o yi,>@sddlmZddlZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZddlmZ d&dedeed d eed efd d Z   d'dededed eeded ef ddZ  d(ded eedeedd dfddZ   d)dedededeedd eeded efddZ   d*deded eedeedd df ddZ    d+dededededeedd eeded efd d!Z      d,deded"ed#dedeedeedeedd eeded efd$d%ZdS)-)OptionalN)Tensor)Literal) '_binary_confusion_matrix_arg_validation_binary_confusion_matrix_format*_binary_confusion_matrix_tensor_validation_binary_confusion_matrix_update+_multiclass_confusion_matrix_arg_validation#_multiclass_confusion_matrix_format._multiclass_confusion_matrix_tensor_validation#_multiclass_confusion_matrix_update+_multilabel_confusion_matrix_arg_validation#_multilabel_confusion_matrix_format._multilabel_confusion_matrix_tensor_validation#_multilabel_confusion_matrix_update) _safe_divideconfmataverage)micromacroweightednonebinary ignore_indexreturnc Csgd}||vrtd|d|d|}|dkr+|d|d|d|dS|d uo=d |ko;|jd kn}|jd krh|d d d d f}|d d d d f|d d d d f|d d d d f}nt|}|d |d |}|d kr|}||r||nd}t||}|d us|dks|d kr|S|dkr|jd kr|d d d d f|d d d d fn|d }n t|}|rd||<|||S)aPerform reduction of an un-normalized confusion matrix into jaccard score. Args: confmat: tensor with un-normalized confusionmatrix average: reduction method - ``'binary'``: binary reduction, expects a 2x2 matrix - ``'macro'``: Calculate the metric for each class separately, and average the metrics across classes (with equal weights for each class). - ``'micro'``: Calculate the metric globally, across all samples and classes. - ``'weighted'``: Calculate the metric for each class separately, and average the metrics across classes, weighting each class by its support (``tp + fn``). - ``'none'`` or ``None``: Calculate the metric for each class separately, and return the metric for every class. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation )rrrrrNzThe `average` has to be one of z, got .r)r)rr)rrNrrrgrr) ValueErrorfloatshapendimtorchdiagsumr ones_like) rrrallowed_averageignore_index_condnumdenomjaccardweightsr,b/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/classification/jaccard.py_jaccard_index_reduce%s0 & 8  : r.?Tpredstarget threshold validate_argscCsB|r t||t|||t||||\}}t||}t|ddS)awCalculates the Jaccard index for binary tasks. The `Jaccard index`_ (also known as the intersetion over union or jaccard similarity coefficient) is an statistic that can be used to determine the similarity and diversity of a sample set. It is defined as the size of the intersection divided by the union of the sample sets: .. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|} Accepts the following input tensors: - ``preds`` (int or float tensor): ``(N, ...)``. If preds is a floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (int tensor): ``(N, ...)`` Additional dimension ``...`` will be flattened into the batch dimension. Args: threshold: Threshold for transforming probability to binary (0,1) predictions ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (preds is int tensor): >>> from torchmetrics.functional.classification import binary_jaccard_index >>> target = torch.tensor([1, 1, 0, 0]) >>> preds = torch.tensor([0, 1, 0, 0]) >>> binary_jaccard_index(preds, target) tensor(0.5000) Example (preds is float tensor): >>> from torchmetrics.functional.classification import binary_jaccard_index >>> target = torch.tensor([1, 1, 0, 0]) >>> preds = torch.tensor([0.35, 0.85, 0.48, 0.01]) >>> binary_jaccard_index(preds, target) tensor(0.5000) r)r)rrrrr.)r0r1r2rr3rr,r,r-binary_jaccard_index\s -    r4 num_classes)rrrrcCs0t||d}||vrtd|d|ddSN)rrrrNz)Expected argument `average` to be one of z , but got r)r r)r5rrr&r,r,r-(_multiclass_jaccard_index_arg_validations r7rcCsH|rt|||t||||t|||\}}t|||}t|||dS)a Calculates the Jaccard index for multiclass tasks. The `Jaccard index`_ (also known as the intersetion over union or jaccard similarity coefficient) is an statistic that can be used to determine the similarity and diversity of a sample set. It is defined as the size of the intersection divided by the union of the sample sets: .. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|} Accepts the following input tensors: - ``preds``: ``(N, ...)