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from typing import Optional |
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|
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from torch import Tensor |
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from typing_extensions import Literal |
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from torchmetrics.functional.classification.stat_scores import ( |
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_binary_stat_scores_arg_validation, |
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_binary_stat_scores_format, |
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_binary_stat_scores_tensor_validation, |
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_binary_stat_scores_update, |
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_multiclass_stat_scores_arg_validation, |
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_multiclass_stat_scores_format, |
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_multiclass_stat_scores_tensor_validation, |
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_multiclass_stat_scores_update, |
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_multilabel_stat_scores_arg_validation, |
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_multilabel_stat_scores_format, |
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_multilabel_stat_scores_tensor_validation, |
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_multilabel_stat_scores_update, |
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) |
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from torchmetrics.utilities.compute import _adjust_weights_safe_divide, _safe_divide |
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from torchmetrics.utilities.enums import ClassificationTask |
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def _accuracy_reduce( |
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tp: Tensor, |
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fp: Tensor, |
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tn: Tensor, |
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fn: Tensor, |
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average: Optional[Literal["binary", "micro", "macro", "weighted", "none"]], |
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multidim_average: Literal["global", "samplewise"] = "global", |
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multilabel: bool = False, |
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top_k: int = 1, |
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) -> Tensor: |
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"""Reduce classification statistics into accuracy score. |
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|
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Args: |
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tp: number of true positives |
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fp: number of false positives |
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tn: number of true negatives |
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fn: number of false negatives |
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average: |
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Defines the reduction that is applied over labels. Should be one of the following: |
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|
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- ``binary``: for binary reduction |
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- ``micro``: sum score over all classes/labels |
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- ``macro``: salculate score for each class/label and average them |
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- ``weighted``: calculates score for each class/label and computes weighted average using their support |
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- ``"none"`` or ``None``: calculates score for each class/label and applies no reduction |
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multidim_average: |
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Defines how additionally dimensions ``...`` should be handled. Should be one of the following: |
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|
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- ``global``: Additional dimensions are flatted along the batch dimension |
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- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. |
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|
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multilabel: If input is multilabel or not |
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top_k: value for top-k accuracy, else 1 |
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Returns: |
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Accuracy score |
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|
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""" |
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if average == "binary": |
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return _safe_divide(tp + tn, tp + tn + fp + fn) |
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if average == "micro": |
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tp = tp.sum(dim=0 if multidim_average == "global" else 1) |
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fn = fn.sum(dim=0 if multidim_average == "global" else 1) |
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if multilabel: |
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fp = fp.sum(dim=0 if multidim_average == "global" else 1) |
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tn = tn.sum(dim=0 if multidim_average == "global" else 1) |
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return _safe_divide(tp + tn, tp + tn + fp + fn) |
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return _safe_divide(tp, tp + fn) |
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score = _safe_divide(tp + tn, tp + tn + fp + fn) if multilabel else _safe_divide(tp, tp + fn) |
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return _adjust_weights_safe_divide(score, average, multilabel, tp, fp, fn, top_k) |
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def binary_accuracy( |
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preds: Tensor, |
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target: Tensor, |
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threshold: float = 0.5, |
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multidim_average: Literal["global", "samplewise"] = "global", |
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ignore_index: Optional[int] = None, |
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validate_args: bool = True, |
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) -> Tensor: |
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r"""Compute `Accuracy`_ for binary tasks. |
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.. math:: |
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\text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i) |
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Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a |
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tensor of predictions. |
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Accepts the following input tensors: |
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- ``preds`` (int or float tensor): ``(N, ...)``. If preds is a floating point tensor with values outside |
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[0,1] range we consider the input to be logits and will auto apply sigmoid per element. Additionally, |
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we convert to int tensor with thresholding using the value in ``threshold``. |
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- ``target`` (int tensor): ``(N, ...)`` |
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|
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Args: |
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preds: Tensor with predictions |
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target: Tensor with true labels |
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threshold: Threshold for transforming probability to binary {0,1} predictions |
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multidim_average: |
