-
Notifications
You must be signed in to change notification settings - Fork 96
/
net.py
366 lines (300 loc) · 16.4 KB
/
net.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
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.basemodel import BaseModel
from model.basenet import BaseNet
from model.loss import WGANLoss, IDMRFLoss
from model.layer import init_weights, PureUpsampling, ConfidenceDrivenMaskLayer, SpectralNorm
import numpy as np
# generative multi-column convolutional neural net
class GMCNN(BaseNet):
def __init__(self, in_channels, out_channels, cnum=32, act=F.elu, norm=F.instance_norm, using_norm=False):
super(GMCNN, self).__init__()
self.act = act
self.using_norm = using_norm
if using_norm is True:
self.norm = norm
else:
self.norm = None
ch = cnum
# network structure
self.EB1 = []
self.EB2 = []
self.EB3 = []
self.decoding_layers = []
self.EB1_pad_rec = []
self.EB2_pad_rec = []
self.EB3_pad_rec = []
self.EB1.append(nn.Conv2d(in_channels, ch, kernel_size=7, stride=1))
self.EB1.append(nn.Conv2d(ch, ch * 2, kernel_size=7, stride=2))
self.EB1.append(nn.Conv2d(ch * 2, ch * 2, kernel_size=7, stride=1))
self.EB1.append(nn.Conv2d(ch * 2, ch * 4, kernel_size=7, stride=2))
self.EB1.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=7, stride=1))
self.EB1.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=7, stride=1))
self.EB1.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=7, stride=1, dilation=2))
self.EB1.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=7, stride=1, dilation=4))
self.EB1.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=7, stride=1, dilation=8))
self.EB1.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=7, stride=1, dilation=16))
self.EB1.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=7, stride=1))
self.EB1.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=7, stride=1))
self.EB1.append(PureUpsampling(scale=4))
self.EB1_pad_rec = [3, 3, 3, 3, 3, 3, 6, 12, 24, 48, 3, 3, 0]
self.EB2.append(nn.Conv2d(in_channels, ch, kernel_size=5, stride=1))
self.EB2.append(nn.Conv2d(ch, ch * 2, kernel_size=5, stride=2))
self.EB2.append(nn.Conv2d(ch * 2, ch * 2, kernel_size=5, stride=1))
self.EB2.append(nn.Conv2d(ch * 2, ch * 4, kernel_size=5, stride=2))
self.EB2.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=5, stride=1))
self.EB2.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=5, stride=1))
self.EB2.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=5, stride=1, dilation=2))
self.EB2.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=5, stride=1, dilation=4))
self.EB2.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=5, stride=1, dilation=8))
self.EB2.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=5, stride=1, dilation=16))
self.EB2.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=5, stride=1))
self.EB2.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=5, stride=1))
self.EB2.append(PureUpsampling(scale=2, mode='nearest'))
self.EB2.append(nn.Conv2d(ch * 4, ch * 2, kernel_size=5, stride=1))
self.EB2.append(nn.Conv2d(ch * 2, ch * 2, kernel_size=5, stride=1))
self.EB2.append(PureUpsampling(scale=2))
self.EB2_pad_rec = [2, 2, 2, 2, 2, 2, 4, 8, 16, 32, 2, 2, 0, 2, 2, 0]
self.EB3.append(nn.Conv2d(in_channels, ch, kernel_size=3, stride=1))
self.EB3.append(nn.Conv2d(ch, ch * 2, kernel_size=3, stride=2))
self.EB3.append(nn.Conv2d(ch * 2, ch * 2, kernel_size=3, stride=1))
self.EB3.append(nn.Conv2d(ch * 2, ch * 4, kernel_size=3, stride=2))
self.EB3.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=3, stride=1))
