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74 lines
2.8 KiB
74 lines
2.8 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 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 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) # save results.png
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