o .wi.@sddlmZddlZddlmZddlmZddlmZmZm Z m Z m Z m Z ddl mZddlmZded ed efd d Z d%dedededdeed eeeff ddZ  d&dededededdeeded efddZ d%dededededdeed eeeff ddZ   d'dedededededdeeded efdd Z     d(deded!ed"deedeedededdeeded efd#d$ZdS)))OptionalN)Tensor)Literal)&_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 _safe_divide)ClassificationTaskNoBinarycorrecttotalreturncCs t||S)zReduce exact match.r )rrro/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/classification/exact_match.py_exact_match_reduce s rglobalpredstargetmultidim_average)r samplewise ignore_indexcCsr|dur|}||||k<||kd|jdk}|dkr |n|}tj|dkr/|jdnd|jd}||fS)Compute the statistics.Nrrrdevice)clonesumshapetorchtensorr)rrrrrrrrr_multiclass_exact_match_update(s "r#T num_classes validate_argsc CsXd\}}|rt|||||t|||||t|||\}}t||||\}} t|| S)a 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. Accepts the following input tensors: - ``preds``: ``(N, ...)`` (int tensor) or ``(N, C, ..)`` (float tensor). If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into an int tensor. - ``target`` (int tensor): ``(N, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels 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. Returns: The 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,)`` Example (multidim tensors): >>> from torch import tensor >>> from torchmetrics.functional.classification import multiclass_exact_match >>> 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]]]) >>> multiclass_exact_match(preds, target, num_classes=3, multidim_average='global') tensor(0.5000) Example (multidim tensors): >>> from torchmetrics.functional.classification import multiclass_exact_match >>> 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]]]) >>> multiclass_exact_match(preds, target, num_classes=3, multidim_average='samplewise') tensor([1., 0.]) )rN)rrrr#r) rrr$rrr%top_kaveragerrrrrmulticlass_exact_match9s: r( num_labelscCs|dur|dk}t|||}|dkr+t|ddd|}t|ddd|}||kd|kjdd}tj|j|dkrBdnd|jd}||fS) rNrr)dimrr) r!wherelongmovedimreshaperr"r r)rrr)rrmaskrrrrr_multilabel_exact_match_update|s"r2? thresholdc CsZd}|rt|||||t|||||t|||||\}}t|||||\}} t|| S)a 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. Accepts the following input tensors: - ``preds`` (int or float tensor): ``(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`` (int tensor): ``(N, C, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels 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. Returns: The 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,)`` Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.functional.classification import multilabel_exact_match >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_exact_match(preds, target, num_labels=3) tensor(0.5000) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_exact_match >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]]) >>> multilabel_exact_match(preds, target, num_labels=3) tensor(0.5000) Example (multidim tensors): >>> from torchmetrics.functional.classification import multilabel_exact_match >>> 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]]]) >>> multilabel_exact_match(preds, target, num_labels=3, multidim_average='samplewise') tensor([0., 0.]) N)rr r r2r) rrr)r4rrr%r'rrrrrmultilabel_exact_matchsD r5task) multiclass multilabelc Csjt|}|tjkr|dusJt||||||S|tjkr.|dus$Jt|||||||Std|)aCompute Exact match (also known as subset accuracy). Exact Match is a stricter version of accuracy where all classes/labels have to match exactly for the sample to be correctly classified. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'multiclass'`` or ``'multilabel'``. See the documentation of :func:`~torchmetrics.functional.classification.multiclass_exact_match` and :func:`~torchmetrics.functional.classification.multilabel_exact_match` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]]) >>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='global') tensor(0.5000) >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 1], [2, 1], [0, 2]], [[2, 2], [2, 1], [1, 0]]]) >>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='samplewise') tensor([1., 0.]) NzNot handled value: )r from_str MULTICLASSr( MULTILABELr5 ValueError) rrr6r$r)r4rrr%rrr exact_matchs #    r=)rN)rNT)r3rNT)NNr3rNT)typingrr!rtyping_extensionsr2torchmetrics.functional.classification.stat_scoresrrrrr r torchmetrics.utilities.computer torchmetrics.utilities.enumsr rinttupler#boolr(r2floatr5r=rrrrs           G   Q