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