rscd/losses/RSMambaLoss.py

import torch
import torch.nn as nn


class dice_loss(nn.Module):
    def __init__(self, batch=True):
        super(dice_loss, self).__init__()
        # batch equal to True means views all batch images as an entity and calculate loss
        # batch equal to False means calculate loss of every single image in batch and get their mean
        self.batch = batch

    def soft_dice_coeff(self, y_pred, y_true):
        smooth = 0.00001
        if self.batch:
            i = torch.sum(y_true)
            j = torch.sum(y_pred)
            intersection = torch.sum(y_true * y_pred)
        else:
            i = y_true.sum(1).sum(1).sum(1)
            j = y_pred.sum(1).sum(1).sum(1)
            intersection = (y_true * y_pred).sum(1).sum(1).sum(1)

        score = (2. * intersection + smooth) / (i + j + smooth)
        return score.mean()

    def soft_dice_loss(self, y_pred, y_true):
        loss = 1 - self.soft_dice_coeff(y_pred, y_true)
        return loss

    def __call__(self, y_pred, y_true):
        return self.soft_dice_loss(y_pred.to(dtype=torch.float32), y_true)


class dice_focal_loss(nn.Module):

    def __init__(self):
        super(dice_focal_loss, self).__init__()
        self.focal_loss = nn.BCEWithLogitsLoss()
        self.binnary_dice = dice_loss()

    def __call__(self, scores, labels):
        diceloss = self.binnary_dice(torch.sigmoid(scores.clone()), labels)
        foclaloss = self.focal_loss(scores.clone(), labels)

        return diceloss, foclaloss

class FCCDN_loss_without_seg(nn.Module):
    def __init__(self):
        super(FCCDN_loss_without_seg, self).__init__()
    
    def __call__(self, scores, labels):
        # scores = change_pred
        # labels = binary_cd_labels
        scores = scores.squeeze(1) if len(scores.shape) > 3 else scores
        labels = labels.squeeze(1) if len(labels.shape) > 3 else labels
        # if len(scores.shape) > 3:
        #     scores = scores.squeeze(1)
        # if len(labels.shape) > 3:
        #     labels = labels.squeeze(1)
        """ for binary change detection task"""
        criterion_change = dice_focal_loss()

        # change loss
        diceloss, foclaloss = criterion_change(scores, labels.float())

        loss_change = diceloss + foclaloss

        return loss_change.mean()