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metrics.py
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266 lines (219 loc) · 8.78 KB
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/12/14 下午4:41
# @Author : chuyu zhang
# @File : metrics.py
# @Software: PyCharm
import numpy as np
# from medpy import metric
import torch
def cal_dice(prediction, label, num=2):
total_dice = np.zeros(num-1)
for i in range(1, num):
prediction_tmp = (prediction == i)
label_tmp = (label == i)
prediction_tmp = prediction_tmp.astype(np.float)
label_tmp = label_tmp.astype(np.float)
dice = 2 * np.sum(prediction_tmp * label_tmp) / (np.sum(prediction_tmp) + np.sum(label_tmp))
total_dice[i - 1] += dice
return total_dice
# def calculate_metric_percase(pred, gt):
# dc = metric.binary.dc(pred, gt)
# jc = metric.binary.jc(pred, gt)
# hd = metric.binary.hd95(pred, gt)
# asd = metric.binary.asd(pred, gt)
# return dc, jc, hd, asd
def dice(input, target, ignore_index=None):
smooth = 1.
# using clone, so that it can do change to original target.
iflat = input.clone().view(-1)
tflat = target.clone().view(-1)
if ignore_index is not None:
mask = tflat == ignore_index
tflat[mask] = 0
iflat[mask] = 0
intersection = (iflat * tflat).sum()
return (2. * intersection + smooth) / (iflat.sum() + tflat.sum() + smooth)
bce = torch.nn.BCEWithLogitsLoss(reduction='none')
def _upscan(f):
for i, fi in enumerate(f):
if fi == np.inf: continue
for j in range(1,i+1):
x = fi+j*j
if f[i-j] < x: break
f[i-j] = x
def distance_transform(bitmap):
f = np.where(bitmap, 0.0, np.inf)
for ibatch in range(f.shape[0]):
for i in range(f.shape[1]):
_upscan(f[ibatch, i, :])
_upscan(f[ibatch, i,::-1])
for i in range(f.shape[2]):
_upscan(f[ibatch, :,i])
_upscan(f[ibatch, ::-1,i])
np.sqrt(f[ibatch], f[ibatch])
return f
def WatershedCrossEntropy(input, target):
# Distance Transform
discmap = target.data.cpu()[:, 0, :, :]
cupmap = target.data.cpu()[:, 1, :, :]
disc_DT = distance_transform(discmap)
cup_DT = distance_transform(cupmap)
disc_DT = torch.from_numpy(disc_DT).float()
cup_DT = torch.from_numpy(cup_DT).float()
disc_DT = discmap * (1.0 - disc_DT/torch.max(disc_DT)) + 1.0
cup_DT = cupmap * (1.0 - cup_DT/torch.max(cup_DT)) + 1.0
disc_DT = disc_DT.cuda()
cup_DT = cup_DT.cuda()
CEloss = bce(input, target)
return torch.mean(disc_DT* CEloss[:, 0 , :, :]+
cup_DT*CEloss[:, 1 , :, :])
def cross_entropy2d(input, target, weight=None, size_average=False):
# input: (n, c, h, w), target: (n, h, w)
n, c, h, w = input.size()
# log_p: (n, c, h, w)
log_p = torch.nn.functional.log_softmax(input, dim=1)
# log_p: (n*h*w, c)
log_p = log_p.transpose(1, 2).transpose(2, 3).contiguous()
log_p = log_p[target.view(n, h, w, 1).repeat(1, 1, 1, c) >= 0]
log_p = log_p.view(-1, c)
# target: (n*h*w,)
mask = target >= 0
target = target[mask]
loss = torch.nn.functional.nll_loss(log_p, target, weight=weight)
loss = loss.float()
if size_average:
temp = mask.data.sum().float()
loss = loss / temp
return loss
def dice_coefficient_numpy(binary_segmentation, binary_gt_label):
'''
Compute the Dice coefficient between two binary segmentation.
Dice coefficient is defined as here: https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
Input:
binary_segmentation: binary 2D numpy array representing the region of interest as segmented by the algorithm
binary_gt_label: binary 2D numpy array representing the region of interest as provided in the database
Output:
dice_value: Dice coefficient between the segmentation and the ground truth
'''
# turn all variables to booleans, just in case
binary_segmentation = np.asarray(binary_segmentation, dtype=bool)
binary_gt_label = np.asarray(binary_gt_label, dtype=bool)
# compute the intersection
intersection = np.logical_and(binary_segmentation, binary_gt_label)
# count the number of True pixels in the binary segmentation
segmentation_pixels = float(np.sum(binary_segmentation.flatten()))
# same for the ground truth
gt_label_pixels = float(np.sum(binary_gt_label.flatten()))
# same for the intersection
intersection = float(np.sum(intersection.flatten()))
# if segmentation_pixels == 0 and gt_label_pixels == 0:
# return 0.0
# compute the Dice coefficient
dice_value = (2 * intersection + 1.0) / (1.001 + segmentation_pixels + gt_label_pixels)
# return it
return dice_value
def dice_coeff(pred, target, ret_arr = False):
"""This definition generalize to real valued pred and target vector.
