forked from fourmi1995/IronSegExperiment-PSPNet
-
Notifications
You must be signed in to change notification settings - Fork 0
/
model.py
737 lines (664 loc) · 41.3 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
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
from network import Network
import tensorflow as tf
class PSPNet101(Network):
def setup(self, is_training, num_classes):
'''Network definition.
Args:
is_training: whether to update the running mean and variance of the batch normalisation layer.
If the batch size is small, it is better to keep the running mean and variance of
the-pretrained model frozen.
num_classes: number of classes to predict (including background).
'''
(self.feed('data')
.conv(3, 3, 64, 2, 2, biased=False, relu=False, padding='SAME', name='conv1_1_3x3_s2')
.batch_normalization(relu=False, name='conv1_1_3x3_s2_bn')
.relu(name='conv1_1_3x3_s2_bn_relu')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, padding='SAME', name='conv1_2_3x3')
.batch_normalization(relu=True, name='conv1_2_3x3_bn')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, padding='SAME', name='conv1_3_3x3')
.batch_normalization(relu=True, name='conv1_3_3x3_bn')
.max_pool(3, 3, 2, 2, padding='SAME', name='pool1_3x3_s2')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_1_1x1_proj')
.batch_normalization(relu=False, name='conv2_1_1x1_proj_bn'))
(self.feed('pool1_3x3_s2')
.conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_1_1x1_reduce')
.batch_normalization(relu=True, name='conv2_1_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding1')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_1_3x3')
.batch_normalization(relu=True, name='conv2_1_3x3_bn')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_1_1x1_increase')
.batch_normalization(relu=False, name='conv2_1_1x1_increase_bn'))
(self.feed('conv2_1_1x1_proj_bn',
'conv2_1_1x1_increase_bn')
.add(name='conv2_1')
.relu(name='conv2_1/relu')
.conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_2_1x1_reduce')
.batch_normalization(relu=True, name='conv2_2_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding2')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_2_3x3')
.batch_normalization(relu=True, name='conv2_2_3x3_bn')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_2_1x1_increase')
.batch_normalization(relu=False, name='conv2_2_1x1_increase_bn'))
(self.feed('conv2_1/relu',
'conv2_2_1x1_increase_bn')
.add(name='conv2_2')
.relu(name='conv2_2/relu')
.conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_3_1x1_reduce')
.batch_normalization(relu=True, name='conv2_3_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding3')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_3_3x3')
.batch_normalization(relu=True, name='conv2_3_3x3_bn')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_3_1x1_increase')
.batch_normalization(relu=False, name='conv2_3_1x1_increase_bn'))
(self.feed('conv2_2/relu',
'conv2_3_1x1_increase_bn')
.add(name='conv2_3')
.relu(name='conv2_3/relu')
.conv(1, 1, 512, 2, 2, biased=False, relu=False, name='conv3_1_1x1_proj')
.batch_normalization(relu=False, name='conv3_1_1x1_proj_bn'))
(self.feed('conv2_3/relu')
.conv(1, 1, 128, 2, 2, biased=False, relu=False, name='conv3_1_1x1_reduce')
.batch_normalization(relu=True, name='conv3_1_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding4')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_1_3x3')
.batch_normalization(relu=True, name='conv3_1_3x3_bn')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_1_1x1_increase')
.batch_normalization(relu=False, name='conv3_1_1x1_increase_bn'))
(self.feed('conv3_1_1x1_proj_bn',
'conv3_1_1x1_increase_bn')
.add(name='conv3_1')
.relu(name='conv3_1/relu')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_2_1x1_reduce')
.batch_normalization(relu=True, name='conv3_2_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding5')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_2_3x3')
.batch_normalization(relu=True, name='conv3_2_3x3_bn')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_2_1x1_increase')
.batch_normalization(relu=False, name='conv3_2_1x1_increase_bn'))
(self.feed('conv3_1/relu',
'conv3_2_1x1_increase_bn')
.add(name='conv3_2')
.relu(name='conv3_2/relu')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_3_1x1_reduce')
.batch_normalization(relu=True, name='conv3_3_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding6')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_3_3x3')
.batch_normalization(relu=True, name='conv3_3_3x3_bn')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_3_1x1_increase')
