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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.
import typing
from collections.abc import Sequence
from typing import Any, Optional, Union
from torch import Tensor
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
from torchmetrics.wrappers.abstract import WrapperMetric
if typing.TYPE_CHECKING:
from torch.nn import Module
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["ClasswiseWrapper.plot"]
class ClasswiseWrapper(WrapperMetric):
"""Wrapper metric for altering the output of classification metrics.
This metric works together with classification metrics that returns multiple values (one value per class) such that
label information can be automatically included in the output.
Args:
metric: base metric that should be wrapped. It is assumed that the metric outputs a single
tensor that is split along the first dimension.
labels: list of strings indicating the different classes.
prefix: string that is prepended to the metric names.
postfix: string that is appended to the metric names.
Example::
Basic example where the output of a metric is unwrapped into a dictionary with the class index as keys:
>>> from torch import randint, randn
>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy
>>> metric = ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None))
>>> preds = randn(10, 3).softmax(dim=-1)
>>> target = randint(3, (10,))
>>> metric(preds, target) # doctest: +NORMALIZE_WHITESPACE
{'multiclassaccuracy_0': tensor(0.5000),
'multiclassaccuracy_1': tensor(0.7500),
'multiclassaccuracy_2': tensor(0.)}
Example::
Using custom name via prefix and postfix:
>>> from torch import randint, randn
>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy
>>> metric_pre = ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None), prefix="acc-")
>>> metric_post = ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None), postfix="-acc")
>>> preds = randn(10, 3).softmax(dim=-1)
>>> target = randint(3, (10,))
>>> metric_pre(preds, target) # doctest: +NORMALIZE_WHITESPACE
{'acc-0': tensor(0.3333), 'acc-1': tensor(0.6667), 'acc-2': tensor(0.)}
>>> metric_post(preds, target) # doctest: +NORMALIZE_WHITESPACE
{'0-acc': tensor(0.3333), '1-acc': tensor(0.6667), '2-acc': tensor(0.)}
Example::
Providing labels as a list of strings:
>>> from torch import randint, randn
>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy
>>> metric = ClasswiseWrapper(
... MulticlassAccuracy(num_classes=3, average=None),
... labels=["horse", "fish", "dog"]
... )
>>> preds = randn(10, 3).softmax(dim=-1)
>>> target = randint(3, (10,))
>>> metric(preds, target) # doctest: +NORMALIZE_WHITESPACE
{'multiclassaccuracy_horse': tensor(0.),
'multiclassaccuracy_fish': tensor(0.3333),
'multiclassaccuracy_dog': tensor(0.4000)}
Example::
Classwise can also be used in combination with :class:`~torchmetrics.MetricCollection`. In this case, everything
will be flattened into a single dictionary:
>>> from torch import randint, randn
>>> from torchmetrics import MetricCollection
>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy, MulticlassRecall
>>> labels = ["horse", "fish", "dog"]
>>> metric = MetricCollection(
... {'multiclassaccuracy': ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None), labels),
... 'multiclassrecall': ClasswiseWrapper(MulticlassRecall(num_classes=3, average=None), labels)}
... )
>>> preds = randn(10, 3).softmax(dim=-1)
>>> target = randint(3, (10,))
>>> metric(preds, target) # doctest: +NORMALIZE_WHITESPACE
{'multiclassaccuracy_horse': tensor(0.6667),
'multiclassaccuracy_fish': tensor(0.3333),
'multiclassaccuracy_dog': tensor(0.5000),
'multiclassrecall_horse': tensor(0.6667),
'multiclassrecall_fish': tensor(0.3333),
'multiclassrecall_dog': tensor(0.5000)}
"""
def __init__(
self,
metric: Metric,
labels: Optional[list[str]] = None,
prefix: Optional[str] = None,
postfix: Optional[str] = None,
) -> None:
super().__init__()
if not isinstance(metric, Metric):
raise ValueError(f"Expected argument `metric` to be an instance of `torchmetrics.Metric` but got {metric}")
self.metric = metric
if labels is not None and not (isinstance(labels, list) and all(isinstance(lab, str) for lab in labels)):
raise ValueError(f"Expected argument `labels` to either be `None` or a list of strings but got {labels}")
self.labels = labels
if prefix is not None and not isinstance(prefix, str):
raise ValueError(f"Expected argument `prefix` to either be `None` or a string but got {prefix}")
self._prefix = prefix
if postfix is not None and not isinstance(postfix, str):
raise ValueError(f"Expected argument `postfix` to either be `None` or a string but got {postfix}")
self._postfix = postfix
self._update_count = 1
def _filter_kwargs(self, **kwargs: Any) -> dict[str, Any]:
"""Filter kwargs for the metric."""
return self.metric._filter_kwargs(**kwargs)
def _convert_output(self, x: Tensor) -> dict[str, Any]:
"""Convert output to dictionary with labels as keys."""
# Will set the class name as prefix if neither prefix nor postfix is given
if not self._prefix and not self._postfix:
prefix = f"{self.metric.__class__.__name__.lower()}_"
postfix = ""
else:
prefix = self._prefix or ""
postfix = self._postfix or ""
if self.labels is None:
return {f"{prefix}{i}{postfix}": val for i, val in enumerate(x)}
return {f"{prefix}{lab}{postfix}": val for lab, val in zip(self.labels, x)}
def forward(self, *args: Any, **kwargs: Any) -> Any:
"""Calculate on batch and accumulate to global state."""
return self._convert_output(self.metric(*args, **kwargs))
def update(self, *args: Any, **kwargs: Any) -> None:
"""Update state."""
self.metric.update(*args, **kwargs)
def compute(self) -> dict[str, Tensor]:
"""Compute metric."""
return self._convert_output(self.metric.compute())
def reset(self) -> None:
"""Reset metric."""
self.metric.reset()
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
>>> import torch
>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy
>>> metric = ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None))
>>> metric.update(torch.randint(3, (20,)), torch.randint(3, (20,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.wrappers import ClasswiseWrapper
>>> from torchmetrics.classification import MulticlassAccuracy
>>> metric = ClasswiseWrapper(MulticlassAccuracy(num_classes=3, average=None))
>>> values = [ ]
>>> for _ in range(3):
... values.append(metric(torch.randint(3, (20,)), torch.randint(3, (20,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
def __getattr__(self, name: str) -> Union[Tensor, "Module"]:
"""Get attribute from classwise wrapper."""
if name == "metric" or (name in self.__dict__ and name not in self.metric.__dict__):
# we need this to prevent from infinite getattribute loop.
return super().__getattr__(name)
return getattr(self.metric, name)
def __setattr__(self, name: str, value: Any) -> None:
"""Set attribute to classwise wrapper."""
if hasattr(self, "metric") and name in self.metric._defaults:
setattr(self.metric, name, value)
else:
super().__setattr__(name, value)
if name == "metric":
self._defaults = self.metric._defaults
self._persistent = self.metric._persistent
self._reductions = self.metric._reductions
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