Python object_detection.builders.box_predictor_builder.build_keras() Examples

The following are 2 code examples of object_detection.builders.box_predictor_builder.build_keras(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module object_detection.builders.box_predictor_builder , or try the search function .
Example #1
Source File: context_rcnn_meta_arch_tf1_test.py    From models with Apache License 2.0 5 votes vote down vote up 
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)
Example #2
Source File: faster_rcnn_meta_arch_test_lib.py    From models with Apache License 2.0 5 votes vote down vote up 
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)