-
Notifications
You must be signed in to change notification settings - Fork 0
/
util_unet_train.py
686 lines (578 loc) · 29.5 KB
/
util_unet_train.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
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
import sys
sys.path.append('..')
import numpy as np
import os
from datetime import datetime
import torch
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable
import sys
import pickle
import pandas as pd
# import nibabel as nib
import SimpleITK as sitk
from tqdm import tqdm
import math
from skimage.measure import label as la
from PIL import Image
from scipy.ndimage import zoom
from PIL import ImageEnhance
img_depth, img_weight, img_height = 64, 128, 128
# def fun_Contrast(image, coefficient):
# # 对比度,增强因子为1.0是原始图片; 对比度增强 1.5; 对比度减弱 0.8
# enh_con = ImageEnhance.Contrast(image)
# image_contrasted1 = enh_con.enhance(coefficient)
# return image_contrasted1
#
#
# def fun_Sharpness(image, coefficient):
# # 锐度,增强因子为1.0是原始图片; 锐度增强 3; 锐度减弱 0.8
# enh_sha = ImageEnhance.Sharpness(image)
# image_sharped1 = enh_sha.enhance(coefficient)
# return image_sharped1
#
#
# def fun_bright(image, coefficient):
# # 变亮 1.5; 变暗 0.8; 亮度增强,增强因子为0.0将产生黑色图像; 为1.0将保持原始图像。
# enh_bri = ImageEnhance.Brightness(image)
# image_brightened1 = enh_bri.enhance(coefficient)
# return image_brightened1
num_seg = 8
points = 20000
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class CrossEntropyLoss(nn.Module):
def forward(self, uout, target):
# cel = nn.CrossEntropyLoss()
# # return cel(uout.transpose(1,2).float(),target[:,0,:].long())/(points*uout.shape[0])
# C = 0
# for i in range(uout.shape[0]):
# y_p = uout[i]
# y_p = torch.transpose(y_p,dim0=1,dim1=0)
# y_true = target[i,0]
# print("y_pred:",y_p.shape)
# print("y_true:",y_true.shape)
# C += cel(y_p.float(),y_true.long())
# return torch.tensor(C/uout.shape[0], requires_grad=True)
# # for i in range(uout.shape[0]):
# #
# # C = 0
# # for i in range(len(uout)):
# # print(i)
# # C += cel(uout[i],target[i])
# #
y_pred = uout
organ_target = torch.zeros((target.size(0),num_seg, target.size(2)))
# print("y_pred", y_pred.shape)
for organ_index in range(num_seg):
temp_target = torch.zeros(target.shape)
temp_target[target == organ_index + 1] = 1
temp_target = temp_target.squeeze(1)
# print(temp_target.shape)
organ_target[:,organ_index, :] = temp_target
# y_true = organ_target.transpose((1,0))
organ_target = organ_target.cuda()
dice = 0.0
for organ_index in range(num_seg):
iflat = (y_pred[:,organ_index, :].contiguous().view(-1)).float()
tflat = organ_target[:,organ_index, :].contiguous().view(-1)
intersection = (iflat * tflat).sum()
dice += 2. * intersection / ((iflat ** 2).sum() + (tflat ** 2).sum())
dice_loss = 1 - dice / (num_seg)
# loss = 0.5*dice_loss + 0.5*(C/uout.shape[0])
return dice_loss
# y_pred = y_pred.transpose(1,0)
# organ_target = organ_target.transpose(1,0)
# print(y_pred.shape)
# C = 0
class dice_loss(nn.Module):
# def forward(self, uout, label, label_1, label_2):
def forward(self, uout, label):
# def forward(self, uout, uout_1, label, label_1):
"""soft dice loss"""
eps = 1e-7
L2_sum = 0
L2_mean = 0
all_sort = []
iflat = uout.contiguous().view(-1)
tflat = label.view(-1)
intersection = (iflat * tflat).sum()
dice_loss = 1 - 2. * intersection / ((iflat ** 2).sum() + (tflat ** 2).sum() + eps)
return dice_loss
criterion_cld = nn.CrossEntropyLoss().cuda() # 会自动加上softmax
# torch.manual_seed(2023)
# torch.cuda.manual_seed(2023)
class CenterLoss(nn.Module):
"""Center loss.
