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
  import sys
  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 center_net_meta_arch
  from object_detection.meta_architectures import context_rcnn_meta_arch
  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.predictors.heads import mask_head
  from object_detection.protos import losses_pb2
  from object_detection.protos import model_pb2
  from object_detection.utils import label_map_util
  from object_detection.utils import ops
  from object_detection.utils import spatial_transform_ops as spatial_ops
  from object_detection.utils import tf_version
  
  ## Feature Extractors for TF
  ## This section conditionally imports different feature extractors based on the
  ## Tensorflow version.
  ##
  # pylint: disable=g-import-not-at-top
  if tf_version.is_tf2():
    from object_detection.models import center_net_hourglass_feature_extractor
    from object_detection.models import center_net_mobilenet_v2_feature_extractor
    from object_detection.models import center_net_mobilenet_v2_fpn_feature_extractor
    from object_detection.models import center_net_resnet_feature_extractor
    from object_detection.models import center_net_resnet_v1_fpn_feature_extractor
    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_resnet_keras_feature_extractor as frcnn_resnet_keras
    from object_detection.models import ssd_resnet_v1_fpn_keras_feature_extractor as ssd_resnet_v1_fpn_keras
    from object_detection.models import faster_rcnn_resnet_v1_fpn_keras_feature_extractor as frcnn_resnet_fpn_keras
    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_v2_fpn_keras_feature_extractor import SSDMobileNetV2FpnKerasFeatureExtractor
    from object_detection.models.ssd_mobilenet_v2_keras_feature_extractor import SSDMobileNetV2KerasFeatureExtractor
    from object_detection.predictors import rfcn_keras_box_predictor
    if sys.version_info[0] >= 3:
      from object_detection.models import ssd_efficientnet_bifpn_feature_extractor as ssd_efficientnet_bifpn
  
  if tf_version.is_tf1():
    from object_detection.models import faster_rcnn_inception_resnet_v2_feature_extractor as frcnn_inc_res
    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_mobilenet_v2_fpn_feature_extractor import SSDMobileNetV2FpnFeatureExtractor
    from object_detection.models.ssd_mobilenet_v2_mnasfpn_feature_extractor import SSDMobileNetV2MnasFPNFeatureExtractor
    from object_detection.models.ssd_inception_v3_feature_extractor import SSDInceptionV3FeatureExtractor
    from object_detection.models.ssd_mobilenet_edgetpu_feature_extractor import SSDMobileNetEdgeTPUFeatureExtractor
    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_ppn_feature_extractor import SSDMobileNetV1PpnFeatureExtractor
    from object_detection.models.ssd_mobilenet_v2_feature_extractor import SSDMobileNetV2FeatureExtractor
    from object_detection.models.ssd_mobilenet_v3_feature_extractor import SSDMobileNetV3LargeFeatureExtractor
    from object_detection.models.ssd_mobilenet_v3_feature_extractor import SSDMobileNetV3SmallFeatureExtractor
    from object_detection.models.ssd_mobiledet_feature_extractor import SSDMobileDetCPUFeatureExtractor
    from object_detection.models.ssd_mobiledet_feature_extractor import SSDMobileDetDSPFeatureExtractor
    from object_detection.models.ssd_mobiledet_feature_extractor import SSDMobileDetEdgeTPUFeatureExtractor
    from object_detection.models.ssd_mobiledet_feature_extractor import SSDMobileDetGPUFeatureExtractor
    from object_detection.models.ssd_pnasnet_feature_extractor import SSDPNASNetFeatureExtractor
    from object_detection.predictors import rfcn_box_predictor
  # pylint: enable=g-import-not-at-top
  