self.EB3.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=3, stride=1))
self.EB3.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=3, stride=1, dilation=2))
self.EB3.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=3, stride=1, dilation=4))
self.EB3.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=3, stride=1, dilation=8))
self.EB3.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=3, stride=1, dilation=16))
self.EB3.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=3, stride=1))
self.EB3.append(nn.Conv2d(ch * 4, ch * 4, kernel_size=3, stride=1))
self.EB3.append(PureUpsampling(scale=2, mode='nearest'))
self.EB3.append(nn.Conv2d(ch * 4, ch * 2, kernel_size=3, stride=1))
self.EB3.append(nn.Conv2d(ch * 2, ch * 2, kernel_size=3, stride=1))
self.EB3.append(PureUpsampling(scale=2, mode='nearest'))
self.EB3.append(nn.Conv2d(ch * 2, ch, kernel_size=3, stride=1))
self.EB3.append(nn.Conv2d(ch, ch, kernel_size=3, stride=1))
self.EB3_pad_rec = [1, 1, 1, 1, 1, 1, 2, 4, 8, 16, 1, 1, 0, 1, 1, 0, 1, 1]
self.decoding_layers.append(nn.Conv2d(ch * 7, ch // 2, kernel_size=3, stride=1))
self.decoding_layers.append(nn.Conv2d(ch // 2, out_channels, kernel_size=3, stride=1))
self.decoding_pad_rec = [1, 1]
self.EB1 = nn.ModuleList(self.EB1)
self.EB2 = nn.ModuleList(self.EB2)
self.EB3 = nn.ModuleList(self.EB3)
self.decoding_layers = nn.ModuleList(self.decoding_layers)
# padding operations
padlen = 49
self.pads = [0] * padlen
for i in range(padlen):
self.pads[i] = nn.ReflectionPad2d(i)
self.pads = nn.ModuleList(self.pads)
def forward(self, x):
x1, x2, x3 = x, x, x
for i, layer in enumerate(self.EB1):
pad_idx = self.EB1_pad_rec[i]
x1 = layer(self.pads[pad_idx](x1))
if self.using_norm:
x1 = self.norm(x1)
if pad_idx != 0:
x1 = self.act(x1)
for i, layer in enumerate(self.EB2):
pad_idx = self.EB2_pad_rec[i]
x2 = layer(self.pads[pad_idx](x2))
if self.using_norm:
x2 = self.norm(x2)
if pad_idx != 0:
x2 = self.act(x2)
for i, layer in enumerate(self.EB3):
pad_idx = self.EB3_pad_rec[i]
x3 = layer(self.pads[pad_idx](x3))
if self.using_norm:
x3 = self.norm(x3)
if pad_idx != 0:
x3 = self.act(x3)
x_d = torch.cat((x1, x2, x3), 1)
x_d = self.act(self.decoding_layers[0](self.pads[self.decoding_pad_rec[0]](x_d)))
x_d = self.decoding_layers[1](self.pads[self.decoding_pad_rec[1]](x_d))
x_out = torch.clamp(x_d, -1, 1)
return x_out
# return one dimensional output indicating the probability of realness or fakeness
class Discriminator(BaseNet):
def __init__(self, in_channels, cnum=32, fc_channels=8*8*32*4, act=F.elu, norm=None, spectral_norm=True):
super(Discriminator, self).__init__()
self.act = act
self.norm = norm
self.embedding = None
self.logit = None
ch = cnum
self.layers = []
if spectral_norm:
self.layers.append(SpectralNorm(nn.Conv2d(in_channels, ch, kernel_size=5, padding=2, stride=2)))
self.layers.append(SpectralNorm(nn.Conv2d(ch, ch * 2, kernel_size=5, padding=2, stride=2)))
self.layers.append(SpectralNorm(nn.Conv2d(ch * 2, ch * 4, kernel_size=5, padding=2, stride=2)))
self.layers.append(SpectralNorm(nn.Conv2d(ch * 4, ch * 4, kernel_size=5, padding=2, stride=2)))
self.layers.append(SpectralNorm(nn.Linear(fc_channels, 1)))
else:
self.layers.append(nn.Conv2d(in_channels, ch, kernel_size=5, padding=2, stride=2))
self.layers.append(nn.Conv2d(ch, ch * 2, kernel_size=5, padding=2, stride=2))
self.layers.append(nn.Conv2d(ch*2, ch*4, kernel_size=5, padding=2, stride=2))
self.layers.append(nn.Conv2d(ch*4, ch*4, kernel_size=5, padding=2, stride=2))
