hjSSKJr SSKJr SSKJr SSKJr SSKJrJ r SSK J r J r "SS \5r g ) ) annotations)copy)DetectionTrainer)RTDETRDetectionModel)RANKcolorstr) RTDETRDatasetRTDETRValidatorc:\rSrSrSrSS SjjrS S SjjrSrSrg) RTDETRTrainera[ Trainer class for the RT-DETR model developed by Baidu for real-time object detection. This class extends the DetectionTrainer class for YOLO to adapt to the specific features and architecture of RT-DETR. The model leverages Vision Transformers and has capabilities like IoU-aware query selection and adaptable inference speed. Attributes: loss_names (tuple): Names of the loss components used for training. data (dict): Dataset configuration containing class count and other parameters. args (dict): Training arguments and hyperparameters. save_dir (Path): Directory to save training results. test_loader (DataLoader): DataLoader for validation/testing data. Methods: get_model: Initialize and return an RT-DETR model for object detection tasks. build_dataset: Build and return an RT-DETR dataset for training or validation. get_validator: Return a DetectionValidator suitable for RT-DETR model validation. Notes: - F.grid_sample used in RT-DETR does not support the `deterministic=True` argument. - AMP training can lead to NaN outputs and may produce errors during bipartite graph matching. Examples: >>> from ultralytics.models.rtdetr.train import RTDETRTrainer >>> args = dict(model="rtdetr-l.yaml", data="coco8.yaml", imgsz=640, epochs=3) >>> trainer = RTDETRTrainer(overrides=args) >>> trainer.train() Nc[XRSURSU=(a [S:HS9nU(aURU5 U$)a  Initialize and return an RT-DETR model for object detection tasks. Args: cfg (dict, optional): Model configuration. weights (str, optional): Path to pre-trained model weights. verbose (bool): Verbose logging if True. Returns: (RTDETRDetectionModel): Initialized model. ncchannels)rchverbose)rdatarload)selfcfgweightsrmodels Y/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/models/rtdetr/train.py get_modelRTDETRTrainer.get_model-sD%SYYt_:AV`g`vlptvlvw  JJw  cp[UURRUUS:HURSURR=(d SURR=(d S[ US35URR URUS:XaURRS9 $SS9 $)a< Build and return an RT-DETR dataset for training or validation. Args: img_path (str): Path to the folder containing images. mode (str): Dataset mode, either 'train' or 'val'. batch (int, optional): Batch size for rectangle training. Returns: (RTDETRDataset): Dataset object for the specific mode. trainFNz: g?) img_pathimgsz batch_sizeaugmenthyprectcache single_clsprefixclassesrfraction) r argsr"r'r(rr*rr+)rr!modebatchs r build_datasetRTDETRTrainer.build_dataset>s))//GO ))//)Tyy++4utfBK(II%%+/7?TYY''  AD  rctSUl[URUR[ UR 5S9$)zBReturn a DetectionValidator suitable for RT-DETR model validation.) giou_losscls_lossl1_loss)save_dirr,) loss_namesr test_loaderr5rr,)rs r get_validatorRTDETRTrainer.get_validatorYs-<t//$--dSWS\S\o^^r)r6)NNT)rz dict | Nonerz str | Nonerbool)valN)r!strr-r<r.z int | None) __name__ __module__ __qualname____firstlineno____doc__rr/r8__static_attributes__rrr r s<" 6_rr N) __future__rrultralytics.models.yolo.detectrultralytics.nn.tasksrultralytics.utilsrrr;r r r rCrrrHs*#;5,/N_$N_r