symbol_resnet.py

# --------------------------------------------------------
# Deformable Convolutional Networks
# Copyright (c) 2017 Microsoft
# Licensed under The Apache-2.0 License [see LICENSE for details]
# Written by Guodong Zhang
# Modified by Bin Xiao
# --------------------------------------------------------

import cPickle
import mxnet as mx
from symbol import Symbol
from proposal import *
from proposal_target import *
#from operator_py.box_annotator_ohem import *


class symbol_resnet(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 = mx.symbol.Convolution(name='res5a_branch2b', data=res5a_branch2a_relu, num_filter=512, pad=(2, 2),
                                               kernel=(3, 3), 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 = mx.symbol.Convolution(name='res5b_branch2b', data=res5b_branch2a_relu, num_filter=512, pad=(2, 2),
                                               kernel=(3, 3), 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 = mx.symbol.Convolution(name='res5c_branch2b', data=res5c_branch2a_relu, num_filter=512, pad=(2, 2),
                                               kernel=(3, 3), 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 encoder(self,seq_len,cfg,expression):
         vocab_size = cfg.RNN.VOCAB_SIZE
         num_embed = cfg.RNN.NUM_EMBED
         embed = mx.symbol.Embedding(data=expression,input_dim=vocab_size,output_dim=num_embed,name='embed')
         #TODO:1024 dim seems need lots of memory
         cell = cfg.ENCODER_CELL
         outputs,_ = cell.unroll(seq_len,inputs=embed)
         output = mx.symbol.slice_axis(data=outputs,axis=1,begin=-2,end=-1)
         output = mx.symbol.reshape(data=output,shape=(-1,256))
         return output
    def get_dfn_global_feat(self,global_feat,global_roi,sent_feat):
        global_conv_1 = mx.symbol.Convolution(data=global_feat,kernel=(1,1),num_filter=256,stride=(1,1),name="global_conv_1")
        global_conv_1_relu = mx.symbol.Activation(data=global_conv_1,act_type='relu',name="global_conv_1_relu")
        offset_t = mx.contrib.sym.DeformablePSROIPooling(name='offset_t', data=global_conv_1_relu, rois=global_roi, group_size=1, pooled_size=7,
                                                         sample_per_part=4, no_trans=True, part_size=7, output_dim=256, spatial_scale=0.0625)
        offset_global_pool = mx.sym.Pooling(data=offset_t,kernel=(7,7),global_pool=True,pool_type='avg',name='offset_global_pool')
        offset_global_pool = mx.sym.reshape(data=offset_global_pool,shape=(1,256),name='offset_global_pool_reshape')
        offset_origin = mx.sym.concat(offset_global_pool,sent_feat,dim=1)
        offset = mx.sym.FullyConnected(name='offset', data=offset_origin, num_hidden=7 * 7 * 2, lr_mult=0.01)
        offset_reshape = mx.sym.Reshape(data=offset, shape=(-1, 2, 7, 7), name="offset_reshape")

        global_df_roi_pool = mx.contrib.sym.DeformablePSROIPooling(name='deformable_roi_pool', data=global_feat, rois=global_roi,
                                                                    trans=offset_reshape, group_size=1, pooled_size=7, sample_per_part=4,
                                                                    no_trans=False, part_size=7, output_dim=256, spatial_scale=0.0625, trans_std=0.1)
        global_conv_2 = mx.symbol.Convolution(data=global_df_roi_pool,kernel=(3,3),stride=(1,1),num_filter=256,name='global_conv_2')
        global_conv_2_relu = mx.sym.Activation(data=global_conv_2,act_type='relu',name='global_conv_2_relu')
        global_feat = mx.sym.Pooling(data=global_conv_2_relu,kernel=(7,7),global_pool=True,pool_type='avg',name="global_feat_pool")
        global_feat = mx.sym.reshape(data=global_feat,shape=(1,256),name='global_feat_reshape')
        return global_feat
    def HighWay_MLP(self,data,name):
        norm = mx.sym.FullyConnected(data=data,num_hidden=1024,name=name+"_norm")
        norm_act = mx.sym.Activation(data=norm,act_type='relu',name=name+'_norm_act')
        gate = mx.sym.FullyConnected(data=data,num_hidden=1024,name=name+'_gate')
        gate_act = mx.sym.Activation(data=gate,act_type='relu',name=name+'_gate_act')
        mlp_gate_and_norm = norm_act * gate_act
        mlp_gate_and_input = (1-gate_act) * data
        return mlp_gate_and_norm + mlp_gate_and_input
        
    def get_symbol(self, seq_len,cfg, is_train=True):

