-
Notifications
You must be signed in to change notification settings - Fork 643
/
Copy pathmodel_gan.py
307 lines (271 loc) · 12.5 KB
/
model_gan.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
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
from torch.optim import Adam
from torch.nn.parallel import DataParallel # , DistributedDataParallel
from models.select_network import define_G, define_D, define_F
from models.model_base import ModelBase
from models.loss import GANLoss
from models.loss_ssim import SSIMLoss
class ModelGAN(ModelBase):
"""Train with pixel-VGG-GAN loss"""
def __init__(self, opt):
super(ModelGAN, self).__init__(opt)
# ------------------------------------
# define network
# ------------------------------------
self.netG = define_G(opt).to(self.device)
self.netG = DataParallel(self.netG)
if self.is_train:
self.netF = define_F(opt).to(self.device)
self.netD = define_D(opt).to(self.device)
self.netF = DataParallel(self.netF)
self.netD = DataParallel(self.netD)
"""
# ----------------------------------------
# Preparation before training with data
# Save model during training
# ----------------------------------------
"""
# ----------------------------------------
# initialize training
# ----------------------------------------
def init_train(self):
self.opt_train = self.opt['train'] # training option
self.load() # load model
self.netG.train() # set training mode,for BN
self.netD.train() # set training mode,for BN
self.define_loss() # define loss
self.define_optimizer() # define optimizer
self.define_scheduler() # define scheduler
self.log_dict = OrderedDict() # log
# ----------------------------------------
# load pre-trained G and D model
# ----------------------------------------
def load(self):
load_path_G = self.opt['path']['pretrained_netG']
if load_path_G is not None:
print('Loading model for G [{:s}] ...'.format(load_path_G))
self.load_network(load_path_G, self.netG)
load_path_D = self.opt['path']['pretrained_netD']
if self.opt['is_train'] and load_path_D is not None:
print('Loading model for D [{:s}] ...'.format(load_path_D))
self.load_network(load_path_D, self.netD)
# ----------------------------------------
# save model
# ----------------------------------------
def save(self, iter_label):
self.save_network(self.save_dir, self.netG, 'G', iter_label)
self.save_network(self.save_dir, self.netD, 'D', iter_label)
# ----------------------------------------
# define loss
# ----------------------------------------
def define_loss(self):
# ------------------------------------
# G_loss
# ------------------------------------
if self.opt_train['G_lossfn_weight'] > 0:
G_lossfn_type = self.opt_train['G_lossfn_type']
if G_lossfn_type == 'l1':
self.G_lossfn = nn.L1Loss().to(self.device)
elif G_lossfn_type == 'l2':
self.G_lossfn = nn.MSELoss().to(self.device)
elif G_lossfn_type == 'l2sum':
self.G_lossfn = nn.MSELoss(reduction='sum').to(self.device)
elif G_lossfn_type == 'ssim':
self.G_lossfn = SSIMLoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] is not found.'.format(G_lossfn_type))
self.G_lossfn_weight = self.opt_train['G_lossfn_weight']
else:
print('Do not use pixel loss.')
self.G_lossfn = None
# ------------------------------------
# F_loss
# ------------------------------------
if self.opt_train['F_lossfn_weight'] > 0:
F_lossfn_type = self.opt_train['F_lossfn_type']
if F_lossfn_type == 'l1':
self.F_lossfn = nn.L1Loss().to(self.device)
elif F_lossfn_type == 'l2':
self.F_lossfn = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(F_lossfn_type))
self.F_lossfn_weight = self.opt_train['F_lossfn_weight']
# self.netF = define_F(self.opt, use_bn=False).to(self.device)
else:
print('Do not use feature loss.')
self.F_lossfn = None
# ------------------------------------
# D_loss
# ------------------------------------
self.D_lossfn = GANLoss(self.opt_train['gan_type'], 1.0, 0.0).to(self.device)
self.D_lossfn_weight = self.opt_train['D_lossfn_weight']
self.D_update_ratio = self.opt_train['D_update_ratio'] if self.opt_train['D_update_ratio'] else 1
self.D_init_iters = self.opt_train['D_init_iters'] if self.opt_train['D_init_iters'] else 0
# ----------------------------------------
# define optimizer, G and D
# ----------------------------------------
def define_optimizer(self):
G_optim_params = []
for k, v in self.netG.named_parameters():
if v.requires_grad:
G_optim_params.append(v)
else:
print('Params [{:s}] will not optimize.'.format(k))
self.G_optimizer = Adam(G_optim_params, lr=self.opt_train['G_optimizer_lr'], weight_decay=0)
self.D_optimizer = Adam(self.netD.parameters(), lr=self.opt_train['D_optimizer_lr'], weight_decay=0)
# ----------------------------------------
# define scheduler, only "MultiStepLR"
# ----------------------------------------
def define_scheduler(self):
self.schedulers.append(lr_scheduler.MultiStepLR(self.G_optimizer,
self.opt_train['G_scheduler_milestones'],
self.opt_train['G_scheduler_gamma']
))
self.schedulers.append(lr_scheduler.MultiStepLR(self.D_optimizer,
self.opt_train['D_scheduler_milestones'],
self.opt_train['D_scheduler_gamma']
))
"""
