(self, master_checkpoint,
                                inception_checkpoint=None)
return init_assign_fn
Creates an init operations to restore weights from various checkpoints.

    Args:
      master_checkpoint: path to a checkpoint which contains all weights for
        the whole model.
      inception_checkpoint: path to a checkpoint which contains weights for the
        inception part only.

    Returns:
      a function to run initialization ops.
Creates an init operations to restore weights from various checkpoints.
Creates an init operations to restore weights from various checkpoints .
def create_init_fn_to_restore(self, master_checkpoint,
                                inception_checkpoint=None):
    """Creates an init operations to restore weights from various checkpoints.

    Args:
      master_checkpoint: path to a checkpoint which contains all weights for
        the whole model.
      inception_checkpoint: path to a checkpoint which contains weights for the
        inception part only.

    Returns:
      a function to run initialization ops.
    """
    all_assign_ops = []
    all_feed_dict = {}

    def assign_from_checkpoint(variables, checkpoint):
      logging.info('Request to re-store %d weights from %s',
                   len(variables), checkpoint)
      if not variables:
        logging.error('Can\'t find any variables to restore.')
        sys.exit(1)
      assign_op, feed_dict = slim.assign_from_checkpoint(checkpoint, variables)
      all_assign_ops.append(assign_op)
      all_feed_dict.update(feed_dict)

    logging.info('variables_to_restore:\n%s' % utils.variables_to_restore().keys())
    logging.info('moving_average_variables:\n%s' % [v.op.name for v in tf.moving_average_variables()])
    logging.info('trainable_variables:\n%s' % [v.op.name for v in tf.trainable_variables()])
    if master_checkpoint:
      assign_from_checkpoint(utils.variables_to_restore(), master_checkpoint)

    if inception_checkpoint:
      variables = utils.variables_to_restore(
        'AttentionOcr_v1/conv_tower_fn/INCE', strip_scope=True)
      assign_from_checkpoint(variables, inception_checkpoint)

    def init_assign_fn(sess):
      logging.info('Restoring checkpoint(s)')
      sess.run(all_assign_ops, all_feed_dict)

    return init_assign_fn
def create_init_fn_to_restore ( self , master_checkpoint , inception_checkpoint = None ) : all_assign_ops = [ ] all_feed_dict = { } def assign_from_checkpoint ( variables , checkpoint ) : logging . info ( 'Request to re-store %d weights from %s' , len ( variables ) , checkpoint ) if not variables : logging . error ( 'Can\'t find any variables to restore.' ) sys . exit ( 1 ) assign_op , feed_dict = slim . assign_from_checkpoint ( checkpoint , variables ) all_assign_ops . append ( assign_op ) all_feed_dict . update ( feed_dict ) logging . info ( 'variables_to_restore:\n%s' % utils . variables_to_restore ( ) . keys ( ) ) logging . info ( 'moving_average_variables:\n%s' % [ v . op . name for v in tf . moving_average_variables ( ) ] ) logging . info ( 'trainable_variables:\n%s' % [ v . op . name for v in tf . trainable_variables ( ) ] ) if master_checkpoint : assign_from_checkpoint ( utils . variables_to_restore ( ) , master_checkpoint ) if inception_checkpoint : variables = utils . variables_to_restore ( 'AttentionOcr_v1/conv_tower_fn/INCE' , strip_scope = True ) assign_from_checkpoint ( variables , inception_checkpoint ) def init_assign_fn ( sess ) : logging . info ( 'Restoring checkpoint(s)' ) sess . run ( all_assign_ops , all_feed_dict ) return init_assign_fn