You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
77 lines
2.7 KiB
77 lines
2.7 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
|
|
|
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 |
|
|
|
|
|
# BaseTrainer python usage |
|
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) # save results.png |
|
|
|
|
|
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()
|
|
|