models/research/object_detection/builders/input_reader_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 __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow.compat.v1 as tf
import tf_slim as slim

from object_detection.data_decoders import tf_example_decoder
from object_detection.data_decoders import tf_sequence_example_decoder
from object_detection.protos import input_reader_pb2

parallel_reader = slim.parallel_reader


def build(input_reader_config):
  """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.')

  if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
    config = input_reader_config.tf_record_input_reader
    if not config.input_path:
      raise ValueError('At least one input path must be specified in '
                       '`input_reader_config`.')
    _, string_tensor = parallel_reader.parallel_read(
        config.input_path[:],  # Convert `RepeatedScalarContainer` to list.
        reader_class=tf.TFRecordReader,
        num_epochs=(input_reader_config.num_epochs
                    if input_reader_config.num_epochs else None),
        num_readers=input_reader_config.num_readers,
        shuffle=input_reader_config.shuffle,
        dtypes=[tf.string, tf.string],
        capacity=input_reader_config.queue_capacity,
        min_after_dequeue=input_reader_config.min_after_dequeue)

    label_map_proto_file = None
    if input_reader_config.HasField('label_map_path'):
      label_map_proto_file = input_reader_config.label_map_path
    input_type = input_reader_config.input_type
    if input_type == input_reader_pb2.InputType.Value('TF_EXAMPLE'):
      decoder = tf_example_decoder.TfExampleDecoder(
          load_instance_masks=input_reader_config.load_instance_masks,
          instance_mask_type=input_reader_config.mask_type,
          label_map_proto_file=label_map_proto_file,
          load_context_features=input_reader_config.load_context_features)
      return decoder.decode(string_tensor)
    elif input_type == input_reader_pb2.InputType.Value('TF_SEQUENCE_EXAMPLE'):
      decoder = tf_sequence_example_decoder.TfSequenceExampleDecoder(
          label_map_proto_file=label_map_proto_file,
          load_context_features=input_reader_config.load_context_features,
          load_context_image_ids=input_reader_config.load_context_image_ids)
      return decoder.decode(string_tensor)
    raise ValueError('Unsupported input_type.')
  raise ValueError('Unsupported input_reader_config.')