o .wi@sddlZddlZddlmZmZddlZddlmZddlmZm Z m Z m Z ddl m Z ddlmZddlmZded ed eeeffd d Zd ed dfddZd edeedfd efddZdeded efddZ d:dedeedeeedfded ef ddZ   d;d ed eeeeefd!e d"d#e d$d ef d%d&Z ' dded ed,e d"d)eeeeefd0ed eeeeefff d1d2Zej d?d)eeeefeeeeffd3eej!d eeeffd4d5Z"d?d)eeefd3eej!d eeeffd6d7Z#ej d?d)eeeefd3eej!d eeeffd8d9Z$dS)@N)OptionalUnion)Tensor)conv2dconv3dpadunfold)Literal)_check_same_shape)_SCIPY_AVAILABLEpredstargetreturncCsT|jddkr|ddddfn|}|jddkr$|ddddfn|}||fS)zQIgnore the background class in the computation assuming it is the first, index 0.Nshape)r r rg/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/segmentation/utils.py_ignore_backgrounds&&rxcCst||ks tddS)zCheck if tensor is binarized. Example: >>> from torchmetrics.functional.segmentation.utils import check_if_binarized >>> import torch >>> check_if_binarized(torch.tensor([0, 1, 1, 0])) zInput x should be binarizedN)torchallbool ValueError)rrrrcheck_if_binarized"s r kernel_size.c Cs|jdd\}}|jd}|dkrt||S|d|d|d}|dgdd|D}tj||j|jd}|j|dg|Rj|}t ||dd}||||d S) a.Unfold the input tensor to a matrix. Function supports 3d images e.g. (B, C, D, H, W). Inspired by: https://github.com/f-dangel/unfoldNd/blob/main/unfoldNd/unfold.py Args: x: Input tensor to be unfolded. kernel_size: The size of the sliding blocks in each dimension. NrrcSsg|]}dqSrr.0_rrr @sz_unfold..devicedtype)weightbias) rndimrreyer#r$reshaperepeatr) rr batch_sizechannelsnkernel_size_numelr+r%unfold_xrrr_unfold/s  r1rank connectivitycCsp|dkrd}|dkrtjdgtjdStjddt|Ddd}ttj|ddd}tj|dd}||kS) aTranslated version of the function from scipy.ndimage.morphology. Args: rank: The rank of the structuring element. connectivity: The number of neighbors connected to a given pixel. Returns: The structuring element. Examples:: >>> from torchmetrics.functional.segmentation.utils import generate_binary_structure >>> import torch >>> generate_binary_structure(2, 1) tensor([[False, True, False], [ True, True, True], [False, True, False]]) >>> generate_binary_structure(2, 2) tensor([[True, True, True], [True, True, True], [True, True, True]]) >>> generate_binary_structure(3, 2) # doctest: +NORMALIZE_WHITESPACE tensor([[[False, True, False], [ True, True, True], [False, True, False]], [[ True, True, True], [ True, True, True], [ True, True, True]], [[False, True, False], [ True, True, True], [False, True, False]]]) r)r$cSsg|]}tdqS))rarangerrrrr!lsz-generate_binary_structure..ij)indexingrdim)rtensoruint8meshgridrangeabsstacksum)r2r3gridsoutputrrrgenerate_binary_structureGs!rCimage structureorigin border_valuec st|tstdt||jdvrtd|jt|dur1t|jdd |j tdur>jdt |fdd t t Dd |d }t|jd }td d}||}|jdd\}} t||jdS)aBinary erosion of a tensor image. Implementation inspired by answer to this question: https://stackoverflow.com/questions/56235733/ Args: image: The image to be eroded, must be a binary tensor with shape ``(batch_size, channels, height, width)``. structure: The structuring element used for the erosion. If no structuring element is provided, an element is generated with a square connectivity equal to one. origin: The origin of the structuring element. border_value: The value to be