class_docstr: Fast-SCNN: Fast Semantic Segmentation Network
  
  Parameters
  ----------
  nclass : int
      Number of categories for the training dataset.
  norm_layer : object
      Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`).
  aux : bool
      Auxiliary loss.
  
  
  Reference:
  
      Rudra P K Poudel, et al. https://bmvc2019.org/wp-content/uploads/papers/0959-paper.pdf
      "Fast-SCNN: Fast Semantic Segmentation Network." *BMVC*, 2019
class_docstr_tok: ["Fast-SCNN",":","Fast","Semantic","Segmentation","Network","Parameters","--","--","--","--","--","nclass",":","int","Number","of","categories","for","the","training","dataset",".","norm_layer",":","object","Normalization","layer","used","in","backbone","network","(","default",":",":","class",":","`","mxnet.gluon.nn.BatchNorm","`",")",".","aux",":","bool","Auxiliary","loss",".","Reference",":","Rudra","P","K","Poudel",",","et","al",".","https",":","\/\/bmvc2019.org\/wp-content\/uploads\/papers\/0959-paper.pdf","``","Fast-SCNN",":","Fast","Semantic","Segmentation","Network",".","''","*","BMVC","*",",","2019"]
code: def __init__(
      self,
      nclass,
      aux=True,
      ctx=cpu(),
      pretrained_base=False,
      height=None,
      width=None,
      base_size=2048,
      crop_size=1024,
      **kwargs,
  ):
      super(FastSCNN, self).__init__()
  
      height = height if height is not None else crop_size
      width = width if width is not None else crop_size
      self._up_kwargs = {"height": height, "width": width}
      self.base_size = base_size
      self.crop_size = crop_size
  
      self.aux = aux
      with self.name_scope():
          self.learning_to_downsample = LearningToDownsample(
              32, 48, 64, **kwargs
          )
          self.learning_to_downsample.initialize(ctx=ctx)
          self.global_feature_extractor = GlobalFeatureExtractor(
              64,
              [64, 96, 128],
              128,
              6,
              [3, 3, 3],
              height=height // 32,
              width=width // 32,
              **kwargs,
          )
          self.global_feature_extractor.initialize(ctx=ctx)
          self.feature_fusion = FeatureFusionModule(
              64,
              128,
              128,
              height=height // 8,
              width=width // 8,
              **kwargs,
          )
          self.feature_fusion.initialize(ctx=ctx)
          self.classifier = Classifer(128, nclass, **kwargs)
          self.classifier.initialize(ctx=ctx)
  
          if self.aux:
              self.auxlayer = _auxHead(
                  in_channels=64, channels=64, nclass=nclass, **kwargs
              )
              self.auxlayer.initialize(ctx=ctx)
              self.auxlayer.collect_params().setattr("lr_mult", 10)
  
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 1 to create a new FastSCNN object from inherited base classes: HybridBlock with example: obj = FastSCNN(nclass, aux, ctx, pretrained_base, height, width, base_size, crop_size)