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| # 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. | |
| # ============================================================================== | |
| r"""Evaluation executable for detection models. | |
| This executable is used to evaluate DetectionModels. There are two ways of | |
| configuring the eval job. | |
| 1) A single pipeline_pb2.TrainEvalPipelineConfig file maybe specified instead. | |
| In this mode, the --eval_training_data flag may be given to force the pipeline | |
| to evaluate on training data instead. | |
| Example usage: | |
| ./eval \ | |
| --logtostderr \ | |
| --checkpoint_dir=path/to/checkpoint_dir \ | |
| --eval_dir=path/to/eval_dir \ | |
| --pipeline_config_path=pipeline_config.pbtxt | |
| 2) Three configuration files may be provided: a model_pb2.DetectionModel | |
| configuration file to define what type of DetectionModel is being evaluated, an | |
| input_reader_pb2.InputReader file to specify what data the model is evaluating | |
| and an eval_pb2.EvalConfig file to configure evaluation parameters. | |
| Example usage: | |
| ./eval \ | |
| --logtostderr \ | |
| --checkpoint_dir=path/to/checkpoint_dir \ | |
| --eval_dir=path/to/eval_dir \ | |
| --eval_config_path=eval_config.pbtxt \ | |
| --model_config_path=model_config.pbtxt \ | |
| --input_config_path=eval_input_config.pbtxt | |
| """ | |
| import functools | |
| import os | |
| import tensorflow.compat.v1 as tf | |
| from tensorflow.python.util.deprecation import deprecated | |
| from object_detection.builders import dataset_builder | |
| from object_detection.builders import graph_rewriter_builder | |
| from object_detection.builders import model_builder | |
| from object_detection.legacy import evaluator | |
| from 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_string('eval_config_path', '', | |
| 'Path to an eval_pb2.EvalConfig config file.') | |
| flags.DEFINE_string('input_config_path', '', | |
| 'Path to an input_reader_pb2.InputReader config file.') | |
| flags.DEFINE_string('model_config_path', '', | |
| 'Path to a model_pb2.DetectionModel config file.') | |
| 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.' | |
| tf.gfile.MakeDirs(FLAGS.eval_dir) | |
| if FLAGS.pipeline_config_path: | |
| configs = config_util.get_configs_from_pipeline_file( | |
| FLAGS.pipeline_config_path) | |
| tf.gfile.Copy( | |
| FLAGS.pipeline_config_path, | |
| os.path.join(FLAGS.eval_dir, 'pipeline.config'), | |
| overwrite=True) | |
| 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) | |
| for name, config in [('model.config', FLAGS.model_config_path), | |
| ('eval.config', FLAGS.eval_config_path), | |
| ('input.config', FLAGS.input_config_path)]: | |
| tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True) | |
| model_config = configs['model'] | |
| eval_config = configs['eval_config'] | |
| input_config = configs['eval_input_config'] | |
| if FLAGS.eval_training_data: | |
| input_config = configs['train_input_config'] | |
| model_fn = functools.partial( | |
| model_builder.build, model_config=model_config, is_training=False) | |
| def get_next(config): | |
| return dataset_builder.make_initializable_iterator( | |
| dataset_builder.build(config)).get_next() | |
| create_input_dict_fn = functools.partial(get_next, input_config) | |
| categories = label_map_util.create_categories_from_labelmap( | |
| input_config.label_map_path) | |
| if FLAGS.run_once: | |
| eval_config.max_evals = 1 | |
| graph_rewriter_fn = None | |
| if 'graph_rewriter_config' in configs: | |
| graph_rewriter_fn = graph_rewriter_builder.build( | |
| configs['graph_rewriter_config'], is_training=False) | |
| evaluator.evaluate( | |
| create_input_dict_fn, | |
| model_fn, | |
| eval_config, | |
| categories, | |
| FLAGS.checkpoint_dir, | |
| FLAGS.eval_dir, | |
| graph_hook_fn=graph_rewriter_fn) | |
| if __name__ == '__main__': | |
| tf.app.run() | |