Reference:
Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
Args:
num_classes (int): number of classes.
feat_dim (int): feature dimension.
"""
def __init__(self, num_classes=8, feat_dim=128, use_gpu=True):
super(CenterLoss, self).__init__()
self.num_classes = num_classes
self.feat_dim = feat_dim
self.use_gpu = use_gpu
if self.use_gpu:
self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim).cuda())
else:
self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim))
def forward(self, x, labels, loss1):
"""
Args:
x: feature matrix with shape (batch_size, feat_dim).
labels: ground truth labels with shape (batch_size).
"""
# print("x.shape, labels.shape:", x.shape, labels.shape)
# print("self.centers.shape:", self.centers.shape)
ce_loss_1 = loss1.view(-1)
ind_1_sorted = np.argsort(ce_loss_1.cpu().data).cuda() # 从小到大排列,然后输出下标列表
ce_loss_1_sorted = ce_loss_1[ind_1_sorted]
# print("ce_loss_1_sorted:", ce_loss_1_sorted) # 此时的loss为从小到大的排列顺序
num_remember = int(0.9 * len(ce_loss_1_sorted))
ind_1_update = ind_1_sorted[num_remember:]
# print("hard_x.shape, hard_labels.shape, ce_loss_1-ind_1_update-:", hard_x.shape, hard_labels.shape, ce_loss_1[ind_1_update])
batch_size = x.size(0)
# print(torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size, self.num_classes).shape)
distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size, self.num_classes) + \
torch.pow(self.centers, 2).sum(dim=1, keepdim=True).expand(self.num_classes, batch_size).t()
"""distmat = 1 * distmat - 2 * (x @ self.centers.t())"""
distmat.addmm_(1, -2, x, self.centers.t()) # 加速计算欧氏距离,因为distmat已经是两个向量的平方和了,但中心是学习出来的
# 注意上面的中心值是学习出来的,不是计算出的特征中心(为啥不可以计算出特征中心呢?浪费时间效率麽?)
classes = torch.arange(self.num_classes).long()
if self.use_gpu: classes = classes.cuda()
labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
mask = labels.eq(classes.expand(batch_size, self.num_classes))
dist = distmat * mask.float()
loss = dist.clamp(min=1e-12, max=1e+12).sum() / batch_size
# print("batch_size, self.centers.shape, x.shape, cld_lab.shape:", batch_size, self.centers.shape, x.shape, labels[:,0].shape, labels[:8,0])
# 按理说这里应该得到血管那些点和label,先不用计算血管来看看
hard_x = x[ind_1_update]
hard_labels = labels[ind_1_update]
affnity = torch.mm(hard_x, self.centers.t()) # 其实也可以换为中心点自身的乘积,表示自身的相似性,使得中心点feature互相具有差异
CLD_loss = criterion_cld(affnity.div_(1), hard_labels[:,0]-1)
# print("affnity, classes:", affnity, classes)
# CLD_loss = criterion_cld(affnity.div_(1), classes)
# print("CLD_loss:", CLD_loss)
return loss, CLD_loss
"""其实上面可以通过CEloss先得到loss较大的那些点,然后仅对那些点进行距离上的拉近以及鉴别,使得模型更好训练!!!"""
