层迭代剪枝过程

图2 端到端迭代剪枝过程

图3 ALE总体流程框架

表1 ALE中符号定义及描述

符号	描述	符号	描述
N	卷积神经网络模型	$O H S$	模型原始层信息熵区间
L	卷积层个数	$α m a x$	卷积层过滤器最大保留率
$c i$	第 $i$ 层过滤器个数	$H S$	模型层信息熵区间
$W i$	第 $i$ 层网络的权重	$α i$	第i层过滤器保留率
$H ∙$	信息熵	$N i_l 1$	网络 $N i$ 的卷积第一层
$β$	自适应嫁接系数	$N i_b 1$	网络 $N i$ 的BN第一层
SE	最小信息熵	$p k i$	$W i$ 落在第k份区间的概率
BE	最大信息熵	$c i,$	剪枝后第 $i$ 层过滤器的个数

表1 ALE中符号定义及描述

符号	描述	符号	描述
N	卷积神经网络模型	$O H S$	模型原始层信息熵区间
L	卷积层个数	$α m a x$	卷积层过滤器最大保留率
$c i$	第 $i$ 层过滤器个数	$H S$	模型层信息熵区间
$W i$	第 $i$ 层网络的权重	$α i$	第i层过滤器保留率
$H ∙$	信息熵	$N i_l 1$	网络 $N i$ 的卷积第一层
$β$	自适应嫁接系数	$N i_b 1$	网络 $N i$ 的BN第一层
SE	最小信息熵	$p k i$	$W i$ 落在第k份区间的概率
BE	最大信息熵	$c i,$	剪枝后第 $i$ 层过滤器的个数

图4 ResNet-56在CIFAR-100上层信息熵变化过程

图5 网络各层过滤器保留率获取过程

图6 压缩网络结构的过程

图7 具有短连接块的网络压缩方法

图8 联合嫁接示意图

图9 自适应嫁接系数β变化关系图

图10 多网络联合嫁接示意图

表2 ResNet-56在CIFAR-10上结果

模型	准确率	计算量(压缩率)	参数量(压缩率)
ResNet-56	93.26%	125.49M(0.0%)	0.85M(0.0%)
PF-A^[16]	93.10%	112.00M(10.7%)	0.77M(9.4%)
PF-B^[16]	93.06%	90.90M(27.6%)	0.73M(14.1%)
SSS^[32]	93.39%	89.35M(28.8%)	0.59M(30.6%)
HRank1^[33]	93.52%	88.72M(29.3%)	0.71M(16.8%)
LEGR1^[34]	94.10%	87.80M(30.0%)
NISP^[35]	93.01%	81.00M(35.5%)	0.49M(42.4%)
ALE-100%	94.26%	80.10M(36.2%)	0.54M(32.9%)
GAL-0.6^[21]	93.38%	78.30M(37.6%)	0.75M(11.8%)
HRank2^[33]	93.17%	62.72M(50.0%)	0.49M(42.4%)
Hinge^[36]	93.69%	62.72M(50.0%)	0.44M(51.27%)
KSE^[26]	93.23%	60.00M(50.0%)	0.43M(49.4%)
FPGM^[19]	93.26%	59.40M(52.6%)
LEGR2^[34]	93.70%	58.90M(53.1%)
ABC-70%^[22]	93.23%	58.54M(54.1%)	0.39M(54.2%)
ALE-60%	93.64%	49.53M(60.5%)	0.36M(57.6%)
C-SGD^[4]	93.31%	49.13M(60.85%)

表3 ResNet-110在CIFAR-10上结果

模型	准确率	计算量(压缩率)	参数量(压缩率)
ResNet-110	93.50%	252.89M(0.0%)	1.72M(0.0%)
GAL-0.1^[21]	93.59%	205.7M(18.7%)	1.65M(4.07%)
NISP^[35]	93.32%	143.35M(43.3%)
GAL-0.5^[21]	92.74%	130.20M(48.5%)	0.95M(44.8%)
FPGM^[19]	93.74%	121.00M(52.2%)
ALE-80%	94.92%	120.29M(52.4%)	0.89M(48.3%)
HRank^[33]	93.36%	105.70M(58.2%)	0.70M(59.3%)
ALE-60%	94.36%	92.68M(63.4%)	0.69M(59.9%)
ABC-60%^[22]	93.58%	89.87M(64.5%)	0.56M(67.4%)

表4 VGGNet在CIFAR-10上结果

模型	准确率	计算量(压缩率)	参数量(压缩率)
VGGNet	93.96%	313.74M(0.0%)	14.95M(0.0%)
SSS^[32]	93.02%	183.13M(42.3%)	3.93M(73.7%)
GAL-0.1^[21]	93.42%	171.89M(45.2%)	2.67M(82.2%)
HRank1^[33]	92.34%	108.61M(65.4%)	2.64M(82.3%)
HRank2^[33]	91.23%	73.70M(76.5%)	1.78M(88.2%)
ALE-90%	92.85%	71.29M(77.28%)	2.87M(80.8%)
LEGR^[34]	92.40%	70.30M(77.6%)	---
GAL-0.2^[21]	91.89%	65.85M(79.0%)	3.35M(77.6%)
ALE-80%	92.81%	60.23M(80.88%)	2.46M(83.6%)

表5 MobileNetV2在CIFAR-10上结果

模型	准确率	计算量(压缩率)	参数量(压缩率)
MobileNetV2	92.25%	91.15M(0.0%)	2.26M(0.0%)
ALE-90%	93.54%	40.86M(55.2%)	0.94M(58.4%)
ALE-70%	92.79%	32.66M(64.2%)	0.83M(63.3%)

