-
Notifications
You must be signed in to change notification settings - Fork 1
/
sceneflow_stereonet.log
939 lines (939 loc) · 66.4 KB
/
sceneflow_stereonet.log
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
argv: ['--model', 'stereonet', '--logdir', 'results/sceneflow_stereonet']
################################ args ################################
model stereonet <class 'str'>
maxdisp 192 <class 'int'>
dataset sceneflow <class 'str'>
datapath /data/luming/scene/ <class 'str'>
test_dataset sceneflow <class 'str'>
test_datapath /data/luming/scene/ <class 'str'>
trainlist ./filenames/sceneflow_train.txt <class 'str'>
testlist ./filenames/sceneflow_test.txt <class 'str'>
lr 0.001 <class 'float'>
batch_size 16 <class 'int'>
test_batch_size 8 <class 'int'>
epochs 16 <class 'int'>
lrepochs 10,12,14,16:2 <class 'str'>
logdir results/sceneflow_stereonet <class 'str'>
loadckpt None <class 'NoneType'>
resume False <class 'bool'>
seed 1 <class 'int'>
summary_freq 50 <class 'int'>
mode train <class 'str'>
crop_height 256 <class 'int'>
crop_width 512 <class 'int'>
test_crop_height 512 <class 'int'>
test_crop_width 960 <class 'int'>
########################################################################
creating new summary file
Number of model parameters: 624612
start at epoch 0
Let's use 4 GPUs!
Epoch 0/16, Iter 0/2215, lr 0.00100, train loss = 234.937, time = 8.897
Epoch 0/16, Iter 50/2215, lr 0.00100, train loss = 63.177, time = 1.368
Epoch 0/16, Iter 100/2215, lr 0.00100, train loss = 39.718, time = 1.108
Epoch 0/16, Iter 150/2215, lr 0.00100, train loss = 52.184, time = 1.425
Epoch 0/16, Iter 200/2215, lr 0.00100, train loss = 45.855, time = 1.218
Epoch 0/16, Iter 250/2215, lr 0.00100, train loss = 38.950, time = 1.402
Epoch 0/16, Iter 300/2215, lr 0.00100, train loss = 33.241, time = 1.301
Epoch 0/16, Iter 350/2215, lr 0.00100, train loss = 36.362, time = 1.349
Epoch 0/16, Iter 400/2215, lr 0.00100, train loss = 23.330, time = 1.412
Epoch 0/16, Iter 450/2215, lr 0.00100, train loss = 29.960, time = 1.413
Epoch 0/16, Iter 500/2215, lr 0.00100, train loss = 28.452, time = 1.416
Epoch 0/16, Iter 550/2215, lr 0.00100, train loss = 32.940, time = 1.448
Epoch 0/16, Iter 600/2215, lr 0.00100, train loss = 45.977, time = 1.228
Epoch 0/16, Iter 650/2215, lr 0.00100, train loss = 34.697, time = 1.451
Epoch 0/16, Iter 700/2215, lr 0.00100, train loss = 24.782, time = 1.294
Epoch 0/16, Iter 750/2215, lr 0.00100, train loss = 21.399, time = 1.348
Epoch 0/16, Iter 800/2215, lr 0.00100, train loss = 23.581, time = 1.455
Epoch 0/16, Iter 850/2215, lr 0.00100, train loss = 21.478, time = 1.462
Epoch 0/16, Iter 900/2215, lr 0.00100, train loss = 26.532, time = 1.470
Epoch 0/16, Iter 950/2215, lr 0.00100, train loss = 21.211, time = 1.433
Epoch 0/16, Iter 1000/2215, lr 0.00100, train loss = 25.963, time = 1.352
Epoch 0/16, Iter 1050/2215, lr 0.00100, train loss = 29.961, time = 1.345
Epoch 0/16, Iter 1100/2215, lr 0.00100, train loss = 14.627, time = 1.455
Epoch 0/16, Iter 1150/2215, lr 0.00100, train loss = 19.973, time = 1.373
Epoch 0/16, Iter 1200/2215, lr 0.00100, train loss = 36.406, time = 1.410
Epoch 0/16, Iter 1250/2215, lr 0.00100, train loss = 21.054, time = 1.218
Epoch 0/16, Iter 1300/2215, lr 0.00100, train loss = 17.132, time = 1.206
Epoch 0/16, Iter 1350/2215, lr 0.00100, train loss = 21.004, time = 1.289
Epoch 0/16, Iter 1400/2215, lr 0.00100, train loss = 17.737, time = 1.230
Epoch 0/16, Iter 1450/2215, lr 0.00100, train loss = 23.707, time = 1.447
Epoch 0/16, Iter 1500/2215, lr 0.00100, train loss = 25.047, time = 1.361
Epoch 0/16, Iter 1550/2215, lr 0.00100, train loss = 13.095, time = 1.192
Epoch 0/16, Iter 1600/2215, lr 0.00100, train loss = 18.461, time = 1.056
Epoch 0/16, Iter 1650/2215, lr 0.00100, train loss = 22.386, time = 1.238
Epoch 0/16, Iter 1700/2215, lr 0.00100, train loss = 19.873, time = 1.148
Epoch 0/16, Iter 1750/2215, lr 0.00100, train loss = 22.629, time = 1.046
Epoch 0/16, Iter 1800/2215, lr 0.00100, train loss = 24.322, time = 1.258
Epoch 0/16, Iter 1850/2215, lr 0.00100, train loss = 14.971, time = 1.321
Epoch 0/16, Iter 1900/2215, lr 0.00100, train loss = 23.426, time = 1.113
Epoch 0/16, Iter 1950/2215, lr 0.00100, train loss = 12.477, time = 1.224
Epoch 0/16, Iter 2000/2215, lr 0.00100, train loss = 20.859, time = 1.217
Epoch 0/16, Iter 2050/2215, lr 0.00100, train loss = 24.111, time = 1.084
Epoch 0/16, Iter 2100/2215, lr 0.00100, train loss = 22.816, time = 1.292
Epoch 0/16, Iter 2150/2215, lr 0.00100, train loss = 16.993, time = 1.278
Epoch 0/16, Iter 2200/2215, lr 0.00100, train loss = 20.437, time = 1.093
Epoch 0/16, Iter 0/547, test loss = 0.000, time = 1.454602
Epoch 0/16, Iter 50/547, test loss = 0.000, time = 0.518368
Epoch 0/16, Iter 100/547, test loss = 0.000, time = 0.564990
Epoch 0/16, Iter 150/547, test loss = 0.000, time = 0.537946
Epoch 0/16, Iter 200/547, test loss = 0.000, time = 0.570375
Epoch 0/16, Iter 250/547, test loss = 0.000, time = 0.538829
Epoch 0/16, Iter 300/547, test loss = 0.000, time = 0.544867
Epoch 0/16, Iter 350/547, test loss = 0.000, time = 0.542211
Epoch 0/16, Iter 400/547, test loss = 0.000, time = 0.513072
Epoch 0/16, Iter 450/547, test loss = 0.000, time = 0.547758
Epoch 0/16, Iter 500/547, test loss = 0.000, time = 0.532969
avg_test_scalars {'loss': 0.0, 'D1': [0.14574737953774672], 'EPE': [3.3618861046744004], 'Thres1': [0.39602866796078584], 'Thres2': [0.21864950994686844], 'Thres3': [0.1613035857663839]}
Best Checkpoint epoch_idx:0
Epoch 1/16, Iter 35/2215, lr 0.00100, train loss = 21.209, time = 1.539
Epoch 1/16, Iter 85/2215, lr 0.00100, train loss = 16.757, time = 1.363
Epoch 1/16, Iter 135/2215, lr 0.00100, train loss = 23.680, time = 1.409
Epoch 1/16, Iter 185/2215, lr 0.00100, train loss = 25.691, time = 1.434
Epoch 1/16, Iter 235/2215, lr 0.00100, train loss = 27.611, time = 1.374
Epoch 1/16, Iter 285/2215, lr 0.00100, train loss = 11.535, time = 1.373
Epoch 1/16, Iter 335/2215, lr 0.00100, train loss = 21.769, time = 1.347
Epoch 1/16, Iter 385/2215, lr 0.00100, train loss = 12.269, time = 1.444
Epoch 1/16, Iter 435/2215, lr 0.00100, train loss = 22.169, time = 1.368
Epoch 1/16, Iter 485/2215, lr 0.00100, train loss = 22.799, time = 1.486
Epoch 1/16, Iter 535/2215, lr 0.00100, train loss = 15.464, time = 1.241
Epoch 1/16, Iter 585/2215, lr 0.00100, train loss = 12.634, time = 1.321
Epoch 1/16, Iter 635/2215, lr 0.00100, train loss = 14.595, time = 1.478
Epoch 1/16, Iter 685/2215, lr 0.00100, train loss = 17.445, time = 1.416
Epoch 1/16, Iter 735/2215, lr 0.00100, train loss = 20.351, time = 1.393
Epoch 1/16, Iter 785/2215, lr 0.00100, train loss = 21.031, time = 1.476
Epoch 1/16, Iter 835/2215, lr 0.00100, train loss = 15.321, time = 1.449
Epoch 1/16, Iter 885/2215, lr 0.00100, train loss = 14.021, time = 1.313
Epoch 1/16, Iter 935/2215, lr 0.00100, train loss = 14.552, time = 1.233
Epoch 1/16, Iter 985/2215, lr 0.00100, train loss = 10.002, time = 1.459
Epoch 1/16, Iter 1035/2215, lr 0.00100, train loss = 18.743, time = 1.358
Epoch 1/16, Iter 1085/2215, lr 0.00100, train loss = 22.158, time = 1.480
Epoch 1/16, Iter 1135/2215, lr 0.00100, train loss = 16.731, time = 1.431
Epoch 1/16, Iter 1185/2215, lr 0.00100, train loss = 15.343, time = 1.446
Epoch 1/16, Iter 1235/2215, lr 0.00100, train loss = 21.498, time = 1.413
Epoch 1/16, Iter 1285/2215, lr 0.00100, train loss = 26.693, time = 1.345
Epoch 1/16, Iter 1335/2215, lr 0.00100, train loss = 16.415, time = 1.451
Epoch 1/16, Iter 1385/2215, lr 0.00100, train loss = 9.948, time = 1.473
Epoch 1/16, Iter 1435/2215, lr 0.00100, train loss = 18.978, time = 1.325
Epoch 1/16, Iter 1485/2215, lr 0.00100, train loss = 15.371, time = 1.384
Epoch 1/16, Iter 1535/2215, lr 0.00100, train loss = 19.898, time = 1.240
Epoch 1/16, Iter 1585/2215, lr 0.00100, train loss = 16.058, time = 1.223
Epoch 1/16, Iter 1635/2215, lr 0.00100, train loss = 24.284, time = 1.194
Epoch 1/16, Iter 1685/2215, lr 0.00100, train loss = 11.742, time = 1.225
Epoch 1/16, Iter 1735/2215, lr 0.00100, train loss = 15.499, time = 1.073
Epoch 1/16, Iter 1785/2215, lr 0.00100, train loss = 19.606, time = 1.079
Epoch 1/16, Iter 1835/2215, lr 0.00100, train loss = 15.586, time = 1.142
Epoch 1/16, Iter 1885/2215, lr 0.00100, train loss = 18.705, time = 1.310
Epoch 1/16, Iter 1935/2215, lr 0.00100, train loss = 19.248, time = 1.221
Epoch 1/16, Iter 1985/2215, lr 0.00100, train loss = 15.231, time = 1.242
