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metrics.py
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metrics.py
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import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import *
from medpy import metric
# def iou_score(output, target):
# num = target.size(0)
# if torch.is_tensor(output):
# output = torch.sigmoid(output).view(num, -1).data.cpu().numpy()
# if torch.is_tensor(target):
# target = target.view(num, -1).data.cpu().numpy()
# smooth = 1e-5
# tp = (target * output)
# fp = ((1 - target) * output)
# fn = (target * (1 - output))
# iou = (tp.sum(1) + smooth) / (fp.sum(1) + tp.sum(1) + fn.sum(1) + smooth)
# iou = iou.sum() / num
# return iou
def iou_score(output, target):
smooth = 1e-5
output = torch.sigmoid(output).view(-1).data.cpu().numpy()
target = target.view(-1).data.cpu().numpy()
tp = (target * output)
fp = ((1 - target) * output)
fn = (target * (1 - output))
iou = (tp.sum() + smooth) / (fp.sum() + tp.sum() + fn.sum() + smooth)
return iou
# def dice_coef(output, target):
# num = target.size(0)
# if torch.is_tensor(output):
# output = torch.sigmoid(output).view(num, -1).data.cpu().numpy()
# a = output.shape
# if torch.is_tensor(target):
# target = target.view(num, -1).data.cpu().numpy()
# b = target.shape
# smooth = 1e-5
# tp = (target * output)
# fp = ((1 - target) * output)
# fn = (target * (1 - output))
# dice = (2 * tp.sum(1) + smooth) / (target.sum(1) + output.sum(1) + smooth)
# dice = dice.sum() / num
# return dice
def dice_coef(output, target):
smooth = 1e-5
output = torch.sigmoid(output).view(-1).data.cpu().numpy()
target = target.view(-1).data.cpu().numpy()
intersection = (output * target).sum()
return (2. * intersection + smooth) / \
(output.sum() + target.sum() + smooth)
# def dice_coef(output, target):
# smooth = 1e-5
# output = torch.sigmoid(output).view(-1).data.cpu().numpy()
# target = target.view(-1).data.cpu().numpy()
# #intersection = (output * target).sum()
# output[output > 0] = 1
# target[target > 0] = 1
# dice = metric.binary.dc(output, target)
# return dice
def sensitivity(output, target):
num = target.size(0)
if torch.is_tensor(output):
output = torch.sigmoid(output).view(num, -1).data.cpu().numpy()
if torch.is_tensor(target):
target = target.view(num, -1).data.cpu().numpy()
smooth = 1e-5
tp = (target * output)
fp = ((1 - target) * output)
fn = (target * (1 - output))
Recall = (tp.sum(1) + smooth) / (tp.sum(1) + fn.sum(1) + smooth)
Recall = Recall.sum() / num
return Recall
def ppv(output, target):
num = target.size(0)
if torch.is_tensor(output):
output = torch.sigmoid(output).view(num, -1).data.cpu().numpy()
if torch.is_tensor(target):
target = target.view(num, -1).data.cpu().numpy()
smooth = 1e-5
tp = (target * output)
fp = ((1 - target) * output)
fn = (target * (1 - output))
Precision = (tp.sum(1) + smooth) / (tp.sum(1) + fp.sum(1) + smooth)
Precision = Precision.sum() / num
return Precision
#intersection TP
# def dice_coef(output, target):
# smooth = 1.
# N = target.size(0)
# pred_flat = torch.sigmoid(output).view(N, -1).data.cpu().numpy()
# gt_flat = target.view(N, -1).data.cpu().numpy()
#
# intersection = (pred_flat * gt_flat).sum(1)
# union = pred_flat.sum(1) + gt_flat.sum(1)
# c = ((2. * intersection + smooth) / (union + smooth)).sum()
# return c / N
# def iou_score(SR, GT):
# # DC : Dice Coefficient
# # threshold = 0.5
# # SR = SR > threshold
# # GT = GT == torch.max(GT)
# # Inter = torch.sum((SR + GT) == 2)
# # # Inter = torch.sum(SR * GT)
# # DC = float(2 * Inter + 1e-5) / (float(torch.sum(SR) + torch.sum(GT)) + 1e-5)
# iou = dice_coef(SR, GT) / 2 - dice_coef(SR, GT)
# return iou
# def dice_coef(SR, GT):
# # DC : Dice Coefficient
# threshold = 0.5
# SR = SR > threshold
# GT = GT == torch.max(GT)
# Inter = torch.sum((SR + GT) == 2)
# # Inter = torch.sum(SR * GT)
# DC = float(2 * Inter + 1e-5) / (float(torch.sum(SR) + torch.sum(GT)) + 1e-5)
# return DC
# def sensitivity(SR, GT):
# # DC : Dice Coefficient
# threshold = 0.5
# 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) + 1e-6) / (float(torch.sum(TP + FN)) + 1e-6)
#
# return SE
# def ppv(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) + 1e-6) / (float(torch.sum(TP + FP)) + 1e-6)
# return PC
def tp(output, target):
num = target.size(0)
if torch.is_tensor(output):
output = torch.sigmoid(output).view(num, -1).data.cpu().numpy()
if torch.is_tensor(target):
target = target.view(num, -1).data.cpu().numpy()
smooth = 1e-5
tp = (target * output)
return tp.sum()/ num
def tn(output, target):
num = target.size(0)
if torch.is_tensor(output):
output = torch.sigmoid(output).view(num, -1).data.cpu().numpy()
if torch.is_tensor(target):
target = target.view(num, -1).data.cpu().numpy()
smooth = 1e-5
tn = ((1 - target) * (1 - output))
return tn.sum()/ num
def fp(output, target):
num = target.size(0)
if torch.is_tensor(output):
output = torch.sigmoid(output).view(num, -1).data.cpu().numpy()
if torch.is_tensor(target):
target = target.view(num, -1).data.cpu().numpy()
smooth = 1e-5
fp = ((1 - target) * output)
return fp.sum()/ num
def fn(output, target):
num = target.size(0)
if torch.is_tensor(output):
output = torch.sigmoid(output).view(num, -1).data.cpu().numpy()
if torch.is_tensor(target):
target = target.view(num, -1).data.cpu().numpy()
smooth = 1e-5
fn = (target * (1 - output))
return fn.sum() / num
def accuracy(output, target):
num = target.size(0)
if torch.is_tensor(output):
output = torch.sigmoid(output).view(num, -1).data.cpu().numpy()
if torch.is_tensor(target):
target = target.view(num, -1).data.cpu().numpy()
smooth = 1e-5
tp = (target * output)
tn = ((1 - target) * (1 - output))
fp = ((1 - target) * output)
fn = (target * (1 - output))
acc = (tp.sum(1) + tn.sum(1) + smooth) / (tn.sum(1) + tp.sum(1) + fn.sum(1) + fp.sum(1) + smooth)
acc = acc.sum() / num
return acc