class_docstr: A Multigrid Method for Efficiently Training Video Models
  Chao-Yuan Wu, Ross Girshick, Kaiming He, Christoph Feichtenhofer, Philipp Krähenbühl
  CVPR 2020, https://arxiv.org/abs/1912.00998
class_docstr_tok: ["A","Multigrid","Method","for","Efficiently","Training","Video","Models","Chao-Yuan","Wu",",","Ross","Girshick",",","Kaiming","He",",","Christoph","Feichtenhofer",",","Philipp","Kr\u00e4henb\u00fchl","CVPR","2020",",","https",":","\/\/arxiv.org\/abs\/1912.00998"]
code: def __init__(self):
      # Scale: [T, H, W]
      self.long_cycle = np.asarray(
          [[1, 1, 1], [2, 1, 1], [2, sq2, sq2]]
      )[::-1]
      self.short_cycle = np.asarray(
          [[1, 1, 1], [1, sq2, sq2], [1, 2, 2]]
      )[::-1]
      self.short_cycle_sp = np.asarray(
          [[1, 1, 1], [1, sq2, sq2], [1, sq2, sq2]]
      )[::-1]
      self.mod_long = len(self.long_cycle)
      self.mod_short = len(self.short_cycle)
  
file_path: gluon-cv/gluoncv/torch/data/video_cls/multigrid_helper.py
imports: from functools import reduce
  import numpy
  from torch.utils.data import Sampler
label_desc: Use image node_id 1 to create a new multiGridHelper object from inherited base classes: object with example: obj = multiGridHelper()