a  dgM@sddlmZmZmZmZddlmZddlmZddl m Z ddl m Z m Z mZddlmZmZmZddlmZddlmZdd lmZdd lmZmZesgd ZGd d d e ZGddde ZGdddeZGddde Z dS))AnyOptionalSequenceUnion)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)BinaryJaccardIndex.plotMulticlassJaccardIndex.plotMultilabelJaccardIndex.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <de e e eed d fdd ZedddZde eeeefe eedddZZS)BinaryJaccardIndexaCalculate 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|} 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. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. .. note:: 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. 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_bound?N) threshold ignore_index validate_argskwargsreturnc s tjf||d|d|dS)N)r r! normalizer")super__init__)selfr r!r"r# __class__l/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/classification/jaccard.pyr'^s zBinaryJaccardIndex.__init__r$cCst|jddS)Compute metric.binaryaverage)r confmatr(r+r+r,computeiszBinaryJaccardIndex.computevalaxr$cCs |||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) Z_plotr(r6r7r+r+r,plotms(r)rNT)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrintrr'rr4rrrrr: __classcell__r+r+r)r,r's, 0      rcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <d Z e ed <deeedeeeeddfdd ZedddZdeeeeefeeedddZZS)MulticlassJaccardIndexa 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|} 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, ...)``. .. note:: 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 specifing 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. 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) FrTrrrrrrZClassplot_legend_namemacroNmicrorFZweightednone) num_classesr1r!r"r#r$c s<tjf||ddd||r,t|||||_||_dS)NF)rJr!r%r")r&r'r r"r1)r(rJr1r!r"r#r)r+r,r's zMulticlassJaccardIndex.__init__r-cCst|j|j|jdS)r.)r1r!)r r2r1r!r3r+r+r,r4szMulticlassJaccardIndex.computer5cCs |||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) r8r9r+r+r,r:s(r)rFNT)NNr;r<r=r>rr?r@rrrrArrEstrrBrrrr'rr4rrrrr:rCr+r+r)r,rDs0 ;       rDc seZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <d Z e ed <dee eedeeeeddfdd ZedddZdeeeeefeeedddZZS)MultilabelJaccardIndexaM 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|} 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. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)`` .. note:: 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 specifing 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. 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) FrTrrrrrrZLabelrErrFNrG) num_labelsr r1r!r"r#r$c s@tjf|||ddd||r0t||||||_||_dS)NF)rNr r!r%r")r&r'rr"r1)r(rNr r1r!r"r#r)r+r,r'\s zMultilabelJaccardIndex.__init__r-cCst|j|jdS)r.r0)r r2r1r3r+r+r,r4rszMultilabelJaccardIndex.computer5cCs |||S)a~Plot 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 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 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) r8r9r+r+r,r:vs(r)rrFNT)NNrKr+r+r)r,rMs4 :       rMc @sHeZdZdZd edeeeeeeedeeee e d d d Z dS) JaccardIndexaCalculate 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|} 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) rNrFT)r/Z multiclassZ multilabelrG) taskr rJrNr1r!r"r#r$c Kst|}|||d|tjkr4t|fi|S|tjkrnt|ts\tdt |dt ||fi|S|tj krt|tstdt |dt |||fi|Std|ddS)zInitialize task metric.)r!r"z+`num_classes` is expected to be `int` but `z was passed.`z*`num_labels` is expected to be `int` but `zTask z not supported!N) rZfrom_strupdateBINARYrZ MULTICLASS isinstancerB ValueErrortyperDZ MULTILABELrM) clsrPr rJrNr1r!r"r#r+r+r,__new__s      zJaccardIndex.__new__)rNNrFNT) r;r<r=r>rrArrBr?rrrWr+r+r+r,rOs$ rON)!typingrrrrZtorchrZtyping_extensionsrZ torchmetrics.classification.baserZ,torchmetrics.classification.confusion_matrixr r r Z.torchmetrics.functional.classification.jaccardr r rZtorchmetrics.metricrZtorchmetrics.utilities.enumsrZtorchmetrics.utilities.importsrZtorchmetrics.utilities.plotrrZ__doctest_skip__rrDrMrOr+r+r+r,s"      q