a  da@sddlmZmZmZmZmZmZddlmZddl m Z ddl m Z ddl mZmZmZddlmZmZmZmZmZmZddlmZddlmZdd lmZdd lmZdd l m!Z!m"Z"esgd Z#Gd ddeZ$GdddeZ%GdddeZ&Gddde Z'dS))AnyListOptionalSequenceTupleUnion)Tensor)Literal)_ClassificationTaskWrapper)BinaryPrecisionRecallCurveMulticlassPrecisionRecallCurveMultilabelPrecisionRecallCurve)0_binary_recall_at_fixed_precision_arg_validation)_binary_recall_at_fixed_precision_compute1_multiclass_recall_at_fixed_precision_arg_compute4_multiclass_recall_at_fixed_precision_arg_validation1_multilabel_recall_at_fixed_precision_arg_compute4_multilabel_recall_at_fixed_precision_arg_validation)Metric) dim_zero_cat)ClassificationTask)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE)!BinaryRecallAtFixedPrecision.plot%MulticlassRecallAtFixedPrecision.plot%MultilabelRecallAtFixedPrecision.plotcseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <de ee eee efeeeedd fd d ZeeefdddZdee eeefeeedddZZS)BinaryRecallAtFixedPrecisiona8 Compute the highest possible recall value given the minimum precision thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class. .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``recall`` (:class:`~torch.Tensor`): A scalar tensor with the maximum recall for the given precision level - ``threshold`` (:class:`~torch.Tensor`): A scalar tensor with the corresponding threshold level .. note:: The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to ``None`` will activate the non-binned version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size :math:`\mathcal{O}(n_{thresholds})` (constant memory). Args: min_precision: float value specifying minimum precision threshold. thresholds: Can be one of: - If set to ``None``, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach. - If set to an ``int`` (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation. - If set to an ``list`` of floats, will use the indicated thresholds in the list as bins for the calculation - If set to an 1d :class:`~torch.Tensor` of floats, will use the indicated thresholds in the tensor as bins for the 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: >>> from torch import tensor >>> from torchmetrics.classification import BinaryRecallAtFixedPrecision >>> preds = tensor([0, 0.5, 0.7, 0.8]) >>> target = tensor([0, 1, 1, 0]) >>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5, thresholds=None) >>> metric(preds, target) (tensor(1.), tensor(0.5000)) >>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5, thresholds=5) >>> metric(preds, target) (tensor(1.), tensor(0.5000)) Fis_differentiableNhigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundT) min_precision thresholds ignore_index validate_argskwargsreturnc s:tj||fddi||r*t|||||_||_dS)Nr(F)super__init__rr(r%)selfr%r&r'r(r) __class__{/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/classification/recall_fixed_precision.pyr,qs  z%BinaryRecallAtFixedPrecision.__init__r*cCs4|jdurt|jt|jfn|j}t||j|jSzCompute metric.N)r&rpredstargetconfmatrr%r-stater0r0r1computes$z$BinaryRecallAtFixedPrecision.computevalaxr*cCs|p|d}|||S)a)Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import BinaryRecallAtFixedPrecision >>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5) >>> metric.update(rand(10), randint(2,(10,))) >>> fig_, ax_ = metric.plot() # the returned plot only shows the maximum recall value by default .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import BinaryRecallAtFixedPrecision >>> metric = BinaryRecallAtFixedPrecision(min_precision=0.5) >>> values = [ ] >>> for _ in range(10): ... # we index by 0 such that only the maximum recall value is plotted ... values.append(metric(rand(10), randint(2,(10,)))[0]) >>> fig_, ax_ = metric.plot(values) rr9Z_plotr-r;r<r0r0r1plots)r)NNT)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrr r"floatr$rintrrrr,rr9rrrr? __classcell__r0r0r.r1r/s. ;    rc seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <dee eeeee efeeeeddfdd ZeeefdddZdeeeeefeeedddZZS) MulticlassRecallAtFixedPrecisionaVCompute the highest possible recall value given the minimum precision thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply softmax per sample. