# Ultralytics YOLO 🚀, AGPL-3.0 license from pathlib import Path import numpy as np import torch from ultralytics.models.yolo.detect import DetectionValidator from ultralytics.utils import LOGGER, ops from ultralytics.utils.checks import check_requirements from ultralytics.utils.metrics import OKS_SIGMA, PoseMetrics, box_iou, kpt_iou from ultralytics.utils.plotting import output_to_target, plot_images class PoseValidator(DetectionValidator): """ A class extending the DetectionValidator class for validation based on a pose model. Example: ```python from ultralytics.models.yolo.pose import PoseValidator args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml') validator = PoseValidator(args=args) validator() ``` """ def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None): """Initialize a 'PoseValidator' object with custom parameters and assigned attributes.""" super().__init__(dataloader, save_dir, pbar, args, _callbacks) self.sigma = None self.kpt_shape = None self.args.task = "pose" self.metrics = PoseMetrics(save_dir=self.save_dir, on_plot=self.on_plot) if isinstance(self.args.device, str) and self.args.device.lower() == "mps": LOGGER.warning( "WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. " "See https://github.com/ultralytics/ultralytics/issues/4031." ) def preprocess(self, batch): """Preprocesses the batch by converting the 'keypoints' data into a float and moving it to the device.""" batch = super().preprocess(batch) batch["keypoints"] = batch["keypoints"].to(self.device).float() return batch def get_desc(self): """Returns description of evaluation metrics in string format.""" return ("%22s" + "%11s" * 10) % ( "Class", "Images", "Instances", "Box(P", "R", "mAP50", "mAP50-95)", "Pose(P", "R", "mAP50", "mAP50-95)", ) def postprocess(self, preds): """Apply non-maximum suppression and return detections with high confidence scores.""" return 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, ) def init_metrics(self, model): """Initiate pose estimation metrics for YOLO 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 self.stats = dict(tp_p=[], tp=[], conf=[], pred_cls=[], target_cls=[]) def _prepare_batch(self, si, batch): """Prepares a batch for processing by converting keypoints to float and moving to device.""" pbatch = super()._prepare_batch(si, batch) kpts = batch["keypoints"][batch["batch_idx"] == si] h, w = pbatch["imgsz"] kpts = kpts.clone() kpts[..., 0] *= w kpts[..., 1] *= h kpts = ops.scale_coords(pbatch["imgsz"], kpts, pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"]) pbatch["kpts"] = kpts return pbatch def _prepare_pred(self, pred, pbatch): """Prepares and scales keypoints in a batch for pose processing.""" predn = super()._prepare_pred(pred, pbatch) nk = pbatch["kpts"].shape[1] pred_kpts = predn[:, 6:].view(len(predn), nk, -1) ops.scale_coords(pbatch["imgsz"], pred_kpts, pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"]) return predn, pred_kpts def update_metrics(self, preds, batch): """Metrics.""" for si, pred in enumerate(preds): self.seen += 1 npr = len(pred) stat = dict( conf=torch.zeros(0, device=self.device), pred_cls=torch.zeros(0, device=self.device), tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device), tp_p=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device), ) pbatch = self._prepare_batch(si, batch) cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox") nl = len(cls) stat["target_cls"] = cls if npr == 0: if nl: for k in self.stats.keys(): self.stats[k].append(stat[k]) if self.args.plots: self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls) continue # Predictions if self.args.single_cls: pred[:, 5] = 0 predn, pred_kpts = self._prepare_pred(pred, pbatch) stat["conf"] = predn[:, 4] stat["pred_cls"] = predn[:, 5] # Evaluate if nl: stat["tp"] = self._process_batch(predn, bbox, cls) stat["tp_p"] = self._process_batch(predn, bbox, cls, pred_kpts, pbatch["kpts"]) if self.args.plots: self.confusion_matrix.process_batch(predn, bbox, cls) for k in self.stats.keys(): self.stats[k].append(stat[k]) # 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, gt_bboxes, gt_cls, pred_kpts=None, gt_kpts=None): """ Return correct prediction matrix. Args: detections (torch.Tensor): Tensor of shape [N, 6] representing detections. Each detection is of the format: x1, y1, x2, y2, conf, class. labels (torch.Tensor): Tensor of shape [M, 5] representing labels. Each label is of the format: class, x1, y1, x2, y2. pred_kpts (torch.Tensor, optional): Tensor of shape [N, 51] representing predicted keypoints. 51 corresponds to 17 keypoints each with 3 values. gt_kpts (torch.Tensor, optional): Tensor of shape [N, 51] representing ground truth keypoints. Returns: torch.Tensor: Correct prediction matrix of shape [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(gt_bboxes)[:, 2:].prod(1) * 0.53 iou = kpt_iou(gt_kpts, pred_kpts, sigma=self.sigma, area=area) else: # boxes iou = box_iou(gt_bboxes, detections[:, :4]) return self.match_predictions(detections[:, 5], gt_cls, iou) def plot_val_samples(self, batch, ni): """Plots and saves validation set samples with predicted bounding boxes and keypoints.""" 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, on_plot=self.on_plot, ) def plot_predictions(self, batch, preds, ni): """Plots predictions for YOLO model.""" pred_kpts = torch.cat([p[:, 6:].view(-1, *self.kpt_shape) for p in preds], 0) plot_images( batch["img"], *output_to_target(preds, max_det=self.args.max_det), kpts=pred_kpts, paths=batch["im_file"], fname=self.save_dir / f"val_batch{ni}_pred.jpg", names=self.names, on_plot=self.on_plot, ) # pred def pred_to_json(self, predn, filename): """Converts YOLO predictions to COCO JSON format.""" 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): """Evaluates object detection model using COCO JSON format.""" 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