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Updated U-Net implementation to Keras 2 #39

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Updated U-Net implementation to Keras 2 #39

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245 changes: 135 additions & 110 deletions unet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import numpy as np
from keras.models import *
from keras.layers import Input, merge, Conv2D, MaxPooling2D, UpSampling2D, Dropout, Cropping2D
from keras.layers import Input, merge, Conv2D, MaxPooling2D, UpSampling2D, Dropout, Cropping2D, Concatenate
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras
Expand All @@ -24,129 +24,154 @@ def load_data(self):

def get_unet(self):

inputs = Input((self.img_rows, self.img_cols,1))

'''
unet with crop(because padding = valid)

conv1 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(inputs)
print "conv1 shape:",conv1.shape
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv1)
print "conv1 shape:",conv1.shape
crop1 = Cropping2D(cropping=((90,90),(90,90)))(conv1)
print "crop1 shape:",crop1.shape
inputs = Input((self.img_rows, self.img_cols,1))

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)
print "pool1 shape:",pool1.shape

conv2 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool1)
print "conv2 shape:",conv2.shape
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv2)
print "conv2 shape:",conv2.shape
crop2 = Cropping2D(cropping=((41,41),(41,41)))(conv2)
print "crop2 shape:",crop2.shape
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
print "pool2 shape:",pool2.shape

conv3 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool2)
print "conv3 shape:",conv3.shape
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv3)
print "conv3 shape:",conv3.shape
crop3 = Cropping2D(cropping=((16,17),(16,17)))(conv3)
print "crop3 shape:",crop3.shape
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
print "pool3 shape:",pool3.shape

conv4 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
crop4 = Cropping2D(cropping=((4,4),(4,4)))(drop4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'valid', 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 = merge([crop4,up6], mode = 'concat', concat_axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv6)

up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = merge([crop3,up7], mode = 'concat', concat_axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv7)

up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = merge([crop2,up8], mode = 'concat', concat_axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv8)

up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = merge([crop1,up9], mode = 'concat', concat_axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'valid', kernel_initializer = 'he_normal')(conv9)
'''

conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
print "conv1 shape:",conv1.shape
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
print "conv1 shape:",conv1.shape
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
print "pool1 shape:",pool1.shape

conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
print "conv2 shape:",conv2.shape
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
print "conv2 shape:",conv2.shape
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)
print "pool2 shape:",pool2.shape

conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
print "conv3 shape:",conv3.shape
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
print "conv3 shape:",conv3.shape
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)
print "pool3 shape:",pool3.shape

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)
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)
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 = merge([drop4,up6], mode = 'concat', concat_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 = merge([conv3,up7], mode = 'concat', concat_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 = merge([conv2,up8], mode = 'concat', concat_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 = merge([conv1,up9], mode = 'concat', concat_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(input = inputs, output = conv10)

model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
up6 = UpSampling2D(size=(2, 2))(drop5)
up6 = Conv2D(512,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up6)
merge6 = Concatenate(axis=3)([drop4, up6])
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 = UpSampling2D(size=(2, 2))(conv6)
up7 = Conv2D(256,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up7)
merge7 = Concatenate(axis=3)([conv3, up7])
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 = UpSampling2D(size=(2, 2))(conv7)
up8 = Conv2D(128,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up8)
merge8 = Concatenate(axis=3)([conv2, up8])
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 = UpSampling2D(size=(2, 2))(conv8)
up9 = Conv2D(64,
2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up9)
merge9 = Concatenate(axis=3)([conv1, up9])
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'])

return model


def train(self):

print("loading data")
Expand Down