class LossFunction(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, target, linear_out, voxel_output_list):
"""
:param pred: (B, 14, 128, 256, 256)
:param target: (B, 128, 256, 256)
:return: Dice距离
"""
cel = nn.CrossEntropyLoss(reduction='none')
loss1 = cel(pred, torch.squeeze(target - 1, dim=1))
msl = nn.MSELoss()
ceter_loss = CenterLoss()
# for i in range(len(voxel_output_list)):
# lab = voxel_output_list[i][1].view(-1)
# mask = voxel_output_list[i][0].view(-1)
# # print("mask.shape, lab.shape:", mask.shape, lab.shape)
# lab_loss = lab[lab>=1]
# mask_loss = mask[lab>=1]
# mean_loss = msl(mask_loss, lab_loss)
# # print("mean_loss:", mean_loss)
# loss1 = loss1+0.01*mean_loss
# print("target.shape, linear_out.shape:", target.shape, linear_out.shape, pred.shape)
tar = target.squeeze(0).squeeze(0)
lin = linear_out.squeeze(0).permute(1, 0)
# print("loss1.shape, ")
# print("loss1.shape, tar.shape, pred.shape, lin.shape:", loss1.shape, tar.shape, pred.shape, lin.shape)
loss_center, cld_loss = ceter_loss(lin, tar, loss1)
# print("loss_center:", loss_center)
# lab_64 = torch.zeros(voxel_output_list[0][1].shape)
# lab_128 = torch.zeros(voxel_output_list[1][1].shape)
# lab_256 = torch.zeros(voxel_output_list[2][1].shape)
# print("lab_64.shape, lab_128.shape, lab_256.shape:", lab_64.shape, lab_128.shape, lab_256.shape)
# for i in range(1, 9):
# lab_64[voxel_output_list[0][1]==i], lab_128[voxel_output_list[1][1]==i], lab_256[voxel_output_list[2][1]==i] = i, i, i
# print("lab_64[lab_64>0].sum(), lab_128[lab_128>0].sum(), lab_256[lab_256>0].sum():", lab_64[lab_64>0].sum(), lab_128[lab_128>0].sum(), lab_256[lab_256>0].sum())
# b_0, c_0 = voxel_output_list[0][0].shape[0], voxel_output_list[0][0].shape[1]
# b_1, c_1 = voxel_output_list[1][0].shape[0], voxel_output_list[1][0].shape[1]
# b_2, c_2 = voxel_output_list[2][0].shape[0], voxel_output_list[2][0].shape[1]
# mask_64, mask_128, mask_256 = voxel_output_list[0][0].view(b_0, c_0, -1), voxel_output_list[1][0].view(b_1, c_1, -1), voxel_output_list[2][0].view(b_2, c_2, -1)
# print("mask_64.shape, mask_128.shape, mask_256.shape:", mask_64.shape, mask_128.shape, mask_256.shape)
# lab_64, lab_128, lab_256 = lab_64.view(b_0, 1, -1), lab_128.view(b_1, 1, -1), lab_256.view(b_2, 1, -1)
# print("lab_64.shape, lab_128.shape, lab_256.shape:", lab_64.shape, lab_128.shape, lab_256.shape)
# label_64, label_128, label_256 = lab_64[lab_64>0], lab_128[lab_128>0], lab_256[lab_256>0]
# print("label_64.shape, label_128.shape, label_256.shape:", label_64.shape, label_128.shape, label_256.shape)
# 首先将金标准拆开
pred = torch.softmax(pred,dim=1)
y_pred = pred
# print(pred.shape)
# print(target.shape)
organ_target = torch.zeros((target.size(0), num_seg, target.size(2)))
# print(organ_target.shape)
# print("y_pred", y_pred.shape)
for organ_index in range(num_seg):
temp_target = torch.zeros(target.shape)
temp_target[target == organ_index+1] = 1
temp_target = temp_target.squeeze(1)
# print(temp_target.shape)
organ_target[:, organ_index, :] = temp_target
# y_true = organ_target.transpose((1,0))
organ_target = organ_target.cuda()
dice = 0.0
for organ_index in range(num_seg):
iflat = (y_pred[:, organ_index, :].contiguous().view(-1)).float()
tflat = organ_target[:, organ_index, :].contiguous().view(-1)
intersection = (iflat * tflat).sum()
dice += 2. * intersection / ((iflat ** 2).sum() + (tflat ** 2).sum())
dice_loss = 1 - dice / (num_seg)
# 返回的是dice距离
loss1 = loss1.mean()
# loss = loss1 + dice_loss + 0.01*(0.9*loss_center + 0.1*cld_loss)
loss = loss1 + dice_loss + 0.01*(0.1*loss_center + 0.9*cld_loss)
