o yi@@sddlmZmZddlZddlmZddlmZddlmZddl m Z ddl m Z m Z mZGdd d e ZGd d d e ZGd d d eZGdddZdS))AnyOptionalN)Tensor)Literal)_accuracy_reduce)Metric)BinaryStatScoresMulticlassStatScoresMultilabelStatScoresc@4eZdZUdZdZdZdZeed<de fddZ dS) BinaryAccuracya Computes `Accuracy`_ for binary tasks: .. math:: \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i) Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An int or float tensor of shape ``(N, ...)``. 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, ...)`` As output to ``forward`` and ``compute`` the metric returns the following output: - ``ba`` (:class:`~torch.Tensor`): If ``multidim_average`` is set to ``global``, the metric returns a scalar value. If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value per sample. Args: 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 BinaryAccuracy >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0, 0, 1, 1, 0, 1]) >>> metric = BinaryAccuracy() >>> metric(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.classification import BinaryAccuracy >>> 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 = BinaryAccuracy() >>> metric(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.classification import BinaryAccuracy >>> 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 = BinaryAccuracy(multidim_average='samplewise') >>> metric(preds, target) tensor([0.3333, 0.1667]) FTfull_state_updatereturncCs&|\}}}}t||||d|jdS)EComputes accuracy based on inputs passed in to ``update`` previously.binaryaveragemultidim_average) _final_staterrselftpfptnfnrX/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/classification/accuracy.pycomputebszBinaryAccuracy.computeN __name__ __module__ __qualname____doc__is_differentiablehigher_is_betterr bool__annotations__rrrrrrr s ? r c@r ) MulticlassAccuracyaqComputes `Accuracy`_ for multiclass tasks: .. math:: \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i) Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. 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: - ``mca`` (:class:`~torch.Tensor`): A tensor with the accuracy score whose returned shape depends on the ``average`` and ``multidim_average`` arguments: - If ``multidim_average`` is set to ``global``: - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor - If ``average=None/'none'``, the shape will be ``(C,)`` - If ``multidim_average`` is set to ``samplewise``: - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)`` - If ``average=None/'none'``, the shape will be ``(N, C)`` Args: num_classes: Integer specifing the number of classes average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: Calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: Calculates statistic for each label and applies no reduction top_k: Number of highest probability or logit score predictions considered to find the correct label. Only works when ``preds`` contain probabilities/logits. 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 MulticlassAccuracy >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([2, 1, 0, 1]) >>> metric = MulticlassAccuracy(num_classes=3) >>> metric(preds, target) tensor(0.8333) >>> mca = MulticlassAccuracy(num_classes=3, average=None) >>> mca(preds, target) tensor([0.5000, 1.0000, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.classification import MulticlassAccuracy >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([ ... [0.16, 0.26, 0.58], ... [0.22, 0.61, 0.17], ... [0.71, 0.09, 0.20], ... [0.05, 0.82, 0.13], ... ]) >>> metric = MulticlassAccuracy(num_classes=3) >>> metric(preds, target) tensor(0.8333) >>> mca = MulticlassAccuracy(num_classes=3, average=None) >>> mca(preds, target) tensor([0.5000, 1.0000, 1.0000]) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassAccuracy >>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]]) >>> metric = MulticlassAccuracy(num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.5000, 0.2778]) >>> mca = MulticlassAccuracy(num_classes=3, multidim_average='samplewise', average=None) >>> mca(preds, target) tensor([[1.0000, 0.0000, 0.5000], [0.0000, 0.3333, 0.5000]]) FTr rcCs(|\}}}}t|||||j|jdS)rrrrrrrrrrrszMulticlassAccuracy.computeNrrrrrr'hs ] r'c@r ) MultilabelAccuracyafComputes `Accuracy`_ for multilabel tasks: .. math:: \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i) Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An int 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: - ``mla`` (:class:`~torch.Tensor`): A tensor with the accuracy score whose returned shape depends on the ``average`` and ``multidim_average`` arguments: - If ``multidim_average`` is set to ``global``: - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor - If ``average=None/'none'``, the shape will be ``(C,)`` - If ``multidim_average`` is set to ``samplewise``: - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)`` - If ``average=None/'none'``, the shape will be ``(N, C)`` Args: num_labels: Integer specifing the number of labels threshold: Threshold for transforming probability to binary (0,1) predictions average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: Calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: Calculates statistic for each label and applies no reduction 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 MultilabelAccuracy >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelAccuracy(num_labels=3) >>> metric(preds, target) tensor(0.6667) >>> mla = MultilabelAccuracy(num_labels=3, average=None) >>> mla(preds, target) tensor([1.0000, 0.5000, 0.5000]) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelAccuracy >>> 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 = MultilabelAccuracy(num_labels=3) >>> metric(preds, target) tensor(0.6667) >>> mla = MultilabelAccuracy(num_labels=3, average=None) >>> mla(preds, target) tensor([1.0000, 0.5000, 0.5000]) Example (multidim tensors): >>> from torchmetrics.classification import MultilabelAccuracy >>> 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]], ... ] ... ) >>> mla = MultilabelAccuracy(num_labels=3, multidim_average='samplewise') >>> mla(preds, target) tensor([0.3333, 0.1667]) >>> mla = MultilabelAccuracy(num_labels=3, multidim_average='samplewise', average=None) >>> mla(preds, target) tensor([[0.5000, 0.5000, 0.0000], [0.0000, 0.0000, 0.5000]]) FTr rc Cs*|\}}}}t|||||j|jddS)rT)rr multilabelr(rrrrr0szMultilabelAccuracy.computeNrrrrrr)s [ r)c@sxeZdZdZ        dded d ed eed eed eeddeddeedeedede de fddZ dS)AccuracyaVComputes `Accuracy`_ .. math:: \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i) Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. 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 ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of :mod:`BinaryAccuracy`, :mod:`MulticlassAccuracy` and :mod:`MultilabelAccuracy` for the specific details of each argument influence and examples. Legacy Example: >>> import torch >>> target = torch.tensor([0, 1, 2, 3]) >>> preds = torch.tensor([0, 2, 1, 3]) >>> accuracy = Accuracy(task="multiclass", num_classes=4) >>> accuracy(preds, target) tensor(0.5000) >>> target = torch.tensor([0, 1, 2]) >>> preds = torch.tensor([[0.1, 0.9, 0], [0.3, 0.1, 0.6], [0.2, 0.5, 0.3]]) >>> accuracy = Accuracy(task="multiclass", num_classes=3, top_k=2) >>> accuracy(preds, target) tensor(0.6667) ?NmicroglobalTtask)r multiclassr* threshold num_classes num_labelsr)r-macroweightednoner)r. samplewisetop_k ignore_index validate_argskwargsrc Ks| t||| d|dkrt|fi| S|dkr2t|ts!Jt|ts(Jt|||fi| S|dkrGt|ts=Jt|||fi| Std|)N)rr:r;rr1r*z[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )updatedictr isinstanceintr'r) ValueError) clsr0r2r3r4rrr9r:r;r<rrr__new__Ts zAccuracy.__new__)r,NNr-r.r/NT) rr r!r"rfloatrr@r%rrrCrrrrr+8sB     r+)typingrrtorchrtyping_extensionsr/torchmetrics.functional.classification.accuracyrtorchmetrics.metricr'torchmetrics.classification.stat_scoresrr r r r'r)r+rrrrs     Jhh