        # config alias for convenient
        num_classes = cfg.NUM_CLASSES
        num_reg_classes = num_classes
        num_anchors = cfg.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')
            global_roi = mx.sym.Variable(name='global_roi')
            expression = mx.sym.Variable(name='expression')
        else:
            data = mx.sym.Variable(name="data")
            im_info = mx.sym.Variable(name="im_info")
            global_roi = mx.sym.Variable(name='global_roi')
            expression = mx.sym.Variable(name='expression')
        
        data_bn = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=self.eps, use_global_stats=self.use_global_stats, name='bn_data')

        # shared convolutional layers
        conv_feat = self.get_resnet_v1_conv4(data_bn)
        # 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.RPN_FEAT_STRIDE, scales=tuple(cfg.ANCHOR_SCALES),
                    ratios=tuple(cfg.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.RPN_FEAT_STRIDE,
                    scales=tuple(cfg.ANCHOR_SCALES), ratios=tuple(cfg.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')
            group = mx.symbol.Custom(rois=rois, gt_boxes=gt_boxes_reshape, op_type='proposal_target',
                             num_classes=num_classes, batch_images=cfg.TRAIN.BATCH_IMAGES,
                             batch_rois=cfg.TRAIN.BATCH_ROIS, fg_fraction=cfg.TRAIN.FG_FRACTION)
            rois = group[0]
            label = group[1]
            bbox_target = group[2]
            bbox_weight = group[3]
        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.RPN_FEAT_STRIDE, scales=tuple(cfg.ANCHOR_SCALES),
                    ratios=tuple(cfg.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.RPN_FEAT_STRIDE,
                    scales=tuple(cfg.ANCHOR_SCALES), ratios=tuple(cfg.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 = mx.sym.Convolution(data=relu1, kernel=(1, 1), num_filter=256, name="conv_new_1")
        conv_new_1_relu = mx.sym.Activation(data=conv_new_1, act_type='relu', name='conv_new_1_relu')
        #region of interest feat
        roi_pool = mx.symbol.ROIPooling(
            name='roi_pool', data=conv_new_1_relu, rois=rois, pooled_size=(7, 7), spatial_scale=0.0625)
        roi_conv = mx.symbol.Convolution(data=roi_pool,kernel=(3,3),stride=(1,1),num_filter=256,name='roi_conv')
        roi_conv_relu = mx.symbol.Activation(data=roi_conv,act_type='relu',name='roi_conv_relu')
        roi_conv_pool = mx.symbol.Pooling(data=roi_conv_relu,kernel=(7,7),global_pool=True,pool_type='avg',name='roi_conv_pool')
        roi_feat = mx.symbol.reshape(data=roi_conv_pool,shape=(-1,256),name="roi_feat_reshape")
        #sent feat
        sent_feat = self.encoder(seq_len,cfg,expression)
        #global region(jointly predicted by global image feat and sent_feat)
        global_feat = self.get_dfn_global_feat(conv_new_1_relu,global_roi,sent_feat)
        #broadcast and concat
        if is_train:
            sent_feat = mx.sym.broadcast_to(data=sent_feat,shape=(cfg.TRAIN.BATCH_ROIS,256))
            global_feat = mx.sym.broadcast_to(data=global_feat,shape=(cfg.TRAIN.BATCH_ROIS,256))
        else:
            sent_feat = mx.sym.broadcast_to(data=sent_feat,shape=(cfg.TEST.RPN_POST_NMS_TOP_N,256))
            global_feat = mx.sym.broadcast_to(data=global_feat,shape=(cfg.TEST.RPN_POST_NMS_TOP_N,256))
        #feat = mx.sym.concat(roi_feat,sent_feat,global_feat,dim=1)
        feat = roi_feat + sent_feat + global_feat
        # 2 fc
        fc_new_1 = mx.symbol.FullyConnected(name='fc_new_1', data=feat, num_hidden=1024)
        fc_new_1_relu = mx.sym.Activation(data=fc_new_1, act_type='relu', name='fc_new_1_relu')

