parameters: (num_classes, num_layers, image_shape, num_group=32, conv_workspace=256, dtype='float32', **kwargs)
return_statement: return resnext(units      = units,
                    num_stages  = num_stages,
                    filter_list = filter_list,
                    num_classes = num_classes,
                    num_group   = num_group,
                    image_shape = image_shape,
                    bottle_neck = bottle_neck,
                    workspace   = conv_workspace,
                    dtype       = dtype)
docstring: Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
      Original author Wei Wu
docstring_summary: Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
      Original author Wei Wu
docstring_tokens: Adapted from https : // github . com / tornadomeet / ResNet / blob / master / train_resnet . py Original author Wei Wu
function: def get_symbol(num_classes, num_layers, image_shape, num_group=32, conv_workspace=256, dtype='float32', **kwargs):
      """
      Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
      Original author Wei Wu
      """
      image_shape = [int(l) for l in image_shape.split(',')]
      (nchannel, height, width) = image_shape
      if height <= 32:
          num_stages = 3
          if (num_layers-2) % 9 == 0 and num_layers >= 164:
              per_unit = [(num_layers-2)//9]
              filter_list = [16, 64, 128, 256]
              bottle_neck = True
          elif (num_layers-2) % 6 == 0 and num_layers < 164:
              per_unit = [(num_layers-2)//6]
              filter_list = [16, 16, 32, 64]
              bottle_neck = False
          else:
              raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
          units = per_unit * num_stages
      else:
          if num_layers >= 50:
              filter_list = [64, 256, 512, 1024, 2048]
              bottle_neck = True
          else:
              filter_list = [64, 64, 128, 256, 512]
              bottle_neck = False
          num_stages = 4
          if num_layers == 18:
              units = [2, 2, 2, 2]
          elif num_layers == 34:
              units = [3, 4, 6, 3]
          elif num_layers == 50:
              units = [3, 4, 6, 3]
          elif num_layers == 101:
              units = [3, 4, 23, 3]
          elif num_layers == 152:
              units = [3, 8, 36, 3]
          elif num_layers == 200:
              units = [3, 24, 36, 3]
          elif num_layers == 269:
              units = [3, 30, 48, 8]
          else:
              raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers))
  
      return resnext(units      = units,
                    num_stages  = num_stages,
                    filter_list = filter_list,
                    num_classes = num_classes,
                    num_group   = num_group,
                    image_shape = image_shape,
                    bottle_neck = bottle_neck,
                    workspace   = conv_workspace,
                    dtype       = dtype)
function_tokens: def get_symbol ( num_classes , num_layers , image_shape , num_group = 32 , conv_workspace = 256 , dtype = 'float32' , * * kwargs ) : image_shape = [ int ( l ) for l in image_shape . split ( ',' ) ] ( nchannel , height , width ) = image_shape if height <= 32 : num_stages = 3 if ( num_layers - 2 ) % 9 == 0 and num_layers >= 164 : per_unit = [ ( num_layers - 2 ) // 9 ] filter_list = [ 16 , 64 , 128 , 256 ] bottle_neck = True elif ( num_layers - 2 ) % 6 == 0 and num_layers < 164 : per_unit = [ ( num_layers - 2 ) // 6 ] filter_list = [ 16 , 16 , 32 , 64 ] bottle_neck = False else : raise ValueError ( "no experiments done on num_layers {}, you can do it yourself" . format ( num_layers ) ) units = per_unit * num_stages else : if num_layers >= 50 : filter_list = [ 64 , 256 , 512 , 1024 , 2048 ] bottle_neck = True else : filter_list = [ 64 , 64 , 128 , 256 , 512 ] bottle_neck = False num_stages = 4 if num_layers == 18 : units = [ 2 , 2 , 2 , 2 ] elif num_layers == 34 : units = [ 3 , 4 , 6 , 3 ] elif num_layers == 50 : units = [ 3 , 4 , 6 , 3 ] elif num_layers == 101 : units = [ 3 , 4 , 23 , 3 ] elif num_layers == 152 : units = [ 3 , 8 , 36 , 3 ] elif num_layers == 200 : units = [ 3 , 24 , 36 , 3 ] elif num_layers == 269 : units = [ 3 , 30 , 48 , 8 ] else : raise ValueError ( "no experiments done on num_layers {}, you can do it yourself" . format ( num_layers ) ) return resnext ( units = units , num_stages = num_stages , filter_list = filter_list , num_classes = num_classes , num_group = num_group , image_shape = image_shape , bottle_neck = bottle_neck , workspace = conv_workspace , dtype = dtype )