-
Notifications
You must be signed in to change notification settings - Fork 19
/
reverse_eng.py
322 lines (242 loc) · 15.4 KB
/
reverse_eng.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
from torchvision import datasets, models, transforms
import os
import torch
from torch.autograd import Variable
from skimage import io
from scipy import fftpack
import numpy as np
from torch import nn
import datetime
from models import encoder_rev_eng
from models import fen
import torch.nn.functional as F
import argparse
#################################################################################################################
# HYPER PARAMETERS INITIALIZING
parser = argparse.ArgumentParser()
parser.add_argument('--lr', default=0.0001, type=float, help='learning rate')
parser.add_argument('--data_train',default='/mnt/scratch/asnanivi/GAN_data_6/set_1/train',help='root directory for training data')
parser.add_argument('--data_test',default='/mnt/scratch/asnanivi/GAN_data_6/set_1/test',help='root directory for testing data')
parser.add_argument('--ground_truth_dir',default='./',help='directory for ground truth')
parser.add_argument('--seed', default=1, type=int, help='manual seed')
parser.add_argument('--batch_size', default=16, type=int, help='batch size')
parser.add_argument('--savedir', default='/mnt/scratch/asnanivi/runs')
parser.add_argument('--model_dir', default='./models')
parser.add_argument('--N_given', nargs='+', help='position number of GM from list of GMs used in testing', default=[1,2,3,4,5,6])
opt = parser.parse_args()
print(opt)
print("Random Seed: ", opt.seed)
device=torch.device("cuda:0")
torch.backends.deterministic = True
torch.manual_seed(opt.seed)
torch.cuda.manual_seed_all(opt.seed)
sig = str(datetime.datetime.now())
set_name="set_1"
train_path=opt.data_train
test_path=opt.data_test
save_dir=opt.savedir
ground_truth_net_all=torch.from_numpy(np.load(opt.ground_truth_dir+ "ground_truth_net_arch_100_15dim.npy"))
ground_truth_loss_all=torch.from_numpy(np.load(opt.ground_truth_dir+ "ground_truth_loss_100_3dim.npy"))
ground_truth_loss_9_all=torch.from_numpy(np.load(opt.ground_truth_dir+ "ground_truth_loss_100_9dim.npy"))
N_all=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99]
N = [x for x in N_all if x not in opt.N_given]
ground_truth_net=ground_truth_net_all[N]
ground_truth_loss=ground_truth_loss_all[N]
ground_truth_loss_9=ground_truth_loss_9_all[N]
os.makedirs('%s/result_3/%s' % (save_dir, sig), exist_ok=True)
transform_train = transforms.Compose([
transforms.Resize((64,64)),
transforms.ToTensor(),
transforms.Normalize((0.6490, 0.6490, 0.6490), (0.1269, 0.1269, 0.1269))
])
train_set=datasets.ImageFolder(train_path, transform_train)
test_set=datasets.ImageFolder(test_path, transform_train)
b_s=opt.batch_size
train_loader = torch.utils.data.DataLoader(train_set,batch_size=b_s,shuffle =True, num_workers=1)
test_loader = torch.utils.data.DataLoader(test_set,batch_size=b_s,shuffle =True, num_workers=1)
model=fen.DnCNN().to(device)
model_params = list(model.parameters())
optimizer = torch.optim.Adam(model_params, lr=opt.lr)
model_2=encoder_rev_eng.encoder(num_hidden=512).to(device)
optimizer_2 = torch.optim.Adam(model_2.parameters(), lr=opt.lr)
weightn1 = torch.tensor([100/69, 100/19, 100/6,100/6])
weightn2 = torch.tensor([100/6, 100/74, 100/7,100/13])
weightn3 = torch.tensor([100/3, 100/74, 100/21,100/2])
weightn4 = torch.tensor([100/70, 100/30])
weightn5 = torch.tensor([100/34, 100/66])
weightn6 = torch.tensor([100/55, 100/45])
weight3L1 = torch.tensor([100/35, 100/65])
weight3L2 = torch.tensor([100/35, 100/65])
