Source code for torchvision.datasets.stanford_cars

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

from PIL import Image

from .utils import verify_str_arg
from .vision import VisionDataset

[docs]class StanfordCars(VisionDataset): """Stanford Cars 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 The original URL is, but it is broken. .. note:: This class needs `scipy <>`_ to load target files from `.mat` format. Args: root (str or ``pathlib.Path``): 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): This parameter exists for backward compatibility but it does not download the dataset, since the original URL is not available anymore. The dataset seems to be available on Kaggle so you can try to manually download it using `these instructions <>`_. """ def __init__( self, root: Union[str, pathlib.Path], split: str = "train", transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, ) -> None: try: import 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: if not self._check_exists(): raise RuntimeError( "Dataset not found. Try to manually download following the instructions in " "" ) 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 ="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 _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() def download(self): raise ValueError( "The original URL is broken so the StanfordCars dataset is not available for automatic " "download anymore. You can try to download it manually following " ", " "and set download=False to avoid this error." )


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