|hjddlmZddlmZddlmZddlmZddlmZm Z ddl m Z m Z Gdd eZ y ) )copy)Optional)DetectionTrainer)RTDETRDetectionModel)RANKcolorstr) RTDETRDatasetRTDETRValidatorcTeZdZdZd deedeedefdZd deded ee fd Z d Z y) 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() Ncfgweightsverbosect||jd|jd|xr tdk(}|r|j||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)rchr)rdatarload)selfrrrmodels ^/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/models/rtdetr/train.py get_modelzRTDETRTrainer.get_model,sF%STYYt_:AV`g`vlptvlvw  JJw  img_pathmodebatchcft||jj||dk(|jd|jjxsd|jjxsdt |d|jj |j|dk(r|jj Sd 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?) rimgsz batch_sizeaugmenthyprectcache single_clsprefixclassesrfraction) r argsr"r'r(rr*rr+)rrrrs r build_datasetzRTDETRTrainer.build_dataset=s))//GO ))//)Tyy++4utfBK(II%%+/7?TYY''  AD  rczd|_t|j|jt |j S)zBReturn a DetectionValidator suitable for RT-DETR model validation.) giou_losscls_lossl1_loss)save_dirr,) loss_namesr test_loaderr2rr,)rs r get_validatorzRTDETRTrainer.get_validatorXs-<t//$--dSWS\S\o^^r)NNT)valN) __name__ __module__ __qualname____doc__rdictstrboolrintr-r5rrr r sN<Xd^Xc]\`" c  Xc] 6_rr N)rtypingrultralytics.models.yolo.detectrultralytics.nn.tasksrultralytics.utilsrrr6r r r r?rrrDs*;5,/N_$N_r