注意力机制论文:Squeeze-and-Excitation Networks及其PyTorch实现

注意力机制论文:Squeeze-and-Excitation Networks及其PyTorch实现

Squeeze-and-Excitation Networks PDF: ​​​​​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)

4 SENet

PyTorch代码:

import torchimport torch.nn as nnimport torchvisiondef 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 outclass 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 xdef 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)

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