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# Ultralytics YOLO 🚀, AGPL-3.0 license
from copy import copy
from ultralytics.models import yolo
from ultralytics.nn.tasks import SegmentationModel
from ultralytics.utils import DEFAULT_CFG, RANK
from ultralytics.utils.plotting import plot_images, plot_results
class SegmentationTrainer(yolo.detect.DetectionTrainer):
"""
A class extending the DetectionTrainer class for training based on a segmentation model.
Example:
```python
from ultralytics.models.yolo.segment import SegmentationTrainer
args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml', epochs=3)
trainer = SegmentationTrainer(overrides=args)
trainer.train()
```
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
"""Initialize a SegmentationTrainer object with given arguments."""
if overrides is None:
overrides = {}
overrides['task'] = 'segment'
super().__init__(cfg, overrides, _callbacks)
def get_model(self, cfg=None, weights=None, verbose=True):
"""Return SegmentationModel initialized with specified config and weights."""
model = SegmentationModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
if weights:
model.load(weights)
return model
def get_validator(self):
"""Return an instance of SegmentationValidator for validation of YOLO model."""
self.loss_names = 'box_loss', 'seg_loss', 'cls_loss', 'dfl_loss', 'reg_loss'
return yolo.segment.SegmentationValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
def plot_training_samples(self, batch, ni):
"""Creates a plot of training sample images with labels and box coordinates."""
plot_images(batch['img'],
batch['batch_idx'],
batch['cls'].squeeze(-1),
batch['bboxes'],
batch['masks'],
paths=batch['im_file'],
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, segment=True, on_plot=self.on_plot) # save results.png
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