/
build_model.py
101 lines (89 loc) · 4.72 KB
/
build_model.py
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from keras.models import Model
from keras.layers.merge import concatenate
from keras.layers import Input, Convolution3D, MaxPooling3D, UpSampling3D
from keras.layers import Reshape, Activation
from keras.layers.normalization import BatchNormalization
def build_model(inp_shape, k_size=3):
merge_axis = -1 # Feature maps are concatenated along last axis (for tf backend)
data = Input(shape=inp_shape)
conv1 = Convolution3D(padding='same', filters=32, kernel_size=k_size)(data)
conv1 = BatchNormalization()(conv1)
conv1 = Activation('relu')(conv1)
conv2 = Convolution3D(padding='same', filters=32, kernel_size=k_size)(conv1)
conv2 = BatchNormalization()(conv2)
conv2 = Activation('relu')(conv2)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
conv3 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(pool1)
conv3 = BatchNormalization()(conv3)
conv3 = Activation('relu')(conv3)
conv4 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(conv3)
conv4 = BatchNormalization()(conv4)
conv4 = Activation('relu')(conv4)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv4)
conv5 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(pool2)
conv5 = BatchNormalization()(conv5)
conv5 = Activation('relu')(conv5)
conv6 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(conv5)
conv6 = BatchNormalization()(conv6)
conv6 = Activation('relu')(conv6)
pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv6)
conv7 = Convolution3D(padding='same', filters=128, kernel_size=k_size)(pool3)
conv7 = BatchNormalization()(conv7)
conv7 = Activation('relu')(conv7)
conv8 = Convolution3D(padding='same', filters=128, kernel_size=k_size)(conv7)
conv8 = BatchNormalization()(conv8)
conv8 = Activation('relu')(conv8)
pool4 = MaxPooling3D(pool_size=(2, 2, 2))(conv8)
conv9 = Convolution3D(padding='same', filters=128, kernel_size=k_size)(pool4)
conv9 = BatchNormalization()(conv9)
conv9 = Activation('relu')(conv9)
up1 = UpSampling3D(size=(2, 2, 2))(conv9)
conv10 = Convolution3D(padding='same', filters=128, kernel_size=k_size)(up1)
conv10 = BatchNormalization()(conv10)
conv10 = Activation('relu')(conv10)
conv11 = Convolution3D(padding='same', filters=128, kernel_size=k_size)(conv10)
conv11 = BatchNormalization()(conv11)
conv11 = Activation('relu')(conv11)
merged1 = concatenate([conv11, conv8], axis=merge_axis)
conv12 = Convolution3D(padding='same', filters=128, kernel_size=k_size)(merged1)
conv12 = BatchNormalization()(conv12)
conv12 = Activation('relu')(conv12)
up2 = UpSampling3D(size=(2, 2, 2))(conv12)
conv13 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(up2)
conv13 = BatchNormalization()(conv13)
conv13 = Activation('relu')(conv13)
conv14 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(conv13)
conv14 = BatchNormalization()(conv14)
conv14 = Activation('relu')(conv14)
merged2 = concatenate([conv14, conv6], axis=merge_axis)
conv15 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(merged2)
conv15 = BatchNormalization()(conv15)
conv15 = Activation('relu')(conv15)
up3 = UpSampling3D(size=(2, 2, 2))(conv15)
conv16 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(up3)
conv16 = BatchNormalization()(conv16)
conv16 = Activation('relu')(conv16)
conv17 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(conv16)
conv17 = BatchNormalization()(conv17)
conv17 = Activation('relu')(conv17)
merged3 = concatenate([conv17, conv4], axis=merge_axis)
conv18 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(merged3)
conv18 = BatchNormalization()(conv18)
conv18 = Activation('relu')(conv18)
up4 = UpSampling3D(size=(2, 2, 2))(conv18)
conv19 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(up4)
conv19 = BatchNormalization()(conv19)
conv19 = Activation('relu')(conv19)
conv20 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(conv19)
conv20 = BatchNormalization()(conv20)
conv20 = Activation('relu')(conv20)
merged4 = concatenate([conv20, conv2], axis=merge_axis)
conv21 = Convolution3D(padding='same', filters=64, kernel_size=k_size)(merged4)
conv21 = BatchNormalization()(conv21)
conv21 = Activation('relu')(conv21)
conv22 = Convolution3D(padding='same', filters=2, kernel_size=k_size)(conv21)
output = Reshape([-1, 2])(conv22)
output = Activation('softmax')(output)
output = Reshape(inp_shape[:-1] + (2,))(output)
model = Model(data, output)
return model