# Copyright The Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional from torch import Tensor from typing_extensions import Literal from torchmetrics.functional.classification.stat_scores import ( _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, ) from torchmetrics.utilities.compute import _adjust_weights_safe_divide, _safe_divide from torchmetrics.utilities.enums import ClassificationTask def _accuracy_reduce( tp: Tensor, fp: Tensor, tn: Tensor, fn: Tensor, average: Optional[Literal["binary", "micro", "macro", "weighted", "none"]], multidim_average: Literal["global", "samplewise"] = "global", multilabel: bool = False, top_k: int = 1, ) -> Tensor: """Reduce 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 average: Defines the reduction that is applied over labels. Should be one of the following: - ``binary``: for binary reduction - ``micro``: sum score over all classes/labels - ``macro``: salculate score for each class/label and average them - ``weighted``: calculates score for each class/label and computes weighted average using their support - ``"none"`` or ``None``: calculates score for each class/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. multilabel: If input is multilabel or not top_k: value for top-k accuracy, else 1 Returns: Accuracy score """ if average == "binary": return _safe_divide(tp + tn, tp + tn + fp + fn) if average == "micro": tp = tp.sum(dim=0 if multidim_average == "global" else 1) fn = fn.sum(dim=0 if multidim_average == "global" else 1) if multilabel: fp = fp.sum(dim=0 if multidim_average == "global" else 1) tn = tn.sum(dim=0 if multidim_average == "global" else 1) return _safe_divide(tp + tn, tp + tn + fp + fn) return _safe_divide(tp, tp + fn) score = _safe_divide(tp + tn, tp + tn + fp + fn) if multilabel else _safe_divide(tp, tp + fn) return _adjust_weights_safe_divide(score, average, multilabel, tp, fp, fn, top_k) def binary_accuracy( preds: Tensor, target: Tensor, threshold: float = 0.5, multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, ) -> Tensor: r"""Compute `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. Additionally, 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 torch import tensor >>> from torchmetrics.functional.classification import binary_accuracy >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = 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]]]) >>> binary_accuracy(preds, target, multidim_average='samplewise') tensor([0.3333, 0.1667]) """ if validate_args: _binary_stat_scores_arg_validation(threshold, multidim_average, ignore_index) _binary_stat_scores_tensor_validation(preds, target, multidim_average, ignore_index) preds, target = _binary_stat_scores_format(preds, target, threshold, ignore_index) tp, fp, tn, fn = _binary_stat_scores_update(preds, target, multidim_average) return _accuracy_reduce(tp, fp, tn, fn, average="binary", multidim_average=multidim_average) def multiclass_accuracy( preds: Tensor, target: Tensor, num_classes: Optional[int] = None, average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro", top_k: int = 1, multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, ) -> Tensor: r"""Compute `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 specifying 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 torch import tensor >>> from torchmetrics.functional.classification import multiclass_accuracy >>> target = tensor([2, 1, 0, 0]) >>> preds = 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 = tensor([2, 1, 0, 0]) >>> preds = 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 = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = 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]]) """ if validate_args: _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( preds, target, num_classes or 1, top_k, average, multidim_average, ignore_index ) return _accuracy_reduce(tp, fp, tn, fn, average=average, multidim_average=multidim_average, top_k=top_k) def multilabel_accuracy( preds: Tensor, 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], [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}")