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from collections import OrderedDict |
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from mmcv.utils import print_log |
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from mmdet.core import eval_map, eval_recalls |
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from .builder import DATASETS |
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from .xml_style import XMLDataset |
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@DATASETS.register_module() |
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class VOCDataset(XMLDataset): |
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CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', |
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'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', |
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'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', |
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'tvmonitor') |
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def __init__(self, **kwargs): |
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super(VOCDataset, self).__init__(**kwargs) |
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if 'VOC2007' in self.img_prefix: |
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self.year = 2007 |
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elif 'VOC2012' in self.img_prefix: |
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self.year = 2012 |
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else: |
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raise ValueError('Cannot infer dataset year from img_prefix') |
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def evaluate(self, |
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results, |
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metric='mAP', |
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logger=None, |
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proposal_nums=(100, 300, 1000), |
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iou_thr=0.5, |
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scale_ranges=None): |
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"""Evaluate in VOC protocol. |
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Args: |
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results (list[list | tuple]): Testing results of the dataset. |
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metric (str | list[str]): Metrics to be evaluated. Options are |
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'mAP', 'recall'. |
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logger (logging.Logger | str, optional): Logger used for printing |
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related information during evaluation. Default: None. |
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proposal_nums (Sequence[int]): Proposal number used for evaluating |
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recalls, such as recall@100, recall@1000. |
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Default: (100, 300, 1000). |
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iou_thr (float | list[float]): IoU threshold. Default: 0.5. |
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scale_ranges (list[tuple], optional): Scale ranges for evaluating |
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mAP. If not specified, all bounding boxes would be included in |
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evaluation. Default: None. |
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Returns: |
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dict[str, float]: AP/recall metrics. |
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""" |
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if not isinstance(metric, str): |
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assert len(metric) == 1 |
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metric = metric[0] |
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allowed_metrics = ['mAP', 'recall'] |
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if metric not in allowed_metrics: |
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raise KeyError(f'metric {metric} is not supported') |
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annotations = [self.get_ann_info(i) for i in range(len(self))] |
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eval_results = OrderedDict() |
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iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr |
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if metric == 'mAP': |
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assert isinstance(iou_thrs, list) |
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if self.year == 2007: |
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ds_name = 'voc07' |
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else: |
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ds_name = self.CLASSES |
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mean_aps = [] |
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for iou_thr in iou_thrs: |
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print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}') |
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mean_ap, _ = eval_map( |
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results, |
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annotations, |
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scale_ranges=None, |
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iou_thr=iou_thr, |
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dataset=ds_name, |
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logger=logger) |
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mean_aps.append(mean_ap) |
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eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3) |
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eval_results['mAP'] = sum(mean_aps) / len(mean_aps) |
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elif metric == 'recall': |
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gt_bboxes = [ann['bboxes'] for ann in annotations] |
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recalls = eval_recalls( |
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gt_bboxes, results, proposal_nums, iou_thr, logger=logger) |
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for i, num in enumerate(proposal_nums): |
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for j, iou in enumerate(iou_thr): |
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eval_results[f'recall@{num}@{iou}'] = recalls[i, j] |
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if recalls.shape[1] > 1: |
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ar = recalls.mean(axis=1) |
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for i, num in enumerate(proposal_nums): |
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eval_results[f'AR@{num}'] = ar[i] |
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return eval_results |
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