-
Notifications
You must be signed in to change notification settings - Fork 25
/
crfcnn_combine_ddsm.py
241 lines (239 loc) · 11.5 KB
/
crfcnn_combine_ddsm.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
import tensorflow as tf
import scipy
import numpy as np
import scipy.io as sio
import os
import scipy.misc
#import matplotlib.pyplot as plt
import utils_combine
from utils_combine import fetchdatalabel, calfilter, init, initcomb, model, crfatmodel, cnnatmodel, meanstd, cnnmodel, dice
rng = np.random.RandomState(1234)
tf.set_random_seed(1)
lrbig = 0.01 #0.01
lrmid = 0.003 #5
lr = 0.003 #2#5 #2 #3 #4 #3#5
lrsmall = 0.0001
l2factor = 0.5 #0.01 # 0.01
totalepoch = 7000 #7000 #300 #200 #120
atepsilon = 1e0 #0 #-1
modelname = 'cnn' #'crfcomb'
debug = False #False
savename = str(lr)+str(totalepoch)+str(atepsilon)+'1nnlrfsave'+str(5)+modelname+'norm'
fusion=None #'late' #None #'late'
saveperepoch = True #False #True #True # True
savefreq = 200
print(savename)
boxheight = 40
boxwidth = 40
batchsize = 29
#thresh = 0.885
postfix='roienhance.mat' # 'roienhance.mat' 'roienhance.jpeg'
traindatapath = './train1/'
trainlabelpath = './train1/'
testdatapath = './test1/'
testlabelpath = './test1/'
if __name__ == '__main__':
assert(modelname!='crf' or modelname!='cnn' or modelname!='cnnat' or modelname!='crfat'
or modelname!='cnncomb' or modelname!='crfcomb' or modelname!='cnncombat' or modelname!='crfcombat')
traindata, trainlabel, trainfname = fetchdatalabel(path=traindatapath, postfix=postfix, flag='train')
testdata, testlabel, testfname = fetchdatalabel(path=testdatapath, postfix=postfix, flag='test')
if savename[-4:] == 'norm':
print('norm')
meandata = traindata.mean(axis=0)
stddata = traindata.std(axis=0)
traindata = meanstd(traindata, meandata, stddata)
testdata = meanstd(testdata, meandata, stddata)
traink1, traink2 = calfilter(traindata)
testk1, testk2 = calfilter(testdata)
X = tf.placeholder(tf.float32, [None, boxheight, boxwidth])
Y = tf.placeholder(tf.float32, [None, boxheight, boxwidth])
k1 = tf.placeholder(tf.float32, [None, boxheight*boxwidth, boxheight*boxwidth])
k2 = tf.placeholder(tf.float32, [None, boxheight*boxwidth, boxheight*boxwidth])
print(modelname[3:7])
if modelname[3:7] == 'comb':
paras = initcomb(nhid221=37, nhid222=12, nhid223=355, lrf221=2, lrf222=2, lrf223=9,
nhid331=16, nhid332=13, nhid333=415, lrf331=3, lrf332=3, lrf333=8,
nhid441=9, nhid442=12, nhid443=588, lrf441=4, lrf442=4, lrf443=7,
nhid551=6, nhid552=12, nhid553=588, lrf551=5, lrf552=5, lrf553=7, fusion=fusion)
else:
paras = init(nhid1=9, nhid2=12, nhid3=588, lrf1=4, lrf2=4, lrf3=7)
if modelname == 'cnn':
trainenergy, accuracy, di, testenergy, qtest = cnnmodel(X, Y, paras)
elif modelname == 'cnnat':
trainenergy, accuracy, di, testenergy, qtest, advloss = cnnatmodel(X, Y, paras, atepsilon)
elif modelname == 'crfat':
trainenergy, accuracy, di, testenergy, qtest, advloss= crfatmodel(X, Y, k1, k2, paras, atepsilon)
elif modelname == 'crf':
trainenergy, accuracy, di, testenergy, qtest = model(X, Y, k1, k2, paras)
elif modelname == 'cnncomb':
trainenergy, accuracy, di, testenergy, qtest = cnnmodel(X, Y, paras, flag='combine')
elif modelname == 'cnncombat':
trainenergy, accuracy, di, testenergy, qtest, advloss = cnnatmodel(X, Y, paras, atepsilon, flag='combine')
elif modelname == 'crfcomb':
trainenergy, accuracy, di, testenergy, qtest = model(X, Y, k1, k2, paras, flag='combine', fusion=fusion)
