add Imagenette dataset (#8139)

docs/source/datasets.rst

@@ -54,6 +54,7 @@ Image classification
     GTSRB
     INaturalist
     ImageNet
+    Imagenette
     KMNIST
     LFWPeople
     LSUN

test/test_datasets.py

@@ -3377,6 +3377,41 @@ class Middlebury2014StereoTestCase(datasets_utils.ImageDatasetTestCase):
                 pass
 
 
+class ImagenetteTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.Imagenette
+    ADDITIONAL_CONFIGS = combinations_grid(split=["train", "val"], size=["full", "320px", "160px"])
+
+    _WNIDS = [
+        "n01440764",
+        "n02102040",
+        "n02979186",
+        "n03000684",
+        "n03028079",
+        "n03394916",
+        "n03417042",
+        "n03425413",
+        "n03445777",
+        "n03888257",
+    ]
+
+    def inject_fake_data(self, tmpdir, config):
+        archive_root = "imagenette2"
+        if config["size"] != "full":
+            archive_root += f"-{config['size'].replace('px', '')}"
+        image_root = pathlib.Path(tmpdir) / archive_root / config["split"]
+
+        num_images_per_class = 3
+        for wnid in self._WNIDS:
+            datasets_utils.create_image_folder(
+                root=image_root,
+                name=wnid,
+                file_name_fn=lambda idx: f"{wnid}_{idx}.JPEG",
+                num_examples=num_images_per_class,
+            )
+
+        return num_images_per_class * len(self._WNIDS)
+
+
 class TestDatasetWrapper:
     def test_unknown_type(self):
         unknown_object = object()

torchvision/datasets/__init__.py

@@ -30,6 +30,7 @@ from .food101 import Food101
 from .gtsrb import GTSRB
 from .hmdb51 import HMDB51
 from .imagenet import ImageNet
+from .imagenette import Imagenette
 from .inaturalist import INaturalist
 from .kinetics import Kinetics
 from .kitti import Kitti
@@ -128,6 +129,7 @@ __all__ = (
     "InStereo2k",
     "ETH3DStereo",
     "wrap_dataset_for_transforms_v2",
+    "Imagenette",
 )
 
 

torchvision/datasets/imagenette.py

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

torchvision/tv_tensors/_dataset_wrapper.py

@@ -284,6 +284,7 @@ for dataset_cls in [
     datasets.GTSRB,
     datasets.DatasetFolder,
     datasets.ImageFolder,
+    datasets.Imagenette,
 ]:
     WRAPPER_FACTORIES.register(dataset_cls)(classification_wrapper_factory)