-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathlog_gpu_1.txt
More file actions
2913 lines (2907 loc) · 127 KB
/
log_gpu_1.txt
File metadata and controls
2913 lines (2907 loc) · 127 KB
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
2022-07-18 16:16:56.603857: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/statsmodels/compat/pandas.py:65: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
from pandas import Int64Index as NumericIndex
/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/deeplabcut/__init__.py:81: UserWarning:
As PyTorch is not installed, unsupervised identity learning will not be available.
Please run `pip install torch`, or ignore this warning.
warnings.warn(
Config:
{'all_joints': [[0],
[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]],
'all_joints_names': ['L1hip',
'L1knee',
'L1ankle',
'L1toe',
'L2hip',
'L2knee',
'L2ankle',
'L2toe',
'L3hip',
'L3knee',
'L3ankle',
'L3toe',
'R1hip',
'R1knee',
'R1ankle',
'R1toe',
'R2hip',
'R2knee',
'R2ankle',
'R2toe',
'R3hip',
'R3knee',
'R3ankle',
'R3toe',
'Leye',
'Reye',
'nose',
'Lantenna1',
'Lantenna2',
'Lantenna3',
'Lantenna4',
'Rantenna1',
'Rantenna2',
'Rantenna3',
'Rantenna4',
'Lbody',
'Rbody',
'rear',
'centerbody',
'Lshoulder',
'Rshoulder'],
'alpha_r': 0.02,
'apply_prob': 0.5,
'batch_size': 1,
'contrast': {'clahe': True,
'claheratio': 0.1,
'histeq': True,
'histeqratio': 0.1},
'convolution': {'edge': False, 'emboss': False, 'sharpen': False},
'covering': True,
'crop_by': 0.15,
'crop_pad': 0,
'cropratio': 0.4,
'dataset': 'training-datasets/iteration-1/UnaugmentedDataSet_stinkbugsJul15/stinkbugs_DLC80shuffle0.mat',
'dataset_type': 'imgaug',
'decay_steps': 30000,
'deterministic': False,
'display_iters': 1000,
'elastic_transform': True,
'fg_fraction': 0.25,
'gaussian_noise': False,
'global_scale': 0.8,
'grayscale': False,
'init_weights': '/media/data/model-weights/resnet_v1_50.ckpt',
'intermediate_supervision': False,
'intermediate_supervision_layer': 12,
'location_refinement': True,
'locref_huber_loss': True,
'locref_loss_weight': 0.05,
'locref_stdev': 7.2801,
'log_dir': 'log',
'lr_init': 0.0005,
'max_input_size': 1500,
'mean_pixel': [123.68, 116.779, 103.939],
'metadataset': 'training-datasets/iteration-1/UnaugmentedDataSet_stinkbugsJul15/Documentation_data-stinkbugs_80shuffle0.pickle',
'min_input_size': 64,
'mirror': True,
'motion_blur': True,
'motion_blur_params': {'angle': [-90, 90], 'k': 7},
'multi_stage': False,
'multi_step': [[0.005, 10000],
[0.02, 430000],
[0.002, 730000],
[0.001, 1030000]],
'net_type': 'resnet_50',
'num_joints': 41,
'optimizer': 'sgd',
'pairwise_huber_loss': False,
'pairwise_predict': False,
'partaffinityfield_predict': False,
'pos_dist_thresh': 17,
'pre_resize': [],
'project_path': '/media/data/stinkbugs-DLC-2022-07-15',
'regularize': False,
'rotation': 25,
'rotratio': 0.4,
'save_iters': 50000,
'scale_jitter_lo': 0.5,
'scale_jitter_up': 1.25,
'scoremap_dir': 'test',
'shuffle': True,
'snapshot_prefix': '/media/data/stinkbugs-DLC-2022-07-15/data_augm_04_mirror/dlc-models/iteration-1/stinkbugsJul15-trainset80shuffle0/train/snapshot',
'stride': 8.0,
'weigh_negatives': False,
'weigh_only_present_joints': False,
'weigh_part_predictions': False,
'weight_decay': 0.0001}
2022-07-18 16:17:01.146275: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-07-18 16:17:01.587312: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22344 MB memory: -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:1a:00.0, compute capability: 8.6
2022-07-18 16:17:01.981617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22344 MB memory: -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:1a:00.0, compute capability: 8.6
2022-07-18 16:17:03.166962: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
2022-07-18 16:17:08.622031: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8401
/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.
