写出resnet18的训练过程伪代码

好的，以下是resnet18的训练过程伪代码：

// 定义超参数
learning_rate = 0.001
num_epochs = 10
batch_size = 64

// 加载数据集
train_loader, test_loader = load_data(batch_size)

// 定义模型
model = ResNet18()

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

// 训练模型
for epoch in range(num_epochs):
    for images, labels in train_loader:
        // 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

        // 反向传播和优化参数
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    // 每个epoch后在测试集上计算准确率
    total_correct = 0
    total_images = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total_images += labels.size(0)
        total_correct += (predicted == labels).sum().item()

    accuracy = total_correct / total_images
    print('Epoch [{}/{}], Loss: {:.4f}, Test Accuracy: {:.2f}%'
          .format(epoch+1, num_epochs, loss.item(), accuracy * 100))

以上是resnet18的训练过程伪代码，包括加载数据集、定义模型、损失函数和优化器、训练模型等步骤。