DatasetFolder¶

class paddle.vision.datasets. DatasetFolder ( root, loader=None, extensions=None, transform=None, is_valid_file=None ) [source]

A generic data loader where the samples are arranged in this way:

root/class_a/1.ext
root/class_a/2.ext
root/class_a/3.ext

root/class_b/123.ext
root/class_b/456.ext
root/class_b/789.ext

Parameters

• root (str) – Root directory path.

• loader (Callable, optional) – A function to load a sample given its path. Default: None.

• extensions (list[str]|tuple[str], optional) – A list of allowed extensions. Both extensions and is_valid_file should not be passed. If this value is not set, the default is to use (‘.jpg’, ‘.jpeg’, ‘.png’, ‘.ppm’, ‘.bmp’, ‘.pgm’, ‘.tif’, ‘.tiff’, ‘.webp’). Default: None.

• transform (Callable, optional) – A function/transform that takes in a sample and returns a transformed version. Default: None.

• is_valid_file (Callable, optional) – A function that takes path of a file and check if the file is a valid file. Both extensions and is_valid_file should not be passed. Default: None.

Returns

Dataset. An instance of DatasetFolder.

classes

List of the class names.

Type

list[str]

class_to_idx

Dict with items (class_name, class_index).

Type

dict[str, int]

samples

List of (sample_path, class_index) tuples.

Type

list[tuple[str, int]]

targets

The class_index value for each image in the dataset.

Type

list[int]

Example

>>> import shutil
>>> import tempfile
>>> import cv2
>>> import numpy as np
>>> import paddle.vision.transforms as T
>>> from pathlib import Path
>>> from paddle.vision.datasets import DatasetFolder


>>> def make_fake_file(img_path: str):
...     if img_path.endswith((".jpg", ".png", ".jpeg")):
...         fake_img = np.random.randint(0, 256, (32, 32, 3), dtype=np.uint8)
...         cv2.imwrite(img_path, fake_img)
...     elif img_path.endswith(".txt"):
...         with open(img_path, "w") as f:
...             f.write("This is a fake file.")

>>> def make_directory(root, directory_hierarchy, file_maker=make_fake_file):
...     root = Path(root)
...     root.mkdir(parents=True, exist_ok=True)
...     for subpath in directory_hierarchy:
...         if isinstance(subpath, str):
...             filepath = root / subpath
...             file_maker(str(filepath))
...         else:
...             dirname = list(subpath.keys())[0]
...             make_directory(root / dirname, subpath[dirname])

>>> directory_hierarchy = [
...     {"class_0": [
...         "abc.jpg",
...         "def.png"]},
...     {"class_1": [
...         "ghi.jpeg",
...         "jkl.png",
...         {"mno": [
...             "pqr.jpeg",
...             "stu.jpg"]}]},
...     "this_will_be_ignored.txt",
... ]

>>> # You can replace this with any directory to explore the structure
>>> # of generated data. e.g. fake_data_dir = "./temp_dir"
>>> fake_data_dir = tempfile.mkdtemp()
>>> make_directory(fake_data_dir, directory_hierarchy)
>>> data_folder_1 = DatasetFolder(fake_data_dir)
>>> print(data_folder_1.classes)
['class_0', 'class_1']
>>> print(data_folder_1.class_to_idx)
{'class_0': 0, 'class_1': 1}
>>> print(data_folder_1.samples)
>>> 
[('./temp_dir/class_0/abc.jpg', 0), ('./temp_dir/class_0/def.png', 0),
 ('./temp_dir/class_1/ghi.jpeg', 1), ('./temp_dir/class_1/jkl.png', 1),
 ('./temp_dir/class_1/mno/pqr.jpeg', 1), ('./temp_dir/class_1/mno/stu.jpg', 1)]
>>> 
>>> print(data_folder_1.targets)
[0, 0, 1, 1, 1, 1]
>>> print(len(data_folder_1))
6

>>> for i in range(len(data_folder_1)):
...     img, label = data_folder_1[i]
...     # do something with img and label
...     print(type(img), img.size, label)
...     # <class 'PIL.Image.Image'> (32, 32) 0


>>> transform = T.Compose(
...     [
...         T.Resize(64),
...         T.ToTensor(),
...         T.Normalize(
...             mean=[0.5, 0.5, 0.5],
...             std=[0.5, 0.5, 0.5],
...             to_rgb=True,
...         ),
...     ]
... )

>>> data_folder_2 = DatasetFolder(
...     fake_data_dir,
...     loader=lambda x: cv2.imread(x),  # load image with OpenCV
...     extensions=(".jpg",),  # only load *.jpg files
...     transform=transform,  # apply transform to every image
... )

>>> print([img_path for img_path, label in data_folder_2.samples])
>>> 
['./temp_dir/class_0/abc.jpg', './temp_dir/class_1/mno/stu.jpg']
>>> 
>>> print(len(data_folder_2))
2

>>> for img, label in iter(data_folder_2):
...     # do something with img and label
...     print(type(img), img.shape, label)
...     # <class 'paddle.Tensor'> [3, 64, 64] 0

>>> shutil.rmtree(fake_data_dir)