/jAYddlmZddlZddlmZddlmZddlZddl Z ddl m Z ddl mZmZmZddlmZddlmZmZmZmZddlmZdd lmZmZmZdd lmZGd d eZ dS) ) annotationsN)Path)Any)build_dataloaderbuild_yolo_dataset converter) BaseValidator)LOGGERRANKnmsops)check_requirements)ConfusionMatrix DetMetricsbox_iou) plot_imagesceZdZdZdDdEfd ZdFd ZdGd ZdHdZdIdZdJdZ dKdZ dLdZ dMdZ dMdZ dNdZdMdZdOd ZdPdQd&ZdRd*ZdSd,Z dTdUd.ZdVd6ZdWd8ZdXd9ZdYd;Z dZd[dCZxZS)\DetectionValidatorayA class extending the BaseValidator class for validation based on a detection model. This class implements validation functionality specific to object detection tasks, including metrics calculation, prediction processing, and visualization of results. Attributes: is_coco (bool): Whether the dataset is COCO. is_lvis (bool): Whether the dataset is LVIS. class_map (list[int]): Mapping from model class indices to dataset class indices. metrics (DetMetrics): Object detection metrics calculator. iouv (torch.Tensor): IoU thresholds for mAP calculation. niou (int): Number of IoU thresholds. lb (list[Any]): List for storing ground truth labels for hybrid saving. jdict (list[dict[str, Any]]): List for storing JSON detection results. stats (dict[str, list[torch.Tensor]]): Dictionary for storing statistics during validation. Examples: >>> from ultralytics.models.yolo.detect import DetectionValidator >>> args = dict(model="yolo26n.pt", data="coco8.yaml") >>> validator = DetectionValidator(args=args) >>> validator() N _callbacks dict | NonereturnNonec*t||||d|_d|_d|_d|j_tjddd|_ |j |_ t|_ dS)aInitialize detection validator with necessary variables and settings. Args: dataloader (torch.utils.data.DataLoader, optional): DataLoader to use for validation. save_dir (Path, optional): Directory to save results. args (dict[str, Any], optional): Arguments for the validator. _callbacks (dict, optional): Dictionary of callback functions. FNdetectg?gffffff? )super__init__is_cocois_lvis class_mapargstasktorchlinspaceiouvnumelniourmetrics)self dataloadersave_dirr!r __class__s g/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/ultralytics/models/yolo/detect/val.pyrzDetectionValidator.__init__-sx XtZ@@@  ! N3b11 IOO%% !|| batchdict[str, Any]cT|D]L\}}t|tjr-||j|jjdk||<M|jjr|dn|d dz |d<|S)zPreprocess batch of images for YOLO validation. Args: batch (dict[str, Any]): Batch containing images and annotations. Returns: (dict[str, Any]): Preprocessed batch. cuda) non_blockingimg) items isinstancer#Tensortodevicetyper!halffloat)r)r/kvs r- preprocesszDetectionValidator.preprocess?sKKMM V VDAq!U\** V44 $+:Jf:T4UUa/3y~We ))+++5z2DetectionValidator.postprocess..~sdjjj]^1QQQU8Qqqq!tWQqqq!tWqQRQRQRTUTVTVQVxXXjjjr.) r non_max_suppressionr!rxiour"rY single_cls agnostic_nmsrqrH)r)rjoutputss r- postprocesszDetectionValidator.postprocessis)  IN IMINh..qqDGY)CTY-CI%LINe+    kjbijjjjr.siintc|d|k}|d|d}|d|}|d|}|djdd}|d |}|jd r8tj|t j||j gd z}||||||d |dS)a3Prepare a batch of images and annotations for validation. Args: si (int): Sample index within the batch. batch (dict[str, Any]): Batch data containing images and annotations. Returns: (dict[str, Any]): Prepared batch with processed annotations. batch_idxryrw ori_shaper4N