Cleanups for FLAVA datasets (#5164)

* Change default of download for Food101 and DTD

* Set download default to False and put it at the end

* Keep stuff private

* GTSRB: train -> split. Also use pathlib

* mypy

* Remove split and partition for SUN397

* mypy

* mypy

* move download param for SST2

* Use make_dataset in SST2

* Use a base URL for GTSRB

* Let's make this code more complictaed than it needs to be because why not

mypy.ini

@@ -117,3 +117,7 @@ ignore_missing_imports = True
 [mypy-torchdata.*]
 
 ignore_missing_imports = True
+
+[mypy-h5py.*]
+
+ignore_missing_imports = True

test/test_datasets.py

@@ -2281,11 +2281,6 @@ class FGVCAircraftTestCase(datasets_utils.ImageDatasetTestCase):
 class SUN397TestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.SUN397
 
-    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(
-        split=("train", "test"),
-        partition=(1, 10, None),
-    )
-
     def inject_fake_data(self, tmpdir: str, config):
         data_dir = pathlib.Path(tmpdir) / "SUN397"
         data_dir.mkdir()
@@ -2308,17 +2303,6 @@ class SUN397TestCase(datasets_utils.ImageDatasetTestCase):
         with open(data_dir / "ClassName.txt", "w") as file:
             file.writelines("\n".join(f"/{cls[0]}/{cls}" for cls in sampled_classes))
 
-        if config["partition"] is not None:
-            num_samples = max(len(im_paths) // (2 if config["split"] == "train" else 3), 1)
-
-            with open(data_dir / f"{config['split'].title()}ing_{config['partition']:02d}.txt", "w") as file:
-                file.writelines(
-                    "\n".join(
-                        f"/{f_path.relative_to(data_dir).as_posix()}"
-                        for f_path in random.choices(im_paths, k=num_samples)
-                    )
-                )
-        else:
         num_samples = len(im_paths)
 
         return num_samples
@@ -2397,17 +2381,17 @@ class GTSRBTestCase(datasets_utils.ImageDatasetTestCase):
     DATASET_CLASS = datasets.GTSRB
     FEATURE_TYPES = (PIL.Image.Image, int)
 
-    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(train=(True, False))
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(split=("train", "test"))
 
     def inject_fake_data(self, tmpdir: str, config):
-        root_folder = os.path.join(tmpdir, "GTSRB")
+        root_folder = os.path.join(tmpdir, "gtsrb")
         os.makedirs(root_folder, exist_ok=True)
 
         # Train data
-        train_folder = os.path.join(root_folder, "Training")
+        train_folder = os.path.join(root_folder, "GTSRB", "Training")
         os.makedirs(train_folder, exist_ok=True)
 
-        num_examples = 3
+        num_examples = 3 if config["split"] == "train" else 4
         classes = ("00000", "00042", "00012")
         for class_idx in classes:
             datasets_utils.create_image_folder(
@@ -2419,7 +2403,7 @@ class GTSRBTestCase(datasets_utils.ImageDatasetTestCase):
 
         total_number_of_examples = num_examples * len(classes)
         # Test data
-        test_folder = os.path.join(root_folder, "Final_Test", "Images")
+        test_folder = os.path.join(root_folder, "GTSRB", "Final_Test", "Images")
         os.makedirs(test_folder, exist_ok=True)
 
         with open(os.path.join(root_folder, "GT-final_test.csv"), "w") as csv_file:

torchvision/datasets/clevr.py

@@ -34,7 +34,7 @@ class CLEVRClassification(VisionDataset):
         split: str = "train",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
-        download: bool = True,
+        download: bool = False,
     ) -> None:
         self._split = verify_str_arg(split, "split", ("train", "val", "test"))
         super().__init__(root, transform=transform, target_transform=target_transform)

torchvision/datasets/country211.py

@@ -32,7 +32,7 @@ class Country211(ImageFolder):
         split: str = "train",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
-        download: bool = True,
+        download: bool = False,
     ) -> None:
         self._split = verify_str_arg(split, "split", ("train", "valid", "test"))
 

torchvision/datasets/dtd.py

@@ -21,12 +21,12 @@ class DTD(VisionDataset):
                 The partition only changes which split each image belongs to. Thus, regardless of the selected
                 partition, combining all splits will result in all images.
 
