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metrics.py
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from typing import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
def getTPTNFPFN(p, g, dim):
g = 1.0*g
TP = (p * g).sum(dim=dim)
FP = (p * (1 - g)).sum(dim=dim)
FN = ((1 - p) * g).sum(dim=dim)
TN = ((1 - p) * (1 - g)).sum(dim=dim)
return torch.stack((TP, TN, FP, FN))
def classLabels2oneHot(classLabels, labels: list):
'''
Converts a class label tensor with shape (B, 1, (D,) H, W) to a onehot tensor with shape (B, C, (D,) H, W)
'''
shape = list(classLabels.shape)
shape[1] = max(labels)+1
onehot = torch.zeros(*tuple(shape))
return onehot.scatter_(1, classLabels, 1)[:, labels]
class DiceLoss(nn.Module):
def __init__(self, nDims, batchDice=False, smooth=1e-5):
super(DiceLoss, self).__init__()
assert nDims in [1, 2, 3]
self.nDims = nDims
self.name = 'Dice Loss'
self.batchDice = batchDice
self.smooth = smooth
self.sumDims = tuple(range(2, nDims+2)) if not batchDice else (0,) + tuple(range(2, nDims+2))
def forward(self, p, g):
num = (p * g).sum(dim=self.sumDims)
den = (p + g).sum(dim=self.sumDims)
dice = ((2. * num + self.smooth) / (den + self.smooth)).mean()
return 1 - dice
class GeneralizedDiceLoss(nn.Module):
def __init__(self, nDims, batchDice=False, smooth=1e-5):
super(GeneralizedDiceLoss, self).__init__()
assert nDims in [1, 2, 3]
self.nDims = nDims
self.name = 'Generalized Dice Loss'
self.batchDice = batchDice
self.smooth = smooth
self.sumDims = tuple(range(2, nDims+2)) if not batchDice else (0,) + tuple(range(2, nDims+2))
def forward(self, p, g):
w = 1 / (g.sum(dim=self.sumDims) + self.smooth)**2
num = (w * (p * g).sum(dim=self.sumDims)).sum(dim=-1)
den = (w * (p + g).sum(dim=self.sumDims)).sum(dim=-1)
dice = ((2. * num + self.smooth) / (den + self.smooth)).mean()
return 1 - dice
class FrequencyWeightedDiceLoss(nn.Module):
def __init__(self, nDims, batchDice=False, smooth=1e-5):
super(FrequencyWeightedDiceLoss, self).__init__()
self.name = 'Generalized Dice Loss'
self.batchDice = batchDice
self.smooth = smooth
self.sumDims = tuple(range(2, nDims+2)) if not batchDice else (0,) + tuple(range(2, nDims+2))
def forward(self, p, g):
w = (g.roll(1,1) + g.roll(-1,1)).sum(dim=self.sumDims) / g.sum(self.sumDims).sum(dim=-1, keepdim=True) / 2
print(w)
num = (p * g).sum(dim=self.sumDims)
den = (p + g).sum(dim=self.sumDims)
dice = (2. * num + self.smooth) / (den + self.smooth)
return (w*(1 - dice)).sum()
class TverskyLoss(nn.Module):
def __init__(self, nDims, alpha=0.3, beta=0.7, batchTversky=False, smooth=1e-5):
super(TverskyLoss, self).__init__()
assert alpha + beta == 1
assert nDims in [1, 2, 3]
self.nDims = nDims
self.name = 'Tversky Loss'
self.alpha = alpha
self.beta = beta
self.batchTversky = batchTversky
self.smooth = smooth
self.sumDims = tuple(range(2, nDims+2)) if not batchTversky else (0,) + tuple(range(2, nDims+2))
def forward(self, p, g):
TP, TN, FP, FN = getTPTNFPFN(p, g, self.sumDims)
tversky = ((TP + self.smooth) / (TP + self.alpha*FP + self.beta*FN + self.smooth)).mean()
return 1- tversky
class GeneralizedTverskyLoss(nn.Module):
def __init__(self, nDims, alpha=0.3, beta=0.7, batchTversky=False, smooth=1e-5):
super(GeneralizedTverskyLoss, self).__init__()
assert alpha + beta == 1
assert nDims in [1, 2, 3]
self.nDims = nDims
self.name = 'Generalized Tversky Loss'
self.alpha = alpha
self.beta = beta
self.batchTversky = batchTversky
self.smooth = smooth
