# This File will contains Loss Function
# This File is to eable Package import, will mostly keep it blank

import os
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import warnings
warnings.simplefilter("ignore")



class DiceLoss(nn.Module):
    """Calculate dice loss."""
    def __init__(self, eps: float = 1e-9):
        super(DiceLoss, self).__init__()
        self.eps = eps
        
    def forward(self,
                logits: torch.Tensor,
                targets: torch.Tensor) -> torch.Tensor:
        
        num = targets.size(0)
        probability = torch.sigmoid(logits)
        probability = probability.view(num, -1)
        targets = targets.view(num, -1)
        assert(probability.shape == targets.shape)
    
        intersection = 2.0 * (probability * targets).sum()
        union = probability.sum() + targets.sum()
        dice_score = (intersection + self.eps) / union
        return 1.0 - dice_score

        
class BCEDiceLoss(nn.Module):
    def __init__(self):
        super(BCEDiceLoss, self).__init__()
        self.bce = nn.BCEWithLogitsLoss()
        self.dice = DiceLoss()
        
    def forward(self, 
                logits: torch.Tensor,
                targets: torch.Tensor) -> torch.Tensor:
        assert(logits.shape == targets.shape)
        dice_loss = self.dice(logits, targets)
        bce_loss = self.bce(logits, targets)
        
        return bce_loss + dice_loss
    
# This File will contains Loss Function
# This File is to eable Package import, will mostly keep it blank

import os
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import warnings
warnings.simplefilter("ignore")



class DiceLoss(nn.Module):
    """Calculate dice loss."""
    def __init__(self, eps: float = 1e-9):
        super(DiceLoss, self).__init__()
        self.eps = eps
        
    def forward(self,
                logits: torch.Tensor,
                targets: torch.Tensor) -> torch.Tensor:
        
        num = targets.size(0)
        probability = torch.sigmoid(logits)
        probability = probability.view(num, -1)
        targets = targets.view(num, -1)
        assert(probability.shape == targets.shape)
    
        intersection = 2.0 * (probability * targets).sum()
        union = probability.sum() + targets.sum()
        dice_score = (intersection + self.eps) / union
        return 1.0 - dice_score

        
class BCEDiceLoss(nn.Module):
    def __init__(self):
        super(BCEDiceLoss, self).__init__()
        self.bce = nn.BCEWithLogitsLoss()
        self.dice = DiceLoss()
        
    def forward(self, 
                logits: torch.Tensor,
                targets: torch.Tensor) -> torch.Tensor:
        assert(logits.shape == targets.shape)
        dice_loss = self.dice(logits, targets)
        bce_loss = self.bce(logits, targets)
        
        return bce_loss + dice_loss
    
# This File will contains Loss Function # This File is to eable Package import, will mostly keep it blank Calculate dice loss.