model.py 文件源码

def build_model(dropout_rate = 0.2):
    input_image = Input(shape = IMAGE_SHAPE,
                        dtype = 'float32',
                        name = INPUT_IMAGE)
    x = MaxPooling2D()(input_image)
    x = MaxPooling2D()(x)
    x = MaxPooling2D()(x)
    x = MaxPooling2D()(x)
    x = Dropout(dropout_rate)(x)
    x = Conv2D(32, kernel_size=3, strides=(2,2))(x)
    x = MaxPooling2D()(x)
    x = Conv2D(32, kernel_size=3, strides=(2,2))(x)
    x = MaxPooling2D()(x)
    x = Dropout(dropout_rate)(x)
    image_out = Flatten()(x)
    # image_out = Dense(32, activation='relu')(conv)

    input_lidar_panorama = Input(shape = PANORAMA_SHAPE,
                                 dtype = 'float32',
                                 name = INPUT_LIDAR_PANORAMA)
    x = pool_and_conv(input_lidar_panorama)
    x = pool_and_conv(x)
    x = Dropout(dropout_rate)(x)
    panorama_out = Flatten()(x)

    input_lidar_slices = Input(shape = SLICES_SHAPE,
                               dtype = 'float32',
                               name = INPUT_LIDAR_SLICES)
    x = MaxPooling3D(pool_size=(2,2,1))(input_lidar_slices)
    x = Conv3D(32, kernel_size=3, strides=(2,2,1))(x)
    x = MaxPooling3D(pool_size=(2,2,1))(x)
    x = Dropout(dropout_rate)(x)
    x = Conv3D(32, kernel_size=2, strides=(2,2,1))(x)
    x = MaxPooling3D(pool_size=(2,2,1))(x)
    x = Dropout(dropout_rate)(x)
    slices_out = Flatten()(x)

    x = keras.layers.concatenate([image_out, panorama_out, slices_out])

    x = Dense(32, activation='relu')(x)
    x = Dense(32, activation='relu')(x)
    x = Dense(32, activation='relu')(x)

    pose_output = Dense(9, name=OUTPUT_POSE)(x)

    model = Model(inputs=[input_image, input_lidar_panorama, input_lidar_slices],
                  outputs=[pose_output])

    # Fix error with TF and Keras
    import tensorflow as tf
    tf.python.control_flow_ops = tf

    model.compile(loss='mean_squared_error', optimizer='adam')

    return model