^j"0dZddlmZddlZddlZddlmcmZ ddl m Z ddl m Z dejdejdejfd Zd ejd ejd ejd ejdededeej&ej&ej&effdZ ddeeeejfdeeeejfdededeeeeefff dZdeeeeefffdZdeeeeeffdeeeeeffdeeeeefffdZdeeeeeffdeeeeefffdZy)zBGreedy matching and accumulation functions for evaluation metrics.)AnyN)box_iou) all_gather pred_masksgt_masksreturnc|jd}|jd}|jdd|jddk7rX|jdd\}}tj|jj d||fdj d}|j j|dj}|j j|dj}tj||j}|jdd } |jdd } | | jz|z } tj| dkD|| z tj|S) aCompute pairwise boolean-mask IoU between N predictions and M ground truths. Args: pred_masks: Boolean mask tensor of shape [N, H, W]. gt_masks: Boolean mask tensor of shape [M, H, W]. Returns: IoU tensor of shape [N, M]. rNnearest)sizemodeT)dimkeepdim)shapeF interpolatefloat unsqueezesqueezeboolviewtorchmmtsumwhere zeros_like) rrnmhw pred_flatgt_flatinter pred_areagt_areaunions e/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/rfdetr/evaluation/matching.py_compute_mask_iour+sE AqArs 33$1==!1!;!;A!>aVR[\ddefg!&&q"-335Immo""1b)//1G HHY ,E !T 2Ikkak.G  #e +E ;;uqy%%-1A1A%1H II pred_scores pred_itemsgt_itemsgt_crowd iou_thresholdiou_typech|jd}|jd}tj|d}||} ||} |dk(r t| |} n t | |} |j } tj |tj| } tj |tj| }tj |tj| }t|D]}| |}|j}d||<d|| |z<|jd\}}||k\r d||<d| |<K|js\|j}d||<|j|k\sd||<t|jj}| j!j#j%j't(j*|j#j%|j#j%j't|fS) u>Greedy highest-score-first matching for one class in one image. Implements the COCO matching algorithm: each GT is matched at most once; detections are processed in descending score order; detections matched to crowd GTs are marked as ignored rather than false positives. Args: pred_scores: Float tensor of shape [N] with detection confidences. pred_items: Predictions — boxes [N, 4] in xyxy coords or masks [N, H, W]. gt_items: Ground truths — boxes [M, 4] in xyxy coords or masks [M, H, W]. gt_crowd: Bool tensor of shape [M], True for crowd instances. iou_threshold: Minimum IoU to count as a positive match. iou_type: ``"bbox"`` for box IoU or ``"segm"`` for mask IoU. Returns: Tuple ``(scores_np, matches_np, ignore_np, total_gt)`` where: - scores_np: float32 array [N] ordered by descending score. - matches_np: int array [N], 1 = TP, 0 = FP. - ignore_np: bool array [N], True if matched to a crowd GT. - total_gt: number of non-crowd GT instances. rT) descendingbboxdtypedeviceg)rr )rrargsortrr+r8zerosrlongrangeclonemaxanyintritemrcpunumpyastypenpfloat32)r-r.r/r0r1r2r r!sort_idxpred_scores_sorted pred_sorted iou_matrixr8 gt_matched pred_match pred_ignoreiiousnc_ious best_nc_iou best_nc_idx crowd_ioustotal_gts r*_match_single_classrU4s8 !AqA}}[T:H$X.X&K6[(3 &{H=   FQejj@JQejj@J++auzz&AK 1X*!}**, *. hY&'#*;;1;#5 [ - 'JqM&*J{ #||~!ZZ\ (, H9%>>#}4%)KN%**XI??$))+,H  "&&(..077 C !((.  r, preds_list targets_listc i}t||D]\}}|d}|d}|d} |jd} |d} |d} |jd} |jdtjt | tj | j }|j}t| jt| jz}|D]}| |k(}| |k(}||}||}t|jj}t|jj}|j|gggdd}|dk(r4|d xxt|jjz cc<|dk(r|jjj!}t#j$| }|dj'||j|d j'dg|z|d j'd g|zT|d k(r ||}| |}n| | t)d| |}| |}t+||||||\}}}} |dj'|j|d j'|j|d j'|j|d xx| z cc<|j-D!cic]x\}}!|t#j.|!dt"j0t#j.|!d t"j2t#j.|!d t|!d dzc}!}Scc}!