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Defines how additionally dimensions ``...`` should be handled. Should be one of the following: |
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|
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- ``global``: Additional dimensions are flatted along the batch dimension |
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- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. |
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The statistics in this case are calculated over the additional dimensions. |
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|
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ignore_index: |
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Specifies a target value that is ignored and does not contribute to the metric calculation |
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validate_args: bool indicating if input arguments and tensors should be validated for correctness. |
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Set to ``False`` for faster computations. |
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|
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Returns: |
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If ``multidim_average`` is set to ``global``, the metric returns a scalar value. If ``multidim_average`` |
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is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value per sample. |
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|
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Example (preds is int tensor): |
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>>> from torch import tensor |
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>>> from torchmetrics.functional.classification import binary_accuracy |
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>>> target = tensor([0, 1, 0, 1, 0, 1]) |
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>>> preds = tensor([0, 0, 1, 1, 0, 1]) |
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>>> binary_accuracy(preds, target) |
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tensor(0.6667) |
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Example (preds is float tensor): |
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>>> from torchmetrics.functional.classification import binary_accuracy |
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>>> target = tensor([0, 1, 0, 1, 0, 1]) |
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>>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92]) |
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>>> binary_accuracy(preds, target) |
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tensor(0.6667) |
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Example (multidim tensors): |
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>>> from torchmetrics.functional.classification import binary_accuracy |
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>>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) |
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>>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], |
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... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) |
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>>> binary_accuracy(preds, target, multidim_average='samplewise') |
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tensor([0.3333, 0.1667]) |
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|
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""" |
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if validate_args: |
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_binary_stat_scores_arg_validation(threshold, multidim_average, ignore_index) |
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_binary_stat_scores_tensor_validation(preds, target, multidim_average, ignore_index) |
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preds, target = _binary_stat_scores_format(preds, target, threshold, ignore_index) |
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tp, fp, tn, fn = _binary_stat_scores_update(preds, target, multidim_average) |
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return _accuracy_reduce(tp, fp, tn, fn, average="binary", multidim_average=multidim_average) |
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def multiclass_accuracy( |
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preds: Tensor, |
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target: Tensor, |
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num_classes: Optional[int] = None, |
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average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro", |
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top_k: int = 1, |
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multidim_average: Literal["global", "samplewise"] = "global", |
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ignore_index: Optional[int] = None, |
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validate_args: bool = True, |
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) -> Tensor: |
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r"""Compute `Accuracy`_ for multiclass tasks. |
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|
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.. math:: |
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\text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i) |
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|
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Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a |
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tensor of predictions. |
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|
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Accepts the following input tensors: |
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|
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- ``preds``: ``(N, ...)`` (int tensor) or ``(N, C, ..)`` (float tensor). If preds is a floating point |
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we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into |
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an int tensor. |
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- ``target`` (int tensor): ``(N, ...)`` |
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|
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Args: |
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preds: Tensor with predictions |
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target: Tensor with true labels |
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num_classes: Integer specifying the number of classes |
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average: |
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Defines the reduction that is applied over labels. Should be one of the following: |
|
|
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- ``micro``: Sum statistics over all labels |
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- ``macro``: Calculate statistics for each label and average them |
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- ``weighted``: calculates statistics for each label and computes weighted average using their support |
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- ``"none"`` or ``None``: calculates statistic for each label and applies no reduction |
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|
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top_k: |
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Number of highest probability or logit score predictions considered to find the correct label. |
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Only works when ``preds`` contain probabilities/logits. |
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multidim_average: |
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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. |
|
|
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ignore_index: |
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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. |
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Set to ``False`` for faster computations. |
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|
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Returns: |
