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seg_unet.py
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seg_unet.py
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from keras.models import Model
from keras.layers import Input, BatchNormalization, Conv2D, Dropout, concatenate, merge, UpSampling2D,Dense
from keras.optimizers import Adam
from keras.layers import Activation,Reshape,Add,Concatenate,Lambda,multiply
from keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D,MaxPooling2D
import keras.backend as K
def activation(x, func='relu'):
'''
Activation layer.
'''
return Activation(func)(x)
def CBAM(x, ratio=4, attention='both'): # 8
"""Contains the implementation of Convolutional Block Attention Module(CBAM) block.
As described in https://arxiv.org/abs/1807.06521.
"""
if attention == 'channel':
cbam_feature = CBAM_channel_attention(x, ratio)
elif attention == 'spatial':
cbam_feature = CBAM_spatial_attention(x)
else:
cbam_feature = CBAM_channel_attention(x, ratio)
cbam_feature = CBAM_spatial_attention(cbam_feature)
return cbam_feature
def CBAM_channel_attention(x, ratio=8):
channel = x._keras_shape[-1]
shared_layer_one = Dense(channel // ratio,
activation='relu',
kernel_initializer='glorot_uniform', #he_normal
use_bias=True,
bias_initializer='zeros')
shared_layer_two = Dense(channel,
kernel_initializer='glorot_uniform',
use_bias=True,
bias_initializer='zeros')
avg_pool = GlobalAveragePooling2D()(x)
avg_pool = Reshape((1, 1, channel))(avg_pool)
avg_pool = shared_layer_one(avg_pool)
avg_pool = shared_layer_two(avg_pool)
max_pool = GlobalMaxPooling2D()(x)
max_pool = Reshape((1, 1, channel))(max_pool)
max_pool = shared_layer_one(max_pool)
max_pool = shared_layer_two(max_pool)
cbam_feature = Add()([avg_pool, max_pool])
cbam_feature = activation(cbam_feature, 'sigmoid')
return multiply([x, cbam_feature])
def CBAM_spatial_attention(x):
kernel_size = 7
channel = x._keras_shape[-1]
cbam_feature = x
avg_pool = Lambda(lambda x: K.mean(x, axis=3, keepdims=True))(cbam_feature)
max_pool = Lambda(lambda x: K.max(x, axis=3, keepdims=True))(cbam_feature)
concat = Concatenate(axis=3)([avg_pool, max_pool])
cbam_feature = Conv2D(filters=1,
kernel_size=kernel_size,
strides=1,
padding='same',
activation='sigmoid',
kernel_initializer='he_normal',
use_bias=False)(concat)
return multiply([x, cbam_feature])
def attunet(pretrained_weights=None, input_size=(256, 256, 4), classNum=2, learning_rate=1e-5):
inputs = Input(input_size)
# 2D卷积层
conv1 = BatchNormalization()(
Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs))
conv1 = BatchNormalization()(
Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1))
# 对于空间数据的最大池化
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = BatchNormalization()(
Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1))
conv2 = BatchNormalization()(
Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2))
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = BatchNormalization()(
Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2))
conv3 = BatchNormalization()(
Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3))
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = BatchNormalization()(
Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3))
conv4 = BatchNormalization()(
Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4))
# Dropout正规化,防止过拟合
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = BatchNormalization()(
Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4))
conv5 = BatchNormalization()(
Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5))
drop5 = Dropout(0.5)(conv5)
# 上采样之后再进行卷积,相当于转置卷积操作
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(drop5))
try:
merge6 = concatenate([CBAM(drop4), up6], axis=3)
except:
merge6 = merge([CBAM(drop4), up6], mode='concat', concat_axis=3)
conv6 = BatchNormalization()(
Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6))
conv6 = BatchNormalization()(
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))
try:
merge7 = concatenate([CBAM(conv3), up7], axis=3)
except:
merge7 = merge([CBAM(conv3), up7], mode='concat', concat_axis=3)
conv7 = BatchNormalization()(
Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7))
conv7 = BatchNormalization()(
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))
try:
merge8 = concatenate([CBAM(conv2), up8], axis=3)
except:
merge8 = merge([CBAM(conv2), up8], mode='concat', concat_axis=3)
conv8 = BatchNormalization()(
Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8))
conv8 = BatchNormalization()(
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))
try:
merge9 = concatenate([CBAM(conv1), up9], axis=3)
except:
merge9 = merge([CBAM(conv1), up9], mode='concat', concat_axis=3)
conv9 = BatchNormalization()(
Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9))
conv9 = BatchNormalization()(
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(classNum, 1, activation='softmax')(conv9)
model = Model(inputs=inputs, outputs=conv10)
# 用于配置训练模型(优化器、目标函数、模型评估标准)
model.compile(optimizer=Adam(lr=learning_rate), loss='categorical_crossentropy', metrics=['accuracy'])
# 如果有预训练的权重
if (pretrained_weights):
model.load_weights(pretrained_weights)
return model
def unet(pretrained_weights = None, input_size = (256, 256, 4), classNum = 2, learning_rate = 1e-5):
inputs = Input(input_size)
# 2D卷积层
conv1 = BatchNormalization()(Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs))
conv1 = BatchNormalization()(Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1))
# 对于空间数据的最大池化
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = BatchNormalization()(Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1))
conv2 = BatchNormalization()(Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2))
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = BatchNormalization()(Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2))
conv3 = BatchNormalization()(Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3))
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = BatchNormalization()(Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3))
conv4 = BatchNormalization()(Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4))
# Dropout正规化,防止过拟合
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = BatchNormalization()(Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4))
conv5 = BatchNormalization()(Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5))
drop5 = Dropout(0.5)(conv5)
# 上采样之后再进行卷积,相当于转置卷积操作
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
try:
merge6 = concatenate([drop4,up6],axis = 3)
except:
merge6 = merge([drop4,up6], mode = 'concat', concat_axis = 3)
conv6 = BatchNormalization()(Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6))
conv6 = BatchNormalization()(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))
try:
merge7 = concatenate([conv3,up7],axis = 3)
except:
merge7 = merge([conv3,up7], mode = 'concat', concat_axis = 3)
conv7 = BatchNormalization()(Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7))
conv7 = BatchNormalization()(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))
try:
merge8 = concatenate([conv2,up8],axis = 3)
except:
merge8 = merge([conv2,up8],mode = 'concat', concat_axis = 3)
conv8 = BatchNormalization()(Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8))
conv8 = BatchNormalization()(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))
try:
merge9 = concatenate([conv1,up9],axis = 3)
except:
merge9 = merge([conv1,up9],mode = 'concat', concat_axis = 3)
conv9 = BatchNormalization()(Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9))
conv9 = BatchNormalization()(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(classNum, 1, activation = 'softmax')(conv9)
model = Model(inputs = inputs, outputs = conv10)
# 用于配置训练模型(优化器、目标函数、模型评估标准)
model.compile(optimizer = Adam(lr = learning_rate), loss = 'categorical_crossentropy', metrics = ['accuracy'])
# 如果有预训练的权重
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
if __name__ == '__main__':
model=attunet()
model.summary()