iHPddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZmZmZddlmZdd lmZdd lmZdd lmZmZesgd ZGd de ZGddeZGddeZ Gdde Z!y))Sequence)AnyOptionalUnion)Tensor)Literal)_ClassificationTaskWrapper)BinaryConfusionMatrixMulticlassConfusionMatrixMultilabelConfusionMatrix)_jaccard_index_reduce(_multiclass_jaccard_index_arg_validation(_multilabel_jaccard_index_arg_validation)Metric)ClassificationTask)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE)zBinaryJaccardIndex.plotzMulticlassJaccardIndex.plotzMultilabelJaccardIndex.plotc eZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed < dd e d e e dede dedd f fd ZdefdZ dde eeeefde edefdZxZS)BinaryJaccardIndexa? Calculate the Jaccard index for binary tasks. The `Jaccard index`_ (also known as the intersection 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|} As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A int or float tensor of shape ``(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. Additionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``bji`` (:class:`~torch.Tensor`): A tensor containing the Binary Jaccard Index. 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. zero_division: Value to replace when there is a division by zero. Should be `0` or `1`. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import BinaryJaccardIndex >>> target = tensor([1, 1, 0, 0]) >>> preds = tensor([0, 1, 0, 0]) >>> metric = BinaryJaccardIndex() >>> metric(preds, target) tensor(0.5000) Example (preds is float tensor): >>> from torchmetrics.classification import BinaryJaccardIndex >>> target = tensor([1, 1, 0, 0]) >>> preds = tensor([0.35, 0.85, 0.48, 0.01]) >>> metric = BinaryJaccardIndex() >>> metric(preds, target) tensor(0.5000) Fis_differentiableThigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundN threshold ignore_index validate_args zero_divisionkwargsreturnc <t|d||d|d|||_y)N)rr normalizer )super__init__r!)selfrrr r!r" __class__s x/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/classification/jaccard.pyr(zBinaryJaccardIndex.__init__bs3  ldZg kq +cFt|jd|jS)Compute metric.binaryaverager!)r confmatr!r)s r+computezBinaryJaccardIndex.computeos$T\\8SWSeSeffr,valaxc&|j||S)aAPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> from torch import rand, randint >>> from torchmetrics.classification import BinaryJaccardIndex >>> metric = BinaryJaccardIndex() >>> metric.update(rand(10), randint(2,(10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand, randint >>> from torchmetrics.classification import BinaryJaccardIndex >>> metric = BinaryJaccardIndex() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(10), randint(2,(10,)))) >>> fig_, ax_ = metric.plot(values) _plotr)r5r6s r+plotzBinaryJaccardIndex.plotsPzz#r""r,)?NTrNN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrintrr(rr4rrrrr; __classcell__r*s@r+rr(s1f$t#!d!#t#!e!!e!&*" + +sm + +  +  +  +gg _c(#E&(6*:":;<(#IQRZI[(# (#r,rceZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <d Z e ed < ddedeeddeedede dedd ffd ZdefdZ ddeeeeefdeedefdZxZS)MulticlassJaccardIndexa~ Calculate the Jaccard index for multiclass tasks. The `Jaccard index`_ (also known as the intersection 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|} As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A int tensor of shape ``(N, ...)`` or float tensor of shape ``(N, C, ..)``. If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into an int tensor. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mcji`` (:class:`~torch.Tensor`): A tensor containing the Multi-class Jaccard Index. Args: num_classes: Integer specifying the number of classes ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation 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 validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. zero_division: Value to replace when there is a division by zero. Should be `0` or `1`. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (pred is integer tensor): >>> from torch import tensor >>> from torchmetrics.classification import MulticlassJaccardIndex >>> target = tensor([2, 1, 0, 0]) >>> preds = tensor([2, 1, 0, 1]) >>> metric = MulticlassJaccardIndex(num_classes=3) >>> metric(preds, target) tensor(0.6667) Example (pred is float tensor): >>> from torchmetrics.classification import MulticlassJaccardIndex >>> 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]]) >>> metric = MulticlassJaccardIndex(num_classes=3) >>> metric(preds, target) tensor(0.6667) FrTrrrrrrClassplot_legend_nameN num_classesr1micromacroweightednonerr r!r"r#c vt|d||ddd||r t|||||_||_||_y)NF)rMrr%r r&)r'r(rr r1r!)r)rMr1rr r!r"r*s r+r(zMulticlassJaccardIndex.