self.layers.append(nn.Linear(fc_channels, 1))
self.layers = nn.ModuleList(self.layers)
def forward(self, x):
for layer in self.layers[:-1]:
x = layer(x)
if self.norm is not None:
x = self.norm(x)
x = self.act(x)
self.embedding = x.view(x.size(0), -1)
self.logit = self.layers[-1](self.embedding)
return self.logit
class GlobalLocalDiscriminator(BaseNet):
def __init__(self, in_channels, cnum=32, g_fc_channels=16*16*32*4, l_fc_channels=8*8*32*4, act=F.elu, norm=None,
spectral_norm=True):
super(GlobalLocalDiscriminator, self).__init__()
self.act = act
self.norm = norm
self.global_discriminator = Discriminator(in_channels=in_channels, fc_channels=g_fc_channels, cnum=cnum,
act=act, norm=norm, spectral_norm=spectral_norm)
self.local_discriminator = Discriminator(in_channels=in_channels, fc_channels=l_fc_channels, cnum=cnum,
act=act, norm=norm, spectral_norm=spectral_norm)
def forward(self, x_g, x_l):
x_global = self.global_discriminator(x_g)
x_local = self.local_discriminator(x_l)
return x_global, x_local
from util.utils import generate_mask
class InpaintingModel_GMCNN(BaseModel):
def __init__(self, in_channels, act=F.elu, norm=None, opt=None):
super(InpaintingModel_GMCNN, self).__init__()
self.opt = opt
self.init(opt)
self.confidence_mask_layer = ConfidenceDrivenMaskLayer()
self.netGM = GMCNN(in_channels, out_channels=3, cnum=opt.g_cnum, act=act, norm=norm).cuda()
init_weights(self.netGM)
self.model_names = ['GM']
if self.opt.phase == 'test':
return
self.netD = None
self.optimizer_G = torch.optim.Adam(self.netGM.parameters(), lr=opt.lr, betas=(0.5, 0.9))
self.optimizer_D = None
self.wganloss = None
self.recloss = nn.L1Loss()
self.aeloss = nn.L1Loss()
self.mrfloss = None
self.lambda_adv = opt.lambda_adv
self.lambda_rec = opt.lambda_rec
self.lambda_ae = opt.lambda_ae
self.lambda_gp = opt.lambda_gp
self.lambda_mrf = opt.lambda_mrf
self.G_loss = None
self.G_loss_reconstruction = None
self.G_loss_mrf = None
self.G_loss_adv, self.G_loss_adv_local = None, None
self.G_loss_ae = None
self.D_loss, self.D_loss_local = None, None
self.GAN_loss = None
self.gt, self.gt_local = None, None
self.mask, self.mask_01 = None, None
self.rect = None
self.im_in, self.gin = None, None
self.completed, self.completed_local = None, None
self.completed_logit, self.completed_local_logit = None, None
self.gt_logit, self.gt_local_logit = None, None
self.pred = None
if self.opt.pretrain_network is False:
if self.opt.mask_type == 'rect':
self.netD = GlobalLocalDiscriminator(3, cnum=opt.d_cnum, act=act,
g_fc_channels=opt.img_shapes[0]//16*opt.img_shapes[1]//16*opt.d_cnum*4,
l_fc_channels=opt.mask_shapes[0]//16*opt.mask_shapes[1]//16*opt.d_cnum*4,
spectral_norm=self.opt.spectral_norm).cuda()
else:
self.netD = GlobalLocalDiscriminator(3, cnum=opt.d_cnum, act=act,
spectral_norm=self.opt.spectral_norm,
g_fc_channels=opt.img_shapes[0]//16*opt.img_shapes[1]//16*opt.d_cnum*4,
l_fc_channels=opt.img_shapes[0]//16*opt.img_shapes[1]//16*opt.d_cnum*4).cuda()
init_weights(self.netD)
self.optimizer_D = torch.optim.Adam(filter(lambda x: x.requires_grad, self.netD.parameters()), lr=opt.lr,
betas=(0.5, 0.9))
self.wganloss = WGANLoss()
self.mrfloss = IDMRFLoss()
def initVariables(self):
self.gt = self.input['gt']
mask, rect = generate_mask(self.opt.mask_type, self.opt.img_shapes, self.opt.mask_shapes)
self.mask_01 = torch.from_numpy(mask).cuda().repeat([self.opt.batch_size, 1, 1, 1])
self.mask = self.confidence_mask_layer(self.mask_01)
if self.opt.mask_type == 'rect':
