帮我写一个基于transformer的图像分类代码

好的，以下是一个基于Transformer的图像分类代码的示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

# 定义超参数
batch_size = 128
epochs = 10
learning_rate = 0.001

# 加载数据集并进行预处理
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_set = datasets.CIFAR10(root="./data", train=True, transform=transform, download=True)
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
test_set = datasets.CIFAR10(root="./data", train=False, transform=transform, download=True)
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)

# 定义模型
class TransformerClassifier(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(TransformerClassifier, self).__init__()
        self.transformer_encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=input_dim, nhead=8),
            num_layers=6
        )
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)
        self.relu = nn.ReLU()
        self.softmax = nn.Softmax(dim=1)
        
    def forward(self, x):
        x = self.transformer_encoder(x)
        x = x.mean(dim=1)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.softmax(x)
        return x

# 初始化模型、损失函数和优化器
model = TransformerClassifier(input_dim=224*224*3, hidden_dim=256, output_dim=10)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.view(-1, 224*224*3)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        if (i+1) % 100 == 0:
            print("Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}"
                  .format(epoch+1, epochs, i+1, len(train_loader), loss.item()))

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for images, labels in test_loader:
        images = images.view(-1, 224*224*3)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print("Accuracy of the model on the test images: {:.2f}%".format(100 * correct / total))

在这个示例中，我们使用了PyTorch来构建一个基于Transformer的图像分类器。我们使用了CIFAR-10数据集，并对图像进行了预处理。我们定义了一个`TransformerClassifier`类，该类包含一个Transformer编码器和两个全连接层。我们使用交叉熵损失函数和Adam优化器进行训练，并在测试集上进行了评估。

请注意，由于Transformer的计算量较大，因此我们在这个示例中使用了较小的隐藏层维度和较少的Transformer层数。您可以根据需要对这些参数进行调整。