Add Rendered sst2 dataset

docs/source/datasets.rst

@@ -70,6 +70,7 @@ You can also create your own datasets using the provided :ref:`base classes <bas
     PCAM
     PhotoTour
     Places365
+    RenderedSST2
     QMNIST
     SBDataset
     SBU

test/test_datasets.py

@@ -2665,5 +2665,27 @@ def inject_fake_data(self, tmpdir: str, config):
         return num_images
 
 
+class RenderedSST2TestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.RenderedSST2
+    FEATURE_TYPES = (PIL.Image.Image, int)
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(split=("train", "valid", "test"))
+
+    def inject_fake_data(self, tmpdir: str, config):
+        root_folder = pathlib.Path(tmpdir) / "rendered-sst2"
+        image_folder = root_folder / config["split"]
+
+        num_images_per_class = 5
+        sampled_classes = ["positive", "negative"]
+        for cls in sampled_classes:
+            datasets_utils.create_image_folder(
+                image_folder,
+                cls,
+                file_name_fn=lambda idx: f"{idx}.png",
+                num_examples=num_images_per_class,
+            )
+
+        return len(sampled_classes) * num_images_per_class
+
+
 if __name__ == "__main__":
     unittest.main()

torchvision/datasets/__init__.py

@@ -29,6 +29,7 @@
 from .pcam import PCAM
 from .phototour import PhotoTour
 from .places365 import Places365
+from .rendered_sst2 import RenderedSST2
 from .sbd import SBDataset
 from .sbu import SBU
 from .semeion import SEMEION
@@ -102,4 +103,5 @@
     "Country211",
     "FGVCAircraft",
     "EuroSAT",
+    "RenderedSST2",
 )

torchvision/datasets/rendered_sst2.py

@@ -0,0 +1,91 @@
+from pathlib import Path
+from typing import Any, Tuple, Callable, Optional
+
+import PIL.Image
+
+from .utils import verify_str_arg, download_and_extract_archive
+from .vision import VisionDataset
+
+
+class RenderedSST2(VisionDataset):
+    """`The Rendered SST2 Dataset <https://github.com/openai/CLIP/blob/main/data/rendered-sst2.md>`_.
+
+    Rendered SST2 is a image classification dataset used to evaluate the models capability on optical
+    character recognition. This dataset was generated bu rendering sentences in the Standford Sentiment
+    Treebank v2 dataset.
+
+    This dataset contains two classes (positive and negative) and is divided in three splits: a  train
+    split containing 6920 images (3610 positive and 3310 negative), a validation split containing 872 images
+    (444 positive and 428 negative), and a test split containing 1821 images (909 positive and 912 negative).
+
+    Args:
+        root (string): Root directory of the dataset.
+        split (string, optional): The dataset split, supports ``"train"`` (default), `"valid"` and ``"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.
+    """
+
+    _URL = "https://openaipublic.azureedge.net/clip/data/rendered-sst2.tgz"
+    _MD5 = "2384d08e9dcfa4bd55b324e610496ee5"
+
+    def __init__(
+        self,
+        root: str,
+        split: str = "train",
+        download: bool = True,
+        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", "valid", "test"))
+        self._base_folder = Path(self.root) / "rendered-sst2"
+        self.classes = ["negative", "positive"]
+        self.class_to_idx = {"negative": 0, "positive": 1}
+
+        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 = []
+
+        for p in (self._base_folder / self._split).glob("**/*.png"):
+            self._labels.append(self.class_to_idx[p.parent.name])
+            self._image_files.append(p)
+        print(self._labels)
+        print(self._image_files)
+
+    def __len__(self) -> int:
+        return len(self._image_files)
+
+    def __getitem__(self, idx) -> Tuple[Any, Any]:
+        image_file, label = self._image_files[idx], self._labels[idx]
+        image = PIL.Image.open(image_file).convert("RGB")
+
+        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:
+        for class_label in set(self.classes):
+            if not (
+                (self._base_folder / self._split / class_label).exists()
+                and (self._base_folder / self._split / class_label).is_dir()
+            ):
+                return False
+        return True
+
+    def _download(self) -> None:
+        if self._check_exists():
+            return
+        download_and_extract_archive(self._URL, download_root=self.root, md5=self._MD5)