        #fc_new_2 = mx.symbol.FullyConnected(name='fc_new_2', data=fc_new_1_relu, num_hidden=1024)
        fc_new_2_relu = self.HighWay_MLP(data=fc_new_1_relu,name='Highway_1')

        # cls_score/bbox_pred
        cls_score = mx.symbol.FullyConnected(name='cls_score', data=fc_new_2_relu, num_hidden=num_classes)
        bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=fc_new_2_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.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.RPN_FEAT_STRIDE, scales=tuple(cfg.ANCHOR_SCALES),
                    ratios=tuple(cfg.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.RPN_FEAT_STRIDE,
                    scales=tuple(cfg.ANCHOR_SCALES), ratios=tuple(cfg.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_rcnn(self, cfg, is_train=True):
        # config alias for convenient
        num_classes = cfg.NUM_CLASSES
        num_reg_classes = 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_classes), name='bbox_target_reshape')
            bbox_weight = mx.symbol.Reshape(data=bbox_weight, shape=(-1, 4 * num_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)
        # res5
        relu1 = self.get_resnet_v1_conv5(conv_feat)

        conv_new_1 = mx.sym.Convolution(data=relu1, kernel=(1, 1), num_filter=256, name="conv_new_1")
        conv_new_1_relu = mx.sym.Activation(data=conv_new_1, act_type='relu', name='conv_new_1_relu')

        roi_pool = mx.symbol.ROIPooling(
            name='roi_pool', data=conv_new_1_relu, rois=rois, pooled_size=(7, 7), spatial_scale=0.0625)

        # 2 fc
        fc_new_1 = mx.symbol.FullyConnected(name='fc_new_1', data=roi_pool, num_hidden=1024)
        fc_new_1_relu = mx.sym.Activation(data=fc_new_1, act_type='relu', name='fc_new_1_relu')

        fc_new_2 = mx.symbol.FullyConnected(name='fc_new_2', data=fc_new_1_relu, num_hidden=1024)
        fc_new_2_relu = mx.sym.Activation(data=fc_new_2, act_type='relu', name='fc_new_2_relu')

        # cls_score/bbox_pred
        cls_score = mx.symbol.FullyConnected(name='cls_score', data=fc_new_2_relu, num_hidden=num_classes)
        bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=fc_new_2_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, 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)
            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])
        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_rcnn(self, cfg, arg_params, aux_params):
        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['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['fc_new_2_weight'] = mx.random.normal(0, 0.01, shape=self.arg_shape_dict['fc_new_2_weight'])
        #arg_params['fc_new_2_bias'] = mx.nd.zeros(shape=self.arg_shape_dict['fc_new_2_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, 0.001, 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_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_extra(self,cfg,arg_params,aux_params):
        arg_params['roi_conv_weight'] = mx.random.normal(0,0.01,shape=self.arg_shape_dict['roi_conv_weight'])
        arg_params['roi_conv_bias'] = mx.nd.zeros(shape= self.arg_shape_dict['roi_conv_bias'])
        arg_params['global_conv_1_weight'] = mx.random.normal(0,0.01,shape=self.arg_shape_dict['global_conv_1_weight'])
        arg_params['global_conv_1_bias'] = mx.nd.zeros(shape= self.arg_shape_dict['global_conv_1_bias'])
        arg_params['global_conv_2_weight'] = mx.random.normal(0,0.01,shape=self.arg_shape_dict['global_conv_2_weight'])
        arg_params['global_conv_2_bias'] = mx.nd.zeros(shape= self.arg_shape_dict['global_conv_2_bias'])
     

    def init_weight(self, cfg, arg_params, aux_params):
        self.init_weight_rpn(cfg, arg_params, aux_params)
        self.init_weight_rcnn(cfg, arg_params, aux_params)
        self.init_weight_extra(cfg,arg_params,aux_params)