tensorflow/models · error · ValueError

Exact one metric must be present, but {0} are present.

Error message

Exact one metric must be present, but {0} are present.

What it means

Error "Exact one metric must be present, but {0} are present." thrown in tensorflow/models.

Source

Thrown at official/projects/volumetric_models/tasks/semantic_segmentation_3d.py:338

  def reduce_aggregated_logs(
      self,
      aggregated_logs: Optional[Mapping[str, Any]] = None,
      global_step: Optional[tf.Tensor] = None) -> Mapping[str, float]:
    """Reduces logs to obtain per-class metrics if needed.

    Args:
      aggregated_logs: An optional dictionary containing aggregated logs.
      global_step: An optional `tf.Tensor` of current global training steps.

    Returns:
      The reduced logs containing per-class metrics and overall metrics.

    Raises:
      ValueError: If `self.metrics` does not contain exactly 1 metric object.
    """
    result = {}
    if len(self.metrics) != 1:
      raise ValueError('Exact one metric must be present, but {0} are '
                       'present.'.format(len(self.metrics)))

    metric = self.metrics[0].result().numpy()
    if self.task_config.evaluation.report_per_class_metric:
      for i, metric_val in enumerate(metric):
        metric_name = self.metrics[0].name + '/class_{0}'.format(
            i - 1) if i > 0 else self.metrics[0].name
        result.update({metric_name: metric_val})
    else:
      result.update({self.metrics[0].name: metric})
    return result

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/projects/volumetric_models/tasks/semantic_segmentation_3d.py:338 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/9e345487075538bb. Report an issue: GitHub.