# Ultralytics YOLO 🚀, AGPL-3.0 license from pathlib import Path import numpy as np import torch from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, ops from ultralytics.yolo.utils.checks import check_requirements from ultralytics.yolo.utils.metrics import OKS_SIGMA, PoseMetrics, box_iou, kpt_iou from ultralytics.yolo.utils.plotting import output_to_target, plot_images from ultralytics.yolo.v8.detect import DetectionValidator class PoseValidator(DetectionValidator): def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None): super().__init__(dataloader, save_dir, pbar, args, _callbacks) self.args.task = 'pose' self.metrics = PoseMetrics(save_dir=self.save_dir) def preprocess(self, batch): batch = super().preprocess(batch) batch['keypoints'] = batch['keypoints'].to(self.device).float() return batch def get_desc(self): return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Pose(P', 'R', 'mAP50', 'mAP50-95)') def postprocess(self, preds): preds = ops.non_max_suppression(preds, self.args.conf, self.args.iou, labels=self.lb, multi_label=True, agnostic=self.args.single_cls, max_det=self.args.max_det, nc=self.nc) return preds def init_metrics(self, model): super().init_metrics(model) self.kpt_shape = self.data['kpt_shape'] is_pose = self.kpt_shape == [17, 3] nkpt = self.kpt_shape[0] self.sigma = OKS_SIGMA if is_pose else np.ones(nkpt) / nkpt def update_metrics(self, preds, batch): """Metrics.""" for si, pred in enumerate(preds): idx = batch['batch_idx'] == si cls = batch['cls'][idx] bbox = batch['bboxes'][idx] kpts = batch['keypoints'][idx] nl, npr = cls.shape[0], pred.shape[0] # number of labels, predictions nk = kpts.shape[1] # number of keypoints shape = batch['ori_shape'][si] correct_kpts = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device) # init self.seen += 1 if npr == 0: if nl: self.stats.append((correct_bboxes, correct_kpts, *torch.zeros( (2, 0), device=self.device), cls.squeeze(-1))) if self.args.plots: self.confusion_matrix.process_batch(detections=None, labels=cls.squeeze(-1)) continue # Predictions if self.args.single_cls: pred[:, 5] = 0 predn = pred.clone() ops.scale_boxes(batch['img'][si].shape[1:], predn[:, :4], shape, ratio_pad=batch['ratio_pad'][si]) # native-space pred pred_kpts = predn[:, 6:].view(npr, nk, -1) ops.scale_coords(batch['img'][si].shape[1:], pred_kpts, shape, ratio_pad=batch['ratio_pad'][si]) # Evaluate if nl: height, width = batch['img'].shape[2:] tbox = ops.xywh2xyxy(bbox) * torch.tensor( (width, height, width, height), device=self.device) # target boxes ops.scale_boxes(batch['img'][si].shape[1:], tbox, shape, ratio_pad=batch['ratio_pad'][si]) # native-space labels tkpts = kpts.clone() tkpts[..., 0] *= width tkpts[..., 1] *= height tkpts = ops.scale_coords(batch['img'][si].shape[1:], tkpts, shape, ratio_pad=batch['ratio_pad'][si]) labelsn = torch.cat((cls, tbox), 1) # native-space labels correct_bboxes = self._process_batch(predn[:, :6], labelsn) correct_kpts = self._process_batch(predn[:, :6], labelsn, pred_kpts, tkpts) if self.args.plots: self.confusion_matrix.process_batch(predn, labelsn) # Append correct_masks, correct_boxes, pconf, pcls, tcls self.stats.append((correct_bboxes, correct_kpts, pred[:, 4], pred[:, 5], cls.squeeze(-1))) # Save if self.args.save_json: self.pred_to_json(predn, batch['im_file'][si]) # if self.args.save_txt: # save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') def _process_batch(self, detections, labels, pred_kpts=None, gt_kpts=None): """ Return correct prediction matrix Arguments: detections (array[N, 6]), x1, y1, x2, y2, conf, class labels (array[M, 5]), class, x1, y1, x2, y2 pred_kpts (array[N, 51]), 51 = 17 * 3 gt_kpts (array[N, 51]) Returns: correct (array[N, 10]), for 10 IoU levels """ if pred_kpts is not None and gt_kpts is not None: # `0.53` is from https://github.com/jin-s13/xtcocoapi/blob/master/xtcocotools/cocoeval.py#L384 area = ops.xyxy2xywh(labels[:, 1:])[:, 2:].prod(1) * 0.53 iou = kpt_iou(gt_kpts, pred_kpts, sigma=self.sigma, area=area) else: # boxes iou = box_iou(labels[:, 1:], detections[:, :4]) correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool) correct_class = labels[:, 0:1] == detections[:, 5] for i in range(len(self.iouv)): x = torch.where((iou >= self.iouv[i]) & correct_class) # IoU > threshold and classes match if x[0].shape[0]: matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] if x[0].shape[0] > 1: matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 1], return_index=True)[1]] # matches = matches[matches[:, 2].argsort()[::-1]] matches = matches[np.unique(matches[:, 0], return_index=True)[1]] correct[matches[:, 1].astype(int), i] = True return torch.tensor(correct, dtype=torch.bool, device=detections.device) def plot_val_samples(self, batch, ni): plot_images(batch['img'], batch['batch_idx'], batch['cls'].squeeze(-1), batch['bboxes'], kpts=batch['keypoints'], paths=batch['im_file'], fname=self.save_dir / f'val_batch{ni}_labels.jpg', names=self.names) def plot_predictions(self, batch, preds, ni): pred_kpts = torch.cat([p[:, 6:].view(-1, *self.kpt_shape)[:15] for p in preds], 0) plot_images(batch['img'], *output_to_target(preds, max_det=15), kpts=pred_kpts, paths=batch['im_file'], fname=self.save_dir / f'val_batch{ni}_pred.jpg', names=self.names) # pred def pred_to_json(self, predn, filename): stem = Path(filename).stem image_id = int(stem) if stem.isnumeric() else stem box = ops.xyxy2xywh(predn[:, :4]) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner for p, b in zip(predn.tolist(), box.tolist()): self.jdict.append({ 'image_id': image_id, 'category_id': self.class_map[int(p[5])], 'bbox': [round(x, 3) for x in b], 'keypoints': p[6:], 'score': round(p[4], 5)}) def eval_json(self, stats): if self.args.save_json and self.is_coco and len(self.jdict): anno_json = self.data['path'] / 'annotations/person_keypoints_val2017.json' # annotations pred_json = self.save_dir / 'predictions.json' # predictions LOGGER.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...') try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb check_requirements('pycocotools>=2.0.6') from pycocotools.coco import COCO # noqa from pycocotools.cocoeval import COCOeval # noqa for x in anno_json, pred_json: assert x.is_file(), f'{x} file not found' anno = COCO(str(anno_json)) # init annotations api pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path) for i, eval in enumerate([COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'keypoints')]): if self.is_coco: eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # im to eval eval.evaluate() eval.accumulate() eval.summarize() idx = i * 4 + 2 stats[self.metrics.keys[idx + 1]], stats[ self.metrics.keys[idx]] = eval.stats[:2] # update mAP50-95 and mAP50 except Exception as e: LOGGER.warning(f'pycocotools unable to run: {e}') return stats def val(cfg=DEFAULT_CFG, use_python=False): model = cfg.model or 'yolov8n-pose.pt' data = cfg.data or 'coco8-pose.yaml' args = dict(model=model, data=data) if use_python: from ultralytics import YOLO YOLO(model).val(**args) else: validator = PoseValidator(args=args) validator(model=args['model']) if __name__ == '__main__': val()