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2267636 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 | """Segmentation losses: Lovasz-Softmax, Focal, Dice, and a combined loss."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, List
import numpy as np
def lovasz_grad(gt_sorted):
"""
Compute gradient of the Lovasz extension w.r.t sorted errors.
See Algorithm 1 in paper.
"""
p = len(gt_sorted)
gts = gt_sorted.sum()
intersection = gts - gt_sorted.float().cumsum(0)
union = gts + (1 - gt_sorted).float().cumsum(0)
jaccard = 1.0 - intersection / union
if p > 1:
jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
return jaccard
def lovasz_softmax_flat(probas, labels, classes='present', ignore_index=255):
"""Multi-class Lovasz-Softmax loss over flat [P,C] probabilities and [P] labels."""
if probas.numel() == 0:
return probas * 0.0
C = probas.size(1)
losses = []
class_to_sum = list(range(C)) if classes == 'all' else []
for c in range(C):
fg = (labels == c).float() # foreground for class c
if classes == 'present' and fg.sum() == 0:
continue
if c == ignore_index:
continue
class_to_sum.append(c) if classes != 'all' else None
errors = (fg - probas[:, c]).abs()
errors_sorted, perm = torch.sort(errors, 0, descending=True)
perm = perm.data
fg_sorted = fg[perm]
losses.append(torch.dot(errors_sorted, lovasz_grad(fg_sorted)))
if len(losses) == 0:
return probas.sum() * 0.0
return torch.stack(losses).mean()
def flatten_probas(probas, labels, ignore_index=255):
"""Flatten [B,C,H,W] preds and [B,H,W] labels to [P,C]/[P], dropping ignored pixels."""
B, C, H, W = probas.size()
probas = probas.permute(0, 2, 3, 1).contiguous().view(-1, C) # [B*H*W, C]
labels = labels.view(-1) # [B*H*W]
if ignore_index is not None:
valid = (labels != ignore_index)
probas = probas[valid]
labels = labels[valid]
return probas, labels
class LovaszSoftmaxLoss(nn.Module):
"""
Lovasz-Softmax loss for multi-class semantic segmentation.
Directly optimizes the mean IoU (Jaccard index).
"""
def __init__(self, classes='present', ignore_index=255):
super().__init__()
self.classes = classes
self.ignore_index = ignore_index
def forward(self, logits, labels):
probas = F.softmax(logits, dim=1)
probas, labels = flatten_probas(probas, labels, self.ignore_index)
return lovasz_softmax_flat(probas, labels, self.classes, self.ignore_index)
class FocalLoss(nn.Module):
"""
Focal Loss for multi-class classification.
FL(p_t) = -alpha_t * (1 - p_t)^gamma * log(p_t)
"""
def __init__(
self,
gamma: float = 2.0,
alpha: Optional[torch.Tensor] = None,
ignore_index: int = 255,
reduction: str = 'mean'
):
super().__init__()
self.gamma = gamma
self.alpha = alpha
self.ignore_index = ignore_index
self.reduction = reduction
def forward(self, logits, labels):
B, C, H, W = logits.shape
ce_loss = F.cross_entropy(
logits, labels,
weight=self.alpha,
ignore_index=self.ignore_index,
reduction='none'
) # [B, H, W]
logits_flat = logits.permute(0, 2, 3, 1).contiguous().view(-1, C) # [B*H*W, C]
labels_flat = labels.view(-1) # [B*H*W]
valid_mask = (labels_flat != self.ignore_index)
probs = F.softmax(logits_flat, dim=1) # [B*H*W, C]
labels_clamped = labels_flat.clamp(0, C-1) # clamp for safe indexing of ignored pixels
p_t = probs.gather(1, labels_clamped.unsqueeze(1)).squeeze(1) # [B*H*W]
focal_weight = (1 - p_t) ** self.gamma # [B*H*W]
focal_weight = focal_weight.view(B, H, W) # [B, H, W]
focal_loss = focal_weight * ce_loss
valid_mask_2d = (labels != self.ignore_index)
if self.reduction == 'mean':
return focal_loss[valid_mask_2d].mean() if valid_mask_2d.sum() > 0 else focal_loss.sum() * 0.0
elif self.reduction == 'sum':
return focal_loss[valid_mask_2d].sum()
else:
return focal_loss
class DiceLoss(nn.Module):
"""
Multi-class Dice loss with optional class weighting.
"""
def __init__(
self,
num_classes: int = 21,
ignore_index: int = 255,
smooth: float = 1e-6,
class_weights: Optional[torch.Tensor] = None,
reduction: str = 'mean'
):
super().__init__()
self.num_classes = num_classes
self.ignore_index = ignore_index
self.smooth = smooth
self.class_weights = class_weights
self.reduction = reduction
def forward(self, logits, labels):
probs = F.softmax(logits, dim=1) # [B, C, H, W]
valid_mask = (labels != self.ignore_index).float() # [B, H, W]
dice_loss = 0.0
valid_classes = 0
class_losses = []
for c in range(self.num_classes):
target_c = (labels == c).float() # [B, H, W]
pred_c = probs[:, c, :, :] # [B, H, W]
target_c = target_c * valid_mask
pred_c = pred_c * valid_mask
intersection = (pred_c * target_c).sum()
union = pred_c.sum() + target_c.sum()
if union > 0:
dice = (2.0 * intersection + self.smooth) / (union + self.smooth)
class_loss = 1.0 - dice
if self.class_weights is not None:
class_loss = class_loss * self.class_weights[c]
class_losses.append(class_loss)
valid_classes += 1
if valid_classes > 0:
if self.reduction == 'mean':
return torch.stack(class_losses).mean()
else:
return torch.stack(class_losses).sum()
return logits.sum() * 0.0
class CombinedSegmentationLoss(nn.Module):
"""Weighted combination of CE/Focal, Dice, and Lovasz-Softmax losses."""
def __init__(
self,
num_classes: int = 21,
ignore_index: int = 255,
weight_ce: float = 1.0,
weight_dice: float = 1.0,
weight_lovasz: float = 1.0,
use_focal: bool = True,
focal_gamma: float = 2.0,
class_weights: Optional[torch.Tensor] = None,
):
super().__init__()
self.weight_ce = weight_ce
self.weight_dice = weight_dice
self.weight_lovasz = weight_lovasz
self.use_focal = use_focal
if use_focal:
self.ce_loss = FocalLoss(
gamma=focal_gamma,
alpha=class_weights,
ignore_index=ignore_index,
)
else:
self.ce_loss = nn.CrossEntropyLoss(
weight=class_weights,
ignore_index=ignore_index,
)
self.dice_loss = DiceLoss(
num_classes=num_classes,
ignore_index=ignore_index,
class_weights=class_weights,
)
self.lovasz_loss = LovaszSoftmaxLoss(
classes='present',
ignore_index=ignore_index,
)
def forward(self, logits, labels):
loss_ce = self.ce_loss(logits, labels)
loss_dice = self.dice_loss(logits, labels)
loss_lovasz = self.lovasz_loss(logits, labels)
loss_total = (
self.weight_ce * loss_ce +
self.weight_dice * loss_dice +
self.weight_lovasz * loss_lovasz
)
return {
'loss_ce': loss_ce,
'loss_dice': loss_dice,
'loss_lovasz': loss_lovasz,
'loss_total': loss_total,
}
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