| import json | |
| from pathlib import Path | |
| from typing import Any, Callable, Optional, Tuple, Union | |
| from .folder import default_loader | |
| from .utils import download_and_extract_archive, verify_str_arg | |
| from .vision import VisionDataset | |
| class Food101(VisionDataset): | |
| """`The Food-101 Data Set <https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/>`_. | |
| The Food-101 is a challenging data set of 101 food categories with 101,000 images. | |
| For each class, 250 manually reviewed test images are provided as well as 750 training images. | |
| On purpose, the training images were not cleaned, and thus still contain some amount of noise. | |
| This comes mostly in the form of intense colors and sometimes wrong labels. All images were | |
| rescaled to have a maximum side length of 512 pixels. | |
| Args: | |
| root (str or ``pathlib.Path``): Root directory of the dataset. | |
| split (string, optional): The dataset split, supports ``"train"`` (default) and ``"test"``. | |
| transform (callable, optional): A function/transform that takes in a PIL image or torch.Tensor, depends on the given loader, | |
| 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. Default is False. | |
| loader (callable, optional): A function to load an image given its path. | |
| By default, it uses PIL as its image loader, but users could also pass in | |
| ``torchvision.io.decode_image`` for decoding image data into tensors directly. | |
| """ | |
| _URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz" | |
| _MD5 = "85eeb15f3717b99a5da872d97d918f87" | |
| def __init__( | |
| self, | |
| root: Union[str, Path], | |
| split: str = "train", | |
| transform: Optional[Callable] = None, | |
| target_transform: Optional[Callable] = None, | |
| download: bool = False, | |
| loader: Callable[[Union[str, Path]], Any] = default_loader, | |
| ) -> None: | |
| super().__init__(root, transform=transform, target_transform=target_transform) | |
| self._split = verify_str_arg(split, "split", ("train", "test")) | |
| self._base_folder = Path(self.root) / "food-101" | |
| self._meta_folder = self._base_folder / "meta" | |
| self._images_folder = self._base_folder / "images" | |
| if download: | |
| self._download() | |
| if not self._check_exists(): | |
| raise RuntimeError("Dataset not found. You can use download=True to download it") | |
| self._labels = [] | |
| self._image_files = [] | |
| with open(self._meta_folder / f"{split}.json") as f: | |
| metadata = json.loads(f.read()) | |
| self.classes = sorted(metadata.keys()) | |
| self.class_to_idx = dict(zip(self.classes, range(len(self.classes)))) | |
| for class_label, im_rel_paths in metadata.items(): | |
| self._labels += [self.class_to_idx[class_label]] * len(im_rel_paths) | |
| self._image_files += [ | |
| self._images_folder.joinpath(*f"{im_rel_path}.jpg".split("/")) for im_rel_path in im_rel_paths | |
| ] | |
| self.loader = loader | |
| 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 = self.loader(image_file) | |
| if self.transform: | |
| image = self.transform(image) | |
| if self.target_transform: | |
| label = self.target_transform(label) | |
| return image, label | |
| def extra_repr(self) -> str: | |
| return f"split={self._split}" | |
| def _check_exists(self) -> bool: | |
| return all(folder.exists() and folder.is_dir() for folder in (self._meta_folder, self._images_folder)) | |
| def _download(self) -> None: | |
| if self._check_exists(): | |
| return | |
| download_and_extract_archive(self._URL, download_root=self.root, md5=self._MD5) | |