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unet_model_deeper.py
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unet_model_deeper.py
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# u-net model with up-convolution or up-sampling and weighted binary-crossentropy as loss func
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout
from keras.optimizers import Adam
from keras.utils import plot_model
from keras import backend as K
def unet_model(n_classes=5, im_sz=320, n_channels=8, n_filters_start=32, growth_factor=2, upconv=True,
class_weights=[0.2, 0.3, 0.1, 0.1, 0.3]):
droprate=0.25
n_filters = n_filters_start
inputs = Input((im_sz, im_sz, n_channels))
#inputs = BatchNormalization()(inputs)
conv1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
pool1 = Dropout(droprate)(pool1)
n_filters *= growth_factor
pool1 = BatchNormalization()(pool1)
conv2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
pool2 = Dropout(droprate)(pool2)
n_filters *= growth_factor
pool2 = BatchNormalization()(pool2)
conv3 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
pool3 = Dropout(droprate)(pool3)
n_filters *= growth_factor
pool3 = BatchNormalization()(pool3)
conv4_0 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool3)
conv4_0 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv4_0)
pool4_0 = MaxPooling2D(pool_size=(2, 2))(conv4_0)
pool4_0 = Dropout(droprate)(pool4_0)
n_filters *= growth_factor
pool4_0 = BatchNormalization()(pool4_0)
conv4_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool4_0)
conv4_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv4_1)
pool4_1 = MaxPooling2D(pool_size=(2, 2))(conv4_1)
pool4_1 = Dropout(droprate)(pool4_1)
n_filters *= growth_factor
pool4_1 = BatchNormalization()(pool4_1)
conv4_2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool4_1)
conv4_2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv4_2)
pool4_2 = MaxPooling2D(pool_size=(2, 2))(conv4_2)
pool4_2 = Dropout(droprate)(pool4_2)
n_filters *= growth_factor
pool4_2 = BatchNormalization()(pool4_2)
conv5 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool4_2)
conv5 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv5)
conv5 = Dropout(droprate)(conv5)
n_filters //= growth_factor
if upconv:
up6 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv5), conv4_2])
else:
up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4_2])
up6 = BatchNormalization()(up6)
conv6 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv6)
conv6 = Dropout(droprate)(conv6)
n_filters //= growth_factor
if upconv:
up6_1 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv6), conv4_1])
else:
up6_1 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv4_1])
up6_1 = BatchNormalization()(up6_1)
conv6_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up6_1)
conv6_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv6_1)
conv6_1 = Dropout(droprate)(conv6_1)
n_filters //= growth_factor
if upconv:
up6_2 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv6_1), conv4_0])
else:
up6_2 = concatenate([UpSampling2D(size=(2, 2))(conv6_1), conv4_0])
up6_2 = BatchNormalization()(up6_2)
conv6_2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up6_2)
conv6_2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv6_2)
conv6_2 = Dropout(droprate)(conv6_2)
n_filters //= growth_factor
if upconv:
up7 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv6_2), conv3])
else:
up7 = concatenate([UpSampling2D(size=(2, 2))(conv6_2), conv3])
up7 = BatchNormalization()(up7)
conv7 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv7)
conv7 = Dropout(droprate)(conv7)
n_filters //= growth_factor
if upconv:
up8 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv7), conv2])
else:
up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2])
up8 = BatchNormalization()(up8)
conv8 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv8)
conv8 = Dropout(droprate)(conv8)
n_filters //= growth_factor
if upconv:
up9 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv8), conv1])
else:
up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1])
up9 = BatchNormalization()(up9)
conv9 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv9)
conv10 = Conv2D(n_classes, (1, 1), activation='sigmoid')(conv9)
model = Model(inputs=inputs, outputs=conv10)
def weighted_binary_crossentropy(y_true, y_pred):
class_loglosses = K.mean(K.binary_crossentropy(y_true, y_pred), axis=[0, 1, 2])
return K.sum(class_loglosses * K.constant(class_weights))
model.compile(optimizer=Adam(), loss=weighted_binary_crossentropy)
return model