used for the border. Examples:: >>> from torchmetrics.functional.segmentation.utils import binary_erosion >>> import torch >>> image = torch.tensor([[[[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]]]]) >>> binary_erosion(image) tensor([[[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]], dtype=torch.uint8) >>> binary_erosion(image, structure=torch.ones(4, 4)) tensor([[[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]], dtype=torch.uint8) z9Expected argument `image` to be of type Tensor but found )z>Expected argument `image` to be of rank 4 or 5 but found rank Nrrrcs4g|]}|j||dfD]}|qqSrr)rirrFrErrr!s4z"binary_erosion..constant)modevalue)rrr'r8) isinstancer TypeErrortyper(rrrCinttor#rr=lenr1floatrrflatten unsqueezeminr*byte) rDrErFrG image_pad image_unfold strel_flattensumsresultr rrKrbinary_erosionrs* $   r_ euclideanpytorchsamplingmetricr` chessboardtaxicabenginerascipycCs<t|tstdt|d|jdkrtd|jd|dur0t|ts0tdt|d|dvr>> from torchmetrics.functional.segmentation.utils import distance_transform >>> import torch >>> x = torch.tensor([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]]) >>> distance_transform(x) tensor([[0., 0., 0., 0., 0.], [0., 1., 1., 1., 0.], [0., 1., 2., 1., 0.], [0., 1., 1., 1., 0.], [0., 0., 0., 0., 0.]]) zExpected argument `sampling` to have length 2 but got length `rarr'r`rerfr8zhThe `scipy` engine requires `scipy` to be installed. Either install `scipy` or use the `pytorch` engine.)ndimage)rc)rOrrrQr(listrTrUrwhereviewr>rsqrtmaxrX zeros_liker rirkdistance_transform_edtcpunumpydistance_transform_cdt)rrbrcrgi0j0i1j1dis_rowdis_colhr dismindiszrkrrrdistance_transformsV -     $    rTcropspacingcCst|||jdvrtd|jdt|t||rK||B}|s6t|t|}}||||fSt||jddgt||jddg}}|durot| d d |A}t| d d |A}||fSt ||j d\} } t |} | dkrtnt} tj| d| dgdd } | | | | \}}t | d}|dk||k@}|dk||k@}t| d|d |}t| d|d |}|d|d|d|dfS) aGet the edges of binary segmentation masks. Args: preds: The predicted binary segmentation mask target: The ground truth binary segmentation mask crop: Whether to crop the edges to the region of interest. If ``True``, the edges are cropped to the bounding spacing: The pixel spacing of the input images. If provided, the edges are calculated using the euclidean Returns: If spacing is not provided, a 2-tuple containing the edges of the predicted and target mask respectively is returned. If spacing is provided, a 4-tuple containing the edges and areas of the predicted and target mask respectively is returned. )rr4z=Expected argument `preds` to be of rank 2 or 3 but got rank `rjrNrr#rr8r')r r(rranyrrqrr_rWsqueezeget_neighbour_tablesr#rTrrr?rUrS index_selectrnrRview_as)r r rror_valptbe_pred be_targettablekernel spatial_dims conv_operatorvolume code_preds code_targetall_ones edges_preds edges_target areas_preds areas_targetrrr mask_edgess4   *" rdistance_metriccCs|jtjkr |jtjkstd|jd|jdt|s*tjt|}||St|s;tjt|}||St|||d}||S)aCalculate the surface distance between two binary edge masks. May return infinity if the predicted mask is empty and the target mask is not, or vice versa. Args: preds: The predicted binary edge mask. target: The target binary edge