deepfashion的Category and Attribute Prediction Benchmark数据集如何使用，请帮我编写一段基于torch的示例

好的，以下是一个基于PyTorch的DeepFashion分类和属性预测的示例代码：

import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms, models
from PIL import Image

# 读取标注文件
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')

# 定义数据预处理的transforms
data_transforms = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 自定义Dataset类
class FashionDataset(Dataset):
    def __init__(self, df, transform=None):
        self.df = df
        self.transform = transform

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

    def __getitem__(self, idx):
        img_path = self.df.loc[idx, 'image_path']
        img = Image.open(img_path).convert('RGB')
        if self.transform:
            img = self.transform(img)
        category = torch.tensor(self.df.loc[idx, 'category'])
        attributes = torch.tensor(self.df.loc[idx, 'attributes'].split(';'))
        return img, category, attributes

# 定义模型
model = models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 50)  # 50为类别数

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 定义训练函数
def train(model, dataloader, criterion, optimizer):
    model.train()
    running_loss = 0.0
    for inputs, category, attributes in dataloader:
        inputs = inputs.to(device)
        category = category.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, category)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * inputs.size(0)
    epoch_loss = running_loss / len(dataloader.dataset)
    return epoch_loss

# 定义测试函数
def test(model, dataloader, criterion):
    model.eval()
    running_loss = 0.0
    running_corrects = 0
    for inputs, category, attributes in dataloader:
        inputs = inputs.to(device)
        category = category.to(device)
        outputs = model(inputs)
        _, preds = torch.max(outputs, 1)
        loss = criterion(outputs, category)
        running_loss += loss.item() * inputs.size(0)
        running_corrects += torch.sum(preds == category.data)
    epoch_loss = running_loss / len(dataloader.dataset)
    epoch_acc = running_corrects.double() / len(dataloader.dataset)
    return epoch_loss, epoch_acc

# 定义训练集和测试集
train_dataset = FashionDataset(train_df, data_transforms)
test_dataset = FashionDataset(test_df, data_transforms)

# 定义DataLoader
train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)

# 训练模型
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model.to(device)
best_acc = 0.0
for epoch in range(10):
    epoch_loss = train(model, train_dataloader, criterion, optimizer)
    _, epoch_acc = test(model, test_dataloader, criterion)
    print('Epoch {} loss: {:.4f} accuracy: {:.4f}'.format(epoch+1, epoch_loss, epoch_acc))
    if epoch_acc > best_acc:
        best_acc = epoch_acc
        torch.save(model.state_dict(), 'best_model.pth')

在这个示例中，我们使用ResNet18预训练模型来进行分类，训练了10个epochs，每个epoch中进行了训练和测试，并记录了loss和accuracy。需要注意的是，这里仅仅是一个示例，实际使用中需要根据任务的具体需求进行模型和数据处理的调整。