-
Notifications
You must be signed in to change notification settings - Fork 26
/
train_face.py
303 lines (217 loc) · 11.3 KB
/
train_face.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
import yaml
import os
import sys
import shutil
import numpy as np
import torch
from torch.backends import cudnn
import torch.optim as optim
import torch.nn as nn
from torch.autograd import Variable, grad
from src.data import LoadDataset
from src.ufdn import LoadModel
from src.util import vae_loss, calc_gradient_penalty, interpolate_vae_3d
from tensorboardX import SummaryWriter
# Experiment Setting
cudnn.benchmark = True
config_path = sys.argv[1]
conf = yaml.load(open(config_path,'r'))
exp_name = conf['exp_setting']['exp_name']
img_size = conf['exp_setting']['img_size']
img_depth = conf['exp_setting']['img_depth']
trainer_conf = conf['trainer']
if trainer_conf['save_checkpoint']:
model_path = conf['exp_setting']['checkpoint_dir'] + exp_name+'/'
if not os.path.exists(model_path):
os.makedirs(model_path)
model_path = model_path+'{}'
if trainer_conf['save_log'] or trainer_conf['save_fig']:
if os.path.exists(conf['exp_setting']['log_dir']+exp_name):
shutil.rmtree(conf['exp_setting']['log_dir']+exp_name)
writer = SummaryWriter(conf['exp_setting']['log_dir']+exp_name)
# Fix seed
np.random.seed(conf['exp_setting']['seed'])
_ = torch.manual_seed(conf['exp_setting']['seed'])
# Load dataset
domain_a = conf['exp_setting']['domain_a']
doamin_b = conf['exp_setting']['doamin_b']
doamin_c = conf['exp_setting']['doamin_c']
data_root = conf['exp_setting']['data_root']
batch_size = conf['trainer']['batch_size']
a_loader = LoadDataset('face',data_root,batch_size,'train',style=domain_a)
b_loader = LoadDataset('face',data_root,batch_size,'train',style=doamin_b)
c_loader = LoadDataset('face',data_root,batch_size,'train',style=doamin_c)
a_test = LoadDataset('face',data_root,batch_size,'test',style=domain_a)
b_test = LoadDataset('face',data_root,batch_size,'test',style=doamin_b)
c_test = LoadDataset('face',data_root,batch_size,'test',style=doamin_c)
for d1,d2,d3 in zip(a_test,b_test,c_test):
a_test_sample = d1[9].type(torch.FloatTensor)
b_test_sample = d2[0].clone().repeat(3,1,1).type(torch.FloatTensor)
c_test_sample = d3[0].type(torch.FloatTensor)
break
# Load Model
enc_dim = conf['model']['vae']['encoder'][-1][1]
code_dim = conf['model']['vae']['code_dim']
vae_learning_rate = conf['model']['vae']['lr']
vae_betas = tuple(conf['model']['vae']['betas'])
df_learning_rate = conf['model']['D_feat']['lr']
df_betas = tuple(conf['model']['D_feat']['betas'])
dp_learning_rate = conf['model']['D_pix']['lr']
dp_betas = tuple(conf['model']['D_pix']['betas'])
vae = LoadModel('vae',conf['model']['vae'],img_size,img_depth)
d_feat = LoadModel('nn',conf['model']['D_feat'],img_size,enc_dim)
d_pix = LoadModel('cnn',conf['model']['D_pix'],img_size,img_depth)
reconstruct_loss = torch.nn.MSELoss()
clf_loss = nn.BCEWithLogitsLoss()
# Use cuda
vae = vae.cuda()
d_feat = d_feat.cuda()
d_pix = d_pix.cuda()
reconstruct_loss = reconstruct_loss.cuda()
