Fi)ddlmZddlmZddlmZddlZddlmZ ddl m Z m Z ddl mZddlmZmZddlmZmZdd lmZGd d eZy) ) annotations)Path)AnyN)ClassificationDatasetbuild_dataloader) BaseValidator)LOGGERRANK)ClassifyMetricsConfusionMatrix) plot_imagesceZdZdZddfd ZddZddZddZddZddZ ddZ dd Z dd Z dd Z dd Zdd ZddZddZxZS)ClassificationValidatoraA class extending the BaseValidator class for validation based on a classification model. This validator handles the validation process for classification models, including metrics calculation, confusion matrix generation, and visualization of results. Attributes: targets (list[torch.Tensor]): Ground truth class labels. pred (list[torch.Tensor]): Model predictions. metrics (ClassifyMetrics): Object to calculate and store classification metrics. names (dict): Mapping of class indices to class names. nc (int): Number of classes. confusion_matrix (ConfusionMatrix): Matrix to evaluate model performance across classes. Methods: get_desc: Return a formatted string summarizing classification metrics. init_metrics: Initialize confusion matrix, class names, and tracking containers. preprocess: Preprocess input batch by moving data to device. update_metrics: Update running metrics with model predictions and batch targets. finalize_metrics: Finalize metrics including confusion matrix and processing speed. postprocess: Extract the primary prediction from model output. get_stats: Calculate and return a dictionary of metrics. build_dataset: Create a ClassificationDataset instance for validation. get_dataloader: Build and return a data loader for classification validation. print_results: Print evaluation metrics for the classification model. plot_val_samples: Plot validation image samples with their ground truth labels. plot_predictions: Plot images with their predicted class labels. Examples: >>> from ultralytics.models.yolo.classify import ClassificationValidator >>> args = dict(model="yolo26n-cls.pt", data="imagenet10") >>> validator = ClassificationValidator(args=args) >>> validator() Notes: Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'. ct|||||d|_d|_d|j_t |_y)aInitialize ClassificationValidator with dataloader, save directory, and other parameters. Args: dataloader (torch.utils.data.DataLoader, optional): DataLoader to use for validation. save_dir (str | Path, optional): Directory to save results. args (dict, optional): Arguments containing model and validation configuration. _callbacks (dict, optional): Dictionary of callback functions to be called during validation. Nclassify)super__init__targetspredargstaskr metrics)self dataloadersave_dirr _callbacks __class__s f/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/models/yolo/classify/val.pyrz ClassificationValidator.__init__8s; XtZ@  # &( c ddzS)z=Return a formatted string summarizing classification metrics.z %22s%11s%11s)classestop1_acctop5_accrs rget_descz ClassificationValidator.get_descGs#'JJJrc|j|_t|j|_g|_g|_t |j|_y)z^Initialize confusion matrix, class names, and tracking containers for predictions and targets.)namesN)r(lenncrrr confusion_matrix)rmodels r init_metricsz$ClassificationValidator.init_metricsKs<[[ ekk"  /ekk Brcl|dj|j|jjdk(|d<|jjr|dj n|dj |d<|dj|j|jjdk(|d<|S)zTPreprocess input batch by moving data to device and converting to appropriate dtype.imgcuda) non_blockingcls)todevicetyperhalffloat)rbatchs r preprocessz"ClassificationValidator.preprocessSsU|t{{AQAQU[A[\e .2iinnuU|((*%,BTBTBVe U|t{{AQAQU[A[\e  rctt|jd}|jj |j ddddd|fj tjj|jj |dj tjjy)aUpdate running metrics with model predictions and batch targets. Args: preds (torch.Tensor): Model predictions, typically logits or probabilities for each class. batch (dict): Batch data containing images and class labels. Notes: This method appends the top-N predictions (sorted by confidence in descending order) to the prediction list for later evaluation. N is limited to the minimum of 5 and the number of classes. T) descendingNr2) minr)r(rappendargsortr5torchint32cpur)rpredsr8n5s