Epoch 1/16, Iter 2035/2215, lr 0.00100, train loss = 12.212, time = 1.236
Epoch 1/16, Iter 2085/2215, lr 0.00100, train loss = 9.397, time = 1.175
Epoch 1/16, Iter 2135/2215, lr 0.00100, train loss = 11.145, time = 1.226
Epoch 1/16, Iter 2185/2215, lr 0.00100, train loss = 15.655, time = 1.225
Epoch 1/16, Iter 3/547, test loss = 0.000, time = 0.593300
Epoch 1/16, Iter 53/547, test loss = 0.000, time = 0.508765
Epoch 1/16, Iter 103/547, test loss = 0.000, time = 0.530883
Epoch 1/16, Iter 153/547, test loss = 0.000, time = 0.573533
Epoch 1/16, Iter 203/547, test loss = 0.000, time = 0.487036
Epoch 1/16, Iter 253/547, test loss = 0.000, time = 0.546659
Epoch 1/16, Iter 303/547, test loss = 0.000, time = 0.462514
Epoch 1/16, Iter 353/547, test loss = 0.000, time = 0.511408
Epoch 1/16, Iter 403/547, test loss = 0.000, time = 0.480870
Epoch 1/16, Iter 453/547, test loss = 0.000, time = 0.471906
Epoch 1/16, Iter 503/547, test loss = 0.000, time = 0.519558
avg_test_scalars {'loss': 0.0, 'D1': [0.20711568567963143], 'EPE': [3.4769499340048657], 'Thres1': [0.4442677628623502], 'Thres2': [0.30302975844367114], 'Thres3': [0.23484219340318083]}
Epoch 2/16, Iter 20/2215, lr 0.00100, train loss = 9.732, time = 1.257
Epoch 2/16, Iter 70/2215, lr 0.00100, train loss = 15.034, time = 1.317
Epoch 2/16, Iter 120/2215, lr 0.00100, train loss = 21.039, time = 1.178
Epoch 2/16, Iter 170/2215, lr 0.00100, train loss = 16.624, time = 1.402
Epoch 2/16, Iter 220/2215, lr 0.00100, train loss = 11.365, time = 1.299
Epoch 2/16, Iter 270/2215, lr 0.00100, train loss = 23.819, time = 1.493
Epoch 2/16, Iter 320/2215, lr 0.00100, train loss = 15.435, time = 1.117
Epoch 2/16, Iter 370/2215, lr 0.00100, train loss = 15.517, time = 1.056
Epoch 2/16, Iter 420/2215, lr 0.00100, train loss = 17.763, time = 1.137
Epoch 2/16, Iter 470/2215, lr 0.00100, train loss = 11.206, time = 1.254
Epoch 2/16, Iter 520/2215, lr 0.00100, train loss = 16.679, time = 1.098
Epoch 2/16, Iter 570/2215, lr 0.00100, train loss = 12.111, time = 1.232
Epoch 2/16, Iter 620/2215, lr 0.00100, train loss = 14.273, time = 1.425
Epoch 2/16, Iter 670/2215, lr 0.00100, train loss = 24.239, time = 1.232
Epoch 2/16, Iter 720/2215, lr 0.00100, train loss = 14.509, time = 1.148
Epoch 2/16, Iter 770/2215, lr 0.00100, train loss = 14.094, time = 1.182
Epoch 2/16, Iter 820/2215, lr 0.00100, train loss = 15.052, time = 1.439
Epoch 2/16, Iter 870/2215, lr 0.00100, train loss = 17.778, time = 1.170
Epoch 2/16, Iter 920/2215, lr 0.00100, train loss = 12.063, time = 1.319
Epoch 2/16, Iter 970/2215, lr 0.00100, train loss = 19.958, time = 1.212
Epoch 2/16, Iter 1020/2215, lr 0.00100, train loss = 20.511, time = 1.239
Epoch 2/16, Iter 1070/2215, lr 0.00100, train loss = 20.899, time = 1.161
Epoch 2/16, Iter 1120/2215, lr 0.00100, train loss = 18.251, time = 1.302
Epoch 2/16, Iter 1170/2215, lr 0.00100, train loss = 23.261, time = 1.359
Epoch 2/16, Iter 1220/2215, lr 0.00100, train loss = 15.889, time = 1.299
Epoch 2/16, Iter 1270/2215, lr 0.00100, train loss = 24.402, time = 1.058
Epoch 2/16, Iter 1320/2215, lr 0.00100, train loss = 19.989, time = 1.078
Epoch 2/16, Iter 1370/2215, lr 0.00100, train loss = 30.637, time = 1.136
Epoch 2/16, Iter 1420/2215, lr 0.00100, train loss = 24.081, time = 1.195
Epoch 2/16, Iter 1470/2215, lr 0.00100, train loss = 13.801, time = 1.419
Epoch 2/16, Iter 1520/2215, lr 0.00100, train loss = 17.049, time = 1.138
Epoch 2/16, Iter 1570/2215, lr 0.00100, train loss = 14.199, time = 1.398
Epoch 2/16, Iter 1620/2215, lr 0.00100, train loss = 9.854, time = 1.126
Epoch 2/16, Iter 1670/2215, lr 0.00100, train loss = 13.909, time = 1.131
Epoch 2/16, Iter 1720/2215, lr 0.00100, train loss = 10.679, time = 1.139
Epoch 2/16, Iter 1770/2215, lr 0.00100, train loss = 13.509, time = 1.136
Epoch 2/16, Iter 1820/2215, lr 0.00100, train loss = 21.158, time = 1.413
Epoch 2/16, Iter 1870/2215, lr 0.00100, train loss = 12.369, time = 1.417
Epoch 2/16, Iter 1920/2215, lr 0.00100, train loss = 18.388, time = 1.485
Epoch 2/16, Iter 1970/2215, lr 0.00100, train loss = 13.717, time = 1.454
Epoch 2/16, Iter 2020/2215, lr 0.00100, train loss = 18.129, time = 1.156
Epoch 2/16, Iter 2070/2215, lr 0.00100, train loss = 13.729, time = 1.122
Epoch 2/16, Iter 2120/2215, lr 0.00100, train loss = 16.970, time = 1.174
Epoch 2/16, Iter 2170/2215, lr 0.00100, train loss = 13.462, time = 1.361
Epoch 2/16, Iter 6/547, test loss = 0.000, time = 0.568463
Epoch 2/16, Iter 56/547, test loss = 0.000, time = 0.626941
Epoch 2/16, Iter 106/547, test loss = 0.000, time = 0.601084
Epoch 2/16, Iter 156/547, test loss = 0.000, time = 0.578050
Epoch 2/16, Iter 206/547, test loss = 0.000, time = 0.532277
Epoch 2/16, Iter 256/547, test loss = 0.000, time = 0.553015
Epoch 2/16, Iter 306/547, test loss = 0.000, time = 0.502459
Epoch 2/16, Iter 356/547, test loss = 0.000, time = 0.576066
Epoch 2/16, Iter 406/547, test loss = 0.000, time = 0.590077
Epoch 2/16, Iter 456/547, test loss = 0.000, time = 0.575776
Epoch 2/16, Iter 506/547, test loss = 0.000, time = 0.706767
avg_test_scalars {'loss': 0.0, 'D1': [0.27615777055785684], 'EPE': [4.3039631973236965], 'Thres1': [0.4801173394024917], 'Thres2': [0.349127784337324], 'Thres3': [0.2873977796741552]}
Epoch 3/16, Iter 5/2215, lr 0.00100, train loss = 12.486, time = 1.504
Epoch 3/16, Iter 55/2215, lr 0.00100, train loss = 17.671, time = 1.338
Epoch 3/16, Iter 105/2215, lr 0.00100, train loss = 18.253, time = 1.478
Epoch 3/16, Iter 155/2215, lr 0.00100, train loss = 16.245, time = 1.252
Epoch 3/16, Iter 205/2215, lr 0.00100, train loss = 9.598, time = 1.412
Epoch 3/16, Iter 255/2215, lr 0.00100, train loss = 11.814, time = 1.286
Epoch 3/16, Iter 305/2215, lr 0.00100, train loss = 13.457, time = 1.457
Epoch 3/16, Iter 355/2215, lr 0.00100, train loss = 13.021, time = 1.461
Epoch 3/16, Iter 405/2215, lr 0.00100, train loss = 17.078, time = 1.419
Epoch 3/16, Iter 455/2215, lr 0.00100, train loss = 14.110, time = 1.397
Epoch 3/16, Iter 505/2215, lr 0.00100, train loss = 14.970, time = 1.264
Epoch 3/16, Iter 555/2215, lr 0.00100, train loss = 11.776, time = 1.387
Epoch 3/16, Iter 605/2215, lr 0.00100, train loss = 10.711, time = 1.362
Epoch 3/16, Iter 655/2215, lr 0.00100, train loss = 17.900, time = 1.357
Epoch 3/16, Iter 705/2215, lr 0.00100, train loss = 15.849, time = 1.170
Epoch 3/16, Iter 755/2215, lr 0.00100, train loss = 16.633, time = 1.056
Epoch 3/16, Iter 805/2215, lr 0.00100, train loss = 14.824, time = 1.180
Epoch 3/16, Iter 855/2215, lr 0.00100, train loss = 17.937, time = 1.069
Epoch 3/16, Iter 905/2215, lr 0.00100, train loss = 21.009, time = 1.154
Epoch 3/16, Iter 955/2215, lr 0.00100, train loss = 14.642, time = 1.207
Epoch 3/16, Iter 1005/2215, lr 0.00100, train loss = 10.958, time = 1.171
Epoch 3/16, Iter 1055/2215, lr 0.00100, train loss = 14.609, time = 1.221
Epoch 3/16, Iter 1105/2215, lr 0.00100, train loss = 12.601, time = 1.200
Epoch 3/16, Iter 1155/2215, lr 0.00100, train loss = 10.433, time = 1.078
Epoch 3/16, Iter 1205/2215, lr 0.00100, train loss = 12.399, time = 1.236
Epoch 3/16, Iter 1255/2215, lr 0.00100, train loss = 9.650, time = 1.405
Epoch 3/16, Iter 1305/2215, lr 0.00100, train loss = 18.963, time = 1.321
Epoch 3/16, Iter 1355/2215, lr 0.00100, train loss = 12.510, time = 1.267
Epoch 3/16, Iter 1405/2215, lr 0.00100, train loss = 14.015, time = 1.126
Epoch 3/16, Iter 1455/2215, lr 0.00100, train loss = 12.999, time = 1.184
Epoch 3/16, Iter 1505/2215, lr 0.00100, train loss = 13.450, time = 1.179
Epoch 3/16, Iter 1555/2215, lr 0.00100, train loss = 22.960, time = 1.168
Epoch 3/16, Iter 1605/2215, lr 0.00100, train loss = 11.221, time = 1.270
Epoch 3/16, Iter 1655/2215, lr 0.00100, train loss = 15.251, time = 1.239
Epoch 3/16, Iter 1705/2215, lr 0.00100, train loss = 13.853, time = 1.199
Epoch 3/16, Iter 1755/2215, lr 0.00100, train loss = 12.336, time = 1.185
Epoch 3/16, Iter 1805/2215, lr 0.00100, train loss = 8.151, time = 1.188
Epoch 3/16, Iter 1855/2215, lr 0.00100, train loss = 11.019, time = 1.312
Epoch 3/16, Iter 1905/2215, lr 0.00100, train loss = 15.412, time = 1.074
Epoch 3/16, Iter 1955/2215, lr 0.00100, train loss = 10.276, time = 1.230
Epoch 3/16, Iter 2005/2215, lr 0.00100, train loss = 11.622, time = 1.457
Epoch 3/16, Iter 2055/2215, lr 0.00100, train loss = 11.688, time = 1.061
Epoch 3/16, Iter 2105/2215, lr 0.00100, train loss = 12.797, time = 1.283
