# 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 Optional import torch from torch import Tensor from torchmetrics.functional.classification.stat_scores import _reduce_stat_scores, _stat_scores_update from torchmetrics.utilities.checks import _input_squeeze from torchmetrics.utilities.enums import AverageMethod, MDMCAverageMethod def _dice_compute( tp: Tensor, fp: Tensor, fn: Tensor, average: Optional[str], mdmc_average: Optional[str], zero_division: int = 0, ) -> Tensor: """Computes dice from the stat scores: true positives, false positives, false negatives. Args: tp: True positives fp: False positives fn: False negatives average: Defines the reduction that is applied mdmc_average: Defines how averaging is done for multi-dimensional multi-class inputs (on top of the ``average`` parameter) """ numerator = 2 * tp denominator = 2 * tp + fp + fn if average == AverageMethod.MACRO and mdmc_average != MDMCAverageMethod.SAMPLEWISE: cond = tp + fp + fn == 0 numerator = numerator[~cond] denominator = denominator[~cond] if average == AverageMethod.NONE and mdmc_average != MDMCAverageMethod.SAMPLEWISE: # a class is not present if there exists no TPs, no FPs, and no FNs meaningless_indeces = torch.nonzero((tp | fn | fp) == 0).cpu() numerator[meaningless_indeces, ...] = -1 denominator[meaningless_indeces, ...] = -1 return _reduce_stat_scores( numerator=numerator, denominator=denominator, weights=None if average != "weighted" else tp + fn, average=average, mdmc_average=mdmc_average, zero_division=zero_division, ) def dice( preds: Tensor, target: Tensor, zero_division: int = 0, average: Optional[str] = "micro", mdmc_average: Optional[str] = "global", threshold: float = 0.5, top_k: Optional[int] = None, num_classes: Optional[int] = None, multiclass: Optional[bool] = None, ignore_index: Optional[int] = None, ) -> Tensor: r"""Computes `Dice`_: .. math:: \text{Dice} = \frac{\text{2 * TP}}{\text{2 * TP} + \text{FP} + \text{FN}} Where :math:`\text{TP}` and :math:`\text{FN}` represent the number of true positives and false negatives respecitively. It is recommend set `ignore_index` to index of background class. The reduction method (how the recall scores are aggregated) is controlled by the ``average`` parameter, and additionally by the ``mdmc_average`` parameter in the multi-dimensional multi-class case. Args: preds: Predictions from model (probabilities, logits or labels) target: Ground truth values 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). .. note:: What is considered a sample in the multi-dimensional multi-class case depends on the value of ``mdmc_average``. .. note:: If ``'none'`` and a given class doesn't occur in the ``preds`` or ``target``, the value for the class will be ``nan``. 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``. num_classes: Number of classes. Necessary for ``'macro'``, ``'weighted'`` 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. 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. Return: The shape of the returned tensor depends on the ``average`` parameter - 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 Raises: ValueError: If ``average`` is not one of ``"micro"``, ``"macro"``, ``"weighted"``, ``"samples"``, ``"none"`` or ``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 torchmetrics.functional import dice >>> preds = torch.tensor([2, 0, 2, 1]) >>> target = torch.tensor([1, 1, 2, 0]) >>> dice(preds, target, average='micro') tensor(0.2500) """ allowed_average = ("micro", "macro", "weighted", "samples", "none", None) if average not in allowed_average: raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.") if average in ["macro", "weighted", "none", None] and (not num_classes or num_classes < 1): raise ValueError(f"When you set `average` as {average}, you have to provide the number of classes.") allowed_mdmc_average = [None, "samplewise", "global"] if mdmc_average not in allowed_mdmc_average: raise ValueError(f"The `mdmc_average` has to be one of {allowed_mdmc_average}, got {mdmc_average}.") 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") if top_k is not None and (not isinstance(top_k, int) or top_k <= 0): raise ValueError(f"The `top_k` should be an integer larger than 0, got {top_k}") preds, target = _input_squeeze(preds, target) reduce = "macro" if average in ("weighted", "none", None) else average tp, fp, _, fn = _stat_scores_update( preds, target, reduce=reduce, mdmc_reduce=mdmc_average, threshold=threshold, num_classes=num_classes, top_k=top_k, multiclass=multiclass, ignore_index=ignore_index, ) return _dice_compute(tp, fp, fn, average, mdmc_average, zero_division)