Source code for torchvision.datasets.sun397

from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union

import PIL.Image

from .utils import download_and_extract_archive
from .vision import VisionDataset

[docs]class SUN397(VisionDataset): """`The SUN397 Data Set <>`_. 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 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. """ _DATASET_URL = "" _DATASET_MD5 = "8ca2778205c41d23104230ba66911c7a" def __init__( self, root: Union[str, Path], transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, ) -> 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 ] def __len__(self) -> int: return len(self._image_files)
[docs] def __getitem__(self, idx: int) -> Tuple[Any, Any]: image_file, label = self._image_files[idx], self._labels[idx] image ="RGB") 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)


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