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# Kinetics

class torchvision.datasets.Kinetics(root: str, frames_per_clip: int, num_classes: str = '400', split: str = 'train', frame_rate: Optional[int] = None, step_between_clips: int = 1, transform: Optional[Callable] = None, extensions: Tuple[str, ...] = ('avi', 'mp4'), download: bool = False, num_download_workers: int = 1, num_workers: int = 1, _precomputed_metadata: Optional[Dict[str, Any]] = None, _video_width: int = 0, _video_height: int = 0, _video_min_dimension: int = 0, _audio_samples: int = 0, _audio_channels: int = 0, _legacy: bool = False, output_format: str = 'TCHW')

  Generic Kinetics dataset.

  Kinetics-400/600/700 are action recognition video datasets. This dataset consider every video as a collection of video clips of fixed size, specified by `frames_per_clip`, where the step in frames between each clip is given by `step_between_clips`.

  To give an example, for 2 videos with 10 and 15 frames respectively, if `frames_per_clip=5` and `step_between_clips=5`, the dataset size will be (2 + 3) = 5, where the first two elements will come from video 1, and the next three elements from video 2. Note that we drop clips which do not have exactly `frames_per_clip` elements, so not all frames in a video might be present.

  Parameters:

    - root (string) –

      Root directory of the Kinetics Dataset. Directory should be structured as follows: .. code:

          root/
          ├── split
          │   ├──  class1
          │   │   ├──  clip1.mp4
          │   │   ├──  clip2.mp4
          │   │   ├──  clip3.mp4
          │   │   ├──  ...
          │   ├──  class2
          │   │   ├──   clipx.mp4
          │   │    └── ...

      Note: split is appended automatically using the split argument.

    - frames_per_clip (int) – number of frames in a clip

    - num_classes (int) – select between Kinetics-400 (default), Kinetics-600, and Kinetics-700

    - split (str) – split of the dataset to consider; supports `"train"` (default) `"val"` `"test"`

    - frame_rate (float) – If omitted, interpolate different frame rate for each clip.

    - step_between_clips (int) – number of frames between each clip

    - transform (callable, optional) – A function/transform that takes in a TxHxWxC video and returns a transformed version.

    - download (bool) – Download the official version of the dataset to root folder.

    - num_workers (int) – Use multiple workers for VideoClips creation

    - num_download_workers (int) – Use multiprocessing in order to speed up download.

    - output_format (str, optional) – The format of the output video tensors (before transforms). Can be either “THWC” or “TCHW” (default). Note that in most other utils and datasets, the default is actually “THWC”.

  Returns:

    A 3-tuple with the following entries:

    > - video (Tensor[T, C, H, W] or Tensor[T, H, W, C]): the T video frames in torch.uint8 tensor

    > - audio(Tensor[K, L]): the audio frames, where K is the number of channels and L is the number of points in torch.float tensor

    > - label (int): class of the video clip

  Return type:

    tuple

  Raises:

    RuntimeError – If `download is True` and the video archives are already extracted.

  Special-members:

  __getitem__(idx: int) → Tuple[Tensor, Tensor, int]
    Parameters:

      index (int) – Index

    Returns:

      Sample and meta data, optionally transformed by the respective transforms.

    Return type:

      (Any)

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