toolbox/torchaudio/losses/dice_loss.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
from typing import List, Tuple

import torch
import torch.nn as nn


class DiceLoss(nn.Module):
    def __init__(self,
                 reduction: str = "mean",
                 eps: float = 1e-6,
                 ):
        super(DiceLoss, self).__init__()
        self.reduction = reduction
        self.eps = eps

        if reduction not in ("sum", "mean"):
            raise AssertionError(f"param reduction must be sum or mean.")

    def forward(self, inputs: torch.Tensor, targets: torch.Tensor):
        """
        :param inputs: torch.Tensor, shape: [b, t, 1]. vad prob, after sigmoid activation.
        :param targets: shape as `inputs`.
        :return:
        """
        inputs_ = torch.squeeze(inputs, dim=-1)
        targets_ = torch.squeeze(targets, dim=-1)
        # shape: [b, t]

        intersection = (inputs_ * targets_).sum(dim=-1)
        union = (inputs_ + targets_).sum(dim=-1)
        # shape: [b,]

        dice = (2. * intersection + self.eps) / (union + self.eps)
        # shape: [b,]

        loss = 1. - dice
        # shape: [b,]

        if self.reduction == "mean":
            loss = torch.mean(loss)
        elif self.reduction == "sum":
            loss = torch.sum(loss)
        else:
            raise AssertionError
        return loss


def main():
    inputs = torch.zeros(size=(1, 198, 1), dtype=torch.float32)

    loss_fn = DiceLoss()

    loss = loss_fn.forward(inputs, inputs)
    print(loss)
    return


if __name__ == "__main__":
    main()