elif modelname == 'crfcombat':
trainenergy, accuracy, di, testenergy, qtest, advloss= crfatmodel(X, Y, k1, k2, paras, atepsilon,
flag='combine', fusion=fusion)
learningrate = tf.Variable(lr, trainable=False)
opt = tf.train.AdamOptimizer(learning_rate=learningrate)
params = tf.trainable_variables()
if modelname[:3] == 'crf':
if fusion=='late':
l2loss = tf.nn.l2_loss(paras['wsmooth22']) + tf.nn.l2_loss(paras['wcontra22'])
l2loss = l2loss + tf.nn.l2_loss(paras['wsmooth33']) + tf.nn.l2_loss(paras['wcontra33'])
l2loss = l2loss + tf.nn.l2_loss(paras['wsmooth44']) + tf.nn.l2_loss(paras['wcontra44'])
l2loss = l2loss + tf.nn.l2_loss(paras['wsmooth55']) + tf.nn.l2_loss(paras['wcontra55'])
else:
l2loss = tf.nn.l2_loss(paras['wsmooth']) + tf.nn.l2_loss(paras['wcontra'])
l2loss = l2loss * l2factor
trainenergy = trainenergy + l2loss
grads = tf.gradients(trainenergy, params, aggregation_method=2)
optimizer = opt.apply_gradients(zip(grads, params))
#optimizer = tf.train.AdamOptimizer(learning_rate=learningrate).minimize(trainenergy)
init = tf.initialize_all_variables()
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
#with tf.Session() as sess:
sess.run(init)
tf.set_random_seed(1)
trenerls, traccls, trdils, teenerls, teaccls, tedils = [], [], [], [], [], []
besttrener, tolstep, besttedi, besttrdi = 1e6, 0, 0, 0
predmat = {}
for epoch in range(0,totalepoch):
randindex = np.random.permutation(batchsize*12)
trainloss, trainacc, traindi, trainl2 = 0, 0, 0, 0
for iindex in range(12):
#print(epoch)
randindexii = randindex[iindex*batchsize : (iindex+1)*batchsize]
traindataii = traindata[randindexii, :, :]
trainlabelii = trainlabel[randindexii, :, :]
#print(len(randindexii))
if modelname[:3] == 'cnn':
if not debug:
energy, acc, dival, _ = sess.run([trainenergy, accuracy, di, optimizer], feed_dict={X: traindataii,
Y: trainlabelii, learningrate: lr})
if debug and modelname[-2:] == 'at':
energy, acc, dival, adv, _ = sess.run([trainenergy, accuracy, di, advloss, optimizer], feed_dict={X: traindataii,
Y: trainlabelii, learningrate: lr})
print(adv)
else:
traink1ii = traink1[randindexii, :, :]
traink2ii = traink2[randindexii, :, :]
if not debug:
energy, acc, dival, l2val, _ = sess.run([trainenergy, accuracy, di, l2loss, optimizer], feed_dict={X: traindataii,
Y: trainlabelii, learningrate: lrbig, k1: traink1ii, k2: traink2ii})
if debug and modelname[-2:] == 'at':
energy, acc, dival, l2val, adv,_ = sess.run([trainenergy, accuracy, di, l2loss, advloss, optimizer], feed_dict={X: traindataii,
Y: trainlabelii, learningrate: lrbig, k1: traink1ii, k2: traink2ii})
print(adv)
trainl2 += l2val
traindi += dival
trainloss += energy
trainacc += acc
print('epoch'+str(epoch)+', '+str(trainloss/12)+' '+str(trainl2/12)+' '+str(trainacc/12)+' '+str(traindi/12))
#if trainloss/4 < besttrainloss:
# besttrainloss = trainloss/4
# tolstep = 0
#else: tolstep += 1
#if tolstep == 8: lr *= .99
for iindex in range(3):
testdataii = testdata[iindex*batchsize:(iindex+1)*batchsize, :, :]
testlabelii = testlabel[iindex*batchsize:(iindex+1)*batchsize, :, :]
if modelname[:3] == 'cnn':
#print(testlabelii.shape, type(testlabelii))
energy, acc, dival, qtestval = sess.run([testenergy, accuracy, di, qtest], feed_dict={X: testdataii,
Y: testlabelii, learningrate: 0})
else:
testk1ii = testk1[iindex*batchsize:(iindex+1)*batchsize, :, :]
testk2ii = testk2[iindex*batchsize:(iindex+1)*batchsize, :, :]
