text: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  """A function to build a DetectionModel from configuration."""
  
  import functools
  
  from object_detection.builders import anchor_generator_builder
  from object_detection.builders import box_coder_builder
  from object_detection.builders import box_predictor_builder
  from object_detection.builders import hyperparams_builder
  from object_detection.builders import image_resizer_builder
  from object_detection.builders import losses_builder
  from object_detection.builders import matcher_builder
  from object_detection.builders import post_processing_builder
  from object_detection.builders import region_similarity_calculator_builder as sim_calc
  from object_detection.core import balanced_positive_negative_sampler as sampler
  from object_detection.core import post_processing
  from object_detection.core import target_assigner
  from object_detection.meta_architectures import faster_rcnn_meta_arch
  from object_detection.meta_architectures import rfcn_meta_arch
  from object_detection.meta_architectures import ssd_meta_arch
  from object_detection.models import faster_rcnn_inception_resnet_v2_feature_extractor as frcnn_inc_res
  from object_detection.models import faster_rcnn_inception_resnet_v2_keras_feature_extractor as frcnn_inc_res_keras
  from object_detection.models import faster_rcnn_inception_v2_feature_extractor as frcnn_inc_v2
  from object_detection.models import faster_rcnn_nas_feature_extractor as frcnn_nas
  from object_detection.models import faster_rcnn_pnas_feature_extractor as frcnn_pnas
  from object_detection.models import faster_rcnn_resnet_v1_feature_extractor as frcnn_resnet_v1
  from object_detection.models import ssd_resnet_v1_fpn_feature_extractor as ssd_resnet_v1_fpn
  from object_detection.models import ssd_resnet_v1_ppn_feature_extractor as ssd_resnet_v1_ppn
  from object_detection.models.embedded_ssd_mobilenet_v1_feature_extractor import EmbeddedSSDMobileNetV1FeatureExtractor
  from object_detection.models.ssd_inception_v2_feature_extractor import SSDInceptionV2FeatureExtractor
  from object_detection.models.ssd_inception_v3_feature_extractor import SSDInceptionV3FeatureExtractor
  from object_detection.models.ssd_mobilenet_v1_feature_extractor import SSDMobileNetV1FeatureExtractor
  from object_detection.models.ssd_mobilenet_v1_fpn_feature_extractor import SSDMobileNetV1FpnFeatureExtractor
  from object_detection.models.ssd_mobilenet_v1_fpn_keras_feature_extractor import SSDMobileNetV1FpnKerasFeatureExtractor
  from object_detection.models.ssd_mobilenet_v1_keras_feature_extractor import SSDMobileNetV1KerasFeatureExtractor
  from object_detection.models.ssd_mobilenet_v1_ppn_feature_extractor import SSDMobileNetV1PpnFeatureExtractor
  from object_detection.models.ssd_mobilenet_v2_feature_extractor import SSDMobileNetV2FeatureExtractor
  from object_detection.models.ssd_mobilenet_v2_fpn_feature_extractor import SSDMobileNetV2FpnFeatureExtractor
  from object_detection.models.ssd_mobilenet_v2_fpn_keras_feature_extractor import SSDMobileNetV2FpnKerasFeatureExtractor
  from object_detection.models.ssd_mobilenet_v2_keras_feature_extractor import SSDMobileNetV2KerasFeatureExtractor
  from object_detection.models.ssd_pnasnet_feature_extractor import SSDPNASNetFeatureExtractor
  from object_detection.predictors import rfcn_box_predictor
  from object_detection.predictors import rfcn_keras_box_predictor
  from object_detection.predictors.heads import mask_head
  from object_detection.protos import model_pb2
  from object_detection.utils import ops
  