.batch_normalization(relu=False, name='conv3_3_1x1_increase_bn'))
(self.feed('conv3_2/relu',
'conv3_3_1x1_increase_bn')
.add(name='conv3_3')
.relu(name='conv3_3/relu')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_4_1x1_reduce')
.batch_normalization(relu=True, name='conv3_4_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding7')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_4_3x3')
.batch_normalization(relu=True, name='conv3_4_3x3_bn')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_4_1x1_increase')
.batch_normalization(relu=False, name='conv3_4_1x1_increase_bn'))
(self.feed('conv3_3/relu',
'conv3_4_1x1_increase_bn')
.add(name='conv3_4')
.relu(name='conv3_4/relu')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_1_1x1_proj')
.batch_normalization(relu=False, name='conv4_1_1x1_proj_bn'))
(self.feed('conv3_4/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_1_1x1_reduce')
.batch_normalization(relu=True, name='conv4_1_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding8')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_1_3x3')
.batch_normalization(relu=True, name='conv4_1_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_1_1x1_increase')
.batch_normalization(relu=False, name='conv4_1_1x1_increase_bn'))
(self.feed('conv4_1_1x1_proj_bn',
'conv4_1_1x1_increase_bn')
.add(name='conv4_1')
.relu(name='conv4_1/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_2_1x1_reduce')
.batch_normalization(relu=True, name='conv4_2_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding9')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_2_3x3')
.batch_normalization(relu=True, name='conv4_2_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_2_1x1_increase')
.batch_normalization(relu=False, name='conv4_2_1x1_increase_bn'))
(self.feed('conv4_1/relu',
'conv4_2_1x1_increase_bn')
.add(name='conv4_2')
.relu(name='conv4_2/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_3_1x1_reduce')
.batch_normalization(relu=True, name='conv4_3_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding10')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_3_3x3')
.batch_normalization(relu=True, name='conv4_3_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_3_1x1_increase')
.batch_normalization(relu=False, name='conv4_3_1x1_increase_bn'))
(self.feed('conv4_2/relu',
'conv4_3_1x1_increase_bn')
.add(name='conv4_3')
.relu(name='conv4_3/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_4_1x1_reduce')
.batch_normalization(relu=True, name='conv4_4_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding11')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_4_3x3')
.batch_normalization(relu=True, name='conv4_4_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_4_1x1_increase')
.batch_normalization(relu=False, name='conv4_4_1x1_increase_bn'))
(self.feed('conv4_3/relu',
'conv4_4_1x1_increase_bn')
.add(name='conv4_4')
.relu(name='conv4_4/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_5_1x1_reduce')
.batch_normalization(relu=True, name='conv4_5_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding12')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_5_3x3')
.batch_normalization(relu=True, name='conv4_5_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_5_1x1_increase')
.batch_normalization(relu=False, name='conv4_5_1x1_increase_bn'))
(self.feed('conv4_4/relu',
'conv4_5_1x1_increase_bn')
.add(name='conv4_5')
.relu(name='conv4_5/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_6_1x1_reduce')
.batch_normalization(relu=True, name='conv4_6_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding13')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_6_3x3')
.batch_normalization(relu=True, name='conv4_6_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_6_1x1_increase')
.batch_normalization(relu=False, name='conv4_6_1x1_increase_bn'))
(self.feed('conv4_5/relu',
'conv4_6_1x1_increase_bn')
.add(name='conv4_6')
.relu(name='conv4_6/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_7_1x1_reduce')
.batch_normalization(relu=True, name='conv4_7_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding14')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_7_3x3')
.batch_normalization(relu=True, name='conv4_7_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_7_1x1_increase')
.batch_normalization(relu=False, name='conv4_7_1x1_increase_bn'))
(self.feed('conv4_6/relu',
'conv4_7_1x1_increase_bn')
.add(name='conv4_7')
.relu(name='conv4_7/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_8_1x1_reduce')
