text: # --------------------------------------------------------
  # Deformable Convolutional Networks
  # Copyright (c) 2017 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Yuwen Xiong, Xizhou Zhu
  # --------------------------------------------------------
  
  import cPickle
  import mxnet as mx
  from utils.symbol import Symbol
  from operator_py.proposal import *
  from operator_py.proposal_target import *
  from operator_py.box_annotator_ohem import *
  
  
  class resnet_v1_101_rfcn_dcn_light(Symbol):
  
      def __init__(self):
          """
          Use __init__ to define parameter network needs
          """
          self.eps = 1e-5
          self.use_global_stats = True
          self.workspace = 512
          self.units = (3, 4, 23, 3) # use for 101
          self.filter_list = [256, 512, 1024, 2048]
  
      def get_resnet_v1_conv4(self, data):
          conv1 = mx.symbol.Convolution(name='conv1', data=data, num_filter=64, pad=(3, 3), kernel=(7, 7), stride=(2, 2),
                                        no_bias=True)
          bn_conv1 = mx.symbol.BatchNorm(name='bn_conv1', data=conv1, use_global_stats=True, fix_gamma=False, eps=self.eps)
          scale_conv1 = bn_conv1
          conv1_relu = mx.symbol.Activation(name='conv1_relu', data=scale_conv1, act_type='relu')
          pool1 = mx.symbol.Pooling(name='pool1', data=conv1_relu, pooling_convention='full', pad=(0, 0), kernel=(3, 3),
                                    stride=(2, 2), pool_type='max')
          res2a_branch1 = mx.symbol.Convolution(name='res2a_branch1', data=pool1, num_filter=256, pad=(0, 0), kernel=(1, 1),
                                                stride=(1, 1), no_bias=True)
          bn2a_branch1 = mx.symbol.BatchNorm(name='bn2a_branch1', data=res2a_branch1, use_global_stats=True, fix_gamma=False, eps=self.eps)
          scale2a_branch1 = bn2a_branch1
          res2a_branch2a = mx.symbol.Convolution(name='res2a_branch2a', data=pool1, num_filter=64, pad=(0, 0), kernel=(1, 1),
                                                 stride=(1, 1), no_bias=True)
          bn2a_branch2a = mx.symbol.BatchNorm(name='bn2a_branch2a', data=res2a_branch2a, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale2a_branch2a = bn2a_branch2a
          res2a_branch2a_relu = mx.symbol.Activation(name='res2a_branch2a_relu', data=scale2a_branch2a, act_type='relu')
          res2a_branch2b = mx.symbol.Convolution(name='res2a_branch2b', data=res2a_branch2a_relu, num_filter=64, pad=(1, 1),
                                                 kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn2a_branch2b = mx.symbol.BatchNorm(name='bn2a_branch2b', data=res2a_branch2b, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale2a_branch2b = bn2a_branch2b
          res2a_branch2b_relu = mx.symbol.Activation(name='res2a_branch2b_relu', data=scale2a_branch2b, act_type='relu')
          res2a_branch2c = mx.symbol.Convolution(name='res2a_branch2c', data=res2a_branch2b_relu, num_filter=256, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn2a_branch2c = mx.symbol.BatchNorm(name='bn2a_branch2c', data=res2a_branch2c, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale2a_branch2c = bn2a_branch2c
          res2a = mx.symbol.broadcast_add(name='res2a', *[scale2a_branch1, scale2a_branch2c])
          res2a_relu = mx.symbol.Activation(name='res2a_relu', data=res2a, act_type='relu')
          res2b_branch2a = mx.symbol.Convolution(name='res2b_branch2a', data=res2a_relu, num_filter=64, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn2b_branch2a = mx.symbol.BatchNorm(name='bn2b_branch2a', data=res2b_branch2a, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale2b_branch2a = bn2b_branch2a
          res2b_branch2a_relu = mx.symbol.Activation(name='res2b_branch2a_relu', data=scale2b_branch2a, act_type='relu')
          res2b_branch2b = mx.symbol.Convolution(name='res2b_branch2b', data=res2b_branch2a_relu, num_filter=64, pad=(1, 1),
                                                 kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn2b_branch2b = mx.symbol.BatchNorm(name='bn2b_branch2b', data=res2b_branch2b, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale2b_branch2b = bn2b_branch2b
          res2b_branch2b_relu = mx.symbol.Activation(name='res2b_branch2b_relu', data=scale2b_branch2b, act_type='relu')
          res2b_branch2c = mx.symbol.Convolution(name='res2b_branch2c', data=res2b_branch2b_relu, num_filter=256, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn2b_branch2c = mx.symbol.BatchNorm(name='bn2b_branch2c', data=res2b_branch2c, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale2b_branch2c = bn2b_branch2c
          res2b = mx.symbol.broadcast_add(name='res2b', *[res2a_relu, scale2b_branch2c])
          res2b_relu = mx.symbol.Activation(name='res2b_relu', data=res2b, act_type='relu')
          res2c_branch2a = mx.symbol.Convolution(name='res2c_branch2a', data=res2b_relu, num_filter=64, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn2c_branch2a = mx.symbol.BatchNorm(name='bn2c_branch2a', data=res2c_branch2a, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale2c_branch2a = bn2c_branch2a
          res2c_branch2a_relu = mx.symbol.Activation(name='res2c_branch2a_relu', data=scale2c_branch2a, act_type='relu')
          res2c_branch2b = mx.symbol.Convolution(name='res2c_branch2b', data=res2c_branch2a_relu, num_filter=64, pad=(1, 1),
                                                 kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn2c_branch2b = mx.symbol.BatchNorm(name='bn2c_branch2b', data=res2c_branch2b, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale2c_branch2b = bn2c_branch2b
          res2c_branch2b_relu = mx.symbol.Activation(name='res2c_branch2b_relu', data=scale2c_branch2b, act_type='relu')
          res2c_branch2c = mx.symbol.Convolution(name='res2c_branch2c', data=res2c_branch2b_relu, num_filter=256, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn2c_branch2c = mx.symbol.BatchNorm(name='bn2c_branch2c', data=res2c_branch2c, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale2c_branch2c = bn2c_branch2c
          res2c = mx.symbol.broadcast_add(name='res2c', *[res2b_relu, scale2c_branch2c])
          res2c_relu = mx.symbol.Activation(name='res2c_relu', data=res2c, act_type='relu')
          res3a_branch1 = mx.symbol.Convolution(name='res3a_branch1', data=res2c_relu, num_filter=512, pad=(0, 0),
                                                kernel=(1, 1), stride=(2, 2), no_bias=True)
          bn3a_branch1 = mx.symbol.BatchNorm(name='bn3a_branch1', data=res3a_branch1, use_global_stats=True, fix_gamma=False, eps=self.eps)
          scale3a_branch1 = bn3a_branch1
          res3a_branch2a = mx.symbol.Convolution(name='res3a_branch2a', data=res2c_relu, num_filter=128, pad=(0, 0),
                                                 kernel=(1, 1), stride=(2, 2), no_bias=True)
          bn3a_branch2a = mx.symbol.BatchNorm(name='bn3a_branch2a', data=res3a_branch2a, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale3a_branch2a = bn3a_branch2a
          res3a_branch2a_relu = mx.symbol.Activation(name='res3a_branch2a_relu', data=scale3a_branch2a, act_type='relu')
          res3a_branch2b = mx.symbol.Convolution(name='res3a_branch2b', data=res3a_branch2a_relu, num_filter=128, pad=(1, 1),
                                                 kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn3a_branch2b = mx.symbol.BatchNorm(name='bn3a_branch2b', data=res3a_branch2b, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale3a_branch2b = bn3a_branch2b
          res3a_branch2b_relu = mx.symbol.Activation(name='res3a_branch2b_relu', data=scale3a_branch2b, act_type='relu')
          res3a_branch2c = mx.symbol.Convolution(name='res3a_branch2c', data=res3a_branch2b_relu, num_filter=512, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn3a_branch2c = mx.symbol.BatchNorm(name='bn3a_branch2c', data=res3a_branch2c, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale3a_branch2c = bn3a_branch2c
          res3a = mx.symbol.broadcast_add(name='res3a', *[scale3a_branch1, scale3a_branch2c])
          res3a_relu = mx.symbol.Activation(name='res3a_relu', data=res3a, act_type='relu')
          res3b1_branch2a = mx.symbol.Convolution(name='res3b1_branch2a', data=res3a_relu, num_filter=128, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn3b1_branch2a = mx.symbol.BatchNorm(name='bn3b1_branch2a', data=res3b1_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale3b1_branch2a = bn3b1_branch2a
          res3b1_branch2a_relu = mx.symbol.Activation(name='res3b1_branch2a_relu', data=scale3b1_branch2a, act_type='relu')
          res3b1_branch2b = mx.symbol.Convolution(name='res3b1_branch2b', data=res3b1_branch2a_relu, num_filter=128,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn3b1_branch2b = mx.symbol.BatchNorm(name='bn3b1_branch2b', data=res3b1_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale3b1_branch2b = bn3b1_branch2b
          res3b1_branch2b_relu = mx.symbol.Activation(name='res3b1_branch2b_relu', data=scale3b1_branch2b, act_type='relu')
          res3b1_branch2c = mx.symbol.Convolution(name='res3b1_branch2c', data=res3b1_branch2b_relu, num_filter=512,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn3b1_branch2c = mx.symbol.BatchNorm(name='bn3b1_branch2c', data=res3b1_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale3b1_branch2c = bn3b1_branch2c
          res3b1 = mx.symbol.broadcast_add(name='res3b1', *[res3a_relu, scale3b1_branch2c])
          res3b1_relu = mx.symbol.Activation(name='res3b1_relu', data=res3b1, act_type='relu')
          res3b2_branch2a = mx.symbol.Convolution(name='res3b2_branch2a', data=res3b1_relu, num_filter=128, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn3b2_branch2a = mx.symbol.BatchNorm(name='bn3b2_branch2a', data=res3b2_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale3b2_branch2a = bn3b2_branch2a
          res3b2_branch2a_relu = mx.symbol.Activation(name='res3b2_branch2a_relu', data=scale3b2_branch2a, act_type='relu')
          res3b2_branch2b = mx.symbol.Convolution(name='res3b2_branch2b', data=res3b2_branch2a_relu, num_filter=128,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn3b2_branch2b = mx.symbol.BatchNorm(name='bn3b2_branch2b', data=res3b2_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale3b2_branch2b = bn3b2_branch2b
          res3b2_branch2b_relu = mx.symbol.Activation(name='res3b2_branch2b_relu', data=scale3b2_branch2b, act_type='relu')
          res3b2_branch2c = mx.symbol.Convolution(name='res3b2_branch2c', data=res3b2_branch2b_relu, num_filter=512,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn3b2_branch2c = mx.symbol.BatchNorm(name='bn3b2_branch2c', data=res3b2_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale3b2_branch2c = bn3b2_branch2c
          res3b2 = mx.symbol.broadcast_add(name='res3b2', *[res3b1_relu, scale3b2_branch2c])
          res3b2_relu = mx.symbol.Activation(name='res3b2_relu', data=res3b2, act_type='relu')
          res3b3_branch2a = mx.symbol.Convolution(name='res3b3_branch2a', data=res3b2_relu, num_filter=128, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn3b3_branch2a = mx.symbol.BatchNorm(name='bn3b3_branch2a', data=res3b3_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale3b3_branch2a = bn3b3_branch2a
