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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 collections.abc import Sequence
from typing import Any, Callable, Optional, Union, no_type_check
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.classification.dice import _dice_compute
from torchmetrics.functional.classification.stat_scores import _stat_scores_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.enums import AverageMethod, MDMCAverageMethod
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["Dice.plot"]
class Dice(Metric):
r"""Compute `Dice`_.
.. math:: \text{Dice} = \frac{\text{2 * TP}}{\text{2 * TP} + \text{FP} + \text{FN}}
Where :math:`\text{TP}` and :math:`\text{FP}` represent the number of true positives and
false positives respecitively.
It is recommend set `ignore_index` to index of background class.
The reduction method (how the precision scores are aggregated) is controlled by the
``average`` parameter, and additionally by the ``mdmc_average`` parameter in the
multi-dimensional multi-class case.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): Predictions from model (probabilities, logits or labels)
- ``target`` (:class:`~torch.Tensor`): Ground truth values
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``dice`` (:class:`~torch.Tensor`): A tensor containing the dice score.
- If ``average in ['micro', 'macro', 'weighted', 'samples']``, a one-element tensor will be returned
- If ``average in ['none', None]``, the shape will be ``(C,)``, where ``C`` stands for the number of classes
Args:
num_classes:
Number of classes. Necessary for ``'macro'``, and ``None`` average methods.
threshold:
Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case
of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.
zero_division:
The value to use for the score if denominator equals zero.
average:
Defines the reduction that is applied. Should be one of the following:
- ``'micro'`` [default]: Calculate the metric globally, across all samples and classes.
- ``'macro'``: Calculate the metric for each class separately, and average the
metrics across classes (with equal weights for each class).
- ``'weighted'``: Calculate the metric for each class separately, and average the
metrics across classes, weighting each class by its support (``tp + fn``).
- ``'none'`` or ``None``: Calculate the metric for each class separately, and return
the metric for every class.
- ``'samples'``: Calculate the metric for each sample, and average the metrics
across samples (with equal weights for each sample).
.. hint::
What is considered a sample in the multi-dimensional multi-class case
depends on the value of ``mdmc_average``.
mdmc_average:
Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
``average`` parameter). Should be one of the following:
- ``None`` [default]: Should be left unchanged if your data is not multi-dimensional
multi-class.
- ``'samplewise'``: In this case, the statistics are computed separately for each
sample on the ``N`` axis, and then averaged over samples.
The computation for each sample is done by treating the flattened extra axes ``...``
as the ``N`` dimension within the sample,
and computing the metric for the sample based on that.
- ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
are flattened into a new ``N_X`` sample axis, i.e.
the inputs are treated as if they were ``(N_X, C)``.
From here on the ``average`` parameter applies as usual.
ignore_index:
Integer specifying a target class to ignore. If given, this class index does not contribute
to the returned score, regardless of reduction method. If an index is ignored, and ``average=None``
or ``'none'``, the score for the ignored class will be returned as ``nan``.
top_k:
Number of the highest probability or logit score predictions considered finding the correct label,
relevant only for (multi-dimensional) multi-class inputs. The
default value (``None``) will be interpreted as 1 for these inputs.
Should be left at default (``None``) for all other types of inputs.
multiclass:
Used only in certain special cases, where you want to treat inputs as a different type
than what they appear to be.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
.. warning::
The ``dice`` metrics is being deprecated from the classification subpackage in v1.6.0 of torchmetrics and will
be removed in v1.7.0. Please instead consider using ``f1score`` metric from the classification subpackage as it
provides the same functionality. Additionally, we are going to re-add the ``dice`` metric in the segmentation
domain in v1.6.0 with slight modifications to functionality.
Raises:
ValueError:
If ``average`` is none of ``"micro"``, ``"macro"``, ``"samples"``, ``"none"``, ``None``.
ValueError:
If ``mdmc_average`` is not one of ``None``, ``"samplewise"``, ``"global"``.
ValueError:
If ``average`` is set but ``num_classes`` is not provided.
ValueError:
If ``num_classes`` is set and ``ignore_index`` is not in the range ``[0, num_classes)``.