rupdate_metricsz&ClassificationValidator.update_metricsZsTZZ! $ qT:1crc6BGG TXXZ[ E%L--ekk:>>@Arc|jj|j|j|jj r9dD]4}|jj |j||j6|j|j_ |j|j_|j|j_y)aFinalize metrics including confusion matrix and processing speed. Examples: >>> validator = ClassificationValidator() >>> validator.pred = [torch.tensor([[0, 1, 2]])] # Top-3 predictions for one sample >>> validator.targets = [torch.tensor([0])] # Ground truth class >>> validator.finalize_metrics() >>> print(validator.metrics.confusion_matrix) # Access the confusion matrix Notes: This method processes the accumulated predictions and targets to generate the confusion matrix, optionally plots it, and updates the metrics object with speed information. )TF)r normalizeon_plotN) r+process_cls_predsrrrplotsplotrrIspeedr)rrHs rfinalize_metricsz(ClassificationValidator.finalize_metricsis // 4<<H 99??( n %%**DMMY`d`l`l*m n!ZZ  $  (,(=(= %rc<t|ttfr|dS|S)zSExtract the primary prediction from model output if it's in a list or tuple format.r) isinstancelisttuple)rrDs r postprocessz#ClassificationValidator.postprocesss%edE];uQxFFrc|jj|j|j|jjS)zSCalculate and return a dictionary of metrics by processing targets and predictions.)rprocessrr results_dictr%s r get_statsz!ClassificationValidator.get_statss. T\\4995||(((rc,tdk(rdgtjz}dgtjz}tj|j|dtj|j |d|Dcgc] }|D]}| c}}|_|Dcgc] }|D]}| c}}|_ytdkDrEtj|jddtj|j ddyycc}}wcc}}w)zGather stats from all GPUs.rN)dst)r distget_world_size gather_objectrr)rgathered_predsgathered_targetsrankrrs r gather_statsz$ClassificationValidator.gather_statss 19"Vd&9&9&;;N $v(;(;(==    tyy.a @   t||-=1 E*8J$TJTJJDI0@UPTUWGUGUDL AX   tyy$A 6   t||Tq 9KUs D !Dc\t||jd|jjS)z7Create a ClassificationDataset instance for validation.F)rootraugmentprefix)rrsplit)rimg_paths r build_datasetz%ClassificationValidator.build_datasets$$(EZ^ZcZcZiZijjrcj|j|}t|||jjdS)aPBuild and return a data loader for classification validation. Args: dataset_path (str | Path): Path to the dataset directory. batch_size (int): Number of samples per batch. Returns: (torch.utils.data.DataLoader): DataLoader object for the classification validation dataset. )r_)rgrrworkers)r dataset_path batch_sizedatasets rget_dataloaderz&ClassificationValidator.get_dataloaders/$$\2TYY5F5FRPPrcddt|jjzz}tj|d|jj |jj fzy)z6Print evaluation metrics for the classification model.z%22sz%11.3gallN)r)rkeysr infotop1top5)rpfs r print_resultsz%ClassificationValidator.print_resultssL hT\\%6%6!77 7 B%!2!2DLL4E4EFFGrctj|djd|d<t||jd|dz |j |j y)aPlot validation image samples with their ground truth labels. Args: batch (dict[str, Any]): Dictionary containing batch data with 'img' (images) and 'cls' (class labels). ni (int): Batch index used for naming the output file. Examples: >>> validator = ClassificationValidator() >>> batch = {"img": torch.rand(16, 3, 224, 224), "cls": torch.randint(0, 10, (16,))} >>> validator.plot_val_samples(batch, 0) r/r batch_idx val_batchz _labels.jpg)labelsfnamer(rIN)rAarangeshaper rr(rI)rr8nis rplot_val_samplesz(ClassificationValidator.plot_val_samplessT#\\%,*<*>> validator = ClassificationValidator() >>> batch = {"img": torch.rand(16, 3, 224, 224)} >>> preds = torch.rand(16, 10) # 16 images, 10 classes >>> validator.plot_predictions(batch, preds, 0) r/rr<)dim)r/rxr2confryz _pred.jpg)r{r(rIN) dictrAr|r}argmaxamaxr rr(rI)rr8rDr~ batched_predss rplot_predictionsz(ClassificationValidator.plot_predictionss|e ll5<#5#5a#89 U*Eq)   --IbT";;**LL  r)NNNN)rz dict | NonereturnNone)rstr)r,ztorch.nn.Modulerr)r8dict[str, Any]rr)rD torch.Tensorr8rrr)rr)rDz7torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor]rr)rzdict[str, float])rfrrr)rkz Path | strrlintrztorch.utils.data.DataLoader)r8rr~rrr)r8rrDrr~rrr)__name__ __module__ __qualname____doc__rr&r-r9rFrNrSrWr`rgrnrvrr __classcell__)rs@rrrsV#J )KC B>,G) :k QH  ( rr) __future__rpathlibrtypingrrAtorch.distributed distributedrZultralytics.datarrultralytics.engine.validatorrultralytics.utilsr r ultralytics.utils.metricsr r ultralytics.utils.plottingr rr$rrrs3# D6*F2F mF r