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helper_functions.py
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helper_functions.py
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'''
'''
import keras
import tensorflow as tf
from keras.models import Model
from keras import backend as K
from keras.layers import Input, merge, Conv2D, ZeroPadding2D, UpSampling2D, Dense, concatenate, Conv2DTranspose
from keras.layers.pooling import MaxPooling2D, GlobalAveragePooling2D, MaxPooling2D
from keras.layers.core import Dense, Dropout, Activation
from keras.layers import BatchNormalization, Dropout, Flatten, Lambda
from keras.layers.advanced_activations import ELU, LeakyReLU
from keras.optimizers import Adam, RMSprop, SGD
from keras.regularizers import l2
from keras.layers.noise import GaussianDropout
import numpy as np
smooth = 1.
dropout_rate = 0.5
act = "relu"
def mean_iou(y_true, y_pred):
prec = []
for t in np.arange(0.5, 1.0, 0.05):
y_pred_ = tf.to_int32(y_pred > t)
score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2)
K.get_session().run(tf.local_variables_initializer())
with tf.control_dependencies([up_opt]):
score = tf.identity(score)
prec.append(score)
return K.mean(K.stack(prec), axis=0)
# Custom loss function
def dice_coef(y_true, y_pred):
smooth = 1.
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_coef_loss(y_true, y_pred):
return 1. - dice_coef(y_true, y_pred)
def bce_dice_loss(y_true, y_pred):
return 0.5 * keras.losses.binary_crossentropy(y_true, y_pred) - dice_coef(y_true, y_pred)
# Evaluation metric: IoU
def compute_iou(im1, im2):
overlap = (im1>0.5) * (im2>0.5)
union = (im1>0.5) + (im2>0.5)
return overlap.sum()/float(union.sum())
# Evaluation metric: Dice
def compute_dice(im1, im2, empty_score=1.0):
im1 = np.asarray(im1>0.5).astype(np.bool)
im2 = np.asarray(im2>0.5).astype(np.bool)
if im1.shape != im2.shape:
raise ValueError("Shape mismatch: im1 and im2 must have the same shape.")
im_sum = im1.sum() + im2.sum()
if im_sum == 0:
return empty_score
intersection = np.logical_and(im1, im2)
return 2. * intersection.sum() / im_sum
########################################
# 2D Standard
########################################
def standard_unit(input_tensor, stage, nb_filter, kernel_size=3):
x = Conv2D(nb_filter, (kernel_size, kernel_size), activation=act, name='conv'+stage+'_1', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(input_tensor)
x = Dropout(dropout_rate, name='dp'+stage+'_1')(x)
x = Conv2D(nb_filter, (kernel_size, kernel_size), activation=act, name='conv'+stage+'_2', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(x)
x = Dropout(dropout_rate, name='dp'+stage+'_2')(x)
return x
########################################
"""
Standard U-Net [Ronneberger et.al, 2015]
Total params: 7,759,521
"""
def U_Net(img_rows, img_cols, color_type=1, num_class=1):
nb_filter = [32,64,128,256,512]
# Handle Dimension Ordering for different backends
global bn_axis
if K.image_dim_ordering() == 'tf':
bn_axis = 3
img_input = Input(shape=(img_rows, img_cols, color_type), name='main_input')
else:
bn_axis = 1
img_input = Input(shape=(color_type, img_rows, img_cols), name='main_input')
conv1_1 = standard_unit(img_input, stage='11', nb_filter=nb_filter[0])
pool1 = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(conv1_1)
conv2_1 = standard_unit(pool1, stage='21', nb_filter=nb_filter[1])
pool2 = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(conv2_1)
conv3_1 = standard_unit(pool2, stage='31', nb_filter=nb_filter[2])
pool3 = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(conv3_1)
conv4_1 = standard_unit(pool3, stage='41', nb_filter=nb_filter[3])
pool4 = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(conv4_1)
conv5_1 = standard_unit(pool4, stage='51', nb_filter=nb_filter[4])
up4_2 = Conv2DTranspose(nb_filter[3], (2, 2), strides=(2, 2), name='up42', padding='same')(conv5_1)
