Keras implementation of Generator for DeblurGAN

generator.py

from keras.layers import Input, Activation, Add
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.core import Lambda
from keras.layers.normalization import BatchNormalization
from keras.models import Model

from layer_utils import ReflectionPadding2D, res_block

ngf = 64
input_nc = 3
output_nc = 3
input_shape_generator = (256, 256, input_nc)
n_blocks_gen = 9


def generator_model():
    """Build generator architecture."""
    # Current version : ResNet block
    inputs = Input(shape=image_shape)

    x = ReflectionPadding2D((3, 3))(inputs)
    x = Conv2D(filters=ngf, kernel_size=(7,7), padding='valid')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    # Increase filter number
    n_downsampling = 2
    for i in range(n_downsampling):
        mult = 2**i
        x = Conv2D(filters=ngf*mult*2, kernel_size=(3,3), strides=2, padding='same')(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)

    # Apply 9 ResNet blocks
    mult = 2**n_downsampling
    for i in range(n_blocks_gen):
        x = res_block(x, ngf*mult, use_dropout=True)

    # Decrease filter number to 3 (RGB)
    for i in range(n_downsampling):
        mult = 2**(n_downsampling - i)
        x = Conv2DTranspose(filters=int(ngf * mult / 2), kernel_size=(3,3), strides=2, padding='same')(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)

    x = ReflectionPadding2D((3,3))(x)
    x = Conv2D(filters=output_nc, kernel_size=(7,7), padding='valid')(x)
    x = Activation('tanh')(x)

    # Add direct connection from input to output and recenter to [-1, 1]
    outputs = Add()([x, inputs])
    outputs = Lambda(lambda z: z/2)(outputs)

    model = Model(inputs=inputs, outputs=outputs, name='Generator')
    return model