forked from alibaba/TinyNeuralNetwork
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathefficientnet_v2_xl.py
2737 lines (2727 loc) · 188 KB
/
efficientnet_v2_xl.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
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
import torch
import torch.nn
import torch.functional
import torch.nn.functional
class efficientnet_v2_xl(torch.nn.Module):
def __init__(self):
super().__init__()
self.features_0_0 = torch.nn.modules.conv.Conv2d(3, 24, 3, 2, 1, bias=False)
self.features_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_1_conv_0 = torch.nn.modules.conv.Conv2d(24, 24, 3, 1, 1, bias=False)
self.features_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(24)
self.features_1_conv_3 = torch.nn.modules.conv.Conv2d(24, 32, 1, 1, 0, bias=False)
self.features_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_2_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False)
self.features_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_2_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False)
self.features_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_3_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False)
self.features_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_3_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False)
self.features_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_4_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False)
self.features_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_4_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False)
self.features_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
self.features_5_conv_0 = torch.nn.modules.conv.Conv2d(32, 128, 3, 2, 1, bias=False)
self.features_5_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(128)
self.features_5_conv_3 = torch.nn.modules.conv.Conv2d(128, 64, 1, 1, 0, bias=False)
self.features_5_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_6_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_6_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_6_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_6_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_7_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_7_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_7_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_7_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_8_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_8_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_8_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_8_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_9_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_9_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_9_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_9_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_10_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_10_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_10_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_10_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_11_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_11_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_11_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_11_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_12_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False)
self.features_12_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_12_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False)
self.features_12_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
self.features_13_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 2, 1, bias=False)
self.features_13_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_13_conv_3 = torch.nn.modules.conv.Conv2d(256, 96, 1, 1, 0, bias=False)
self.features_13_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_14_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_14_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_14_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_14_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_15_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_15_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_15_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_15_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_16_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_16_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_16_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_16_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_17_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_17_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_17_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_17_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_18_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_18_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_18_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_18_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_19_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_19_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_19_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_19_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_20_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 3, 1, 1, bias=False)
self.features_20_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_20_conv_3 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
self.features_20_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
self.features_21_conv_0 = torch.nn.modules.conv.Conv2d(96, 384, 1, 1, 0, bias=False)
self.features_21_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_21_conv_3 = torch.nn.modules.conv.Conv2d(384, 384, 3, 2, 1, groups=384, bias=False)
self.features_21_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(384)
self.features_21_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_21_conv_6_fc_0 = torch.nn.modules.linear.Linear(384, 24)
self.features_21_conv_6_fc_2 = torch.nn.modules.linear.Linear(24, 384)
self.features_21_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_21_conv_7 = torch.nn.modules.conv.Conv2d(384, 192, 1, 1, 0, bias=False)
self.features_21_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_22_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_22_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_22_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_22_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_22_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_22_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_22_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_22_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_22_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_22_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_23_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_23_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_23_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_23_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_23_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_23_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_23_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_23_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_23_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_23_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_24_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_24_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_24_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_24_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_24_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_24_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_24_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_24_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_24_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_24_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_25_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_25_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_25_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_25_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_25_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_25_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_25_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_25_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_25_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_25_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_26_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_26_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_26_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_26_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_26_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_26_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_26_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_26_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_26_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_26_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_27_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_27_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_27_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_27_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_27_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_27_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_27_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_27_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_27_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_27_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_28_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_28_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_28_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_28_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_28_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_28_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_28_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_28_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_28_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_28_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_29_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_29_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_29_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_29_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_29_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_29_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_29_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_29_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_29_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_29_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_30_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_30_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_30_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_30_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_30_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_30_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_30_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_30_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_30_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_30_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_31_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_31_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_31_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_31_