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modelCPMWeight.py
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#import caffe
#from caffe.proto import caffe_pb2
#use_caffe = True
import re
from google.protobuf import text_format
import mxnet as mx
from generateLabelCPMWeight import *
numofparts = 15
numoflinks = 13
def CPMModel():
data = mx.symbol.Variable(name='data')
## heat map of human parts
heatmaplabel = mx.sym.Variable("heatmaplabel")
## part affinity graph
partaffinityglabel = mx.sym.Variable('partaffinityglabel')
heatweight = mx.sym.Variable('heatweight')
vecweight = mx.sym.Variable('vecweight')
conv1_1 = mx.symbol.Convolution(name='conv1_1', data=data , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu1_1 = mx.symbol.Activation(name='relu1_1', data=conv1_1 , act_type='relu')
conv1_2 = mx.symbol.Convolution(name='conv1_2', data=relu1_1 , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu1_2 = mx.symbol.Activation(name='relu1_2', data=conv1_2 , act_type='relu')
pool1_stage1 = mx.symbol.Pooling(name='pool1_stage1', data=relu1_2 , pooling_convention='full', pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='max')
conv2_1 = mx.symbol.Convolution(name='conv2_1', data=pool1_stage1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu2_1 = mx.symbol.Activation(name='relu2_1', data=conv2_1 , act_type='relu')
conv2_2 = mx.symbol.Convolution(name='conv2_2', data=relu2_1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu2_2 = mx.symbol.Activation(name='relu2_2', data=conv2_2 , act_type='relu')
pool2_stage1 = mx.symbol.Pooling(name='pool2_stage1', data=relu2_2 , pooling_convention='full', pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='max')
conv3_1 = mx.symbol.Convolution(name='conv3_1', data=pool2_stage1 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu3_1 = mx.symbol.Activation(name='relu3_1', data=conv3_1 , act_type='relu')
conv3_2 = mx.symbol.Convolution(name='conv3_2', data=relu3_1 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu3_2 = mx.symbol.Activation(name='relu3_2', data=conv3_2 , act_type='relu')
conv3_3 = mx.symbol.Convolution(name='conv3_3', data=relu3_2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu3_3 = mx.symbol.Activation(name='relu3_3', data=conv3_3 , act_type='relu')
conv3_4 = mx.symbol.Convolution(name='conv3_4', data=relu3_3 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu3_4 = mx.symbol.Activation(name='relu3_4', data=conv3_4 , act_type='relu')
pool3_stage1 = mx.symbol.Pooling(name='pool3_stage1', data=relu3_4 , pooling_convention='full', pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='max')
conv4_1 = mx.symbol.Convolution(name='conv4_1', data=pool3_stage1 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu4_1 = mx.symbol.Activation(name='relu4_1', data=conv4_1 , act_type='relu')
conv4_2 = mx.symbol.Convolution(name='conv4_2', data=relu4_1 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu4_2 = mx.symbol.Activation(name='relu4_2', data=conv4_2 , act_type='relu')
conv4_3_CPM = mx.symbol.Convolution(name='conv4_3_CPM', data=relu4_2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu4_3_CPM = mx.symbol.Activation(name='relu4_3_CPM', data=conv4_3_CPM , act_type='relu')
conv4_4_CPM = mx.symbol.Convolution(name='conv4_4_CPM', data=relu4_3_CPM , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu4_4_CPM = mx.symbol.Activation(name='relu4_4_CPM', data=conv4_4_CPM , act_type='relu')
conv5_1_CPM_L1 = mx.symbol.Convolution(name='conv5_1_CPM_L1', data=relu4_4_CPM , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_1_CPM_L1 = mx.symbol.Activation(name='relu5_1_CPM_L1', data=conv5_1_CPM_L1 , act_type='relu')
conv5_1_CPM_L2 = mx.symbol.Convolution(name='conv5_1_CPM_L2', data=relu4_4_CPM , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_1_CPM_L2 = mx.symbol.Activation(name='relu5_1_CPM_L2', data=conv5_1_CPM_L2 , act_type='relu')
conv5_2_CPM_L1 = mx.symbol.Convolution(name='conv5_2_CPM_L1', data=relu5_1_CPM_L1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_2_CPM_L1 = mx.symbol.Activation(name='relu5_2_CPM_L1', data=conv5_2_CPM_L1 , act_type='relu')
conv5_2_CPM_L2 = mx.symbol.Convolution(name='conv5_2_CPM_L2', data=relu5_1_CPM_L2 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_2_CPM_L2 = mx.symbol.Activation(name='relu5_2_CPM_L2', data=conv5_2_CPM_L2 , act_type='relu')
conv5_3_CPM_L1 = mx.symbol.Convolution(name='conv5_3_CPM_L1', data=relu5_2_CPM_L1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_3_CPM_L1 = mx.symbol.Activation(name='relu5_3_CPM_L1', data=conv5_3_CPM_L1 , act_type='relu')
conv5_3_CPM_L2 = mx.symbol.Convolution(name='conv5_3_CPM_L2', data=relu5_2_CPM_L2 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_3_CPM_L2 = mx.symbol.Activation(name='relu5_3_CPM_L2', data=conv5_3_CPM_L2 , act_type='relu')
conv5_4_CPM_L1 = mx.symbol.Convolution(name='conv5_4_CPM_L1', data=relu5_3_CPM_L1 , num_filter=512, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
relu5_4_CPM_L1 = mx.symbol.Activation(name='relu5_4_CPM_L1', data=conv5_4_CPM_L1 , act_type='relu')
conv5_4_CPM_L2 = mx.symbol.Convolution(name='conv5_4_CPM_L2', data=relu5_3_CPM_L2 , num_filter=512, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
relu5_4_CPM_L2 = mx.symbol.Activation(name='relu5_4_CPM_L2', data=conv5_4_CPM_L2 , act_type='relu')
conv5_5_CPM_L1 = mx.symbol.Convolution(name='conv5_5_CPM_L1', data=relu5_4_CPM_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
conv5_5_CPM_L2 = mx.symbol.Convolution(name='conv5_5_CPM_L2', data=relu5_4_CPM_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
concat_stage2 = mx.symbol.Concat(name='concat_stage2', *[conv5_5_CPM_L1,conv5_5_CPM_L2,relu4_4_CPM] )
Mconv1_stage2_L1 = mx.symbol.Convolution(name='Mconv1_stage2_L1', data=concat_stage2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage2_L1 = mx.symbol.Activation(name='Mrelu1_stage2_L1', data=Mconv1_stage2_L1 , act_type='relu')
Mconv1_stage2_L2 = mx.symbol.Convolution(name='Mconv1_stage2_L2', data=concat_stage2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage2_L2 = mx.symbol.Activation(name='Mrelu1_stage2_L2', data=Mconv1_stage2_L2 , act_type='relu')