.batch_normalization(relu=True, name='conv4_8_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding15')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_8_3x3')
.batch_normalization(relu=True, name='conv4_8_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_8_1x1_increase')
.batch_normalization(relu=False, name='conv4_8_1x1_increase_bn'))
(self.feed('conv4_7/relu',
'conv4_8_1x1_increase_bn')
.add(name='conv4_8')
.relu(name='conv4_8/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_9_1x1_reduce')
.batch_normalization(relu=True, name='conv4_9_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding16')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_9_3x3')
.batch_normalization(relu=True, name='conv4_9_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_9_1x1_increase')
.batch_normalization(relu=False, name='conv4_9_1x1_increase_bn'))
(self.feed('conv4_8/relu',
'conv4_9_1x1_increase_bn')
.add(name='conv4_9')
.relu(name='conv4_9/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_10_1x1_reduce')
.batch_normalization(relu=True, name='conv4_10_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding17')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_10_3x3')
.batch_normalization(relu=True, name='conv4_10_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_10_1x1_increase')
.batch_normalization(relu=False, name='conv4_10_1x1_increase_bn'))
(self.feed('conv4_9/relu',
'conv4_10_1x1_increase_bn')
.add(name='conv4_10')
.relu(name='conv4_10/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_11_1x1_reduce')
.batch_normalization(relu=True, name='conv4_11_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding18')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_11_3x3')
.batch_normalization(relu=True, name='conv4_11_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_11_1x1_increase')
.batch_normalization(relu=False, name='conv4_11_1x1_increase_bn'))
(self.feed('conv4_10/relu',
'conv4_11_1x1_increase_bn')
.add(name='conv4_11')
.relu(name='conv4_11/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_12_1x1_reduce')
.batch_normalization(relu=True, name='conv4_12_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding19')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_12_3x3')
.batch_normalization(relu=True, name='conv4_12_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_12_1x1_increase')
.batch_normalization(relu=False, name='conv4_12_1x1_increase_bn'))
(self.feed('conv4_11/relu',
'conv4_12_1x1_increase_bn')
.add(name='conv4_12')
.relu(name='conv4_12/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_13_1x1_reduce')
.batch_normalization(relu=True, name='conv4_13_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding20')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_13_3x3')
.batch_normalization(relu=True, name='conv4_13_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_13_1x1_increase')
.batch_normalization(relu=False, name='conv4_13_1x1_increase_bn'))
(self.feed('conv4_12/relu',
'conv4_13_1x1_increase_bn')
.add(name='conv4_13')
.relu(name='conv4_13/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_14_1x1_reduce')
.batch_normalization(relu=True, name='conv4_14_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding21')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_14_3x3')
.batch_normalization(relu=True, name='conv4_14_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_14_1x1_increase')
.batch_normalization(relu=False, name='conv4_14_1x1_increase_bn'))
(self.feed('conv4_13/relu',
'conv4_14_1x1_increase_bn')
.add(name='conv4_14')
.relu(name='conv4_14/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_15_1x1_reduce')
.batch_normalization(relu=True, name='conv4_15_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding22')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_15_3x3')
.batch_normalization(relu=True, name='conv4_15_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_15_1x1_increase')
.batch_normalization(relu=False, name='conv4_15_1x1_increase_bn'))
(self.feed('conv4_14/relu',
'conv4_15_1x1_increase_bn')
.add(name='conv4_15')
.relu(name='conv4_15/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_16_1x1_reduce')
.batch_normalization(relu=True, name='conv4_16_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding23')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_16_3x3')
.batch_normalization(relu=True, name='conv4_16_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_16_1x1_increase')
.batch_normalization(relu=False, name='conv4_16_1x1_increase_bn'))
(self.feed('conv4_15/relu',
'conv4_16_1x1_increase_bn')
.add(name='conv4_16')
