def image_transform_net(img_width, img_height, tv_weight=1):
x = Input(shape=(img_width, img_height, 3), name='itn_input')
a = InputNormalize(name='itn_input_norm')(x)
a = ReflectionPadding2D(padding=(40, 40),
input_shape=(img_width, img_height, 3),
name='itn_reflectpad')(a)
a = Conv2D(32, (9, 9), strides=1, padding='same', name='conv_1')(a)
a = BatchNormalization(name='batch_norm_1')(a)
a = Activation('relu', name='act_1')(a)
a = Conv2D(64, (3, 3), strides=2, padding='same', name='conv_2')(a)
a = BatchNormalization(name='batch_norm_2')(a)
a = Activation('relu', name='act_2')(a)
a = Conv2D(128, (3, 3), strides=2, padding='same', name='conv_3')(a)
a = BatchNormalization(name='batch_norm_3')(a)
a = Activation('relu', name='act_3')(a)
# Residual 1
a = res_conv(128, 3, 3)(a)
# Residual 2
a = res_conv(128, 3, 3)(a)
# Residual 3
a = res_conv(128, 3, 3)(a)
# Residual 4
a = res_conv(128, 3, 3)(a)
# Residual 5
a = res_conv(128, 3, 3)(a)
a = Conv2DTranspose(64, (3, 3), strides=2, padding='same',
name='conv_4')(a)
a = BatchNormalization(name='batch_norm_4')(a)
a = Activation('relu', name='act_4')(a)
a = Conv2DTranspose(32, (3, 3), strides=2, padding='same',
name='conv_5')(a)
a = BatchNormalization(name='batch_norm_5')(a)
a = Activation('relu', name='act_5')(a)
a = Conv2D(3, (9, 9), strides=1, padding='same', name='conv_6')(a)
a = BatchNormalization(name='batch_norm_6')(a)
a = Activation('tanh', name='act_6')(a) #output_image
# Scale output to range [0, 255] via custom Denormalize layer
y_hat = Scale_tanh(name='transform_output')(a)
itn_model = Model(inputs=x, outputs=y_hat)
#itn_model.load_weights('wave_crop_weights.h5', by_name=True)
#print(model.output.shape)
add_total_variation_loss(itn_model.layers[-1], tv_weight)
return itn_model
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
