/
dense_pdd_net_v01.py
312 lines (223 loc) · 12.1 KB
/
dense_pdd_net_v01.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
import torch
import torch.nn as nn
import torch.optim as optim
import time
import torch.nn.functional as F
#import matplotlib.pyplot as plt
import nibabel as nib
import numpy as np
import pydicom
from torch.utils.checkpoint import checkpoint
fold = 1#int(sys.argv[1])
list_train = torch.Tensor([1,4,5,6,9,10]).long()
if(fold==2):
list_train = torch.Tensor([2,3,5,6,7,8,9]).long()
if(fold==3):
list_train = torch.Tensor([1,2,3,4,7,8,10]).long()
B = 10#len(list_train)
H = 233; W = 168; D = 286;
imgs = torch.zeros(B,1,H,W,D)
segs = torch.zeros(B,H,W,D).long()
label_select = torch.Tensor([0,1,2,3,4,5,6,7,0,0,8,9]).long()
for i in range(B):
#case_train = int(list_train[i])
imgs[i,0,:,:,:] = torch.from_numpy(nib.load('/share/data_zoe2/heinrich/DatenPMBV/img'+str(i+1)+'v2.nii.gz').get_data())/500.0#.unsqueeze(0).unsqueeze(0)
segs[i,:,:,:] = label_select[torch.from_numpy(nib.load('/share/data_zoe2/heinrich/DatenPMBV/seg'+str(i+1)+'v2.nii.gz').get_data()).long()]
#img00 = torch.from_numpy(nib.load('/share/data_zoe2/heinrich/DatenPMBV/img10v2.nii.gz').get_data()).unsqueeze(0).unsqueeze(0)
#img50 = torch.from_numpy(nib.load('/share/data_zoe2/heinrich/DatenPMBV/img5v2.nii.gz').get_data()).unsqueeze(0).unsqueeze(0)
#seg00 = torch.from_numpy(nib.load('/share/data_zoe2/heinrich/DatenPMBV/seg10v2.nii.gz').get_data()).long().unsqueeze(0)
#seg50 = torch.from_numpy(nib.load('/share/data_zoe2/heinrich/DatenPMBV/seg5v2.nii.gz').get_data()).long().unsqueeze(0)
#seg00 = label_select[seg00]
#seg50 = label_select[seg50]
def dice_coeff(outputs, labels, max_label):
dice = torch.FloatTensor(max_label-1).fill_(0)
for label_num in range(1, max_label):
iflat = (outputs==label_num).view(-1).float()
tflat = (labels==label_num).view(-1).float()
intersection = torch.mean(iflat * tflat)
dice[label_num-1] = (2. * intersection) / (1e-8 + torch.mean(iflat) + torch.mean(tflat))
return dice
print(np.unique(segs.view(-1).numpy()))
# mask_train[i,:,:,:] = torch.from_numpy(nib.load('/share/data_zoe2/heinrich/DatenPMBV/mask'+str(case_train)+'v2.nii.gz').get_data())#.long()
d0 = dice_coeff(segs[9,:,:,:], segs[4,:,:,:], 10)
print(d0.mean(),d0)
o_m = H//3
o_n = W//3
o_o = D//3
print('numel_o',o_m*o_n*o_o)
ogrid_xyz = F.affine_grid(torch.eye(3,4).unsqueeze(0),(1,1,o_m,o_n,o_o)).view(1,1,-1,1,3).cuda()
def init_weights(m):
if isinstance(m, nn.Linear) or isinstance(m, nn.Conv3d) or isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
nn.init.xavier_normal(m.weight)
if m.bias is not None:
nn.init.constant(m.bias, 0.0)
def countParameters(model):
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
return params
class OBELISK(nn.Module):
def __init__(self):
super(OBELISK, self).__init__()
channels = 16#16