  # A map of names to SSD feature extractors.
  SSD_FEATURE_EXTRACTOR_CLASS_MAP = {
      'ssd_inception_v2': SSDInceptionV2FeatureExtractor,
      'ssd_inception_v3': SSDInceptionV3FeatureExtractor,
      'ssd_mobilenet_v1': SSDMobileNetV1FeatureExtractor,
      'ssd_mobilenet_v1_fpn': SSDMobileNetV1FpnFeatureExtractor,
      'ssd_mobilenet_v1_ppn': SSDMobileNetV1PpnFeatureExtractor,
      'ssd_mobilenet_v2': SSDMobileNetV2FeatureExtractor,
      'ssd_mobilenet_v2_fpn': SSDMobileNetV2FpnFeatureExtractor,
      'ssd_resnet50_v1_fpn': ssd_resnet_v1_fpn.SSDResnet50V1FpnFeatureExtractor,
      'ssd_resnet101_v1_fpn': ssd_resnet_v1_fpn.SSDResnet101V1FpnFeatureExtractor,
      'ssd_resnet152_v1_fpn': ssd_resnet_v1_fpn.SSDResnet152V1FpnFeatureExtractor,
      'ssd_resnet50_v1_ppn': ssd_resnet_v1_ppn.SSDResnet50V1PpnFeatureExtractor,
      'ssd_resnet101_v1_ppn':
          ssd_resnet_v1_ppn.SSDResnet101V1PpnFeatureExtractor,
      'ssd_resnet152_v1_ppn':
          ssd_resnet_v1_ppn.SSDResnet152V1PpnFeatureExtractor,
      'embedded_ssd_mobilenet_v1': EmbeddedSSDMobileNetV1FeatureExtractor,
      'ssd_pnasnet': SSDPNASNetFeatureExtractor,
  }
  
  SSD_KERAS_FEATURE_EXTRACTOR_CLASS_MAP = {
      'ssd_mobilenet_v1_keras': SSDMobileNetV1KerasFeatureExtractor,
      'ssd_mobilenet_v1_fpn_keras': SSDMobileNetV1FpnKerasFeatureExtractor,
      'ssd_mobilenet_v2_keras': SSDMobileNetV2KerasFeatureExtractor,
      'ssd_mobilenet_v2_fpn_keras': SSDMobileNetV2FpnKerasFeatureExtractor,
  }
  
  # A map of names to Faster R-CNN feature extractors.
  FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP = {
      'faster_rcnn_nas':
      frcnn_nas.FasterRCNNNASFeatureExtractor,
      'faster_rcnn_pnas':
      frcnn_pnas.FasterRCNNPNASFeatureExtractor,
      'faster_rcnn_inception_resnet_v2':
      frcnn_inc_res.FasterRCNNInceptionResnetV2FeatureExtractor,
      'faster_rcnn_inception_v2':
      frcnn_inc_v2.FasterRCNNInceptionV2FeatureExtractor,
      'faster_rcnn_resnet50':
      frcnn_resnet_v1.FasterRCNNResnet50FeatureExtractor,
      'faster_rcnn_resnet101':
      frcnn_resnet_v1.FasterRCNNResnet101FeatureExtractor,
      'faster_rcnn_resnet152':
      frcnn_resnet_v1.FasterRCNNResnet152FeatureExtractor,
  }
  
  FASTER_RCNN_KERAS_FEATURE_EXTRACTOR_CLASS_MAP = {
      'faster_rcnn_inception_resnet_v2_keras':
      frcnn_inc_res_keras.FasterRCNNInceptionResnetV2KerasFeatureExtractor,
  }
  
  
  def build(model_config, is_training, add_summaries=True):
    """Builds a DetectionModel based on the model config.
  
    Args:
      model_config: A model.proto object containing the config for the desired
        DetectionModel.
      is_training: True if this model is being built for training purposes.
      add_summaries: Whether to add tensorflow summaries in the model graph.
    Returns:
      DetectionModel based on the config.
  
    Raises:
      ValueError: On invalid meta architecture or model.
    """
    if not isinstance(model_config, model_pb2.DetectionModel):
      raise ValueError('model_config not of type model_pb2.DetectionModel.')
    meta_architecture = model_config.WhichOneof('model')
    if meta_architecture == 'ssd':
      return _build_ssd_model(model_config.ssd, is_training, add_summaries)
    if meta_architecture == 'faster_rcnn':
      return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
                                      add_summaries)
    raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
  
  
  def _build_ssd_feature_extractor(feature_extractor_config,
                                   is_training,
                                   freeze_batchnorm,
                                   reuse_weights=None,
                                   num_input_channels=3):
    """Builds a ssd_meta_arch.SSDFeatureExtractor based on config.
  
