本文整理汇总了Python中object_detection.core.losses.WeightedIOULocalizationLoss方法的典型用法代码示例。如果您正苦于以下问题：Python losses.WeightedIOULocalizationLoss方法的具体用法？Python losses.WeightedIOULocalizationLoss怎么用？Python losses.WeightedIOULocalizationLoss使用的例子？那么, 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object_detection.core.losses的用法示例。
在下文中一共展示了losses.WeightedIOULocalizationLoss方法的6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: test_build_weighted_iou_localization_loss
# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedIOULocalizationLoss [as 别名]
def test_build_weighted_iou_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_iou {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedIOULocalizationLoss)) 示例2: testReturnsCorrectLossWithNoLabels
# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedIOULocalizationLoss [as 别名]
def testReturnsCorrectLossWithNoLabels(self):
prediction_tensor = tf.constant([[[1.5, 0, 2.4, 1],
[0, 0, 1, 1],
[0, 0, .5, .25]]])
target_tensor = tf.constant([[[1.5, 0, 2.4, 1],
[0, 0, 1, 1],
[50, 50, 500.5, 100.25]]])
weights = [[1.0, .5, 2.0]]
losses_mask = tf.constant([False], tf.bool)
loss_op = losses.WeightedIOULocalizationLoss()
loss = loss_op(prediction_tensor, target_tensor, weights=weights,
losses_mask=losses_mask)
loss = tf.reduce_sum(loss)
exp_loss = 0.0
with self.test_session() as sess:
loss_output = sess.run(loss)
self.assertAllClose(loss_output, exp_loss) 示例3: test_build_weighted_iou_localization_loss
# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedIOULocalizationLoss [as 别名]
def test_build_weighted_iou_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_iou {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedIOULocalizationLoss)) 示例4: test_build_weighted_iou_localization_loss
# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedIOULocalizationLoss [as 别名]
def test_build_weighted_iou_localization_loss(self):
losses_text_proto = """
localization_loss {
weighted_iou {
}
}
classification_loss {
weighted_softmax {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
_, localization_loss, _, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(isinstance(localization_loss,
losses.WeightedIOULocalizationLoss)) 开发者ID:ShivangShekhar，项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代码行数:18，代码来源:losses_builder_test.py
示例5: testReturnsCorrectLoss
# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedIOULocalizationLoss [as 别名]
def testReturnsCorrectLoss(self):
prediction_tensor = tf.constant([[[1.5, 0, 2.4, 1],
[0, 0, 1, 1],
[0, 0, .5, .25]]])
target_tensor = tf.constant([[[1.5, 0, 2.4, 1],
[0, 0, 1, 1],
[50, 50, 500.5, 100.25]]])
weights = [[1.0, .5, 2.0]]
loss_op = losses.WeightedIOULocalizationLoss()
loss = loss_op(prediction_tensor, target_tensor, weights=weights)
exp_loss = 2.0
with self.test_session() as sess:
loss_output = sess.run(loss)
self.assertAllClose(loss_output, exp_loss) 示例6: _build_localization_loss
# 需要导入模块: from object_detection.core import losses [as 别名]
# 或者: from object_detection.core.losses import WeightedIOULocalizationLoss [as 别名]
def _build_localization_loss(loss_config):
"""Builds a localization loss based on the loss config.
Args:
loss_config: A losses_pb2.LocalizationLoss object.
Returns:
Loss based on the config.
Raises:
ValueError: On invalid loss_config.
"""
if not isinstance(loss_config, losses_pb2.LocalizationLoss):
raise ValueError('loss_config not of type losses_pb2.LocalizationLoss.')
loss_type = loss_config.WhichOneof('localization_loss')
if loss_type == 'weighted_l2':
config = loss_config.weighted_l2
return losses.WeightedL2LocalizationLoss(
anchorwise_output=config.anchorwise_output)
if loss_type == 'weighted_smooth_l1':
config = loss_config.weighted_smooth_l1
return losses.WeightedSmoothL1LocalizationLoss(
anchorwise_output=config.anchorwise_output)
if loss_type == 'weighted_iou':
return losses.WeightedIOULocalizationLoss()
raise ValueError('Empty loss config.')