-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathmodel.py
376 lines (306 loc) · 13.3 KB
/
model.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
367
368
369
370
371
372
373
374
375
376
import tensorflow as tf
from ops import fully_connect
from ops import conv2d
from ops import pooling
from ops import transconv2d
from ops import batch_norm
from ops import lrelu
import numpy as np
def generator(masked_image, batch_size, image_dim, is_train=True, no_reuse=False):
with tf.variable_scope('generator') as scope:
if not (is_train or no_reuse):
scope.reuse_variables()
# input 180x256ximage_dim
#conv0_1
layer_num = 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = conv2d(masked_image, 32, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv0_2
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = conv2d(hidden, 32, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
# output 180*256*32
#pool0
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = pooling(hidden, (2, 2), (2,2))
# output 90*128*32
#conv1_1_new
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = conv2d(hidden, 64, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv1_2
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = conv2d(hidden, 64, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#pool1
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = pooling(hidden, (2, 2), (2,2))
#output 45*64*64
#conv2_1
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = conv2d(hidden, 128, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv2_2
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = conv2d(hidden, 128, (3, 3), (3, 2), trainable=is_train) ###
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#pool2
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = pooling(hidden, (2, 2), (1,1)) ###
#output 15*32*128
#conv3_1
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = conv2d(hidden, 256, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv3_2
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = conv2d(hidden, 256, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv3_3
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = conv2d(hidden, 256, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv3_4
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = conv2d(hidden, 512, (3, 3), (3, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#pool3
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = pooling(hidden, (2, 2), (1,2))
#output 5*8*512
#fc3
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = tf.reshape(hidden, [batch_size, 5 * 8 * 512])
hidden = fully_connect(hidden, 5120, trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
# output 5120
#defc3
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = fully_connect(hidden, 20480, trainable=is_train)
hidden = tf.reshape(hidden, [batch_size, 5, 8, 512])
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
# output 5*8*512
#conv_decode3_4
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=512,
kernel=(3, 3), stride=(1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 5*8*512
#conv_decode3_3
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=512,
kernel=(3, 3), stride=(1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv_decode3_2
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=512,
kernel=(3, 3), stride=(3, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 15*16*512
#conv_decode3_1
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=512,
kernel=(3, 3), stride=(1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv_decode2_2
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=256,
kernel=(3, 3), stride=(3, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 45*16*256
#conv_decode2_1
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=256,
kernel=(3, 3), stride=(1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv_decode1_2
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=128,
kernel=(3, 3), stride=(1, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 45*32*128
#conv_decode1_1
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=128,
kernel=(3, 3), stride=(1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv_decode0_2
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=64,
kernel=(3, 3), stride=(2, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 90*64*64
#conv_decode0_1
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=64,
kernel=(3, 3), stride=(1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#reconstruction
layer_num += 1
with tf.variable_scope('hidden' + str(layer_num)):
hidden = transconv2d(hidden, output_channel=1,
kernel=(3, 3), stride=(2, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 180*64*image_dim
return hidden
def global_discriminator(full_image, batch_size, reuse=False, is_train=True):
with tf.variable_scope('global_discriminator') as scope:
if reuse:
scope.reuse_variables()
#input 180*256*1
#conv0_1
layer_num = 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = conv2d(full_image, 32, (3, 3), (2, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 90*256*32
#conv0_2
layer_num += 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = conv2d(hidden, 32, (4, 4), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 90*256*32
#conv1_1
layer_num += 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = conv2d(hidden, 64, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv1_2
layer_num += 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = conv2d(hidden, 64, (4, 4), (1, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 90*128*64
#conv2_1
layer_num += 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = conv2d(hidden, 128, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv2_2
layer_num += 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = conv2d(hidden, 128, (4, 4), (2, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 45*64*128
#conv3_1
layer_num += 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = conv2d(hidden, 256, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv3_2
layer_num += 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = conv2d(hidden, 256, (4, 4), (3, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 15*32*256
#conv4_1
layer_num += 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = conv2d(hidden, 512, (3, 3), (1, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv4_2
layer_num += 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = conv2d(hidden, 512, (4, 4), (3, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 5*8*512
# #conv5
# layer_num += 1
# with tf.variable_scope('hidden' + str(layer_num)):
# hidden = conv2d(hidden, 1, (4, 4), (1, 1), trainable=is_train)
# hidden = lrelu(batch_norm(hidden, train=is_train))
# #
layer_num += 1
with tf.variable_scope('hidden_glo' + str(layer_num)):
hidden = tf.reshape(hidden, [batch_size, 5 * 8 * 512])
hidden = fully_connect(hidden, 1, trainable=is_train)
#输出尺寸为true 或者false
return hidden[:, 0]
def local_discriminator(fake_image, batch_size, reuse=False, is_train=True):
with tf.variable_scope('local_discriminator1') as scope:
if reuse:
scope.reuse_variables()
#input 180*64*image_dim
#conv1_1
layer_num = 1
with tf.variable_scope('hidden_loc' + str(layer_num)):
hidden = conv2d(fake_image, 64, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv1_2
layer_num += 1
with tf.variable_scope('hidden_loc' + str(layer_num)):
hidden = conv2d(hidden, 64, (4, 4), (2, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 90*64*64
#conv2_1
layer_num += 1
with tf.variable_scope('hidden_loc' + str(layer_num)):
hidden = conv2d(hidden, 128, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv2_2
layer_num += 1
with tf.variable_scope('hidden_loc' + str(layer_num)):
hidden = conv2d(hidden, 128, (4, 4), (2, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 45*32*128
#conv3_1
layer_num += 1
with tf.variable_scope('hidden_loc' + str(layer_num)):
hidden = conv2d(hidden, 256, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 45*32*256
#conv3_2
layer_num += 1
with tf.variable_scope('hidden_loc' + str(layer_num)):
hidden = conv2d(hidden, 256, (4, 4), (3, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 15*16*256
#conv4_1
layer_num += 1
with tf.variable_scope('hidden_loc' + str(layer_num)):
hidden = conv2d(hidden, 512, (3, 3), (1, 1), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#conv4_2
layer_num += 1
with tf.variable_scope('hidden_loc' + str(layer_num)):
hidden = conv2d(hidden, 512, (4, 4), (3, 2), trainable=is_train)
hidden = tf.nn.relu(batch_norm(hidden, train=is_train))
#output 5*8*512
# #conv5
# layer_num += 1
# with tf.variable_scope('hidden' + str(layer_num)):
# hidden = conv2d(hidden, 1, (4, 4), (1, 1), trainable=is_train)
# hidden = lrelu(batch_norm(hidden, train=is_train))
# #
layer_num += 1
with tf.variable_scope('hidden_loc' + str(layer_num)):
hidden = tf.reshape(hidden, [batch_size, 5* 8 * 512])
hidden = fully_connect(hidden, 1, trainable=is_train)
#输出尺寸为true 或者false
return hidden[:, 0]