o yi  @sddlmZmZddlZddlmmZddlmZddl m Z ddl m Z de dedejd ejd ef d d Zd e dee deedejd eejefd ef ddZd e dee deedejd ejd ef ddZdede de d efddZdede de de d ef ddZdS))SequenceUnionN)Tensor)rank_zero_warn)_TORCH_GREATER_EQUAL_1_10 kernel_sizesigmadtypedevicereturncCsTtjd|dd|dd||d}tt||d d}||jddS)aYComputes 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 r)dim)torcharangeexppowsum unsqueeze)rrr r distgaussrX/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/image/helper.py _gaussian s$ rchannelcCsTt|d|d||}t|d|d||}t||}||d|d|dS)aComputes 2D gaussian kernel. Args: channel: number of channels in the image kernel_size: size of the gaussian kernel as a tuple (h, w) sigma: Standard deviation of the gaussian kernel dtype: data type of the output tensor device: device of the output tensor Example: >>> _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_ykernelrrr_gaussian_kernel_2dsr$c Cst|d|d||}t|d|d||}t|d|d||}t||}t|ddd|d||d|d|d} | |d|d|d|dS)a6Computes 3D gaussian kernel. Args: channel: number of channels in the image kernel_size: size of the gaussian kernel as a tuple (h, w, d) sigma: Standard deviation of the gaussian kernel dtype: data type of the output tensor device: device of the output tensor rr r )rrrrmulrrepeatr ) rrrr r r!r"gaussian_kernel_z kernel_xyr#rrr_gaussian_kernel_3d=s r*inputsrpadc Cs`|j|d}t||tj|dd|jd}t||tj|||d|jd}t|||f|S)zReflective padding of input along a specific dimension. Args: inputs: tensor to pad dim: dimension to pad along pad: amount of padding to add Returns: padded input r rr%)r )shaper index_selectrr cat)r+rr,_maxxyrrr_single_dimension_padUs "r3pad_hpad_wpad_dcCsXtrtj|||||||fdd}|Stdt|||gD] \}}t||d|}q|S)aTReflective padding of 3d input. Args: inputs: tensor to pad, should be a 3D tensor of shape ``[N, C, H, W, D]`` pad_w: amount of padding in the height dimension pad_h: amount of padding in the width dimension pad_d: amount of padding in the depth dimension Returns: padded input tensor reflect)modez`An older version of pyTorch is used. For optimal speed, please upgrade to at least pyTorch 1.10.r )rFr,r enumerater3)r+r4r5r6rr,rrr_reflection_pad_3dfs r;)typingrrrtorch.nn.functionalnn functionalr9rtorchmetrics.utilitiesrtorchmetrics.utilities.importsrintfloatr r rstrr$r*r3r;rrrrsF   "    "