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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 based on a pose model. |
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Example: |
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```python |
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from ultralytics.models.yolo.pose import PoseTrainer |
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args = dict(model='yolov8n-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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""" |
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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("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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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(self.test_loader, save_dir=self.save_dir, args=copy(self.args)) |
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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(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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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) |
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