Medico-Multimedia/model.py at main · PhucPhamSy/Medico-Multimedia · GitHub

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import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Conv2DTranspose, Concatenate, Input
from tensorflow.keras.layers import AveragePooling2D, GlobalAveragePooling2D, UpSampling2D, Reshape, Dense, LayerNormalization, Dropout, Attention


def SqueezeAndExcite(inputs, ratio=8):
    init = inputs
    filters = init.shape[-1]
    se_shape = (1, 1, filters)

    se = GlobalAveragePooling2D()(init)
    se = Reshape(se_shape)(se)
    se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
    se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
    x = init * se
    return x

def ASPP(inputs):
    """ Image Pooling """
    shape = inputs.shape
    y1 = AveragePooling2D(pool_size=(shape[1], shape[2]))(inputs)
    y1 = Conv2D(256, 1, padding="same", use_bias=False)(y1)
    y1 = BatchNormalization()(y1)
    y1 = Activation("relu")(y1)
    y1 = UpSampling2D((shape[1], shape[2]), interpolation="bilinear")(y1)
    y1 = Attention()([y1, y1])

        """ 1x1 conv """
    y2 = Conv2D(256, 1, padding="same", use_bias=False)(inputs)
    y2 = BatchNormalization()(y2)
    y2 = Activation("relu")(y2)
    #y2 = reshape(y2, y2.shape[1:])
    y2 = Attention()([y2, y2])

        """ 3x3 conv rate=6 """
    y3 = Conv2D(256, 3, padding="same", use_bias=False, dilation_rate=6)(inputs)
    y3 = BatchNormalization()(y3)
    y3 = Activation("relu")(y3)
    #y3 = reshape(y3, y3.shape[1:])
    y3 = Attention()([y3, y3])

        """ 3x3 conv rate=12 """
    y4 = Conv2D(256, 3, padding="same", use_bias=False, dilation_rate=12)(inputs)
    y4 = BatchNormalization()(y4)
    y4 = Activation("relu")(y4)
    #y4 = reshape(y4, y4.shape[1:])
    y4 = Attention()([y4, y4])


        """ 3x3 conv rate=18 """
    y5 = Conv2D(256, 3, padding="same", use_bias=False, dilation_rate=18)(inputs)
    y5 = BatchNormalization()(y5)
    y5 = Activation("relu")(y5)
    #y5 = reshape(y5, y5.shape[1:])
    y5 = Attention()([y5, y5])


    y = Concatenate()([y1, y2, y3, y4, y5])
    y = Conv2D(256, 1, padding="same", use_bias=False)(y)
    y = BatchNormalization()(y)
    y = Activation("relu")(y)

    return y

def build_DLV3SA(shape):
    """ Input """
    inputs = Input(shape)
    """ Encoder """
    encoder = ResNet50(weights="imagenet", include_top=False, input_tensor=inputs)

    image_features = encoder.get_layer("conv4_block6_out").output

    x_a = ASPP(image_features)
    x_a = UpSampling2D((4, 4), interpolation="bilinear")(x_a)

    x_b = encoder.get_layer("conv2_block2_out").output
    x_b = Conv2D(filters=48, kernel_size=1, padding='same', use_bias=False)(x_b)
    x_b = BatchNormalization()(x_b)
    x_b = Activation('relu')(x_b)

    # cr_a_b = Attention()([x_a, x_b, x_a])
    # print(cr_a_b.shape)
    # cr_b_a = Attention()([x_b, x_a, x_b])
    # print(cr_b_a.shape)

    x = Concatenate()([x_a, x_b])
    # x = Concatenate()([cr_a_b, cr_b_a])
    x = SqueezeAndExcite(x)


    x = Conv2D(filters=256, kernel_size=3, padding='same', use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(filters=256, kernel_size=3, padding='same', use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    # x = Attention()([x, x])
    x = SqueezeAndExcite(x)

    x = UpSampling2D((4, 4), interpolation="bilinear")(x)
    x = Conv2D(1, 1)(x)
    x = Activation("sigmoid")(x)

    model = Model(inputs, x)
    return model

if __name__ == "__main__":
    model = build_DLV3SA((256, 256, 3))