一个神经网络去融合红外图像和可将光图像，用pytorch写的代码

好的，以下是一个使用 PyTorch 实现的神经网络，用于融合红外图像和可见光图像：

import torch
import torch.nn as nn

class FusionNet(nn.Module):
    def __init__(self):
        super(FusionNet, self).__init__()
        self.conv1_ir = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.conv1_vi = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        
        self.conv2_ir = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
        self.conv2_vi = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        
        self.conv3_ir = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
        self.conv3_vi = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        
        self.up3 = nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1)
        self.up2 = nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)
        self.up1 = nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1)
        self.conv4 = nn.Conv2d(32, 1, kernel_size=3, stride=1, padding=1)
        
    def forward(self, ir, vi):
        x1 = self.conv1_ir(ir)
        x2 = self.conv1_vi(vi)
        x = torch.cat([x1, x2], dim=1)
        x = self.bn1(x)
        x = self.relu(x)
        
        x1 = self.conv2_ir(x1)
        x2 = self.conv2_vi(x2)
        x = torch.cat([x1, x2], dim=1)
        x = self.bn2(x)
        x = self.relu(x)
        
        x1 = self.conv3_ir(x1)
        x2 = self.conv3_vi(x2)
        x = torch.cat([x1, x2], dim=1)
        x = self.bn3(x)
        x = self.relu(x)
        
        x = self.up3(x)
        x = self.relu(x)
        x = self.up2(x)
        x = self.relu(x)
        x = self.up1(x)
        x = self.relu(x)
        
        x = self.conv4(x)
        x = torch.sigmoid(x)
        return x

这个网络的输入有两个：一个红外图像、一个可见光图像。网络有两个分支，分别处理这两个输入。每个分支都包括几个卷积层和池化层，以提取图像特征。然后，这些特征被合并在一起，并通过反卷积层进行上采样，以生成融合的图像。最后一层是一个 sigmoid 激活函数，它将输出缩放到 0 到 1 之间的范围。