# Copyright (c) OpenMMLab. All rights reserved. import itertools import logging import os.path as osp import tempfile import warnings from collections import OrderedDict, defaultdict from typing import Dict, List, Optional, Sequence, Union import numpy as np import torch from mmengine.dist import (all_gather_object, broadcast_object_list, is_main_process) from mmengine.evaluator import BaseMetric from mmengine.evaluator.metric import _to_cpu from mmengine.fileio import get_local_path from mmengine.logging import MMLogger, print_log from terminaltables import AsciiTable from mmdet.registry import METRICS from mmdet.structures.mask import encode_mask_results from ..functional import eval_recalls from .coco_metric import CocoMetric try: import lvis if getattr(lvis, '__version__', '0') >= '10.5.3': warnings.warn( 'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"', # noqa: E501 UserWarning) from lvis import LVIS, LVISEval, LVISResults except ImportError: lvis = None LVISEval = None LVISResults = None @METRICS.register_module() class LVISMetric(CocoMetric): """LVIS evaluation metric. Args: ann_file (str, optional): Path to the coco format annotation file. If not specified, ground truth annotations from the dataset will be converted to coco format. Defaults to None. metric (str | List[str]): Metrics to be evaluated. Valid metrics include 'bbox', 'segm', 'proposal', and 'proposal_fast'. Defaults to 'bbox'. classwise (bool): Whether to evaluate the metric class-wise. Defaults to False. proposal_nums (Sequence[int]): Numbers of proposals to be evaluated. Defaults to (100, 300, 1000). iou_thrs (float | List[float], optional): IoU threshold to compute AP and AR. If not specified, IoUs from 0.5 to 0.95 will be used. Defaults to None. metric_items (List[str], optional): Metric result names to be recorded in the evaluation result. Defaults to None. format_only (bool): Format the output results without perform evaluation. It is useful when you want to format the result to a specific format and submit it to the test server. Defaults to False. outfile_prefix (str, optional): The prefix of json files. It includes the file path and the prefix of filename, e.g., "a/b/prefix". If not specified, a temp file will be created. Defaults to None. collect_device (str): Device name used for collecting results from different ranks during distributed training. Must be 'cpu' or 'gpu'. Defaults to 'cpu'. prefix (str, optional): The prefix that will be added in the metric names to disambiguate homonymous metrics of different evaluators. If prefix is not provided in the argument, self.default_prefix will be used instead. Defaults to None. file_client_args (dict, optional): Arguments to instantiate the corresponding backend in mmdet <= 3.0.0rc6. Defaults to None. backend_args (dict, optional): Arguments to instantiate the corresponding backend. Defaults to None. """ default_prefix: Optional[str] = 'lvis' def __init__(self, ann_file: Optional[str] = None, metric: Union[str, List[str]] = 'bbox', classwise: bool = False, proposal_nums: Sequence[int] = (100, 300, 1000), iou_thrs: Optional[Union[float, Sequence[float]]] = None, metric_items: Optional[Sequence[str]] = None, format_only: bool = False, outfile_prefix: Optional[str] = None, collect_device: str = 'cpu', prefix: Optional[str] = None, file_client_args: dict = None, backend_args: dict = None) -> None: if lvis is None: raise RuntimeError( 'Package lvis is not installed. Please run "pip install ' 'git+https://github.com/lvis-dataset/lvis-api.git".') super().__init__(collect_device=collect_device, prefix=prefix) # coco evaluation metrics self.metrics = metric if isinstance(metric, list) else [metric] allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast'] for metric in self.metrics: if metric not in allowed_metrics: raise KeyError( "metric should be one of 'bbox', 'segm', 'proposal', " f"'proposal_fast', but got {metric}.") # do class wise evaluation, default False self.classwise = classwise # proposal_nums used to compute recall or precision. self.proposal_nums = list(proposal_nums) # iou_thrs used to compute recall or precision. if iou_thrs is None: iou_thrs = np.linspace( .5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True) self.iou_thrs = iou_thrs self.metric_items = metric_items self.format_only = format_only if self.format_only: assert outfile_prefix is not None, 'outfile_prefix must be not' 'None when format_only is True, otherwise the result files will' 'be saved to a temp directory which will be cleaned up at the end.' self.outfile_prefix = outfile_prefix self.backend_args = backend_args if file_client_args is not None: raise RuntimeError( 'The `file_client_args` is deprecated, ' 'please use `backend_args` instead, please refer to' 'https://github.com/open-mmlab/mmdetection/blob/main/configs/_base_/datasets/coco_detection.py' # noqa: E501 ) # if ann_file is not specified, # initialize lvis api with the