3j SSKrSSKJrJr SSKJrJrJr S S\RS\RS\S\R4Sjjr"S S \R5r \r g) N)Tensornn) KORNIA_CHECKKORNIA_CHECK_IS_TENSORKORNIA_CHECK_SHAPEinputtarget reductionreturnc[U5 [U5 [USS/5 [USS/5 [URUR:HSURSUR35 USUS- S-USUS- S--R 5nUS:XaUR 5nU$US :XaUR 5nU$US :XaU$[S 5e) aCreate a function that calculates the average endpoint error (AEPE) between 2 flow maps. AEPE is the endpoint error between two 2D vectors (e.g., optical flow). Given a h x w x 2 optical flow map, the AEPE is: .. math:: \text{AEPE}=\frac{1}{hw}\sum_{i=1, j=1}^{h, w}\sqrt{(I_{i,j,1}-T_{i,j,1})^{2}+(I_{i,j,2}-T_{i,j,2})^{2}} Args: input: the input flow map with shape :math:`(*, 2)`. target: the target flow map with shape :math:`(*, 2)`. reduction : Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, ``'mean'``: the sum of the output will be divided by the number of elements in the output, ``'sum'``: the output will be summed. Return: the computed AEPE as a scalar. Examples: >>> ones = torch.ones(4, 4, 2) >>> aepe(ones, 1.2 * ones) tensor(0.2828) Reference: https://link.springer.com/content/pdf/10.1007/s11263-010-0390-2.pdf *2z/input and target shapes must be the same. Got: z and ).r).meansumnonezInvalid reduction option.)rrrshapesqrtrrNotImplementedError)rr r epes W/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/metrics/endpoint_error.pyaepers<5!6"usCj)vSz* v||#'VW\WbWbVcchioiuiuhv%w&MF6N2q8E&MFSYN>c^\rSrSrSrS S\SS4U4SjjjrS\RS\RS\R4S jr S r U=r $) AEPELaXComputes the average endpoint error (AEPE) between 2 flow maps. EPE is the endpoint error between two 2D vectors (e.g., optical flow). Given a h x w x 2 optical flow map, the AEPE is: .. math:: \text{AEPE}=\frac{1}{hw}\sum_{i=1, j=1}^{h, w}\sqrt{(I_{i,j,1}-T_{i,j,1})^{2}+(I_{i,j,2}-T_{i,j,2})^{2}} Args: reduction : Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, ``'mean'``: the sum of the output will be divided by the number of elements in the output, ``'sum'``: the output will be summed. Shape: - input: :math:`(*, 2)`. - target :math:`(*, 2)`. - output: :math:`(1)`. Examples: >>> input1 = torch.rand(1, 4, 5, 2) >>> input2 = torch.rand(1, 4, 5, 2) >>> epe = AEPE(reduction="mean") >>> epe = epe(input1, input2) r r Nc.>[TU]5 XlgN)super__init__r )selfr __class__s rr! AEPE.__init__is 'rrr c.[XUR5$r)rr )r"rr s rforward AEPE.forwardmsE4>>22r)r r) __name__ __module__ __qualname____firstlineno____doc__strr!torchrr&__static_attributes__ __classcell__)r#s@rrrLsK8(#(4((3U\\35<<3ELL33rrr() r/rrkornia.core.checkrrrr.rModuleraverage_endpoint_errorrrr6s]$ VV1 1ell1s1PUP\P\1h"3299"3Jr