    Args:
      feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
      is_training: True if this feature extractor is being built for training.
      freeze_batchnorm: Whether to freeze batch norm parameters during
        training or not. When training with a small batch size (e.g. 1), it is
        desirable to freeze batch norm update and use pretrained batch norm
        params.
      reuse_weights: if the feature extractor should reuse weights.
  
    Returns:
      ssd_meta_arch.SSDFeatureExtractor based on config.
  
    Raises:
      ValueError: On invalid feature extractor type.
    """
    feature_type = feature_extractor_config.type
    is_keras_extractor = feature_type in SSD_KERAS_FEATURE_EXTRACTOR_CLASS_MAP
    depth_multiplier = feature_extractor_config.depth_multiplier
    min_depth = feature_extractor_config.min_depth
    pad_to_multiple = feature_extractor_config.pad_to_multiple
    use_explicit_padding = feature_extractor_config.use_explicit_padding
    use_depthwise = feature_extractor_config.use_depthwise
  
    if is_keras_extractor:
      conv_hyperparams = hyperparams_builder.KerasLayerHyperparams(
          feature_extractor_config.conv_hyperparams)
    else:
      conv_hyperparams = hyperparams_builder.build(
          feature_extractor_config.conv_hyperparams, is_training)
    override_base_feature_extractor_hyperparams = (
        feature_extractor_config.override_base_feature_extractor_hyperparams)
  
    if (feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP) and (
        not is_keras_extractor):
      raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
  
    if is_keras_extractor:
      feature_extractor_class = SSD_KERAS_FEATURE_EXTRACTOR_CLASS_MAP[
          feature_type]
    else:
      feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
    kwargs = {
        'is_training':
            is_training,
        'depth_multiplier':
            depth_multiplier,
        'min_depth':
            min_depth,
        'pad_to_multiple':
            pad_to_multiple,
        'use_explicit_padding':
            use_explicit_padding,
        'use_depthwise':
            use_depthwise,
        'override_base_feature_extractor_hyperparams':
            override_base_feature_extractor_hyperparams,
        'num_input_channels':
            num_input_channels,
    }
  
    if feature_extractor_config.HasField('replace_preprocessor_with_placeholder'):
      kwargs.update({
          'replace_preprocessor_with_placeholder':
              feature_extractor_config.replace_preprocessor_with_placeholder
      })
  
    if is_keras_extractor:
      kwargs.update({
          'conv_hyperparams': conv_hyperparams,
          'inplace_batchnorm_update': False,
          'freeze_batchnorm': freeze_batchnorm
      })
    else:
      kwargs.update({
          'conv_hyperparams_fn': conv_hyperparams,
          'reuse_weights': reuse_weights,
      })
  
    if feature_extractor_config.HasField('fpn'):
      kwargs.update({
          'fpn_min_level':
              feature_extractor_config.fpn.min_level,
          'fpn_max_level':
              feature_extractor_config.fpn.max_level,
          'additional_layer_depth':
              feature_extractor_config.fpn.additional_layer_depth,
      })
  
    return feature_extractor_class(**kwargs)
  
  
  def _build_ssd_model(ssd_config, is_training, add_summaries):
    """Builds an SSD detection model based on the model config.
  
    Args:
      ssd_config: A ssd.proto object containing the config for the desired
        SSDMetaArch.
      is_training: True if this model is being built for training purposes.
      add_summaries: Whether to add tf summaries in the model.
    Returns:
      SSDMetaArch based on the config.
  
