o yi~,@sddlmZmZddlZddlmZddlmZddlmZm Z m Z m Z m Z m Z ddlmZdeded efd d Z d$d edededd eeeffddZ  d%d ededededdeeded efddZ d$d ededededd eeeff ddZ   d&d edededededdeeded efddZ     d'd eded ed!deedeedededdeeded efd"d#ZdS)()OptionalTupleN)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_dividecorrecttotalreturncCs t||S)z Final reduction for exact match.r )rrrf/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/classification/exact_match.py_exact_match_reduces rglobalpredstargetmultidim_average)r samplewisecCsV||kd|jdk}|dkr|n|}tj|dkr!|jdnd|jd}||fS)Computes the statistics.rrrdevice)sumshapetorchtensorr)rrrrrrrr_multiclass_exact_match_update's"r!T num_classes ignore_index validate_argsc CsVd\}}|rt|||||t|||||t|||\}}t|||\}} t|| S)a Computes 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 specifing 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 torchmetrics.functional.classification import multiclass_exact_match >>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = torch.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 = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = torch.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"rr#r$top_kaveragerrrrrmulticlass_exact_match3s6 r' num_labelscCsx|dkrt|ddd|}t|ddd|}||kd|kjdd}tj|j|dkr1dnd|jd}||fS)rrr)dimrr)rmovedimreshaperr rr)rrr(rrrrrr_multilabel_exact_match_updaters "r.? thresholdc CsXd}|rt|||||t|||||t|||||\}}t||||\}} t|| S)a Computes 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. Addtionally, 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 specifing 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 torchmetrics.functional.classification import multilabel_exact_match >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.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 = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.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 = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = torch.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)r r r r.r) rrr(r0rr#r$r&rrrrrmultilabel_exact_matchsD r1task) multiclass multilabelc Cs\|dkr|dus Jt||||||S|dkr'|dusJt|||||||Std|)aComputes 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:`multiclass_exact_match` and :func:`multilabel_exact_match` for the specific details of each argument influence and examples. Legacy Example: >>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = torch.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 = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = torch.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.]) r3Nr4zOExpected argument `task` to either be `'multiclass'` or `'multilabel'` but got )r'r1 ValueError) rrr2r"r(r0rr#r$rrr exact_matchs  r6)r)rNT)r/rNT)NNr/rNT)typingrrrrtyping_extensionsr2torchmetrics.functional.classification.stat_scoresrrrr r r torchmetrics.utilities.computer rr!intboolr'r.floatr1r6rrrrs          @   Q