代码实现验证swin transformer准确度

Swin Transformer是一种新型的Transformer模型，可以用于图像分类等任务。其准确度与其他先进的模型相当。下面是使用PyTorch实现Swin Transformer进行图像分类的代码，可以用于验证其准确度：

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

# 加载数据集
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
                                          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=128,
                                         shuffle=False, num_workers=2)

# 定义Swin Transformer模型
model = SwinTransformer(
    img_size=32,
    patch_size=4,
    in_chans=3,
    num_classes=10,
    embed_dim=96,
    depths=[2, 2, 18, 2],
    num_heads=[3, 6, 12, 24],
    window_size=7,
    mlp_ratio=4.,
    qkv_bias=True,
    qk_scale=None,
    drop_rate=0.,
    drop_path_rate=0.2,
    ape=False,
    patch_norm=True,
    use_checkpoint=False
)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

# 训练模型
for epoch in range(50):

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # 获取输入数据
        inputs, labels = data

        # 清零梯度
        optimizer.zero_grad()

        # 前向传播、计算损失、反向传播
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 统计损失
        running_loss += loss.item()

    # 打印训练进度
    print('[%d] loss: %.3f' %
          (epoch + 1, running_loss / (len(trainloader) * trainloader.batch_size)))

    # 在测试集上计算准确率
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = model(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))

print('Finished Training')

其中，使用的是CIFAR-10数据集，训练50个epoch，学习率为0.1，动量为0.9，权重衰减为5e-4。在测试集上计算准确率。