回传数据解析，兼容v5和v10
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jiajie555
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ultralytics/models/yolo/pose/val.py
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248
ultralytics/models/yolo/pose/val.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from pathlib import Path
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import numpy as np
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import torch
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from ultralytics.models.yolo.detect import DetectionValidator
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from ultralytics.utils import LOGGER, ops
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from ultralytics.utils.checks import check_requirements
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from ultralytics.utils.metrics import OKS_SIGMA, PoseMetrics, box_iou, kpt_iou
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from ultralytics.utils.plotting import output_to_target, plot_images
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class PoseValidator(DetectionValidator):
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"""
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A class extending the DetectionValidator class for validation based on a pose model.
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Example:
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```python
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from ultralytics.models.yolo.pose import PoseValidator
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args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml')
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validator = PoseValidator(args=args)
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validator()
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```
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"""
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def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
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"""Initialize a 'PoseValidator' object with custom parameters and assigned attributes."""
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super().__init__(dataloader, save_dir, pbar, args, _callbacks)
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self.sigma = None
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self.kpt_shape = None
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self.args.task = "pose"
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self.metrics = PoseMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
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if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
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LOGGER.warning(
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"WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
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"See https://github.com/ultralytics/ultralytics/issues/4031."
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)
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def preprocess(self, batch):
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"""Preprocesses the batch by converting the 'keypoints' data into a float and moving it to the device."""
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batch = super().preprocess(batch)
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batch["keypoints"] = batch["keypoints"].to(self.device).float()
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return batch
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def get_desc(self):
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"""Returns description of evaluation metrics in string format."""
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return ("%22s" + "%11s" * 10) % (
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"Class",
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"Images",
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"Instances",
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"Box(P",
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"R",
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"mAP50",
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"mAP50-95)",
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"Pose(P",
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"R",
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"mAP50",
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"mAP50-95)",
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)
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def postprocess(self, preds):
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"""Apply non-maximum suppression and return detections with high confidence scores."""
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return ops.non_max_suppression(
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preds,
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self.args.conf,
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self.args.iou,
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labels=self.lb,
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multi_label=True,
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agnostic=self.args.single_cls,
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max_det=self.args.max_det,
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nc=self.nc,
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)
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def init_metrics(self, model):
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"""Initiate pose estimation metrics for YOLO model."""
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super().init_metrics(model)
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self.kpt_shape = self.data["kpt_shape"]
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is_pose = self.kpt_shape == [17, 3]
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nkpt = self.kpt_shape[0]
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self.sigma = OKS_SIGMA if is_pose else np.ones(nkpt) / nkpt
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self.stats = dict(tp_p=[], tp=[], conf=[], pred_cls=[], target_cls=[])
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def _prepare_batch(self, si, batch):
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"""Prepares a batch for processing by converting keypoints to float and moving to device."""
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pbatch = super()._prepare_batch(si, batch)
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kpts = batch["keypoints"][batch["batch_idx"] == si]
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h, w = pbatch["imgsz"]
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kpts = kpts.clone()
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kpts[..., 0] *= w
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kpts[..., 1] *= h
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kpts = ops.scale_coords(pbatch["imgsz"], kpts, pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"])
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pbatch["kpts"] = kpts
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return pbatch
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def _prepare_pred(self, pred, pbatch):
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"""Prepares and scales keypoints in a batch for pose processing."""
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predn = super()._prepare_pred(pred, pbatch)
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nk = pbatch["kpts"].shape[1]
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pred_kpts = predn[:, 6:].view(len(predn), nk, -1)
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ops.scale_coords(pbatch["imgsz"], pred_kpts, pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"])
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return predn, pred_kpts
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def update_metrics(self, preds, batch):
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"""Metrics."""
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for si, pred in enumerate(preds):
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self.seen += 1
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npr = len(pred)
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stat = dict(
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conf=torch.zeros(0, device=self.device),
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pred_cls=torch.zeros(0, device=self.device),
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tp=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
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tp_p=torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device),
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)
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pbatch = self._prepare_batch(si, batch)
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cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
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nl = len(cls)
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stat["target_cls"] = cls
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if npr == 0:
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if nl:
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for k in self.stats.keys():
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self.stats[k].append(stat[k])
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if self.args.plots:
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self.confusion_matrix.process_batch(detections=None, gt_bboxes=bbox, gt_cls=cls)
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continue
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# Predictions
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if self.args.single_cls:
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pred[:, 5] = 0
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predn, pred_kpts = self._prepare_pred(pred, pbatch)
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stat["conf"] = predn[:, 4]
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stat["pred_cls"] = predn[:, 5]
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# Evaluate
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if nl:
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stat["tp"] = self._process_batch(predn, bbox, cls)
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stat["tp_p"] = self._process_batch(predn, bbox, cls, pred_kpts, pbatch["kpts"])
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if self.args.plots:
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self.confusion_matrix.process_batch(predn, bbox, cls)
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for k in self.stats.keys():
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self.stats[k].append(stat[k])
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# Save
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if self.args.save_json:
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self.pred_to_json(predn, batch["im_file"][si])
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# if self.args.save_txt:
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# save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
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def _process_batch(self, detections, gt_bboxes, gt_cls, pred_kpts=None, gt_kpts=None):
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"""
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Return correct prediction matrix.
