text: from tensorflow.keras.models import *
  from tensorflow.keras.layers import *
  from tensorflow.keras.optimizers import *
  
  
  def unet(pretrained_weights=None, input_size=(256, 256, 1)):
      """
      UNet from https://github.com/zhixuhao/unet
      :param pretrained_weights:
      :param input_size:
      :return:
      """
      inputs = Input(input_size)
      conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
      conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
      pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
      conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
      conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
      pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
      conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
      conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
      pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
      conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
      conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
      drop4 = Dropout(0.5)(conv4)
      pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
  
      conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
      conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
      drop5 = Dropout(0.5)(conv5)
  
      up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
          UpSampling2D(size=(2, 2))(drop5))
      merge6 = concatenate([drop4, up6], axis=3)
      conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
      conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
  
      up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
          UpSampling2D(size=(2, 2))(conv6))
      merge7 = concatenate([conv3, up7], axis=3)
      conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
      conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
  
      up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
          UpSampling2D(size=(2, 2))(conv7))
      merge8 = concatenate([conv2, up8], axis=3)
      conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
      conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
  
      up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
          UpSampling2D(size=(2, 2))(conv8))
      merge9 = concatenate([conv1, up9], axis=3)
      conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
      conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
      conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
      conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
  
      model = Model(inputs=inputs, outputs=conv10)
      model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
  
      # model.summary()
  
      if (pretrained_weights):
          model.load_weights(pretrained_weights)
  
      return model
  
  def unet2(pretrained_weights=None, input_size=(256, 256, 1)):
      """
      UNet from https://github.com/zhixuhao/unet
      :param pretrained_weights:
      :param input_size:
      :return:
      """
      inputs = Input(input_size)
      conv1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
      conv1 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
      pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
      conv2 = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
      conv2 = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
      pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
      conv3 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
      conv3 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
      pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
      conv4 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
      conv4 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
      drop4 = Dropout(0.5)(conv4)
      pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
  
      conv5 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
      conv5 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
      drop5 = Dropout(0.5)(conv5)
  
      up6 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
          UpSampling2D(size=(2, 2))(drop5))
      merge6 = concatenate([drop4, up6], axis=3)
      conv6 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
      conv6 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
  
      up7 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
          UpSampling2D(size=(2, 2))(conv6))
      merge7 = concatenate([conv3, up7], axis=3)
      conv7 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
      conv7 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
  
      up8 = Conv2D(32, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
          UpSampling2D(size=(2, 2))(conv7))
      merge8 = concatenate([conv2, up8], axis=3)
      conv8 = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
      conv8 = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
  
      up9 = Conv2D(16, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
          UpSampling2D(size=(2, 2))(conv8))
      merge9 = concatenate([conv1, up9], axis=3)
      conv9 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
      conv9 = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
      conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
      conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
  
      model = Model(inputs=inputs, outputs=conv10)
      model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
  
      # model.summary()
  
      if (pretrained_weights):
          model.load_weights(pretrained_weights)
  
      return model
  