.relu(name='conv4_16/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_17_1x1_reduce')
.batch_normalization(relu=True, name='conv4_17_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding24')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_17_3x3')
.batch_normalization(relu=True, name='conv4_17_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_17_1x1_increase')
.batch_normalization(relu=False, name='conv4_17_1x1_increase_bn'))
(self.feed('conv4_16/relu',
'conv4_17_1x1_increase_bn')
.add(name='conv4_17')
.relu(name='conv4_17/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_18_1x1_reduce')
.batch_normalization(relu=True, name='conv4_18_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding25')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_18_3x3')
.batch_normalization(relu=True, name='conv4_18_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_18_1x1_increase')
.batch_normalization(relu=False, name='conv4_18_1x1_increase_bn'))
(self.feed('conv4_17/relu',
'conv4_18_1x1_increase_bn')
.add(name='conv4_18')
.relu(name='conv4_18/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_19_1x1_reduce')
.batch_normalization(relu=True, name='conv4_19_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding26')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_19_3x3')
.batch_normalization(relu=True, name='conv4_19_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_19_1x1_increase')
.batch_normalization(relu=False, name='conv4_19_1x1_increase_bn'))
(self.feed('conv4_18/relu',
'conv4_19_1x1_increase_bn')
.add(name='conv4_19')
.relu(name='conv4_19/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_20_1x1_reduce')
.batch_normalization(relu=True, name='conv4_20_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding27')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_20_3x3')
.batch_normalization(relu=True, name='conv4_20_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_20_1x1_increase')
.batch_normalization(relu=False, name='conv4_20_1x1_increase_bn'))
(self.feed('conv4_19/relu',
'conv4_20_1x1_increase_bn')
.add(name='conv4_20')
.relu(name='conv4_20/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_21_1x1_reduce')
.batch_normalization(relu=True, name='conv4_21_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding28')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_21_3x3')
.batch_normalization(relu=True, name='conv4_21_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_21_1x1_increase')
.batch_normalization(relu=False, name='conv4_21_1x1_increase_bn'))
(self.feed('conv4_20/relu',
'conv4_21_1x1_increase_bn')
.add(name='conv4_21')
.relu(name='conv4_21/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_22_1x1_reduce')
.batch_normalization(relu=True, name='conv4_22_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding29')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_22_3x3')
.batch_normalization(relu=True, name='conv4_22_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_22_1x1_increase')
.batch_normalization(relu=False, name='conv4_22_1x1_increase_bn'))
(self.feed('conv4_21/relu',
'conv4_22_1x1_increase_bn')
.add(name='conv4_22')
.relu(name='conv4_22/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_23_1x1_reduce')
.batch_normalization(relu=True, name='conv4_23_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding30')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_23_3x3')
.batch_normalization(relu=True, name='conv4_23_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_23_1x1_increase')
.batch_normalization(relu=False, name='conv4_23_1x1_increase_bn'))
(self.feed('conv4_22/relu',
'conv4_23_1x1_increase_bn')
.add(name='conv4_23')
.relu(name='conv4_23/relu')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_1_1x1_proj')
.batch_normalization(relu=False, name='conv5_1_1x1_proj_bn'))
(self.feed('conv4_23/relu')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_1_1x1_reduce')
.batch_normalization(relu=True, name='conv5_1_1x1_reduce_bn')
.zero_padding(paddings=4, name='padding31')
.atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_1_3x3')
.batch_normalization(relu=True, name='conv5_1_3x3_bn')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_1_1x1_increase')
.batch_normalization(relu=False, name='conv5_1_1x1_increase_bn'))
(self.feed('conv5_1_1x1_proj_bn',
'conv5_1_1x1_increase_bn')
.add(name='conv5_1')
.relu(name='conv5_1/relu')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_2_1x1_reduce')
.batch_normalization(relu=True, name='conv5_2_1x1_reduce_bn')
.zero_padding(paddings=4, name='padding32')
.atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_2_3x3')