Mconv2_stage2_L1 = mx.symbol.Convolution(name='Mconv2_stage2_L1', data=Mrelu1_stage2_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage2_L1 = mx.symbol.Activation(name='Mrelu2_stage2_L1', data=Mconv2_stage2_L1 , act_type='relu')
Mconv2_stage2_L2 = mx.symbol.Convolution(name='Mconv2_stage2_L2', data=Mrelu1_stage2_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage2_L2 = mx.symbol.Activation(name='Mrelu2_stage2_L2', data=Mconv2_stage2_L2 , act_type='relu')
Mconv3_stage2_L1 = mx.symbol.Convolution(name='Mconv3_stage2_L1', data=Mrelu2_stage2_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage2_L1 = mx.symbol.Activation(name='Mrelu3_stage2_L1', data=Mconv3_stage2_L1 , act_type='relu')
Mconv3_stage2_L2 = mx.symbol.Convolution(name='Mconv3_stage2_L2', data=Mrelu2_stage2_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage2_L2 = mx.symbol.Activation(name='Mrelu3_stage2_L2', data=Mconv3_stage2_L2 , act_type='relu')
Mconv4_stage2_L1 = mx.symbol.Convolution(name='Mconv4_stage2_L1', data=Mrelu3_stage2_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage2_L1 = mx.symbol.Activation(name='Mrelu4_stage2_L1', data=Mconv4_stage2_L1 , act_type='relu')
Mconv4_stage2_L2 = mx.symbol.Convolution(name='Mconv4_stage2_L2', data=Mrelu3_stage2_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage2_L2 = mx.symbol.Activation(name='Mrelu4_stage2_L2', data=Mconv4_stage2_L2 , act_type='relu')
Mconv5_stage2_L1 = mx.symbol.Convolution(name='Mconv5_stage2_L1', data=Mrelu4_stage2_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage2_L1 = mx.symbol.Activation(name='Mrelu5_stage2_L1', data=Mconv5_stage2_L1 , act_type='relu')
Mconv5_stage2_L2 = mx.symbol.Convolution(name='Mconv5_stage2_L2', data=Mrelu4_stage2_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage2_L2 = mx.symbol.Activation(name='Mrelu5_stage2_L2', data=Mconv5_stage2_L2 , act_type='relu')
Mconv6_stage2_L1 = mx.symbol.Convolution(name='Mconv6_stage2_L1', data=Mrelu5_stage2_L1 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage2_L1 = mx.symbol.Activation(name='Mrelu6_stage2_L1', data=Mconv6_stage2_L1 , act_type='relu')
Mconv6_stage2_L2 = mx.symbol.Convolution(name='Mconv6_stage2_L2', data=Mrelu5_stage2_L2 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage2_L2 = mx.symbol.Activation(name='Mrelu6_stage2_L2', data=Mconv6_stage2_L2 , act_type='relu')
Mconv7_stage2_L1 = mx.symbol.Convolution(name='Mconv7_stage2_L1', data=Mrelu6_stage2_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mconv7_stage2_L2 = mx.symbol.Convolution(name='Mconv7_stage2_L2', data=Mrelu6_stage2_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
concat_stage3 = mx.symbol.Concat(name='concat_stage3', *[Mconv7_stage2_L1,Mconv7_stage2_L2,relu4_4_CPM] )
Mconv1_stage3_L1 = mx.symbol.Convolution(name='Mconv1_stage3_L1', data=concat_stage3 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage3_L1 = mx.symbol.Activation(name='Mrelu1_stage3_L1', data=Mconv1_stage3_L1 , act_type='relu')
Mconv1_stage3_L2 = mx.symbol.Convolution(name='Mconv1_stage3_L2', data=concat_stage3 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage3_L2 = mx.symbol.Activation(name='Mrelu1_stage3_L2', data=Mconv1_stage3_L2 , act_type='relu')
Mconv2_stage3_L1 = mx.symbol.Convolution(name='Mconv2_stage3_L1', data=Mrelu1_stage3_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage3_L1 = mx.symbol.Activation(name='Mrelu2_stage3_L1', data=Mconv2_stage3_L1 , act_type='relu')
Mconv2_stage3_L2 = mx.symbol.Convolution(name='Mconv2_stage3_L2', data=Mrelu1_stage3_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage3_L2 = mx.symbol.Activation(name='Mrelu2_stage3_L2', data=Mconv2_stage3_L2 , act_type='relu')
Mconv3_stage3_L1 = mx.symbol.Convolution(name='Mconv3_stage3_L1', data=Mrelu2_stage3_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage3_L1 = mx.symbol.Activation(name='Mrelu3_stage3_L1', data=Mconv3_stage3_L1 , act_type='relu')
Mconv3_stage3_L2 = mx.symbol.Convolution(name='Mconv3_stage3_L2', data=Mrelu2_stage3_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage3_L2 = mx.symbol.Activation(name='Mrelu3_stage3_L2', data=Mconv3_stage3_L2 , act_type='relu')
Mconv4_stage3_L1 = mx.symbol.Convolution(name='Mconv4_stage3_L1', data=Mrelu3_stage3_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage3_L1 = mx.symbol.Activation(name='Mrelu4_stage3_L1', data=Mconv4_stage3_L1 , act_type='relu')
Mconv4_stage3_L2 = mx.symbol.Convolution(name='Mconv4_stage3_L2', data=Mrelu3_stage3_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage3_L2 = mx.symbol.Activation(name='Mrelu4_stage3_L2', data=Mconv4_stage3_L2 , act_type='relu')
Mconv5_stage3_L1 = mx.symbol.Convolution(name='Mconv5_stage3_L1', data=Mrelu4_stage3_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage3_L1 = mx.symbol.Activation(name='Mrelu5_stage3_L1', data=Mconv5_stage3_L1 , act_type='relu')
Mconv5_stage3_L2 = mx.symbol.Convolution(name='Mconv5_stage3_L2', data=Mrelu4_stage3_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage3_L2 = mx.symbol.Activation(name='Mrelu5_stage3_L2', data=Mconv5_stage3_L2 , act_type='relu')
Mconv6_stage3_L1 = mx.symbol.Convolution(name='Mconv6_stage3_L1', data=Mrelu5_stage3_L1 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage3_L1 = mx.symbol.Activation(name='Mrelu6_stage3_L1', data=Mconv6_stage3_L1 , act_type='relu')
Mconv6_stage3_L2 = mx.symbol.Convolution(name='Mconv6_stage3_L2', data=Mrelu5_stage3_L2 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage3_L2 = mx.symbol.Activation(name='Mrelu6_stage3_L2', data=Mconv6_stage3_L2 , act_type='relu')
Mconv7_stage3_L1 = mx.symbol.Convolution(name='Mconv7_stage3_L1', data=Mrelu6_stage3_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mconv7_stage3_L2 = mx.symbol.Convolution(name='Mconv7_stage3_L2', data=Mrelu6_stage3_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
concat_stage4 = mx.symbol.Concat(name='concat_stage4', *[Mconv7_stage3_L1,Mconv7_stage3_L2,relu4_4_CPM] )
Mconv1_stage4_L1 = mx.symbol.Convolution(name='Mconv1_stage4_L1', data=concat_stage4 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage4_L1 = mx.symbol.Activation(name='Mrelu1_stage4_L1', data=Mconv1_stage4_L1 , act_type='relu')
