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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import io
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import tarfile
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from typing import TYPE_CHECKING, Any, Literal
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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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if TYPE_CHECKING:
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import numpy.typing as npt
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from paddle._typing import DTypeLike
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from paddle.vision.transforms.transforms import _Transform
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from ..image import _ImageDataType
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_ImageBackend = Literal["cv2", "pil"]
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_DatasetMode = Literal["train", "valid", "test"]
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__all__ = []
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VOC_URL = 'https://dataset.bj.bcebos.com/voc/VOCtrainval_11-May-2012.tar'
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VOC_MD5 = '6cd6e144f989b92b3379bac3b3de84fd'
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SET_FILE = 'VOCdevkit/VOC2012/ImageSets/Segmentation/{}.txt'
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DATA_FILE = 'VOCdevkit/VOC2012/JPEGImages/{}.jpg'
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LABEL_FILE = 'VOCdevkit/VOC2012/SegmentationClass/{}.png'
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CACHE_DIR = 'voc2012'
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MODE_FLAG_MAP = {'train': 'trainval', 'test': 'train', 'valid': "val"}
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class VOC2012(Dataset[tuple["_ImageDataType", "npt.NDArray[Any]"]]):
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"""
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Implementation of `VOC2012 <http://host.robots.ox.ac.uk/pascal/VOC/voc2012/>`_ 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/voc2012.
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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, 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 VOC2012 dataset.
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Examples:
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.. code-block:: pycon
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>>> # doctest: +TIMEOUT(120)
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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 VOC2012
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>>> voc2012 = VOC2012()
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>>> print(len(voc2012))
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2913
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>>> for i in range(5): # only show first 5 images
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... img, label = voc2012[i]
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... # do something with img and label
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... print(type(img), img.size)
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... # <class 'PIL.JpegImagePlugin.JpegImageFile'> (500, 281)
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... print(type(label), label.size)
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... # <class 'PIL.PngImagePlugin.PngImageFile'> (500, 281)
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>>> transform = T.Compose(
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... [
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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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>>> voc2012_test = VOC2012(
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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(voc2012_test))
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1464
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>>> for img, label in itertools.islice(iter(voc2012_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)
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... # <class 'paddle.Tensor'> [3, 281, 500]
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... print(type(label), label.shape)
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... # <class 'numpy.ndarray'> (281, 500)
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"""
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data_file: str | None
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mode: _DatasetMode
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transform: _Transform[Any, Any] | None
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backend: _ImageBackend
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flag: str
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dtype: DTypeLike
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def __init__(
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self,
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data_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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self.flag = MODE_FLAG_MAP[mode.lower()]
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self.data_file = data_file
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if self.data_file is None:
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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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self.data_file = _check_exists_and_download(
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data_file, VOC_URL, VOC_MD5, CACHE_DIR, download
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)
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self.transform = transform
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# read dataset into memory
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self._load_anno()
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self.dtype = paddle.get_default_dtype()
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def _load_anno(self):
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self.name2mem = {}
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self.data_tar = tarfile.open(self.data_file)
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for ele in self.data_tar.getmembers():
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self.name2mem[ele.name] = ele
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set_file = SET_FILE.format(self.flag)
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sets = self.data_tar.extractfile(self.name2mem[set_file])
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self.data = []
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self.labels = []
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for line in sets:
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line = line.strip()
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data = DATA_FILE.format(line.decode('utf-8'))
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label = LABEL_FILE.format(line.decode('utf-8'))
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self.data.append(data)
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self.labels.append(label)
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def __getitem__(self, idx: int) -> tuple[_ImageDataType, npt.NDArray[Any]]:
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data_file = self.data[idx]
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label_file = self.labels[idx]
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data = self.data_tar.extractfile(self.name2mem[data_file]).read()
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label = self.data_tar.extractfile(self.name2mem[label_file]).read()
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data = Image.open(io.BytesIO(data))
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label = Image.open(io.BytesIO(label))
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if self.backend == 'cv2':
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data = np.array(data)
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label = np.array(label)
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if self.transform is not None:
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data = self.transform(data)
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if self.backend == 'cv2':
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return data.astype(self.dtype), label.astype(self.dtype)
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return data, label
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def __len__(self) -> int:
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return len(self.data)
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def __del__(self) -> None:
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if self.data_tar:
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self.data_tar.close()
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