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_31_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_31_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_31_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_31_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_31_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_31_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_32_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_32_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_32_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_32_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_32_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_32_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_32_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_32_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_32_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_32_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_33_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_33_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_33_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_33_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_33_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_33_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_33_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_33_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_33_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_33_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_34_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_34_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_34_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_34_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_34_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_34_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_34_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_34_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_34_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_34_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_35_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_35_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_35_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_35_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_35_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_35_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_35_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_35_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_35_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_35_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_36_conv_0 = torch.nn.modules.conv.Conv2d(192, 768, 1, 1, 0, bias=False)
self.features_36_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_36_conv_3 = torch.nn.modules.conv.Conv2d(768, 768, 3, 1, 1, groups=768, bias=False)
self.features_36_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
self.features_36_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_36_conv_6_fc_0 = torch.nn.modules.linear.Linear(768, 48)
self.features_36_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 768)
self.features_36_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_36_conv_7 = torch.nn.modules.conv.Conv2d(768, 192, 1, 1, 0, bias=False)
self.features_36_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(192)
self.features_37_conv_0 = torch.nn.modules.conv.Conv2d(192, 1152, 1, 1, 0, bias=False)
self.features_37_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1152)
self.features_37_conv_3 = torch.nn.modules.conv.Conv2d(1152, 1152, 3, 1, 1, groups=1152, bias=False)
self.features_37_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1152)
self.features_37_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_37_conv_6_fc_0 = torch.nn.modules.linear.Linear(1152, 48)
self.features_37_conv_6_fc_2 = torch.nn.modules.linear.Linear(48, 1152)
self.features_37_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_37_conv_7 = torch.nn.modules.conv.Conv2d(1152, 256, 1, 1, 0, bias=False)
self.features_37_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_38_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_38_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_38_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_38_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_38_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_38_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_38_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_38_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_38_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_38_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_39_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_39_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_39_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_39_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_39_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_39_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_39_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_39_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_39_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_39_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_40_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_40_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_40_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_40_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_40_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_40_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_40_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_40_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_40_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_40_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_41_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_41_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_41_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_41_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_41_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_41_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_41_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_41_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_41_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_41_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_42_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_42_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_42_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_42_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_42_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_42_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_42_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_42_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_42_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_42_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_43_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_43_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_43_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_43_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_43_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_43_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_43_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_43_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_43_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_43_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_44_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_44_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_44_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_44_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_44_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_44_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_44_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_44_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_44_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_44_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_45_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_45_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_45_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_45_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_45_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_45_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_45_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_45_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_45_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_45_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_46_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_46_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_46_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_46_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_46_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_46_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_46_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_46_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_46_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_46_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_47_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_47_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_47_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_47_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_47_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_47_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_47_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_47_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_47_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_47_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_48_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_48_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_48_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_48_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_48_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_48_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_48_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_48_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_48_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_48_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_49_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_49_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_49_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_49_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_49_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_49_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_49_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_49_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_49_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_49_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_50_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_50_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_50_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_50_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_50_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_50_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_50_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_50_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_50_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_50_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_51_