# Arda Mavi
import os
from keras.models import Model
from keras import backend as K
from keras.optimizers import Adadelta
from keras.utils import multi_gpu_model
from keras.models import model_from_json
from keras.layers import Input, Conv3D, Dense, UpSampling3D, Activation, MaxPooling3D, Dropout, concatenate, Flatten, Multiply, Subtract
def save_model(model, path='Data/Model/', model_name = 'model', weights_name = 'weights'):
if not os.path.exists(path):
os.makedirs(path)
model_json = model.to_json()
with open(path+model_name+'.json', 'w') as model_file:
model_file.write(model_json)
# serialize weights to HDF5
model.save_weights(path+weights_name+'.h5')
print('Model and weights saved to ' + path+model_name+'.json and ' + path+weights_name+'.h5')
return
def get_model(model_path, weights_path):
if not os.path.exists(model_path):
print('Model file not exists!')
return None
elif not os.path.exists(weights_path):
print('Weights file not exists!')
return None
# Getting model:
with open(model_path, 'r') as model_file:
model = model_file.read()
model = model_from_json(model)
# Getting weights
model.load_weights(weights_path)
return model
# Loss Function:
def dice_coefficient(y_true, y_pred):
smoothing_factor = 1
flat_y_true = K.flatten(y_true)
flat_y_pred = K.flatten(y_pred)
return (2. * K.sum(flat_y_true * flat_y_pred) + smoothing_factor) / (K.sum(flat_y_true) + K.sum(flat_y_pred) + smoothing_factor)
def dice_coefficient_loss(y_true, y_pred):
return 1 - dice_coefficient(y_true, y_pred)
# Segment Model:
def get_segment_model(data_shape):
# U-Net:
inputs = Input(shape=(data_shape))
conv_block_1 = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(inputs)
conv_block_1 = Activation('relu')(conv_block_1)
conv_block_1 = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(conv_block_1)
conv_block_1 = Activation('relu')(conv_block_1)
pool_block_1 = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2))(conv_block_1)
conv_block_2 = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(pool_block_1)
conv_block_2 = Activation('relu')(conv_block_2)
conv_block_2 = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(conv_block_2)
conv_block_2 = Activation('relu')(conv_block_2)
pool_block_2 = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2))(conv_block_2)
conv_block_3 = Conv3D(128, (3, 3, 3), strides=(1, 1, 1), padding='same')(pool_block_2)
conv_block_3 = Activation('relu')(conv_block_3)
conv_block_3 = Conv3D(128, (3, 3, 3), strides=(1, 1, 1), padding='same')(conv_block_3)
conv_block_3 = Activation('relu')(conv_block_3)
pool_block_3 = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2))(conv_block_3)
conv_block_4 = Conv3D(256, (3, 3, 3), strides=(1, 1, 1), padding='same')(pool_block_3)
conv_block_4 = Activation('relu')(conv_block_4)
conv_block_4 = Conv3D(256, (3, 3, 3), strides=(1, 1, 1), padding='same')(conv_block_4)
conv_block_4 = Activation('relu')(conv_block_4)
pool_block_4 = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2))(conv_block_4)
conv_block_5 = Conv3D(256, (3, 3, 3), strides=(1, 1, 1), padding='same')(pool_block_4)
conv_block_5 = Activation('relu')(conv_block_5)
conv_block_5 = Conv3D(256, (3, 3, 3), strides=(1, 1, 1), padding='same')(conv_block_5)
conv_block_5 = Activation('relu')(conv_block_5)
encoder = Model(inputs=inputs, outputs=conv_block_5)
up_block_1 = UpSampling3D((2, 2, 2))(conv_block_5)
up_block_1 = Conv3D(512, (3, 3, 3), strides=(1, 1, 1), padding='same')(up_block_1)
merge_1 = concatenate([conv_block_4, up_block_1])
conv_block_6 = Conv3D(256, (3, 3, 3), strides=(1, 1, 1), padding='same')(merge_1)
conv_block_6 = Activation('relu')(conv_block_6)
conv_block_6 = Conv3D(256, (3, 3, 3), strides=(1, 1, 1), padding='same')(conv_block_6)
conv_block_6 = Activation('relu')(conv_block_6)
up_block_2 = UpSampling3D((2, 2, 2))(conv_block_6)
up_block_2 = Conv3D(256, (3, 3, 3), strides=(1, 1, 1), padding='same')(up_block_2)
merge_2 = concatenate([conv_block_3, up_block_2])
conv_block_7 = Conv3D(128, (3, 3, 3), strides=(1, 1, 1), padding='same')(merge_2)
conv_block_7 = Activation('relu')(conv_block_7)
conv_block_7 = Conv3D(128, (3, 3, 3), strides=(1, 1, 1), padding='same')(conv_block_7)
