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Python losses_pb2.LocalizationLoss方法代碼示例

本文整理匯總了Python中object_detection.protos.losses_pb2.LocalizationLoss方法的典型用法代碼示例。如果您正苦於以下問題:Python losses_pb2.LocalizationLoss方法的具體用法?Python losses_pb2.LocalizationLoss怎麽用?Python losses_pb2.LocalizationLoss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object_detection.protos.losses_pb2的用法示例。


在下文中一共展示了losses_pb2.LocalizationLoss方法的5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_localization_loss

# 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 LocalizationLoss [as 別名]
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.') 
開發者ID:ringringyi,項目名稱:DOTA_models,代碼行數:33,代碼來源:losses_builder.py

示例2: _build_localization_loss

# 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 LocalizationLoss [as 別名]
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':
    return losses.WeightedL2LocalizationLoss()

  if loss_type == 'weighted_smooth_l1':
    return losses.WeightedSmoothL1LocalizationLoss(
        loss_config.weighted_smooth_l1.delta)

  if loss_type == 'weighted_iou':
    return losses.WeightedIOULocalizationLoss()

  raise ValueError('Empty loss config.') 
開發者ID:ahmetozlu,項目名稱:vehicle_counting_tensorflow,代碼行數:30,代碼來源:losses_builder.py

示例3: _build_localization_loss

# 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 LocalizationLoss [as 別名]
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':
    return losses.WeightedL2LocalizationLoss()

  if loss_type == 'weighted_smooth_l1':
    return losses.WeightedSmoothL1LocalizationLoss()

  if loss_type == 'weighted_iou':
    return losses.WeightedIOULocalizationLoss()

  raise ValueError('Empty loss config.') 
開發者ID:ShreyAmbesh,項目名稱:Traffic-Rule-Violation-Detection-System,代碼行數:29,代碼來源:losses_builder.py

示例4: _build_localization_loss

# 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 LocalizationLoss [as 別名]
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':
    return losses.WeightedL2LocalizationLoss()

  if loss_type == 'weighted_smooth_l1':
    return losses.WeightedSmoothL1LocalizationLoss(
        loss_config.weighted_smooth_l1.delta)

  if loss_type == 'weighted_giou':
    return losses.WeightedGIoULocalizationLoss()

  if loss_type == 'weighted_iou':
    return losses.WeightedIOULocalizationLoss()

  raise ValueError('Empty loss config.') 
開發者ID:generalized-iou,項目名稱:g-tensorflow-models,代碼行數:33,代碼來源:losses_builder.py

示例5: _build_localization_loss

# 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 LocalizationLoss [as 別名]
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':
    return losses.WeightedL2LocalizationLoss()

  if loss_type == 'weighted_smooth_l1':
    return losses.WeightedSmoothL1LocalizationLoss(
        loss_config.weighted_smooth_l1.delta)

  if loss_type == 'weighted_iou':
    return losses.WeightedIOULocalizationLoss()

  if loss_type == 'l1_localization_loss':
    return losses.L1LocalizationLoss()

  raise ValueError('Empty loss config.') 
開發者ID:tensorflow,項目名稱:models,代碼行數:33,代碼來源:losses_builder.py


注:本文中的object_detection.protos.losses_pb2.LocalizationLoss方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台,相關代碼片段篩選自各路編程大神貢獻的開源項目,源碼版權歸原作者所有,傳播和使用請參考對應項目的License;未經允許,請勿轉載。