src/dinov2/eval/segmentation_m2f/models/losses/dice_loss.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the Apache License, Version 2.0
# found in the LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
from mmseg.models.builder import LOSSES
from mmseg.models.losses.utils import weight_reduce_loss


def dice_loss(pred, target, weight=None, eps=1e-3, reduction="mean", avg_factor=None):
    """Calculate dice loss, which is proposed in
    `V-Net: Fully Convolutional Neural Networks for Volumetric
    Medical Image Segmentation <https://arxiv.org/abs/1606.04797>`_.

    Args:
        pred (torch.Tensor): The prediction, has a shape (n, *)
        target (torch.Tensor): The learning label of the prediction,
            shape (n, *), same shape of pred.
        weight (torch.Tensor, optional): The weight of loss for each
            prediction, has a shape (n,). Defaults to None.
        eps (float): Avoid dividing by zero. Default: 1e-3.
        reduction (str, optional): The method used to reduce the loss into
            a scalar. Defaults to 'mean'.
            Options are "none", "mean" and "sum".
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
    """

    input = pred.flatten(1)
    target = target.flatten(1).float()

    a = torch.sum(input * target, 1)
    b = torch.sum(input * input, 1) + eps
    c = torch.sum(target * target, 1) + eps
    d = (2 * a) / (b + c)
    loss = 1 - d
    if weight is not None:
        assert weight.ndim == loss.ndim
        assert len(weight) == len(pred)
    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
    return loss


def naive_dice_loss(pred, target, weight=None, eps=1e-3, reduction="mean", avg_factor=None):
    """Calculate naive dice loss, the coefficient in the denominator is the
    first power instead of the second power.

    Args:
        pred (torch.Tensor): The prediction, has a shape (n, *)
        target (torch.Tensor): The learning label of the prediction,
            shape (n, *), same shape of pred.
        weight (torch.Tensor, optional): The weight of loss for each
            prediction, has a shape (n,). Defaults to None.
        eps (float): Avoid dividing by zero. Default: 1e-3.
        reduction (str, optional): The method used to reduce the loss into
            a scalar. Defaults to 'mean'.
            Options are "none", "mean" and "sum".
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
    """
    input = pred.flatten(1)
    target = target.flatten(1).float()

    a = torch.sum(input * target, 1)
    b = torch.sum(input, 1)
    c = torch.sum(target, 1)
    d = (2 * a + eps) / (b + c + eps)
    loss = 1 - d
    if weight is not None:
        assert weight.ndim == loss.ndim
        assert len(weight) == len(pred)
    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
    return loss


@LOSSES.register_module(force=True)
class DiceLoss(nn.Module):
    def __init__(self, use_sigmoid=True, activate=True, reduction="mean", naive_dice=False, loss_weight=1.0, eps=1e-3):
        """Dice Loss, there are two forms of dice loss is supported:

            - the one proposed in `V-Net: Fully Convolutional Neural
                Networks for Volumetric Medical Image Segmentation
                <https://arxiv.org/abs/1606.04797>`_.
            - the dice loss in which the power of the number in the
                denominator is the first power instead of the second
                power.

        Args:
            use_sigmoid (bool, optional): Whether to the prediction is
                used for sigmoid or softmax. Defaults to True.
            activate (bool): Whether to activate the predictions inside,
                this will disable the inside sigmoid operation.
                Defaults to True.
            reduction (str, optional): The method used
                to reduce the loss. Options are "none",
                "mean" and "sum". Defaults to 'mean'.
            naive_dice (bool, optional): If false, use the dice
                loss defined in the V-Net paper, otherwise, use the
                naive dice loss in which the power of the number in the
                denominator is the first power instead of the second
                power.Defaults to False.
            loss_weight (float, optional): Weight of loss. Defaults to 1.0.
            eps (float): Avoid dividing by zero. Defaults to 1e-3.
        """

        super(DiceLoss, self).__init__()
        self.use_sigmoid = use_sigmoid
        self.reduction = reduction
        self.naive_dice = naive_dice
        self.loss_weight = loss_weight
        self.eps = eps
        self.activate = activate

    def forward(self, pred, target, weight=None, reduction_override=None, avg_factor=None):
        """Forward function.

        Args:
            pred (torch.Tensor): The prediction, has a shape (n, *).
            target (torch.Tensor): The label of the prediction,
                shape (n, *), same shape of pred.
            weight (torch.Tensor, optional): The weight of loss for each
                prediction, has a shape (n,). Defaults to None.
            avg_factor (int, optional): Average factor that is used to average
                the loss. Defaults to None.
            reduction_override (str, optional): The reduction method used to
                override the original reduction method of the loss.
                Options are "none", "mean" and "sum".

        Returns:
            torch.Tensor: The calculated loss
        """

        assert reduction_override in (None, "none", "mean", "sum")
        reduction = reduction_override if reduction_override else self.reduction

        if self.activate:
            if self.use_sigmoid:
                pred = pred.sigmoid()
            else:
                raise NotImplementedError

        if self.naive_dice:
            loss = self.loss_weight * naive_dice_loss(
                pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor
            )
        else:
            loss = self.loss_weight * dice_loss(
                pred, target, weight, eps=self.eps, reduction=reduction, avg_factor=avg_factor
            )

        return loss