a d" @s*ddlZdejejeeejdddZdS)NF)inputtarget num_classes normalizedreturncCsNt|s(|jtjur(tdt|t|sP|jtjurPtdt||j|jksptd|j|j|j |j kstd|j |j t |t r|dkrtd||jd}|||}| |d}g}t |D](}||} tj| |dd } || qt|} | |||tj} |rJtj| d d d } | | d } | S)a Compute confusion matrix to evaluate the accuracy of a classification. Args: input: 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.]]]) zEInput input type is not a torch.Tensor with torch.int64 dtype. Got {}zFInput target type is not a torch.Tensor with torch.int64 dtype. Got {}z@Inputs input and target must have the same shape. Got: {} and {}z/Inputs must be in the same device. Got: {} - {}zAThe number of classes must be an integer bigger than two. Got: {}r)Z minlengthT)ZdimZkeepdimgư>)torchZ is_tensorZdtypeint64 TypeErrorformattypeshape ValueErrorZdevice isinstanceintviewrangeZbincountappendstacktofloat32sum)rrrrZ batch_sizeZ pre_bincountZpre_bincount_vecZconfusion_listZiter_idZpbZ bin_countZ confusion_vecZ confusion_matZnorm_valrh/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/metrics/confusion_matrix.pyconfusion_matrixs8          r)F)r ZTensorrboolrrrrrs