o W hV @sddlmZddlmZddlmZmZmZddlZddl Z ddl m Z m Z mZmZmZddlmZmZmZmZmZz3er?JeddusGJddlZeed sRJddlZdd lmZd d gZd ZdZdZdZ dZ!da"Wn e#e$fyzdZYnwde%fddZ&de%fddZ'de(fddZ)de(fddZ*de+de+fddZ,de-fddZ.de-fd d!Z/d"eddfd#d$Z0de1fd%d&Z2dee+fd'd(Z3dKdee1fd)d*Z4dLdee1fd+d,Z5d-e%d.edeeeefd/d0Z6deefd1d2Z7de1fd3d4Z8dMd5d6Z9dKdMd7d8Z:dMd9d:Z;dMd;d<ZZ=d?e(ddfd@dAZ>dMdBdCZ?dMdDdEZ@dMdFdGZAdMdHdIZBer?e?e@eAeBdJZCdSiZCdS)N)Callable)SimpleNamespace)AnyListOptionalN)LOGGERRANKSETTINGS TESTS_RUNNINGops)ClassifyMetrics DetMetrics OBBMetrics PoseMetricsSegmentMetricscometT __version__)Pathdetectsegment)confusion_matrixconfusion_matrix_normalized)F1_curveP_curveR_curvePR_curve)labelslabels_correlogram)BoxMask)rPosereturncCs$td}|durtd|SdS)z\Returns the mode of comet set in the environment variables, defaults to 'online' if not set. COMET_MODENaThe COMET_MODE environment variable is deprecated. Please use COMET_START_ONLINE to set the Comet experiment mode. To start an offline Comet experiment, use 'export COMET_START_ONLINE=0'. If COMET_START_ONLINE is not set or is set to '1', an online Comet experiment will be created.online)osgetenvrwarning) comet_moder(U/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/utils/callbacks/comet.py_get_comet_mode's r*cCs tddS)zmReturns the model name for Comet from the environment variable COMET_MODEL_NAME or defaults to 'Ultralytics'.COMET_MODEL_NAME Ultralytics)r$r%r(r(r(r)_get_comet_model_name6s r-cCttddS)z[Get the evaluation batch logging interval from environment variable or use default value 1.!COMET_EVAL_BATCH_LOGGING_INTERVALintr$r%r(r(r(r) _get_eval_batch_logging_interval;r3cCr.)zRGet the maximum number of image predictions to log from the environment variables.COMET_MAX_IMAGE_PREDICTIONSdr1r(r(r(r)!_get_max_image_predictions_to_log@r4r7scorecCsttdd}||S)zSScales the given confidence score by a factor specified in an environment variable.COMET_MAX_CONFIDENCE_SCOREgY@)floatr$r%)r8scaler(r(r)_scale_confidence_scoreEsr<cCstdddkS)z_Determines if the confusion matrix should be logged based on the environment variable settings.COMET_EVAL_LOG_CONFUSION_MATRIXfalsetruer$r%lowerr(r(r(r)_should_log_confusion_matrixKrBcCstdddkS)zVDetermines whether to log image predictions based on a specified environment variable. COMET_EVAL_LOG_IMAGE_PREDICTIONSr?r@r(r(r(r)_should_log_image_predictionsPrCrEargsc CstdvrdStddurt}|dkrdndtjd<z+td|j}tj|d}|t || t t t td |d d WdStya}ztd |WYd}~dSd}~ww) z Resumes CometML experiment or creates a new experiment based on args. Ensures that the experiment object is only created in a single process during distributed training. >rNCOMET_START_ONLINEoffline10COMET_PROJECT_NAME) project_name)eval_batch_logging_intervallog_confusion_matrix_on_evallog_image_predictionsmax_image_predictionsz Created from ultralyticszEComet installed but not initialized correctly, not logging this run. )rr$r%r*environprojectcomet_mlstartlog_parametersvars log_othersr3rBrEr7 log_other Exceptionrr&)rFr' _project_name experimenter(r(r)_resume_or_create_experimentUs* r_c Csp|jd}t|jj|j}||}||jk}|jj}|jj}||dk}|o/|dko/|o/| }t ||||dS)zKReturns metadata for YOLO training including epoch and asset saving status.r0r) curr_epoch curr_step save_assets final_epoch) epochlen train_loaderdataset batch_sizeepochsrFsave save_perioddict) trainerr`train_num_steps_per_epochrarcrjrk save_intervalrbr(r(r)_fetch_trainer_metadatavs   rpcCs^|\}}tj|||d}t||||}t|}|dd|ddd8<|}|S)z YOLO resizes images during training and the label values are normalized based on this resized shape. This function rescales the bounding box labels to the original image shape. )hwN)r xywhn2xyxy scale_boxes xyxy2xywhtolist)boxresized_image_shapeoriginal_image_shape ratio_padresized_image_heightresized_image_widthr(r(r)+_scale_bounding_box_to_original_image_shapes  r~c s|d|k}|d|}t|dkrtd|ddS|d|d}r3fd d |D}|d |}|d |}|d |} g} t||D]\} } t| ||| } | | gd| tddqLd| dS)aD Format ground truth annotations for object detection. This function processes ground truth annotations from a batch of images for object detection tasks. It extracts bounding boxes, class labels, and other metadata for a specific image in the