Example:
>>> from torch import tensor
>>> from torchmetrics.classification import Dice
>>> preds = tensor([2, 0, 2, 1])
>>> target = tensor([1, 1, 2, 0])
>>> dice = Dice(average='micro')
>>> dice(preds, target)
tensor(0.2500)
"""
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"
@no_type_check
def __init__(
self,
zero_division: int = 0,
num_classes: Optional[int] = None,
threshold: float = 0.5,
average: Optional[Literal["micro", "macro", "none"]] = "micro",
mdmc_average: Optional[str] = "global",
ignore_index: Optional[int] = None,
top_k: Optional[int] = None,
multiclass: Optional[bool] = None,
**kwargs: Any,
) -> None:
rank_zero_warn(
"The `dice` metrics is being deprecated from the classification subpackage in v1.6.0 of torchmetrics and"
" will removed in v1.7.0. Please instead consider using `f1score` metric from the classification subpackage"
" as it provides the same functionality. Additionally, we are going to re-add the `dice` metric in the"
" segmentation domain in v1.6.0 with slight modifications to functionality.",
DeprecationWarning,
)
super().__init__(**kwargs)
allowed_average = ("micro", "macro", "samples", "none", None)
if average not in allowed_average:
raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")
_reduce_options = (AverageMethod.WEIGHTED, AverageMethod.NONE, None)
if "reduce" not in kwargs:
kwargs["reduce"] = AverageMethod.MACRO if average in _reduce_options else average
if "mdmc_reduce" not in kwargs:
kwargs["mdmc_reduce"] = mdmc_average
self.reduce = average
self.mdmc_reduce = mdmc_average
self.num_classes = num_classes
self.threshold = threshold
self.multiclass = multiclass
self.ignore_index = ignore_index
self.top_k = top_k
if average not in ["micro", "macro", "samples"]:
raise ValueError(f"The `reduce` {average} is not valid.")
if mdmc_average not in [None, "samplewise", "global"]:
raise ValueError(f"The `mdmc_reduce` {mdmc_average} is not valid.")
if average == "macro" and (not num_classes or num_classes < 1):
raise ValueError("When you set `average` as 'macro', you have to provide the number of classes.")
if num_classes and ignore_index is not None and (not ignore_index < num_classes or num_classes == 1):
raise ValueError(f"The `ignore_index` {ignore_index} is not valid for inputs with {num_classes} classes")
default: Callable = list
reduce_fn: Optional[str] = "cat"
if mdmc_average != "samplewise" and average != "samples":
if average == "micro":
zeros_shape = []
elif average == "macro":
zeros_shape = [num_classes]
else:
raise ValueError(f'Wrong reduce="{average}"')
default = lambda: torch.zeros(zeros_shape, dtype=torch.long)
reduce_fn = "sum"
for s in ("tp", "fp", "tn", "fn"):
self.add_state(s, default=default(), dist_reduce_fx=reduce_fn)
self.average = average
self.zero_division = zero_division
@no_type_check
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
tp, fp, tn, fn = _stat_scores_update(
preds,
target,
reduce=self.reduce,
mdmc_reduce=self.mdmc_reduce,
threshold=self.threshold,
num_classes=self.num_classes,
top_k=self.top_k,
multiclass=self.multiclass,
ignore_index=self.ignore_index,
)
# Update states
if self.reduce != AverageMethod.SAMPLES and self.mdmc_reduce != MDMCAverageMethod.SAMPLEWISE:
self.tp += tp
self.fp += fp
self.tn += tn
self.fn += fn
else:
self.tp.append(tp)
self.fp.append(fp)
self.tn.append(tn)
self.fn.append(fn)
@no_type_check
def _get_final_stats(self) -> tuple[Tensor, Tensor, Tensor, Tensor]:
"""Perform concatenation on the stat scores if necessary, before passing them to a compute function."""
tp = torch.cat(self.tp) if isinstance(self.tp, list) else self.tp
fp = torch.cat(self.fp) if isinstance(self.fp, list) else self.fp
tn = torch.cat(self.tn) if isinstance(self.tn, list) else self.tn
fn = torch.cat(self.fn) if isinstance(self.fn, list) else self.fn
return tp, fp, tn, fn
@no_type_check
def compute(self) -> Tensor:
"""Compute metric."""
tp, fp, _, fn = self._get_final_stats()
return _dice_compute(tp, fp, fn, self.average, self.mdmc_reduce, self.zero_division)
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
>>> from torch import randint
>>> from torchmetrics.classification import Dice
>>> metric = Dice()
>>> metric.update(randint(2,(10,)), randint(2,(10,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> from torch import randint
>>> from torchmetrics.classification import Dice
>>> metric = Dice()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(randint(2,(10,)), randint(2,(10,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
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