帮我写一个基于Vision Transformer将一个化学结构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的化学结构图片分类代码的示例：

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
from torch.utils.data import Dataset
from PIL import Image
import os

# 定义数据集类
class ChemicalDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.images = os.listdir(root_dir)

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        img_name = os.path.join(self.root_dir, self.images[idx])
        image = Image.open(img_name).convert('RGB')
        if self.transform:
            image = self.transform(image)
        return image, int(self.images[idx].split('_')[])

# 定义模型
class VisionTransformer(nn.Module):
    def __init__(self, num_classes=10, dim=512, depth=6, heads=8, mlp_dim=2048):
        super(VisionTransformer, self).__init__()
        self.patch_size = 16
        self.num_classes = num_classes
        self.dim = dim
        self.depth = depth
        self.heads = heads
        self.mlp_dim = mlp_dim

        self.patch_embed = nn.Conv2d(3, dim, kernel_size=self.patch_size, stride=self.patch_size)
        self.pos_embed = nn.Parameter(torch.zeros(1, (224 // self.patch_size) ** 2 + 1, dim))
        self.cls_token = nn.Parameter(torch.zeros(1, 1, dim))
        self.transformer = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=dim, nhead=heads, dim_feedforward=mlp_dim), num_layers=depth)
        self.fc = nn.Linear(dim, num_classes)

    def forward(self, x):
        x = self.patch_embed(x)
        x = x.flatten(2).transpose(1, 2)
        cls_tokens = self.cls_token.expand(x.shape[], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x = x + self.pos_embed
        x = self.transformer(x)
        x = x[:, , :]
        x = self.fc(x)
        return x

# 定义训练函数
def train(model, train_loader, optimizer, criterion, device):
    model.train()
    train_loss = 
    train_acc = 
    for images, labels in train_loader:
        images, labels = images.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        train_acc += (predicted == labels).sum().item()
    train_loss /= len(train_loader)
    train_acc /= len(train_loader.dataset)
    return train_loss, train_acc

# 定义测试函数
def test(model, test_loader, criterion, device):
    model.eval()
    test_loss = 
    test_acc = 
    with torch.no_grad():
        for images, labels in test_loader:
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            loss = criterion(outputs, labels)
            test_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            test_acc += (predicted == labels).sum().item()
    test_loss /= len(test_loader)
    test_acc /= len(test_loader.dataset)
    return test_loss, test_acc

# 定义训练参数
batch_size = 32
learning_rate = .001
num_epochs = 10

# 定义数据增强和数据加载器
transform_train = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[.485, .456, .406], std=[.229, .224, .225])
])

transform_test = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[.485, .456, .406], std=[.229, .224, .225])
])

train_dataset = ChemicalDataset('train/', transform_train)
test_dataset = ChemicalDataset('test/', transform_test)

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义设备和模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = VisionTransformer(num_classes=10).to(device)

# 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):
    train_loss, train_acc = train(model, train_loader, optimizer, criterion, device)
    test_loss, test_acc = test(model, test_loader, criterion, device)
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'.format(epoch+1, num_epochs, train_loss, train_acc, test_loss, test_acc))

这个代码可以用于对一个化学结构图片集进行分类，其中使用了Vision Transformer模型，并且使用了数据增强和数据加载器来提高模型的性能。