  if tf_version.is_tf2():
    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,
        'ssd_resnet50_v1_fpn_keras':
            ssd_resnet_v1_fpn_keras.SSDResNet50V1FpnKerasFeatureExtractor,
        'ssd_resnet101_v1_fpn_keras':
            ssd_resnet_v1_fpn_keras.SSDResNet101V1FpnKerasFeatureExtractor,
        'ssd_resnet152_v1_fpn_keras':
            ssd_resnet_v1_fpn_keras.SSDResNet152V1FpnKerasFeatureExtractor,
        'ssd_efficientnet-b0_bifpn_keras':
            ssd_efficientnet_bifpn.SSDEfficientNetB0BiFPNKerasFeatureExtractor,
        'ssd_efficientnet-b1_bifpn_keras':
            ssd_efficientnet_bifpn.SSDEfficientNetB1BiFPNKerasFeatureExtractor,
        'ssd_efficientnet-b2_bifpn_keras':
            ssd_efficientnet_bifpn.SSDEfficientNetB2BiFPNKerasFeatureExtractor,
        'ssd_efficientnet-b3_bifpn_keras':
            ssd_efficientnet_bifpn.SSDEfficientNetB3BiFPNKerasFeatureExtractor,
        'ssd_efficientnet-b4_bifpn_keras':
            ssd_efficientnet_bifpn.SSDEfficientNetB4BiFPNKerasFeatureExtractor,
        'ssd_efficientnet-b5_bifpn_keras':
            ssd_efficientnet_bifpn.SSDEfficientNetB5BiFPNKerasFeatureExtractor,
        'ssd_efficientnet-b6_bifpn_keras':
            ssd_efficientnet_bifpn.SSDEfficientNetB6BiFPNKerasFeatureExtractor,
        'ssd_efficientnet-b7_bifpn_keras':
            ssd_efficientnet_bifpn.SSDEfficientNetB7BiFPNKerasFeatureExtractor,
    }
  
    FASTER_RCNN_KERAS_FEATURE_EXTRACTOR_CLASS_MAP = {
        'faster_rcnn_resnet50_keras':
            frcnn_resnet_keras.FasterRCNNResnet50KerasFeatureExtractor,
        'faster_rcnn_resnet101_keras':
            frcnn_resnet_keras.FasterRCNNResnet101KerasFeatureExtractor,
        'faster_rcnn_resnet152_keras':
            frcnn_resnet_keras.FasterRCNNResnet152KerasFeatureExtractor,
        'faster_rcnn_inception_resnet_v2_keras':
        frcnn_inc_res_keras.FasterRCNNInceptionResnetV2KerasFeatureExtractor,
        'faster_rcnn_resnet50_fpn_keras':
            frcnn_resnet_fpn_keras.FasterRCNNResnet50FpnKerasFeatureExtractor,
        'faster_rcnn_resnet101_fpn_keras':
            frcnn_resnet_fpn_keras.FasterRCNNResnet101FpnKerasFeatureExtractor,
        'faster_rcnn_resnet152_fpn_keras':
            frcnn_resnet_fpn_keras.FasterRCNNResnet152FpnKerasFeatureExtractor,
    }
  
    CENTER_NET_EXTRACTOR_FUNCTION_MAP = {
        'resnet_v2_50':
            center_net_resnet_feature_extractor.resnet_v2_50,
        'resnet_v2_101':
            center_net_resnet_feature_extractor.resnet_v2_101,
        'resnet_v1_18_fpn':
            center_net_resnet_v1_fpn_feature_extractor.resnet_v1_18_fpn,
        'resnet_v1_34_fpn':
            center_net_resnet_v1_fpn_feature_extractor.resnet_v1_34_fpn,
        'resnet_v1_50_fpn':
            center_net_resnet_v1_fpn_feature_extractor.resnet_v1_50_fpn,
        'resnet_v1_101_fpn':
            center_net_resnet_v1_fpn_feature_extractor.resnet_v1_101_fpn,
        'hourglass_10':
            center_net_hourglass_feature_extractor.hourglass_10,
        'hourglass_20':
            center_net_hourglass_feature_extractor.hourglass_20,
        'hourglass_32':
            center_net_hourglass_feature_extractor.hourglass_32,
        'hourglass_52':
            center_net_hourglass_feature_extractor.hourglass_52,
        'hourglass_104':
            center_net_hourglass_feature_extractor.hourglass_104,
        'mobilenet_v2':
            center_net_mobilenet_v2_feature_extractor.mobilenet_v2,
        'mobilenet_v2_fpn':
            center_net_mobilenet_v2_fpn_feature_extractor.mobilenet_v2_fpn,
        'mobilenet_v2_fpn_sep_conv':
            center_net_mobilenet_v2_fpn_feature_extractor
            .mobilenet_v2_fpn_sep_conv,
    }
  