conv_block_7 = Activation('relu')(conv_block_7)
up_block_3 = UpSampling3D((2, 2, 2))(conv_block_7)
up_block_3 = Conv3D(128, (3, 3, 3), strides=(1, 1, 1), padding='same')(up_block_3)
merge_3 = concatenate([conv_block_2, up_block_3])
conv_block_8 = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(merge_3)
conv_block_8 = Activation('relu')(conv_block_8)
conv_block_8 = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(conv_block_8)
conv_block_8 = Activation('relu')(conv_block_8)
up_block_4 = UpSampling3D((2, 2, 2))(conv_block_8)
up_block_4 = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(up_block_4)
merge_4 = concatenate([conv_block_1, up_block_4])
conv_block_9 = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(merge_4)
conv_block_9 = Activation('relu')(conv_block_9)
conv_block_9 = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(conv_block_9)
conv_block_9 = Activation('relu')(conv_block_9)
conv_block_10 = Conv3D(data_shape[-1], (1, 1, 1), strides=(1, 1, 1), padding='same')(conv_block_9)
outputs = Activation('sigmoid')(conv_block_10)
model = Model(inputs=inputs, outputs=outputs)
"""
# For Multi-GPU:
try:
model = multi_gpu_model(model)
except:
pass
"""
model.compile(optimizer = Adadelta(lr=0.01), loss=dice_coefficient_loss, metrics=[dice_coefficient, 'acc'])
print('Segment Model Architecture:')
print(model.summary())
return model, encoder
# GAN:
def get_GAN(input_shape, Generator, Discriminator):
input_gan = Input(shape=(input_shape))
generated_seg = Generator(input_gan)
gan_output = Discriminator([input_gan, generated_seg])
# Compile GAN:
gan = Model(input_gan, gan_output)
gan.compile(optimizer=Adadelta(lr=0.01), loss='mse', metrics=['accuracy'])
print('GAN Architecture:')
print(gan.summary())
return gan
def get_Generator(input_shape):
Generator, _ = get_segment_model(input_shape)
print('Generator Architecture:')
print(Generator.summary())
return Generator
def get_Discriminator(input_shape_1, input_shape_2, Encoder):
dis_inputs_1 = Input(shape=input_shape_1) # Image
dis_inputs_2 = Input(shape=input_shape_2) # Segmentation
mul_1 = Multiply()([dis_inputs_1, dis_inputs_2]) # Getting segmented part
encoder_output_1 = Encoder(dis_inputs_1)
encoder_output_2 = Encoder(mul_1)
subtract_dis = Subtract()([encoder_output_1, encoder_output_2])
dis_conv_block = Conv3D(128, (3, 3, 3), strides=(1, 1, 1), padding='same')(subtract_dis)
dis_conv_block = Activation('relu')(dis_conv_block)
dis_conv_block = Conv3D(128, (3, 3, 3), strides=(1, 1, 1), padding='same')(dis_conv_block)
dis_conv_block = Activation('relu')(dis_conv_block)
dis_conv_block = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2))(dis_conv_block)
dis_conv_block = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(dis_conv_block)
dis_conv_block = Activation('relu')(dis_conv_block)
dis_conv_block = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(dis_conv_block)
dis_conv_block = Activation('relu')(dis_conv_block)
dis_conv_block = Conv3D(32, (3, 3, 3), strides=(1, 1, 1), padding='same')(dis_conv_block)
dis_conv_block = Activation('relu')(dis_conv_block)
dis_conv_block = Conv3D(32, (3, 3, 3), strides=(1, 1, 1), padding='same')(dis_conv_block)
dis_conv_block = Activation('relu')(dis_conv_block)
flat_1 = Flatten()(dis_conv_block)
dis_fc_1 = Dense(256)(flat_1)
dis_fc_1 = Activation('relu')(dis_fc_1)
dis_drp_1 = Dropout(0.5)(dis_fc_1)
dis_fc_2 = Dense(128)(dis_drp_1)
dis_fc_2 = Activation('relu')(dis_fc_2)
dis_drp_2 = Dropout(0.5)(dis_fc_2)
dis_fc_3 = Dense(1)(dis_drp_2)
dis_similarity_output = Activation('sigmoid')(dis_fc_3)
Discriminator = Model(inputs=[dis_inputs_1, dis_inputs_2], outputs=dis_similarity_output)
Discriminator.compile(optimizer=Adadelta(lr=0.01), loss='binary_crossentropy', metrics=['accuracy'])
print('Discriminator Architecture:')
print(Discriminator.summary())
return Discriminator
if __name__ == '__main__':
segment_model, encoder = get_segment_model((128,128,128,1))
generator = get_Generator((128,128,128,1))
discriminator = get_Discriminator((128,128,128,1), (128,128,128,1), encoder)
gan = get_GAN((128,128,128,1), generator, discriminator)
