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66 lines
2.4 KiB
66 lines
2.4 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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from copy import copy
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from ultralytics.models import yolo
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from ultralytics.nn.tasks import SegmentationModel
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from ultralytics.utils import DEFAULT_CFG, RANK
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from ultralytics.utils.plotting import plot_images, plot_results
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class SegmentationTrainer(yolo.detect.DetectionTrainer):
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""Initialize a SegmentationTrainer object with given arguments."""
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if overrides is None:
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overrides = {}
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overrides['task'] = 'segment'
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super().__init__(cfg, overrides, _callbacks)
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def get_model(self, cfg=None, weights=None, verbose=True):
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"""Return SegmentationModel initialized with specified config and weights."""
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model = SegmentationModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
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if weights:
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model.load(weights)
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return model
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def get_validator(self):
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"""Return an instance of SegmentationValidator for validation of YOLO model."""
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self.loss_names = 'box_loss', 'seg_loss', 'cls_loss', 'dfl_loss'
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return yolo.segment.SegmentationValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
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def plot_training_samples(self, batch, ni):
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"""Creates a plot of training sample images with labels and box coordinates."""
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plot_images(batch['img'],
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batch['batch_idx'],
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batch['cls'].squeeze(-1),
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batch['bboxes'],
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batch['masks'],
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paths=batch['im_file'],
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fname=self.save_dir / f'train_batch{ni}.jpg',
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on_plot=self.on_plot)
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def plot_metrics(self):
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"""Plots training/val metrics."""
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plot_results(file=self.csv, segment=True, on_plot=self.on_plot) # save results.png
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def train(cfg=DEFAULT_CFG, use_python=False):
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"""Train a YOLO segmentation model based on passed arguments."""
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model = cfg.model or 'yolov8n-seg.pt'
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data = cfg.data or 'coco8-seg.yaml'
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device = cfg.device if cfg.device is not None else ''
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args = dict(model=model, data=data, device=device)
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if use_python:
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from ultralytics import YOLO
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YOLO(model).train(**args)
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else:
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trainer = SegmentationTrainer(overrides=args)
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trainer.train()
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if __name__ == '__main__':
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train()
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