1. nn.BCEWithLogitsLoss() 与 nn.CrossEntropyLoss()
Engineering code reference: https://github.com/milesial/Pytorch-UNet.git
(1) nn.BCEWithLogitsLoss() is generally used for segmentation of two categories, only foreground and background.
(2) nn.CrossEntropyLoss() is generally used for segmentation of multiple categories.
See the code and comments for specific applications:
import torch
import torch.nn as nn
if __name__ == "__main__":
# 测试nn.BCEWithLogitsLoss()
# img = np.expand_dims(img, axis=0) 用来扩展维度
loss = nn.BCEWithLogitsLoss()
inputs = torch.randn((32, 1, 224, 224), requires_grad=True)
targets = torch.empty((32, 1, 224,224)).random_(2)
output = loss(inputs, targets)
output.backward()
# 测试nn.CrossEntropyLoss()
# 以分20类为例(包括背景)，targets里面的只为0~19
loss = nn.CrossEntropyLoss()
inputs = torch.randn((32, 20, 224, 224), requires_grad=True)
targets = torch.empty((32, 224, 224)).random_(20).long()
output = loss(inputs, targets)
output.backward()2. LovaszLossHinge () 与 LovaszLossSoftmax ()
Engineering code reference: https://github.com/zonasw/unet-nested-multiple-classification.git
The usage is basically the same as the usage of nn.BCEWithLogitsLoss() and nn.CrossEntropyLoss() above.
(1) LovaszLossHinge() is generally used for segmentation of two categories, only foreground and background.
(2) LovaszLossSoftmax() is generally used for segmentation of multiple categories.
See the code and comments for specific applications:
# -*- coding: utf-8 -*-
# @Time : 2020-02-26 17:46
# @Author : Zonas
# @Email : [email protected]
# @File : losses.py
"""
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
import lovasz_losses as L
class LovaszLossSoftmax(nn.Module):
def __init__(self):
super(LovaszLossSoftmax, self).__init__()
def forward(self, input, target):
out = F.softmax(input, dim=1)
loss = L.lovasz_softmax(out, target)
return loss
class LovaszLossHinge(nn.Module):
def __init__(self):
super(LovaszLossHinge, self).__init__()
def forward(self, input, target):
loss = L.lovasz_hinge(input, target)
return loss
class DiceCoeff(Function):
"""Dice coeff for individual examples"""
def forward(self, input, target):
self.save_for_backward(input, target)
eps = 0.0001
self.inter = torch.dot(input.view(-1), target.view(-1))
self.union = torch.sum(input) + torch.sum(target) + eps
t = (2 * self.inter.float() + eps) / self.union.float()
return t
# This function has only a single output, so it gets only one gradient
def backward(self, grad_output):
input, target = self.saved_variables
grad_input = grad_target = None
if self.needs_input_grad[0]:
grad_input = grad_output * 2 * (target * self.union - self.inter) \
/ (self.union * self.union)
if self.needs_input_grad[1]:
grad_target = None
return grad_input, grad_target
def dice_coeff(input, target):
"""Dice coeff for batches"""
if input.is_cuda:
s = torch.FloatTensor(1).cuda().zero_()
else:
s = torch.FloatTensor(1).zero_()
for i, c in enumerate(zip(input, target)):
s = s + DiceCoeff().forward(c[0], c[1])
return s / (i + 1)
if __name__ == "__main__":
# 测试多类别分割
loss = LovaszLossSoftmax()
inputs = torch.randn((32, 20, 224, 224), requires_grad=True)
targets = torch.empty((32, 224, 224)).random_(20).long()
output = loss(inputs, targets)
output.backward()
# 测试2类别分割
loss = LovaszLossHinge()
inputs = torch.randn((32, 1, 224, 224), requires_grad=True)
targets = torch.empty((32, 1, 224,224)).random_(2)
output = loss(inputs, targets)
output.backward()The implementation of lovasz_losses.py is as follows:
"""
Lovasz-Softmax and Jaccard hinge loss in PyTorch
Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License)
"""
from __future__ import print_function, division
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
try:
from itertools import ifilterfalse
except ImportError: # py3k
from itertools import filterfalse as ifilterfalse
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 iou_binary(preds, labels, EMPTY=1., ignore=None, per_image=True):
"""
IoU for foreground class
binary: 1 foreground, 0 background
"""
if not per_image:
preds, labels = (preds,), (labels,)
ious = []
for pred, label in zip(preds, labels):
intersection = ((label == 1) & (pred == 1)).sum()
union = ((label == 1) | ((pred == 1) & (label != ignore))).sum()
if not union:
iou = EMPTY
else:
iou = float(intersection) / float(union)
ious.append(iou)
iou = mean(ious) # mean accross images if per_image
return 100 * iou
def iou(preds, labels, C, EMPTY=1., ignore=None, per_image=False):
"""
Array of IoU for each (non ignored) class
"""
if not per_image:
preds, labels = (preds,), (labels,)
ious = []
for pred, label in zip(preds, labels):
iou = []
for i in range(C):
if i != ignore: # The ignored label is sometimes among predicted classes (ENet - CityScapes)
intersection = ((label == i) & (pred == i)).sum()
union = ((label == i) | ((pred == i) & (label != ignore))).sum()
if not union:
iou.append(EMPTY)
else:
iou.append(float(intersection) / float(union))
ious.append(iou)
ious = [mean(iou) for iou in zip(*ious)] # mean accross images if per_image
return 100 * np.array(ious)
# --------------------------- BINARY LOSSES ---------------------------
def lovasz_hinge(logits, labels, per_image=True, ignore=None):
"""
Binary Lovasz hinge loss
logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
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
logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
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.