          res3b3_branch2a_relu = mx.symbol.Activation(name='res3b3_branch2a_relu', data=scale3b3_branch2a, act_type='relu')
          res3b3_branch2b = mx.symbol.Convolution(name='res3b3_branch2b', data=res3b3_branch2a_relu, num_filter=128,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn3b3_branch2b = mx.symbol.BatchNorm(name='bn3b3_branch2b', data=res3b3_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale3b3_branch2b = bn3b3_branch2b
          res3b3_branch2b_relu = mx.symbol.Activation(name='res3b3_branch2b_relu', data=scale3b3_branch2b, act_type='relu')
          res3b3_branch2c = mx.symbol.Convolution(name='res3b3_branch2c', data=res3b3_branch2b_relu, num_filter=512,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn3b3_branch2c = mx.symbol.BatchNorm(name='bn3b3_branch2c', data=res3b3_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale3b3_branch2c = bn3b3_branch2c
          res3b3 = mx.symbol.broadcast_add(name='res3b3', *[res3b2_relu, scale3b3_branch2c])
          res3b3_relu = mx.symbol.Activation(name='res3b3_relu', data=res3b3, act_type='relu')
          res4a_branch1 = mx.symbol.Convolution(name='res4a_branch1', data=res3b3_relu, num_filter=1024, pad=(0, 0),
                                                kernel=(1, 1), stride=(2, 2), no_bias=True)
          bn4a_branch1 = mx.symbol.BatchNorm(name='bn4a_branch1', data=res4a_branch1, use_global_stats=True, fix_gamma=False, eps=self.eps)
          scale4a_branch1 = bn4a_branch1
          res4a_branch2a = mx.symbol.Convolution(name='res4a_branch2a', data=res3b3_relu, num_filter=256, pad=(0, 0),
                                                 kernel=(1, 1), stride=(2, 2), no_bias=True)
          bn4a_branch2a = mx.symbol.BatchNorm(name='bn4a_branch2a', data=res4a_branch2a, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale4a_branch2a = bn4a_branch2a
          res4a_branch2a_relu = mx.symbol.Activation(name='res4a_branch2a_relu', data=scale4a_branch2a, act_type='relu')
          res4a_branch2b = mx.symbol.Convolution(name='res4a_branch2b', data=res4a_branch2a_relu, num_filter=256, pad=(1, 1),
                                                 kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4a_branch2b = mx.symbol.BatchNorm(name='bn4a_branch2b', data=res4a_branch2b, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale4a_branch2b = bn4a_branch2b
          res4a_branch2b_relu = mx.symbol.Activation(name='res4a_branch2b_relu', data=scale4a_branch2b, act_type='relu')
          res4a_branch2c = mx.symbol.Convolution(name='res4a_branch2c', data=res4a_branch2b_relu, num_filter=1024, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4a_branch2c = mx.symbol.BatchNorm(name='bn4a_branch2c', data=res4a_branch2c, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale4a_branch2c = bn4a_branch2c
          res4a = mx.symbol.broadcast_add(name='res4a', *[scale4a_branch1, scale4a_branch2c])
          res4a_relu = mx.symbol.Activation(name='res4a_relu', data=res4a, act_type='relu')
          res4b1_branch2a = mx.symbol.Convolution(name='res4b1_branch2a', data=res4a_relu, num_filter=256, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b1_branch2a = mx.symbol.BatchNorm(name='bn4b1_branch2a', data=res4b1_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b1_branch2a = bn4b1_branch2a
          res4b1_branch2a_relu = mx.symbol.Activation(name='res4b1_branch2a_relu', data=scale4b1_branch2a, act_type='relu')
          res4b1_branch2b = mx.symbol.Convolution(name='res4b1_branch2b', data=res4b1_branch2a_relu, num_filter=256,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b1_branch2b = mx.symbol.BatchNorm(name='bn4b1_branch2b', data=res4b1_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b1_branch2b = bn4b1_branch2b
          res4b1_branch2b_relu = mx.symbol.Activation(name='res4b1_branch2b_relu', data=scale4b1_branch2b, act_type='relu')
          res4b1_branch2c = mx.symbol.Convolution(name='res4b1_branch2c', data=res4b1_branch2b_relu, num_filter=1024,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b1_branch2c = mx.symbol.BatchNorm(name='bn4b1_branch2c', data=res4b1_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b1_branch2c = bn4b1_branch2c
          res4b1 = mx.symbol.broadcast_add(name='res4b1', *[res4a_relu, scale4b1_branch2c])
          res4b1_relu = mx.symbol.Activation(name='res4b1_relu', data=res4b1, act_type='relu')
          res4b2_branch2a = mx.symbol.Convolution(name='res4b2_branch2a', data=res4b1_relu, num_filter=256, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b2_branch2a = mx.symbol.BatchNorm(name='bn4b2_branch2a', data=res4b2_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b2_branch2a = bn4b2_branch2a
          res4b2_branch2a_relu = mx.symbol.Activation(name='res4b2_branch2a_relu', data=scale4b2_branch2a, act_type='relu')
          res4b2_branch2b = mx.symbol.Convolution(name='res4b2_branch2b', data=res4b2_branch2a_relu, num_filter=256,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b2_branch2b = mx.symbol.BatchNorm(name='bn4b2_branch2b', data=res4b2_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b2_branch2b = bn4b2_branch2b
          res4b2_branch2b_relu = mx.symbol.Activation(name='res4b2_branch2b_relu', data=scale4b2_branch2b, act_type='relu')
          res4b2_branch2c = mx.symbol.Convolution(name='res4b2_branch2c', data=res4b2_branch2b_relu, num_filter=1024,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b2_branch2c = mx.symbol.BatchNorm(name='bn4b2_branch2c', data=res4b2_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b2_branch2c = bn4b2_branch2c
          res4b2 = mx.symbol.broadcast_add(name='res4b2', *[res4b1_relu, scale4b2_branch2c])
          res4b2_relu = mx.symbol.Activation(name='res4b2_relu', data=res4b2, act_type='relu')
          res4b3_branch2a = mx.symbol.Convolution(name='res4b3_branch2a', data=res4b2_relu, num_filter=256, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b3_branch2a = mx.symbol.BatchNorm(name='bn4b3_branch2a', data=res4b3_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b3_branch2a = bn4b3_branch2a
          res4b3_branch2a_relu = mx.symbol.Activation(name='res4b3_branch2a_relu', data=scale4b3_branch2a, act_type='relu')
          res4b3_branch2b = mx.symbol.Convolution(name='res4b3_branch2b', data=res4b3_branch2a_relu, num_filter=256,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b3_branch2b = mx.symbol.BatchNorm(name='bn4b3_branch2b', data=res4b3_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b3_branch2b = bn4b3_branch2b
          res4b3_branch2b_relu = mx.symbol.Activation(name='res4b3_branch2b_relu', data=scale4b3_branch2b, act_type='relu')
          res4b3_branch2c = mx.symbol.Convolution(name='res4b3_branch2c', data=res4b3_branch2b_relu, num_filter=1024,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b3_branch2c = mx.symbol.BatchNorm(name='bn4b3_branch2c', data=res4b3_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b3_branch2c = bn4b3_branch2c
          res4b3 = mx.symbol.broadcast_add(name='res4b3', *[res4b2_relu, scale4b3_branch2c])
          res4b3_relu = mx.symbol.Activation(name='res4b3_relu', data=res4b3, act_type='relu')
          res4b4_branch2a = mx.symbol.Convolution(name='res4b4_branch2a', data=res4b3_relu, num_filter=256, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b4_branch2a = mx.symbol.BatchNorm(name='bn4b4_branch2a', data=res4b4_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b4_branch2a = bn4b4_branch2a
          res4b4_branch2a_relu = mx.symbol.Activation(name='res4b4_branch2a_relu', data=scale4b4_branch2a, act_type='relu')
          res4b4_branch2b = mx.symbol.Convolution(name='res4b4_branch2b', data=res4b4_branch2a_relu, num_filter=256,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b4_branch2b = mx.symbol.BatchNorm(name='bn4b4_branch2b', data=res4b4_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b4_branch2b = bn4b4_branch2b
          res4b4_branch2b_relu = mx.symbol.Activation(name='res4b4_branch2b_relu', data=scale4b4_branch2b, act_type='relu')
          res4b4_branch2c = mx.symbol.Convolution(name='res4b4_branch2c', data=res4b4_branch2b_relu, num_filter=1024,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b4_branch2c = mx.symbol.BatchNorm(name='bn4b4_branch2c', data=res4b4_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b4_branch2c = bn4b4_branch2c
          res4b4 = mx.symbol.broadcast_add(name='res4b4', *[res4b3_relu, scale4b4_branch2c])
          res4b4_relu = mx.symbol.Activation(name='res4b4_relu', data=res4b4, act_type='relu')
          res4b5_branch2a = mx.symbol.Convolution(name='res4b5_branch2a', data=res4b4_relu, num_filter=256, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b5_branch2a = mx.symbol.BatchNorm(name='bn4b5_branch2a', data=res4b5_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b5_branch2a = bn4b5_branch2a
          res4b5_branch2a_relu = mx.symbol.Activation(name='res4b5_branch2a_relu', data=scale4b5_branch2a, act_type='relu')
          res4b5_branch2b = mx.symbol.Convolution(name='res4b5_branch2b', data=res4b5_branch2a_relu, num_filter=256,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b5_branch2b = mx.symbol.BatchNorm(name='bn4b5_branch2b', data=res4b5_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b5_branch2b = bn4b5_branch2b
          res4b5_branch2b_relu = mx.symbol.Activation(name='res4b5_branch2b_relu', data=scale4b5_branch2b, act_type='relu')
          res4b5_branch2c = mx.symbol.Convolution(name='res4b5_branch2c', data=res4b5_branch2b_relu, num_filter=1024,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b5_branch2c = mx.symbol.BatchNorm(name='bn4b5_branch2c', data=res4b5_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b5_branch2c = bn4b5_branch2c
          res4b5 = mx.symbol.broadcast_add(name='res4b5', *[res4b4_relu, scale4b5_branch2c])
          res4b5_relu = mx.symbol.Activation(name='res4b5_relu', data=res4b5, act_type='relu')
          res4b6_branch2a = mx.symbol.Convolution(name='res4b6_branch2a', data=res4b5_relu, num_filter=256, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b6_branch2a = mx.symbol.BatchNorm(name='bn4b6_branch2a', data=res4b6_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b6_branch2a = bn4b6_branch2a
          res4b6_branch2a_relu = mx.symbol.Activation(name='res4b6_branch2a_relu', data=scale4b6_branch2a, act_type='relu')
          res4b6_branch2b = mx.symbol.Convolution(name='res4b6_branch2b', data=res4b6_branch2a_relu, num_filter=256,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b6_branch2b = mx.symbol.BatchNorm(name='bn4b6_branch2b', data=res4b6_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b6_branch2b = bn4b6_branch2b
          res4b6_branch2b_relu = mx.symbol.Activation(name='res4b6_branch2b_relu', data=scale4b6_branch2b, act_type='relu')
          res4b6_branch2c = mx.symbol.Convolution(name='res4b6_branch2c', data=res4b6_branch2b_relu, num_filter=1024,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b6_branch2c = mx.symbol.BatchNorm(name='bn4b6_branch2c', data=res4b6_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b6_branch2c = bn4b6_branch2c
