# Path: tf_slim/training/evaluation.py
# def wait_for_new_checkpoint(checkpoint_dir,
#                             last_checkpoint=None,
#                             seconds_to_sleep=1,
#                             timeout=None):
# def checkpoints_iterator(checkpoint_dir,
#                          min_interval_secs=0,
#                          timeout=None,
#                          timeout_fn=None):
#   def __init__(self,
#                log_dir=None,
#                summary_writer=None,
#                summary_op=None,
#                feed_dict=None):
#   def begin(self):
#   def after_create_session(self, session, coord):
#   def end(self, session):
# def _scaffold_with_init(scaffold, saver, checkpoint_path):
#   def restore_checkpoint(_, session):
# def evaluate_repeatedly(checkpoint_dir,
#                         master='',
#                         scaffold=None,
#                         eval_ops=None,
#                         feed_dict=None,
#                         final_ops=None,
#                         final_ops_feed_dict=None,
#                         eval_interval_secs=60,
#                         hooks=None,
#                         config=None,
#                         max_number_of_evaluations=None,
#                         timeout=None,
#                         timeout_fn=None):
# class SummaryAtEndHook(session_run_hook.SessionRunHook):


from tf_slim.training import evaluation
from tensorflow.python.summary import summary
from tensorflow.python.training import monitored_session
from tensorflow.python.training import saver as tf_saver
  log_dir = '/tmp/my_model_eval/'

  # Evaluate once every 10 minutes.
  slim.evaluation_loop(
      master='',
      checkpoint_dir,
      logdir,
      num_evals=1,
      summary_op=tf.compat.v1.summary.merge(summary_ops),
      eval_interval_secs=600)

"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# pylint:disable=g-direct-tensorflow-import
# pylint:enable=g-direct-tensorflow-import

__all__ = [
    'StopAfterNEvalsHook',
    'SummaryAtEndHook',
    'checkpoints_iterator',
    'evaluate_once',
    'evaluation_loop',
    'get_or_create_eval_step',
    'wait_for_new_checkpoint',
]


# Path: tf_slim/training/evaluation.py
# def wait_for_new_checkpoint(checkpoint_dir,
#                             last_checkpoint=None,
#                             seconds_to_sleep=1,
#                             timeout=None):
# def checkpoints_iterator(checkpoint_dir,
#                          min_interval_secs=0,
#                          timeout=None,
#                          timeout_fn=None):
#   def __init__(self,
#                log_dir=None,
#                summary_writer=None,
#                summary_op=None,
#                feed_dict=None):
#   def begin(self):
#   def after_create_session(self, session, coord):
#   def end(self, session):
# def _scaffold_with_init(scaffold, saver, checkpoint_path):
#   def restore_checkpoint(_, session):
# def evaluate_repeatedly(checkpoint_dir,
#                         master='',
#                         scaffold=None,
#                         eval_ops=None,
#                         feed_dict=None,
#                         final_ops=None,
#                         final_ops_feed_dict=None,
#                         eval_interval_secs=60,
#                         hooks=None,
#                         config=None,
#                         max_number_of_evaluations=None,
#                         timeout=None,
#                         timeout_fn=None):
# class SummaryAtEndHook(session_run_hook.SessionRunHook):


# Path: tf_slim/evaluation.py
from tf_slim.training import evaluation
from tensorflow.python.summary import summary
from tensorflow.python.training import monitored_session
from tensorflow.python.training import saver as tf_saver

  log_dir = '/tmp/my_model_eval/'

  # Evaluate once every 10 minutes.
  slim.evaluation_loop(
      master='',
      checkpoint_dir,
      logdir,
      num_evals=1,
      summary_op=tf.compat.v1.summary.merge(summary_ops),
      eval_interval_secs=600)

"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# pylint:disable=g-direct-tensorflow-import
# pylint:enable=g-direct-tensorflow-import

__all__ = [
    'StopAfterNEvalsHook',
    'SummaryAtEndHook',
    'checkpoints_iterator',
    'evaluate_once',
    'evaluation_loop',
    'get_or_create_eval_step',
    'wait_for_new_checkpoint',
]


StopAfterNEvalsHook = evaluation.StopAfterNEvalsHook