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model.py
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from keras.layers import (
Input,
merge,
Flatten,
BatchNormalization,
Dropout
)
from keras.layers.convolutional import (
Convolution2D,
UpSampling2D
)
from keras.layers.advanced_activations import ELU
from keras.models import Model
from keras.optimizers import Adam
from metric import dice_loss, dice
from data import DataManager
# Helper to build a conv -> BN -> relu block
def _conv_bn_relu(nb_filter, nb_row, nb_col, subsample=(1, 1)):
def f(input):
conv = Convolution2D(nb_filter=nb_filter, nb_row=nb_row, nb_col=nb_col,
subsample=subsample, init="he_normal",
border_mode="same")(input)
norm = BatchNormalization()(conv)
return ELU()(norm)
return f
def build_model(optimizer=None):
if optimizer is None:
optimizer = Adam(lr=1e-4)
inputs = Input((1, DataManager.IMG_TARGET_ROWS, DataManager.IMG_TARGET_COLS), name='main_input')
conv1 = _conv_bn_relu(32, 7, 7)(inputs)
conv1 = _conv_bn_relu(32, 3, 3)(conv1)
pool1 = _conv_bn_relu(32, 2, 2, subsample=(2, 2))(conv1)
drop1 = Dropout(0.5)(pool1)
conv2 = _conv_bn_relu(64, 3, 3)(drop1)
conv2 = _conv_bn_relu(64, 3, 3)(conv2)
pool2 = _conv_bn_relu(64, 2, 2, subsample=(2, 2))(conv2)
drop2 = Dropout(0.5)(pool2)
conv3 = _conv_bn_relu(128, 3, 3)(drop2)
conv3 = _conv_bn_relu(128, 3, 3)(conv3)
pool3 = _conv_bn_relu(128, 2, 2, subsample=(2, 2))(conv3)
drop3 = Dropout(0.5)(pool3)
conv4 = _conv_bn_relu(256, 3, 3)(drop3)
conv4 = _conv_bn_relu(256, 3, 3)(conv4)
pool4 = _conv_bn_relu(256, 2, 2, subsample=(2, 2))(conv4)
drop4 = Dropout(0.5)(pool4)
conv5 = _conv_bn_relu(512, 3, 3)(drop4)
conv5 = _conv_bn_relu(512, 3, 3)(conv5)
drop5 = Dropout(0.5)(conv5)
# Using conv to mimic fully connected layer.
aux = Convolution2D(nb_filter=1, nb_row=drop5._keras_shape[2], nb_col=drop5._keras_shape[3],
subsample=(1, 1), init="he_normal", activation='sigmoid')(drop5)
aux = Flatten(name='aux_output')(aux)
up6 = merge([UpSampling2D()(drop5), conv4], mode='concat', concat_axis=1)
conv6 = _conv_bn_relu(256, 3, 3)(up6)
conv6 = _conv_bn_relu(256, 3, 3)(conv6)
drop6 = Dropout(0.5)(conv6)
up7 = merge([UpSampling2D()(drop6), conv3], mode='concat', concat_axis=1)
conv7 = _conv_bn_relu(128, 3, 3)(up7)
conv7 = _conv_bn_relu(128, 3, 3)(conv7)
drop7 = Dropout(0.5)(conv7)
up8 = merge([UpSampling2D()(drop7), conv2], mode='concat', concat_axis=1)
conv8 = _conv_bn_relu(64, 3, 3)(up8)
conv8 = _conv_bn_relu(64, 3, 3)(conv8)
drop8 = Dropout(0.5)(conv8)
up9 = merge([UpSampling2D()(drop8), conv1], mode='concat', concat_axis=1)
conv9 = _conv_bn_relu(32, 3, 3)(up9)
conv9 = _conv_bn_relu(32, 3, 3)(conv9)
drop9 = Dropout(0.5)(conv9)
conv10 = Convolution2D(1, 1, 1, activation='sigmoid', init="he_normal", name='main_output')(drop9)
model = Model(input=inputs, output=[conv10, aux])
model.compile(optimizer=optimizer,
loss={'main_output': dice_loss, 'aux_output': 'binary_crossentropy'},
metrics={'main_output': dice, 'aux_output': 'acc'},
loss_weights={'main_output': 1, 'aux_output': 0.5})
return model
if __name__ == '__main__':
model = build_model()
model.summary()