Epoch 3/16, Iter 2155/2215, lr 0.00100, train loss = 10.744, time = 1.208
Epoch 3/16, Iter 2205/2215, lr 0.00100, train loss = 10.768, time = 1.123
Epoch 3/16, Iter 9/547, test loss = 0.000, time = 0.571217
Epoch 3/16, Iter 59/547, test loss = 0.000, time = 0.549194
Epoch 3/16, Iter 109/547, test loss = 0.000, time = 0.560727
Epoch 3/16, Iter 159/547, test loss = 0.000, time = 0.546848
Epoch 3/16, Iter 209/547, test loss = 0.000, time = 0.561335
Epoch 3/16, Iter 259/547, test loss = 0.000, time = 0.534331
Epoch 3/16, Iter 309/547, test loss = 0.000, time = 0.559714
Epoch 3/16, Iter 359/547, test loss = 0.000, time = 0.545423
Epoch 3/16, Iter 409/547, test loss = 0.000, time = 0.546256
Epoch 3/16, Iter 459/547, test loss = 0.000, time = 0.538035
Epoch 3/16, Iter 509/547, test loss = 0.000, time = 0.552776
avg_test_scalars {'loss': 0.0, 'D1': [0.09210645794950194], 'EPE': [2.2265418758122095], 'Thres1': [0.304975116519867], 'Thres2': [0.17002330761287068], 'Thres3': [0.11912583767809315]}
Best Checkpoint epoch_idx:3
Epoch 4/16, Iter 40/2215, lr 0.00100, train loss = 10.891, time = 1.567
Epoch 4/16, Iter 90/2215, lr 0.00100, train loss = 14.203, time = 1.336
Epoch 4/16, Iter 140/2215, lr 0.00100, train loss = 13.046, time = 1.334
Epoch 4/16, Iter 190/2215, lr 0.00100, train loss = 21.266, time = 1.338
Epoch 4/16, Iter 240/2215, lr 0.00100, train loss = 14.666, time = 1.290
Epoch 4/16, Iter 290/2215, lr 0.00100, train loss = 13.650, time = 1.278
Epoch 4/16, Iter 340/2215, lr 0.00100, train loss = 14.111, time = 1.145
Epoch 4/16, Iter 390/2215, lr 0.00100, train loss = 10.835, time = 1.356
Epoch 4/16, Iter 440/2215, lr 0.00100, train loss = 14.795, time = 1.524
Epoch 4/16, Iter 490/2215, lr 0.00100, train loss = 16.790, time = 1.429
Epoch 4/16, Iter 540/2215, lr 0.00100, train loss = 5.692, time = 1.282
Epoch 4/16, Iter 590/2215, lr 0.00100, train loss = 10.839, time = 1.279
Epoch 4/16, Iter 640/2215, lr 0.00100, train loss = 15.110, time = 1.420
Epoch 4/16, Iter 690/2215, lr 0.00100, train loss = 14.712, time = 1.049
Epoch 4/16, Iter 740/2215, lr 0.00100, train loss = 16.380, time = 1.126
Epoch 4/16, Iter 790/2215, lr 0.00100, train loss = 12.555, time = 1.117
Epoch 4/16, Iter 840/2215, lr 0.00100, train loss = 12.714, time = 1.140
Epoch 4/16, Iter 890/2215, lr 0.00100, train loss = 12.185, time = 1.275
Epoch 4/16, Iter 940/2215, lr 0.00100, train loss = 11.371, time = 1.222
Epoch 4/16, Iter 990/2215, lr 0.00100, train loss = 11.146, time = 1.423
Epoch 4/16, Iter 1040/2215, lr 0.00100, train loss = 11.804, time = 1.517
Epoch 4/16, Iter 1090/2215, lr 0.00100, train loss = 18.490, time = 1.351
Epoch 4/16, Iter 1140/2215, lr 0.00100, train loss = 18.929, time = 1.343
Epoch 4/16, Iter 1190/2215, lr 0.00100, train loss = 10.918, time = 1.311
Epoch 4/16, Iter 1240/2215, lr 0.00100, train loss = 13.382, time = 1.055
Epoch 4/16, Iter 1290/2215, lr 0.00100, train loss = 14.247, time = 1.199
Epoch 4/16, Iter 1340/2215, lr 0.00100, train loss = 15.870, time = 1.437
Epoch 4/16, Iter 1390/2215, lr 0.00100, train loss = 7.912, time = 1.101
Epoch 4/16, Iter 1440/2215, lr 0.00100, train loss = 9.872, time = 1.434
Epoch 4/16, Iter 1490/2215, lr 0.00100, train loss = 17.676, time = 1.407
Epoch 4/16, Iter 1540/2215, lr 0.00100, train loss = 13.053, time = 1.293
Epoch 4/16, Iter 1590/2215, lr 0.00100, train loss = 12.204, time = 1.083
Epoch 4/16, Iter 1640/2215, lr 0.00100, train loss = 11.385, time = 1.098
Epoch 4/16, Iter 1690/2215, lr 0.00100, train loss = 10.901, time = 1.262
Epoch 4/16, Iter 1740/2215, lr 0.00100, train loss = 24.088, time = 1.191
Epoch 4/16, Iter 1790/2215, lr 0.00100, train loss = 13.758, time = 1.247
Epoch 4/16, Iter 1840/2215, lr 0.00100, train loss = 10.310, time = 1.105
Epoch 4/16, Iter 1890/2215, lr 0.00100, train loss = 24.527, time = 1.188
Epoch 4/16, Iter 1940/2215, lr 0.00100, train loss = 7.172, time = 1.407
Epoch 4/16, Iter 1990/2215, lr 0.00100, train loss = 9.719, time = 1.417
Epoch 4/16, Iter 2040/2215, lr 0.00100, train loss = 16.597, time = 1.123
Epoch 4/16, Iter 2090/2215, lr 0.00100, train loss = 15.291, time = 1.238
Epoch 4/16, Iter 2140/2215, lr 0.00100, train loss = 14.188, time = 1.197
Epoch 4/16, Iter 2190/2215, lr 0.00100, train loss = 14.765, time = 1.193
Epoch 4/16, Iter 12/547, test loss = 0.000, time = 0.578478
Epoch 4/16, Iter 62/547, test loss = 0.000, time = 0.527786
Epoch 4/16, Iter 112/547, test loss = 0.000, time = 0.510440
Epoch 4/16, Iter 162/547, test loss = 0.000, time = 0.551147
Epoch 4/16, Iter 212/547, test loss = 0.000, time = 0.538553
Epoch 4/16, Iter 262/547, test loss = 0.000, time = 0.509398
Epoch 4/16, Iter 312/547, test loss = 0.000, time = 0.509061
Epoch 4/16, Iter 362/547, test loss = 0.000, time = 0.552139
Epoch 4/16, Iter 412/547, test loss = 0.000, time = 0.485842
Epoch 4/16, Iter 462/547, test loss = 0.000, time = 0.517485
Epoch 4/16, Iter 512/547, test loss = 0.000, time = 0.521139
avg_test_scalars {'loss': 0.0, 'D1': [0.11733577708957182], 'EPE': [2.4012917755290837], 'Thres1': [0.3215860531578552], 'Thres2': [0.18351460639149006], 'Thres3': [0.13532733657759985]}
Epoch 5/16, Iter 25/2215, lr 0.00100, train loss = 9.938, time = 1.531
Epoch 5/16, Iter 75/2215, lr 0.00100, train loss = 18.593, time = 1.393
Epoch 5/16, Iter 125/2215, lr 0.00100, train loss = 13.117, time = 1.447
Epoch 5/16, Iter 175/2215, lr 0.00100, train loss = 9.599, time = 1.402
Epoch 5/16, Iter 225/2215, lr 0.00100, train loss = 15.234, time = 1.436
Epoch 5/16, Iter 275/2215, lr 0.00100, train loss = 9.961, time = 1.393
Epoch 5/16, Iter 325/2215, lr 0.00100, train loss = 12.013, time = 1.422
Epoch 5/16, Iter 375/2215, lr 0.00100, train loss = 9.733, time = 1.374
Epoch 5/16, Iter 425/2215, lr 0.00100, train loss = 11.448, time = 1.318
Epoch 5/16, Iter 475/2215, lr 0.00100, train loss = 13.216, time = 1.499
Epoch 5/16, Iter 525/2215, lr 0.00100, train loss = 11.947, time = 1.397
Epoch 5/16, Iter 575/2215, lr 0.00100, train loss = 11.360, time = 1.381
Epoch 5/16, Iter 625/2215, lr 0.00100, train loss = 21.854, time = 1.299
Epoch 5/16, Iter 675/2215, lr 0.00100, train loss = 11.541, time = 1.297
Epoch 5/16, Iter 725/2215, lr 0.00100, train loss = 14.372, time = 1.517
Epoch 5/16, Iter 775/2215, lr 0.00100, train loss = 15.468, time = 1.255
Epoch 5/16, Iter 825/2215, lr 0.00100, train loss = 13.240, time = 1.421
Epoch 5/16, Iter 875/2215, lr 0.00100, train loss = 9.589, time = 1.424
Epoch 5/16, Iter 925/2215, lr 0.00100, train loss = 13.752, time = 1.541
Epoch 5/16, Iter 975/2215, lr 0.00100, train loss = 7.576, time = 1.393
Epoch 5/16, Iter 1025/2215, lr 0.00100, train loss = 12.123, time = 1.580
Epoch 5/16, Iter 1075/2215, lr 0.00100, train loss = 11.672, time = 1.436
Epoch 5/16, Iter 1125/2215, lr 0.00100, train loss = 20.300, time = 1.512
Epoch 5/16, Iter 1175/2215, lr 0.00100, train loss = 11.143, time = 1.343
Epoch 5/16, Iter 1225/2215, lr 0.00100, train loss = 13.160, time = 1.408
Epoch 5/16, Iter 1275/2215, lr 0.00100, train loss = 16.790, time = 1.481
Epoch 5/16, Iter 1325/2215, lr 0.00100, train loss = 12.534, time = 1.489
Epoch 5/16, Iter 1375/2215, lr 0.00100, train loss = 12.814, time = 1.507
Epoch 5/16, Iter 1425/2215, lr 0.00100, train loss = 13.734, time = 1.378
Epoch 5/16, Iter 1475/2215, lr 0.00100, train loss = 15.655, time = 1.512
Epoch 5/16, Iter 1525/2215, lr 0.00100, train loss = 11.245, time = 1.476
Epoch 5/16, Iter 1575/2215, lr 0.00100, train loss = 12.059, time = 1.174
Epoch 5/16, Iter 1625/2215, lr 0.00100, train loss = 14.035, time = 1.295
Epoch 5/16, Iter 1675/2215, lr 0.00100, train loss = 9.563, time = 1.336
Epoch 5/16, Iter 1725/2215, lr 0.00100, train loss = 21.642, time = 1.374
Epoch 5/16, Iter 1775/2215, lr 0.00100, train loss = 19.159, time = 1.416
Epoch 5/16, Iter 1825/2215, lr 0.00100, train loss = 12.555, time = 1.415
Epoch 5/16, Iter 1875/2215, lr 0.00100, train loss = 13.618, time = 1.321
Epoch 5/16, Iter 1925/2215, lr 0.00100, train loss = 8.083, time = 1.562
Epoch 5/16, Iter 1975/2215, lr 0.00100, train loss = 13.230, time = 1.302
Epoch 5/16, Iter 2025/2215, lr 0.00100, train loss = 12.514, time = 1.378
Epoch 5/16, Iter 2075/2215, lr 0.00100, train loss = 9.766, time = 1.391
Epoch 5/16, Iter 2125/2215, lr 0.00100, train loss = 16.756, time = 1.184
Epoch 5/16, Iter 2175/2215, lr 0.00100, train loss = 7.883, time = 1.149
Epoch 5/16, Iter 15/547, test loss = 0.000, time = 0.788236
Epoch 5/16, Iter 65/547, test loss = 0.000, time = 0.543088
