o ϯi$@sddlZddlZddlZddlZddlZddlmZddlmZm Z m Z m Z m Z m Z ddlZddlZddlZddlmZddlmZddlmZddlmZGd d d ejjjZ dd ed ededede e ee eff ddZdS)N) defaultdict)AnyCallableListOptionalTupleType) g_pathmgr) ClipSampler) FrameVideo)MultiProcessSamplerc@seZdZdZejjjddddfdededede d e ejjj d e e egefd ed e ed eddfddZedeed ededeefddZeddZdefddZddZdS)SSv2aq Action recognition video dataset for `Something-something v2 (SSv2) `_ stored as image frames. This dataset handles the parsing of frames, loading and clip sampling for the videos. All io is done through :code:`iopath.common.file_io.PathManager`, enabling non-local storage uri's to be used. NFlabel_name_filevideo_label_filevideo_path_label_file clip_sampler video_sampler transformvideo_path_prefixframes_per_cliprand_sample_framesreturnc Csrtjd||_||_t||||d\|_|_||j|_d|_ |dur.t j t j || dnd|_d|_d|_dS)a Args: label_name_file (str): SSV2 label file that contains the label names and indexes. video_label_file (str): a file that contains video ids and the corresponding video label. video_path_label_file (str): a file that contains frame paths for each video and the corresponding frame label. The file must be a space separated csv of the format: (original_vido_id video_id frame_id path labels). clip_sampler (ClipSampler): Defines how clips should be sampled from each video. See the clip sampling documentation for more information. video_sampler (Type[torch.utils.data.Sampler]): Sampler for the internal video container. This defines the order videos are decoded and, if necessary, the distributed split. transform (Optional[Callable]): This callable is evaluated on the clip output before the clip is returned. It can be used for user defined preprocessing and augmentations on the clips. The clip output format is described in __next__(). video_path_prefix (str): prefix path to add to all paths from data_path. frames_per_clip (Optional[int]): The number of frames per clip to sample. rand_sample_frames (bool): If True, randomly sampling frames for each clip. z"PYTORCHVIDEO.dataset.SSv2.__init__)prefixN)r rand_sample)torch_C_log_api_usage_once _transform _clip_sampler_read_video_paths_and_labels_path_to_videos_labels_video_sampler_video_sampler_iter functoolspartialr_sample_clip_frames _frame_filter _loaded_video_next_clip_start_time) selfrrrrrrrrrr.J/home/ubuntu/.local/lib/python3.10/site-packages/pytorchvideo/data/ssv2.py__init__ s* *  z SSv2.__init__ frame_indicesrc st}t|d|}g}t|D]+}tt||}tt||d}|r4|t||q|||dqfdd|DS)a Use segment-based input frame sampling that splits eachvideo into segments, and from each of them, we sample one frame to form a clip. Args: frame_indices (list): list of frame indices. frames_per_clip (int): The number of frames per clip to sample. rand_sample (bool): if True, randomly sampling frames. Returns: (list): Outputs a subsampled list with num_samples frames. r csg|]}|qSr.r.).0idxr1r.r/ sz,SSv2._sample_clip_frames..) lenfloatrangeintnproundappendrandomrandint) r1rr num_framesseg_sizeseqistartendr.r5r/r)fs zSSv2._sample_clip_framescCs|jSN)r%r-r.r.r/rszSSv2.video_samplerc Cs|js tt|j|_|jr|j\}}nt|j}|j|}t|}||f|_| |j |j i\}}}}}|dkrD| d|j |j |_||_ |rOd|_d|_ |jd|j|t||||d} |jdurj|| } | S)a Retrieves the next clip based on the clip sampling strategy and video sampler. Returns: A dictionary with the following format. .. code-block:: text { 'video': , 'label': , 'video_label': 'video_index': , 'clip_index': , 'aug_index': , } rNrvideo)rHlabel video_name video_index clip_index aug_index)r&iterr r%r+nextr#r from_frame_pathsr!r,durationget_clipr* _loaded_clipr$strr ) r-rHrKpath_to_video_frames clip_startclip_endrLrM is_last_clip sample_dictr.r.r/__next__s6        z SSv2.__next__cCs|SrFr.rGr.r.r/__iter__sz SSv2.__iter__)__name__ __module__ __qualname____doc__rutilsdata RandomSamplerrTr rSamplerrrdictrr:boolr0 staticmethodrr)propertyrrZr[r.r.r.r/rsT    F   9rrrrrrrcCs^tt}t|d0}tj|dd}|D]}t|dksJ|d}tj ||d} || | qWdn1s=wYt|d }t |} Wdn1sXwYt|d }t |} Wdn1sswYg} g} | D],}|d}||vr|d }| d d }| d d }t| |}| ||| |q~| | fS) aA Args: label_name_file (str): ssv2 label file that contians the label names and indexes. ('/path/to/folder/something-something-v2-labels.json') video_label_file (str): a file that contains video ids and the corresponding video label. (e.g., '/path/to/folder/something-something-v2-train.json') video_path_label_file (str): a file that contains frame paths for each video and the corresponding frame label. The file must be a space separated csv of the format: `original_vido_id video_id frame_id path labels` prefix (str): prefix path to add to all paths from video_path_label_file. Returns: image_paths (list): list of list containing path to each frame. labels (list): list containing label of each video. r ) delimiteroriginal_vido_idpathNidtemplate[r])rlistr opencsv DictReaderr7osrmjoinr=jsonloadreplacer:)rrrrpathsf csv_readerrowrJrmlabel_name_dictvideo_label_jsonlabels image_pathsrHrorIr.r.r/r"s:      r")r) rtr'rxrvr> collectionsrtypingrrrrrrnumpyr;rtorch.utils.dataiopath.common.file_ior pytorchvideo.data.clip_samplingr pytorchvideo.data.frame_videor r`r raIterableDatasetrrTr:r"r.r.r.r/s8      5