Mconv1_stage4_L2 = mx.symbol.Convolution(name='Mconv1_stage4_L2', data=concat_stage4 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage4_L2 = mx.symbol.Activation(name='Mrelu1_stage4_L2', data=Mconv1_stage4_L2 , act_type='relu')
Mconv2_stage4_L1 = mx.symbol.Convolution(name='Mconv2_stage4_L1', data=Mrelu1_stage4_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage4_L1 = mx.symbol.Activation(name='Mrelu2_stage4_L1', data=Mconv2_stage4_L1 , act_type='relu')
Mconv2_stage4_L2 = mx.symbol.Convolution(name='Mconv2_stage4_L2', data=Mrelu1_stage4_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage4_L2 = mx.symbol.Activation(name='Mrelu2_stage4_L2', data=Mconv2_stage4_L2 , act_type='relu')
Mconv3_stage4_L1 = mx.symbol.Convolution(name='Mconv3_stage4_L1', data=Mrelu2_stage4_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage4_L1 = mx.symbol.Activation(name='Mrelu3_stage4_L1', data=Mconv3_stage4_L1 , act_type='relu')
Mconv3_stage4_L2 = mx.symbol.Convolution(name='Mconv3_stage4_L2', data=Mrelu2_stage4_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage4_L2 = mx.symbol.Activation(name='Mrelu3_stage4_L2', data=Mconv3_stage4_L2 , act_type='relu')
Mconv4_stage4_L1 = mx.symbol.Convolution(name='Mconv4_stage4_L1', data=Mrelu3_stage4_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage4_L1 = mx.symbol.Activation(name='Mrelu4_stage4_L1', data=Mconv4_stage4_L1 , act_type='relu')
Mconv4_stage4_L2 = mx.symbol.Convolution(name='Mconv4_stage4_L2', data=Mrelu3_stage4_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage4_L2 = mx.symbol.Activation(name='Mrelu4_stage4_L2', data=Mconv4_stage4_L2 , act_type='relu')
Mconv5_stage4_L1 = mx.symbol.Convolution(name='Mconv5_stage4_L1', data=Mrelu4_stage4_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage4_L1 = mx.symbol.Activation(name='Mrelu5_stage4_L1', data=Mconv5_stage4_L1 , act_type='relu')
Mconv5_stage4_L2 = mx.symbol.Convolution(name='Mconv5_stage4_L2', data=Mrelu4_stage4_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage4_L2 = mx.symbol.Activation(name='Mrelu5_stage4_L2', data=Mconv5_stage4_L2 , act_type='relu')
Mconv6_stage4_L1 = mx.symbol.Convolution(name='Mconv6_stage4_L1', data=Mrelu5_stage4_L1 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage4_L1 = mx.symbol.Activation(name='Mrelu6_stage4_L1', data=Mconv6_stage4_L1 , act_type='relu')
Mconv6_stage4_L2 = mx.symbol.Convolution(name='Mconv6_stage4_L2', data=Mrelu5_stage4_L2 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage4_L2 = mx.symbol.Activation(name='Mrelu6_stage4_L2', data=Mconv6_stage4_L2 , act_type='relu')
Mconv7_stage4_L1 = mx.symbol.Convolution(name='Mconv7_stage4_L1', data=Mrelu6_stage4_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mconv7_stage4_L2 = mx.symbol.Convolution(name='Mconv7_stage4_L2', data=Mrelu6_stage4_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
concat_stage5 = mx.symbol.Concat(name='concat_stage5', *[Mconv7_stage4_L1,Mconv7_stage4_L2,relu4_4_CPM] )
Mconv1_stage5_L1 = mx.symbol.Convolution(name='Mconv1_stage5_L1', data=concat_stage5 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage5_L1 = mx.symbol.Activation(name='Mrelu1_stage5_L1', data=Mconv1_stage5_L1 , act_type='relu')
Mconv1_stage5_L2 = mx.symbol.Convolution(name='Mconv1_stage5_L2', data=concat_stage5 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage5_L2 = mx.symbol.Activation(name='Mrelu1_stage5_L2', data=Mconv1_stage5_L2 , act_type='relu')
Mconv2_stage5_L1 = mx.symbol.Convolution(name='Mconv2_stage5_L1', data=Mrelu1_stage5_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage5_L1 = mx.symbol.Activation(name='Mrelu2_stage5_L1', data=Mconv2_stage5_L1 , act_type='relu')
Mconv2_stage5_L2 = mx.symbol.Convolution(name='Mconv2_stage5_L2', data=Mrelu1_stage5_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage5_L2 = mx.symbol.Activation(name='Mrelu2_stage5_L2', data=Mconv2_stage5_L2 , act_type='relu')
Mconv3_stage5_L1 = mx.symbol.Convolution(name='Mconv3_stage5_L1', data=Mrelu2_stage5_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage5_L1 = mx.symbol.Activation(name='Mrelu3_stage5_L1', data=Mconv3_stage5_L1 , act_type='relu')
Mconv3_stage5_L2 = mx.symbol.Convolution(name='Mconv3_stage5_L2', data=Mrelu2_stage5_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage5_L2 = mx.symbol.Activation(name='Mrelu3_stage5_L2', data=Mconv3_stage5_L2 , act_type='relu')
Mconv4_stage5_L1 = mx.symbol.Convolution(name='Mconv4_stage5_L1', data=Mrelu3_stage5_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage5_L1 = mx.symbol.Activation(name='Mrelu4_stage5_L1', data=Mconv4_stage5_L1 , act_type='relu')
Mconv4_stage5_L2 = mx.symbol.Convolution(name='Mconv4_stage5_L2', data=Mrelu3_stage5_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage5_L2 = mx.symbol.Activation(name='Mrelu4_stage5_L2', data=Mconv4_stage5_L2 , act_type='relu')
Mconv5_stage5_L1 = mx.symbol.Convolution(name='Mconv5_stage5_L1', data=Mrelu4_stage5_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage5_L1 = mx.symbol.Activation(name='Mrelu5_stage5_L1', data=Mconv5_stage5_L1 , act_type='relu')
Mconv5_stage5_L2 = mx.symbol.Convolution(name='Mconv5_stage5_L2', data=Mrelu4_stage5_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage5_L2 = mx.symbol.Activation(name='Mrelu5_stage5_L2', data=Mconv5_stage5_L2 , act_type='relu')
Mconv6_stage5_L1 = mx.symbol.Convolution(name='Mconv6_stage5_L1', data=Mrelu5_stage5_L1 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage5_L1 = mx.symbol.Activation(name='Mrelu6_stage5_L1', data=Mconv6_stage5_L1 , act_type='relu')
Mconv6_stage5_L2 = mx.symbol.Convolution(name='Mconv6_stage5_L2', data=Mrelu5_stage5_L2 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage5_L2 = mx.symbol.Activation(name='Mrelu6_stage5_L2', data=Mconv6_stage5_L2 , act_type='relu')
Mconv7_stage5_L1 = mx.symbol.Convolution(name='Mconv7_stage5_L1', data=Mrelu6_stage5_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mconv7_stage5_L2 = mx.symbol.Convolution(name='Mconv7_stage5_L2', data=Mrelu6_stage5_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
