420 lines
17 KiB
Python
420 lines
17 KiB
Python
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import bisect
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import math
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import warnings
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from fractions import Fraction
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from typing import Any, Callable, cast, Dict, List, Optional, Tuple, TypeVar, Union
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import torch
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from torchvision.io import _probe_video_from_file, _read_video_from_file, read_video, read_video_timestamps
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from .utils import tqdm
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T = TypeVar("T")
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def pts_convert(pts: int, timebase_from: Fraction, timebase_to: Fraction, round_func: Callable = math.floor) -> int:
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"""convert pts between different time bases
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Args:
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pts: presentation timestamp, float
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timebase_from: original timebase. Fraction
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timebase_to: new timebase. Fraction
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round_func: rounding function.
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"""
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new_pts = Fraction(pts, 1) * timebase_from / timebase_to
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return round_func(new_pts)
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def unfold(tensor: torch.Tensor, size: int, step: int, dilation: int = 1) -> torch.Tensor:
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"""
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similar to tensor.unfold, but with the dilation
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and specialized for 1d tensors
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Returns all consecutive windows of `size` elements, with
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`step` between windows. The distance between each element
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in a window is given by `dilation`.
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"""
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if tensor.dim() != 1:
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raise ValueError(f"tensor should have 1 dimension instead of {tensor.dim()}")
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o_stride = tensor.stride(0)
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numel = tensor.numel()
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new_stride = (step * o_stride, dilation * o_stride)
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new_size = ((numel - (dilation * (size - 1) + 1)) // step + 1, size)
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if new_size[0] < 1:
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new_size = (0, size)
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return torch.as_strided(tensor, new_size, new_stride)
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class _VideoTimestampsDataset:
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"""
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Dataset used to parallelize the reading of the timestamps
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of a list of videos, given their paths in the filesystem.
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Used in VideoClips and defined at top level, so it can be
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pickled when forking.
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"""
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def __init__(self, video_paths: List[str]) -> None:
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self.video_paths = video_paths
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def __len__(self) -> int:
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return len(self.video_paths)
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def __getitem__(self, idx: int) -> Tuple[List[int], Optional[float]]:
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return read_video_timestamps(self.video_paths[idx])
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def _collate_fn(x: T) -> T:
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"""
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Dummy collate function to be used with _VideoTimestampsDataset
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"""
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return x
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class VideoClips:
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"""
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Given a list of video files, computes all consecutive subvideos of size
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`clip_length_in_frames`, where the distance between each subvideo in the
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same video is defined by `frames_between_clips`.
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If `frame_rate` is specified, it will also resample all the videos to have
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the same frame rate, and the clips will refer to this frame rate.
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Creating this instance the first time is time-consuming, as it needs to
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decode all the videos in `video_paths`. It is recommended that you
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cache the results after instantiation of the class.
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Recreating the clips for different clip lengths is fast, and can be done
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with the `compute_clips` method.
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Args:
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video_paths (List[str]): paths to the video files
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clip_length_in_frames (int): size of a clip in number of frames
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frames_between_clips (int): step (in frames) between each clip
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frame_rate (float, optional): if specified, it will resample the video
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so that it has `frame_rate`, and then the clips will be defined
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on the resampled video
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num_workers (int): how many subprocesses to use for data loading.
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0 means that the data will be loaded in the main process. (default: 0)
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output_format (str): The format of the output video tensors. Can be either "THWC" (default) or "TCHW".
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"""
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def __init__(
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self,
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video_paths: List[str],
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clip_length_in_frames: int = 16,
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frames_between_clips: int = 1,
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frame_rate: Optional[float] = None,
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_precomputed_metadata: Optional[Dict[str, Any]] = None,
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num_workers: int = 0,
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_video_width: int = 0,
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_video_height: int = 0,
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_video_min_dimension: int = 0,
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_video_max_dimension: int = 0,
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_audio_samples: int = 0,
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_audio_channels: int = 0,
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output_format: str = "THWC",
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) -> None:
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self.video_paths = video_paths
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self.num_workers = num_workers
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# these options are not valid for pyav backend
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self._video_width = _video_width
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self._video_height = _video_height
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self._video_min_dimension = _video_min_dimension
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self._video_max_dimension = _video_max_dimension
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self._audio_samples = _audio_samples
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self._audio_channels = _audio_channels
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self.output_format = output_format.upper()
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if self.output_format not in ("THWC", "TCHW"):
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raise ValueError(f"output_format should be either 'THWC' or 'TCHW', got {output_format}.")
