o yiK@sddlmZddlZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZddlmZ  d-d ed ed ed ed eeddeddedefddZ    d.dededededdeededefddZ     d/deded ed eed!d"ededdeededefd#d$Z     d0deded%eded eed!deddeededefd&d'Z    (    d1deded)ed*ded eed%eed eed!deedd"eedeededefd+d,ZdS)2)OptionalN)Tensor)Literal) "_binary_stat_scores_arg_validation_binary_stat_scores_format%_binary_stat_scores_tensor_validation_binary_stat_scores_update&_multiclass_stat_scores_arg_validation_multiclass_stat_scores_format)_multiclass_stat_scores_tensor_validation_multiclass_stat_scores_update&_multilabel_stat_scores_arg_validation_multilabel_stat_scores_format)_multilabel_stat_scores_tensor_validation_multilabel_stat_scores_update) _safe_divideglobalFtpfptnfnaverage)binarymicromacroweightednonemultidim_average)r samplewise multilabelreturnc Cs2|dkrt||||||S|dkr[|j|dkrdndd}|j|dkr)dndd}|rT|j|dkr7dndd}|j|dkrCdndd}t||||||St|||S|rkt||||||}nt|||}|dusz|dkr||S|d kr||}nt|}t|||jd d d d S) atReduce classification statistics into accuracy score Args: tp: number of true positives fp: number of false positives tn: number of true negatives fn: number of false negatives normalize: normalization method. - `"true"` will divide by the sum of the column dimension. - `"pred"` will divide by the sum of the row dimension. - `"all"` will divide by the sum of the full matrix - `"none"` or `None` will apply no reduction multilabel: bool indicating if reduction is for multilabel tasks Returns: Accuracy score rrrr)dimNrrT)keepdim)rsumtorch ones_like) rrrrrrrscoreweightsr*c/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/classification/accuracy.py_accuracy_reduce%s&  r,?Tpredstarget threshold ignore_index validate_argsc CsX|rt|||t||||t||||\}}t|||\}}}} t|||| d|dS)a 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. Accepts the following input tensors: - ``preds`` (int or float tensor): ``(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`` (int tensor): ``(N, ...)`` Args: preds: Tensor with predictions target: Tensor with true 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: 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. Example (preds is int tensor): >>> from torchmetrics.functional.classification import binary_accuracy >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0, 0, 1, 1, 0, 1]) >>> binary_accuracy(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.functional.classification import binary_accuracy >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) >>> binary_accuracy(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.functional.classification import binary_accuracy >>> 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]], ... ] ... ) >>> binary_accuracy(preds, target, multidim_average='samplewise') tensor([0.3333, 0.1667]) rrr)rrrrr,) r.r/r0rr1r2rrrrr*r*r+binary_accuracyVs E r4rr! num_classes)rrrrtop_kc Csd|rt|||||t|||||t|||\}}t|||||||\}} } } t|| | | ||dS)aNComputes `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. 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 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. Returns: The 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)`` Example (preds is int tensor): >>> from torchmetrics.functional.classification import multiclass_accuracy >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([2, 1, 0, 1]) >>> multiclass_accuracy(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_accuracy(preds, target, num_classes=3, average=None) tensor([0.5000, 1.0000, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multiclass_accuracy >>> 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], ... ]) >>> multiclass_accuracy(preds, target, num_classes=3) tensor(0.8333) >>> multiclass_accuracy(preds, target, num_classes=3, average=None) tensor([0.5000, 1.0000, 1.0000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multiclass_accuracy >>> 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]]]) >>> multiclass_accuracy(preds, target, num_classes=3, multidim_average='samplewise') tensor([0.5000, 0.2778]) >>> multiclass_accuracy(preds, target, num_classes=3, multidim_average='samplewise', average=None) tensor([[1.0000, 0.0000, 0.5000], [0.0000, 0.3333, 0.5000]]) r3)r r r r r,) r.r/r5rr6rr1r2rrrrr*r*r+multiclass_accuracysb r7 num_labelsc Csb|rt|||||t|||||t|||||\}}t|||\}} } } t|| | | ||ddS)aPComputes `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. 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 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. Returns: The 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)`` Example (preds is int tensor): >>> from torchmetrics.functional.classification import multilabel_accuracy >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_accuracy(preds, target, num_labels=3) tensor(0.6667) >>> multilabel_accuracy(preds, target, num_labels=3, average=None) tensor([1.0000, 0.5000, 0.5000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_accuracy >>> 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_accuracy(preds, target, num_labels=3) tensor(0.6667) >>> multilabel_accuracy(preds, target, num_labels=3, average=None) tensor([1.0000, 0.5000, 0.5000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multilabel_accuracy >>> 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_accuracy(preds, target, num_labels=3, multidim_average='samplewise') tensor([0.3333, 0.1667]) >>> multilabel_accuracy(preds, target, num_labels=3, multidim_average='samplewise', average=None) tensor([[0.5000, 0.5000, 0.0000], [0.0000, 0.0000, 0.5000]]) T)rrr)r rrrr,) r.r/r8r0rrr1r2rrrrr*r*r+multilabel_accuracys `r9rtask)r multiclassrc Cs|dusJ|dkrt||||| | S|dkr0t|tsJt|ts%Jt||||||| | S|dkrFt|ts;Jt||||||| | Std|)aComputes `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 function 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 :func:`binary_accuracy`, :func:`multiclass_accuracy` and :func:`multilabel_accuracy` 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(preds, target, task="multiclass", num_classes=4) 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(preds, target, task="multiclass", num_classes=3, top_k=2) tensor(0.6667) Nrr;rz[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )r4 isinstanceintr7r9 ValueError) r.r/r:r0r5r8rrr6r1r2r*r*r+accuracyws" %r?)rF)r-rNT)rr!rNT)r-rrNT)r-NNrrr!NT)typingrr&rtyping_extensionsr2torchmetrics.functional.classification.stat_scoresrrrrr r r r r rrrtorchmetrics.utilities.computerboolr,floatr=r4r7r9r?r*r*r*r+s   8    4 Q  p  l