          res4b6 = mx.symbol.broadcast_add(name='res4b6', *[res4b5_relu, scale4b6_branch2c])
          res4b6_relu = mx.symbol.Activation(name='res4b6_relu', data=res4b6, act_type='relu')
          res4b7_branch2a = mx.symbol.Convolution(name='res4b7_branch2a', data=res4b6_relu, num_filter=256, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b7_branch2a = mx.symbol.BatchNorm(name='bn4b7_branch2a', data=res4b7_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b7_branch2a = bn4b7_branch2a
          res4b7_branch2a_relu = mx.symbol.Activation(name='res4b7_branch2a_relu', data=scale4b7_branch2a, act_type='relu')
          res4b7_branch2b = mx.symbol.Convolution(name='res4b7_branch2b', data=res4b7_branch2a_relu, num_filter=256,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b7_branch2b = mx.symbol.BatchNorm(name='bn4b7_branch2b', data=res4b7_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b7_branch2b = bn4b7_branch2b
          res4b7_branch2b_relu = mx.symbol.Activation(name='res4b7_branch2b_relu', data=scale4b7_branch2b, act_type='relu')
          res4b7_branch2c = mx.symbol.Convolution(name='res4b7_branch2c', data=res4b7_branch2b_relu, num_filter=1024,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b7_branch2c = mx.symbol.BatchNorm(name='bn4b7_branch2c', data=res4b7_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b7_branch2c = bn4b7_branch2c
          res4b7 = mx.symbol.broadcast_add(name='res4b7', *[res4b6_relu, scale4b7_branch2c])
          res4b7_relu = mx.symbol.Activation(name='res4b7_relu', data=res4b7, act_type='relu')
          res4b8_branch2a = mx.symbol.Convolution(name='res4b8_branch2a', data=res4b7_relu, num_filter=256, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b8_branch2a = mx.symbol.BatchNorm(name='bn4b8_branch2a', data=res4b8_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b8_branch2a = bn4b8_branch2a
          res4b8_branch2a_relu = mx.symbol.Activation(name='res4b8_branch2a_relu', data=scale4b8_branch2a, act_type='relu')
          res4b8_branch2b = mx.symbol.Convolution(name='res4b8_branch2b', data=res4b8_branch2a_relu, num_filter=256,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b8_branch2b = mx.symbol.BatchNorm(name='bn4b8_branch2b', data=res4b8_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b8_branch2b = bn4b8_branch2b
          res4b8_branch2b_relu = mx.symbol.Activation(name='res4b8_branch2b_relu', data=scale4b8_branch2b, act_type='relu')
          res4b8_branch2c = mx.symbol.Convolution(name='res4b8_branch2c', data=res4b8_branch2b_relu, num_filter=1024,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b8_branch2c = mx.symbol.BatchNorm(name='bn4b8_branch2c', data=res4b8_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b8_branch2c = bn4b8_branch2c
          res4b8 = mx.symbol.broadcast_add(name='res4b8', *[res4b7_relu, scale4b8_branch2c])
          res4b8_relu = mx.symbol.Activation(name='res4b8_relu', data=res4b8, act_type='relu')
          res4b9_branch2a = mx.symbol.Convolution(name='res4b9_branch2a', data=res4b8_relu, num_filter=256, pad=(0, 0),
                                                  kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b9_branch2a = mx.symbol.BatchNorm(name='bn4b9_branch2a', data=res4b9_branch2a, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b9_branch2a = bn4b9_branch2a
          res4b9_branch2a_relu = mx.symbol.Activation(name='res4b9_branch2a_relu', data=scale4b9_branch2a, act_type='relu')
          res4b9_branch2b = mx.symbol.Convolution(name='res4b9_branch2b', data=res4b9_branch2a_relu, num_filter=256,
                                                  pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b9_branch2b = mx.symbol.BatchNorm(name='bn4b9_branch2b', data=res4b9_branch2b, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b9_branch2b = bn4b9_branch2b
          res4b9_branch2b_relu = mx.symbol.Activation(name='res4b9_branch2b_relu', data=scale4b9_branch2b, act_type='relu')
          res4b9_branch2c = mx.symbol.Convolution(name='res4b9_branch2c', data=res4b9_branch2b_relu, num_filter=1024,
                                                  pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b9_branch2c = mx.symbol.BatchNorm(name='bn4b9_branch2c', data=res4b9_branch2c, use_global_stats=True,
                                               fix_gamma=False, eps=self.eps)
          scale4b9_branch2c = bn4b9_branch2c
          res4b9 = mx.symbol.broadcast_add(name='res4b9', *[res4b8_relu, scale4b9_branch2c])
          res4b9_relu = mx.symbol.Activation(name='res4b9_relu', data=res4b9, act_type='relu')
          res4b10_branch2a = mx.symbol.Convolution(name='res4b10_branch2a', data=res4b9_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b10_branch2a = mx.symbol.BatchNorm(name='bn4b10_branch2a', data=res4b10_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b10_branch2a = bn4b10_branch2a
          res4b10_branch2a_relu = mx.symbol.Activation(name='res4b10_branch2a_relu', data=scale4b10_branch2a, act_type='relu')
          res4b10_branch2b = mx.symbol.Convolution(name='res4b10_branch2b', data=res4b10_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b10_branch2b = mx.symbol.BatchNorm(name='bn4b10_branch2b', data=res4b10_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b10_branch2b = bn4b10_branch2b
          res4b10_branch2b_relu = mx.symbol.Activation(name='res4b10_branch2b_relu', data=scale4b10_branch2b, act_type='relu')
          res4b10_branch2c = mx.symbol.Convolution(name='res4b10_branch2c', data=res4b10_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b10_branch2c = mx.symbol.BatchNorm(name='bn4b10_branch2c', data=res4b10_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b10_branch2c = bn4b10_branch2c
          res4b10 = mx.symbol.broadcast_add(name='res4b10', *[res4b9_relu, scale4b10_branch2c])
          res4b10_relu = mx.symbol.Activation(name='res4b10_relu', data=res4b10, act_type='relu')
          res4b11_branch2a = mx.symbol.Convolution(name='res4b11_branch2a', data=res4b10_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b11_branch2a = mx.symbol.BatchNorm(name='bn4b11_branch2a', data=res4b11_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b11_branch2a = bn4b11_branch2a
          res4b11_branch2a_relu = mx.symbol.Activation(name='res4b11_branch2a_relu', data=scale4b11_branch2a, act_type='relu')
          res4b11_branch2b = mx.symbol.Convolution(name='res4b11_branch2b', data=res4b11_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b11_branch2b = mx.symbol.BatchNorm(name='bn4b11_branch2b', data=res4b11_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b11_branch2b = bn4b11_branch2b
          res4b11_branch2b_relu = mx.symbol.Activation(name='res4b11_branch2b_relu', data=scale4b11_branch2b, act_type='relu')
          res4b11_branch2c = mx.symbol.Convolution(name='res4b11_branch2c', data=res4b11_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b11_branch2c = mx.symbol.BatchNorm(name='bn4b11_branch2c', data=res4b11_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b11_branch2c = bn4b11_branch2c
          res4b11 = mx.symbol.broadcast_add(name='res4b11', *[res4b10_relu, scale4b11_branch2c])
          res4b11_relu = mx.symbol.Activation(name='res4b11_relu', data=res4b11, act_type='relu')
          res4b12_branch2a = mx.symbol.Convolution(name='res4b12_branch2a', data=res4b11_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b12_branch2a = mx.symbol.BatchNorm(name='bn4b12_branch2a', data=res4b12_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b12_branch2a = bn4b12_branch2a
          res4b12_branch2a_relu = mx.symbol.Activation(name='res4b12_branch2a_relu', data=scale4b12_branch2a, act_type='relu')
          res4b12_branch2b = mx.symbol.Convolution(name='res4b12_branch2b', data=res4b12_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b12_branch2b = mx.symbol.BatchNorm(name='bn4b12_branch2b', data=res4b12_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b12_branch2b = bn4b12_branch2b
          res4b12_branch2b_relu = mx.symbol.Activation(name='res4b12_branch2b_relu', data=scale4b12_branch2b, act_type='relu')
          res4b12_branch2c = mx.symbol.Convolution(name='res4b12_branch2c', data=res4b12_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b12_branch2c = mx.symbol.BatchNorm(name='bn4b12_branch2c', data=res4b12_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b12_branch2c = bn4b12_branch2c
          res4b12 = mx.symbol.broadcast_add(name='res4b12', *[res4b11_relu, scale4b12_branch2c])
          res4b12_relu = mx.symbol.Activation(name='res4b12_relu', data=res4b12, act_type='relu')
          res4b13_branch2a = mx.symbol.Convolution(name='res4b13_branch2a', data=res4b12_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b13_branch2a = mx.symbol.BatchNorm(name='bn4b13_branch2a', data=res4b13_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b13_branch2a = bn4b13_branch2a
          res4b13_branch2a_relu = mx.symbol.Activation(name='res4b13_branch2a_relu', data=scale4b13_branch2a, act_type='relu')
          res4b13_branch2b = mx.symbol.Convolution(name='res4b13_branch2b', data=res4b13_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b13_branch2b = mx.symbol.BatchNorm(name='bn4b13_branch2b', data=res4b13_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b13_branch2b = bn4b13_branch2b
          res4b13_branch2b_relu = mx.symbol.Activation(name='res4b13_branch2b_relu', data=scale4b13_branch2b, act_type='relu')
          res4b13_branch2c = mx.symbol.Convolution(name='res4b13_branch2c', data=res4b13_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b13_branch2c = mx.symbol.BatchNorm(name='bn4b13_branch2c', data=res4b13_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b13_branch2c = bn4b13_branch2c
          res4b13 = mx.symbol.broadcast_add(name='res4b13', *[res4b12_relu, scale4b13_branch2c])
          res4b13_relu = mx.symbol.Activation(name='res4b13_relu', data=res4b13, act_type='relu')
          res4b14_branch2a = mx.symbol.Convolution(name='res4b14_branch2a', data=res4b13_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b14_branch2a = mx.symbol.BatchNorm(name='bn4b14_branch2a', data=res4b14_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b14_branch2a = bn4b14_branch2a
          res4b14_branch2a_relu = mx.symbol.Activation(name='res4b14_branch2a_relu', data=scale4b14_branch2a, act_type='relu')
          res4b14_branch2b = mx.symbol.Convolution(name='res4b14_branch2b', data=res4b14_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b14_branch2b = mx.symbol.BatchNorm(name='bn4b14_branch2b', data=res4b14_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b14_branch2b = bn4b14_branch2b
          res4b14_branch2b_relu = mx.symbol.Activation(name='res4b14_branch2b_relu', data=scale4b14_branch2b, act_type='relu')
          res4b14_branch2c = mx.symbol.Convolution(name='res4b14_branch2c', data=res4b14_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b14_branch2c = mx.symbol.BatchNorm(name='bn4b14_branch2c', data=res4b14_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b14_branch2c = bn4b14_branch2c
          res4b14 = mx.symbol.broadcast_add(name='res4b14', *[res4b13_relu, scale4b14_branch2c])
