dataset 的cache工作原理

Question

问题遇到的现象和发生背景

关于您在一篇博客中分析了YOLOv5中dataset部分关于cache的用法我想了解一下 问您几个相关问题

用代码块功能插入代码，请勿粘贴截图

Check cache

    self.label_files = img2label_paths(self.img_files)  # labels
    cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')  # cached labels
    if cache_path.is_file():
        cache, exists = torch.load(cache_path), True  # load
        #if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache:  # changed
        #    cache, exists = self.cache_labels(cache_path, prefix), False  # re-cache
    else:
        cache, exists = self.cache_labels(cache_path, prefix), False  # cache

    # Display cache
    nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupted, total
    if exists:
        d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
        tqdm(None, desc=prefix + d, total=n, initial=n)  # display cache results
    assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'

我的解答思路和尝试过的方法

我其实想过 是否可以不应用这个cache缓存 直接读取图片呢