clf_loss = clf_loss.cuda()
# Optmizer
opt_vae = optim.Adam(list(vae.parameters()), lr=vae_learning_rate, betas=vae_betas)
opt_df = optim.Adam(list(d_feat.parameters()), lr=df_learning_rate, betas=df_betas)
opt_dp = optim.Adam(list(d_pix.parameters()), lr=dp_learning_rate, betas=dp_betas)
# Training
vae.train()
d_feat.train()
d_pix.train()
# Domain code setting
domain_code = np.concatenate([np.repeat(np.array([[*([1]*int(code_dim/3)),
*([0]*int(code_dim/3)),
*([0]*int(code_dim/3))]]),batch_size,axis=0),
np.repeat(np.array([[*([0]*int(code_dim/3)),
*([1]*int(code_dim/3)),
*([0]*int(code_dim/3))]]),batch_size,axis=0),
np.repeat(np.array([[*([0]*int(code_dim/3)),
*([0]*int(code_dim/3)),
*([1]*int(code_dim/3))]]),batch_size,axis=0)],
axis=0)
domain_code = torch.FloatTensor(domain_code)
### Messy, torch.randperm will be better approach
# forword translation code : A->B->C->A
forword_code = np.concatenate([np.repeat(np.array([[*([0]*int(code_dim/3)),
*([1]*int(code_dim/3)),
*([0]*int(code_dim/3))]]),batch_size,axis=0),
np.repeat(np.array([[*([0]*int(code_dim/3)),
*([0]*int(code_dim/3)),
*([1]*int(code_dim/3))]]),batch_size,axis=0),
np.repeat(np.array([[*([1]*int(code_dim/3)),
*([0]*int(code_dim/3)),
*([0]*int(code_dim/3))]]),batch_size,axis=0)],
axis=0)
forword_code = torch.FloatTensor(forword_code)
# backword translation code : C->B->A->C
backword_code = np.concatenate([np.repeat(np.array([[*([0]*int(code_dim/3)),
*([0]*int(code_dim/3)),
*([1]*int(code_dim/3))]]),batch_size,axis=0),
np.repeat(np.array([[*([1]*int(code_dim/3)),
*([0]*int(code_dim/3)),
*([0]*int(code_dim/3))]]),batch_size,axis=0),
np.repeat(np.array([[*([0]*int(code_dim/3)),
*([1]*int(code_dim/3)),
*([0]*int(code_dim/3))]]),batch_size,axis=0)],
axis=0)
backword_code = torch.FloatTensor(backword_code)
# Loss weight setting
loss_lambda = {}
for k in trainer_conf['lambda'].keys():
init = trainer_conf['lambda'][k]['init']
final = trainer_conf['lambda'][k]['final']
step = trainer_conf['lambda'][k]['step']
loss_lambda[k] = {}
loss_lambda[k]['cur'] = init
loss_lambda[k]['inc'] = (final-init)/step
loss_lambda[k]['final'] = final
# Training
global_step = 0
while global_step < trainer_conf['total_step']:
for a_img,b_img,c_img in zip(a_loader,b_loader,c_loader):
# data augmentation
input_img = torch.cat([a_img.type(torch.FloatTensor),
b_img.clone().repeat(1,3,1,1).type(torch.FloatTensor),
c_img.type(torch.FloatTensor)],dim=0)
input_img = Variable(input_img.cuda(),requires_grad=False)
code = Variable(torch.FloatTensor(domain_code).cuda(),requires_grad=False)
invert_code = 1-code
if global_step%2 == 0:
trans_code = Variable(torch.FloatTensor(forword_code).cuda(),requires_grad=False)
else:
trans_code = Variable(torch.FloatTensor(backword_code).cuda(),requires_grad=False)
# Train Feature Discriminator
opt_df.zero_grad()
enc_x = vae(input_img,return_enc=True).detach()
code_pred = d_feat(enc_x)
df_loss = clf_loss(code_pred,code)
df_loss.backward()
opt_df.step()
# Train Pixel Discriminator
opt_dp.zero_grad()
