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models.py
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from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, Callback
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
def dice_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
coef = (2. * intersection + K.epsilon()) / (K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon())
return coef
def unet_small(pretrained_weights=None, image_size=(128, 256, 3), input_chan=3, output_chan=3):
print("Generating UNET-Small Model using settings: ")
print("\t- Pretrained Weights= " + str(pretrained_weights))
print("\t- image_size= " + str(image_size))
inputs = Input(image_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(3, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(3, 3, activation='sigmoid')(conv9)
model = Model(input=inputs, output=conv10)
model.compile(optimizer=Adam(lr=5e-4),
loss='binary_crossentropy', metrics=[dice_coef])
print(model.summary())
if(pretrained_weights):
print('Loading Weights from ' + pretrained_weights)
model.load_weights(pretrained_weights)
print("Saving to JSON")
model_json = model.to_json()
with open("unet-small.json", "w") as json_file:
json_file.write(model_json)
return model
def unet_mid(pretrained_weights=None, image_size=(128, 256, 3), input_chan=3, output_chan=3):
print("Generating UNET-Mid Model using settings: ")
print("\t- Pretrained Weights= " + str(pretrained_weights))
print("\t- image_size= " + str(image_size))
inputs = Input(image_size)
conv1 = Conv2D(32, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(32, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(64, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(128, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(256, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(512, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(512, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(256, 2, activation='relu', padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
merge6 = concatenate([drop4, up6], axis=3)
conv6 = Conv2D(256, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(256, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv6)
up7 = Conv2D(128, 2, activation='relu', padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv6))
merge7 = concatenate([conv3, up7], axis=3)
conv7 = Conv2D(128, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(128, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv7)
up8 = Conv2D(64, 2, activation='relu', padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv7))
merge8 = concatenate([conv2, up8], axis=3)
conv8 = Conv2D(64, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(64, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv8)
up9 = Conv2D(32, 2, activation='relu', padding='same',
kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv8))
merge9 = concatenate([conv1, up9], axis=3)
conv9 = Conv2D(32, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(32, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(3, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(3, 3, activation='sigmoid')(conv9)
model = Model(input=inputs, output=conv10)
model.compile(optimizer=Adam(lr=0.005),
loss='binary_crossentropy', metrics=[dice_coef])
print(model.summary())
if(pretrained_weights):
print('Loading Weights from ' + pretrained_weights)
model.load_weights(pretrained_weights)
print("Saving to JSON")
model_json = model.to_json()
with open("unet-mid.json", "w") as json_file:
json_file.write(model_json)
return model
def unet_large(pretrained_weights=None, image_size=(128, 256, 3), input_chan=3, output_chan=3):
print("Generating UNET-Mid Model using settings: ")
print("\t- Pretrained Weights= " + str(pretrained_weights))
print("\t- image_size= " + str(image_size))
inputs = Input(image_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(3, 3, activation='relu', padding='same',
kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(3, 3, activation='sigmoid')(conv9)
model = Model(input=inputs, output=conv10)
model.compile(optimizer=Adam(lr=5e-4),
loss='binary_crossentropy', metrics=[dice_coef])
print(model.summary())
if(pretrained_weights):
print('Loading Weights from ' + pretrained_weights)
model.load_weights(pretrained_weights)
print("Saving to JSON")
model_json = model.to_json()
with open("unet-large.json", "w") as json_file:
json_file.write(model_json)
return model