    FEATURE_EXTRACTOR_MAPS = [
        CENTER_NET_EXTRACTOR_FUNCTION_MAP,
        FASTER_RCNN_KERAS_FEATURE_EXTRACTOR_CLASS_MAP,
        SSD_KERAS_FEATURE_EXTRACTOR_CLASS_MAP
    ]
  
  if tf_version.is_tf1():
    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_mobilenet_v2_mnasfpn':
            SSDMobileNetV2MnasFPNFeatureExtractor,
        'ssd_mobilenet_v3_large':
            SSDMobileNetV3LargeFeatureExtractor,
        'ssd_mobilenet_v3_small':
            SSDMobileNetV3SmallFeatureExtractor,
        'ssd_mobilenet_edgetpu':
            SSDMobileNetEdgeTPUFeatureExtractor,
        '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_mobiledet_cpu':
            SSDMobileDetCPUFeatureExtractor,
        'ssd_mobiledet_dsp':
            SSDMobileDetDSPFeatureExtractor,
        'ssd_mobiledet_edgetpu':
            SSDMobileDetEdgeTPUFeatureExtractor,
        'ssd_mobiledet_gpu':
            SSDMobileDetGPUFeatureExtractor,
    }
  
    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,
    }
  
    CENTER_NET_EXTRACTOR_FUNCTION_MAP = {}
  
    FEATURE_EXTRACTOR_MAPS = [
        SSD_FEATURE_EXTRACTOR_CLASS_MAP,
        FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP,
        CENTER_NET_EXTRACTOR_FUNCTION_MAP
    ]
  
  
  def _check_feature_extractor_exists(feature_extractor_type):
    feature_extractors = set().union(*FEATURE_EXTRACTOR_MAPS)
    if feature_extractor_type not in feature_extractors:
      raise ValueError('{} is not supported. See `model_builder.py` for features '
                       'extractors compatible with different versions of '
                       'Tensorflow'.format(feature_extractor_type))
  
  
  def _build_ssd_feature_extractor(feature_extractor_config,
                                   is_training,
                                   freeze_batchnorm,
                                   reuse_weights=None):
    """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
    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
  
    is_keras = tf_version.is_tf2()
    if is_keras:
      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 not is_keras and feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
      raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))
  
    if is_keras:
      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
    }
  
    if feature_extractor_config.HasField('replace_preprocessor_with_placeholder'):
      kwargs.update({
          'replace_preprocessor_with_placeholder':
              feature_extractor_config.replace_preprocessor_with_placeholder
      })
  
    if feature_extractor_config.HasField('num_layers'):
      kwargs.update({'num_layers': feature_extractor_config.num_layers})
  
    if is_keras:
      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,
      })
  
    if feature_extractor_config.HasField('bifpn'):
      kwargs.update({
          'bifpn_min_level': feature_extractor_config.bifpn.min_level,
          'bifpn_max_level': feature_extractor_config.bifpn.max_level,
          'bifpn_num_iterations': feature_extractor_config.bifpn.num_iterations,
          'bifpn_num_filters': feature_extractor_config.bifpn.num_filters,
          'bifpn_combine_method': feature_extractor_config.bifpn.combine_method,
      })
  
    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
    _check_feature_extractor_exists(ssd_config.feature_extractor.type)
  