.batch_normalization(relu=True, name='conv5_2_3x3_bn')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_2_1x1_increase')
.batch_normalization(relu=False, name='conv5_2_1x1_increase_bn'))
(self.feed('conv5_1/relu',
'conv5_2_1x1_increase_bn')
.add(name='conv5_2')
.relu(name='conv5_2/relu')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_1x1_reduce')
.batch_normalization(relu=True, name='conv5_3_1x1_reduce_bn')
.zero_padding(paddings=4, name='padding33')
.atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_3_3x3')
.batch_normalization(relu=True, name='conv5_3_3x3_bn')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_3_1x1_increase')
.batch_normalization(relu=False, name='conv5_3_1x1_increase_bn'))
(self.feed('conv5_2/relu',
'conv5_3_1x1_increase_bn')
.add(name='conv5_3')
.relu(name='conv5_3/relu'))
conv5_3 = self.layers['conv5_3/relu']
shape = tf.shape(conv5_3)[1:3]
(self.feed('conv5_3/relu')
#.avg_pool(90, 90, 90, 90, name='conv5_3_pool1')
.avg_pool(45, 45, 45, 45, name='conv5_3_pool1')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool1_conv')
.batch_normalization(relu=True, name='conv5_3_pool1_conv_bn')
.resize_bilinear(shape, name='conv5_3_pool1_interp'))
(self.feed('conv5_3/relu')
.avg_pool(45, 45, 45, 45, name='conv5_3_pool2')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool2_conv')
.batch_normalization(relu=True, name='conv5_3_pool2_conv_bn')
.resize_bilinear(shape, name='conv5_3_pool2_interp'))
(self.feed('conv5_3/relu')
.avg_pool(30, 30, 30, 30, name='conv5_3_pool3')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool3_conv')
.batch_normalization(relu=True, name='conv5_3_pool3_conv_bn')
.resize_bilinear(shape, name='conv5_3_pool3_interp'))
(self.feed('conv5_3/relu')
.avg_pool(15, 15, 15, 15, name='conv5_3_pool6')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool6_conv')
.batch_normalization(relu=True, name='conv5_3_pool6_conv_bn')
.resize_bilinear(shape, name='conv5_3_pool6_interp'))
(self.feed('conv5_3/relu',
'conv5_3_pool6_interp',
'conv5_3_pool3_interp',
'conv5_3_pool2_interp',
'conv5_3_pool1_interp')
.concat(axis=-1, name='conv5_3_concat')
.conv(3, 3, 512, 1, 1, biased=False, relu=False, padding='SAME', name='conv5_4')
.batch_normalization(relu=True, name='conv5_4_bn')
.conv(1, 1, num_classes, 1, 1, biased=True, relu=False, name='conv6'))
class PSPNet50(Network):
def setup(self, is_training, num_classes):
'''Network definition.
Args:
is_training: whether to update the running mean and variance of the batch normalisation layer.
If the batch size is small, it is better to keep the running mean and variance of
the-pretrained model frozen.
num_classes: number of classes to predict (including background).
'''
(self.feed('data')
.conv(3, 3, 64, 2, 2, biased=False, relu=False, padding='SAME', name='conv1_1_3x3_s2')
.batch_normalization(relu=False, name='conv1_1_3x3_s2_bn')
.relu(name='conv1_1_3x3_s2_bn_relu')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, padding='SAME', name='conv1_2_3x3')
.batch_normalization(relu=True, name='conv1_2_3x3_bn')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, padding='SAME', name='conv1_3_3x3')
.batch_normalization(relu=True, name='conv1_3_3x3_bn')
.max_pool(3, 3, 2, 2, padding='SAME', name='pool1_3x3_s2')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_1_1x1_proj')
.batch_normalization(relu=False, name='conv2_1_1x1_proj_bn'))
(self.feed('pool1_3x3_s2')
.conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_1_1x1_reduce')
.batch_normalization(relu=True, name='conv2_1_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding1')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_1_3x3')
.batch_normalization(relu=True, name='conv2_1_3x3_bn')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_1_1x1_increase')
.batch_normalization(relu=False, name='conv2_1_1x1_increase_bn'))
(self.feed('conv2_1_1x1_proj_bn',
'conv2_1_1x1_increase_bn')
.add(name='conv2_1')
.relu(name='conv2_1/relu')
.conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_2_1x1_reduce')
.batch_normalization(relu=True, name='conv2_2_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding2')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_2_3x3')
.batch_normalization(relu=True, name='conv2_2_3x3_bn')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_2_1x1_increase')
.batch_normalization(relu=False, name='conv2_2_1x1_increase_bn'))
(self.feed('conv2_1/relu',
'conv2_2_1x1_increase_bn')
.add(name='conv2_2')
.relu(name='conv2_2/relu')
.conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_3_1x1_reduce')
