使用pytorch训练一个基于多尺度自编码网络的红外与可见光图像融合的模型，第一阶段训练一个自动编码器网络，数据集COCO用于训练自编码器网络，第二阶段为融合阶段，融合网络包含三个主要部分：编码器、融合策略和解码器,并用模型对TNO数据集进行图像融合,给出具体代码

这是一个比较复杂的项目，需要较多的代码和训练时间，下面给出一个简单的代码示例，以供参考：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets

# 定义自编码器网络
class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()
        # 编码器部分
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
            nn.ReLU()
        )
        # 解码器部分
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(32, 3, kernel_size=3, stride=1, padding=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

# 定义融合网络
class FusionNet(nn.Module):
    def __init__(self):
        super(FusionNet, self).__init__()
        # 编码器部分
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
            nn.ReLU()
        )
        # 融合策略
        self.fusion = nn.Sequential(
            nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(32, 3, kernel_size=3, stride=1, padding=1),
            nn.Tanh()
        )
        # 解码器部分
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(32, 3, kernel_size=3, stride=1, padding=1),
            nn.Sigmoid()
        )

    def forward(self, x1, x2):
        # 编码器
        x1 = self.encoder(x1)
        x2 = self.encoder(x2)
        # 融合
        x = torch.cat((x1, x2), dim=1)
        x = self.fusion(x)
        # 解码器
        x = self.decoder(x)
        return x

# 数据集
train_transforms = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor()
])
train_dataset = datasets.CocoDetection(root='path/to/coco', annFile='path/to/annotations',
                                       transform=train_transforms)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)

# 训练自编码器网络
autoencoder = Autoencoder()
criterion = nn.MSELoss()
optimizer = optim.Adam(autoencoder.parameters(), lr=0.001)
for epoch in range(10):
    running_loss = 0.0
    for data in train_loader:
        inputs, _ = data
        optimizer.zero_grad()
        outputs = autoencoder(inputs)
        loss = criterion(outputs, inputs)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * inputs.size(0)
    epoch_loss = running_loss / len(train_loader.dataset)
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, epoch_loss))

# 训练融合网络
fusion_net = FusionNet()
criterion = nn.MSELoss()
optimizer = optim.Adam(fusion_net.parameters(), lr=0.001)
for epoch in range(10):
    running_loss = 0.0
    for data in train_loader:
        inputs1, inputs2 = data
        optimizer.zero_grad()
        outputs = fusion_net(inputs1, inputs2)
        loss = criterion(outputs, inputs1)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * inputs1.size(0)
    epoch_loss = running_loss / len(train_loader.dataset)
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, epoch_loss))

# 使用模型进行图像融合
test_transforms = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor()
])
test_dataset = datasets.ImageFolder(root='path/to/TNO', transform=test_transforms)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False)

for data in test_loader:
    inputs, _ = data
    inputs1 = inputs[:, :, :, :3]
    inputs2 = inputs[:, :, :, 3:]
    outputs = fusion_net(inputs1, inputs2)
    # 显示结果

请注意，这只是一个简单的示例代码，需要根据具体情况进行修改和完善。同时，需要注意数据集的下载和路径设置，以及模型的保存和加载等问题。