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| # Lint as: python2, python3 | |
| # 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) | |
| return decoder.decode(string_tensor) | |
| raise ValueError('Unsupported input_type.') | |
| raise ValueError('Unsupported input_reader_config.') | |