weight3L3 = torch.tensor([100/67, 100/33])
weight9L1 = torch.tensor([100/56, 100/44])
weight9L2 = torch.tensor([100/80, 100/20])
weight9L3 = torch.tensor([100/86, 100/14])
weight9L4 = torch.tensor([100/96, 100/4])
weight9L5 = torch.tensor([100/52, 100/48])
weight9L6 = torch.tensor([100/86, 100/14])
weight9L7 = torch.tensor([100/93, 100/7])
weight9L8 = torch.tensor([100/68, 100/32])
l1=torch.nn.MSELoss().to(device)
l_cn1 = torch.nn.CrossEntropyLoss(weightn1).to(device)
l_cn2 = torch.nn.CrossEntropyLoss(weightn2).to(device)
l_cn3 = torch.nn.CrossEntropyLoss(weightn3).to(device)
l_cn4 = torch.nn.CrossEntropyLoss(weightn4).to(device)
l_cn5 = torch.nn.CrossEntropyLoss(weightn5).to(device)
l_cn6 = torch.nn.CrossEntropyLoss(weightn6).to(device)
l_c3L1 = torch.nn.CrossEntropyLoss(weight3L1).to(device)
l_c3L2 = torch.nn.CrossEntropyLoss(weight3L2).to(device)
l_c3L3 = torch.nn.CrossEntropyLoss(weight3L3).to(device)
l_c9L1 = torch.nn.CrossEntropyLoss(weight9L1).to(device)
l_c9L2 = torch.nn.CrossEntropyLoss(weight9L2).to(device)
l_c9L3 = torch.nn.CrossEntropyLoss(weight9L3).to(device)
l_c9L4 = torch.nn.CrossEntropyLoss(weight9L4).to(device)
l_c9L5 = torch.nn.CrossEntropyLoss(weight9L5).to(device)
l_c9L6 = torch.nn.CrossEntropyLoss(weight9L6).to(device)
l_c9L7 = torch.nn.CrossEntropyLoss(weight9L7).to(device)
l_c9L8 = torch.nn.CrossEntropyLoss(weight9L8).to(device)
state = {
'state_dict_cnn':model.state_dict(),
'optimizer_1': optimizer.state_dict(),
'state_dict_class':model_2.state_dict(),
'optimizer_2': optimizer_2.state_dict()
}
state1 = torch.load(opt.model_dir)
optimizer.load_state_dict(state1['optimizer_1'])
model.load_state_dict(state1['state_dict_cnn'])
optimizer_2.load_state_dict(state1['optimizer_2'])
model_2.load_state_dict(state1['state_dict_class'])
def train(batch, labels,g_t_net,g_t_loss, g_t_loss_9):
model.train()
model_2.train()
y,low_freq_part,max_value ,y_orig,residual, y_trans,residual_gray =model(batch.type(torch.cuda.FloatTensor))
y_2=torch.unsqueeze(y.clone(),1)
outn1,outn2,outn3,outn4, outn5,outn6,outn7,out3L1,out3L2,out3L3,outh9L1,outh9L2,outh9L3,outh9L4,outh9L5,outh9L6,outh9L7,outh9L8=model_2(residual)
n=25
zero=torch.zeros([y.shape[0],2*n+1,2*n+1], dtype=torch.float32).to(device)
zero_1=torch.zeros(residual_gray.shape, dtype=torch.float32).to(device)
loss1=0.05*l1(low_freq_part,zero).to(device)
loss2=-0.001*max_value.to(device)
loss3 = 0.1*l1(residual_gray,zero_1).to(device)
loss4=l1(y,y_trans).to(device)
loss5_1=10*l1(outn1,g_t_net[:,0:9].type(torch.cuda.FloatTensor)).to(device)
loss5_2=10*l_cn1(outn2,g_t_net[:,9].type(torch.cuda.LongTensor)).to(device)
loss5_3=10*l_cn2(outn3,g_t_net[:,10].type(torch.cuda.LongTensor)).to(device)
loss5_4=10*l_cn3(outn4,g_t_net[:,11].type(torch.cuda.LongTensor)).to(device)
loss5_5=10*l_cn4(outn5,g_t_net[:,12].type(torch.cuda.LongTensor)).to(device)
loss5_6=10*l_cn5(outn6,g_t_net[:,13].type(torch.cuda.LongTensor)).to(device)
loss5_7=10*l_cn6(outn7,g_t_net[:,14].type(torch.cuda.LongTensor)).to(device)
loss6_1=10*l_c3L1(out3L1,g_t_loss[:,0].type(torch.cuda.LongTensor)).to(device)
loss6_2=10*l_c3L2(out3L2,g_t_loss[:,1].type(torch.cuda.LongTensor)).to(device)
loss6_3=10*l_c3L3(out3L3,g_t_loss[:,2].type(torch.cuda.LongTensor)).to(device)
loss7_1=10*l_c9L1(outh9L1,g_t_loss_9[:,0].type(torch.cuda.LongTensor)).to(device)
loss7_2=10*l_c9L2(outh9L2,g_t_loss_9[:,1].type(torch.cuda.LongTensor)).to(device)
loss7_3=10*l_c9L3(outh9L3,g_t_loss_9[:,2].type(torch.cuda.LongTensor)).to(device)
loss7_4=10*l_c9L4(outh9L4,g_t_loss_9[:,3].type(torch.cuda.LongTensor)).to(device)
