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78 lines
2.7 KiB
78 lines
2.7 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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from copy import copy
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from ultralytics.nn.tasks import PoseModel
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from ultralytics.yolo import v8
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from ultralytics.yolo.utils import DEFAULT_CFG
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from ultralytics.yolo.utils.plotting import plot_images, plot_results
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# BaseTrainer python usage
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class PoseTrainer(v8.detect.DetectionTrainer):
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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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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 v8.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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def train(cfg=DEFAULT_CFG, use_python=False):
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"""Train the YOLO model on the given data and device."""
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model = cfg.model or 'yolov8n-pose.yaml'
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data = cfg.data or 'coco8-pose.yaml'
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device = cfg.device if cfg.device is not None else ''
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args = dict(model=model, data=data, device=device)
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if use_python:
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from ultralytics import YOLO
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YOLO(model).train(**args)
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else:
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trainer = PoseTrainer(overrides=args)
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trainer.train()
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if __name__ == '__main__':
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train()
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