U h(@sddlZddlZddlZddlZddlmZddlmZddlmZddl m Z ddl m Z m Z mZmZmZmZddlmZdd lmZmZdd lmZmZmZmZdd lmZdd lmZe e e dd ddZ!GdddeZ"dS)N)partial)Pool)path)Path)AnyCallableDictOptionalTupleUnion)Tensor) find_classes make_dataset)check_integritydownload_and_extract_archive download_urlverify_str_arg) VideoClips) VisionDataset)tarpath videopathlinereturncCst|||dSN)r)rrrrO/var/www/html/venv/lib/python3.8/site-packages/torchvision/datasets/kinetics.py_dl_wrapsrcseZdZdZddddZddddZd#eeefe eee e e e e e edfe e e e eeefe e e e e e ed dfdd Zd dddZd dddZd dddZeeeefdddZe dddZe e eee fd d!d"ZZS)$Kineticsu `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. Args: root (str or ``pathlib.Path``): Root directory of the Kinetics Dataset. Directory should be structured as follows: .. code:: root/ ├── split │ ├── class1 │ │ ├── vid1.mp4 │ │ ├── vid2.mp4 │ │ ├── vid3.mp4 │ │ ├── ... │ ├── class2 │ │ ├── vidx.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: tuple: 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 Raises: RuntimeError: If ``download is True`` and the video archives are already extracted. zChttps://s3.amazonaws.com/kinetics/400/{split}/k400_{split}_path.txtzChttps://s3.amazonaws.com/kinetics/600/{split}/k600_{split}_path.txtzMhttps://s3.amazonaws.com/kinetics/700_2020/{split}/k700_2020_{split}_path.txt)400600700z=https://s3.amazonaws.com/kinetics/400/annotations/{split}.csvz=https://s3.amazonaws.com/kinetics/600/annotations/{split}.csvzBhttps://s3.amazonaws.com/kinetics/700_2020/annotations/{split}.csvrtrainNr ZaviZmp4FrTCHW.)rootframes_per_clip num_classessplit frame_ratestep_between_clips transform extensionsdownloadnum_download_workers num_workers_precomputed_metadata _video_width _video_height_video_min_dimension_audio_samples_audio_channels_legacy output_formatrcst|ddddgd|_||_| |_||_||_|rXtd||_d|_d}| r|t d n$t |||_t|d d d d gd|_| r| t |jt|j\|_}t|j||dd|_dd|jD}t||||| | | |||||d |_||_dS)Nr'rr r!)argZ valid_valueszUsing legacy structureunknownZTHWCz2Cannot download the videos using legacy_structure.r(r"valtest)Z is_valid_filecSsg|] }|dqS)rr).0xrrr sz%Kinetics.__init__..)r/r1r2r3r4r5r7)rr'r,r.r%r6print split_folderr( ValueErrorrjoindownload_and_process_videossuper__init__rclassesrsamplesr video_clipsr+)selfr%r&r'r(r)r*r+r,r-r.r/r0r1r2r3r4r5r6r7Z class_to_idxZ video_list __class__rrrE\sF zKinetics.__init__)rcCsbt}|t}td||d|t}td||dtd||ddS)zEDownloads all the videos to the _root_ folder in the expected format.z%Elapsed time for downloading in mins <z$Elapsed time for processing in mins zElapsed time overall in mins N)time_download_videosr?_make_ds_structure)rIZticZtocZtoc2rrrrCsz$Kinetics.download_and_process_videosc Cst|jrtd|jdt|jd}t|jd}|j|jj|j d}t|t |}t |stt ||t |}dd|D}W5QRX|jdkr|D]}t|||jqn$tt||j}t|j} | ||d S) adownload tarballs containing the video to "tars" folder and extract them into the _split_ folder where split is one of the official dataset splits. Raises: RuntimeError: if download folder exists, break to prevent downloading entire dataset again. zThe directory z[ already exists. If you want to re-download or re-extract the images, delete the directory.Ztarsfilesr(cSsg|]}tjj|ddqS)z/,:)safe)urllibparsequote)r<rrrrr>sz-Kinetics._download_videos..r N)rexistsr@ RuntimeErrorrBr% _TAR_URLSr'formatr(basenamerropenread splitlinesr.rrrrmap) rIZtar_pathZfile_list_pathZ split_urlZsplit_url_filepathfileZlist_video_urlsrpartZpoolprocrrrrNs$       zKinetics._download_videosc Cst|jd}tt||jdsBt|j|jj|jd|t||jd}d}t |}t |}|D]}|j|dt |dt |dd}|d  d d  d d  dd  dd }tjt|j|ddt|j|} t| rrt | t|j||qrW5QRXdS)umove videos from split_folder/ ├── clip1.avi ├── clip2.avi to the correct format as described below: split_folder/ ├── class1 │ ├── clip1.avi annotationsz.csvrQz{ytid}_{start:06}_{end:06}.mp4Z youtube_idZ time_startZtime_end)Zytidstartendlabel _'()T)exist_okN)rrBr%rr(r_ANNOTATION_URLSr'rYr[csv DictReaderintreplaceosmakedirsr@isfile) rIZannotation_pathraZ file_fmtstrZcsvfilereaderrowfrdZdownloaded_filerrrrOs*     ( zKinetics._make_ds_structurecCs|jjSr)rHmetadatarIrrrrwszKinetics.metadatacCs |jSr)rHZ num_clipsrxrrr__len__szKinetics.__len__)idxrcCs@|j|\}}}}|j|d}|jdk r6||}|||fS)Nr )rHZget_cliprGr+)rIrzZvideoZaudioinfoZ video_idxrdrrr __getitem__s   zKinetics.__getitem__)rr"Nr Nr#Fr r NrrrrrFr$)__name__ __module__ __qualname____doc__rXrlr strrror rr boolrrrErCrNrOpropertyrwryr r| __classcell__rrrJrrsn:   B #r)#rmrqrMrS functoolsrmultiprocessingrrpathlibrtypingrrrr r r Ztorchr folderrrutilsrrrrZ video_utilsrZvisionrrrrrrrrs