self.offsets = nn.Parameter(torch.randn(2,channels*2,3)*0.05)
self.layer0 = nn.Conv3d(1, 4, 5, stride=2, bias=False, padding=2)
self.batch0 = nn.BatchNorm3d(4)
self.layer1 = nn.Conv3d(channels*8, channels*4, 1, bias=False, groups=1)
self.batch1 = nn.BatchNorm3d(channels*4)
self.layer2 = nn.Conv3d(channels*4, channels*4, 3, bias=False, padding=1)
self.batch2 = nn.BatchNorm3d(channels*4)
self.layer3 = nn.Conv3d(channels*4, channels*1, 1)
def forward(self, input_img):
img_in = F.avg_pool3d(input_img,3,padding=1,stride=2)
img_in = F.relu(self.batch0(self.layer0(img_in)))
sampled = F.grid_sample(img_in,ogrid_xyz + self.offsets[0,:,:].view(1,-1,1,1,3)).view(1,-1,o_m,o_n,o_o)
sampled -= F.grid_sample(img_in,ogrid_xyz + self.offsets[1,:,:].view(1,-1,1,1,3)).view(1,-1,o_m,o_n,o_o)
x = F.relu(self.batch1(self.layer1(sampled)))
x = F.relu(self.batch2(self.layer2(x)))
features = self.layer3(x)
return features
disp_range = 0.4#0.25
displacement_width = 15#11#17
shift_xyz = F.affine_grid(disp_range*torch.eye(3,4).unsqueeze(0),(1,1,displacement_width,displacement_width,displacement_width)).view(1,1,-1,1,3).cuda()
#_,_,H,W,D = img00.size()
grid_size = 32#25#30
grid_xyz = F.affine_grid(torch.eye(3,4).unsqueeze(0),(1,1,grid_size,grid_size,grid_size)).view(1,-1,1,1,3).cuda()
# print('moving_unfold',torch.numel(moving_unfold)*4e-6,'MBytes')
#print('deeds_cost',torch.numel(deeds_cost)*4e-6,'MBytes')
#minconv_seq = nn.Sequential(pad1,max1,avg1,avg1)
#cost = checkpoint(deeds_cost,minconv_seq)
#print('cost',torch.numel(cost)*4e-6,'MBytes')
#
# avg_seq = nn.Sequential(pad2,avg1,avg1)
#>>> input_var = checkpoint_sequential(model, chunks, input_var)
#cost_avg = checkpoint(avg_seq,cost_permute)#
# print('cost_avg',torch.numel(cost_avg)*4e-6,'MBytes')
def augmentAffine(img_in, seg_in, strength=0.05):
"""
3D affine augmentation on image and segmentation mini-batch on GPU.
(affine transf. is centered: trilinear interpolation and zero-padding used for sampling)
:input: img_in batch (torch.cuda.FloatTensor), seg_in batch (torch.cuda.LongTensor)
:return: augmented BxCxTxHxW image batch (torch.cuda.FloatTensor), augmented BxTxHxW seg batch (torch.cuda.LongTensor)
"""
B,C,D,H,W = img_in.size()
affine_matrix = (torch.eye(3,4).unsqueeze(0) + torch.randn(B, 3, 4) * strength).to(img_in.device)
meshgrid = F.affine_grid(affine_matrix,torch.Size((B,1,D,H,W)))
img_out = F.grid_sample(img_in, meshgrid,padding_mode='border')
seg_out = F.grid_sample(seg_in.float().unsqueeze(1), meshgrid, mode='nearest').long().squeeze(1)
return img_out, seg_out
class deeds(nn.Module):
def __init__(self):
super(deeds, self).__init__()
self.alpha = nn.Parameter(torch.Tensor([1,.1,1,1,.1,1]))#.cuda()
self.pad1 = nn.ReplicationPad3d(3)#.cuda()
self.avg1 = nn.AvgPool3d(3,stride=1)#.cuda()
self.max1 = nn.MaxPool3d(3,stride=1)#.cuda()
