一共有12类不同的图像，分别被划分为训练集，验证集，分别在data文件夹下的train文件夹和val文件夹中。这12类图像中，其中有11类图像描述着各种各样的问题，分别在data文件夹下的train1文件夹和val1文件夹中。有1类图像是无缺陷的图像，分别在data文件夹下的train2文件夹和val2文件夹中。现在准备在一个分支上将11类有问题的图像用resnet34网络进行训练，再在另一个分支上将1类无问题的图像用resnet34网络进行训练，然后将这两个分支得到的结果进行相减，相减之后得到的结果，再次通过resnet34网络进行训练，对12类图像的数据集进行训练、验证。请给出相应代码，并使用ContrastiveLoss作为损失函数。

以下是相应的代码，使用PyTorch实现。

首先，我们需要导入所需的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder

然后，定义训练和验证数据集的路径：

train_data_path = 'data/train'
val_data_path = 'data/val'
train1_data_path = 'data/train1'
val1_data_path = 'data/val1'
train2_data_path = 'data/train2'
val2_data_path = 'data/val2'

接下来，定义数据预处理方式：

transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

然后，定义11类有问题图像的数据集和1类无问题图像的数据集：

train_dataset1 = ImageFolder(train1_data_path, transform=transform)
val_dataset1 = ImageFolder(val1_data_path, transform=transform)
train_dataset2 = ImageFolder(train2_data_path, transform=transform)
val_dataset2 = ImageFolder(val2_data_path, transform=transform)

接下来，定义ResNet34模型：

class ResNet34(nn.Module):
    def __init__(self, num_classes=1000):
        super().__init__()
        self.resnet = torch.hub.load('pytorch/vision:v0.9.0', 'resnet34', pretrained=False)
        self.resnet.fc = nn.Linear(self.resnet.fc.in_features, num_classes)

    def forward(self, x):
        x = self.resnet(x)
        return x

接下来，定义训练和验证函数：

def train(model, dataloader, optimizer, criterion, device):
    model.train()
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(dataloader):
        inputs = inputs.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * inputs.size(0)
    epoch_loss = running_loss / len(dataloader.dataset)
    return epoch_loss

def validate(model, dataloader, criterion, device):
    model.eval()
    running_loss = 0.0
    running_corrects = 0
    with torch.no_grad():
        for i, (inputs, labels) in enumerate(dataloader):
            inputs = inputs.to(device)
            labels = labels.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            _, preds = torch.max(outputs, 1)
            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds == labels.data)
    epoch_loss = running_loss / len(dataloader.dataset)
    epoch_acc = running_corrects.double() / len(dataloader.dataset)
    return epoch_loss, epoch_acc

然后，定义训练和验证参数：

batch_size = 32
num_epochs1 = 10
num_epochs2 = 10
lr1 = 0.001
lr2 = 0.001
num_classes1 = 11
num_classes2 = 1

接下来，定义11类有问题图像的数据集和1类无问题图像的数据集的数据加载器：

train_loader1 = data.DataLoader(train_dataset1, batch_size=batch_size, shuffle=True, num_workers=4)
val_loader1 = data.DataLoader(val_dataset1, batch_size=batch_size, shuffle=False, num_workers=4)
train_loader2 = data.DataLoader(train_dataset2, batch_size=batch_size, shuffle=True, num_workers=4)
val_loader2 = data.DataLoader(val_dataset2, batch_size=batch_size, shuffle=False, num_workers=4)

然后，定义11类有问题图像的ResNet34模型和1类无问题图像的ResNet34模型：

model1 = ResNet34(num_classes=num_classes1)
model2 = ResNet34(num_classes=num_classes2)

接下来，定义优化器和损失函数：

optimizer1 = optim.Adam(model1.parameters(), lr=lr1)
optimizer2 = optim.Adam(model2.parameters(), lr=lr2)
criterion1 = nn.CrossEntropyLoss()
criterion2 = nn.CrossEntropyLoss()

然后，训练11类有问题图像的ResNet34模型：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model1.to(device)
best_loss1 = float('inf')
for epoch in range(num_epochs1):
    train_loss1 = train(model1, train_loader1, optimizer1, criterion1, device)
    val_loss1, val_acc1 = validate(model1, val_loader1, criterion1, device)
    print(f'Epoch {epoch + 1}/{num_epochs1}, Train Loss: {train_loss1:.4f}, Val Loss: {val_loss1:.4f}, Val Acc: {val_acc1:.4f}')
    if val_loss1 < best_loss1:
        best_loss1 = val_loss1
        torch.save(model1.state_dict(), 'resnet34_1.pt')

接下来，训练1类无问题图像的ResNet34模型：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model2.to(device)
best_loss2 = float('inf')
for epoch in range(num_epochs2):
    train_loss2 = train(model2, train_loader2, optimizer2, criterion2, device)
    val_loss2, val_acc2 = validate(model2, val_loader2, criterion2, device)
    print(f'Epoch {epoch + 1}/{num_epochs2}, Train Loss: {train_loss2:.4f}, Val Loss: {val_loss2:.4f}, Val Acc: {val_acc2:.4f}')
    if val_loss2 < best_loss2:
        best_loss2 = val_loss2
        torch.save(model2.state_dict(), 'resnet34_2.pt')

接下来，定义相减模型：

class SubtractModel(nn.Module):
    def __init__(self, model1, model2):
        super().__init__()
        self.model1 = model1
        self.model2 = model2

    def forward(self, x1, x2):
        x1 = self.model1(x1)
        x2 = self.model2(x2)
        return x1 - x2

接下来，定义相减模型和12类图像的ResNet34模型：

sub_model = SubtractModel(model1, model2)
model3 = ResNet34(num_classes=12)

接下来，定义优化器和损失函数：

optimizer3 = optim.Adam(model3.parameters(), lr=lr1)
criterion3 = nn.ContrastiveLoss()

然后，定义12类图像的数据集：

train_dataset = ImageFolder(train_data_path, transform=transform)
val_dataset = ImageFolder(val_data_path, transform=transform)

接下来，定义12类图像的数据集的数据加载器：

train_loader = data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
val_loader = data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)

接下来，训练相减模型和12类图像的ResNet34模型：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
sub_model.to(device)
model3.to(device)
best_loss3 = float('inf')
for epoch in range(num_epochs1):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(train_loader):
        inputs1 = inputs
        inputs2 = inputs
        inputs1[:, :3, :, :] = inputs[:, 3:, :, :]
        inputs2[:, :3, :, :] = inputs[:, :3, :, :]
        inputs1 = inputs1.to(device)
        inputs2 = inputs2.to(device)
        labels = labels.to(device)
        optimizer3.zero_grad()
        outputs = sub_model(inputs1, inputs2)
        loss = criterion3(outputs, labels)
        loss.backward()
        optimizer3.step()
        running_loss += loss.item() * inputs.size(0)
    epoch_loss = running_loss / len(train_loader.dataset)
    val_loss, val_acc = validate(model3, val_loader, criterion1, device)
    print(f'Epoch {epoch + 1}/{num_epochs1}, Train Loss: {epoch_loss:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}')
    if val_loss < best_loss3:
        best_loss3 = val_loss
        torch.save(model3.state_dict(), 'resnet34_3.pt')

最后，我们可以使用训练好的模型进行预测，评估模型的性能。