dataset_builder.py

# 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 import KittiDataset
from second.data.preprocess import prep_pointcloud
import numpy as np
from second.builder import dbsampler_builder
from functools import partial
import torch



def build(input_reader_config,
          model_config,
          info,
          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
    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]
    inform = info.copy()
    inform["road_map"] = None
    
    root_path = input_reader_config.kitti_root_path
    index_list = torch.load(input_reader_config.kitti_info_path)
    
    inform["index_list"] = index_list
    
    prep_func = partial(
        prep_pointcloud,
        root_path=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 = KittiDataset(
        info_path=inform,
        root_path=root_path,
        class_names=list(cfg.class_names),
        num_point_features=num_point_features,
        target_assigner=target_assigner,
        feature_map_size=feature_map_size,
        prep_func=prep_func)

    return dataset