# 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 collections.abc import Sequence from typing import Any, Optional, Union import torch from torch import Tensor from typing_extensions import Literal from torchmetrics.classification.base import _ClassificationTaskWrapper from torchmetrics.functional.classification.exact_match import ( _exact_match_reduce, _multiclass_exact_match_update, _multilabel_exact_match_update, ) from torchmetrics.functional.classification.stat_scores import ( _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, ) from torchmetrics.metric import Metric from torchmetrics.utilities.data import dim_zero_cat from torchmetrics.utilities.enums import ClassificationTaskNoBinary from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["MulticlassExactMatch.plot", "MultilabelExactMatch.plot"] class MulticlassExactMatch(Metric): r"""Compute 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,)`` If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present, which the reduction will then be applied over instead of the sample dimension ``N``. Args: num_classes: Integer specifying 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 torch import tensor >>> from torchmetrics.classification import MulticlassExactMatch >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = 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 = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = 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.]) """ total: Tensor is_differentiable: bool = False higher_is_better: bool = True full_state_update: bool = False plot_lower_bound: float = 0.0 plot_upper_bound: float = 1.0 plot_legend_name: str = "Class" def __init__( self, num_classes: int, multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any, ) -> None: super().__init__(**kwargs) top_k, average = 1, None if validate_args: _multiclass_stat_scores_arg_validation(num_classes, top_k, average, multidim_average, ignore_index) self.num_classes = num_classes self.multidim_average = multidim_average self.ignore_index = ignore_index self.validate_args = validate_args self.add_state( "correct", torch.zeros(1, dtype=torch.long) if self.multidim_average == "global" else [], dist_reduce_fx="sum" if self.multidim_average == "global" else "cat", ) self.add_state( "total", torch.zeros(1, dtype=torch.long), dist_reduce_fx="sum" if self.multidim_average == "global" else "mean", ) def update(self, preds: Tensor, target: Tensor) -> None: """Update metric states with predictions and targets.""" if self.validate_args: _multiclass_stat_scores_tensor_validation( preds, target, self.num_classes, self.multidim_average, self.ignore_index ) preds, target = _multiclass_stat_scores_format(preds, target, 1) correct, total = _multiclass_exact_match_update(preds, target, self.multidim_average, self.ignore_index) if self.multidim_average == "samplewise": if not isinstance(self.correct, list): raise TypeError("Expected `self.correct` to be a list in samplewise mode.") self.correct.append(correct) if not isinstance(self.total, Tensor): raise TypeError("Expected `self.total` to be a Tensor in samplewise mode.") self.total = total else: if not isinstance(self.correct, Tensor): raise TypeError("Expected `self.correct` to be a tensor in global mode.") self.correct += correct if not isinstance(self.total, Tensor): raise TypeError("Expected `self.total` to be a Tensor in samplewise mode.") self.total += total def compute(self) -> Tensor: """Compute metric.""" correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct # Validate that `correct` and `total` are tensors if not isinstance(correct, Tensor) or not isinstance(self.total, Tensor): raise TypeError("Expected `correct` and `total` to be tensors after processing.") return _exact_match_reduce(correct, self.total) def plot( self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None ) -> _PLOT_OUT_TYPE: """Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value per class >>> from torch import randint >>> from torchmetrics.classification import MulticlassExactMatch >>> metric = MulticlassExactMatch(num_classes=3) >>> metric.update(randint(3, (20,5)), randint(3, (20,5))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassExactMatch >>> metric = MulticlassExactMatch(num_classes=3) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,5)), randint(3, (20,5)))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax) class MultilabelExactMatch(Metric): r"""Compute 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. Additionally, 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,)`` If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present, which the reduction will then be applied over instead of the sample dimension ``N``. Args: num_labels: Integer specifying 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 torch import tensor >>> from torchmetrics.classification import MultilabelExactMatch >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = 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]]]) >>> metric = MultilabelExactMatch(num_labels=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0., 0.]) """ total: Tensor is_differentiable: bool = False higher_is_better: bool = True full_state_update: bool = False plot_lower_bound: float = 0.0 plot_upper_bound: float = 1.0 plot_legend_name: str = "Label" def __init__( self, num_labels: int, threshold: float = 0.5, multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any, ) -> None: super().