# 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, Optional, Union import torch from torch import Tensor from typing_extensions import Literal from torchmetrics.functional.segmentation.generalized_dice import ( _generalized_dice_compute, _generalized_dice_update, _generalized_dice_validate_args, ) from torchmetrics.metric import Metric from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["GeneralizedDiceScore.plot"] class GeneralizedDiceScore(Metric): r"""Compute `Generalized Dice Score`_. The metric can be used to evaluate the performance of image segmentation models. The Generalized Dice Score is defined as: .. math:: GDS = \frac{2 \\sum_{i=1}^{N} w_i \\sum_{j} t_{ij} p_{ij}}{ \\sum_{i=1}^{N} w_i \\sum_{j} t_{ij} + \\sum_{i=1}^{N} w_i \\sum_{j} p_{ij}} where :math:`N` is the number of classes, :math:`t_{ij}` is the target tensor, :math:`p_{ij}` is the prediction tensor, and :math:`w_i` is the weight for class :math:`i`. The weight can be computed in three different ways: - `square`: :math:`w_i = 1 / (\\sum_{j} t_{ij})^2` - `simple`: :math:`w_i = 1 / \\sum_{j} t_{ij}` - `linear`: :math:`w_i = 1` Note that the generalized dice loss can be computed as one minus the generalized dice score. 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 generalized dice score. If ``per_class`` is set to ``True``, the output will be a tensor of shape ``(C,)`` with the generalized dice score for each class. If ``per_class`` is set to ``False``, the output will be a scalar tensor. Args: num_classes: The number of classes in the segmentation problem. include_background: Whether to include the background class in the computation per_class: Whether to compute the metric for each class separately. weight_type: The type of weight to apply to each class. Can be one of ``"square"``, ``"simple"``, or ``"linear"``. 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 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 ``per_class`` is not a boolean ValueError: If ``weight_type`` is not one of ``"square"``, ``"simple"``, or ``"linear"`` ValueError: If ``input_format`` is not one of ``"one-hot"``, ``"index"`` or ``"mixed"`` Example: >>> from torch import randint >>> from torchmetrics.segmentation import GeneralizedDiceScore >>> gds = GeneralizedDiceScore(num_classes=3) >>> preds = randint(0, 2, (10, 3, 128, 128)) >>> target = randint(0, 2, (10, 3, 128, 128)) >>> gds(preds, target) tensor(0.4992) >>> gds = GeneralizedDiceScore(num_classes=3, per_class=True) >>> gds(preds, target) tensor([0.5001, 0.4993, 0.4982]) >>> gds = GeneralizedDiceScore(num_classes=3, per_class=True, include_background=False) >>> gds(preds, target) tensor([0.4993, 0.4982]) """ score: Tensor samples: Tensor 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 def __init__( self, num_classes: int, include_background: bool = True, per_class: bool = False, weight_type: Literal["square", "simple", "linear"] = "square", input_format: Literal["one-hot", "index", "mixed"] = "one-hot", **kwargs: Any, ) -> None: super().__init__(**kwargs) _generalized_dice_validate_args(num_classes, include_background, per_class, weight_type, input_format) self.num_classes = num_classes self.include_background = include_background self.per_class = per_class self.weight_type = weight_type self.input_format = input_format num_classes = num_classes - 1 if not include_background else num_classes self.add_state("score", default=torch.zeros(num_classes if per_class else 1), dist_reduce_fx="sum") self.add_state("samples", default=torch.zeros(1), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: """Update the state with new data.""" numerator, denominator = _generalized_dice_update( preds, target, self.num_classes, self.include_background, self.weight_type, self.input_format ) self.score += _generalized_dice_compute(numerator, denominator, self.per_class).sum(dim=0) self.samples += preds.shape[0] def compute(self) -> Tensor: """Compute the final generalized dice score.""" return self.score / self.samples 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 GeneralizedDiceScore >>> metric = GeneralizedDiceScore(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 GeneralizedDiceScore >>> metric = GeneralizedDiceScore(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)