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# Ultralytics YOLO 🚀, AGPL-3.0 license
from copy import copy
from ultralytics.models import yolo
from ultralytics.nn.tasks import PoseModel
from ultralytics.utils import DEFAULT_CFG, LOGGER
from ultralytics.utils.plotting import plot_images, plot_results
class PoseTrainer(yolo.detect.DetectionTrainer):
"""
A class extending the DetectionTrainer class for training based on a pose model.
Example:
```python
from ultralytics.models.yolo.pose import PoseTrainer
args = dict(model='yolov8n-pose.pt', data='coco8-pose.yaml', epochs=3)
trainer = PoseTrainer(overrides=args)
trainer.train()
```
"""
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)
if isinstance(self.args.device, str) and self.args.device.lower() == "mps":
LOGGER.warning(
"WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
"See https://github.com/ultralytics/ultralytics/issues/4031."
)
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 yolo.pose.PoseValidator(
self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks
)
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
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