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import tensorflow as tf
def sigmoid_cross_entropy_balanced(logits, label, name='cross_entrony_loss'):
"""
Initially proposed in: 'Holistically-Nested Edge Detection (CVPR 15)'
Implements Equation [2] in https://arxiv.org/pdf/1504.06375.pdf
Compute edge pixels for each training sample and set as pos_weights to
tf.nn.weighted_cross_entropy_with_logits
"""
y = tf.cast(label, tf.float32)
count_neg = tf.reduce_sum(1.-y)
count_pos = tf.reduce_sum(y)
# Equation [2]
beta = count_neg / (count_neg + count_pos)
# Equation [2] divide by 1 - beta
pos_weight = beta / (1 - beta)
cost = tf.nn.weighted_cross_entropy_with_logits(logits=logits, targets=y, pos_weight=pos_weight)
# cost = tf.
# Multiply by 1 - beta
cost = tf.reduce_mean(cost * (1 - beta))
# check if image has no edge pixels return 0 else return complete error function
return tf.where(tf.equal(count_pos, 0.0), 0.0, cost, name=name)
# def class_balanced_cross_entropy_with_logits(logits,label,name='class_ballanced_cross_entropy'):
#
# # Initialy proposed in: 'Holistically-Nested Edge Detection (CVPR 15)'
# with tf.name_scope(name) as scope:
# logits= tf.cast(logits, tf.float32)
# label = tf.cast(label, tf.float32)
#
# n_positives = tf.reduce_sum(label)
# n_negatives = tf.reduce_sum(1.0-label)
#
# beta = n_negatives/(n_negatives+n_positives)
# pos_weight = beta / (1-beta)
# check_weight = tf.identity(beta,name='check')
#
# cost = tf.nn.weighted_cross_entropy_with_logits(targets=label,logits=logits,pos_weight=pos_weight)
# loss = tf.reduce_mean((1-beta)*cost)
#
# return tf.where(tf.equal(beta,1.0),0.0,loss)
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