本文整理汇总了Python中object_detection.protos.losses_pb2.ClassificationLoss方法的典型用法代码示例。如果您正苦于以下问题：Python losses_pb2.ClassificationLoss方法的具体用法？Python losses_pb2.ClassificationLoss怎么用？Python losses_pb2.ClassificationLoss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.protos.losses_pb2的用法示例。
在下文中一共展示了losses_pb2.ClassificationLoss方法的10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: _build_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import ClassificationLoss [as 别名]
def _build_classification_loss(loss_config):
"""Builds a classification loss based on the loss config.
Args:
loss_config: A losses_pb2.ClassificationLoss object.
Returns:
Loss based on the config.
Raises:
ValueError: On invalid loss_config.
"""
if not isinstance(loss_config, losses_pb2.ClassificationLoss):
raise ValueError('loss_config not of type losses_pb2.ClassificationLoss.')
loss_type = loss_config.WhichOneof('classification_loss')
if loss_type == 'weighted_sigmoid':
config = loss_config.weighted_sigmoid
return losses.WeightedSigmoidClassificationLoss(
anchorwise_output=config.anchorwise_output)
if loss_type == 'weighted_softmax':
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
anchorwise_output=config.anchorwise_output)
if loss_type == 'bootstrapped_sigmoid':
config = loss_config.bootstrapped_sigmoid
return losses.BootstrappedSigmoidClassificationLoss(
alpha=config.alpha,
bootstrap_type=('hard' if config.hard_bootstrap else 'soft'),
anchorwise_output=config.anchorwise_output)
raise ValueError('Empty loss config.') 示例2: build_faster_rcnn_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import ClassificationLoss [as 别名]
def build_faster_rcnn_classification_loss(loss_config):
"""Builds a classification loss for Faster RCNN based on the loss config.
Args:
loss_config: A losses_pb2.ClassificationLoss object.
Returns:
Loss based on the config.
Raises:
ValueError: On invalid loss_config.
"""
if not isinstance(loss_config, losses_pb2.ClassificationLoss):
raise ValueError('loss_config not of type losses_pb2.ClassificationLoss.')
loss_type = loss_config.WhichOneof('classification_loss')
if loss_type == 'weighted_sigmoid':
return losses.WeightedSigmoidClassificationLoss()
if loss_type == 'weighted_softmax':
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale)
if loss_type == 'weighted_logits_softmax':
config = loss_config.weighted_logits_softmax
return losses.WeightedSoftmaxClassificationAgainstLogitsLoss(
logit_scale=config.logit_scale)
if loss_type == 'weighted_sigmoid_focal':
config = loss_config.weighted_sigmoid_focal
alpha = None
if config.HasField('alpha'):
alpha = config.alpha
return losses.SigmoidFocalClassificationLoss(
gamma=config.gamma,
alpha=alpha)
# By default, Faster RCNN second stage classifier uses Softmax loss
# with anchor-wise outputs.
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale) 示例3: test_build_sigmoid_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import ClassificationLoss [as 别名]
def test_build_sigmoid_loss(self):
losses_text_proto = """
weighted_sigmoid {
}
"""
losses_proto = losses_pb2.ClassificationLoss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss = losses_builder.build_faster_rcnn_classification_loss(
losses_proto)
self.assertTrue(isinstance(classification_loss,
losses.WeightedSigmoidClassificationLoss)) 示例4: test_build_softmax_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import ClassificationLoss [as 别名]
def test_build_softmax_loss(self):
losses_text_proto = """
weighted_softmax {
}
"""
losses_proto = losses_pb2.ClassificationLoss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss = losses_builder.build_faster_rcnn_classification_loss(
losses_proto)
self.assertTrue(isinstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss)) 示例5: test_build_logits_softmax_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import ClassificationLoss [as 别名]
def test_build_logits_softmax_loss(self):
losses_text_proto = """
weighted_logits_softmax {
}
"""
losses_proto = losses_pb2.ClassificationLoss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss = losses_builder.build_faster_rcnn_classification_loss(
losses_proto)
self.assertTrue(
isinstance(classification_loss,
losses.WeightedSoftmaxClassificationAgainstLogitsLoss)) 示例6: test_build_softmax_loss_by_default
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import ClassificationLoss [as 别名]
def test_build_softmax_loss_by_default(self):
losses_text_proto = """
"""
losses_proto = losses_pb2.ClassificationLoss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss = losses_builder.build_faster_rcnn_classification_loss(
losses_proto)
self.assertTrue(isinstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss)) 示例7: build_faster_rcnn_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import ClassificationLoss [as 别名]
def build_faster_rcnn_classification_loss(loss_config):
"""Builds a classification loss for Faster RCNN based on the loss config.