Epoch 5/16, Iter 115/547, test loss = 0.000, time = 0.641635
Epoch 5/16, Iter 165/547, test loss = 0.000, time = 0.717980
Epoch 5/16, Iter 215/547, test loss = 0.000, time = 0.556396
Epoch 5/16, Iter 265/547, test loss = 0.000, time = 0.631888
Epoch 5/16, Iter 315/547, test loss = 0.000, time = 0.627804
Epoch 5/16, Iter 365/547, test loss = 0.000, time = 0.537126
Epoch 5/16, Iter 415/547, test loss = 0.000, time = 0.605628
Epoch 5/16, Iter 465/547, test loss = 0.000, time = 0.610493
Epoch 5/16, Iter 515/547, test loss = 0.000, time = 0.527460
avg_test_scalars {'loss': 0.0, 'D1': [0.14068709339749005], 'EPE': [2.4830906793229977], 'Thres1': [0.40647494550487895], 'Thres2': [0.24155754660693776], 'Thres3': [0.1673093146050325]}
Epoch 6/16, Iter 10/2215, lr 0.00100, train loss = 8.020, time = 1.251
Epoch 6/16, Iter 60/2215, lr 0.00100, train loss = 13.229, time = 1.413
Epoch 6/16, Iter 110/2215, lr 0.00100, train loss = 11.588, time = 1.354
Epoch 6/16, Iter 160/2215, lr 0.00100, train loss = 11.585, time = 1.442
Epoch 6/16, Iter 210/2215, lr 0.00100, train loss = 9.988, time = 1.507
Epoch 6/16, Iter 260/2215, lr 0.00100, train loss = 15.714, time = 1.329
Epoch 6/16, Iter 310/2215, lr 0.00100, train loss = 20.550, time = 1.197
Epoch 6/16, Iter 360/2215, lr 0.00100, train loss = 13.963, time = 1.462
Epoch 6/16, Iter 410/2215, lr 0.00100, train loss = 16.138, time = 1.312
Epoch 6/16, Iter 460/2215, lr 0.00100, train loss = 12.130, time = 1.441
Epoch 6/16, Iter 510/2215, lr 0.00100, train loss = 10.389, time = 1.336
Epoch 6/16, Iter 560/2215, lr 0.00100, train loss = 14.891, time = 1.394
Epoch 6/16, Iter 610/2215, lr 0.00100, train loss = 14.502, time = 1.355
Epoch 6/16, Iter 660/2215, lr 0.00100, train loss = 10.885, time = 1.331
Epoch 6/16, Iter 710/2215, lr 0.00100, train loss = 13.609, time = 1.381
Epoch 6/16, Iter 760/2215, lr 0.00100, train loss = 8.027, time = 1.219
Epoch 6/16, Iter 810/2215, lr 0.00100, train loss = 13.655, time = 1.416
Epoch 6/16, Iter 860/2215, lr 0.00100, train loss = 10.012, time = 1.406
Epoch 6/16, Iter 910/2215, lr 0.00100, train loss = 9.863, time = 1.407
Epoch 6/16, Iter 960/2215, lr 0.00100, train loss = 10.045, time = 1.306
Epoch 6/16, Iter 1010/2215, lr 0.00100, train loss = 11.482, time = 1.424
Epoch 6/16, Iter 1060/2215, lr 0.00100, train loss = 15.222, time = 1.293
Epoch 6/16, Iter 1110/2215, lr 0.00100, train loss = 12.161, time = 1.335
Epoch 6/16, Iter 1160/2215, lr 0.00100, train loss = 13.904, time = 1.459
Epoch 6/16, Iter 1210/2215, lr 0.00100, train loss = 10.874, time = 1.329
Epoch 6/16, Iter 1260/2215, lr 0.00100, train loss = 11.091, time = 1.343
Epoch 6/16, Iter 1310/2215, lr 0.00100, train loss = 12.511, time = 1.281
Epoch 6/16, Iter 1360/2215, lr 0.00100, train loss = 10.386, time = 1.292
Epoch 6/16, Iter 1410/2215, lr 0.00100, train loss = 14.160, time = 1.227
Epoch 6/16, Iter 1460/2215, lr 0.00100, train loss = 11.438, time = 1.417
Epoch 6/16, Iter 1510/2215, lr 0.00100, train loss = 19.217, time = 1.309
Epoch 6/16, Iter 1560/2215, lr 0.00100, train loss = 10.023, time = 1.291
Epoch 6/16, Iter 1610/2215, lr 0.00100, train loss = 16.642, time = 1.354
Epoch 6/16, Iter 1660/2215, lr 0.00100, train loss = 10.766, time = 1.453
Epoch 6/16, Iter 1710/2215, lr 0.00100, train loss = 13.653, time = 1.453
Epoch 6/16, Iter 1760/2215, lr 0.00100, train loss = 11.996, time = 1.440
Epoch 6/16, Iter 1810/2215, lr 0.00100, train loss = 12.191, time = 1.293
Epoch 6/16, Iter 1860/2215, lr 0.00100, train loss = 13.660, time = 1.363
Epoch 6/16, Iter 1910/2215, lr 0.00100, train loss = 11.435, time = 1.327
Epoch 6/16, Iter 1960/2215, lr 0.00100, train loss = 16.314, time = 1.541
Epoch 6/16, Iter 2010/2215, lr 0.00100, train loss = 12.799, time = 1.437
Epoch 6/16, Iter 2060/2215, lr 0.00100, train loss = 17.394, time = 1.358
Epoch 6/16, Iter 2110/2215, lr 0.00100, train loss = 16.405, time = 1.373
Epoch 6/16, Iter 2160/2215, lr 0.00100, train loss = 7.845, time = 1.474
Epoch 6/16, Iter 2210/2215, lr 0.00100, train loss = 9.227, time = 1.379
Epoch 6/16, Iter 18/547, test loss = 0.000, time = 0.581084
Epoch 6/16, Iter 68/547, test loss = 0.000, time = 0.516020
Epoch 6/16, Iter 118/547, test loss = 0.000, time = 0.546057
Epoch 6/16, Iter 168/547, test loss = 0.000, time = 0.507125
Epoch 6/16, Iter 218/547, test loss = 0.000, time = 0.556957
Epoch 6/16, Iter 268/547, test loss = 0.000, time = 0.507446
Epoch 6/16, Iter 318/547, test loss = 0.000, time = 0.531343
Epoch 6/16, Iter 368/547, test loss = 0.000, time = 0.506698
Epoch 6/16, Iter 418/547, test loss = 0.000, time = 0.527923
Epoch 6/16, Iter 468/547, test loss = 0.000, time = 0.561804
Epoch 6/16, Iter 518/547, test loss = 0.000, time = 0.521468
avg_test_scalars {'loss': 0.0, 'D1': [0.07609969207598152], 'EPE': [1.8008141419569361], 'Thres1': [0.22281890265793425], 'Thres2': [0.12288820485446528], 'Thres3': [0.08885586540294739]}
Best Checkpoint epoch_idx:6
Epoch 7/16, Iter 45/2215, lr 0.00100, train loss = 14.667, time = 1.391
Epoch 7/16, Iter 95/2215, lr 0.00100, train loss = 7.633, time = 1.440
Epoch 7/16, Iter 145/2215, lr 0.00100, train loss = 10.314, time = 1.187
Epoch 7/16, Iter 195/2215, lr 0.00100, train loss = 10.276, time = 1.338
Epoch 7/16, Iter 245/2215, lr 0.00100, train loss = 15.836, time = 1.440
Epoch 7/16, Iter 295/2215, lr 0.00100, train loss = 7.914, time = 1.479
Epoch 7/16, Iter 345/2215, lr 0.00100, train loss = 14.690, time = 1.384
Epoch 7/16, Iter 395/2215, lr 0.00100, train loss = 11.154, time = 1.245
Epoch 7/16, Iter 445/2215, lr 0.00100, train loss = 12.679, time = 1.141
Epoch 7/16, Iter 495/2215, lr 0.00100, train loss = 11.147, time = 1.332
Epoch 7/16, Iter 545/2215, lr 0.00100, train loss = 12.159, time = 1.267
Epoch 7/16, Iter 595/2215, lr 0.00100, train loss = 12.786, time = 1.192
Epoch 7/16, Iter 645/2215, lr 0.00100, train loss = 9.750, time = 1.072
Epoch 7/16, Iter 695/2215, lr 0.00100, train loss = 13.637, time = 1.090
Epoch 7/16, Iter 745/2215, lr 0.00100, train loss = 11.779, time = 1.229
Epoch 7/16, Iter 795/2215, lr 0.00100, train loss = 10.883, time = 1.300
Epoch 7/16, Iter 845/2215, lr 0.00100, train loss = 11.226, time = 1.150
Epoch 7/16, Iter 895/2215, lr 0.00100, train loss = 7.750, time = 1.259
Epoch 7/16, Iter 945/2215, lr 0.00100, train loss = 9.047, time = 1.220
Epoch 7/16, Iter 995/2215, lr 0.00100, train loss = 13.332, time = 1.086
Epoch 7/16, Iter 1045/2215, lr 0.00100, train loss = 9.786, time = 1.163
Epoch 7/16, Iter 1095/2215, lr 0.00100, train loss = 13.408, time = 1.302
Epoch 7/16, Iter 1145/2215, lr 0.00100, train loss = 11.145, time = 1.158
Epoch 7/16, Iter 1195/2215, lr 0.00100, train loss = 8.027, time = 1.155
Epoch 7/16, Iter 1245/2215, lr 0.00100, train loss = 7.412, time = 1.412
Epoch 7/16, Iter 1295/2215, lr 0.00100, train loss = 13.028, time = 1.159
Epoch 7/16, Iter 1345/2215, lr 0.00100, train loss = 12.844, time = 1.171
Epoch 7/16, Iter 1395/2215, lr 0.00100, train loss = 12.534, time = 1.165
Epoch 7/16, Iter 1445/2215, lr 0.00100, train loss = 11.885, time = 1.205
Epoch 7/16, Iter 1495/2215, lr 0.00100, train loss = 17.479, time = 1.170
Epoch 7/16, Iter 1545/2215, lr 0.00100, train loss = 12.275, time = 1.215
Epoch 7/16, Iter 1595/2215, lr 0.00100, train loss = 12.714, time = 1.184
Epoch 7/16, Iter 1645/2215, lr 0.00100, train loss = 12.393, time = 1.141
Epoch 7/16, Iter 1695/2215, lr 0.00100, train loss = 13.191, time = 1.142
Epoch 7/16, Iter 1745/2215, lr 0.00100, train loss = 12.075, time = 1.214
Epoch 7/16, Iter 1795/2215, lr 0.00100, train loss = 12.890, time = 1.119
Epoch 7/16, Iter 1845/2215, lr 0.00100, train loss = 8.980, time = 1.143
Epoch 7/16, Iter 1895/2215, lr 0.00100, train loss = 9.843, time = 1.041
Epoch 7/16, Iter 1945/2215, lr 0.00100, train loss = 8.334, time = 1.225
Epoch 7/16, Iter 1995/2215, lr 0.00100, train loss = 9.458, time = 1.187
Epoch 7/16, Iter 2045/2215, lr 0.00100, train loss = 10.124, time = 1.188
Epoch 7/16, Iter 2095/2215, lr 0.00100, train loss = 10.679, time = 1.056
Epoch 7/16, Iter 2145/2215, lr 0.00100, train loss = 11.187, time = 1.175
Epoch 7/16, Iter 2195/2215, lr 0.00100, train loss = 11.980, time = 1.221
Epoch 7/16, Iter 21/547, test loss = 0.000, time = 0.542111
Epoch 7/16, Iter 71/547, test loss = 0.000, time = 0.532781
Epoch 7/16, Iter 121/547, test loss = 0.000, time = 0.471699
Epoch 7/16, Iter 171/547, test loss = 0.000, time = 0.473600
Epoch 7/16, Iter 221/547, test loss = 0.000, time = 0.478323
