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exact_match.py
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exact_match.py
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# 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 Any, Optional, Sequence, Type, 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.])
"""
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":
self.correct.append(correct)
self.total = total
else:
self.correct += correct
self.total += total
def compute(self) -> Tensor:
"""Compute metric."""
correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct
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.])
"""
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":
self.correct.append(correct)
self.total = total
else:
self.correct += correct
self.total += total
def compute(self) -> Tensor:
"""Compute metric."""
correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct
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__(
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!")