mask. distance_metric: The distance metric to use. One of `["euclidean", "chessboard", "taxicab"]`. spacing: The spacing between pixels along each spatial dimension. Returns: A tensor with length equal to the number of edges in predictions e.g. `preds.sum()`. Each element is the distance from the corresponding edge in `preds` to the closest edge in `target`. Example:: >>> import torch >>> from torchmetrics.functional.segmentation.utils import surface_distance >>> preds = torch.tensor([[1, 1, 1, 1, 1], ... [1, 0, 0, 0, 1], ... [1, 0, 0, 0, 1], ... [1, 0, 0, 0, 1], ... [1, 1, 1, 1, 1]], dtype=torch.bool) >>> target = torch.tensor([[1, 1, 1, 1, 0], ... [1, 0, 0, 1, 0], ... [1, 0, 0, 1, 0], ... [1, 0, 0, 1, 0], ... [1, 1, 1, 1, 0]], dtype=torch.bool) >>> surface_distance(preds, target, distance_metric="euclidean", spacing=[1, 1]) tensor([0., 0., 0., 0., 1., 0., 1., 0., 1., 0., 1., 0., 0., 0., 0., 1.]) z9Expected both inputs to be of type `torch.bool`, but got z and .)rbrc)r$rrrrinf ones_liker)r r rrr}rrrsurface_distanceVs%  rF symmetriccCsXt||}|d|d}}|r$t||||dt||||dfSt||||dS)a5Extracts the edges from the input masks and calculates the surface distance between them. Args: preds: The predicted binary edge mask. target: The target binary edge mask. distance_metric: The distance metric to use. One of `["euclidean", "chessboard", "taxicab"]`. spacing: The spacing between pixels along each spatial dimension. symmetric: Whether to calculate the symmetric distance between the edges. Returns: A tensor with length equal to the number of edges in predictions e.g. `preds.sum()`. Each element is the distance from the corresponding edge in `preds` to the closest edge in `target`. If `symmetric` is `True`, the function returns a tuple containing the distances from the predicted edges to the target edges and vice versa. rr)rr)rrr)r r rrrrBrrrrredge_surface_distances rr#cCsHt|trt|dkrt||St|tr t|dkr t||Std)aCreate a table that maps neighbour codes to the contour length or surface area of the corresponding contour. Args: spacing: The spacing between pixels along each spatial dimension. device: The device on which the table should be created. Returns: A tuple containing as its first element the table that maps neighbour codes to the contour length or surface area of the corresponding contour and as its second element the kernel used to compute the neighbour codes. rr4z-The spacing must be a tuple of length 2 or 3.)rOtuplerTtable_contour_lengthtable_surface_arear)rr#rrrrs   rcCst|tst|dkrtd|\}}dt|d|d}tjdtj|d}dD]}|||<q+dD]}|||<q4dD]}|||<q=d D]}d|||<qFtj d d gdd gggg|d }||fS)a Create a table that maps neighbour codes to the contour length of the corresponding contour. Adopted from: https://github.com/deepmind/surface-distance/blob/master/surface_distance/lookup_tables.py Args: spacing: The spacing between pixels along each spatial dimension. Should be a tuple of length 2. device: The device on which the table should be created. Returns: A tuple containing as its first element the table that maps neighbour codes to the contour length of the corresponding contour and as its second element the