self.sumDims = tuple(range(2, nDims+2)) if not batchTversky else (0,) + tuple(range(2, nDims+2))
def forward(self, p, g):
TP, TN, FP, FN = getTPTNFPFN(p, g, self.sumDims)
w = 1 / (g.sum(dim=self.sumDims) + self.smooth)**2
num = (w * TP).sum(dim=-1)
den = (w * (TP + self.alpha*FP + self.beta*FN)).sum(dim=-1)
tversky = ((num + self.smooth) / (den + self.smooth)).mean()
return 1- tversky
class FocalTverskyLoss(nn.Module):
def __init__(self, nDims, alpha=0.3, beta=0.7, gamma=0.75, batchTversky=False, smooth=1e-5):
super(FocalTverskyLoss, self).__init__()
assert alpha + beta == 1
assert nDims in [1, 2, 3]
self.nDims = nDims
self.name = 'Focal Tversky Loss'
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.batchTversky = batchTversky
self.smooth = smooth
self.sumDims = tuple(range(2, nDims+2)) if not batchTversky else (0,) + tuple(range(2, nDims+2))
def forward(self, p, g):
TP, TN, FP, FN = getTPTNFPFN(p, g, self.sumDims)
focalTverskyLoss = torch.pow(1 - (TP + self.smooth) / (TP + self.alpha*FP + self.beta*FN + self.smooth), self.gamma).mean()
return focalTverskyLoss
class GeneralizedFocalTverskyLoss(nn.Module):
def __init__(self, nDims, alpha=0.3, beta=0.7, gamma=0.75, batchTversky=False, smooth=1e-5):
super(GeneralizedFocalTverskyLoss, self).__init__()
assert alpha + beta == 1
assert nDims in [1, 2, 3]
self.nDims = nDims
self.name = 'Generalized Focal Tversky Loss'
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.batchTversky = batchTversky
self.smooth = smooth
self.sumDims = tuple(range(2, nDims+2)) if not batchTversky else (0,) + tuple(range(2, nDims+2))
def forward(self, p, g):
TP, TN, FP, FN = getTPTNFPFN(p, g, self.sumDims)
w = 1 / (g.sum(dim=self.sumDims) + self.smooth)**2
num = (w * TP).sum(dim=-1)
den = (w * (TP + self.alpha*FP + self.beta*FN)).sum(dim=-1)
generalizedFocalTverskyLoss = torch.pow(1 - (num + self.smooth) / (den + self.smooth), self.gamma).mean()
return generalizedFocalTverskyLoss
class Dice(nn.Module):
def __init__(self, nDims, smooth=1.):
super(Dice, self).__init__()
assert nDims in [1, 2, 3]
self.nDims = nDims
self.abbreviation = 'DSC'
self.name = 'Dice'
self.smooth = smooth
self.sumDims = tuple(range(1, nDims+1))
@torch.no_grad()
def forward(self, p, g):
p = p.round()
g = g.round()
num = (p * g).sum(dim=self.sumDims)
den = (p + g).sum(dim=self.sumDims)
dice = ((2. * num + self.smooth) / (den + self.smooth)).mean()
return dice
@torch.no_grad()
def TPTNFPFN(self, TP, TN, FP, FN):
return 2*TP / (2*TP + FP + FN)
class IoU(nn.Module):
def __init__(self, nDims, smooth=1.):
super(IoU, self).__init__()
assert nDims in [1, 2, 3]
self.nDims = nDims
self.abbreviation = 'IoU'
self.name = 'IoU'
self.smooth = smooth
self.sumDims = tuple(range(1, nDims+1))
@torch.no_grad()
def forward(self, p, g):
p = p.round()
g = g.round()
TP, TN, FP, FN = getTPTNFPFN(p, g, self.sumDims)
intersection = TP
union = TP + FP + FN
iou = ((intersection + self.smooth) / (union + self.smooth)).mean()
return iou
@torch.no_grad()
def TPTNFPFN(self, TP, TN, FP, FN):
return TP / (TP + FP + FN)
class Sensetivity(nn.Module):
def __init__(self, nDims, smooth=1.):
super(Sensetivity, self).__init__()
assert nDims in [1, 2, 3]
self.nDims = nDims
self.abbreviation = 'Sen'
self.name = 'Sensetivity'
self.smooth = smooth
self.sumDims = tuple(range(1, nDims+1))
@torch.no_grad()
def forward(self, p, g):
p = p.round()
g = g.round()
TP, TN, FP, FN = getTPTNFPFN(p, g, self.sumDims)
sensetivity = ((TP + self.smooth) / (TP + FN + self.smooth)).mean()
return sensetivity
@torch.no_grad()
