FPhjddlmZmZddlmZ erJeddusJddlZeedsJddlZ ddl Z iZ ddZ ddZd Zd Zd Zd Zd ZereeeedZyiZy#e ef$rdZY6wxYw))SETTINGS TESTS_RUNNING)model_info_for_loggerswandbTN __version__ctj|||djd}dddd}|||d}tjdtj | || S) a Create and log a custom metric visualization to wandb.plot.pr_curve. This function crafts a custom metric visualization that mimics the behavior of wandb's default precision-recall curve while allowing for enhanced customization. The visual metric is useful for monitoring model performance across different classes. Args: x (List): Values for the x-axis; expected to have length N. y (List): Corresponding values for the y-axis; also expected to have length N. classes (List): Labels identifying the class of each point; length N. title (str, optional): Title for the plot; defaults to 'Precision Recall Curve'. x_title (str, optional): Label for the x-axis; defaults to 'Recall'. y_title (str, optional): Label for the y-axis; defaults to 'Precision'. Returns: (wandb.Object): A wandb object suitable for logging, showcasing the crafted metric visualization. )classyxr r r )r r r )titlez x-axis-titlez y-axis-titlezwandb/area-under-curve/v0) dataframe)fields string_fields)pd DataFrameroundwb plot_tableTable) r r classesr x_titley_titledfrrs iC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\ultralytics/utils/callbacks/wb.py _custom_tablersb$ aa8 9 ? ? BBS7 3F#WgVM ==4B/ &'4 66c :|g}tj|d|d|jd} | j} tj| |tj |djdj} |rot jtt| | ||g} t jj|t jj| |||iyd gt| z} t|D]]\}}| j!| | j!tj| ||| j!||gt| z_t j|t#| | | |||id y) a Log a metric curve visualization. This function generates a metric curve based on input data and logs the visualization to wandb. The curve can represent aggregated data (mean) or individual class data, depending on the 'only_mean' flag. Args: x (np.ndarray): Data points for the x-axis with length N. y (np.ndarray): Corresponding data points for the y-axis with shape CxN, where C represents the number of classes. names (list, optional): Names of the classes corresponding to the y-axis data; length C. Defaults to an empty list. id (str, optional): Unique identifier for the logged data in wandb. Defaults to 'precision-recall'. title (str, optional): Title for the visualization plot. Defaults to 'Precision Recall Curve'. x_title (str, optional): Label for the x-axis. Defaults to 'Recall'. y_title (str, optional): Label for the y-axis. Defaults to 'Precision'. num_x (int, optional): Number of interpolated data points for visualization. Defaults to 100. only_mean (bool, optional): Flag to indicate if only the mean curve should be plotted. Defaults to True. Note: The function leverages the '_custom_table' function to generate the actual visualization. Nr)axisr )datacolumns)r meanF)commit)nplinspacertolistinterpr$rrlistziprunlogplotlinelen enumerateextendr)r r namesidr rrnum_x only_meanx_newx_logy_logtableriyis r _plot_curver=1sF< } KK!aeU + 1 1! 4E LLNE IIeQ 2 3 9 9! < C C EEd3ue#45?QR  E277<<wu<MNO(SZ'q\EAr LL  LL5!R0 1 NNE!H:E 2 3" M%QR[`arc |jD]u\}}|d}tj||k7s$tjj |j tjt|i||t|<wy)z_Logs plots from the input dictionary if they haven't been logged already at the specified step. timestampstepN) items_processed_plotsgetrr,r-stemImagestr)plotsrAnameparamsr?s r _log_plotsrKcsh f;'    % 2 FFJJ 288CI#67dJ C%. T " &rctjxsYtj|jjxsd|jj t |jyy)z0Initiate and start project if module is present.YOLOv8)projectrIconfigN)rr,initargsrNrIvarstrainers ron_pretrain_routine_startrUlsDFFrbgggll22>hW\\EVEV_cdkdpdp_qrrrctjj|j|jdzt |j |jdzt |jj |jdz|jdk(r8tjjt||jdzyy)zCLogs training metrics and model information at the end of an epoch.r@rN) rr,r-metricsepochrKrH validatorrrSs ron_fit_epoch_endr[qsFFJJwW]]Q%6J7w}}7==1#45w  &&W]]Q->?}}  )'29J Krcntjj|j|jd|j dztjj|j |j dz|j dk(r%t|j|j dzyy)z>Log metrics and save images at the end of each training epoch.train)prefixrWr@N) rr,r-label_loss_itemstlossrYlrrKrHrSs ron_train_epoch_endrbzs}FFJJw'' g'FW]]]^M^J_FFJJwzz  1J2}}7==w}}q'89rc :t|jj|jdzt|j|jdzt j ddtj jd}|jjr=|j|jtj j|dgt|jjj|jjjD]V\}}|\}}}}t!||t#|jjj$j'd |||| Xtj j)y ) z6Save the best model as an artifact at end of training.rWr@modelrun__model)typerIbest)aliaseszcurves/)r3r4r rrN)rKrZrHrYrArtifactr,r4rhexistsadd_file log_artifactr+rXcurvescurves_resultsr=r*r3valuesfinish)rTart curve_name curve_valuesr r rrs r on_train_endrus.w  &&W]]Q->?w}}7==1#45 ++74 {&)A BC|| W\\" C&2$'(9(9(A(A(H(H'J[J[JcJcJrJr$s L!-1gw w((0066==?@ % %tFFMMOr)rUrbr[ru)Precision Recall CurveRecall Precision)Nzprecision-recallrvrwrxdF)ultralytics.utilsrrultralytics.utils.torch_utilsrrrhasattrnumpyr&pandasrrC ImportErrorAssertionErrorrr=rKrUr[rbru callbacksrrrs6@   G  $$ $ 2} %% % 6:%. #/bd/s L:4&(";,(  " .0  M ^$ Bs&A!! A-,A-