def _convtrans_norm_activation(
self,
x,
filters,
kernel_size,
strides=1,
padding='same',
kernel_initializer=tf_utils.xavier_initializer(),
normalization=None,
activation=None,
trainable=True,
name=None,
reuse=None):
x = self._convtrans(x,
filters,
kernel_size,
strides=strides,
padding=padding,
kernel_initializer=kernel_initializer,
trainable=trainable,
name=name,
reuse=reuse)
x = tf_utils.norm_activation(x,
normalization=normalization,
activation=activation,
training=getattr(self, 'is_training',
False))
return x
def _conv_norm_activation(self,
x,
filters,
kernel_size,
strides=1,
padding='same',
kernel_initializer=tf_utils.xavier_initializer(),
normalization=None,
activation=None,
name=None,
reuse=None):
x = self._conv(x,
filters,
kernel_size,
strides=strides,
padding=padding,
kernel_initializer=kernel_initializer,
name=name,
reuse=reuse)
x = tf_utils.norm_activation(x,
normalization=normalization,
activation=activation,
training=self.training,
trainable=self.trainable)
return x