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Reference for ultralytics/models/yolo/pose/val.py

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/pose/val.py. If you spot a problem please help fix it by contributing a Pull Request ūüõ†ÔłŹ. Thank you ūüôŹ!


ultralytics.models.yolo.pose.val.PoseValidator

PoseValidator(dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None)

Bases: DetectionValidator

A class extending the DetectionValidator class for validation based on a pose model.

Example
from ultralytics.models.yolo.pose import PoseValidator

args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml')
validator = PoseValidator(args=args)
validator()
Source code in ultralytics/models/yolo/pose/val.py
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 ‚ö†ÔłŹ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
            "See https://github.com/ultralytics/ultralytics/issues/4031."
        )

eval_json

eval_json(stats)

Evaluates object detection model using COCO JSON format.

Source code in ultralytics/models/yolo/pose/val.py
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

get_desc

get_desc()

Returns description of evaluation metrics in string format.

Source code in ultralytics/models/yolo/pose/val.py
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)",
    )

init_metrics

init_metrics(model)

Initiate pose estimation metrics for YOLO model.

Source code in ultralytics/models/yolo/pose/val.py
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=[], target_img=[])

plot_predictions

plot_predictions(batch, preds, ni)

Plots predictions for YOLO model.

Source code in ultralytics/models/yolo/pose/val.py
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

plot_val_samples

plot_val_samples(batch, ni)

Plots and saves validation set samples with predicted bounding boxes and keypoints.

Source code in ultralytics/models/yolo/pose/val.py
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,
    )

postprocess

postprocess(preds)

Apply non-maximum suppression and return detections with high confidence scores.

Source code in ultralytics/models/yolo/pose/val.py
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,
    )

pred_to_json

pred_to_json(predn, filename)

Converts YOLO predictions to COCO JSON format.

Source code in ultralytics/models/yolo/pose/val.py
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),
            }
        )

preprocess

preprocess(batch)

Preprocesses the batch by converting the 'keypoints' data into a float and moving it to the device.

Source code in ultralytics/models/yolo/pose/val.py
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

save_one_txt

save_one_txt(predn, pred_kpts, save_conf, shape, file)

Save YOLO detections to a txt file in normalized coordinates in a specific format.

Source code in ultralytics/models/yolo/pose/val.py
def save_one_txt(self, predn, pred_kpts, save_conf, shape, file):
    """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
    from ultralytics.engine.results import Results

    Results(
        np.zeros((shape[0], shape[1]), dtype=np.uint8),
        path=None,
        names=self.names,
        boxes=predn[:, :6],
        keypoints=pred_kpts,
    ).save_txt(file, save_conf=save_conf)

update_metrics

update_metrics(preds, batch)

Metrics.

Source code in ultralytics/models/yolo/pose/val.py
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
        stat["target_img"] = cls.unique()
        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:
            self.save_one_txt(
                predn,
                pred_kpts,
                self.args.save_conf,
                pbatch["ori_shape"],
                self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt',
            )





Created 2023-11-12, Updated 2024-07-21
Authors: glenn-jocher (6), Burhan-Q (1)