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SENet代码复现+超详细注释(PyTorch)

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+11卷积核+ReLu+11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!


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