pytorch resnet18分类器 代码

你可以使用以下代码实现PyTorch中的resnet18分类器：

import torch
import torchvision.models as models

# 加载预训练的resnet18模型
model = models.resnet18(pretrained=True)

# 替换全连接层
num_features = model.fc.in_features
model.fc = torch.nn.Linear(num_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)

# 训练和测试模型
for epoch in range(num_epochs):
    # 训练模型
    model.train()
    for images, labels in train_loader:
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    
    # 测试模型
    model.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        for images, labels in test_loader:
            outputs = model(images)
            _, 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))