ratio_padr)r:)rGrrGrim_file)ryrwrimgszrr)squeezeshaper xywh2xyxyr#tensorr:) r)rr/idxrybboxrrrs r-_prepare_batchz!DetectionValidator._prepare_batchsK B&El3''++Xs#+&r* e "122&+&r* 9Q< _=&&eDK)P)P)PQ]Q]Q])^^D""Y'+    r.preddict[str, torch.Tensor]c>|jjr|dxxdzcc<|S)aPrepare predictions for evaluation against ground truth. Args: pred (dict[str, torch.Tensor]): Post-processed predictions from the model. Returns: (dict[str, torch.Tensor]): Prepared predictions in native space. ryr)r!r)r)rs r- _prepare_predz DetectionValidator._prepare_preds, 9   KKK1 KKK r.c t|D]m\}}|xjdz c_|||}||}|d}|djddk}|ji| |||tj ||rtj dn+|d|rtj dn+|dd|j jrf|j|||j j|j jr3|j|d||d|j|r|j js |j jr|||} |j jr|| ||j jrN|| |j j|d |jd z t7|djd z od S) zUpdate metrics with new predictions and ground truth. Args: preds (list[dict[str, torch.Tensor]]): List of predictions from the model. batch (dict[str, Any]): Batch data containing ground truth. rGryrrx) target_cls target_imgrxpred_cls)rxr4rrlabelsz.txtN) enumerater[rrcpunumpyrr( update_stats_process_batchnpuniquezerosr!r]r_ process_batchrxr^ plot_matchesr+rVsave_txt scale_preds pred_to_json save_one_txt save_confrstem) r)rjr/rrpbatchprednryno_pred predn_scaleds r-update_metricsz!DetectionValidator.update_metricssw"%(($ $ HB IINII((U33F&&t,,E-##%%++--CEl(+q0G L % %))%88"%"$)C..+2SBHQKKKf 8I8I8K8K8Q8Q8S8S/6 V E%L> y" 8!!,777y! !! I';'MH,$vi7H2I2I2N/T/T/TT ?$ $ r.c|jjr,dD])}|j|j||j*|j|j_|j|j_|j|j_dS)z8Set final values for metrics speed and confusion matrix.)TF)r+ normalizeon_plotN)r!r]r_plotr+rspeedr()r)rs r-finalize_metricsz#DetectionValidator.finalize_metricssr 9? n( n n %**DMY`d`l*mmmm!Z (,(= % $  r.ctdkrdgtjz}tj|jj|dd|jjD}|D](}|D]#}||||$)dgtjz}tj|j|dg|_|D]}|j|||j_t|j j |_ dStdkr_tj|jjddtj|jddg|_|j dSdS)zGather stats from all GPUs.rN)dstci|]}|gSrgrg)r|keys r- z3DetectionValidator.gather_stats..sIIICIIIr.)r distget_world_size gather_objectr(statskeysextendr\rUr*datasetr[ clear_stats)r)gathered_stats merged_stats stats_dictrgathered_jdictr\s r- gather_statszDetectionValidator.gather_statss 199"Vd&9&;&;;N  t|1>q I I I IIIt|/A/F/F/H/HIIIL, > > '>>C %,,Z_====>"Vd&9&;&;;N  tz>q A A A ADJ' ) ) !!%((((!-DL DO344DIII AXX  t|14Q ? ? ? ?  tz4Q 7 7 7 7DJ L $ $ & & & & & Xr.c|j|j|jj|j|j|jjS)zCalculate and return metrics statistics. Returns: (dict[str, Any]): Dictionary containing metrics results. )r+rr)r(processr+r!r]rr results_dictrhs r- get_statszDetectionValidator.get_statssJ dm$)/SWS_```   """|((r.cddt|jjzz}tj|d|j|jjg|jRz|jjdkr"tj d|j j d|j j r|j s|jdkrt|jjrt!|jjD]m\}}tj||j||jj||jj|g|j|RzfdSdSdSdSdS) z0Print training/validation set metrics per class.z %22s%11i%11iz%11.3gallrzno labels found in z+ set, cannot compute metrics without labelsrGN)rUr(rr infor[ nt_per_classsum mean_resultswarningr!r"verboserXrYrrap_class_indexrI nt_per_image class_result)r)pfics r- print_resultsz DetectionValidator.print_resultss 8c$,2C.D.D#D D B%DL,E,I,I,K,KjdlNgNgNiNijjjkkk < $ ( ( * *a / / Nllll m m m 9  T] tw{{s4>  1  1  1!4 1!422155     {{{{  r.dict[str, np.ndarray]c|djddks|djddkr5dtj|djd|jftiSt |d|d}d||d|d|iS)aReturn correct prediction matrix. Args: preds (dict[str, torch.Tensor]): Dictionary containing prediction data with 'bboxes' and 'cls' keys. batch (dict[str, Any]): Batch dictionary containing ground truth data with 'bboxes' and 'cls' keys. Returns: (dict[str, np.ndarray]): Dictionary containing 'tp' key with correct prediction matrix of shape (N, 10) for 10 IoU levels. ryrtpdtyperw) rrrr'boolrmatch_predictionsrr)r)rjr/rs r-rz!DetectionValidator._process_batchs < a A % %u);A)>!)C)C"(E%L$6q$949#ETRRRS SeHouX77d,,U5\5<MMQQSSYY[[\\r.rWimg_pathmode int | Nonetorch.utils.data.DatasetcJt|j|||j||jS)acBuild YOLO Dataset. Args: img_path (str): Path to the folder containing images. mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode. batch (int, optional): Size of batches, this is for `rect`. Returns: (Dataset): YOLO dataset. )rstride)rr!rKr)r)rrr/s r- build_datasetz DetectionValidator.build_dataset"s'"$)Xudid[_[fggggr. dataset_path batch_sizetorch.utils.data.DataLoaderc |||d}t|||jjdd|jj|jS)zConstruct and return dataloader. Args: dataset_path (str): Path to the dataset. batch_size (int): Size of each batch. Returns: (torch.utils.data.DataLoader): DataLoader for validation. rW)r/rFr)shufflerank drop_last pin_memory)rrr!workerscompilerX)r)rrrs r-get_dataloaderz!DetectionValidator.get_dataloader/sV$$\%$PP   I i'}    r.nicft||d|jd|dz |j|jdS)zPlot validation image samples. Args: batch (dict[str, Any]): Batch containing images and annotations. ni (int): Batch index. r val_batchz _labels.jpg)rpathsfnamerIrN)rr+rIr)r)r/rs r-plot_val_samplesz#DetectionValidator.plot_val_samplesDsP  "-"=b"="="==*L       r.rqc sdStD]%\}}tj|d|z|d<&d}p |jjfd|D}t j|d|d<t|d||d|j d |d z |j |j dS) aiPlot predicted bounding boxes on input images and save the result. Args: batch (dict[str, Any]): Batch containing images and annotations. preds (list[dict[str, torch.Tensor]]): List of predictions from the model. ni (int): Batch index. max_det (int | None): Maximum number of detections to plot. NrxrrcZi|]&tjfdDd'S)c0g|]}|dSNrg)r|r}r>rqs r-r~zBDetectionValidator.plot_predictions...ds%&E&E&E!qtHWH~&E&E&Er.rdim)r#cat)r|r>rqrjs @r-rz7DetectionValidator.plot_predictions..dsD\\\RSEI&E&E&E&E&Eu&E&E&E1MMM\\\r.rwr4rrz _pred.jpg)imagesrrrrIr) rr# ones_likerr!rqr xyxy2xywhrr+rIr) r)r/rjrrqrrr batched_predss ` ` r-plot_predictionsz#DetectionValidator.plot_predictionsSs  F '' B BGAt %V = = AD  Qx}}.TY.\\\\\W[\\\ "%- h0G"H"H h<  "-";b";";";;*L       r.rrrrtuple[int, int]filerc Zddlm}|tj|d|dftjd|jt j|d|dd|d dgd   || dS) aSave YOLO detections to a txt file in normalized coordinates in a specific format. Args: predn (dict[str, torch.Tensor]): Dictionary containing predictions with keys 'bboxes', 'conf', and 'cls'. save_conf (bool): Whether to save confidence scores. shape (tuple[int, int]): Shape of the original image (height, width). file (Path): File path to save the detections. r)ResultsrGrNrwrxrryr)pathrIboxes)r) ultralytics.engine.resultsr rruint8rIr#r unsqueezer)r)rrrr r s r-rzDetectionValidator.save_one_txtos 766666 HeAha) : : :*)U8_eFm.E.Eb.I.I5QV>> result = { ... "image_id": 42, ... "file_name": "42.jpg", ... "category_id": 18, ... "bbox": [258.15, 41.29, 348.26, 243.78], ... "score": 0.236, ... } rrwNrrxryc.g|]}t|dS))roundr{s r-r~z3DetectionValidator.pred_to_json..s 444QU1a[[444r.ru)image_id file_name category_idrscore) rr isnumericrr rziptolistr\appendnamer r) r)rrrrrboxbsrs r-rzDetectionValidator.pred_to_jsons4"F9%&&y $ 0 0:3t999dmE(O,, AAArrE c!!!QRR%j1n$ 3::<<v)=)=)?)