def evaluate(
        self,
        results,
        metric="mAP",
        logger=None,
        proposal_nums=(100, 300, 1000),
        iou_thr=0.5,  #
        scale_ranges=None,
        **kwargs,
    ):
        """Evaluate the dataset.

        Args:
            **kwargs (dict): Keyword arguments to pass to self.evaluate object
            results (list): Testing results of the dataset.
            metric (str | list[str]): Metrics to be evaluated.
            logger (logging.Logger | None | str): Logger used for printing
                related information during evaluation. Default: None.
            proposal_nums (Sequence[int]): Proposal number used for evaluating
                recalls, such as recall@100, recall@1000.
                Default: (100, 300, 1000).
            iou_thr (float | list[float]): IoU threshold. Default: 0.5.
            scale_ranges (list[tuple] | None): Scale ranges for evaluating mAP.
                Default: None.

        Raises:
            KeyError: if a specified metric format is not supported

        Returns:
            OrderedDict: Evaluation metrics dictionary
        """
        if self.num_gpus > 1:
            results_ordered = []
            for i in range(self.num_gpus):
                results_ordered += results[i :: self.num_gpus]
            results = results_ordered

        if self.evaluator is None:
            if not isinstance(metric, str):
                assert len(metric) == 1
                metric = metric[0]
            allowed_metrics = ["mAP", "recall"]
            if metric not in allowed_metrics:
                raise KeyError(f"metric {metric} is not supported")
            annotations = [
                self.get_ann_info(i) for i in range(len(self))
            ]  # directly evaluate from hub
            eval_results = OrderedDict()
            iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr
            if metric == "mAP":
                assert isinstance(iou_thrs, list)
                mean_aps = []
                for iou_thr in iou_thrs:
                    print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}')
                    mean_ap, _ = eval_map(
                        results,
                        annotations,
                        scale_ranges=scale_ranges,
                        iou_thr=iou_thr,
                        dataset=self.CLASSES,
                        logger=logger,
                    )
                    mean_aps.append(mean_ap)
                    eval_results[f"AP{int(iou_thr * 100):02d}"] = round(mean_ap, 3)
                eval_results["mAP"] = sum(mean_aps) / len(mean_aps)
            elif metric == "recall":
                gt_bboxes = [ann["bboxes"] for ann in annotations]  # evaluate from hub
                recalls = eval_recalls(
                    gt_bboxes, results, proposal_nums, iou_thr, logger=logger
                )
                for i, num in enumerate(proposal_nums):
                    for j, iou in enumerate(iou_thrs):
                        eval_results[f"recall@{num}@{iou}"] = recalls[i, j]
                if recalls.shape[1] > 1:
                    ar = recalls.mean(axis=1)
                    for i, num in enumerate(proposal_nums):
                        eval_results[f"AR@{num}"] = ar[i]
            return eval_results

        return self.evaluator.evaluate(
            results,
            metric=metric,
            logger=logger,
            proposal_nums=proposal_nums,
            **kwargs,
        )
Evaluate the dataset.

        Args:
            **kwargs (dict): Keyword arguments to pass to self.evaluate object
            results (list): Testing results of the dataset.
            metric (str | list[str]): Metrics to be evaluated.
            logger (logging.Logger | None | str): Logger used for printing
                related information during evaluation. Default: None.
            proposal_nums (Sequence[int]): Proposal number used for evaluating
                recalls, such as recall@100, recall@1000.
                Default: (100, 300, 1000).
            iou_thr (float | list[float]): IoU threshold. Default: 0.5.
            scale_ranges (list[tuple] | None): Scale ranges for evaluating mAP.
                Default: None.

        Raises:
            KeyError: if a specified metric format is not supported

        Returns:
            OrderedDict: Evaluation metrics dictionary
        
python/deeplake/integrations/mmdet/mmdet_dataset_.py