converted dataset if ann_file is not None: with get_local_path( ann_file, backend_args=self.backend_args) as local_path: self._lvis_api = LVIS(local_path) else: self._lvis_api = None # handle dataset lazy init self.cat_ids = None self.img_ids = None def fast_eval_recall(self, results: List[dict], proposal_nums: Sequence[int], iou_thrs: Sequence[float], logger: Optional[MMLogger] = None) -> np.ndarray: """Evaluate proposal recall with LVIS's fast_eval_recall. Args: results (List[dict]): Results of the dataset. proposal_nums (Sequence[int]): Proposal numbers used for evaluation. iou_thrs (Sequence[float]): IoU thresholds used for evaluation. logger (MMLogger, optional): Logger used for logging the recall summary. Returns: np.ndarray: Averaged recall results. """ gt_bboxes = [] pred_bboxes = [result['bboxes'] for result in results] for i in range(len(self.img_ids)): ann_ids = self._lvis_api.get_ann_ids(img_ids=[self.img_ids[i]]) ann_info = self._lvis_api.load_anns(ann_ids) if len(ann_info) == 0: gt_bboxes.append(np.zeros((0, 4))) continue bboxes = [] for ann in ann_info: x1, y1, w, h = ann['bbox'] bboxes.append([x1, y1, x1 + w, y1 + h]) bboxes = np.array(bboxes, dtype=np.float32) if bboxes.shape[0] == 0: bboxes = np.zeros((0, 4)) gt_bboxes.append(bboxes) recalls = eval_recalls( gt_bboxes, pred_bboxes, proposal_nums, iou_thrs, logger=logger) ar = recalls.mean(axis=1) return ar # TODO: data_batch is no longer needed, consider adjusting the # parameter position def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: """Process one batch of data samples and predictions. The processed results should be stored in ``self.results``, which will be used to compute the metrics when all batches have been processed. Args: data_batch (dict): A batch of data from the dataloader. data_samples (Sequence[dict]): A batch of data samples that contain annotations and predictions. """ for data_sample in data_samples: result = dict() pred = data_sample['pred_instances'] result['img_id'] = data_sample['img_id'] result['bboxes'] = pred['bboxes'].cpu().numpy() result['scores'] = pred['scores'].cpu().numpy() result['labels'] = pred['labels'].cpu().numpy() # encode mask to RLE if 'masks' in pred: result['masks'] = encode_mask_results( pred['masks'].detach().cpu().numpy()) # some detectors use different scores for bbox and mask if 'mask_scores' in pred: result['mask_scores'] = pred['mask_scores'].cpu().numpy() # parse gt gt = dict() gt['width'] = data_sample['ori_shape'][1] gt['height'] = data_sample['ori_shape'][0] gt['img_id'] = data_sample['img_id'] if self._lvis_api is None: # TODO: Need to refactor to support LoadAnnotations assert 'instances' in data_sample, \ 'ground truth is required for evaluation when ' \ '`ann_file` is not provided' gt['anns'] = data_sample['instances'] # add converted result to the results list self.results.append((gt, result)) 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._lvis_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._lvis_api = LVIS(coco_json_path) # handle lazy init if self.cat_ids is None: self.cat_ids = self._lvis_api.get_cat_ids() if self.img_ids is None: self.img_ids = self._lvis_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 lvis_gt = self._lvis_api 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 try: lvis_dt = LVISResults(lvis_gt, result_files[metric]) except IndexError: logger.info( 'The testing results of the whole dataset is empty.') break iou_type = 'bbox' if metric == 'proposal' else metric lvis_eval = LVISEval(lvis_gt, lvis_dt, iou_type) lvis_eval.params.imgIds = self.img_ids metric_items = self.metric_items if metric == 'proposal': lvis_eval.params.useCats = 0 lvis_eval.params.maxDets = list(self.proposal_nums) lvis_eval.evaluate() lvis_eval.accumulate() lvis_eval.summarize() if metric_items is None: metric_items = ['AR@300', 'ARs@300', 'ARm@300', 'ARl@300'] for k, v in lvis_eval.get_results().items(): if k in metric_items: val = float('{:.3f}'.format(float(v))) eval_results[k] = val else: lvis_eval.evaluate() lvis_eval.accumulate() lvis_eval.summarize() lvis_results = lvis_eval.get_results() if self.classwise: # Compute per-category AP # Compute per-category AP # from https://github.com/facebookresearch/detectron2/ precisions = lvis_eval.eval['precision'] # precision: (iou, recall, cls, area range, max dets) assert len(self.cat_ids) == precisions.shape[2] results_per_category = [] for idx, catId in enumerate(self.cat_ids): # area range index 0: all area ranges # max dets index -1: typically 100 per image # the dimensions of precisions are # [num_thrs, num_recalls, num_cats, num_area_rngs] nm = self._lvis_api.load_cats([catId])[0] precision = precisions[:, :, idx, 0] precision = precision[precision > -1] if precision.size: ap = np.mean(precision) else: ap = float('nan') results_per_category.append( (f'{nm["name"]}', f'{float(ap):0.3f}')) eval_results[f'{nm["name"]}_precision'] = round(ap, 3) num_columns = min(6, len(results_per_category) * 2) results_flatten = list( itertools.chain(*results_per_category)) headers = ['category', 'AP'] * (num_columns // 2) 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 = [ 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'APr', 'APc', 'APf' ] for k, v in lvis_results.items(): if k in metric_items: key = '{}_{}'.format(metric, k) val = float('{:.3f}'.format(float(v))) eval_results[key] = val lvis_eval.print_results() if tmp_dir is not None: tmp_dir.cleanup() return eval_results def _merge_lists(listA, listB, maxN, key): result = [] indA, indB = 0, 0 while (indA < len(listA) or indB < len(listB)) and len(result) < maxN: if (indB < len(listB)) and (indA >= len(listA) or key(listA[indA]) < key(listB[indB])): result.append(listB[indB]) indB += 1 else: result.append(listA[indA]) indA += 1 return result @METRICS.register_module() class LVISFixedAPMetric(BaseMetric): default_prefix: Optional[str] = 'lvis_fixed_ap' def __init__(self, ann_file: str, topk: int = 10000, format_only: bool = False, outfile_prefix: Optional[str] = None, collect_device: str = 'cpu', prefix: Optional[str] = None, backend_args: dict = None) -> None: if lvis is None: raise RuntimeError( 'Package lvis is not installed. Please run "pip install ' 'git+https://github.com/lvis-dataset/lvis-api.git".') super().__init__(collect_device=collect_device, prefix=prefix) self.format_only = format_only if self.format_only: assert outfile_prefix is not None, 'outfile_prefix must be not' 'None when format_only is True, otherwise the result files will' 'be saved to a temp directory which will be cleaned up at the end.' self.outfile_prefix = outfile_prefix self.backend_args = backend_args with get_local_path( ann_file, backend_args=self.backend_args) as local_path: self._lvis_api = LVIS(local_path) self.cat_ids = self._lvis_api.get_cat_ids() self.results = {} self.topk = topk def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: """Process one batch of data samples and predictions. The processed results should be stored in ``self.results``, which will be used to compute the metrics when all batches have been processed. Args: data_batch (dict): A batch of data from the dataloader. data_samples (Sequence[dict]): A batch of data samples that contain annotations and predictions. """ cur_results = [] for data_sample in data_samples: pred = data_sample['pred_instances'] xmin, ymin, xmax, ymax = pred['bboxes'].cpu().unbind(1) boxes = torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1).tolist() scores = pred['scores'].cpu().numpy() labels = pred['labels'].cpu().numpy() if len(boxes) == 0: continue cur_results.extend([{ 'image_id': data_sample['img_id'], 'category_id': self.cat_ids[labels[k]], 'bbox': box, 'score': scores[k], } for k, box in enumerate(boxes)]) by_cat = defaultdict(list) for ann in cur_results: by_cat[ann['category_id']].append(ann) for cat, cat_anns in by_cat.items(): if cat not in self.results: self.results[cat] = [] cur = sorted( cat_anns, key=lambda x: x['score'], reverse=True)[:self.topk] self.results[cat] = _merge_lists( self.results[cat], cur, self.topk, key=lambda x: x['score']) def compute_metrics(self, results: dict) -> dict: logger: MMLogger = MMLogger.get_current_instance() new_results = [] missing_dets_cats = set() for cat, cat_anns in results.items(): if len(cat_anns) < self.topk: missing_dets_cats.add(cat) new_results.extend( sorted(cat_anns, key=lambda x: x['score'], reverse=True)[:self.topk]) if missing_dets_cats: logger.info( f'\n===\n' f'{len(missing_dets_cats)} classes had less than {self.topk} ' f'detections!\n Outputting {self.topk} detections for each ' f'class will improve AP further.\n ===') new_results = LVISResults(self._lvis_api, new_results, max_dets=-1) lvis_eval = LVISEval(self._lvis_api, new_results, iou_type='bbox') params = lvis_eval.params params.max_dets = -1 # No limit on detections per image. lvis_eval.run() lvis_eval.print_results() metrics = { k: v for k, v in lvis_eval.results.items() if k.startswith('AP') } logger.info(f'mAP_copypaste: {metrics}') return metrics def evaluate(self, size: int) -> dict: if len(self.results) == 0: print_log( f'{self.__class__.__name__} got empty `self.results`. Please ' 'ensure that the processed results are properly added into ' '`self.results` in `process` method.', logger='current', level=logging.WARNING) all_cats = all_gather_object(self.results) results = defaultdict(list) for cats in all_cats: for cat, cat_anns in cats.items(): results[cat].extend(cat_anns) if is_main_process(): # cast all tensors in results list to cpu results = _to_cpu(results) _metrics = self.compute_metrics(results) # type: ignore # Add prefix to metric names if self.prefix: _metrics = { '/'.join((self.prefix, k)): v for k, v in _metrics.items() } metrics = [_metrics] else: metrics = [None] # type: ignore broadcast_object_list(metrics) # reset the results self.results = {} return metrics[0]