本文整理汇总了Python中losses.log_quaternion_loss方法的典型用法代码示例。如果您正苦于以下问题：Python losses.log_quaternion_loss方法的具体用法？Python losses.log_quaternion_loss怎么用？Python losses.log_quaternion_loss使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类losses的用法示例。
在下文中一共展示了losses.log_quaternion_loss方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: add_task_loss
# 需要导入模块: import losses [as 别名]
# 或者: from losses import log_quaternion_loss [as 别名]
def add_task_loss(source_images, source_labels, basic_tower, params):
"""Adds a classification and/or pose estimation loss to the model.
Args:
source_images: images from the source domain, a tensor of size
[batch_size, height, width, channels]
source_labels: labels from the source domain, a tensor of size [batch_size].
or a tuple of (quaternions, class_labels)
basic_tower: a function that creates the single tower of the model.
params: A dictionary of parameters. Expecting 'weight_decay', 'pose_weight'.
Returns:
The source endpoints.
Raises:
RuntimeError: if basic tower does not support pose estimation.
"""
with tf.variable_scope('towers'):
source_logits, source_endpoints = basic_tower(
source_images, weight_decay=params['weight_decay'], prefix='Source')
if 'quaternions' in source_labels: # We have pose estimation as well
if 'quaternion_pred' not in source_endpoints:
raise RuntimeError('Please use a model for estimation e.g. pose_mini')
loss = losses.log_quaternion_loss(source_labels['quaternions'],
source_endpoints['quaternion_pred'],
params)
assert_op = tf.Assert(tf.is_finite(loss), [loss])
with tf.control_dependencies([assert_op]):
quaternion_loss = loss
tf.summary.histogram('log_quaternion_loss_hist', quaternion_loss)
slim.losses.add_loss(quaternion_loss * params['pose_weight'])
tf.summary.scalar('losses/quaternion_loss', quaternion_loss)
classification_loss = tf.losses.softmax_cross_entropy(
source_labels['classes'], source_logits)
tf.summary.scalar('losses/classification_loss', classification_loss)
return source_endpoints