def create_bce_loss(output, label, num_classes, ignore_label):
raw_pred = tf.reshape(output, [-1, num_classes])
label = prepare_label(label,
tf.stack(output.get_shape()[1:3]),
num_classes=num_classes,
one_hot=False)
label = tf.reshape(label, [
-1,
])
indices = get_mask(label, num_classes, ignore_label)
gt = tf.cast(tf.gather(label, indices), tf.int32)
gt_one_hot = tf.one_hot(gt, num_classes)
pred = tf.gather(raw_pred, indices)
BCE = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred,
labels=gt_one_hot))
# inse = tf.reduce_sum(pred * tf.cast(gt_one_hot,tf.float32))
# l = tf.reduce_sum(pred)
# r = tf.reduce_sum(tf.cast(gt_one_hot,tf.float32))
# dice = tf.math.log((2. * inse + 1e-5) / (l + r + 1e-5))
dice = dice_coef_theoretical(pred, gt)
# tf.Print(dice)
loss = BCE - tf.math.log(dice)
reduced_loss = loss
return reduced_loss
def create_loss(output, label, num_classes, ignore_label):
raw_pred = tf.reshape(output, [-1, num_classes])
label = prepare_label(label, tf.stack(output.get_shape()[1:3]), num_classes=num_classes, one_hot=False)
label = tf.reshape(label, [-1,])
indices = get_mask(label, num_classes, ignore_label)
gt = tf.cast(tf.gather(label, indices), tf.int32)
pred = tf.gather(raw_pred, indices)
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=gt)
reduced_loss = tf.reduce_mean(loss)
return reduced_loss