写一段基于pytorch的深度学习图像融合代码

以下是一个简单的基于PyTorch的图像融合代码，它使用了两个预训练的神经网络进行图像融合：VGG16和UNet。

import torch
import torch.nn as nn
import torchvision.models as models

class ImageFusion(nn.Module):
    def __init__(self):
        super(ImageFusion, self).__init__()
        self.vgg_model = models.vgg16(pretrained=True)
        self.vgg_model.eval()
        self.unet_model = UNet()

    def forward(self, img1, img2):
        img1_features = self.vgg_model.features(img1)
        img2_features = self.vgg_model.features(img2)
        fused_features = self.unet_model(img1_features, img2_features)
        return fused_features

class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()

        # Encoder
        self.conv1 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 64, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(64, 32, kernel_size=3, padding=1)

        # Decoder
        self.upconv1 = nn.ConvTranspose2d(32, 64, kernel_size=2, stride=2)
        self.upconv2 = nn.ConvTranspose2d(64, 128, kernel_size=2, stride=2)
        self.upconv3 = nn.ConvTranspose2d(128, 256, kernel_size=2, stride=2)
        self.upconv4 = nn.ConvTranspose2d(256, 512, kernel_size=2, stride=2)

    def forward(self, img1_features, img2_features):
        # Encoder
        x = torch.cat((img1_features, img2_features), dim=1)
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)

        # Decoder
        x = self.upconv1(x)
        x = torch.cat((x, img2_features), dim=1)
        x = self.upconv2(x)
        x = torch.cat((x, img1_features), dim=1)
        x = self.upconv3(x)
        x = self.upconv4(x)

        return x

该代码定义了两个模型：VGG16和UNet。VGG16用于提取输入图像的特征，UNet用于将两个输入图像的特征融合在一起。在进行融合之前，我们首先将两个输入图像的特征送入VGG16模型中进行特征提取，然后再将提取出来的特征送入UNet模型中进行融合。最后，融合后的特征将被送回给调用该模型的函数。