帮我用写一个基于vit模型的图像分类代码

以下是一个基于vit模型的图像分类代码示例：

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

# 定义模型
class ViT(nn.Module):
    def __init__(self, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim):
        super().__init__()
        assert image_size % patch_size == , "image size must be divisible by patch size"
        num_patches = (image_size // patch_size) ** 2
        patch_dim = 3 * patch_size ** 2
        self.patch_size = patch_size
        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
        self.patch_embedding = nn.Sequential(
            nn.Conv2d(3, dim, patch_size, stride=patch_size),
            nn.BatchNorm2d(dim),
            nn.GELU()
        )
        self.transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(dim, heads, mlp_dim),
            depth
        )
        self.fc = nn.Linear(dim, num_classes)

    def forward(self, x):
        x = self.patch_embedding(x)
        x = x.flatten(2).transpose(1, 2)
        x = torch.cat([self.pos_embedding[:, :1, :].repeat(x.shape[], 1, 1), self.pos_embedding[:, 1:, :]], dim=1)
        x = self.transformer(x)
        x = x.mean(dim=1)
        x = self.fc(x)
        return x

# 定义超参数
image_size = 224
patch_size = 16
num_classes = 10
dim = 768
depth = 12
heads = 12
mlp_dim = 3072
lr = 1e-4
batch_size = 64
num_epochs = 10

# 加载数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.CIFAR10(root='./data', train=False, transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 初始化模型和优化器
model = ViT(image_size, patch_size, num_classes, dim, depth, heads, mlp_dim)
optimizer = optim.Adam(model.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()

# 训练模型
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == :
            print(f"Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(train_loader)}], Loss: {loss.item():.4f}")

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

    print(f"Accuracy: {100 * correct / total:.2f}%")

希望这个代码示例能够帮助你！