o oi@sdddlmZddlZddlmZddlmZddlmZmZm Z m Z ddd dZ GdddeZ dS)) annotationsN)Tensor)Module) KORNIA_CHECKKORNIA_CHECK_IS_TENSORKORNIA_CHECK_SAME_DEVICEKORNIA_CHECK_SAME_SHAPEnoneimg1rimg2 reductionstrreturncCst|t|t||t||t|dvd|tdt||}|dkr2|}|S|dkr<|}|S|dksD|durG |St d) aCriterion 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 tensor with shape :math:`(*)`. img2: the target 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() )meansumr Nz/Given type of reduction is not supported. Got: g?rrr NzInvalid reduction option.) rrrrtorchlog1psquarerrNotImplementedError)r r r lossrH/home/ubuntu/.local/lib/python3.10/site-packages/kornia/losses/cauchy.py cauchy_losss$$  rcs.eZdZdZddfdd Zdd d ZZS) CauchyLossaCriterion 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 tensor with shape :math:`(*)`. - img2: the target 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() r r r rNonecst||_dS)N)super__init__r )selfr  __class__rrrs  zCauchyLoss.__init__r rr cCst|||jdS)N)r r r )rr )rr r rrrforwardszCauchyLoss.forwardr )r r rr)r rr rrr)__name__ __module__ __qualname____doc__rr __classcell__rrrrr[s$rr!)r rr rr r rr) __future__rrr kornia.corerkornia.core.checkrrrrrrrrrrs    @