remove torchvision.prototype module and related tests / CI from release branch (#7983)

torchvision/prototype/datasets/_builtin/semeion.py

-import pathlib
-from typing import Any, Dict, List, Tuple, Union
-
-import torch
-from torchdata.datapipes.iter import CSVParser, IterDataPipe, Mapper
-from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
-from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
-from torchvision.prototype.tv_tensors import OneHotLabel
-from torchvision.tv_tensors import Image
-
-from .._api import register_dataset, register_info
-
-NAME = "semeion"
-
-
-@register_info(NAME)
-def _info() -> Dict[str, Any]:
-    return dict(categories=[str(i) for i in range(10)])
-
-
-@register_dataset(NAME)
-class SEMEION(Dataset):
-    """Semeion dataset
-    homepage="https://archive.ics.uci.edu/ml/datasets/Semeion+Handwritten+Digit",
-    """
-
-    def __init__(self, root: Union[str, pathlib.Path], *, skip_integrity_check: bool = False) -> None:
-
-        self._categories = _info()["categories"]
-        super().__init__(root, skip_integrity_check=skip_integrity_check)
-
-    def _resources(self) -> List[OnlineResource]:
-        data = HttpResource(
-            "http://archive.ics.uci.edu/ml/machine-learning-databases/semeion/semeion.data",
-            sha256="f43228ae3da5ea6a3c95069d53450b86166770e3b719dcc333182128fe08d4b1",
-        )
-        return [data]
-
-    def _prepare_sample(self, data: Tuple[str, ...]) -> Dict[str, Any]:
-        image_data, label_data = data[:256], data[256:-1]
-
-        return dict(
-            image=Image(torch.tensor([float(pixel) for pixel in image_data], dtype=torch.float).reshape(16, 16)),
-            label=OneHotLabel([int(label) for label in label_data], categories=self._categories),
-        )
-
-    def _datapipe(self, resource_dps: List[IterDataPipe]) -> IterDataPipe[Dict[str, Any]]:
-        dp = resource_dps[0]
-        dp = CSVParser(dp, delimiter=" ")
-        dp = hint_shuffling(dp)
-        dp = hint_sharding(dp)
-        return Mapper(dp, self._prepare_sample)
-
-    def __len__(self) -> int:
-        return 1_593

torchvision/prototype/datasets/_builtin/voc.categories

-__background__
-aeroplane
-bicycle
-bird
-boat
-bottle
-bus
-car
-cat
-chair
-cow
-diningtable
-dog
-horse
-motorbike
-person
-pottedplant
-sheep
-sofa
-train
-tvmonitor

torchvision/prototype/datasets/utils/__init__.py

-from . import _internal  # usort: skip
-from ._dataset import Dataset
-from ._encoded import EncodedData, EncodedImage
-from ._resource import GDriveResource, HttpResource, KaggleDownloadResource, ManualDownloadResource, OnlineResource

torchvision/prototype/models/__init__.py

-from . import depth

torchvision/prototype/models/depth/__init__.py

-from . import stereo

torchvision/prototype/models/depth/stereo/__init__.py

-from .raft_stereo import *
-from .crestereo import *