mmocr/models/common/losses/dice_loss.py

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn

from mmocr.models.builder import LOSSES


@LOSSES.register_module()
class DiceLoss(nn.Module):

    def __init__(self, eps=1e-6):
        super().__init__()
        assert isinstance(eps, float)
        self.eps = eps

    def forward(self, pred, target, mask=None):

        pred = pred.contiguous().view(pred.size()[0], -1)
        target = target.contiguous().view(target.size()[0], -1)

        if mask is not None:
            mask = mask.contiguous().view(mask.size()[0], -1)
            pred = pred * mask
            target = target * mask

        a = torch.sum(pred * target)
        b = torch.sum(pred)
        c = torch.sum(target)
        d = (2 * a) / (b + c + self.eps)

        return 1 - d