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exact_match.py
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exact_match.py
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# Copyright The PyTorch 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
import torch
from torch import Tensor
from typing_extensions import Literal
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
class MulticlassExactMatch(Metric):
r"""Computes 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, ...)``
The influence of the additional dimension ``...`` (if present) will be determined by the `multidim_average`
argument.
Args:
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 torchmetrics.classification import MulticlassExactMatch
>>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = torch.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 = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = torch.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 = False
higher_is_better = True
full_state_update: bool = False
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, target) -> None:
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)
if self.multidim_average == "samplewise":
self.correct.append(correct)
self.total = total
else:
self.correct += correct
self.total += total
def compute(self) -> Tensor:
correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct
return _exact_match_reduce(correct, self.total)
class MultilabelExactMatch(Metric):
r"""Computes 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, ...)``
The influence of the additional dimension ``...`` (if present) will be determined by the `multidim_average`
argument.
Args:
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 torchmetrics.classification import MultilabelExactMatch
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.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 = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.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 = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = torch.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 = False
higher_is_better = True
full_state_update: bool = False
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: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Tensor with predictions
target: Tensor with true labels
"""
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:
correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct
return _exact_match_reduce(correct, self.total)
class ExactMatch:
r"""Computes 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
:mod:`MulticlassExactMatch` and :mod:`MultilabelExactMatch` for the specific details of
each argument influence and examples.
Legacy Example:
>>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = torch.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 = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = torch.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,
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:
kwargs.update(dict(multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args))
if task == "multiclass":
assert isinstance(num_classes, int)
return MulticlassExactMatch(num_classes, **kwargs)
if task == "multilabel":
assert isinstance(num_labels, int)
return MultilabelExactMatch(num_labels, threshold, **kwargs)
raise ValueError(f"Expected argument `task` to either be `'multiclass'` or `'multilabel'` but got {task}")