3j" lSSKrSS\RS\RS\S\S\R4 Sjjrg) Npredtarget num_classes normalizedreturnc2[R"U5(d4UR[RLa[ S[ U535e[R"U5(d4UR[RLa[ S[ U535eUR UR :Xd%[SUR SUR 35eURUR:Xd%[SURSUR35e[U[5(aUS:a[SU35eUR S nXU--nURUS 5n/n[U5H/nXhn [R"XS-S 9n URU 5 M1 [R"U5n U RXBU5R![R"5n U(a[R$"U S S S9n XS-- n U $)aCompute confusion matrix to evaluate the accuracy of a classification. Args: pred: tensor with estimated targets returned by a classifier. The shape can be :math:`(B, *)` and must contain integer values between 0 and K-1. target: tensor with ground truth (correct) target values. The shape can be :math:`(B, *)` and must contain integer values between 0 and K-1, where targets are assumed to be provided as one-hot vectors. num_classes: total possible number of classes in target. normalized: whether to return the confusion matrix normalized. Returns: a tensor containing the confusion matrix with shape :math:`(B, K, K)` where K is the number of classes. Example: >>> logits = torch.tensor([[0, 1, 0]]) >>> target = torch.tensor([[0, 1, 0]]) >>> confusion_matrix(logits, target, num_classes=3) tensor([[[2., 0., 0.], [0., 1., 0.], [0., 0., 0.]]]) zBInput pred type is not a torch.Tensor with torch.int64 dtype. Got zDInput target type is not a torch.Tensor with torch.int64 dtype. Got z6Inputs pred and target must have the same shape. Got: z and z(Inputs must be in the same device. Got: z - z?The number of classes must be an integer bigger than two. Got: r) minlengthT)dimkeepdimgư>)torch is_tensordtypeint64 TypeErrortypeshape ValueErrordevice isinstanceintviewrangebincountappendstacktofloat32sum)rrrr batch_size pre_bincountpre_bincount_vecconfusion_listiter_idpb bin_count confusion_vec confusion_matnorm_vals Y/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/metrics/confusion_matrix.pyconfusion_matrixr-s: ??4 TZZu{{%B\]abf]g\hijj ??6 " "v||5;;'F^_cdj_k^lmnn :: %QRVR\R\Q]]bcicocobpqrr ;;&-- 'CDKK=PSTZTaTaSbcdd k3 ' ';?Z[fZghiijjmJ"&(r2sJ$ TY> ,,> % >;>>LP> \\>r.