2d34259281a6251cbdc67103ce8a1310010b8ceb,orbit/controller.py,Controller,evaluate,#Controller#,177

Before Change

      ValueError: If no checkpoint found in `self.checkpoint_manager.directory`.
      ValueError: If `evaluator` is not provided.
    
    if self.evaluator is None:
      raise ValueError("`evaluator` must be provided to call `evaluate()` "
                       "method.")

    steps = steps or -1
    current_step = self.global_step.numpy()
    if steps > 0:
      logging.info("Running %s steps of evaluation at train step: %s", steps,
                   current_step)
      steps = tf.convert_to_tensor(steps, dtype=tf.int32)
    else:
      logging.info("Evaluating at train step: %s", current_step)

    with self.eval_summary_manager.summary_writer().as_default():
      eval_outputs = self.evaluator.evaluate(steps)

    if eval_outputs:
      eval_outputs = tf.nest.map_structure(utils.get_value, eval_outputs)

    info = "step: {}        evaluation metric: {}".format(
        current_step, eval_outputs)
    _log_info(info)

After Change

    eval_output = tf.nest.map_structure(utils.get_value, eval_output or {})
    elapsed = time.time() - start

    _log(f" eval | step: {current_step: 6d} | "
         f"eval time: {elapsed: 6.1f} | "
         f"output: {_format_output(eval_output)}")

    self.eval_summary_manager.write_summaries(eval_output)
    self.eval_summary_manager.flush()

Instances