帮我写一个基于vit模型的图像分类代码

好的，我可以回答这个问题。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 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):
        super(ViT, self).__init__()
        self.patch_size = patch_size
        self.num_patches = (image_size // patch_size) ** 2
        self.patch_embedding = nn.Conv2d(3, dim, kernel_size=patch_size, stride=patch_size)
        self.transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=dim, nhead=8), num_layers=6)
        self.classifier = nn.Linear(dim, num_classes)

    def forward(self, x):
        x = self.patch_embedding(x).flatten(2).transpose(1, 2)
        x = self.transformer(x)
        x = x.mean(dim=1)
        x = self.classifier(x)
        return x

# 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=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# 训练模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ViT(image_size=32, patch_size=8, num_classes=10, dim=256).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
num_epochs = 10
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if (i+1) % 100 == :
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 测试模型
model.eval()
with torch.no_grad():
    correct = 
    total = 
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size()
        correct += (predicted == labels).sum().item()
    print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))

希望这个代码示例能够帮助你。