pytorch写的梯度反转层代码如何使用

使用梯度反转层的代码步骤如下：

1. 导入必要的库

import torch
import torch.nn as nn
from torch.autograd import Function

2. 定义梯度反转层类

class GradReverse(Function):
    @staticmethod
    def forward(ctx, x, alpha):
        ctx.alpha = alpha
        return x.view_as(x)

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

3. 定义网络模型

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=5),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=5),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Linear(128 * 4 * 4, 1024),
            nn.ReLU(inplace=True),
            nn.Linear(1024, 2),
        )
        self.domain_classifier = nn.Sequential(
            nn.Linear(128 * 4 * 4, 1024),
            nn.ReLU(inplace=True),
            nn.Linear(1024, 2),
        )

    def forward(self, x, alpha=None):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        if alpha is not None:
            x = GradReverse.apply(x, alpha)
        y = self.classifier(x)
        d = self.domain_classifier(x)
        return y, d

4. 训练模型

net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs, _ = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))

5. 进行测试

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

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

这样就可以使用梯度反转层进行域适应了。