concat_stage6 = mx.symbol.Concat(name='concat_stage6', *[Mconv7_stage5_L1,Mconv7_stage5_L2,relu4_4_CPM] )
Mconv1_stage6_L1 = mx.symbol.Convolution(name='Mconv1_stage6_L1', data=concat_stage6 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage6_L1 = mx.symbol.Activation(name='Mrelu1_stage6_L1', data=Mconv1_stage6_L1 , act_type='relu')
Mconv1_stage6_L2 = mx.symbol.Convolution(name='Mconv1_stage6_L2', data=concat_stage6 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage6_L2 = mx.symbol.Activation(name='Mrelu1_stage6_L2', data=Mconv1_stage6_L2 , act_type='relu')
Mconv2_stage6_L1 = mx.symbol.Convolution(name='Mconv2_stage6_L1', data=Mrelu1_stage6_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage6_L1 = mx.symbol.Activation(name='Mrelu2_stage6_L1', data=Mconv2_stage6_L1 , act_type='relu')
Mconv2_stage6_L2 = mx.symbol.Convolution(name='Mconv2_stage6_L2', data=Mrelu1_stage6_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage6_L2 = mx.symbol.Activation(name='Mrelu2_stage6_L2', data=Mconv2_stage6_L2 , act_type='relu')
Mconv3_stage6_L1 = mx.symbol.Convolution(name='Mconv3_stage6_L1', data=Mrelu2_stage6_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage6_L1 = mx.symbol.Activation(name='Mrelu3_stage6_L1', data=Mconv3_stage6_L1 , act_type='relu')
Mconv3_stage6_L2 = mx.symbol.Convolution(name='Mconv3_stage6_L2', data=Mrelu2_stage6_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage6_L2 = mx.symbol.Activation(name='Mrelu3_stage6_L2', data=Mconv3_stage6_L2 , act_type='relu')
Mconv4_stage6_L1 = mx.symbol.Convolution(name='Mconv4_stage6_L1', data=Mrelu3_stage6_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage6_L1 = mx.symbol.Activation(name='Mrelu4_stage6_L1', data=Mconv4_stage6_L1 , act_type='relu')
Mconv4_stage6_L2 = mx.symbol.Convolution(name='Mconv4_stage6_L2', data=Mrelu3_stage6_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage6_L2 = mx.symbol.Activation(name='Mrelu4_stage6_L2', data=Mconv4_stage6_L2 , act_type='relu')
Mconv5_stage6_L1 = mx.symbol.Convolution(name='Mconv5_stage6_L1', data=Mrelu4_stage6_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage6_L1 = mx.symbol.Activation(name='Mrelu5_stage6_L1', data=Mconv5_stage6_L1 , act_type='relu')
Mconv5_stage6_L2 = mx.symbol.Convolution(name='Mconv5_stage6_L2', data=Mrelu4_stage6_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage6_L2 = mx.symbol.Activation(name='Mrelu5_stage6_L2', data=Mconv5_stage6_L2 , act_type='relu')
Mconv6_stage6_L1 = mx.symbol.Convolution(name='Mconv6_stage6_L1', data=Mrelu5_stage6_L1 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage6_L1 = mx.symbol.Activation(name='Mrelu6_stage6_L1', data=Mconv6_stage6_L1 , act_type='relu')
Mconv6_stage6_L2 = mx.symbol.Convolution(name='Mconv6_stage6_L2', data=Mrelu5_stage6_L2 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage6_L2 = mx.symbol.Activation(name='Mrelu6_stage6_L2', data=Mconv6_stage6_L2 , act_type='relu')
Mconv7_stage6_L1 = mx.symbol.Convolution(name='Mconv7_stage6_L1', data=Mrelu6_stage6_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mconv7_stage6_L2 = mx.symbol.Convolution(name='Mconv7_stage6_L2', data=Mrelu6_stage6_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
conv5_5_CPM_L1r = mx.symbol.Reshape(data=conv5_5_CPM_L1, shape=(-1,), name='conv5_5_CPM_L1r')
partaffinityglabelr = mx.symbol.Reshape(data=partaffinityglabel, shape=(-1, ), name='partaffinityglabelr')
stage1_loss_L1s = mx.symbol.square(conv5_5_CPM_L1r-partaffinityglabelr)
vecweightw = mx.symbol.Reshape(data=vecweight, shape=(-1,), name='conv5_5_CPM_L1w')
stage1_loss_L1w = stage1_loss_L1s*vecweightw
stage1_loss_L1 = mx.symbol.MakeLoss(stage1_loss_L1w)
conv5_5_CPM_L2r = mx.symbol.Reshape(data=conv5_5_CPM_L2, shape=(-1,), name='conv5_5_CPM_L2r')
heatmaplabelr = mx.symbol.Reshape(data=heatmaplabel, shape=(-1, ), name='heatmaplabelr')
stage1_loss_L2s = mx.symbol.square(conv5_5_CPM_L2r-heatmaplabelr)
heatweightw = mx.symbol.Reshape(data=heatweight, shape=(-1,), name='conv5_5_CPM_L2w')
stage1_loss_L2w = stage1_loss_L2s*heatweightw
stage1_loss_L2 = mx.symbol.MakeLoss(stage1_loss_L2w)
Mconv7_stage2_L1r = mx.symbol.Reshape(data=Mconv7_stage2_L1, shape=(-1,), name='Mconv7_stage2_L1')
#partaffinityglabelr = mx.symbol.Reshape(data=partaffinityglabel, shape=(-1, ), name='partaffinityglabelr')
stage2_loss_L1s = mx.symbol.square(Mconv7_stage2_L1r - partaffinityglabelr)
#vecweightw = mx.symbol.Reshape(data=vecweight, shape=(-1,), name='Mconv7_stage2_L1r')
stage2_loss_L1w = stage2_loss_L1s*vecweightw
stage2_loss_L1 = mx.symbol.MakeLoss(stage2_loss_L1w)
Mconv7_stage2_L2r = mx.symbol.Reshape(data=Mconv7_stage2_L2, shape=(-1,), name='Mconv7_stage2_L2')
#heatmaplabelr = mx.symbol.Reshape(data=heatmaplabel, shape=(-1, ), name='heatmaplabelr')
stage2_loss_L2s = mx.symbol.square(Mconv7_stage2_L2r-heatmaplabelr)
#heatweightw = mx.symbol.Reshape(data=heatweight, shape=(-1,), name='conv5_5_CPM_L1r')
stage2_loss_L2w = stage2_loss_L2s*heatweightw
stage2_loss_L2 = mx.symbol.MakeLoss(stage2_loss_L2w)
Mconv7_stage3_L1r = mx.symbol.Reshape(data=Mconv7_stage3_L1, shape=(-1,), name='Mconv7_stage3_L1')
#partaffinityglabelr = mx.symbol.Reshape(data=partaffinityglabel, shape=(-1, ), name='partaffinityglabelr')
stage3_loss_L1s = mx.symbol.square(Mconv7_stage3_L1r - partaffinityglabelr)
#vecweightw = mx.symbol.Reshape(data=vecweight, shape=(-1,), name='Mconv7_stage2_L1r')
stage3_loss_L1w = stage3_loss_L1s*vecweightw
stage3_loss_L1 = mx.symbol.MakeLoss(stage3_loss_L1w)
Mconv7_stage3_L2r = mx.symbol.Reshape(data=Mconv7_stage3_L2, shape=(-1,), name='Mconv7_stage3_L2')
#heatmaplabelr = mx.symbol.Reshape(data=heatmaplabel, shape=(-1, ), name='heatmaplabelr')
stage3_loss_L2s = mx.symbol.square(Mconv7_stage3_L2r-heatmaplabelr)
#heatweightw = mx.symbol.Reshape(data=heatweight, shape=(-1,), name='conv5_5_CPM_L1r')
stage3_loss_L2w = stage3_loss_L2s*heatweightw
stage3_loss_L2 = mx.symbol.MakeLoss(stage3_loss_L2w)
Mconv7_stage4_L1r = mx.symbol.Reshape(data=Mconv7_stage4_L1, shape=(-1,), name='Mconv7_stage4_L1')
#partaffinityglabelr = mx.symbol.Reshape(data=partaffinityglabel, shape=(-1, ), name='partaffinityglabelr')
stage4_loss_L1s = mx.symbol.square(Mconv7_stage4_L1r - partaffinityglabelr)
