chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Literal, TypeAlias
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if TYPE_CHECKING:
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from collections.abc import Callable, Sequence
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from paddle._typing.dtype_like import _DTypeLiteral
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from paddle.vision.transforms.transforms import _Transform
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from ..image import _ImageDataType
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_AllowedExtensions: TypeAlias = Literal[
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'.jpg',
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'.jpeg',
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'.png',
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'.ppm',
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'.bmp',
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'.pgm',
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'.tif',
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'.tiff',
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'.webp',
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]
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import os
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from PIL import Image
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import paddle
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from paddle.io import Dataset
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from paddle.utils import try_import
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__all__ = []
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def has_valid_extension(filename: str, extensions: Sequence[str]) -> bool:
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"""Checks if a file is a valid extension.
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Args:
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filename (str): path to a file
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extensions (list[str]|tuple[str]): extensions to consider
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Returns:
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bool: True if the filename ends with one of given extensions
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"""
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assert isinstance(extensions, (list, tuple)), (
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"`extensions` must be list or tuple."
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)
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extensions = tuple([x.lower() for x in extensions])
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return filename.lower().endswith(extensions)
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def make_dataset(dir, class_to_idx, extensions, is_valid_file=None):
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images = []
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dir = os.path.expanduser(dir)
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if extensions is not None:
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def is_valid_file(x):
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return has_valid_extension(x, extensions)
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for target in sorted(class_to_idx.keys()):
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d = os.path.join(dir, target)
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if not os.path.isdir(d):
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continue
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for root, _, fnames in sorted(os.walk(d, followlinks=True)):
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for fname in sorted(fnames):
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path = os.path.join(root, fname)
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if is_valid_file(path):
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item = (path, class_to_idx[target])
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images.append(item)
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return images
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class DatasetFolder(Dataset[tuple["_ImageDataType", int]]):
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"""A generic data loader where the samples are arranged in this way:
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.. code-block:: text
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root/class_a/1.ext
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root/class_a/2.ext
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root/class_a/3.ext
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root/class_b/123.ext
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root/class_b/456.ext
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root/class_b/789.ext
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Args:
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root (str): Root directory path.
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loader (Callable|None, optional): A function to load a sample given its path. Default: None.
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extensions (list[str]|tuple[str]|None, optional): A list of allowed extensions.
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Both :attr:`extensions` and :attr:`is_valid_file` should not be passed.
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If this value is not set, the default is to use ('.jpg', '.jpeg', '.png',
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'.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'). Default: None.
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transform (Callable|None, optional): A function/transform that takes in
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a sample and returns a transformed version. Default: None.
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is_valid_file (Callable|None, optional): A function that takes path of a file
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and check if the file is a valid file. Both :attr:`extensions` and
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:attr:`is_valid_file` should not be passed. Default: None.
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Returns:
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:ref:`api_paddle_io_Dataset`. An instance of DatasetFolder.
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Attributes:
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classes (list[str]): List of the class names.
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class_to_idx (dict[str, int]): Dict with items (class_name, class_index).
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samples (list[tuple[str, int]]): List of (sample_path, class_index) tuples.
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targets (list[int]): The class_index value for each image in the dataset.
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Example:
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.. code-block:: pycon
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>>> import shutil
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>>> import tempfile
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>>> import cv2
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>>> import numpy as np
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>>> import paddle
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>>> import paddle.vision.transforms as T
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>>> from pathlib import Path
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>>> from paddle.vision.datasets import DatasetFolder
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>>> def make_fake_file(img_path: str):
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... if img_path.endswith((".jpg", ".png", ".jpeg")):
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... fake_img = np.random.randint(0, 256, (32, 32, 3), dtype=np.uint8)
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... cv2.imwrite(img_path, fake_img)
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... elif img_path.endswith(".txt"):
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... with open(img_path, "w") as f:
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... f.write("This is a fake file.")
