()
return slim.assign_from_checkpoint_fn(
      checkpoint_path,
      variables_to_restore,
      ignore_missing_vars=FLAGS.ignore_missing_vars)
Returns a function run by the chief worker to warm-start the training.

  Note that the init_fn is only run when initializing the model during the very
  first global step.

  Returns:
    An init function run by the supervisor.
Returns a function run by the chief worker to warm-start the training.
Returns a function run by the chief worker to warm - start the training .
def _get_init_fn():
  """Returns a function run by the chief worker to warm-start the training.

  Note that the init_fn is only run when initializing the model during the very
  first global step.

  Returns:
    An init function run by the supervisor.
  """
  if FLAGS.checkpoint_path is None:
    return None

  # Warn the user if a checkpoint exists in the train_dir. Then we'll be
  # ignoring the checkpoint anyway.
  if tf.train.latest_checkpoint(FLAGS.train_dir):
    tf.logging.info(
        'Ignoring --checkpoint_path because a checkpoint already exists in %s'
        % FLAGS.train_dir)
    return None

  exclusions = []
  if FLAGS.checkpoint_exclude_scopes:
    exclusions = [scope.strip()
                  for scope in FLAGS.checkpoint_exclude_scopes.split(',')]

  # TODO(sguada) variables.filter_variables()
  variables_to_restore = []
  for var in slim.get_model_variables():
    excluded = False
    for exclusion in exclusions:
      if var.op.name.startswith(exclusion):
        excluded = True
        break
    if not excluded:
      variables_to_restore.append(var)

  if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
    checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
  else:
    checkpoint_path = FLAGS.checkpoint_path

  tf.logging.info('Fine-tuning from %s' % checkpoint_path)

  return slim.assign_from_checkpoint_fn(
      checkpoint_path,
      variables_to_restore,
      ignore_missing_vars=FLAGS.ignore_missing_vars)
def _get_init_fn ( ) : if FLAGS . checkpoint_path is None : return None # Warn the user if a checkpoint exists in the train_dir. Then we'll be # ignoring the checkpoint anyway. if tf . train . latest_checkpoint ( FLAGS . train_dir ) : tf . logging . info ( 'Ignoring --checkpoint_path because a checkpoint already exists in %s' % FLAGS . train_dir ) return None exclusions = [ ] if FLAGS . checkpoint_exclude_scopes : exclusions = [ scope . strip ( ) for scope in FLAGS . checkpoint_exclude_scopes . split ( ',' ) ] # TODO(sguada) variables.filter_variables() variables_to_restore = [ ] for var in slim . get_model_variables ( ) : excluded = False for exclusion in exclusions : if var . op . name . startswith ( exclusion ) : excluded = True break if not excluded : variables_to_restore . append ( var ) if tf . gfile . IsDirectory ( FLAGS . checkpoint_path ) : checkpoint_path = tf . train . latest_checkpoint ( FLAGS . checkpoint_path ) else : checkpoint_path = FLAGS . checkpoint_path tf . logging . info ( 'Fine-tuning from %s' % checkpoint_path ) return slim . assign_from_checkpoint_fn ( checkpoint_path , variables_to_restore , ignore_missing_vars = FLAGS . ignore_missing_vars )