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified). .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing: - ``recall`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum recall for the given precision level per class - ``threshold`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the corresponding threshold level per class .. note:: The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to ``None`` will activate the non-binned version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory). Args: num_classes: Integer specifing the number of classes min_precision: float value specifying minimum precision threshold. thresholds: Can be one of: - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach. - If set to an ``int`` (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation. - If set to an ``list`` of floats, will use the indicated thresholds in the list as bins for the calculation - If set to an 1d :class:`~torch.Tensor` of floats, will use the indicated thresholds in the tensor as bins for the 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: >>> from torch import tensor >>> from torchmetrics.classification import MulticlassRecallAtFixedPrecision >>> preds = tensor([[0.75, 0.05, 0.05, 0.05, 0.05], ... [0.05, 0.75, 0.05, 0.05, 0.05], ... [0.05, 0.05, 0.75, 0.05, 0.05], ... [0.05, 0.05, 0.05, 0.75, 0.05]]) >>> target = tensor([0, 1, 3, 2]) >>> metric = MulticlassRecallAtFixedPrecision(num_classes=5, min_precision=0.5, thresholds=None) >>> metric(preds, target) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06])) >>> mcrafp = MulticlassRecallAtFixedPrecision(num_classes=5, min_precision=0.5, thresholds=5) >>> mcrafp(preds, target) (tensor([1., 1., 0., 0., 0.]), tensor([7.5000e-01, 7.5000e-01, 1.0000e+06, 1.0000e+06, 1.0000e+06])) FrNrr r!r"r#r$ZClassplot_legend_nameT) num_classesr%r&r'r(r)r*c s>tjf|||dd||r.t||||||_||_dS)NF)rKr&r'r()r+r,rr(r%)r-rKr%r&r'r(r)r.r0r1r,s z)MulticlassRecallAtFixedPrecision.__init__r2cCs8|jdurt|jt|jfn|j}t||j|j|jSr3)r&rr4r5r6rrKr%r7r0r0r1r9 s$z(MulticlassRecallAtFixedPrecision.computer:cCs|p|d}|||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 >>> from torch import rand, randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassRecallAtFixedPrecision >>> metric = MulticlassRecallAtFixedPrecision(num_classes=3, min_precision=0.5) >>> metric.update(rand(20, 3).softmax(dim=-1), randint(3, (20,))) >>> fig_, ax_ = metric.plot() # the returned plot only shows the maximum recall value by default .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassRecallAtFixedPrecision >>> metric = MulticlassRecallAtFixedPrecision(num_classes=3, min_precision=0.5) >>> values = [] >>> for _ in range(20): ... # we index by 0 such that only the maximum recall value is plotted ... values.append(metric(rand(20, 3).softmax(dim=-1), randint(3, (20,)))[0]) >>> fig_, ax_ = metric.plot(values) rr=r>r0r0r1r?s)r)NNT)NNr@rArBrCrrDrErrr r"rFr$rJstrrGrrrrr,rr9rrrr?rHr0r0r.r1rIs2 A     rIc seZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <dee eeeee efeeeeddfdd ZeeefdddZdeeeeefeeedddZZS) MultilabelRecallAtFixedPrecisiona\Compute the highest possible recall value given the minimum precision thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class. .. note:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing: - ``recall`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum recall for the given precision level per class - ``threshold`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the corresponding threshold level per class .. note:: The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to ```None``` will activate the non-binned version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size :math:`\mathcal{O}(n_{thresholds} \times n_{labels})` (constant memory). Args: num_labels: Integer specifing the number of labels min_precision: float value specifying minimum precision threshold. thresholds: Can be one of: - If set to ``None``, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach. - If set to an ``int`` (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation. - If set to an ``list`` of floats, will use the indicated thresholds in the list as bins for the calculation - If set to an 1d :class:`~torch.Tensor` of floats, will use the indicated thresholds in the tensor as bins for the 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: >>> from torch import tensor >>> from torchmetrics.classification import MultilabelRecallAtFixedPrecision >>> preds = tensor([[0.75, 0.05, 0.35], ... [0.45, 0.75, 0.05], ... [0.05, 0.55, 0.75], ... [0.05, 0.65, 0.05]]) >>> target = tensor([[1, 0, 1], ... [0, 0, 0], ... [0, 1, 1], ... [1, 1, 1]]) >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5, thresholds=None) >>> metric(preds, target) (tensor([1., 1., 1.]), tensor([0.0500, 0.5500, 0.0500])) >>> mlrafp = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5, thresholds=5) >>> mlrafp(preds, target) (tensor([1., 1., 1.]), tensor([0.0000, 0.5000, 0.0000])) FrNrr r!r"r#r$ZLabelrJT) num_labelsr%r&r'r(r)r*c s>tjf|||dd||r.t||||||_||_dS)NF)rOr&r'r()r+r,rr(r%)r-rOr%r&r'r(r)r.r0r1r,s z)MultilabelRecallAtFixedPrecision.__init__r2cCs<|jdurt|jt|jfn|j}t||j|j|j|jSr3) r&rr4r5r6rrOr'r%r7r0r0r1r9s$z(MultilabelRecallAtFixedPrecision.computer:cCs|p|d}|||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 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import MultilabelRecallAtFixedPrecision >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5) >>> metric.update(rand(20, 3), randint(2, (20, 3))) >>> fig_, ax_ = metric.plot() # the returned plot only shows the maximum recall value by default .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import MultilabelRecallAtFixedPrecision >>> metric = MultilabelRecallAtFixedPrecision(num_labels=3, min_precision=0.5) >>> values = [ ] >>> for _ in range(10): ... # we index by 0 such that only the maximum recall value is plotted ... values.append(metric(rand(20, 3), randint(2, (20, 3)))[0]) >>> fig_, ax_ = metric.plot(values) rr=r>r0r0r1r?s)r)NNT)NNrLr0r0r.r1rN?s2 D     rNc @sReZdZdZdedeeeee ee feeeeeee e e d ddZdS) RecallAtFixedPrecisionaCompute the highest possible recall value given the minimum precision thresholds provided. This is done by first calculating the precision-recall curve for different thresholds and the find the recall for a given precision level. 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.BinaryRecallAtFixedPrecision`, :class:`~torchmetrics.classification.MulticlassRecallAtFixedPrecision` and :class:`~torchmetrics.classification.MultilabelRecallAtFixedPrecision` for the specific details of each argument influence and examples. NT)binaryZ multiclassZ multilabel) taskr%r&rKrOr'r(r)r*c Kst|}|tjkr*t||||fi|S|tjkrjt|tsRtdt|dt |||||fi|S|tj krt|tstdt|dt |||||fi|Std|ddS)zInitialize task metric.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_strBINARYrZ MULTICLASS isinstancerG ValueErrortyperIZ MULTILABELrN) clsrRr%r&rKrOr'r(r)r0r0r1__new__s(        zRecallAtFixedPrecision.__new__)NNNNT)r@rArBrCr rFrrrGrrrDrrrXr0r0r0r1rPs"rPN)(typingrrrrrrZtorchrZtyping_extensionsr Z torchmetrics.classification.baser Z2torchmetrics.classification.precision_recall_curver r r Z=torchmetrics.functional.classification.recall_fixed_precisionrrrrrrZtorchmetrics.metricrZtorchmetrics.utilities.datarZtorchmetrics.utilities.enumsrZtorchmetrics.utilities.importsrZtorchmetrics.utilities.plotrrZ__doctest_skip__rrIrNrPr0r0r0r1s&