swin transformer实现猫狗图像分类的代码

以下是使用 Swin Transformer 实现猫狗图像分类的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from swin_transformer import SwinTransformer

# 定义数据预处理
transform = transforms.Compose(
    [transforms.Resize((224, 224)),
     transforms.ToTensor(),
     transforms.Normalize((.5, .5, .5), (.5, .5, .5))])

# 加载数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=32,
                                         shuffle=False, num_workers=2)

# 定义模型
model = SwinTransformer(img_size=224, patch_size=4, in_chans=3, num_classes=2)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=.001, momentum=.9)

# 训练模型
for epoch in range(10):  # 训练 10 轮
    running_loss = .
    for i, data in enumerate(trainloader, ):
        inputs, labels = data

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 200 == 199:    # 每 200 个 batch 输出一次损失值
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 200))
            running_loss = .

print('Finished Training')

# 测试模型
correct = 
total = 
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size()
        correct += (predicted == labels).sum().item()

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

注意：这只是一个示例代码，实际使用时需要根据具体情况进行修改。