def _evaluate_predictions_on_oid()

in detic/evaluation/oideval.py [0:0]

• 55 lines of code
• 7 McCabe index (conditional complexity)

def _evaluate_predictions_on_oid(
    oid_gt, oid_results_path, eval_seg=False,
    class_names=None):
    logger = logging.getLogger(__name__)
    metrics = ["AP50", "AP50_expand"]

    results = {}
    oid_eval = OIDEval(oid_gt, oid_results_path, 'bbox', expand_pred_label=False)
    oid_eval.run()
    oid_eval.print_results()
    results["AP50"] = oid_eval.get_results()["AP50"]

    if eval_seg:
        oid_eval = OIDEval(oid_gt, oid_results_path, 'segm', expand_pred_label=False)
        oid_eval.run()
        oid_eval.print_results()
        results["AP50_segm"] = oid_eval.get_results()["AP50"]
    else:
        oid_eval = OIDEval(oid_gt, oid_results_path, 'bbox', expand_pred_label=True)
        oid_eval.run()
        oid_eval.print_results()
        results["AP50_expand"] = oid_eval.get_results()["AP50"]

    mAP = np.zeros(len(class_names)) - 1
    precisions = oid_eval.eval['precision']
    assert len(class_names) == precisions.shape[2]
    results_per_category = []
    id2apiid = sorted(oid_gt.get_cat_ids())
    inst_aware_ap, inst_count = 0, 0
    for idx, name in enumerate(class_names):
        precision = precisions[:, :, idx, 0]
        precision = precision[precision > -1]
        ap = np.mean(precision) if precision.size else float("nan")
        inst_num = len(oid_gt.get_ann_ids(cat_ids=[id2apiid[idx]]))
        if inst_num > 0:
            results_per_category.append(("{} {}".format(
                name.replace(' ', '_'), 
                inst_num if inst_num < 1000 else '{:.1f}k'.format(inst_num / 1000)), 
                float(ap * 100)))
            inst_aware_ap += inst_num * ap
            inst_count += inst_num
            mAP[idx] = ap
            # logger.info("{} {} {:.2f}".format(name, inst_num, ap * 100))
    inst_aware_ap = inst_aware_ap * 100 / inst_count
    N_COLS = min(6, len(results_per_category) * 2)
    results_flatten = list(itertools.chain(*results_per_category))
    results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
    table = tabulate(
        results_2d,
        tablefmt="pipe",
        floatfmt=".3f",
        headers=["category", "AP"] * (N_COLS // 2),
        numalign="left",
    )
    logger.info("Per-category {} AP: \n".format('bbox') + table)
    logger.info("Instance-aware {} AP: {:.4f}".format('bbox', inst_aware_ap))

    logger.info("Evaluation results for bbox: \n" + \
        create_small_table(results))
    return results, mAP