    # 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)
  
    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,
        return_raw_detections_during_predict=(
            ssd_config.return_raw_detections_during_predict),
        **kwargs)
  
  
  def _build_faster_rcnn_feature_extractor(
      feature_extractor_config, is_training, reuse_weights=True,
      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]
  
    kwargs = {}
  
    if feature_extractor_config.HasField('conv_hyperparams'):
      kwargs.update({
          'conv_hyperparams':
              hyperparams_builder.KerasLayerHyperparams(
                  feature_extractor_config.conv_hyperparams),
          'override_base_feature_extractor_hyperparams':
              feature_extractor_config.override_base_feature_extractor_hyperparams
      })
  
    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(
        is_training, first_stage_features_stride,
        batch_norm_trainable, **kwargs)
  
  
  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)
    _check_feature_extractor_exists(frcnn_config.feature_extractor.type)
    is_keras = tf_version.is_tf2()
  
    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,
        use_partitioned_nms=frcnn_config.use_partitioned_nms_in_first_stage,
        use_combined_nms=frcnn_config.use_combined_nms_in_first_stage)
    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 = (
        spatial_ops.multilevel_matmul_crop_and_resize
        if frcnn_config.use_matmul_crop_and_resize
        else spatial_ops.multilevel_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,
        'return_raw_detections_during_predict':
            frcnn_config.return_raw_detections_during_predict,
        'output_final_box_features':
            frcnn_config.output_final_box_features,
        'output_final_box_rpn_features':
            frcnn_config.output_final_box_rpn_features,
    }
  
    if ((not is_keras and isinstance(second_stage_box_predictor,
                                     rfcn_box_predictor.RfcnBoxPredictor)) or
        (is_keras and
         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)
    elif frcnn_config.HasField('context_config'):
      context_config = frcnn_config.context_config
      common_kwargs.update({
          'attention_bottleneck_dimension':
              context_config.attention_bottleneck_dimension,
          'attention_temperature':
              context_config.attention_temperature,
          'use_self_attention':
              context_config.use_self_attention,
          'use_long_term_attention':
              context_config.use_long_term_attention,
          'self_attention_in_sequence':
              context_config.self_attention_in_sequence,
          'num_attention_heads':
              context_config.num_attention_heads,
          'num_attention_layers':
              context_config.num_attention_layers,
          'attention_position':
              context_config.attention_position
      })
      return context_rcnn_meta_arch.ContextRCNNMetaArch(
          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)
    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)
  
  EXPERIMENTAL_META_ARCH_BUILDER_MAP = {
  }
  
  
  def _build_experimental_model(config, is_training, add_summaries=True):
    return EXPERIMENTAL_META_ARCH_BUILDER_MAP[config.name](
        is_training, add_summaries)
  
  
  # The class ID in the groundtruth/model architecture is usually 0-based while
  # the ID in the label map is 1-based. The offset is used to convert between the
  # the two.
  CLASS_ID_OFFSET = 1
  KEYPOINT_STD_DEV_DEFAULT = 1.0
  
  
  def keypoint_proto_to_params(kp_config, keypoint_map_dict):
    """Converts CenterNet.KeypointEstimation proto to parameter namedtuple."""
    label_map_item = keypoint_map_dict[kp_config.keypoint_class_name]
  
    classification_loss, localization_loss, _, _, _, _, _ = (
        losses_builder.build(kp_config.loss))
  
