Source code for torch_uncertainty.metrics.classification.categorical_nll

from typing import Any, Literal

import torch
import torch.nn.functional as F
from torch import Tensor
from torchmetrics import Metric
from torchmetrics.utilities.data import dim_zero_cat


class CategoricalNLL(Metric):
    is_differentiable = False
    higher_is_better = False
    full_state_update = False

    def __init__(
        self,
        reduction: Literal["mean", "sum", "none", None] = "mean",
        **kwargs: Any,
    ) -> None:
        """The Negative Log Likelihood Metric.

        Args:
            reduction (str, optional): Determines how to reduce over the
                :math:`B`/batch dimension:

                - ``'mean'`` [default]: Averages score across samples
                - ``'sum'``: Sum score across samples
                - ``'none'`` or ``None``: Returns score per sample

            kwargs: Additional keyword arguments, see `Advanced metric settings
                <https://torchmetrics.readthedocs.io/en/stable/pages/overview.html#metric-kwargs>`_.

        Inputs:
            - :attr:`probs`: :math:`(B, C)`
            - :attr:`target`: :math:`(B)`

            where :math:`B` is the batch size and :math:`C` is the number of
            classes.

        Warning:
            Make sure that the probabilities in :attr:`probs` are normalized to sum
            to one.

        Raises:
            ValueError:
                If :attr:`reduction` is not one of ``'mean'``, ``'sum'``,
                ``'none'`` or ``None``.
        """
        super().__init__(**kwargs)

        allowed_reduction = ("sum", "mean", "none", None)
        if reduction not in allowed_reduction:
            raise ValueError(
                "Expected argument `reduction` to be one of ",
                f"{allowed_reduction} but got {reduction}",
            )

        self.reduction = reduction

        if self.reduction in ["mean", "sum"]:
            self.add_state(
                "values",
                default=torch.tensor(0.0),
                dist_reduce_fx="sum",
            )
        else:
            self.add_state("values", default=[], dist_reduce_fx="cat")
        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")

    def update(self, probs: Tensor, target: Tensor) -> None:
        """Update state with prediction probabilities and targets.

        Args:
            probs (Tensor): Probabilities from the model.
            target (Tensor): Ground truth labels.
        """
        if self.reduction is None or self.reduction == "none":
            self.values.append(
                F.nll_loss(torch.log(probs), target, reduction="none")
            )
        else:
            self.values += F.nll_loss(torch.log(probs), target, reduction="sum")
            self.total += target.size(0)

    def compute(self) -> Tensor:
        """Computes NLL based on inputs passed in to ``update`` previously."""
        values = dim_zero_cat(self.values)

        if self.reduction == "sum":
            return values.sum(dim=-1)
        if self.reduction == "mean":
            return values.sum(dim=-1) / self.total
        # reduction is None or "none"
        return values