Source code for torchvision.datasets.kinetics

from .utils import list_dir
from .folder import make_dataset
from .video_utils import VideoClips
from .vision import VisionDataset

[docs]class Kinetics400(VisionDataset): """ `Kinetics-400 <>`_ dataset. Kinetics-400 is an action recognition video dataset. 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. Internally, it uses a VideoClips object to handle clip creation. Args: root (string): Root directory of the Kinetics-400 Dataset. frames_per_clip (int): number of frames in a 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. Returns: video (Tensor[T, H, W, C]): the `T` video frames audio(Tensor[K, L]): the audio frames, where `K` is the number of channels and `L` is the number of points label (int): class of the video clip """ def __init__(self, root, frames_per_clip, step_between_clips=1, frame_rate=None, extensions=('avi',), transform=None, _precomputed_metadata=None, num_workers=1, _video_width=0, _video_height=0, _video_min_dimension=0, _audio_samples=0, _audio_channels=0): super(Kinetics400, self).__init__(root) classes = list(sorted(list_dir(root))) class_to_idx = {classes[i]: i for i in range(len(classes))} self.samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file=None) self.classes = classes video_list = [x[0] for x in self.samples] self.video_clips = VideoClips( video_list, frames_per_clip, step_between_clips, frame_rate, _precomputed_metadata, num_workers=num_workers, _video_width=_video_width, _video_height=_video_height, _video_min_dimension=_video_min_dimension, _audio_samples=_audio_samples, _audio_channels=_audio_channels, ) self.transform = transform @property def metadata(self): return self.video_clips.metadata def __len__(self): return self.video_clips.num_clips() def __getitem__(self, idx): video, audio, info, video_idx = self.video_clips.get_clip(idx) label = self.samples[video_idx][1] if self.transform is not None: video = self.transform(video) return video, audio, label


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