pix_real_pred,pix_real_code_pred = d_pix(input_img)
fake_img = vae(input_img,insert_attrs=trans_code)[0].detach()
pix_fake_pred, _ = d_pix(fake_img)
pix_real_pred = pix_real_pred.mean()
pix_fake_pred = pix_fake_pred.mean()
gp = loss_lambda['gp']['cur']*calc_gradient_penalty(d_pix,input_img.data,fake_img.data)
pix_code_loss = clf_loss(pix_real_code_pred,code)
d_pix_loss = pix_code_loss + pix_fake_pred - pix_real_pred + gp
d_pix_loss.backward()
opt_dp.step()
# Train VAE
opt_vae.zero_grad()
### Reconstruction Phase
recon_batch, mu, logvar = vae(input_img,insert_attrs = code)
mse,kl = vae_loss(recon_batch, input_img, mu, logvar, reconstruct_loss) #.view(batch_size,-1)
recon_loss = (loss_lambda['pix_recon']['cur']*mse+loss_lambda['kl']['cur']*kl)
recon_loss.backward()
### Feature space adversarial Phase
enc_x = vae(input_img,return_enc=True)
domain_pred = d_feat(enc_x)
adv_code_loss = clf_loss(domain_pred,invert_code)
feature_loss = loss_lambda['feat_domain']['cur']*adv_code_loss
feature_loss.backward()
### Pixel space adversarial Phase
enc_x = vae(input_img,return_enc=True).detach()
fake_img = vae.decode(enc_x,trans_code)
recon_enc_x = vae(fake_img,return_enc=True)
adv_pix_loss, pix_code_pred = d_pix(fake_img)
adv_pix_loss = adv_pix_loss.mean()
pix_clf_loss = clf_loss(pix_code_pred,trans_code)
pixel_loss = - loss_lambda['pix_adv']['cur']*adv_pix_loss + loss_lambda['pix_clf']['cur']*pix_clf_loss
pixel_loss.backward()
opt_vae.step()
# End of step
print('Step',global_step,end='\r',flush=True)
global_step += 1
# Records
if trainer_conf['save_log'] and (global_step % trainer_conf['verbose_step'] ==0):
writer.add_scalar('MSE', mse.data[0], global_step)
writer.add_scalar('KL', kl.data[0], global_step)
writer.add_scalar('gp', gp.data[0], global_step)
writer.add_scalars('Pixel_Distance',{'real':pix_real_pred.data[0],
'fake':pix_fake_pred.data[0]}, global_step)
writer.add_scalars('Code_loss',{'feature':df_loss.data[0],
'pixel':pix_code_loss.data[0],
'adv_feature':feature_loss.data[0],
'adv_pixel':pix_clf_loss.data[0]}, global_step)
# update lambda
for k in loss_lambda.keys():
if loss_lambda[k]['inc']*loss_lambda[k]['cur'] < loss_lambda[k]['inc']*loss_lambda[k]['final']:
loss_lambda[k]['cur'] += loss_lambda[k]['inc']
if global_step%trainer_conf['checkpoint_step']==0 and trainer_conf['save_checkpoint'] and not trainer_conf['save_best_only']:
torch.save(vae,model_path.format(global_step)+'.vae')
### Show result
if global_step% trainer_conf['plot_step'] ==0:
vae.eval()
# Reconstruct
tmp = interpolate_vae_3d(vae,a_test_sample,b_test_sample,c_test_sample,attr_max=1.0,attr_dim=code_dim)
fig1 = (tmp+1)/2
# Generate
tmp = interpolate_vae_3d(vae,a_test_sample,b_test_sample,c_test_sample,attr_max=1.0,random_test=True,
sd =conf['exp_setting']['seed'],attr_dim=code_dim)
fig2 = (tmp+1)/2
if trainer_conf['save_fig']:
writer.add_image('interpolate', torch.FloatTensor(np.transpose(fig1,(2,0,1))), global_step)
writer.add_image('random generate', torch.FloatTensor(np.transpose(fig2,(2,0,1))), global_step)
vae.train()