self.rect = [rect[0, 0], rect[0, 1], rect[0, 2], rect[0, 3]]
self.gt_local = self.gt[:, :, self.rect[0]:self.rect[0] + self.rect[1],
self.rect[2]:self.rect[2] + self.rect[3]]
else:
self.gt_local = self.gt
self.im_in = self.gt * (1 - self.mask_01)
self.gin = torch.cat((self.im_in, self.mask_01), 1)
def forward_G(self):
self.G_loss_reconstruction = self.recloss(self.completed * self.mask, self.gt.detach() * self.mask)
self.G_loss_reconstruction = self.G_loss_reconstruction / torch.mean(self.mask_01)
self.G_loss_ae = self.aeloss(self.pred * (1 - self.mask_01), self.gt.detach() * (1 - self.mask_01))
self.G_loss_ae = self.G_loss_ae / torch.mean(1 - self.mask_01)
self.G_loss = self.lambda_rec * self.G_loss_reconstruction + self.lambda_ae * self.G_loss_ae
if self.opt.pretrain_network is False:
# discriminator
self.completed_logit, self.completed_local_logit = self.netD(self.completed, self.completed_local)
self.G_loss_mrf = self.mrfloss((self.completed_local+1)/2.0, (self.gt_local.detach()+1)/2.0)
self.G_loss = self.G_loss + self.lambda_mrf * self.G_loss_mrf
self.G_loss_adv = -self.completed_logit.mean()
self.G_loss_adv_local = -self.completed_local_logit.mean()
self.G_loss = self.G_loss + self.lambda_adv * (self.G_loss_adv + self.G_loss_adv_local)
def forward_D(self):
self.completed_logit, self.completed_local_logit = self.netD(self.completed.detach(), self.completed_local.detach())
self.gt_logit, self.gt_local_logit = self.netD(self.gt, self.gt_local)
# hinge loss
self.D_loss_local = nn.ReLU()(1.0 - self.gt_local_logit).mean() + nn.ReLU()(1.0 + self.completed_local_logit).mean()
self.D_loss = nn.ReLU()(1.0 - self.gt_logit).mean() + nn.ReLU()(1.0 + self.completed_logit).mean()
self.D_loss = self.D_loss + self.D_loss_local
def backward_G(self):
self.G_loss.backward()
def backward_D(self):
self.D_loss.backward(retain_graph=True)
def optimize_parameters(self):
self.initVariables()
self.pred = self.netGM(self.gin)
self.completed = self.pred * self.mask_01 + self.gt * (1 - self.mask_01)
if self.opt.mask_type == 'rect':
self.completed_local = self.completed[:, :, self.rect[0]:self.rect[0] + self.rect[1],
self.rect[2]:self.rect[2] + self.rect[3]]
else:
self.completed_local = self.completed
if self.opt.pretrain_network is False:
for i in range(self.opt.D_max_iters):
self.optimizer_D.zero_grad()
self.optimizer_G.zero_grad()
self.forward_D()
self.backward_D()
self.optimizer_D.step()
self.optimizer_G.zero_grad()
self.forward_G()
self.backward_G()
self.optimizer_G.step()
def get_current_losses(self):
l = {'G_loss': self.G_loss.item(), 'G_loss_rec': self.G_loss_reconstruction.item(),
'G_loss_ae': self.G_loss_ae.item()}
if self.opt.pretrain_network is False:
l.update({'G_loss_adv': self.G_loss_adv.item(),
'G_loss_adv_local': self.G_loss_adv_local.item(),
'D_loss': self.D_loss.item(),
'G_loss_mrf': self.G_loss_mrf.item()})
return l
def get_current_visuals(self):
return {'input': self.im_in.cpu().detach().numpy(), 'gt': self.gt.cpu().detach().numpy(),
'completed': self.completed.cpu().detach().numpy()}
def get_current_visuals_tensor(self):
return {'input': self.im_in.cpu().detach(), 'gt': self.gt.cpu().detach(),
'completed': self.completed.cpu().detach()}
def evaluate(self, im_in, mask):
im_in = torch.from_numpy(im_in).type(torch.FloatTensor).cuda() / 127.5 - 1
mask = torch.from_numpy(mask).type(torch.FloatTensor).cuda()
im_in = im_in * (1-mask)
xin = torch.cat((im_in, mask), 1)
ret = self.netGM(xin) * mask + im_in * (1-mask)
ret = (ret.cpu().detach().numpy() + 1) * 127.5
return ret.astype(np.uint8)