jaccard.py

# Copyright The PyTorch Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Any, Optional

import torch
from torch import Tensor

from torchmetrics.classification.confusion_matrix import ConfusionMatrix
from torchmetrics.functional.classification.jaccard import _jaccard_from_confmat


class JaccardIndex(ConfusionMatrix):
    r"""Computes Intersection over union, or `Jaccard index`_:

    .. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|}

    Where: :math:`A` and :math:`B` are both tensors of the same size, containing integer class values.
    They may be subject to conversion from input data (see description below). Note that it is different from box IoU.

    Works with binary, multiclass and multi-label data.
    Accepts probabilities from a model output or integer class values in prediction.
    Works with multi-dimensional preds and target.

    Forward accepts

    - ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
    - ``target`` (long tensor): ``(N, ...)``

    If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument
    to convert into integer labels. This is the case for binary and multi-label probabilities.

    If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.

    Args:
        num_classes: Number of classes in the dataset.
        average:
            Defines the reduction that is applied. Should be one of the following:

            - ``'macro'`` [default]: Calculate the metric for each class separately, and average the
              metrics across classes (with equal weights for each class).
            - ``'micro'``: Calculate the metric globally, across all samples and classes.
            - ``'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. Note that if a given class doesn't occur in the
              `preds` or `target`, the value for the class will be ``nan``.

        ignore_index: optional int specifying a target class to ignore. If given, this class index does not contribute
            to the returned score, regardless of reduction method. Has no effect if given an int that is not in the
            range [0, num_classes-1]. By default, no index is ignored, and all classes are used.
        absent_score: score to use for an individual class, if no instances of the class index were present in
            ``preds`` AND no instances of the class index were present in ``target``. For example, if we have 3 classes,
            [0, 0] for ``preds``, and [0, 2] for ``target``, then class 1 would be assigned the `absent_score`.
        threshold: Threshold value for binary or multi-label probabilities.
        multilabel: determines if data is multilabel or not.
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example:
        >>> from torchmetrics import JaccardIndex
        >>> target = torch.randint(0, 2, (10, 25, 25))
        >>> pred = torch.tensor(target)
        >>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15]
        >>> jaccard = JaccardIndex(num_classes=2)
        >>> jaccard(pred, target)
        tensor(0.9660)

    """
    is_differentiable: bool = False
    higher_is_better: bool = True
    full_state_update: bool = False

    def __init__(
        self,
        num_classes: int,
        average: Optional[str] = "macro",
        ignore_index: Optional[int] = None,
        absent_score: float = 0.0,
        threshold: float = 0.5,
        multilabel: bool = False,
        **kwargs: Any,
    ) -> None:
        kwargs["normalize"] = kwargs.get("normalize")

        super().__init__(
            num_classes=num_classes,
            threshold=threshold,
            multilabel=multilabel,
            **kwargs,
        )
        self.average = average
        self.ignore_index = ignore_index
        self.absent_score = absent_score

    def compute(self) -> Tensor:
        """Computes intersection over union (IoU)"""

        if self.multilabel:
            return torch.stack(
                [
                    _jaccard_from_confmat(
                        confmat,
                        2,
                        self.average,
                        self.ignore_index,
                        self.absent_score,
                    )[1]
                    for confmat in self.confmat
                ]
            )
        else:
            return _jaccard_from_confmat(
                self.confmat,
                self.num_classes,
                self.average,
                self.ignore_index,
                self.absent_score,
            )