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if _precomputed_metadata is None:
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self._compute_frame_pts()
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else:
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self._init_from_metadata(_precomputed_metadata)
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self.compute_clips(clip_length_in_frames, frames_between_clips, frame_rate)
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def _compute_frame_pts(self) -> None:
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self.video_pts = [] # len = num_videos. Each entry is a tensor of shape (num_frames_in_video,)
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self.video_fps: List[float] = [] # len = num_videos
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# strategy: use a DataLoader to parallelize read_video_timestamps
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# so need to create a dummy dataset first
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import torch.utils.data
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dl: torch.utils.data.DataLoader = torch.utils.data.DataLoader(
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_VideoTimestampsDataset(self.video_paths), # type: ignore[arg-type]
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batch_size=16,
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num_workers=self.num_workers,
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collate_fn=_collate_fn,
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)
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with tqdm(total=len(dl)) as pbar:
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for batch in dl:
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pbar.update(1)
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batch_pts, batch_fps = list(zip(*batch))
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# we need to specify dtype=torch.long because for empty list,
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# torch.as_tensor will use torch.float as default dtype. This
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# happens when decoding fails and no pts is returned in the list.
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batch_pts = [torch.as_tensor(pts, dtype=torch.long) for pts in batch_pts]
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self.video_pts.extend(batch_pts)
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self.video_fps.extend(batch_fps)
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def _init_from_metadata(self, metadata: Dict[str, Any]) -> None:
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self.video_paths = metadata["video_paths"]
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assert len(self.video_paths) == len(metadata["video_pts"])
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self.video_pts = metadata["video_pts"]
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assert len(self.video_paths) == len(metadata["video_fps"])
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self.video_fps = metadata["video_fps"]
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@property
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def metadata(self) -> Dict[str, Any]:
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_metadata = {
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"video_paths": self.video_paths,
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"video_pts": self.video_pts,
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"video_fps": self.video_fps,
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}
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return _metadata
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def subset(self, indices: List[int]) -> "VideoClips":
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video_paths = [self.video_paths[i] for i in indices]
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video_pts = [self.video_pts[i] for i in indices]
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video_fps = [self.video_fps[i] for i in indices]
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metadata = {
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"video_paths": video_paths,
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"video_pts": video_pts,
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"video_fps": video_fps,
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}
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return type(self)(
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video_paths,
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clip_length_in_frames=self.num_frames,
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frames_between_clips=self.step,
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frame_rate=self.frame_rate,
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_precomputed_metadata=metadata,
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num_workers=self.num_workers,
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_video_width=self._video_width,
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_video_height=self._video_height,
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_video_min_dimension=self._video_min_dimension,
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_video_max_dimension=self._video_max_dimension,
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_audio_samples=self._audio_samples,
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_audio_channels=self._audio_channels,
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output_format=self.output_format,
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)
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@staticmethod
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def compute_clips_for_video(
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video_pts: torch.Tensor, num_frames: int, step: int, fps: Optional[float], frame_rate: Optional[float] = None
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) -> Tuple[torch.Tensor, Union[List[slice], torch.Tensor]]:
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if fps is None:
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# if for some reason the video doesn't have fps (because doesn't have a video stream)
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# set the fps to 1. The value doesn't matter, because video_pts is empty anyway
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fps = 1
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if frame_rate is None:
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frame_rate = fps
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total_frames = len(video_pts) * frame_rate / fps
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_idxs = VideoClips._resample_video_idx(int(math.floor(total_frames)), fps, frame_rate)
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video_pts = video_pts[_idxs]
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clips = unfold(video_pts, num_frames, step)
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if not clips.numel():
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warnings.warn(
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"There aren't enough frames in the current video to get a clip for the given clip length and "
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"frames between clips. The video (and potentially others) will be skipped."
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)
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idxs: Union[List[slice], torch.Tensor]
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if isinstance(_idxs, slice):
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idxs = [_idxs] * len(clips)
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else:
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idxs = unfold(_idxs, num_frames, step)
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return clips, idxs
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def compute_clips(self, num_frames: int, step: int, frame_rate: Optional[float] = None) -> None:
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"""
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Compute all consecutive sequences of clips from video_pts.
