o
    )i`                     @   sp   d dl Z d dlmZmZmZmZmZmZ d dlm	Z	 ddl
mZmZ ddlmZ ddlmZ G dd	 d	eZdS )
    N)AnyCallableDictListOptionalTuple)Tensor   )find_classesmake_dataset)
VideoClips)VisionDatasetc                !       s   e Zd ZdZ												d#deded	ed
edee dededee dee	ee
f  dededededededdf  fddZede	ee
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ddZdefddZd edeeeef fd!d"Z  ZS )$UCF101a  
    `UCF101 <https://www.crcv.ucf.edu/data/UCF101.php>`_ dataset.

    UCF101 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``. The dataset itself can be downloaded from the dataset website;
    annotations that ``annotation_path`` should be pointing to can be downloaded from `here
    <https://www.crcv.ucf.edu/data/UCF101/UCF101TrainTestSplits-RecognitionTask.zip>`_.

    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 UCF101 Dataset.
        annotation_path (str): path to the folder containing the split files;
            see docstring above for download instructions of these files
        frames_per_clip (int): number of frames in a clip.
        step_between_clips (int, optional): number of frames between each clip.
        fold (int, optional): which fold to use. Should be between 1 and 3.
        train (bool, optional): if ``True``, creates a dataset from the train split,
            otherwise from the ``test`` split.
        transform (callable, optional): A function/transform that  takes in a TxHxWxC video
            and returns a transformed version.
        output_format (str, optional): The format of the output video tensors (before transforms).
            Can be either "THWC" (default) or "TCHW".

    Returns:
        tuple: A 3-tuple with the following entries:

            - video (Tensor[T, H, W, C] or Tensor[T, C, H, W]): 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
    r	   NTr   THWCrootannotation_pathframes_per_clipstep_between_clips
frame_ratefoldtrain	transform_precomputed_metadatanum_workers_video_width_video_height_video_min_dimension_audio_samplesoutput_formatreturnc                    s   t  | d|  krdksn td| d}|| _|| _t| j\| _}t| j||d d| _	dd | j	D }t
|||||	|
|||||d}|| _| ||||| _|| j| _|| _d S )	Nr	      z$fold should be between 1 and 3, got )Zavi)Zis_valid_filec                 S   s   g | ]}|d  qS )r    .0xr!   r!   j/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/torchvision/datasets/ucf101.py
<listcomp>Q   s    z#UCF101.__init__.<locals>.<listcomp>)r   r   r   r   r   r   )super__init__
ValueErrorr   r   r
   r   classesr   samplesr   full_video_clips_select_foldindicesZsubsetvideo_clipsr   )selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   r   
extensionsZclass_to_idx
video_listr/   	__class__r!   r%   r(   5   s4   
zUCF101.__init__c                 C   s   | j jS N)r,   metadatar0   r!   r!   r%   r6   g   s   zUCF101.metadatar2   c           
         s   |rdnd}| d|dd}t j||}t  t|!}| }dd |D }fdd|D } | W d    n1 sAw   Y   fd	dttD }	|	S )
Nr   testlist02dz.txtc                 S   s   g | ]}|  d d qS ) r   )stripsplitr"   r!   r!   r%   r&   r   s    z'UCF101._select_fold.<locals>.<listcomp>c                    s(   g | ]}t jj jg|d R  qS )/)ospathjoinr   r=   r"   r7   r!   r%   r&   s   s   ( c                    s   g | ]
}|  v r|qS r!   r!   )r#   i)selected_filesr2   r!   r%   r&   u   s    )	r?   r@   rA   setopen	readlinesupdaterangelen)
r0   r2   r   r   r   namefZfiddatar.   r!   )rC   r0   r2   r%   r-   k   s   
zUCF101._select_foldc                 C   s
   | j  S r5   )r/   Z	num_clipsr7   r!   r!   r%   __len__x   s   
zUCF101.__len__idxc                 C   sF   | j |\}}}}| j| j|  d }| jd ur| |}|||fS )Nr	   )r/   Zget_clipr+   r.   r   )r0   rN   ZvideoZaudioinfoZ	video_idxlabelr!   r!   r%   __getitem__{   s
   


zUCF101.__getitem__)r	   Nr	   TNNr	   r   r   r   r   r   )__name__
__module____qualname____doc__strintr   boolr   r   r   r(   propertyr6   r   r-   rM   r   r   rQ   __classcell__r!   r!   r3   r%   r      sh    .	
2&$r   )r?   typingr   r   r   r   r   r   Ztorchr   folderr
   r   Zvideo_utilsr   Zvisionr   r   r!   r!   r!   r%   <module>   s     