#vecweightw = mx.symbol.Reshape(data=vecweight, shape=(-1,), name='Mconv7_stage2_L1r')
stage4_loss_L1w = stage4_loss_L1s*vecweightw
stage4_loss_L1 = mx.symbol.MakeLoss(stage4_loss_L1w)
Mconv7_stage4_L2r = mx.symbol.Reshape(data=Mconv7_stage4_L2, shape=(-1,), name='Mconv7_stage4_L2')
#heatmaplabelr = mx.symbol.Reshape(data=heatmaplabel, shape=(-1, ), name='heatmaplabelr')
stage4_loss_L2s = mx.symbol.square(Mconv7_stage4_L2r-heatmaplabelr)
#heatweightw = mx.symbol.Reshape(data=heatweight, shape=(-1,), name='conv5_5_CPM_L1r')
stage4_loss_L2w = stage4_loss_L2s*heatweightw
stage4_loss_L2 = mx.symbol.MakeLoss(stage4_loss_L2w)
Mconv7_stage5_L1r = mx.symbol.Reshape(data=Mconv7_stage5_L1, shape=(-1,), name='Mconv7_stage5_L1')
#partaffinityglabelr = mx.symbol.Reshape(data=partaffinityglabel, shape=(-1, ), name='partaffinityglabelr')
stage5_loss_L1s = mx.symbol.square(Mconv7_stage5_L1r - partaffinityglabelr)
#vecweightw = mx.symbol.Reshape(data=vecweight, shape=(-1,), name='Mconv7_stage2_L1r')
stage5_loss_L1w = stage5_loss_L1s*vecweightw
stage5_loss_L1 = mx.symbol.MakeLoss(stage5_loss_L1w)
Mconv7_stage5_L2r = mx.symbol.Reshape(data=Mconv7_stage5_L2, shape=(-1,), name='Mconv7_stage5_L2')
#heatmaplabelr = mx.symbol.Reshape(data=heatmaplabel, shape=(-1, ), name='heatmaplabelr')
stage5_loss_L2s = mx.symbol.square(Mconv7_stage5_L2r-heatmaplabelr)
#heatweightw = mx.symbol.Reshape(data=heatweight, shape=(-1,), name='conv5_5_CPM_L1r')
stage5_loss_L2w = stage5_loss_L2s*heatweightw
stage5_loss_L2 = mx.symbol.MakeLoss(stage5_loss_L2w)
Mconv7_stage6_L1r = mx.symbol.Reshape(data=Mconv7_stage6_L1, shape=(-1,), name='Mconv7_stage3_L1')
#partaffinityglabelr = mx.symbol.Reshape(data=partaffinityglabel, shape=(-1, ), name='partaffinityglabelr')
stage6_loss_L1s = mx.symbol.square(Mconv7_stage6_L1r - partaffinityglabelr)
#vecweightw = mx.symbol.Reshape(data=vecweight, shape=(-1,), name='Mconv7_stage2_L1r')
stage6_loss_L1w = stage6_loss_L1s*vecweightw
stage6_loss_L1 = mx.symbol.MakeLoss(stage6_loss_L1w)
Mconv7_stage6_L2r = mx.symbol.Reshape(data=Mconv7_stage6_L2, shape=(-1,), name='Mconv7_stage3_L2')
#heatmaplabelr = mx.symbol.Reshape(data=heatmaplabel, shape=(-1, ), name='heatmaplabelr')
stage6_loss_L2s = mx.symbol.square(Mconv7_stage6_L2r-heatmaplabelr)
#heatweightw = mx.symbol.Reshape(data=heatweight, shape=(-1,), name='conv5_5_CPM_L1r')
stage6_loss_L2w = stage6_loss_L2s*heatweightw
stage6_loss_L2 = mx.symbol.MakeLoss(stage6_loss_L2w)
group = mx.symbol.Group([stage1_loss_L1, stage1_loss_L2,
stage2_loss_L1, stage2_loss_L2,
stage3_loss_L1, stage3_loss_L2,
stage4_loss_L1, stage4_loss_L2,
stage5_loss_L1, stage5_loss_L2,
stage6_loss_L1, stage6_loss_L2])
return group
def CPMModel_test():
data = mx.symbol.Variable(name='data')
## heat map of human parts
heatmaplabel = mx.sym.Variable("heatmaplabel")
## part affinity graph
partaffinityglabel = mx.sym.Variable('partaffinityglabel')
heatweight = mx.sym.Variable('heatweight')
vecweight = mx.sym.Variable('vecweight')
conv1_1 = mx.symbol.Convolution(name='conv1_1', data=data , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu1_1 = mx.symbol.Activation(name='relu1_1', data=conv1_1 , act_type='relu')
conv1_2 = mx.symbol.Convolution(name='conv1_2', data=relu1_1 , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu1_2 = mx.symbol.Activation(name='relu1_2', data=conv1_2 , act_type='relu')
pool1_stage1 = mx.symbol.Pooling(name='pool1_stage1', data=relu1_2 , pooling_convention='full', pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='max')
conv2_1 = mx.symbol.Convolution(name='conv2_1', data=pool1_stage1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu2_1 = mx.symbol.Activation(name='relu2_1', data=conv2_1 , act_type='relu')
conv2_2 = mx.symbol.Convolution(name='conv2_2', data=relu2_1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu2_2 = mx.symbol.Activation(name='relu2_2', data=conv2_2 , act_type='relu')
pool2_stage1 = mx.symbol.Pooling(name='pool2_stage1', data=relu2_2 , pooling_convention='full', pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='max')
conv3_1 = mx.symbol.Convolution(name='conv3_1', data=pool2_stage1 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu3_1 = mx.symbol.Activation(name='relu3_1', data=conv3_1 , act_type='relu')
conv3_2 = mx.symbol.Convolution(name='conv3_2', data=relu3_1 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu3_2 = mx.symbol.Activation(name='relu3_2', data=conv3_2 , act_type='relu')
conv3_3 = mx.symbol.Convolution(name='conv3_3', data=relu3_2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu3_3 = mx.symbol.Activation(name='relu3_3', data=conv3_3 , act_type='relu')
conv3_4 = mx.symbol.Convolution(name='conv3_4', data=relu3_3 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu3_4 = mx.symbol.Activation(name='relu3_4', data=conv3_4 , act_type='relu')
pool3_stage1 = mx.symbol.Pooling(name='pool3_stage1', data=relu3_4 , pooling_convention='full', pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='max')
conv4_1 = mx.symbol.Convolution(name='conv4_1', data=pool3_stage1 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu4_1 = mx.symbol.Activation(name='relu4_1', data=conv4_1 , act_type='relu')
conv4_2 = mx.symbol.Convolution(name='conv4_2', data=relu4_1 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu4_2 = mx.symbol.Activation(name='relu4_2', data=conv4_2 , act_type='relu')
conv4_3_CPM = mx.symbol.Convolution(name='conv4_3_CPM', data=relu4_2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu4_3_CPM = mx.symbol.Activation(name='relu4_3_CPM', data=conv4_3_CPM , act_type='relu')
conv4_4_CPM = mx.symbol.Convolution(name='conv4_4_CPM', data=relu4_3_CPM , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu4_4_CPM = mx.symbol.Activation(name='relu4_4_CPM', data=conv4_4_CPM , act_type='relu')
conv5_1_CPM_L1 = mx.symbol.Convolution(name='conv5_1_CPM_L1', data=relu4_4_CPM , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_1_CPM_L1 = mx.symbol.Activation(name='relu5_1_CPM_L1', data=conv5_1_CPM_L1 , act_type='relu')
conv5_1_CPM_L2 = mx.symbol.Convolution(name='conv5_1_CPM_L2', data=relu4_4_CPM , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_1_CPM_L2 = mx.symbol.Activation(name='relu5_1_CPM_L2', data=conv5_1_CPM_L2 , act_type='relu')