energy, acc, dival, qtestval = sess.run([testenergy, accuracy, di, qtest], feed_dict={X: testdataii,
Y: testlabelii, learningrate: 0, k1: testk1ii, k2: testk2ii})
if iindex == 0:
testenergyval, testacc, testpred = energy, acc, qtestval
else:
testenergyval += energy
testacc += acc
testpred = np.concatenate((testpred, qtestval), axis=0)
testdi = dice(testlabel[:], (testpred.argmax(3))[:])
if saveperepoch and (epoch%savefreq==0): #(totalepoch<200 or testdi>thresh): # because of model size, save the result and tune further
for iindex in range(12):
traindataii = traindata[iindex*batchsize:(iindex+1)*batchsize, :, :]
trainlabelii = trainlabel[iindex*batchsize:(iindex+1)*batchsize, :, :]
if modelname[:3] == 'cnn':
qtestval = sess.run(qtest, feed_dict={X: traindataii,
Y: trainlabelii, learningrate: 0})
else:
traink1ii = traink1[iindex*batchsize:(iindex+1)*batchsize, :, :]
traink2ii = traink2[iindex*batchsize:(iindex+1)*batchsize, :, :]
qtestval = sess.run(qtest, feed_dict={X: traindataii,
Y: trainlabelii, learningrate: 0, k1: traink1ii, k2: traink2ii})
if iindex == 0:
trainpred = qtestval
else:
trainpred = np.concatenate((trainpred, qtestval), axis=0)
savemat={}
savemat['testpred'] = testpred
#savemat['testlabel'] = testlabel
savemat['trainpred'] = trainpred
#savemat['trainlabel'] = trainlabel
sio.savemat(savename+str(epoch)+'.mat', savemat)
print('epoch'+str(epoch)+', '+str(testenergyval/3)+' '+str(testacc/3)+' '+str(testdi))
if traindi/12>0.90 and testdi > besttedi:
saver.save(sess, savename+'.ckpt')
besttedi = testdi
besttrdi = traindi/12
predmat['test'] = testpred
lrbig = lrmid
if traindi/12 > 0.885:
lrbig = lr
trenerls.append(trainloss/12)
traccls.append(trainacc/12)
trdils.append(traindi/12)
teenerls.append(testenergyval/12)
teaccls.append(testacc/12)
tedils.append(testdi)
saver.restore(sess, savename+'.ckpt')
#argpred, dival = sess.run([predarg, di], feed_dict={X: testdata,
# Y: testlabel, learningrate: 0})#, k1: testk1, k2: testk2})
#assert(dival==besttedi)
print(str(besttedi)+' '+str(max(trdils)))
predmat['trainloss'] = trenerls
predmat['trainacc'] = traccls
predmat['traindi'] = trdils
predmat['testfname'] = testfname
predmat['testloss'] = teenerls
predmat['testacc'] = teaccls
predmat['testdi'] = tedils
predmat['testlabel'] = testlabel
predmat['trainlabel'] = trainlabel
predmat['traindata'] = traindata
predmat['trainfname'] = trainfname
predmat['testdata'] = testdata
for iindex in range(12):
traindataii = traindata[iindex*batchsize:(iindex+1)*batchsize, :, :]
trainlabelii = trainlabel[iindex*batchsize:(iindex+1)*batchsize, :, :]
if modelname[:3] == 'cnn':
qtestval = sess.run(qtest, feed_dict={X: traindataii,
Y: trainlabelii, learningrate: 0})
else:
traink1ii = traink1[iindex*batchsize:(iindex+1)*batchsize, :, :]
traink2ii = traink2[iindex*batchsize:(iindex+1)*batchsize, :, :]
qtestval = sess.run(qtest, feed_dict={X: traindataii,
Y: trainlabelii, learningrate: 0, k1: traink1ii, k2: traink2ii})
if iindex == 0:
trainpred = qtestval
else:
trainpred = np.concatenate((trainpred, qtestval), axis=0)
predmat['trainpred'] = trainpred
print(savename+'.mat', str(max(tedils)))
sio.savemat(savename+'.mat', predmat)
plt.figure(1)
t = np.arange(0, totalepoch, 1)
plt.subplot(311)
plt.plot(t, trenerls, 'r', t, teenerls, 'b')
plt.subplot(312)
plt.plot(t, traccls, 'r', t, teaccls, 'b')
plt.subplot(313)
plt.plot(t, trdils, 'r', t, tedils, 'b')
plt.savefig(savename+'.png')