__init__sR  #,$^c gm   4[,PW X* *r,cpt|j|j|j|jS)r.)r1rr!)r r2r1rr!r3s r+r4zMulticlassJaccardIndex.computes-$ LL$,,T=N=N^b^p^p  r,r5r6c&|j||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value per class >>> from torch import randint >>> from torchmetrics.classification import MulticlassJaccardIndex >>> metric = MulticlassJaccardIndex(num_classes=3, average=None) >>> metric.update(randint(3, (20,)), randint(3, (20,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting a multiple values per class >>> from torch import randint >>> from torchmetrics.classification import MulticlassJaccardIndex >>> metric = MulticlassJaccardIndex(num_classes=3, average=None) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,)), randint(3, (20,)))) >>> fig_, ax_ = metric.plot(values) r8r:s r+r;zMulticlassJaccardIndex.plotr<r,)rPNTrr>r?r@rArBrrCrDrrrrErrLstrrFrrrr(rr4rrrrr;rGrHs@r+rJrJs<|$t#!d!#t#!e!!e!#c# LS&*" ++'"FGH+sm +  +  ++ +$  _c(#E&(6*:":;<(#IQRZI[(# (#r,rJceZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <d Z e ed < ddede deeddeedede dedd ffd ZdefdZ ddeeeeefdeedefdZxZS)MultilabelJaccardIndexa Calculate the Jaccard index for multilabel tasks. The `Jaccard index`_ (also known as the intersection 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|} As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A int tensor or float tensor of shape ``(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. Additionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)`` .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mlji`` (:class:`~torch.Tensor`): A tensor containing the Multi-label Jaccard Index loss. Args: num_classes: Integer specifying the number of 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 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 validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. zero_division: Value to replace when there is a division by zero. Should be `0` or `1`. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import MultilabelJaccardIndex >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelJaccardIndex(num_labels=3) >>> metric(preds, target) tensor(0.5000) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelJaccardIndex >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> metric = MultilabelJaccardIndex(num_labels=3) >>> metric(preds, target) tensor(0.5000) FrTrrrrrrLabelrLN num_labelsrr1rNrr r!r"r#c zt|d|||ddd||rt||||||_||_||_y)NF)r[rrr%r r&)r'r(rr r1r!) r)r[rr1rr r!r"r*s r+r(zMultilabelJaccardIndex.__init__ks[  !%      4ZLZa b* *r,cZt|j|j|jS)r.r0)r r2r1r!r3s r+r4zMultilabelJaccardIndex.computes $T\\4<>> # Example plotting a single value >>> from torch import rand, randint >>> from torchmetrics.classification import MultilabelJaccardIndex >>> metric = MultilabelJaccardIndex(num_labels=3) >>> metric.update(randint(2, (20, 3)), randint(2, (20, 3))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand, randint >>> from torchmetrics.classification import MultilabelJaccardIndex >>> metric = MultilabelJaccardIndex(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(randint(2, (20, 3)), randint(2, (20, 3)))) >>> fig_, ax_ = metric.plot(values) r8r:s r+r;zMultilabelJaccardIndex.plotr<r,)r=rPNTrr>rVrHs@r+rYrY's :x$t#!d!#t#!e!!e!#c# KR&*" +++'"FGH + sm +  +++ +0kk _c(#E&(6*:":;<(#IQRZI[(# (#r,rYcxeZdZdZ ddeddeddedeedeed eed d eed e d e de fdZ y) JaccardIndexaCalculate the Jaccard index for multilabel tasks. The `Jaccard index`_ (also known as the intersection 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 :class:`~torchmetrics.classification.BinaryJaccardIndex`, :class:`~torchmetrics.classification.MulticlassJaccardIndex` and :class:`~torchmetrics.classification.MultilabelJaccardIndex` 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 = JaccardIndex(task="multiclass", num_classes=2) >>> jaccard(pred, target) tensor(0.9660) Nclstask)r/ multiclass multilabelrrMr[r1rNrr r"r#c tj|}|j||d|tjk(r t |fi|S|tj k(r5t |tstdt|dt||fi|S|tjk(r6t |tstdt|dt|||fi|Std|d)zInitialize task metric.)rr z+`num_classes` is expected to be `int` but `z was passed.`z*`num_labels` is expected to be `int` but `zTask z not supported!) rfrom_strupdateBINARYr MULTICLASS isinstancerF ValueErrortyperJ MULTILABELrY) rarbrrMr[r1rr r"s r+__new__zJaccardIndex.__new__s"**40 |mTU %,, ,%i:6: : %00 0k3/ #NtT_O`Naan!opp)+wI&I I %00 0j#. #MdS]N^M__l!mnn)*iSFS S5o677r,)r=NNrPNT) r?r@rArBrlrrErrFrCrrrnr&r,r+r`r`s:%)$(KR&*"8 . !8:;88c] 8 SM 8 '"FGH 8sm888 8r,r`N)"collections.abcrtypingrrrtorchrtyping_extensionsr torchmetrics.classification.baser ,torchmetrics.classification.confusion_matrixr r r .torchmetrics.functional.classification.jaccardr rrtorchmetrics.metricrtorchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rrJrYr`r&r,r+r{s%''%G  ';@@ps#.s#lF#6F#RH#6H#V38-38r,