          res4b14_relu = mx.symbol.Activation(name='res4b14_relu', data=res4b14, act_type='relu')
          res4b15_branch2a = mx.symbol.Convolution(name='res4b15_branch2a', data=res4b14_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b15_branch2a = mx.symbol.BatchNorm(name='bn4b15_branch2a', data=res4b15_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b15_branch2a = bn4b15_branch2a
          res4b15_branch2a_relu = mx.symbol.Activation(name='res4b15_branch2a_relu', data=scale4b15_branch2a, act_type='relu')
          res4b15_branch2b = mx.symbol.Convolution(name='res4b15_branch2b', data=res4b15_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b15_branch2b = mx.symbol.BatchNorm(name='bn4b15_branch2b', data=res4b15_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b15_branch2b = bn4b15_branch2b
          res4b15_branch2b_relu = mx.symbol.Activation(name='res4b15_branch2b_relu', data=scale4b15_branch2b, act_type='relu')
          res4b15_branch2c = mx.symbol.Convolution(name='res4b15_branch2c', data=res4b15_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b15_branch2c = mx.symbol.BatchNorm(name='bn4b15_branch2c', data=res4b15_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b15_branch2c = bn4b15_branch2c
          res4b15 = mx.symbol.broadcast_add(name='res4b15', *[res4b14_relu, scale4b15_branch2c])
          res4b15_relu = mx.symbol.Activation(name='res4b15_relu', data=res4b15, act_type='relu')
          res4b16_branch2a = mx.symbol.Convolution(name='res4b16_branch2a', data=res4b15_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b16_branch2a = mx.symbol.BatchNorm(name='bn4b16_branch2a', data=res4b16_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b16_branch2a = bn4b16_branch2a
          res4b16_branch2a_relu = mx.symbol.Activation(name='res4b16_branch2a_relu', data=scale4b16_branch2a, act_type='relu')
          res4b16_branch2b = mx.symbol.Convolution(name='res4b16_branch2b', data=res4b16_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b16_branch2b = mx.symbol.BatchNorm(name='bn4b16_branch2b', data=res4b16_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b16_branch2b = bn4b16_branch2b
          res4b16_branch2b_relu = mx.symbol.Activation(name='res4b16_branch2b_relu', data=scale4b16_branch2b, act_type='relu')
          res4b16_branch2c = mx.symbol.Convolution(name='res4b16_branch2c', data=res4b16_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b16_branch2c = mx.symbol.BatchNorm(name='bn4b16_branch2c', data=res4b16_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b16_branch2c = bn4b16_branch2c
          res4b16 = mx.symbol.broadcast_add(name='res4b16', *[res4b15_relu, scale4b16_branch2c])
          res4b16_relu = mx.symbol.Activation(name='res4b16_relu', data=res4b16, act_type='relu')
          res4b17_branch2a = mx.symbol.Convolution(name='res4b17_branch2a', data=res4b16_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b17_branch2a = mx.symbol.BatchNorm(name='bn4b17_branch2a', data=res4b17_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b17_branch2a = bn4b17_branch2a
          res4b17_branch2a_relu = mx.symbol.Activation(name='res4b17_branch2a_relu', data=scale4b17_branch2a, act_type='relu')
          res4b17_branch2b = mx.symbol.Convolution(name='res4b17_branch2b', data=res4b17_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b17_branch2b = mx.symbol.BatchNorm(name='bn4b17_branch2b', data=res4b17_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b17_branch2b = bn4b17_branch2b
          res4b17_branch2b_relu = mx.symbol.Activation(name='res4b17_branch2b_relu', data=scale4b17_branch2b, act_type='relu')
          res4b17_branch2c = mx.symbol.Convolution(name='res4b17_branch2c', data=res4b17_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b17_branch2c = mx.symbol.BatchNorm(name='bn4b17_branch2c', data=res4b17_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b17_branch2c = bn4b17_branch2c
          res4b17 = mx.symbol.broadcast_add(name='res4b17', *[res4b16_relu, scale4b17_branch2c])
          res4b17_relu = mx.symbol.Activation(name='res4b17_relu', data=res4b17, act_type='relu')
          res4b18_branch2a = mx.symbol.Convolution(name='res4b18_branch2a', data=res4b17_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b18_branch2a = mx.symbol.BatchNorm(name='bn4b18_branch2a', data=res4b18_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b18_branch2a = bn4b18_branch2a
          res4b18_branch2a_relu = mx.symbol.Activation(name='res4b18_branch2a_relu', data=scale4b18_branch2a, act_type='relu')
          res4b18_branch2b = mx.symbol.Convolution(name='res4b18_branch2b', data=res4b18_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b18_branch2b = mx.symbol.BatchNorm(name='bn4b18_branch2b', data=res4b18_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b18_branch2b = bn4b18_branch2b
          res4b18_branch2b_relu = mx.symbol.Activation(name='res4b18_branch2b_relu', data=scale4b18_branch2b, act_type='relu')
          res4b18_branch2c = mx.symbol.Convolution(name='res4b18_branch2c', data=res4b18_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b18_branch2c = mx.symbol.BatchNorm(name='bn4b18_branch2c', data=res4b18_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b18_branch2c = bn4b18_branch2c
          res4b18 = mx.symbol.broadcast_add(name='res4b18', *[res4b17_relu, scale4b18_branch2c])
          res4b18_relu = mx.symbol.Activation(name='res4b18_relu', data=res4b18, act_type='relu')
          res4b19_branch2a = mx.symbol.Convolution(name='res4b19_branch2a', data=res4b18_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b19_branch2a = mx.symbol.BatchNorm(name='bn4b19_branch2a', data=res4b19_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b19_branch2a = bn4b19_branch2a
          res4b19_branch2a_relu = mx.symbol.Activation(name='res4b19_branch2a_relu', data=scale4b19_branch2a, act_type='relu')
          res4b19_branch2b = mx.symbol.Convolution(name='res4b19_branch2b', data=res4b19_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b19_branch2b = mx.symbol.BatchNorm(name='bn4b19_branch2b', data=res4b19_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b19_branch2b = bn4b19_branch2b
          res4b19_branch2b_relu = mx.symbol.Activation(name='res4b19_branch2b_relu', data=scale4b19_branch2b, act_type='relu')
          res4b19_branch2c = mx.symbol.Convolution(name='res4b19_branch2c', data=res4b19_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b19_branch2c = mx.symbol.BatchNorm(name='bn4b19_branch2c', data=res4b19_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b19_branch2c = bn4b19_branch2c
          res4b19 = mx.symbol.broadcast_add(name='res4b19', *[res4b18_relu, scale4b19_branch2c])
          res4b19_relu = mx.symbol.Activation(name='res4b19_relu', data=res4b19, act_type='relu')
          res4b20_branch2a = mx.symbol.Convolution(name='res4b20_branch2a', data=res4b19_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b20_branch2a = mx.symbol.BatchNorm(name='bn4b20_branch2a', data=res4b20_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b20_branch2a = bn4b20_branch2a
          res4b20_branch2a_relu = mx.symbol.Activation(name='res4b20_branch2a_relu', data=scale4b20_branch2a, act_type='relu')
          res4b20_branch2b = mx.symbol.Convolution(name='res4b20_branch2b', data=res4b20_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b20_branch2b = mx.symbol.BatchNorm(name='bn4b20_branch2b', data=res4b20_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b20_branch2b = bn4b20_branch2b
          res4b20_branch2b_relu = mx.symbol.Activation(name='res4b20_branch2b_relu', data=scale4b20_branch2b, act_type='relu')
          res4b20_branch2c = mx.symbol.Convolution(name='res4b20_branch2c', data=res4b20_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b20_branch2c = mx.symbol.BatchNorm(name='bn4b20_branch2c', data=res4b20_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b20_branch2c = bn4b20_branch2c
          res4b20 = mx.symbol.broadcast_add(name='res4b20', *[res4b19_relu, scale4b20_branch2c])
          res4b20_relu = mx.symbol.Activation(name='res4b20_relu', data=res4b20, act_type='relu')
          res4b21_branch2a = mx.symbol.Convolution(name='res4b21_branch2a', data=res4b20_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b21_branch2a = mx.symbol.BatchNorm(name='bn4b21_branch2a', data=res4b21_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b21_branch2a = bn4b21_branch2a
          res4b21_branch2a_relu = mx.symbol.Activation(name='res4b21_branch2a_relu', data=scale4b21_branch2a, act_type='relu')
          res4b21_branch2b = mx.symbol.Convolution(name='res4b21_branch2b', data=res4b21_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b21_branch2b = mx.symbol.BatchNorm(name='bn4b21_branch2b', data=res4b21_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b21_branch2b = bn4b21_branch2b
          res4b21_branch2b_relu = mx.symbol.Activation(name='res4b21_branch2b_relu', data=scale4b21_branch2b, act_type='relu')
          res4b21_branch2c = mx.symbol.Convolution(name='res4b21_branch2c', data=res4b21_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b21_branch2c = mx.symbol.BatchNorm(name='bn4b21_branch2c', data=res4b21_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b21_branch2c = bn4b21_branch2c
          res4b21 = mx.symbol.broadcast_add(name='res4b21', *[res4b20_relu, scale4b21_branch2c])
          res4b21_relu = mx.symbol.Activation(name='res4b21_relu', data=res4b21, act_type='relu')
          res4b22_branch2a = mx.symbol.Convolution(name='res4b22_branch2a', data=res4b21_relu, num_filter=256, pad=(0, 0),
                                                   kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b22_branch2a = mx.symbol.BatchNorm(name='bn4b22_branch2a', data=res4b22_branch2a, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b22_branch2a = bn4b22_branch2a
          res4b22_branch2a_relu = mx.symbol.Activation(name='res4b22_branch2a_relu', data=scale4b22_branch2a, act_type='relu')
          res4b22_branch2b = mx.symbol.Convolution(name='res4b22_branch2b', data=res4b22_branch2a_relu, num_filter=256,
                                                   pad=(1, 1), kernel=(3, 3), stride=(1, 1), no_bias=True)
          bn4b22_branch2b = mx.symbol.BatchNorm(name='bn4b22_branch2b', data=res4b22_branch2b, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b22_branch2b = bn4b22_branch2b
          res4b22_branch2b_relu = mx.symbol.Activation(name='res4b22_branch2b_relu', data=scale4b22_branch2b, act_type='relu')
          res4b22_branch2c = mx.symbol.Convolution(name='res4b22_branch2c', data=res4b22_branch2b_relu, num_filter=1024,
                                                   pad=(0, 0), kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn4b22_branch2c = mx.symbol.BatchNorm(name='bn4b22_branch2c', data=res4b22_branch2c, use_global_stats=True,
                                                fix_gamma=False, eps=self.eps)
          scale4b22_branch2c = bn4b22_branch2c
          res4b22 = mx.symbol.broadcast_add(name='res4b22', *[res4b21_relu, scale4b22_branch2c])
          res4b22_relu = mx.symbol.Activation(name='res4b22_relu', data=res4b22, act_type='relu')
          return res4b22_relu
          