conv5_2_CPM_L1 = mx.symbol.Convolution(name='conv5_2_CPM_L1', data=relu5_1_CPM_L1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_2_CPM_L1 = mx.symbol.Activation(name='relu5_2_CPM_L1', data=conv5_2_CPM_L1 , act_type='relu')
conv5_2_CPM_L2 = mx.symbol.Convolution(name='conv5_2_CPM_L2', data=relu5_1_CPM_L2 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_2_CPM_L2 = mx.symbol.Activation(name='relu5_2_CPM_L2', data=conv5_2_CPM_L2 , act_type='relu')
conv5_3_CPM_L1 = mx.symbol.Convolution(name='conv5_3_CPM_L1', data=relu5_2_CPM_L1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_3_CPM_L1 = mx.symbol.Activation(name='relu5_3_CPM_L1', data=conv5_3_CPM_L1 , act_type='relu')
conv5_3_CPM_L2 = mx.symbol.Convolution(name='conv5_3_CPM_L2', data=relu5_2_CPM_L2 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False)
relu5_3_CPM_L2 = mx.symbol.Activation(name='relu5_3_CPM_L2', data=conv5_3_CPM_L2 , act_type='relu')
conv5_4_CPM_L1 = mx.symbol.Convolution(name='conv5_4_CPM_L1', data=relu5_3_CPM_L1 , num_filter=512, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
relu5_4_CPM_L1 = mx.symbol.Activation(name='relu5_4_CPM_L1', data=conv5_4_CPM_L1 , act_type='relu')
conv5_4_CPM_L2 = mx.symbol.Convolution(name='conv5_4_CPM_L2', data=relu5_3_CPM_L2 , num_filter=512, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
relu5_4_CPM_L2 = mx.symbol.Activation(name='relu5_4_CPM_L2', data=conv5_4_CPM_L2 , act_type='relu')
conv5_5_CPM_L1 = mx.symbol.Convolution(name='conv5_5_CPM_L1', data=relu5_4_CPM_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
conv5_5_CPM_L2 = mx.symbol.Convolution(name='conv5_5_CPM_L2', data=relu5_4_CPM_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
concat_stage2 = mx.symbol.Concat(name='concat_stage2', *[conv5_5_CPM_L1,conv5_5_CPM_L2,relu4_4_CPM] )
Mconv1_stage2_L1 = mx.symbol.Convolution(name='Mconv1_stage2_L1', data=concat_stage2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage2_L1 = mx.symbol.Activation(name='Mrelu1_stage2_L1', data=Mconv1_stage2_L1 , act_type='relu')
Mconv1_stage2_L2 = mx.symbol.Convolution(name='Mconv1_stage2_L2', data=concat_stage2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage2_L2 = mx.symbol.Activation(name='Mrelu1_stage2_L2', data=Mconv1_stage2_L2 , act_type='relu')
Mconv2_stage2_L1 = mx.symbol.Convolution(name='Mconv2_stage2_L1', data=Mrelu1_stage2_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage2_L1 = mx.symbol.Activation(name='Mrelu2_stage2_L1', data=Mconv2_stage2_L1 , act_type='relu')
Mconv2_stage2_L2 = mx.symbol.Convolution(name='Mconv2_stage2_L2', data=Mrelu1_stage2_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage2_L2 = mx.symbol.Activation(name='Mrelu2_stage2_L2', data=Mconv2_stage2_L2 , act_type='relu')
Mconv3_stage2_L1 = mx.symbol.Convolution(name='Mconv3_stage2_L1', data=Mrelu2_stage2_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage2_L1 = mx.symbol.Activation(name='Mrelu3_stage2_L1', data=Mconv3_stage2_L1 , act_type='relu')
Mconv3_stage2_L2 = mx.symbol.Convolution(name='Mconv3_stage2_L2', data=Mrelu2_stage2_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage2_L2 = mx.symbol.Activation(name='Mrelu3_stage2_L2', data=Mconv3_stage2_L2 , act_type='relu')
Mconv4_stage2_L1 = mx.symbol.Convolution(name='Mconv4_stage2_L1', data=Mrelu3_stage2_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage2_L1 = mx.symbol.Activation(name='Mrelu4_stage2_L1', data=Mconv4_stage2_L1 , act_type='relu')
Mconv4_stage2_L2 = mx.symbol.Convolution(name='Mconv4_stage2_L2', data=Mrelu3_stage2_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage2_L2 = mx.symbol.Activation(name='Mrelu4_stage2_L2', data=Mconv4_stage2_L2 , act_type='relu')
Mconv5_stage2_L1 = mx.symbol.Convolution(name='Mconv5_stage2_L1', data=Mrelu4_stage2_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage2_L1 = mx.symbol.Activation(name='Mrelu5_stage2_L1', data=Mconv5_stage2_L1 , act_type='relu')
Mconv5_stage2_L2 = mx.symbol.Convolution(name='Mconv5_stage2_L2', data=Mrelu4_stage2_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage2_L2 = mx.symbol.Activation(name='Mrelu5_stage2_L2', data=Mconv5_stage2_L2 , act_type='relu')
Mconv6_stage2_L1 = mx.symbol.Convolution(name='Mconv6_stage2_L1', data=Mrelu5_stage2_L1 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage2_L1 = mx.symbol.Activation(name='Mrelu6_stage2_L1', data=Mconv6_stage2_L1 , act_type='relu')
Mconv6_stage2_L2 = mx.symbol.Convolution(name='Mconv6_stage2_L2', data=Mrelu5_stage2_L2 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage2_L2 = mx.symbol.Activation(name='Mrelu6_stage2_L2', data=Mconv6_stage2_L2 , act_type='relu')
Mconv7_stage2_L1 = mx.symbol.Convolution(name='Mconv7_stage2_L1', data=Mrelu6_stage2_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mconv7_stage2_L2 = mx.symbol.Convolution(name='Mconv7_stage2_L2', data=Mrelu6_stage2_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
concat_stage3 = mx.symbol.Concat(name='concat_stage3', *[Mconv7_stage2_L1,Mconv7_stage2_L2,relu4_4_CPM] )
Mconv1_stage3_L1 = mx.symbol.Convolution(name='Mconv1_stage3_L1', data=concat_stage3 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage3_L1 = mx.symbol.Activation(name='Mrelu1_stage3_L1', data=Mconv1_stage3_L1 , act_type='relu')
Mconv1_stage3_L2 = mx.symbol.Convolution(name='Mconv1_stage3_L2', data=concat_stage3 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage3_L2 = mx.symbol.Activation(name='Mrelu1_stage3_L2', data=Mconv1_stage3_L2 , act_type='relu')
Mconv2_stage3_L1 = mx.symbol.Convolution(name='Mconv2_stage3_L1', data=Mrelu1_stage3_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage3_L1 = mx.symbol.Activation(name='Mrelu2_stage3_L1', data=Mconv2_stage3_L1 , act_type='relu')
Mconv2_stage3_L2 = mx.symbol.Convolution(name='Mconv2_stage3_L2', data=Mrelu1_stage3_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage3_L2 = mx.symbol.Activation(name='Mrelu2_stage3_L2', data=Mconv2_stage3_L2 , act_type='relu')
