帮我用代码写一个vit模型将一个图片集进行分类

好的，以下是使用 Python 代码实现 VIT 模型对图片集进行分类的示例：

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 VIT 模型
class VIT(nn.Module):
    def __init__(self, num_classes=10):
        super(VIT, self).__init__()
        self.transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=768, nhead=12), num_layers=12)
        self.fc = nn.Linear(768, num_classes)

    def forward(self, x):
        x = self.transformer(x)
        x = x.mean(dim=1)
        x = self.fc(x)
        return x

# 加载数据集
train_dataset = datasets.ImageFolder(root='path/to/train', transform=transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[.485, .456, .406], std=[.229, .224, .225])
]))
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 初始化模型和优化器
model = VIT(num_classes=10)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

# 训练模型
for epoch in range(10):
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = nn.CrossEntropyLoss()(outputs, labels)
        loss.backward()
        optimizer.step()
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, loss.item()))

# 对测试集进行预测
test_dataset = datasets.ImageFolder(root='path/to/test', transform=transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[.485, .456, .406], std=[.229, .224, .225])
]))
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

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

请注意，这只是一个示例代码，实际应用中需要根据具体情况进行修改和调整。