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metrics.py
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metrics.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
""" Loss Functions -------------------------------------- """
class DiceLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceLoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
inputs = torch.sigmoid(inputs)
inputs = inputs.view(-1)
targets = targets.view(-1)
intersection = (inputs * targets).sum()
dice = (2.*intersection + smooth)/(inputs.sum() + targets.sum() + smooth)
return 1 - dice
class DiceBCELoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(DiceBCELoss, self).__init__()
def forward(self, inputs, targets, smooth=1):
inputs = torch.sigmoid(inputs)
inputs = inputs.view(-1)
targets = targets.view(-1)
intersection = (inputs * targets).sum()
dice_loss = 1 - (2.*intersection + smooth)/(inputs.sum() + targets.sum() + smooth)
BCE = F.binary_cross_entropy(inputs, targets, reduction='mean')
Dice_BCE = BCE + dice_loss
return Dice_BCE
""" Metrics ------------------------------------------ """
def precision(y_true, y_pred):
intersection = (y_true * y_pred).sum()
return (intersection + 1e-15) / (y_pred.sum() + 1e-15)
def recall(y_true, y_pred):
intersection = (y_true * y_pred).sum()
return (intersection + 1e-15) / (y_true.sum() + 1e-15)
def F2(y_true, y_pred, beta=2):
p = precision(y_true,y_pred)
r = recall(y_true, y_pred)
return (1+beta**2.) *(p*r) / float(beta**2*p + r + 1e-15)
def dice_score(y_true, y_pred):
return (2 * (y_true * y_pred).sum() + 1e-15) / (y_true.sum() + y_pred.sum() + 1e-15)
def jac_score(y_true, y_pred):
intersection = (y_true * y_pred).sum()
union = y_true.sum() + y_pred.sum() - intersection
return (intersection + 1e-15) / (union + 1e-15)