kernel used to compute the neighbour codes. Example:: >>> from torchmetrics.functional.segmentation.utils import table_contour_length >>> table, kernel = table_contour_length((2,2)) >>> table tensor([0.0000, 1.4142, 1.4142, 2.0000, 1.4142, 2.0000, 2.8284, 1.4142, 1.4142, 2.8284, 2.0000, 1.4142, 2.0000, 1.4142, 1.4142, 0.0000]) >>> kernel tensor([[[[8, 4], [2, 1]]]]) rz(The spacing must be a tuple of length 2.?r$r#)rrrH )r4 )rI ) rrHrr) rOrrTrmathrorzerosfloat32 as_tensor)rr#firstseconddiagrrJrrrrrs   rcCs<t|tst|dkrtdgd}tjg||||ggd|||ggd|||ggdgd||ggd|||ggd gd ||ggdgd||ggd gd gd|ggd |||ggdgd ||ggdgd||ggdgdgd|ggdgd||ggdgdgd|ggdgdgd |ggdgd||ggd|||ggdgd||ggdgd||ggdgd gd|ggdgd||ggdgd gd|ggdgdgd|ggdgdgd gdggd gd||ggdgdgd |ggdgdgd|ggdgdgdgdggdgdgd|ggdgdgdgd ggdgdgdgdggdgdgd|ggd|||ggdgd||ggdgd||ggdgd!gd"|ggdgd||ggdgd gd |ggdgdgd|ggd#gd$gd!gd ggd gd||ggdgdgd |ggd%gdgd|ggd"gd"gd!gdggdgdgd|ggdgdgdgdggdgd&gd gd'ggdgdgd|ggdgd ||ggd(gdgd|ggd(gdgd)|ggd(gd||ggdgd gd|ggd*gdgdgd ggdgdgdgdggdgdgd|ggd gdgd |ggd(gd"gdgdggd+gdgdgdggd(gd"gd|ggdgd gdgdggdgdgd|ggdgdgd|ggdgd||ggd|||ggdgd||ggdgd||ggdgdgd|ggdgd||ggd,gdgd|ggdgdgd|ggd-gdgdgd'ggdgd ||ggdgdgd |ggdgdgd|ggdgdgdgdggd.gd)gd|ggd)gd)gdgdggd/gd&gdgd ggdgd)gd|ggdgd0||ggd%gdgd |ggdgdgd0|ggd gd1gdgd ggdgd"gd|ggd%gd,||ggdgdgd gd,ggdgd gd,|ggdgd0gd |ggd%gdgdgdggdgdgdgd0ggdgd gd |ggd gd#gd gdggd%gdgd|ggdgd gd |ggd gd ||ggdgd||ggdgdgd|ggdgdgd|ggdgd!gd"gdggdgdgd|ggd,gdgdgdggdgdgdgdggdgdgd|ggd gdgd|ggd gdgdgdggd%gdgdgdggdgdgd|ggd.gd)gdgdggdgdgd|ggdgdgd|ggdgd||ggd gd2gd|ggdgd gdgdggd-gdgdgdggdgdgd |ggd#gdgdgdggd gdgd,|ggdgdgd|ggdgd||ggd gd gdgdggdgdgd|ggd gd gd|ggdgd||ggdgdgd|ggdgd||ggdgd||ggd|||ggd|||ggdgd||ggdgd||ggdgdgd|ggdgd||ggd gd gd|ggdgdgd|ggd gd gdgdggdgd||ggdgdgd|ggd gdgd,|ggd#gdgdgdggdgdgd |ggd-gdgdgdggdgd gdgdggd gd2gd|ggdgd||ggdgdgd|ggdgdgd|ggd.gd)gdgdggdgdgd|ggd%gdgdgdggd gdgdgdggd gdgd|ggdgdgd|ggdgdgdgdggd,gdgdgdggdgdgd|ggdgd!gd"gdggdgdgd|ggdgdgd|ggdgd||ggd gd ||ggdgd gd |ggd%gdgd|ggd gd#gd gdggdgd gd |ggd gd gd gd ggd%gdgdgdggdgd0gd |ggdgd gd,|ggdgdgd gd,ggd%gd,||ggdgd"gd|ggd gd1gdgd ggdgdgd0|ggd%gdgd |ggdgd0||ggdgd)gd|ggd/gd&gdgd ggd)gd)gdgdggd.gd)gd|ggdgdgdgdggdgdgd|ggdgdgd |ggdgd ||ggd-gdgdgd'ggdgdgd|ggd,gdgd|ggdgd||ggdgdgd|ggdgd||ggdgd||ggd|||ggdgd||ggdgdgd|ggdgdgd|ggdgdgdgdggd(gd"gd|ggd+gdgdgdggd(gd"gdgdggd gdgd |ggdgdgd|ggdgdgdgdggd*gdgdgd ggdgd gd|ggd(gd||ggd(gdgd)|ggd(gdgd|ggdgd ||ggdgdgd|ggdgd&gd gd'ggdgdgdgdggdgdgd|ggd"gd"gd!gdggd%gdgd|ggdgdgd |ggd gd||ggd#gd$gd!gd ggdgdgd|ggdgd gd |ggdgd||ggdgd!gd"|ggdgd||ggdgd||ggd|||ggdgdgd|ggdgdgdgdggdgdgdgd ggdgdgd|ggdgdgdgdggdgdgd|ggdgdgd |ggd gd||ggdgdgd gdggdgdgd|ggdgd gd|ggdgd||ggdgd gd|ggdgd||ggdgd||ggd|||ggdgd||ggdgdgd |ggdgdgd|ggdgd||ggdgdgd|ggdgd||ggdgd ||ggd |||ggd gd gd|ggdgd||ggd gd ||ggd|||ggdgd||ggd|||ggd|||g||||gtj|d3}tj|d4|d5|d6|d5|d6|d4ggg||jd7}tj j ||d8d9}| d8}tjd:d;gdd?gd5d4ggggg|d@}||fS)Aa Create a table that maps neighbour codes to the surface area of the corresponding surface. Adopted from: https://github.com/deepmind/surface-distance/blob/master/surface_distance/lookup_tables.py Args: spacing: The spacing between pixels along each spatial dimension. Should