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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. | |
| # ============================================================================== | |
| """Binary to run train and evaluation on object detection model.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| from absl import flags | |
| import tensorflow.compat.v1 as tf | |
| from object_detection import model_hparams | |
| from object_detection import model_lib | |
| flags.DEFINE_string( | |
| 'model_dir', None, 'Path to output model directory ' | |
| 'where event and checkpoint files will be written.') | |
| flags.DEFINE_string('pipeline_config_path', None, 'Path to pipeline config ' | |
| 'file.') | |
| flags.DEFINE_integer('num_train_steps', None, 'Number of train steps.') | |
| flags.DEFINE_boolean('eval_training_data', False, | |
| 'If training data should be evaluated for this job. Note ' | |
| 'that one call only use this in eval-only mode, and ' | |
| '`checkpoint_dir` must be supplied.') | |
| flags.DEFINE_integer('sample_1_of_n_eval_examples', 1, 'Will sample one of ' | |
| 'every n eval input examples, where n is provided.') | |
| flags.DEFINE_integer('sample_1_of_n_eval_on_train_examples', 5, 'Will sample ' | |
| 'one of every n train input examples for evaluation, ' | |
| 'where n is provided. This is only used if ' | |
| '`eval_training_data` is True.') | |
| flags.DEFINE_string( | |
| 'hparams_overrides', None, 'Hyperparameter overrides, ' | |
| 'represented as a string containing comma-separated ' | |
| 'hparam_name=value pairs.') | |
| flags.DEFINE_string( | |
| 'checkpoint_dir', None, 'Path to directory holding a checkpoint. If ' | |
| '`checkpoint_dir` is provided, this binary operates in eval-only mode, ' | |
| 'writing resulting metrics to `model_dir`.') | |
| flags.DEFINE_boolean( | |
| 'run_once', False, 'If running in eval-only mode, whether to run just ' | |
| 'one round of eval vs running continuously (default).' | |
| ) | |
| flags.DEFINE_integer( | |
| 'max_eval_retries', 0, 'If running continuous eval, the maximum number of ' | |
| 'retries upon encountering tf.errors.InvalidArgumentError. If negative, ' | |
| 'will always retry the evaluation.' | |
| ) | |
| FLAGS = flags.FLAGS | |
| def main(unused_argv): | |
| flags.mark_flag_as_required('model_dir') | |
| flags.mark_flag_as_required('pipeline_config_path') | |
| config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir) | |
| train_and_eval_dict = model_lib.create_estimator_and_inputs( | |
| run_config=config, | |
| hparams=model_hparams.create_hparams(FLAGS.hparams_overrides), | |
| pipeline_config_path=FLAGS.pipeline_config_path, | |
| train_steps=FLAGS.num_train_steps, | |
| sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples, | |
| sample_1_of_n_eval_on_train_examples=( | |
| FLAGS.sample_1_of_n_eval_on_train_examples)) | |
| estimator = train_and_eval_dict['estimator'] | |
| train_input_fn = train_and_eval_dict['train_input_fn'] | |
| eval_input_fns = train_and_eval_dict['eval_input_fns'] | |
| eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn'] | |
| predict_input_fn = train_and_eval_dict['predict_input_fn'] | |
| train_steps = train_and_eval_dict['train_steps'] | |
| if FLAGS.checkpoint_dir: | |
| if FLAGS.eval_training_data: | |
| name = 'training_data' | |
| input_fn = eval_on_train_input_fn | |
| else: | |
| name = 'validation_data' | |
| # The first eval input will be evaluated. | |
| input_fn = eval_input_fns[0] | |
| if FLAGS.run_once: | |
| estimator.evaluate(input_fn, | |
| steps=None, | |
| checkpoint_path=tf.train.latest_checkpoint( | |
| FLAGS.checkpoint_dir)) | |
| else: | |
| model_lib.continuous_eval(estimator, FLAGS.checkpoint_dir, input_fn, | |
| train_steps, name, FLAGS.max_eval_retries) | |
| else: | |
| train_spec, eval_specs = model_lib.create_train_and_eval_specs( | |
| train_input_fn, | |
| eval_input_fns, | |
| eval_on_train_input_fn, | |
| predict_input_fn, | |
| train_steps, | |
| eval_on_train_data=False) | |
| # Currently only a single Eval Spec is allowed. | |
| tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0]) | |
| if __name__ == '__main__': | |
| tf.app.run() | |