Function tem_block

src/models/blocks.py:171–226

Texture Enhancement Module Args: inputs: input to the tem module Returns: permute_2: output from the tem module

(inputs)

    return tf.keras.layers.concatenate(concat_list)

def tem_block(inputs):
    """
    Texture Enhancement Module
    Args:
        inputs: input to the tem module
    Returns:
        permute_2: output from the tem module
    """
    conv_1 = tf.keras.layers.Conv2D(filters=256,
                                    kernel_size=(3, 3),
                                    padding="same",
                                    name="conv_1")(inputs)
    gap_1 = tf.keras.layers.GlobalAveragePooling2D(keepdims=True,
                                                   name="gap_1")(conv_1)
    conv_2 = tf.keras.layers.Conv2D(filters=256,
                                    kernel_size=(1, 1),
                                    activation="relu",
                                    padding="same",
                                    name="conv_2")(gap_1)
    conv_3 = tf.keras.layers.Conv2D(filters=256,
                                    kernel_size=(1, 1),
                                    activation="sigmoid",
                                    padding="same",
                                    name="conv_3")(conv_2)
    mult_1 = tf.keras.layers.Multiply(name="mult_1")([conv_1, conv_3])
    add_1 = tf.keras.layers.Add(name="add_123")([conv_1, mult_1])

    gap_2 = tf.keras.layers.GlobalAveragePooling2D(keepdims=True,
                                                   name="gap_2")(add_1)
    cos_sim_1 = tf.keras.layers.Dot(axes=(3),
                                    normalize=True,
                                    name="cos_sim1")([gap_2, add_1])
    reshape_1 = tf.keras.layers.Flatten()(cos_sim_1)
    transpose_1 = tf.keras.backend.transpose(reshape_1)
    mat_mul_1 = tf.keras.backend.dot(transpose_1,
                                     reshape_1)

    conv_4 = tf.keras.layers.Conv2D(filters=256,
                                    kernel_size=(1, 1),
                                    padding="same",
                                    name="conv_4")(add_1)
    permute_1 = tf.keras.layers.Permute((3, 1, 2),
                                        name="permute_1")(conv_4)
    reshape_2 = tf.keras.layers.Reshape((-1,
                                         int(conv_4.shape[-2]) * int(conv_4.shape[-3])),
                                         name="reshape_2")(permute_1)
    mat_mul_2 = tf.keras.backend.dot(reshape_2,
                                     mat_mul_1)

    reshape_3 = tf.keras.layers.Reshape((-1,
                                         int(conv_4.shape[-2]),
                                         int(conv_4.shape[-3])), name="reshape_3")(mat_mul_2)
    permute_2 = tf.keras.layers.Permute((2, 3, 1),
                                        name="permute_2")(reshape_3)

    return permute_2

def learning_module(input_layer):