def model()

in src/cloud/pipelines/semantic_segmentation/train_tf.py [0:0]

• 21 lines of code
• 2 McCabe index (conditional complexity)

def model():
    inputs = Input(shape=(IMAGE_WIDTH, IMAGE_HEIGHT, 3), name="input_image")
    
    encoder = MobileNetV2(input_tensor=inputs, weights="imagenet", include_top=False, alpha=0.35)
    skip_connection_names = ["input_image", "block_1_expand_relu", "block_3_expand_relu", "block_6_expand_relu"]
    encoder_output = encoder.get_layer("block_13_expand_relu").output
    
    f = [16, 32, 48, 64]
    x = encoder_output
    for i in range(1, len(skip_connection_names)+1, 1):
        x_skip = encoder.get_layer(skip_connection_names[-i]).output
        x = UpSampling2D((2, 2))(x)
        x = Concatenate()([x, x_skip])
        
        x = Conv2D(f[-i], (3, 3), padding="same")(x)
        x = BatchNormalization()(x)
        x = Activation("relu")(x)
        
        x = Conv2D(f[-i], (3, 3), padding="same")(x)
        x = BatchNormalization()(x)
        x = Activation("relu")(x)
        
    x = Conv2D(1, (1, 1), padding="same")(x)
    x = Activation("sigmoid")(x)
    
    model = Model(inputs, x)
    return model