wehub-resource-sync
539 lines
18 KiB
Python
539 lines
18 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import TYPE_CHECKING, Any, Literal, TypeAlias
|
|
|
|
if TYPE_CHECKING:
|
|
from collections.abc import Callable, Sequence
|
|
|
|
from paddle._typing.dtype_like import _DTypeLiteral
|
|
from paddle.vision.transforms.transforms import _Transform
|
|
|
|
from ..image import _ImageDataType
|
|
|
|
_AllowedExtensions: TypeAlias = Literal[
|
|
'.jpg',
|
|
'.jpeg',
|
|
'.png',
|
|
'.ppm',
|
|
'.bmp',
|
|
'.pgm',
|
|
'.tif',
|
|
'.tiff',
|
|
'.webp',
|
|
]
|
|
|
|
import os
|
|
|
|
from PIL import Image
|
|
|
|
import paddle
|
|
from paddle.io import Dataset
|
|
from paddle.utils import try_import
|
|
|
|
__all__ = []
|
|
|
|
|
|
def has_valid_extension(filename: str, extensions: Sequence[str]) -> bool:
|
|
"""Checks if a file is a valid extension.
|
|
|
|
Args:
|
|
filename (str): path to a file
|
|
extensions (list[str]|tuple[str]): extensions to consider
|
|
|
|
Returns:
|
|
bool: True if the filename ends with one of given extensions
|
|
"""
|
|
assert isinstance(extensions, (list, tuple)), (
|
|
"`extensions` must be list or tuple."
|
|
)
|
|
extensions = tuple([x.lower() for x in extensions])
|
|
return filename.lower().endswith(extensions)
|
|
|
|
|
|
def make_dataset(dir, class_to_idx, extensions, is_valid_file=None):
|
|
images = []
|
|
dir = os.path.expanduser(dir)
|
|
|
|
if extensions is not None:
|
|
|
|
def is_valid_file(x):
|
|
return has_valid_extension(x, extensions)
|
|
|
|
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, followlinks=True)):
|
|
for fname in sorted(fnames):
|
|
path = os.path.join(root, fname)
|
|
if is_valid_file(path):
|
|
item = (path, class_to_idx[target])
|
|
images.append(item)
|
|
|
|
return images
|
|
|
|
|
|
class DatasetFolder(Dataset[tuple["_ImageDataType", int]]):
|
|
"""A generic data loader where the samples are arranged in this way:
|
|
|
|
.. code-block:: text
|
|
|
|
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
|
|
|
|
Args:
|
|
root (str): Root directory path.
|
|
loader (Callable|None, optional): A function to load a sample given its path. Default: None.
|
|
extensions (list[str]|tuple[str]|None, optional): A list of allowed extensions.
|
|
Both :attr:`extensions` and :attr:`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|None, optional): A function/transform that takes in
|
|
a sample and returns a transformed version. Default: None.
|
|
is_valid_file (Callable|None, optional): A function that takes path of a file
|
|
and check if the file is a valid file. Both :attr:`extensions` and
|
|
:attr:`is_valid_file` should not be passed. Default: None.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_io_Dataset`. An instance of DatasetFolder.
|
|
|
|
Attributes:
|
|
classes (list[str]): List of the class names.
|
|
class_to_idx (dict[str, int]): Dict with items (class_name, class_index).
|
|
samples (list[tuple[str, int]]): List of (sample_path, class_index) tuples.
|
|
targets (list[int]): The class_index value for each image in the dataset.
|
|
|
|
Example:
|
|
|
|
.. code-block:: pycon
|
|
|
|
>>> import shutil
|
|
>>> import tempfile
|
|
>>> import cv2
|
|
>>> import numpy as np
|
|
>>> import paddle
|
|
>>> 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",
|
|
... ] # fmt: skip
|
|
|
|
>>> # 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)
|
|
>>> # doctest: +SKIP(it's different with windows)
|
|
[('./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)]
|
|
>>> # doctest: -SKIP
|
|
>>> 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,
|
|
... ),
|
|
... ]
|
|
... )
|
|
|
|
>>> def cv2_loader(path: str):
|
|
... image = cv2.imread(path)
|
|
... assert image is not None
|
|
... return image
|
|
|
|
>>> data_folder_2 = DatasetFolder(
|
|
... fake_data_dir,
|
|
... loader=cv2_loader, # 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])
|
|
>>> # doctest: +SKIP(it's different with windows)
|
|
['./temp_dir/class_0/abc.jpg', './temp_dir/class_1/mno/stu.jpg']
|
|
>>> # doctest: -SKIP
|
|
>>> print(len(data_folder_2))
|
|
2
|
|
|
|
>>> for img, label in iter(data_folder_2):
|
|
... # do something with img and label
|
|
... assert isinstance(img, paddle.Tensor)
|
|
... print(type(img), img.shape, label)
|
|
... # <class 'paddle.Tensor'> [3, 64, 64] 0
|
|
|
|
>>> shutil.rmtree(fake_data_dir)
|
|
"""
|
|
|
|
loader: Callable[..., _ImageDataType] | None
|
|
extensions: Sequence[_AllowedExtensions] | None
|
|
transform: _Transform[Any, Any] | None
|
|
classes: list[str]
|
|
class_to_idx: dict[str, int]
|
|
samples: list[tuple[str, int]]
|
|
targets: list[str]
|
|
dtype: _DTypeLiteral
|
|
|
|
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
|
|
self.transform = transform
|
|
if extensions is None:
|
|
extensions = IMG_EXTENSIONS
|
|
classes, class_to_idx = self._find_classes(self.root)
|
|
samples = make_dataset(
|
|
self.root, class_to_idx, extensions, is_valid_file
|
|
)
|
|
if len(samples) == 0:
|
|
raise (
|
|
RuntimeError(
|
|
"Found 0 directories 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.classes = classes
|
|
self.class_to_idx = class_to_idx
|
|
self.samples = samples
|
|
self.targets = [s[1] for s in samples]
|
|
|
|
self.dtype = paddle.get_default_dtype()
|
|
|
|
def _find_classes(self, dir: str) -> tuple[list[str], dict[str, int]]:
|
|
"""
|
|
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.