loss7_5=10*l_c9L5(outh9L5,g_t_loss_9[:,4].type(torch.cuda.LongTensor)).to(device)
loss7_6=10*l_c9L6(outh9L6,g_t_loss_9[:,5].type(torch.cuda.LongTensor)).to(device)
loss7_7=10*l_c9L7(outh9L7,g_t_loss_9[:,6].type(torch.cuda.LongTensor)).to(device)
loss7_8=10*l_c9L8(outh9L8,g_t_loss_9[:,7].type(torch.cuda.LongTensor)).to(device)
print("loss1:",loss1.item()," loss2:",loss2.item()," loss3:",loss3.item()," loss4:",loss4.item())
print("loss5_1:",loss5_1.item()," loss 5_2:",loss5_2.item()," loss5_3:",loss5_3.item(),
" loss5_4:",loss5_4.item()," loss5_5:",loss5_5.item()," loss5_6:",loss5_6.item()," loss5_7:",loss5_7.item())
print("loss6_1:",loss6_1.item()," loss6_2:",loss6_2.item()," loss6_3:",loss6_3.item())
print("loss7_1:",loss7_1.item()," loss7_2:",loss7_2.item()," loss7_3:",loss7_3.item()," loss7_4:",loss7_4.item(), " loss7_5:",loss7_5.item()," loss7_6:",loss7_6.item()," loss7_7:",loss7_7.item()," loss7_8:",loss7_8.item())
loss=(loss1+loss2+loss3+loss4+loss5_1+loss5_2+loss5_3+loss5_4+loss5_5+loss5_6+loss5_7+loss6_1+loss6_2+loss6_3+loss7_1+loss7_2+loss7_3+loss7_4+loss7_5+loss7_6+loss7_7+loss7_8)
optimizer.zero_grad()
optimizer_2.zero_grad()
loss.backward()
optimizer.step()
optimizer_2.step()
return y, loss.item(), y_orig,residual, outn1,outn2,outn3,outn4,outn5,outn6,outn7,out3L1,out3L2,out3L3,outh9L1,outh9L2,outh9L3,outh9L4,outh9L5,outh9L6,outh9L7,outh9L8
def test(batch,labels):
model.eval()
model_2.eval()
with torch.no_grad():
y,low_freq_part,max_value ,y_orig,residual, y_trans,residual_gray =model(batch.type(torch.cuda.FloatTensor))
y_2=torch.unsqueeze(y.clone(),1)
outn1,outn2,outn3,outn4,outn5 ,outn6,outn7,out3L1,out3L2,out3L3,outh9L1,outh9L2,outh9L3,outh9L4,outh9L5,outh9L6,outh9L7,outh9L8=model_2(residual)
return y,y_orig,residual, outn1,outn2,outn3,outn4,outn5,outn6,outn7,out3L1,out3L2,out3L3,outh9L1,outh9L2,outh9L3,outh9L4,outh9L5,outh9L6,outh9L7,outh9L8
print(len(train_set))
print(len(test_set))
print(train_set.class_to_idx)
print(test_set.class_to_idx)
epochs=20
for epoch in range(epochs):
all_y=[]
all_y_test=[]
flag=0
flag1=0
count=0
number=0
for batch_idx, (inputs,labels) in enumerate(train_loader):
g_t_loss_batch= torch.empty(labels.shape[0],3 )
g_t_loss_batch_9= torch.empty(labels.shape[0],8 )
g_t_net_batch= torch.empty(labels.shape[0], 15)
for i in range(labels.shape[0]):
g_t_net_batch[i,:]=ground_truth_net[labels[i]]
g_t_loss_batch[i,:]=ground_truth_loss[labels[i]]
g_t_loss_batch_9[i,:]=ground_truth_loss_9[labels[i]]
out,loss, out_orig,residual,outn1,outn2,outn3,outn4,outn5,outn6,outn7,out3L1,out3L2,out3L3,outh9L1, outh9L2,outh9L3,outh9L4,outh9L5,outh9L6,outh9L7,outh9L8=train(Variable(torch.FloatTensor(inputs)),Variable(torch.LongTensor(labels)), Variable(torch.FloatTensor(g_t_net_batch)),Variable(torch.FloatTensor(g_t_loss_batch)),Variable(torch.FloatTensor(g_t_loss_batch_9)))
all_y.append(np.asarray(labels))
count+=1
for batch_idx_test, (inputs_test,labels_test) in enumerate(test_loader):
out,out_orig,residual,outn1,outn2,outn3,outn4,outn5,outn6,outn7,out3L1,out3L2,out3L3,outh9L1, outh9L2,outh9L3,outh9L4,outh9L5,outh9L6,outh9L7,outh9L8=test(Variable(torch.FloatTensor(inputs_test)),Variable(torch.LongTensor(labels_test)))
all_y_test.append(np.asarray(labels_test))
if flag1==0:
flag1=1
all_features_net1=outn1.detach()
all_features_net2=outn2.detach()
all_features_net3=outn3.detach()
all_features_net4=outn4.detach()
all_features_net5=outn5.detach()
all_features_net6=outn6.detach()
all_features_net7=outn7.detach()
all_features_loss1=out3L1.detach()
all_features_loss2=out3L2.detach()
all_features_loss3=out3L3.detach()
