写一个基于transformer的图像分类代码

写一个基于 Transformer 的图像分类代码需要一定的深度学习和 PyTorch 编程经验。但是，我可以提供一些基本的指导：

1. 导入所需的库：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

2. 定义超参数和数据加载：

# 超参数
num_epochs = 10
batch_size = 64
learning_rate = 0.001

# 加载 CIFAR10 数据集
transform = transforms.Compose([
    transforms.Resize(224),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

3. 定义 Transformer 模型：

class TransformerModel(nn.Module):
    def __init__(self):
        super(TransformerModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(64 * 32 * 32, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        return x

4. 定义损失函数和优化器：

model = TransformerModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

5. 训练模型：

total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        outputs =