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Python losses_pb2.ClassificationLoss方法代碼示例

本文整理匯總了Python中object_detection.protos.losses_pb2.ClassificationLoss方法的典型用法代碼示例。如果您正苦於以下問題：Python losses_pb2.ClassificationLoss方法的具體用法？Python losses_pb2.ClassificationLoss怎麽用？Python losses_pb2.ClassificationLoss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.ClassificationLoss方法的10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_classification_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 ClassificationLoss [as 別名]
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.')
開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:37，代碼來源:losses_builder.py

示例2: build_faster_rcnn_classification_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 ClassificationLoss [as 別名]
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)
開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:43，代碼來源:losses_builder.py

示例3: test_build_sigmoid_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 ClassificationLoss [as 別名]
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))
開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:13，代碼來源:losses_builder_test.py

示例4: test_build_softmax_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 ClassificationLoss [as 別名]
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))
開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:13，代碼來源:losses_builder_test.py

示例5: test_build_logits_softmax_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 ClassificationLoss [as 別名]
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))
開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:14，代碼來源:losses_builder_test.py

示例6: test_build_softmax_loss_by_default

# 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 ClassificationLoss [as 別名]
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))
開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:11，代碼來源:losses_builder_test.py

示例7: build_faster_rcnn_classification_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 ClassificationLoss [as 別名]
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)
開發者ID:cagbal，項目名稱:ros_people_object_detection_tensorflow，代碼行數:31，代碼來源:losses_builder.py

示例8: _build_classification_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 ClassificationLoss [as 別名]
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.')
開發者ID:cagbal，項目名稱:ros_people_object_detection_tensorflow，代碼行數:43，代碼來源:losses_builder.py

示例9: build_faster_rcnn_classification_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 ClassificationLoss [as 別名]
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)
開發者ID:ambakick，項目名稱:Person-Detection-and-Tracking，代碼行數:35，代碼來源:losses_builder.py

示例10: build_faster_rcnn_classification_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 ClassificationLoss [as 別名]
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)
開發者ID:rky0930，項目名稱:yolo_v2，代碼行數:32，代碼來源:losses_builder.py


注：本文中的object_detection.protos.losses_pb2.ClassificationLoss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。