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Model_MNet.py
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from __future__ import print_function
from __future__ import absolute_import
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, AveragePooling2D, Conv2DTranspose, UpSampling2D
from keras.layers import BatchNormalization, Activation, average
def DeepModel(size_set = 800):
img_input = Input(shape=(size_set, size_set, 3))
scale_img_2 = AveragePooling2D(pool_size=(2, 2))(img_input)
scale_img_3 = AveragePooling2D(pool_size=(2, 2))(scale_img_2)
scale_img_4 = AveragePooling2D(pool_size=(2, 2))(scale_img_3)
conv1 = Conv2D(32, (3, 3), padding='same', activation='relu',name='block1_conv1')(img_input)
conv1 = Conv2D(32, (3, 3), padding='same', activation='relu',name='block1_conv2')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
input2 = Conv2D(64, (3, 3), padding='same', activation='relu',name='block2_input1')(scale_img_2)
input2 = concatenate([input2, pool1], axis=3)
conv2 = Conv2D(64, (3, 3), padding='same', activation='relu',name='block2_conv1')(input2)
conv2 = Conv2D(64, (3, 3), padding='same', activation='relu',name='block2_conv2')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
input3 = Conv2D(128, (3, 3), padding='same', activation='relu',name='block3_input1')(scale_img_3)
input3 = concatenate([input3, pool2], axis=3)
conv3 = Conv2D(128, (3, 3), padding='same', activation='relu',name='block3_conv1')(input3)
conv3 = Conv2D(128, (3, 3), padding='same', activation='relu',name='block3_conv2')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
input4 = Conv2D(256, (3, 3), padding='same', activation='relu',name='block4_input1')(scale_img_4)
input4 = concatenate([input4, pool3], axis=3)
conv4 = Conv2D(256, (3, 3), padding='same', activation='relu',name='block4_conv1')(input4)
conv4 = Conv2D(256, (3, 3), padding='same', activation='relu',name='block4_conv2')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), padding='same', activation='relu',name='block5_conv1')(pool4)
conv5 = Conv2D(512, (3, 3), padding='same', activation='relu',name='block5_conv2')(conv5)
up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same', name='block6_dconv')(conv5), conv4], axis=3)
conv6 = Conv2D(256, (3, 3), padding='same', activation='relu',name='block6_conv1')(up6)
conv6 = Conv2D(256, (3, 3), padding='same', activation='relu',name='block6_conv2')(conv6)
up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same', name='block7_dconv')(conv6), conv3], axis=3)
conv7 = Conv2D(128, (3, 3), padding='same', activation='relu',name='block7_conv1')(up7)
conv7 = Conv2D(128, (3, 3), padding='same', activation='relu',name='block7_conv2')(conv7)
up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same', name='block8_dconv')(conv7), conv2], axis=3)
conv8 = Conv2D(64, (3, 3), padding='same', activation='relu',name='block8_conv1')(up8)
conv8 = Conv2D(64, (3, 3), padding='same', activation='relu', name='block8_conv2')(conv8)
up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same', name='block9_dconv')(conv8), conv1], axis=3)
conv9 = Conv2D(32, (3, 3), padding='same', activation='relu', name='block9_conv1')(up9)
conv9 = Conv2D(32, (3, 3), padding='same', activation='relu', name='block9_conv2')(conv9)
side6 = UpSampling2D(size=(8, 8))(conv6)
side7 = UpSampling2D(size=(4, 4))(conv7)
side8 = UpSampling2D(size=(2, 2))(conv8)
out6 = Conv2D(2, (1, 1), activation='sigmoid', name='side_63')(side6)
out7 = Conv2D(2, (1, 1), activation='sigmoid', name='side_73')(side7)
out8 = Conv2D(2, (1, 1), activation='sigmoid', name='side_83')(side8)
out9 = Conv2D(2, (1, 1), activation='sigmoid', name='side_93')(conv9)
out10 = average([out6, out7, out8, out9])
model = Model(inputs=[img_input], outputs=[out6, out7, out8, out9, out10])
return model