def TPTNFPFN(self, TP, TN, FP, FN):
return TP / (TP + FN)
class Specificity(nn.Module):
def __init__(self, nDims, smooth=1.):
super(Specificity, self).__init__()
assert nDims in [1, 2, 3]
self.nDims = nDims
self.abbreviation = 'Spe'
self.name = 'Specificity'
self.smooth = smooth
self.sumDims = tuple(range(1, nDims+1))
@torch.no_grad()
def forward(self, p, g):
p = p.round()
g = g.round()
TP, TN, FP, FN = getTPTNFPFN(p, g, self.sumDims)
specificity = ((TN + self.smooth) / (TN + FP + self.smooth)).mean()
return specificity
@torch.no_grad()
def TPTNFPFN(self, TP, TN, FP, FN):
return TN / (TN + FP)
class F2(nn.Module):
def __init__(self, nDims, smooth=1.):
super(F2, self).__init__()
assert nDims in [1, 2, 3]
self.nDims = nDims
self.abbreviation = 'F2'
self.name = 'F2'
self.smooth = smooth
self.sumDims = tuple(range(1, nDims+1))
@torch.no_grad()
def forward(self, p, g):
p = p.round()
g = g.round()
TP, TN, FP, FN = getTPTNFPFN(p, g, self.sumDims)
specificity = ((5*TP + self.smooth) / (5*TP + 4*FN + FP + self.smooth)).mean()
return specificity
@torch.no_grad()
def TPTNFPFN(self, TP, TN, FP, FN):
return 5*TP / (5*TP + FP + 4*FN)
class MetricsCalculator():
def __init__(self, metrics) -> None:
self.metrics = metrics
@torch.no_grad()
def __call__(self, p, g):
values = torch.zeros(len(self.metrics))
for i, metric in enumerate(self.metrics):
values[i] = metric(p, g)
return values
@torch.no_grad()
def useTP(self, TP, TN, FP, FN):
values = torch.zeros(len(self.metrics))
for i, metric in enumerate(self.metrics):
values[i] = metric.TPTNFPFN(TP, TN, FP, FN)
return values
def getDict(self, lossValue, metricsValues):
names = ['loss', *[metric.abbreviation for metric in self.metrics]]
values = ['{:.4f}'.format(lossValue.item()), *['{:.4f}'.format(metricValue.item()) for metricValue in metricsValues]]
d = OrderedDict(zip(names, values))
return d
if __name__ == '__main__':
torch.manual_seed(313)
nDims = 2
testp = F.softmax(torch.rand((1,3,*(nDims*(32,))), requires_grad=True), dim=1)
testg = testp.round().detach()
criterion = DiceLoss(nDims=nDims, batchDice=False)
print(criterion(testp, testg))
criterion = GeneralizedDiceLoss(nDims=nDims, batchDice=False)
print(criterion(testp, testg))
criterion = FrequencyWeightedDiceLoss(nDims=nDims, batchDice=True)
print(criterion(testp, testg))
criterion = TverskyLoss(nDims=nDims, alpha=0.3, beta=0.7, batchTversky=False)
print(criterion(testp, testg))
criterion = GeneralizedTverskyLoss(nDims=nDims, alpha=0.3, beta=0.7, batchTversky=False)
print(criterion(testp, testg))
criterion = FocalTverskyLoss(nDims=nDims, alpha=0.3, beta=0.7, gamma=0.75, batchTversky=False)
print(criterion(testp, testg))
criterion = GeneralizedFocalTverskyLoss(nDims=nDims, alpha=0.3, beta=0.7, gamma=0.75, batchTversky=False)
print(criterion(testp, testg))
criterion = nn.CrossEntropyLoss()
print(criterion(testp, torch.argmax(testg, dim=1)))
metric = Dice(nDims)
print(metric(testp[:, 0], testg[:, 0]))
metric = IoU(nDims)
print(metric(testp[:, 0], testg[:, 0]))
metric = Sensetivity(nDims)
print(metric(testp[:, 0], testg[:, 0]))
metric = Specificity(nDims)
print(metric(testp[:, 0], testg[:, 0]))
metric = F2(nDims)
print(metric(testp[:, 0], testg[:, 0]))
# metrics = [
# Dice(nDims),
# IoU(nDims),
# F2(nDims),
# Sensetivity(nDims),
# Specificity(nDims),
# ]
# calc = MetricsCalculator(metrics)
# print(calc(testp[:, 0], testg[:, 0]))
# y = torch.LongTensor(1,1,2,2).random_() % 3
# y_onehot = classLabels2oneHot(y, [0, 1, 2])
# print(y)
# print(y_onehot)