all_features_loss_91=outh9L1.detach()
all_features_loss_92=outh9L2.detach()
all_features_loss_93=outh9L3.detach()
all_features_loss_94=outh9L4.detach()
all_features_loss_95=outh9L5.detach()
all_features_loss_96=outh9L6.detach()
all_features_loss_97=outh9L7.detach()
all_features_loss_98=outh9L8.detach()
else:
all_features_net1=torch.cat([all_features_net1,outn1.detach()], dim=0)
all_features_net2=torch.cat([all_features_net2,outn2.detach()], dim=0)
all_features_net3=torch.cat([all_features_net3,outn3.detach()], dim=0)
all_features_net4=torch.cat([all_features_net4,outn4.detach()], dim=0)
all_features_net5=torch.cat([all_features_net5,outn5.detach()], dim=0)
all_features_net6=torch.cat([all_features_net6,outn6.detach()], dim=0)
all_features_net7=torch.cat([all_features_net7,outn7.detach()], dim=0)
all_features_loss1=torch.cat([all_features_loss1,out3L1.detach()], dim=0)
all_features_loss2=torch.cat([all_features_loss2,out3L2.detach()], dim=0)
all_features_loss3=torch.cat([all_features_loss3,out3L3.detach()], dim=0)
all_features_loss_91=torch.cat([all_features_loss_91,outh9L1.detach()], dim=0)
all_features_loss_92=torch.cat([all_features_loss_92,outh9L2.detach()], dim=0)
all_features_loss_93=torch.cat([all_features_loss_93,outh9L3.detach()], dim=0)
all_features_loss_94=torch.cat([all_features_loss_94,outh9L4.detach()], dim=0)
all_features_loss_95=torch.cat([all_features_loss_95,outh9L5.detach()], dim=0)
all_features_loss_96=torch.cat([all_features_loss_96,outh9L6.detach()], dim=0)
all_features_loss_97=torch.cat([all_features_loss_97,outh9L7.detach()], dim=0)
all_features_loss_98=torch.cat([all_features_loss_98,outh9L8.detach()], dim=0)
torch.save(all_features_net1, '%s/result_3/%s/out_features_net_test_1_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_net2, '%s/result_3/%s/out_features_net_test_2_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_net3, '%s/result_3/%s/out_features_net_test_3_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_net4, '%s/result_3/%s/out_features_net_test_4_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_net5, '%s/result_3/%s/out_features_net_test_5_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_net6, '%s/result_3/%s/out_features_net_test_6_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_net7, '%s/result_3/%s/out_features_net_test_7_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss1, '%s/result_3/%s/out_features_loss3_test_1_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss2, '%s/result_3/%s/out_features_loss3_test_2_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss3, '%s/result_3/%s/out_features_loss3_test_3_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss_91, '%s/result_3/%s/out_features_loss9_test_1_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss_91, '%s/result_3/%s/out_features_loss9_test_2_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss_93, '%s/result_3/%s/out_features_loss9_test_3_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss_94, '%s/result_3/%s/out_features_loss9_test_4_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss_95, '%s/result_3/%s/out_features_loss9_test_5_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss_96, '%s/result_3/%s/out_features_loss9_test_6_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss_97, '%s/result_3/%s/out_features_loss9_test_7_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_features_loss_98, '%s/result_3/%s/out_features_loss9_test_8_%d.pickle' % (save_dir, sig, epoch))
torch.save(all_y_test, '%s/result_3/%s/out_y_test_%d.pickle' % (save_dir, sig, epoch))