Args:
loss_config: A losses_pb2.ClassificationLoss object.
Returns:
Loss based on the config.
Raises:
ValueError: On invalid loss_config.
"""
if not isinstance(loss_config, losses_pb2.ClassificationLoss):
raise ValueError('loss_config not of type losses_pb2.ClassificationLoss.')
loss_type = loss_config.WhichOneof('classification_loss')
if loss_type == 'weighted_sigmoid':
return losses.WeightedSigmoidClassificationLoss()
if loss_type == 'weighted_softmax':
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale)
# By default, Faster RCNN second stage classifier uses Softmax loss
# with anchor-wise outputs.
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale) 示例8: _build_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import ClassificationLoss [as 别名]
def _build_classification_loss(loss_config):
"""Builds a classification loss based on the loss config.
Args:
loss_config: A losses_pb2.ClassificationLoss object.
Returns:
Loss based on the config.
Raises:
ValueError: On invalid loss_config.
"""
if not isinstance(loss_config, losses_pb2.ClassificationLoss):
raise ValueError('loss_config not of type losses_pb2.ClassificationLoss.')
loss_type = loss_config.WhichOneof('classification_loss')
if loss_type == 'weighted_sigmoid':
return losses.WeightedSigmoidClassificationLoss()
if loss_type == 'weighted_sigmoid_focal':
config = loss_config.weighted_sigmoid_focal
alpha = None
if config.HasField('alpha'):
alpha = config.alpha
return losses.SigmoidFocalClassificationLoss(
gamma=config.gamma,
alpha=alpha)
if loss_type == 'weighted_softmax':
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale)
if loss_type == 'bootstrapped_sigmoid':
config = loss_config.bootstrapped_sigmoid
return losses.BootstrappedSigmoidClassificationLoss(
alpha=config.alpha,
bootstrap_type=('hard' if config.hard_bootstrap else 'soft'))
raise ValueError('Empty loss config.') 示例9: build_faster_rcnn_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import ClassificationLoss [as 别名]
def build_faster_rcnn_classification_loss(loss_config):
"""Builds a classification loss for Faster RCNN based on the loss config.
Args:
loss_config: A losses_pb2.ClassificationLoss object.
Returns:
Loss based on the config.
Raises:
ValueError: On invalid loss_config.
"""
if not isinstance(loss_config, losses_pb2.ClassificationLoss):
raise ValueError('loss_config not of type losses_pb2.ClassificationLoss.')
loss_type = loss_config.WhichOneof('classification_loss')
if loss_type == 'weighted_sigmoid':
return losses.WeightedSigmoidClassificationLoss()
if loss_type == 'weighted_softmax':
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale)
if loss_type == 'weighted_logits_softmax':
config = loss_config.weighted_logits_softmax
return losses.WeightedSoftmaxClassificationAgainstLogitsLoss(
logit_scale=config.logit_scale)
# By default, Faster RCNN second stage classifier uses Softmax loss
# with anchor-wise outputs.
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale) 示例10: build_faster_rcnn_classification_loss
# 需要导入模块: from object_detection.protos import losses_pb2 [as 别名]
# 或者: from object_detection.protos.losses_pb2 import ClassificationLoss [as 别名]
def build_faster_rcnn_classification_loss(loss_config):
"""Builds a classification loss for Faster RCNN based on the loss config.
Args:
loss_config: A losses_pb2.ClassificationLoss object.
Returns:
Loss based on the config.
Raises:
ValueError: On invalid loss_config.
"""
if not isinstance(loss_config, losses_pb2.ClassificationLoss):
raise ValueError('loss_config not of type losses_pb2.ClassificationLoss.')
loss_type = loss_config.WhichOneof('classification_loss')
if loss_type == 'weighted_sigmoid':
config = loss_config.weighted_sigmoid
return losses.WeightedSigmoidClassificationLoss(
anchorwise_output=config.anchorwise_output)
if loss_type == 'weighted_softmax':
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
anchorwise_output=config.anchorwise_output)
# By default, Faster RCNN second stage classifier uses Softmax loss
# with anchor-wise outputs.
return losses.WeightedSoftmaxClassificationLoss(
anchorwise_output=True)