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| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| from copy import copy | |
| from ultralytics.models import yolo | |
| from ultralytics.nn.tasks import PoseModel | |
| from ultralytics.utils import DEFAULT_CFG, LOGGER | |
| from ultralytics.utils.plotting import plot_images, plot_results | |
| 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 | |