Vehicle-cpp/hkpc/ultralytics-main/ultralytics/models/yolo/segment/train.py

# 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))

    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