| from pathlib import Path | |
| from typing import Any, Callable, Optional, Tuple, Union | |
| from .folder import default_loader | |
| from .utils import download_and_extract_archive | |
| from .vision import VisionDataset | |
| class SUN397(VisionDataset): | |
| """`The SUN397 Data Set <https://vision.princeton.edu/projects/2010/SUN/>`_. | |
| The SUN397 or Scene UNderstanding (SUN) is a dataset for scene recognition consisting of | |
| 397 categories with 108'754 images. | |
| Args: | |
| root (str or ``pathlib.Path``): Root directory of the dataset. | |
| transform (callable, optional): A function/transform that takes in a PIL image or torch.Tensor, depends on the given loader, | |
| and returns a transformed version. E.g, ``transforms.RandomCrop`` | |
| target_transform (callable, optional): A function/transform that takes in the target and transforms it. | |
| download (bool, optional): If true, downloads the dataset from the internet and | |
| puts it in root directory. If dataset is already downloaded, it is not | |
| downloaded again. | |
| loader (callable, optional): A function to load an image given its path. | |
| By default, it uses PIL as its image loader, but users could also pass in | |
| ``torchvision.io.decode_image`` for decoding image data into tensors directly. | |
| """ | |
| _DATASET_URL = "http://vision.princeton.edu/projects/2010/SUN/SUN397.tar.gz" | |
| _DATASET_MD5 = "8ca2778205c41d23104230ba66911c7a" | |
| def __init__( | |
| self, | |
| root: Union[str, Path], | |
| transform: Optional[Callable] = None, | |
| target_transform: Optional[Callable] = None, | |
| download: bool = False, | |
| loader: Callable[[Union[str, Path]], Any] = default_loader, | |
| ) -> None: | |
| super().__init__(root, transform=transform, target_transform=target_transform) | |
| self._data_dir = Path(self.root) / "SUN397" | |
| if download: | |
| self._download() | |
| if not self._check_exists(): | |
| raise RuntimeError("Dataset not found. You can use download=True to download it") | |
| with open(self._data_dir / "ClassName.txt") as f: | |
| self.classes = [c[3:].strip() for c in f] | |
| self.class_to_idx = dict(zip(self.classes, range(len(self.classes)))) | |
| self._image_files = list(self._data_dir.rglob("sun_*.jpg")) | |
| self._labels = [ | |
| self.class_to_idx["/".join(path.relative_to(self._data_dir).parts[1:-1])] for path in self._image_files | |
| ] | |
| self.loader = loader | |
| def __len__(self) -> int: | |
| return len(self._image_files) | |
| def __getitem__(self, idx: int) -> Tuple[Any, Any]: | |
| image_file, label = self._image_files[idx], self._labels[idx] | |
| image = self.loader(image_file) | |
| if self.transform: | |
| image = self.transform(image) | |
| if self.target_transform: | |
| label = self.target_transform(label) | |
| return image, label | |
| def _check_exists(self) -> bool: | |
| return self._data_dir.is_dir() | |
| def _download(self) -> None: | |
| if self._check_exists(): | |
| return | |
| download_and_extract_archive(self._DATASET_URL, download_root=self.root, md5=self._DATASET_MD5) | |