self.pad2 = nn.ReplicationPad3d(2)#.cuda()##
def forward(self, feat00,feat50):
#deeds correlation layer (slightly unrolled)
deeds_cost = torch.zeros(1,grid_size**3,displacement_width,displacement_width,displacement_width).cuda()
xyz8 = grid_size**2
for i in range(grid_size):
moving_unfold = F.grid_sample(feat50,grid_xyz[:,i*xyz8:(i+1)*xyz8,:,:,:] + shift_xyz,padding_mode='border')
fixed_grid = F.grid_sample(feat00,grid_xyz[:,i*xyz8:(i+1)*xyz8,:,:,:])
deeds_cost[:,i*xyz8:(i+1)*xyz8,:,:,:] = self.alpha[1]+self.alpha[0]*torch.sum(torch.pow(fixed_grid-moving_unfold,2),1).view(1,-1,displacement_width,displacement_width,displacement_width)
# remove mean (not really necessary)
#deeds_cost = deeds_cost.view(-1,displacement_width**3) - deeds_cost.view(-1,displacement_width**3).mean(1,keepdim=True)[0]
deeds_cost = deeds_cost.view(1,-1,displacement_width,displacement_width,displacement_width)
# approximate min convolution / displacement compatibility
cost = self.avg1(self.avg1(-self.max1(-self.pad1(deeds_cost))))
# grid-based mean field inference (one iteration)
cost_permute = cost.permute(2,3,4,0,1).view(1,displacement_width**3,grid_size,grid_size,grid_size)
cost_avg = self.avg1(self.avg1(self.pad2(cost_permute))).permute(0,2,3,4,1).view(1,-1,displacement_width,displacement_width,displacement_width)
# second path
cost = self.alpha[4]+self.alpha[2]*deeds_cost+self.alpha[3]*cost_avg
cost = self.avg1(self.avg1(-self.max1(-self.pad1(cost))))
# grid-based mean field inference (one iteration)
cost_permute = cost.permute(2,3,4,0,1).view(1,displacement_width**3,grid_size,grid_size,grid_size)
cost_avg = self.avg1(self.avg1(self.pad2(cost_permute))).permute(0,2,3,4,1).view(grid_size**3,displacement_width**3)
#cost = alpha[4]+alpha[2]*deeds_cost+alpha[3]*cost.view(1,-1,displacement_width,displacement_width,displacement_width)
#cost = avg1(avg1(-max1(-pad1(cost))))
#probabilistic and continuous output
cost_soft = F.softmax(-self.alpha[5]*cost_avg,1)
# pred_xyz = torch.sum(F.softmax(-5self.alpha[2]*cost_avg,1).unsqueeze(2)*shift_xyz.view(1,-1,3),1)
pred_xyz = torch.sum(cost_soft.unsqueeze(2)*shift_xyz.view(1,-1,3),1)
return cost_soft,pred_xyz
net = OBELISK()
net.apply(init_weights)
net.cuda()
net.train()
class_weight = torch.sqrt(1.0/(torch.bincount(segs.view(-1)).float()))
class_weight = class_weight/class_weight.mean()
class_weight[0] = 0.15
class_weight = class_weight.cuda()
print('inv sqrt class_weight',class_weight)
criterion = nn.CrossEntropyLoss(class_weight)
t0 = time.time()
reg = deeds()
reg.cuda()
print('alpha_before',reg.alpha)
list_train = torch.Tensor([1,4,5,6,9,10]).long()-1
#img00.requires_grad = True
#img50.requires_grad = True
iterations = 1000
lambda_weight = 2#2.5#1.5
run_labelloss = torch.zeros(iterations)#/0
run_diffloss = torch.zeros(iterations)#/0