# loss = loss1 + dice_loss + 0.001*loss_center
# loss = loss1 + dice_loss
# print("loss1, dice_loss, loss_center, cld_loss:", loss1.item(), dice_loss.item(), loss_center.item(), cld_loss.item())
return loss
class DiceLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, target):
"""
:param pred: (B, 14, 128, 256, 256)
:param target: (B, 128, 256, 256)
:return: Dice距离
"""
# 首先将金标准拆开
organ_target = torch.zeros((target.size(0), num_seg + 1, target.size(2), target.size(3), target.size(4)))
for organ_index in range(num_seg + 1):
temp_target = torch.zeros(target.shape)
temp_target[target == organ_index] = 1
temp_target = temp_target.squeeze(1)
organ_target[:, organ_index, :, :, :] = temp_target
# organ_target: (B, 14, 128, 256, 256)
organ_target = organ_target.cuda()
dice = 0.0
for organ_index in range(num_seg + 1):
iflat = (pred[:, organ_index, :, :, :].contiguous().view(-1)).float()
tflat = organ_target[:, organ_index, :, :, :].contiguous().view(-1)
intersection = (iflat * tflat).sum()
dice += 2. * intersection / ((iflat ** 2).sum() + (tflat ** 2).sum())
dice_loss = 1 - dice / (num_seg + 1)
# 返回的是dice距离
return dice_loss
def get_batch_acc(uout, label):
# def get_acc(uout, uout_1, label, label_1):
"""soft dice score"""
eps = 1e-7
uout = torch.Tensor(uout)
label = torch.Tensor(label)
# print("type(uout), uout.shape, type(label), label.shape:", type(uout), uout.shape, type(label), label.shape)
iflat = uout.view(-1).float()
tflat = label.view(-1).float()
intersection = (iflat * tflat).sum()
dice_0 = 2. * intersection / ((iflat ** 2).sum() + (tflat ** 2).sum() + eps)
return dice_0
'''
是否可以设置一个3、5循环,然后train函数放在循环里面,每一次循环使用的train_data不同,
但是每一次循环都加载上一次保存最优的那个模型!
'''
import matplotlib.pyplot as plt
save_results_data = "./save_result"
def train(net, train_data, valid_data, num_epochs, optimizer, criterion):
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
net = net.cuda()
print("使用了 cuda")
else:
print("没使用cuda")
prev_time = datetime.now()
# 超算上用于保存模型的路径
save = './model' # /home/zxk/Code/self-sub/code/save
save_results_data = "./save_result"
# 定义初始化正确率为 0
best_acc = 0
forget_rate = 0.5
v_name = 'xxx.nii.gz'
for epoch in range(num_epochs):
print(f'\n==> training epoch {epoch}/{num_epochs}')
if epoch % 20 == 0:
for p in optimizer.param_groups:
# p['lr'] *= 0.9
p['lr'] *= 1.0
print("当前学习率为{:.6f}".format(p['lr']))
train_loss = 0
IoU = [0, 0, 0, 0, 0, 0, 0, 0]
avg_IoU = 0
number_tra, number_val = 0, 0
train_case_n, val_case_n = 0, 0
net = net.train()
for xyz_origin, features, target,name in tqdm(train_data, desc='train', ncols=0):
number_tra += 1
# print("xyz",xyz_origin.shape)
# print("f",features.shape)
# print("t",target.shape)
xyz_origin, features, target = xyz_origin.cuda(), features.cuda(), target.cuda() #
# print("im:",im.shape)
inputs = torch.cat((xyz_origin,features),dim=1)
# print("input:",inputs.shape)
# print("xyz_origin.shape, features.shape, target.shape:", xyz_origin.shape, features.shape, target.shape)
uout, linear_out, uout_aux_list = net(inputs, target) # 因为术中所有的轮廓都是label,所以先使用全1的label进行训练;
# print("len(uout_aux_list):", len(uout_aux_list))
# print("1:", uout_aux_list[0][0].shape, uout_aux_list[0][1].shape)
# print("2:", uout_aux_list[1][0].shape, uout_aux_list[1][1].shape)
# print("3:", uout_aux_list[2][0].shape, uout_aux_list[2][1].shape)
# print("uout.shape, target.shape:", uout.shape, uout.max(), uout.min(), target.shape, target.max(), target.min())
loss = criterion(uout,target, linear_out, uout_aux_list)
