class_docstr: Mobilenet with DUC upsampling block.
  
  Parameters
  ----------
  base_network : str
      Name of the base feature extraction network.
  up_filters : list of int
      Number of filters for DUC layers.
  up_scales : list of int
      Upsampling scales for DUC layers.
  pretrained_base : bool
      Whether load pretrained base network.
  norm_layer : mxnet.gluon.nn.HybridBlock
      Type of Norm layers, can be BatchNorm, SyncBatchNorm, GroupNorm, etc.
  norm_kwargs : dict
      Additional kwargs for `norm_layer`.
class_docstr_tok: ["Mobilenet","with","DUC","upsampling","block",".","Parameters","--","--","--","--","--","base_network",":","str","Name","of","the","base","feature","extraction","network",".","up_filters",":","list","of","int","Number","of","filters","for","DUC","layers",".","up_scales",":","list","of","int","Upsampling","scales","for","DUC","layers",".","pretrained_base",":","bool","Whether","load","pretrained","base","network",".","norm_layer",":","mxnet.gluon.nn.HybridBlock","Type","of","Norm","layers",",","can","be","BatchNorm",",","SyncBatchNorm",",","GroupNorm",",","etc",".","norm_kwargs",":","dict","Additional","kwargs","for","`","norm_layer","`","."]
code: def __init__(
      self,
      base_network="mobilenetv3_small",
      up_filters=(512, 256, 128),
      up_scales=(2, 2, 2),
      pretrained_base=True,
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(DUCMobilenet, self).__init__(**kwargs)
      if norm_layer != nn.BatchNorm:
          raise NotImplementedError(
              "Only standard BatchNorm layer is supported in DUC module"
          )
      assert "mobilenet" in base_network
      net = get_model(base_network, pretrained=pretrained_base)
      feat = net.features
      idx = [type(l) for l in feat].index(
          nn.conv_layers.GlobalAvgPool2D
      )
      with self.name_scope():
          self.feature = feat[:idx]
          self.upsampling = nn.HybridSequential(prefix="upsampling_")
          for f, s in zip(up_filters, up_scales):
              self.upsampling.add(DUC(f, s))
  
file_path: gluon-cv/gluoncv/model_zoo/center_net/duc_mobilenet.py
imports: from __future__ import absolute_import
  import warnings
  from mxnet.context import cpu
  from mxnet.gluon import nn
  from ..model_zoo import get_model
  from ...nn.block import DUC
label_desc: Use image node_id 1 to create a new DUCMobilenet object from inherited base classes: nn with example: obj = DUCMobilenet(base_network, up_filters, up_scales, pretrained_base, norm_layer, norm_kwargs)