khU dZddlZddlmZdejfdZ d dededed efd Z d dededed efd Z y)z PyTorch Soft-NMS This code was adapted from a PR for detectron2 submitted by https://github.com/alekseynp https://github.com/facebookresearch/detectron2/pull/1183/files Detectron2 is licensed Apache 2.0, Copyright Facebook Inc. N)Listreturnc 8|dddf|dddfz |dddf|dddfz z}|dddf|dddfz |dddf|dddfz z}tj|dddddf|ddddftj|dddddf|ddddfz }|jd|j d}tj |dkD||dddf|z|z z tj d|j|j}|S) aH Given two lists of boxes of size N and M, compute the IoU (intersection over union) between __all__ N x M pairs of boxes. The box order must be (xmin, ymin, xmax, ymax). Args: boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. Returns: Tensor: IoU, sized [N,M]. Nr)min)dimdtypedevice) torchr maxclamp_prodwherezerosr r )boxes1boxes2area1area2 width_heightinterious K/var/www/teggl/fontify/venv/lib/python3.12/site-packages/effdet/soft_nms.py pairwise_iour s:AqD\F1a4L (VAqD\F1a4L-H IE AqD\F1a4L (VAqD\F1a4L-H IE99VAtQRK0&AB-@599q${VArrE]DLA   !  $E ++   q$w%'%/0 AU[[> C Jmethod_gaussiansigma iou_thresholdscore_thresholdcR|j}|j}|j}|jd} tj| } tj | tj |} tj | tj|} d} |jdkDrtj|}| || | <||| | <| dz } ||}t|jd|d}|r.tjtj|d |z }n%tj|}||kD}d||z ||<||z}||kD}tjd|||<||}||}| |} |jdkDr| d| | d| fS)a Soft non-max suppression algorithm. Implementation of [Soft-NMS -- Improving Object Detection With One Line of Codec] (https://arxiv.org/abs/1704.04503) Args: boxes_remain (Tensor[N, ?]): boxes where NMS will be performed if Boxes, in (x1, y1, x2, y2) format if RotatedBoxes, in (x_ctr, y_ctr, width, height, angle_degrees) format scores_remain (Tensor[N]): scores for each one of the boxes method_gaussian (bool): use gaussian method if True, otherwise linear sigma (float): parameter for Gaussian penalty function iou_threshold (float): iou threshold for applying linear decay. Nt from the paper re-used as threshold for standard "hard" nms score_threshold (float): boxes with scores below this threshold are pruned at each iteration. Dramatically reduces computation time. Authors use values in [10e-4, 10e-2] Returns: tuple(Tensor, Tensor): [0]: int64 tensor with the indices of the elements that have been kept by Soft NMS, sorted in decreasing order of scores [1]: float tensor with the re-scored scores of the elements that were kept rr rrF)r N)r clonesizerarangerint64float32numelargmaxr unsqueezeexppow ones_liketensor)boxesscoresrrr r!r boxes_remain scores_remainnum_elemidxsidxs_out scores_outcounttop_idxtop_boxiousdecay decay_maskkeeps rsoft_nmsr>*sJ\\F;;=LLLNM!!#A&H << !D{{85;;vFHXU]]6JJE    ! #,,}-w-)'2 5  w'G--a0,?B IIuyyq11E9:EOOD)E -J !D$4 4E*  . U6:W #D) %d+ Dz-    ! #0 FU Z/ //rcp|jdk(r`tjdtj|jtjdtj |jfS|j }|j||dzz}||dddfz} t| |||||S)a Performs soft non-maximum suppression in a batched fashion. Each index value correspond to a category, and NMS will not be applied between elements of different categories. Args: boxes (Tensor[N, 4]): boxes where NMS will be performed. They are expected to be in (x1, y1, x2, y2) format scores (Tensor[N]): scores for each one of the boxes idxs (Tensor[N]): indices of the categories for each one of the boxes. method (str): one of ['gaussian', 'linear', 'hard'] see paper for details. users encouraged not to use "hard", as this is the same nms available elsewhere in detectron2 sigma (float): parameter for Gaussian penalty function iou_threshold (float): iou threshold for applying linear decay. Nt from the paper re-used as threshold for standard "hard" nms score_threshold (float): boxes with scores below this threshold are pruned at each iteration. Dramatically reduces computation time. Authors use values in [10e-4, 10e-2] Returns: tuple(Tensor, Tensor): [0]: int64 tensor with the indices of the elements that have been kept by Soft NMS, sorted in decreasing order of scores [1]: float tensor with the re-scored scores of the elements that were kept r)rr rN)rrr r!) r(remptyr&r r'rtor>) r/r0r4rrr r!max_coordinateoffsets boxes_for_nmss rbatched_soft_nmsrEssN {{} KKEKK E KKEMM&-- H  YY[Nggen 23GGAtG,,M ve#_ r)T?rFg{Gzt?)TrFrFgMbP?) __doc__rtypingrTensorrboolfloatr>rErrrMs ELLB!" F0F0  F0  F0  F0V!" 66 6 6  6r