class_docstr: Default Mask RCNN validation transform.
  
  Parameters
  ----------
  short : int, default is 600
      Resize image shorter side to ``short``.
  max_size : int, default is 1000
      Make sure image longer side is smaller than ``max_size``.
  mean : array-like of size 3
      Mean pixel values to be subtracted from image tensor. Default is [0.485, 0.456, 0.406].
  std : array-like of size 3
      Standard deviation to be divided from image. Default is [0.229, 0.224, 0.225].
class_docstr_tok: ["Default","Mask","RCNN","validation","transform",".","Parameters","--","--","--","--","--","short",":","int",",","default","is","600","Resize","image","shorter","side","to","``","short","``",".","max_size",":","int",",","default","is","1000","Make","sure","image","longer","side","is","smaller","than","``","max_size","``",".","mean",":","array-like","of","size","3","Mean","pixel","values","to","be","subtracted","from","image","tensor",".","Default","is","[","0.485",",","0.456",",","0.406","]",".","std",":","array-like","of","size","3","Standard","deviation","to","be","divided","from","image",".","Default","is","[","0.229",",","0.224",",","0.225","]","."]
code: def __init__(
      self,
      short=600,
      max_size=1000,
      mean=(0.485, 0.456, 0.406),
      std=(0.229, 0.224, 0.225),
  ):
      self._mean = mean
      self._std = std
      self._short = short
      self._max_size = max_size
  
imports: from __future__ import absolute_import
  import copy
  from random import randint
  import mxnet
  from ..None import bbox
  from ..None import image
  from ..None import mask
label_desc: Use image node_id 1 to create a new MaskRCNNDefaultValTransform object from inherited base classes: object with example: obj = MaskRCNNDefaultValTransform(short, max_size, mean, std)