    Raises:
      ValueError: If ssd_config.type is not recognized (i.e. not registered in
        model_class_map).
    """
    num_classes = ssd_config.num_classes
    num_input_channels = ssd_config.num_input_channels
  
    # Feature extractor
    feature_extractor = _build_ssd_feature_extractor(
        feature_extractor_config=ssd_config.feature_extractor,
        freeze_batchnorm=ssd_config.freeze_batchnorm,
        is_training=is_training,
        num_input_channels=num_input_channels)
  
    box_coder = box_coder_builder.build(ssd_config.box_coder)
    matcher = matcher_builder.build(ssd_config.matcher)
    region_similarity_calculator = sim_calc.build(
        ssd_config.similarity_calculator)
    encode_background_as_zeros = ssd_config.encode_background_as_zeros
    negative_class_weight = ssd_config.negative_class_weight
    anchor_generator = anchor_generator_builder.build(
        ssd_config.anchor_generator)
    if feature_extractor.is_keras_model:
      ssd_box_predictor = box_predictor_builder.build_keras(
          hyperparams_fn=hyperparams_builder.KerasLayerHyperparams,
          freeze_batchnorm=ssd_config.freeze_batchnorm,
          inplace_batchnorm_update=False,
          num_predictions_per_location_list=anchor_generator
          .num_anchors_per_location(),
          box_predictor_config=ssd_config.box_predictor,
          is_training=is_training,
          num_classes=num_classes,
          add_background_class=ssd_config.add_background_class)
    else:
      ssd_box_predictor = box_predictor_builder.build(
          hyperparams_builder.build, ssd_config.box_predictor, is_training,
          num_classes, ssd_config.add_background_class)
    image_resizer_fn = image_resizer_builder.build(ssd_config.image_resizer)
    non_max_suppression_fn, score_conversion_fn = post_processing_builder.build(
        ssd_config.post_processing)
    (classification_loss, localization_loss, classification_weight,
     localization_weight, hard_example_miner, random_example_sampler,
     expected_loss_weights_fn) = losses_builder.build(ssd_config.loss)
    normalize_loss_by_num_matches = ssd_config.normalize_loss_by_num_matches
    normalize_loc_loss_by_codesize = ssd_config.normalize_loc_loss_by_codesize
  
    equalization_loss_config = ops.EqualizationLossConfig(
        weight=ssd_config.loss.equalization_loss.weight,
        exclude_prefixes=ssd_config.loss.equalization_loss.exclude_prefixes)
  
    target_assigner_instance = target_assigner.TargetAssigner(
        region_similarity_calculator,
        matcher,
        box_coder,
        negative_class_weight=negative_class_weight)
  
    ssd_meta_arch_fn = ssd_meta_arch.SSDMetaArch
    kwargs = {}
  
    return ssd_meta_arch_fn(
        is_training=is_training,
        anchor_generator=anchor_generator,
        box_predictor=ssd_box_predictor,
        box_coder=box_coder,
        feature_extractor=feature_extractor,
        encode_background_as_zeros=encode_background_as_zeros,
        image_resizer_fn=image_resizer_fn,
        non_max_suppression_fn=non_max_suppression_fn,
        score_conversion_fn=score_conversion_fn,
        classification_loss=classification_loss,
        localization_loss=localization_loss,
        classification_loss_weight=classification_weight,
        localization_loss_weight=localization_weight,
        normalize_loss_by_num_matches=normalize_loss_by_num_matches,
        hard_example_miner=hard_example_miner,
        target_assigner_instance=target_assigner_instance,
        add_summaries=add_summaries,
        normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize,
        freeze_batchnorm=ssd_config.freeze_batchnorm,
        inplace_batchnorm_update=ssd_config.inplace_batchnorm_update,
        add_background_class=ssd_config.add_background_class,
        explicit_background_class=ssd_config.explicit_background_class,
        random_example_sampler=random_example_sampler,
        expected_loss_weights_fn=expected_loss_weights_fn,
        use_confidences_as_targets=ssd_config.use_confidences_as_targets,
        implicit_example_weight=ssd_config.implicit_example_weight,
        equalization_loss_config=equalization_loss_config,
        **kwargs)
  
  
  def _build_faster_rcnn_feature_extractor(
      feature_extractor_config, is_training, reuse_weights=None,
      inplace_batchnorm_update=False):
    """Builds a faster_rcnn_meta_arch.FasterRCNNFeatureExtractor based on config.
  