signs = 2. * labels.float() - 1.
errors = (1. - logits * Variable(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), Variable(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
class StableBCELoss(torch.nn.modules.Module):
def __init__(self):
super(StableBCELoss, self).__init__()
def forward(self, input, target):
neg_abs = - input.abs()
loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
return loss.mean()
def binary_xloss(logits, labels, ignore=None):
"""
Binary Cross entropy loss
logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
ignore: void class id
"""
logits, labels = flatten_binary_scores(logits, labels, ignore)
loss = StableBCELoss()(logits, Variable(labels.float()))
return loss
# --------------------------- MULTICLASS LOSSES ---------------------------
def lovasz_softmax(probas, labels, classes='present', per_image=False, ignore=None):
"""
Multi-class Lovasz-Softmax loss
probas: [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1).
Interpreted as binary (sigmoid) output with outputs of size [B, H, W].
labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
per_image: compute the loss per image instead of per batch
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
probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
labels: [P] Tensor, ground truth labels (between 0 and C - 1)
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.
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).float() # foreground for class c
if (classes is '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 = (Variable(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, Variable(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)
B, C, H, W = probas.size()
probas = probas.permute(0, 2, 3, 1).contiguous().view(-1, C) # B * H * W, C = P, C
labels = labels.view(-1)
if ignore is None:
return probas, labels
valid = (labels != ignore)
vprobas = probas[valid.nonzero().squeeze()]
vlabels = labels[valid]
return vprobas, vlabels
def xloss(logits, labels, ignore=None):
"""
Cross entropy loss
"""
return F.cross_entropy(logits, Variable(labels), ignore_index=255)
# --------------------------- HELPER FUNCTIONS ---------------------------
def isnan(x):
return x != x
def mean(l, ignore_nan=False, empty=0):
"""
nanmean compatible with generators.
"""
l = iter(l)
if ignore_nan:
l = ifilterfalse(isnan, l)
try:
n = 1
acc = next(l)
except StopIteration:
if empty == 'raise':
raise ValueError('Empty mean')
return empty
for n, v in enumerate(l, 2):
acc += v
if n == 1:
return acc
return acc / n3. DiceLoss()
Engineering code reference: https://github.com/ooooverflow/BiSeNet.git
It can be used to split two categories and split multiple categories.
See the code and comments for specific usage:
import torch.nn as nn
import torch
import torch.nn.functional as F
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
# new axis order
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
# Transpose: (N, C, D, H, W) -> (C, N, D, H, W)
transposed = tensor.permute(axis_order)
# Flatten: (C, N, D, H, W) -> (C, N * D * H * W)
return transposed.contiguous().view(C, -1)
class DiceLoss(nn.Module):
def __init__(self):
super().__init__()
self.epsilon = 1e-5
def forward(self, output, target):
assert output.size() == target.size(), "'input' and 'target' must have the same shape"
output = F.softmax(output, dim=1)
output = flatten(output)
target = flatten(target)
# intersect = (output * target).sum(-1).sum() + self.epsilon
# denominator = ((output + target).sum(-1)).sum() + self.epsilon
intersect = (output * target).sum(-1)
denominator = (output + target).sum(-1)
dice = intersect / denominator
dice = torch.mean(dice)
return 1 - dice
# return 1 - 2. * intersect / denominator
if __name__ == "__main__":
# 可用于多个类别的分割，下面以2类分割为例说明
# target 每个像素点的值都要转化为独热编码的形式
loss = DiceLoss()
inputs = torch.randn((32, 2, 224, 224), requires_grad=True)
targets = torch.empty((32, 2, 224, 224)).random_(2).long()
output = loss(inputs, targets)
output.backward()If you want to convert the color map into the form of one-hot encoding, please refer to: Blog
If you have any questions, please leave a message, welcome to discuss.