a  dA @sddlmZddlZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZddlmZddlmZd#eeedeeed d d Zd$eeeeeeedddZd%eeeeedddddZd&eeeeedeeeedddZd'eeeeeedddddZd(eeeeeedeeeedddZd)eeedeeeeeeedeeeed d!d"Z dS)*)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_divide)ClassificationTask)micromacroweightednonebinary)confmataverage ignore_indexreturnc Csgd}||vr&td|d|d|}|dkrV|d|d|d|dS|d uozd |kov|jd kn}|jd k}|r|d d d d f}|d d d d f|d d d d f|d d d d f}n"t|}|d |d |}|d kr"|}||r||nd}t||}|d usJ|dksJ|d krN|S|dkr|jd kr|d d d d f|d d d d fn|d } n:t|} |rd| |<|sd| |d |d d k<| || 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) ValueErrorfloatshapendimtorchZdiagsumrZ ones_like) rrrallowed_averageZignore_index_cond multilabelnumZdenomZjaccardweightsr)w/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/classification/jaccard.py_jaccard_index_reduce&s6 & 8    < r+?T)predstarget thresholdr validate_argsrcCsB|rt||t|||t||||\}}t||}t|ddS)aCalculate 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: preds: Tensor with predictions target: Tensor with true labels 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 torch import tensor >>> from torchmetrics.functional.classification import binary_jaccard_index >>> target = tensor([1, 1, 0, 0]) >>> preds = 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 = tensor([1, 1, 0, 0]) >>> preds = tensor([0.35, 0.85, 0.48, 0.01]) >>> binary_jaccard_index(preds, target) tensor(0.5000) r)r)rrrrr+)r-r.r/rr0rr)r)r*binary_jaccard_indexas 2   r1)rrrr) num_classesrrrcCs0t||d}||vr,td|d|ddSN)rrrrNz)Expected argument `average` to be one of z , but got r)r r)r2rrr%r)r)r*(_multiclass_jaccard_index_arg_validations r4r)r-r.r2rrr0rcCsH|rt|||t||||t|||\}}t|||}t|||dS)a Calculate 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: preds: Tensor with predictions target: Tensor with true labels 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 torch import tensor >>> from torchmetrics.functional.classification import multiclass_jaccard_index >>> target = tensor([2, 1, 0, 0]) >>> preds = 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 = tensor([2, 1, 0, 0]) >>> preds = 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)r4r r r r+)r-r.r2rrr0rr)r)r*multiclass_jaccard_indexs >  r6) num_labelsr/rrrcCs2t|||d}||vr.td|d|ddSr3)r r)r7r/rrr%r)r)r*(_multilabel_jaccard_index_arg_validations r8)r-r.r7r/rrr0rcCsL|rt|||t||||t|||||\}}t|||}t|||dS)aA Calculate 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: preds: Tensor with predictions target: Tensor with true labels num_labels: 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 torch import tensor >>> from torchmetrics.functional.classification import multilabel_jaccard_index >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> multilabel_jaccard_index(preds, target, num_labels=3) tensor(0.5000) r5)r8rrrr+)r-r.r7r/rrr0rr)r)r*multilabel_jaccard_indexs =  r9)rZ multiclassr&) r-r.taskr/r2r7rrr0rc Cst|}|tjkr$t|||||S|tjkr^t|tsLtdt|dt ||||||S|tj krt|tstdt|dt |||||||Std|dS)aCalculate 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:`~torchmetrics.functional.classification.binary_jaccard_index`, :func:`~torchmetrics.functional.classification.multiclass_jaccard_index` and :func:`~torchmetrics.functional.classification.multilabel_jaccard_index` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import randint, tensor >>> target = randint(0, 2, (10, 25, 25)) >>> pred = 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) z+`num_classes` is expected to be `int` but `z was passed.`z*`num_labels` is expected to be `int` but `zNot handled value: N) rZfrom_strBINARYr1Z MULTICLASS isinstanceintrtyper6Z MULTILABELr9) r-r.r:r/r2r7rrr0r)r)r* jaccard_index=s#      r?)N)r,NT)NN)rNT)r,Nr)r,rNT)r,NNrNT)!typingrr#rZtyping_extensionsrZ7torchmetrics.functional.classification.confusion_matrixrrrrr r r r r rrrZtorchmetrics.utilities.computerZtorchmetrics.utilities.enumsrr=r+r boolr1r4r6r8r9r?r)r)r)r*s   8    > <    H    I