      def get_resnet_v1_conv5(self, conv_feat):
          res5a_branch1 = mx.symbol.Convolution(name='res5a_branch1', data=conv_feat, num_filter=2048, pad=(0, 0),
                                                kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn5a_branch1 = mx.symbol.BatchNorm(name='bn5a_branch1', data=res5a_branch1, use_global_stats=True, fix_gamma=False, eps=self.eps)
          scale5a_branch1 = bn5a_branch1
          res5a_branch2a = mx.symbol.Convolution(name='res5a_branch2a', data=conv_feat, num_filter=512, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn5a_branch2a = mx.symbol.BatchNorm(name='bn5a_branch2a', data=res5a_branch2a, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale5a_branch2a = bn5a_branch2a
          res5a_branch2a_relu = mx.symbol.Activation(name='res5a_branch2a_relu', data=scale5a_branch2a, act_type='relu')
          res5a_branch2b_offset = mx.symbol.Convolution(name='res5a_branch2b_offset', data = res5a_branch2a_relu,
                                                        num_filter=72, pad=(2, 2), kernel=(3, 3), stride=(1, 1), dilate=(2, 2), cudnn_off=True)
          res5a_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5a_branch2b', data=res5a_branch2a_relu, offset=res5a_branch2b_offset,
                                                                   num_filter=512, pad=(2, 2), kernel=(3, 3), num_deformable_group=4,
                                                                   stride=(1, 1), dilate=(2, 2), no_bias=True)
          bn5a_branch2b = mx.symbol.BatchNorm(name='bn5a_branch2b', data=res5a_branch2b, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale5a_branch2b = bn5a_branch2b
          res5a_branch2b_relu = mx.symbol.Activation(name='res5a_branch2b_relu', data=scale5a_branch2b, act_type='relu')
          res5a_branch2c = mx.symbol.Convolution(name='res5a_branch2c', data=res5a_branch2b_relu, num_filter=2048, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn5a_branch2c = mx.symbol.BatchNorm(name='bn5a_branch2c', data=res5a_branch2c, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale5a_branch2c = bn5a_branch2c
          res5a = mx.symbol.broadcast_add(name='res5a', *[scale5a_branch1, scale5a_branch2c])
          res5a_relu = mx.symbol.Activation(name='res5a_relu', data=res5a, act_type='relu')
          res5b_branch2a = mx.symbol.Convolution(name='res5b_branch2a', data=res5a_relu, num_filter=512, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn5b_branch2a = mx.symbol.BatchNorm(name='bn5b_branch2a', data=res5b_branch2a, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale5b_branch2a = bn5b_branch2a
          res5b_branch2a_relu = mx.symbol.Activation(name='res5b_branch2a_relu', data=scale5b_branch2a, act_type='relu')
          res5b_branch2b_offset = mx.symbol.Convolution(name='res5b_branch2b_offset', data = res5b_branch2a_relu,
                                                        num_filter=72, pad=(2, 2), kernel=(3, 3), stride=(1, 1), dilate=(2, 2), cudnn_off=True)
          res5b_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5b_branch2b', data=res5b_branch2a_relu, offset=res5b_branch2b_offset,
                                                                   num_filter=512, pad=(2, 2), kernel=(3, 3), num_deformable_group=4,
                                                                   stride=(1, 1), dilate=(2, 2), no_bias=True)
          bn5b_branch2b = mx.symbol.BatchNorm(name='bn5b_branch2b', data=res5b_branch2b, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale5b_branch2b = bn5b_branch2b
          res5b_branch2b_relu = mx.symbol.Activation(name='res5b_branch2b_relu', data=scale5b_branch2b, act_type='relu')
          res5b_branch2c = mx.symbol.Convolution(name='res5b_branch2c', data=res5b_branch2b_relu, num_filter=2048, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn5b_branch2c = mx.symbol.BatchNorm(name='bn5b_branch2c', data=res5b_branch2c, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale5b_branch2c = bn5b_branch2c
          res5b = mx.symbol.broadcast_add(name='res5b', *[res5a_relu, scale5b_branch2c])
          res5b_relu = mx.symbol.Activation(name='res5b_relu', data=res5b, act_type='relu')
          res5c_branch2a = mx.symbol.Convolution(name='res5c_branch2a', data=res5b_relu, num_filter=512, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn5c_branch2a = mx.symbol.BatchNorm(name='bn5c_branch2a', data=res5c_branch2a, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale5c_branch2a = bn5c_branch2a
          res5c_branch2a_relu = mx.symbol.Activation(name='res5c_branch2a_relu', data=scale5c_branch2a, act_type='relu')
          res5c_branch2b_offset = mx.symbol.Convolution(name='res5c_branch2b_offset', data = res5c_branch2a_relu,
                                                        num_filter=72, pad=(2, 2), kernel=(3, 3), stride=(1, 1), dilate=(2, 2), cudnn_off=True)
          res5c_branch2b = mx.contrib.symbol.DeformableConvolution(name='res5c_branch2b', data=res5c_branch2a_relu, offset=res5c_branch2b_offset,
                                                                   num_filter=512, pad=(2, 2), kernel=(3, 3), num_deformable_group=4,
                                                                   stride=(1, 1), dilate=(2, 2), no_bias=True)
          bn5c_branch2b = mx.symbol.BatchNorm(name='bn5c_branch2b', data=res5c_branch2b, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale5c_branch2b = bn5c_branch2b
          res5c_branch2b_relu = mx.symbol.Activation(name='res5c_branch2b_relu', data=scale5c_branch2b, act_type='relu')
          res5c_branch2c = mx.symbol.Convolution(name='res5c_branch2c', data=res5c_branch2b_relu, num_filter=2048, pad=(0, 0),
                                                 kernel=(1, 1), stride=(1, 1), no_bias=True)
          bn5c_branch2c = mx.symbol.BatchNorm(name='bn5c_branch2c', data=res5c_branch2c, use_global_stats=True,
                                              fix_gamma=False, eps=self.eps)
          scale5c_branch2c = bn5c_branch2c
          res5c = mx.symbol.broadcast_add(name='res5c', *[res5b_relu, scale5c_branch2c])
          res5c_relu = mx.symbol.Activation(name='res5c_relu', data=res5c, act_type='relu')
          return res5c_relu
  
