Source code for catalyst.utils.metrics.focal

import torch
import torch.nn.functional as F


def sigmoid_focal_loss(
    outputs: torch.Tensor,
    targets: torch.Tensor,
    gamma: float = 2.0,
    alpha: float = 0.25,
    reduction: str = "mean"
):
    """
    Compute binary focal loss between target and output logits.

    Source https://github.com/BloodAxe/pytorch-toolbelt
    See :class:`~pytorch_toolbelt.losses` for details.

    Args:
        outputs: Tensor of arbitrary shape
        targets: Tensor of the same shape as input
        reduction (string, optional):
            Specifies the reduction to apply to the output:
            "none" | "mean" | "sum" | "batchwise_mean".
            "none": no reduction will be applied,
            "mean": the sum of the output will be divided by the number of
            elements in the output,
            "sum": the output will be summed.

    See https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/loss/losses.py  # noqa: E501
    """
    targets = targets.type(outputs.type())

    logpt = -F.binary_cross_entropy_with_logits(
        outputs, targets, reduction="none"
    )
    pt = torch.exp(logpt)

    # compute the loss
    loss = -((1 - pt).pow(gamma)) * logpt

    if alpha is not None:
        loss = loss * (alpha * targets + (1 - alpha) * (1 - targets))

    if reduction == "mean":
        loss = loss.mean()
    if reduction == "sum":
        loss = loss.sum()
    if reduction == "batchwise_mean":
        loss = loss.sum(0)

    return loss


def reduced_focal_loss(
    outputs: torch.Tensor,
    targets: torch.Tensor,
    threshold: float = 0.5,
    gamma: float = 2.0,
    reduction="mean"
):
    """
    Compute reduced focal loss between target and output logits.

    Source https://github.com/BloodAxe/pytorch-toolbelt
    See :class:`~pytorch_toolbelt.losses` for details.

    Args:
        outputs: Tensor of arbitrary shape
        targets: Tensor of the same shape as input
        reduction (string, optional):
            Specifies the reduction to apply to the output:
            "none" | "mean" | "sum" | "batchwise_mean".

            "none": no reduction will be applied,
            "mean": the sum of the output will be divided by the number of
            elements in the output,
            "sum": the output will be summed.

            Note: :attr:`size_average` and :attr:`reduce`
            are in the process of being deprecated,
            and in the meantime, specifying either of those two args
            will override :attr:`reduction`.

            "batchwise_mean" computes mean loss per sample in batch.
            Default: "mean"

    See https://arxiv.org/abs/1903.01347
    """
    targets = targets.type(outputs.type())

    logpt = -F.binary_cross_entropy_with_logits(
        outputs, targets, reduction="none"
    )
    pt = torch.exp(logpt)

    # compute the loss
    focal_reduction = ((1. - pt) / threshold).pow(gamma)
    focal_reduction[pt < threshold] = 1

    loss = -focal_reduction * logpt

    if reduction == "mean":
        loss = loss.mean()
    if reduction == "sum":
        loss = loss.sum()
    if reduction == "batchwise_mean":
        loss = loss.sum(0)

    return loss


__all__ = ["sigmoid_focal_loss", "reduced_focal_loss"]