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