为每一行加注释

好的，下面是注释版的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

# 定义模型
class TransferModel(nn.Module):
    def __init__(self, num_classes):
        super(TransferModel, self).__init__()
        # 使用预训练的ResNet-18模型
        self.resnet = torchvision.models.resnet18(pretrained=True)
        # 将ResNet-18的最后一层全连接层替换为一个新的全连接层
        self.resnet.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        # 前向传播
        output = self.resnet(x)
        return output

# 数据预处理
transform_train = transforms.Compose([
    # 随机裁剪和大小变换
    transforms.RandomResizedCrop(224),
    # 随机水平翻转
    transforms.RandomHorizontalFlip(),
    # 将数据转换为tensor类型
    transforms.ToTensor(),
    # 归一化
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])

transform_test = transforms.Compose([
    # 将图像大小调整为256x256
    transforms.Resize(256),
    # 中心裁剪为224x224
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])

# 加载数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform_train)
trainloader = DataLoader(trainset, batch_size=32, shuffle=True, num_workers=4)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform_test)
testloader = DataLoader(testset, batch_size=32, shuffle=False, num_workers=4)

# 定义模型和损失函数
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

num_classes = 10
model = TransferModel(num_classes).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # 将输入数据和标签转移到指定设备上
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        # 清空优化器的梯度信息
        optimizer.zero_grad()
        # 计算输出和损失
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        # 反向传播和优化
        loss.backward()
        optimizer.step()
        # 计算平均损失
        running_loss += loss.item()
        if i % 200 == 199:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 200))
            running_loss = 0.0

print('Finished Training')

# 评估模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        # 将输入数据和标签转移到指定设备上
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        # 计算模型的输出
        outputs = model(images)
        # 找到每个输出向量中的最大值，并返回其所在的索引，即预测的类别
        _, predicted = torch.max(outputs.data, 1)
        # 计算正确预测的样本数和总样本数
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the test images: %d %%' % (
    100 * correct / total))

希望这样更容易理解些。