o mik,@sddlmZmZmZmZmZddlmZddlm Z zddl m Z ddl m Z mZWneyAddlm Z ddlm Z mZYnwddlmZddlZddlmZGd d d Z    dd ed eed eedeeedeedeedefddZdS))AnyCallableDictListOptional)YOLO) BaseTrainer)RANK) get_flopsget_num_params)ClassificationTrainerN) telemetryc@seZdZdZ    d dedeedeedeeedeedeed dfd d Z d e d dfd dZ d e d dfddZ d e d dfddZ d e d dfddZd e d dfddZd e d dfddZd e d dfddZde d dfddZed eeeffddZdS)! 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) ``` Nyolorun_nameprojecttagsresumekwargsreturncKs(||_||_||_||_||_||_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)rrrrrr)selfrrrrrrr]/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/wandb/integration/yolov8/yolov8.py__init__"s  zWandbCallback.__init__trainercCs"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 modelroundr rrrrron_pretrain_routine_endds 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&logrr5rrron_train_epoch_startmsz"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&r8metricslabel_loss_itemstlosslr isinstancer save_dirglobr5rrron_train_epoch_endss$  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)rEkeyrrrrGs z2WandbCallback.on_fit_epoch_end..)r7 inferencer7zmodel/speed(ms/img)r1z best/epochcSsi|] \}}d||qS)zbest/r)rErTvalrrr sz2WandbCallback.on_fit_epoch_end..) r&rr#r2r4 best_fitnessfitnessrIitems)rrspeedsrRrrUron_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>r@rDrrrrGrHz.WandbCallback.on_train_end..z*.pngcSr=r>r@rDrrrrGrHzval*.jpg)plots val_imagesr3_.ptbestepoch_r7typeraliases)r&rMr r8rNrOrQrbexists log_artifactrBrr)taskrr5rrr 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.Nr3r`rar%rcr7rd)r&rhrBr%rr)rirr5rrr 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)rrlrrrteardowns  zWandbCallback.teardownc 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.r0r6r9rPr]rjrkrnro)rrrr callbacksszWandbCallback.callbacksNNNN)__name__ __module__ __qualname____doc__rrrBrrrrr0r6r9rPr]rjrkrnpropertyrrrprrrrrsB  (   rrrrrrrrc 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'termwarnr rrpr[ add_callback termerror) rrrrrr wandb_loggerevent callback_fnrrr add_callbackss.! rrq)typingrrrrrultralytics.yolo.engine.modelrultralytics.yolo.engine.trainerrultralytics.yolo.utilsr "ultralytics.yolo.utils.torch_utilsr r ModuleNotFoundErrorultralytics.utilsultralytics.utils.torch_utils"ultralytics.yolo.v8.classify.trainr r' wandb.sdk.libr rrBrrrrrsD       O