Source code for torchvision.datasets.folder

import as data

from PIL import Image
import os
import os.path

    '.jpg', '.JPG', '.jpeg', '.JPEG',
    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',

def is_image_file(filename):
    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)

def find_classes(dir):
    classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))]
    class_to_idx = {classes[i]: i for i in range(len(classes))}
    return classes, class_to_idx

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

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

    return images

def pil_loader(path):
    # open path as file to avoid ResourceWarning (
    with open(path, 'rb') as f:
        with as img:
            return img.convert('RGB')

def accimage_loader(path):
    import accimage
        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)
        return pil_loader(path)

[docs]class ImageFolder(data.Dataset): """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): classes, class_to_idx = find_classes(root) imgs = make_dataset(root, class_to_idx) if len(imgs) == 0: raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n" "Supported image extensions are: " + ",".join(IMG_EXTENSIONS))) self.root = root self.imgs = imgs self.classes = classes self.class_to_idx = class_to_idx self.transform = transform self.target_transform = target_transform self.loader = loader
[docs] def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class. """ path, target = self.imgs[index] img = self.loader(path) if self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target) return img, target
def __len__(self): return len(self.imgs)