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Args:
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detections (torch.Tensor): Tensor of shape [N, 6] representing detections.
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Each detection is of the format: x1, y1, x2, y2, conf, class.
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labels (torch.Tensor): Tensor of shape [M, 5] representing labels.
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Each label is of the format: class, x1, y1, x2, y2.
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pred_kpts (torch.Tensor, optional): Tensor of shape [N, 51] representing predicted keypoints.
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51 corresponds to 17 keypoints each with 3 values.
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gt_kpts (torch.Tensor, optional): Tensor of shape [N, 51] representing ground truth keypoints.
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Returns:
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torch.Tensor: Correct prediction matrix of shape [N, 10] for 10 IoU levels.
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"""
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if pred_kpts is not None and gt_kpts is not None:
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# `0.53` is from https://github.com/jin-s13/xtcocoapi/blob/master/xtcocotools/cocoeval.py#L384
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area = ops.xyxy2xywh(gt_bboxes)[:, 2:].prod(1) * 0.53
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iou = kpt_iou(gt_kpts, pred_kpts, sigma=self.sigma, area=area)
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else: # boxes
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iou = box_iou(gt_bboxes, detections[:, :4])
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return self.match_predictions(detections[:, 5], gt_cls, iou)
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def plot_val_samples(self, batch, ni):
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"""Plots and saves validation set samples with predicted bounding boxes and keypoints."""
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plot_images(
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batch["img"],
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batch["batch_idx"],
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batch["cls"].squeeze(-1),
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batch["bboxes"],
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kpts=batch["keypoints"],
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paths=batch["im_file"],
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fname=self.save_dir / f"val_batch{ni}_labels.jpg",
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names=self.names,
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on_plot=self.on_plot,
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)
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def plot_predictions(self, batch, preds, ni):
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"""Plots predictions for YOLO model."""
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pred_kpts = torch.cat([p[:, 6:].view(-1, *self.kpt_shape) for p in preds], 0)
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plot_images(
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batch["img"],
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*output_to_target(preds, max_det=self.args.max_det),
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kpts=pred_kpts,
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paths=batch["im_file"],
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fname=self.save_dir / f"val_batch{ni}_pred.jpg",
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names=self.names,
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on_plot=self.on_plot,
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) # pred
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def pred_to_json(self, predn, filename):
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"""Converts YOLO predictions to COCO JSON format."""
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stem = Path(filename).stem
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image_id = int(stem) if stem.isnumeric() else stem
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box = ops.xyxy2xywh(predn[:, :4]) # xywh
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box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
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for p, b in zip(predn.tolist(), box.tolist()):
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self.jdict.append(
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{
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"image_id": image_id,
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"category_id": self.class_map[int(p[5])],
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"bbox": [round(x, 3) for x in b],
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"keypoints": p[6:],
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"score": round(p[4], 5),
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}
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)
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def eval_json(self, stats):
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"""Evaluates object detection model using COCO JSON format."""
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if self.args.save_json and self.is_coco and len(self.jdict):
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anno_json = self.data["path"] / "annotations/person_keypoints_val2017.json" # annotations
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pred_json = self.save_dir / "predictions.json" # predictions
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LOGGER.info(f"\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...")
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try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
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check_requirements("pycocotools>=2.0.6")
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from pycocotools.coco import COCO # noqa
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from pycocotools.cocoeval import COCOeval # noqa
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for x in anno_json, pred_json:
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assert x.is_file(), f"{x} file not found"
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anno = COCO(str(anno_json)) # init annotations api
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pred = anno.loadRes(str(pred_json)) # init predictions api (must pass string, not Path)
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for i, eval in enumerate([COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "keypoints")]):
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if self.is_coco:
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eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files] # im to eval
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eval.evaluate()
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eval.accumulate()
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eval.summarize()
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idx = i * 4 + 2
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stats[self.metrics.keys[idx + 1]], stats[self.metrics.keys[idx]] = eval.stats[
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:2
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] # update mAP50-95 and mAP50
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except Exception as e:
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LOGGER.warning(f"pycocotools unable to run: {e}")
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return stats
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