      def get_rpn(self, conv_feat, num_anchors):
          rpn_conv = mx.sym.Convolution(
              data=conv_feat, kernel=(3, 3), pad=(1, 1), num_filter=512, name="rpn_conv_3x3")
          rpn_relu = mx.sym.Activation(data=rpn_conv, act_type="relu", name="rpn_relu")
          rpn_cls_score = mx.sym.Convolution(
              data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=2 * num_anchors, name="rpn_cls_score")
          rpn_bbox_pred = mx.sym.Convolution(
              data=rpn_relu, kernel=(1, 1), pad=(0, 0), num_filter=4 * num_anchors, name="rpn_bbox_pred")
          return rpn_cls_score, rpn_bbox_pred
  
      def get_symbol(self, cfg, is_train=True):
  
          # config alias for convenient
          num_classes = cfg.dataset.NUM_CLASSES
          num_reg_classes = (2 if cfg.CLASS_AGNOSTIC else num_classes)
          num_anchors = cfg.network.NUM_ANCHORS
  
          # input init
          if is_train:
              data = mx.sym.Variable(name="data")
              im_info = mx.sym.Variable(name="im_info")
              gt_boxes = mx.sym.Variable(name="gt_boxes")
              rpn_label = mx.sym.Variable(name='label')
              rpn_bbox_target = mx.sym.Variable(name='bbox_target')
              rpn_bbox_weight = mx.sym.Variable(name='bbox_weight')
          else:
              data = mx.sym.Variable(name="data")
              im_info = mx.sym.Variable(name="im_info")
  
          # shared convolutional layers
          conv_feat = self.get_resnet_v1_conv4(data)
          # res5
          relu1 = self.get_resnet_v1_conv5(conv_feat)
  
          rpn_cls_score, rpn_bbox_pred = self.get_rpn(conv_feat, num_anchors)
  
          if is_train:
              # prepare rpn data
              rpn_cls_score_reshape = mx.sym.Reshape(
                  data=rpn_cls_score, shape=(0, 2, -1, 0), name="rpn_cls_score_reshape")
  
              # classification
              rpn_cls_prob = mx.sym.SoftmaxOutput(data=rpn_cls_score_reshape, label=rpn_label, multi_output=True,
                                                  normalization='valid', use_ignore=True, ignore_label=-1, name="rpn_cls_prob")
              # bounding box regression
              rpn_bbox_loss_ = rpn_bbox_weight * mx.sym.smooth_l1(name='rpn_bbox_loss_', scalar=3.0, data=(rpn_bbox_pred - rpn_bbox_target))
              rpn_bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=rpn_bbox_loss_, grad_scale=1.0 / cfg.TRAIN.RPN_BATCH_SIZE)
  
              # ROI proposal
              rpn_cls_act = mx.sym.SoftmaxActivation(
                  data=rpn_cls_score_reshape, mode="channel", name="rpn_cls_act")
              rpn_cls_act_reshape = mx.sym.Reshape(
                  data=rpn_cls_act, shape=(0, 2 * num_anchors, -1, 0), name='rpn_cls_act_reshape')
              if cfg.TRAIN.CXX_PROPOSAL:
                  rois = mx.contrib.sym.Proposal(
                      cls_prob=rpn_cls_act_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
                      feature_stride=cfg.network.RPN_FEAT_STRIDE, scales=tuple(cfg.network.ANCHOR_SCALES), ratios=tuple(cfg.network.ANCHOR_RATIOS),
                      rpn_pre_nms_top_n=cfg.TRAIN.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=cfg.TRAIN.RPN_POST_NMS_TOP_N,
                      threshold=cfg.TRAIN.RPN_NMS_THRESH, rpn_min_size=cfg.TRAIN.RPN_MIN_SIZE)
              else:
                  rois = mx.sym.Custom(
                      cls_prob=rpn_cls_act_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
                      op_type='proposal', feat_stride=cfg.network.RPN_FEAT_STRIDE,
                      scales=tuple(cfg.network.ANCHOR_SCALES), ratios=tuple(cfg.network.ANCHOR_RATIOS),
                      rpn_pre_nms_top_n=cfg.TRAIN.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=cfg.TRAIN.RPN_POST_NMS_TOP_N,
                      threshold=cfg.TRAIN.RPN_NMS_THRESH, rpn_min_size=cfg.TRAIN.RPN_MIN_SIZE)
              # ROI proposal target
              gt_boxes_reshape = mx.sym.Reshape(data=gt_boxes, shape=(-1, 5), name='gt_boxes_reshape')
              rois, label, bbox_target, bbox_weight = mx.sym.Custom(rois=rois, gt_boxes=gt_boxes_reshape,
                                                                    op_type='proposal_target',
                                                                    num_classes=num_reg_classes,
                                                                    batch_images=cfg.TRAIN.BATCH_IMAGES,
                                                                    batch_rois=cfg.TRAIN.BATCH_ROIS,
                                                                    cfg=cPickle.dumps(cfg),
                                                                    fg_fraction=cfg.TRAIN.FG_FRACTION)
          else:
              # ROI Proposal
              rpn_cls_score_reshape = mx.sym.Reshape(
                  data=rpn_cls_score, shape=(0, 2, -1, 0), name="rpn_cls_score_reshape")
              rpn_cls_prob = mx.sym.SoftmaxActivation(
                  data=rpn_cls_score_reshape, mode="channel", name="rpn_cls_prob")
              rpn_cls_prob_reshape = mx.sym.Reshape(
                  data=rpn_cls_prob, shape=(0, 2 * num_anchors, -1, 0), name='rpn_cls_prob_reshape')
              if cfg.TEST.CXX_PROPOSAL:
                  rois = mx.contrib.sym.Proposal(
                      cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
                      feature_stride=cfg.network.RPN_FEAT_STRIDE, scales=tuple(cfg.network.ANCHOR_SCALES),
                      ratios=tuple(cfg.network.ANCHOR_RATIOS),
                      rpn_pre_nms_top_n=cfg.TEST.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=cfg.TEST.RPN_POST_NMS_TOP_N,
                      threshold=cfg.TEST.RPN_NMS_THRESH, rpn_min_size=cfg.TEST.RPN_MIN_SIZE)
              else:
                  rois = mx.sym.Custom(
                      cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
                      op_type='proposal', feat_stride=cfg.network.RPN_FEAT_STRIDE,
                      scales=tuple(cfg.network.ANCHOR_SCALES), ratios=tuple(cfg.network.ANCHOR_RATIOS),
                      rpn_pre_nms_top_n=cfg.TEST.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=cfg.TEST.RPN_POST_NMS_TOP_N,
                      threshold=cfg.TEST.RPN_NMS_THRESH, rpn_min_size=cfg.TEST.RPN_MIN_SIZE)
  
  
  
          # conv_new_1
          # conv_new_1 = mx.sym.Convolution(data=relu1, kernel=(1, 1), num_filter=1024, name="conv_new_1", lr_mult=3.0)
          # relu_new_1 = mx.sym.Activation(data=conv_new_1, act_type='relu', name='relu1')
  
          # start light head
          conv_new_1 = mx.sym.Convolution(data=relu1, kernel=(15, 1), pad=(7, 0), num_filter=256,
                                          name="conv_new_1", lr_mult=3.0)
          # relu_new_1 = mx.sym.Activation(data=conv_new_1, act_type='relu', name='relu1')
          conv_new_2 = mx.sym.Convolution(data=conv_new_1, kernel=(1, 15), pad=(0, 7), num_filter=10 * 7 * 7,
                                          name="conv_new_2", lr_mult=3.0)
          # relu_new_2 = mx.sym.Activation(data=conv_new_2, act_type='relu', name='relu2')
          conv_new_3 = mx.sym.Convolution(data=relu1, kernel=(1, 15), pad=(0, 7), num_filter=256,
                                          name="conv_new_3", lr_mult=3.0)
          # relu_new_3 = mx.sym.Activation(data=conv_new_3, act_type='relu', name='relu3')
          conv_new_4 = mx.sym.Convolution(data=conv_new_3, kernel=(15, 1), pad=(7, 0), num_filter=10 * 7 * 7,
                                          name="conv_new_4", lr_mult=3.0)
          # relu_new_4 = mx.sym.Activation(data=conv_new_4, act_type='relu', name='relu4')
          light_head = mx.symbol.broadcast_add(name='light_head', *[conv_new_2, conv_new_4])
          relu_new_1 = mx.sym.Activation(data=light_head, act_type='relu', name='light_head_relu')
          # end light head
  
