93 lines
3.7 KiB
Python
93 lines
3.7 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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from copy import copy
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from ultralytics.models import yolo
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from ultralytics.nn.tasks import PoseModel
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from ultralytics.utils import DEFAULT_CFG, LOGGER
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from ultralytics.utils.plotting import plot_images, plot_results
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class PoseTrainer(yolo.detect.DetectionTrainer):
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"""
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A class extending the DetectionTrainer class for training YOLO pose estimation models.
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This trainer specializes in handling pose estimation tasks, managing model training, validation, and visualization
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of pose keypoints alongside bounding boxes.
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Attributes:
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args (Dict): Configuration arguments for training.
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model (PoseModel): The pose estimation model being trained.
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data (Dict): Dataset configuration including keypoint shape information.
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loss_names (Tuple[str]): Names of the loss components used in training.
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Methods:
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get_model: Retrieves a pose estimation model with specified configuration.
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set_model_attributes: Sets keypoints shape attribute on the model.
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get_validator: Creates a validator instance for model evaluation.
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plot_training_samples: Visualizes training samples with keypoints.
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plot_metrics: Generates and saves training/validation metric plots.
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Examples:
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>>> from ultralytics.models.yolo.pose import PoseTrainer
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>>> args = dict(model="yolo11n-pose.pt", data="coco8-pose.yaml", epochs=3)
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>>> trainer = PoseTrainer(overrides=args)
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>>> trainer.train()
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""Initialize a PoseTrainer object with specified configurations and overrides."""
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if overrides is None:
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overrides = {}
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overrides["task"] = "pose"
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super().__init__(cfg, overrides, _callbacks)
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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 get_model(self, cfg=None, weights=None, verbose=True):
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"""Get pose estimation model with specified configuration and weights."""
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model = PoseModel(cfg, ch=3, nc=self.data["nc"], data_kpt_shape=self.data["kpt_shape"], verbose=verbose)
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if weights:
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model.load(weights)
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return model
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def set_model_attributes(self):
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"""Sets keypoints shape attribute of PoseModel."""
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super().set_model_attributes()
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self.model.kpt_shape = self.data["kpt_shape"]
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def get_validator(self):
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"""Returns an instance of the PoseValidator class for validation."""
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self.loss_names = "box_loss", "pose_loss", "kobj_loss", "cls_loss", "dfl_loss"
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return yolo.pose.PoseValidator(
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self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
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)
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def plot_training_samples(self, batch, ni):
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"""Plot a batch of training samples with annotated class labels, bounding boxes, and keypoints."""
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images = batch["img"]
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kpts = batch["keypoints"]
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cls = batch["cls"].squeeze(-1)
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bboxes = batch["bboxes"]
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paths = batch["im_file"]
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batch_idx = batch["batch_idx"]
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plot_images(
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images,
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batch_idx,
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cls,
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bboxes,
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kpts=kpts,
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paths=paths,
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fname=self.save_dir / f"train_batch{ni}.jpg",
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on_plot=self.on_plot,
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)
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def plot_metrics(self):
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"""Plots training/val metrics."""
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plot_results(file=self.csv, pose=True, on_plot=self.on_plot) # save results.png
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