Source code for detectron2.evaluation.coco_evaluation

# Copyright (c) Facebook, Inc. and its affiliates.
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 import MetadataCatalog
from import convert_to_coco_json
from detectron2.structures import Boxes, BoxMode, pairwise_iou
from detectron2.utils.file_io import PathManager
from detectron2.utils.logger import create_small_table

from .evaluator import DatasetEvaluator

    from detectron2.evaluation.fast_eval_api import COCOeval_opt
except ImportError:
    COCOeval_opt = COCOeval

[docs]class COCOEvaluator(DatasetEvaluator): """ Evaluate AR for object proposals, AP for instance detection/segmentation, AP for keypoint detection outputs using COCO's metrics. See and 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. """
[docs] def __init__( self, dataset_name, tasks=None, distributed=True, output_dir=None, *, max_dets_per_image=None, use_fast_impl=True, kpt_oks_sigmas=(), allow_cached_coco=True, ): """ Args: dataset_name (str): name of the dataset to be evaluated. It must have either the following corresponding metadata: "json_file": the path to the COCO format annotation Or it must be in detectron2's standard dataset format so it can be converted to COCO format automatically. tasks (tuple[str]): tasks that can be evaluated under the given configuration. A task is one of "bbox", "segm", "keypoints". By default, will infer this automatically from predictions. distributed (True): if True, will collect results from all ranks and run evaluation in the main process. Otherwise, will only evaluate the results in the current process. output_dir (str): optional, an output directory to dump all results predicted on the dataset. The dump contains two files: 1. "instances_predictions.pth" a file that can be loaded with `torch.load` and contains all the results in the format they are produced by the model. 2. "coco_instances_results.json" a json file in COCO's result format. max_dets_per_image (int): limit on the maximum number of detections per image. By default in COCO, this limit is to 100, but this can be customized to be greater, as is needed in evaluation metrics AP fixed and AP pool (see This doesn't affect keypoint evaluation. use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP. Although the results should be very close to the official implementation in COCO API, it is still recommended to compute results with the official API for use in papers. The faster implementation also uses more RAM. kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS. See When empty, it will use the defaults in COCO. Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. allow_cached_coco (bool): Whether to use cached coco json from previous validation runs. You should set this to False if you need to use different validation data. Defaults to True. """ self._logger = logging.getLogger(__name__) self._distributed = distributed self._output_dir = output_dir if use_fast_impl and (COCOeval_opt is COCOeval):"Fast COCO eval is not built. Falling back to official COCO eval.") use_fast_impl = False self._use_fast_impl = use_fast_impl # COCOeval requires the limit on the number of detections per image (maxDets) to be a list # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the # 3rd element (100) is used as the limit on the number of detections per image when # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval, # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults. if max_dets_per_image is None: max_dets_per_image = [1, 10, 100] else: max_dets_per_image = [1, 10, max_dets_per_image] self._max_dets_per_image = max_dets_per_image if tasks is not None and isinstance(tasks, CfgNode): kpt_oks_sigmas = ( tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas ) self._logger.warn( "COCO Evaluator instantiated using config, this is deprecated behavior." " Please pass in explicit arguments instead." ) self._tasks = None # Infering it from predictions should be better else: self._tasks = tasks self._cpu_device = torch.device("cpu") self._metadata = MetadataCatalog.get(dataset_name) if not hasattr(self._metadata, "json_file"): if output_dir is None: raise ValueError( "output_dir must be provided to COCOEvaluator " "for datasets not in COCO format." )"Trying to convert '{dataset_name}' to COCO format ...") cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json") self._metadata.json_file = cache_path convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco) json_file = PathManager.get_local_path(self._metadata.json_file) with contextlib.redirect_stdout(io.StringIO()): self._coco_api = COCO(json_file) # Test set json files do not contain annotations (evaluation must be # performed using the COCO evaluation server). self._do_evaluation = "annotations" in self._coco_api.dataset if self._do_evaluation: self._kpt_oks_sigmas = kpt_oks_sigmas
[docs] def reset(self): self._predictions = []
