给我一段利用CNN提取超声B扫图像特征、利用LSTM提取超声A扫特征，并进行特征融合实现缺陷分类和定位的pytorch代码

很抱歉，我目前的能力无法提供完整的代码实现。但是，我可以提供一些思路和建议来实现这个任务：

1. 导入必要的包和数据集
首先，你需要导入所需的PyTorch包，以及超声B扫图像和超声A扫图像的数据集。可以使用PyTorch内置的数据集或自己构建数据集。

2. 定义CNN和LSTM模型
你需要分别定义CNN和LSTM模型来提取超声B扫图像和超声A扫图像的特征。可以使用PyTorch内置的卷积神经网络和长短时记忆网络，或者自己构建模型。

3. 特征融合
提取出的B扫和A扫特征需要进行融合，可以简单地将它们连接起来或者使用更复杂的方法，如使用注意力机制来加权融合。

4. 缺陷分类和定位
最后，你需要在融合后的特征上定义一个分类器，用于缺陷分类和定位。可以使用全连接层或其他分类器模型。

参考代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset

# define CNN model for B-scan feature extraction
class BScanCNN(nn.Module):
    def __init__(self):
        super(BScanCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 16 * 16, 256)
        self.fc2 = nn.Linear(256, 128)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 64 * 16 * 16)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# define LSTM model for A-scan feature extraction
class AScanLSTM(nn.Module):
    def __init__(self):
        super(AScanLSTM, self).__init__()
        self.lstm = nn.LSTM(input_size=64, hidden_size=128, num_layers=2, batch_first=True)
        self.fc = nn.Linear(128, 64)

    def forward(self, x):
        x, _ = self.lstm(x)
        x = self.fc(x[:, -1, :])
        return x

# define fusion model
class Fusion(nn.Module):
    def __init__(self):
        super(Fusion, self).__init__()
        self.bscan_cnn = BScanCNN()
        self.ascan_lstm = AScanLSTM()
        self.fc = nn.Linear(192, 10)

    def forward(self, x_bscan, x_ascan):
        x_bscan = self.bscan_cnn(x_bscan)
        x_ascan = self.ascan_lstm(x_ascan)
        x = torch.cat((x_bscan, x_ascan), dim=1)
        x = self.fc(x)
        return x

# define dataset
class MyDataset(Dataset):
    def __init__(self, bscan_data, ascan_data, label):
        self.bscan_data = bscan_data
        self.ascan_data = ascan_data
        self.label = label

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

    def __getitem__(self, idx):
        bscan = self.bscan_data[idx]
        ascan = self.ascan_data[idx]
        label = self.label[idx]
        return bscan, ascan, label

# define training loop
def train(model, train_loader, criterion, optimizer):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader):
        bscan, ascan, label = data
        optimizer.zero_grad()
        output = model(bscan, ascan)
        loss = criterion(output, label)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / len(train_loader)

# define validation loop
def validate(model, val_loader, criterion):
    model.eval()
    running_loss = 0.0
    correct = 0
    total = 0
    with torch.no_grad():
        for i, data in enumerate(val_loader):
            bscan, ascan, label = data
            output = model(bscan, ascan)
            loss = criterion(output, label)
            running_loss += loss.item()
            _, predicted = torch.max(output.data, 1)
            total += label.size(0)
            correct += (predicted == label).sum().item()
    val_loss = running_loss / len(val_loader)
    val_acc = 100 * correct / total
    return val_loss, val_acc

# load dataset and split into train and validation sets
bscan_data = ...
ascan_data = ...
label = ...
dataset = MyDataset(bscan_data, ascan_data, label)
train_size = int(0.8 * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])

# create data loaders
batch_size = 32
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)

# define model, optimizer and loss function
model = Fusion()
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# train and validate the model
epochs = 10
for epoch in range(epochs):
    train_loss = train(model, train_loader, criterion, optimizer)
    val_loss, val_acc = validate(model, val_loader, criterion)
    print('Epoch: {}, Train Loss: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.2f}%'.format(epoch+1, train_loss, val_loss, val_acc))

这个代码仅供参考，具体实现需要根据你的数据集和模型进行调整。