    keypoint_indices = [
        keypoint.id for keypoint in label_map_item.keypoints
    ]
    keypoint_labels = [
        keypoint.label for keypoint in label_map_item.keypoints
    ]
    keypoint_std_dev_dict = {
        label: KEYPOINT_STD_DEV_DEFAULT for label in keypoint_labels
    }
    if kp_config.keypoint_label_to_std:
      for label, value in kp_config.keypoint_label_to_std.items():
        keypoint_std_dev_dict[label] = value
    keypoint_std_dev = [keypoint_std_dev_dict[label] for label in keypoint_labels]
    return center_net_meta_arch.KeypointEstimationParams(
        task_name=kp_config.task_name,
        class_id=label_map_item.id - CLASS_ID_OFFSET,
        keypoint_indices=keypoint_indices,
        classification_loss=classification_loss,
        localization_loss=localization_loss,
        keypoint_labels=keypoint_labels,
        keypoint_std_dev=keypoint_std_dev,
        task_loss_weight=kp_config.task_loss_weight,
        keypoint_regression_loss_weight=kp_config.keypoint_regression_loss_weight,
        keypoint_heatmap_loss_weight=kp_config.keypoint_heatmap_loss_weight,
        keypoint_offset_loss_weight=kp_config.keypoint_offset_loss_weight,
        heatmap_bias_init=kp_config.heatmap_bias_init,
        keypoint_candidate_score_threshold=(
            kp_config.keypoint_candidate_score_threshold),
        num_candidates_per_keypoint=kp_config.num_candidates_per_keypoint,
        peak_max_pool_kernel_size=kp_config.peak_max_pool_kernel_size,
        unmatched_keypoint_score=kp_config.unmatched_keypoint_score,
        box_scale=kp_config.box_scale,
        candidate_search_scale=kp_config.candidate_search_scale,
        candidate_ranking_mode=kp_config.candidate_ranking_mode,
        offset_peak_radius=kp_config.offset_peak_radius,
        per_keypoint_offset=kp_config.per_keypoint_offset,
        predict_depth=kp_config.predict_depth,
        per_keypoint_depth=kp_config.per_keypoint_depth,
        keypoint_depth_loss_weight=kp_config.keypoint_depth_loss_weight,
        score_distance_offset=kp_config.score_distance_offset,
        clip_out_of_frame_keypoints=kp_config.clip_out_of_frame_keypoints,
        rescore_instances=kp_config.rescore_instances)
  
  
  def object_detection_proto_to_params(od_config):
    """Converts CenterNet.ObjectDetection proto to parameter namedtuple."""
    loss = losses_pb2.Loss()
    # Add dummy classification loss to avoid the loss_builder throwing error.
    # TODO(yuhuic): update the loss builder to take the classification loss
    # directly.
    loss.classification_loss.weighted_sigmoid.CopyFrom(
        losses_pb2.WeightedSigmoidClassificationLoss())
    loss.localization_loss.CopyFrom(od_config.localization_loss)
    _, localization_loss, _, _, _, _, _ = (losses_builder.build(loss))
    return center_net_meta_arch.ObjectDetectionParams(
        localization_loss=localization_loss,
        scale_loss_weight=od_config.scale_loss_weight,
        offset_loss_weight=od_config.offset_loss_weight,
        task_loss_weight=od_config.task_loss_weight)
  
  
  def object_center_proto_to_params(oc_config):
    """Converts CenterNet.ObjectCenter proto to parameter namedtuple."""
    loss = losses_pb2.Loss()
    # Add dummy localization loss to avoid the loss_builder throwing error.
    # TODO(yuhuic): update the loss builder to take the localization loss
    # directly.
    loss.localization_loss.weighted_l2.CopyFrom(
        losses_pb2.WeightedL2LocalizationLoss())
    loss.classification_loss.CopyFrom(oc_config.classification_loss)
    classification_loss, _, _, _, _, _, _ = (losses_builder.build(loss))
    keypoint_weights_for_center = []
    if oc_config.keypoint_weights_for_center:
      keypoint_weights_for_center = list(oc_config.keypoint_weights_for_center)
    return center_net_meta_arch.ObjectCenterParams(
        classification_loss=classification_loss,
        object_center_loss_weight=oc_config.object_center_loss_weight,
        heatmap_bias_init=oc_config.heatmap_bias_init,
        min_box_overlap_iou=oc_config.min_box_overlap_iou,
        max_box_predictions=oc_config.max_box_predictions,
        use_labeled_classes=oc_config.use_labeled_classes,
        keypoint_weights_for_center=keypoint_weights_for_center)
  