Epoch 7/16, Iter 271/547, test loss = 0.000, time = 0.532849
Epoch 7/16, Iter 321/547, test loss = 0.000, time = 0.464107
Epoch 7/16, Iter 371/547, test loss = 0.000, time = 0.481821
Epoch 7/16, Iter 421/547, test loss = 0.000, time = 0.544007
Epoch 7/16, Iter 471/547, test loss = 0.000, time = 0.520519
Epoch 7/16, Iter 521/547, test loss = 0.000, time = 0.551103
avg_test_scalars {'loss': 0.0, 'D1': [0.07770233780785823], 'EPE': [1.8532437844511795], 'Thres1': [0.24180337150582884], 'Thres2': [0.13156737348361033], 'Thres3': [0.0940815759158712]}
Epoch 8/16, Iter 30/2215, lr 0.00100, train loss = 11.737, time = 1.389
Epoch 8/16, Iter 80/2215, lr 0.00100, train loss = 11.736, time = 1.497
Epoch 8/16, Iter 130/2215, lr 0.00100, train loss = 16.640, time = 1.374
Epoch 8/16, Iter 180/2215, lr 0.00100, train loss = 15.929, time = 1.382
Epoch 8/16, Iter 230/2215, lr 0.00100, train loss = 7.094, time = 1.391
Epoch 8/16, Iter 280/2215, lr 0.00100, train loss = 7.985, time = 1.420
Epoch 8/16, Iter 330/2215, lr 0.00100, train loss = 14.311, time = 1.347
Epoch 8/16, Iter 380/2215, lr 0.00100, train loss = 14.880, time = 1.419
Epoch 8/16, Iter 430/2215, lr 0.00100, train loss = 13.056, time = 1.305
Epoch 8/16, Iter 480/2215, lr 0.00100, train loss = 12.970, time = 1.393
Epoch 8/16, Iter 530/2215, lr 0.00100, train loss = 11.802, time = 1.320
Epoch 8/16, Iter 580/2215, lr 0.00100, train loss = 8.525, time = 1.345
Epoch 8/16, Iter 630/2215, lr 0.00100, train loss = 10.404, time = 1.410
Epoch 8/16, Iter 680/2215, lr 0.00100, train loss = 13.113, time = 1.364
Epoch 8/16, Iter 730/2215, lr 0.00100, train loss = 9.794, time = 1.443
Epoch 8/16, Iter 780/2215, lr 0.00100, train loss = 8.743, time = 1.446
Epoch 8/16, Iter 830/2215, lr 0.00100, train loss = 11.592, time = 1.402
Epoch 8/16, Iter 880/2215, lr 0.00100, train loss = 15.892, time = 1.385
Epoch 8/16, Iter 930/2215, lr 0.00100, train loss = 9.358, time = 1.483
Epoch 8/16, Iter 980/2215, lr 0.00100, train loss = 13.049, time = 1.521
Epoch 8/16, Iter 1030/2215, lr 0.00100, train loss = 8.704, time = 1.443
Epoch 8/16, Iter 1080/2215, lr 0.00100, train loss = 12.746, time = 1.353
Epoch 8/16, Iter 1130/2215, lr 0.00100, train loss = 12.217, time = 1.167
Epoch 8/16, Iter 1180/2215, lr 0.00100, train loss = 8.675, time = 1.442
Epoch 8/16, Iter 1230/2215, lr 0.00100, train loss = 10.063, time = 1.432
Epoch 8/16, Iter 1280/2215, lr 0.00100, train loss = 15.983, time = 1.555
Epoch 8/16, Iter 1330/2215, lr 0.00100, train loss = 14.407, time = 1.502
Epoch 8/16, Iter 1380/2215, lr 0.00100, train loss = 14.182, time = 1.419
Epoch 8/16, Iter 1430/2215, lr 0.00100, train loss = 10.614, time = 1.273
Epoch 8/16, Iter 1480/2215, lr 0.00100, train loss = 16.109, time = 1.367
Epoch 8/16, Iter 1530/2215, lr 0.00100, train loss = 8.978, time = 1.474
Epoch 8/16, Iter 1580/2215, lr 0.00100, train loss = 13.394, time = 1.480
Epoch 8/16, Iter 1630/2215, lr 0.00100, train loss = 13.445, time = 1.369
Epoch 8/16, Iter 1680/2215, lr 0.00100, train loss = 12.855, time = 1.363
Epoch 8/16, Iter 1730/2215, lr 0.00100, train loss = 12.478, time = 1.377
Epoch 8/16, Iter 1780/2215, lr 0.00100, train loss = 12.608, time = 1.384
Epoch 8/16, Iter 1830/2215, lr 0.00100, train loss = 14.635, time = 1.270
Epoch 8/16, Iter 1880/2215, lr 0.00100, train loss = 11.251, time = 1.428
Epoch 8/16, Iter 1930/2215, lr 0.00100, train loss = 12.063, time = 1.457
Epoch 8/16, Iter 1980/2215, lr 0.00100, train loss = 9.082, time = 1.549
Epoch 8/16, Iter 2030/2215, lr 0.00100, train loss = 16.016, time = 1.429
Epoch 8/16, Iter 2080/2215, lr 0.00100, train loss = 9.720, time = 1.372
Epoch 8/16, Iter 2130/2215, lr 0.00100, train loss = 12.567, time = 1.515
Epoch 8/16, Iter 2180/2215, lr 0.00100, train loss = 9.169, time = 1.337
Epoch 8/16, Iter 24/547, test loss = 0.000, time = 0.583899
Epoch 8/16, Iter 74/547, test loss = 0.000, time = 0.498785
Epoch 8/16, Iter 124/547, test loss = 0.000, time = 0.537254
Epoch 8/16, Iter 174/547, test loss = 0.000, time = 0.524379
Epoch 8/16, Iter 224/547, test loss = 0.000, time = 0.570261
Epoch 8/16, Iter 274/547, test loss = 0.000, time = 0.539446
Epoch 8/16, Iter 324/547, test loss = 0.000, time = 0.548743
Epoch 8/16, Iter 374/547, test loss = 0.000, time = 0.477252
Epoch 8/16, Iter 424/547, test loss = 0.000, time = 0.546947
Epoch 8/16, Iter 474/547, test loss = 0.000, time = 0.509715
Epoch 8/16, Iter 524/547, test loss = 0.000, time = 0.523953
avg_test_scalars {'loss': 0.0, 'D1': [0.10214168689395871], 'EPE': [2.062037591197591], 'Thres1': [0.32996067575007076], 'Thres2': [0.1842336993071998], 'Thres3': [0.12682689484583395]}
Epoch 9/16, Iter 15/2215, lr 0.00100, train loss = 9.977, time = 1.427
Epoch 9/16, Iter 65/2215, lr 0.00100, train loss = 9.059, time = 1.209
Epoch 9/16, Iter 115/2215, lr 0.00100, train loss = 13.412, time = 1.328
Epoch 9/16, Iter 165/2215, lr 0.00100, train loss = 11.324, time = 1.419
Epoch 9/16, Iter 215/2215, lr 0.00100, train loss = 15.843, time = 1.358
Epoch 9/16, Iter 265/2215, lr 0.00100, train loss = 10.754, time = 1.390
Epoch 9/16, Iter 315/2215, lr 0.00100, train loss = 14.617, time = 1.390
Epoch 9/16, Iter 365/2215, lr 0.00100, train loss = 11.687, time = 1.303
Epoch 9/16, Iter 415/2215, lr 0.00100, train loss = 12.903, time = 1.469
Epoch 9/16, Iter 465/2215, lr 0.00100, train loss = 16.216, time = 1.418
Epoch 9/16, Iter 515/2215, lr 0.00100, train loss = 18.941, time = 1.317
Epoch 9/16, Iter 565/2215, lr 0.00100, train loss = 11.623, time = 1.446
Epoch 9/16, Iter 615/2215, lr 0.00100, train loss = 12.005, time = 1.349
Epoch 9/16, Iter 665/2215, lr 0.00100, train loss = 10.183, time = 1.439
Epoch 9/16, Iter 715/2215, lr 0.00100, train loss = 16.971, time = 1.480
Epoch 9/16, Iter 765/2215, lr 0.00100, train loss = 6.486, time = 1.219
Epoch 9/16, Iter 815/2215, lr 0.00100, train loss = 17.744, time = 1.407
Epoch 9/16, Iter 865/2215, lr 0.00100, train loss = 9.913, time = 1.391
Epoch 9/16, Iter 915/2215, lr 0.00100, train loss = 10.884, time = 1.322
Epoch 9/16, Iter 965/2215, lr 0.00100, train loss = 6.524, time = 1.369
Epoch 9/16, Iter 1015/2215, lr 0.00100, train loss = 8.902, time = 1.387
Epoch 9/16, Iter 1065/2215, lr 0.00100, train loss = 13.967, time = 1.434
Epoch 9/16, Iter 1115/2215, lr 0.00100, train loss = 11.339, time = 1.389
Epoch 9/16, Iter 1165/2215, lr 0.00100, train loss = 10.241, time = 1.342
Epoch 9/16, Iter 1215/2215, lr 0.00100, train loss = 11.646, time = 1.400
Epoch 9/16, Iter 1265/2215, lr 0.00100, train loss = 14.191, time = 1.477
Epoch 9/16, Iter 1315/2215, lr 0.00100, train loss = 10.954, time = 1.335
Epoch 9/16, Iter 1365/2215, lr 0.00100, train loss = 7.337, time = 1.317
Epoch 9/16, Iter 1415/2215, lr 0.00100, train loss = 12.396, time = 1.408
Epoch 9/16, Iter 1465/2215, lr 0.00100, train loss = 17.014, time = 1.372
Epoch 9/16, Iter 1515/2215, lr 0.00100, train loss = 11.106, time = 1.329
Epoch 9/16, Iter 1565/2215, lr 0.00100, train loss = 16.675, time = 1.320
Epoch 9/16, Iter 1615/2215, lr 0.00100, train loss = 12.159, time = 1.245
Epoch 9/16, Iter 1665/2215, lr 0.00100, train loss = 7.570, time = 1.390
Epoch 9/16, Iter 1715/2215, lr 0.00100, train loss = 21.083, time = 1.356
Epoch 9/16, Iter 1765/2215, lr 0.00100, train loss = 12.708, time = 1.428
Epoch 9/16, Iter 1815/2215, lr 0.00100, train loss = 9.700, time = 1.521
Epoch 9/16, Iter 1865/2215, lr 0.00100, train loss = 10.444, time = 1.378
Epoch 9/16, Iter 1915/2215, lr 0.00100, train loss = 10.586, time = 1.328
Epoch 9/16, Iter 1965/2215, lr 0.00100, train loss = 10.086, time = 1.362
Epoch 9/16, Iter 2015/2215, lr 0.00100, train loss = 12.907, time = 1.204
Epoch 9/16, Iter 2065/2215, lr 0.00100, train loss = 10.100, time = 1.225
Epoch 9/16, Iter 2115/2215, lr 0.00100, train loss = 9.817, time = 1.113
Epoch 9/16, Iter 2165/2215, lr 0.00100, train loss = 9.925, time = 1.167
Epoch 9/16, Iter 27/547, test loss = 0.000, time = 0.551450
Epoch 9/16, Iter 77/547, test loss = 0.000, time = 0.531414
Epoch 9/16, Iter 127/547, test loss = 0.000, time = 0.570839
Epoch 9/16, Iter 177/547, test loss = 0.000, time = 0.548686
Epoch 9/16, Iter 227/547, test loss = 0.000, time = 0.524444
Epoch 9/16, Iter 277/547, test loss = 0.000, time = 0.528269
Epoch 9/16, Iter 327/547, test loss = 0.000, time = 0.553571
Epoch 9/16, Iter 377/547, test loss = 0.000, time = 0.528248
Epoch 9/16, Iter 427/547, test loss = 0.000, time = 0.474395
Epoch 9/16, Iter 477/547, test loss = 0.000, time = 0.519895
