a yff0@sddlmZddlZddlZddlmZddlmZm Z ddl m Z ddl m Z mZmZmZddlmZmZGdd d eZdS) )PathN)DetectionValidator)LOGGERops)check_requirements) OKS_SIGMA PoseMetricsbox_ioukpt_iou)output_to_target plot_imagescseZdZdZdfdd ZfddZddZd d Zfd d Zfd dZ fddZ ddZ d ddZ ddZ ddZddZddZddZZS)! PoseValidatoraP 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() ``` Ncsft|||||d|_d|_d|j_t|j|jd|_ t |jj t rb|jj dkrbtddS)zSInitialize a 'PoseValidator' object with custom parameters and assigned attributes.NZpose)save_diron_plotZmpsuWARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. 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Args: detections (torch.Tensor): Tensor with shape (N, 6) representing detection boxes and scores, where each detection is of the format (x1, y1, x2, y2, conf, class). gt_bboxes (torch.Tensor): Tensor with shape (M, 4) representing ground truth bounding boxes, where each box is of the format (x1, y1, x2, y2). gt_cls (torch.Tensor): Tensor with shape (M,) representing ground truth class indices. pred_kpts (torch.Tensor | None): Optional tensor with shape (N, 51) representing predicted keypoints, where 51 corresponds to 17 keypoints each having 3 values. gt_kpts (torch.Tensor | None): Optional tensor with shape (N, 51) representing ground truth keypoints. Returns: torch.Tensor: A tensor with shape (N, 10) representing the correct prediction matrix for 10 IoU levels, where N is the number of detections. Example: ```python detections = torch.rand(100, 6) # 100 predictions: (x1, y1, x2, y2, conf, class) gt_bboxes = torch.rand(50, 4) # 50 ground truth boxes: (x1, y1, x2, y2) gt_cls = torch.randint(0, 2, (50,)) # 50 ground truth class indices pred_kpts = torch.rand(100, 51) # 100 predicted keypoints gt_kpts = torch.rand(50, 51) # 50 ground truth keypoints correct_preds = _process_batch(detections, gt_bboxes, gt_cls, pred_kpts, gt_kpts) ``` Note: `0.53` scale factor used in area computation is referenced from https://github.com/jin-s13/xtcocoapi/blob/master/xtcocotools/cocoeval.py#L384. 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