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The returned shape depends on the ``average`` and ``multidim_average`` arguments: |
|
|
|
- If ``multidim_average`` is set to ``global``: |
|
|
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- If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor |
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- If ``average=None/'none'``, the shape will be ``(C,)`` |
|
|
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- If ``multidim_average`` is set to ``samplewise``: |
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|
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- If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)`` |
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- If ``average=None/'none'``, the shape will be ``(N, C)`` |
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|
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Example (preds is int tensor): |
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>>> from torch import tensor |
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>>> from torchmetrics.functional.classification import multiclass_accuracy |
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>>> target = tensor([2, 1, 0, 0]) |
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>>> preds = tensor([2, 1, 0, 1]) |
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>>> multiclass_accuracy(preds, target, num_classes=3) |
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tensor(0.8333) |
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>>> multiclass_accuracy(preds, target, num_classes=3, average=None) |
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tensor([0.5000, 1.0000, 1.0000]) |
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|
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Example (preds is float tensor): |
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>>> from torchmetrics.functional.classification import multiclass_accuracy |
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>>> target = tensor([2, 1, 0, 0]) |
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>>> preds = tensor([[0.16, 0.26, 0.58], |
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... [0.22, 0.61, 0.17], |
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... [0.71, 0.09, 0.20], |
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... [0.05, 0.82, 0.13]]) |
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>>> multiclass_accuracy(preds, target, num_classes=3) |
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tensor(0.8333) |
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>>> multiclass_accuracy(preds, target, num_classes=3, average=None) |
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tensor([0.5000, 1.0000, 1.0000]) |
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|
|
Example (multidim tensors): |
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>>> from torchmetrics.functional.classification import multiclass_accuracy |
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>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) |
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>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]]) |
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>>> multiclass_accuracy(preds, target, num_classes=3, multidim_average='samplewise') |
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tensor([0.5000, 0.2778]) |
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>>> multiclass_accuracy(preds, target, num_classes=3, multidim_average='samplewise', average=None) |
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tensor([[1.0000, 0.0000, 0.5000], |
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[0.0000, 0.3333, 0.5000]]) |
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|
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""" |
|
if validate_args: |
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_multiclass_stat_scores_arg_validation(num_classes, top_k, average, multidim_average, ignore_index) |
|
_multiclass_stat_scores_tensor_validation(preds, target, num_classes, multidim_average, ignore_index) |
|
preds, target = _multiclass_stat_scores_format(preds, target, top_k) |
|
tp, fp, tn, fn = _multiclass_stat_scores_update( |
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preds, target, num_classes or 1, top_k, average, multidim_average, ignore_index |
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) |
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return _accuracy_reduce(tp, fp, tn, fn, average=average, multidim_average=multidim_average, top_k=top_k) |
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|
|
|
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def multilabel_accuracy( |
|
preds: Tensor, |
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target: Tensor, |
|
num_labels: int, |
|
threshold: float = 0.5, |
|
average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro", |
|
multidim_average: Literal["global", "samplewise"] = "global", |
|
ignore_index: Optional[int] = None, |
|
validate_args: bool = True, |
|
) -> Tensor: |
|
r"""Compute `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. 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 |
|
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 torch import tensor |
|
>>> from torchmetrics.functional.classification import multilabel_accuracy |
|
>>> target = tensor([[0, 1, 0], [1, 0, 1]]) |
|
>>> preds = 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 = tensor([[0, 1, 0], [1, 0, 1]]) |
|
>>> preds = 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 = 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_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], |
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[0.0000, 0.0000, 0.5000]]) |
|
|
|
""" |
|
if validate_args: |
|
_multilabel_stat_scores_arg_validation(num_labels, threshold, average, multidim_average, ignore_index) |
|
_multilabel_stat_scores_tensor_validation(preds, target, num_labels, multidim_average, ignore_index) |
|
preds, target = _multilabel_stat_scores_format(preds, target, num_labels, threshold, ignore_index) |
|
tp, fp, tn, fn = _multilabel_stat_scores_update(preds, target, multidim_average) |
|
return _accuracy_reduce(tp, fp, tn, fn, average=average, multidim_average=multidim_average, multilabel=True) |
|
|
|
|
|
def accuracy( |
|
preds: Tensor, |
|
target: Tensor, |
|
task: Literal["binary", "multiclass", "multilabel"], |
|
threshold: float = 0.5, |
|
num_classes: Optional[int] = None, |
|
num_labels: Optional[int] = None, |
|
average: Literal["micro", "macro", "weighted", "none"] = "micro", |
|
multidim_average: Literal["global", "samplewise"] = "global", |
|
top_k: Optional[int] = 1, |
|
ignore_index: Optional[int] = None, |
|
validate_args: bool = True, |
|
) -> Tensor: |
|
r"""Compute `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:`~torchmetrics.functional.classification.binary_accuracy`, |
|
:func:`~torchmetrics.functional.classification.multiclass_accuracy` and |
|
:func:`~torchmetrics.functional.classification.multilabel_accuracy` for the specific details of |
|
each argument influence and examples. |
|
|
|
Legacy Example: |
|
>>> from torch import tensor |
|
>>> target = tensor([0, 1, 2, 3]) |
|
>>> preds = tensor([0, 2, 1, 3]) |
|
>>> accuracy(preds, target, task="multiclass", num_classes=4) |
|
tensor(0.5000) |
|
|
|
>>> target = tensor([0, 1, 2]) |
|
>>> preds = 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) |
|
|
|
""" |
|
task = ClassificationTask.from_str(task) |
|
|
|
if task == ClassificationTask.BINARY: |
|
return binary_accuracy(preds, target, threshold, multidim_average, ignore_index, validate_args) |
|
if task == ClassificationTask.MULTICLASS: |
|
if not isinstance(num_classes, int): |
|
raise ValueError( |
|
f"Optional arg `num_classes` must be type `int` when task is {task}. Got {type(num_classes)}" |
|
) |
|
if not isinstance(top_k, int): |
|
raise ValueError(f"Optional arg `top_k` must be type `int` when task is {task}. Got {type(top_k)}") |
|
return multiclass_accuracy( |
|
preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args |
|
) |
|
if task == ClassificationTask.MULTILABEL: |
|
if not isinstance(num_labels, int): |
|
raise ValueError( |
|
f"Optional arg `num_labels` must be type `int` when task is {task}. Got {type(num_labels)}" |
|
) |
|
return multilabel_accuracy( |
|
preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args |
|
) |
|
raise ValueError(f"Not handled value: {task}") |
|
|