  
  def mask_proto_to_params(mask_config):
    """Converts CenterNet.MaskEstimation proto to parameter namedtuple."""
    loss = losses_pb2.Loss()
    # Add dummy localization loss to avoid the loss_builder throwing error.
    loss.localization_loss.weighted_l2.CopyFrom(
        losses_pb2.WeightedL2LocalizationLoss())
    loss.classification_loss.CopyFrom(mask_config.classification_loss)
    classification_loss, _, _, _, _, _, _ = (losses_builder.build(loss))
    return center_net_meta_arch.MaskParams(
        classification_loss=classification_loss,
        task_loss_weight=mask_config.task_loss_weight,
        mask_height=mask_config.mask_height,
        mask_width=mask_config.mask_width,
        score_threshold=mask_config.score_threshold,
        heatmap_bias_init=mask_config.heatmap_bias_init)
  
  
  def densepose_proto_to_params(densepose_config):
    """Converts CenterNet.DensePoseEstimation proto to parameter namedtuple."""
    classification_loss, localization_loss, _, _, _, _, _ = (
        losses_builder.build(densepose_config.loss))
    return center_net_meta_arch.DensePoseParams(
        class_id=densepose_config.class_id,
        classification_loss=classification_loss,
        localization_loss=localization_loss,
        part_loss_weight=densepose_config.part_loss_weight,
        coordinate_loss_weight=densepose_config.coordinate_loss_weight,
        num_parts=densepose_config.num_parts,
        task_loss_weight=densepose_config.task_loss_weight,
        upsample_to_input_res=densepose_config.upsample_to_input_res,
        heatmap_bias_init=densepose_config.heatmap_bias_init)
  
  
  def tracking_proto_to_params(tracking_config):
    """Converts CenterNet.TrackEstimation proto to parameter namedtuple."""
    loss = losses_pb2.Loss()
    # Add dummy localization loss to avoid the loss_builder throwing error.
    # TODO(yuhuic): update the loss builder to take the localization loss
    # directly.
    loss.localization_loss.weighted_l2.CopyFrom(
        losses_pb2.WeightedL2LocalizationLoss())
    loss.classification_loss.CopyFrom(tracking_config.classification_loss)
    classification_loss, _, _, _, _, _, _ = losses_builder.build(loss)
    return center_net_meta_arch.TrackParams(
        num_track_ids=tracking_config.num_track_ids,
        reid_embed_size=tracking_config.reid_embed_size,
        classification_loss=classification_loss,
        num_fc_layers=tracking_config.num_fc_layers,
        task_loss_weight=tracking_config.task_loss_weight)
  
  
  def temporal_offset_proto_to_params(temporal_offset_config):
    """Converts CenterNet.TemporalOffsetEstimation proto to param-tuple."""
    loss = losses_pb2.Loss()
    # Add dummy classification loss to avoid the loss_builder throwing error.
    # TODO(yuhuic): update the loss builder to take the classification loss
    # directly.
    loss.classification_loss.weighted_sigmoid.CopyFrom(
        losses_pb2.WeightedSigmoidClassificationLoss())
    loss.localization_loss.CopyFrom(temporal_offset_config.localization_loss)
    _, localization_loss, _, _, _, _, _ = losses_builder.build(loss)
    return center_net_meta_arch.TemporalOffsetParams(
        localization_loss=localization_loss,
        task_loss_weight=temporal_offset_config.task_loss_weight)
  
  
  def _build_center_net_model(center_net_config, is_training, add_summaries):
    """Build a CenterNet detection model.
  
    Args:
      center_net_config: A CenterNet proto object with model configuration.
      is_training: True if this model is being built for training purposes.
      add_summaries: Whether to add tf summaries in the model.
  
    Returns:
      CenterNetMetaArch based on the config.
  