conv4_2 = concatenate([up4_2, conv4_1], name='merge42', axis=bn_axis)
conv4_2 = standard_unit(conv4_2, stage='42', nb_filter=nb_filter[3])
up3_3 = Conv2DTranspose(nb_filter[2], (2, 2), strides=(2, 2), name='up33', padding='same')(conv4_2)
conv3_3 = concatenate([up3_3, conv3_1], name='merge33', axis=bn_axis)
conv3_3 = standard_unit(conv3_3, stage='33', nb_filter=nb_filter[2])
up2_4 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up24', padding='same')(conv3_3)
conv2_4 = concatenate([up2_4, conv2_1], name='merge24', axis=bn_axis)
conv2_4 = standard_unit(conv2_4, stage='24', nb_filter=nb_filter[1])
up1_5 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up15', padding='same')(conv2_4)
conv1_5 = concatenate([up1_5, conv1_1], name='merge15', axis=bn_axis)
conv1_5 = standard_unit(conv1_5, stage='15', nb_filter=nb_filter[0])
unet_output = Conv2D(num_class, (1, 1), activation='sigmoid', name='output', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv1_5)
model = Model(input=img_input, output=unet_output)
return model
"""
wU-Net for comparison
Total params: 9,282,246
"""
def wU_Net(img_rows, img_cols, color_type=1, num_class=1):
# nb_filter = [32,64,128,256,512]
nb_filter = [35,70,140,280,560]
# Handle Dimension Ordering for different backends
global bn_axis
if K.image_dim_ordering() == 'tf':
bn_axis = 3
img_input = Input(shape=(img_rows, img_cols, color_type), name='main_input')
else:
bn_axis = 1
img_input = Input(shape=(color_type, img_rows, img_cols), name='main_input')
conv1_1 = standard_unit(img_input, stage='11', nb_filter=nb_filter[0])
pool1 = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(conv1_1)
conv2_1 = standard_unit(pool1, stage='21', nb_filter=nb_filter[1])
pool2 = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(conv2_1)
conv3_1 = standard_unit(pool2, stage='31', nb_filter=nb_filter[2])
pool3 = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(conv3_1)
conv4_1 = standard_unit(pool3, stage='41', nb_filter=nb_filter[3])
pool4 = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(conv4_1)
conv5_1 = standard_unit(pool4, stage='51', nb_filter=nb_filter[4])
up4_2 = Conv2DTranspose(nb_filter[3], (2, 2), strides=(2, 2), name='up42', padding='same')(conv5_1)
conv4_2 = concatenate([up4_2, conv4_1], name='merge42', axis=bn_axis)
conv4_2 = standard_unit(conv4_2, stage='42', nb_filter=nb_filter[3])
up3_3 = Conv2DTranspose(nb_filter[2], (2, 2), strides=(2, 2), name='up33', padding='same')(conv4_2)
conv3_3 = concatenate([up3_3, conv3_1], name='merge33', axis=bn_axis)
conv3_3 = standard_unit(conv3_3, stage='33', nb_filter=nb_filter[2])
up2_4 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up24', padding='same')(conv3_3)
conv2_4 = concatenate([up2_4, conv2_1], name='merge24', axis=bn_axis)
conv2_4 = standard_unit(conv2_4, stage='24', nb_filter=nb_filter[1])
up1_5 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up15', padding='same')(conv2_4)
conv1_5 = concatenate([up1_5, conv1_1], name='merge15', axis=bn_axis)
conv1_5 = standard_unit(conv1_5, stage='15', nb_filter=nb_filter[0])
unet_output = Conv2D(num_class, (1, 1), activation='sigmoid', name='output', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv1_5)
model = Model(input=img_input, output=unet_output)
return model
"""
Standard UNet++ [Zhou et.al, 2018]
Total params: 9,041,601
"""
def UNetPlusPlus(img_rows, img_cols, color_type=1, num_class=1, deep_supervision=False):
nb_filter = [32,64,128,256,512]
# Handle Dimension Ordering for different backends
global bn_axis
if K.image_dim_ordering() == 'tf':
bn_axis = 3
img_input = Input(shape=(img_rows, img_cols, color_type), name='main_input')
else:
bn_axis = 1
img_input = Input(shape=(color_type, img_rows, img_cols), name='main_input')
conv1_1 = standard_unit(img_input, stage='11', nb_filter=nb_filter[0])
