[Request] Making a python file to get AP/AR for each class? about simple-hrnet HOT 1 CLOSED

Hi,

To evaluate the accuracy on COCO, I used the official cocoapi and mirrored the code used in the official HRNet repository.
If you want to change the default behaviour, you can take a look at them and update the functions

• evaluate_overall_accuracy
  

  simple-HRNet/datasets/COCO.py

  Lines 328 to 390 in 2de1176

  	def evaluate_overall_accuracy(self, predictions, bounding_boxes, image_paths, output_dir, rank=0.):
  	res_folder = os.path.join(output_dir, 'results')
  	if not os.path.exists(res_folder):
  	os.makedirs(res_folder, 0o755, exist_ok=True)
  	res_file = os.path.join(res_folder, 'keypoints_{}_results_{}.json'.format(self.data_version, rank))
  	# person x (keypoints)
  	_kpts = []
  	for idx, kpt in enumerate(predictions):
  	_kpts.append({
  	'keypoints': kpt,
  	'center': bounding_boxes[idx][0:2],
  	'scale': bounding_boxes[idx][2:4],
  	'area': bounding_boxes[idx][4],
  	'score': bounding_boxes[idx][5],
  	'image': int(image_paths[idx][-16:-4])
  	})
  	# image x person x (keypoints)
  	kpts = defaultdict(list)
  	for kpt in _kpts:
  	kpts[kpt['image']].append(kpt)
  	# rescoring and oks nms
  	num_joints = self.nof_joints
  	in_vis_thre = self.in_vis_thre
  	oks_thre = self.oks_thre
  	oks_nmsed_kpts = []
  	for img in kpts.keys():
  	img_kpts = kpts[img]
  	for n_p in img_kpts:
  	box_score = n_p['score']
  	kpt_score = 0
  	valid_num = 0
  	for n_jt in range(0, num_joints):
  	t_s = n_p['keypoints'][n_jt][2]
  	if t_s > in_vis_thre:
  	kpt_score = kpt_score + t_s
  	valid_num = valid_num + 1
  	if valid_num != 0:
  	kpt_score = kpt_score / valid_num
  	# rescoring
  	n_p['score'] = kpt_score * box_score
  	if self.soft_nms:
  	keep = soft_oks_nms([img_kpts[i] for i in range(len(img_kpts))], oks_thre)
  	else:
  	keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))], oks_thre)
  	if len(keep) == 0:
  	oks_nmsed_kpts.append(img_kpts)
  	else:
  	oks_nmsed_kpts.append([img_kpts[_keep] for _keep in keep])
  	self._write_coco_keypoint_results(oks_nmsed_kpts, res_file)
  	if 'test' not in self.data_version:
  	info_str = self._do_python_keypoint_eval(res_file)
  	name_value = OrderedDict(info_str)
  	return name_value, name_value['AP']
  	else:
  	return {'Null': 0}, 0

• _do_python_keypoint_eval
  

  simple-HRNet/datasets/COCO.py

  Lines 574 to 588 in 2de1176

  	def _do_python_keypoint_eval(self, res_file):
  	coco_dt = self.coco.loadRes(res_file)
  	coco_eval = COCOeval(self.coco, coco_dt, 'keypoints')
  	coco_eval.params.useSegm = None
  	coco_eval.evaluate()
  	coco_eval.accumulate()
  	coco_eval.summarize()
  	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]))
  	return info_str

from simple-hrnet.