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Source code for torchvision.datasets.stanford_cars

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

from PIL import Image

from .utils import download_and_extract_archive, download_url, verify_str_arg
from .vision import VisionDataset


[docs]class StanfordCars(VisionDataset): """`Stanford Cars <https://ai.stanford.edu/~jkrause/cars/car_dataset.html>`_ Dataset The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split .. note:: This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format. Args: root (string): Root directory of dataset split (string, optional): The dataset split, supports ``"train"`` (default) or ``"test"``. 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.""" def __init__( self, root: str, split: str = "train", transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, ) -> None: try: import scipy.io as sio except ImportError: raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: pip install scipy") super().__init__(root, transform=transform, target_transform=target_transform) self._split = verify_str_arg(split, "split", ("train", "test")) self._base_folder = pathlib.Path(root) / "stanford_cars" devkit = self._base_folder / "devkit" if self._split == "train": self._annotations_mat_path = devkit / "cars_train_annos.mat" self._images_base_path = self._base_folder / "cars_train" else: self._annotations_mat_path = self._base_folder / "cars_test_annos_withlabels.mat" self._images_base_path = self._base_folder / "cars_test" if download: self.download() if not self._check_exists(): raise RuntimeError("Dataset not found. You can use download=True to download it") self._samples = [ ( str(self._images_base_path / annotation["fname"]), annotation["class"] - 1, # Original target mapping starts from 1, hence -1 ) for annotation in sio.loadmat(self._annotations_mat_path, squeeze_me=True)["annotations"] ] self.classes = sio.loadmat(str(devkit / "cars_meta.mat"), squeeze_me=True)["class_names"].tolist() self.class_to_idx = {cls: i for i, cls in enumerate(self.classes)} def __len__(self) -> int: return len(self._samples)
[docs] def __getitem__(self, idx: int) -> Tuple[Any, Any]: """Returns pil_image and class_id for given index""" image_path, target = self._samples[idx] pil_image = Image.open(image_path).convert("RGB") if self.transform is not None: pil_image = self.transform(pil_image) if self.target_transform is not None: target = self.target_transform(target) return pil_image, target
def download(self) -> None: if self._check_exists(): return download_and_extract_archive( url="https://ai.stanford.edu/~jkrause/cars/car_devkit.tgz", download_root=str(self._base_folder), md5="c3b158d763b6e2245038c8ad08e45376", ) if self._split == "train": download_and_extract_archive( url="https://ai.stanford.edu/~jkrause/car196/cars_train.tgz", download_root=str(self._base_folder), md5="065e5b463ae28d29e77c1b4b166cfe61", ) else: download_and_extract_archive( url="https://ai.stanford.edu/~jkrause/car196/cars_test.tgz", download_root=str(self._base_folder), md5="4ce7ebf6a94d07f1952d94dd34c4d501", ) download_url( url="https://ai.stanford.edu/~jkrause/car196/cars_test_annos_withlabels.mat", root=str(self._base_folder), md5="b0a2b23655a3edd16d84508592a98d10", ) def _check_exists(self) -> bool: if not (self._base_folder / "devkit").is_dir(): return False return self._annotations_mat_path.exists() and self._images_base_path.is_dir()

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