File size: 1,412 Bytes
89ce6b3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
import tensorflow as tf
epsilon = 1e-7
def conf_criterion_lp(im1, im2, conf_sigma): # factorized laplacian distribution
loss = tf.abs(im1 - im2)
if conf_sigma is not None:
loss = loss * 2 / (conf_sigma + epsilon) + tf.log(conf_sigma * 2 + epsilon)
loss = tf.reduce_mean(loss)
else:
loss = tf.reduce_mean(loss)
return loss
def conf_criterion(im1, im2, conf_sigma): # gaussian distribution
loss = tf.abs(im1 - im2)
if conf_sigma is not None:
loss = tf.math.exp(-conf_sigma) * 5 * loss + conf_sigma / 2
loss = tf.reduce_mean(loss)
else:
loss = tf.reduce_mean(loss)
return loss
def abs_criterion(in_, target):
return tf.reduce_mean(tf.abs(in_ - target))
def mae_criterion(in_, target):
return tf.reduce_mean((in_ - target) ** 2)
def sce_criterion(logits, labels):
return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))
def mae_criterion_list(in_, target):
loss = 0.0
for i in range(len(target)):
loss += tf.reduce_mean((in_[i] - target[i]) ** 2)
return loss / len(target)
def sce_criterion_list(logits, labels):
loss = 0.0
for i in range(len(labels)):
loss += tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits[i], labels=labels[i]))
return loss / len(labels) |