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>>> def make_directory(root, directory_hierarchy, file_maker=make_fake_file):
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... root = Path(root)
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... root.mkdir(parents=True, exist_ok=True)
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... for subpath in directory_hierarchy:
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... if isinstance(subpath, str):
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... filepath = root / subpath
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... file_maker(str(filepath))
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... else:
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... dirname = list(subpath.keys())[0]
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... make_directory(root / dirname, subpath[dirname])
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>>> directory_hierarchy = [
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... {"class_0": [
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... "abc.jpg",
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... "def.png"]},
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... {"class_1": [
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... "ghi.jpeg",
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... "jkl.png",
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... {"mno": [
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... "pqr.jpeg",
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... "stu.jpg"]}]},
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... "this_will_be_ignored.txt",
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... ] # fmt: skip
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>>> # You can replace this with any directory to explore the structure
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>>> # of generated data. e.g. fake_data_dir = "./temp_dir"
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>>> fake_data_dir = tempfile.mkdtemp()
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>>> make_directory(fake_data_dir, directory_hierarchy)
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>>> data_folder_1 = DatasetFolder(fake_data_dir)
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>>> print(data_folder_1.classes)
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['class_0', 'class_1']
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>>> print(data_folder_1.class_to_idx)
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{'class_0': 0, 'class_1': 1}
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>>> print(data_folder_1.samples)
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>>> # doctest: +SKIP(it's different with windows)
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[('./temp_dir/class_0/abc.jpg', 0), ('./temp_dir/class_0/def.png', 0),
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('./temp_dir/class_1/ghi.jpeg', 1), ('./temp_dir/class_1/jkl.png', 1),
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('./temp_dir/class_1/mno/pqr.jpeg', 1), ('./temp_dir/class_1/mno/stu.jpg', 1)]
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>>> # doctest: -SKIP
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>>> print(data_folder_1.targets)
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[0, 0, 1, 1, 1, 1]
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>>> print(len(data_folder_1))
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6
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>>> for i in range(len(data_folder_1)):
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... img, label = data_folder_1[i]
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... # do something with img and label
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... print(type(img), img.size, label)
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... # <class 'PIL.Image.Image'> (32, 32) 0
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>>> transform = T.Compose(
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... [
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... T.Resize(64),
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... T.ToTensor(),
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... T.Normalize(
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... mean=[0.5, 0.5, 0.5],
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... std=[0.5, 0.5, 0.5],
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... to_rgb=True,
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... ),
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... ]
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... )
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>>> def cv2_loader(path: str):
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... image = cv2.imread(path)
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... assert image is not None
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... return image
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>>> data_folder_2 = DatasetFolder(
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... fake_data_dir,
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... loader=cv2_loader, # load image with OpenCV
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... extensions=(".jpg",), # only load *.jpg files
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... transform=transform, # apply transform to every image
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... )
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>>> print([img_path for img_path, label in data_folder_2.samples])
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>>> # doctest: +SKIP(it's different with windows)
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['./temp_dir/class_0/abc.jpg', './temp_dir/class_1/mno/stu.jpg']
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>>> # doctest: -SKIP
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>>> print(len(data_folder_2))
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2
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>>> for img, label in iter(data_folder_2):
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... # do something with img and label
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... assert isinstance(img, paddle.Tensor)
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... print(type(img), img.shape, label)
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... # <class 'paddle.Tensor'> [3, 64, 64] 0
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>>> shutil.rmtree(fake_data_dir)
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"""
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loader: Callable[..., _ImageDataType] | None
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extensions: Sequence[_AllowedExtensions] | None
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transform: _Transform[Any, Any] | None
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classes: list[str]
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class_to_idx: dict[str, int]
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samples: list[tuple[str, int]]
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targets: list[str]
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dtype: _DTypeLiteral
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def __init__(
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self,
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root: str,
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loader: Callable[..., _ImageDataType] | None = None,
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extensions: Sequence[_AllowedExtensions] | None = None,
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transform: _Transform[Any, Any] | None = None,
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is_valid_file: _ImageDataType | None = None,
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) -> None:
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self.root = root
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self.transform = transform
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if extensions is None:
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extensions = IMG_EXTENSIONS
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classes, class_to_idx = self._find_classes(self.root)
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samples = make_dataset(
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self.root, class_to_idx, extensions, is_valid_file
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)
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if len(samples) == 0:
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raise (
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RuntimeError(
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"Found 0 directories in subfolders of: " + self.root + "\n"
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"Supported extensions are: " + ",".join(extensions)
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)
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)
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self.loader = default_loader if loader is None else loader
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self.extensions = extensions
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self.classes = classes
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self.class_to_idx = class_to_idx
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self.samples = samples
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self.targets = [s[1] for s in samples]
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self.dtype = paddle.get_default_dtype()
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def _find_classes(self, dir: str) -> tuple[list[str], dict[str, int]]:
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"""
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Finds the class folders in a dataset.