?uATATAVAVWW  GAq! J   (!%#'>#a& 44!444"1a[[       r.c i|dtj|d|d|d|diS)z.Scales predictions to the original image size.rwrrr)r)r scale_boxesclone)r)rrs r-rzDetectionValidator.scale_predss^  cowh%%''{# -    r.rc|jdz }|jddz |jrdnd|jjdz }||||S)aEvaluate YOLO output in JSON format and return performance statistics. Args: stats (dict[str, Any]): Current statistics dictionary. Returns: (dict[str, Any]): Updated statistics dictionary with COCO/LVIS evaluation results. zpredictions.jsonrrzinstances_val2017.jsonlvis_v1_z.json)r+rKrr!rM coco_evaluate)r)r pred_json anno_jsons r- eval_jsonzDetectionValidator.eval_jsonshM$66 If  +/<^''=^ =^=^=^ `  !!%I>>>r.rBoxr)r* iou_typesstr | list[str]suffixcl|jjr|js|jrt |jrt jd|d|d ||fD]#}|s J|d$t|tr|gn|}t|tr|gn|}tdddl m }m}||} | |} t!|D]\} } || | | |jt j} d |jjjD| j_| | | | jd |d || dd <| jd |d|| dd <| jd|d<| jd|d<| jd|d<d| jd zd| jd zz|d<|jr`| jd|d|| dd <| jd|d|| dd <| jd|d|| dd <|jr |d|d<n.#t4$r!}t jd|Yd }~nd }~wwxYw|S)!amEvaluate COCO/LVIS metrics using faster-coco-eval library. Performs evaluation using the faster-coco-eval library to compute mAP metrics for object detection. Updates the provided stats dictionary with computed metrics including mAP50, mAP50-95, and LVIS-specific metrics if applicable. Args: stats (dict[str, Any]): Dictionary to store computed metrics and statistics. pred_json (str | Path): Path to JSON file containing predictions in COCO format. anno_json (str | Path): Path to JSON file containing ground truth annotations in COCO format. iou_types (str | list[str]): IoU type(s) for evaluation. Can be single string or list of strings. Common values include "bbox", "segm", "keypoints". Defaults to "bbox". suffix (str | list[str]): Suffix to append to metric names in stats dictionary. Should correspond to iou_types if multiple types provided. Defaults to "Box". Returns: (dict[str, Any]): Updated stats dictionary containing the computed COCO/LVIS evaluation metrics. z' Evaluating faster-coco-eval mAP using z and z...z file not foundzfaster-coco-eval>=1.6.7r)COCOCOCOeval_faster)iouType lvis_styleprint_functioncPg|]#}tt|j$Srg)rrrr{s r-r~z4DetectionValidator.coco_evaluate..s((e(e(eqT!WW\):):(e(e(er.AP_50zmetrics/mAP50()AP_allzmetrics/mAP50-95(AP_smallzmetrics/mAP_small(B) AP_mediumzmetrics/mAP_medium(B)AP_largezmetrics/mAP_large(B)g?g?fitnessAPrz metrics/APr(APcz metrics/APc(APfz metrics/APf(zmetrics/mAP50-95(B)z faster-coco-eval unable to run: N)r!rVrrrUr\r ris_filer7rNrfaster_coco_evalr1r2loadResrr*rim_filesparamsimgIdsevaluate accumulate summarize stats_as_dict Exceptionr)r)rr)r*r-r/r}r1r2annorriou_typerWes r-r(z DetectionValidator.coco_evaluatesH4 9 ' GDL' GDL' Gc$*oo' G Ka9aaS\aaa b b b% G"I->>A99;;==1(=(=(===;=+5i+E+ETYKK9 %/%<%<H&&"#<===BBBBBBBBtI||I..#,Y#7#7YYKAx)/dH^d^iC)f(eDOD[Dd(e(e(eCJ%LLNNNNN$$$MMOOO?B>OPW>XE:6!9Q<:::;ADARS[A\E=fQil===>474Ej4QE01585F{5SE12474Ej4QE01'*S->x-H'H3QTQbcjQkKk'kE)$|Y@C@QRW@Xr_sA#"""""  LLLLLLLLLL666666444444444444777777JJJJJJJJJJ222222llllllllllr.