FER2013 dataset

docs/source/datasets.rst

@@ -42,6 +42,7 @@ You can also create your own datasets using the provided :ref:`base classes <bas
     EMNIST
     FakeData
     FashionMNIST
+    FER2013
     Flickr8k
     Flickr30k
     FlyingChairs

test/test_datasets.py

@@ -1,5 +1,6 @@
 import bz2
 import contextlib
+import csv
 import io
 import itertools
 import json
@@ -2241,5 +2242,38 @@ def inject_fake_data(self, tmpdir: str, config):
         return len(image_ids_in_config)
 
 
+class FER2013TestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.FER2013
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(split=("train", "test"))
+
+    FEATURE_TYPES = (PIL.Image.Image, (int, type(None)))
+
+    def inject_fake_data(self, tmpdir, config):
+        base_folder = os.path.join(tmpdir, "fer2013")
+        os.makedirs(base_folder)
+
+        num_samples = 5
+        with open(os.path.join(base_folder, f"{config['split']}.csv"), "w", newline="") as file:
+            writer = csv.DictWriter(
+                file,
+                fieldnames=("emotion", "pixels") if config["split"] == "train" else ("pixels",),
+                quoting=csv.QUOTE_NONNUMERIC,
+                quotechar='"',
+            )
+            writer.writeheader()
+            for _ in range(num_samples):
+                row = dict(
+                    pixels=" ".join(
+                        str(pixel) for pixel in datasets_utils.create_image_or_video_tensor((48, 48)).view(-1).tolist()
+                    )
+                )
+                if config["split"] == "train":
+                    row["emotion"] = str(int(torch.randint(0, 7, ())))
+
+                writer.writerow(row)
+
+        return num_samples
+
+
 if __name__ == "__main__":
     unittest.main()

torchvision/datasets/__init__.py

@@ -6,6 +6,7 @@
 from .coco import CocoCaptions, CocoDetection
 from .dtd import DTD
 from .fakedata import FakeData
+from .fer2013 import FER2013
 from .flickr import Flickr8k, Flickr30k
 from .folder import ImageFolder, DatasetFolder
 from .food101 import Food101
@@ -81,4 +82,5 @@
     "HD1K",
     "Food101",
     "DTD",
+    "FER2013",
 )

torchvision/datasets/fer2013.py

@@ -0,0 +1,75 @@
+import csv
+import pathlib
+from typing import Any, Callable, Optional, Tuple
+
+import torch
+from PIL import Image
+
+from .utils import verify_str_arg, check_integrity
+from .vision import VisionDataset
+
+
+class FER2013(VisionDataset):
+    """`FER2013
+    <https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge>`_ Dataset.
+
+    Args:
+        root (string): Root directory of dataset where directory
+            ``root/fer2013`` exists.
+        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.
+    """
+
+    _RESOURCES = {
+        "train": ("train.csv", "3f0dfb3d3fd99c811a1299cb947e3131"),
+        "test": ("test.csv", "b02c2298636a634e8c2faabbf3ea9a23"),
+    }
+
+    def __init__(
+        self,
+        root: str,
+        split: str = "train",
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+    ) -> None:
+        self._split = verify_str_arg(split, "split", self._RESOURCES.keys())
+        super().__init__(root, transform=transform, target_transform=target_transform)
+
+        base_folder = pathlib.Path(self.root) / "fer2013"
+        file_name, md5 = self._RESOURCES[self._split]
+        data_file = base_folder / file_name
+        if not check_integrity(str(data_file), md5=md5):
+            raise RuntimeError(
+                f"{file_name} not found in {base_folder} or corrupted. "
+                f"You can download it from "
+                f"https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge"
+            )
+
+        with open(data_file, "r", newline="") as file:
+            self._samples = [
+                (
+                    torch.tensor([int(idx) for idx in row["pixels"].split()], dtype=torch.uint8).reshape(48, 48),
+                    int(row["emotion"]) if "emotion" in row else None,
+                )
+                for row in csv.DictReader(file)
+            ]
+
+    def __len__(self) -> int:
+        return len(self._samples)
+
+    def __getitem__(self, idx: int) -> Tuple[Any, Any]:
+        image_tensor, target = self._samples[idx]
+        image = Image.fromarray(image_tensor.numpy())
+
+        if self.transform is not None:
+            image = self.transform(image)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return image, target
+
+    def extra_repr(self) -> str:
+        return f"split={self._split}"

