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symbol_utils.py
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import mxnet as mx
def Conv(**kwargs):
#name = kwargs.get('name')
#_weight = mx.symbol.Variable(name+'_weight')
#_bias = mx.symbol.Variable(name+'_bias', lr_mult=2.0, wd_mult=0.0)
#body = mx.sym.Convolution(weight = _weight, bias = _bias, **kwargs)
body = mx.sym.Convolution(**kwargs)
return body
def Act(data, act_type, name):
#ignore param act_type, set it in this function
body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name)
return body
bn_mom = 0.9
def Linear(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):
conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))
bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=False,momentum=bn_mom)
return bn
def get_fc1(last_conv, num_classes, fc_type):
bn_mom = 0.9
body = last_conv
if fc_type=='Z':
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
body = mx.symbol.Dropout(data=body, p=0.4)
fc1 = body
elif fc_type=='E':
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
body = mx.symbol.Dropout(data=body, p=0.4)
fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
elif fc_type=='GAP':
bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
relu1 = Act(data=bn1, act_type='relu', name='relu1')
# Although kernel is not used here when global_pool=True, we should put one
pool1 = mx.sym.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
flat = mx.sym.Flatten(data=pool1)
fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='pre_fc1')
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
elif fc_type=="GDC": #mobilefacenet_v1
filters_in = 512 # 512 for mobilefacenet
# _weight = mx.symbol.Variable("fc1_weight", shape=(num_classes, filters_in), lr_mult=1.0, wd_mult=10.0)
# conv_6_f = mx.sym.FullyConnected(data=conv_6_dw, weight = _weight, num_hidden=num_classes, name='pre_fc1')
conv_6_dw = Linear(last_conv, num_filter=filters_in, num_group=filters_in, kernel=(4,4), pad=(0, 0), stride=(1, 1), name="conv_6dw7_7")
conv_6_f = mx.sym.FullyConnected(data=conv_6_dw, num_hidden=num_classes, name='pre_fc1')
fc1 = mx.sym.BatchNorm(data=conv_6_f, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
elif fc_type=='GNAP': #mobilefacenet++
filters_in = 512 # param in mobilefacenet
if num_classes>filters_in:
body = mx.sym.Convolution(data=last_conv, num_filter=num_classes, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, name='convx')
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=0.9, name='convx_bn')
body = Act(data=body, act_type='relu', name='convx_relu')
filters_in = num_classes
else:
body = last_conv
body = mx.sym.BatchNorm(data=body, fix_gamma=True, eps=2e-5, momentum=0.9, name='bn6f')
spatial_norm=body*body
spatial_norm=mx.sym.sum(data=spatial_norm, axis=1, keepdims=True)
spatial_sqrt=mx.sym.sqrt(spatial_norm)
#spatial_mean=mx.sym.mean(spatial_sqrt, axis=(1,2,3), keepdims=True)
spatial_mean=mx.sym.mean(spatial_sqrt)
spatial_div_inverse=mx.sym.broadcast_div(spatial_mean, spatial_sqrt)
spatial_attention_inverse=mx.symbol.tile(spatial_div_inverse, reps=(1,filters_in,1,1))
body=body*spatial_attention_inverse
#body = mx.sym.broadcast_mul(body, spatial_div_inverse)
fc1 = mx.sym.Pooling(body, kernel=(7, 7), global_pool=True, pool_type='avg')
if num_classes<filters_in:
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=0.9, name='bn6w')
fc1 = mx.sym.FullyConnected(data=fc1, num_hidden=num_classes, name='pre_fc1')
else:
fc1 = mx.sym.Flatten(data=fc1)
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=0.9, name='fc1')
elif fc_type=='F':
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
body = mx.symbol.Dropout(data=body, p=0.4)
fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='fc1')
elif fc_type=='G':
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='fc1')
elif fc_type=='H':
fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='fc1')
elif fc_type=='I':
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
elif fc_type=='J':
fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
else:
bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')
relu1 = Act(data=bn1, act_type='relu', name='relu1')
# Although kernel is not used here when global_pool=True, we should put one
pool1 = mx.sym.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
flat = mx.sym.Flatten(data=pool1)
if len(fc_type)>1:
if fc_type[1]=='X':
print('dropout mode')
flat = mx.symbol.Dropout(data=flat, p=0.2)
fc_type = fc_type[0]
if fc_type=='A':
fc1 = flat
else:
#B-D
#B
fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='pre_fc1')
if fc_type=='C':
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
elif fc_type=='D':
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')
fc1 = Act(data=fc1, act_type='relu', name='fc1_relu')
return fc1
def residual_unit_v3(data, num_filter, stride, dim_match, name, **kwargs):
"""Return ResNet Unit symbol for building ResNet
Parameters
----------
data : str
Input data
num_filter : int
Number of output channels
bnf : int
Bottle neck channels factor with regard to num_filter
stride : tuple
Stride used in convolution
dim_match : Boolean
True means channel number between input and output is the same, otherwise means differ
name : str
Base name of the operators
workspace : int
Workspace used in convolution operator
"""
bn_mom = kwargs.get('bn_mom', 0.9)
workspace = kwargs.get('workspace', 256)
memonger = kwargs.get('memonger', False)
#print('in unit3')
bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')
conv1 = Conv(data=bn1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv1')
bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')
act1 = Act(data=bn2, act_type='relu', name=name + '_relu1')
conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),
no_bias=True, workspace=workspace, name=name + '_conv2')
bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')
if dim_match:
shortcut = data
else:
conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,
workspace=workspace, name=name+'_conv1sc')
shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')
if memonger:
shortcut._set_attr(mirror_stage='True')
return bn3 + shortcut
def get_head(data, version_input, num_filter):
bn_mom = 0.9
workspace = 256
kwargs = {'bn_mom': bn_mom, 'workspace' : workspace}
data = data-127.5
data = data*0.0078125
#data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')
if version_input==0:
body = Conv(data=data, num_filter=num_filter, kernel=(7, 7), stride=(2,2), pad=(3, 3),
no_bias=True, name="conv0", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
body = Act(data=body, act_type='relu', name='relu0')
body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')
else:
body = data
_num_filter = min(num_filter, 64)
body = Conv(data=body, num_filter=_num_filter, kernel=(3,3), stride=(1,1), pad=(1, 1),
no_bias=True, name="conv0", workspace=workspace)
body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')
body = Act(data=body, act_type='relu', name='relu0')
body = residual_unit_v3(body, _num_filter, (2, 2), False, name='head', **kwargs)
return body