iEOddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZmZmZmZmZmZdd lmZdd lmZdd lmZdd lmZdd lm Z m!Z!esddgZ"GddeZ#GddeZ$Gdde Z%y))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_TYPEzMulticlassExactMatch.plotzMultilabelExactMatch.plotc eZdZUdZeed<dZeed<dZeed<dZ eed<dZ e ed <d Z e ed <d Z eed < ddededdeedededdf fd ZdededdfdZdefdZ ddeeeeefdeedefdZxZS) 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_nameN num_classesmultidim_averageglobal samplewise ignore_index validate_argskwargsreturnc t|d i|d\}}|rt|||||||_||_||_||_|jd|jdk(r%tjdtjng|jdk(rdnd|jd tjdtj|jdk(rdyd y) N)Ncorrectr(r/dtypesumcatdist_reduce_fxrmean) super__init__r r%r&r*r+ add_statetorchzeroslong) selfr%r&r*r+r,top_kaverage __class__s |/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/classification/exact_match.pyr:zMulticlassExactMatch.__init__ms "6" w  2;wP`bn o& 0(*  040E0E0QEKK ,WY$($9$9X$E55    KK ,$($9$9X$E5  LR  predstargetc|jr-t|||j|j|jt ||d\}}t |||j|j\}}|jdk(rmt|jts td|jj|t|jts td||_ yt|jts td|xj|z c_t|jts td|xj|z c_ y)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*rr isinstancer0list TypeErrorappendrrr?rErFr0rs rCupdatezMulticlassExactMatch.updates    5vt//1F1FHYHY 7ufaH v7vtG\G\^b^o^op  L 0dllD1 Z[[ LL   (djj&1 Z[[DJdllF3 XYY LLG #Ldjj&1 Z[[ JJ% JrDct|jtrt|jn |j}t|trt|j ts t dt||j SzCompute metric.z>Expected `correct` and `total` to be tensors after processing.rKr0rLrrrrMr r?r0s rCcomputezMulticlassExactMatch.compute\0:4<<0N,t||,TXT`T`'6**TZZ2P\] ]"7DJJ77rDvalaxc&|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 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?rWrXs rCplotzMulticlassExactMatch.plotPzz#r""rD)r(NTNN__name__ __module__ __qualname____doc__r__annotations__rboolrrr floatr"r$strintrrrr:rPrUrrrrr] __classcell__rBs@rCrr-s 5n M#t#!d!#t#!e!!e!#c# =E&*"   ""89 sm        8 F F t 488_c(#E&(6*:":;<(#IQRZI[(# (#rDrc eZdZUdZeed<dZeed<dZeed<dZ eed<dZ e ed <d Z e ed <d Z eed < ddede deddeedededdffd ZdededdfdZdefdZ d deeeeefdeedefdZxZS)!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$N num_labels thresholdr&r'r*r+r,r-c t|d i||rt||d||||_||_||_||_||_|jd|j dk(r%tjdtjng|j dk(rdnd|jd tjdtj|j dk(rdyd y) N)rAr&r*r0r(r/r1r3r4r5rr7r8) r9r:rrorpr&r*r+r;r<r=r>)r?rorpr&r*r+r,rBs rCr:zMultilabelExactMatch.__init__"s "6"  2ItFVeq %" 0(*  040E0E0QEKK ,WY$($9$9X$E55    KK ,$($9$9X$E5  LR  rDrErFc|jr-t|||j|j|jt |||j|j |j\}}t|||j|j|j\}}|jdk(rmt|jts td|jj|t|jts td||_ yt|jts td|xj|z c_ t|jts td|xj|z c_ y)z*Update state with predictions and targets.)rErFror&r*r)rHrIrJN)r+rror&r*rrpr rKr0rLrMrNrrrOs rCrPzMultilabelExactMatch.updateAs3    5vt0E0EtGXGX 7 64??DNND>> # 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) rZr\s rCr]zMultilabelExactMatch.plotmr^rD)?r(NTr_r`rks@rCrmrms?B M#t#!d!#t#!e!!e!#c#   F  F  t  D88_c(#E&(6*:":;<(#IQRZI[(# (#rDrmcreZdZdZ ddeddeddedeedeed ed d eed e d e de fdZ y) 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.]) Nclstask)binary multiclass multilabelrpr%ror&r'r*r+r,r-c tj|}|j|||d|tjk(r4t |t st dt|dt|fi|S|tjk(r5t |t st dt|dt||fi|St d|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_strrP MULTICLASSrKri ValueErrortyper MULTILABELrm) rxryrpr%ror&r*r+r,s rC__new__zExactMatch.__new__s*2248 0(*  -88 8k3/ #NtT_O`Naan!opp' >v> > -88 8j#. #MdS]N^M__l!mnn' IHH H5o677rD)ruNNr(NT) rarbrcrdrrrgrrirfrrrr8rDrCrwrws<%)$(rs{%'' %G '4C@@35PQh#6h#V}#6}#@68+68rD