def _evaluate_field(
        self,
        task_name: str,
        metric: Any,
        field: str,
        response_obj: Dict[str, Any],
        correct_answer: Dict[str, Any],
        query: Dict[str, Any],
        is_aux: bool = False,
    ) -> float:
        """Compute score for a single field using the given metric."""
        eval_context = self._get_eval_context(task_name, query)

        if metric == MetricType.UNSUPPORTED:
            print(f"The metric for {field} in task {task_name} is not supported")
            return 0.0
        elif metric == MetricType.SYMBOLIC_PLANNING_TEST or metric == MetricType.PROGRAM_JUDGE:
            query["scores"]["field"][field] = metric.match(
                response_obj.get(field),
                eval_context,
            )
        elif metric == MetricType.CONSTRAINED_GENERATION:
            score, eval_info = metric.match(response_obj, eval_context)
            query["scores"]["field"][field] = score
            query["scores"]["info"][field] = eval_info
        elif metric == MetricType.XML_NORM_POINT_IN_BBOX:
            score, eval_info = metric.match(response_obj.get(field), eval_context)
            query["scores"]["field"][field] = score
            query["scores"]["info"][field] = eval_info
        elif isinstance(metric, MetricType.VLM_AS_JUDGE.class_impl):
            images = query.get("images", [])
            question = query.get("question", "")
            correct_val = correct_answer.get(field, "") if not is_aux else correct_answer
            response_info = (
                response_obj.get(field)
                if isinstance(response_obj, dict)
                else response_obj
            )
            query["scores"]["field"][field] = metric.match(
                response_info,
                correct_val,
                images=images,
                question=question,
                eval_context=eval_context,
            )
        else:
            correct_val = correct_answer.get(field, "") if not is_aux else correct_answer
            correct_val = evaluate_as_string(correct_val)  # remove extra formatting
            predicted_val = response_obj.get(field, "")
            query["scores"]["field"][field] = metric.match(predicted_val, correct_val)
Compute score for a single field using the given metric.
vlmeval/dataset/utils/megabench/evaluator.py