Source code for gluoncv.utils.metrics.coco_keypoints

"""MS COCO Key Points Evaluate Metrics."""
from __future__ import absolute_import

import os
from os import path as osp
from collections import OrderedDict
import warnings
    from mxnet.metric import EvalMetric
except ImportError:
    from mxnet.gluon.metric import EvalMetric

[docs]class COCOKeyPointsMetric(EvalMetric): """Detection metric for COCO bbox task. Parameters ---------- dataset : instance of The validation dataset. save_prefix : str Prefix for the saved JSON results. use_time : bool Append unique datetime string to created JSON file name if ``True``. cleanup : bool Remove created JSON file if ``True``. in_vis_thresh : float Detection results with confident scores smaller than ``in_vis_thresh`` will be discarded before saving to results. data_shape : tuple of int, default is None If `data_shape` is provided as (height, width), we will rescale bounding boxes when saving the predictions. This is helpful when SSD/YOLO box predictions cannot be rescaled conveniently. Note that the data_shape must be fixed for all validation images. """ def __init__(self, dataset, save_prefix, use_time=True, cleanup=False, in_vis_thresh=0.2, data_shape=None): super(COCOKeyPointsMetric, self).__init__('COCOMeanAP') self.dataset = dataset self._img_ids = sorted(dataset.coco.getImgIds()) self._recorded_ids = {} self._cleanup = cleanup self._results = [] self._in_vis_thresh = in_vis_thresh if isinstance(data_shape, (tuple, list)): assert len(data_shape) == 2, "Data shape must be (height, width)" elif not data_shape: data_shape = None else: raise ValueError("data_shape must be None or tuple of int as (height, width)") self._data_shape = data_shape if use_time: import datetime t ='_%Y_%m_%d_%H_%M_%S') else: t = '' self._filename = osp.abspath(osp.expanduser(save_prefix) + t + '.json') try: f = open(self._filename, 'w') except IOError as e: raise RuntimeError("Unable to open json file to dump. What(): {}".format(str(e))) else: f.close() def __del__(self): if self._cleanup: try: os.remove(self._filename) except IOError as err: warnings.warn(str(err))
[docs] def reset(self): self._recorded_ids = {} self._results = []
def _update(self): """Use coco to get real scores. """ import json try: with open(self._filename, 'w') as f: json.dump(self._results, f) except IOError as e: raise RuntimeError("Unable to dump json file, ignored. What(): {}".format(str(e))) pred = self.dataset.coco.loadRes(self._filename) gt = self.dataset.coco # lazy import pycocotools from import try_import_pycocotools try_import_pycocotools() from pycocotools.cocoeval import COCOeval coco_eval = COCOeval(gt, pred, 'keypoints') coco_eval.params.useSegm = None coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() self._coco_eval = coco_eval return coco_eval
[docs] def get(self): """Get evaluation metrics. """ # call real update coco_eval = self._update() stats_names = ['AP', 'Ap .5', 'AP .75', 'AP (M)', 'AP (L)', 'AR', 'AR .5', 'AR .75', 'AR (M)', 'AR (L)'] info_str = [] for ind, name in enumerate(stats_names): info_str.append((name, coco_eval.stats[ind])) name_value = OrderedDict(info_str) return name_value, name_value['AP']
# pylint: disable=arguments-differ, unused-argument, missing-docstring
[docs] def update(self, preds, maxvals, score, imgid, *args, **kwargs): # import pdb; pdb.set_trace() num_joints = preds.shape[1] in_vis_thresh = self._in_vis_thresh for idx, kpt in enumerate(preds): kpt = [] kpt_score = 0 count = 0 for i in range(num_joints): kpt += preds[idx][i].asnumpy().tolist() mval = float(maxvals[idx][i].asscalar()) kpt.append(mval) if mval > in_vis_thresh: kpt_score += mval count += 1 if count > 0: kpt_score /= count rescore = kpt_score * score[idx].asscalar() self._results.append({'image_id': int(imgid[idx].asscalar()), 'category_id': 1, 'keypoints': kpt, 'score': rescore}) self._recorded_ids[int(imgid[idx].asscalar())] = True