[fbsync] Fix int/float typing in video_utils.py (#8234)

Summary: Co-authored-by: Nicolas Hug <nh.nicolas.hug@gmail.com> Reviewed By: vmoens Differential Revision: D55062804 fbshipit-source-id: a880482450d6275597bec500648ff1e085b65a39

Author: NicolasHug

torchvision/datasets/video_utils.py

@@ -89,7 +89,7 @@ class VideoClips:
  video_paths (List[str]): paths to the video files
  clip_length_in_frames (int): size of a clip in number of frames
  frames_between_clips (int): step (in frames) between each clip
- frame_rate (int, optional): if specified, it will resample the video
+ frame_rate (float, optional): if specified, it will resample the video
  so that it has `frame_rate`, and then the clips will be defined
  on the resampled video
  num_workers (int): how many subprocesses to use for data loading.
@@ -102,7 +102,7 @@ def __init__(
  video_paths: List[str],
  clip_length_in_frames: int = 16,
  frames_between_clips: int = 1,
- frame_rate: Optional[int] = None,
+ frame_rate: Optional[float] = None,
  _precomputed_metadata: Optional[Dict[str, Any]] = None,
  num_workers: int = 0,
  _video_width: int = 0,
@@ -136,7 +136,7 @@ def __init__(
 
  def _compute_frame_pts(self) -> None:
  self.video_pts = [] # len = num_videos. Each entry is a tensor of shape (num_frames_in_video,)
- self.video_fps: List[int] = [] # len = num_videos
+ self.video_fps: List[float] = [] # len = num_videos
 
  # strategy: use a DataLoader to parallelize read_video_timestamps
  # so need to create a dummy dataset first
@@ -203,15 +203,15 @@ def subset(self, indices: List[int]) -> "VideoClips":
 
  @staticmethod
  def compute_clips_for_video(
- video_pts: torch.Tensor, num_frames: int, step: int, fps: int, frame_rate: Optional[int] = None
+ video_pts: torch.Tensor, num_frames: int, step: int, fps: Optional[float], frame_rate: Optional[float] = None
  ) -> Tuple[torch.Tensor, Union[List[slice], torch.Tensor]]:
  if fps is None:
  # if for some reason the video doesn't have fps (because doesn't have a video stream)
  # set the fps to 1. The value doesn't matter, because video_pts is empty anyway
  fps = 1
  if frame_rate is None:
  frame_rate = fps
- total_frames = len(video_pts) * (float(frame_rate) / fps)
+ total_frames = len(video_pts) * frame_rate / fps
  _idxs = VideoClips._resample_video_idx(int(math.floor(total_frames)), fps, frame_rate)
  video_pts = video_pts[_idxs]
  clips = unfold(video_pts, num_frames, step)
@@ -227,7 +227,7 @@ def compute_clips_for_video(
  idxs = unfold(_idxs, num_frames, step)
  return clips, idxs
 
- def compute_clips(self, num_frames: int, step: int, frame_rate: Optional[int] = None) -> None:
+ def compute_clips(self, num_frames: int, step: int, frame_rate: Optional[float] = None) -> None:
  """
  Compute all consecutive sequences of clips from video_pts.
  Always returns clips of size `num_frames`, meaning that the
@@ -275,8 +275,8 @@ def get_clip_location(self, idx: int) -> Tuple[int, int]:
  return video_idx, clip_idx
 
  @staticmethod
- def _resample_video_idx(num_frames: int, original_fps: int, new_fps: int) -> Union[slice, torch.Tensor]:
- step = float(original_fps) / new_fps
+ def _resample_video_idx(num_frames: int, original_fps: float, new_fps: float) -> Union[slice, torch.Tensor]:
+ step = original_fps / new_fps
  if step.is_integer():
  # optimization: if step is integer, don't need to perform
  # advanced indexing