resnet18实现cifar10分类

下面是一个使用ResNet-18模型实现CIFAR-10分类的示例代码：

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

# 数据预处理
transform_train = 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))
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载CIFAR-10数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)

# 定义ResNet-18模型
class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.resnet18 = torchvision.models.resnet18(pretrained=False)
        self.fc = nn.Linear(1000, 10)  # CIFAR-10有10个类别

    def forward(self, x):
        x = self.resnet18(x)
        x = self.fc(x)
        return x

model = ResNet()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        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')

# 在测试集上评估模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

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

这段代码使用了PyTorch框架，首先定义了数据预处理的方式，然后使用`torchvision.datasets.CIFAR10`加载CIFAR-10数据集，并使用`torch.utils.data.DataLoader`创建数据加载器。接着定义了一个基于ResNet-18的模型，包含一个全连接层用于分类。然后定义了损失函数和优化器。最后，使用训练数据对模型进行训练，并在测试集上评估模型的准确率。