帮我写一个基于Vision Transformer将一个图片集进行分类的代码

好的，我可以回答这个问题。以下是一个基于Vision Transformer的图片分类代码示例：

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

# 定义超参数
batch_size = 32
learning_rate = .001
num_epochs = 10

# 加载数据集
transform = transforms.Compose(
    [transforms.Resize((224, 224)),
     transforms.ToTensor(),
     transforms.Normalize((.5, .5, .5), (.5, .5, .5))])

train_dataset = torchvision.datasets.ImageFolder(root='./train', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

test_dataset = torchvision.datasets.ImageFolder(root='./test', transform=transform)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义模型
class VisionTransformer(nn.Module):
    def __init__(self, num_classes):
        super(VisionTransformer, 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

model = VisionTransformer(num_classes=10)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 每100个batch打印一次日志
        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
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))

这个代码使用了PyTorch框架，通过Vision Transformer模型对一个图片集进行分类。具体来说，它将图片集分为训练集和测试集，然后使用数据加载器将它们加载到模型中。模型使用了一个Transformer编码器和一个全连接层，其中Transformer编码器用于提取图片的特征，全连接层用于将这些特征映射到类别标签。在训练过程中，模型使用交叉熵损失函数和Adam优化器进行优化。在测试过程中，模型计算准确率并输出结果。