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_51_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_51_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_51_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_51_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_51_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_51_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_51_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_51_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_51_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_52_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_52_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_52_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_52_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_52_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_52_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_52_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_52_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_52_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_52_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_53_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_53_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_53_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_53_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_53_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_53_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_53_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_53_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_53_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_53_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_54_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_54_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_54_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_54_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_54_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_54_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_54_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_54_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_54_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_54_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_55_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_55_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_55_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_55_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_55_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_55_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_55_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_55_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_55_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_55_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_56_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_56_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_56_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_56_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_56_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_56_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_56_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_56_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_56_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_56_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_57_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_57_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_57_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_57_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_57_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_57_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_57_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_57_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_57_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_57_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_58_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_58_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_58_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_58_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_58_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_58_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_58_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_58_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_58_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_58_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_59_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_59_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_59_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_59_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_59_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_59_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_59_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_59_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_59_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_59_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_60_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_60_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_60_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 1, 1, groups=1536, bias=False)
self.features_60_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_60_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_60_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_60_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_60_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_60_conv_7 = torch.nn.modules.conv.Conv2d(1536, 256, 1, 1, 0, bias=False)
self.features_60_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(256)
self.features_61_conv_0 = torch.nn.modules.conv.Conv2d(256, 1536, 1, 1, 0, bias=False)
self.features_61_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_61_conv_3 = torch.nn.modules.conv.Conv2d(1536, 1536, 3, 2, 1, groups=1536, bias=False)
self.features_61_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(1536)
self.features_61_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_61_conv_6_fc_0 = torch.nn.modules.linear.Linear(1536, 64)
self.features_61_conv_6_fc_2 = torch.nn.modules.linear.Linear(64, 1536)
self.features_61_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_61_conv_7 = torch.nn.modules.conv.Conv2d(1536, 512, 1, 1, 0, bias=False)
self.features_61_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_62_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_62_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_62_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_62_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_62_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_62_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_62_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_62_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_62_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_62_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_63_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_63_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_63_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_63_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_63_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_63_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_63_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_63_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_63_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_63_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_64_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_64_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_64_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_64_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_64_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_64_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_64_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_64_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_64_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_64_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_65_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_65_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_65_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_65_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_65_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_65_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_65_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_65_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_65_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_65_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_66_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_66_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_66_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_66_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_66_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_66_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_66_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_66_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_66_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_66_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_67_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_67_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_67_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_67_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_67_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_67_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_67_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_67_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_67_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_67_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_68_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_68_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_68_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_68_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_68_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_68_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_68_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_68_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_68_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_68_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_69_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_69_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_69_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_69_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_69_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_69_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_69_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_69_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_69_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_69_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_70_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_70_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_70_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_70_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_70_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_70_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_70_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_70_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_70_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_70_