Source code for torchvision.datasets.places365
import os
from os import path
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from urllib.parse import urljoin
from .folder import default_loader
from .utils import check_integrity, download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
[docs]class Places365(VisionDataset):
r"""`Places365 <http://places2.csail.mit.edu/index.html>`_ classification dataset.
Args:
root (str or ``pathlib.Path``): Root directory of the Places365 dataset.
split (string, optional): The dataset split. Can be one of ``train-standard`` (default), ``train-challenge``,
``val``.
small (bool, optional): If ``True``, uses the small images, i.e. resized to 256 x 256 pixels, instead of the
high resolution ones.
download (bool, optional): If ``True``, downloads the dataset components and places them in ``root``. Already
downloaded archives are not downloaded again.
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.
loader (callable, optional): A function to load an image given its path.
Attributes:
classes (list): List of the class names.
class_to_idx (dict): Dict with items (class_name, class_index).
imgs (list): List of (image path, class_index) tuples
targets (list): The class_index value for each image in the dataset
Raises:
RuntimeError: If ``download is False`` and the meta files, i.e. the devkit, are not present or corrupted.
RuntimeError: If ``download is True`` and the image archive is already extracted.
"""
_SPLITS = ("train-standard", "train-challenge", "val")
_BASE_URL = "http://data.csail.mit.edu/places/places365/"
# {variant: (archive, md5)}
_DEVKIT_META = {
"standard": ("filelist_places365-standard.tar", "35a0585fee1fa656440f3ab298f8479c"),
"challenge": ("filelist_places365-challenge.tar", "70a8307e459c3de41690a7c76c931734"),
}
# (file, md5)
_CATEGORIES_META = ("categories_places365.txt", "06c963b85866bd0649f97cb43dd16673")
# {split: (file, md5)}
_FILE_LIST_META = {
"train-standard": ("places365_train_standard.txt", "30f37515461640559006b8329efbed1a"),
"train-challenge": ("places365_train_challenge.txt", "b2931dc997b8c33c27e7329c073a6b57"),
"val": ("places365_val.txt", "e9f2fd57bfd9d07630173f4e8708e4b1"),
}
# {(split, small): (file, md5)}
_IMAGES_META = {
("train-standard", False): ("train_large_places365standard.tar", "67e186b496a84c929568076ed01a8aa1"),
("train-challenge", False): ("train_large_places365challenge.tar", "605f18e68e510c82b958664ea134545f"),
("val", False): ("val_large.tar", "9b71c4993ad89d2d8bcbdc4aef38042f"),
("train-standard", True): ("train_256_places365standard.tar", "53ca1c756c3d1e7809517cc47c5561c5"),
("train-challenge", True): ("train_256_places365challenge.tar", "741915038a5e3471ec7332404dfb64ef"),
("val", True): ("val_256.tar", "e27b17d8d44f4af9a78502beb927f808"),
}
def __init__(
self,
root: Union[str, Path],
split: str = "train-standard",
small: bool = False,
download: bool = False,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
loader: Callable[[str], Any] = default_loader,
) -> None:
super().__init__(root, transform=transform, target_transform=target_transform)
self.split = self._verify_split(split)
self.small = small
self.loader = loader
self.classes, self.class_to_idx = self.load_categories(download)
self.imgs, self.targets = self.load_file_list(download)
if download:
self.download_images()
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]:
file, target = self.imgs[index]
image = self.loader(file)
if self.transforms is not None:
image, target = self.transforms(image, target)
return image, target
def __len__(self) -> int:
return len(self.imgs)
@property
def variant(self) -> str:
return "challenge" if "challenge" in self.split else "standard"
@property
def images_dir(self) -> str:
size = "256" if self.small else "large"
if self.split.startswith("train"):
dir = f"data_{size}_{self.variant}"
else:
dir = f"{self.split}_{size}"
return path.join(self.root, dir)
def load_categories(self, download: bool = True) -> Tuple[List[str], Dict[str, int]]:
def process(line: str) -> Tuple[str, int]:
cls, idx = line.split()
return cls, int(idx)
file, md5 = self._CATEGORIES_META
file = path.join(self.root, file)
if not self._check_integrity(file, md5, download):
self.download_devkit()
with open(file) as fh:
class_to_idx = dict(process(line) for line in fh)
return sorted(class_to_idx.keys()), class_to_idx
def load_file_list(self, download: bool = True) -> Tuple[List[Tuple[str, int]], List[int]]:
def process(line: str, sep="/") -> Tuple[str, int]:
image, idx = line.split()
return path.join(self.images_dir, image.lstrip(sep).replace(sep, os.sep)), int(idx)
file, md5 = self._FILE_LIST_META[self.split]
file = path.join(self.root, file)
if not self._check_integrity(file, md5, download):
self.download_devkit()
with open(file) as fh:
images = [process(line) for line in fh]
_, targets = zip(*images)
return images, list(targets)
def download_devkit(self) -> None:
file, md5 = self._DEVKIT_META[self.variant]
download_and_extract_archive(urljoin(self._BASE_URL, file), self.root, md5=md5)
def download_images(self) -> None:
if path.exists(self.images_dir):
return
file, md5 = self._IMAGES_META[(self.split, self.small)]
download_and_extract_archive(urljoin(self._BASE_URL, file), self.root, md5=md5)
if self.split.startswith("train"):
os.rename(self.images_dir.rsplit("_", 1)[0], self.images_dir)
def extra_repr(self) -> str:
return "\n".join(("Split: {split}", "Small: {small}")).format(**self.__dict__)
def _verify_split(self, split: str) -> str:
return verify_str_arg(split, "split", self._SPLITS)
def _check_integrity(self, file: str, md5: str, download: bool) -> bool:
integrity = check_integrity(file, md5=md5)
if not integrity and not download:
raise RuntimeError(
f"The file {file} does not exist or is corrupted. You can set download=True to download it."
)
return integrity
