注意力机制论文:Squeeze-and-Excitation Networks及其PyTorch实现_squeeze and execute 模块 pytorch

Squeeze-and-Excitation Networks
PDF: https://arxiv.org/pdf/1709.01507.pdf
PyTorch代码: https://github.com/shanglianlm0525/PyTorch-Networks

Squeeze-and-Excitation Networks（SENet）是由自动驾驶公司Momenta在2017年公布的一种全新的图像识别结构，它通过对特征通道间的相关性进行建模，把重要的特征进行强化来提升准确率。这个结构是2017 ILSVR竞赛的冠军，top5的错误率达到了2.251%，比2016年的第一名还要低25%，可谓提升巨大。

1 概述

SENet通过学习channel之间的相关性，筛选出了针对通道的注意力，稍微增加了一点计算量，但是效果提升较明显
Squeeze-and-Excitation(SE) block是一个子结构，可以有效地嵌到其他分类或检测模型中。
SENet的核心思想在于通过网络根据loss去学习feature map的特征权重来使模型达到更好的结果
SE模块本质上是一种attention机制

2 Squeeze-and-Excitation模块

Squeeze 操作对 C x H x W 进行global average pooling，得到大小为 C x 1 x 1 的特征图

Excitation 操作 使用一个全连接神经网络，对Sequeeze之后的结果做一个非线性变换

Reweight 操作 使用Excitation 得到的结果作为权重，乘到输入特征上

3 SE模块应用举例

SE-Inception 和 SE-ResNet

class SE_Module(nn.Module):
    def __init__(self, channel,ratio = 16):
        super(SE_Module, self).__init__()
        self.squeeze = nn.AdaptiveAvgPool2d(1)
        self.excitation = nn.Sequential(
                nn.Linear(in_features=channel, out_features=channel // ratio),
                nn.ReLU(inplace=True),
                nn.Linear(in_features=channel // ratio, out_features=channel),
                nn.Sigmoid()
            )
    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.squeeze(x).view(b, c)
        z = self.excitation(y).view(b, c, 1, 1)
        return x * z.expand_as(x)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

4 SENet

PyTorch代码:

import torch
import torch.nn as nn
import torchvision


def Conv1(in_planes, places, stride=2):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_planes,out_channels=places,kernel_size=7,stride=stride,padding=3, bias=False),
        nn.BatchNorm2d(places),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
    )

class SE_Module(nn.Module):
    def __init__(self, channel,ratio = 16):
        super(SE_Module, self).__init__()
        self.squeeze = nn.AdaptiveAvgPool2d(1)
        self.excitation = nn.Sequential(
                nn.Linear(in_features=channel, out_features=channel // ratio),
                nn.ReLU(inplace=True),
                nn.Linear(in_features=channel // ratio, out_features=channel),
                nn.Sigmoid()
            )
    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.squeeze(x).view(b, c)
        z = self.excitation(y).view(b, c, 1, 1)
        return x * z.expand_as(x)


class SE_ResNetBlock(nn.Module):
    def __init__(self,in_places,places, stride=1,downsampling=False, expansion = 4):
        super(SE_ResNetBlock,self).__init__()
        self.expansion = expansion
        self.downsampling = downsampling

        self.bottleneck = nn.Sequential(
            nn.Conv2d(in_channels=in_places,out_channels=places,kernel_size=1,stride=1, bias=False),
            nn.BatchNorm2d(places),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(places),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=places, out_channels=places*self.expansion, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(places*self.expansion),
        )

        if self.downsampling:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_channels=in_places, out_channels=places*self.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(places*self.expansion)
            )
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        residual = x
        out = self.bottleneck(x)

        if self.downsampling:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)
        return out

class SE_ResNet(nn.Module):
    def __init__(self,blocks, num_classes=1000, expansion = 4):
        super(SE_ResNet,self).__init__()
        self.expansion = expansion

        self.conv1 = Conv1(in_planes = 3, places= 64)

        self.layer1 = self.make_layer(in_places = 64, places= 64, block=blocks[0], stride=1)
        self.layer2 = self.make_layer(in_places = 256,places=128, block=blocks[1], stride=2)
        self.layer3 = self.make_layer(in_places=512,places=256, block=blocks[2], stride=2)
        self.layer4 = self.make_layer(in_places=1024,places=512, block=blocks[3], stride=2)

        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(2048,num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def make_layer(self, in_places, places, block, stride):
        layers = []
        layers.append(SE_ResNetBlock(in_places, places,stride, downsampling =True))
        for i in range(1, block):
            layers.append(SE_ResNetBlock(places*self.expansion, places))

        return nn.Sequential(*layers)


    def forward(self, x):
        x = self.conv1(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

def SE_ResNet50():
    return SE_ResNet([3, 4, 6, 3])

if __name__=='__main__':
    model = SE_ResNet50()
    print(model)

    input = torch.randn(1, 3, 224, 224)
    out = model(input)
    print(out.shape)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120