ResNet-18模型

import torch
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn as nn
import torchvision.models as models

# 定义数据预处理和加载
transform = transforms.Compose([transforms.RandomCrop(32, padding=4),
                                transforms.RandomHorizontalFlip(),
                                transforms.ToTensor(),
                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False, num_workers=2)

# 定义ResNet-18模型
class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()
        self.resnet = models.resnet18(pretrained=False)
        self.resnet.fc = nn.Linear(512, 10)  # 更改输出层为10个类别

    def forward(self, x):
        return self.resnet(x)

# 初始化模型、损失函数和优化器
net = ResNet18()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

# 训练模型
def train(net, trainloader, criterion, optimizer, epochs=10):
    net.train()
    for epoch in range(epochs):
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data
            optimizer.zero_grad()
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
            if i % 100 == 99:  # 每100个小批次打印一次损失
                print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
                running_loss = 0.0

    print('Finished Training')

# 训练模型
train(net, trainloader, criterion, optimizer, epochs=10)

# 保存模型权重
torch.save(net.state_dict(), 'resnet18_cifar10.pth')

# 测试模型
def test(net, testloader):
    net.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            inputs, labels = data
            outputs = net(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    accuracy = 100 * correct / total
    print('Accuracy on test set: {:.2f}%'.format(accuracy))


# 加载保存的模型权重并测试模型
net = ResNet18()
net.load_state_dict(torch.load('resnet18_cifar10.pth'))
test(net, testloader)