o oi @shddlZddlmZmZddlmZmZmZd dejdejdedejfd d ZGd d d ej Z eZ dS)N)Tensornn) KORNIA_CHECKKORNIA_CHECK_IS_TENSORKORNIA_CHECK_SHAPEmeaninputtarget reductionreturncCst|t|t|ddgt|ddgt|j|jkd|jd|j|d|dd|d|dd}|dkrG|}|S|d krQ|}|S|d krX |Std ) 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).rsumnonezInvalid reduction option.)rrrshapesqrtrrNotImplementedError)rr r eperQ/home/ubuntu/.local/lib/python3.10/site-packages/kornia/metrics/endpoint_error.pyaepes$,rcsHeZdZdZd deddffdd Zdejd ejdejfd d ZZ S) AEPEaComputes the average endpoint error (AEPE) between 2 flow maps. EPE is the endpoint error between two 2D vectors (e.g., optical flow). Given a h x w x 2 optical flow map, the AEPE is: .. math:: \text{AEPE}=\frac{1}{hw}\sum_{i=1, j=1}^{h, w}\sqrt{(I_{i,j,1}-T_{i,j,1})^{2}+(I_{i,j,2}-T_{i,j,2})^{2}} Args: reduction : Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, ``'mean'``: the sum of the output will be divided by the number of elements in the output, ``'sum'``: the output will be summed. Shape: - input: :math:`(*, 2)`. - target :math:`(*, 2)`. - output: :math:`(1)`. Examples: >>> input1 = torch.rand(1, 4, 5, 2) >>> input2 = torch.rand(1, 4, 5, 2) >>> epe = AEPE(reduction="mean") >>> epe = epe(input1, input2) rr r Ncst||_dSN)super__init__r )selfr  __class__rrris  z AEPE.__init__rr cCst|||jSr)rr )rrr rrrforwardmsz AEPE.forwardr) __name__ __module__ __qualname____doc__strrtorchrr __classcell__rrrrrLs$rr!) r'rrkornia.core.checkrrrr&rModuleraverage_endpoint_errorrrrrs "4%