Source code for mmdet.datasets.voc

from collections import OrderedDict

from mmcv.utils import print_log

from mmdet.core import eval_map, eval_recalls
from .builder import DATASETS
from .xml_style import XMLDataset

[docs]@DATASETS.register_module() class VOCDataset(XMLDataset): CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor') def __init__(self, **kwargs): super(VOCDataset, self).__init__(**kwargs) if 'VOC2007' in self.img_prefix: self.year = 2007 elif 'VOC2012' in self.img_prefix: self.year = 2012 else: raise ValueError('Cannot infer dataset year from img_prefix')
[docs] def evaluate(self, results, metric='mAP', logger=None, proposal_nums=(100, 300, 1000), iou_thr=0.5, scale_ranges=None): """Evaluate in VOC protocol. Args: results (list[list | tuple]): Testing results of the dataset. metric (str | list[str]): Metrics to be evaluated. Options are 'mAP', 'recall'. logger (logging.Logger | str, optional): 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], optional): Scale ranges for evaluating mAP. If not specified, all bounding boxes would be included in evaluation. Default: None. Returns: dict[str, float]: AP/recall metrics. """ 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))] eval_results = OrderedDict() iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr if metric == 'mAP': assert isinstance(iou_thrs, list) if self.year == 2007: ds_name = 'voc07' else: ds_name = self.CLASSES 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=None, iou_thr=iou_thr, dataset=ds_name, 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] recalls = eval_recalls( gt_bboxes, results, proposal_nums, iou_thrs, logger=logger) for i, num in enumerate(proposal_nums): for j, iou_thr in enumerate(iou_thrs): eval_results[f'recall@{num}@{iou_thr}'] = 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