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_71_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_71_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_71_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_71_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_71_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_71_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_71_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_71_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_71_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_71_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_72_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_72_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_72_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_72_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_72_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_72_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_72_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_72_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_72_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_72_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_73_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_73_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_73_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_73_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_73_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_73_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_73_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_73_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_73_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_73_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_74_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_74_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_74_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_74_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_74_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_74_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_74_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_74_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_74_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_74_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_75_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_75_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_75_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_75_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_75_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_75_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_75_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_75_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_75_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_75_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_76_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_76_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_76_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_76_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_76_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_76_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_76_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_76_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_76_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_76_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_77_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_77_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_77_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_77_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_77_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_77_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_77_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_77_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_77_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_77_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_78_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_78_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_78_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_78_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_78_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_78_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_78_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_78_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_78_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_78_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_79_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_79_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_79_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_79_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_79_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_79_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_79_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_79_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_79_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_79_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_80_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_80_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_80_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_80_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_80_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_80_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_80_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_80_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_80_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_80_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_81_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_81_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_81_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_81_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_81_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_81_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_81_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_81_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_81_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_81_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_82_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_82_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_82_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_82_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_82_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_82_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_82_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_82_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_82_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_82_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_83_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_83_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_83_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_83_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_83_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_83_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_83_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_83_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_83_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_83_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_84_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_84_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_84_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_84_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_84_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_84_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_84_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_84_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_84_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_84_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_85_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_85_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_85_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_85_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_85_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_85_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_85_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_85_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_85_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_85_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_86_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_86_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_86_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_86_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_86_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_86_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_86_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_86_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_86_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_86_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_87_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_87_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_87_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_87_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_87_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_87_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_87_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_87_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_87_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_87_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_88_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_88_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_88_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_88_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_88_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_88_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_88_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_88_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_88_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_88_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_89_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_89_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_89_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_89_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_89_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_89_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_89_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_89_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_89_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_89_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_90_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_90_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_90_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_90_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_90_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_90_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_90_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_90_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_90_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_90_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_91_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_91_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_91_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_91_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_91_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_91_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_91_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_91_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_91_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_91_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_92_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_92_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_92_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_92_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_92_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_92_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_92_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_92_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_92_conv_7 = torch.nn.modules.conv.Conv2d(3072, 512, 1, 1, 0, bias=False)
