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Unet_modified.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
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
def UNetPlusPlus(img_rows, img_cols, color_type=3, num_class=1, deep_supervision=True):
nb_filter = [32, 64, 128, 256, 512]
import efficientnet.keras as efn
from keras.models import Model
from keras.layers.convolutional import Conv2D
from keras.layers import LeakyReLU, Add, Input, MaxPool2D, UpSampling2D, concatenate, Conv2DTranspose, \
BatchNormalization, Dropout
base_model = efn.EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
input_model = base_model.input
# Handle Dimension Ordering for different backends
global bn_axis
bn_axis = 3
conv6_1 = base_model.get_layer('top_activation').output
conv5_1 = base_model.get_layer('block6a_expand_activation').output
conv4_1 = base_model.get_layer('block4a_expand_activation').output
conv3_1 = base_model.get_layer('block3a_expand_activation').output
conv2_1 = base_model.get_layer('block2a_expand_activation').output
print(conv6_1.shape) # 7*7
print(conv5_1.shape) # 14*14
print(conv4_1.shape) # 28*28
print(conv3_1.shape) # 56*56
print(conv2_1.shape) # 112*112
conv1_1 = standard_unit(input_model, 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])
conv5_1_upsampling_16 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(16, 16), name='conv5_1_upsampling_16',
padding='same')(conv5_1) # changes made
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])
conv4_2_upsampling_8 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(8, 8), name='conv4_2_upsampling_8',
padding='same')(conv4_2) # changes made
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])
conv3_3_upsampling_4 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(4, 4), name='conv3_3_upsampling_4',
padding='same')(conv3_3) # changes made
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])
conv2_4_upsampling_2 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='conv2_4_upsampling_2',
padding='same')(conv2_4) # changes made
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)
# changes code
nested_conv2_4_upsampling_2 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_5',
kernel_initializer='he_normal',
padding='same', kernel_regularizer=l2(1e-4))(conv2_4_upsampling_2)
nested_conv3_3_upsampling_4 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_6',
kernel_initializer='he_normal',
padding='same', kernel_regularizer=l2(1e-4))(conv3_3_upsampling_4)
nested_conv4_2_upsampling_8 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_7',
kernel_initializer='he_normal',
padding='same', kernel_regularizer=l2(1e-4))(conv4_2_upsampling_8)
nested_conv5_1_upsampling_16 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_8',
kernel_initializer='he_normal',
padding='same', kernel_regularizer=l2(1e-4))(conv5_1_upsampling_16)
nestnet_output_all = keras.layers.Average()(
[nestnet_output_1, nestnet_output_2, nestnet_output_3, nestnet_output_4, nested_conv2_4_upsampling_2,
nested_conv3_3_upsampling_4, nested_conv4_2_upsampling_8, nested_conv5_1_upsampling_16])
model = Model(inputs=[input_model], outputs=[nestnet_output_all])
# if deep_supervision:
# model = Model(input=img_input, output=[nestnet_output_1,
# nestnet_output_2,
# nestnet_output_3,
# nestnet_output_4,
# nested_conv2_4_upsampling_2,
# nested_conv3_3_upsampling_4,
# nested_conv4_2_upsampling_8,
# nested_conv5_1_upsampling_16
# ])
# else:
# model = Model(input=img_input, output=[nestnet_output_4])
return model
if __name__ == '__main__':
model = UNetPlusPlus(224, 224, 3)
model.summary()