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| # Copyright 2018 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. | |
| # ============================================================================== | |
| """Tests for object_detection.utils.config_util.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import os | |
| import tensorflow.compat.v1 as tf | |
| from google.protobuf import text_format | |
| from lstm_object_detection.protos import pipeline_pb2 as internal_pipeline_pb2 | |
| from lstm_object_detection.utils import config_util | |
| from object_detection.protos import pipeline_pb2 | |
| def _write_config(config, config_path): | |
| """Writes a config object to disk.""" | |
| config_text = text_format.MessageToString(config) | |
| with tf.gfile.Open(config_path, "wb") as f: | |
| f.write(config_text) | |
| class ConfigUtilTest(tf.test.TestCase): | |
| def test_get_configs_from_pipeline_file(self): | |
| """Test that proto configs can be read from pipeline config file.""" | |
| pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config") | |
| pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() | |
| pipeline_config.model.ssd.num_classes = 10 | |
| pipeline_config.train_config.batch_size = 32 | |
| pipeline_config.train_input_reader.label_map_path = "path/to/label_map" | |
| pipeline_config.eval_config.num_examples = 20 | |
| pipeline_config.eval_input_reader.add().queue_capacity = 100 | |
| pipeline_config.Extensions[ | |
| internal_pipeline_pb2.lstm_model].train_unroll_length = 5 | |
| pipeline_config.Extensions[ | |
| internal_pipeline_pb2.lstm_model].eval_unroll_length = 10 | |
| _write_config(pipeline_config, pipeline_config_path) | |
| configs = config_util.get_configs_from_pipeline_file(pipeline_config_path) | |
| self.assertProtoEquals(pipeline_config.model, configs["model"]) | |
| self.assertProtoEquals(pipeline_config.train_config, | |
| configs["train_config"]) | |
| self.assertProtoEquals(pipeline_config.train_input_reader, | |
| configs["train_input_config"]) | |
| self.assertProtoEquals(pipeline_config.eval_config, configs["eval_config"]) | |
| self.assertProtoEquals(pipeline_config.eval_input_reader, | |
| configs["eval_input_configs"]) | |
| self.assertProtoEquals( | |
| pipeline_config.Extensions[internal_pipeline_pb2.lstm_model], | |
| configs["lstm_model"]) | |
| def test_create_pipeline_proto_from_configs(self): | |
| """Tests that proto can be reconstructed from configs dictionary.""" | |
| pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config") | |
| pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() | |
| pipeline_config.model.ssd.num_classes = 10 | |
| pipeline_config.train_config.batch_size = 32 | |
| pipeline_config.train_input_reader.label_map_path = "path/to/label_map" | |
| pipeline_config.eval_config.num_examples = 20 | |
| pipeline_config.eval_input_reader.add().queue_capacity = 100 | |
| pipeline_config.Extensions[ | |
| internal_pipeline_pb2.lstm_model].train_unroll_length = 5 | |
| pipeline_config.Extensions[ | |
| internal_pipeline_pb2.lstm_model].eval_unroll_length = 10 | |
| _write_config(pipeline_config, pipeline_config_path) | |
| configs = config_util.get_configs_from_pipeline_file(pipeline_config_path) | |
| pipeline_config_reconstructed = ( | |
| config_util.create_pipeline_proto_from_configs(configs)) | |
| self.assertEqual(pipeline_config, pipeline_config_reconstructed) | |
| if __name__ == "__main__": | |
| tf.test.main() | |