o xi[,@sddlmZddlmZmZddlmZddlmZzddl m Z ddl m Z m Z WneyAddlm Z ddlm Z m Z YnwddlmZddlZdd lmZGd d d Z    ddddZdS)) annotations)AnyCallable)YOLO) BaseTrainer)RANK) get_flopsget_num_params)ClassificationTrainerN) telemetryc@seZdZdZ    d'd(ddZd)ddZd)ddZd)ddZd)ddZd)ddZ d)ddZ d)dd Z d*d"d#Z e d+d%d&ZdS), WandbCallbackaAn internal YOLO model wrapper that tracks metrics, and logs models to Weights & Biases. Usage: ```python from wandb.integration.yolov8.yolov8 import WandbCallback model = YOLO("yolov8n.pt") wandb_logger = WandbCallback( model, ) for event, callback_fn in wandb_logger.callbacks.items(): model.add_callback(event, callback_fn) ``` Nyolorrun_name str | Noneprojecttagslist[str] | Noneresumekwargs Any | NonereturnNonecKs(||_||_||_||_||_||_dS)a A utility class to manage wandb run and various callbacks for the ultralytics YOLOv8 framework. Args: yolo: A YOLOv8 model that's inherited from `:class:ultralytics.yolo.engine.model.YOLO` run_name, str: The name of the Weights & Biases run, defaults to an auto generated run_name if `trainer.args.name` is not defined. project, str: The name of the Weights & Biases project, defaults to `"YOLOv8"` if `trainer.args.project` is not defined. tags, List[str]: A list of tags to be added to the Weights & Biases run, defaults to `["YOLOv8"]`. resume, str: Whether to resume a previous run on Weights & Biases, defaults to `None`. **kwargs: Additional arguments to be passed to `wandb.init()`. N)r rrrrr)selfr rrrrrrS/home/ubuntu/.local/lib/python3.10/site-packages/wandb/integration/yolov8/yolov8.py__init__$s  zWandbCallback.__init__trainerrcCs"tjdur8tjd|jr|jn|jj|jr|jn|jjpd|jr"|jndgt|j|j r.|j ndd|j |_ntj|_|jdusCJ|jj ddd|jj ddddd |jj d dddd |jj d ddd d |jj d dddd t j tjd }d|j_WddS1swYdS)a'Starts a new wandb run to track the training process and log to Weights & Biases. Args: trainer: A task trainer that's inherited from `:class:ultralytics.yolo.engine.trainer.BaseTrainer` that contains the model training and optimization routine. NYOLOv8)namerrconfigrepochT)hiddenztrain/*min) step_metric step_syncsummaryzval/*z metrics/*maxzlr/*last)runr)wandbr(initrargsrrrvarsrr define_metricr contextfeatureultralytics_yolov8)rrtelrrron_pretrain_routine_start>s<     "z'WandbCallback.on_pretrain_routine_startcCs8|jdusJ|jjt|jtt|jdddS)N)zmodel/parametersz model/GFLOPs)r(r%updater modelroundrrrrrron_pretrain_routine_endfs z%WandbCallback.on_pretrain_routine_endcCs(|jdusJ|jd|jdidS)zJOn train epoch start we only log epoch number to the Weights & Biases run.Nr )r(logr r7rrron_train_epoch_startosz"WandbCallback.on_train_epoch_startcCsj|jdusJ|ji|j|j|jdd|jt|ts3|jddd|j dDidSdS)zFOn train epoch end we log all the metrics to the Weights & Biases run.Ntrain)prefixtrain_batch_imagescS g|] }tjt||jdqS)captionr)Imagestrstem.0 image_pathrrr z4WandbCallback.on_train_epoch_end..ztrain_batch*.jpg) r(r:metricslabel_loss_itemstlosslr isinstancer save_dirglobr7rrron_train_epoch_endus$  z WandbCallback.on_train_epoch_endcs|jdusJjdkr/fdddD}|dr|dn|d}|r/|jjdt|dijjkrM|jjd jdid d jDdSdS) z^On fit epoch end we log all the best metrics and model detail to Weights & Biases run summary.Nrcsg|] }jj|qSr) validatorspeedget)rGkeyrrrrIs z2WandbCallback.on_fit_epoch_end..)r9 inferencer9zmodel/speed(ms/img)r3z best/epochcSsi|] \}}d||qS)zbest/r)rGrVvalrrr sz2WandbCallback.on_fit_epoch_end..) r(r r%r4r6 best_fitnessfitnessrKitems)rrspeedsrTrrWron_fit_epoch_ends&     