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105 lines
4.4 KiB
105 lines
4.4 KiB
from pathlib import Path
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from typing import Any, Callable, Optional, Tuple, Union
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from PIL import Image
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from .folder import find_classes, make_dataset
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from .utils import download_and_extract_archive, verify_str_arg
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from .vision import VisionDataset
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class Imagenette(VisionDataset):
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"""`Imagenette <https://github.com/fastai/imagenette#imagenette-1>`_ image classification dataset.
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Args:
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root (str or ``pathlib.Path``): Root directory of the Imagenette dataset.
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split (string, optional): The dataset split. Supports ``"train"`` (default), and ``"val"``.
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size (string, optional): The image size. Supports ``"full"`` (default), ``"320px"``, and ``"160px"``.
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download (bool, optional): If ``True``, downloads the dataset components and places them in ``root``. Already
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downloaded archives are not downloaded again.
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version, e.g. ``transforms.RandomCrop``.
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target_transform (callable, optional): A function/transform that takes in the target and transforms it.
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Attributes:
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classes (list): List of the class name tuples.
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class_to_idx (dict): Dict with items (class name, class index).
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wnids (list): List of the WordNet IDs.
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wnid_to_idx (dict): Dict with items (WordNet ID, class index).
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"""
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_ARCHIVES = {
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"full": ("https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz", "fe2fc210e6bb7c5664d602c3cd71e612"),
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"320px": ("https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz", "3df6f0d01a2c9592104656642f5e78a3"),
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"160px": ("https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-160.tgz", "e793b78cc4c9e9a4ccc0c1155377a412"),
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}
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_WNID_TO_CLASS = {
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"n01440764": ("tench", "Tinca tinca"),
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"n02102040": ("English springer", "English springer spaniel"),
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"n02979186": ("cassette player",),
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"n03000684": ("chain saw", "chainsaw"),
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"n03028079": ("church", "church building"),
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"n03394916": ("French horn", "horn"),
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"n03417042": ("garbage truck", "dustcart"),
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"n03425413": ("gas pump", "gasoline pump", "petrol pump", "island dispenser"),
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"n03445777": ("golf ball",),
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"n03888257": ("parachute", "chute"),
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}
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def __init__(
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self,
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root: Union[str, Path],
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split: str = "train",
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size: str = "full",
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download=False,
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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) -> None:
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super().__init__(root, transform=transform, target_transform=target_transform)
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self._split = verify_str_arg(split, "split", ["train", "val"])
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self._size = verify_str_arg(size, "size", ["full", "320px", "160px"])
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self._url, self._md5 = self._ARCHIVES[self._size]
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self._size_root = Path(self.root) / Path(self._url).stem
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self._image_root = str(self._size_root / self._split)
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if download:
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self._download()
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elif not self._check_exists():
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raise RuntimeError("Dataset not found. You can use download=True to download it.")
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self.wnids, self.wnid_to_idx = find_classes(self._image_root)
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self.classes = [self._WNID_TO_CLASS[wnid] for wnid in self.wnids]
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self.class_to_idx = {
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class_name: idx for wnid, idx in self.wnid_to_idx.items() for class_name in self._WNID_TO_CLASS[wnid]
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}
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self._samples = make_dataset(self._image_root, self.wnid_to_idx, extensions=".jpeg")
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def _check_exists(self) -> bool:
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return self._size_root.exists()
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def _download(self):
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if self._check_exists():
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raise RuntimeError(
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f"The directory {self._size_root} already exists. "
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f"If you want to re-download or re-extract the images, delete the directory."
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)
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download_and_extract_archive(self._url, self.root, md5=self._md5)
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def __getitem__(self, idx: int) -> Tuple[Any, Any]:
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path, label = self._samples[idx]
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image = Image.open(path).convert("RGB")
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if self.transform is not None:
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image = self.transform(image)
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if self.target_transform is not None:
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label = self.target_transform(label)
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return image, label
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def __len__(self) -> int:
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return len(self._samples)
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