def __init__(self, filters, sn=False, use_bias=False, **kwargs):
super(rk_3D, self).__init__()
self.l1_conv3d = BaseLayers.Conv3D(filters=filters,
kernel_size=[3, 3, 3],
strides=[1, 1, 1],
padding="REFLECT",
use_bias=use_bias,
sn=sn,
**kwargs)
self.l2_norm = BaseLayers.InstanceNorm3D()
self.l3_activation = BaseLayers.ReLU()
self.l4_conv3d = BaseLayers.Conv3D(filters=filters,
kernel_size=[3, 3, 3],
strides=[1, 1, 1],
padding="REFLECT",
use_bias=use_bias,
sn=sn,
**kwargs)
self.l5_norm = BaseLayers.InstanceNorm3D()
def __init__(self,filters,sn=False,use_bias=False,activation="relu",**kwargs):
super(c7s1_k_3D,self).__init__()
self.filters_zp = filters
self.l1_conv3d = BaseLayers.Conv3D(filters=filters,kernel_size=[7,7,7],strides=[1,1,1],padding="REFLECT",use_bias=use_bias,sn=sn,**kwargs)
self.l2_norm = BaseLayers.InstanceNorm3D()
if activation.lower()=="relu":
self.l3_activation = BaseLayers.ReLU()
elif activation.lower()=="sigmoid":
self.l3_activation = tf.keras.layers.Activation("sigmoid")
elif activation.lower()=="tanh":
self.l3_activation = tf.keras.layers.Activation("tanh")
else:
raise ValueError("Un supported activation "+activation)
def __init__(self, use_sigmoid=True, sn=False, **kwargs):
super(last_conv_3D, self).__init__()
self.l1_conv3d = BaseLayers.Conv3D(filters=1,
kernel_size=[4, 4, 4],
strides=[1, 1, 1],
padding='SAME',
use_bias=True,
sn=sn,
**kwargs)
if use_sigmoid:
self.l2_activation = tf.keras.layers.Activation("sigmoid")
else:
self.l2_activation = tf.keras.layers.Activation("linear")
def __init__(self, filters, sn=False, norm=True, use_bias=False, **kwargs):
super(ck_3D, self).__init__()
self.filters_zp = filters
self.l1_conv3d = BaseLayers.Conv3D(filters=filters,
kernel_size=[4, 4, 4],
strides=[2, 2, 2],
padding='SAME',
use_bias=use_bias,
sn=sn,
**kwargs)
if norm:
self.l2_norm = BaseLayers.InstanceNorm3D()
else:
self.l2_norm = tf.keras.layers.Activation("linear")
self.l3_activation = BaseLayers.LeakyReLU(alpha=0.2)