o .wiEO@sddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZmZmZmZmZmZdd lmZdd lmZdd lmZdd lmZdd lm Z m!Z!esfddgZ"GdddeZ#GdddeZ$Gddde Z%dS))Sequence)AnyOptionalUnionN)Tensor)Literal)_ClassificationTaskWrapper)_exact_match_reduce_multiclass_exact_match_update_multilabel_exact_match_update)&_multiclass_stat_scores_arg_validation_multiclass_stat_scores_format)_multiclass_stat_scores_tensor_validation&_multilabel_stat_scores_arg_validation_multilabel_stat_scores_format)_multilabel_stat_scores_tensor_validation)Metric) dim_zero_cat)ClassificationTaskNoBinary)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEMulticlassExactMatch.plotMultilabelExactMatch.plotc seZdZUdZeed<dZeed<dZeed<dZ eed<dZ e ed <d Z e ed <d Z eed <   d#dededdeedededdf fdd ZdededdfddZdefddZ d$deeeeefd eedefd!d"ZZS)%MulticlassExactMatcha] Compute Exact match (also known as subset accuracy) for multiclass tasks. Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An 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, ...)``. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mcem`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` argument: - If ``multidim_average`` is set to ``global`` the output will be a scalar tensor - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)`` If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present, which the reduction will then be applied over instead of the sample dimension ``N``. Args: num_classes: Integer specifying the number of labels multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. 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. Example (multidim tensors): >>> from torch import tensor >>> from torchmetrics.classification import MulticlassExactMatch >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]]) >>> metric = MulticlassExactMatch(num_classes=3, multidim_average='global') >>> metric(preds, target) tensor(0.5000) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassExactMatch >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]]) >>> metric = MulticlassExactMatch(num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([1., 0.]) totalFis_differentiableThigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundClassplot_legend_nameglobalN num_classesmultidim_averager% samplewise ignore_index validate_argskwargsreturnc stjd i|d\}}|rt|||||||_||_||_||_|jd|jdkr3tj dtj dng|jdkr;dndd|jd tj dtj d|jdkrPdnd ddS) N)Ncorrectr%r.dtypesumcatdist_reduce_fxrmean) super__init__r r&r'r*r+ add_statetorchzeroslong)selfr&r'r*r+r,top_kaverage __class__r7d/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/classification/exact_match.pyr9ms$ zMulticlassExactMatch.__init__predstargetcCs|jrt|||j|j|jt||d\}}t|||j|j\}}|jdkrEt|jt s0t d|j |t|j t s@t d||_ dSt|jt sOt d|j|7_t|j t s`t d|j |7_ dS)z2Update metric states with predictions and targets.r.r)8Expected `self.correct` to be a list in samplewise mode.8Expected `self.total` to be a Tensor in samplewise mode.6Expected `self.correct` to be a tensor in global mode.N)r+rr&r'r*r r isinstancer/list TypeErrorappendrrr>rDrEr/rr7r7rCupdates&       zMulticlassExactMatch.updatecCFt|jtr t|jn|j}t|trt|jtstdt||jSzCompute metric.z>Expected `correct` and `total` to be tensors after processing.rIr/rJrrrrKr r>r/r7r7rCcompute zMulticlassExactMatch.computevalaxcC |||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 MulticlassExactMatch >>> metric = MulticlassExactMatch(num_classes=3) >>> metric.update(randint(3, (20,5)), randint(3, (20,5))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassExactMatch >>> metric = MulticlassExactMatch(num_classes=3) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,5)), randint(3, (20,5)))) >>> fig_, ax_ = metric.plot(values) _plotr>rUrVr7r7rCplot (r)r%NTNN__name__ __module__ __qualname____doc__r__annotations__rboolrrr floatr"r$strintrrrr9rNrSrrrrr[ __classcell__r7r7rArCr-sF 7       rcseZdZUdZeed<dZeed<dZeed<dZ eed<dZ e ed <d Z e ed <d Z eed <    d%dede deddeedededdffdd ZdededdfddZdefdd Z d&d!eeeeefd"eedefd#d$ZZS)'MultilabelExactMatcha; Compute Exact match (also known as subset accuracy) for multilabel tasks. Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An 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, ...)``. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mlem`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` argument: - If ``multidim_average`` is set to ``global`` the output will be a scalar tensor - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)`` If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present, which the reduction will then be applied over instead of the sample dimension ``N``. Args: num_labels: Integer specifying the number of labels threshold: Threshold for transforming probability to binary (0,1) predictions multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. 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. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import MultilabelExactMatch >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelExactMatch(num_labels=3) >>> metric(preds, target) tensor(0.5000) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelExactMatch >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> metric = MultilabelExactMatch(num_labels=3) >>> metric(preds, target) tensor(0.5000) Example (multidim tensors): >>> from torchmetrics.classification import MultilabelExactMatch >>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) >>> metric = MultilabelExactMatch(num_labels=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0., 0.]) rFrTrrrr r!r"Labelr$?r%N num_labels thresholdr'r(r*r+r,r-c stjd i||rt||d||d||_||_||_||_||_|jd|jdkr3t j dt j dng|jdkr;dndd|jd t j dt j d|jdkrPdnd ddS) N)r@r'r*r/r%r.r0r2r3r4rr6r7) r8r9rrlrmr'r*r+r:r;r<r=)r>rlrmr'r*r+r,rAr7rCr9"s(   zMultilabelExactMatch.__init__rDrEcCs|jrt|||j|j|jt|||j|j|j\}}t|||j|j|jd\}}|jdkrMt|j t s8t d|j |t|j tsHt d||_ dSt|j tsWt d|j |7_ t|j tsht d|j |7_ dS)z*Update state with predictions and targets.)rDrErlr'r*r)rFrGrHN)r+rrlr'r*rrmr rIr/rJrKrLrrrMr7r7rCrNAs6       zMultilabelExactMatch.updatecCrOrPrQrRr7r7rCrScrTzMultilabelExactMatch.computerUrVcCrW)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 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 MultilabelExactMatch >>> metric = MultilabelExactMatch(num_labels=3) >>> metric.update(randint(2, (20, 3, 5)), randint(2, (20, 3, 5))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand, randint >>> from torchmetrics.classification import MultilabelExactMatch >>> metric = MultilabelExactMatch(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(randint(2, (20, 3, 5)), randint(2, (20, 3, 5)))) >>> fig_, ax_ = metric.plot(values) rXrZr7r7rCr[mr\r)rkr%NTr]r^r7r7rArCrisL A      " ric@sheZdZdZ      ddeddedd ed eed eed ed deede de de fddZ dS) ExactMatchaCompute Exact match (also known as subset accuracy). Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be correctly classified. This module is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'multiclass'`` or ``'multilabel'``. See the documentation of :class:`~torchmetrics.classification.MulticlassExactMatch` and :class:`~torchmetrics.classification.MultilabelExactMatch` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]]) >>> metric = ExactMatch(task="multiclass", num_classes=3, multidim_average='global') >>> metric(preds, target) tensor(0.5000) >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]]) >>> metric = ExactMatch(task="multiclass", num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([1., 0.]) rkNr%Tclstask)binary multiclass multilabelrmr&rlr'r(r*r+r,r-c Kst|}||||d|tjkr*t|ts"tdt|dt|fi|S|tj krGt|ts>tdt|dt ||fi|Std|d)zInitialize task metric.)r'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!) rfrom_strrN MULTICLASSrIrg ValueErrortyper MULTILABELri) rorprmr&rlr'r*r+r,r7r7rC__new__s     zExactMatch.__new__)rkNNr%NT) r_r`rarbrwrrerrgrdrrryr7r7r7rCrns:  rn)&collections.abcrtypingrrrr;rtyping_extensionsr torchmetrics.classification.baser2torchmetrics.functional.classification.exact_matchr r r 2torchmetrics.functional.classification.stat_scoresr r rrrrtorchmetrics.metricrtorchmetrics.utilities.datartorchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rrirnr7r7r7rCs(         ,A