    """
  
    image_resizer_fn = image_resizer_builder.build(
        center_net_config.image_resizer)
    _check_feature_extractor_exists(center_net_config.feature_extractor.type)
    feature_extractor = _build_center_net_feature_extractor(
        center_net_config.feature_extractor, is_training)
    object_center_params = object_center_proto_to_params(
        center_net_config.object_center_params)
  
    object_detection_params = None
    if center_net_config.HasField('object_detection_task'):
      object_detection_params = object_detection_proto_to_params(
          center_net_config.object_detection_task)
  
    keypoint_params_dict = None
    if center_net_config.keypoint_estimation_task:
      label_map_proto = label_map_util.load_labelmap(
          center_net_config.keypoint_label_map_path)
      keypoint_map_dict = {
          item.name: item for item in label_map_proto.item if item.keypoints
      }
      keypoint_params_dict = {}
      keypoint_class_id_set = set()
      all_keypoint_indices = []
      for task in center_net_config.keypoint_estimation_task:
        kp_params = keypoint_proto_to_params(task, keypoint_map_dict)
        keypoint_params_dict[task.task_name] = kp_params
        all_keypoint_indices.extend(kp_params.keypoint_indices)
        if kp_params.class_id in keypoint_class_id_set:
          raise ValueError(('Multiple keypoint tasks map to the same class id is '
                            'not allowed: %d' % kp_params.class_id))
        else:
          keypoint_class_id_set.add(kp_params.class_id)
      if len(all_keypoint_indices) > len(set(all_keypoint_indices)):
        raise ValueError('Some keypoint indices are used more than once.')
  
    mask_params = None
    if center_net_config.HasField('mask_estimation_task'):
      mask_params = mask_proto_to_params(center_net_config.mask_estimation_task)
  
    densepose_params = None
    if center_net_config.HasField('densepose_estimation_task'):
      densepose_params = densepose_proto_to_params(
          center_net_config.densepose_estimation_task)
  
    track_params = None
    if center_net_config.HasField('track_estimation_task'):
      track_params = tracking_proto_to_params(
          center_net_config.track_estimation_task)
  
    temporal_offset_params = None
    if center_net_config.HasField('temporal_offset_task'):
      temporal_offset_params = temporal_offset_proto_to_params(
          center_net_config.temporal_offset_task)
    non_max_suppression_fn = None
    if center_net_config.HasField('post_processing'):
      non_max_suppression_fn, _ = post_processing_builder.build(
          center_net_config.post_processing)
    return center_net_meta_arch.CenterNetMetaArch(
        is_training=is_training,
        add_summaries=add_summaries,
        num_classes=center_net_config.num_classes,
        feature_extractor=feature_extractor,
        image_resizer_fn=image_resizer_fn,
        object_center_params=object_center_params,
        object_detection_params=object_detection_params,
        keypoint_params_dict=keypoint_params_dict,
        mask_params=mask_params,
        densepose_params=densepose_params,
        track_params=track_params,
        temporal_offset_params=temporal_offset_params,
        use_depthwise=center_net_config.use_depthwise,
        compute_heatmap_sparse=center_net_config.compute_heatmap_sparse,
        non_max_suppression_fn=non_max_suppression_fn)
  
  
  def _build_center_net_feature_extractor(feature_extractor_config, is_training):
    """Build a CenterNet feature extractor from the given config."""
  
    if feature_extractor_config.type not in CENTER_NET_EXTRACTOR_FUNCTION_MAP:
      raise ValueError('\'{}\' is not a known CenterNet feature extractor type'
                       .format(feature_extractor_config.type))
    kwargs = {
        'channel_means': list(feature_extractor_config.channel_means),
        'channel_stds': list(feature_extractor_config.channel_stds),
        'bgr_ordering': feature_extractor_config.bgr_ordering,
    }
  
  
    return CENTER_NET_EXTRACTOR_FUNCTION_MAP[feature_extractor_config.type](
        **kwargs)
  
  
  META_ARCH_BUILDER_MAP = {
      'ssd': _build_ssd_model,
      'faster_rcnn': _build_faster_rcnn_model,
      'experimental_model': _build_experimental_model,
      'center_net': _build_center_net_model
  }
  
  
  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 not in META_ARCH_BUILDER_MAP:
      raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
    else:
      build_func = META_ARCH_BUILDER_MAP[meta_architecture]
      return build_func(getattr(model_config, meta_architecture), is_training,
                        add_summaries)
  