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

class PoseTrainer(yolo.detect.DetectionTrainer):
    """A class extending the DetectionTrainer class for training YOLO pose estimation models.

    This trainer specializes in handling pose estimation tasks, managing model training, validation, and visualization
    of pose keypoints alongside bounding boxes.

    Attributes:
        args (dict): Configuration arguments for training.
        model (PoseModel): The pose estimation model being trained.
        data (dict): Dataset configuration including keypoint shape information.
        loss_names (tuple): Names of the loss components used in training.

    Methods:
        get_model: Retrieve a pose estimation model with specified configuration.
        set_model_attributes: Set keypoints shape attribute on the model.
        get_validator: Create a validator instance for model evaluation.
        plot_training_samples: Visualize training samples with keypoints.
        get_dataset: Retrieve the dataset and ensure it contains required kpt_shape key.

    Examples:
        >>> from ultralytics.models.yolo.pose import PoseTrainer
        >>> args = dict(model="yolo11n-pose.pt", data="coco8-pose.yaml", epochs=3)
        >>> trainer = PoseTrainer(overrides=args)
        >>> trainer.train()
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides: dict[str, Any] | None = None, _callbacks=None):
        """Initialize a PoseTrainer object for training YOLO pose estimation models.

        Args:
            cfg (dict, optional): Default configuration dictionary containing training parameters.
            overrides (dict, optional): Dictionary of parameter overrides for the default configuration.
            _callbacks (list, optional): List of callback functions to be executed during training.

        Notes:
            This trainer will automatically set the task to 'pose' regardless of what is provided in overrides.
            A warning is issued when using Apple MPS device due to known bugs with pose models.
        """
        if overrides is None:
            overrides = {}
        overrides["task"] = "pose"
        super().__init__(cfg, overrides, _callbacks)

def get_dataset(self) -> dict[str, Any]:
    """Retrieve the dataset and ensure it contains the required `kpt_shape` key.

    Returns:
        (dict): A dictionary containing the training/validation/test dataset and category names.

    Raises:
        KeyError: If the `kpt_shape` key is not present in the dataset.
    """
    data = super().get_dataset()
    if "kpt_shape" not in data:
        raise KeyError(f"No `kpt_shape` in the {self.args.data}. See https://docs.ultralytics.com/datasets/pose/")
    return data

def get_model(
    self,
    cfg: str | Path | dict[str, Any] | None = None,
    weights: str | Path | None = None,
    verbose: bool = True,
) -> PoseModel:
    """Get pose estimation model with specified configuration and weights.

    Args:
        cfg (str | Path | dict, optional): Model configuration file path or dictionary.
        weights (str | Path, optional): Path to the model weights file.
        verbose (bool): Whether to display model information.

    Returns:
        (PoseModel): Initialized pose estimation model.
    """
    model = PoseModel(
        cfg, nc=self.data["nc"], ch=self.data["channels"], data_kpt_shape=self.data["kpt_shape"], verbose=verbose
    )
    if weights:
        model.load(weights)

    return model

def get_validator(self):
    """Return an instance of the PoseValidator class for validation."""
    self.loss_names = "box_loss", "pose_loss", "kobj_loss", "cls_loss", "dfl_loss"
    return yolo.pose.PoseValidator(
        self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
    )

def set_model_attributes(self):
    """Set keypoints shape attribute of PoseModel."""
    super().set_model_attributes()
    self.model.kpt_shape = self.data["kpt_shape"]
    kpt_names = self.data.get("kpt_names")
    if not kpt_names:
        names = list(map(str, range(self.model.kpt_shape[0])))
        kpt_names = {i: names for i in range(self.model.nc)}
    self.model.kpt_names = kpt_names