    Args:
      feature_extractor_config: A FasterRcnnFeatureExtractor proto config from
        faster_rcnn.proto.
      is_training: True if this feature extractor is being built for training.
      reuse_weights: if the feature extractor should reuse weights.
      inplace_batchnorm_update: Whether to update batch_norm inplace during
        training. This is required for batch norm to work correctly on TPUs. When
        this is false, user must add a control dependency on
        tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
        norm moving average parameters.
  
    Returns:
      faster_rcnn_meta_arch.FasterRCNNFeatureExtractor based on config.
  
    Raises:
      ValueError: On invalid feature extractor type.
    """
    if inplace_batchnorm_update:
      raise ValueError('inplace batchnorm updates not supported.')
    feature_type = feature_extractor_config.type
    first_stage_features_stride = (
        feature_extractor_config.first_stage_features_stride)
    batch_norm_trainable = feature_extractor_config.batch_norm_trainable
  
    if feature_type not in FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP:
      raise ValueError('Unknown Faster R-CNN feature_extractor: {}'.format(
          feature_type))
    feature_extractor_class = FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP[
        feature_type]
    return feature_extractor_class(
        is_training, first_stage_features_stride,
        batch_norm_trainable, reuse_weights=reuse_weights)
  
  
  def _build_faster_rcnn_keras_feature_extractor(
      feature_extractor_config, is_training,
      inplace_batchnorm_update=False):
    """Builds a faster_rcnn_meta_arch.FasterRCNNKerasFeatureExtractor from config.
  
    Args:
      feature_extractor_config: A FasterRcnnFeatureExtractor proto config from
        faster_rcnn.proto.
      is_training: True if this feature extractor is being built for training.
      inplace_batchnorm_update: Whether to update batch_norm inplace during
        training. This is required for batch norm to work correctly on TPUs. When
        this is false, user must add a control dependency on
        tf.GraphKeys.UPDATE_OPS for train/loss op in order to update the batch
        norm moving average parameters.
  
    Returns:
      faster_rcnn_meta_arch.FasterRCNNKerasFeatureExtractor based on config.
  
    Raises:
      ValueError: On invalid feature extractor type.
    """
    if inplace_batchnorm_update:
      raise ValueError('inplace batchnorm updates not supported.')
    feature_type = feature_extractor_config.type
    first_stage_features_stride = (
        feature_extractor_config.first_stage_features_stride)
    batch_norm_trainable = feature_extractor_config.batch_norm_trainable
  
    if feature_type not in FASTER_RCNN_KERAS_FEATURE_EXTRACTOR_CLASS_MAP:
      raise ValueError('Unknown Faster R-CNN feature_extractor: {}'.format(
          feature_type))
    feature_extractor_class = FASTER_RCNN_KERAS_FEATURE_EXTRACTOR_CLASS_MAP[
        feature_type]
    return feature_extractor_class(
        is_training, first_stage_features_stride,
        batch_norm_trainable)
  
  
  def _build_faster_rcnn_model(frcnn_config, is_training, add_summaries):
    """Builds a Faster R-CNN or R-FCN detection model based on the model config.
  
    Builds R-FCN model if the second_stage_box_predictor in the config is of type
    `rfcn_box_predictor` else builds a Faster R-CNN model.
  
    Args:
      frcnn_config: A faster_rcnn.proto object containing the config for the
        desired FasterRCNNMetaArch or RFCNMetaArch.
      is_training: True if this model is being built for training purposes.
      add_summaries: Whether to add tf summaries in the model.
  
    Returns:
      FasterRCNNMetaArch based on the config.
  
    Raises:
      ValueError: If frcnn_config.type is not recognized (i.e. not registered in
        model_class_map).
    """
    num_classes = frcnn_config.num_classes
    image_resizer_fn = image_resizer_builder.build(frcnn_config.image_resizer)
  
    is_keras = (frcnn_config.feature_extractor.type in
                FASTER_RCNN_KERAS_FEATURE_EXTRACTOR_CLASS_MAP)
  
    if is_keras:
      feature_extractor = _build_faster_rcnn_keras_feature_extractor(
          frcnn_config.feature_extractor, is_training,
          inplace_batchnorm_update=frcnn_config.inplace_batchnorm_update)
    else:
      feature_extractor = _build_faster_rcnn_feature_extractor(
          frcnn_config.feature_extractor, is_training,
          inplace_batchnorm_update=frcnn_config.inplace_batchnorm_update)
  