          # rfcn_cls/rfcn_bbox
          rfcn_cls = mx.sym.Convolution(data=relu_new_1, kernel=(1, 1), num_filter=7*7*num_classes, name="rfcn_cls")
          rfcn_bbox = mx.sym.Convolution(data=relu_new_1, kernel=(1, 1), num_filter=7*7*4*num_reg_classes, name="rfcn_bbox")
          # trans_cls / trans_cls
          rfcn_cls_offset_t = mx.sym.Convolution(data=relu_new_1, kernel=(1, 1), num_filter=2 * 7 * 7 * num_classes, name="rfcn_cls_offset_t")
          rfcn_bbox_offset_t = mx.sym.Convolution(data=relu_new_1, kernel=(1, 1), num_filter=7 * 7 * 2, name="rfcn_bbox_offset_t")
  
          rfcn_cls_offset = mx.contrib.sym.DeformablePSROIPooling(name='rfcn_cls_offset', data=rfcn_cls_offset_t, rois=rois, group_size=7, pooled_size=7,
                                                                  sample_per_part=4, no_trans=True, part_size=7, output_dim=2 * num_classes, spatial_scale=0.0625)
          rfcn_bbox_offset = mx.contrib.sym.DeformablePSROIPooling(name='rfcn_bbox_offset', data=rfcn_bbox_offset_t, rois=rois, group_size=7, pooled_size=7,
                                                                   sample_per_part=4, no_trans=True, part_size=7, output_dim=2, spatial_scale=0.0625)
  
          psroipooled_cls_rois = mx.contrib.sym.DeformablePSROIPooling(name='psroipooled_cls_rois', data=rfcn_cls, rois=rois, trans=rfcn_cls_offset,
                                                                       group_size=7, pooled_size=7, sample_per_part=4, no_trans=False, trans_std=0.1,
                                                                       output_dim=num_classes, spatial_scale=0.0625, part_size=7)
          psroipooled_loc_rois = mx.contrib.sym.DeformablePSROIPooling(name='psroipooled_loc_rois', data=rfcn_bbox, rois=rois, trans=rfcn_bbox_offset,
                                                                       group_size=7, pooled_size=7, sample_per_part=4, no_trans=False, trans_std=0.1,
                                                                       output_dim=8, spatial_scale=0.0625, part_size=7)
          # cls_score = mx.sym.Pooling(name='ave_cls_scors_rois', data=psroipooled_cls_rois, pool_type='avg', global_pool=True, kernel=(7, 7))
          # bbox_pred = mx.sym.Pooling(name='ave_bbox_pred_rois', data=psroipooled_loc_rois, pool_type='avg', global_pool=True, kernel=(7, 7))
          # RCM pooling
          cls_score_h = mx.sym.Pooling(name='ave_cls_scors_rois_h_', data=psroipooled_cls_rois, pool_type='max', kernel=(7, 1), stride=(7,1))
          cls_score_h = mx.sym.Pooling(name='ave_cls_scors_rois_h', data=cls_score_h, pool_type='avg', kernel=(1, 7), stride=(1, 7))
          cls_score_w = mx.sym.Pooling(name='ave_cls_scors_rois_w_', data=psroipooled_cls_rois, pool_type='max', kernel=(1, 7), stride=(1, 7))
          cls_score_w = mx.sym.Pooling(name='ave_cls_scors_rois_w', data=cls_score_w, pool_type='avg', kernel=(7, 1), stride=(7, 1))
          cls_score = mx.symbol.broadcast_add(name='ave_cls_scors_rois', *[cls_score_h, cls_score_w])
          bbox_pred_h = mx.sym.Pooling(name='ave_bbox_pred_rois_h_', data=psroipooled_loc_rois, pool_type='max', kernel=(7, 1), stride=(7, 1))
          bbox_pred_h = mx.sym.Pooling(name='ave_bbox_pred_rois_h', data=bbox_pred_h, pool_type='avg', kernel=(1, 7), stride=(1, 7))
          bbox_pred_w = mx.sym.Pooling(name='ave_bbox_pred_rois_w_', data=psroipooled_loc_rois, pool_type='max', kernel=(1, 7), stride=(1, 7))
          bbox_pred_w = mx.sym.Pooling(name='ave_bbox_pred_rois_w', data=bbox_pred_w, pool_type='avg', kernel=(7, 1), stride=(7, 1))
          bbox_pred = mx.symbol.broadcast_add(name='ave_bbox_pred_rois', *[bbox_pred_h, bbox_pred_w])
  
          cls_score = mx.sym.Reshape(name='cls_score_reshape', data=cls_score, shape=(-1, num_classes))
          bbox_pred = mx.sym.Reshape(name='bbox_pred_reshape', data=bbox_pred, shape=(-1, 4 * num_reg_classes))
  
          """
          # light head
          roi_pool = mx.contrib.sym.PSROIPooling(name='roi_pool', data=light_head, rois=rois, group_size=7, pooled_size=7,
                                                 output_dim=10, spatial_scale=0.0625)
          fc_new_1 = mx.symbol.FullyConnected(name='fc_new_1', data=roi_pool, num_hidden=2048)
          fc_new_1_relu = mx.sym.Activation(data=fc_new_1, act_type='relu', name='fc_new_1_relu')
          cls_score = mx.symbol.FullyConnected(name='cls_score', data=fc_new_1_relu, num_hidden=num_classes)
          bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=fc_new_1_relu, num_hidden=num_reg_classes * 4)
          """
  
          if is_train:
              if cfg.TRAIN.ENABLE_OHEM:
                  labels_ohem, bbox_weights_ohem = mx.sym.Custom(op_type='BoxAnnotatorOHEM', num_classes=num_classes,
                                                                 num_reg_classes=num_reg_classes, roi_per_img=cfg.TRAIN.BATCH_ROIS_OHEM,
                                                                 cls_score=cls_score, bbox_pred=bbox_pred, labels=label,
                                                                 bbox_targets=bbox_target, bbox_weights=bbox_weight)
                  cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=labels_ohem, normalization='valid', use_ignore=True, ignore_label=-1)
                  bbox_loss_ = bbox_weights_ohem * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target))
                  bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=1.0 / cfg.TRAIN.BATCH_ROIS_OHEM)
                  rcnn_label = labels_ohem
              else:
                  cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='valid')
                  bbox_loss_ = bbox_weight * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target))
                  bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=1.0 / cfg.TRAIN.BATCH_ROIS)
                  rcnn_label = label
  
              # reshape output
              rcnn_label = mx.sym.Reshape(data=rcnn_label, shape=(cfg.TRAIN.BATCH_IMAGES, -1), name='label_reshape')
              cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TRAIN.BATCH_IMAGES, -1, num_classes), name='cls_prob_reshape')
              bbox_loss = mx.sym.Reshape(data=bbox_loss, shape=(cfg.TRAIN.BATCH_IMAGES, -1, 4 * num_reg_classes), name='bbox_loss_reshape')
              group = mx.sym.Group([rpn_cls_prob, rpn_bbox_loss, cls_prob, bbox_loss, mx.sym.BlockGrad(rcnn_label)])
          else:
              cls_prob = mx.sym.SoftmaxActivation(name='cls_prob', data=cls_score)
              cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TEST.BATCH_IMAGES, -1, num_classes),
                                        name='cls_prob_reshape')
              bbox_pred = mx.sym.Reshape(data=bbox_pred, shape=(cfg.TEST.BATCH_IMAGES, -1, 4 * num_reg_classes),
                                         name='bbox_pred_reshape')
              group = mx.sym.Group([rois, cls_prob, bbox_pred])
  
          self.sym = group
          return group
  
      def get_symbol_rpn(self, cfg, is_train=True):
          # config alias for convenient
          num_anchors = cfg.network.NUM_ANCHORS
  
          # input init
          if is_train:
              data = mx.sym.Variable(name="data")
              rpn_label = mx.sym.Variable(name='label')
              rpn_bbox_target = mx.sym.Variable(name='bbox_target')
              rpn_bbox_weight = mx.sym.Variable(name='bbox_weight')
          else:
              data = mx.sym.Variable(name="data")
              im_info = mx.sym.Variable(name="im_info")
  
          # shared convolutional layers
          conv_feat = self.get_resnet_v1_conv4(data)
          rpn_cls_score, rpn_bbox_pred = self.get_rpn(conv_feat, num_anchors)
          if is_train:
              # prepare rpn data
              rpn_cls_score_reshape = mx.sym.Reshape(
                  data=rpn_cls_score, shape=(0, 2, -1, 0), name="rpn_cls_score_reshape")
  
              # classification
              rpn_cls_prob = mx.sym.SoftmaxOutput(data=rpn_cls_score_reshape, label=rpn_label, multi_output=True,
                                                  normalization='valid', use_ignore=True, ignore_label=-1, name="rpn_cls_prob",
                                                  grad_scale=1.0)
              # bounding box regression
              rpn_bbox_loss_ = rpn_bbox_weight * mx.sym.smooth_l1(name='rpn_bbox_loss_', scalar=3.0, data=(rpn_bbox_pred - rpn_bbox_target))
              rpn_bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=rpn_bbox_loss_, grad_scale=1.0 / cfg.TRAIN.RPN_BATCH_SIZE)
              group = mx.symbol.Group([rpn_cls_prob, rpn_bbox_loss])
          else:
              # ROI Proposal
              rpn_cls_score_reshape = mx.sym.Reshape(
                  data=rpn_cls_score, shape=(0, 2, -1, 0), name="rpn_cls_score_reshape")
              rpn_cls_prob = mx.sym.SoftmaxActivation(
                  data=rpn_cls_score_reshape, mode="channel", name="rpn_cls_prob")
              rpn_cls_prob_reshape = mx.sym.Reshape(
                  data=rpn_cls_prob, shape=(0, 2 * num_anchors, -1, 0), name='rpn_cls_prob_reshape')
              if cfg.TEST.CXX_PROPOSAL:
                  rois, score = mx.contrib.sym.Proposal(
                      cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois', output_score=True,
                      feature_stride=cfg.network.RPN_FEAT_STRIDE, scales=tuple(cfg.network.ANCHOR_SCALES),
                      ratios=tuple(cfg.network.ANCHOR_RATIOS),
                      rpn_pre_nms_top_n=cfg.TEST.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=cfg.TEST.RPN_POST_NMS_TOP_N,
                      threshold=cfg.TEST.RPN_NMS_THRESH, rpn_min_size=cfg.TEST.RPN_MIN_SIZE)
              else:
                  rois, score = mx.sym.Custom(
                      cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois', output_score=True,
                      op_type='proposal', feat_stride=cfg.network.RPN_FEAT_STRIDE,
                      scales=tuple(cfg.network.ANCHOR_SCALES), ratios=tuple(cfg.network.ANCHOR_RATIOS),
                      rpn_pre_nms_top_n=cfg.TEST.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=cfg.TEST.RPN_POST_NMS_TOP_N,
                      threshold=cfg.TEST.RPN_NMS_THRESH, rpn_min_size=cfg.TEST.RPN_MIN_SIZE)
                  group = mx.symbol.Group([rois, score])
          self.sym = group
          return group
  
      def get_symbol_rfcn(self, cfg, is_train=True):
  
