def add_forward(self, data: sym.Variable):
"""Add neural network model."""
conv1 = sym.Convolution(
data, name='conv1', num_filter=64, kernel=(3, 3), stride=(2, 2))
relu1 = sym.Activation(conv1, act_type='relu')
pool1 = sym.Pooling(relu1, pool_type='max', kernel=(3, 3), stride=(2, 2))
fire2 = self._fire_layer('fire2', pool1, s1x1=16, e1x1=64, e3x3=64)
fire3 = self._fire_layer('fire3', fire2, s1x1=16, e1x1=64, e3x3=64)
pool3 = sym.Pooling(fire3, name='pool3', kernel=(3, 3), stride=(2, 2), pool_type='max')
fire4 = self._fire_layer('fire4', pool3, s1x1=32, e1x1=128, e3x3=128)
fire5 = self._fire_layer('fire5', fire4, s1x1=32, e1x1=128, e3x3=128)
pool5 = sym.Pooling(fire5, name='pool5', kernel=(3, 3), stride=(2, 2), pool_type='max')
fire6 = self._fire_layer('fire6', pool5, s1x1=48, e1x1=192, e3x3=192)
fire7 = self._fire_layer('fire7', fire6, s1x1=48, e1x1=192, e3x3=192)
fire8 = self._fire_layer('fire8', fire7, s1x1=64, e1x1=256, e3x3=256)
fire9 = self._fire_layer('fire9', fire8, s1x1=64, e1x1=256, e3x3=256)
fire10 = self._fire_layer('fire10', fire9, s1x1=96, e1x1=384, e3x3=384)
fire11 = self._fire_layer('fire11', fire10, s1x1=96, e1x1=384, e3x3=384)
dropout11 = sym.Dropout(fire11, p=0.1, name='drop11')
return sym.Convolution(
dropout11, name='conv12', num_filter=NUM_OUT_CHANNELS,
kernel=(3, 3), stride=(1, 1), pad=(1, 1))
def ConvFactory(data,
num_filter,
kernel,
stride=(1, 1),
pad=(0, 0),
name=None,
suffix='',
attr={}):
conv = mxy.Convolution(data=data,
num_filter=num_filter,
kernel=kernel,
stride=stride,
pad=pad,
name='conv_%s%s' % (name, suffix))
bn = mxy.BatchNorm(data=conv,
fix_gamma=fix_gamma,
eps=eps,
momentum=bn_mom,
name='bn_%s%s' % (name, suffix))
act = mxy.Activation(data=bn,
act_type='relu',
name='relu_%s%s' % (name, suffix),
attr=attr)
return act
def SEModule(data, num_filter, name):
body = sym.Pooling(data=data,
global_pool=True,
kernel=(7, 7),
pool_type='avg',
name=name + '_se_pool1')
body = sym.Convolution(data=body,
num_filter=num_filter // 8,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv1")
body = Act(data=body, act_type="prelu", name=name + '_se_relu1')
body = sym.Convolution(data=body,
num_filter=num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=name + "_se_conv2")
body = sym.Activation(data=body,
act_type='sigmoid',
name=name + "_se_sigmoid")
data = sym.broadcast_mul(data, body)
return data
def _get_activation(self, inputs, activation, **kwargs):
"""Get activation function. Convert if is string"""
if isinstance(activation, string_types):
return symbol.Activation(inputs, act_type=activation, **kwargs)
else:
return activation(inputs, **kwargs)