torchvision/prototype/datasets/_builtin/__init__.py

@@ -3,6 +3,7 @@
 from .cifar import Cifar10, Cifar100
 from .coco import Coco
 from .dtd import DTD
+from .fer2013 import FER2013
 from .imagenet import ImageNet
 from .mnist import MNIST, FashionMNIST, KMNIST, EMNIST, QMNIST
 from .sbd import SBD

torchvision/prototype/datasets/_builtin/fer2013.py

@@ -0,0 +1,80 @@
+import functools
+import io
+from typing import Any, Callable, Dict, List, Optional, Union, cast
+
+import torch
+from torchdata.datapipes.iter import IterDataPipe, Mapper, CSVDictParser
+from torchvision.prototype.datasets.decoder import raw
+from torchvision.prototype.datasets.utils import (
+    Dataset,
+    DatasetConfig,
+    DatasetInfo,
+    OnlineResource,
+    DatasetType,
+    KaggleDownloadResource,
+)
+from torchvision.prototype.datasets.utils._internal import (
+    hint_sharding,
+    hint_shuffling,
+    image_buffer_from_array,
+)
+from torchvision.prototype.features import Label, Image
+
+
+class FER2013(Dataset):
+    def _make_info(self) -> DatasetInfo:
+        return DatasetInfo(
+            "fer2013",
+            type=DatasetType.RAW,
+            homepage="https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge",
+            categories=("angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"),
+            valid_options=dict(split=("train", "test")),
+        )
+
+    _CHECKSUMS = {
+        "train": "a2b7c9360cc0b38d21187e5eece01c2799fce5426cdeecf746889cc96cda2d10",
+        "test": "dec8dfe8021e30cd6704b85ec813042b4a5d99d81cb55e023291a94104f575c3",
+    }
+
+    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+        archive = KaggleDownloadResource(
+            cast(str, self.info.homepage),
+            file_name=f"{config.split}.csv.zip",
+            sha256=self._CHECKSUMS[config.split],
+        )
+        return [archive]
+
+    def _collate_and_decode_sample(
+        self,
+        data: Dict[str, Any],
+        *,
+        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
+    ) -> Dict[str, Any]:
+        raw_image = torch.tensor([int(idx) for idx in data["pixels"].split()], dtype=torch.uint8).reshape(48, 48)
+        label_id = data.get("emotion")
+        label_idx = int(label_id) if label_id is not None else None
+
+        image: Union[Image, io.BytesIO]
+        if decoder is raw:
+            image = Image(raw_image)
+        else:
+            image_buffer = image_buffer_from_array(raw_image.numpy())
+            image = decoder(image_buffer) if decoder else image_buffer  # type: ignore[assignment]
+
+        return dict(
+            image=image,
+            label=Label(label_idx, category=self.info.categories[label_idx]) if label_idx is not None else None,
+        )
+
+    def _make_datapipe(
+        self,
+        resource_dps: List[IterDataPipe],
+        *,
+        config: DatasetConfig,
+        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
+    ) -> IterDataPipe[Dict[str, Any]]:
+        dp = resource_dps[0]
+        dp = CSVDictParser(dp)
+        dp = hint_sharding(dp)
+        dp = hint_shuffling(dp)
+        return Mapper(dp, functools.partial(self._collate_and_decode_sample, decoder=decoder))

torchvision/prototype/datasets/utils/__init__.py

@@ -1,4 +1,4 @@
 from . import _internal
 from ._dataset import DatasetType, DatasetConfig, DatasetInfo, Dataset
 from ._query import SampleQuery
-from ._resource import OnlineResource, HttpResource, GDriveResource, ManualDownloadResource
+from ._resource import OnlineResource, HttpResource, GDriveResource, ManualDownloadResource, KaggleDownloadResource

torchvision/prototype/datasets/utils/_resource.py

@@ -176,3 +176,17 @@ def _download(self, root: pathlib.Path) -> NoReturn:
             f"Please follow the instructions below and place it in {root}\n\n"
             f"{self.instructions}"
         )
+
+
+class KaggleDownloadResource(ManualDownloadResource):
+    def __init__(self, challenge_url: str, *, file_name: str, **kwargs: Any) -> None:
+        instructions = "\n".join(
+            (
+                "1. Register and login at https://www.kaggle.com",
+                f"2. Navigate to {challenge_url}",
+                "3. Click 'Join Competition' and follow the instructions there",
+                "4. Navigate to the 'Data' tab",
+                f"5. Select {file_name} in the 'Data Explorer' and click the download button",
+            )
+        )
+        super().__init__(instructions, file_name=file_name, **kwargs)