Mconv3_stage3_L1 = mx.symbol.Convolution(name='Mconv3_stage3_L1', data=Mrelu2_stage3_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage3_L1 = mx.symbol.Activation(name='Mrelu3_stage3_L1', data=Mconv3_stage3_L1 , act_type='relu')
Mconv3_stage3_L2 = mx.symbol.Convolution(name='Mconv3_stage3_L2', data=Mrelu2_stage3_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage3_L2 = mx.symbol.Activation(name='Mrelu3_stage3_L2', data=Mconv3_stage3_L2 , act_type='relu')
Mconv4_stage3_L1 = mx.symbol.Convolution(name='Mconv4_stage3_L1', data=Mrelu3_stage3_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage3_L1 = mx.symbol.Activation(name='Mrelu4_stage3_L1', data=Mconv4_stage3_L1 , act_type='relu')
Mconv4_stage3_L2 = mx.symbol.Convolution(name='Mconv4_stage3_L2', data=Mrelu3_stage3_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage3_L2 = mx.symbol.Activation(name='Mrelu4_stage3_L2', data=Mconv4_stage3_L2 , act_type='relu')
Mconv5_stage3_L1 = mx.symbol.Convolution(name='Mconv5_stage3_L1', data=Mrelu4_stage3_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage3_L1 = mx.symbol.Activation(name='Mrelu5_stage3_L1', data=Mconv5_stage3_L1 , act_type='relu')
Mconv5_stage3_L2 = mx.symbol.Convolution(name='Mconv5_stage3_L2', data=Mrelu4_stage3_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage3_L2 = mx.symbol.Activation(name='Mrelu5_stage3_L2', data=Mconv5_stage3_L2 , act_type='relu')
Mconv6_stage3_L1 = mx.symbol.Convolution(name='Mconv6_stage3_L1', data=Mrelu5_stage3_L1 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage3_L1 = mx.symbol.Activation(name='Mrelu6_stage3_L1', data=Mconv6_stage3_L1 , act_type='relu')
Mconv6_stage3_L2 = mx.symbol.Convolution(name='Mconv6_stage3_L2', data=Mrelu5_stage3_L2 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage3_L2 = mx.symbol.Activation(name='Mrelu6_stage3_L2', data=Mconv6_stage3_L2 , act_type='relu')
Mconv7_stage3_L1 = mx.symbol.Convolution(name='Mconv7_stage3_L1', data=Mrelu6_stage3_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mconv7_stage3_L2 = mx.symbol.Convolution(name='Mconv7_stage3_L2', data=Mrelu6_stage3_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
concat_stage4 = mx.symbol.Concat(name='concat_stage4', *[Mconv7_stage3_L1,Mconv7_stage3_L2,relu4_4_CPM] )
Mconv1_stage4_L1 = mx.symbol.Convolution(name='Mconv1_stage4_L1', data=concat_stage4 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage4_L1 = mx.symbol.Activation(name='Mrelu1_stage4_L1', data=Mconv1_stage4_L1 , act_type='relu')
Mconv1_stage4_L2 = mx.symbol.Convolution(name='Mconv1_stage4_L2', data=concat_stage4 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage4_L2 = mx.symbol.Activation(name='Mrelu1_stage4_L2', data=Mconv1_stage4_L2 , act_type='relu')
Mconv2_stage4_L1 = mx.symbol.Convolution(name='Mconv2_stage4_L1', data=Mrelu1_stage4_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage4_L1 = mx.symbol.Activation(name='Mrelu2_stage4_L1', data=Mconv2_stage4_L1 , act_type='relu')
Mconv2_stage4_L2 = mx.symbol.Convolution(name='Mconv2_stage4_L2', data=Mrelu1_stage4_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage4_L2 = mx.symbol.Activation(name='Mrelu2_stage4_L2', data=Mconv2_stage4_L2 , act_type='relu')
Mconv3_stage4_L1 = mx.symbol.Convolution(name='Mconv3_stage4_L1', data=Mrelu2_stage4_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage4_L1 = mx.symbol.Activation(name='Mrelu3_stage4_L1', data=Mconv3_stage4_L1 , act_type='relu')
Mconv3_stage4_L2 = mx.symbol.Convolution(name='Mconv3_stage4_L2', data=Mrelu2_stage4_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage4_L2 = mx.symbol.Activation(name='Mrelu3_stage4_L2', data=Mconv3_stage4_L2 , act_type='relu')
Mconv4_stage4_L1 = mx.symbol.Convolution(name='Mconv4_stage4_L1', data=Mrelu3_stage4_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage4_L1 = mx.symbol.Activation(name='Mrelu4_stage4_L1', data=Mconv4_stage4_L1 , act_type='relu')
Mconv4_stage4_L2 = mx.symbol.Convolution(name='Mconv4_stage4_L2', data=Mrelu3_stage4_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage4_L2 = mx.symbol.Activation(name='Mrelu4_stage4_L2', data=Mconv4_stage4_L2 , act_type='relu')
Mconv5_stage4_L1 = mx.symbol.Convolution(name='Mconv5_stage4_L1', data=Mrelu4_stage4_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage4_L1 = mx.symbol.Activation(name='Mrelu5_stage4_L1', data=Mconv5_stage4_L1 , act_type='relu')
Mconv5_stage4_L2 = mx.symbol.Convolution(name='Mconv5_stage4_L2', data=Mrelu4_stage4_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage4_L2 = mx.symbol.Activation(name='Mrelu5_stage4_L2', data=Mconv5_stage4_L2 , act_type='relu')
Mconv6_stage4_L1 = mx.symbol.Convolution(name='Mconv6_stage4_L1', data=Mrelu5_stage4_L1 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage4_L1 = mx.symbol.Activation(name='Mrelu6_stage4_L1', data=Mconv6_stage4_L1 , act_type='relu')
Mconv6_stage4_L2 = mx.symbol.Convolution(name='Mconv6_stage4_L2', data=Mrelu5_stage4_L2 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage4_L2 = mx.symbol.Activation(name='Mrelu6_stage4_L2', data=Mconv6_stage4_L2 , act_type='relu')
Mconv7_stage4_L1 = mx.symbol.Convolution(name='Mconv7_stage4_L1', data=Mrelu6_stage4_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mconv7_stage4_L2 = mx.symbol.Convolution(name='Mconv7_stage4_L2', data=Mrelu6_stage4_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
concat_stage5 = mx.symbol.Concat(name='concat_stage5', *[Mconv7_stage4_L1,Mconv7_stage4_L2,relu4_4_CPM] )
Mconv1_stage5_L1 = mx.symbol.Convolution(name='Mconv1_stage5_L1', data=concat_stage5 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage5_L1 = mx.symbol.Activation(name='Mrelu1_stage5_L1', data=Mconv1_stage5_L1 , act_type='relu')
Mconv1_stage5_L2 = mx.symbol.Convolution(name='Mconv1_stage5_L2', data=concat_stage5 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage5_L2 = mx.symbol.Activation(name='Mrelu1_stage5_L2', data=Mconv1_stage5_L2 , act_type='relu')
Mconv2_stage5_L1 = mx.symbol.Convolution(name='Mconv2_stage5_L1', data=Mrelu1_stage5_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage5_L1 = mx.symbol.Activation(name='Mrelu2_stage5_L1', data=Mconv2_stage5_L1 , act_type='relu')
