将senet模块加在池化层之后，通过卷积层与反卷积进行特征融合，再将融合得到的特征传递给剩下的卷积层进行再次学习代码

以下是一个使用senet模块进行特征融合的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
from senet import SEBlock

class MyNet(nn.Module):
    def __init__(self):
        super(MyNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.se1 = SEBlock(64)
        self.se2 = SEBlock(128)
        self.se3 = SEBlock(256)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(512)
        self.conv5 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1)
        self.bn5 = nn.BatchNorm2d(256)
        self.conv6 = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1)
        self.bn6 = nn.BatchNorm2d(128)
        self.conv7 = nn.Conv2d(128, 10, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.pool(x)
        x = self.se1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = F.relu(x)
        x = self.pool(x)
        x = self.se2(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = F.relu(x)
        x = self.pool(x)
        x = self.se3(x)
        x = self.conv4(x)
        x = self.bn4(x)
        x = F.relu(x)
        x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
        x = self.conv5(x)
        x = self.bn5(x)
        x = F.relu(x)
        x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
        x = self.conv6(x)
        x = self.bn6(x)
        x = F.relu(x)
        x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
        x = self.conv7(x)
        return x

在这个示例中，我们在池化层之后加入了SE模块，以获得更具表现力的特征表示。然后，我们将融合后的特征通过反卷积层进行尺寸上的扩展，并在剩余的卷积层中使用它们进行再次学习。