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David Feaster
DavidFeaster/
DR-UNet
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[606337]: / drunet / model / dr_unet.py

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from tensorflow import keras

import tensorflow as tf





def conv_layer(inputs, filters, kernel_size=3, strides=1, need_activate=True):

    out = keras.layers.Conv2D(filters, kernel_size, strides, padding='same')(inputs)

    out = keras.layers.BatchNormalization()(out)

    if need_activate:

        out = keras.layers.ELU()(out)

    return out





def block_1(inputs, filters):

    out = conv_layer(inputs, filters // 4, 1)

    out = conv_layer(out, filters // 4, 3)

    out = conv_layer(out, filters // 4, 3, need_activate=False)

    res = conv_layer(inputs, filters // 4, 1)

    out = keras.layers.ELU()(out + res)

    return out





def block_2(inputs, filters):

    out = conv_layer(inputs, filters // 4, 1)

    out = conv_layer(out, filters // 4, 3)

    out = conv_layer(out, filters // 4, 3, need_activate=False)

    res = conv_layer(inputs, filters // 4, 1)

    out = keras.layers.ELU()(out + res)

    return out





def block_3(inputs, filters):

    out = conv_layer(inputs, filters // 4, 1)

    out = conv_layer(out, filters // 4, 3)

    out = conv_layer(out, filters, 3, need_activate=False)

    res = conv_layer(inputs, filters, 1)

    out = keras.layers.ELU()(out + res)

    return out





def dr_unet(input_shape=(256, 256, 1), dims=32):

    inputs = keras.Input(input_shape)

    out = conv_layer(inputs, 16, 1)



    out = block_1(out, dims)

    out_256 = block_3(out, dims)

    out = keras.layers.MaxPool2D(2, 2, padding='same')(out_256)



    out = block_1(out, dims * 2)

    out_128 = block_3(out, dims * 2)

    out = keras.layers.MaxPool2D(2, 2, padding='same')(out_128)



    out = block_1(out, dims * 4)

    out_64 = block_3(out, dims * 4)

    out = keras.layers.MaxPool2D(2, 2, padding='same')(out_64)



    out = block_1(out, dims * 8)

    out_32 = block_3(out, dims * 8)

    out = keras.layers.MaxPool2D(2, 2, padding='same')(out_32)



    out = block_1(out, dims * 16)

    out_16 = block_3(out, dims * 16)

    out = keras.layers.MaxPool2D(2, 2, padding='same')(out_16)



    out = block_1(out, dims * 32)

    out = block_3(out, dims * 32)



    up_16 = keras.layers.Conv2DTranspose(filters=dims * 16, kernel_size=2, strides=2, padding='same')(out)

    up = keras.layers.Concatenate()([up_16, out_16])

    up = keras.layers.BatchNormalization()(up)

    up = keras.layers.ELU()(up)



    up = block_2(up, dims * 16)

    up = block_3(up, dims * 16)

    up_32 = keras.layers.Conv2DTranspose(filters=dims * 8, kernel_size=2, strides=2, padding='same')(up)

    up = keras.layers.Concatenate()([up_32, out_32])

    up = keras.layers.BatchNormalization()(up)

    up = keras.layers.ELU()(up)



    up = block_2(up, dims * 8)

    up = block_3(up, dims * 8)

    up_64 = keras.layers.Conv2DTranspose(filters=dims * 4, kernel_size=2, strides=2, padding='same')(up)

    up = keras.layers.Concatenate()([up_64, out_64])

    up = keras.layers.BatchNormalization()(up)

    up = keras.layers.ELU()(up)



    up = block_2(up, dims * 4)

    up = block_3(up, dims * 4)

    up_128 = keras.layers.Conv2DTranspose(filters=dims * 2, kernel_size=2, strides=2, padding='same')(up)

    up = keras.layers.Concatenate()([up_128, out_128])

    up = keras.layers.BatchNormalization()(up)

    up = keras.layers.ELU()(up)



    up = block_2(up, dims * 2)

    up = block_3(up, dims * 2)

    up_256 = keras.layers.Conv2DTranspose(filters=dims * 1, kernel_size=2, strides=2, padding='same')(up)

    up = keras.layers.Concatenate()([up_256, out_256])

    up = keras.layers.BatchNormalization()(up)

    up = keras.layers.ELU()(up)



    up = block_2(up, dims)

    up = block_3(up, dims)

    up = keras.layers.Conv2D(filters=1, kernel_size=1, strides=(1, 1), padding='same')(up)

    up = keras.activations.sigmoid(up)

    return keras.Model(inputs, up)





if __name__ == '__main__':

    model = dr_unet()

    model.summary()