Added missing typing annotations in datasets/ucf101 (#4171)

torchvision/datasets/ucf101.py

 import os
+from typing import Any, Dict, List, Tuple, Optional, Callable
+from torch import Tensor
 
 from .folder import find_classes, make_dataset
 from .video_utils import VideoClips
@@ -42,10 +44,23 @@ class UCF101(VisionDataset):
             - label (int): class of the video clip
     """
 
-    def __init__(self, root, annotation_path, frames_per_clip, step_between_clips=1,
-                 frame_rate=None, fold=1, train=True, transform=None,
-                 _precomputed_metadata=None, num_workers=1, _video_width=0,
-                 _video_height=0, _video_min_dimension=0, _audio_samples=0):
+    def __init__(
+        self,
+        root: str,
+        annotation_path: str,
+        frames_per_clip: int,
+        step_between_clips: int = 1,
+        frame_rate: Optional[int] = None,
+        fold: int = 1,
+        train: bool = True,
+        transform: Optional[Callable] = None,
+        _precomputed_metadata: Optional[Dict[str, Any]] = None,
+        num_workers: int = 1,
+        _video_width: int = 0,
+        _video_height: int = 0,
+        _video_min_dimension: int = 0,
+        _audio_samples: int = 0
+    ) -> None:
         super(UCF101, self).__init__(root)
         if not 1 <= fold <= 3:
             raise ValueError("fold should be between 1 and 3, got {}".format(fold))
@@ -78,27 +93,26 @@ class UCF101(VisionDataset):
         self.transform = transform
 
     @property
-    def metadata(self):
+    def metadata(self) -> Dict[str, Any]:
         return self.full_video_clips.metadata
 
-    def _select_fold(self, video_list, annotation_path, fold, train):
+    def _select_fold(self, video_list: List[str], annotation_path: str, fold: int, train: bool) -> List[int]:
         name = "train" if train else "test"
         name = "{}list{:02d}.txt".format(name, fold)
         f = os.path.join(annotation_path, name)
-        selected_files = []
+        selected_files = set()
         with open(f, "r") as fid:
             data = fid.readlines()
-            data = [x.strip().split(" ") for x in data]
-            data = [os.path.join(self.root, x[0]) for x in data]
-            selected_files.extend(data)
-        selected_files = set(selected_files)
+            data = [x.strip().split(" ")[0] for x in data]
+            data = [os.path.join(self.root, x) for x in data]
+            selected_files.update(data)
         indices = [i for i in range(len(video_list)) if video_list[i] in selected_files]
         return indices
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.video_clips.num_clips()
 
-    def __getitem__(self, idx):
+    def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, int]:
         video, audio, info, video_idx = self.video_clips.get_clip(idx)
         label = self.samples[self.indices[video_idx]][1]