Source code for

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.

from typing import Any, Callable, Dict, Optional, Type

import torch
from import ClipSampler

from .labeled_video_dataset import LabeledVideoDataset, labeled_video_dataset

    Action recognition video dataset for Kinetics-{400,600,700}

[docs]def Kinetics( data_path: str, clip_sampler: ClipSampler, video_sampler: Type[] =, transform: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None, video_path_prefix: str = "", decode_audio: bool = True, decoder: str = "pyav", ) -> LabeledVideoDataset: """ A helper function to create ``LabeledVideoDataset`` object for the Kinetics dataset. Args: data_path (str): Path to the data. The path type defines how the data should be read: * For a file path, the file is read and each line is parsed into a video path and label. * For a directory, the directory structure defines the classes (i.e. each subdirectory is a class). clip_sampler (ClipSampler): Defines how clips should be sampled from each video. See the clip sampling documentation for more information. video_sampler (Type[]): Sampler for the internal video container. This defines the order videos are decoded and, if necessary, the distributed split. transform (Callable): This callable is evaluated on the clip output before the clip is returned. It can be used for user defined preprocessing and augmentations to the clips. See the ``LabeledVideoDataset`` class for clip output format. video_path_prefix (str): Path to root directory with the videos that are loaded in ``LabeledVideoDataset``. All the video paths before loading are prefixed with this path. decode_audio (bool): If True, also decode audio from video. decoder (str): Defines what type of decoder used to decode a video. """ torch._C._log_api_usage_once("PYTORCHVIDEO.dataset.Kinetics") return labeled_video_dataset( data_path, clip_sampler, video_sampler, transform, video_path_prefix, decode_audio, decoder, )
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