在卷积网络中通道注意力经常用到SENet模块，来增强网络模型在通道权重的选择能力，进而提点。关于SENet的原理和具体细节，我们在上一篇已经详细的介绍了：经典神经网络论文超详细解读（七）——SENet（注意力机制）学习笔记（翻译＋精读＋代码复现）
接下来我们来复现一下代码。
因为SENet不是一个全新的网络模型，而是相当于提出了一个即插即用的高性能小插件，所以代码实现也是比较简单的。本文是在ResNet基础上加入SEblock模块进行实现ResNet_SE50。
一、SENet结构组成介绍
上图为一个SEblock，由SEblock块构成的网络叫做SENet；可以基于原生网络，添加SEblock块构成SE-NameNet，如基于AlexNet等添加SE结构，称作SE-AlexNet、SE-ResNet等
SE块与先进的架构Inception、ResNet的结合效果
原理：通过一个全局平均池化层加两个全连接层以及全连接层对应激活【ReLU和sigmoid】组成的结构输出和输入特征同样数目的权重值，也就是每个特征通道的权重系数，学习一个通道的注意力出来，用于决定哪些通道应该重点提取特征，哪些部分放弃。
SE块详细过程
1.首先由 Inception结构 或 ResNet结构处理后的C×W×H特征图开始，通过Squeeze操作对特征图进行全局平均池化（GAP），得到1×1×C 的特征向量
2.紧接着两个 FC 层组成一个 Bottleneck 结构去建模通道间的相关性：
（1）经过第一个FC层，将C个通道变成 C/ r ，减少参数量，然后通过ReLU的非线性激活，到达第二个FC层
（2）经过第二个FC层，再将特征通道数恢复到C个，得到带有注意力机制的权重参数
3.最后经过Sigmoid激活函数，最后通过一个 Scale 的操作来将归一化后的权重加权到每个通道的特征上。
二、SEblock的具体介绍
Sequeeze：Fsq操作就是使用通道的全局平均池化，将包含全局信息的W×H×C 的特征图直接压缩成一个1×1×C的特征向量，即将每个二维通道变成一个具有全局感受野的数值，此时1个像素表示1个通道，屏蔽掉空间上的分布信息，更好的利用通道间的相关性。
具体操作：对原特征图50×512×7×7进行全局平均池化,然后得到了一个50×512×1×1大小的特征图，这个特征图具有全局感受野。
Excitation ：基于特征通道间的相关性，每个特征通道生成一个权重，用来代表特征通道的重要程度。由原本全为白色的C个通道的特征，得到带有不同深浅程度的颜色的特征向量，也就是不同的重要程度。
具体操作：输出的50×512×1×1特征图，经过两个全连接层，最后用一 个类似于循环神经网络中门控机制，通过参数来为每个特征通道生成权重，参数被学习用来显式地建模特征通道间的相关性(论文中使用的是sigmoid)。50×512×1×1变成50×512 / 16×1×1，最后再还原回来：50×512×1×1
Reweight：将Excitation输出的权重看做每个特征通道的重要性，也就是对于U每个位置上的所有H×W上的值都乘上对应通道的权值，完成对原始特征的重校准。
具体操作：50×512×1×1通过expand_as得到50×512×7×7, 完成在通道维度上对原始特征的重标定，并作为下一级的输入数据。
三、PyTorch代码实现
（1）SEblock搭建
全局平均池化+1*1卷积核+ReLu+1*1卷积核+Sigmoid
'''-------------一、SE模块-----------------------------''' #全局平均池化+1*1卷积核+ReLu+1*1卷积核+Sigmoid class SE_Block(nn.Module): def __init__(self, inchannel, ratio=16): super(SE_Block, self).__init__() # 全局平均池化(Fsq操作) self.gap = nn.AdaptiveAvgPool2d((1, 1)) # 两个全连接层(Fex操作) self.fc = nn.Sequential( nn.Linear(inchannel, inchannel // ratio, bias=False), # 从 c -> c/r nn.ReLU(), nn.Linear(inchannel // ratio, inchannel, bias=False), # 从 c/r -> c nn.Sigmoid() ) def forward(self, x): # 读取批数据图片数量及通道数 b, c, h, w = x.size() # Fsq操作：经池化后输出b*c的矩阵 y = self.gap(x).view(b, c) # Fex操作：经全连接层输出（b，c，1，1）矩阵 y = self.fc(y).view(b, c, 1, 1) # Fscale操作：将得到的权重乘以原来的特征图x return x * y.expand_as(x)
（2）将SEblock嵌入残差模块
SEblock可以灵活的加入到resnet等相关完整模型中，通常加在残差之前。【因为激活是sigmoid原因，存在梯度弥散问题，所以尽量不放到主信号通道去，即使本个残差模块有弥散问题，以不至于影响整个网络模型】
这里我们将SE模块分别嵌入ResNet的BasicBlock和Bottleneck中，得到 SEBasicBlock和SEBottleneck（具体解释可以看我之前写的ResNet代码复现＋超详细注释（PyTorch））
BasicBlock模块
'''-------------二、BasicBlock模块-----------------------------''' # 左侧的 residual block 结构（18-layer、34-layer） class BasicBlock(nn.Module): expansion = 1 def __init__(self, inchannel, outchannel, stride=1): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(outchannel) self.conv2 = nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(outchannel) # SE_Block放在BN之后，shortcut之前 self.SE = SE_Block(outchannel) self.shortcut = nn.Sequential() if stride != 1 or inchannel != self.expansion*outchannel: self.shortcut = nn.Sequential( nn.Conv2d(inchannel, self.expansion*outchannel, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion*outchannel) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) SE_out = self.SE(out) out = out * SE_out out += self.shortcut(x) out = F.relu(out) return out
