o V hC @sXddlmZddlmZddlmZddlmZmZddlm Z m Z GdddeZ d S) )copy)DetectionTrainer)RTDETRDetectionModel)RANKcolorstr) RTDETRDatasetRTDETRValidatorc@s,eZdZdZd ddZd ddZd d ZdS) 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[str]): 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. 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() NTcCs6t||jd|jd|otdkd}|r|||S)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)r chverbose)rdatarload)selfcfgweightsrmodelrS/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/models/rtdetr/train.py get_model&s$  zRTDETRTrainer.get_modelvalcCs^t||jj||dk|jd|jjpd|jjpdt|d|jj|j|dkr+|jjd Sdd S)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) rargsrr!r"rr$rr%)rrmodebatchrrr build_dataset7s      zRTDETRTrainer.build_datasetcCsd|_t|j|jt|jdS)zCReturns a DetectionValidator suitable for RT-DETR model validation.) giou_losscls_lossl1_loss)save_dirr&) loss_namesr test_loaderr-rr&)rrrr get_validatorRszRTDETRTrainer.get_validator)NNT)rN)__name__ __module__ __qualname____doc__rr)r0rrrrr s    r N) rultralytics.models.yolo.detectrultralytics.nn.tasksrultralytics.utilsrrrrr r rrrrs