Epoch 9/16, Iter 527/547, test loss = 0.000, time = 0.537153
avg_test_scalars {'loss': 0.0, 'D1': [0.17981900852275723], 'EPE': [2.858533688175613], 'Thres1': [0.43792402567222743], 'Thres2': [0.28048034968498203], 'Thres3': [0.2058103957249216]}
Epoch 10/16, Iter 0/2215, lr 0.00050, train loss = 10.800, time = 1.465
Epoch 10/16, Iter 50/2215, lr 0.00050, train loss = 10.232, time = 1.088
Epoch 10/16, Iter 100/2215, lr 0.00050, train loss = 14.103, time = 1.361
Epoch 10/16, Iter 150/2215, lr 0.00050, train loss = 11.328, time = 1.208
Epoch 10/16, Iter 200/2215, lr 0.00050, train loss = 11.871, time = 1.274
Epoch 10/16, Iter 250/2215, lr 0.00050, train loss = 11.151, time = 1.162
Epoch 10/16, Iter 300/2215, lr 0.00050, train loss = 14.004, time = 1.406
Epoch 10/16, Iter 350/2215, lr 0.00050, train loss = 10.491, time = 1.302
Epoch 10/16, Iter 400/2215, lr 0.00050, train loss = 8.328, time = 1.179
Epoch 10/16, Iter 450/2215, lr 0.00050, train loss = 14.730, time = 1.377
Epoch 10/16, Iter 500/2215, lr 0.00050, train loss = 10.571, time = 1.129
Epoch 10/16, Iter 550/2215, lr 0.00050, train loss = 12.193, time = 1.485
Epoch 10/16, Iter 600/2215, lr 0.00050, train loss = 14.479, time = 1.206
Epoch 10/16, Iter 650/2215, lr 0.00050, train loss = 8.165, time = 1.242
Epoch 10/16, Iter 700/2215, lr 0.00050, train loss = 8.549, time = 1.339
Epoch 10/16, Iter 750/2215, lr 0.00050, train loss = 12.854, time = 1.442
Epoch 10/16, Iter 800/2215, lr 0.00050, train loss = 10.299, time = 1.457
Epoch 10/16, Iter 850/2215, lr 0.00050, train loss = 8.795, time = 1.394
Epoch 10/16, Iter 900/2215, lr 0.00050, train loss = 10.042, time = 1.424
Epoch 10/16, Iter 950/2215, lr 0.00050, train loss = 15.765, time = 1.454
Epoch 10/16, Iter 1000/2215, lr 0.00050, train loss = 11.824, time = 1.450
Epoch 10/16, Iter 1050/2215, lr 0.00050, train loss = 7.780, time = 1.316
Epoch 10/16, Iter 1100/2215, lr 0.00050, train loss = 7.807, time = 1.333
Epoch 10/16, Iter 1150/2215, lr 0.00050, train loss = 8.092, time = 1.456
Epoch 10/16, Iter 1200/2215, lr 0.00050, train loss = 9.950, time = 1.409
Epoch 10/16, Iter 1250/2215, lr 0.00050, train loss = 6.669, time = 1.207
Epoch 10/16, Iter 1300/2215, lr 0.00050, train loss = 5.396, time = 1.136
Epoch 10/16, Iter 1350/2215, lr 0.00050, train loss = 16.307, time = 1.106
Epoch 10/16, Iter 1400/2215, lr 0.00050, train loss = 8.084, time = 1.198
Epoch 10/16, Iter 1450/2215, lr 0.00050, train loss = 14.290, time = 1.192
Epoch 10/16, Iter 1500/2215, lr 0.00050, train loss = 7.774, time = 1.416
Epoch 10/16, Iter 1550/2215, lr 0.00050, train loss = 16.963, time = 1.049
Epoch 10/16, Iter 1600/2215, lr 0.00050, train loss = 7.654, time = 1.311
Epoch 10/16, Iter 1650/2215, lr 0.00050, train loss = 8.746, time = 1.265
Epoch 10/16, Iter 1700/2215, lr 0.00050, train loss = 7.177, time = 1.177
Epoch 10/16, Iter 1750/2215, lr 0.00050, train loss = 9.671, time = 1.209
Epoch 10/16, Iter 1800/2215, lr 0.00050, train loss = 18.428, time = 1.279
Epoch 10/16, Iter 1850/2215, lr 0.00050, train loss = 8.650, time = 1.101
Epoch 10/16, Iter 1900/2215, lr 0.00050, train loss = 6.868, time = 1.141
Epoch 10/16, Iter 1950/2215, lr 0.00050, train loss = 7.805, time = 1.176
Epoch 10/16, Iter 2000/2215, lr 0.00050, train loss = 11.047, time = 1.175
Epoch 10/16, Iter 2050/2215, lr 0.00050, train loss = 10.314, time = 1.089
Epoch 10/16, Iter 2100/2215, lr 0.00050, train loss = 13.243, time = 1.188
Epoch 10/16, Iter 2150/2215, lr 0.00050, train loss = 11.305, time = 1.435
Epoch 10/16, Iter 2200/2215, lr 0.00050, train loss = 13.159, time = 1.191
Epoch 10/16, Iter 30/547, test loss = 0.000, time = 0.558604
Epoch 10/16, Iter 80/547, test loss = 0.000, time = 0.524295
Epoch 10/16, Iter 130/547, test loss = 0.000, time = 0.534618
Epoch 10/16, Iter 180/547, test loss = 0.000, time = 0.504958
Epoch 10/16, Iter 230/547, test loss = 0.000, time = 0.491819
Epoch 10/16, Iter 280/547, test loss = 0.000, time = 0.588047
Epoch 10/16, Iter 330/547, test loss = 0.000, time = 0.530170
Epoch 10/16, Iter 380/547, test loss = 0.000, time = 0.457757
Epoch 10/16, Iter 430/547, test loss = 0.000, time = 0.507326
Epoch 10/16, Iter 480/547, test loss = 0.000, time = 0.450721
Epoch 10/16, Iter 530/547, test loss = 0.000, time = 0.444252
avg_test_scalars {'loss': 0.0, 'D1': [0.06390948048784266], 'EPE': [1.5255181802474167], 'Thres1': [0.19258534833380248], 'Thres2': [0.10372663127561482], 'Thres3': [0.07452572950960513]}
Best Checkpoint epoch_idx:10
Epoch 11/16, Iter 35/2215, lr 0.00050, train loss = 9.205, time = 1.432
Epoch 11/16, Iter 85/2215, lr 0.00050, train loss = 11.691, time = 1.344
Epoch 11/16, Iter 135/2215, lr 0.00050, train loss = 12.883, time = 1.243
Epoch 11/16, Iter 185/2215, lr 0.00050, train loss = 10.155, time = 1.289
Epoch 11/16, Iter 235/2215, lr 0.00050, train loss = 11.835, time = 1.174
Epoch 11/16, Iter 285/2215, lr 0.00050, train loss = 12.073, time = 1.373
Epoch 11/16, Iter 335/2215, lr 0.00050, train loss = 11.512, time = 1.406
Epoch 11/16, Iter 385/2215, lr 0.00050, train loss = 13.939, time = 1.410
Epoch 11/16, Iter 435/2215, lr 0.00050, train loss = 9.576, time = 1.458
Epoch 11/16, Iter 485/2215, lr 0.00050, train loss = 7.726, time = 1.288
Epoch 11/16, Iter 535/2215, lr 0.00050, train loss = 9.108, time = 1.033
Epoch 11/16, Iter 585/2215, lr 0.00050, train loss = 16.359, time = 1.220
Epoch 11/16, Iter 635/2215, lr 0.00050, train loss = 6.773, time = 1.307
Epoch 11/16, Iter 685/2215, lr 0.00050, train loss = 15.477, time = 1.245
Epoch 11/16, Iter 735/2215, lr 0.00050, train loss = 9.385, time = 1.440
Epoch 11/16, Iter 785/2215, lr 0.00050, train loss = 11.047, time = 1.423
Epoch 11/16, Iter 835/2215, lr 0.00050, train loss = 8.440, time = 1.365
Epoch 11/16, Iter 885/2215, lr 0.00050, train loss = 14.542, time = 1.250
Epoch 11/16, Iter 935/2215, lr 0.00050, train loss = 9.094, time = 1.179
Epoch 11/16, Iter 985/2215, lr 0.00050, train loss = 9.129, time = 1.165
Epoch 11/16, Iter 1035/2215, lr 0.00050, train loss = 8.489, time = 1.205
Epoch 11/16, Iter 1085/2215, lr 0.00050, train loss = 9.585, time = 1.489
Epoch 11/16, Iter 1135/2215, lr 0.00050, train loss = 10.848, time = 1.499
Epoch 11/16, Iter 1185/2215, lr 0.00050, train loss = 7.639, time = 1.467
Epoch 11/16, Iter 1235/2215, lr 0.00050, train loss = 10.834, time = 1.502
Epoch 11/16, Iter 1285/2215, lr 0.00050, train loss = 8.528, time = 1.171
Epoch 11/16, Iter 1335/2215, lr 0.00050, train loss = 12.828, time = 1.114
Epoch 11/16, Iter 1385/2215, lr 0.00050, train loss = 10.765, time = 1.114
Epoch 11/16, Iter 1435/2215, lr 0.00050, train loss = 9.293, time = 1.127
Epoch 11/16, Iter 1485/2215, lr 0.00050, train loss = 9.538, time = 1.512
Epoch 11/16, Iter 1535/2215, lr 0.00050, train loss = 9.965, time = 1.064
Epoch 11/16, Iter 1585/2215, lr 0.00050, train loss = 12.398, time = 1.095
Epoch 11/16, Iter 1635/2215, lr 0.00050, train loss = 8.165, time = 1.148
Epoch 11/16, Iter 1685/2215, lr 0.00050, train loss = 9.196, time = 1.430
Epoch 11/16, Iter 1735/2215, lr 0.00050, train loss = 10.686, time = 1.118
Epoch 11/16, Iter 1785/2215, lr 0.00050, train loss = 12.088, time = 1.201
Epoch 11/16, Iter 1835/2215, lr 0.00050, train loss = 8.470, time = 1.352
Epoch 11/16, Iter 1885/2215, lr 0.00050, train loss = 10.835, time = 1.083
Epoch 11/16, Iter 1935/2215, lr 0.00050, train loss = 13.203, time = 1.136
Epoch 11/16, Iter 1985/2215, lr 0.00050, train loss = 14.123, time = 1.220
Epoch 11/16, Iter 2035/2215, lr 0.00050, train loss = 9.202, time = 1.130
Epoch 11/16, Iter 2085/2215, lr 0.00050, train loss = 7.875, time = 1.190
Epoch 11/16, Iter 2135/2215, lr 0.00050, train loss = 7.371, time = 1.127
Epoch 11/16, Iter 2185/2215, lr 0.00050, train loss = 10.436, time = 1.247
Epoch 11/16, Iter 33/547, test loss = 0.000, time = 0.607240
Epoch 11/16, Iter 83/547, test loss = 0.000, time = 0.487234
Epoch 11/16, Iter 133/547, test loss = 0.000, time = 0.566740
Epoch 11/16, Iter 183/547, test loss = 0.000, time = 0.548603
Epoch 11/16, Iter 233/547, test loss = 0.000, time = 0.522856
Epoch 11/16, Iter 283/547, test loss = 0.000, time = 0.515908
Epoch 11/16, Iter 333/547, test loss = 0.000, time = 0.539999
Epoch 11/16, Iter 383/547, test loss = 0.000, time = 0.474554
Epoch 11/16, Iter 433/547, test loss = 0.000, time = 0.481163
Epoch 11/16, Iter 483/547, test loss = 0.000, time = 0.482255
Epoch 11/16, Iter 533/547, test loss = 0.000, time = 0.551207
