style_transfer.py 文件源码

def st_convt(input_shape, weights_path=None, mode=0, nb_res_layer=5):
    if K.image_dim_ordering() == 'tf':
        channel_axis = 3
    else:
        channel_axis = 1

    input = Input(shape=input_shape, name='input_node', dtype=K.floatx())
    # Downsampling
    c11 = Convolution2D(32, 9, 9, dim_ordering=K.image_dim_ordering(), 
        init='he_normal', subsample=(1, 1), border_mode='same', activation='linear')(input)
    bn11 = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(c11)
    a11 = Activation('relu')(bn11)

    c12 = Convolution2D(64, 3, 3, dim_ordering=K.image_dim_ordering(), 
        init='he_normal', subsample=(2, 2),  border_mode='same', activation='linear')(a11)
    bn12 = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(c12)
    a12 = Activation('relu')(bn12)

    c13 = Convolution2D(128, 3, 3, dim_ordering=K.image_dim_ordering(), 
        init='he_normal', subsample=(2, 2), border_mode='same', activation='linear')(a12)
    bn13 = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(c13)
    last_out = Activation('relu')(bn13)

    for i in range(nb_res_layer):
        c = Convolution2D(128, 3, 3, dim_ordering=K.image_dim_ordering(), 
            init='he_normal', subsample=(1, 1), border_mode='same', activation='linear')(last_out)
        bn = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(c)
        a = Activation('relu')(bn)
        c = Convolution2D(128, 3, 3, dim_ordering=K.image_dim_ordering(), 
            init='he_normal', subsample=(1, 1), border_mode='same', activation='linear')(a)
        bn = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(c)
        # a = Activation('relu')(bn)
        last_out = merge([last_out, bn], mode='sum')
        # last_out = a

    ct71 = ConvolutionTranspose2D(64, 3, 3, dim_ordering=K.image_dim_ordering(), 
        init='he_normal', subsample=(2, 2), border_mode='same', activation='linear')(last_out)
    bn71 = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(ct71)
    a71 = Activation('relu')(bn71)

    ct81 = ConvolutionTranspose2D(32, 3, 3, dim_ordering=K.image_dim_ordering(), 
        init='he_normal', subsample=(2, 2), border_mode='same', activation='linear')(a71)
    bn81 = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.1, gamma_init='he_normal')(ct81)
    a81 = Activation('relu')(bn81)    

    c91 = Convolution2D(3, 9, 9, dim_ordering=K.image_dim_ordering(), 
        init='he_normal', subsample=(1, 1), border_mode='same', activation='linear')(a81)
    out = ScaledSigmoid(scaling=255., name="output_node")(c91)    


    model = Model(input=[input], output=[out])

    if weights_path:
        model.load_weights(weights_path)

    return model

# Moving from 4 to 12 layers doesn't seem to improve much