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# coding: utf-8
from keras import layers as L
from keras import backend as K
from keras.models import Model
import tensorflow as tf
import numpy as np
_epsilon = tf.convert_to_tensor(K.epsilon(), np.float32)
def my_loss(target, output):
"""
A custom function defined to simply sum the pixelwise loss.
This function doesn't compute the crossentropy loss, since that is made a
part of the model's computational graph itself.
Parameters
----------
target : tf.tensor
A tensor corresponding to the true labels of an image.
output : tf.tensor
Model output
Returns
-------
tf.tensor
A tensor holding the aggregated loss.
"""
return - tf.reduce_sum(target * output,
len(output.get_shape()) - 1)
def make_weighted_loss_unet(input_shape, n_classes):
# two inputs, one for the image and one for the weight maps
ip = L.Input(shape=input_shape)
# the shape of the weight maps has to be such that it can be element-wise
# multiplied to the softmax output.
weight_ip = L.Input(shape=input_shape[:2] + (n_classes,))
# adding the layers
conv1 = L.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(ip)
conv1 = L.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
conv1 = L.Dropout(0.1)(conv1)
mpool1 = L.MaxPool2D()(conv1)
conv2 = L.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(mpool1)
conv2 = L.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
conv2 = L.Dropout(0.2)(conv2)
mpool2 = L.MaxPool2D()(conv2)
conv3 = L.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(mpool2)
conv3 = L.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
conv3 = L.Dropout(0.3)(conv3)
mpool3 = L.MaxPool2D()(conv3)
conv4 = L.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(mpool3)
conv4 = L.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
conv4 = L.Dropout(0.4)(conv4)
mpool4 = L.MaxPool2D()(conv4)
conv5 = L.Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(mpool4)
conv5 = L.Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
conv5 = L.Dropout(0.5)(conv5)
up6 = L.Conv2DTranspose(512, 2, strides=2, kernel_initializer='he_normal', padding='same')(conv5)
conv6 = L.Concatenate()([up6, conv4])
conv6 = L.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
conv6 = L.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
conv6 = L.Dropout(0.4)(conv6)
up7 = L.Conv2DTranspose(256, 2, strides=2, kernel_initializer='he_normal', padding='same')(conv6)
conv7 = L.Concatenate()([up7, conv3])
conv7 = L.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
conv7 = L.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
conv7 = L.Dropout(0.3)(conv7)
up8 = L.Conv2DTranspose(128, 2, strides=2, kernel_initializer='he_normal', padding='same')(conv7)
conv8 = L.Concatenate()([up8, conv2])
conv8 = L.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
conv8 = L.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
conv8 = L.Dropout(0.2)(conv8)
up9 = L.Conv2DTranspose(64, 2, strides=2, kernel_initializer='he_normal', padding='same')(conv8)
conv9 = L.Concatenate()([up9, conv1])
conv9 = L.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = L.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = L.Dropout(0.1)(conv9)
c10 = L.Conv2D(n_classes, 1, activation='softmax', kernel_initializer='he_normal')(conv9)
# Add a few non trainable layers to mimic the computation of the crossentropy
# loss, so that the actual loss function just has to peform the
# aggregation.
c11 = L.Lambda(lambda x: x / tf.reduce_sum(x, len(x.get_shape()) - 1, True))(c10)
c11 = L.Lambda(lambda x: tf.clip_by_value(x, _epsilon, 1. - _epsilon))(c11)
c11 = L.Lambda(lambda x: K.log(x))(c11)
weighted_sm = L.multiply([c11, weight_ip])
model = Model(inputs=[ip, weight_ip], outputs=[weighted_sm])
return model
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