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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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""" |
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A class extending the DetectionTrainer class for training based on a segmentation model. |
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Example: |
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```python |
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from ultralytics.models.yolo.segment import SegmentationTrainer |
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args = dict(model='yolov8n-seg.pt', data='coco8-seg.yaml', epochs=3) |
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trainer = SegmentationTrainer(overrides=args) |
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trainer.train() |
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``` |
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""" |
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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', 'reg_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) |
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