def coco_aggregation_result()

in lmms_eval/tasks/coco_cap/utils.py [0:0]

• 38 lines of code
• 8 McCabe index (conditional complexity)

def coco_aggregation_result(results, metric, args):
    scorers = [(Bleu(4), "Bleu_1"), (Bleu(4), "Bleu_2"), (Bleu(4), "Bleu_3"), (Bleu(4), "Bleu_4"), (Meteor(), "METEOR"), (Rouge(), "ROUGE_L"), (Cider(), "CIDEr"), (Spice(), "SPICE")]
    scorers_dict = {s[1]: s for s in scorers}

    stored_results = []
    # In order to make the coco eval tools to successfully create index
    # We need at least two dict in the dataset
    # 'annotation' and 'images'
    # 'annotation' exactly reproduce the original annotation
    # 'images' however only need the image id which is contained in the file name
    dataset = {"annotations": [], "images": []}
    idx = 0
    for result in results:
        stored_results.append({"image_id": int(result["image_id"]), "caption": result["pred"]})
        for a in result["answer"]:
            dataset["annotations"].append({"image_id": int(result["image_id"]), "caption": a, "id": idx})
            idx += 1
        dataset["images"].append({"id": result["image_id"]})

    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]

    path = generate_submission_file("coco_captions_val2014_alg_results.json", args)
    if not os.path.exists(path):
        eval_logger.info("Storing prediction that can be submitted to the server ...")
        with open(path, "w") as f:
            json.dump(stored_results, f, indent=4)

    return score