给出模型断点恢复训练的pytorch代码，要求详细注释

好的，下面是一个简单的 PyTorch 代码示例，演示了如何使用 `torch.save()` 和 `torch.load()` 函数实现模型断点恢复训练。代码中使用的是 ResNet18 模型和 CIFAR10 数据集。

import torch
import torchvision
import torchvision.transforms as transforms

# 定义是否使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 定义超参数
num_epochs = 10
batch_size = 128
learning_rate = 0.001

# 加载 CIFAR10 数据集
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transforms.ToTensor())
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=2)

# 定义 ResNet18 模型
model = torchvision.models.resnet18(pretrained=False)
num_ftrs = model.fc.in_features
model.fc = torch.nn.Linear(num_ftrs, 10)
model = model.to(device)

# 定义损失函数和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)

# 定义模型保存路径和文件名
checkpoint_path = './checkpoint.pth'

# 加载之前保存的模型参数
try:
    checkpoint = torch.load(checkpoint_path)
    model.load_state_dict(checkpoint['model_state_dict'])
    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
    start_epoch = checkpoint['epoch']
    print("Successfully loaded checkpoint '{}' (epoch {})".format(checkpoint_path, start_epoch))
except:
    print("No checkpoint found at '{}'".format(checkpoint_path))
    start_epoch = 0

# 训练模型
for epoch in range(start_epoch, num_epochs):
    for i, (images, labels) in enumerate(trainloader):
        images = images.to(device)
        labels = labels.to(device)

        # 前向传播和计算损失
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(trainloader), loss.item()))

    # 保存模型参数
    torch.save({
        'epoch': epoch+1,
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
        'loss': loss
    }, checkpoint_path)

print('Finished Training')

在这个代码示例中，我们首先加载 CIFAR10 数据集，并定义 ResNet18 模型、损失函数和优化器。然后我们定义了一个模型保存路径和文件名，并尝试加载之前保存的模型参数。如果成功加载，就从加载的 epoch 开始训练，否则从第 0 个 epoch 开始训练。在训练过程中，每训练完一个 epoch 就保存一次模型参数，以便恢复训练时可以从最近一次保存的参数开始训练。最后输出训练完成信息。