self.features_92_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(512)
self.features_93_conv_0 = torch.nn.modules.conv.Conv2d(512, 3072, 1, 1, 0, bias=False)
self.features_93_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_93_conv_3 = torch.nn.modules.conv.Conv2d(3072, 3072, 3, 1, 1, groups=3072, bias=False)
self.features_93_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3072)
self.features_93_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_93_conv_6_fc_0 = torch.nn.modules.linear.Linear(3072, 128)
self.features_93_conv_6_fc_2 = torch.nn.modules.linear.Linear(128, 3072)
self.features_93_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_93_conv_7 = torch.nn.modules.conv.Conv2d(3072, 640, 1, 1, 0, bias=False)
self.features_93_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_94_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_94_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_94_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_94_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_94_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_94_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_94_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_94_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_94_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_94_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_95_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_95_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_95_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_95_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_95_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_95_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_95_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_95_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_95_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_95_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_96_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_96_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_96_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_96_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_96_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_96_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_96_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_96_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_96_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_96_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_97_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_97_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_97_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_97_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_97_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_97_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_97_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_97_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_97_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_97_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_98_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_98_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_98_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_98_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_98_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_98_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_98_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_98_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_98_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_98_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_99_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_99_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_99_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_99_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_99_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_99_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_99_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_99_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_99_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_99_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.features_100_conv_0 = torch.nn.modules.conv.Conv2d(640, 3840, 1, 1, 0, bias=False)
self.features_100_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_100_conv_3 = torch.nn.modules.conv.Conv2d(3840, 3840, 3, 1, 1, groups=3840, bias=False)
self.features_100_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(3840)
self.features_100_conv_6_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
self.features_100_conv_6_fc_0 = torch.nn.modules.linear.Linear(3840, 160)
self.features_100_conv_6_fc_2 = torch.nn.modules.linear.Linear(160, 3840)
self.features_100_conv_6_fc_3 = torch.nn.modules.activation.Sigmoid()
self.features_100_conv_7 = torch.nn.modules.conv.Conv2d(3840, 640, 1, 1, 0, bias=False)
self.features_100_conv_8 = torch.nn.modules.batchnorm.BatchNorm2d(640)
self.conv_0 = torch.nn.modules.conv.Conv2d(640, 1792, 1, 1, 0, bias=False)
self.conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(1792)
self.avgpool = torch.nn.modules.pooling.AdaptiveAvgPool2d((1, 1))
self.classifier = torch.nn.modules.linear.Linear(1792, 1000)
def forward(self, input_1):
features_0_0 = self.features_0_0(input_1)
features_0_1 = self.features_0_1(features_0_0)
sigmoid_1 = torch.sigmoid(features_0_1)
mul_1 = features_0_1.__mul__(sigmoid_1)
features_1_conv_0 = self.features_1_conv_0(mul_1)
features_1_conv_1 = self.features_1_conv_1(features_1_conv_0)
sigmoid_2 = torch.sigmoid(features_1_conv_1)
mul_2 = features_1_conv_1.__mul__(sigmoid_2)
features_1_conv_3 = self.features_1_conv_3(mul_2)
features_1_conv_4 = self.features_1_conv_4(features_1_conv_3)
features_2_conv_0 = self.features_2_conv_0(features_1_conv_4)
features_2_conv_1 = self.features_2_conv_1(features_2_conv_0)
sigmoid_3 = torch.sigmoid(features_2_conv_1)
mul_3 = features_2_conv_1.__mul__(sigmoid_3)
features_2_conv_3 = self.features_2_conv_3(mul_3)
features_2_conv_4 = self.features_2_conv_4(features_2_conv_3)
add_1 = features_1_conv_4.__add__(features_2_conv_4)
features_3_conv_0 = self.features_3_conv_0(add_1)
features_3_conv_1 = self.features_3_conv_1(features_3_conv_0)
sigmoid_4 = torch.sigmoid(features_3_conv_1)
mul_4 = features_3_conv_1.__mul__(sigmoid_4)
features_3_conv_3 = self.features_3_conv_3(mul_4)
features_3_conv_4 = self.features_3_conv_4(features_3_conv_3)
add_2 = add_1.__add__(features_3_conv_4)
features_4_conv_0 = self.features_4_conv_0(add_2)
features_4_conv_1 = self.features_4_conv_1(features_4_conv_0)
sigmoid_5 = torch.sigmoid(features_4_conv_1)
mul_5 = features_4_conv_1.__mul__(sigmoid_5)
features_4_conv_3 = self.features_4_conv_3(mul_5)
features_4_conv_4 = self.features_4_conv_4(features_4_conv_3)
add_3 = add_2.__add__(features_4_conv_4)
features_5_conv_0 = self.features_5_conv_0(add_3)
features_5_conv_1 = self.features_5_conv_1(features_5_conv_0)
sigmoid_6 = torch.sigmoid(features_5_conv_1)
mul_6 = features_5_conv_1.__mul__(sigmoid_6)
features_5_conv_3 = self.features_5_conv_3(mul_6)
features_5_conv_4 = self.features_5_conv_4(features_5_conv_3)
features_6_conv_0 = self.features_6_conv_0(features_5_conv_4)
features_6_conv_1 = self.features_6_conv_1(features_6_conv_0)
sigmoid_7 = torch.sigmoid(features_6_conv_1)
mul_7 = features_6_conv_1.__mul__(sigmoid_7)
features_6_conv_3 = self.features_6_conv_3(mul_7)
features_6_conv_4 = self.features_6_conv_4(features_6_conv_3)
add_4 = features_5_conv_4.__add__(features_6_conv_4)
features_7_conv_0 = self.features_7_conv_0(add_4)
features_7_conv_1 = self.features_7_conv_1(features_7_conv_0)
sigmoid_8 = torch.sigmoid(features_7_conv_1)
mul_8 = features_7_conv_1.__mul__(sigmoid_8)
features_7_conv_3 = self.features_7_conv_3(mul_8)
features_7_conv_4 = self.features_7_conv_4(features_7_conv_3)
add_5 = add_4.__add__(features_7_conv_4)
features_8_conv_0 = self.features_8_conv_0(add_5)
features_8_conv_1 = self.features_8_conv_1(features_8_conv_0)
sigmoid_9 = torch.sigmoid(features_8_conv_1)
mul_9 = features_8_conv_1.__mul__(sigmoid_9)
features_8_conv_3 = self.features_8_conv_3(mul_9)
features_8_conv_4 = self.features_8_conv_4(features_8_conv_3)
add_6 = add_5.__add__(features_8_conv_4)
features_9_conv_0 = self.features_9_conv_0(add_6)
features_9_conv_1 = self.features_9_conv_1(features_9_conv_0)
sigmoid_10 = torch.sigmoid(features_9_conv_1)
mul_10 = features_9_conv_1.__mul__(sigmoid_10)
features_9_conv_3 = self.features_9_conv_3(mul_10)
features_9_conv_4 = self.features_9_conv_4(features_9_conv_3)
add_7 = add_6.__add__(features_9_conv_4)
features_10_conv_0 = self.features_10_conv_0(add_7)
features_10_conv_1 = self.features_10_conv_1(features_10_conv_0)
sigmoid_11 = torch.sigmoid(features_10_conv_1)
mul_11 = features_10_conv_1.__mul__(sigmoid_11)
features_10_conv_3 = self.features_10_conv_3(mul_11)
features_10_conv_4 = self.features_10_conv_4(features_10_conv_3)
add_8 = add_7.__add__(features_10_conv_4)
features_11_conv_0 = self.features_11_conv_0(add_8)
features_11_conv_1 = self.features_11_conv_1(features_11_conv_0)
sigmoid_12 = torch.sigmoid(features_11_conv_1)
mul_12 = features_11_conv_1.__mul__(sigmoid_12)
features_11_conv_3 = self.features_11_conv_3(mul_12)
features_11_conv_4 = self.features_11_conv_4(features_11_conv_3)
add_9 = add_8.__add__(features_11_conv_4)
features_12_conv_0 = self.features_12_conv_0(add_9)
features_12_conv_1 = self.features_12_conv_1(features_12_conv_0)
sigmoid_13 = torch.sigmoid(features_12_conv_1)
mul_13 = features_12_conv_1.__mul__(sigmoid_13)
features_12_conv_3 = self.features_12_conv_3(mul_13)
features_12_conv_4 = self.features_12_conv_4(features_12_conv_3)
add_10 = add_9.__add__(features_12_conv_4)
features_13_conv_0 = self.features_13_conv_0(add_10)
features_13_conv_1 = self.features_13_conv_1(features_13_conv_0)
sigmoid_14 = torch.sigmoid(features_13_conv_1)
mul_14 = features_13_conv_1.__mul__(sigmoid_14)
features_13_conv_3 = self.features_13_conv_3(mul_14)
features_13_conv_4 = self.features_13_conv_4(features_13_conv_3)
features_14_conv_0 = self.features_14_conv_0(features_13_conv_4)
features_14_conv_1 = self.features_14_conv_1(features_14_conv_0)
sigmoid_15 = torch.sigmoid(features_14_conv_1)
mul_15 = features_14_conv_1.__mul__(sigmoid_15)
features_14_conv_3 = self.features_14_conv_3(mul_15)
features_14_conv_4 = self.features_14_conv_4(features_14_conv_3)
add_11 = features_13_conv_4.__add__(features_14_conv_4)
features_15_conv_0 = self.features_15_conv_0(add_11)
features_15_conv_1 = self.features_15_conv_1(features_15_conv_0)