o ϯi~%@s@ddlZddlZddlZddlmZmZmZmZmZddl Z ddl m Z ddl m Z mZddlmZeeZe jjjddddfd ed ed ee jjjd eeeeefgeeeffd ededefddZe jjjddddfd ed ed ee jjjd eeeeefgeeeffd ededefddZGdddZdS)N)AnyCallableDictOptionalType) g_pathmgr)ClipInfo ClipSampler)LabeledVideoDatasetTpyav data_path clip_sampler video_sampler transformvideo_path_prefix decode_audiodecoderc stjdt|rCzt|d }t|}Wdn1s"wYWnty6t |dwfdd| D} nt |dt | |||||d} | S) ae Builds a LabeledVideoDataset with no annotations from a json file with the following format: .. code-block:: text { "video_name1": {...} "video_name2": {...} .... "video_nameN": {...} } Args: labeled_video_paths (List[Tuple[str, Optional[dict]]]): List containing video file paths and associated labels. If video paths are a folder it's interpreted as a frame video, otherwise it must be an encoded video. 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 (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__(). decode_audio (bool): If True, also decode audio from video. decoder (str): Defines what type of decoder used to decode a video. Not used for frame videos. z4PYTORCHVIDEO.dataset.json_dataset.video_only_datasetrN must be json for Ego4D datasetcsg|] }tj|ifqS)ospathjoin).0xrrR/home/ubuntu/.local/lib/python3.10/site-packages/pytorchvideo/data/json_dataset.py Isz&video_only_dataset.. not found.rr) torch_C_log_api_usage_oncerisfileopenjsonload ExceptionFileNotFoundErrorkeysr ) r rrrrrrf annotations video_pathsdatasetrrrvideo_only_datasets. -    r/c sJt|rzt|d }t|}Wdn1swYWnty0t|dwg|D]I\} } tj || } | ddD]7} | d} | d}| d}|d }|d }|d }|d }|dusr|dusr|sr|rsqH | | |||d fqHq7nt|dfdd}|d|dt t |||||d}|S)a Builds a LabeledVideoDataset with noun, verb annotations from a json file with the following format: .. code-block:: text { "video_name1": { { "benchmarks": { "forecasting_hands_objects": [ { "critical_frame_selection_parent_start_sec": "critical_frame_selection_parent_end_sec": { "taxonomy: { "noun":