lmms_eval/tasks/coco_cap/utils.py [64:90]:
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    coco = COCO()
    # Manually create index here
    coco.dataset = dataset
    coco.createIndex()

    coco_result = coco.loadRes(stored_results)
    coco_eval = COCOEvalCap(coco, coco_result)

    imgIds = coco_eval.params["image_id"]
    gts = {}
    res = {}
    for imgId in imgIds:
        gts[imgId] = coco_eval.coco.imgToAnns[imgId]
        res[imgId] = coco_eval.cocoRes.imgToAnns[imgId]

    eval_logger.info("tokenization...")
    tokenizer = PTBTokenizer()
    gts = tokenizer.tokenize(gts)
    res = tokenizer.tokenize(res)

    eval_logger.info(f"Computing {metric} scores...")

    score, scores = scorers_dict[metric][0].compute_score(gts, res)
    # When metric is one of the Bleu, score will be a list
    if type(score) == list:
        n = int(metric.split("_")[-1])
        score = score[n - 1]
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lmms_eval/tasks/refcoco/utils.py [73:101]:
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    coco = COCO()
    # Manually create index here
    coco.dataset = dataset
    coco.createIndex()

    coco_result = coco.loadRes(stored_results)
    coco_eval = COCOEvalCap(coco, coco_result)

    imgIds = coco_eval.params["image_id"]
    gts = {}
    res = {}
    for imgId in imgIds:
        gts[imgId] = coco_eval.coco.imgToAnns[imgId]
        res[imgId] = coco_eval.cocoRes.imgToAnns[imgId]

    eval_logger.info("tokenization...")
    tokenizer = PTBTokenizer()
    gts = tokenizer.tokenize(gts)
    res = tokenizer.tokenize(res)

    eval_logger.info(f"Computing {metric} scores...")

    score, scores = scorers_dict[metric][0].compute_score(gts, res)
    # coco_eval.setEval(score, metric)

    # When metric is one of the Bleu, score will be a list
    if type(score) == list:
        n = int(metric.split("_")[-1])
        score = score[n - 1]
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