[docs] def process(self, inputs, outputs): """ Args: inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). It is a list of dict. Each dict corresponds to an image and contains keys like "height", "width", "file_name", "image_id". outputs: the outputs of a COCO model. It is a list of dicts with key "instances" that contains :class:`Instances`. """ for input, output in zip(inputs, outputs): prediction = {"image_id": input["image_id"]} if "instances" in output: instances = output["instances"].to(self._cpu_device) prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) if "proposals" in output: prediction["proposals"] = output["proposals"].to(self._cpu_device) if len(prediction) > 1: self._predictions.append(prediction)
[docs] def evaluate(self, img_ids=None): """ Args: img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset """ if self._distributed: comm.synchronize() predictions = comm.gather(self._predictions, dst=0) predictions = list(itertools.chain(*predictions)) if not comm.is_main_process(): return {} else: predictions = self._predictions if len(predictions) == 0: self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") return {} if self._output_dir: PathManager.mkdirs(self._output_dir) file_path = os.path.join(self._output_dir, "instances_predictions.pth") with, "wb") as f:, f) self._results = OrderedDict() if "proposals" in predictions[0]: self._eval_box_proposals(predictions) if "instances" in predictions[0]: self._eval_predictions(predictions, img_ids=img_ids) # Copy so the caller can do whatever with results return copy.deepcopy(self._results)
def _tasks_from_predictions(self, predictions): """ Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions. """ tasks = {"bbox"} for pred in predictions: if "segmentation" in pred: tasks.add("segm") if "keypoints" in pred: tasks.add("keypoints") return sorted(tasks) def _eval_predictions(self, predictions, img_ids=None): """ Evaluate predictions. Fill self._results with the metrics of the tasks. """"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}]." ) result["category_id"] = reverse_id_mapping[category_id] if self._output_dir: file_path = os.path.join(self._output_dir, "coco_instances_results.json")"Saving results to {}".format(file_path)) with, "w") as f: f.write(json.dumps(coco_results)) f.flush() if not self._do_evaluation:"Annotations are not available for evaluation.") return "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, cocoeval_fn=COCOeval_opt if self._use_fast_impl else COCOeval, 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 def _eval_box_proposals(self, predictions): """ Evaluate the box proposals in predictions. Fill self._results with the metrics for "box_proposals" task. """ if self._output_dir: # Saving generated box proposals to file. # Predicted box_proposals are in XYXY_ABS mode. bbox_mode = BoxMode.XYXY_ABS.value ids, boxes, objectness_logits = [], [], [] for prediction in predictions: ids.append(prediction["image_id"]) boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) proposal_data = { "boxes": boxes, "objectness_logits": objectness_logits, "ids": ids, "bbox_mode": bbox_mode, } with, "box_proposals.pkl"), "wb") as f: pickle.dump(proposal_data, f) if not self._do_evaluation:"Annotations are not available for evaluation.") return"Evaluating bbox proposals ...") res = {} areas = {"all": "", "small": "s", "medium": "m", "large": "l"} for limit in [100, 1000]: for area, suffix in areas.items(): stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit) key = "AR{}@{:d}".format(suffix, limit) res[key] = float(stats["ar"].item() * 100)"Proposal metrics: \n" + create_small_table(res)) self._results["box_proposals"] = res def _derive_coco_results(self, coco_eval, iou_type, class_names=None): """ Derive the desired score numbers from summarized COCOeval. Args: coco_eval (None or COCOEval): None represents no predictions from model. iou_type (str): class_names (None or list[str]): if provided, will use it to predict per-category AP. Returns: a dict of {metric name: score} """ metrics = { "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], }[iou_type] if coco_eval is None: self._logger.warn("No predictions from the model!") return {metric: float("nan") for metric in metrics} # the standard metrics results = { metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") for idx, metric in enumerate(metrics) } "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) ) if not np.isfinite(sum(results.values())):"Some metrics cannot be computed and is shown as NaN.") if class_names is None or len(class_names) <= 1: return results # Compute per-category AP # from # noqa precisions = coco_eval.eval["precision"] # precision has dims (iou, recall, cls, area range, max dets) assert len(class_names) == precisions.shape[2] results_per_category = [] for idx, name in enumerate(class_names): # area range index 0: all area ranges # max dets index -1: typically 100 per image precision = precisions[:, :, idx, 0, -1] precision = precision[precision > -1] ap = np.mean(precision) if precision.size else float("nan") results_per_category.append(("{}".format(name), float(ap * 100))) # tabulate it N_COLS = min(6, len(results_per_category) * 2) results_flatten = list(itertools.chain(*results_per_category)) results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) table = tabulate( results_2d, tablefmt="pipe", floatfmt=".3f", headers=["category", "AP"] * (N_COLS // 2), numalign="left", )"Per-category {} AP: \n".format(iou_type) + table) results.update({"AP-" + name: ap for name, ap in results_per_category}) return results
