|hddlmZddlmZddlmZmZmZddlmZddl m Z ddl m Z m Z ddlmZGdd ej j"Zy ) )copy)Path)DictOptionalUnion)yolo)SegmentationModel) DEFAULT_CFGRANK) plot_resultsczeZdZdZeddfdeeffd Z d deeee fdeee e fde fdZ d Z d ZxZS) SegmentationTrainera A class extending the DetectionTrainer class for training based on a segmentation model. This trainer specializes in handling segmentation tasks, extending the detection trainer with segmentation-specific functionality including model initialization, validation, and visualization. Attributes: loss_names (Tuple[str]): Names of the loss components used during training. Examples: >>> from ultralytics.models.yolo.segment import SegmentationTrainer >>> args = dict(model="yolo11n-seg.pt", data="coco8-seg.yaml", epochs=3) >>> trainer = SegmentationTrainer(overrides=args) >>> trainer.train() N overridesc:|i}d|d<t||||y)ad Initialize a SegmentationTrainer object. This initializes a trainer for segmentation tasks, extending the detection trainer with segmentation-specific functionality. It sets the task to 'segment' and prepares the trainer for training segmentation models. Args: cfg (dict): Configuration dictionary with default training settings. overrides (dict, optional): Dictionary of parameter overrides for the default configuration. _callbacks (list, optional): List of callback functions to be executed during training. Examples: >>> from ultralytics.models.yolo.segment import SegmentationTrainer >>> args = dict(model="yolo11n-seg.pt", data="coco8-seg.yaml", epochs=3) >>> trainer = SegmentationTrainer(overrides=args) >>> trainer.train() Nsegmenttask)super__init__)selfcfgr _callbacks __class__s d/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/models/yolo/segment/train.pyrzSegmentationTrainer.__init__s+$  I% & i4rweightsverbosect||jd|jd|xr tdk(}|r|j||S)a Initialize and return a SegmentationModel with specified configuration and weights. Args: cfg (dict | str, optional): Model configuration. Can be a dictionary, a path to a YAML file, or None. weights (str | Path, optional): Path to pretrained weights file. verbose (bool): Whether to display model information during initialization. Returns: (SegmentationModel): Initialized segmentation model with loaded weights if specified. Examples: >>> trainer = SegmentationTrainer() >>> model = trainer.get_model(cfg="yolo11n-seg.yaml") >>> model = trainer.get_model(weights="yolo11n-seg.pt", verbose=False) ncchannels)rchr)r datar load)rrrrmodels r get_modelzSegmentationTrainer.get_model5sF&"#$))D/dii >S]d]simqsist  JJw  rcd|_tjj|j|j t |j|jS)zIReturn an instance of SegmentationValidator for validation of YOLO model.)box_lossseg_losscls_lossdfl_loss)save_dirargsr) loss_namesrrSegmentationValidator test_loaderr+rr, callbacksrs r get_validatorz!SegmentationTrainer.get_validatorNsGH||11   t}}4 ?W[WeWe2  rcHt|jd|jy)z!Plot training/validation metrics.T)fileron_plotN)r csvr5r1s r plot_metricsz SegmentationTrainer.plot_metricsUs$((D$,,Gr)NNT)__name__ __module__ __qualname____doc__r rrrrstrrboolr%r2r7 __classcell__)rs@rrr sn 'DUY58D>50swE$),-?GcSWiHX?Yko2 HrrN)rpathlibrtypingrrrultralytics.modelsrultralytics.nn.tasksr ultralytics.utilsr r ultralytics.utils.plottingr detectDetectionTrainerrrrrHs9((#2/3JH$++66JHr