.batch_normalization(relu=True, name='conv2_3_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding3')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_3_3x3')
.batch_normalization(relu=True, name='conv2_3_3x3_bn')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_3_1x1_increase')
.batch_normalization(relu=False, name='conv2_3_1x1_increase_bn'))
(self.feed('conv2_2/relu',
'conv2_3_1x1_increase_bn')
.add(name='conv2_3')
.relu(name='conv2_3/relu')
.conv(1, 1, 512, 2, 2, biased=False, relu=False, name='conv3_1_1x1_proj')
.batch_normalization(relu=False, name='conv3_1_1x1_proj_bn'))
(self.feed('conv2_3/relu')
.conv(1, 1, 128, 2, 2, biased=False, relu=False, name='conv3_1_1x1_reduce')
.batch_normalization(relu=True, name='conv3_1_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding4')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_1_3x3')
.batch_normalization(relu=True, name='conv3_1_3x3_bn')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_1_1x1_increase')
.batch_normalization(relu=False, name='conv3_1_1x1_increase_bn'))
(self.feed('conv3_1_1x1_proj_bn',
'conv3_1_1x1_increase_bn')
.add(name='conv3_1')
.relu(name='conv3_1/relu')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_2_1x1_reduce')
.batch_normalization(relu=True, name='conv3_2_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding5')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_2_3x3')
.batch_normalization(relu=True, name='conv3_2_3x3_bn')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_2_1x1_increase')
.batch_normalization(relu=False, name='conv3_2_1x1_increase_bn'))
(self.feed('conv3_1/relu',
'conv3_2_1x1_increase_bn')
.add(name='conv3_2')
.relu(name='conv3_2/relu')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_3_1x1_reduce')
.batch_normalization(relu=True, name='conv3_3_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding6')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_3_3x3')
.batch_normalization(relu=True, name='conv3_3_3x3_bn')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_3_1x1_increase')
.batch_normalization(relu=False, name='conv3_3_1x1_increase_bn'))
(self.feed('conv3_2/relu',
'conv3_3_1x1_increase_bn')
.add(name='conv3_3')
.relu(name='conv3_3/relu')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_4_1x1_reduce')
.batch_normalization(relu=True, name='conv3_4_1x1_reduce_bn')
.zero_padding(paddings=1, name='padding7')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_4_3x3')
.batch_normalization(relu=True, name='conv3_4_3x3_bn')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_4_1x1_increase')
.batch_normalization(relu=False, name='conv3_4_1x1_increase_bn'))
(self.feed('conv3_3/relu',
'conv3_4_1x1_increase_bn')
.add(name='conv3_4')
.relu(name='conv3_4/relu')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_1_1x1_proj')
.batch_normalization(relu=False, name='conv4_1_1x1_proj_bn'))
(self.feed('conv3_4/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_1_1x1_reduce')
.batch_normalization(relu=True, name='conv4_1_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding8')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_1_3x3')
.batch_normalization(relu=True, name='conv4_1_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_1_1x1_increase')
.batch_normalization(relu=False, name='conv4_1_1x1_increase_bn'))
(self.feed('conv4_1_1x1_proj_bn',
'conv4_1_1x1_increase_bn')
.add(name='conv4_1')
.relu(name='conv4_1/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_2_1x1_reduce')
.batch_normalization(relu=True, name='conv4_2_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding9')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_2_3x3')
.batch_normalization(relu=True, name='conv4_2_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_2_1x1_increase')
.batch_normalization(relu=False, name='conv4_2_1x1_increase_bn'))
(self.feed('conv4_1/relu',
'conv4_2_1x1_increase_bn')
.add(name='conv4_2')
.relu(name='conv4_2/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_3_1x1_reduce')
.batch_normalization(relu=True, name='conv4_3_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding10')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_3_3x3')
.batch_normalization(relu=True, name='conv4_3_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_3_1x1_increase')
.batch_normalization(relu=False, name='conv4_3_1x1_increase_bn'))
(self.feed('conv4_2/relu',
'conv4_3_1x1_increase_bn')
.add(name='conv4_3')
.relu(name='conv4_3/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_4_1x1_reduce')
.batch_normalization(relu=True, name='conv4_4_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding11')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_4_3x3')
