本文整理汇总了Python中object_detection.builders.box_predictor_builder.build_keras方法的典型用法代码示例。如果您正苦于以下问题：Python box_predictor_builder.build_keras方法的具体用法？Python box_predictor_builder.build_keras怎么用？Python box_predictor_builder.build_keras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.builders.box_predictor_builder的用法示例。
在下文中一共展示了box_predictor_builder.build_keras方法的2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: _get_second_stage_box_predictor
# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build_keras [as 别名]
def _get_second_stage_box_predictor(self,
num_classes,
is_training,
predict_masks,
masks_are_class_agnostic,
share_box_across_classes=False,
use_keras=False):
box_predictor_proto = box_predictor_pb2.BoxPredictor()
text_format.Merge(
self._get_second_stage_box_predictor_text_proto(
share_box_across_classes), box_predictor_proto)
if predict_masks:
text_format.Merge(
self._add_mask_to_second_stage_box_predictor_text_proto(
masks_are_class_agnostic), box_predictor_proto)
if use_keras:
return box_predictor_builder.build_keras(
hyperparams_builder.KerasLayerHyperparams,
inplace_batchnorm_update=False,
freeze_batchnorm=False,
box_predictor_config=box_predictor_proto,
num_classes=num_classes,
num_predictions_per_location_list=None,
is_training=is_training)
else:
return box_predictor_builder.build(
hyperparams_builder.build,
box_predictor_proto,
num_classes=num_classes,
is_training=is_training) 示例2: _get_second_stage_box_predictor
# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build_keras [as 别名]
def _get_second_stage_box_predictor(self, num_classes, is_training,
predict_masks, masks_are_class_agnostic,
share_box_across_classes=False,
use_keras=False):
box_predictor_proto = box_predictor_pb2.BoxPredictor()
text_format.Merge(self._get_second_stage_box_predictor_text_proto(
share_box_across_classes), box_predictor_proto)
if predict_masks:
text_format.Merge(
self._add_mask_to_second_stage_box_predictor_text_proto(
masks_are_class_agnostic),
box_predictor_proto)
if use_keras:
return box_predictor_builder.build_keras(
hyperparams_builder.KerasLayerHyperparams,
inplace_batchnorm_update=False,
freeze_batchnorm=False,
box_predictor_config=box_predictor_proto,
num_classes=num_classes,
num_predictions_per_location_list=None,
is_training=is_training)
else:
return box_predictor_builder.build(
hyperparams_builder.build,
box_predictor_proto,
num_classes=num_classes,
is_training=is_training)