You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
77 lines
2.7 KiB
77 lines
2.7 KiB
5 months ago
|
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
|
||
|
|
|
||
|
|
|
||
|
|
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 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 = "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,
|
||
|
|
) -> 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)
|
||
|
|
|
||
|
|
def __getitem__(self, idx: int) -> Tuple[Any, Any]:
|
||
|
|
image_file, label = self._image_files[idx], self._labels[idx]
|
||
|
|
image = PIL.Image.open(image_file).convert("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)
|