h:)SSKJr SSKrSSKrSSKJr SSKJr SSKrSSK r SSK J r SSK J r Jr SSKJr SSKJr SSKJr SS KJrJrJr SS KJr SS KJrJr SS KJrJ r "S S\5r!g)) annotationsN)copy)Any)build_dataloaderbuild_yolo_dataset) BaseTrainer)yolo)DetectionModel) DEFAULT_CFGLOGGERRANK)override_configs) plot_images plot_labels)torch_distributed_zero_first unwrap_modelc^\rSrSrSr\SS4SU4SjjjrSSSjjrSSSjjrSSjr Sr SSS jjr S r SSS jjr S rSS jrSrU4SjrSrU=r$)DetectionTraineraJ A class extending the BaseTrainer class for training based on a detection model. This trainer specializes in object detection tasks, handling the specific requirements for training YOLO models for object detection including dataset building, data loading, preprocessing, and model configuration. Attributes: model (DetectionModel): The YOLO detection model being trained. data (dict): Dictionary containing dataset information including class names and number of classes. loss_names (tuple): Names of the loss components used in training (box_loss, cls_loss, dfl_loss). Methods: build_dataset: Build YOLO dataset for training or validation. get_dataloader: Construct and return dataloader for the specified mode. preprocess_batch: Preprocess a batch of images by scaling and converting to float. set_model_attributes: Set model attributes based on dataset information. get_model: Return a YOLO detection model. get_validator: Return a validator for model evaluation. label_loss_items: Return a loss dictionary with labeled training loss items. progress_string: Return a formatted string of training progress. plot_training_samples: Plot training samples with their annotations. plot_training_labels: Create a labeled training plot of the YOLO model. auto_batch: Calculate optimal batch size based on model memory requirements. Examples: >>> from ultralytics.models.yolo.detect import DetectionTrainer >>> args = dict(model="yolo11n.pt", data="coco8.yaml", epochs=3) >>> trainer = DetectionTrainer(overrides=args) >>> trainer.train() Nc&>[TU]XU5 g)ax Initialize a DetectionTrainer object for training YOLO object detection model training. Args: cfg (dict, optional): Default configuration dictionary containing training parameters. overrides (dict, optional): Dictionary of parameter overrides for the default configuration. _callbacks (list, optional): List of callback functions to be executed during training. N)super__init__)selfcfg overrides _callbacks __class__s ^/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/models/yolo/detect/train.pyrDetectionTrainer.__init__8s 4c [[UR(a-[UR5RR5OS5S5n[ UR XURX"S:HUS9$)au Build YOLO Dataset for training or validation. Args: img_path (str): Path to the folder containing images. mode (str): 'train' mode or 'val' mode, users are able to customize different augmentations for each mode. batch (int, optional): Size of batches, this is for 'rect' mode. Returns: (Dataset): YOLO dataset object configured for the specified mode. r val)moderectstride)maxintmodelrr&rargsdata)rimg_pathr$batchgss r build_datasetDetectionTrainer.build_datasetCsXdjj\$**-4488:aPRT U!$))XdiidafYfoqrrr c US;d SUS35e[U5 URXU5nSSS5 US:Hn[WSS5(aU(a[R"S5 Sn[ UUUS:XaUR ROUR RS -UUUR R=(a US:HS 9$!,(df  N=f) a_ Construct and return dataloader for the specified mode. Args: dataset_path (str): Path to the dataset. batch_size (int): Number of images per batch. rank (int): Process rank for distributed training. mode (str): 'train' for training dataloader, 'val' for validation dataloader. Returns: (DataLoader): PyTorch dataloader object. >r#trainz#Mode must be 'train' or 'val', not .Nr2r%FzJ'rect=True' is incompatible with DataLoader shuffle, setting shuffle=False)r-workersshufflerank drop_last) rr/getattrr warningrr*r5compile)r dataset_path batch_sizer7r$datasetr6s rget_dataloaderDetectionTrainer.get_dataloaderRs''V+NtfTU)VV' )$ /((ZHG0'/ 7FE * *w NNg hG )-DII%%dii>O>ORS>Sii'';DGO   0 /s C CcUR5HYup#[U[R5(dM&UR UR UR R S:HS9X'M[ USR5S- US'URR(GaUSn[R"[URRS-5[URRS-UR-55UR-UR-nU[UR SS5- nUS :wanUR SSVs/sH5n["R$"Xv-UR- 5UR-PM7 nn[&R(R+XHS S S 9nXAS'U$s snf) z Preprocess a batch of images by scaling and converting to float. Args: batch (dict): Dictionary containing batch data with 'img' tensor. Returns: (dict): Preprocessed batch with normalized images. cuda) non_blockingimgg?g?r4NbilinearF)sizer$ align_corners)items isinstancetorchTensortodevicetypefloatr* multi_scalerandom randranger(imgszr&r'shapemathceilnn functional interpolate) rr-kvimgsszsfxnss rpreprocess_batch!DetectionTrainer.preprocess_batchosxKKMDA!U\\**44 $++:J:Jf:T4U"U|))+c1e 99 KYaDIIaft{{23dkkA>}}00Z_d0e%L srrsqueezer+rxr)rlbboxesrs rplot_training_labels%DetectionTrainer.plot_training_labelsst7H7H7P7P7W7WX7W8 7WXZ[\nn$2C2C2K2K2R2RS2RBi2RSUVWE;;= '0BT]]dhdpdpq YSs C ;Cc>[URSS0S9UlURURSSSS9nSSS5 [ SWR 55S -nA[ TU]U5$!,(df  N==f) zl Get optimal batch size by calculating memory occupation of model. Returns: (int): Optimal batch size. cacheF)rr2)r$r-Nc3># UHn[US5v M g7f)rN)r).0labels r .DetectionTrainer.auto_batch..sN9M#eEl++9Msr)rr*r/r+r'rr auto_batch)r train_dataset max_num_objrs rrDetectionTrainer.auto_batchs|diiGU3C D  ..tyy/AWY.ZMEN9M9MNNQRR w!+.. E Ds $A33 B)r*ry)rzdict[str, Any] | None)r2N)r,strr$rr-z int | None)rrr2)r<rr=r(r7r(r$r)r-rreturnr)NNT)r str | Nonerqrrobool)Nr2)rzlist[float] | Nonerr)r-zdict[str, Any]rr(rNone)__name__ __module__ __qualname____firstlineno____doc__r rr/r?rcrirrr~rrrrr__static_attributes__ __classcell__)rs@rrrsZ>'4\` 5 5 s :<$" $   r / /r r)" __future__rrWrSrtypingrnumpyrrLtorch.nnrYultralytics.datarrultralytics.engine.trainerrultralytics.modelsr ultralytics.nn.tasksr ultralytics.utilsr r r ultralytics.utils.patchesrultralytics.utils.plottingrrultralytics.utils.torch_utilsrrrr rrsH#  A2#/776?TT/{T/r