:j} ddlmZddlmZddlZddlmZddlmZde de dejd eeje fd ef d Zd e dee dee dejd eeje fd ef d Zdede d eeeffdZddede de de d ef dZddede de d efdZdede d efdZd e dee dee dejd ejd ef dZdede de de d ef dZy))Sequence)UnionN)Tensor) functional kernel_sizesigmadtypedevicereturnctjd|z dz d|zdz d||}tjtj||z d dz }||j z j dS)aYCompute 1D gaussian kernel. Args: kernel_size: size of the gaussian kernel sigma: Standard deviation of the gaussian kernel dtype: data type of the output tensor device: device of the output tensor Example: >>> _gaussian(3, 1, torch.float, 'cpu') tensor([[0.2741, 0.4519, 0.2741]]) )startendstepr r rdim)torcharangeexppowsum unsqueeze)rrr r distgausss x/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/image/utils.py _gaussianr ss <>> _gaussian_kernel_2d(1, (5,5), (1,1), torch.float, "cpu") tensor([[[[0.0030, 0.0133, 0.0219, 0.0133, 0.0030], [0.0133, 0.0596, 0.0983, 0.0596, 0.0133], [0.0219, 0.0983, 0.1621, 0.0983, 0.0219], [0.0133, 0.0596, 0.0983, 0.0596, 0.0133], [0.0030, 0.0133, 0.0219, 0.0133, 0.0030]]]]) rr )rrmatmultexpand)rrrr r gaussian_kernel_xgaussian_kernel_ykernels r_gaussian_kernel_2dr'sn2"+a.%(E6J!+a.%(E6J \\+--/1B CF ==![^[^ DDrinputs window_sizectjdd|||j|j}||dzz}tjd|j|j}||fS)zConstruct uniform weight and bias for a 2d convolution. Args: inputs: Input image window_size: size of convolutional kernel Return: The weight and bias for 2d convolution r )r r r)ronesr r zeros)r(r) kernel_weight kernel_biass r_uniform_weight_bias_conv2dr/<sZJJq![+V\\Z`ZgZghM[!^#M++av||FMMJK + %%rrpad outer_padc ~|j|}tj||tj|dz ddj |j }tj||tj|dz ||z |z dj |j }tj |||f|S)a,Apply single-dimension reflection padding to match scipy implementation. Args: inputs: Input image dim: A dimension the image should be padded over pad: Number of pads outer_pad: Number of outer pads Return: Image padded over a single dimension r )shaper index_selectrtor cat)r(rr0r1_maxxys r_single_dimension_padr;Ms << D 63 S1Wb"(E(H(H(WXA 63 TAXtczI?UWY(Z(](]^d^k^k(lmA 99a^S ))rc0dD]}t||||}|S)zApply reflection padding to the input image. Args: inputs: Input image pad: Number of pads outer_pad: Number of outer pads Return: Padded image )r)r;)r(r0r1rs r_reflection_pad_2dr>`s*D&vsCCD Mrc  t||dz|dz}t||\}}tjt |j dDcgc]1}t j|dd|fjd||d3c}dScc}w)zApply uniform filter with a window of a given size over the input image. Args: inputs: Input image window_size: Sliding window used for rmse calculation Return: Image transformed with the uniform input rr Nr)paddingr) r>r/rr7ranger4Fconv2dr)r(r)r-r.rs r_uniform_filterrDqs  q(8+/ JF!tj|||||||fdS)aUReflective padding of 3d input. Args: inputs: tensor to pad, should be a 3D tensor of shape ``[N, C, H, W, D]`` pad_w: amount of padding in the height dimension pad_h: amount of padding in the width dimension pad_d: amount of padding in the depth dimension Returns: padded input tensor reflect)mode)rBr0)r(rKrLrMs r_reflection_pad_3drQs$ 55%ueUC) TTr)r)collections.abcrtypingrrrtorch.nnrrBintfloatr r strrr'tupler/r;r>rDrJrQrrrZs$ $232u2U[[2%PUP\P\^aPaJb2gm2&E E#E E?E ;; E %,,# $ E  E@&&S&U6SY>EZ&"*&*s***U[*&vCC"F.U U'}U5=e_UMR[[UbgbnbnU U0 Uv Uc U# Uc Uf Ur