tj=ddlZddlmZddlmcmZddlmZddlm Z ddl m Z m Z erddl mZdZ dd Zd ed ejd ed ejfdZdejd ed ed ejfdZddZGdde Z ddZdS)N)is_vision_available)box_iou)RTDetrHungarianMatcher RTDetrLoss)center_to_corners_formatc6dt||DS)Ncg|] \}}||d S))logits pred_boxes).0abs [/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/loss/loss_d_fine.py z!_set_aux_loss..s$ Y Y Ytq!q * * Y Y Yzip) outputs_class outputs_coords r _set_aux_lossrs Y Ys=-7X7X Y Y YYrcDfdt||||DS)Nc ,g|]\}}}}||||dS))r r pred_corners ref_pointsteacher_cornersteacher_logitsr)rrrcdrrs rrz"_set_aux_loss2..$sL    Aq!Q.,      rr)rroutputs_corners outputs_refrrs ``r_set_aux_loss2r$!sD     m]O[YY    r max_num_binsup reg_scalereturnct|dt|z}t|dt|zdz}|dzd|dz z zfdt|dzdz ddD}fdtd|dzD}| g|ztj|ddgz|z|gz}d|D}tj|d}|S) uK Generates the non-uniform Weighting Function W(n) for bounding box regression. Args: max_num_bins (int): Max number of the discrete bins. up (Tensor): Controls upper bounds of the sequence, where maximum offset is ±up * H / W. reg_scale (float): Controls the curvature of the Weighting Function. Larger values result in flatter weights near the central axis W(max_num_bins/2)=0 and steeper weights at both ends. Returns: Tensor: Sequence of Weighting Function. rrrc"g|] }|z dz Srrristeps rrz&weighting_function..Bs$SSS!dq[>A%SSSrc g|] }|zdz  Sr+rr,s rrz&weighting_function..Cs!IIITaK!OIIIrNclg|]1}|dkr|n|d2S)r)dim unsqueeze)rvs rrz&weighting_function..Es6 C C Cq15577Q;;aaAKKNN C C Cr)absrangetorch zeros_likecat) r%r&r' upper_bound1 upper_bound2 left_values right_valuesvaluesr.s @rweighting_functionr?1sr!u::I.Lr!u::I.2L 1 !|a'7"8 9DSSSSu\Q5F5JAr/R/RSSSKIIIIU1la6G-H-HIIILm_{ *e.>r!uT{.K.K-L L| [_k^l lF C CF C C CF Yvq ! !F Mrgtc|d}t|||}|d|dz }|dk}tj|ddz }|}tj|} tj|} |dk||kz} || } || } || dz}tj|| | z }tj||| z }|||zz | | <d| | z | | <|dk}d| |<d| |<d||<||k}d| |<d| |<|dz ||<|| | fS)a  Decodes bounding box ground truth (GT) values into distribution-based GT representations. This function maps continuous GT values into discrete distribution bins, which can be used for regression tasks in object detection models. It calculates the indices of the closest bins to each GT value and assigns interpolation weights to these bins based on their proximity to the GT value. Args: gt (Tensor): Ground truth bounding box values, shape (N, ). max_num_bins (int): Maximum number of discrete bins for the distribution. reg_scale (float): Controls the curvature of the Weighting Function. up (Tensor): Controls the upper bounds of the Weighting Function. Returns: tuple[Tensor, Tensor, Tensor]: - indices (Tensor): Index of the left bin closest to each GT value, shape (N, ). - weight_right (Tensor): Weight assigned to the right bin, shape (N, ). - weight_left (Tensor): Weight assigned to the left bin, shape (N, ). r/rrr2g?g皙?) reshaper?r3r7sumfloatr8longr5)r@r%r'r&function_valuesdiffsmaskclosest_left_indicesindices weight_right weight_leftvalid_idx_mask valid_indicesr<r= left_diffs right_diffsinvalid_idx_mask_neginvalid_idx_mask_poss r translate_gtrUJs* BB(r9EEO  % %a ( (2<<?? :E A:D 9Tq111A5#((**G#G,,L"7++Klw'=>NN+0022M"-0K"=1#45L2n- ;<>K$.k1I#JL "% ^(D"DK#Q;),L%&(+K$%$'G !"l2),L%&(+K$%$03$6G ! L+ --rrCct|}|dddf|dddfz |d|z dzz d|zz }|dddf|dddfz |d|z dzz d|zz }|dddf|dddfz |d|z dzz d|zz }|ddd f|dddfz |d|z dzz d|zz } tj|||| gd } t| |||\} } } || d||z } | d | | fS) a Converts bounding box coordinates to distances from a reference point. Args: points (Tensor): (n, 4) [x, y, w, h], where (x, y) is the center. bbox (Tensor): (n, 4) bounding boxes in "xyxy" format. max_num_bins (float): Maximum bin value. reg_scale (float): Controlling curvarture of W(n). up (Tensor): Controlling upper bounds of W(n). eps (float): Small value to ensure target < max_num_bins. Returns: Tensor: Decoded distances. Nr).rgؗҜ<?r).rrXr/minmax)r5r7stackrUclamprDdetach) pointsbboxr%r'r&epslefttoprightbottom four_lensrMrNs r bbox2distancergs II 111a4L41: %&.9*Du*L MPSV_P_ _D !!!Q$<$qqq!t* $))Ce)K LsU^ ^C !!!Q$Z&A, &6&>I+E+M NQTW`Q` `E111a4j6!!!Q$<'F6NY,F,N ORUXaRa aF T3v6;;I+7 +>+@+@+BTBTBVBV VVrc:eZdZdZfdZ d dZd dZdZxZS) DFineLossa This class computes the losses for D-FINE. The process happens in two steps: 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched ground-truth / prediction (supervise class and box). Args: matcher (`DetrHungarianMatcher`): Module able to compute a matching between targets and proposals. weight_dict (`Dict`): Dictionary relating each loss with its weights. These losses are configured in DFineConf as `weight_loss_vfl`, `weight_loss_bbox`, `weight_loss_giou`, `weight_loss_fgl`, `weight_loss_ddf` losses (`list[str]`): List of all the losses to be applied. See `get_loss` for a list of all available losses. alpha (`float`): Parameter alpha used to compute the focal loss. gamma (`float`): Parameter gamma used to compute the focal loss. eos_coef (`float`): Relative classification weight applied to the no-object category. num_classes (`int`): Number of object categories, omitting the special no-object category. cft|t||_|j|_|j|j|j|j|j d|_ gd|_ |j |_ tjtj|jgd|_dS)N)loss_vfl loss_bbox loss_giouloss_fglloss_ddf)vflboxeslocalF) requires_grad)super__init__rmatcherr%weight_loss_vflweight_loss_bboxweight_loss_giouweight_loss_fglweight_loss_ddf weight_dictlossesr'nn Parameterr7tensorr&)selfconfig __class__s rruzDFineLoss.__init__s    -f55 "/.00..   0// ),u|VYK88NNNrNrEc|}|dz} tj||d|dztj|| d|dzz} ||}| |z} || |z } n5|dkr| } n|dkr| } | S)Nrnone reductionr/meanrE)rGF cross_entropyrDrFrEr) rpredlabelrMrNweightr avg_factordis_left dis_rightlosss r unimodal_distribution_focal_lossz*DFineLoss.unimodal_distribution_focal_losss::<<qL tX@@@;CVCVWYCZCZZ]^]l )v^ ^ ^   $ $^%%  \\^^F&=D  !88:: *DD & 99;;DD %  88::D rcV i}d|vr||}tjdt||Dd}|d|d|jdz} |d|} tj5t| t||j|j |j |_ dddn #1swxYwY|j \} } } tj tt|d |t|d}|dddd d}|| | | | || |d <|dd|jdz} |d d|jdz} tj| | r| dz|d<nv|ddd}tj|tj}d||<|dddd d}||||j||<|dddd d}||dzzt7jdt;j| |z dt;j| |z ddz}d|d j dz }||zdz||zdzc|_!|_"|#r||$nd}|#r||$nd}||j!z||j"zz|j!|j"zz |d<|S)zaCompute Fine-Grained Localization (FGL) Loss and Decoupled Distillation Focal (DDF) Loss.rc6g|]\}\}}|d|S)rqr)rt_r-s rrz(DFineLoss.loss_local..s(%[%[%[ 6Aqajm%[%[%[rrrBr/rrNr )rrnrror)dtypeTrrrrW)%_get_source_permutation_idxr7r9rrDr%r^no_gradrgr r'r& fgl_targetsdiagrr3repeatrequalrEsigmoidr[r8bool reshape_astorr~ KLDivLossr log_softmaxsoftmaxshapenum_posnum_neganyr)routputstargetsrL num_boxesTr}idx target_boxesrrtarget_cornersrMrNiousweight_targetsweight_targets_localrJloss_match_local batch_scaleloss_match_local1loss_match_local2s r loss_localzDFineLoss.loss_locals W $ $227;;C 9%[%[SRYEZEZ%[%[%[abcccL">237??TEVYZEZ\\L .s3::<2::2@QTU@UWWL$%67??TEVYZEZ\\N{<88 %1%5%5%7%7!%;z""'./?'@'H'H'J'J'N'NSU'N'V'VWX'Y$'()r labelsdevicer rrrenc_topk_logitsenc_topk_bboxesrpredicted_cornersinitial_reference_pointskwargs criterion outputs_lossr dn_out_coordnormal_out_coord dn_out_classnormal_out_classdn_out_corners out_corners dn_out_refsout_refsr loss_dictrs rDFineForObjectDetectionLossr<s1&!!I LLL#L!+!1!1aQ!1!?!?L &N ,-2[##q#113H3X^_... *L*.3[H]^lHmst-u-u-u *L**/+6GI^_mIntu*v*v*v 'NK$)K0HJ_`nJouv$w$w$w !K,22qa2@@ , +K/H   N . 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