forked from lfz/DSB2017
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathnet_classifier.py
173 lines (154 loc) · 6.88 KB
/
net_classifier.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
import torch
from torch import nn
from layers import *
from torch.nn import DataParallel
from torch.backends import cudnn
from torch.utils.data import DataLoader
from torch import optim
from torch.autograd import Variable
from torch.utils.data import Dataset
from scipy.ndimage.interpolation import rotate
import numpy as np
import os
config = {}
config['topk'] = 5
config['resample'] = None
config['datadir'] = '/run/shm/preprocess_1_3/'
config['preload_train'] = True
config['bboxpath'] = '../cpliangming/results/res18_prep3/bbox/'
config['labelfile'] = '../stage1_labels.csv'
config['preload_val'] = True
config['padmask'] = False
config['crop_size'] = [96,96,96]
config['scaleLim'] = [0.85,1.15]
config['radiusLim'] = [6,100]
config['jitter_range'] = 0.15
config['isScale'] = True
config['random_sample'] = True
config['T'] = 1
config['topk'] = 5
config['stride'] = 4
config['augtype'] = {'flip':True,'swap':False,'rotate':False,'scale':False}
config['detect_th'] = 0.05
config['conf_th'] = -1
config['nms_th'] = 0.05
config['filling_value'] = 160
config['startepoch'] = 20
config['lr_stage'] = np.array([50,100,140,160])
config['lr'] = [0.01,0.001,0.0001,0.00001]
config['miss_ratio'] = 1
config['miss_thresh'] = 0.03
config['anchors'] = [10,30,60]
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# The first few layers consumes the most memory, so use simple convolution to save memory.
# Call these layers preBlock, i.e., before the residual blocks of later layers.
self.preBlock = nn.Sequential(
nn.Conv3d(1, 24, kernel_size = 3, padding = 1),
nn.BatchNorm3d(24),
nn.ReLU(inplace = True),
nn.Conv3d(24, 24, kernel_size = 3, padding = 1),
nn.BatchNorm3d(24),
nn.ReLU(inplace = True))
# 3 poolings, each pooling downsamples the feature map by a factor 2.
# 3 groups of blocks. The first block of each group has one pooling.
num_blocks_forw = [2,2,3,3]
num_blocks_back = [3,3]
self.featureNum_forw = [24,32,64,64,64]
self.featureNum_back = [128,64,64]
for i in range(len(num_blocks_forw)):
blocks = []
for j in range(num_blocks_forw[i]):
if j == 0:
blocks.append(PostRes(self.featureNum_forw[i], self.featureNum_forw[i+1]))
else:
blocks.append(PostRes(self.featureNum_forw[i+1], self.featureNum_forw[i+1]))
setattr(self, 'forw' + str(i + 1), nn.Sequential(*blocks))
for i in range(len(num_blocks_back)):
blocks = []
for j in range(num_blocks_back[i]):
if j == 0:
if i==0:
addition = 3
else:
addition = 0
blocks.append(PostRes(self.featureNum_back[i+1]+self.featureNum_forw[i+2]+addition, self.featureNum_back[i]))
else:
blocks.append(PostRes(self.featureNum_back[i], self.featureNum_back[i]))
setattr(self, 'back' + str(i + 2), nn.Sequential(*blocks))
self.maxpool1 = nn.MaxPool3d(kernel_size=2,stride=2,return_indices =True)
self.maxpool2 = nn.MaxPool3d(kernel_size=2,stride=2,return_indices =True)
self.maxpool3 = nn.MaxPool3d(kernel_size=2,stride=2,return_indices =True)
self.maxpool4 = nn.MaxPool3d(kernel_size=2,stride=2,return_indices =True)
self.unmaxpool1 = nn.MaxUnpool3d(kernel_size=2,stride=2)
self.unmaxpool2 = nn.MaxUnpool3d(kernel_size=2,stride=2)
self.path1 = nn.Sequential(
nn.ConvTranspose3d(64, 64, kernel_size = 2, stride = 2),
nn.BatchNorm3d(64),
nn.ReLU(inplace = True))
self.path2 = nn.Sequential(
nn.ConvTranspose3d(64, 64, kernel_size = 2, stride = 2),
nn.BatchNorm3d(64),
nn.ReLU(inplace = True))
self.drop = nn.Dropout3d(p = 0.2, inplace = False)
self.output = nn.Sequential(nn.Conv3d(self.featureNum_back[0], 64, kernel_size = 1),
nn.ReLU(),
#nn.Dropout3d(p = 0.3),
nn.Conv3d(64, 5 * len(config['anchors']), kernel_size = 1))
def forward(self, x, coord):
out = self.preBlock(x)#16
out_pool,indices0 = self.maxpool1(out)
out1 = self.forw1(out_pool)#32
out1_pool,indices1 = self.maxpool2(out1)
out2 = self.forw2(out1_pool)#64
#out2 = self.drop(out2)
out2_pool,indices2 = self.maxpool3(out2)
out3 = self.forw3(out2_pool)#96
out3_pool,indices3 = self.maxpool4(out3)
out4 = self.forw4(out3_pool)#96
#out4 = self.drop(out4)
rev3 = self.path1(out4)
comb3 = self.back3(torch.cat((rev3, out3), 1))#96+96
#comb3 = self.drop(comb3)
rev2 = self.path2(comb3)
feat = self.back2(torch.cat((rev2, out2,coord), 1))#64+64
comb2 = self.drop(feat)
out = self.output(comb2)
size = out.size()
out = out.view(out.size(0), out.size(1), -1)
#out = out.transpose(1, 4).transpose(1, 2).transpose(2, 3).contiguous()
out = out.transpose(1, 2).contiguous().view(size[0], size[2], size[3], size[4], len(config['anchors']), 5)
#out = out.view(-1, 5)
return feat,out
class CaseNet(nn.Module):
def __init__(self,topk):
super(CaseNet,self).__init__()
self.NoduleNet = Net()
self.fc1 = nn.Linear(128,64)
self.fc2 = nn.Linear(64,1)
self.pool = nn.MaxPool3d(kernel_size=2)
self.dropout = nn.Dropout(0.5)
self.baseline = nn.Parameter(torch.Tensor([-30.0]).float())
self.Relu = nn.ReLU()
def forward(self,xlist,coordlist):
# xlist: n x k x 1x 96 x 96 x 96
# coordlist: n x k x 3 x 24 x 24 x 24
xsize = xlist.size()
corrdsize = coordlist.size()
xlist = xlist.view(-1,xsize[2],xsize[3],xsize[4],xsize[5])
coordlist = coordlist.view(-1,corrdsize[2],corrdsize[3],corrdsize[4],corrdsize[5])
noduleFeat,nodulePred = self.NoduleNet(xlist,coordlist)
nodulePred = nodulePred.contiguous().view(corrdsize[0],corrdsize[1],-1)
featshape = noduleFeat.size()#nk x 128 x 24 x 24 x24
centerFeat = self.pool(noduleFeat[:,:,featshape[2]/2-1:featshape[2]/2+1,
featshape[3]/2-1:featshape[3]/2+1,
featshape[4]/2-1:featshape[4]/2+1])
centerFeat = centerFeat[:,:,0,0,0]
out = self.dropout(centerFeat)
out = self.Relu(self.fc1(out))
out = torch.sigmoid(self.fc2(out))
out = out.view(xsize[0],xsize[1])
base_prob = torch.sigmoid(self.baseline)
casePred = 1-torch.prod(1-out,dim=1)*(1-base_prob.expand(out.size()[0]))
return nodulePred,casePred,out