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.
  # ==============================================================================
  """Input reader builder.
  
  Creates data sources for DetectionModels from an InputReader config. See
  input_reader.proto for options.
  
  Note: If users wishes to also use their own InputReaders with the Object
  Detection configuration framework, they should define their own builder function
  that wraps the build function.
  """
  
  from second.protos import input_reader_pb2
  from second.data.dataset_JRDB import JRDBDataset
  from second.data.preprocess_JRDB import prep_pointcloud
  import numpy as np
  from second.builder import dbsampler_builder
  from functools import partial
  
  
  def build(input_reader_config,
            model_config,
            training,
            voxel_generator,
            target_assigner=None):
      """Builds a tensor dictionary based on the InputReader config.
  
      Args:
          input_reader_config: A input_reader_pb2.InputReader object.
  
      Returns:
          A tensor dict based on the input_reader_config.
  
      Raises:
          ValueError: On invalid input reader proto.
          ValueError: If no input paths are specified.
      """
      if not isinstance(input_reader_config, input_reader_pb2.InputReader):
          raise ValueError('input_reader_config not of type '
                           'input_reader_pb2.InputReader.')
      generate_bev = model_config.use_bev
      without_reflectivity = model_config.without_reflectivity
      num_point_features = model_config.num_point_features
      out_size_factor = model_config.rpn.layer_strides[0] // model_config.rpn.upsample_strides[0]
  
      cfg = input_reader_config
      """
      <GT sampling>
      
      # db_sampler_cfg = input_reader_config.database_sampler
      # db_sampler = None
      # if len(db_sampler_cfg.sample_groups) > 0:  # enable sample
      #     db_sampler = dbsampler_builder.build(db_sampler_cfg)
      # u_db_sampler_cfg = input_reader_config.unlabeled_database_sampler
      # u_db_sampler = None
      # if len(u_db_sampler_cfg.sample_groups) > 0:  # enable sample
      #     u_db_sampler = dbsampler_builder.build(u_db_sampler_cfg)
      
      db_sampler_cfg = input_reader_config.database_sampler
      db_sampler = None
      if len(db_sampler_cfg.sample_groups) > 0:  # enable sample
          db_sampler = dbsampler_builder.build(db_sampler_cfg)
      u_db_sampler_cfg = input_reader_config.unlabeled_database_sampler
      u_db_sampler = None
      if len(u_db_sampler_cfg.sample_groups) > 0:  # enable sample
          u_db_sampler = dbsampler_builder.build(u_db_sampler_cfg)
      """
      grid_size = voxel_generator.grid_size
      # [352, 400]
      feature_map_size = grid_size[:2] // out_size_factor
      feature_map_size = [*feature_map_size, 1][::-1]
      prep_func = partial(
          prep_pointcloud,
          root_path=cfg.kitti_root_path,
          class_names=list(cfg.class_names),
          voxel_generator=voxel_generator,
          target_assigner=target_assigner,
          training=training,
          max_voxels=cfg.max_number_of_voxels,
          remove_outside_points=False,
          remove_unknown=cfg.remove_unknown_examples,
          create_targets=training,
          shuffle_points=cfg.shuffle_points,
          gt_rotation_noise=list(cfg.groundtruth_rotation_uniform_noise),
          gt_loc_noise_std=list(cfg.groundtruth_localization_noise_std),
          global_rotation_noise=list(cfg.global_rotation_uniform_noise),
          global_scaling_noise=list(cfg.global_scaling_uniform_noise),
          global_loc_noise_std=(0.2, 0.2, 0.2),
          global_random_rot_range=list(
              cfg.global_random_rotation_range_per_object),
          # db_sampler=db_sampler,
          # unlabeled_db_sampler=u_db_sampler,
          generate_bev=generate_bev,
          without_reflectivity=without_reflectivity,
          num_point_features=num_point_features,
          anchor_area_threshold=cfg.anchor_area_threshold,
          gt_points_drop=cfg.groundtruth_points_drop_percentage,
          gt_drop_max_keep=cfg.groundtruth_drop_max_keep_points,
          remove_points_after_sample=cfg.remove_points_after_sample,
          remove_environment=cfg.remove_environment,
          use_group_id=cfg.use_group_id,
          out_size_factor=out_size_factor)
      dataset = JRDBDataset(
          info_path=cfg.kitti_info_path,
          root_path=cfg.kitti_root_path,
          num_point_features=num_point_features,
          target_assigner=target_assigner,
          feature_map_size=feature_map_size,
          prep_func=prep_func)
          
      return dataset
  