class_docstr: An abstraction of meter for evaluating video models.
  
  Video-level metric is computed by averaging clip-level predictions and
  compare the result with video-level groundtruth label.
  
  This meter abstraction can wrap conventional classy meters by passing
  averaged clip-level predictions to the meter needed for video level metrics.
docstr: Constructor of VideoMeter class.
  
  Args:
      clips_per_video_train: No. of clips sampled per video at train time
      clips_per_video_test: No. of clips sampled per video at test time
docstr_tok: ["Constructor","of","VideoMeter","class",".","Args",":","clips_per_video_train",":","No",".","of","clips","sampled","per","video","at","train","time","clips_per_video_test",":","No",".","of","clips","sampled","per","video","at","test","time"]
code: def __init__(self, clips_per_video_train, clips_per_video_test):
      """Constructor of VideoMeter class.
  
      Args:
          clips_per_video_train: No. of clips sampled per video at train time
          clips_per_video_test: No. of clips sampled per video at test time
      """
      super().__init__()
  
      self._clips_per_video_train = clips_per_video_train
      self._clips_per_video_test = clips_per_video_test
  
file_path: ClassyVision/classy_vision/meters/video_meter.py
imports: import logging
  import torch
  from classy_vision.meters import ClassyMeter
label_desc: Use image node_id 1 to create a new VideoMeter object from inherited base classes: ClassyMeter with example: obj = VideoMeter(clips_per_video_train, clips_per_video_test)