表6 ResNet-56在CIFAR-100上结果

模型	准确率	计算量(压缩率)	参数量(压缩率)
ResNet-56	71.92%	125.49M(0.0%)	0.85M(0.0%)
PF-A^[16]	70.42%	112.44M(10.4%)	0.77M(9.4%)
PF-B^[16]	69.95%	90.85M(27.6%)	0.73M(13.7%)
ALE-90%	70.91%	64.77M(48.4%)	0.53M(37.7%)
ALE-80%	70.72%	57.97M(53.8%)	0.47M(44.7%)

表7 ResNet-56和ResNet-110在SVHN上结果

模型	准确率	计算量(压缩率)	参数量(压缩率)
ResNet-56	96.38%	125.49M(0.0%)	0.85M(0.0%)
GAL-0.6^[21]	95.82%	73.58M(41.4%)	0.56M(34.1%)
ALE-60%	96.52%	50.17M(60.0%)	0.35M(58.8%)
ResNet-110	96.36%	252.89M(0.0%)	1.72M(0.0%)
GAL-0.06^[21]	96.63%	120.77M(52.2%)	0.83M(51.7%)
ALE-60%	96.65%	98.50M(61.1%)	0.71M(58.7%)

表8 ResNet-50在Market1501上结果

模型

mAP

Rank1

计算量

(压缩率)

参数量

(压缩率)

ResNet-50

65.1%

84.1%

4087.14M

(0.0%)

24.99M

(0.0%)

ALE-90%

62.2%

82.1%

1577.95M

(61.4%)

8.28M

(66.9%)

ALE-80%

61.5%

81.7%

1419.49M

(65.3%)

7.48M

(70.1%)

表9 ResNet-50在Duke上结果

模型

mAP

Rank1

计算量

(压缩率)

参数量

(压缩率)

ResNet-50

52.3%

72.1%

4087.14M

(0.0%)

24.89M

(0.0%)

ALE-90%

51.7%

71.1%

1639.11M

(59.9%)

7.41M

(70.2%)

ALE-80%

51.0%

70.5%

1508.50M

(63.1%)

7.22M

(71.0%)

图11 ResNet-56在CIFAR-10上最大保留率αmax对网络性能的影响

图12 MobileNetV2在CIFAR-10上压缩前后层信息熵关系比较

图13 嫁接对比实验结果

表10 ResNet-56在CIFAR-100上结果

模型	准确率	计算量(压缩率)	参数量(压缩率)
ResNet-56	71.92%	125.49M(0.0%)	0.85M(0.0%)
ALE-80%-10	69.31%	69.93M(44.3%)	0.52M(38.8%)
ALE-80%-15	70.06%	68.31M(45.6%)	0.53M(37.7%)
ALE-90%-122	70.91%	64.77M(48.4%)	0.53M(37.7%)
ALE-80%-60	70.48%	63.74M(49.2%)	0.48M(43.5%)
ALE-80%-20	70.26%	61.97M(50.6%)	0.50M(41.2%)
ALE-80%-90	70.93%	61.67M(50.9%)	0.49M(42.4%)
ALE-80%-30	70.19%	61.50M(51.0%)	0.47M(44.7%)
ALE-80%-122	70.72%	57.97M(53.8%)	0.47M(44.7%)
ALE-80%-150	70.65%	55.35M(55.9%)	0.44M(48.2%)
ALE-80%-5	69.17%	50.32M(59.9%)	0.36M(57.6%)

表11 层最小保留率的影响

模型	准确率	计算量(压缩率)	参数量(压缩率)
ResNet-56	93.26%	125.49M(0.0%)	0.85M(0.0%)
ALE-60%	93.64%	49.53M(60.5%)	0.36M(57.6%)
20%-ALE-60%	93.50%	52.80M(57.9%)	0.37M(56.5%)
ResNet-110	93.50%	252.89M(0.0%)	1.72M(0.0%)
ALE-80%	94.92%	120.29M(52.4%)	0.89M(48.3%)
20%-ALE-80%	94.55%	131.50M(48.0%)	0.96M(44.2%)
MobileNetV2	92.25%	91.15M(0.0%)	2.26M(0.0%)
ALE-90%	93.54%	40.86M(55.2%)	0.94M(58.4%)
20%- ALE-90%	94.11%	46.20M(49.3%)	1.08M(52.2%)

表12 嫁接网络个数的影响

模型	嫁接个数	准确率
ResNet-110		93.50%
ALE-80%	1	93.39%
	2	93.86%
	3	94.55%
	4	94.21%
	5	94.71%
	6	94.92%

表13 是否扩充信息熵实验对比

模型	准确率	计算量(压缩率)	参数量 (压缩率)
ResNet-56	93.26%	125.49M(0.0%)	0.85M(0.0%)
ALE-100%(扩)	94.26%	80.10M(36.2%)	0.54M(32.9%)
ALE-100%	94.11%	81.47M(35.1%)	0.59M(30.6%)
ALE-60%(扩)	93.64%	49.53M(60.5%)	0.36M(57.6%)
ALE-60%	93.37%	49.38M(60.7%)	0.36M(57.6%)
ResNet-110	93.50%	252.89M(0.0%)	1.72M(0.0%)
ALE-80%(扩)	94.92%	120.29M(52.4%)	0.89M(48.3%)
ALE-80%	94.81%	120.88M(52.2%)	0.91M(47.1%)
MobileNetV2	92.25%	91.15M(0.0%)	2.26M(0.0%)
ALE-90%(扩)	93.54%	40.86M(55.2%)	0.94M(58.4%)
ALE-90%	93.42%	38.90M(57.3%)	0.89M(60.6%)

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