avg_test_scalars {'loss': 0.0, 'D1': [0.06240706925213991], 'EPE': [1.5136574476793871], 'Thres1': [0.19816410151979388], 'Thres2': [0.10500423495455133], 'Thres3': [0.07426200752441092]}
Best Checkpoint epoch_idx:11
Epoch 12/16, Iter 20/2215, lr 0.00025, train loss = 11.110, time = 1.288
Epoch 12/16, Iter 70/2215, lr 0.00025, train loss = 11.961, time = 1.151
Epoch 12/16, Iter 120/2215, lr 0.00025, train loss = 7.879, time = 1.090
Epoch 12/16, Iter 170/2215, lr 0.00025, train loss = 12.849, time = 1.187
Epoch 12/16, Iter 220/2215, lr 0.00025, train loss = 12.893, time = 1.329
Epoch 12/16, Iter 270/2215, lr 0.00025, train loss = 11.185, time = 1.337
Epoch 12/16, Iter 320/2215, lr 0.00025, train loss = 13.088, time = 1.261
Epoch 12/16, Iter 370/2215, lr 0.00025, train loss = 9.785, time = 1.178
Epoch 12/16, Iter 420/2215, lr 0.00025, train loss = 8.704, time = 1.199
Epoch 12/16, Iter 470/2215, lr 0.00025, train loss = 10.660, time = 1.199
Epoch 12/16, Iter 520/2215, lr 0.00025, train loss = 10.802, time = 1.235
Epoch 12/16, Iter 570/2215, lr 0.00025, train loss = 9.939, time = 1.458
Epoch 12/16, Iter 620/2215, lr 0.00025, train loss = 8.310, time = 1.447
Epoch 12/16, Iter 670/2215, lr 0.00025, train loss = 14.868, time = 1.160
Epoch 12/16, Iter 720/2215, lr 0.00025, train loss = 15.837, time = 1.241
Epoch 12/16, Iter 770/2215, lr 0.00025, train loss = 8.722, time = 1.134
Epoch 12/16, Iter 820/2215, lr 0.00025, train loss = 9.721, time = 1.407
Epoch 12/16, Iter 870/2215, lr 0.00025, train loss = 9.579, time = 1.140
Epoch 12/16, Iter 920/2215, lr 0.00025, train loss = 11.320, time = 1.226
Epoch 12/16, Iter 970/2215, lr 0.00025, train loss = 8.670, time = 1.077
Epoch 12/16, Iter 1020/2215, lr 0.00025, train loss = 9.351, time = 1.047
Epoch 12/16, Iter 1070/2215, lr 0.00025, train loss = 8.604, time = 1.109
Epoch 12/16, Iter 1120/2215, lr 0.00025, train loss = 9.847, time = 1.401
Epoch 12/16, Iter 1170/2215, lr 0.00025, train loss = 11.341, time = 1.073
Epoch 12/16, Iter 1220/2215, lr 0.00025, train loss = 7.106, time = 1.047
Epoch 12/16, Iter 1270/2215, lr 0.00025, train loss = 13.936, time = 1.194
Epoch 12/16, Iter 1320/2215, lr 0.00025, train loss = 7.406, time = 1.203
Epoch 12/16, Iter 1370/2215, lr 0.00025, train loss = 9.200, time = 1.130
Epoch 12/16, Iter 1420/2215, lr 0.00025, train loss = 9.895, time = 1.202
Epoch 12/16, Iter 1470/2215, lr 0.00025, train loss = 11.438, time = 1.164
Epoch 12/16, Iter 1520/2215, lr 0.00025, train loss = 11.356, time = 1.131
Epoch 12/16, Iter 1570/2215, lr 0.00025, train loss = 8.803, time = 1.181
Epoch 12/16, Iter 1620/2215, lr 0.00025, train loss = 10.697, time = 1.159
Epoch 12/16, Iter 1670/2215, lr 0.00025, train loss = 9.524, time = 1.179
Epoch 12/16, Iter 1720/2215, lr 0.00025, train loss = 9.109, time = 1.240
Epoch 12/16, Iter 1770/2215, lr 0.00025, train loss = 7.438, time = 1.136
Epoch 12/16, Iter 1820/2215, lr 0.00025, train loss = 12.203, time = 1.171
Epoch 12/16, Iter 1870/2215, lr 0.00025, train loss = 13.213, time = 1.427
Epoch 12/16, Iter 1920/2215, lr 0.00025, train loss = 9.326, time = 1.164
Epoch 12/16, Iter 1970/2215, lr 0.00025, train loss = 6.913, time = 1.143
Epoch 12/16, Iter 2020/2215, lr 0.00025, train loss = 12.907, time = 1.181
Epoch 12/16, Iter 2070/2215, lr 0.00025, train loss = 9.113, time = 1.201
Epoch 12/16, Iter 2120/2215, lr 0.00025, train loss = 11.567, time = 1.166
Epoch 12/16, Iter 2170/2215, lr 0.00025, train loss = 8.452, time = 1.235
Epoch 12/16, Iter 36/547, test loss = 0.000, time = 0.580140
Epoch 12/16, Iter 86/547, test loss = 0.000, time = 0.521858
Epoch 12/16, Iter 136/547, test loss = 0.000, time = 0.558500
Epoch 12/16, Iter 186/547, test loss = 0.000, time = 0.553850
Epoch 12/16, Iter 236/547, test loss = 0.000, time = 0.551558
Epoch 12/16, Iter 286/547, test loss = 0.000, time = 0.542836
Epoch 12/16, Iter 336/547, test loss = 0.000, time = 0.553081
Epoch 12/16, Iter 386/547, test loss = 0.000, time = 0.566878
Epoch 12/16, Iter 436/547, test loss = 0.000, time = 0.527839
Epoch 12/16, Iter 486/547, test loss = 0.000, time = 0.536958
Epoch 12/16, Iter 536/547, test loss = 0.000, time = 0.606031
avg_test_scalars {'loss': 0.0, 'D1': [0.05828505729783115], 'EPE': [1.4146983577738732], 'Thres1': [0.1806350925177935], 'Thres2': [0.09705324340237124], 'Thres3': [0.06921654248906392]}
Best Checkpoint epoch_idx:12
Epoch 13/16, Iter 5/2215, lr 0.00025, train loss = 11.458, time = 1.338
Epoch 13/16, Iter 55/2215, lr 0.00025, train loss = 11.616, time = 1.417
Epoch 13/16, Iter 105/2215, lr 0.00025, train loss = 5.525, time = 1.487
Epoch 13/16, Iter 155/2215, lr 0.00025, train loss = 10.106, time = 1.409
Epoch 13/16, Iter 205/2215, lr 0.00025, train loss = 9.591, time = 1.443
Epoch 13/16, Iter 255/2215, lr 0.00025, train loss = 7.919, time = 1.414
Epoch 13/16, Iter 305/2215, lr 0.00025, train loss = 7.236, time = 1.286
Epoch 13/16, Iter 355/2215, lr 0.00025, train loss = 5.153, time = 1.203
Epoch 13/16, Iter 405/2215, lr 0.00025, train loss = 12.442, time = 1.140
Epoch 13/16, Iter 455/2215, lr 0.00025, train loss = 11.597, time = 1.091
Epoch 13/16, Iter 505/2215, lr 0.00025, train loss = 9.729, time = 1.394
Epoch 13/16, Iter 555/2215, lr 0.00025, train loss = 12.380, time = 1.342
Epoch 13/16, Iter 605/2215, lr 0.00025, train loss = 10.176, time = 1.331
Epoch 13/16, Iter 655/2215, lr 0.00025, train loss = 8.812, time = 1.135
Epoch 13/16, Iter 705/2215, lr 0.00025, train loss = 11.198, time = 1.199
Epoch 13/16, Iter 755/2215, lr 0.00025, train loss = 11.171, time = 1.097
Epoch 13/16, Iter 805/2215, lr 0.00025, train loss = 16.148, time = 1.249
Epoch 13/16, Iter 855/2215, lr 0.00025, train loss = 10.645, time = 1.230
Epoch 13/16, Iter 905/2215, lr 0.00025, train loss = 11.348, time = 1.213
Epoch 13/16, Iter 955/2215, lr 0.00025, train loss = 6.496, time = 1.336
Epoch 13/16, Iter 1005/2215, lr 0.00025, train loss = 14.983, time = 1.416
Epoch 13/16, Iter 1055/2215, lr 0.00025, train loss = 13.498, time = 1.280
Epoch 13/16, Iter 1105/2215, lr 0.00025, train loss = 10.302, time = 1.214
Epoch 13/16, Iter 1155/2215, lr 0.00025, train loss = 9.473, time = 1.100
Epoch 13/16, Iter 1205/2215, lr 0.00025, train loss = 7.805, time = 1.519
Epoch 13/16, Iter 1255/2215, lr 0.00025, train loss = 8.023, time = 1.123
Epoch 13/16, Iter 1305/2215, lr 0.00025, train loss = 11.856, time = 1.486
Epoch 13/16, Iter 1355/2215, lr 0.00025, train loss = 8.011, time = 1.364
Epoch 13/16, Iter 1405/2215, lr 0.00025, train loss = 10.855, time = 1.462
Epoch 13/16, Iter 1455/2215, lr 0.00025, train loss = 8.342, time = 1.342
Epoch 13/16, Iter 1505/2215, lr 0.00025, train loss = 15.167, time = 1.366
Epoch 13/16, Iter 1555/2215, lr 0.00025, train loss = 11.242, time = 1.017
Epoch 13/16, Iter 1605/2215, lr 0.00025, train loss = 9.196, time = 1.193
Epoch 13/16, Iter 1655/2215, lr 0.00025, train loss = 16.919, time = 1.134
Epoch 13/16, Iter 1705/2215, lr 0.00025, train loss = 11.885, time = 1.194
Epoch 13/16, Iter 1755/2215, lr 0.00025, train loss = 7.838, time = 1.303
Epoch 13/16, Iter 1805/2215, lr 0.00025, train loss = 8.521, time = 1.159
Epoch 13/16, Iter 1855/2215, lr 0.00025, train loss = 9.095, time = 1.145
Epoch 13/16, Iter 1905/2215, lr 0.00025, train loss = 7.521, time = 1.243
Epoch 13/16, Iter 1955/2215, lr 0.00025, train loss = 8.540, time = 1.151
Epoch 13/16, Iter 2005/2215, lr 0.00025, train loss = 15.745, time = 1.134
Epoch 13/16, Iter 2055/2215, lr 0.00025, train loss = 8.396, time = 1.203
Epoch 13/16, Iter 2105/2215, lr 0.00025, train loss = 7.999, time = 1.470
Epoch 13/16, Iter 2155/2215, lr 0.00025, train loss = 5.995, time = 1.424
Epoch 13/16, Iter 2205/2215, lr 0.00025, train loss = 10.975, time = 1.470
Epoch 13/16, Iter 39/547, test loss = 0.000, time = 0.587964
Epoch 13/16, Iter 89/547, test loss = 0.000, time = 0.527727
Epoch 13/16, Iter 139/547, test loss = 0.000, time = 0.450545
Epoch 13/16, Iter 189/547, test loss = 0.000, time = 0.470509
Epoch 13/16, Iter 239/547, test loss = 0.000, time = 0.500093
Epoch 13/16, Iter 289/547, test loss = 0.000, time = 0.576947
Epoch 13/16, Iter 339/547, test loss = 0.000, time = 0.488402
Epoch 13/16, Iter 389/547, test loss = 0.000, time = 0.455441
Epoch 13/16, Iter 439/547, test loss = 0.000, time = 0.540526
Epoch 13/16, Iter 489/547, test loss = 0.000, time = 0.690592
Epoch 13/16, Iter 539/547, test loss = 0.000, time = 0.534788