__init__(**kwargs) if validate_args: _multilabel_stat_scores_arg_validation( num_labels, threshold, average=None, multidim_average=multidim_average, ignore_index=ignore_index ) self.num_labels = num_labels self.threshold = threshold self.multidim_average = multidim_average self.ignore_index = ignore_index self.validate_args = validate_args self.add_state( "correct", torch.zeros(1, dtype=torch.long) if self.multidim_average == "global" else [], dist_reduce_fx="sum" if self.multidim_average == "global" else "cat", ) self.add_state( "total", torch.zeros(1, dtype=torch.long), dist_reduce_fx="sum" if self.multidim_average == "global" else "mean", ) def update(self, preds: Tensor, target: Tensor) -> None: """Update state with predictions and targets.""" if self.validate_args: _multilabel_stat_scores_tensor_validation( preds, target, self.num_labels, self.multidim_average, self.ignore_index ) preds, target = _multilabel_stat_scores_format( preds, target, self.num_labels, self.threshold, self.ignore_index ) correct, total = _multilabel_exact_match_update(preds, target, self.num_labels, self.multidim_average) if self.multidim_average == "samplewise": if not isinstance(self.correct, list): raise TypeError("Expected `self.correct` to be a list in samplewise mode.") self.correct.append(correct) if not isinstance(self.total, Tensor): raise TypeError("Expected `self.total` to be a Tensor in samplewise mode.") self.total = total else: if not isinstance(self.correct, Tensor): raise TypeError("Expected `self.correct` to be a tensor in global mode.") self.correct += correct if not isinstance(self.total, Tensor): raise TypeError("Expected `self.total` to be a Tensor in samplewise mode.") self.total += total def compute(self) -> Tensor: """Compute metric.""" correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct # Validate that `correct` and `total` are tensors if not isinstance(correct, Tensor) or not isinstance(self.total, Tensor): raise TypeError("Expected `correct` and `total` to be tensors after processing.") return _exact_match_reduce(correct, self.total) def plot( self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None ) -> _PLOT_OUT_TYPE: """Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> from torch import rand, randint >>> from torchmetrics.classification import MultilabelExactMatch >>> metric = MultilabelExactMatch(num_labels=3) >>> metric.update(randint(2, (20, 3, 5)), randint(2, (20, 3, 5))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> from torch import rand, randint >>> from torchmetrics.classification import MultilabelExactMatch >>> metric = MultilabelExactMatch(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(randint(2, (20, 3, 5)), randint(2, (20, 3, 5)))) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax) class ExactMatch(_ClassificationTaskWrapper): r"""Compute 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 :class:`~torchmetrics.classification.MulticlassExactMatch` and :class:`~torchmetrics.classification.MultilabelExactMatch` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = 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 = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = 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.]) """ def __new__( # type: ignore[misc] cls: type["ExactMatch"], task: Literal["binary", "multiclass", "multilabel"], threshold: float = 0.5, num_classes: Optional[int] = None, num_labels: Optional[int] = None, multidim_average: Literal["global", "samplewise"] = "global", ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any, ) -> Metric: """Initialize task metric.""" task = ClassificationTaskNoBinary.from_str(task) kwargs.update({ "multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args, }) if task == ClassificationTaskNoBinary.MULTICLASS: if not isinstance(num_classes, int): raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`") return MulticlassExactMatch(num_classes, **kwargs) if task == ClassificationTaskNoBinary.MULTILABEL: if not isinstance(num_labels, int): raise ValueError(f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`") return MultilabelExactMatch(num_labels, threshold, **kwargs) raise ValueError(f"Task {task} not supported!")