Bottleneck模块
'''-------------三、Bottleneck模块-----------------------------''' # 右侧的 residual block 结构（50-layer、101-layer、152-layer） class Bottleneck(nn.Module): expansion = 4 def __init__(self, inchannel, outchannel, stride=1): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inchannel, outchannel, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(outchannel) self.conv2 = nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(outchannel) self.conv3 = nn.Conv2d(outchannel, self.expansion*outchannel, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(self.expansion*outchannel) # SE_Block放在BN之后，shortcut之前 self.SE = SE_Block(self.expansion*outchannel) self.shortcut = nn.Sequential() if stride != 1 or inchannel != self.expansion*outchannel: self.shortcut = nn.Sequential( nn.Conv2d(inchannel, self.expansion*outchannel, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion*outchannel) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) SE_out = self.SE(out) out = out * SE_out out += self.shortcut(x) out = F.relu(out) return out
（3）搭建SE_ResNet结构
'''-------------四、搭建SE_ResNet结构-----------------------------''' class SE_ResNet(nn.Module): def __init__(self, block, num_blocks, num_classes=10): super(SE_ResNet, self).__init__() self.in_planes = 64 self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) # conv1 self.bn1 = nn.BatchNorm2d(64) self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) # conv2_x self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) # conv3_x self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) # conv4_x self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) # conv5_x self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.linear = nn.Linear(512 * block.expansion, num_classes) def _make_layer(self, block, planes, num_blocks, stride): strides = [stride] + [1]*(num_blocks-1) layers = [] for stride in strides: layers.append(block(self.in_planes, planes, stride)) self.in_planes = planes * block.expansion return nn.Sequential(*layers) def forward(self, x): x = F.relu(self.bn1(self.conv1(x))) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = torch.flatten(x, 1) out = self.linear(x) return out
（4）网络模型的创建和测试
网络模型创建打印 SE_ResNet50
# test() if __name__ == '__main__': model = SE_ResNet50() print(model) input = torch.randn(1, 3, 224, 224) out = model(input) print(out.shape)
打印模型如下
SE_ResNet( (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (layer1): Sequential( (0): Bottleneck( (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=256, out_features=16, bias=False) (1): ReLU() (2): Linear(in_features=16, out_features=256, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential( (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=256, out_features=16, bias=False) (1): ReLU() (2): Linear(in_features=16, out_features=256, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) (2): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=256, out_features=16, bias=False) (1): ReLU() (2): Linear(in_features=16, out_features=256, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) ) (layer2): Sequential( (0): Bottleneck( (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=512, out_features=32, bias=False) (1): ReLU() (2): Linear(in_features=32, out_features=512, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=512, out_features=32, bias=False) (1): ReLU() (2): Linear(in_features=32, out_features=512, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) (2): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=512, out_features=32, bias=False) (1): ReLU() (2): Linear(in_features=32, out_features=512, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) (3): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=512, out_features=32, bias=False) (1): ReLU() (2): Linear(in_features=32, out_features=512, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) ) (layer3): Sequential( (0): Bottleneck( (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=1024, out_features=64, bias=False) (1): ReLU() (2): Linear(in_features=64, out_features=1024, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential( (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=1024, out_features=64, bias=False) (1): ReLU() (2): Linear(in_features=64, out_features=1024, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) (2): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=1024, out_features=64, bias=False) (1): ReLU() (2): Linear(in_features=64, out_features=1024, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) (3): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=1024, out_features=64, bias=False) (1): ReLU() (2): Linear(in_features=64, out_features=1024, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) (4): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=1024, out_features=64, bias=False) (1): ReLU() (2): Linear(in_features=64, out_features=1024, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) (5): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=1024, out_features=64, bias=False) (1): ReLU() (2): Linear(in_features=64, out_features=1024, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) ) (layer4): Sequential( (0): Bottleneck( (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=2048, out_features=128, bias=False) (1): ReLU() (2): Linear(in_features=128, out_features=2048, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential( (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=2048, out_features=128, bias=False) (1): ReLU() (2): Linear(in_features=128, out_features=2048, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) (2): Bottleneck( (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (SE): SE_Block( (gap): AdaptiveAvgPool2d(output_size=(1, 1)) (fc): Sequential( (0): Linear(in_features=2048, out_features=128, bias=False) (1): ReLU() (2): Linear(in_features=128, out_features=2048, bias=False) (3): Sigmoid() ) ) (shortcut): Sequential() ) ) (avgpool): AdaptiveAvgPool2d(output_size=(1, 1)) (linear): Linear(in_features=2048, out_features=10, bias=True) ) torch.Size([1, 10])
使用torchsummary打印每个网络模型的详细信息
if __name__ == '__main__': net = SE_ResNet50().cuda() summary(net, (3, 224, 224))
