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Ссылка для ultralytics/models/yolo/pose/train.py

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ultralytics.models.yolo.pose.train.PoseTrainer

Базы: DetectionTrainer

Класс, расширяющий класс DetectionTrainer, для обучения на основе модели позы.

Пример
from ultralytics.models.yolo.pose import PoseTrainer

args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml', epochs=3)
trainer = PoseTrainer(overrides=args)
trainer.train()
Исходный код в ultralytics/models/yolo/pose/train.py
class PoseTrainer(yolo.detect.DetectionTrainer):
    """
    A class extending the DetectionTrainer class for training based on a pose model.

    Example:
        ```python
        from ultralytics.models.yolo.pose import PoseTrainer

        args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml', epochs=3)
        trainer = PoseTrainer(overrides=args)
        trainer.train()
        ```
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """Initialize a PoseTrainer object with specified configurations and overrides."""
        if overrides is None:
            overrides = {}
        overrides["task"] = "pose"
        super().__init__(cfg, overrides, _callbacks)

        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 get_model(self, cfg=None, weights=None, verbose=True):
        """Get pose estimation model with specified configuration and weights."""
        model = PoseModel(cfg, ch=3, nc=self.data["nc"], data_kpt_shape=self.data["kpt_shape"], verbose=verbose)
        if weights:
            model.load(weights)

        return model

    def set_model_attributes(self):
        """Sets keypoints shape attribute of PoseModel."""
        super().set_model_attributes()
        self.model.kpt_shape = self.data["kpt_shape"]

    def get_validator(self):
        """Returns 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 plot_training_samples(self, batch, ni):
        """Plot a batch of training samples with annotated class labels, bounding boxes, and keypoints."""
        images = batch["img"]
        kpts = batch["keypoints"]
        cls = batch["cls"].squeeze(-1)
        bboxes = batch["bboxes"]
        paths = batch["im_file"]
        batch_idx = batch["batch_idx"]
        plot_images(
            images,
            batch_idx,
            cls,
            bboxes,
            kpts=kpts,
            paths=paths,
            fname=self.save_dir / f"train_batch{ni}.jpg",
            on_plot=self.on_plot,
        )

    def plot_metrics(self):
        """Plots training/val metrics."""
        plot_results(file=self.csv, pose=True, on_plot=self.on_plot)  # save results.png

__init__(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Инициализируй объект PoseTrainer с указанными конфигурациями и переопределениями.

Исходный код в ultralytics/models/yolo/pose/train.py
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """Initialize a PoseTrainer object with specified configurations and overrides."""
    if overrides is None:
        overrides = {}
    overrides["task"] = "pose"
    super().__init__(cfg, overrides, _callbacks)

    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."
        )

get_model(cfg=None, weights=None, verbose=True)

Получи модель оценки позы с заданной конфигурацией и весами.

Исходный код в ultralytics/models/yolo/pose/train.py
def get_model(self, cfg=None, weights=None, verbose=True):
    """Get pose estimation model with specified configuration and weights."""
    model = PoseModel(cfg, ch=3, nc=self.data["nc"], data_kpt_shape=self.data["kpt_shape"], verbose=verbose)
    if weights:
        model.load(weights)

    return model

get_validator()

Возвращает экземпляр класса PoseValidator для проверки.

Исходный код в ultralytics/models/yolo/pose/train.py
def get_validator(self):
    """Returns 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
    )

plot_metrics()

Строит графики тренировочных/вальных показателей.

Исходный код в ultralytics/models/yolo/pose/train.py
def plot_metrics(self):
    """Plots training/val metrics."""
    plot_results(file=self.csv, pose=True, on_plot=self.on_plot)  # save results.png

plot_training_samples(batch, ni)

Нарисуй партию тренировочных образцов с аннотированными метками классов, ограничительными рамками и ключевыми точками.

Исходный код в ultralytics/models/yolo/pose/train.py
def plot_training_samples(self, batch, ni):
    """Plot a batch of training samples with annotated class labels, bounding boxes, and keypoints."""
    images = batch["img"]
    kpts = batch["keypoints"]
    cls = batch["cls"].squeeze(-1)
    bboxes = batch["bboxes"]
    paths = batch["im_file"]
    batch_idx = batch["batch_idx"]
    plot_images(
        images,
        batch_idx,
        cls,
        bboxes,
        kpts=kpts,
        paths=paths,
        fname=self.save_dir / f"train_batch{ni}.jpg",
        on_plot=self.on_plot,
    )

set_model_attributes()

Устанавливает атрибут формы ключевых точек модели PoseModel.

Исходный код в ultralytics/models/yolo/pose/train.py
def set_model_attributes(self):
    """Sets keypoints shape attribute of PoseModel."""
    super().set_model_attributes()
    self.model.kpt_shape = self.data["kpt_shape"]





Создано 2023-11-12, Обновлено 2023-11-25
Авторы: glenn-jocher (3)