|
|
|
|
"""
|
|
classes = [d.name for d in os.scandir(dir) if d.is_dir()]
|
|
classes.sort()
|
|
class_to_idx = {classes[i]: i for i in range(len(classes))}
|
|
return classes, class_to_idx
|
|
|
|
def __getitem__(self, index: int) -> tuple[_ImageDataType, int]:
|
|
"""
|
|
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)
|
|
|
|
return sample, target
|
|
|
|
def __len__(self):
|
|
return len(self.samples)
|
|
|
|
|
|
IMG_EXTENSIONS = (
|
|
'.jpg',
|
|
'.jpeg',
|
|
'.png',
|
|
'.ppm',
|
|
'.bmp',
|
|
'.pgm',
|
|
'.tif',
|
|
'.tiff',
|
|
'.webp',
|
|
)
|
|
|
|
|
|
def pil_loader(path):
|
|
with open(path, 'rb') as f:
|
|
img = Image.open(f)
|
|
return img.convert('RGB')
|
|
|
|
|
|
def cv2_loader(path):
|
|
cv2 = try_import('cv2')
|
|
return cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
|
|
|
|
|
|
def default_loader(path):
|
|
from paddle.vision import get_image_backend
|
|
|
|
if get_image_backend() == 'cv2':
|
|
return cv2_loader(path)
|
|
else:
|
|
return pil_loader(path)
|
|
|
|
|
|
class ImageFolder(Dataset[list["_ImageDataType"]]):
|
|
"""A generic data loader where the samples are arranged in this way:
|
|
|
|
.. code-block:: text
|
|
|
|
root/1.ext
|
|
root/2.ext
|
|
root/sub_dir/3.ext
|
|
|
|
Args:
|
|
root (str): Root directory path.
|
|
loader (Callable|None, optional): A function to load a sample given its path. Default: None.
|
|
extensions (list[str]|tuple[str]|None, optional): A list of allowed extensions.
|
|
Both :attr:`extensions` and :attr:`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|None, optional): A function/transform that takes in
|
|
a sample and returns a transformed version. Default: None.
|
|
is_valid_file (Callable|None, optional): A function that takes path of a file
|
|
and check if the file is a valid file. Both :attr:`extensions` and
|
|
:attr:`is_valid_file` should not be passed. Default: None.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_io_Dataset`. An instance of ImageFolder.
|
|
|
|
Attributes:
|
|
samples (list[str]): List of sample path.
|
|
|
|
Example:
|
|
|
|
.. code-block:: pycon
|
|
|
|
>>> import shutil
|
|
>>> import tempfile
|
|
>>> import cv2
|
|
>>> import numpy as np
|
|
>>> import paddle
|
|
>>> import paddle.vision.transforms as T
|
|
>>> from pathlib import Path
|
|
>>> from paddle.vision.datasets import ImageFolder
|
|
|
|
>>> 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 = [
|
|
... "abc.jpg",
|
|
... "def.png",
|
|
... {"ghi": [
|
|
... "jkl.jpeg",
|
|
... {"mno": [
|
|
... "pqr.jpg"]}]},
|
|
... "this_will_be_ignored.txt",
|
|
... ] # fmt: skip
|
|
|
|
>>> # 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)
|
|
>>> image_folder_1 = ImageFolder(fake_data_dir)
|
|
>>> print(image_folder_1.samples)
|
|
>>> # doctest: +SKIP(it's different with windows)
|
|
['./temp_dir/abc.jpg', './temp_dir/def.png',
|
|
'./temp_dir/ghi/jkl.jpeg', './temp_dir/ghi/mno/pqr.jpg']
|
|
>>> # doctest: -SKIP
|
|
>>> print(len(image_folder_1))
|
|
4
|
|
|
|
>>> for i in range(len(image_folder_1)):
|
|
... (img,) = image_folder_1[i]
|
|
... # do something with img
|
|
... print(type(img), img.size)
|
|
... # <class 'PIL.Image.Image'> (32, 32)
|
|
|
|
|
|
>>> 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,
|
|
... ),
|
|
... ]
|
|
... )
|
|
|
|
>>> def cv2_loader(path: str):
|
|
... image = cv2.imread(path)
|
|
... assert image is not None
|
|
... return image
|
|
|
|
>>> image_folder_2 = ImageFolder(
|
|
... 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)
|