warnings.warn('`layer.apply` is deprecated and '
iteration: 1000 loss: 0.0279 lr: 0.005
iteration: 2000 loss: 0.0194 lr: 0.005
iteration: 3000 loss: 0.0178 lr: 0.005
iteration: 4000 loss: 0.0166 lr: 0.005
iteration: 5000 loss: 0.0154 lr: 0.005
iteration: 6000 loss: 0.0148 lr: 0.005
iteration: 7000 loss: 0.0136 lr: 0.005
iteration: 8000 loss: 0.0129 lr: 0.005
iteration: 9000 loss: 0.0128 lr: 0.005
iteration: 10000 loss: 0.0121 lr: 0.005
iteration: 11000 loss: 0.0119 lr: 0.02
iteration: 12000 loss: 0.0106 lr: 0.02
iteration: 13000 loss: 0.0099 lr: 0.02
iteration: 14000 loss: 0.0091 lr: 0.02
iteration: 15000 loss: 0.0087 lr: 0.02
iteration: 16000 loss: 0.0083 lr: 0.02
iteration: 17000 loss: 0.0082 lr: 0.02
iteration: 18000 loss: 0.0081 lr: 0.02
iteration: 19000 loss: 0.0079 lr: 0.02
iteration: 20000 loss: 0.0078 lr: 0.02
iteration: 21000 loss: 0.0083 lr: 0.02
iteration: 22000 loss: 0.0074 lr: 0.02
iteration: 23000 loss: 0.0069 lr: 0.02
iteration: 24000 loss: 0.0065 lr: 0.02
iteration: 25000 loss: 0.0065 lr: 0.02
iteration: 26000 loss: 0.0061 lr: 0.02
iteration: 27000 loss: 0.0061 lr: 0.02
iteration: 28000 loss: 0.0059 lr: 0.02
iteration: 29000 loss: 0.0060 lr: 0.02
iteration: 30000 loss: 0.0060 lr: 0.02
iteration: 31000 loss: 0.0059 lr: 0.02
iteration: 32000 loss: 0.0055 lr: 0.02
iteration: 33000 loss: 0.0055 lr: 0.02
iteration: 34000 loss: 0.0054 lr: 0.02
iteration: 35000 loss: 0.0053 lr: 0.02
iteration: 36000 loss: 0.0054 lr: 0.02
iteration: 37000 loss: 0.0052 lr: 0.02
iteration: 38000 loss: 0.0053 lr: 0.02
iteration: 39000 loss: 0.0051 lr: 0.02
iteration: 40000 loss: 0.0051 lr: 0.02
iteration: 41000 loss: 0.0050 lr: 0.02
iteration: 42000 loss: 0.0048 lr: 0.02
iteration: 43000 loss: 0.0047 lr: 0.02
iteration: 44000 loss: 0.0047 lr: 0.02
iteration: 45000 loss: 0.0046 lr: 0.02
iteration: 46000 loss: 0.0048 lr: 0.02
iteration: 47000 loss: 0.0045 lr: 0.02
iteration: 48000 loss: 0.0045 lr: 0.02
iteration: 49000 loss: 0.0046 lr: 0.02
iteration: 50000 loss: 0.0044 lr: 0.02
iteration: 51000 loss: 0.0044 lr: 0.02
iteration: 52000 loss: 0.0044 lr: 0.02
iteration: 53000 loss: 0.0044 lr: 0.02
iteration: 54000 loss: 0.0043 lr: 0.02
iteration: 55000 loss: 0.0042 lr: 0.02
iteration: 56000 loss: 0.0043 lr: 0.02
iteration: 57000 loss: 0.0042 lr: 0.02
iteration: 58000 loss: 0.0043 lr: 0.02
iteration: 59000 loss: 0.0041 lr: 0.02
iteration: 60000 loss: 0.0042 lr: 0.02
iteration: 61000 loss: 0.0040 lr: 0.02
iteration: 62000 loss: 0.0040 lr: 0.02
iteration: 63000 loss: 0.0038 lr: 0.02
iteration: 64000 loss: 0.0039 lr: 0.02
iteration: 65000 loss: 0.0039 lr: 0.02
iteration: 66000 loss: 0.0038 lr: 0.02
iteration: 67000 loss: 0.0039 lr: 0.02
iteration: 68000 loss: 0.0038 lr: 0.02
iteration: 69000 loss: 0.0037 lr: 0.02
iteration: 70000 loss: 0.0037 lr: 0.02
iteration: 71000 loss: 0.0037 lr: 0.02
iteration: 72000 loss: 0.0037 lr: 0.02
iteration: 73000 loss: 0.0036 lr: 0.02
iteration: 74000 loss: 0.0036 lr: 0.02
