Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,775 Bytes
4893ce0 |
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 |
"""
Lovasz Loss
refer https://arxiv.org/abs/1705.08790
Author: Xiaoyang Wu ([email protected])
Please cite our work if the code is helpful to you.
"""
from typing import Optional
from itertools import filterfalse
import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from .builder import LOSSES
BINARY_MODE: str = "binary"
MULTICLASS_MODE: str = "multiclass"
MULTILABEL_MODE: str = "multilabel"
def _lovasz_grad(gt_sorted):
"""Compute gradient of the Lovasz extension w.r.t sorted errors
See Alg. 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: # cover 1-pixel case
jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
return jaccard
def _lovasz_hinge(logits, labels, per_image=True, ignore=None):
"""
Binary Lovasz hinge loss
logits: [B, H, W] Logits at each pixel (between -infinity and +infinity)
labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
per_image: compute the loss per image instead of per batch
ignore: void class id
"""
if per_image:
loss = mean(
_lovasz_hinge_flat(
*_flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore)
)
for log, lab in zip(logits, labels)
)
else:
loss = _lovasz_hinge_flat(*_flatten_binary_scores(logits, labels, ignore))
return loss
def _lovasz_hinge_flat(logits, labels):
"""Binary Lovasz hinge loss
Args:
logits: [P] Logits at each prediction (between -infinity and +infinity)
labels: [P] Tensor, binary ground truth labels (0 or 1)
"""
if len(labels) == 0:
# only void pixels, the gradients should be 0
return logits.sum() * 0.0
signs = 2.0 * labels.float() - 1.0
errors = 1.0 - logits * signs
errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
perm = perm.data
gt_sorted = labels[perm]
grad = _lovasz_grad(gt_sorted)
loss = torch.dot(F.relu(errors_sorted), grad)
return loss
def _flatten_binary_scores(scores, labels, ignore=None):
"""Flattens predictions in the batch (binary case)
Remove labels equal to 'ignore'
"""
scores = scores.view(-1)
labels = labels.view(-1)
if ignore is None:
return scores, labels
valid = labels != ignore
vscores = scores[valid]
vlabels = labels[valid]
return vscores, vlabels
def _lovasz_softmax(
probas, labels, classes="present", class_seen=None, per_image=False, ignore=None
):
"""Multi-class Lovasz-Softmax loss
Args:
@param probas: [B, C, H, W] Class probabilities at each prediction (between 0 and 1).
Interpreted as binary (sigmoid) output with outputs of size [B, H, W].
@param labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
@param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
@param per_image: compute the loss per image instead of per batch
@param ignore: void class labels
"""
if per_image:
loss = mean(
_lovasz_softmax_flat(
*_flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore),
classes=classes
)
for prob, lab in zip(probas, labels)
)
else:
loss = _lovasz_softmax_flat(
*_flatten_probas(probas, labels, ignore),
classes=classes,
class_seen=class_seen
)
return loss
def _lovasz_softmax_flat(probas, labels, classes="present", class_seen=None):
"""Multi-class Lovasz-Softmax loss
Args:
@param probas: [P, C] Class probabilities at each prediction (between 0 and 1)
@param labels: [P] Tensor, ground truth labels (between 0 and C - 1)
@param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
"""
if probas.numel() == 0:
# only void pixels, the gradients should be 0
return probas * 0.0
C = probas.size(1)
losses = []
class_to_sum = list(range(C)) if classes in ["all", "present"] else classes
# for c in class_to_sum:
for c in labels.unique():
if class_seen is None:
fg = (labels == c).type_as(probas) # foreground for class c
if classes == "present" and fg.sum() == 0:
continue
if C == 1:
if len(classes) > 1:
raise ValueError("Sigmoid output possible only with 1 class")
class_pred = probas[:, 0]
else:
class_pred = probas[:, c]
errors = (fg - class_pred).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)))
else:
if c in class_seen:
fg = (labels == c).type_as(probas) # foreground for class c
if classes == "present" and fg.sum() == 0:
continue
if C == 1:
if len(classes) > 1:
raise ValueError("Sigmoid output possible only with 1 class")
class_pred = probas[:, 0]
else:
class_pred = probas[:, c]
errors = (fg - class_pred).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)))
return mean(losses)
def _flatten_probas(probas, labels, ignore=None):
"""Flattens predictions in the batch"""
if probas.dim() == 3:
# assumes output of a sigmoid layer
B, H, W = probas.size()
probas = probas.view(B, 1, H, W)
C = probas.size(1)
probas = torch.movedim(probas, 1, -1) # [B, C, Di, Dj, ...] -> [B, Di, Dj, ..., C]
probas = probas.contiguous().view(-1, C) # [P, C]
labels = labels.view(-1)
if ignore is None:
return probas, labels
valid = labels != ignore
vprobas = probas[valid]
vlabels = labels[valid]
return vprobas, vlabels
def isnan(x):
return x != x
def mean(values, ignore_nan=False, empty=0):
"""Nan-mean compatible with generators."""
values = iter(values)
if ignore_nan:
values = filterfalse(isnan, values)
try:
n = 1
acc = next(values)
except StopIteration:
if empty == "raise":
raise ValueError("Empty mean")
return empty
for n, v in enumerate(values, 2):
acc += v
if n == 1:
return acc
return acc / n
@LOSSES.register_module()
class LovaszLoss(_Loss):
def __init__(
self,
mode: str,
class_seen: Optional[int] = None,
per_image: bool = False,
ignore_index: Optional[int] = None,
loss_weight: float = 1.0,
):
"""Lovasz loss for segmentation task.
It supports binary, multiclass and multilabel cases
Args:
mode: Loss mode 'binary', 'multiclass' or 'multilabel'
ignore_index: Label that indicates ignored pixels (does not contribute to loss)
per_image: If True loss computed per each image and then averaged, else computed per whole batch
Shape
- **y_pred** - torch.Tensor of shape (N, C, H, W)
- **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W)
Reference
https://github.com/BloodAxe/pytorch-toolbelt
"""
assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE}
super().__init__()
self.mode = mode
self.ignore_index = ignore_index
self.per_image = per_image
self.class_seen = class_seen
self.loss_weight = loss_weight
def forward(self, y_pred, y_true):
if self.mode in {BINARY_MODE, MULTILABEL_MODE}:
loss = _lovasz_hinge(
y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index
)
elif self.mode == MULTICLASS_MODE:
y_pred = y_pred.softmax(dim=1)
loss = _lovasz_softmax(
y_pred,
y_true,
class_seen=self.class_seen,
per_image=self.per_image,
ignore=self.ignore_index,
)
else:
raise ValueError("Wrong mode {}.".format(self.mode))
return loss * self.loss_weight
|