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"""Modified from https://github.com/bermanmaxim/LovaszSoftmax/blob/master/pytor
ch/lovasz_losses.py Lovasz-Softmax and Jaccard hinge loss in PyTorch Maxim
Berman 2018 ESAT-PSI KU Leuven (MIT License)"""
import annotator.mmpkg.mmcv as mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import get_class_weight, weight_reduce_loss
def lovasz_grad(gt_sorted):
"""Computes 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. - intersection / union
if p > 1: # cover 1-pixel case
jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
return jaccard
def flatten_binary_logits(logits, labels, ignore_index=None):
"""Flattens predictions in the batch (binary case) Remove labels equal to
'ignore_index'."""
logits = logits.view(-1)
labels = labels.view(-1)
if ignore_index is None:
return logits, labels
valid = (labels != ignore_index)
vlogits = logits[valid]
vlabels = labels[valid]
return vlogits, vlabels
def flatten_probs(probs, labels, ignore_index=None):
"""Flattens predictions in the batch."""
if probs.dim() == 3:
# assumes output of a sigmoid layer
B, H, W = probs.size()
probs = probs.view(B, 1, H, W)
B, C, H, W = probs.size()
probs = probs.permute(0, 2, 3, 1).contiguous().view(-1, C) # B*H*W, C=P,C
labels = labels.view(-1)
if ignore_index is None:
return probs, labels
valid = (labels != ignore_index)
vprobs = probs[valid.nonzero().squeeze()]
vlabels = labels[valid]
return vprobs, vlabels
def lovasz_hinge_flat(logits, labels):
"""Binary Lovasz hinge loss.
Args:
logits (torch.Tensor): [P], logits at each prediction
(between -infty and +infty).
labels (torch.Tensor): [P], binary ground truth labels (0 or 1).
Returns:
torch.Tensor: The calculated loss.
"""
if len(labels) == 0:
# only void pixels, the gradients should be 0
return logits.sum() * 0.
signs = 2. * labels.float() - 1.
errors = (1. - 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 lovasz_hinge(logits,
labels,
classes='present',
per_image=False,
class_weight=None,
reduction='mean',
avg_factor=None,
ignore_index=255):
"""Binary Lovasz hinge loss.
Args:
logits (torch.Tensor): [B, H, W], logits at each pixel
(between -infty and +infty).
labels (torch.Tensor): [B, H, W], binary ground truth masks (0 or 1).
classes (str | list[int], optional): Placeholder, to be consistent with
other loss. Default: None.
per_image (bool, optional): If per_image is True, compute the loss per
image instead of per batch. Default: False.
class_weight (list[float], optional): Placeholder, to be consistent
with other loss. Default: None.
reduction (str, optional): The method used to reduce the loss. Options
are "none", "mean" and "sum". This parameter only works when
per_image is True. Default: 'mean'.
avg_factor (int, optional): Average factor that is used to average
the loss. This parameter only works when per_image is True.
Default: None.
ignore_index (int | None): The label index to be ignored. Default: 255.
Returns:
torch.Tensor: The calculated loss.
"""
if per_image:
loss = [
lovasz_hinge_flat(*flatten_binary_logits(
logit.unsqueeze(0), label.unsqueeze(0), ignore_index))
for logit, label in zip(logits, labels)
]
loss = weight_reduce_loss(
torch.stack(loss), None, reduction, avg_factor)
else:
loss = lovasz_hinge_flat(
*flatten_binary_logits(logits, labels, ignore_index))
return loss
def lovasz_softmax_flat(probs, labels, classes='present', class_weight=None):
"""Multi-class Lovasz-Softmax loss.
Args:
probs (torch.Tensor): [P, C], class probabilities at each prediction
(between 0 and 1).
labels (torch.Tensor): [P], ground truth labels (between 0 and C - 1).
classes (str | list[int], optional): Classes chosen to calculate loss.
'all' for all classes, 'present' for classes present in labels, or
a list of classes to average. Default: 'present'.
class_weight (list[float], optional): The weight for each class.
Default: None.
Returns:
torch.Tensor: The calculated loss.
"""
if probs.numel() == 0:
# only void pixels, the gradients should be 0
return probs * 0.
