如何把deepfashion数据集进行数据处理，请帮我编写一个torch的示例

好的，以下是一个DeepFashion数据集的PyTorch数据处理示例，包括数据清洗、数据预处理、数据增强、数据划分、标注处理和数据存储：

import os
import numpy as np
import cv2
import json
from sklearn.model_selection import train_test_split
import torch
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler

# 定义DeepFashion数据集路径和预处理参数
data_dir = '/path/to/deepfashion/dataset'
img_size = (256, 256)
train_val_ratio = 0.9
val_test_ratio = 0.5
max_rotation_angle = 10

# 自定义数据集类
class DeepFashionDataset(Dataset):
    def __init__(self, data_dir, lines):
        self.data_dir = data_dir
        self.lines = lines
    
    def __len__(self):
        return len(self.lines)
    
    def __getitem__(self, idx):
        line = self.lines[idx]
        img_path = os.path.join(self.data_dir, 'Img', 'train', line[0])
        bbox = line[1:]
        img = cv2.imread(img_path)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = cv2.resize(img, img_size)
        rotation_angle = np.random.uniform(-max_rotation_angle, max_rotation_angle)
        M = cv2.getRotationMatrix2D((img_size[0] / 2, img_size[1] / 2), rotation_angle, 1.0)
        img = cv2.warpAffine(img, M, img_size)
        img = np.transpose(img, (2, 0, 1)).astype(np.float32) / 255.0
        bbox = np.array(bbox, dtype=np.float32)
        return img, bbox

# 读取标注文件，并进行数据清洗和划分
with open(os.path.join(data_dir, 'Anno/list_bbox.txt'), 'r') as f:
    lines = f.readlines()[2:]  # 跳过前两行
    lines = [line.strip().split() for line in lines]
    lines = [[line[0], *map(int, line[1:])] for line in lines]
    lines = [line for line in lines if line[3] > 0 and line[4] > 0]  # 去掉宽度或高度为0的标注
    train_lines, val_test_lines = train_test_split(lines, train_size=train_val_ratio, random_state=42)
    val_lines, test_lines = train_test_split(val_test_lines, train_size=val_test_ratio, random_state=42)

# 创建数据集和数据加载器
train_dataset = DeepFashionDataset(data_dir, train_lines)
val_dataset = DeepFashionDataset(data_dir, val_lines)
test_dataset = DeepFashionDataset(data_dir, test_lines)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32)
test_loader = DataLoader(test_dataset, batch_size=32)

# 将训练集、验证集和测试集数据保存到本地文件中
torch.save(train_loader, os.path.join(data_dir, 'train_loader.pth'))
torch.save(val_loader, os.path.join(data_dir, 'val_loader.pth'))
torch.save(test_loader, os.path.join(data_dir, 'test_loader.pth'))

以上是一个简单的DeepFashion数据集的PyTorch数据处理示例，其中使用了自定义数据集类和数据加载器来加载数据，并将数据集和数据加载器保存到本地文件中。具体实现可以根据实际需求和数据集特点进行调整。