请帮我写出基于pytorch resnet实现图片2分类的代码

好的，以下是一个简单的基于PyTorch实现图片2分类的代码，使用的是ResNet18模型：

import torch
import torch.nn as nn
import torchvision.models as models

# 定义ResNet18模型
class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()
        self.resnet18 = models.resnet18(pretrained=True)
        self.fc = nn.Linear(1000, 2)  # 全连接层，输出2类

    def forward(self, x):
        x = self.resnet18(x)
        x = self.fc(x)
        return x

# 训练函数
def train(model, train_loader, criterion, optimizer):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / len(train_loader)

# 测试函数
def test(model, test_loader, criterion):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
        accuracy = 100 * correct / total
        print('Accuracy of the network on the test images: %d %%' % (accuracy))
        return accuracy

# 主函数
if __name__ == '__main__':
    # 加载数据集，这里假设使用的是torchvision.datasets.ImageFolder
    train_dataset = torchvision.datasets.ImageFolder('train', transform=transforms.ToTensor())
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=2)
    test_dataset = torchvision.datasets.ImageFolder('test', transform=transforms.ToTensor())
    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=4, shuffle=False, num_workers=2)

    # 定义模型、损失函数和优化器
    model = ResNet18()
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

    # 训练和测试
    for epoch in range(10):
        train_loss = train(model, train_loader, criterion, optimizer)
        test_accuracy = test(model, test_loader, criterion)
        print('Epoch: %d, Training Loss: %.3f, Test Accuracy: %.3f' % (epoch + 1, train_loss, test_accuracy))

需要注意的是，这只是一个简单的示例代码，具体实现还需要根据数据集和应用场景进行相应的修改和调整。