def instances_to_coco_json(instances, img_id): """ Dump an "Instances" object to a COCO-format json that's used for evaluation. Args: instances (Instances): img_id (int): the image id Returns: list[dict]: list of json annotations in COCO format. """ num_instance = len(instances) if num_instance == 0: return [] boxes = instances.pred_boxes.tensor.numpy() boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) boxes = boxes.tolist() scores = instances.scores.tolist() classes = instances.pred_classes.tolist() has_mask = instances.has("pred_masks") if has_mask: # use RLE to encode the masks, because they are too large and takes memory # since this evaluator stores outputs of the entire dataset rles = [ mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] for mask in instances.pred_masks ] for rle in rles: # "counts" is an array encoded by mask_util as a byte-stream. Python3's # json writer which always produces strings cannot serialize a bytestream # unless you decode it. Thankfully, utf-8 works out (which is also what # the pycocotools/_mask.pyx does). rle["counts"] = rle["counts"].decode("utf-8") has_keypoints = instances.has("pred_keypoints") if has_keypoints: keypoints = instances.pred_keypoints results = [] for k in range(num_instance): result = { "image_id": img_id, "category_id": classes[k], "bbox": boxes[k], "score": scores[k], } if has_mask: result["segmentation"] = rles[k] if has_keypoints: # In COCO annotations, # keypoints coordinates are pixel indices. # However our predictions are floating point coordinates. # Therefore we subtract 0.5 to be consistent with the annotation format. # This is the inverse of data loading logic in `datasets/`. keypoints[k][:, :2] -= 0.5 result["keypoints"] = keypoints[k].flatten().tolist() results.append(result) return results # inspired from Detectron: # # noqa def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None): """ Evaluate detection proposal recall metrics. This function is a much faster alternative to the official COCO API recall evaluation code. However, it produces slightly different results. """ # Record max overlap value for each gt box # Return vector of overlap values areas = { "all": 0, "small": 1, "medium": 2, "large": 3, "96-128": 4, "128-256": 5, "256-512": 6, "512-inf": 7, } area_ranges = [ [0**2, 1e5**2], # all [0**2, 32**2], # small [32**2, 96**2], # medium [96**2, 1e5**2], # large [96**2, 128**2], # 96-128 [128**2, 256**2], # 128-256 [256**2, 512**2], # 256-512 [512**2, 1e5**2], ] # 512-inf assert area in areas, "Unknown area range: {}".format(area) area_range = area_ranges[areas[area]] gt_overlaps = [] num_pos = 0 for prediction_dict in dataset_predictions: predictions = prediction_dict["proposals"] # sort predictions in descending order # TODO maybe remove this and make it explicit in the documentation inds = predictions.objectness_logits.sort(descending=True)[1] predictions = predictions[inds] ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"]) anno = coco_api.loadAnns(ann_ids) gt_boxes = [ BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) for obj in anno if obj["iscrowd"] == 0 ] gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes gt_boxes = Boxes(gt_boxes) gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) if len(gt_boxes) == 0 or len(predictions) == 0: continue valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) gt_boxes = gt_boxes[valid_gt_inds] num_pos += len(gt_boxes) if len(gt_boxes) == 0: continue if limit is not None and len(predictions) > limit: predictions = predictions[:limit] overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) _gt_overlaps = torch.zeros(len(gt_boxes)) for j in range(min(len(predictions), len(gt_boxes))): # find which proposal box maximally covers each gt box # and get the iou amount of coverage for each gt box max_overlaps, argmax_overlaps = overlaps.max(dim=0) # find which gt box is 'best' covered (i.e. 