.batch_normalization(relu=True, name='conv4_4_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_4_1x1_increase')
.batch_normalization(relu=False, name='conv4_4_1x1_increase_bn'))
(self.feed('conv4_3/relu',
'conv4_4_1x1_increase_bn')
.add(name='conv4_4')
.relu(name='conv4_4/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_5_1x1_reduce')
.batch_normalization(relu=True, name='conv4_5_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding12')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_5_3x3')
.batch_normalization(relu=True, name='conv4_5_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_5_1x1_increase')
.batch_normalization(relu=False, name='conv4_5_1x1_increase_bn'))
(self.feed('conv4_4/relu',
'conv4_5_1x1_increase_bn')
.add(name='conv4_5')
.relu(name='conv4_5/relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_6_1x1_reduce')
.batch_normalization(relu=True, name='conv4_6_1x1_reduce_bn')
.zero_padding(paddings=2, name='padding13')
.atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_6_3x3')
.batch_normalization(relu=True, name='conv4_6_3x3_bn')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_6_1x1_increase')
.batch_normalization(relu=False, name='conv4_6_1x1_increase_bn'))
(self.feed('conv4_5/relu',
'conv4_6_1x1_increase_bn')
.add(name='conv4_6')
.relu(name='conv4_6/relu')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_1_1x1_proj')
.batch_normalization(relu=False, name='conv5_1_1x1_proj_bn'))
(self.feed('conv4_6/relu')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_1_1x1_reduce')
.batch_normalization(relu=True, name='conv5_1_1x1_reduce_bn')
.zero_padding(paddings=4, name='padding31')
.atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_1_3x3')
.batch_normalization(relu=True, name='conv5_1_3x3_bn')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_1_1x1_increase')
.batch_normalization(relu=False, name='conv5_1_1x1_increase_bn'))
(self.feed('conv5_1_1x1_proj_bn',
'conv5_1_1x1_increase_bn')
.add(name='conv5_1')
.relu(name='conv5_1/relu')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_2_1x1_reduce')
.batch_normalization(relu=True, name='conv5_2_1x1_reduce_bn')
.zero_padding(paddings=4, name='padding32')
.atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_2_3x3')
.batch_normalization(relu=True, name='conv5_2_3x3_bn')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_2_1x1_increase')
.batch_normalization(relu=False, name='conv5_2_1x1_increase_bn'))
(self.feed('conv5_1/relu',
'conv5_2_1x1_increase_bn')
.add(name='conv5_2')
.relu(name='conv5_2/relu')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_1x1_reduce')
.batch_normalization(relu=True, name='conv5_3_1x1_reduce_bn')
.zero_padding(paddings=4, name='padding33')
.atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_3_3x3')
.batch_normalization(relu=True, name='conv5_3_3x3_bn')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_3_1x1_increase')
.batch_normalization(relu=False, name='conv5_3_1x1_increase_bn'))
(self.feed('conv5_2/relu',
'conv5_3_1x1_increase_bn')
.add(name='conv5_3')
.relu(name='conv5_3/relu'))
conv5_3 = self.layers['conv5_3/relu']
shape = tf.shape(conv5_3)[1:3]
(self.feed('conv5_3/relu')
.avg_pool(60, 60, 60, 60, name='conv5_3_pool1')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool1_conv')
.batch_normalization(relu=True, name='conv5_3_pool1_conv_bn')
.resize_bilinear(shape, name='conv5_3_pool1_interp'))
(self.feed('conv5_3/relu')
.avg_pool(30, 30, 30, 30, name='conv5_3_pool2')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool2_conv')
.batch_normalization(relu=True, name='conv5_3_pool2_conv_bn')
.resize_bilinear(shape, name='conv5_3_pool2_interp'))
(self.feed('conv5_3/relu')
.avg_pool(20, 20, 20, 20, name='conv5_3_pool3')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool3_conv')
.batch_normalization(relu=True, name='conv5_3_pool3_conv_bn')
.resize_bilinear(shape, name='conv5_3_pool3_interp'))
(self.feed('conv5_3/relu')
.avg_pool(10, 10, 10, 10, name='conv5_3_pool6')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool6_conv')
.batch_normalization(relu=True, name='conv5_3_pool6_conv_bn')
.resize_bilinear(shape, name='conv5_3_pool6_interp'))
(self.feed('conv5_3/relu',
'conv5_3_pool6_interp',
'conv5_3_pool3_interp',
'conv5_3_pool2_interp',
'conv5_3_pool1_interp')
.concat(axis=-1, name='conv5_3_concat')
.conv(3, 3, 512, 1, 1, biased=False, relu=False, padding='SAME', name='conv5_4')
.batch_normalization(relu=True, name='conv5_4_bn')
.conv(1, 1, num_classes, 1, 1, biased=True, relu=False, name='conv6'))