219-unet_model_with_functions_of_blocks.py

# Building Unet by dividing encoder and decoder into blocks

from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda
from keras.optimizers import Adam
from keras.layers import Activation, MaxPool2D, Concatenate


def conv_block(input, num_filters):
    x = Conv2D(num_filters, 3, padding="same")(input)
    x = BatchNormalization()(x)   #Not in the original network. 
    x = Activation("relu")(x)

    x = Conv2D(num_filters, 3, padding="same")(x)
    x = BatchNormalization()(x)  #Not in the original network
    x = Activation("relu")(x)

    return x

#Encoder block: Conv block followed by maxpooling


def encoder_block(input, num_filters):
    x = conv_block(input, num_filters)
    p = MaxPool2D((2, 2))(x)
    return x, p   

#Decoder block
#skip features gets input from encoder for concatenation

def decoder_block(input, skip_features, num_filters):
    x = Conv2DTranspose(num_filters, (2, 2), strides=2, padding="same")(input)
    x = Concatenate()([x, skip_features])
    x = conv_block(x, num_filters)
    return x

#Build Unet using the blocks
def build_unet(input_shape):
    inputs = Input(input_shape)

    s1, p1 = encoder_block(inputs, 64)
    s2, p2 = encoder_block(p1, 128)
    s3, p3 = encoder_block(p2, 256)
    s4, p4 = encoder_block(p3, 512)

    b1 = conv_block(p4, 1024) #Bridge

    d1 = decoder_block(b1, s4, 512)
    d2 = decoder_block(d1, s3, 256)
    d3 = decoder_block(d2, s2, 128)
    d4 = decoder_block(d3, s1, 64)

    outputs = Conv2D(1, 1, padding="same", activation="sigmoid")(d4)  #Binary (can be multiclass)

    model = Model(inputs, outputs, name="U-Net")
    return model