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Args:
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dir (string): Root directory path.
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Returns:
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tuple: (classes, class_to_idx) where classes are relative to (dir),
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and class_to_idx is a dictionary.
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"""
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classes = [d.name for d in os.scandir(dir) if d.is_dir()]
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classes.sort()
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class_to_idx = {classes[i]: i for i in range(len(classes))}
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return classes, class_to_idx
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def __getitem__(self, index: int) -> tuple[_ImageDataType, int]:
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"""
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Args:
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index (int): Index
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Returns:
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tuple: (sample, target) where target is class_index of the target class.
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"""
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path, target = self.samples[index]
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sample = self.loader(path)
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if self.transform is not None:
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sample = self.transform(sample)
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return sample, target
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def __len__(self):
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return len(self.samples)
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IMG_EXTENSIONS = (
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'.jpg',
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'.jpeg',
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'.png',
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'.ppm',
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'.bmp',
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'.pgm',
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'.tif',
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'.tiff',
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'.webp',
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)
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def pil_loader(path):
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with open(path, 'rb') as f:
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img = Image.open(f)
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return img.convert('RGB')
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def cv2_loader(path):
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cv2 = try_import('cv2')
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return cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
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def default_loader(path):
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from paddle.vision import get_image_backend
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if get_image_backend() == 'cv2':
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return cv2_loader(path)
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else:
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return pil_loader(path)
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class ImageFolder(Dataset[list["_ImageDataType"]]):
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"""A generic data loader where the samples are arranged in this way:
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.. code-block:: text
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root/1.ext
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root/2.ext
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root/sub_dir/3.ext
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Args:
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root (str): Root directory path.
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loader (Callable|None, optional): A function to load a sample given its path. Default: None.
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extensions (list[str]|tuple[str]|None, optional): A list of allowed extensions.
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Both :attr:`extensions` and :attr:`is_valid_file` should not be passed.
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If this value is not set, the default is to use ('.jpg', '.jpeg', '.png',
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'.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'). Default: None.
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transform (Callable|None, optional): A function/transform that takes in
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a sample and returns a transformed version. Default: None.
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is_valid_file (Callable|None, optional): A function that takes path of a file
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and check if the file is a valid file. Both :attr:`extensions` and
|
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:attr:`is_valid_file` should not be passed. Default: None.
|
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|
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Returns:
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:ref:`api_paddle_io_Dataset`. An instance of ImageFolder.
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Attributes:
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samples (list[str]): List of sample path.
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Example:
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.. code-block:: pycon
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>>> import shutil
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>>> import tempfile
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>>> import cv2
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>>> import numpy as np
|
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>>> import paddle
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>>> import paddle.vision.transforms as T
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>>> from pathlib import Path
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>>> from paddle.vision.datasets import ImageFolder
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>>> def make_fake_file(img_path: str):
|
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... if img_path.endswith((".jpg", ".png", ".jpeg")):
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... fake_img = np.random.randint(0, 256, (32, 32, 3), dtype=np.uint8)
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... cv2.imwrite(img_path, fake_img)
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... elif img_path.endswith(".txt"):
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... with open(img_path, "w") as f:
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... f.write("This is a fake file.")