# if math.isnan(loss):
# print("Loss is NaN!")
# break
# print("loss:", loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
uout = torch.softmax(uout, dim=1)
new_uout = uout.cpu().detach().numpy()
target = target.cpu().detach().numpy()
# print("traget",target.shape)
# label = label.cpu().numpy()
for index in range(len(new_uout)):
mask = new_uout[index]
mask = np.argmax(mask, axis=0)
mask += 1
xyz = xyz_origin[index]
xyz = xyz.cpu().detach().numpy()
label = target[index]
# label = label[np.newaxis,:]
mask = mask[np.newaxis,:]
# print(xyz.shape)
# print(label.shape)
# print(mask.shape)
avg_IoU += np.sum(mask == label) / points
preTxt = np.concatenate((xyz, label,mask), axis=0)
preTxt = preTxt.transpose(1, 0)
# print(preTxt.shape)
# preTxt = np.concatenate((preTxt, new_uout), axis=1)
df = pd.DataFrame(preTxt.tolist())
# print("name[i]",name[index])
path = os.path.join(save_results_data, "train", str(name[index]) + ".txt")
df.to_csv(path, header=None, index=False)
train_case_n = train_case_n + 1
train_loss += loss.item()
print('index in train-data, and the length of train-data:', train_case_n)
cur_time = datetime.now()
h, remainder = divmod((cur_time - prev_time).seconds, 3600)
m, s = divmod(remainder, 60)
time_str = "Time %02d:%02d:%02d" % (h, m, s)
number = 0
# im_list, uout_list, map_list, label_list = [], [], [], []
# name_zero = ["0"]
if valid_data is not None:
val_acc = 0
# val_dice = [0,0,0,0,0,0,0,0]
with torch.no_grad():
net = net.eval()
val_points = 50000
for xyz_origin, features, target,name in valid_data:
number_val += 1
"""得到tensor形式的数据"""
number = number + 1
xyz_origin, features, target = xyz_origin.cuda(), features.cuda(), target.cuda() #
inputs = torch.cat((xyz_origin, features), dim=1)
# print("input:", inputs.shape)
uout, linear_out, uout_aux_list = net(inputs, target) # 因为术中所有的轮廓都是label,所以先使用全1的label进行训练;
# print("im:",im.shape)
# uout = net(features, xyz_origin) # 因为术中所有的轮廓都是label,所以先使用全1的label进行训练;
uout = torch.softmax(uout, dim=1)
new_uout = uout.cpu().detach().numpy()
target = target.cpu().detach().numpy()
for index in range(len(new_uout)):
mask = new_uout[index]
mask = np.argmax(mask, axis=0)
mask += 1
xyz = xyz_origin[index]
xyz = xyz.cpu().detach().numpy()
label = target[index]
mask = mask[np.newaxis, :]
val_acc += np.sum(mask == label) / val_points
preTxt = np.concatenate((xyz, label, mask), axis=0)
preTxt = preTxt.transpose(1, 0)
df = pd.DataFrame(preTxt.tolist())
path = os.path.join(save_results_data, "test", str(name[index]) + ".txt")
df.to_csv(path, header=None, index=False)
val_case_n = val_case_n + 1
print("val_case_n", val_case_n)
print("train_case_n", train_case_n)
epoch_str = (
"Epoch %d. Train Loss: %f,train avg dice:%f,Valid avg dice:%f,len(valid_data): %d"
% (epoch, train_loss / train_case_n,
avg_IoU / (train_case_n),
val_acc / (val_case_n),
val_case_n))
# print('dice list, and conf list:', dice_list, conf_list)
sys.stdout.flush()
else:
epoch_str = ("Epoch %d. Train Loss: %f, Train Acc: %f, " %
