# 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, List, Optional, Union from torch import Tensor from typing_extensions import Literal from torchmetrics.functional.segmentation.dice import ( _dice_score_compute, _dice_score_update, _dice_score_validate_args, ) from torchmetrics.metric import Metric from torchmetrics.utilities import rank_zero_warn from torchmetrics.utilities.data import dim_zero_cat from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["DiceScore.plot"] class DiceScore(Metric): r"""Compute `Dice Score`_. The metric can be used to evaluate the performance of image segmentation models. The Dice Score is defined as: ..math:: DS = \frac{2 \sum_{i=1}^{N} t_i p_i}{\sum_{i=1}^{N} t_i + \sum_{i=1}^{N} p_i} where :math:`N` is the number of classes, :math:`t_i` is the target tensor, and :math:`p_i` is the prediction tensor. In general the Dice Score can be interpreted as the overlap between the prediction and target tensors divided by the total number of elements in the tensors. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): An one-hot boolean tensor of shape ``(N, C, ...)`` with ``N`` being the number of samples and ``C`` the number of classes. Alternatively, an integer tensor of shape ``(N, ...)`` can be provided, where the integer values correspond to the class index. The input type can be controlled with the ``input_format`` argument. - ``target`` (:class:`~torch.Tensor`): An one-hot boolean tensor of shape ``(N, C, ...)`` with ``N`` being the number of samples and ``C`` the number of classes. Alternatively, an integer tensor of shape ``(N, ...)`` can be provided, where the integer values correspond to the class index. The input type can be controlled with the ``input_format`` argument. As output to ``forward`` and ``compute`` the metric returns the following output: - ``gds`` (:class:`~torch.Tensor`): The dice score. If ``average`` is set to ``None`` or ``"none"`` the output will be a tensor of shape ``(C,)`` with the dice score for each class. If ``average`` is set to ``"micro"``, ``"macro"``, or ``"weighted"`` the output will be a scalar tensor. The score is an average over all samples. Args: num_classes: The number of classes in the segmentation problem. include_background: Whether to include the background class in the computation. average: The method to average the dice score. Options are ``"micro"``, ``"macro"``, ``"weighted"``, ``"none"`` or ``None``. This determines how to average the dice score across different classes. aggregation_level: The level at which to aggregate the dice score. Options are ``"samplewise"`` or ``"global"``. For ``"samplewise"`` the dice score is computed for each sample and then averaged. For ``"global"`` the dice score is computed globally over all samples. input_format: What kind of input the function receives. Choose between ``"one-hot"`` for one-hot encoded tensors, ``"index"`` for index tensors or ``"mixed"`` for one one-hot encoded and one index tensor zero_division: The value to return when there is a division by zero. Options are 1.0, 0.0, "warn" or "nan". Setting it to "warn" behaves like 0.0 but will also create a warning. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``num_classes`` is not a positive integer ValueError: If ``include_background`` is not a boolean ValueError: If ``average`` is not one of ``"micro"``, ``"macro"``, ``"weighted"``, ``"none"`` or ``None`` ValueError: If ``input_format`` is not one of ``"one-hot"``, ``"index"`` or ``"mixed"`` Example: >>> from torch import randint >>> from torchmetrics.segmentation import DiceScore >>> preds = randint(0, 2, (4, 5, 16, 16)) # 4 samples, 5 classes, 16x16 prediction >>> target = randint(0, 2, (4, 5, 16, 16)) # 4 samples, 5 classes, 16x16 target >>> dice_score = DiceScore(num_classes=5, average="micro") >>> dice_score(preds, target) tensor(0.4941) >>> dice_score = DiceScore(num_classes=5, average="none") >>> dice_score(preds, target) tensor([0.4860, 0.4999, 0.5014, 0.4885, 0.4915]) """ full_state_update: bool = False is_differentiable: bool = False higher_is_better: bool = True plot_lower_bound: float = 0.0 plot_upper_bound: float = 1.0 numerator: List[Tensor] denominator: List[Tensor] support: List[Tensor] def __init__( self, num_classes: int, include_background: bool = True, average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro", aggregation_level: Optional[Literal["samplewise", "global"]] = "samplewise", input_format: Literal["one-hot", "index", "mixed"] = "one-hot", **kwargs: Any, ) -> None: super().__init__(**kwargs) if average == "micro": rank_zero_warn( "DiceScore metric currently defaults to `average=micro`, but will change to" "`average=macro` in the v1.9 release." " If you've explicitly set this parameter, you can ignore this warning.", UserWarning, ) _dice_score_validate_args(num_classes, include_background, average, input_format, aggregation_level) self.num_classes = num_classes self.include_background = include_background self.average = average self.aggregation_level = aggregation_level self.input_format = input_format num_classes = num_classes - 1 if not include_background else num_classes self.add_state("numerator", [], dist_reduce_fx="cat") self.add_state("denominator", [], dist_reduce_fx="cat") self.add_state("support", [], dist_reduce_fx="cat") def update(self, preds: Tensor, target: Tensor) -> None: """Update the state with new data.""" numerator, denominator, support = _dice_score_update( preds, target, self.num_classes, self.include_background, self.input_format ) self.numerator.append(numerator) self.denominator.append(denominator) self.support.append(support) def compute(self) -> Tensor: """Computes the Dice Score.""" return _dice_score_compute( dim_zero_cat(self.numerator), dim_zero_cat(self.denominator), self.average, self.aggregation_level, support=dim_zero_cat(self.support) if self.average == "weighted" else None, ).nanmean(dim=0) def plot(self, val: Union[Tensor, Sequence[Tensor], None] = 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.segmentation import DiceScore >>> metric = DiceScore(num_classes=3) >>> metric.update(torch.randint(0, 2, (10, 3, 128, 128)), torch.randint(0, 2, (10, 3, 128, 128))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.segmentation import DiceScore >>> metric = DiceScore(num_classes=3) >>> values = [ ] >>> for _ in range(10): ... values.append( ... metric(torch.randint(0, 2, (10, 3, 128, 128)), torch.randint(0, 2, (10, 3, 128, 128))) ... ) >>> fig_, ax_ = metric.plot(values) """ return self._plot(val, ax)