| |
|
|
| from copy import copy |
|
|
| from ultralytics.nn.tasks import PoseModel |
| from ultralytics.yolo import v8 |
| from ultralytics.yolo.utils import DEFAULT_CFG |
| from ultralytics.yolo.utils.plotting import plot_images, plot_results |
|
|
|
|
| |
| class PoseTrainer(v8.detect.DetectionTrainer): |
|
|
| 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) |
|
|
| 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 v8.pose.PoseValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args)) |
|
|
| 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) |
|
|
|
|
| def train(cfg=DEFAULT_CFG, use_python=False): |
| """Train the YOLO model on the given data and device.""" |
| model = cfg.model or 'yolov8n-pose.yaml' |
| data = cfg.data or 'coco8-pose.yaml' |
| device = cfg.device if cfg.device is not None else '' |
|
|
| args = dict(model=model, data=data, device=device) |
| if use_python: |
| from ultralytics import YOLO |
| YOLO(model).train(**args) |
| else: |
| trainer = PoseTrainer(overrides=args) |
| trainer.train() |
|
|
|
|
| if __name__ == '__main__': |
| train() |
|
|
