def __getitem__(self, index):
    # 1. 将传入的索引转换为实际使用的索引（处理线性、打乱或加权采样）
    index = self.indices[index]  
    
    # 2. 检查是否应用mosaic增强（基于配置和随机概率）
    if mosaic := self.mosaic and random.random() < hyp["mosaic"]:
        # 加载mosaic增强的图像和标签
        img, labels = self.load_mosaic(index)  # 这里是您想修改为load_mosaic9的地方
        shapes = None
        
        # 检查是否进一步应用mixup增强
        if random.random() < hyp["mixup"]:
            img, labels = mixup(img, labels, *self.load_mosaic(random.choice(self.indices)))
    else:
        # 3. 不使用mosaic时的常规图像加载和处理流程
        img, (h0, w0), (h, w) = self.load_image(index)
        
        # Letterbox处理
        shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size
        img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
        shapes = (h0, w0), ((h / h0, w / w0), pad)
        
        # 处理标签
        labels = self.labels[index].copy()
        if labels.size:
            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
            
        # 应用随机透视变换等增强
        if self.augment:
            img, labels = random_perspective(...)
    
    # 4. 标签格式转换
    nl = len(labels)
    if nl:
        labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)
    
    # 5. 应用更多的增强技术（如果启用）
    if self.augment:
        # Albumentations增强
        img, labels = self.albumentations(img, labels)
        
        # HSV颜色空间增强
        augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"])
        
        # 上下翻转
        if random.random() < hyp["flipud"]:
            img = np.flipud(img)
            if nl:
                labels[:, 2] = 1 - labels[:, 2]
                
        # 左右翻转
        if random.random() < hyp["fliplr"]:
            img = np.fliplr(img)
            if nl:
                labels[:, 1] = 1 - labels[:, 1]
    
    # 6. 准备输出格式
    labels_out = torch.zeros((nl, 6))
    if nl:
        labels_out[:, 1:] = torch.from_numpy(labels)
        
    # 7. 图像格式转换：HWC->CHW, BGR->RGB
    img = img.transpose((2, 0, 1))[::-1]
    img = np.ascontiguousarray(img)
    
    # 8. 返回最终处理好的数据
    return torch.from_numpy(img), labels_out, self.im_files[index], shapes

train_loader, dataset = create_dataloader(

        train_path,

        imgsz,

        batch_size // WORLD_SIZE,

        gs,

        single_cls,

        hyp=hyp,

        augment=True, # 数据增强在训练中默认开启

        cache=None if opt.cache == "val" else opt.cache,

        rect=opt.rect,

        rank=LOCAL_RANK,

        workers=workers,

        image_weights=opt.image_weights,

        quad=opt.quad,

        prefix=colorstr("train: "),

        shuffle=True,

        seed=opt.seed,

    )

# Trainloader
train_loader, dataset = create_dataloader(
    train_path,
    imgsz,
    batch_size // WORLD_SIZE,
    gs,
    single_cls,
    hyp=hyp,
    augment=True, # 数据增强在训练中默认开启
    cache=None if opt.cache == "val" else opt.cache,
    rect=opt.rect,
    rank=LOCAL_RANK,
    workers=workers,
    image_weights=opt.image_weights,
    quad=opt.quad,
    prefix=colorstr("train: "),
    shuffle=True,
    seed=opt.seed,
)

# 训练循环
for epoch in range(start_epoch, epochs):
    model.train()
    # ...
    pbar = enumerate(train_loader)
    # ...
    for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
        # 处理批次数据
        # ...

# Trainloader
train_loader, dataset = create_dataloader(
    train_path,
    imgsz,
    batch_size // WORLD_SIZE,
    gs,
    single_cls,
    hyp=hyp,
    augment=True, # 数据增强在训练中默认开启
    cache=None if opt.cache == "val" else opt.cache,
    rect=opt.rect,
    rank=LOCAL_RANK,
    workers=workers,
    image_weights=opt.image_weights,
    quad=opt.quad,
    prefix=colorstr("train: "),
    shuffle=True,
    seed=opt.seed,
)

# 训练循环
for epoch in range(start_epoch, epochs):
    model.train()
    # ...
    pbar = enumerate(train_loader)
    # ...
    for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
        # 处理批次数据
        # ...