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# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
#
# All rights reserved.
# This work should only be used for nonprofit purposes.
#
# By downloading and/or using any of these files, you implicitly agree to all the
# terms of the license, as specified in the document LICENSE.txt
# (included in this package) and online at
# http://www.grip.unina.it/download/LICENSE_OPEN.txt
"""
Created in September 2022
@author: fabrizio.guillaro
"""
import torch
import torch.nn as nn
from torch.nn import functional as F
class CrossEntropy(nn.Module):
def __init__(self, ignore_label=-1, weight=None):
super(CrossEntropy, self).__init__()
self.ignore_label = ignore_label
self.criterion = nn.CrossEntropyLoss(weight=weight,
ignore_index=ignore_label)
def forward(self, score, target):
ph, pw = score.size(2), score.size(3)
h, w = target.size(1), target.size(2)
if ph != h or pw != w:
score = F.upsample(
input=score, size=(h, w), mode='bilinear')
loss = self.criterion(score, target)
return loss
class DiceLoss(nn.Module):
def __init__(self, ignore_label=-1, smooth=1, exponent=2): #because padding adds -1 to the targets
super(DiceLoss, self).__init__()
self.ignore_index = ignore_label
self.smooth = smooth
self.exponent = exponent
def dice_loss(self, pred, target, valid_mask):
assert pred.shape[0] == target.shape[0]
total_loss = 0
num_classes = pred.shape[1]
for i in range(num_classes):
if i != self.ignore_index:
dice_loss = self.binary_dice_loss(
pred[:, i],
target[..., i],
valid_mask=valid_mask,)
total_loss += dice_loss
return total_loss / num_classes
def binary_dice_loss(self, pred, target, valid_mask):
assert pred.shape[0] == target.shape[0]
pred = pred.reshape(pred.shape[0], -1)
target = target.reshape(target.shape[0], -1)
valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + self.smooth
den = torch.sum(pred.pow(self.exponent)*valid_mask + target.pow(self.exponent)*valid_mask, dim=1) + max(self.smooth, 1e-5)
dice = num / den
dice = torch.mean(dice)
return 1 - dice
def forward(self, score, target):
ph, pw = score.size(2), score.size(3)
h, w = target.size(1), target.size(2)
if ph != h or pw != w:
score = F.upsample(
input=score, size=(h, w), mode='bilinear')
score = F.softmax(score,dim=1)
num_classes = score.shape[1]
one_hot_target = F.one_hot(
torch.clamp(target.long(), 0, num_classes - 1),
num_classes=num_classes)
valid_mask = (target != self.ignore_index).long()
loss = self.dice_loss(score, one_hot_target, valid_mask)
return loss
class BinaryDiceLoss(nn.Module):
def __init__(self, smooth=1, exponent=2, ignore_label=-1): #because padding adds -1 to the targets
super(BinaryDiceLoss, self).__init__()
self.ignore_index = ignore_label
self.smooth = smooth
self.exponent = exponent
def binary_dice_loss(self, pred, target, valid_mask):
assert pred.shape[0] == target.shape[0]
pred = pred.reshape(pred.shape[0], -1)
target = target.reshape(target.shape[0], -1)
valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + self.smooth
den = torch.sum(pred.pow(self.exponent)*valid_mask + target.pow(self.exponent)*valid_mask, dim=1) + max(self.smooth, 1e-5)
dice = num / den
dice = torch.mean(dice)
return 1 - dice
def forward(self, score, target):
ph, pw = score.size(2), score.size(3)
h, w = target.size(1), target.size(2)
if ph != h or pw != w:
score = F.upsample(
input=score, size=(h, w), mode='bilinear')
score = F.softmax(score,dim=1)
num_classes = score.shape[1]
one_hot_target = F.one_hot(
torch.clamp(target.long(), 0, num_classes - 1),
num_classes=num_classes)
valid_mask = (target != self.ignore_index).long()
loss = self.binary_dice_loss(
score[:, 1],
one_hot_target[..., 1],
valid_mask)
return loss
class DiceEntropyLoss(nn.Module):
def __init__(self, smooth=1, exponent=2, ignore_label=-1, weight=None): #because padding adds -1 to the targets
super(DiceEntropyLoss, self).__init__()
self.ignore_label = ignore_label
self.smooth = smooth
self.exponent = exponent
self.cross_entropy = nn.CrossEntropyLoss(weight=weight,
ignore_index=ignore_label)
def binary_dice_loss(self, pred, target, valid_mask):
print(pred.shape, target.shape, "this is second list")
assert pred.shape[0] == target.shape[0]
pred = pred.reshape(pred.shape[0], -1)
target = target.reshape(target.shape[0], -1)
valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
# print(pred.shape, target.shape)
num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + self.smooth
den = torch.sum(pred.pow(self.exponent)*valid_mask + target.pow(self.exponent)*valid_mask, dim=1) + max(self.smooth, 1e-5)
dice = num / den
dice = torch.mean(dice)
return 1 - dice
def forward(self, score, target):
ph, pw = score.size(2), score.size(3)
h, w = target.size(1), target.size(2)
# if ph != h or pw != w:
# score = F.upsample(
# input=score, size=(h, w), mode='bilinear')
CE_loss = self.cross_entropy(score, target)
score = F.softmax(score,dim=1)
num_classes = score.shape[1]
one_hot_target = F.one_hot(
torch.clamp(target.long(), 0, num_classes - 1),
num_classes=num_classes)
valid_mask = (target != self.ignore_label).long()
# print(score.shape,one_hot_target.shape)
dice_loss = self.binary_dice_loss(
score[:, 1],
one_hot_target[:,1][..., 1],
valid_mask)
return 0.3*CE_loss + 0.7*dice_loss
class FocalLoss(nn.Module):
def __init__(self, alpha=0.25, gamma=2., ignore_label=-1): #alpha 0.25, gamma=2.
super(FocalLoss, self).__init__()
self.alpha=alpha
self.gamma= gamma
self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_label, reduction="none")
def forward(self, score, target):
ph, pw = score.size(2), score.size(3)
h, w = target.size(1), target.size(2)
if ph != h or pw != w:
score = F.upsample(
input=score, size=(h, w), mode='bilinear')
ce_loss = self.criterion(score, target)
pt = torch.exp(-ce_loss)
f_loss = self.alpha * (1-pt)**self.gamma * ce_loss
return f_loss.mean()
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