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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. | |
| # ============================================================================== | |
| r"""Evaluation executable for detection models. | |
| This executable is used to evaluate DetectionModels. Example usage: | |
| ./eval \ | |
| --logtostderr \ | |
| --checkpoint_dir=path/to/checkpoint_dir \ | |
| --eval_dir=path/to/eval_dir \ | |
| --pipeline_config_path=pipeline_config.pbtxt | |
| """ | |
| import functools | |
| import os | |
| import tensorflow.compat.v1 as tf | |
| from google.protobuf import text_format | |
| from lstm_object_detection import evaluator | |
| from lstm_object_detection import model_builder | |
| from lstm_object_detection.inputs import seq_dataset_builder | |
| from lstm_object_detection.utils import config_util | |
| from object_detection.utils import label_map_util | |
| tf.logging.set_verbosity(tf.logging.INFO) | |
| flags = tf.app.flags | |
| flags.DEFINE_boolean('eval_training_data', False, | |
| 'If training data should be evaluated for this job.') | |
| flags.DEFINE_string('checkpoint_dir', '', | |
| 'Directory containing checkpoints to evaluate, typically ' | |
| 'set to `train_dir` used in the training job.') | |
| flags.DEFINE_string('eval_dir', '', 'Directory to write eval summaries to.') | |
| flags.DEFINE_string('pipeline_config_path', '', | |
| 'Path to a pipeline_pb2.TrainEvalPipelineConfig config ' | |
| 'file. If provided, other configs are ignored') | |
| flags.DEFINE_boolean('run_once', False, 'Option to only run a single pass of ' | |
| 'evaluation. Overrides the `max_evals` parameter in the ' | |
| 'provided config.') | |
| FLAGS = flags.FLAGS | |
| def main(unused_argv): | |
| assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.' | |
| assert FLAGS.eval_dir, '`eval_dir` is missing.' | |
| if FLAGS.pipeline_config_path: | |
| configs = config_util.get_configs_from_pipeline_file( | |
| FLAGS.pipeline_config_path) | |
| else: | |
| configs = config_util.get_configs_from_multiple_files( | |
| model_config_path=FLAGS.model_config_path, | |
| eval_config_path=FLAGS.eval_config_path, | |
| eval_input_config_path=FLAGS.input_config_path) | |
| pipeline_proto = config_util.create_pipeline_proto_from_configs(configs) | |
| config_text = text_format.MessageToString(pipeline_proto) | |
| tf.gfile.MakeDirs(FLAGS.eval_dir) | |
| with tf.gfile.Open(os.path.join(FLAGS.eval_dir, 'pipeline.config'), | |
| 'wb') as f: | |
| f.write(config_text) | |
| model_config = configs['model'] | |
| lstm_config = configs['lstm_model'] | |
| eval_config = configs['eval_config'] | |
| input_config = configs['eval_input_config'] | |
| if FLAGS.eval_training_data: | |
| input_config.external_input_reader.CopyFrom( | |
| configs['train_input_config'].external_input_reader) | |
| lstm_config.eval_unroll_length = lstm_config.train_unroll_length | |
| model_fn = functools.partial( | |
| model_builder.build, | |
| model_config=model_config, | |
| lstm_config=lstm_config, | |
| is_training=False) | |
| def get_next(config, model_config, lstm_config, unroll_length): | |
| return seq_dataset_builder.build(config, model_config, lstm_config, | |
| unroll_length) | |
| create_input_dict_fn = functools.partial(get_next, input_config, model_config, | |
| lstm_config, | |
| lstm_config.eval_unroll_length) | |
| label_map = label_map_util.load_labelmap(input_config.label_map_path) | |
| max_num_classes = max([item.id for item in label_map.item]) | |
| categories = label_map_util.convert_label_map_to_categories( | |
| label_map, max_num_classes) | |
| if FLAGS.run_once: | |
| eval_config.max_evals = 1 | |
| evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories, | |
| FLAGS.checkpoint_dir, FLAGS.eval_dir) | |
| if __name__ == '__main__': | |
| tf.app.run() | |