1. nn.BCEWithLogitsLoss（）与nn.CrossEntropyLoss（）
エンジニアリングコードリファレンス：https：//github.com/milesial/Pytorch-UNet.git
（1）nn.BCEWithLogitsLoss（）は通常、フォアグラウンドとバックグラウンドのみの2つのカテゴリのセグメンテーションに使用されます。
（2）nn.CrossEntropyLoss（）は通常、複数のカテゴリのセグメンテーションに使用されます。
特定のアプリケーションのコードとコメントを参照してください。
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（）
エンジニアリングコードリファレンス：https：//github.com/zonasw/unet-nested-multiple-classification.git
使用法は、基本的に上記のnn.BCEWithLogitsLoss（）およびnn.CrossEntropyLoss（）の使用法と同じです。
（1）LovaszLossHinge（）は通常、前景と背景のみの2つのカテゴリのセグメンテーションに使用されます。
（2）LovaszLossSoftmax（）は通常、複数のカテゴリのセグメンテーションに使用されます。
特定のアプリケーションのコードとコメントを参照してください。
# -*- 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()lovasz_losses.pyの実装は次のとおりです。
"""
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（）
エンジニアリングコードリファレンス：https：//github.com/ooooverflow/BiSeNet.git
2つのカテゴリを分割し、複数のカテゴリを分割するために使用できます。
特定の使用法については、コードとコメントを参照してください。
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()カラーマップをワンホットエンコーディングの形式に変換する場合は、ブログを参照してください。
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