在为数据分类训练分类器的时候，比如猫狗分类时，我们经常会使用pytorch的ImageFolder:

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CLASS torchvision.datasets.ImageFolder(root, transform=None, target_transform=None, loader=, is_valid_file=None)

使用可见pytorch torchvision.ImageFolder的用法介绍

这里想实现的是如果想要覆写该函数，即能使用它的特性，又可以实现自己的功能

首先先分析下其源代码：

IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', 'webp']

class ImageFolder(DatasetFolder):
 """A generic data loader where the images are arranged in this way: ::

  root/dog/xxx.png
  root/dog/xxy.png
  root/dog/xxz.png

  root/cat/123.png
  root/cat/nsdf3.png
  root/cat/asd932_.png

 Args:
  root (string): Root directory path.
  transform (callable, optional): A function/transform that takes in an PIL image
   and returns a transformed version. E.g, ``transforms.RandomCrop``
  target_transform (callable, optional): A function/transform that takes in the
   target and transforms it.
  loader (callable, optional): A function to load an image given its path.

  Attributes:
  classes (list): List of the class names.
  class_to_idx (dict): Dict with items (class_name, class_index).
  imgs (list): List of (image path, class_index) tuples
 """
 def __init__(self, root, transform=None, target_transform=None,
     loader=default_loader):
  super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS,
           transform=transform,
           target_transform=target_transform)
  self.imgs = self.samples

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