o yi5@sddlmZmZddlZddlmZddlmZddlmZm Z m Z ddl m Z m Z mZmZmZmZddlmZddlmZGd d d eZGd d d eZGd ddZdS))AnyOptionalN)Tensor)Literal)_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_catc sxeZdZUdZdZdZdZeed<   dde de d d e e d ed e d df fdd Z dddZd efddZZS)MulticlassExactMatchay 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. 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,)`` Args: 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. Example (multidim tensors): >>> from torchmetrics.classification import MulticlassExactMatch >>> 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]]]) >>> metric = MulticlassExactMatch(num_classes=3, multidim_average='global') >>> metric(preds, target) tensor(0.5000) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassExactMatch >>> 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]]]) >>> metric = MulticlassExactMatch(num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([1., 0.]) FTfull_state_updateglobalN 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)Ncorrectrrdtypesumcatdist_reduce_fxtotalmean) super__init__r rrrr add_statetorchzeroslong)selfrrrrrtop_kaverage __class__r&[/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/classification/exact_match.pyr(Zs$ zMulticlassExactMatch.__init__cCs~|jrt|||j|j|jt||d\}}t|||j\}}|jdkr/|j|||_ dS|j|7_|j |7_ dS)Nrr) rr rrrr rrappendr$r-predstargetrr$r&r&r2updatevs   zMulticlassExactMatch.updatecC(t|jtr t|jn|j}t||jSN isinstancerlistrrr$r-rr&r&r2compute zMulticlassExactMatch.compute)rNT)rN)__name__ __module__ __qualname____doc__is_differentiablehigher_is_betterrbool__annotations__intrrrr(r7rr> __classcell__r&r&r0r2r%s. 0  rcseZdZUdZdZdZdZeed<    dde d e d e d d e e d ede ddffdd ZdededdfddZdefddZZS)MultilabelExactMatcha 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. 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. Addtionally, 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,)`` Args: 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. Example (preds is int tensor): >>> from torchmetrics.classification import MultilabelExactMatch >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.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 = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.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 = 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]], ... ] ... ) >>> metric = MultilabelExactMatch(num_labels=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0., 0.]) FTr?rN num_labels thresholdrrrrrrc stjd i||rt||d||d||_||_||_||_||_|jd|jdkr3t j dt j dng|jdkr;dndd|jd t j dt j d|jdkrPdnd ddS) N)r/rrrrrrr r!r"r$r%r&) r'r(r rLrMrrrr)r*r+r,)r-rLrMrrrrr0r&r2r(s(   zMultilabelExactMatch.__init__r5r6cCs|jrt|||j|j|jt|||j|j|j\}}t|||j|j\}}|jdkr6|j |||_ dS|j|7_|j |7_ dS)z*Update state with predictions and targets.rN) rrrLrrr rMrrr3r$r4r&r&r2r7s   zMultilabelExactMatch.updatecCr8r9r:r=r&r&r2r>r?zMultilabelExactMatch.compute)rKrNT)r@rArBrCrDrErrFrGrHfloatrrrr(rr7r>rIr&r&r0r2rJs4 ? rJc@s`eZdZdZ      ddedded eed eed ed d eedede de fddZ dS) ExactMatchaComputes 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 :mod:`MulticlassExactMatch` and :mod:`MultilabelExactMatch` 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]]]) >>> metric = ExactMatch(task="multiclass", num_classes=3, multidim_average='global') >>> metric(preds, target) 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]]]) >>> metric = ExactMatch(task="multiclass", num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([1., 0.]) rKNrTtask)binary multiclass multilabelrMrrLrrrrrrc Ksp|t|||d|dkrt|tsJt|fi|S|dkr1t|ts(Jt||fi|Std|)N)rrrrRrSzOExpected argument `task` to either be `'multiclass'` or `'multilabel'` but got )r7dictr;rHrrJ ValueError) clsrPrMrrLrrrrr&r&r2__new__s zExactMatch.__new__)rKNNrNT) r@rArBrCrrNrrHrFrrrWr&r&r&r2rOs6  rO)typingrrr*rtyping_extensionsr2torchmetrics.functional.classification.exact_matchrrr2torchmetrics.functional.classification.stat_scoresr r r r r rtorchmetrics.metricrtorchmetrics.utilities.datarrrJrOr&r&r&r2s      dy