def Decoder(self):
input_ = self.keras.layers.Input(shape=(4, 4, 1024))
x = input_
x = upscale(self.keras, x, 512)
x = res(self.keras, x, 512)
x = upscale(self.keras, x, 256)
x = res(self.keras, x, 256)
x = upscale(self.keras, x, 128)
x = res(self.keras, x, 128)
x = upscale(self.keras, x, 64)
x = res(self.keras, x, 64)
x = upscale(self.keras, x, 32)
x = res(self.keras, x, 32)
x = self.keras.layers.convolutional.Conv2D(3,
kernel_size=5,
padding='same',
activation='sigmoid')(x)
y = input_ #mask decoder
y = upscale(self.keras, y, 512)
y = upscale(self.keras, y, 256)
y = upscale(self.keras, y, 128)
y = upscale(self.keras, y, 64)
y = upscale(self.keras, y, 32)
y = self.keras.layers.convolutional.Conv2D(1,
kernel_size=5,
padding='same',
activation='sigmoid')(y)
return self.keras.models.Model(input_, [x, y])
def Decoder(self, created_vram_gb):
if created_vram_gb >= 4:
input_ = self.keras.layers.Input(shape=(8, 8, 512))
x = input_
x = upscale(self.keras, x, 512)
x = res(self.keras, x, 512)
x = upscale(self.keras, x, 256)
x = res(self.keras, x, 256)
x = upscale(self.keras, x, 128)
x = res(self.keras, x, 128)
x = upscale(self.keras, x, 64)
x = res(self.keras, x, 64)
else:
input_ = self.keras.layers.Input(shape=(8, 8, 256))
x = input_
x = upscale(self.keras, x, 256)
x = res(self.keras, x, 256)
x = upscale(self.keras, x, 128)
x = res(self.keras, x, 128)
x = upscale(self.keras, x, 64)
x = res(self.keras, x, 64)
x = upscale(self.keras, x, 32)
x = res(self.keras, x, 32)
x = self.keras.layers.convolutional.Conv2D(4,
kernel_size=5,
padding='same',
activation='sigmoid')(x)
return self.keras.models.Model(input_, x)
def DecoderCommon(self):
input_ = self.keras.layers.Input(shape=(4, 4, 1024))
x = input_
x = upscale(self.keras, x, 512)
x = res(self.keras, x, 512)
x = upscale(self.keras, x, 256)
x = res(self.keras, x, 256)
x = upscale(self.keras, x, 128)
x = res(self.keras, x, 128)
x = upscale(self.keras, x, 64)
x = res(self.keras, x, 64)
x = upscale(self.keras, x, 32)
x = res(self.keras, x, 32)
y = input_
y = upscale(self.keras, y, 256)
y = upscale(self.keras, y, 128)
y = upscale(self.keras, y, 64)
y = upscale(self.keras, y, 32)
y = upscale(self.keras, y, 16)
return self.keras.models.Model(input_, [x,y])
def Decoder(self, created_vram_gb):
if created_vram_gb >= 4:
input_ = self.keras.layers.Input(shape=(8, 8, 512))
x = input_
x = upscale(self.keras, x, 512)
x = res(self.keras, x, 512)
x = upscale(self.keras, x, 256)
x = res(self.keras, x, 256)
x = upscale(self.keras, x, 128)
x = res(self.keras, x, 128)
x = upscale(self.keras, x, 64)
x = res(self.keras, x, 64)
y = input_ #mask decoder
y = upscale(self.keras, y, 512)
y = upscale(self.keras, y, 256)
y = upscale(self.keras, y, 128)
y = upscale(self.keras, y, 64)
else:
input_ = self.keras.layers.Input(shape=(8, 8, 256))
x = input_
x = upscale(self.keras, x, 256)
x = res(self.keras, x, 256)
x = upscale(self.keras, x, 128)
x = res(self.keras, x, 128)
x = upscale(self.keras, x, 64)
x = res(self.keras, x, 64)
x = upscale(self.keras, x, 32)
x = res(self.keras, x, 32)
y = input_ #mask decoder
y = self.upscale(256)(y)
y = self.upscale(128)(y)
y = self.upscale(64)(y)
y = self.upscale(32)(y)
x = self.keras.layers.convolutional.Conv2D(3,
kernel_size=5,
padding='same',
activation='sigmoid')(x)
y = self.keras.layers.convolutional.Conv2D(1,
kernel_size=5,
padding='same',
activation='sigmoid')(y)
return self.keras.models.Model(input_, [x, y])