(epoch, train_loss / len(train_data),
avg_IoU / len(train_data)))
prev_time = cur_time
print(epoch_str + time_str)
'''
保存最终的模型:
torch.save(net.state_dict(), os.path.join(save, 'model_half.dat'))
'''
# Determine if model is the best
if (val_acc / (val_case_n)) > best_acc:
best_acc = (val_acc / (val_case_n))
save_path = os.path.join(save, "best/")
# if os.path.exists(save_path):
# os.mkdir(save_path)
torch.save(net.state_dict(), os.path.join(save_path, 'fine_model_co_enhance.dat'))
torch.save(net, os.path.join(save_path, 'fine_model_co_enhance.pth'))
if epoch > 100 and epoch % 100 == 0:
now_acc = (val_acc / (val_case_n))
save_path_epoch = os.path.join(save, "epoch/")
# if os.path.exists(save_path):
# os.mkdir(save_path)
torch.save(net.state_dict(), os.path.join(save_path_epoch, str(now_acc.item())[0:6] + '_' + str(
epoch) + '_fine_model_co_enhance.dat'))
torch.save(net, os.path.join(save_path_epoch,
str(now_acc.item())[0:6] + '_' + str(epoch) + '_fine_model_co_enhance.pth'))
print("best_val_acc:", best_acc)
'''
Compute img-entropy(confidence-level)
'''
def entropy_fn(map, point_number):
map[map < 0.5] = 0
# index = 1
# for i in range(len(map)):
# for j in range(len(map[i])):
# if map[i][j] != 0:
# index = index + 1
# print("index, point_number:", index, point_number)
map = torch.tensor(map)
entropy = ((-1) * map.contiguous().view(-1) * torch.log2(map.contiguous().view(-1) + 1e-7)).sum() / point_number
return entropy.item()
from scipy.ndimage import zoom
def test(net, test_data):
test_points = 400000
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
net = net.cuda()
print("使用了 cuda")
else:
print("没使用cuda")
prev_time = datetime.now()
# 定义初始化正确率为 0
dice_all = 0
val_sum_batch = 0
origh_error_batch = 0
origh_good_batch = 0
new_good_batch = 0
ide = 0
origh_all = 0
Jaccard = 0
val_acc_1 = 0
ASD = 0
HD = 0
# val_acc_2 = 0
# val_acc_3 = 0
with torch.no_grad():
net = net.eval()
name_tem = ''
mask_list = []
data_list = []
ACC_list = []
Conf_list = []
number_val = 0
if test_data is not None:
val_acc = 0
val_case_n = 0
# val_dice = [0,0,0,0,0,0,0,0]
with torch.no_grad():
net = net.eval()
for xyz_list, features_list, target_list, number_list, name, xyz_yw_list in test_data: # 再传入一个列表来形容liver和vessel的数量,用于后续采纳这些数量的点
total_points_num = 0
correct_points_num = 0
txt_list, txt_list_liver, txt_list_vessel = [], [], []
for i in range(len(xyz_list)):
xyz = xyz_list[i]
xyz_sequence = np.array(xyz)[np.newaxis,:,:]
xyz_tensor = torch.Tensor(xyz_sequence)
# print("xyz_sequence.shape:",xyz_sequence.shape) # xyz_sequence.shape: (1, 3, 50000)
features = features_list[i]
features_sequence = np.array(features)[np.newaxis,:,:]
features_tensor = torch.Tensor(features_sequence)
target = target_list[i]
target_sequence = np.array(target)[np.newaxis,:,:]
target_tensor = torch.Tensor(target_sequence)
xyz_tensor, features_tensor, target_tensor = xyz_tensor.cuda(), features_tensor.cuda(), target_tensor.cuda()
num_list = number_list[i]
xyz_yw = xyz_yw_list[i]
