|hQddlmZddlmZddlmZmZmZmZddlm Z ddl m Z ddl m Z mZGdde jj Zy ) )copy)Path)AnyListOptionalUnion)yolo)OBBModel) DEFAULT_CFGRANKc eZdZdZeddfdeedeeeffd Z d dee e efdee e e fde d efd Zd ZxZS) OBBTraineram A class extending the DetectionTrainer class for training based on an Oriented Bounding Box (OBB) model. This trainer specializes in training YOLO models that detect oriented bounding boxes, which are useful for detecting objects at arbitrary angles rather than just axis-aligned rectangles. Attributes: loss_names (tuple): Names of the loss components used during training including box_loss, cls_loss, and dfl_loss. Methods: get_model: Return OBBModel initialized with specified config and weights. get_validator: Return an instance of OBBValidator for validation of YOLO model. Examples: >>> from ultralytics.models.yolo.obb import OBBTrainer >>> args = dict(model="yolo11n-obb.pt", data="dota8.yaml", epochs=3) >>> trainer = OBBTrainer(overrides=args) >>> trainer.train() N overrides _callbacksc:|i}d|d<t||||y)a Initialize an OBBTrainer object for training Oriented Bounding Box (OBB) models. This trainer extends the DetectionTrainer class to specialize in training models that detect oriented bounding boxes. It automatically sets the task to 'obb' in the configuration. Args: cfg (dict, optional): Configuration dictionary for the trainer. Contains training parameters and model configuration. overrides (dict, optional): Dictionary of parameter overrides for the configuration. Any values here will take precedence over those in cfg. _callbacks (List[Any], optional): List of callback functions to be invoked during training. Examples: >>> from ultralytics.models.yolo.obb import OBBTrainer >>> args = dict(model="yolo11n-obb.pt", data="dota8.yaml", epochs=3) >>> trainer = OBBTrainer(overrides=args) >>> trainer.train() Nobbtask)super__init__)selfcfgrr __class__s `/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/models/yolo/obb/train.pyrzOBBTrainer.__init__"s+(  I! & i4rweightsverbosereturnct||jd|jd|xr tdk(}|r|j||S)a Return OBBModel initialized with specified config and weights. Args: cfg (str | dict, optional): Model configuration. Can be a path to a YAML config file, a dictionary containing configuration parameters, or None to use default configuration. weights (str | Path, optional): Path to pretrained weights file. If None, random initialization is used. verbose (bool): Whether to display model information during initialization. Returns: (OBBModel): Initialized OBBModel with the specified configuration and weights. Examples: >>> trainer = OBBTrainer() >>> model = trainer.get_model(cfg="yolo11n-obb.yaml", weights="yolo11n-obb.pt") ncchannels)rchr)r datar load)rrrrmodels r get_modelzOBBTrainer.get_model;sF&4TYYz5JT[Tj`dhj`jk  JJw  rcd|_tjj|j|j t |j|jS)z@Return an instance of OBBValidator for validation of YOLO model.)box_losscls_lossdfl_loss)save_dirargsr) loss_namesr r OBBValidator test_loaderr+rr, callbacks)rs r get_validatorzOBBTrainer.get_validatorTsG<xx$$   t}}4 ?W[WeWe%  r)NNT)__name__ __module__ __qualname____doc__r rdictrrrrstrrboolr r&r1 __classcell__)rs@rrr s*'Dlp58D>5V^_cdg_hVi54swE#t),-?GcSWiHX?Yko 2 rrN)rpathlibrtypingrrrrultralytics.modelsr ultralytics.nn.tasksr ultralytics.utilsr r detectDetectionTrainerrrrrBs4--#)/M --M r