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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" | |
| return yolo.segment.SegmentationValidator( | |
| self.test_loader, save_dir=self.save_dir, args=copy(self.args), _callbacks=self.callbacks | |
| ) | |
| 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"], | |
| masks=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 | |