在PyTorch迁移学习中使用自定义resnet18代码

import torch
import torch.nn as nn
import torchvision.models as models

class CustomResNet18(nn.Module):
    def __init__(self, num_classes):
        super(CustomResNet18, self).__init__()
        self.resnet = models.resnet18(pretrained=True)
        num_ftrs = self.resnet.fc.in_features
        self.resnet.fc = nn.Linear(num_ftrs, num_classes)

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

num_classes = 10  # 根据具体问题设置类别数
model = CustomResNet18(num_classes)

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# TODO: 加载和预处理数据集

num_epochs = 10  # 根据具体问题设置训练轮数
for epoch in range(num_epochs):
    # TODO: 完成训练过程

# TODO: 使用模型进行预测