打印模型如下
---------------------------------------------------------------- Layer (type) Output Shape Param # ================================================================ Conv2d-1 [-1, 64, 224, 224] 1,728 BatchNorm2d-2 [-1, 64, 224, 224] 128 Conv2d-3 [-1, 64, 224, 224] 4,096 BatchNorm2d-4 [-1, 64, 224, 224] 128 Conv2d-5 [-1, 64, 224, 224] 36,864 BatchNorm2d-6 [-1, 64, 224, 224] 128 Conv2d-7 [-1, 256, 224, 224] 16,384 BatchNorm2d-8 [-1, 256, 224, 224] 512 AdaptiveAvgPool2d-9 [-1, 256, 1, 1] 0 Linear-10 [-1, 16] 4,096 ReLU-11 [-1, 16] 0 Linear-12 [-1, 256] 4,096 Sigmoid-13 [-1, 256] 0 SE_Block-14 [-1, 256, 224, 224] 0 Conv2d-15 [-1, 256, 224, 224] 16,384 BatchNorm2d-16 [-1, 256, 224, 224] 512 Bottleneck-17 [-1, 256, 224, 224] 0 Conv2d-18 [-1, 64, 224, 224] 16,384 BatchNorm2d-19 [-1, 64, 224, 224] 128 Conv2d-20 [-1, 64, 224, 224] 36,864 BatchNorm2d-21 [-1, 64, 224, 224] 128 Conv2d-22 [-1, 256, 224, 224] 16,384 BatchNorm2d-23 [-1, 256, 224, 224] 512 AdaptiveAvgPool2d-24 [-1, 256, 1, 1] 0 Linear-25 [-1, 16] 4,096 ReLU-26 [-1, 16] 0 Linear-27 [-1, 256] 4,096 Sigmoid-28 [-1, 256] 0 SE_Block-29 [-1, 256, 224, 224] 0 Bottleneck-30 [-1, 256, 224, 224] 0 Conv2d-31 [-1, 64, 224, 224] 16,384 BatchNorm2d-32 [-1, 64, 224, 224] 128 Conv2d-33 [-1, 64, 224, 224] 36,864 BatchNorm2d-34 [-1, 64, 224, 224] 128 Conv2d-35 [-1, 256, 224, 224] 16,384 BatchNorm2d-36 [-1, 256, 224, 224] 512 AdaptiveAvgPool2d-37 [-1, 256, 1, 1] 0 Linear-38 [-1, 16] 4,096 ReLU-39 [-1, 16] 0 Linear-40 [-1, 256] 4,096 Sigmoid-41 [-1, 256] 0 SE_Block-42 [-1, 256, 224, 224] 0 Bottleneck-43 [-1, 256, 224, 224] 0 Conv2d-44 [-1, 128, 224, 224] 32,768 BatchNorm2d-45 [-1, 128, 224, 224] 256 Conv2d-46 [-1, 128, 112, 112] 147,456 BatchNorm2d-47 [-1, 128, 112, 112] 256 Conv2d-48 [-1, 512, 112, 112] 65,536 BatchNorm2d-49 [-1, 512, 112, 112] 1,024 AdaptiveAvgPool2d-50 [-1, 512, 1, 1] 0 Linear-51 [-1, 32] 16,384 ReLU-52 [-1, 32] 0 Linear-53 [-1, 512] 16,384 Sigmoid-54 [-1, 512] 0 SE_Block-55 [-1, 512, 112, 112] 0 Conv2d-56 [-1, 512, 112, 112] 131,072 BatchNorm2d-57 [-1, 512, 112, 112] 1,024 Bottleneck-58 [-1, 512, 112, 112] 0 Conv2d-59 [-1, 128, 112, 112] 65,536 BatchNorm2d-60 [-1, 128, 112, 112] 256 Conv2d-61 [-1, 128, 112, 112] 147,456 BatchNorm2d-62 [-1, 128, 112, 112] 256 Conv2d-63 [-1, 512, 112, 112] 65,536 BatchNorm2d-64 [-1, 512, 112, 112] 1,024 AdaptiveAvgPool2d-65 [-1, 512, 1, 1] 0 Linear-66 [-1, 32] 16,384 ReLU-67 [-1, 32] 0 Linear-68 [-1, 512] 16,384 Sigmoid-69 [-1, 512] 0 SE_Block-70 [-1, 512, 112, 112] 0 Bottleneck-71 [-1, 512, 112, 112] 0 Conv2d-72 [-1, 128, 112, 112] 65,536 BatchNorm2d-73 [-1, 128, 112, 112] 256 Conv2d-74 [-1, 128, 112, 112] 147,456 BatchNorm2d-75 [-1, 128, 112, 112] 256 Conv2d-76 [-1, 512, 112, 112] 65,536 BatchNorm2d-77 [-1, 512, 112, 112] 1,024 AdaptiveAvgPool2d-78 [-1, 512, 1, 1] 0 Linear-79 [-1, 32] 16,384 ReLU-80 [-1, 32] 0 Linear-81 [-1, 512] 16,384 Sigmoid-82 [-1, 512] 0 SE_Block-83 [-1, 512, 112, 112] 0 Bottleneck-84 [-1, 512, 112, 112] 0 Conv2d-85 [-1, 128, 112, 112] 65,536 BatchNorm2d-86 [-1, 128, 112, 112] 256 Conv2d-87 [-1, 128, 