cyclegan_generator

cyclegan_generator.py

def make_generator_model(n_blocks):
    # 6 residual blocks
    # c7s1-64,d128,d256,R256,R256,R256,R256,R256,R256,u128,u64,c7s1-3
    # 9 residual blocks
    # c7s1-64,d128,d256,R256,R256,R256,R256,R256,R256,R256,R256,R256,u128,u64,c7s1-3
    model = tf.keras.Sequential()

    # Encoding
    model.add(ReflectionPad2d(3, input_shape=(256, 256, 3)))
    model.add(tf.keras.layers.Conv2D(64, (7, 7), strides=(1, 1), padding='valid', use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.ReLU())

    model.add(tf.keras.layers.Conv2D(128, (3, 3), strides=(2, 2), padding='same', use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.ReLU())

    model.add(tf.keras.layers.Conv2D(256, (3, 3), strides=(2, 2), padding='same', use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.ReLU())

    # Transformation
    for i in range(n_blocks):
        model.add(ResNetBlock(256))

    # Decoding
    model.add(tf.keras.layers.Conv2DTranspose(128, (3, 3), strides=(2, 2), padding='same', use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.ReLU())

    model.add(tf.keras.layers.Conv2DTranspose(64, (3, 3), strides=(2, 2), padding='same', use_bias=False))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.ReLU())

    model.add(ReflectionPad2d(3))
    model.add(tf.keras.layers.Conv2D(3, (7, 7), strides=(1, 1), padding='valid', activation='tanh'))

    return model