batch, and formats them for visualization or evaluation. Args: img_idx (int): Index of the image in the batch to process. image_path (str | Path): Path to the image file. batch (dict): Batch dictionary containing detection data with keys: - 'batch_idx': Tensor of batch indices - 'bboxes': Tensor of bounding boxes in normalized xywh format - 'cls': Tensor of class labels - 'ori_shape': Original image shapes - 'resized_shape': Resized image shapes - 'ratio_pad': Ratio and padding information class_name_map (dict | None, optional): Mapping from class indices to class names. Returns: (dict | None): Formatted ground truth annotations with the following structure: - 'boxes': List of box coordinates [x, y, width, height] - 'label': Label string with format "gt_{class_name}" - 'score': Confidence score (always 1.0, scaled by _scale_confidence_score) Returns None if no bounding boxes are found for the image. batch_idxbboxesr Comet Image: z has no bounding boxes labelsNclsr0csg|]}t|qSr()str).0labelclass_name_mapr(r) zB_format_ground_truth_annotations_for_detection.. ori_shape resized_shaper{gt_g?boxesrr8 ground_truthnamedata) rerdebugsqueezerwzipr~appendr<) img_idx image_pathbatchrindicesr cls_labelsrzryr{rrxrr(rr)._format_ground_truth_annotations_for_detections*       rcs|j}|r t|n|}||}|std|ddS|r.r.fdd|D}zddlm}Wn t yAd}Ynwg}|D]?} | d} t | d } | d } |r^t || } | g| | d } |dur| d d}|durxt ||}|dur|| d <| | qFd|dS)z;Format YOLO predictions for object detection visualization.rz" has no bounding boxes predictionsNcsi|] \}}||qSr(r()rkv class_mapr(r) sz2_format_prediction_annotations..r)decodebboxr8 category_idr segmentationpoints predictionr)stem isnumericr2getrritemspycocotools.maskr ImportErrorr<r _extract_segmentation_annotationr)rmetadataclass_label_maprrimage_id predictionsrrrrr8 cls_labelannotation_datasegmentsr(rr)_format_prediction_annotationss:        rsegmentation_rawrc Cstz||}t|tjtj\}}dd|D}dd|DWSty9}ztd|WYd}~dSd}~ww)aZ Extracts segmentation annotation from compressed segmentations as list of polygons. Args: segmentation_raw: Raw segmentation data in compressed format. decode: Function to decode the compressed segmentation data. Returns: (Optional[List[List[Any]]]): List of polygon points or None if extraction fails. cSs&g|]}t|dkrt|qS))renparrayr)rpolygonr(r(r)r s&z4_extract_segmentation_annotation..cSsg|]}|qSr()ravelrwr annotationr(r(r)r rz1Comet Failed to extract segmentation annotation: N)cv2 findContours RETR_LISTCHAIN_APPROX_SIMPLEr[rr&)rrmaskcontours_ annotationsr^r(r(r)rs rc Cs<t||||}t||||}dd||fD}|r|gSdS)z?Join the ground truth and prediction annotations if they exist.cSsg|]}|dur|qSNr(rr(r(r)rsz&_fetch_annotations..N)rr) rrrprediction_metadata_maprrground_truth_annotationsprediction_annotationsrr(r(r)_fetch_annotationssrcCs4i}|D]}||dg||d|q|S)zNCreate metadata map for model predictions by groupings them based on image ID.r) setdefaultr)model_predictionspred_metadata_maprr(r(r)_create_prediction_metadata_map$s rcCs>|jjj}t|jddg}|j||t|||ddS)z-Log the confusion matrix to Comet experiment.names background)matrixrmax_categoriesrdstepN) validatorrrlistrvalueslog_confusion_matrixre)r]rmrar`conf_matrr(r(r)_log_confusion_matrix.s  rcCsP|rt||D]\}}|j||j||dqdS|D] }|j||j|dqdS)a Log images to the experiment with optional annotations. This function logs images to a Comet ML experiment, optionally including annotation data for visualization such as bounding boxes or segmentation masks. Args: experiment (comet_ml.Experiment): The Comet ML experiment to log images to. image_paths (List[Path]): List of paths to images that will be logged. curr_step (int): Current training step/iteration for tracking in the experiment timeline. annotations (List[List[dict]], optional): Nested list of annotation dictionaries for each image. Each annotation contains visualization data like bounding boxes, labels, and confidence scores. Returns: None )rrr)rrN)r log_imager)r] image_pathsrarrrr(r(r) _log_images7src Cs|jj}|tvr dS|j}|sdSt|}|j}|j}t|dd}t} t } t |D]=\} } | d| dkr8q+| d} t | D]'\}}t | krLdSt |}t ||| |||d}t||g||dt d7a q@q+dS)a Log predicted boxes for a single image during training. This