- 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')[source]¶
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
To give an example, for 2 videos with 10 and 15 frames respectively, if
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_clipelements, so not all frames in a video might be present.
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
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”.
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:
RuntimeError – If
download is Trueand the video archives are already extracted.