-        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.
         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. Default is False.
     """
 
     _URL = "https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz"
@@ -37,9 +37,9 @@ class DTD(VisionDataset):
         root: str,
         split: str = "train",
         partition: int = 1,
-        download: bool = True,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
+        download: bool = False,
     ) -> None:
         self._split = verify_str_arg(split, "split", ("train", "val", "test"))
         if not isinstance(partition, int) and not (1 <= partition <= 10):

torchvision/datasets/eurosat.py

 import os
-from typing import Any
+from typing import Callable, Optional
 
 from .folder import ImageFolder
 from .utils import download_and_extract_archive
@@ -10,23 +10,21 @@ class EuroSAT(ImageFolder):
 
     Args:
         root (string): Root directory of dataset where ``root/eurosat`` exists.
-        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.
         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.
+        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.
     """
 
-    url = "https://madm.dfki.de/files/sentinel/EuroSAT.zip"
-    md5 = "c8fa014336c82ac7804f0398fcb19387"
-
     def __init__(
         self,
         root: str,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
         download: bool = False,
-        **kwargs: Any,
     ) -> None:
         self.root = os.path.expanduser(root)
         self._base_folder = os.path.join(self.root, "eurosat")
@@ -38,7 +36,7 @@ class EuroSAT(ImageFolder):
         if not self._check_exists():
             raise RuntimeError("Dataset not found. You can use download=True to download it")
 
-        super().__init__(self._data_folder, **kwargs)
+        super().__init__(self._data_folder, transform=transform, target_transform=target_transform)
         self.root = os.path.expanduser(root)
 
     def __len__(self) -> int:
@@ -53,4 +51,8 @@ class EuroSAT(ImageFolder):
             return
 
         os.makedirs(self._base_folder, exist_ok=True)
-        download_and_extract_archive(self.url, download_root=self._base_folder, md5=self.md5)
+        download_and_extract_archive(
+            "https://madm.dfki.de/files/sentinel/EuroSAT.zip",
+            download_root=self._base_folder,
+            md5="c8fa014336c82ac7804f0398fcb19387",
+        )

torchvision/datasets/fgvc_aircraft.py

@@ -26,15 +26,15 @@ class FGVCAircraft(VisionDataset):
         root (string): Root directory of the FGVC Aircraft dataset.
         split (string, optional): The dataset split, supports ``train``, ``val``,
             ``trainval`` and ``test``.
-        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.
         annotation_level (str, optional): The annotation level, supports ``variant``,
             ``family`` and ``manufacturer``.
         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.
+        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.
     """
 
     _URL = "https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/archives/fgvc-aircraft-2013b.tar.gz"
@@ -43,10 +43,10 @@ class FGVCAircraft(VisionDataset):
         self,
         root: str,
         split: str = "trainval",
-        download: bool = False,
         annotation_level: str = "variant",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
+        download: bool = False,
     ) -> None:
         super().__init__(root, transform=transform, target_transform=target_transform)
         self._split = verify_str_arg(split, "split", ("train", "val", "trainval", "test"))

torchvision/datasets/flowers102.py

@@ -24,12 +24,12 @@ class Flowers102(VisionDataset):
     Args:
         root (string): Root directory of the dataset.
         split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
-        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.
         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.
+        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.
     """
 
     _download_url_prefix = "https://www.robots.ox.ac.uk/~vgg/data/flowers/102/"
@@ -44,9 +44,9 @@ class Flowers102(VisionDataset):
         self,
         root: str,
         split: str = "train",
-        download: bool = True,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
+        download: bool = False,
     ) -> None:
         super().__init__(root, transform=transform, target_transform=target_transform)
         self._split = verify_str_arg(split, "split", ("train", "val", "test"))

torchvision/datasets/food101.py

@@ -24,6 +24,9 @@ class Food101(VisionDataset):
         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.
+        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.
     """
 
     _URL = "http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz"
@@ -33,9 +36,9 @@ class Food101(VisionDataset):
         self,
         root: str,
         split: str = "train",
-        download: bool = True,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
+        download: bool = False,
     ) -> None:
         super().__init__(root, transform=transform, target_transform=target_transform)
         self._split = verify_str_arg(split, "split", ("train", "test"))

torchvision/datasets/gtsrb.py

 import csv
-import os
+import pathlib
 from typing import Any, Callable, Optional, Tuple
 
 import PIL
 
 from .folder import make_dataset
-from .utils import download_and_extract_archive
+from .utils import download_and_extract_archive, verify_str_arg
 from .vision import VisionDataset
 
 
@@ -14,8 +14,7 @@ class GTSRB(VisionDataset):
 
     Args:
         root (string): Root directory of the dataset.
-        train (bool, optional): If True, creates dataset from training set, otherwise
-            creates from test set.
+        split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``.
         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.
@@ -24,23 +23,10 @@ class GTSRB(VisionDataset):
             downloaded again.
     """
 
