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Source code for torchvision.datasets.folder

import torch.utils.data as data

from PIL import Image

import os
import os.path
import sys


def has_file_allowed_extension(filename, extensions):
    """Checks if a file is an allowed extension.

    Args:
        filename (string): path to a file
        extensions (iterable of strings): extensions to consider (lowercase)

    Returns:
        bool: True if the filename ends with one of given extensions
    """
    filename_lower = filename.lower()
    return any(filename_lower.endswith(ext) for ext in extensions)


def is_image_file(filename):
    """Checks if a file is an allowed image extension.

    Args:
        filename (string): path to a file

    Returns:
        bool: True if the filename ends with a known image extension
    """
    return has_file_allowed_extension(filename, IMG_EXTENSIONS)


def make_dataset(dir, class_to_idx, extensions):
    images = []
    dir = os.path.expanduser(dir)
    for target in sorted(class_to_idx.keys()):
        d = os.path.join(dir, target)
        if not os.path.isdir(d):
            continue

        for root, _, fnames in sorted(os.walk(d)):
            for fname in sorted(fnames):
                if has_file_allowed_extension(fname, extensions):
                    path = os.path.join(root, fname)
                    item = (path, class_to_idx[target])
                    images.append(item)

    return images


[docs]class DatasetFolder(data.Dataset): """A generic data loader where the samples are arranged in this way: :: root/class_x/xxx.ext root/class_x/xxy.ext root/class_x/xxz.ext root/class_y/123.ext root/class_y/nsdf3.ext root/class_y/asd932_.ext Args: root (string): Root directory path. loader (callable): A function to load a sample given its path. extensions (list[string]): A list of allowed extensions. transform (callable, optional): A function/transform that takes in a sample and returns a transformed version. E.g, ``transforms.RandomCrop`` for images. target_transform (callable, optional): A function/transform that takes in the target and transforms it. Attributes: classes (list): List of the class names. class_to_idx (dict): Dict with items (class_name, class_index). samples (list): List of (sample path, class_index) tuples targets (list): The class_index value for each image in the dataset """ def __init__(self, root, loader, extensions, transform=None, target_transform=None): classes, class_to_idx = self._find_classes(root) samples = make_dataset(root, class_to_idx, extensions) if len(samples) == 0: raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n" "Supported extensions are: " + ",".join(extensions))) self.root = root self.loader = loader self.extensions = extensions self.classes = classes self.class_to_idx = class_to_idx self.samples = samples self.targets = [s[1] for s in samples] self.transform = transform self.target_transform = target_transform def _find_classes(self, dir): """ Finds the class folders in a dataset. Args: dir (string): Root directory path. Returns: tuple: (classes, class_to_idx) where classes are relative to (dir), and class_to_idx is a dictionary. Ensures: No class is a subdirectory of another. """ if sys.version_info >= (3, 5): # Faster and available in Python 3.5 and above classes = [d.name for d in os.scandir(dir) if d.is_dir()] else: classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))] classes.sort() class_to_idx = {classes[i]: i for i in range(len(classes))} return classes, class_to_idx
[docs] def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (sample, target) where target is class_index of the target class. """ path, target = self.samples[index] sample = self.loader(path) if self.transform is not None: sample = self.transform(sample) if self.target_transform is not None: target = self.target_transform(target) return sample, target
def __len__(self): return len(self.samples) def __repr__(self): fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) fmt_str += ' Root Location: {}\n'.format(self.root) tmp = ' Transforms (if any): ' fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) tmp = ' Target Transforms (if any): ' fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) return fmt_str
IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', 'webp'] def pil_loader(path): # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835) with open(path, 'rb') as f: img = Image.open(f) return img.convert('RGB') def accimage_loader(path): import accimage try: return accimage.Image(path) except IOError: # Potentially a decoding problem, fall back to PIL.Image return pil_loader(path) def default_loader(path): from torchvision import get_image_backend if get_image_backend() == 'accimage': return accimage_loader(path) else: return pil_loader(path)
[docs]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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