iteration: 75000 loss: 0.0037 lr: 0.02
iteration: 76000 loss: 0.0039 lr: 0.02
iteration: 77000 loss: 0.0037 lr: 0.02
iteration: 78000 loss: 0.0037 lr: 0.02
iteration: 79000 loss: 0.0037 lr: 0.02
iteration: 80000 loss: 0.0037 lr: 0.02
iteration: 81000 loss: 0.0036 lr: 0.02
iteration: 82000 loss: 0.0035 lr: 0.02
iteration: 83000 loss: 0.0035 lr: 0.02
iteration: 84000 loss: 0.0035 lr: 0.02
iteration: 85000 loss: 0.0035 lr: 0.02
iteration: 86000 loss: 0.0033 lr: 0.02
iteration: 87000 loss: 0.0036 lr: 0.02
iteration: 88000 loss: 0.0035 lr: 0.02
iteration: 89000 loss: 0.0035 lr: 0.02
iteration: 90000 loss: 0.0034 lr: 0.02
iteration: 91000 loss: 0.0033 lr: 0.02
iteration: 92000 loss: 0.0034 lr: 0.02
iteration: 93000 loss: 0.0033 lr: 0.02
iteration: 94000 loss: 0.0033 lr: 0.02
iteration: 95000 loss: 0.0032 lr: 0.02
iteration: 96000 loss: 0.0033 lr: 0.02
iteration: 97000 loss: 0.0032 lr: 0.02
iteration: 98000 loss: 0.0031 lr: 0.02
iteration: 99000 loss: 0.0032 lr: 0.02
iteration: 100000 loss: 0.0031 lr: 0.02
iteration: 101000 loss: 0.0031 lr: 0.02
iteration: 102000 loss: 0.0032 lr: 0.02
iteration: 103000 loss: 0.0033 lr: 0.02
iteration: 104000 loss: 0.0031 lr: 0.02
iteration: 105000 loss: 0.0032 lr: 0.02
iteration: 106000 loss: 0.0031 lr: 0.02
iteration: 107000 loss: 0.0031 lr: 0.02
iteration: 108000 loss: 0.0030 lr: 0.02
iteration: 109000 loss: 0.0031 lr: 0.02
iteration: 110000 loss: 0.0030 lr: 0.02
iteration: 111000 loss: 0.0029 lr: 0.02
iteration: 112000 loss: 0.0030 lr: 0.02
iteration: 113000 loss: 0.0030 lr: 0.02
iteration: 114000 loss: 0.0029 lr: 0.02
iteration: 115000 loss: 0.0029 lr: 0.02
iteration: 116000 loss: 0.0029 lr: 0.02
iteration: 117000 loss: 0.0030 lr: 0.02
iteration: 118000 loss: 0.0029 lr: 0.02
iteration: 119000 loss: 0.0029 lr: 0.02
iteration: 120000 loss: 0.0029 lr: 0.02
iteration: 121000 loss: 0.0028 lr: 0.02
iteration: 122000 loss: 0.0029 lr: 0.02
iteration: 123000 loss: 0.0029 lr: 0.02
iteration: 124000 loss: 0.0030 lr: 0.02
iteration: 125000 loss: 0.0029 lr: 0.02
iteration: 126000 loss: 0.0029 lr: 0.02
iteration: 127000 loss: 0.0028 lr: 0.02
iteration: 128000 loss: 0.0029 lr: 0.02
iteration: 129000 loss: 0.0028 lr: 0.02
iteration: 130000 loss: 0.0028 lr: 0.02
iteration: 131000 loss: 0.0028 lr: 0.02
iteration: 132000 loss: 0.0028 lr: 0.02
iteration: 133000 loss: 0.0028 lr: 0.02
iteration: 134000 loss: 0.0027 lr: 0.02
iteration: 135000 loss: 0.0028 lr: 0.02
iteration: 136000 loss: 0.0028 lr: 0.02
iteration: 137000 loss: 0.0027 lr: 0.02
iteration: 138000 loss: 0.0028 lr: 0.02
iteration: 139000 loss: 0.0027 lr: 0.02
iteration: 140000 loss: 0.0027 lr: 0.02
iteration: 141000 loss: 0.0027 lr: 0.02
iteration: 142000 loss: 0.0027 lr: 0.02
iteration: 143000 loss: 0.0026 lr: 0.02
iteration: 144000 loss: 0.0026 lr: 0.02
iteration: 145000 loss: 0.0027 lr: 0.02
iteration: 146000 loss: 0.0028 lr: 0.02
iteration: 147000 loss: 0.0026 lr: 0.02