-    # Ground Truth for the test set
-    _gt_url = "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/GTSRB_Final_Test_GT.zip"
-    _gt_csv = "GT-final_test.csv"
-    _gt_md5 = "fe31e9c9270bbcd7b84b7f21a9d9d9e5"
-
-    # URLs for the test and train set
-    _urls = (
-        "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/GTSRB_Final_Test_Images.zip",
-        "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/GTSRB-Training_fixed.zip",
-    )
-
-    _md5s = ("c7e4e6327067d32654124b0fe9e82185", "513f3c79a4c5141765e10e952eaa2478")
-
     def __init__(
         self,
         root: str,
-        train: bool = True,
+        split: str = "train",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         download: bool = False,
@@ -48,12 +34,11 @@ class GTSRB(VisionDataset):
 
         super().__init__(root, transform=transform, target_transform=target_transform)
 
-        self.root = os.path.expanduser(root)
-
-        self.train = train
-
-        self._base_folder = os.path.join(self.root, type(self).__name__)
-        self._target_folder = os.path.join(self._base_folder, "Training" if self.train else "Final_Test/Images")
+        self._split = verify_str_arg(split, "split", ("train", "test"))
+        self._base_folder = pathlib.Path(root) / "gtsrb"
+        self._target_folder = (
+            self._base_folder / "GTSRB" / ("Training" if self._split == "train" else "Final_Test/Images")
+        )
 
         if download:
             self.download()
@@ -61,12 +46,12 @@ class GTSRB(VisionDataset):
         if not self._check_exists():
             raise RuntimeError("Dataset not found. You can use download=True to download it")
 
-        if train:
-            samples = make_dataset(self._target_folder, extensions=(".ppm",))
+        if self._split == "train":
+            samples = make_dataset(str(self._target_folder), extensions=(".ppm",))
         else:
-            with open(os.path.join(self._base_folder, self._gt_csv)) as csv_file:
+            with open(self._base_folder / "GT-final_test.csv") as csv_file:
                 samples = [
-                    (os.path.join(self._target_folder, row["Filename"]), int(row["ClassId"]))
+                    (str(self._target_folder / row["Filename"]), int(row["ClassId"]))
                     for row in csv.DictReader(csv_file, delimiter=";", skipinitialspace=True)
                 ]
 
@@ -91,16 +76,28 @@ class GTSRB(VisionDataset):
         return sample, target
 
     def _check_exists(self) -> bool:
-        return os.path.exists(self._target_folder) and os.path.isdir(self._target_folder)
+        return self._target_folder.is_dir()
 
     def download(self) -> None:
         if self._check_exists():
             return
 
-        download_and_extract_archive(self._urls[self.train], download_root=self.root, md5=self._md5s[self.train])
+        base_url = "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/"
 
-        if not self.train:
-            # Download Ground Truth for the test set
+        if self._split == "train":
+            download_and_extract_archive(
+                f"{base_url}GTSRB-Training_fixed.zip",
+                download_root=str(self._base_folder),
+                md5="513f3c79a4c5141765e10e952eaa2478",
+            )
+        else:
+            download_and_extract_archive(
+                f"{base_url}GTSRB_Final_Test_Images.zip",
+                download_root=str(self._base_folder),
+                md5="c7e4e6327067d32654124b0fe9e82185",
+            )
             download_and_extract_archive(
-                self._gt_url, download_root=self.root, extract_root=self._base_folder, md5=self._gt_md5
+                f"{base_url}GTSRB_Final_Test_GT.zip",
+                download_root=str(self._base_folder),
+                md5="fe31e9c9270bbcd7b84b7f21a9d9d9e5",
             )

torchvision/datasets/oxford_iiit_pet.py

@@ -45,7 +45,7 @@ class OxfordIIITPet(VisionDataset):
         transforms: Optional[Callable] = None,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
-        download: bool = True,
+        download: bool = False,
     ):
         self._split = verify_str_arg(split, "split", ("trainval", "test"))
         if isinstance(target_types, str):

torchvision/datasets/pcam.py

@@ -72,10 +72,10 @@ class PCAM(VisionDataset):
         split: str = "train",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
-        download: bool = True,
+        download: bool = False,
     ):
         try:
-            import h5py  # type: ignore[import]
+            import h5py
 
             self.h5py = h5py
         except ImportError:

torchvision/datasets/rendered_sst2.py

@@ -3,6 +3,7 @@ from typing import Any, Tuple, Callable, Optional
 
 import PIL.Image
 
+from .folder import make_dataset
 from .utils import verify_str_arg, download_and_extract_archive
 from .vision import VisionDataset
 
@@ -21,12 +22,12 @@ class RenderedSST2(VisionDataset):
     Args:
         root (string): Root directory of the dataset.
         split (string, optional): The dataset split, supports ``"train"`` (default), `"val"` and ``"test"``.
-        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.
         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.
+        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.
     """
 
