tensorflow/models · error · ValueError

if `eval_metric_fn` is specified, eval_metric_fn must be a c

Error message

if `eval_metric_fn` is specified, eval_metric_fn must be a callable.

What it means

Error "if `eval_metric_fn` is specified, eval_metric_fn must be a callable." thrown in tensorflow/models.

Source

Thrown at official/legacy/detection/executor/distributed_executor.py:371

      summary_writer_fn: function to create summary writer.
      init_checkpoint: function to load checkpoint.
      custom_callbacks: A list of Keras Callbacks objects to run during
        training. More specifically, `on_batch_begin()`, `on_batch_end()`,
        methods are invoked during training.
      continuous_eval: If `True`, will continously run evaluation on every
        available checkpoints. If `False`, will do the evaluation once after the
        final step.
      save_config: bool. Whether to save params to model_dir.

    Returns:
      The training loss and eval metrics.
    """
    assert train_input_fn is not None
    if train_metric_fn and not callable(train_metric_fn):
      raise ValueError('if `train_metric_fn` is specified, '
                       'train_metric_fn must be a callable.')
    if eval_metric_fn and not callable(eval_metric_fn):
      raise ValueError('if `eval_metric_fn` is specified, '
                       'eval_metric_fn must be a callable.')
    train_metric_fn = train_metric_fn or _no_metric
    eval_metric_fn = eval_metric_fn or _no_metric

    if custom_callbacks and iterations_per_loop != 1:
      logging.warning(
          'It is sematically wrong to run callbacks when '
          'iterations_per_loop is not one (%s)', iterations_per_loop)

    custom_callbacks = custom_callbacks or []

    def _run_callbacks_on_batch_begin(batch):
      """Runs custom callbacks at the start of every step."""
      if not custom_callbacks:
        return
      for callback in custom_callbacks:
        if callback:
          callback.on_batch_begin(batch)

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/legacy/detection/executor/distributed_executor.py:371 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/1de16db7c1a9c922. Report an issue: GitHub.