CNN和领域自适应结合进行样本迁移pytorch

可以使用PyTorch中的DANN（Domain-Adversarial Neural Networks）模型实现CNN和领域自适应结合进行样本迁移。DANN模型在训练过程中，通过引入一个领域分类器来判别输入数据的领域信息，再通过反向传播来最小化领域分类器的误差和原始任务分类器的误差，从而实现领域自适应。

以下是一个简单的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=5, padding=2)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=5, padding=2)
        self.bn2 = nn.BatchNorm2d(128)
        self.fc1 = nn.Linear(128*8*8, 256)
        self.fc2 = nn.Linear(256, 10)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 128*8*8)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

class DANN(nn.Module):
    def __init__(self, cnn):
        super(DANN, self).__init__()
        self.cnn = cnn
        self.domain_classifier = nn.Sequential(
            nn.Linear(128*8*8, 1024),
            nn.ReLU(),
            nn.Dropout(),
            nn.Linear(1024, 1024),
            nn.ReLU(),
            nn.Dropout(),
            nn.Linear(1024, 1)
        )

    def forward(self, x, alpha):
        features = self.cnn(x)
        reverse_features = ReverseLayerF.apply(features, alpha)
        domain_output = self.domain_classifier(reverse_features)
        class_output = F.softmax(features, dim=1)
        return domain_output, class_output

class ReverseLayerF(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x, alpha):
        ctx.alpha = alpha
        return x

    @staticmethod
    def backward(ctx, grad_output):
        output = grad_output.neg() * ctx.alpha
        return output, None

# 定义数据加载器
train_loader = torch.utils.data.DataLoader(
    torchvision.datasets.CIFAR10(root='./data', train=True, download=True,
                                   transform=torchvision.transforms.Compose([
                                       torchvision.transforms.RandomHorizontalFlip(),
                                       torchvision.transforms.RandomCrop(32, 4),
                                       torchvision.transforms.ToTensor(),
                                       torchvision.transforms.Normalize(
                                           (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
                                   ])),
    batch_size=128, shuffle=True, num_workers=2)

test_loader = torch.utils.data.DataLoader(
    torchvision.datasets.CIFAR10(root='./data', train=False, download=True,
                                   transform=torchvision.transforms.Compose([
                                       torchvision.transforms.ToTensor(),
                                       torchvision.transforms.Normalize(
                                           (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
                                   ])),
    batch_size=128, shuffle=False, num_workers=2)

# 定义模型、优化器和损失函数
cnn = CNN()
model = DANN(cnn)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
criterion = nn.CrossEntropyLoss()

# 开始训练
for epoch in range(100):
    for i, (inputs, labels) in enumerate(train_loader):
        # 梯度清零
        optimizer.zero_grad()

        # 计算领域标签，0表示源域，1表示目标域
        domain_labels = torch.zeros(inputs.size(0))
        domain_labels[inputs.size(0)//2:] = 1

        # 计算领域自适应参数alpha
        alpha = 0.1 * epoch / 100

        # 前向传播
        domain_outputs, class_outputs = model(inputs, alpha)

        # 计算损失函数
        class_loss = criterion(class_outputs, labels)
        domain_loss = criterion(domain_outputs.squeeze(), domain_labels)
        loss = class_loss + domain_loss

        # 反向传播和优化
        loss.backward()
        optimizer.step()

        if i % 100 == 0:
            print('Epoch [%d/%d], Iter [%d/%d], Class Loss: %.4f, Domain Loss: %.4f'
                  % (epoch+1, 100, i+1, len(train_loader), class_loss.item(), domain_loss.item()))

    # 在测试集上测试准确率
    correct = 0
    total = 0
    with torch.no_grad():
        for inputs, labels in test_loader:
            _, outputs = model(inputs, 0)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Test Accuracy of the model on the test images: %.4f %%' % (100 * correct / total))

在这个示例代码中，我们首先定义了一个CNN模型，然后用DANN模型将其包装起来，形成一个领域自适应模型。在训练过程中，我们使用了PyTorch中的反向传播函数`torch.autograd.Function`来实现领域自适应参数alpha的自动求导。最后，在训练结束后，在测试集上测试了模型的准确率。