chore: import upstream snapshot with attribution
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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
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if TYPE_CHECKING:
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import numpy.typing as npt
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from paddle.vision.transforms.transforms import _Transform
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from ..image import _ImageBackend, _ImageDataType
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_DatasetMode = Literal["train", "valid", "test"]
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import os
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import tarfile
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import numpy as np
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from PIL import Image
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import paddle
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from paddle.dataset.common import _check_exists_and_download
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from paddle.io import Dataset
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from paddle.utils import try_import
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from paddle.utils.download import _safe_extract_tar
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__all__ = []
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DATA_URL = 'http://paddlemodels.bj.bcebos.com/flowers/102flowers.tgz'
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LABEL_URL = 'http://paddlemodels.bj.bcebos.com/flowers/imagelabels.mat'
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SETID_URL = 'http://paddlemodels.bj.bcebos.com/flowers/setid.mat'
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DATA_MD5 = '52808999861908f626f3c1f4e79d11fa'
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LABEL_MD5 = 'e0620be6f572b9609742df49c70aed4d'
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SETID_MD5 = 'a5357ecc9cb78c4bef273ce3793fc85c'
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# In official 'readme', tstid is the flag of test data
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# and trnid is the flag of train data. But test data is more than train data.
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# So we exchange the train data and test data.
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MODE_FLAG_MAP = {'train': 'tstid', 'test': 'trnid', 'valid': 'valid'}
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class Flowers(Dataset[tuple["_ImageDataType", "npt.NDArray[np.int64]"]]):
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"""
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Implementation of `Flowers102 <https://www.robots.ox.ac.uk/~vgg/data/flowers/>`_
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dataset.
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Args:
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data_file (str|None, optional): Path to data file, can be set None if
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:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.
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label_file (str|None, optional): Path to label file, can be set None if
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:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.
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setid_file (str|None, optional): Path to subset index file, can be set
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None if :attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.
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mode (str, optional): Either train or test mode. Default 'train'.
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transform (Callable|None, optional): transform to perform on image, None for no transform. Default: None.
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download (bool|None, optional): download dataset automatically if :attr:`data_file` is None. Default: True.
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backend (str|None, optional): Specifies which type of image to be returned:
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PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
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If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
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default backend is 'pil'. Default: None.
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Returns:
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:ref:`api_paddle_io_Dataset`. An instance of Flowers dataset.
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Examples:
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.. code-block:: pycon
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>>> # doctest: +TIMEOUT(60)
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>>> import itertools
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>>> import paddle
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>>> import paddle.vision.transforms as T
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>>> from paddle.vision.datasets import Flowers
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>>> flowers = Flowers()
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>>> print(len(flowers))
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6149
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>>> for i in range(5): # only show first 5 images
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... img, label = flowers[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.JpegImagePlugin.JpegImageFile'> (523, 500) [1]
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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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>>> flowers_test = Flowers(
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... mode="test",
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... transform=transform, # apply transform to every image
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... backend="cv2", # use OpenCV as image transform backend
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... )
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>>> print(len(flowers_test))
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1020
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>>> for img, label in itertools.islice(iter(flowers_test), 5): # only show first 5 images
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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, 96] [1]
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"""
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backend: _ImageBackend
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data_file: str | None
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label_file: str | None
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setid_file: str | None
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mode: _DatasetMode
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transform: _Transform[Any, Any] | None
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def __init__(
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self,
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data_file: str | None = None,
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label_file: str | None = None,
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setid_file: str | None = None,
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mode: _DatasetMode = 'train',
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transform: _Transform[Any, Any] | None = None,
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download: bool = True,
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backend: _ImageBackend | None = None,
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) -> None:
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assert mode.lower() in [
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'train',
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'valid',
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'test',
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], f"mode should be 'train', 'valid' or 'test', but got {mode}"
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if backend is None:
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backend = paddle.vision.get_image_backend()
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if backend not in ['pil', 'cv2']:
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raise ValueError(
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f"Expected backend are one of ['pil', 'cv2'], but got {backend}"
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)
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self.backend = backend
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flag = MODE_FLAG_MAP[mode.lower()]
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if not data_file:
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assert download, (
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"data_file is not set and downloading automatically is disabled"
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)
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data_file = _check_exists_and_download(
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data_file, DATA_URL, DATA_MD5, 'flowers', download
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)
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if not label_file:
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assert download, (
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"label_file is not set and downloading automatically is disabled"
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)
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label_file = _check_exists_and_download(
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label_file, LABEL_URL, LABEL_MD5, 'flowers', download
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)
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if not setid_file:
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assert download, (
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"setid_file is not set and downloading automatically is disabled"
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)
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setid_file = _check_exists_and_download(
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setid_file, SETID_URL, SETID_MD5, 'flowers', download
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)
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self.transform = transform
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data_tar = tarfile.open(data_file)
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self.data_path = data_file.replace(".tgz", "/")
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if not os.path.exists(self.data_path):
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os.mkdir(self.data_path)
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jpg_path = os.path.join(self.data_path, "jpg")
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if not os.path.exists(jpg_path):
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_safe_extract_tar(data_tar, self.data_path, on_unsafe='raise')
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scio = try_import('scipy.io')
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self.labels = scio.loadmat(label_file)['labels'][0]
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self.indexes = scio.loadmat(setid_file)[flag][0]
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def __getitem__(
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self, idx: int
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) -> tuple[_ImageDataType, npt.NDArray[np.int64]]:
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index = self.indexes[idx]
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label = np.array([self.labels[index - 1]])
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img_name = f"jpg/image_{index:05}.jpg"
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image = os.path.join(self.data_path, img_name)
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if self.backend == 'pil':
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image = Image.open(image)
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elif self.backend == 'cv2':
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image = np.array(Image.open(image))
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if self.transform is not None:
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image = self.transform(image)
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if self.backend == 'pil':
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return image, label.astype('int64')
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return image.astype(paddle.get_default_dtype()), label.astype('int64')
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def __len__(self):
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return len(self.indexes)
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