# ----------------------------------------
# Optimization during training with data
# Testing/evaluation
# ----------------------------------------
"""
# ----------------------------------------
# feed L/H data
# ----------------------------------------
def feed_data(self, data, need_H=True):
self.L = data['L'].to(self.device)
if need_H:
self.H = data['H'].to(self.device)
input_ref = data['ref'] if 'ref' in data else data['H']
self.var_ref = input_ref.to(self.device)
# ----------------------------------------
# update parameters and get loss
# ----------------------------------------
def optimize_parameters(self, current_step):
# ------------------------------------
# optimize G
# ------------------------------------
for p in self.netD.parameters():
p.requires_grad = False
self.G_optimizer.zero_grad()
self.E = self.netG(self.L)
loss_G_total = 0
if current_step % self.D_update_ratio == 0 and current_step > self.D_init_iters: # updata D first
if self.opt_train['G_lossfn_weight'] > 0:
G_loss = self.G_lossfn_weight * self.G_lossfn(self.E, self.H)
loss_G_total += G_loss # 1) pixel loss
if self.opt_train['F_lossfn_weight'] > 0:
real_fea = self.netF(self.H).detach()
fake_fea = self.netF(self.E)
F_loss = self.F_lossfn_weight * self.F_lossfn(fake_fea, real_fea)
loss_G_total += F_loss # 2) VGG feature loss
pred_g_fake = self.netD(self.E)
if self.opt['train']['gan_type'] == 'gan':
D_loss = self.D_lossfn_weight * self.D_lossfn(pred_g_fake, True)
elif self.opt['train']['gan_type'] == 'ragan':
pred_d_real = self.netD(self.var_ref).detach()
D_loss = self.D_lossfn_weight * (
self.D_lossfn(pred_d_real - torch.mean(pred_g_fake), False) +
self.D_lossfn(pred_g_fake - torch.mean(pred_d_real), True)) / 2
loss_G_total += D_loss # 3) GAN loss
loss_G_total.backward()
self.G_optimizer.step()
# ------------------------------------
# optimize D
# ------------------------------------
for p in self.netD.parameters():
p.requires_grad = True
self.D_optimizer.zero_grad()
loss_D_total = 0
pred_d_real = self.netD(self.var_ref) # 1) real data
pred_d_fake = self.netD(self.E.detach()) # 2) fake data, detach to avoid BP to G
if self.opt['train']['gan_type'] == 'gan':
l_d_real = self.D_lossfn(pred_d_real, True)
l_d_fake = self.D_lossfn(pred_d_fake, False)
loss_D_total = l_d_real + l_d_fake
elif self.opt['train']['gan_type'] == 'ragan':
l_d_real = self.D_lossfn(pred_d_real - torch.mean(pred_d_fake), True)
l_d_fake = self.D_lossfn(pred_d_fake - torch.mean(pred_d_real), False)
loss_D_total = (l_d_real + l_d_fake) / 2
loss_D_total.backward()
self.D_optimizer.step()
# ------------------------------------
# record log
# ------------------------------------
if current_step % self.D_update_ratio == 0 and current_step > self.D_init_iters:
if self.opt_train['G_lossfn_weight'] > 0:
self.log_dict['G_loss'] = G_loss.item() # /self.E.size()[0]
if self.opt_train['F_lossfn_weight'] > 0:
self.log_dict['F_loss'] = F_loss.item() # /self.E.size()[0]
self.log_dict['D_loss'] = D_loss.item() # /self.E.size()[0]
self.log_dict['l_d_real'] = l_d_real.item() # /self.E.size()[0]
self.log_dict['l_d_fake'] = l_d_fake.item() # /self.E.size()[0]
self.log_dict['D_real'] = torch.mean(pred_d_real.detach())
self.log_dict['D_fake'] = torch.mean(pred_d_fake.detach())
# ----------------------------------------
# test and inference
# ----------------------------------------
def test(self):
self.netG.eval()
with torch.no_grad():
self.E = self.netG(self.L)
self.netG.train()
# ----------------------------------------
# get log_dict
# ----------------------------------------
def current_log(self):
return self.log_dict
# ----------------------------------------
# get L, E, H images
# ----------------------------------------
def current_visuals(self, need_H=True):
out_dict = OrderedDict()
out_dict['L'] = self.L.detach()[0].float().cpu()
out_dict['E'] = self.E.detach()[0].float().cpu()
if need_H:
out_dict['H'] = self.H.detach()[0].float().cpu()
return out_dict
"""
# ----------------------------------------
# Information of netG, netD and netF
# ----------------------------------------
"""
# ----------------------------------------
# print network
# ----------------------------------------
def print_network(self):
msg = self.describe_network(self.netG)
print(msg)
if self.is_train:
msg = self.describe_network(self.netD)
print(msg)
if self.opt_train['F_lossfn_weight'] > 0:
msg = self.describe_network(self.netF)
print(msg)
# ----------------------------------------
# print params
# ----------------------------------------
def print_params(self):
msg = self.describe_params(self.netG)
print(msg)
# ----------------------------------------
# network information
# ----------------------------------------
def info_network(self):
msg = self.describe_network(self.netG)
if self.is_train:
msg += self.describe_network(self.netD)
if self.opt_train['F_lossfn_weight'] > 0:
msg += self.describe_network(self.netF)
return msg
# ----------------------------------------
# params information
# ----------------------------------------
def info_params(self):
msg = self.describe_params(self.netG)
return msg