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Always returns clips of size `num_frames`, meaning that the
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last few frames in a video can potentially be dropped.
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Args:
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num_frames (int): number of frames for the clip
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step (int): distance between two clips
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frame_rate (int, optional): The frame rate
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"""
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self.num_frames = num_frames
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self.step = step
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self.frame_rate = frame_rate
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self.clips = []
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self.resampling_idxs = []
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for video_pts, fps in zip(self.video_pts, self.video_fps):
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clips, idxs = self.compute_clips_for_video(video_pts, num_frames, step, fps, frame_rate)
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self.clips.append(clips)
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self.resampling_idxs.append(idxs)
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clip_lengths = torch.as_tensor([len(v) for v in self.clips])
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self.cumulative_sizes = clip_lengths.cumsum(0).tolist()
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def __len__(self) -> int:
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return self.num_clips()
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def num_videos(self) -> int:
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return len(self.video_paths)
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def num_clips(self) -> int:
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"""
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Number of subclips that are available in the video list.
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"""
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return self.cumulative_sizes[-1]
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def get_clip_location(self, idx: int) -> Tuple[int, int]:
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"""
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Converts a flattened representation of the indices into a video_idx, clip_idx
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representation.
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"""
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video_idx = bisect.bisect_right(self.cumulative_sizes, idx)
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if video_idx == 0:
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clip_idx = idx
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else:
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clip_idx = idx - self.cumulative_sizes[video_idx - 1]
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return video_idx, clip_idx
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@staticmethod
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def _resample_video_idx(num_frames: int, original_fps: float, new_fps: float) -> Union[slice, torch.Tensor]:
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step = original_fps / new_fps
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if step.is_integer():
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# optimization: if step is integer, don't need to perform
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# advanced indexing
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step = int(step)
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return slice(None, None, step)
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idxs = torch.arange(num_frames, dtype=torch.float32) * step
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idxs = idxs.floor().to(torch.int64)
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return idxs
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def get_clip(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any], int]:
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"""
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Gets a subclip from a list of videos.
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Args:
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idx (int): index of the subclip. Must be between 0 and num_clips().
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Returns:
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video (Tensor)
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audio (Tensor)
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info (Dict)
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video_idx (int): index of the video in `video_paths`
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"""
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if idx >= self.num_clips():
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raise IndexError(f"Index {idx} out of range ({self.num_clips()} number of clips)")
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video_idx, clip_idx = self.get_clip_location(idx)
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video_path = self.video_paths[video_idx]
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clip_pts = self.clips[video_idx][clip_idx]
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from torchvision import get_video_backend
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backend = get_video_backend()
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if backend == "pyav":
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# check for invalid options
