3jtSSKJr SSKrSSKJr SSKJrJrJrJr SS Sjjr "SS\R5r g) ) annotationsN)nn) KORNIA_CHECKKORNIA_CHECK_IS_TENSORKORNIA_CHECK_SAME_DEVICEKORNIA_CHECK_SAME_SHAPEcj[U5 [U5 [X5 [X5 [US;SU35 [R "S[R "X- 5-5nUS:XaUR5nU$US:XaUR5nU$US:XdUcU$[S5e)a<Criterion that computes the Cauchy [2] (aka. Lorentzian) loss. According to [1], we compute the Cauchy loss as follows: .. math:: \text{WL}(x, y) = log(\frac{1}{2} (x - y)^{2} + 1) Where: - :math:`x` is the prediction. - :math:`y` is the target to be regressed to. Reference: [1] https://arxiv.org/pdf/1701.03077.pdf [2] https://files.is.tue.mpg.de/black/papers/cviu.63.1.1996.pdf Args: img1: the predicted torch.Tensor with shape :math:`(*)`. img2: the target torch.Tensor with the same shape as img1. reduction: Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied (default), ``'mean'``: the sum of the output will be divided by the number of elements in the output, ``'sum'``: the output will be summed. Return: a scalar with the computed loss. Example: >>> img1 = torch.randn(2, 3, 32, 32, requires_grad=True) >>> img2 = torch.randn(2, 3, 32, 32) >>> output = cauchy_loss(img1, img2, reduction="mean") >>> output.backward() )meansumnoneNz/Given type of reduction is not supported. Got: g?r r r zInvalid reduction option.) rrrrtorchlog1psquarer r NotImplementedError)img1img2 reductionlosss N/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/losses/cauchy.py cauchy_lossrsH4 4 D'T(226efoep4q ;;sU\\$+66 7DFyy{ K e xxz K f  1  K""=>>c>^\rSrSrSrSSU4SjjjrSSjrSrU=r$) CauchyLossZa;Criterion that computes the Cauchy [2] (aka. Lorentzian) loss. According to [1], we compute the Cauchy loss as follows: .. math:: \text{WL}(x, y) = log(\frac{1}{2} (x - y)^{2} + 1) Where: - :math:`x` is the prediction. - :math:`y` is the target to be regressed to. Reference: [1] https://arxiv.org/pdf/1701.03077.pdf [2] https://files.is.tue.mpg.de/black/papers/cviu.63.1.1996.pdf Args: reduction: Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied (default), ``'mean'``: the sum of the output will be divided by the number of elements in the output, ``'sum'``: the output will be summed. Shape: - img1: the predicted torch.Tensor with shape :math:`(*)`. - img2: the target torch.Tensor with the same shape as img1. Example: >>> criterion = CauchyLoss(reduction="mean") >>> img1 = torch.randn(2, 3, 32, 2107, requires_grad=True) >>> img2 = torch.randn(2, 3, 32, 2107) >>> output = criterion(img1, img2) >>> output.backward() c.>[TU]5 Xlg)N)super__init__r)selfr __class__s rrCauchyLoss.__init__s "rc*[XURS9$)N)rrr)rr)rrrs rforwardCauchyLoss.forwards4>>JJr)rr )rstrreturnNone)r torch.Tensorrr(r&r() __name__ __module__ __qualname____firstlineno____doc__rr"__static_attributes__ __classcell__)rs@rrrZs"H##KKrrr$)rr(rr(rr%r&r() __future__rr rkornia.core.checkrrrrrModulerrrr4s.$# uu=@*K*Kr