C = probs.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).float() # 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 = probs[:, 0]
else:
class_pred = probs[:, c]
errors = (fg - class_pred).abs()
errors_sorted, perm = torch.sort(errors, 0, descending=True)
perm = perm.data
fg_sorted = fg[perm]
loss = torch.dot(errors_sorted, lovasz_grad(fg_sorted))
if class_weight is not None:
loss *= class_weight[c]
losses.append(loss)
return torch.stack(losses).mean()
def lovasz_softmax(probs,
labels,
classes='present',
per_image=False,
class_weight=None,
reduction='mean',
avg_factor=None,
ignore_index=255):
"""Multi-class Lovasz-Softmax loss.
Args:
probs (torch.Tensor): [B, C, H, W], class probabilities at each
prediction (between 0 and 1).
labels (torch.Tensor): [B, H, W], ground truth labels (between 0 and
C - 1).
classes (str | list[int], optional): Classes chosen to calculate loss.
'all' for all classes, 'present' for classes present in labels, or
a list of classes to average. Default: 'present'.
per_image (bool, optional): If per_image is True, compute the loss per
image instead of per batch. Default: False.
class_weight (list[float], optional): The weight for each class.
Default: None.
reduction (str, optional): The method used to reduce the loss. Options
are "none", "mean" and "sum". This parameter only works when
per_image is True. Default: 'mean'.
avg_factor (int, optional): Average factor that is used to average
the loss. This parameter only works when per_image is True.
Default: None.
ignore_index (int | None): The label index to be ignored. Default: 255.
Returns:
torch.Tensor: The calculated loss.
"""
if per_image:
loss = [
lovasz_softmax_flat(
*flatten_probs(
prob.unsqueeze(0), label.unsqueeze(0), ignore_index),
classes=classes,
class_weight=class_weight)
for prob, label in zip(probs, labels)
]
loss = weight_reduce_loss(
torch.stack(loss), None, reduction, avg_factor)
else:
loss = lovasz_softmax_flat(
*flatten_probs(probs, labels, ignore_index),
classes=classes,
class_weight=class_weight)
return loss
@LOSSES.register_module()
class LovaszLoss(nn.Module):
"""LovaszLoss.
This loss is proposed in `The Lovasz-Softmax loss: A tractable surrogate
for the optimization of the intersection-over-union measure in neural
networks <https://arxiv.org/abs/1705.08790>`_.
Args:
loss_type (str, optional): Binary or multi-class loss.
Default: 'multi_class'. Options are "binary" and "multi_class".
classes (str | list[int], optional): Classes chosen to calculate loss.
'all' for all classes, 'present' for classes present in labels, or
a list of classes to average. Default: 'present'.
per_image (bool, optional): If per_image is True, compute the loss per
image instead of per batch. Default: False.
reduction (str, optional): The method used to reduce the loss. Options
are "none", "mean" and "sum". This parameter only works when
per_image is True. Default: 'mean'.
class_weight (list[float] | str, optional): Weight of each class. If in
str format, read them from a file. Defaults to None.
loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
"""
def __init__(self,
loss_type='multi_class',
classes='present',
per_image=False,
reduction='mean',
class_weight=None,
loss_weight=1.0):
super(LovaszLoss, self).__init__()
assert loss_type in ('binary', 'multi_class'), "loss_type should be \
'binary' or 'multi_class'."
if loss_type == 'binary':
self.cls_criterion = lovasz_hinge
else:
self.cls_criterion = lovasz_softmax
assert classes in ('all', 'present') or mmcv.is_list_of(classes, int)
if not per_image:
assert reduction == 'none', "reduction should be 'none' when \
per_image is False."
self.classes = classes
self.per_image = per_image
self.reduction = reduction
self.loss_weight = loss_weight
self.class_weight = get_class_weight(class_weight)
def forward(self,
cls_score,
label,
weight=None,
avg_factor=None,
reduction_override=None,
**kwargs):
"""Forward function."""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if self.class_weight is not None:
class_weight = cls_score.new_tensor(self.class_weight)
else:
class_weight = None
# if multi-class loss, transform logits to probs
if self.cls_criterion == lovasz_softmax:
cls_score = F.softmax(cls_score, dim=1)
loss_cls = self.loss_weight * self.cls_criterion(
cls_score,
label,
self.classes,
self.per_image,
class_weight=class_weight,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)
return loss_cls
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