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if self._video_width != 0:
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raise ValueError("pyav backend doesn't support _video_width != 0")
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if self._video_height != 0:
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raise ValueError("pyav backend doesn't support _video_height != 0")
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if self._video_min_dimension != 0:
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raise ValueError("pyav backend doesn't support _video_min_dimension != 0")
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if self._video_max_dimension != 0:
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raise ValueError("pyav backend doesn't support _video_max_dimension != 0")
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if self._audio_samples != 0:
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raise ValueError("pyav backend doesn't support _audio_samples != 0")
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if backend == "pyav":
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start_pts = clip_pts[0].item()
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end_pts = clip_pts[-1].item()
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video, audio, info = read_video(video_path, start_pts, end_pts)
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else:
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_info = _probe_video_from_file(video_path)
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video_fps = _info.video_fps
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audio_fps = None
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video_start_pts = cast(int, clip_pts[0].item())
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video_end_pts = cast(int, clip_pts[-1].item())
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audio_start_pts, audio_end_pts = 0, -1
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audio_timebase = Fraction(0, 1)
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video_timebase = Fraction(_info.video_timebase.numerator, _info.video_timebase.denominator)
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if _info.has_audio:
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audio_timebase = Fraction(_info.audio_timebase.numerator, _info.audio_timebase.denominator)
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audio_start_pts = pts_convert(video_start_pts, video_timebase, audio_timebase, math.floor)
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audio_end_pts = pts_convert(video_end_pts, video_timebase, audio_timebase, math.ceil)
|
||
|
|
audio_fps = _info.audio_sample_rate
|
||
|
|
video, audio, _ = _read_video_from_file(
|
||
|
|
video_path,
|
||
|
|
video_width=self._video_width,
|
||
|
|
video_height=self._video_height,
|
||
|
|
video_min_dimension=self._video_min_dimension,
|
||
|
|
video_max_dimension=self._video_max_dimension,
|
||
|
|
video_pts_range=(video_start_pts, video_end_pts),
|
||
|
|
video_timebase=video_timebase,
|
||
|
|
audio_samples=self._audio_samples,
|
||
|
|
audio_channels=self._audio_channels,
|
||
|
|
audio_pts_range=(audio_start_pts, audio_end_pts),
|
||
|
|
audio_timebase=audio_timebase,
|
||
|
|
)
|
||
|
|
|
||
|
|
info = {"video_fps": video_fps}
|
||
|
|
if audio_fps is not None:
|
||
|
|
info["audio_fps"] = audio_fps
|
||
|
|
|
||
|
|
if self.frame_rate is not None:
|
||
|
|
resampling_idx = self.resampling_idxs[video_idx][clip_idx]
|
||
|
|
if isinstance(resampling_idx, torch.Tensor):
|
||
|
|
resampling_idx = resampling_idx - resampling_idx[0]
|
||
|
|
video = video[resampling_idx]
|
||
|
|
info["video_fps"] = self.frame_rate
|
||
|
|
assert len(video) == self.num_frames, f"{video.shape} x {self.num_frames}"
|
||
|
|
|
||
|
|
if self.output_format == "TCHW":
|
||
|
|
# [T,H,W,C] --> [T,C,H,W]
|
||
|
|
video = video.permute(0, 3, 1, 2)
|
||
|
|
|
||
|
|
return video, audio, info, video_idx
|
||
|
|
|
||
|
|
def __getstate__(self) -> Dict[str, Any]:
|
||
|
|
video_pts_sizes = [len(v) for v in self.video_pts]
|
||
|
|
# To be back-compatible, we convert data to dtype torch.long as needed
|
||
|
|
# because for empty list, in legacy implementation, torch.as_tensor will
|
||
|
|
# use torch.float as default dtype. This happens when decoding fails and
|
||
|
|
# no pts is returned in the list.
|
||
|
|
video_pts = [x.to(torch.int64) for x in self.video_pts]
|
||
|
|
# video_pts can be an empty list if no frames have been decoded
|
||
|
|
if video_pts:
|
||
|
|
video_pts = torch.cat(video_pts) # type: ignore[assignment]
|
||
|
|
# avoid bug in https://github.com/pytorch/pytorch/issues/32351
|
||
|
|
# TODO: Revert it once the bug is fixed.
|
||
|
|
video_pts = video_pts.numpy() # type: ignore[attr-defined]
|
||
|
|
|
||
|
|
# make a copy of the fields of self
|
||
|
|
d = self.__dict__.copy()
|
||
|
|
d["video_pts_sizes"] = video_pts_sizes
|
||
|
|
d["video_pts"] = video_pts
|
||
|
|
# delete the following attributes to reduce the size of dictionary. They
|
||
|
|
# will be re-computed in "__setstate__()"
|
||
|
|
del d["clips"]
|
||
|
|
del d["resampling_idxs"]
|
||
|
|
del d["cumulative_sizes"]
|
||
|
|
|
||
|
|
# for backwards-compatibility
|
||
|
|
d["_version"] = 2
|
||
|
|
return d
|
||
|
|
|
||
|
|
def __setstate__(self, d: Dict[str, Any]) -> None:
|
||
|
|
# for backwards-compatibility
|
||
|
|
if "_version" not in d:
|
||
|
|
self.__dict__ = d
|
||
|
|
return
|
||
|
|
|
||
|
|
video_pts = torch.as_tensor(d["video_pts"], dtype=torch.int64)
|
||
|
|
video_pts = torch.split(video_pts, d["video_pts_sizes"], dim=0)
|
||
|
|
# don't need this info anymore
|
||
|
|
del d["video_pts_sizes"]
|
||
|
|
|
||
|
|
d["video_pts"] = video_pts
|
||
|
|
self.__dict__ = d
|
||
|
|
# recompute attributes "clips", "resampling_idxs" and other derivative ones
|
||
|
|
self.compute_clips(self.num_frames, self.step, self.frame_rate)
|