pytorch图像分类代码怎么写

以下是使用 PyTorch 实现图像分类的示例代码：

import torch
import torchvision.transforms as transforms
import torchvision.models as models

# 定义模型
model = models.resnet18(pretrained=True)

# 修改最后一层输出类别数
num_classes = 10
model.fc = torch.nn.Linear(model.fc.in_features, num_classes)

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

# 数据预处理
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, 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=4, shuffle=False, num_workers=2)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data

        optimizer.zero_grad()

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

        running_loss += loss.item()

    print(f"Epoch {epoch + 1}, Loss: {running_loss / (i + 1)}")

print("Finished Training")

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

print(f"Accuracy of the network on the test images: {100 * correct / total}%")

以上代码使用了 PyTorch 中的 ResNet18 模型，并修改了最后一层的输出类别数为 10。同时，定义了损失函数为交叉熵损失函数，优化器为随机梯度下降算法。数据预处理包括调整图像大小、中心裁剪、转换为张量和标准化。最后，加载 CIFAR-10 数据集进行训练和测试。