be a tuple of length 3. device: The device on which the table should be created. Returns: A tuple containing as its first element the table that maps neighbour codes to the surface area of the corresponding surface and as its second element the kernel used to compute the neighbour codes. Example:: >>> from torchmetrics.functional.segmentation.utils import table_surface_area >>> table, kernel = table_surface_area((2,2,2)) >>> table tensor([0.0000, 0.8660, 0.8660, 2.8284, 0.8660, 2.8284, 1.7321, 4.5981, 0.8660, 1.7321, 2.8284, 4.5981, 2.8284, 4.5981, 4.5981, 4.0000, 0.8660, 2.8284, 1.7321, 4.5981, 1.7321, 4.5981, 2.5981, 5.1962, 1.7321, 3.6945, 3.6945, 6.2925, 3.6945, 6.2925, 5.4641, 4.5981, 0.8660, 1.7321, 2.8284, 4.5981, 1.7321, 3.6945, 3.6945, 6.2925, 1.7321, 2.5981, 4.5981, 5.1962, 3.6945, 5.4641, 6.2925, 4.5981, 2.8284, 4.5981, 4.5981, 4.0000, 3.6945, 6.2925, 5.4641, 4.5981, 3.6945, 5.4641, 6.2925, 4.5981, 5.6569, 3.6945, 3.6945, 2.8284, 0.8660, 1.7321, 1.7321, 3.6945, 2.8284, 4.5981, 3.6945, 6.2925, 1.7321, 2.5981, 3.6945, 5.4641, 4.5981, 5.1962, 6.2925, 4.5981, 2.8284, 4.5981, 3.6945, 6.2925, 4.5981, 4.0000, 5.4641, 4.5981, 3.6945, 5.4641, 5.6569, 3.6945, 6.2925, 4.5981, 3.6945, 2.8284, 1.7321, 2.5981, 3.6945, 5.4641, 3.6945, 5.4641, 5.6569, 3.6945, 2.5981, 3.4641, 5.4641, 2.5981, 5.4641, 2.5981, 3.6945, 1.7321, 4.5981, 5.1962, 6.2925, 4.5981, 6.2925, 4.5981, 3.6945, 2.8284, 5.4641, 2.5981, 3.6945, 1.7321, 3.6945, 1.7321, 1.7321, 0.8660, 0.8660, 1.7321, 1.7321, 3.6945, 1.7321, 3.6945, 2.5981, 5.4641, 2.8284, 3.6945, 4.5981, 6.2925, 4.5981, 6.2925, 5.1962, 4.5981, 1.7321, 3.6945, 2.5981, 5.4641, 2.5981, 5.4641, 3.4641, 2.5981, 3.6945, 5.6569, 5.4641, 3.6945, 5.4641, 3.6945, 2.5981, 1.7321, 2.8284, 3.6945, 4.5981, 6.2925, 3.6945, 5.6569, 5.4641, 3.6945, 4.5981, 5.4641, 4.0000, 4.5981, 6.2925, 3.6945, 4.5981, 2.8284, 4.5981, 6.2925, 5.1962, 4.5981, 5.4641, 3.6945, 2.5981, 1.7321, 6.2925, 3.6945, 4.5981, 2.8284, 3.6945, 1.7321, 1.7321, 0.8660, 2.8284, 3.6945, 3.6945, 5.6569, 4.5981, 6.2925, 5.4641, 3.6945, 4.5981, 5.4641, 6.2925, 3.6945, 4.0000, 4.5981, 4.5981, 2.8284, 4.5981, 6.2925, 5.4641, 3.6945, 5.1962, 4.5981, 2.5981, 1.7321, 6.2925, 3.6945, 3.6945, 1.7321, 4.5981, 2.8284, 1.7321, 0.8660, 4.5981, 5.4641, 6.2925, 3.6945, 6.2925, 3.6945, 3.6945, 1.7321, 5.1962, 2.5981, 4.5981, 1.7321, 4.5981, 1.7321, 2.8284, 0.8660, 4.0000, 4.5981, 4.5981, 2.8284, 4.5981, 2.8284, 1.7321, 0.8660, 4.5981, 1.7321, 2.8284, 0.8660, 2.8284, 0.8660, 0.8660, 0.0000]) >>> kernel tensor([[[[[128, 64], [ 32, 16]], [[ 8, 4], [ 2, 1]]]]]) r4z(The spacing must be a tuple of length 3.)rr)?rr)rr)пrr)?r)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)?rr)rrr)rrr)rrr)rrr)rrr)ؿrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrr)rrrrrrrr"r'r8@ rrrHr) rOrrTrrr:rrr$linalgnormr@)rr#rrspacerrrrrrs:7       !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~  0 .r)NNr)Nr`ra)TN)r`N)r`NF)N)% functoolsrtypingrrrrtorch.nn.functionalrrrrtyping_extensionsr torchmetrics.utilities.checksr torchmetrics.utilities.importsr rrrrRr1rCr_rlrUrrrrr lru_cacher#rrrrrrrs      , H g  = 5    .+6