class_docstr: Deep layer aggregation upsampling layer.
  
  Parameters
  ----------
  startp : int
      Start index.
  channels : iterable of int
      Output channels.
  scales : iterable of int
      Upsampling scales.
  in_channels : iterable of int
      Input channels.
  use_dcnv2 : bool
      Whether use ModulatedDeformableConvolution(DCN version 2).
  norm_layer : nn.HybridBlock, default is BatchNorm
      The norm layer type.
  norm_kwargs : dict
      Extra arguments to norm layer.
class_docstr_tok: ["Deep","layer","aggregation","upsampling","layer",".","Parameters","--","--","--","--","--","startp",":","int","Start","index",".","channels",":","iterable","of","int","Output","channels",".","scales",":","iterable","of","int","Upsampling","scales",".","in_channels",":","iterable","of","int","Input","channels",".","use_dcnv2",":","bool","Whether","use","ModulatedDeformableConvolution","(","DCN","version","2",")",".","norm_layer",":","nn.HybridBlock",",","default","is","BatchNorm","The","norm","layer","type",".","norm_kwargs",":","dict","Extra","arguments","to","norm","layer","."]
code: def __init__(
      self,
      startp,
      channels,
      scales,
      in_channels=None,
      use_dcnv2=False,
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(DLAUp, self).__init__(**kwargs)
      self.startp = startp
      if in_channels is None:
          in_channels = channels
      self.channels = channels
      channels = list(channels)
      scales = np.array(scales, dtype=int)
      with self.name_scope():
          self.idas = nn.HybridSequential("ida")
          for i in range(len(channels) - 1):
              j = -i - 2
              self.idas.add(
                  IDAUp(
                      channels[j],
                      in_channels[j:],
                      scales[j:] // scales[j],
                      use_dcnv2=use_dcnv2,
                      norm_layer=norm_layer,
                      norm_kwargs=norm_kwargs,
                  )
              )
              scales[j + 1 :] = scales[j]
              in_channels[j + 1 :] = [
                  channels[j] for _ in channels[j + 1 :]
              ]
  
imports: from __future__ import absolute_import
  import warnings
  import numpy
  from mxnet.context import cpu
  from mxnet.gluon import nn
  from mxnet.gluon import contrib
  from .deconv_resnet import BilinearUpSample
  from ..model_zoo import get_model
label_desc: Use image node_id 1 to create a new DLAUp object from inherited base classes: nn with example: obj = DLAUp(startp, channels, scales, in_channels, use_dcnv2, norm_layer, norm_kwargs)