112, 112] 147,456 BatchNorm2d-88 [-1, 128, 112, 112] 256 Conv2d-89 [-1, 512, 112, 112] 65,536 BatchNorm2d-90 [-1, 512, 112, 112] 1,024 AdaptiveAvgPool2d-91 [-1, 512, 1, 1] 0 Linear-92 [-1, 32] 16,384 ReLU-93 [-1, 32] 0 Linear-94 [-1, 512] 16,384 Sigmoid-95 [-1, 512] 0 SE_Block-96 [-1, 512, 112, 112] 0 Bottleneck-97 [-1, 512, 112, 112] 0 Conv2d-98 [-1, 256, 112, 112] 131,072 BatchNorm2d-99 [-1, 256, 112, 112] 512 Conv2d-100 [-1, 256, 56, 56] 589,824 BatchNorm2d-101 [-1, 256, 56, 56] 512 Conv2d-102 [-1, 1024, 56, 56] 262,144 BatchNorm2d-103 [-1, 1024, 56, 56] 2,048 AdaptiveAvgPool2d-104 [-1, 1024, 1, 1] 0 Linear-105 [-1, 64] 65,536 ReLU-106 [-1, 64] 0 Linear-107 [-1, 1024] 65,536 Sigmoid-108 [-1, 1024] 0 SE_Block-109 [-1, 1024, 56, 56] 0 Conv2d-110 [-1, 1024, 56, 56] 524,288 BatchNorm2d-111 [-1, 1024, 56, 56] 2,048 Bottleneck-112 [-1, 1024, 56, 56] 0 Conv2d-113 [-1, 256, 56, 56] 262,144 BatchNorm2d-114 [-1, 256, 56, 56] 512 Conv2d-115 [-1, 256, 56, 56] 589,824 BatchNorm2d-116 [-1, 256, 56, 56] 512 Conv2d-117 [-1, 1024, 56, 56] 262,144 BatchNorm2d-118 [-1, 1024, 56, 56] 2,048 AdaptiveAvgPool2d-119 [-1, 1024, 1, 1] 0 Linear-120 [-1, 64] 65,536 ReLU-121 [-1, 64] 0 Linear-122 [-1, 1024] 65,536 Sigmoid-123 [-1, 1024] 0 SE_Block-124 [-1, 1024, 56, 56] 0 Bottleneck-125 [-1, 1024, 56, 56] 0 Conv2d-126 [-1, 256, 56, 56] 262,144 BatchNorm2d-127 [-1, 256, 56, 56] 512 Conv2d-128 [-1, 256, 56, 56] 589,824 BatchNorm2d-129 [-1, 256, 56, 56] 512 Conv2d-130 [-1, 1024, 56, 56] 262,144 BatchNorm2d-131 [-1, 1024, 56, 56] 2,048 AdaptiveAvgPool2d-132 [-1, 1024, 1, 1] 0 Linear-133 [-1, 64] 65,536 ReLU-134 [-1, 64] 0 Linear-135 [-1, 1024] 65,536 Sigmoid-136 [-1, 1024] 0 SE_Block-137 [-1, 1024, 56, 56] 0 Bottleneck-138 [-1, 1024, 56, 56] 0 Conv2d-139 [-1, 256, 56, 56] 262,144 BatchNorm2d-140 [-1, 256, 56, 56] 512 Conv2d-141 [-1, 256, 56, 56] 589,824 BatchNorm2d-142 [-1, 256, 56, 56] 512 Conv2d-143 [-1, 1024, 56, 56] 262,144 BatchNorm2d-144 [-1, 1024, 56, 56] 2,048 AdaptiveAvgPool2d-145 [-1, 1024, 1, 1] 0 Linear-146 [-1, 64] 65,536 ReLU-147 [-1, 64] 0 Linear-148 [-1, 1024] 65,536 Sigmoid-149 [-1, 1024] 0 SE_Block-150 [-1, 1024, 56, 56] 0 Bottleneck-151 [-1, 1024, 56, 56] 0 Conv2d-152 [-1, 256, 56, 56] 262,144 BatchNorm2d-153 [-1, 256, 56, 56] 512 Conv2d-154 [-1, 256, 56, 56] 589,824 BatchNorm2d-155 [-1, 256, 56, 56] 512 Conv2d-156 [-1, 1024, 56, 56] 262,144 BatchNorm2d-157 [-1, 1024, 56, 56] 2,048 AdaptiveAvgPool2d-158 [-1, 1024, 1, 1] 0 Linear-159 [-1, 64] 65,536 ReLU-160 [-1, 64] 0 Linear-161 [-1, 1024] 65,536 Sigmoid-162 [-1, 1024] 0 SE_Block-163 [-1, 1024, 56, 56] 0 Bottleneck-164 [-1, 1024, 56, 56] 0 Conv2d-165 [-1, 256, 56, 56] 262,144 BatchNorm2d-166 [-1, 256, 56, 56] 512 Conv2d-167 [-1, 256, 56, 56] 589,824 BatchNorm2d-168 [-1, 256, 56, 56] 512 Conv2d-169 [-1, 1024, 56, 56] 262,144 BatchNorm2d-170 [-1, 1024, 56, 56] 2,048 AdaptiveAvgPool2d-171 [-1, 1024, 1, 1] 0 Linear-172 [-1, 64] 65,536 ReLU-173 [-1, 64] 0 Linear-174 [-1, 1024] 65,536 Sigmoid-175 [-1, 1024] 0 SE_Block-176 [-1, 1024, 56, 56] 0 Bottleneck-177 [-1, 1024, 56, 56] 0 Conv2d-178 [-1, 512, 56, 56] 524,288 