    number_of_stages = frcnn_config.number_of_stages
    first_stage_anchor_generator = anchor_generator_builder.build(
        frcnn_config.first_stage_anchor_generator)
  
    first_stage_target_assigner = target_assigner.create_target_assigner(
        'FasterRCNN',
        'proposal',
        use_matmul_gather=frcnn_config.use_matmul_gather_in_matcher)
    first_stage_atrous_rate = frcnn_config.first_stage_atrous_rate
    if is_keras:
      first_stage_box_predictor_arg_scope_fn = (
          hyperparams_builder.KerasLayerHyperparams(
              frcnn_config.first_stage_box_predictor_conv_hyperparams))
    else:
      first_stage_box_predictor_arg_scope_fn = hyperparams_builder.build(
          frcnn_config.first_stage_box_predictor_conv_hyperparams, is_training)
    first_stage_box_predictor_kernel_size = (
        frcnn_config.first_stage_box_predictor_kernel_size)
    first_stage_box_predictor_depth = frcnn_config.first_stage_box_predictor_depth
    first_stage_minibatch_size = frcnn_config.first_stage_minibatch_size
    use_static_shapes = frcnn_config.use_static_shapes and (
        frcnn_config.use_static_shapes_for_eval or is_training)
    first_stage_sampler = sampler.BalancedPositiveNegativeSampler(
        positive_fraction=frcnn_config.first_stage_positive_balance_fraction,
        is_static=(frcnn_config.use_static_balanced_label_sampler and
                   use_static_shapes))
    first_stage_max_proposals = frcnn_config.first_stage_max_proposals
    if (frcnn_config.first_stage_nms_iou_threshold < 0 or
        frcnn_config.first_stage_nms_iou_threshold > 1.0):
      raise ValueError('iou_threshold not in [0, 1.0].')
    if (is_training and frcnn_config.second_stage_batch_size >
        first_stage_max_proposals):
      raise ValueError('second_stage_batch_size should be no greater than '
                       'first_stage_max_proposals.')
    first_stage_non_max_suppression_fn = functools.partial(
        post_processing.batch_multiclass_non_max_suppression,
        score_thresh=frcnn_config.first_stage_nms_score_threshold,
        iou_thresh=frcnn_config.first_stage_nms_iou_threshold,
        max_size_per_class=frcnn_config.first_stage_max_proposals,
        max_total_size=frcnn_config.first_stage_max_proposals,
        use_static_shapes=use_static_shapes)
    first_stage_loc_loss_weight = (
        frcnn_config.first_stage_localization_loss_weight)
    first_stage_obj_loss_weight = frcnn_config.first_stage_objectness_loss_weight
  
    initial_crop_size = frcnn_config.initial_crop_size
    maxpool_kernel_size = frcnn_config.maxpool_kernel_size
    maxpool_stride = frcnn_config.maxpool_stride
  
    second_stage_target_assigner = target_assigner.create_target_assigner(
        'FasterRCNN',
        'detection',
        use_matmul_gather=frcnn_config.use_matmul_gather_in_matcher)
    if is_keras:
      second_stage_box_predictor = box_predictor_builder.build_keras(
          hyperparams_builder.KerasLayerHyperparams,
          freeze_batchnorm=False,
          inplace_batchnorm_update=False,
          num_predictions_per_location_list=[1],
          box_predictor_config=frcnn_config.second_stage_box_predictor,
          is_training=is_training,
          num_classes=num_classes)
    else:
      second_stage_box_predictor = box_predictor_builder.build(
          hyperparams_builder.build,
          frcnn_config.second_stage_box_predictor,
          is_training=is_training,
          num_classes=num_classes)
    second_stage_batch_size = frcnn_config.second_stage_batch_size
    second_stage_sampler = sampler.BalancedPositiveNegativeSampler(
        positive_fraction=frcnn_config.second_stage_balance_fraction,
        is_static=(frcnn_config.use_static_balanced_label_sampler and
                   use_static_shapes))
    (second_stage_non_max_suppression_fn, second_stage_score_conversion_fn
    ) = post_processing_builder.build(frcnn_config.second_stage_post_processing)
    second_stage_localization_loss_weight = (
        frcnn_config.second_stage_localization_loss_weight)
    second_stage_classification_loss = (
        losses_builder.build_faster_rcnn_classification_loss(
            frcnn_config.second_stage_classification_loss))
    second_stage_classification_loss_weight = (
        frcnn_config.second_stage_classification_loss_weight)
    second_stage_mask_prediction_loss_weight = (
        frcnn_config.second_stage_mask_prediction_loss_weight)
  
