def Encoder(input_shape):
input_layer = self.keras.layers.Input(input_shape)
x = input_layer
if created_vram_gb >= 4:
x = conv(self.keras, x, 128)
x = conv(self.keras, x, 256)
x = conv(self.keras, x, 512)
x = conv(self.keras, x, 1024)
x = self.keras.layers.Dense(1024)(
self.keras.layers.Flatten()(x))
x = self.keras.layers.Dense(4 * 4 * 1024)(x)
x = self.keras.layers.Reshape((4, 4, 1024))(x)
x = upscale(self.keras, x, 512)
else:
x = conv(self.keras, x, 128)
x = conv(self.keras, x, 256)
x = conv(self.keras, x, 512)
x = conv(self.keras, x, 768)
x = self.keras.layers.Dense(512)(
self.keras.layers.Flatten()(x))
x = self.keras.layers.Dense(4 * 4 * 512)(x)
x = self.keras.layers.Reshape((4, 4, 512))(x)
x = upscale(self.keras, x, 256)
return self.keras.models.Model(input_layer, x)
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 Encoder(input_shape):
input_layer = self.keras.layers.Input(input_shape)
x = input_layer
if created_vram_gb >= 5:
x = conv(self.keras, x, 128)
x = conv(self.keras, x, 256)
x = conv(self.keras, x, 512)
x = conv(self.keras, x, 1024)
x = self.keras.layers.Dense(512)(
self.keras.layers.Flatten()(x))
x = self.keras.layers.Dense(8 * 8 * 512)(x)
x = self.keras.layers.Reshape((8, 8, 512))(x)
x = upscale(self.keras, x, 512)
else:
x = conv(self.keras, x, 128)
x = conv(self.keras, x, 256)
x = conv(self.keras, x, 512)
x = conv(self.keras, x, 1024)
x = self.keras.layers.Dense(256)(
self.keras.layers.Flatten()(x))
x = self.keras.layers.Dense(8 * 8 * 256)(x)
x = self.keras.layers.Reshape((8, 8, 256))(x)
x = upscale(self.keras, x, 256)
return self.keras.models.Model(input_layer, x)
def decoder64(encoder):
decoder_input = keras.layers.Input ( K.int_shape(encoder.outputs[0])[1:] )
x = decoder_input
x = self.keras.layers.Dense(8 * 8 * 720)(x)
x = keras.layers.Reshape ( (8, 8, 720) )(x)
x = upscale(keras, x, 360)
x = upscale(keras, x, 180)
x = upscale(keras, x, 90)
x = keras.layers.convolutional.Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x)
return keras.models.Model(decoder_input, x)
def Decoder():
if created_vram_gb >= 4:
input_ = self.keras.layers.Input(shape=(8, 8, 512))
x = input_
x = upscale(self.keras, x, 512)
x = upscale(self.keras, x, 256)
x = upscale(self.keras, x, 128)
else:
input_ = self.keras.layers.Input(shape=(8, 8, 256))
x = input_
x = upscale(self.keras, x, 256)
x = upscale(self.keras, x, 128)
x = upscale(self.keras, x, 64)
y = input_ #mask decoder
y = upscale(self.keras, y, 256)
y = upscale(self.keras, y, 128)
y = upscale(self.keras, y, 64)
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])
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 Intermediate(self):
input_layer = self.keras.layers.Input(shape=(None, 8 * 8 * 1024))
x = input_layer
x = self.keras.layers.Dense(256)(x)
x = self.keras.layers.Dense(8 * 8 * 512)(x)
x = self.keras.layers.Reshape((8, 8, 512))(x)
x = upscale(self.keras, x, 512)
return self.keras.models.Model(input_layer, x)
def Encoder(self, input_layer):
x = input_layer
x = conv(self.keras, x, 128)
x = conv(self.keras, x, 256)
x = conv(self.keras, x, 512)
x = conv(self.keras, x, 1024)
x = self.keras.layers.Dense(512)(self.keras.layers.Flatten()(x))
x = self.keras.layers.Dense(8 * 8 * 512)(x)
x = self.keras.layers.Reshape((8, 8, 512))(x)
x = upscale(self.keras, x, 512)
return self.keras.models.Model(input_layer, 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])
