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"""
Lovasz-Softmax and Jaccard hinge loss in PyTorch
Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License)
"""
from __future__ import print_function, division
from typing import Optional
import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE
try:
from itertools import ifilterfalse
except ImportError: # py3k
from itertools import filterfalse as ifilterfalse
__all__ = ["LovaszLoss"]
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)
ignore: label to ignore
"""
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
# --------------------------- MULTICLASS LOSSES ---------------------------
def _lovasz_softmax(probas, labels, classes="present", 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
)
return loss
def _lovasz_softmax_flat(probas, labels, classes="present"):
"""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:
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
# --------------------------- HELPER FUNCTIONS ---------------------------
def isnan(x):
return x != x
def mean(values, ignore_nan=False, empty=0):
"""Nanmean compatible with generators."""
values = iter(values)
if ignore_nan:
values = ifilterfalse(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
class LovaszLoss(_Loss):
def __init__(
self,
mode: str,
per_image: bool = False,
ignore_index: Optional[int] = None,
from_logits: bool = True,
):
"""Lovasz loss for image 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
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, per_image=self.per_image, ignore=self.ignore_index
)
else:
raise ValueError("Wrong mode {}.".format(self.mode))
return loss
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