init

data/folder_new.py

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+### Modify the ImageFolder function to get the image path in the data loader
+import torch.utils.data as data
+
+from PIL import Image
+import os
+import os.path
+from PIL import ImageFile
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True  ## used to handle some error when loading the special images.
+
+print('the data loader file has been modified')
+IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm']
+
+
+def is_image_file(filename):
+    """Checks if a file is an image.
+    Args:
+        filename (string): path to a file
+    Returns:
+        bool: True if the filename ends with a known image extension
+    """
+    filename_lower = filename.lower()
+    return any(filename_lower.endswith(ext) for ext in IMG_EXTENSIONS)
+
+
+def find_classes(dir):
+    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
+
+
+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):
+            continue
+
+        for root, _, fnames in sorted(os.walk(d)):  # os.walk()是一种遍历目录数的函数
+            for fname in sorted(fnames):
+                if is_image_file(fname):
+                    path = os.path.join(root, fname)
+                    item = (path, class_to_idx[target])
+                    images.append(item)
+
+    return images
+
+
+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)
+
+
+class ImageFolder_new(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
+
+    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, path
+
+    def __len__(self):
+        return len(self.imgs)
+
+    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