pool1 = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(conv1_1)
conv2_1 = standard_unit(pool1, stage='21', nb_filter=nb_filter[1])
pool2 = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(conv2_1)
up1_2 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up12', padding='same')(conv2_1)
conv1_2 = concatenate([up1_2, conv1_1], name='merge12', axis=bn_axis)
conv1_2 = standard_unit(conv1_2, stage='12', nb_filter=nb_filter[0])
conv3_1 = standard_unit(pool2, stage='31', nb_filter=nb_filter[2])
pool3 = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(conv3_1)
up2_2 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up22', padding='same')(conv3_1)
conv2_2 = concatenate([up2_2, conv2_1], name='merge22', axis=bn_axis)
conv2_2 = standard_unit(conv2_2, stage='22', nb_filter=nb_filter[1])
up1_3 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up13', padding='same')(conv2_2)
conv1_3 = concatenate([up1_3, conv1_1, conv1_2], name='merge13', axis=bn_axis)
conv1_3 = standard_unit(conv1_3, stage='13', nb_filter=nb_filter[0])
conv4_1 = standard_unit(pool3, stage='41', nb_filter=nb_filter[3])
pool4 = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(conv4_1)
up3_2 = Conv2DTranspose(nb_filter[2], (2, 2), strides=(2, 2), name='up32', padding='same')(conv4_1)
conv3_2 = concatenate([up3_2, conv3_1], name='merge32', axis=bn_axis)
conv3_2 = standard_unit(conv3_2, stage='32', nb_filter=nb_filter[2])
up2_3 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up23', padding='same')(conv3_2)
conv2_3 = concatenate([up2_3, conv2_1, conv2_2], name='merge23', axis=bn_axis)
conv2_3 = standard_unit(conv2_3, stage='23', nb_filter=nb_filter[1])
up1_4 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up14', padding='same')(conv2_3)
conv1_4 = concatenate([up1_4, conv1_1, conv1_2, conv1_3], name='merge14', axis=bn_axis)
conv1_4 = standard_unit(conv1_4, stage='14', nb_filter=nb_filter[0])
conv5_1 = standard_unit(pool4, stage='51', nb_filter=nb_filter[4])
up4_2 = Conv2DTranspose(nb_filter[3], (2, 2), strides=(2, 2), name='up42', padding='same')(conv5_1)
conv4_2 = concatenate([up4_2, conv4_1], name='merge42', axis=bn_axis)
conv4_2 = standard_unit(conv4_2, stage='42', nb_filter=nb_filter[3])
up3_3 = Conv2DTranspose(nb_filter[2], (2, 2), strides=(2, 2), name='up33', padding='same')(conv4_2)
conv3_3 = concatenate([up3_3, conv3_1, conv3_2], name='merge33', axis=bn_axis)
conv3_3 = standard_unit(conv3_3, stage='33', nb_filter=nb_filter[2])
up2_4 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up24', padding='same')(conv3_3)
conv2_4 = concatenate([up2_4, conv2_1, conv2_2, conv2_3], name='merge24', axis=bn_axis)
conv2_4 = standard_unit(conv2_4, stage='24', nb_filter=nb_filter[1])
up1_5 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up15', padding='same')(conv2_4)
conv1_5 = concatenate([up1_5, conv1_1, conv1_2, conv1_3, conv1_4], name='merge15', axis=bn_axis)
conv1_5 = standard_unit(conv1_5, stage='15', nb_filter=nb_filter[0])
nestnet_output_1 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_1', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv1_2)
nestnet_output_2 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_2', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv1_3)
nestnet_output_3 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_3', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv1_4)
nestnet_output_4 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_4', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv1_5)
if deep_supervision:
model = Model(input=img_input, output=[nestnet_output_1,
nestnet_output_2,
nestnet_output_3,
nestnet_output_4])
else:
model = Model(input=img_input, output=[nestnet_output_4])
return model
if __name__ == '__main__':
model = U_Net(96,96,1)
model.summary()
model = wU_Net(96,96,1)
model.summary()
model = UNetPlusPlus(96,96,1)
model.summary()