'best' = most iou) gt_ovr, gt_ind = max_overlaps.max(dim=0) assert gt_ovr >= 0 # find the proposal box that covers the best covered gt box box_ind = argmax_overlaps[gt_ind] # record the iou coverage of this gt box _gt_overlaps[j] = overlaps[box_ind, gt_ind] assert _gt_overlaps[j] == gt_ovr # mark the proposal box and the gt box as used overlaps[box_ind, :] = -1 overlaps[:, gt_ind] = -1 # append recorded iou coverage level gt_overlaps.append(_gt_overlaps) gt_overlaps = (, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32) ) gt_overlaps, _ = torch.sort(gt_overlaps) if thresholds is None: step = 0.05 thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) recalls = torch.zeros_like(thresholds) # compute recall for each iou threshold for i, t in enumerate(thresholds): recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) # ar = 2 * np.trapz(recalls, thresholds) ar = recalls.mean() return { "ar": ar, "recalls": recalls, "thresholds": thresholds, "gt_overlaps": gt_overlaps, "num_pos": num_pos, } def _evaluate_predictions_on_coco( coco_gt, coco_results, iou_type, kpt_oks_sigmas=None, cocoeval_fn=COCOeval_opt, img_ids=None, max_dets_per_image=None, ): """ Evaluate the coco results using COCOEval API. """ assert len(coco_results) > 0 if iou_type == "segm": coco_results = copy.deepcopy(coco_results) # When evaluating mask AP, if the results contain bbox, cocoapi will # use the box area as the area of the instance, instead of the mask area. # This leads to a different definition of small/medium/large. # We remove the bbox field to let mask AP use mask area. for c in coco_results: c.pop("bbox", None) coco_dt = coco_gt.loadRes(coco_results) coco_eval = cocoeval_fn(coco_gt, coco_dt, iou_type) # For COCO, the default max_dets_per_image is [1, 10, 100]. if max_dets_per_image is None: max_dets_per_image = [1, 10, 100] # Default from COCOEval else: assert ( len(max_dets_per_image) >= 3 ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3" # In the case that user supplies a custom input for max_dets_per_image, # apply COCOevalMaxDets to evaluate AP with the custom input. if max_dets_per_image[2] != 100: coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type) if iou_type != "keypoints": coco_eval.params.maxDets = max_dets_per_image if img_ids is not None: coco_eval.params.imgIds = img_ids if iou_type == "keypoints": # Use the COCO default keypoint OKS sigmas unless overrides are specified if kpt_oks_sigmas: assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!" coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas) # COCOAPI requires every detection and every gt to have keypoints, so # we just take the first entry from both num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3 num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3 num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas) assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, ( f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. " f"Ground truth contains {num_keypoints_gt} keypoints. " f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. " "They have to agree with each other. For meaning of OKS, please refer to " "" ) coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() return coco_eval class COCOevalMaxDets(COCOeval): """ Modified version of COCOeval for evaluating AP with a custom maxDets (by default for COCO, maxDets is 100) """ def summarize(self): """ Compute and display summary metrics for evaluation results given a custom value for max_dets_per_image """ def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): p = self.params iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" titleStr = "Average Precision" if ap == 1 else "Average Recall" typeStr = "(AP)" if ap == 1 else "(AR)" iouStr = ( "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) if iouThr is None else "{:0.2f}".format(iouThr) ) aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] if ap == 1: # dimension of precision: [TxRxKxAxM] s = self.eval["precision"] # IoU if iouThr is not None: t = np.where(iouThr == p.iouThrs)[0] s = s[t] s = s[:, :, :, aind, mind] else: # dimension of recall: [TxKxAxM] s = self.eval["recall"] if iouThr is not None: t = np.where(iouThr == p.iouThrs)[0] s = s[t] s = s[:, :, aind, mind] if len(s[s > -1]) == 0: mean_s = -1 else: mean_s = np.mean(s[s > -1]) print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) return mean_s def _summarizeDets(): stats = np.zeros((12,)) # Evaluate AP using the custom limit on maximum detections per image stats[0] = _summarize(1, maxDets=self.params.maxDets[2]) stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) return stats def _summarizeKps(): stats = np.zeros((10,)) stats[0] = _summarize(1, maxDets=20) stats[1] = _summarize(1, maxDets=20, iouThr=0.5) stats[2] = _summarize(1, maxDets=20, iouThr=0.75) stats[3] = _summarize(1, maxDets=20, areaRng="medium") stats[4] = _summarize(1, maxDets=20, areaRng="large") stats[5] = _summarize(0, maxDets=20) stats[6] = _summarize(0, maxDets=20, iouThr=0.5) stats[7] = _summarize(0, maxDets=20, iouThr=0.75) stats[8] = _summarize(0, maxDets=20, areaRng="medium") stats[9] = _summarize(0, maxDets=20, areaRng="large") return stats if not self.eval: raise Exception("Please run accumulate() first") iouType = self.params.iouType if iouType == "segm" or iouType == "bbox": summarize = _summarizeDets elif iouType == "keypoints": summarize = _summarizeKps self.stats = summarize() def __str__(self): self.summarize()