optimizer = optim.Adam(list(net.parameters())+list(reg.parameters()),lr=0.005)
for i in range(iterations):
idx = list_train[torch.randperm(6)].view(2,3)[:,0]
#print(idx)
optimizer.zero_grad()
label_moving = torch.zeros(size=(1,10,H,W,D)).cuda()
label_moving = label_moving.scatter_(1, segs[idx[1]:idx[1]+1,:,:,:].unsqueeze(1).cuda(), 1).detach()
img00_in = imgs[idx[0]:idx[0]+1,:,:,:,:].cuda()
img50 = imgs[idx[1]:idx[1]+1,:,:,:,:].cuda()
img00, seg50 = augmentAffine(img00_in,segs[idx[0]:idx[0]+1,:,:,:].cuda(),0.0375)
img00.requires_grad = True
img50.requires_grad = True
label_fixed = torch.zeros(size=(1,10,H,W,D)).cuda()
label_fixed = label_fixed.scatter_(1, seg50.unsqueeze(1), 1).detach()
# get features (regular grid)
feat00 = checkpoint(net,img00)#net(img00)# #00 is fixed
feat50 = checkpoint(net,img50)#net(img50)# #50 is moving
# run differentiable deeds (regular grid)
cost_soft,pred_xyz = checkpoint(reg,feat00,feat50)#reg(feat00,feat50)#
pred_xyz = pred_xyz.view(1,grid_size,grid_size,grid_size,3)
# evaluate diffusion regularisation loss
diffloss = lambda_weight*((pred_xyz[0,:,1:,:,:]-pred_xyz[0,:,:-1,:,:])**2).mean()+\
lambda_weight*((pred_xyz[0,1:,:,:,:]-pred_xyz[0,:-1,:,:,:])**2).mean()+\
lambda_weight*((pred_xyz[0,:,:,1:,:]-pred_xyz[0,:,:,:-1,:])**2).mean()
run_diffloss[i] = diffloss.item()
# evaluate non-local loss
nonlocal_label = (F.grid_sample(label_moving,grid_xyz+shift_xyz,padding_mode='border')\
*cost_soft.view(1,-1,grid_size**3,displacement_width**3,1)).sum(3,keepdim=True)
fixed_label = F.grid_sample(label_fixed,grid_xyz,padding_mode='border').detach()#.long().squeeze(1)
labelloss = ((nonlocal_label-fixed_label)**2)*class_weight.view(1,-1,1,1,1)
labelloss = labelloss.mean()
#labelloss = criterion(nonlocal_label,fixed_label)
run_labelloss[i] = labelloss.item()
(labelloss+diffloss).backward()
optimizer.step()
if(i%50==49):
print('epoch',i,'time',time.time()-t0)
#print('grad',reg.layer1.weight.grad.norm().item())
loss_avg = F.avg_pool1d(run_labelloss.view(1,1,-1),5,stride=1).squeeze().numpy()[:i]
print('run_labelloss',loss_avg[-1])
loss_avg = F.avg_pool1d(run_diffloss.view(1,1,-1),5,stride=1).squeeze().numpy()[:i]
print('run_diffloss',loss_avg[-1])
#plt.plot(F.avg_pool1d(run_labelloss.view(1,1,-1),5,stride=1).squeeze().numpy()[:i])
#plt.plot(F.avg_pool1d(run_diffloss.view(1,1,-1),5,stride=1).squeeze().numpy()[:i])
#plt.show()
#plt.imshow(pred_xyz[0,:,12,:,0].cpu().data.numpy())
#plt.colorbar()
#plt.show()
torch.save(net.cpu().state_dict(),'/data_supergrover2/heinrich/dense_reg3_feat_epoch'+str(i)+'.pth')
torch.save(reg.cpu().state_dict(),'/data_supergrover2/heinrich/dense_reg3_deeds_epoch'+str(i)+'.pth')
net.cuda()
reg.cuda()
#
torch.cuda.synchronize()
print('time',time.time()-t0)
print('grad_alpha',reg.alpha.grad.norm())
print('grad_obelisk',net.layer1.weight.grad.norm())
print('alpha_after',reg.alpha)