/
evaluation.py
87 lines (59 loc) · 1.86 KB
/
evaluation.py
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import torch
# SR : Segmentation Result
# GT : Ground Truth
def get_accuracy(SR,GT,threshold=0.5):
SR = SR > threshold
GT = GT == torch.max(GT)
corr = torch.sum(SR==GT)
tensor_size = SR.size(0)*SR.size(1)*SR.size(2)*SR.size(3)
acc = float(corr)/float(tensor_size)
return acc
def get_sensitivity(SR,GT,threshold=0.5):
# Sensitivity == Recall
SR = SR > threshold
GT = GT == torch.max(GT)
# TP : True Positive
# FN : False Negative
TP = ((SR==1)+(GT==1))==2
FN = ((SR==0)+(GT==1))==2
SE = float(torch.sum(TP))/(float(torch.sum(TP+FN)) + 1e-6)
return SE
def get_specificity(SR,GT,threshold=0.5):
SR = SR > threshold
GT = GT == torch.max(GT)
# TN : True Negative
# FP : False Positive
TN = ((SR==0)+(GT==0))==2
FP = ((SR==1)+(GT==0))==2
SP = float(torch.sum(TN))/(float(torch.sum(TN+FP)) + 1e-6)
return SP
def get_precision(SR,GT,threshold=0.5):
SR = SR > threshold
GT = GT == torch.max(GT)
# TP : True Positive
# FP : False Positive
TP = ((SR==1)+(GT==1))==2
FP = ((SR==1)+(GT==0))==2
PC = float(torch.sum(TP))/(float(torch.sum(TP+FP)) + 1e-6)
return PC
def get_F1(SR,GT,threshold=0.5):
# Sensitivity == Recall
SE = get_sensitivity(SR,GT,threshold=threshold)
PC = get_precision(SR,GT,threshold=threshold)
F1 = 2*SE*PC/(SE+PC + 1e-6)
return F1
def get_JS(SR,GT,threshold=0.5):
# JS : Jaccard similarity
SR = SR > threshold
GT = GT == torch.max(GT)
Inter = torch.sum((SR+GT)==2)
Union = torch.sum((SR+GT)>=1)
JS = float(Inter)/(float(Union) + 1e-6)
return JS
def get_DC(SR,GT,threshold=0.5):
# DC : Dice Coefficient
SR = SR > threshold
GT = GT == torch.max(GT)
Inter = torch.sum((SR+GT)==2)
DC = float(2*Inter)/(float(torch.sum(SR)+torch.sum(GT)) + 1e-6)
return DC