def _get_model(self):
x = tf.keras.Input(shape=(None, None, 3))
a = InputNormalize()(x)
#a = ReflectionPadding2D(padding=(40,40),input_shape=(img_width,img_height,3))(a)
a = conv_bn_relu(8, 9, 9, stride=(1, 1))(a)
a = conv_bn_relu(16, 3, 3, stride=(2, 2))(a)
a = conv_bn_relu(32, 3, 3, stride=(2, 2))(a)
for i in range(2):
a = res_conv(32, 3, 3)(a)
a = dconv_bn_nolinear(16, 3, 3)(a)
a = dconv_bn_nolinear(8, 3, 3)(a)
a = dconv_bn_nolinear(3, 9, 9, stride=(1, 1), activation="tanh")(a)
# Scale output to range [0, 255] via custom Denormalize layer
y = Denormalize(name='transform_output')(a)
return tf.keras.Model(x, y, name="transformnet")
def image_transform_net(img_width, img_height, tv_weight=1):
x = Input(shape=(img_width, img_height, 3), name="input")
a = layers.InputNormalize()(x)
a = layers.ReflectionPadding2D(padding=(40, 40),
input_shape=(img_width, img_height, 3))(a)
a = layers.conv_bn_relu(32, 9, 9, stride=(1, 1))(a)
a = layers.conv_bn_relu(64, 9, 9, stride=(2, 2))(a)
a = layers.conv_bn_relu(128, 3, 3, stride=(2, 2))(a)
for _ in range(5):
a = layers.res_conv(128, 3, 3)(a)
a = layers.dconv_bn_nolinear(64, 3, 3)(a)
a = layers.dconv_bn_nolinear(64, 3, 3)(a)
a = layers.conv_bn_relu(3, 9, 9, stride=(1, 1), relu=False)(a)
y = layers.Denormalize(name='transform_output')(a)
model = Model(inputs=x, outputs=y)
if tv_weight > 0:
add_total_variation_loss(model.layers[-1], tv_weight)
return model
def image_transform_net(img_width,img_height,tv_weight=1):
x = Input(shape=(img_width,img_height,3))
a = InputNormalize()(x)
a = ReflectionPadding2D(padding=(40,40),input_shape=(img_width,img_height,3))(a)
a = conv_bn_relu(32, 9, 9, stride=(1,1))(a)
a = conv_bn_relu(64, 9, 9, stride=(2,2))(a)
a = conv_bn_relu(128, 3, 3, stride=(2,2))(a)
for i in range(5):
a = res_conv(128,3,3)(a)
a = dconv_bn_nolinear(64,3,3)(a)
a = dconv_bn_nolinear(32,3,3)(a)
a = dconv_bn_nolinear(3,9,9,stride=(1,1),activation="tanh")(a)
# Scale output to range [0, 255] via custom Denormalize layer
y = Denormalize(name='transform_output')(a)
model = Model(inputs=x, outputs=y)
if tv_weight > 0:
add_total_variation_loss(model.layers[-1],tv_weight)
return model
def image_transform_net(img_width, img_height, tv_weight=1):
"""
Image tranform
network model.
"""
# Input layer as an RGB image
x = Input(shape=(img_width, img_height, 3))
# Normalize input image
a = InputNormalize()(x)
# Pad image
a = ReflectionPadding2D(padding=(40, 40),
input_shape=(img_width, img_height, 3))(a)
# Extract feature maps
a = conv_bn_relu(32, 9, 9, stride=(1, 1))(a)
a = conv_bn_relu(64, 3, 3,
stride=(2, 2))(a) # The previous kernel size was 9x9
a = conv_bn_relu(128, 3, 3, stride=(2, 2))(a)
for _ in range(5):
a = res_conv(128, 3, 3)(a)
a = dconv_bn_nolinear(64, 3, 3)(a)
a = dconv_bn_nolinear(32, 3, 3)(a)
a = dconv_bn_nolinear(3, 9, 9, stride=(1, 1), activation="tanh")(a)
# Scale output to range [0, 255] via custom Denormalize layer
y = Denormalize(name='transform_output')(a)
# Create model
model = Model(inputs=x, outputs=y)
# Total variation regularizer
if tv_weight > 0:
add_total_variation_loss(model.layers[-1], tv_weight)
return model
