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| # Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/evaluation/instance_evaluation.py | |
| import contextlib | |
| import copy | |
| import io | |
| import itertools | |
| import json | |
| import logging | |
| import numpy as np | |
| import os | |
| import pickle | |
| from collections import OrderedDict | |
| import pycocotools.mask as mask_util | |
| import torch | |
| from pycocotools.coco import COCO | |
| from pycocotools.cocoeval import COCOeval | |
| from tabulate import tabulate | |
| import detectron2.utils.comm as comm | |
| from detectron2.config import CfgNode | |
| from detectron2.data import MetadataCatalog | |
| from detectron2.data.datasets.coco import convert_to_coco_json | |
| from detectron2.evaluation.coco_evaluation import COCOEvaluator, _evaluate_predictions_on_coco | |
| from detectron2.evaluation.fast_eval_api import COCOeval_opt | |
| from detectron2.structures import Boxes, BoxMode, pairwise_iou | |
| from detectron2.utils.file_io import PathManager | |
| from detectron2.utils.logger import create_small_table | |
| # modified from COCOEvaluator for instance segmetnat | |
| class InstanceSegEvaluator(COCOEvaluator): | |
| """ | |
| Evaluate AR for object proposals, AP for instance detection/segmentation, AP | |
| for keypoint detection outputs using COCO's metrics. | |
| See http://cocodataset.org/#detection-eval and | |
| http://cocodataset.org/#keypoints-eval to understand its metrics. | |
| The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means | |
| the metric cannot be computed (e.g. due to no predictions made). | |
| In addition to COCO, this evaluator is able to support any bounding box detection, | |
| instance segmentation, or keypoint detection dataset. | |
| """ | |
| def _eval_predictions(self, predictions, img_ids=None): | |
| """ | |
| Evaluate predictions. Fill self._results with the metrics of the tasks. | |
| """ | |
| self._logger.info("Preparing results for COCO format ...") | |
| coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) | |
| tasks = self._tasks or self._tasks_from_predictions(coco_results) | |
| # unmap the category ids for COCO | |
| if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): | |
| dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id | |
| # all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) | |
| # num_classes = len(all_contiguous_ids) | |
| # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 | |
| reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} | |
| for result in coco_results: | |
| category_id = result["category_id"] | |
| # assert category_id < num_classes, ( | |
| # f"A prediction has class={category_id}, " | |
| # f"but the dataset only has {num_classes} classes and " | |
| # f"predicted class id should be in [0, {num_classes - 1}]." | |
| # ) | |
| assert category_id in reverse_id_mapping, ( | |
| f"A prediction has class={category_id}, " | |
| f"but the dataset only has class ids in {dataset_id_to_contiguous_id}." | |
| ) | |
| result["category_id"] = reverse_id_mapping[category_id] | |
| if self._output_dir: | |
| file_path = os.path.join(self._output_dir, "coco_instances_results.json") | |
| self._logger.info("Saving results to {}".format(file_path)) | |
| with PathManager.open(file_path, "w") as f: | |
| f.write(json.dumps(coco_results)) | |
| f.flush() | |
| if not self._do_evaluation: | |
| self._logger.info("Annotations are not available for evaluation.") | |
| return | |
| self._logger.info( | |
| "Evaluating predictions with {} COCO API...".format( | |
| "unofficial" if self._use_fast_impl else "official" | |
| ) | |
| ) | |
| for task in sorted(tasks): | |
| assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" | |
| coco_eval = ( | |
| _evaluate_predictions_on_coco( | |
| self._coco_api, | |
| coco_results, | |
| task, | |
| kpt_oks_sigmas=self._kpt_oks_sigmas, | |
| use_fast_impl=self._use_fast_impl, | |
| img_ids=img_ids, | |
| max_dets_per_image=self._max_dets_per_image, | |
| ) | |
| if len(coco_results) > 0 | |
| else None # cocoapi does not handle empty results very well | |
| ) | |
| res = self._derive_coco_results( | |
| coco_eval, task, class_names=self._metadata.get("thing_classes") | |
| ) | |
| self._results[task] = res | |