}w)a!Build compact per-class matching data from a batch of predictions and targets. Implements greedy highest-score-first matching compatible with the COCO algorithm. The returned dict can be passed directly to ``merge_matching_data()`` and ultimately consumed by ``sweep_confidence_thresholds()`` after conversion to list form. Args: preds_list: Per-image predictions. Each dict must contain: - ``boxes``: float Tensor [N, 4] in absolute xyxy coordinates. - ``scores``: float Tensor [N]. - ``labels``: int64 Tensor [N]. - ``masks`` *(optional)*: bool Tensor [N, H, W] for segmentation. targets_list: Per-image ground truths. Each dict must contain: - ``boxes``: float Tensor [M, 4] in absolute xyxy coordinates. - ``labels``: int64 Tensor [M]. - ``masks`` *(optional)*: bool Tensor [M, H, W] for segmentation. - ``iscrowd`` *(optional)*: int64 Tensor [M], 1 for crowd instances. iou_threshold: IoU threshold for positive matching. Defaults to 0.5. iou_type: ``"bbox"`` for bounding-box IoU; ``"segm"`` for boolean-mask IoU. Defaults to ``"bbox"``. Returns: Dict mapping ``class_id`` (int) to a compact matching dict with keys: - ``"scores"``: float32 ndarray of detection scores. - ``"matches"``: int ndarray (1 = TP, 0 = FP). - ``"ignore"``: bool ndarray (True if matched to a crowd GT). - ``"total_gt"``: int, count of non-crowd GT instances. boxesscoreslabelsmasksiscrowdr6rrZmatchesignorerTrTr_r`Fr5z:iou_type='segm' requires 'masks' in both preds and targets)r7)zipgetrr:lenr;r8rsettolistr@rrA setdefaultrrBrCrEr9extend ValueErrorrUitemsarrayrFint64)"rVrWr1r2accpredstargets pred_boxesr- pred_labelsrgt_boxes gt_labelsr raw_crowdr0 all_class_idsclass_id pred_mask_c gt_mask_cp_scores gt_crowd_cn_predn_gtentryscorderp_itemsr/ scores_np matches_np ignore_nprTdatas" r*build_matching_datarsaN-/Cj,7<*w7^ Ho Ho YYw' 7#H% ;;w'KK  KKIejjAQAQ R >>#"%i&6&6&8"9C @R@R@TciS)zReturn an empty matching accumulator compatible with ``merge_matching_data()``. Returns: Empty dict to be passed as the first argument to ``merge_matching_data()``. rr,r*init_matching_accumulatorrs Ir, accumulatornew_datac|jD]\}}||vrA|dj|dj|dj|dd||<K||}tj|d|dg|d<tj|d|dg|d<tj|d|dg|d<|dxx|dz cc<|S)aMerge *new_data* into *accumulator* in place. Both arguments share the dict schema produced by ``build_matching_data()``: each class-keyed sub-dict contains ``"scores"`` (float32 ndarray), ``"matches"`` (int64 ndarray), ``"ignore"`` (bool ndarray), and ``"total_gt"`` (int). Args: accumulator: Running accumulator, modified in place. new_data: Batch-level matching data to merge in. Returns: The modified *accumulator* (same object, for method chaining). rZr_r`rTr^)ricopyrE concatenate)rrrurr|s r*merge_matching_datars"#..* 2$ ; &x.--/ ?//1x.--/ , %K ! )E nneHotH~-NOE(O!~~uY/?i.QRE)  nneHotH~-NOE(O * j!1 1  2 r, local_datacFt|}i}|D]}t|||S)aGather per-rank matching data from all DDP ranks and merge into one dict. Uses ``rfdetr.utilities.all_gather`` (pickle-based) so the data need not be a tensor. In single-process (non-distributed) mode, returns a merged copy of *local_data* unchanged. Args: local_data: Per-rank accumulator produced by ``merge_matching_data()``. Returns: Merged accumulator containing contributions from all ranks. )rr)rgatheredmerged rank_datas r*distributed_merge_matching_datars11;:0FH(*F/ FI./ Mr,)g?r5)__doc__typingrrCrErtorch.nn.functionalnn functionalrtorchvision.opsrrfdetr.utilitiesrTensorr+rstrtuplendarrayr@rUlistdictrrrrrr,r*rs I #'J%,,J%,,J5<<J0HH HllHll H  H  H 2::rzz2::s 23H\ oT#u||+,-otC-./oo o  #tCH~  od4T#s(^(;#<c4S>)*3S#X&' #tCH~ DS$sCx.() #tCH~ r,