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evaluation.py
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evaluation.py
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import torch
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# SR : Segmentation Result
# GT : Ground Truth
class SoftIoULoss(nn.Module):
def __init__(self, n_classes=2):
super(SoftIoULoss, self).__init__()
self.n_classes = n_classes
# @staticmethod
# def to_one_hot(tensor, n_classes):
# n, h, w = tensor.size()
# one_hot = torch.zeros(n, n_classes, h, w).scatter_(1, tensor.view(n, 1, h, w), 1)
# return one_hot
def forward(self, input, target):
# logit => N x Classes x H x W
# target => N x H x W
N = len(input)
pred = F.softmax(input, dim=1)
# target_onehot = self.to_one_hot(target, self.n_classes)
target_onehot = input
# Numerator Product
inter = pred * target_onehot
# Sum over all pixels N x C x H x W => N x C
inter = inter.view(N, self.n_classes, -1).sum(2)
# Denominator
union = pred + target_onehot - (pred * target_onehot)
# Sum over all pixels N x C x H x W => N x C
union = union.view(N, self.n_classes, -1).sum(2)
loss = inter / (union + 1e-16)
# Return average loss over classes and batch
return -loss.mean()
class FocalLoss(nn.Module):
def __init__(self, alpha=0.25, gamma=2, weight=None, ignore_index=None):
super(FocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.weight = weight
self.ignore_index = ignore_index
self.bce_fn = nn.BCEWithLogitsLoss(weight=self.weight)
def forward(self, preds, labels):
if self.ignore_index is not None:
mask = labels != self.ignore
labels = labels[mask]
preds = preds[mask]
logpt = -self.bce_fn(preds, labels)
pt = torch.exp(logpt)
loss = -((1 - pt) ** self.gamma) * self.alpha * logpt
return loss
def get_best_threshold(SR, GT):
max_dc = 0
best_threhold = None
thresholdlist = torch.linspace(0.,1.,21).cuda()
for i, threshold in enumerate(thresholdlist):
DC = get_DC(SR,GT,threshold)
if max_dc < DC :
max_dc = DC
best_threhold = threshold
return best_threhold
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