def __init__(self,
unit_name,
cardinality,
channels,
bn_mom,
act_type,
res_scale_fac,
use_se=True):
"""
:param channels: Number of channels used in the conv-operations
:param bn_mom: Batch normalization momentum
:param act_type: Activation function to use
:param unit_name: Unit name of the residual block (only used for description (string))
"""
super(ResidualBlockX, self).__init__(unit_name + '_')
self.act_type = act_type
self.unit_name = unit_name
self.res_scale_fac = res_scale_fac
self.body = HybridSequential(prefix='')
with self.name_scope():
self.body.add(
Conv2D(channels=channels,
kernel_size=3,
padding=1,
use_bias=False))
self.body.add(BatchNorm(momentum=bn_mom))
self.body.add(get_act(self.act_type))
self.body.add(
Conv2D(channels=channels,
kernel_size=3,
padding=1,
groups=cardinality,
use_bias=False))
self.body.add(BatchNorm(momentum=bn_mom))
if use_se is True:
# apply squeeze excitation
self.body.add(_SqueezeExcitation('se0', channels, 16,
act_type))
self.act0 = get_act(act_type)
def __init__(self,
unit_name,
cardinality,
channels,
bn_mom=0.9,
act_type='relu',
use_se=True,
res_scale_fac=0.2,
dim_match=True):
"""
:param channels: Number of channels used in the conv-operations
:param bn_mom: Batch normalization momentum
:param act_type: Activation function to use
:param unit_name: Unit name of the residual block (only used for description (string))
"""
super(_ResidualBlockXBottleneck, self).__init__(prefix=unit_name + '_')
self.unit_name = unit_name
self.res_scale_fac = res_scale_fac
self.use_se = use_se
self.dim_match = dim_match
self.body = HybridSequential(prefix='')
with self.name_scope():
self.body.add(get_act(act_type))
self.body.add(
Conv2D(int(channels // 2),
kernel_size=1,
padding=0,
use_bias=False))
self.body.add(BatchNorm(momentum=bn_mom))
self.body.add(get_act(act_type))
self.body.add(
Conv2D(int(channels // 2),
kernel_size=3,
padding=1,
groups=cardinality,
use_bias=False))
self.body.add(BatchNorm(momentum=bn_mom))
self.body.add(get_act(act_type))
# add a 1x1 branch after concatenation
self.body.add(
Conv2D(channels=channels, kernel_size=1, use_bias=False))
self.body.add(BatchNorm(momentum=bn_mom))
if use_se is True:
# apply squeeze excitation
self.body.add(_SqueezeExcitation('se0', channels, 16,
act_type))
self.act0 = get_act(act_type, prefix='%s1' % act_type)
if self.dim_match is False:
self.expander = HybridSequential(prefix='')
self.expander.add(
Conv2D(channels=channels,
kernel_size=1,
use_bias=False,
prefix='expander_conv'))
self.expander.add(BatchNorm())