#网络输入
inputs = torch.cat((xyz_tensor, features_tensor), dim=1)
uout, linear_out, uout_aux_list = net(inputs, target_tensor)
uout = torch.softmax(uout, dim=1)
# print("uout.shape:", uout.shape) # uout.shape: torch.Size([1, 8, 50000])
new_uout = uout.cpu().detach().numpy()
target = target_tensor.cpu().detach().numpy()
total_points_num += xyz_tensor.shape[2]
for index in range(len(new_uout)): # 其实这里没有遍历
mask = new_uout[index]
mask = np.argmax(mask, axis=0)
mask += 1
xyz = xyz_tensor[index]
xyz = xyz.cpu().detach().numpy()
# print("xyz.shpae, xyz_yw.shape:", xyz.shape, xyz_yw.shape)
# print("xyz.shpae, xyz_yw.shape:", xyz[:, :4], xyz_yw[:, :4])
xyz = xyz_yw
label = target[index]
mask = mask[np.newaxis, :]
# print("mask:",mask.shape)
# print("label:",label.shape)
# print(xyz.shape)
# print(label.shape)
# print(mask.shape)
# print("np.sum(mask==label):", np.sum(mask==label))
correct_points_num += np.sum(mask == label)
preTxt = np.concatenate((xyz, label, mask), axis=0)
preTxt = preTxt.transpose(1, 0)
# print("preTxt.shape, num_list:", preTxt.shape, num_list) # 记得vessel是从列表前面开始,liver可以从列表后面开始
# 需要将下面3个列表进行改动即可,即仅保存需要保存的点!
all_vl = np.concatenate((preTxt[:num_list[0], :], preTxt[-num_list[1]:, :]), axis=0)
txt_list_liver.extend(preTxt[-num_list[1]:, :])
txt_list_vessel.extend(preTxt[:num_list[0], :])
txt_list.extend(all_vl)
print("len(txt_list_liver), len(txt_list_vessel), len(txt_list):", len(txt_list_liver), len(txt_list_vessel), len(txt_list))
print("correct_points_num:", correct_points_num)
print("total_points_num:", total_points_num)
temp_acc = correct_points_num / total_points_num
val_acc += temp_acc
ACC_list.append([name[0], temp_acc])
list_array = np.array(txt_list)
list_array_l = np.array(txt_list_liver)
list_array_v = np.array(txt_list_vessel)
lab_num, mask_num = list_array[:, 3], list_array[:, 4]
print("list_array.shape, lab_num.shape, mask_num.shape:", list_array.shape, lab_num.shape, mask_num.shape)
print("当前样例的所有点的ACC为:", np.sum(lab_num == mask_num)/list_array.shape[0])
path = os.path.join(save_results_data, "test_all", name + ".txt") # 输出的肝脏和血管也要分开保存,即将50000个点进行拆分,肝脏保存肝脏,血管保存血管;
df = pd.DataFrame(list_array.tolist())
print("name:", name, list_array.shape, list_array_l.shape, list_array_v.shape)
print("ACC:", temp_acc)
df.to_csv(path, header=None, index=False) # 这里保存的是所有的点
path_l = os.path.join(save_results_data, "test_all", "liver", name + ".txt")
df_l = pd.DataFrame(list_array_l.tolist())
df_l.to_csv(path_l, header=None, index=False)
path_v = os.path.join(save_results_data, "test_all", "vessel", name + ".txt")
df_v = pd.DataFrame(list_array_v.tolist())
df_v.to_csv(path_v, header=None, index=False)
val_case_n = val_case_n + 1
print("val_case_n", val_case_n)
epoch_str = (
" Valid avg dice:%f,len(valid_data): %d"
% (val_acc / (val_case_n),
val_case_n))
print(epoch_str)
df = pd.DataFrame(ACC_list, columns=["name", "ACC"])
df.to_csv(os.path.join(save_results_data, "./test_acc_all.csv"), header=None, index=False)