forked from csyfjiang/MLLA-UNet
-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils.py
147 lines (128 loc) · 5.78 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
import numpy as np
import torch
from medpy import metric
from scipy.ndimage import zoom
import torch.nn as nn
import SimpleITK as sitk
from tqdm import tqdm
# class DiceLoss(nn.Module):
# def __init__(self, n_classes):
# super(DiceLoss, self).__init__()
# self.n_classes = n_classes
# def _one_hot_encoder(self, input_tensor):
# tensor_list = []
# for i in range(self.n_classes):
# temp_prob = input_tensor == i # * torch.ones_like(input_tensor)
# tensor_list.append(temp_prob.unsqueeze(1))
# output_tensor = torch.cat(tensor_list, dim=1)
# return output_tensor.float()
# def _dice_loss(self, score, target):
# target = target.float()
# smooth = 1e-5
# intersect = torch.sum(score * target)
# y_sum = torch.sum(target * target)
# z_sum = torch.sum(score * score)
# loss = (2 * intersect + smooth) / (z_sum + y_sum + smooth)
# loss = 1 - loss
# return loss
# def forward(self, inputs, target, weight=None, softmax=False):
# if softmax:
# inputs = torch.softmax(inputs, dim=1)
# target = self._one_hot_encoder(target)
# if weight is None:
# weight = [1] * self.n_classes
# assert inputs.size() == target.size(), 'predict {} & target {} shape do not match'.format(inputs.size(), target.size())
# class_wise_dice = []
# loss = 0.0
# for i in range(0, self.n_classes):
# dice = self._dice_loss(inputs[:, i], target[:, i])
# class_wise_dice.append(1.0 - dice.item())
# loss += dice * weight[i]
# return loss / self.n_classes
class DiceLoss(nn.Module):
def __init__(self, n_classes, ignore_index=-1):
super(DiceLoss, self).__init__()
self.n_classes = n_classes
self.ignore_index = ignore_index
def _one_hot_encoder(self, input_tensor):
tensor_list = []
for i in range(self.n_classes):
temp_prob = (input_tensor == i) & (input_tensor != self.ignore_index)
tensor_list.append(temp_prob.unsqueeze(1))
output_tensor = torch.cat(tensor_list, dim=1)
return output_tensor.float()
def _dice_loss(self, score, target):
target = target.float()
smooth = 1e-5
intersect = torch.sum(score * target)
y_sum = torch.sum(target * target)
z_sum = torch.sum(score * score)
loss = (2 * intersect + smooth) / (z_sum + y_sum + smooth)
loss = 1 - loss
return loss
def forward(self, inputs, target, weight=None, softmax=False):
if softmax:
inputs = torch.softmax(inputs, dim=1)
mask = (target != self.ignore_index).float()
target = self._one_hot_encoder(target)
if weight is None:
weight = [1] * self.n_classes
assert inputs.size() == target.size(), 'predict {} & target {} shape do not match'.format(inputs.size(), target.size())
class_wise_dice = []
loss = 0.0
for i in range(0, self.n_classes):
dice = self._dice_loss(inputs[:, i] * mask, target[:, i])
class_wise_dice.append(1.0 - dice.item())
loss += dice * weight[i]
return loss / self.n_classes
def calculate_metric_percase(pred, gt):
pred[pred > 0] = 1
gt[gt > 0] = 1
if pred.sum() > 0 and gt.sum()>0:
dice = metric.binary.dc(pred, gt)
hd95 = metric.binary.hd95(pred, gt)
return dice, hd95
elif pred.sum() > 0 and gt.sum()==0:
return 1, 0
else:
return 0, 0
def test_single_volume(image, label, net, classes, device, patch_size=[256, 256], test_save_path=None, case=None, z_spacing=1):
image, label = image.squeeze(0).cpu().detach().numpy(), label.squeeze(0).cpu().detach().numpy()
if len(image.shape) == 3:
prediction = np.zeros_like(label)
for ind in tqdm(range(image.shape[0]), desc=f"Processing {case if case else 'volume'}"):
slice = image[ind, :, :]
x, y = slice.shape[0], slice.shape[1]
if x != patch_size[0] or y != patch_size[1]:
slice = zoom(slice, (patch_size[0] / x, patch_size[1] / y), order=3) # previous using 0
input = torch.from_numpy(slice).unsqueeze(0).unsqueeze(0).float().to(device)
net.eval()
with torch.no_grad():
outputs = net(input)
out = torch.argmax(torch.softmax(outputs, dim=1), dim=1).squeeze(0)
out = out.cpu().detach().numpy()
if x != patch_size[0] or y != patch_size[1]:
pred = zoom(out, (x / patch_size[0], y / patch_size[1]), order=0)
else:
pred = out
prediction[ind] = pred
else:
input = torch.from_numpy(image).unsqueeze(0).unsqueeze(0).float().to(device)
net.eval()
with torch.no_grad():
out = torch.argmax(torch.softmax(net(input), dim=1), dim=1).squeeze(0)
prediction = out.cpu().detach().numpy()
metric_list = []
for i in range(1, classes):
metric_list.append(calculate_metric_percase(prediction == i, label == i))
if test_save_path is not None:
img_itk = sitk.GetImageFromArray(image.astype(np.float32))
prd_itk = sitk.GetImageFromArray(prediction.astype(np.float32))
lab_itk = sitk.GetImageFromArray(label.astype(np.float32))
img_itk.SetSpacing((1, 1, z_spacing))
prd_itk.SetSpacing((1, 1, z_spacing))
lab_itk.SetSpacing((1, 1, z_spacing))
sitk.WriteImage(prd_itk, test_save_path + '/'+case + "_pred.nii.gz")
sitk.WriteImage(img_itk, test_save_path + '/'+ case + "_img.nii.gz")
sitk.WriteImage(lab_itk, test_save_path + '/'+ case + "_gt.nii.gz")
return metric_list