编写dataset类加载CoCo数据集与tno数据集，使用pytorch训练一个基于多尺度自自编码网络，用COCO数据集训练，通过网络中的encoder函数编码输入图像，再由decoder函数解码特征重建图像并计算重建图像与源图像的损失，保存模型，用该模型进行可见光与红外图像的融合，给出代码

以下是根据您的要求编写的代码，其中包含了加载CoCo数据集和tno数据集、训练多尺度自编码网络、保存模型、可见光与红外图像融合等功能。

import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import numpy as np
import os

# 定义CoCo数据集的Dataset类
class CoCoDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.img_names = os.listdir(root_dir)

    def __len__(self):
        return len(self.img_names)

    def __getitem__(self, idx):
        img_name = os.path.join(self.root_dir, self.img_names[idx])
        image = Image.open(img_name).convert('RGB')
        if self.transform:
            image = self.transform(image)
        return image

# 定义tno数据集的Dataset类
class TnoDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.img_names = os.listdir(root_dir)

    def __len__(self):
        return len(self.img_names)

    def __getitem__(self, idx):
        img_name = os.path.join(self.root_dir, self.img_names[idx])
        image = Image.open(img_name).convert('L')
        if self.transform:
            image = self.transform(image)
        return image

# 定义多尺度自编码网络类
class MultiScaleAutoencoder(torch.nn.Module):
    def __init__(self):
        super(MultiScaleAutoencoder, self).__init__()
        self.encoder = torch.nn.Sequential(
            torch.nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2, stride=2),
            torch.nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2, stride=2),
            torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2, stride=2),
            torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.decoder = torch.nn.Sequential(
            torch.nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2),
            torch.nn.ReLU(),
            torch.nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2),
            torch.nn.ReLU(),
            torch.nn.ConvTranspose2d(32, 16, kernel_size=2, stride=2),
            torch.nn.ReLU(),
            torch.nn.ConvTranspose2d(16, 3, kernel_size=2, stride=2),
            torch.nn.Sigmoid()
        )

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

# 定义训练函数
def train(model, train_loader, criterion, optimizer, num_epochs):
    for epoch in range(num_epochs):
        for data in train_loader:
            img = data
            img = img.to(device)
            output = model(img)
            loss = criterion(output, img)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

    # 保存模型
    torch.save(model.state_dict(), 'model.ckpt')

# 定义图像融合函数
def fuse_images(model, visible, infrared):
    visible = visible.to(device)
    infrared = infrared.to(device)
    visible_feature = model.encoder(visible)
    infrared_feature = model.encoder(infrared)
    fused_feature = (visible_feature + infrared_feature) / 2
    fused_image = model.decoder(fused_feature)
    return fused_image

# 设置超参数
num_epochs = 10
batch_size = 16
learning_rate = 0.001

# 加载数据集
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor()
])
coco_dataset = CoCoDataset('coco_data', transform)
tno_dataset = TnoDataset('tno_data', transform)
coco_loader = DataLoader(coco_dataset, batch_size=batch_size, shuffle=True)
tno_loader = DataLoader(tno_dataset, batch_size=batch_size, shuffle=True)

# 创建模型并将其移动到GPU上
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = MultiScaleAutoencoder().to(device)

# 定义损失函数和优化器
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
train(model, coco_loader, criterion, optimizer, num_epochs)

# 加载可见光图像和红外图像
visible_image = Image.open('visible.jpg').convert('RGB')
infrared_image = Image.open('infrared.jpg').convert('L')

# 对图像进行预处理
preprocess = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor()
])
visible_tensor = preprocess(visible_image)
infrared_tensor = preprocess(infrared_image)

# 对图像进行融合
fused_image = fuse_images(model, visible_tensor.unsqueeze(0), infrared_tensor.unsqueeze(0)).squeeze(0)

# 将融合后的图像保存到文件
transforms.ToPILImage()(fused_image.cpu()).save('fused.jpg')

在上面的代码中，我们定义了两个数据集类`CoCoDataset`和`TnoDataset`，用于加载CoCo数据集和tno数据集。我们还定义了一个多尺度自编码网络类`MultiScaleAutoencoder`，其中包含了一个encoder函数和一个decoder函数，用于对输入图像进行编码和解码。我们还定义了一个训练函数`train`，用于训练模型，并将训练好的模型保存到文件。最后，我们定义了一个图像融合函数`fuse_images`，用于将可见光图像和红外图像进行融合，得到融合后的图像。