This should be differentiable.
pred: tensor with first dimension as batch
target: tensor with first dimension as batch
"""
# target = target.data.cpu()
# pred = torch.sigmoid(pred)
# pred = pred.data.cpu()
# pred[pred > 0.5] = 1
# pred[pred <= 0.5] = 0
# return dice_coefficient_numpy(pred, target)
target = target.data.cpu()
if len(pred.shape) == 2:
return dice_coefficient_numpy(pred, target)
else:
all_dice = []
for i in range(pred.shape[0]):
dice = dice_coefficient_numpy(pred[i, ...], target[i, ...])
all_dice.append(dice)
if ret_arr:
return [np.array(all_dice)]
return [sum(all_dice) / len(all_dice)]
def dice_coeff_2label(pred, target, ret_arr = False):
"""This definition generalize to real valued pred and target vector.
This should be differentiable.
pred: tensor with first dimension as batch
target: tensor with first dimension as batch
"""
target = target.data.cpu()
# pred = torch.sigmoid(pred)
# pred = pred.data.cpu()
# pred[pred > 0.75] = 1
# pred[pred <= 0.75] = 0
# print target.shape
# print pred.shape
if len(pred.shape) == 3:
return dice_coefficient_numpy(pred[0, ...], target[0, ...]), dice_coefficient_numpy(pred[1, ...], target[1, ...])
else:
dice_cup = []
dice_disc = []
for i in range(pred.shape[0]):
cup, disc = dice_coefficient_numpy(pred[i, 0, ...], target[i, 0, ...]), dice_coefficient_numpy(pred[i, 1, ...], target[i, 1, ...])
dice_cup.append(cup)
dice_disc.append(disc)
if ret_arr:
return [np.array(dice_cup), np.array(dice_disc)]
return [sum(dice_cup) / len(dice_cup), sum(dice_disc) / len(dice_disc)]
def dice_coeff_3label(pred, target, ret_arr = False):
"""This definition generalize to real valued pred and target vector.
This should be differentiable.
pred: tensor with first dimension as batch
target: tensor with first dimension as batch
"""
target = target.data.cpu()
target = np.array(target)
# pred = torch.sigmoid(pred)
# pred = pred.data.cpu()
# pred[pred > 0.75] = 1
# pred[pred <= 0.75] = 0
# print target.shape
# print pred.shape
if len(pred.shape) == 2:
return dice_coefficient_numpy((pred==1).astype(float), (target==1).astype(float)), dice_coefficient_numpy((pred==2).astype(float), (target==2).astype(float)), dice_coefficient_numpy((pred==3).astype(float), (target==3).astype(float))
else:
dice_lv = []
dice_myo = []
dice_rv = []
for i in range(pred.shape[0]):
lv, myo, rv = dice_coefficient_numpy((pred[i]==1).astype(float), (target[i]==1).astype(float)), dice_coefficient_numpy((pred[i]==2).astype(float), (target[i]==2).astype(float)), dice_coefficient_numpy((pred[i]==3).astype(float), (target[i]==3).astype(float))
dice_lv.append(lv)
dice_myo.append(myo)
dice_rv.append(rv)
if ret_arr:
return [np.array(dice_lv), np.array(dice_myo), np.array(dice_rv)]
return [sum(dice_lv) / len(dice_lv), sum(dice_myo) / len(dice_myo), sum(dice_rv) / len(dice_rv)]
def dice_loss(pred, target):
"""This definition generalize to real valued pred and target vector.
This should be differentiable.
pred: tensor with first dimension as batch
target: tensor with first dimension as batch
"""
return 1 - dice_coeff(pred, target)
def DiceLoss(input, target):
'''
in tensor fomate
:param input:
:param target:
:return:
'''
smooth = 1.
iflat = input.contiguous().view(-1)
tflat = target.contiguous().view(-1)
intersection = (iflat * tflat).sum()
return 1 - ((2. * intersection + smooth) /
(iflat.sum() + tflat.sum() + smooth))
def Balanced_DiceLoss(input, target):
'''
:param input:
:param target:
:return:
'''
input = torch.sigmoid(input)
return 0.5 * (DiceLoss(input[:, 0, ...], target[:, 0, ...]) + DiceLoss(input[:, 1, ...], target[:, 1, ...]))