BatchNorm2d-179 [-1, 512, 56, 56] 1,024 Conv2d-180 [-1, 512, 28, 28] 2,359,296 BatchNorm2d-181 [-1, 512, 28, 28] 1,024 Conv2d-182 [-1, 2048, 28, 28] 1,048,576 BatchNorm2d-183 [-1, 2048, 28, 28] 4,096 AdaptiveAvgPool2d-184 [-1, 2048, 1, 1] 0 Linear-185 [-1, 128] 262,144 ReLU-186 [-1, 128] 0 Linear-187 [-1, 2048] 262,144 Sigmoid-188 [-1, 2048] 0 SE_Block-189 [-1, 2048, 28, 28] 0 Conv2d-190 [-1, 2048, 28, 28] 2,097,152 BatchNorm2d-191 [-1, 2048, 28, 28] 4,096 Bottleneck-192 [-1, 2048, 28, 28] 0 Conv2d-193 [-1, 512, 28, 28] 1,048,576 BatchNorm2d-194 [-1, 512, 28, 28] 1,024 Conv2d-195 [-1, 512, 28, 28] 2,359,296 BatchNorm2d-196 [-1, 512, 28, 28] 1,024 Conv2d-197 [-1, 2048, 28, 28] 1,048,576 BatchNorm2d-198 [-1, 2048, 28, 28] 4,096 AdaptiveAvgPool2d-199 [-1, 2048, 1, 1] 0 Linear-200 [-1, 128] 262,144 ReLU-201 [-1, 128] 0 Linear-202 [-1, 2048] 262,144 Sigmoid-203 [-1, 2048] 0 SE_Block-204 [-1, 2048, 28, 28] 0 Bottleneck-205 [-1, 2048, 28, 28] 0 Conv2d-206 [-1, 512, 28, 28] 1,048,576 BatchNorm2d-207 [-1, 512, 28, 28] 1,024 Conv2d-208 [-1, 512, 28, 28] 2,359,296 BatchNorm2d-209 [-1, 512, 28, 28] 1,024 Conv2d-210 [-1, 2048, 28, 28] 1,048,576 BatchNorm2d-211 [-1, 2048, 28, 28] 4,096 AdaptiveAvgPool2d-212 [-1, 2048, 1, 1] 0 Linear-213 [-1, 128] 262,144 ReLU-214 [-1, 128] 0 Linear-215 [-1, 2048] 262,144 Sigmoid-216 [-1, 2048] 0 SE_Block-217 [-1, 2048, 28, 28] 0 Bottleneck-218 [-1, 2048, 28, 28] 0 AdaptiveAvgPool2d-219 [-1, 2048, 1, 1] 0 Linear-220 [-1, 10] 20,490 ================================================================ Total params: 26,035,786 Trainable params: 26,035,786 Non-trainable params: 0 ---------------------------------------------------------------- Input size (MB): 0.57 Forward/backward pass size (MB): 3914.25 Params size (MB): 99.32 Estimated Total Size (MB): 4014.14 ---------------------------------------------------------------- Process finished with exit code 0
（5）完整代码
import torch import torch.nn as nn import torch.nn.functional as F from torchsummary import summary '''-------------一、SE模块-----------------------------''' #全局平均池化+1*1卷积核+ReLu+1*1卷积核+Sigmoid class SE_Block(nn.Module): def __init__(self, inchannel, ratio=16): super(SE_Block, self).__init__() # 全局平均池化(Fsq操作) self.gap = nn.AdaptiveAvgPool2d((1, 1)) # 两个全连接层(Fex操作) self.fc = nn.Sequential( nn.Linear(inchannel, inchannel // ratio, bias=False), # 从 c -> c/r nn.ReLU(), nn.Linear(inchannel // ratio, inchannel, bias=False), # 从 c/r -> c nn.Sigmoid() ) def forward(self, x): # 读取批数据图片数量及通道数 b, c, h, w = x.size() # Fsq操作：经池化后输出b*c的矩阵 y = self.gap(x).view(b, c) # Fex操作：经全连接层输出（b，c，1，1）矩阵 y = self.fc(y).view(b, c, 1, 1) # Fscale操作：将得到的权重乘以原来的特征图x return x * y.expand_as(x) '''-------------二、BasicBlock模块-----------------------------''' # 左侧的 residual block 结构（18-layer、34-layer） class BasicBlock(nn.Module): expansion = 1 def __init__(self, inchannel, outchannel, stride=1): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(outchannel) self.conv2 = nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(outchannel) # SE_Block放在BN之后，shortcut之前 self.SE = SE_Block(outchannel) self.shortcut = nn.Sequential() if stride != 1 or inchannel != self.expansion*outchannel: self.shortcut = nn.Sequential( nn.Conv2d(inchannel, self.expansion*outchannel, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion*outchannel) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) SE_out = self.SE(out) out = out * SE_out out += self.shortcut(x) out = F.relu(out) return out '''-------------三、Bottleneck模块-----------------------------''' # 右侧的 residual block 结构（50-layer、101-layer、152-layer） class Bottleneck(nn.Module): expansion = 4 def __init__(self, inchannel, outchannel, stride=1): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inchannel, outchannel, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(outchannel) self.conv2 = nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(outchannel) self.conv3 = nn.Conv2d(outchannel, self.expansion*outchannel, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(self.expansion*outchannel) # SE_Block放在BN之后，shortcut之前 self.SE = SE_Block(self.expansion*outchannel) self.shortcut = nn.Sequential() if stride != 1 or inchannel != self.expansion*outchannel: self.shortcut = nn.Sequential( nn.Conv2d(inchannel, self.expansion*outchannel, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion*outchannel) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) out = self.bn3(self.conv3(out)) SE_out = self.SE(out) out = out * SE_out out += self.shortcut(x) out = F.relu(out) return out '''-------------四、搭建SE_ResNet结构-----------------------------''' class SE_ResNet(nn.Module): def __init__(self, block, num_blocks, num_classes=10): super(SE_ResNet, self).__init__() self.in_planes = 64 self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) # conv1 self.bn1 = nn.BatchNorm2d(64) self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) # conv2_x self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) # conv3_x self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) # conv4_x self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) # conv5_x self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.linear = nn.Linear(512 * block.expansion, num_classes) def _make_layer(self, block, planes, num_blocks, stride): strides = [stride] + [1]*(num_blocks-1) layers = [] for stride in strides: layers.append(block(self.in_planes, planes, stride)) self.in_planes = planes * block.expansion return nn.Sequential(*layers) def forward(self, x): x = F.relu(self.bn1(self.conv1(x))) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = torch.flatten(x, 1) out = self.linear(x) return out def SE_ResNet18(): return SE_ResNet(BasicBlock, [2, 2, 2, 2]) def SE_ResNet34(): return SE_ResNet(BasicBlock, [3, 4, 6, 3]) def SE_ResNet50(): return SE_ResNet(Bottleneck, [3, 4, 6, 3]) def SE_ResNet101(): return SE_ResNet(Bottleneck, [3, 4, 23, 3]) def SE_ResNet152(): return SE_ResNet(Bottleneck, [3, 8, 36, 3]) ''' if __name__ == '__main__': model = SE_ResNet50() print(model) input = torch.randn(1, 3, 224, 224) out = model(input) print(out.shape) # test() ''' if __name__ == '__main__': net = SE_ResNet50().cuda() summary(net, (3, 224, 224))
本篇就结束了，欢迎大家留言讨论呀！