     _URL = "https://openaipublic.azureedge.net/clip/data/rendered-sst2.tgz"
@@ -36,9 +37,9 @@ class RenderedSST2(VisionDataset):
         self,
         root: str,
         split: str = "train",
-        download: bool = False,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
+        download: bool = False,
     ) -> None:
         super().__init__(root, transform=transform, target_transform=target_transform)
         self._split = verify_str_arg(split, "split", ("train", "val", "test"))
@@ -53,18 +54,13 @@ class RenderedSST2(VisionDataset):
         if not self._check_exists():
             raise RuntimeError("Dataset not found. You can use download=True to download it")
 
-        self._labels = []
-        self._image_files = []
-
-        for p in (self._base_folder / self._split_to_folder[self._split]).glob("**/*.png"):
-            self._labels.append(self.class_to_idx[p.parent.name])
-            self._image_files.append(p)
+        self._samples = make_dataset(str(self._base_folder / self._split_to_folder[self._split]), extensions=("png",))
 
     def __len__(self) -> int:
-        return len(self._image_files)
+        return len(self._samples)
 
     def __getitem__(self, idx) -> Tuple[Any, Any]:
-        image_file, label = self._image_files[idx], self._labels[idx]
+        image_file, label = self._samples[idx]
         image = PIL.Image.open(image_file).convert("RGB")
 
         if self.transform:

torchvision/datasets/sun397.py

@@ -3,7 +3,7 @@ from typing import Any, Tuple, Callable, Optional
 
 import PIL.Image
 
-from .utils import verify_str_arg, download_and_extract_archive
+from .utils import download_and_extract_archive
 from .vision import VisionDataset
 
 
@@ -11,45 +11,31 @@ class SUN397(VisionDataset):
     """`The SUN397 Data Set <https://vision.princeton.edu/projects/2010/SUN/>`_.
 
     The SUN397 or Scene UNderstanding (SUN) is a dataset for scene recognition consisting of
-    397 categories with 108'754 images. The dataset also provides 10 partitions for training
-    and testing, with each partition consisting of 50 images per class.
+    397 categories with 108'754 images.
 
     Args:
         root (string): Root directory of the dataset.
-        split (string, optional): The dataset split, supports ``"train"`` (default) and ``"test"``.
-        partition (int, optional): A valid partition can be an integer from 1 to 10 or None,
-            for the entire dataset.
-        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.
         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.
+        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.
     """
 
     _DATASET_URL = "http://vision.princeton.edu/projects/2010/SUN/SUN397.tar.gz"
     _DATASET_MD5 = "8ca2778205c41d23104230ba66911c7a"
-    _PARTITIONS_URL = "https://vision.princeton.edu/projects/2010/SUN/download/Partitions.zip"
-    _PARTITIONS_MD5 = "29a205c0a0129d21f36cbecfefe81881"
 
     def __init__(
         self,
         root: str,
-        split: str = "train",
-        partition: Optional[int] = 1,
-        download: bool = True,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
+        download: bool = False,
     ) -> None:
         super().__init__(root, transform=transform, target_transform=target_transform)
-        self.split = verify_str_arg(split, "split", ("train", "test"))
-        self.partition = partition
         self._data_dir = Path(self.root) / "SUN397"
 
-        if self.partition is not None:
-            if self.partition < 0 or self.partition > 10:
-                raise RuntimeError(f"The partition parameter should be an int in [1, 10] or None, got {partition}.")
-
         if download:
             self._download()
 
@@ -60,10 +46,6 @@ class SUN397(VisionDataset):
             self.classes = [c[3:].strip() for c in f]
 
         self.class_to_idx = dict(zip(self.classes, range(len(self.classes))))
-        if self.partition is not None:
-            with open(self._data_dir / f"{self.split.title()}ing_{self.partition:02d}.txt", "r") as f:
-                self._image_files = [self._data_dir.joinpath(*line.strip()[1:].split("/")) for line in f]
-        else:
         self._image_files = list(self._data_dir.rglob("sun_*.jpg"))
 
         self._labels = [
@@ -86,13 +68,9 @@ class SUN397(VisionDataset):
         return image, label
 
     def _check_exists(self) -> bool:
-        return self._data_dir.exists() and self._data_dir.is_dir()
-
-    def extra_repr(self) -> str:
-        return "Split: {split}".format(**self.__dict__)
+        return self._data_dir.is_dir()
 
     def _download(self) -> None:
         if self._check_exists():
             return
         download_and_extract_archive(self._DATASET_URL, download_root=self.root, md5=self._DATASET_MD5)
-        download_and_extract_archive(self._PARTITIONS_URL, download_root=str(self._data_dir), md5=self._PARTITIONS_MD5)