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109 lines
4.4 KiB
109 lines
4.4 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Binary to run train and evaluation on object detection model."""
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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 as tf
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from object_detection import model_hparams
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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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'hparams_overrides', None, 'Hyperparameter overrides, '
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'represented as a string containing comma-separated '
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'hparam_name=value pairs.')
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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 = flags.FLAGS
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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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train_and_eval_dict = model_lib.create_estimator_and_inputs(
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run_config=config,
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hparams=model_hparams.create_hparams(FLAGS.hparams_overrides),
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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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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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# The first eval input will be evaluated.
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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)
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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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# Currently only a single Eval Spec is allowed.
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tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
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if __name__ == '__main__':
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tf.app.run()
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