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# --------------------------------------------------------
# Deformable Convolutional Networks
# Copyright (c) 2017 Microsoft
# Licensed under The Apache-2.0 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_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 = 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, cudnn_off=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, cudnn_off=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, cudnn_off=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)
# 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=relu_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=relu_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', *[relu_new_2, relu_new_4])
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=2.0, 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=2.0, 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")
psroipooled_cls_rois = mx.contrib.sym.PSROIPooling(name='psroipooled_cls_rois', data=rfcn_cls, rois=rois, group_size=7, pooled_size=7,
output_dim=num_classes, spatial_scale=0.0625)
psroipooled_loc_rois = mx.contrib.sym.PSROIPooling(name='psroipooled_loc_rois', data=rfcn_bbox, rois=rois, group_size=7, pooled_size=7,
output_dim=8, spatial_scale=0.0625)
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['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'])
#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'])
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)