ChartQA eval bug

https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/dataset/image_vqa.py
line 57

        elif listinstr(['ChartQA'], dataset):
            res = pool.map(partial(process_line, method='relaxed_accuracy'), lines)

https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/dataset/utils/vqa_eval.py

lines  272
    elif method == 'relaxed_accuracy':
        ret['gt'] = answers
        ret['pred'] = line['prediction'].strip()
        ret['match'] = [relaxed_correctness(ret['pred'], x) for x in ret['gt']]

but function relaxed_correctness is
relaxed_correctness(target: str,
prediction: str,
max_relative_change: float = 0.05

lines 171

# https://github.com/google-research/pix2struct/blob/main/pix2struct/metrics.py# 
def relaxed_correctness(target: str,
                        prediction: str,
                        max_relative_change: float = 0.05) -> bool:
    """Calculates relaxed correctness.

    The correctness tolerates certain error ratio defined by max_relative_change.
    See https://arxiv.org/pdf/2203.10244.pdf, end of section 5.1:
    “Following Methani et al. (2020), we use a relaxed accuracy measure for the
    numeric answers to allow a minor inaccuracy that may result from the automatic
    data extraction process. We consider an answer to be correct if it is within
    5% of the gold answer. For non-numeric answers, we still need an exact match
    to consider an answer to be correct.”

    Args:
      target: Target string.
      prediction: Predicted string.
      max_relative_change: Maximum relative change.

    Returns:
      Whether the prediction was correct given the specified tolerance.
    """

    def _to_float(text: str) -> Optional[float]:
        try:
            if text.endswith('%'):
                # Convert percentages to floats.
                return float(text.rstrip('%')) / 100.0
            else:
                return float(text)
        except ValueError:
            return None
    prediction = str(prediction)
    target = str(target)

Start with the ChartQA branch in vlmeval/dataset/image_vqa.py around line 57, then inspect the relaxed_accuracy handling in vlmeval/dataset/utils/vqa_eval.py around lines 171 and 272. Compare the argument order at the call site with relaxed_correctness and add or run focused evaluation coverage showing the corrected matching behavior.