function logs image predictions to a Comet ML experiment during model validation. It processes validation data and formats both ground truth and prediction annotations for visualization in the Comet dashboard. The function respects configured limits on the number of images to log. Args: experiment (comet_ml.Experiment): The Comet ML experiment to log to. validator (BaseValidator): The validator instance containing validation data and predictions. curr_step (int): The current training step for logging timeline. Notes: This function uses global state to track the number of logged predictions across calls. It only logs predictions for supported tasks defined in COMET_SUPPORTED_TASKS. The number of logged images is limited by the COMET_MAX_IMAGE_PREDICTIONS environment variable. Nrr0rim_filer)r)rFtaskCOMET_SUPPORTED_TASKSjdictr dataloaderrgetattrr3r7 enumerate_comet_image_prediction_countrrr)r]rrarrpredictions_metadata_maprrrbatch_logging_intervalrQrrrrrrr(r(r)_log_image_predictionsQsJ  rcsd}tjjtrjjjdkrfddtD}n#tjjtr+fddtD}ntjjttfr=fddtD}|durGt ||dfddt D}t ||dtjjt snfddt D}t ||ddSdS) a Log evaluation plots and label plots for the experiment. This function logs various evaluation plots and confusion matrices to the experiment tracking system. It handles different types of metrics (SegmentMetrics, PoseMetrics, DetMetrics, OBBMetrics) and logs the appropriate plots for each type. Args: experiment (comet_ml.Experiment): The Comet ML experiment to log plots to. trainer (ultralytics.engine.trainer.BaseTrainer): The trainer object containing validation metrics and save directory information. Examples: >>> from ultralytics.utils.callbacks.comet import _log_plots >>> _log_plots(experiment, trainer) Nrc*g|]}tD] }j||dqqSz.png)SEGMENT_METRICS_PLOT_PREFIXsave_dirrplotsprefixrmr(r)rz_log_plots..crr)POSE_METRICS_PLOT_PREFIXrrrr(r)rrcg|] }j|dqSrrrrrr(r)rcrrrrrr(r)rrcr)z.jpgr)rrrr(r)rr) isinstancermetricsrrEVALUATION_PLOT_NAMESrr rrCONFUSION_MATRIX_PLOT_NAMESr LABEL_PLOT_NAMES)r]rmplot_filenamesconfusion_matrix_filenameslabel_plot_filenamesr(rr) _log_plotss&    rcCs"t}|j|t|jddddS)z'Log the best-trained model to Comet.ml.zbest.ptT)file_or_folder file_name overwriteN)r- log_modelrbest)r]rm model_namer(r(r) _log_modelsrracCs,t||jd|t||jd|dS)z>Log samples of images batches for train, validation, and test.ztrain_batch*.jpgzval_batch*.jpgN)rrglob)r]rmrar(r(r)_log_image_batchessrcCst|jdS)zTCreates or resumes a CometML experiment at the start of a YOLO pre-training routine.N)r_rFrr(r(r)on_pretrain_routine_startsr cCsHt}|sdSt|}|d}|d}|j|j|jdd||ddS)z@Log metrics and save batch images at the end of training epochs.Nr`ratrain)rrrd)rUget_running_experimentrp log_metricslabel_loss_itemstloss)rmr]rr`rar(r(r)on_train_epoch_ends rcCst}|sdSt|}|d}|d}|d}|j|j||d|j|j||d|dkr>ddlm}|j||||d|sBdSt||t rQt ||||t r]t ||j |dSdS) aM Log model assets at the end of each epoch during training. This function is called at the end of each training epoch to log metrics, learning rates, and model information to a Comet ML experiment. It also logs model assets, confusion matrices, and image predictions based on configuration settings. The function retrieves the current Comet ML experiment and logs various training metrics. If it's the first epoch, it also logs model information. On specified save intervals, it logs the model, confusion matrix (if enabled), and image predictions (if enabled). Args: trainer (BaseTrainer): The YOLO trainer object containing training state, metrics, and configuration. Examples: >>> # Inside a training loop >>> on_fit_epoch_end(trainer) # Log metrics and assets to Comet ML Nr`rarbr r0r)model_info_for_loggers)rUr rpr rlrultralytics.utils.torch_utilsrrrBrrErr)rmr]rr`rarbrr(r(r)on_fit_epoch_ends(  rcCst}|sdSt|}|d}|d}|jj}t|||r$t||t||||t||j |t |||| da dS)z*Perform operations at the end of training.Nr`rar) rUr rprFrrrrrrrendr)rmr]rr`rarr(r(r) on_train_ends   r)r rrrr)NN)r!N)Dcollections.abcrtypesrtypingrrrrnumpyrultralytics.utilsrrr r r ultralytics.utils.metricsr r rrrrUhasattrr$pathlibrrrrrrrrrAssertionErrorrr*r-r2r3r7r:r<boolrBrEr_rlrpr~rrrrrrrrrrrr rrr callbacksr(r(r(r)s~   ! 7"*   ? ,   -