o oix @sLddlmZddlmZmZddlmZmZddd d ZGd d d eZ dS)) annotations)ModuleTensor) KORNIA_CHECKKORNIA_CHECK_SHAPEsumimgr reductionstrreturncCs@t|tstdt|t|gdt|dvd|d|dddddf|ddd ddf}|dddddf|ddddd f}|}|}d }|d kr|rt||j |d }||j |d }||S| j |d }| j |d }||S|d kr|j |d }|j |d }||St d)aCompute Total Variation according to [1]. Args: img: the input image with shape :math:`(*, H, W)`. reduction : Specifies the reduction to apply to the output: ``'mean'`` | ``'sum'``. ``'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 tensor with shape :math:`(*,)`. Examples: >>> total_variation(torch.ones(4, 4)) tensor(0.) >>> total_variation(torch.ones(2, 5, 3, 4, 4)).shape torch.Size([2, 5, 3]) .. note:: See a working example `here `__. Total Variation is formulated with summation, however this is not resolution invariant. Thus, `reduction='mean'` was added as an optional reduction method. Reference: [1] https://en.wikipedia.org/wiki/Total_variation zNot a Tensor type. Got: )*HW)meanrz7Expected reduction to be one of 'mean'/'sum', but got 'z'..N)rr)dimrzInvalid reduction option.) isinstancer TypeErrortyperrabsis_floating_pointtorfloatrNotImplementedError)rr pixel_dif1 pixel_dif2res1res2 reduce_axesr!Q/home/ubuntu/.local/lib/python3.10/site-packages/kornia/losses/total_variation.pytotal_variations, ,,   r#c@seZdZdZdddZdS) TotalVariationaCompute the Total Variation according to [1]. Shape: - Input: :math:`(*, H, W)`. - Output: :math:`(*,)`. Examples: >>> tv = TotalVariation() >>> output = tv(torch.ones((2, 3, 4, 4), requires_grad=True)) >>> output.data tensor([[0., 0., 0.], [0., 0., 0.]]) >>> output.sum().backward() # grad can be implicitly created only for scalar outputs Reference: [1] https://en.wikipedia.org/wiki/Total_variation rrr cCst|S)N)r#)selfrr!r!r"forwardeszTotalVariation.forwardN)rrr r)__name__ __module__ __qualname____doc__r&r!r!r!r"r$Qsr$N)r)rrr r r r) __future__r kornia.corerrkornia.core.checkrrr#r$r!r!r!r"s  9