Function fusion_module

src/models/blocks.py:291–325

Feaure fusion module between learning module and feature extractor module outputs: Args: learning_layer: output of learning module fe_layer: output of feature extracted module Returns: fusion_layer: output of feature fusion module

(learning_layer, fe_layer)

    return fe_layer4

def fusion_module(learning_layer, fe_layer):
    """
    Feaure fusion module between learning module and feature extractor module outputs:
    Args:
        learning_layer: output of learning module
        fe_layer: output of feature extracted module
    Returns:
        fusion_layer: output of feature fusion module
    """
    fusion_layer1 = conv_block(learning_layer,
                               conv_type="conv",
                               filters=96,
                               kernel_size=(1, 1),
                               padding="same",
                               strides=(1, 1),
                               relu=True)

    fusion_layer2 = tf.keras.layers.UpSampling2D((4, 4))(fe_layer)
    fusion_layer2 = tf.keras.layers.DepthwiseConv2D(kernel_size=(3, 3),
                                                    strides=(1, 1),
                                                    depth_multiplier=1,
                                                    padding="same")(fusion_layer2)
    fusion_layer2 = tf.keras.layers.BatchNormalization()(fusion_layer2)
    fusion_layer2 = tf.keras.activations.relu(fusion_layer2)
    fusion_layer2 = tf.keras.layers.Conv2D(filters=96,
                                           kernel_size=(1, 1),
                                           strides=(1, 1),
                                           padding="same",
                                           activation=None)(fusion_layer2)

    fusion_layer = tf.keras.layers.add([fusion_layer1, fusion_layer2])
    fusion_layer = tf.keras.layers.BatchNormalization()(fusion_layer)
    fusion_layer = tf.keras.activations.relu(fusion_layer)

    return fusion_layer

def get_encoder(image_height, image_width):
    """