          # config alias for convenient
          num_classes = cfg.dataset.NUM_CLASSES
          num_reg_classes = (2 if cfg.CLASS_AGNOSTIC else num_classes)
  
          # input init
          if is_train:
              data = mx.symbol.Variable(name="data")
              rois = mx.symbol.Variable(name='rois')
              label = mx.symbol.Variable(name='label')
              bbox_target = mx.symbol.Variable(name='bbox_target')
              bbox_weight = mx.symbol.Variable(name='bbox_weight')
              # reshape input
              rois = mx.symbol.Reshape(data=rois, shape=(-1, 5), name='rois_reshape')
              label = mx.symbol.Reshape(data=label, shape=(-1,), name='label_reshape')
              bbox_target = mx.symbol.Reshape(data=bbox_target, shape=(-1, 4 * num_reg_classes), name='bbox_target_reshape')
              bbox_weight = mx.symbol.Reshape(data=bbox_weight, shape=(-1, 4 * num_reg_classes), name='bbox_weight_reshape')
          else:
              data = mx.sym.Variable(name="data")
              rois = mx.symbol.Variable(name='rois')
              # reshape input
              rois = mx.symbol.Reshape(data=rois, shape=(-1, 5), name='rois_reshape')
  
          # shared convolutional layers
          conv_feat = self.get_resnet_v1_conv4(data)
          relu1 = self.get_resnet_v1_conv5(conv_feat)
  
          # conv_new_1
          conv_new_1 = mx.sym.Convolution(data=relu1, kernel=(1, 1), num_filter=1024, name="conv_new_1", lr_mult=3.0)
          relu_new_1 = mx.sym.Activation(data=conv_new_1, act_type='relu', name='relu1')
  
          # rfcn_cls/rfcn_bbox
          rfcn_cls = mx.sym.Convolution(data=relu_new_1, kernel=(1, 1), num_filter=7*7*num_classes, name="rfcn_cls")
          rfcn_bbox = mx.sym.Convolution(data=relu_new_1, kernel=(1, 1), num_filter=7*7*4*num_reg_classes, name="rfcn_bbox")
          # trans_cls / trans_cls
          rfcn_cls_offset_t = mx.sym.Convolution(data=relu_new_1, kernel=(1, 1), num_filter=2 * 7 * 7 * num_classes, name="rfcn_cls_offset_t")
          rfcn_bbox_offset_t = mx.sym.Convolution(data=relu_new_1, kernel=(1, 1), num_filter=7 * 7 * 2, name="rfcn_bbox_offset_t")
  
          rfcn_cls_offset = mx.contrib.sym.DeformablePSROIPooling(name='rfcn_cls_offset', data=rfcn_cls_offset_t, rois=rois, group_size=7, pooled_size=7,
                                                                  sample_per_part=4, no_trans=True, part_size=7, output_dim=2 * num_classes, spatial_scale=0.0625)
          rfcn_bbox_offset = mx.contrib.sym.DeformablePSROIPooling(name='rfcn_bbox_offset', data=rfcn_bbox_offset_t, rois=rois, group_size=7, pooled_size=7,
                                                                   sample_per_part=4, no_trans=True, part_size=7, output_dim=2, spatial_scale=0.0625)
  
          psroipooled_cls_rois = mx.contrib.sym.DeformablePSROIPooling(name='psroipooled_cls_rois', data=rfcn_cls, rois=rois, trans=rfcn_cls_offset,
                                                                       group_size=7, pooled_size=7, sample_per_part=4, no_trans=False, trans_std=0.1,
                                                                       output_dim=num_classes, spatial_scale=0.0625, part_size=7)
          psroipooled_loc_rois = mx.contrib.sym.DeformablePSROIPooling(name='psroipooled_loc_rois', data=rfcn_bbox, rois=rois, trans=rfcn_bbox_offset,
                                                                       group_size=7, pooled_size=7, sample_per_part=4, no_trans=False, trans_std=0.1,
                                                                       output_dim=8, spatial_scale=0.0625, part_size=7)
          cls_score = mx.sym.Pooling(name='ave_cls_scors_rois', data=psroipooled_cls_rois, pool_type='avg', global_pool=True, kernel=(7, 7))
          bbox_pred = mx.sym.Pooling(name='ave_bbox_pred_rois', data=psroipooled_loc_rois, pool_type='avg', global_pool=True, kernel=(7, 7))
          cls_score = mx.sym.Reshape(name='cls_score_reshape', data=cls_score, shape=(-1, num_classes))
          bbox_pred = mx.sym.Reshape(name='bbox_pred_reshape', data=bbox_pred, shape=(-1, 4 * num_reg_classes))
  
          if is_train:
              if cfg.TRAIN.ENABLE_OHEM:
                  labels_ohem, bbox_weights_ohem = mx.sym.Custom(op_type='BoxAnnotatorOHEM', num_classes=num_classes,
                                                                 num_reg_classes=num_reg_classes, roi_per_img=cfg.TRAIN.BATCH_ROIS_OHEM,
                                                                 cls_score=cls_score, bbox_pred=bbox_pred, labels=label,
                                                                 bbox_targets=bbox_target, bbox_weights=bbox_weight)
                  cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=labels_ohem, normalization='valid', use_ignore=True, ignore_label=-1, grad_scale=1.0)
                  bbox_loss_ = bbox_weights_ohem * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target))
                  bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=1.0 / cfg.TRAIN.BATCH_ROIS_OHEM)
                  label = labels_ohem
              else:
                  cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='valid', grad_scale=1.0)
                  bbox_loss_ = bbox_weight * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target))
                  bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=1.0 / cfg.TRAIN.BATCH_ROIS)
  
              # reshape output
              cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TRAIN.BATCH_IMAGES, -1, num_classes), name='cls_prob_reshape')
              bbox_loss = mx.sym.Reshape(data=bbox_loss, shape=(cfg.TRAIN.BATCH_IMAGES, -1, 4 * num_reg_classes), name='bbox_loss_reshape')
              group = mx.sym.Group([cls_prob, bbox_loss, mx.sym.BlockGrad(label)]) if cfg.TRAIN.ENABLE_OHEM else mx.sym.Group([cls_prob, bbox_loss])
          else:
              cls_prob = mx.sym.SoftmaxActivation(name='cls_prob', data=cls_score)
              cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TEST.BATCH_IMAGES, -1, num_classes),
                                        name='cls_prob_reshape')
              bbox_pred = mx.sym.Reshape(data=bbox_pred, shape=(cfg.TEST.BATCH_IMAGES, -1, 4 * num_reg_classes),
                                         name='bbox_pred_reshape')
              group = mx.sym.Group([cls_prob, bbox_pred])
  
          self.sym = group
          return group
  
      def init_weight_rpn(self, cfg, arg_params, aux_params):
          arg_params['rpn_conv_3x3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rpn_conv_3x3_weight'])
          arg_params['rpn_conv_3x3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rpn_conv_3x3_bias'])
          arg_params['rpn_cls_score_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rpn_cls_score_weight'])
          arg_params['rpn_cls_score_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rpn_cls_score_bias'])
          arg_params['rpn_bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rpn_bbox_pred_weight'])
          arg_params['rpn_bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rpn_bbox_pred_bias'])
  
      def init_weight_rfcn(self, cfg, arg_params, aux_params):
          arg_params['res5a_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5a_branch2b_offset_weight'])
          arg_params['res5a_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5a_branch2b_offset_bias'])
          arg_params['res5b_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5b_branch2b_offset_weight'])
          arg_params['res5b_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5b_branch2b_offset_bias'])
          arg_params['res5c_branch2b_offset_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['res5c_branch2b_offset_weight'])
          arg_params['res5c_branch2b_offset_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['res5c_branch2b_offset_bias'])
  
          arg_params['rfcn_cls_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rfcn_cls_weight'])
          arg_params['rfcn_cls_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rfcn_cls_bias'])
          arg_params['rfcn_bbox_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['rfcn_bbox_weight'])
          arg_params['rfcn_bbox_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rfcn_bbox_bias'])
          arg_params['rfcn_cls_offset_t_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['rfcn_cls_offset_t_weight'])
          arg_params['rfcn_cls_offset_t_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rfcn_cls_offset_t_bias'])
          arg_params['rfcn_bbox_offset_t_weight'] = mx.nd.zeros(shape=self.arg_shape_dict['rfcn_bbox_offset_t_weight'])
          arg_params['rfcn_bbox_offset_t_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['rfcn_bbox_offset_t_bias'])
  
          arg_params['conv_new_1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['conv_new_1_weight'])
          arg_params['conv_new_1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['conv_new_1_bias'])
          arg_params['conv_new_2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['conv_new_2_weight'])
          arg_params['conv_new_2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['conv_new_2_bias'])
          arg_params['conv_new_3_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['conv_new_3_weight'])
          arg_params['conv_new_3_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['conv_new_3_bias'])
          arg_params['conv_new_4_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['conv_new_4_weight'])
          arg_params['conv_new_4_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['conv_new_4_bias'])
          # arg_params['fc_new_1_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_1_weight'])
          # arg_params['fc_new_1_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_1_bias'])
          # arg_params['cls_score_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['cls_score_weight'])
          # arg_params['cls_score_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['cls_score_bias'])
          # arg_params['bbox_pred_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['bbox_pred_weight'])
          # arg_params['bbox_pred_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['bbox_pred_bias'])
  
      def init_weight(self, cfg, arg_params, aux_params):
          self.init_weight_rpn(cfg, arg_params, aux_params)
          self.init_weight_rfcn(cfg, arg_params, aux_params)
  
  