`` (int tensor) or ``(N, C, ..)`` (float tensor). If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into an int tensor. - ``target`` (int tensor): ``(N, ...)`` Additional dimension ``...`` will be flattened into the batch dimension. Args: num_classes: Integer specifing the number of classes average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: Calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: Calculates statistic for each label and applies no reduction ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (pred is integer tensor): >>> from torchmetrics.functional.classification import multiclass_jaccard_index >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([2, 1, 0, 1]) >>> multiclass_jaccard_index(preds, target, num_classes=3) tensor(0.6667) Example (pred is float tensor): >>> from torchmetrics.functional.classification import multiclass_jaccard_index >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([ ... [0.16, 0.26, 0.58], ... [0.22, 0.61, 0.17], ... [0.71, 0.09, 0.20], ... [0.05, 0.82, 0.13], ... ]) >>> multiclass_jaccard_index(preds, target, num_classes=3) tensor(0.6667) rr)r7r r r r.)r0r1r5rrr3rr,r,r-multiclass_jaccard_indexs ;  r9 num_labelscCs2t|||d}||vrtd|d|ddSr6)r r)r:r2rrr&r,r,r-(_multilabel_jaccard_index_arg_validations r;cCsL|rt|||t||||t|||||\}}t|||}t|||dS)a Calculates the Jaccard index for multilabel tasks. The `Jaccard index`_ (also known as the intersetion over union or jaccard similarity coefficient) is an statistic that can be used to determine the similarity and diversity of a sample set. It is defined as the size of the intersection divided by the union of the sample sets: .. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|} Accepts the following input tensors: - ``preds`` (int or float tensor): ``(N, C, ...)``. If preds is a floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (int tensor): ``(N, C, ...)`` Additional dimension ``...`` will be flattened into the batch dimension. Args: num_classes: Integer specifing the number of labels threshold: Threshold for transforming probability to binary (0,1) predictions average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: Calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: Calculates statistic for each label and applies no reduction ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (preds is int tensor): >>> from torchmetrics.functional.classification import multilabel_jaccard_index >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_jaccard_index(preds, target, num_labels=3) tensor(0.5000) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_jaccard_index >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> multilabel_jaccard_index(preds, target, num_labels=3) tensor(0.5000) r8)r;rrrr.)r0r1r:r2rrr3rr,r,r-multilabel_jaccard_indexs 8  r<task)r multiclass multilabelc Csx|dkr t|||||S|dkr t|tsJt||||||S|dkr5t|ts+Jt|||||||Std|)aCalculates the Jaccard index. The `Jaccard index`_ (also known as the intersetion over union or jaccard similarity coefficient) is an statistic that can be used to determine the similarity and diversity of a sample set. It is defined as the size of the intersection divided by the union of the sample sets: .. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|} This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of :func:`binary_jaccard_index`, :func:`multiclass_jaccard_index` and :func:`multilabel_jaccard_index` for the specific details of each argument influence and examples. Legacy Example: >>> target = torch.randint(0, 2, (10, 25, 25)) >>> pred = torch.tensor(target) >>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15] >>> jaccard_index(pred, target, task="multiclass", num_classes=2) tensor(0.9660) rr>r?z[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )r4 isinstanceintr9r<r) r0r1r=r2r5r:rrr3r,r,r- jaccard_index+srB)N)r/NT)NN)rNT)r/Nr)r/rNT)r/NNrNT)typingrr"rtyping_extensionsr7torchmetrics.functional.classification.confusion_matrixrrrrr r r r r rrrtorchmetrics.utilities.computerrAr.rboolr4r7r9r;r<rBr,r,r,r-s   8   : 7    E    D