    hard_example_miner = None
    if frcnn_config.HasField('hard_example_miner'):
      hard_example_miner = losses_builder.build_hard_example_miner(
          frcnn_config.hard_example_miner,
          second_stage_classification_loss_weight,
          second_stage_localization_loss_weight)
  
    crop_and_resize_fn = (
        ops.matmul_crop_and_resize if frcnn_config.use_matmul_crop_and_resize
        else ops.native_crop_and_resize)
    clip_anchors_to_image = (
        frcnn_config.clip_anchors_to_image)
  
    common_kwargs = {
        'is_training': is_training,
        'num_classes': num_classes,
        'image_resizer_fn': image_resizer_fn,
        'feature_extractor': feature_extractor,
        'number_of_stages': number_of_stages,
        'first_stage_anchor_generator': first_stage_anchor_generator,
        'first_stage_target_assigner': first_stage_target_assigner,
        'first_stage_atrous_rate': first_stage_atrous_rate,
        'first_stage_box_predictor_arg_scope_fn':
        first_stage_box_predictor_arg_scope_fn,
        'first_stage_box_predictor_kernel_size':
        first_stage_box_predictor_kernel_size,
        'first_stage_box_predictor_depth': first_stage_box_predictor_depth,
        'first_stage_minibatch_size': first_stage_minibatch_size,
        'first_stage_sampler': first_stage_sampler,
        'first_stage_non_max_suppression_fn': first_stage_non_max_suppression_fn,
        'first_stage_max_proposals': first_stage_max_proposals,
        'first_stage_localization_loss_weight': first_stage_loc_loss_weight,
        'first_stage_objectness_loss_weight': first_stage_obj_loss_weight,
        'second_stage_target_assigner': second_stage_target_assigner,
        'second_stage_batch_size': second_stage_batch_size,
        'second_stage_sampler': second_stage_sampler,
        'second_stage_non_max_suppression_fn':
        second_stage_non_max_suppression_fn,
        'second_stage_score_conversion_fn': second_stage_score_conversion_fn,
        'second_stage_localization_loss_weight':
        second_stage_localization_loss_weight,
        'second_stage_classification_loss':
        second_stage_classification_loss,
        'second_stage_classification_loss_weight':
        second_stage_classification_loss_weight,
        'hard_example_miner': hard_example_miner,
        'add_summaries': add_summaries,
        'crop_and_resize_fn': crop_and_resize_fn,
        'clip_anchors_to_image': clip_anchors_to_image,
        'use_static_shapes': use_static_shapes,
        'resize_masks': frcnn_config.resize_masks
    }
  
    if (isinstance(second_stage_box_predictor,
                   rfcn_box_predictor.RfcnBoxPredictor) or
        isinstance(second_stage_box_predictor,
                   rfcn_keras_box_predictor.RfcnKerasBoxPredictor)):
      return rfcn_meta_arch.RFCNMetaArch(
          second_stage_rfcn_box_predictor=second_stage_box_predictor,
          **common_kwargs)
    else:
      return faster_rcnn_meta_arch.FasterRCNNMetaArch(
          initial_crop_size=initial_crop_size,
          maxpool_kernel_size=maxpool_kernel_size,
          maxpool_stride=maxpool_stride,
          second_stage_mask_rcnn_box_predictor=second_stage_box_predictor,
          second_stage_mask_prediction_loss_weight=(
              second_stage_mask_prediction_loss_weight),
          **common_kwargs)
  