写一个pytorch域适应迁移学习代码，损失函数为MMD距离域判别损失和预测的交叉熵损失，特征提取用CNN实现

好的，以下是一个可能的代码示例：

import torch
from torch import nn
from torch.optim import Adam
from torch.utils.data import DataLoader
import torchvision.models as models
from sklearn.metrics.pairwise import rbf_kernel

# 定义 MMD 距离
def mmd(kernel_values):
    return torch.mean(kernel_values)

# 定义域适应的损失函数
class DAMDLoss(nn.Module):
    def __init__(self, num_classes, sigma):
        super(DAMDLoss, self).__init__()
        self.num_classes = num_classes
        self.sigma = sigma

    def forward(self, source_outputs, target_outputs, source_targets, target_targets):
        # 计算预测的交叉熵损失
        source_ce_loss = nn.CrossEntropyLoss()(source_outputs, source_targets)
        target_ce_loss = nn.CrossEntropyLoss()(target_outputs, target_targets)

        # 计算 MMD 距离
        source_features = source_outputs.view(-1, self.num_classes)
        target_features = target_outputs.view(-1, self.num_classes)
        s_s = rbf_kernel(source_features, source_features, gamma=1.0 / (2 * self.sigma ** 2))
        s_t = rbf_kernel(source_features, target_features, gamma=1.0 / (2 * self.sigma ** 2))
        t_t = rbf_kernel(target_features, target_features, gamma=1.0 / (2 * self.sigma ** 2))
        mmd_loss = mmd(s_s) + mmd(t_t) - 2 * mmd(s_t)

        return source_ce_loss + target_ce_loss + mmd_loss

# 定义模型
class CNNModel(nn.Module):
    def __init__(self, num_classes):
        super(CNNModel, self).__init__()
        self.cnn = models.resnet18(pretrained=True)
        self.fc = nn.Linear(self.cnn.fc.in_features, num_classes)
        self.cnn.fc = nn.Identity()

    def forward(self, x):
        features = self.cnn(x)
        outputs = self.fc(features)
        return outputs, features

# 定义数据加载器
source_dataset = ...
target_dataset = ...
source_loader = DataLoader(source_dataset, batch_size=32, shuffle=True)
target_loader = DataLoader(target_dataset, batch_size=32, shuffle=True)

# 定义模型和优化器
model = CNNModel(num_classes=10)
optimizer = Adam(model.parameters(), lr=1e-3)

# 定义损失函数
loss_fn = DAMDLoss(num_classes=10, sigma=10.0)

# 训练模型
for epoch in range(10):
    model.train()
    for i, (source_batch, target_batch) in enumerate(zip(source_loader, target_loader)):
        source_inputs, source_targets = source_batch
        target_inputs, target_targets = target_batch
        optimizer.zero_grad()
        source_outputs, source_features = model(source_inputs)
        target_outputs, target_features = model(target_inputs)
        loss = loss_fn(source_outputs, target_outputs, source_targets, target_targets)
        loss.backward()
        optimizer.step()