avg_test_scalars {'loss': 0.0, 'D1': [0.06191175755188278], 'EPE': [1.4781914197548651], 'Thres1': [0.20173595789854007], 'Thres2': [0.10548234231756418], 'Thres3': [0.0740938502835481]}
Epoch 14/16, Iter 40/2215, lr 0.00013, train loss = 7.234, time = 1.417
Epoch 14/16, Iter 90/2215, lr 0.00013, train loss = 11.096, time = 1.344
Epoch 14/16, Iter 140/2215, lr 0.00013, train loss = 15.156, time = 1.250
Epoch 14/16, Iter 190/2215, lr 0.00013, train loss = 12.543, time = 1.461
Epoch 14/16, Iter 240/2215, lr 0.00013, train loss = 7.080, time = 1.459
Epoch 14/16, Iter 290/2215, lr 0.00013, train loss = 10.543, time = 1.419
Epoch 14/16, Iter 340/2215, lr 0.00013, train loss = 10.998, time = 1.300
Epoch 14/16, Iter 390/2215, lr 0.00013, train loss = 6.756, time = 1.479
Epoch 14/16, Iter 440/2215, lr 0.00013, train loss = 7.674, time = 1.340
Epoch 14/16, Iter 490/2215, lr 0.00013, train loss = 9.651, time = 1.472
Epoch 14/16, Iter 540/2215, lr 0.00013, train loss = 16.774, time = 1.265
Epoch 14/16, Iter 590/2215, lr 0.00013, train loss = 9.601, time = 1.406
Epoch 14/16, Iter 640/2215, lr 0.00013, train loss = 10.040, time = 1.412
Epoch 14/16, Iter 690/2215, lr 0.00013, train loss = 8.879, time = 1.395
Epoch 14/16, Iter 740/2215, lr 0.00013, train loss = 10.777, time = 1.497
Epoch 14/16, Iter 790/2215, lr 0.00013, train loss = 9.972, time = 1.322
Epoch 14/16, Iter 840/2215, lr 0.00013, train loss = 10.074, time = 1.404
Epoch 14/16, Iter 890/2215, lr 0.00013, train loss = 6.011, time = 1.453
Epoch 14/16, Iter 940/2215, lr 0.00013, train loss = 9.196, time = 1.305
Epoch 14/16, Iter 990/2215, lr 0.00013, train loss = 9.121, time = 1.512
Epoch 14/16, Iter 1040/2215, lr 0.00013, train loss = 10.080, time = 1.426
Epoch 14/16, Iter 1090/2215, lr 0.00013, train loss = 7.109, time = 1.448
Epoch 14/16, Iter 1140/2215, lr 0.00013, train loss = 9.584, time = 1.244
Epoch 14/16, Iter 1190/2215, lr 0.00013, train loss = 9.037, time = 1.080
Epoch 14/16, Iter 1240/2215, lr 0.00013, train loss = 6.621, time = 1.538
Epoch 14/16, Iter 1290/2215, lr 0.00013, train loss = 5.554, time = 1.380
Epoch 14/16, Iter 1340/2215, lr 0.00013, train loss = 7.659, time = 1.467
Epoch 14/16, Iter 1390/2215, lr 0.00013, train loss = 6.355, time = 1.235
Epoch 14/16, Iter 1440/2215, lr 0.00013, train loss = 12.853, time = 1.374
Epoch 14/16, Iter 1490/2215, lr 0.00013, train loss = 5.685, time = 1.461
Epoch 14/16, Iter 1540/2215, lr 0.00013, train loss = 6.727, time = 1.430
Epoch 14/16, Iter 1590/2215, lr 0.00013, train loss = 9.660, time = 1.243
Epoch 14/16, Iter 1640/2215, lr 0.00013, train loss = 9.220, time = 1.438
Epoch 14/16, Iter 1690/2215, lr 0.00013, train loss = 11.274, time = 1.219
Epoch 14/16, Iter 1740/2215, lr 0.00013, train loss = 11.137, time = 1.369
Epoch 14/16, Iter 1790/2215, lr 0.00013, train loss = 8.155, time = 1.040
Epoch 14/16, Iter 1840/2215, lr 0.00013, train loss = 9.572, time = 1.189
Epoch 14/16, Iter 1890/2215, lr 0.00013, train loss = 7.815, time = 1.445
Epoch 14/16, Iter 1940/2215, lr 0.00013, train loss = 7.177, time = 1.526
Epoch 14/16, Iter 1990/2215, lr 0.00013, train loss = 10.375, time = 1.304
Epoch 14/16, Iter 2040/2215, lr 0.00013, train loss = 10.987, time = 1.599
Epoch 14/16, Iter 2090/2215, lr 0.00013, train loss = 7.916, time = 1.425
Epoch 14/16, Iter 2140/2215, lr 0.00013, train loss = 7.949, time = 1.312
Epoch 14/16, Iter 2190/2215, lr 0.00013, train loss = 9.077, time = 1.348
Epoch 14/16, Iter 42/547, test loss = 0.000, time = 0.522482
Epoch 14/16, Iter 92/547, test loss = 0.000, time = 0.533513
Epoch 14/16, Iter 142/547, test loss = 0.000, time = 0.447150
Epoch 14/16, Iter 192/547, test loss = 0.000, time = 0.519265
Epoch 14/16, Iter 242/547, test loss = 0.000, time = 0.485824
Epoch 14/16, Iter 292/547, test loss = 0.000, time = 0.441684
Epoch 14/16, Iter 342/547, test loss = 0.000, time = 0.520987
Epoch 14/16, Iter 392/547, test loss = 0.000, time = 0.506403
Epoch 14/16, Iter 442/547, test loss = 0.000, time = 0.574218
Epoch 14/16, Iter 492/547, test loss = 0.000, time = 0.558561
Epoch 14/16, Iter 542/547, test loss = 0.000, time = 0.565634
avg_test_scalars {'loss': 0.0, 'D1': [0.05682909396737541], 'EPE': [1.3670715834584488], 'Thres1': [0.1788410122490997], 'Thres2': [0.09514409031589571], 'Thres3': [0.06766271793625744]}
Best Checkpoint epoch_idx:14
Epoch 15/16, Iter 25/2215, lr 0.00013, train loss = 10.845, time = 1.546
Epoch 15/16, Iter 75/2215, lr 0.00013, train loss = 5.682, time = 1.349
Epoch 15/16, Iter 125/2215, lr 0.00013, train loss = 5.143, time = 1.251
Epoch 15/16, Iter 175/2215, lr 0.00013, train loss = 9.655, time = 1.373
Epoch 15/16, Iter 225/2215, lr 0.00013, train loss = 10.206, time = 1.450
Epoch 15/16, Iter 275/2215, lr 0.00013, train loss = 8.132, time = 1.335
Epoch 15/16, Iter 325/2215, lr 0.00013, train loss = 8.895, time = 1.464
Epoch 15/16, Iter 375/2215, lr 0.00013, train loss = 11.416, time = 1.371
Epoch 15/16, Iter 425/2215, lr 0.00013, train loss = 13.555, time = 1.396
Epoch 15/16, Iter 475/2215, lr 0.00013, train loss = 8.849, time = 1.355
Epoch 15/16, Iter 525/2215, lr 0.00013, train loss = 7.530, time = 1.366
Epoch 15/16, Iter 575/2215, lr 0.00013, train loss = 11.959, time = 1.328
Epoch 15/16, Iter 625/2215, lr 0.00013, train loss = 9.497, time = 1.285
Epoch 15/16, Iter 675/2215, lr 0.00013, train loss = 6.740, time = 1.394
Epoch 15/16, Iter 725/2215, lr 0.00013, train loss = 5.776, time = 1.370
Epoch 15/16, Iter 775/2215, lr 0.00013, train loss = 11.107, time = 1.300
Epoch 15/16, Iter 825/2215, lr 0.00013, train loss = 10.051, time = 1.193
Epoch 15/16, Iter 875/2215, lr 0.00013, train loss = 10.867, time = 1.477
Epoch 15/16, Iter 925/2215, lr 0.00013, train loss = 12.329, time = 1.455
Epoch 15/16, Iter 975/2215, lr 0.00013, train loss = 5.705, time = 1.402
Epoch 15/16, Iter 1025/2215, lr 0.00013, train loss = 5.676, time = 1.365
Epoch 15/16, Iter 1075/2215, lr 0.00013, train loss = 12.642, time = 1.425
Epoch 15/16, Iter 1125/2215, lr 0.00013, train loss = 7.641, time = 1.387
Epoch 15/16, Iter 1175/2215, lr 0.00013, train loss = 11.076, time = 1.515
Epoch 15/16, Iter 1225/2215, lr 0.00013, train loss = 6.844, time = 1.294
Epoch 15/16, Iter 1275/2215, lr 0.00013, train loss = 9.776, time = 1.351
Epoch 15/16, Iter 1325/2215, lr 0.00013, train loss = 9.376, time = 1.408
Epoch 15/16, Iter 1375/2215, lr 0.00013, train loss = 11.145, time = 1.494
Epoch 15/16, Iter 1425/2215, lr 0.00013, train loss = 14.757, time = 1.411
Epoch 15/16, Iter 1475/2215, lr 0.00013, train loss = 5.557, time = 1.493
Epoch 15/16, Iter 1525/2215, lr 0.00013, train loss = 9.272, time = 1.412
Epoch 15/16, Iter 1575/2215, lr 0.00013, train loss = 10.841, time = 1.442
Epoch 15/16, Iter 1625/2215, lr 0.00013, train loss = 9.524, time = 1.308
Epoch 15/16, Iter 1675/2215, lr 0.00013, train loss = 8.628, time = 1.400
Epoch 15/16, Iter 1725/2215, lr 0.00013, train loss = 11.141, time = 1.504
Epoch 15/16, Iter 1775/2215, lr 0.00013, train loss = 11.989, time = 1.479
Epoch 15/16, Iter 1825/2215, lr 0.00013, train loss = 12.630, time = 1.367
Epoch 15/16, Iter 1875/2215, lr 0.00013, train loss = 10.663, time = 1.409
Epoch 15/16, Iter 1925/2215, lr 0.00013, train loss = 13.122, time = 1.355
Epoch 15/16, Iter 1975/2215, lr 0.00013, train loss = 7.234, time = 1.405
Epoch 15/16, Iter 2025/2215, lr 0.00013, train loss = 10.227, time = 1.395
Epoch 15/16, Iter 2075/2215, lr 0.00013, train loss = 5.326, time = 1.200
Epoch 15/16, Iter 2125/2215, lr 0.00013, train loss = 8.325, time = 1.476
Epoch 15/16, Iter 2175/2215, lr 0.00013, train loss = 8.271, time = 1.170
Epoch 15/16, Iter 45/547, test loss = 0.000, time = 0.617016
Epoch 15/16, Iter 95/547, test loss = 0.000, time = 0.511199
Epoch 15/16, Iter 145/547, test loss = 0.000, time = 0.497326
Epoch 15/16, Iter 195/547, test loss = 0.000, time = 0.550936
Epoch 15/16, Iter 245/547, test loss = 0.000, time = 0.497095
Epoch 15/16, Iter 295/547, test loss = 0.000, time = 0.546645
Epoch 15/16, Iter 345/547, test loss = 0.000, time = 0.561604
Epoch 15/16, Iter 395/547, test loss = 0.000, time = 0.509445
Epoch 15/16, Iter 445/547, test loss = 0.000, time = 0.459908
Epoch 15/16, Iter 495/547, test loss = 0.000, time = 0.510891
Epoch 15/16, Iter 545/547, test loss = 0.000, time = 0.641158
avg_test_scalars {'loss': 0.0, 'D1': [0.05503614659725911], 'EPE': [1.3238676964582865], 'Thres1': [0.16929179338748537], 'Thres2': [0.09155908794273405], 'Thres3': [0.06555999057636455]}
Best Checkpoint epoch_idx:15