Port `semeion` dataset to `prototype` namespace (#4840)

* Port semeion dataset

* Update torchvision/prototype/datasets/_builtin/semeion.py
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* explicitly convert the image array to torch.uint8

* explicitly convert the image array to torch.uint8
Co-authored-by: Philip Meier <github.pmeier@posteo.de>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

torchvision/prototype/datasets/_builtin/__init__.py

@@ -5,4 +5,5 @@ from .coco import Coco
 from .imagenet import ImageNet
 from .mnist import MNIST, FashionMNIST, KMNIST, EMNIST, QMNIST
 from .sbd import SBD
+from .semeion import SEMEION
 from .voc import VOC

torchvision/prototype/datasets/_builtin/semeion.py

+import io
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import torch
+from torchdata.datapipes.iter import (
+    IterDataPipe,
+    Mapper,
+    Shuffler,
+    CSVParser,
+)
+from torchvision.prototype.datasets.decoder import raw
+from torchvision.prototype.datasets.utils import (
+    Dataset,
+    DatasetConfig,
+    DatasetInfo,
+    HttpResource,
+    OnlineResource,
+    DatasetType,
+)
+from torchvision.prototype.datasets.utils._internal import INFINITE_BUFFER_SIZE, image_buffer_from_array
+
+
+class SEMEION(Dataset):
+    def _make_info(self) -> DatasetInfo:
+        return DatasetInfo(
+            "semeion",
+            type=DatasetType.RAW,
+            categories=10,
+            homepage="https://archive.ics.uci.edu/ml/datasets/Semeion+Handwritten+Digit",
+        )
+
+    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+        archive = HttpResource(
+            "http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data",
+            sha256="f43228ae3da5ea6a3c95069d53450b86166770e3b719dcc333182128fe08d4b1",
+        )
+        return [archive]
+
+    def _collate_and_decode_sample(
+        self,
+        data: Tuple[str, ...],
+        *,
+        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
+    ) -> Dict[str, Any]:
+        image_data = torch.tensor([float(pixel) for pixel in data[:256]], dtype=torch.uint8).reshape(16, 16)
+        label_data = [int(label) for label in data[256:] if label]
+
+        if decoder is raw:
+            image = image_data.unsqueeze(0)
+        else:
+            image_buffer = image_buffer_from_array(image_data.numpy())
+            image = decoder(image_buffer) if decoder else image_buffer  # type: ignore[assignment]
+
+        label = next((idx for idx, one_hot_label in enumerate(label_data) if one_hot_label))
+        category = self.info.categories[label]
+        return dict(image=image, label=label, category=category)
+
+    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 = CSVParser(dp, delimiter=" ")
+        dp = Shuffler(dp, buffer_size=INFINITE_BUFFER_SIZE)
+        dp = Mapper(dp, self._collate_and_decode_sample, fn_kwargs=dict(decoder=decoder))
+        return dp