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UNet++_MSOF_model.py
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import os
import numpy as np
from keras import Input, Model
from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten, merge, Concatenate,Conv2DTranspose,add,Concatenate,Add,Subtract
from keras.optimizers import Adam,SGD
from keras.utils import plot_model
from keras import backend as K
from keras.layers import add,BatchNormalization,UpSampling2D
from keras.layers import Embedding,Input,Conv2D,Conv3D,Lambda,concatenate,Flatten,Dense,Dropout,MaxPooling2D,Activation,GlobalAveragePooling2D,GlobalAveragePooling3D,BatchNormalization
from keras import regularizers
import tensorflow as tf
from keras import objectives
from keras.regularizers import l2
from keras.callbacks import Callback
SEED = 1998
np.random.seed(SEED)
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1,
inter_op_parallelism_threads=1)
tf.set_random_seed(SEED)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
K.set_session(sess)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
def dice_coef(y_true, y_pred, smooth=1, weight=0.5):
"""
加权后的dice coefficient
"""
y_true = y_true[:, :, :, -1] # y_true[:, :, :, :-1]=y_true[:, :, :, -1] if dim(3)=1 等效于[8,256,256,1]==>[8,256,256]
y_pred = y_pred[:, :, :, -1]
intersection = K.sum(y_true * y_pred)
union = K.sum(y_true) + weight * K.sum(y_pred)
# K.mean((2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))
return ((2. * intersection + smooth) / (union + smooth)) # not working better using mean
def dice_coef_loss(y_true, y_pred):
"""
目标函数
"""
return 1 - dice_coef(y_true, y_pred)
def weighted_bce_dice_loss(y_true,y_pred):
class_loglosses = K.mean(K.binary_crossentropy(y_true, y_pred), axis=[0, 1, 2])
class_weights = [0.1, 0.9]#note that the weights can be computed automatically using the training smaples
weighted_bce = K.sum(class_loglosses * K.constant(class_weights))
# return K.weighted_binary_crossentropy(y_true, y_pred,pos_weight) + 0.35 * (self.dice_coef_loss(y_true, y_pred)) #not work
return weighted_bce + 0.5 * (dice_coef_loss(y_true, y_pred))
def standard_unit(input_tensor, stage, nb_filter, kernel_size=3, mode='None'):
x = Conv2D(nb_filter, (kernel_size, kernel_size), activation='selu', name='conv' + stage + '_1',
kernel_initializer='he_normal', padding='same', kernel_regularizer=l2(1e-4))(input_tensor)
x0 = x
# x = Dropout(0.2, name='dp' + stage + '_1')(x)
x = BatchNormalization(name='bn' + stage + '_1')(x) # much better than dropout
x = Conv2D(nb_filter, (kernel_size, kernel_size), activation='selu', name='conv' + stage + '_2',
kernel_initializer='he_normal', padding='same', kernel_regularizer=l2(1e-4))(x)
# x = Dropout(0.2, name='dp' + stage + '_2')(x)
x = BatchNormalization(name='bn' + stage + '_2')(x)
if mode == 'residual':
# x=Add(name='resi'+stage)([x,input_tensor])# 维度不相同！
x = Add(name='resi' + stage)([x, x0])
return x
def Nest_Net2(input_shape, num_class=1, deep_supervision=False):
nb_filter = [32, 64, 128, 256, 512]
# nb_filter = [16, 32, 64, 128, 256]
mode = 'residual' # mode='residual' seems to improve better than DS
# Handle Dimension Ordering for different backends
bn_axis = 3
inputs = Input(shape=input_shape)
conv1_1 = standard_unit(inputs, stage='11', nb_filter=nb_filter[0]) # add 要求输入输出维度相同
pool1 = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(conv1_1) # (?,128,128,32)
conv2_1 = standard_unit(pool1, stage='21', nb_filter=nb_filter[1], mode=mode)
pool2 = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(conv2_1) # (?,64,64,64)
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) # (?,256,256,64)
conv1_2 = standard_unit(conv1_2, stage='12', nb_filter=nb_filter[0], mode=mode) # (?,256,256,32)
conv3_1 = standard_unit(pool2, stage='31', nb_filter=nb_filter[2], mode=mode)
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], mode=mode)
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], mode=mode) # (?,256,256,32)
conv4_1 = standard_unit(pool3, stage='41', nb_filter=nb_filter[3], mode=mode)
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], mode=mode)
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], mode=mode)
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], mode=mode)
conv5_1 = standard_unit(pool4, stage='51', nb_filter=nb_filter[4], mode=mode)
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], mode=mode)
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], mode=mode)
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], mode=mode)
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], mode=mode)
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)
# using combined loss
conv_fuse = concatenate([conv1_2, conv1_3, conv1_4, conv1_5], name='merge_fuse', axis=bn_axis)
nestnet_output_5 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_5',
kernel_initializer='he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv_fuse)
if deep_supervision:
model = Model(input=inputs, output=[nestnet_output_1,
nestnet_output_2,
nestnet_output_3,
nestnet_output_4, nestnet_output_5])
model.compile(optimizer=Adam(lr=1e-4),
#loss=['binary_crossentropy','binary_crossentropy','binary_crossentropy','binary_crossentropy'],
loss=[weighted_bce_dice_loss, weighted_bce_dice_loss, weighted_bce_dice_loss,
weighted_bce_dice_loss, weighted_bce_dice_loss],
loss_weights=[0.5, 0.5, 0.75, 0.5, 1.0],
metrics=['accuracy']
)
else:
model = Model(input=inputs, output=[nestnet_output_4])
model.compile(optimizer=Adam(lr=1e-4), loss=weighted_bce_dice_loss,
metrics=['accuracy'])
model.summary()
return model
if __name__ == '__main__':
input_shape = [256, 256, 6]
model=Nest_Net2(input_shape,deep_supervision=True)
output_layer=model.get_layer('output_5')
print("the output shape is:")
print(output_layer.output_shape)