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|
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>>> def make_directory(root, directory_hierarchy, file_maker=make_fake_file):
|
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... root = Path(root)
|
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... root.mkdir(parents=True, exist_ok=True)
|
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... for subpath in directory_hierarchy:
|
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... if isinstance(subpath, str):
|
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... filepath = root / subpath
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... file_maker(str(filepath))
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... else:
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... dirname = list(subpath.keys())[0]
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... make_directory(root / dirname, subpath[dirname])
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>>> directory_hierarchy = [
|
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... "abc.jpg",
|
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... "def.png",
|
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... {"ghi": [
|
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... "jkl.jpeg",
|
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... {"mno": [
|
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... "pqr.jpg"]}]},
|
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... "this_will_be_ignored.txt",
|
||||
... ] # fmt: skip
|
||||
|
||||
>>> # You can replace this with any directory to explore the structure
|
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>>> # of generated data. e.g. fake_data_dir = "./temp_dir"
|
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>>> fake_data_dir = tempfile.mkdtemp()
|
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>>> make_directory(fake_data_dir, directory_hierarchy)
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>>> image_folder_1 = ImageFolder(fake_data_dir)
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>>> print(image_folder_1.samples)
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>>> # doctest: +SKIP(it's different with windows)
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['./temp_dir/abc.jpg', './temp_dir/def.png',
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'./temp_dir/ghi/jkl.jpeg', './temp_dir/ghi/mno/pqr.jpg']
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>>> # doctest: -SKIP
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>>> print(len(image_folder_1))
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4
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>>> for i in range(len(image_folder_1)):
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... (img,) = image_folder_1[i]
|
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... # do something with img
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... print(type(img), img.size)
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... # <class 'PIL.Image.Image'> (32, 32)
|
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|
||||
|
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>>> transform = T.Compose(
|
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... [
|
||||
... T.Resize(64),
|
||||
... T.ToTensor(),
|
||||
... T.Normalize(
|
||||
... mean=[0.5, 0.5, 0.5],
|
||||
... std=[0.5, 0.5, 0.5],
|
||||
... to_rgb=True,
|
||||
... ),
|
||||
... ]
|
||||
... )
|
||||
|
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>>> def cv2_loader(path: str):
|
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... image = cv2.imread(path)
|
||||
... assert image is not None
|
||||
... return image
|
||||
|
||||
>>> image_folder_2 = ImageFolder(
|
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... fake_data_dir,
|
||||
... loader=cv2_loader, # load image with OpenCV
|
||||
... extensions=(".jpg",), # only load *.jpg files
|
||||
... transform=transform, # apply transform to every image
|
||||
... )
|
||||
|
||||
>>> print(image_folder_2.samples)
|
||||
>>> # doctest: +SKIP(it's different with windows)
|
||||
['./temp_dir/abc.jpg', './temp_dir/ghi/mno/pqr.jpg']
|
||||
>>> # doctest: -SKIP
|
||||
>>> print(len(image_folder_2))
|
||||
2
|
||||
|
||||
>>> for (img,) in iter(image_folder_2):
|
||||
... # do something with img
|
||||
... assert isinstance(img, paddle.Tensor)
|
||||
... print(type(img), img.shape)
|
||||
... # <class 'paddle.Tensor'> [3, 64, 64]
|
||||
|
||||
>>> shutil.rmtree(fake_data_dir)
|
||||
"""
|
||||
|
||||
loader: Callable[..., _ImageDataType] | None
|
||||
extensions: Sequence[_AllowedExtensions] | None
|
||||
samples: list[str]
|
||||
transform: _Transform[Any, Any] | None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
root: str,
|
||||
loader: Callable[..., _ImageDataType] | None = None,
|
||||
extensions: Sequence[_AllowedExtensions] | None = None,
|
||||
transform: _Transform[Any, Any] | None = None,
|
||||
is_valid_file: _ImageDataType | None = None,
|
||||
) -> None:
|
||||
self.root = root
|
||||
if extensions is None:
|
||||
extensions = IMG_EXTENSIONS
|
||||
|
||||
samples = []
|
||||
path = os.path.expanduser(root)
|
||||
|
||||
if extensions is not None:
|
||||
|
||||
def is_valid_file(x):
|
||||
return has_valid_extension(x, extensions)
|
||||
|
||||
for root, _, fnames in sorted(os.walk(path, followlinks=True)):
|
||||
for fname in sorted(fnames):
|
||||
f = os.path.join(root, fname)
|
||||
if is_valid_file(f):
|
||||
samples.append(f)
|
||||
|
||||
if len(samples) == 0:
|
||||
raise (
|
||||
RuntimeError(
|
||||
"Found 0 files in subfolders of: " + self.root + "\n"
|
||||
"Supported extensions are: " + ",".join(extensions)
|
||||
)
|
||||
)
|
||||
|
||||
self.loader = default_loader if loader is None else loader
|
||||
self.extensions = extensions
|
||||
self.samples = samples
|
||||
self.transform = transform
|
||||
|
||||
def __getitem__(self, index: int) -> list[_ImageDataType]:
|
||||
"""
|
||||
Args:
|
||||
index (int): Index
|
||||
|
||||
Returns:
|
||||
sample of specific index.
|
||||
"""
|
||||
path = self.samples[index]
|
||||
sample = self.loader(path)
|
||||
if self.transform is not None:
|
||||
sample = self.transform(sample)
|
||||
return [sample]
|
||||
|
||||
def __len__(self) -> int:
|
||||
return len(self.samples)
|
||||
Reference in New Issue
Block a user