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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 Optional, Tuple
import torch
from torch import Tensor
from typing_extensions import Literal
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.utilities.compute import _safe_divide
from torchmetrics.utilities.enums import ClassificationTaskNoBinary
def _exact_match_reduce(
correct: Tensor,
total: Tensor,
) -> Tensor:
"""Reduce exact match."""
return _safe_divide(correct, total)
def _multiclass_exact_match_update(
preds: Tensor,
target: Tensor,
multidim_average: Literal["global", "samplewise"] = "global",
ignore_index: Optional[int] = None,
) -> Tuple[Tensor, Tensor]:
"""Compute the statistics."""
if ignore_index is not None:
preds = preds.clone()
preds[target == ignore_index] = ignore_index
correct = (preds == target).sum(1) == preds.shape[1]
correct = correct if multidim_average == "samplewise" else correct.sum()
total = torch.tensor(preds.shape[0] if multidim_average == "global" else 1, device=correct.device)
return correct, total
def multiclass_exact_match(
preds: Tensor,
target: Tensor,
num_classes: int,
multidim_average: Literal["global", "samplewise"] = "global",
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
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.
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 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.
Returns:
The 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,)``
Example (multidim tensors):
>>> from torch import tensor
>>> from torchmetrics.functional.classification import multiclass_exact_match
>>> 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]]])
>>> multiclass_exact_match(preds, target, num_classes=3, multidim_average='global')
tensor(0.5000)
Example (multidim tensors):
>>> from torchmetrics.functional.classification import multiclass_exact_match
>>> 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]]])
>>> multiclass_exact_match(preds, target, num_classes=3, multidim_average='samplewise')
tensor([1., 0.])
"""
top_k, average = 1, None
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)
correct, total = _multiclass_exact_match_update(preds, target, multidim_average, ignore_index)
return _exact_match_reduce(correct, total)
def _multilabel_exact_match_update(
preds: Tensor, target: Tensor, num_labels: int, multidim_average: Literal["global", "samplewise"] = "global"
) -> Tuple[Tensor, Tensor]:
"""Compute the statistics."""
if multidim_average == "global":
preds = torch.movedim(preds, 1, -1).reshape(-1, num_labels)
target = torch.movedim(target, 1, -1).reshape(-1, num_labels)
correct = ((preds == target).sum(1) == num_labels).sum(dim=-1)
total = torch.tensor(preds.shape[0 if multidim_average == "global" else 2], device=correct.device)
return correct, total
def multilabel_exact_match(
preds: Tensor,
target: Tensor,
num_labels: int,
threshold: float = 0.5,
multidim_average: Literal["global", "samplewise"] = "global",
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
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.
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
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 ``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,)``
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.functional.classification import multilabel_exact_match
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0, 0, 1], [1, 0, 1]])
>>> multilabel_exact_match(preds, target, num_labels=3)
tensor(0.5000)
Example (preds is float tensor):
>>> from torchmetrics.functional.classification import multilabel_exact_match
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> multilabel_exact_match(preds, target, num_labels=3)
tensor(0.5000)
Example (multidim tensors):
>>> from torchmetrics.functional.classification import multilabel_exact_match
>>> 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_exact_match(preds, target, num_labels=3, multidim_average='samplewise')
tensor([0., 0.])
"""
average = None
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)
correct, total = _multilabel_exact_match_update(preds, target, num_labels, multidim_average)
return _exact_match_reduce(correct, total)
def exact_match(
preds: Tensor,
target: Tensor,
task: Literal["multiclass", "multilabel"],
num_classes: Optional[int] = None,
num_labels: Optional[int] = None,
threshold: float = 0.5,
multidim_average: Literal["global", "samplewise"] = "global",
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
r"""Compute Exact match (also known as subset accuracy).
Exact Match is a stricter version of accuracy where all classes/labels have to match exactly for the sample to be
correctly classified.
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 ``'multiclass'`` or ``'multilabel'``. See the documentation of
:func:`~torchmetrics.functional.classification.multiclass_exact_match` and
:func:`~torchmetrics.functional.classification.multilabel_exact_match` 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]]])
>>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='global')
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]]])
>>> exact_match(preds, target, task="multiclass", num_classes=3, multidim_average='samplewise')
tensor([1., 0.])
"""
task = ClassificationTaskNoBinary.from_str(task)
if task == ClassificationTaskNoBinary.MULTICLASS:
assert num_classes is not None # noqa: S101 # needed for mypy
return multiclass_exact_match(preds, target, num_classes, multidim_average, ignore_index, validate_args)
if task == ClassificationTaskNoBinary.MULTILABEL:
assert num_labels is not None # noqa: S101 # needed for mypy
return multilabel_exact_match(
preds, target, num_labels, threshold, multidim_average, ignore_index, validate_args
)
raise ValueError(f"Not handled value: {task}")