注释rsnet图像分类代码

以下是对rsnet图像分类代码的注释：

# 导入所需的库
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义设备（CPU或GPU）
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 定义超参数（batch_size、learning_rate、num_epochs）
batch_size = 100
learning_rate = 0.001
num_epochs = 10

# 加载CIFAR-10数据集并进行数据增强
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),  # 随机裁剪
    transforms.RandomHorizontalFlip(),  # 随机水平翻转
    transforms.ToTensor(),  # 转为张量
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # 标准化
])

transform_test = transforms.Compose([
    transforms.ToTensor(),  # 转为张量
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # 标准化
])

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=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=2)

# 定义模型
class RsNet(nn.Module):
    def __init__(self):
        super(RsNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.relu3 = nn.ReLU(inplace=True)
        self.fc1 = nn.Linear(128 * 8 * 8, 512)
        self.bn4 = nn.BatchNorm1d(512)
        self.relu4 = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = x.view(-1, 128 * 8 * 8)
        x = self.fc1(x)
        x = self.bn4(x)
        x = self.relu4(x)
        x = self.fc2(x)
        return x

net = RsNet().to(device)

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

# 训练模型
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data[0].to(device), data[1].to(device)
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data[0].to(device), data[1].to(device)
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))