101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
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import os
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import pathlib
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from typing import Any, Callable, Optional, Tuple, Union
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import PIL.Image
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from .utils import download_and_extract_archive, verify_str_arg
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from .vision import VisionDataset
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class DTD(VisionDataset):
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"""`Describable Textures Dataset (DTD) <https://www.robots.ox.ac.uk/~vgg/data/dtd/>`_.
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Args:
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root (str or ``pathlib.Path``): Root directory of the dataset.
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split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
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partition (int, optional): The dataset partition. Should be ``1 <= partition <= 10``. Defaults to ``1``.
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.. note::
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The partition only changes which split each image belongs to. Thus, regardless of the selected
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partition, combining all splits will result in all images.
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version. E.g, ``transforms.RandomCrop``.
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target_transform (callable, optional): A function/transform that takes in the target and transforms it.
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download (bool, optional): If True, downloads the dataset from the internet and
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puts it in root directory. If dataset is already downloaded, it is not
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downloaded again. Default is False.
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"""
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_URL = "https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz"
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_MD5 = "fff73e5086ae6bdbea199a49dfb8a4c1"
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def __init__(
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self,
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root: Union[str, pathlib.Path],
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split: str = "train",
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partition: int = 1,
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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download: bool = False,
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) -> None:
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self._split = verify_str_arg(split, "split", ("train", "val", "test"))
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if not isinstance(partition, int) and not (1 <= partition <= 10):
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raise ValueError(
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f"Parameter 'partition' should be an integer with `1 <= partition <= 10`, "
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f"but got {partition} instead"
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)
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self._partition = partition
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super().__init__(root, transform=transform, target_transform=target_transform)
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self._base_folder = pathlib.Path(self.root) / type(self).__name__.lower()
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self._data_folder = self._base_folder / "dtd"
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self._meta_folder = self._data_folder / "labels"
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self._images_folder = self._data_folder / "images"
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if download:
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self._download()
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if not self._check_exists():
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raise RuntimeError("Dataset not found. You can use download=True to download it")
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self._image_files = []
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classes = []
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with open(self._meta_folder / f"{self._split}{self._partition}.txt") as file:
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for line in file:
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cls, name = line.strip().split("/")
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self._image_files.append(self._images_folder.joinpath(cls, name))
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classes.append(cls)
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self.classes = sorted(set(classes))
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self.class_to_idx = dict(zip(self.classes, range(len(self.classes))))
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self._labels = [self.class_to_idx[cls] for cls in classes]
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def __len__(self) -> int:
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return len(self._image_files)
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def __getitem__(self, idx: int) -> Tuple[Any, Any]:
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image_file, label = self._image_files[idx], self._labels[idx]
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image = PIL.Image.open(image_file).convert("RGB")
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if self.transform:
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image = self.transform(image)
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if self.target_transform:
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label = self.target_transform(label)
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return image, label
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def extra_repr(self) -> str:
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return f"split={self._split}, partition={self._partition}"
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def _check_exists(self) -> bool:
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return os.path.exists(self._data_folder) and os.path.isdir(self._data_folder)
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def _download(self) -> None:
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if self._check_exists():
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return
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download_and_extract_archive(self._URL, download_root=str(self._base_folder), md5=self._MD5)
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