Function get_decoder

src/models/blocks.py:348–391

(encoder, learning_layer1, learning_layer2, num_classes)

    return model, learning_layer1, learning_layer2

def get_decoder(encoder, learning_layer1, learning_layer2, num_classes):

    inputs = encoder.input
    out = encoder.output
    out = tem_block(out)

    classifier1 = conv_block(out,
                             conv_type="ds",
                             filters=128,
                             kernel_size=(3, 3),
                             strides=(1, 1),
                             padding="same",
                             relu=True,
                             upsampling=True,
                             up_sample_size=2,
                             skip_layer=learning_layer2)
    
    classifier2 = conv_block(classifier1,
                             conv_type="ds",
                             filters=128,
                             kernel_size=(3, 3),
                             strides=(1, 1),
                             padding="same",
                             relu=True,
                             upsampling=True,
                            up_sample_size=2,
                            skip_layer=learning_layer1)
    
    
    classifier2 = tf.keras.layers.UpSampling2D(size=(2, 2),
                                       data_format="channels_last")(classifier2)

    classifier3 = tf.keras.layers.Conv2D(filters=num_classes,
                                         kernel_size=(1,1),
                                         padding="same",
                                         strides=(1, 1))(classifier2)
    classifier3 = tf.keras.layers.BatchNormalization(axis=3)(classifier3)
    classifier3 = tf.keras.activations.softmax(classifier3)

    # get final model
    model = tf.keras.models.Model(inputs=[inputs],
                                  outputs=[classifier3],
                                  name="pcb-decoder")
    return model

def get_classification(encoder, num_classes):
    inputs = encoder.inputs