本文整理匯總了Python中object_detection.protos.losses_pb2.HardExampleMiner方法的典型用法代碼示例。如果您正苦於以下問題：Python losses_pb2.HardExampleMiner方法的具體用法？Python losses_pb2.HardExampleMiner怎麽用？Python losses_pb2.HardExampleMiner使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.protos.losses_pb2的用法示例。
在下文中一共展示了losses_pb2.HardExampleMiner方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: build_hard_example_miner
# 需要導入模塊: from object_detection.protos import losses_pb2 [as 別名]
# 或者: from object_detection.protos.losses_pb2 import HardExampleMiner [as 別名]
def build_hard_example_miner(config,
classification_weight,
localization_weight):
"""Builds hard example miner based on the config.
Args:
config: A losses_pb2.HardExampleMiner object.
classification_weight: Classification loss weight.
localization_weight: Localization loss weight.
Returns:
Hard example miner.
"""
loss_type = None
if config.loss_type == losses_pb2.HardExampleMiner.BOTH:
loss_type = 'both'
if config.loss_type == losses_pb2.HardExampleMiner.CLASSIFICATION:
loss_type = 'cls'
if config.loss_type == losses_pb2.HardExampleMiner.LOCALIZATION:
loss_type = 'loc'
max_negatives_per_positive = None
num_hard_examples = None
if config.max_negatives_per_positive > 0:
max_negatives_per_positive = config.max_negatives_per_positive
if config.num_hard_examples > 0:
num_hard_examples = config.num_hard_examples
hard_example_miner = losses.HardExampleMiner(
num_hard_examples=num_hard_examples,
iou_threshold=config.iou_threshold,
loss_type=loss_type,
cls_loss_weight=classification_weight,
loc_loss_weight=localization_weight,
max_negatives_per_positive=max_negatives_per_positive,
min_negatives_per_image=config.min_negatives_per_image)
return hard_example_miner 示例2: build
# 需要導入模塊: from object_detection.protos import losses_pb2 [as 別名]
# 或者: from object_detection.protos.losses_pb2 import HardExampleMiner [as 別名]
def build(loss_config):
"""Build losses based on the config.
Builds classification, localization losses and optionally a hard example miner
based on the config.
Args:
loss_config: A losses_pb2.Loss object.
Returns:
classification_loss: Classification loss object.
localization_loss: Localization loss object.
classification_weight: Classification loss weight.
localization_weight: Localization loss weight.
hard_example_miner: Hard example miner object.
Raises:
ValueError: If hard_example_miner is used with sigmoid_focal_loss.
"""
classification_loss = _build_classification_loss(
loss_config.classification_loss)
localization_loss = _build_localization_loss(
loss_config.localization_loss)
classification_weight = loss_config.classification_weight
localization_weight = loss_config.localization_weight
hard_example_miner = None
if loss_config.HasField('hard_example_miner'):
if (loss_config.classification_loss.WhichOneof('classification_loss') ==
'weighted_sigmoid_focal'):
raise ValueError('HardExampleMiner should not be used with sigmoid focal '
'loss')
hard_example_miner = build_hard_example_miner(
loss_config.hard_example_miner,
classification_weight,
localization_weight)
return (classification_loss, localization_loss,
classification_weight,
localization_weight, hard_example_miner)