# Copyright (c) OpenMMLab. All rights reserved. import itertools import os.path as osp import tempfile from collections import OrderedDict from typing import Dict import numpy as np from mmengine.fileio import load from mmengine.logging import MMLogger from terminaltables import AsciiTable from mmdet.datasets.api_wrappers import COCO, COCOeval from mmdet.evaluation.metrics import CocoMetric from mmdet.registry import METRICS @METRICS.register_module() class RF100CocoMetric(CocoMetric): """COCO evaluation metric. In the RF100 dataset, there are cases where the classes and sup_names are the same, which is incorrect, such as "bees-jt5in". Therefore, we need to handle this situation. """ def compute_metrics(self, results: list) -> Dict[str, float]: """Compute the metrics from processed results. Args: results (list): The processed results of each batch. Returns: Dict[str, float]: The computed metrics. The keys are the names of the metrics, and the values are corresponding results. """ logger: MMLogger = MMLogger.get_current_instance() # split gt and prediction list gts, preds = zip(*results) tmp_dir = None if self.outfile_prefix is None: tmp_dir = tempfile.TemporaryDirectory() outfile_prefix = osp.join(tmp_dir.name, 'results') else: outfile_prefix = self.outfile_prefix if self._coco_api is None: # use converted gt json file to initialize coco api logger.info('Converting ground truth to coco format...') coco_json_path = self.gt_to_coco_json( gt_dicts=gts, outfile_prefix=outfile_prefix) self._coco_api = COCO(coco_json_path) # handle lazy init if self.cat_ids is None: self.cat_ids = self._coco_api.get_cat_ids( cat_names=self.dataset_meta['classes']) # ---------------------------------- # We only change this if len(self.cat_ids) != len(self.dataset_meta['classes']): sup_id = self._coco_api.get_cat_ids(sup_names=['none']) self.cat_ids = [x for x in self.cat_ids if x not in sup_id] # ---------------------------------- if self.img_ids is None: self.img_ids = self._coco_api.get_img_ids() # convert predictions to coco format and dump to json file result_files = self.results2json(preds, outfile_prefix) eval_results = OrderedDict() if self.format_only: logger.info('results are saved in ' f'{osp.dirname(outfile_prefix)}') return eval_results for metric in self.metrics: logger.info(f'Evaluating {metric}...') # TODO: May refactor fast_eval_recall to an independent metric? # fast eval recall if metric == 'proposal_fast': ar = self.fast_eval_recall( preds, self.proposal_nums, self.iou_thrs, logger=logger) log_msg = [] for i, num in enumerate(self.proposal_nums): eval_results[f'AR@{num}'] = ar[i] log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}') log_msg = ''.join(log_msg) logger.info(log_msg) continue # evaluate proposal, bbox and segm iou_type = 'bbox' if metric == 'proposal' else metric if metric not in result_files: raise KeyError(f'{metric} is not in results') try: predictions = load(result_files[metric]) if iou_type == 'segm': # Refer to https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py#L331 # noqa # When evaluating mask AP, if the results contain bbox, # cocoapi will use the box area instead of the mask area # for calculating the instance area. Though the overall AP # is not affected, this leads to different # small/medium/large mask AP results. for x in predictions: x.pop('bbox') coco_dt = self._coco_api.loadRes(predictions) except IndexError: logger.error( 'The testing results of the whole dataset is empty.') break coco_eval = COCOeval(self._coco_api, coco_dt, iou_type) coco_eval.params.catIds = self.cat_ids coco_eval.params.imgIds = self.img_ids coco_eval.params.maxDets = list(self.proposal_nums) coco_eval.params.iouThrs = self.iou_thrs # mapping of cocoEval.stats coco_metric_names = { 'mAP': 0, 'mAP_50': 1, 'mAP_75': 2, 'mAP_s': 3, 'mAP_m': 4, 'mAP_l': 5, 'AR@100': 6, 'AR@300': 7, 'AR@1000': 8, 'AR_s@1000': 9, 'AR_m@1000': 10, 'AR_l@1000': 11 } metric_items = self.metric_items if metric_items is not None: for metric_item in metric_items: if metric_item not in coco_metric_names: raise KeyError( f'metric item "{metric_item}" is not supported') if metric == 'proposal': coco_eval.params.useCats = 0 coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() if metric_items is None: metric_items = [ 'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ] for item in metric_items: val = float( f'{coco_eval.stats[coco_metric_names[item]]:.3f}') eval_results[item] = val else: coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() if self.classwise: # Compute per-category AP # Compute per-category AP # from https://github.com/facebookresearch/detectron2/ precisions = coco_eval.eval['precision'] # precision: (iou, recall, cls, area range, max dets) assert len(self.cat_ids) == precisions.shape[2] results_per_category = [] for idx, cat_id in enumerate(self.cat_ids): t = [] # area range index 0: all area ranges # max dets index -1: typically 100 per image nm = self._coco_api.loadCats(cat_id)[0] precision = precisions[:, :, idx, 0, -1] precision = precision[precision > -1] if precision.size: ap = np.mean(precision) else: ap = float('nan') t.append(f'{nm["name"]}') t.append(f'{round(ap, 3)}') eval_results[f'{nm["name"]}_precision'] = round(ap, 3) # indexes of IoU @50 and @75 for iou in [0, 5]: precision = precisions[iou, :, idx, 0, -1] precision = precision[precision > -1] if precision.size: ap = np.mean(precision) else: ap = float('nan') t.append(f'{round(ap, 3)}') # indexes of area of small, median and large for area in [1, 2, 3]: precision = precisions[:, :, idx, area, -1] precision = precision[precision > -1] if precision.size: ap = np.mean(precision) else: ap = float('nan') t.append(f'{round(ap, 3)}') results_per_category.append(tuple(t)) num_columns = len(results_per_category[0]) results_flatten = list( itertools.chain(*results_per_category)) headers = [ 'category', 'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l' ] results_2d = itertools.zip_longest(*[ results_flatten[i::num_columns] for i in range(num_columns) ]) table_data = [headers] table_data += [result for result in results_2d] table = AsciiTable(table_data) logger.info('\n' + table.table) if metric_items is None: metric_items = [ 'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l' ] for metric_item in metric_items: key = f'{metric}_{metric_item}' val = coco_eval.stats[coco_metric_names[metric_item]] eval_results[key] = float(f'{round(val, 3)}') ap = coco_eval.stats[:6] logger.info(f'{metric}_mAP_copypaste: {ap[0]:.3f} ' f'{ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} ' f'{ap[4]:.3f} {ap[5]:.3f}') if tmp_dir is not None: tmp_dir.cleanup() return eval_results