以上算法，使用CNN网络做

如果您想使用CNN网络进行图像匹配和质量评价，可以考虑使用基于深度学习的图像匹配方法，例如基于Siamese网络的图像匹配方法。下面是一个简单的基于Siamese网络的图像匹配代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import transforms, datasets
from torch.utils.data import DataLoader, Dataset

class SiameseNetwork(nn.Module):
    def __init__(self):
        super(SiameseNetwork, self).__init__()
        # 定义网络结构
        self.conv1 = nn.Conv2d(1, 64, 10)
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(64, 128, 7)
        self.pool2 = nn.MaxPool2d(2)
        self.conv3 = nn.Conv2d(128, 128, 4)
        self.pool3 = nn.MaxPool2d(2)
        self.conv4 = nn.Conv2d(128, 256, 4)
        self.fc1 = nn.Linear(256 * 6 * 6, 4096)
        self.fc2 = nn.Linear(4096, 1)

    def forward_once(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = F.relu(x)
        x = self.pool3(x)
        x = self.conv4(x)
        x = F.relu(x)
        x = x.view(-1, 256 * 6 * 6)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        return x

    def forward(self, x1, x2):
        # 分别输入两张图片，计算输出结果
        output1 = self.forward_once(x1)
        output2 = self.forward_once(x2)
        return output1, output2


class SiameseDataset(Dataset):
    def __init__(self, dataset):
        self.dataset = dataset
        self.transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.5,), (0.5,))
        ])

    def __getitem__(self, index):
        # 随机选择一对图像，并返回它们的标签
        img1, label1 = self.dataset[index]
        while True:
            # 避免选择相同的图像
            index2 = torch.randint(0, len(self.dataset), (1,)).item()
            if index2 != index:
                break
        img2, label2 = self.dataset[index2]
        if label1 == label2:
            label = torch.FloatTensor([1])
        else:
            label = torch.FloatTensor([0])
        img1 = self.transform(img1)
        img2 = self.transform(img2)
        return img1, img2, label

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


# 训练模型
def train(model, train_loader, optimizer, criterion, epoch):
    for batch_idx, (data1, data2, label) in enumerate(train_loader):
        optimizer.zero_grad()
        output1, output2 = model(data1, data2)
        loss = criterion(output1, output2, label)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data1), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))


# 测试模型
def test(model, test_loader):
    with torch.no_grad():
        correct = 0
        total = 0
        for data1, data2, label in test_loader:
            output1, output2 = model(data1, data2)
            pred = (output1 < output2).float()
            correct += (pred == label).sum().item()
            total += label.size(0)
        print('Test Accuracy: {:.2f}%'.format(100. * correct / total))


# 计算损失函数
class ContrastiveLoss(nn.Module):
    def __init__(self, margin=1.0):
        super(ContrastiveLoss, self).__init__()
        self.margin = margin

    def forward(self, output1, output2, label):
        euclidean_distance = F.pairwise_distance(output1, output2)
        loss_contrastive = torch.mean((label) * torch.pow(euclidean_distance, 2) +
                                      (1 - label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))
        return loss_contrastive


# 加载数据
train_dataset = datasets.ImageFolder('train', transform=transforms.ToTensor())
train_dataset = SiameseDataset(train_dataset)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_dataset = datasets.ImageFolder('test', transform=transforms.ToTensor())
test_dataset = SiameseDataset(test_dataset)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

# 初始化模型和优化器
model = SiameseNetwork()
optimizer = optim.Adam(model.parameters(), lr=0.0001)
criterion = ContrastiveLoss()

# 训练模型
for epoch in range(1, 10):
    train(model, train_loader, optimizer, criterion, epoch)
    test(model, test_loader)

使用Siamese网络进行图像匹配，需要定义一个SiameseNetwork类，并实现forward_once函数和forward函数。其中forward_once函数实现了单个图像的前向传播，forward函数实现了两个图像的前向传播。在SiameseDataset中定义了数据集的读取方式，每次随机选择一对图像进行训练。训练过程中使用ContrastiveLoss计算损失函数，并使用Adam优化器进行优化。最终训练出的模型可以用于图像匹配和质量评价。