zWandbCallback.on_fit_epoch_endcCs|jdusJt|ts/|jdusJ|jdd|jdDdd|jjdDd|jr\|jdus;J|jj t |jd|jj d |j j d d d |jd gddSdS)zgOn train end we log all the media, including plots, images and best model artifact to Weights & Biases.NcSr?r@rBrFrrrrIrJz.WandbCallback.on_train_end..z*.pngcSr?r@rBrFrrrrIrJzval*.jpg)plots val_imagesr5_.ptbestepoch_r9typeraliases)r(rOr r:rPrQrSrdexists log_artifactrDrr+taskr r7rrr on_train_ends*     zWandbCallback.on_train_endcCsP|jdusJ|jjt|jd|jjd|jjddd|jdgddS) zBOn model save we log the model as an artifact to Weights & Biases.Nr5rbrcr'rer9rf)r(rjrDr'rr+rkr r7rrr on_model_saves zWandbCallback.on_model_save_trainercCs"|jdusJ|jd|_dS)zCOn teardown, we finish the Weights & Biases run and set it to None.N)r(finish)rrnrrrteardowns  zWandbCallback.teardowndict[str, Callable]c Cs&|j|j|j|j|j|j|j|jdS)zgProperty contains all the relevant callbacks to add to the YOLO model for the Weights & Biases logging.r2r8r;rRr_rlrmrprr)rrrr callbacksszWandbCallback.callbacksNNNN)r rrrrrrrrrrrrr)rrrr)rnrrr)rrq)__name__ __module__ __qualname____doc__rr2r8r;rRr_rlrmrppropertyrsrrrrr s"  (     r r rrrrrrrrrrc Ksptjdddtjdddtdvr1t|f||||d|}|jD] \}}|||q$|Std|S)aA YOLO model wrapper that tracks metrics, and logs models to Weights & Biases. Args: yolo: A YOLOv8 model that's inherited from `:class:ultralytics.yolo.engine.model.YOLO` run_name, str: The name of the Weights & Biases run, defaults to an auto generated name if `trainer.args.name` is not defined. project, str: The name of the Weights & Biases project, defaults to `"YOLOv8"` if `trainer.args.project` is not defined. tags, List[str]: A list of tags to be added to the Weights & Biases run, defaults to `["YOLOv8"]`. resume, str: Whether to resume a previous run on Weights & Biases, defaults to `None`. **kwargs: Additional arguments to be passed to `wandb.init()`. Usage: ```python from wandb.integration.yolov8 import add_callbacks as add_wandb_callbacks model = YOLO("yolov8n.pt") add_wandb_callbacks( model, ) model.train( data="coco128.yaml", epochs=3, imgsz=640, ) ``` a$The wandb callback is currently in beta and is subject to change based on updates to `ultralytics yolov8`. The callback is tested and supported for ultralytics v8.0.43 and above. Please report any issues to https://github.com/wandb/wandb/issues with the tag `yolov8`. F)repeataThis wandb callback is no longer functional and would be deprecated in the near future. We recommend you to use the updated callback using `from wandb.integration.ultralytics import add_wandb_callback`. The updated callback is tested and supported for ultralytics 8.0.167 and above. You can refer to https://docs.wandb.ai/guides/integrations/ultralytics for the updated documentation. Please report any issues to https://github.com/wandb/wandb/issues with the tag `yolov8`. )r)rrrrzThe RANK of the process to add the callbacks was neither 0 or -1.No Weights & Biases callbacks were added to this instance of the YOLO model.)r)termwarnrr rsr] add_callback termerror) r rrrrr wandb_loggerevent callback_fnrrr add_callbackss.! rrt)r rrrrrrrrrrrrr) __future__rtypingrrultralytics.yolo.engine.modelrultralytics.yolo.engine.trainerrultralytics.yolo.utilsr"ultralytics.yolo.utils.torch_utilsrr ModuleNotFoundErrorultralytics.utilsultralytics.utils.torch_utils"ultralytics.yolo.v8.classify.trainr r) wandb.sdk.libr r rrrrrs*        O