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Dataset-aware loss functions
Implements Critical Fix #2: Dataset-Aware Loss Function
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, Optional
class DiceLoss(nn.Module):
"""Dice loss for segmentation"""
def __init__(self, smooth: float = 1.0):
"""
Initialize Dice loss
Args:
smooth: Smoothing factor to avoid division by zero
"""
super().__init__()
self.smooth = smooth
def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Compute Dice loss
Args:
pred: Predicted probabilities (B, 1, H, W)
target: Ground truth mask (B, 1, H, W)
Returns:
Dice loss value
"""
pred = torch.sigmoid(pred)
# Flatten
pred_flat = pred.view(-1)
target_flat = target.view(-1)
# Dice coefficient
intersection = (pred_flat * target_flat).sum()
dice = (2. * intersection + self.smooth) / (
pred_flat.sum() + target_flat.sum() + self.smooth
)
return 1 - dice
class CombinedLoss(nn.Module):
"""
Combined BCE + Dice loss for segmentation
Dataset-aware: Only uses Dice when pixel masks are available
"""
def __init__(self,
bce_weight: float = 1.0,
dice_weight: float = 1.0):
"""
Initialize combined loss
Args:
bce_weight: Weight for BCE loss
dice_weight: Weight for Dice loss
"""
super().__init__()
self.bce_weight = bce_weight
self.dice_weight = dice_weight
self.bce_loss = nn.BCEWithLogitsLoss()
self.dice_loss = DiceLoss()
def forward(self,
pred: torch.Tensor,
target: torch.Tensor,
has_pixel_mask: bool = True) -> Dict[str, torch.Tensor]:
"""
Compute loss (dataset-aware)
Critical Fix #2: Only use Dice loss for datasets with pixel masks
Args:
pred: Predicted logits (B, 1, H, W)
target: Ground truth mask (B, 1, H, W)
has_pixel_mask: Whether dataset has pixel-level masks
Returns:
Dictionary with 'total', 'bce', and optionally 'dice' losses
"""
# BCE loss (always used)
bce = self.bce_loss(pred, target)
losses = {
'bce': bce
}
if has_pixel_mask:
# Use Dice loss only for datasets with pixel masks
dice = self.dice_loss(pred, target)
losses['dice'] = dice
losses['total'] = self.bce_weight * bce + self.dice_weight * dice
else:
# Critical Fix #2: CASIA only uses BCE
losses['total'] = self.bce_weight * bce
return losses
class DatasetAwareLoss(nn.Module):
"""
Dataset-aware loss function wrapper
Automatically determines appropriate loss based on dataset metadata
"""
def __init__(self, config):
"""
Initialize dataset-aware loss
Args:
config: Configuration object
"""
super().__init__()
self.config = config
bce_weight = config.get('loss.bce_weight', 1.0)
dice_weight = config.get('loss.dice_weight', 1.0)
self.combined_loss = CombinedLoss(
bce_weight=bce_weight,
dice_weight=dice_weight
)
def forward(self,
pred: torch.Tensor,
target: torch.Tensor,
metadata: Dict) -> Dict[str, torch.Tensor]:
"""
Compute loss with dataset awareness
Args:
pred: Predicted logits (B, 1, H, W)
target: Ground truth mask (B, 1, H, W)
metadata: Batch metadata containing 'has_pixel_mask' flags
Returns:
Dictionary with loss components
"""
# Check if batch has pixel masks
has_pixel_mask = all(m.get('has_pixel_mask', True) for m in metadata) \
if isinstance(metadata, list) else metadata.get('has_pixel_mask', True)
return self.combined_loss(pred, target, has_pixel_mask)
def get_loss_function(config) -> DatasetAwareLoss:
"""
Factory function to create loss
Args:
config: Configuration object
Returns:
Loss function instance
"""
return DatasetAwareLoss(config)
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