Class CachedImageFolder

data/cached_image_folder.py:209–258

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 (cal

class CachedImageFolder(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:
        imgs (list): List of (image path, class_index) tuples
    """

    def __init__(self, root, ann_file='', img_prefix='', transform=None, target_transform=None,
                 loader=default_img_loader, cache_mode="no"):
        super(CachedImageFolder, self).__init__(root, loader, IMG_EXTENSIONS,
                                                ann_file=ann_file, img_prefix=img_prefix,
                                                transform=transform, target_transform=target_transform,
                                                cache_mode=cache_mode)
        self.imgs = self.samples
        if not isinstance(self.transform, (tuple, list)) and self.transform is not None:
            self.transform = [self.transform]

    def __getitem__(self, index):
        """
        Args:
            index (int): Index
        Returns:
            tuple: (image, target) where target is class_index of the target class.
        """
        path, target = self.samples[index]
        image = self.loader(path)
        
        ret = []
        if self.transform is not None:
            for t in self.transform:
                ret.append(t(image))
        else:
            ret.append(image)
        if self.target_transform is not None:
            target = self.target_transform(target)
        ret.append(target)

        return ret