if train:
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
return (loss)
dL = layers.Input(shape=(218, 170, 24))
dM = layers.Input(shape=(218, 170, 24))
dLZ = layers.ZeroPadding2D(padding=((3, 3)))(dL)
dMZ = layers.ZeroPadding2D(padding=((3, 3)))(dM)
mul = cl.mulBlock()([dLZ, dMZ])
sConv01 = cl.convBlock(32, 3, 4)(mul)
sConv02 = cl.convBlock(128, 3, 2)(sConv01)
sConv03 = cl.convBlock(256, 3, 2)(sConv02)
res01 = cl.resBlock(256, 3)(sConv03)
res02 = cl.resBlock(256, 3)(res01)
tConv01 = cl.convTransBlock(128, 3, 2)(res02)
add = layers.Add()([tConv01, sConv02])
tConv02 = cl.convTransBlock(64, 3, 2)(add)
tConv03 = cl.convTransBlock(24, 3, 4)(tConv02)
synt = layers.Conv2D(24, 3, padding="same")(tConv03)
con = cl.conBlock()([mul, dMZ, synt])
def dumbelXL():
inL = layers.Input(shape=(256, 256, 2))
conv01 = cl.convBlock(32, 3)(inL)
conv02 = cl.convBlock(32, 3)(conv01)
sConv01 = cl.convBlock(64, 3, 2)(conv02)
conv03 = cl.convBlock(64, 3)(sConv01)
conv04 = cl.convBlock(64, 3)(conv03)
sConv02 = cl.convBlock(128, 3, 2)(conv04)
conv05 = cl.convBlock(128, 3)(sConv02)
conv06 = cl.convBlock(128, 3)(conv05)
sConv03 = cl.convBlock(256, 3, 2)(conv06)
conv07 = cl.convBlock(256, 3)(sConv03)
conv08 = cl.convBlock(256, 3)(conv07)
sConv04 = cl.convBlock(512, 3, 2)(conv08)
res01 = cl.resBlock(512, 3)(sConv04)
res02 = cl.resBlock(512, 3)(res01)
res03 = cl.resBlock(512, 3)(res02)
res04 = cl.resBlock(512, 3)(res03)
tConv01 = cl.convTransBlock(256, 3, 2)(res04)
add01 = layers.Add()([tConv01, conv08])
conv09 = cl.convBlock(256, 3)(add01)
conv10 = cl.convBlock(256, 3)(conv09)
tConv02 = cl.convTransBlock(128, 3, 2)(conv10)
add02 = layers.Add()([tConv02, conv06])
conv11 = cl.convBlock(128, 3)(add02)
conv12 = cl.convBlock(128, 3)(conv11)
tConv03 = cl.convTransBlock(64, 3, 2)(conv12)
add03 = layers.Add()([tConv03, conv04])
conv13 = cl.convBlock(64, 3)(add03)
conv14 = cl.convBlock(64, 3)(conv13)
tConv04 = cl.convTransBlock(32, 3, 2)(conv14)
add04 = layers.Add()([tConv04, conv02])
conv15 = cl.convBlock(32, 3)(add04)
conv16 = cl.convBlock(32, 3)(conv15)
synt = layers.Conv2D(2, 3, padding="same", use_bias=False)(conv16)
model = models.Model(inputs=[inL], outputs=[synt])
return(model)