Mconv2_stage5_L2 = mx.symbol.Convolution(name='Mconv2_stage5_L2', data=Mrelu1_stage5_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage5_L2 = mx.symbol.Activation(name='Mrelu2_stage5_L2', data=Mconv2_stage5_L2 , act_type='relu')
Mconv3_stage5_L1 = mx.symbol.Convolution(name='Mconv3_stage5_L1', data=Mrelu2_stage5_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage5_L1 = mx.symbol.Activation(name='Mrelu3_stage5_L1', data=Mconv3_stage5_L1 , act_type='relu')
Mconv3_stage5_L2 = mx.symbol.Convolution(name='Mconv3_stage5_L2', data=Mrelu2_stage5_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage5_L2 = mx.symbol.Activation(name='Mrelu3_stage5_L2', data=Mconv3_stage5_L2 , act_type='relu')
Mconv4_stage5_L1 = mx.symbol.Convolution(name='Mconv4_stage5_L1', data=Mrelu3_stage5_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage5_L1 = mx.symbol.Activation(name='Mrelu4_stage5_L1', data=Mconv4_stage5_L1 , act_type='relu')
Mconv4_stage5_L2 = mx.symbol.Convolution(name='Mconv4_stage5_L2', data=Mrelu3_stage5_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage5_L2 = mx.symbol.Activation(name='Mrelu4_stage5_L2', data=Mconv4_stage5_L2 , act_type='relu')
Mconv5_stage5_L1 = mx.symbol.Convolution(name='Mconv5_stage5_L1', data=Mrelu4_stage5_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage5_L1 = mx.symbol.Activation(name='Mrelu5_stage5_L1', data=Mconv5_stage5_L1 , act_type='relu')
Mconv5_stage5_L2 = mx.symbol.Convolution(name='Mconv5_stage5_L2', data=Mrelu4_stage5_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage5_L2 = mx.symbol.Activation(name='Mrelu5_stage5_L2', data=Mconv5_stage5_L2 , act_type='relu')
Mconv6_stage5_L1 = mx.symbol.Convolution(name='Mconv6_stage5_L1', data=Mrelu5_stage5_L1 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage5_L1 = mx.symbol.Activation(name='Mrelu6_stage5_L1', data=Mconv6_stage5_L1 , act_type='relu')
Mconv6_stage5_L2 = mx.symbol.Convolution(name='Mconv6_stage5_L2', data=Mrelu5_stage5_L2 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage5_L2 = mx.symbol.Activation(name='Mrelu6_stage5_L2', data=Mconv6_stage5_L2 , act_type='relu')
Mconv7_stage5_L1 = mx.symbol.Convolution(name='Mconv7_stage5_L1', data=Mrelu6_stage5_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mconv7_stage5_L2 = mx.symbol.Convolution(name='Mconv7_stage5_L2', data=Mrelu6_stage5_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
concat_stage6 = mx.symbol.Concat(name='concat_stage6', *[Mconv7_stage5_L1,Mconv7_stage5_L2,relu4_4_CPM] )
Mconv1_stage6_L1 = mx.symbol.Convolution(name='Mconv1_stage6_L1', data=concat_stage6 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage6_L1 = mx.symbol.Activation(name='Mrelu1_stage6_L1', data=Mconv1_stage6_L1 , act_type='relu')
Mconv1_stage6_L2 = mx.symbol.Convolution(name='Mconv1_stage6_L2', data=concat_stage6 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu1_stage6_L2 = mx.symbol.Activation(name='Mrelu1_stage6_L2', data=Mconv1_stage6_L2 , act_type='relu')
Mconv2_stage6_L1 = mx.symbol.Convolution(name='Mconv2_stage6_L1', data=Mrelu1_stage6_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage6_L1 = mx.symbol.Activation(name='Mrelu2_stage6_L1', data=Mconv2_stage6_L1 , act_type='relu')
Mconv2_stage6_L2 = mx.symbol.Convolution(name='Mconv2_stage6_L2', data=Mrelu1_stage6_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu2_stage6_L2 = mx.symbol.Activation(name='Mrelu2_stage6_L2', data=Mconv2_stage6_L2 , act_type='relu')
Mconv3_stage6_L1 = mx.symbol.Convolution(name='Mconv3_stage6_L1', data=Mrelu2_stage6_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage6_L1 = mx.symbol.Activation(name='Mrelu3_stage6_L1', data=Mconv3_stage6_L1 , act_type='relu')
Mconv3_stage6_L2 = mx.symbol.Convolution(name='Mconv3_stage6_L2', data=Mrelu2_stage6_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu3_stage6_L2 = mx.symbol.Activation(name='Mrelu3_stage6_L2', data=Mconv3_stage6_L2 , act_type='relu')
Mconv4_stage6_L1 = mx.symbol.Convolution(name='Mconv4_stage6_L1', data=Mrelu3_stage6_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage6_L1 = mx.symbol.Activation(name='Mrelu4_stage6_L1', data=Mconv4_stage6_L1 , act_type='relu')
Mconv4_stage6_L2 = mx.symbol.Convolution(name='Mconv4_stage6_L2', data=Mrelu3_stage6_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu4_stage6_L2 = mx.symbol.Activation(name='Mrelu4_stage6_L2', data=Mconv4_stage6_L2 , act_type='relu')
Mconv5_stage6_L1 = mx.symbol.Convolution(name='Mconv5_stage6_L1', data=Mrelu4_stage6_L1 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage6_L1 = mx.symbol.Activation(name='Mrelu5_stage6_L1', data=Mconv5_stage6_L1 , act_type='relu')
Mconv5_stage6_L2 = mx.symbol.Convolution(name='Mconv5_stage6_L2', data=Mrelu4_stage6_L2 , num_filter=128, pad=(3,3), kernel=(7,7), stride=(1,1), no_bias=False)
Mrelu5_stage6_L2 = mx.symbol.Activation(name='Mrelu5_stage6_L2', data=Mconv5_stage6_L2 , act_type='relu')
Mconv6_stage6_L1 = mx.symbol.Convolution(name='Mconv6_stage6_L1', data=Mrelu5_stage6_L1 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage6_L1 = mx.symbol.Activation(name='Mrelu6_stage6_L1', data=Mconv6_stage6_L1 , act_type='relu')
Mconv6_stage6_L2 = mx.symbol.Convolution(name='Mconv6_stage6_L2', data=Mrelu5_stage6_L2 , num_filter=128, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mrelu6_stage6_L2 = mx.symbol.Activation(name='Mrelu6_stage6_L2', data=Mconv6_stage6_L2 , act_type='relu')
Mconv7_stage6_L1 = mx.symbol.Convolution(name='Mconv7_stage6_L1', data=Mrelu6_stage6_L1 , num_filter=numoflinks*2, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
Mconv7_stage6_L2 = mx.symbol.Convolution(name='Mconv7_stage6_L2', data=Mrelu6_stage6_L2 , num_filter=numofparts, pad=(0,0), kernel=(1,1), stride=(1,1), no_bias=False)
group = mx.symbol.Group([conv5_5_CPM_L1, conv5_5_CPM_L2,
Mconv7_stage2_L1, Mconv7_stage2_L2,
Mconv7_stage3_L1, Mconv7_stage3_L2,
Mconv7_stage4_L1, Mconv7_stage4_L2,
Mconv7_stage5_L1, Mconv7_stage5_L2,
Mconv7_stage6_L1, Mconv7_stage6_L2])
return group
class DataBatchweight(object):
def __init__(self, data, heatmaplabel, partaffinityglabel, heatweight, vecweight, pad=0):
self.data = [data]
self.label = [heatmaplabel, partaffinityglabel, heatweight, vecweight]
self.pad = pad