port UCF101

Author: pmeier

torchvision/prototype/datasets/_builtin/__init__.py

@@ -15,4 +15,5 @@
 from .sbd import SBD
 from .semeion import SEMEION
 from .svhn import SVHN
+from .ucf101 import UCF101
 from .voc import VOC

torchvision/prototype/datasets/_builtin/hmdb51.py

@@ -26,6 +26,7 @@ def _make_info(self) -> DatasetInfo:
         return DatasetInfo(
             "hmdb51",
             homepage="https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/",
+            dependencies=("rarfile",),
             valid_options=dict(
                 split=("train", "test"),
                 split_number=("1", "2", "3"),

torchvision/prototype/datasets/_builtin/ucf101.categories

@@ -0,0 +1,101 @@
+ApplyEyeMakeup
+ApplyLipstick
+Archery
+BabyCrawling
+BalanceBeam
+BandMarching
+BaseballPitch
+Basketball
+BasketballDunk
+BenchPress
+Biking
+Billiards
+BlowDryHair
+BlowingCandles
+BodyWeightSquats
+Bowling
+BoxingPunchingBag
+BoxingSpeedBag
+BreastStroke
+BrushingTeeth
+CleanAndJerk
+CliffDiving
+CricketBowling
+CricketShot
+CuttingInKitchen
+Diving
+Drumming
+Fencing
+FieldHockeyPenalty
+FloorGymnastics
+FrisbeeCatch
+FrontCrawl
+GolfSwing
+Haircut
+Hammering
+HammerThrow
+HandstandPushups
+HandstandWalking
+HeadMassage
+HighJump
+HorseRace
+HorseRiding
+HulaHoop
+IceDancing
+JavelinThrow
+JugglingBalls
+JumpingJack
+JumpRope
+Kayaking
+Knitting
+LongJump
+Lunges
+MilitaryParade
+Mixing
+MoppingFloor
+Nunchucks
+ParallelBars
+PizzaTossing
+PlayingCello
+PlayingDaf
+PlayingDhol
+PlayingFlute
+PlayingGuitar
+PlayingPiano
+PlayingSitar
+PlayingTabla
+PlayingViolin
+PoleVault
+PommelHorse
+PullUps
+Punch
+PushUps
+Rafting
+RockClimbingIndoor
+RopeClimbing
+Rowing
+SalsaSpin
+ShavingBeard
+Shotput
+SkateBoarding
+Skiing
+Skijet
+SkyDiving
+SoccerJuggling
+SoccerPenalty
+StillRings
+SumoWrestling
+Surfing
+Swing
+TableTennisShot
+TaiChi
+TennisSwing
+ThrowDiscus
+TrampolineJumping
+Typing
+UnevenBars
+VolleyballSpiking
+WalkingWithDog
+WallPushups
+WritingOnBoard
+YoYo

torchvision/prototype/datasets/_builtin/ucf101.py

@@ -0,0 +1,94 @@
+import csv
+import pathlib
+from typing import Any, Dict, List, Tuple, cast, BinaryIO
+
+from torch.utils.data import IterDataPipe
+from torch.utils.data.datapipes.iter import Filter, Mapper
+from torchdata.datapipes.iter import CSVParser, IterKeyZipper
+from torchvision.prototype.datasets.utils import (
+    Dataset,
+    DatasetConfig,
+    DatasetInfo,
+    HttpResource,
+    OnlineResource,
+)
+from torchvision.prototype.datasets.utils._internal import (
+    path_accessor,
+    path_comparator,
+    hint_sharding,
+    hint_shuffling,
+    INFINITE_BUFFER_SIZE,
+)
+from torchvision.prototype.features import Label, EncodedVideo
+
+csv.register_dialect("ucf101", delimiter=" ")
+
+
+class UCF101(Dataset):
+    def _make_info(self) -> DatasetInfo:
+        return DatasetInfo(
+            "ucf101",
+            dependencies=("rarfile",),
+            valid_options=dict(
+                split=("train", "test"),
+                fold=("1", "2", "3"),
+            ),
+            homepage="https://www.crcv.ucf.edu/data/UCF101.php",
+        )
+
+    def _extract_videos_archive(self, path: pathlib.Path) -> pathlib.Path:
+        folder = OnlineResource._extract(path)
+        for rar_file in folder.glob("*.rar"):
+            OnlineResource._extract(rar_file)
+            rar_file.unlink()
+        return folder
+
+    def resources(self, config: DatasetConfig) -> List[OnlineResource]:
+        url_root = "https://www.crcv.ucf.edu/data/UCF101/"
+
+        splits = HttpResource(
+            f"{url_root}/UCF101TrainTestSplits-RecognitionTask.zip",
+            sha256="5c0d1a53b8ed364a2ac830a73f405e51bece7d98ce1254fd19ed4a36b224bd27",
+        )
+
+        videos = HttpResource(
+            f"{url_root}/UCF101.rar",
+            sha256="ca8dfadb4c891cb11316f94d52b6b0ac2a11994e67a0cae227180cd160bd8e55",
+            extract=True,
+        )
+        videos._preprocess = self._extract_videos_archive
+
+        return [splits, videos]
+
+    def _prepare_sample(self, data: Tuple[Tuple[str, str], Tuple[str, BinaryIO]]) -> Dict[str, Any]:
+        _, (path, buffer) = data
+        path = pathlib.Path(path)
+        return dict(
+            label=Label.from_category(path.parent.name, categories=self.categories),
+            video=EncodedVideo.from_file(buffer, path=path),
+        )
+
+    def _make_datapipe(
+        self, resource_dps: List[IterDataPipe], *, config: DatasetConfig
+    ) -> IterDataPipe[Dict[str, Any]]:
+        splits_dp, images_dp = resource_dps
+
+        splits_dp: IterDataPipe[Tuple[str, BinaryIO]] = Filter(
+            splits_dp, path_comparator("name", f"{config.split}list0{config.fold}.txt")
+        )
+        splits_dp = CSVParser(splits_dp, dialect="ucf101")
+        splits_dp = hint_sharding(splits_dp)
+        splits_dp = hint_shuffling(splits_dp)
+
+        dp = IterKeyZipper(splits_dp, images_dp, path_accessor("name"), buffer_size=INFINITE_BUFFER_SIZE)
+        return Mapper(dp, self._prepare_sample)
+
+    def _generate_categories(self, root: pathlib.Path) -> Tuple[str, ...]:
+        config = self.default_config
+        resources = self.resources(config)
+
+        dp = resources[0].load(root)
+        dp = Filter(dp, path_comparator("name", "classInd.txt"))
+        dp = CSVParser(dp, dialect="ucf101")
+        _, categories = zip(*dp)
+        return cast(Tuple[str, ...], categories)

torchvision/prototype/features/_encoded.py

@@ -33,10 +33,10 @@ def from_file(cls: Type[E], file: BinaryIO, **meta: Any) -> E:
         return cls(fromfile(file, dtype=torch.uint8, byte_order=sys.byteorder), **meta)
 
     @classmethod
-    def from_path(cls: Type[E], path: Union[str, os.PathLike]) -> E:
+    def from_path(cls: Type[E], path: Union[str, os.PathLike], **meta: Any) -> E:
         path = pathlib.Path(path)
         with open(path, "rb") as file:
-            return cls.from_file(file, path=path)
+            return cls.from_file(file, path=path, **meta)
 
 
 class EncodedImage(EncodedData):