iteration: 148000 loss: 0.0026 lr: 0.02
iteration: 149000 loss: 0.0026 lr: 0.02
iteration: 150000 loss: 0.0026 lr: 0.02
iteration: 151000 loss: 0.0027 lr: 0.02
iteration: 152000 loss: 0.0026 lr: 0.02
iteration: 153000 loss: 0.0026 lr: 0.02
iteration: 154000 loss: 0.0027 lr: 0.02
iteration: 155000 loss: 0.0027 lr: 0.02
iteration: 156000 loss: 0.0026 lr: 0.02
iteration: 157000 loss: 0.0026 lr: 0.02
iteration: 158000 loss: 0.0026 lr: 0.02
iteration: 159000 loss: 0.0026 lr: 0.02
iteration: 160000 loss: 0.0025 lr: 0.02
iteration: 161000 loss: 0.0025 lr: 0.02
iteration: 162000 loss: 0.0026 lr: 0.02
iteration: 163000 loss: 0.0025 lr: 0.02
iteration: 164000 loss: 0.0025 lr: 0.02
iteration: 165000 loss: 0.0025 lr: 0.02
iteration: 166000 loss: 0.0025 lr: 0.02
iteration: 167000 loss: 0.0025 lr: 0.02
iteration: 168000 loss: 0.0025 lr: 0.02
iteration: 169000 loss: 0.0025 lr: 0.02
iteration: 170000 loss: 0.0025 lr: 0.02
iteration: 171000 loss: 0.0025 lr: 0.02
iteration: 172000 loss: 0.0026 lr: 0.02
iteration: 173000 loss: 0.0025 lr: 0.02
iteration: 174000 loss: 0.0027 lr: 0.02
iteration: 175000 loss: 0.0033 lr: 0.02
iteration: 176000 loss: 0.0028 lr: 0.02
iteration: 177000 loss: 0.0026 lr: 0.02
iteration: 178000 loss: 0.0025 lr: 0.02
iteration: 179000 loss: 0.0025 lr: 0.02
iteration: 180000 loss: 0.0025 lr: 0.02
iteration: 181000 loss: 0.0024 lr: 0.02
iteration: 182000 loss: 0.0026 lr: 0.02
iteration: 183000 loss: 0.0025 lr: 0.02
iteration: 184000 loss: 0.0026 lr: 0.02
iteration: 185000 loss: 0.0028 lr: 0.02
iteration: 186000 loss: 0.0025 lr: 0.02
iteration: 187000 loss: 0.0026 lr: 0.02
iteration: 188000 loss: 0.0027 lr: 0.02
iteration: 189000 loss: 0.0025 lr: 0.02
iteration: 190000 loss: 0.0024 lr: 0.02
iteration: 191000 loss: 0.0025 lr: 0.02
iteration: 192000 loss: 0.0024 lr: 0.02
iteration: 193000 loss: 0.0024 lr: 0.02
iteration: 194000 loss: 0.0024 lr: 0.02
iteration: 195000 loss: 0.0023 lr: 0.02
iteration: 196000 loss: 0.0024 lr: 0.02
iteration: 197000 loss: 0.0024 lr: 0.02
iteration: 198000 loss: 0.0024 lr: 0.02
iteration: 199000 loss: 0.0024 lr: 0.02
iteration: 200000 loss: 0.0024 lr: 0.02
iteration: 201000 loss: 0.0030 lr: 0.02
iteration: 202000 loss: 0.0026 lr: 0.02
iteration: 203000 loss: 0.0025 lr: 0.02
iteration: 204000 loss: 0.0025 lr: 0.02
iteration: 205000 loss: 0.0023 lr: 0.02
iteration: 206000 loss: 0.0024 lr: 0.02
iteration: 207000 loss: 0.0026 lr: 0.02
iteration: 208000 loss: 0.0024 lr: 0.02
iteration: 209000 loss: 0.0024 lr: 0.02
iteration: 210000 loss: 0.0025 lr: 0.02
iteration: 211000 loss: 0.0024 lr: 0.02
iteration: 212000 loss: 0.0024 lr: 0.02
iteration: 213000 loss: 0.0024 lr: 0.02
iteration: 214000 loss: 0.0023 lr: 0.02
iteration: 215000 loss: 0.0025 lr: 0.02
iteration: 216000 loss: 0.0023 lr: 0.02
iteration: 217000 loss: 0.0023 lr: 0.02
iteration: 218000 loss: 0.0024 lr: 0.02
iteration: 219000 loss: 0.0023 lr: 0.02
iteration: 220000 loss: 0.0024 lr: 0.02
iteration: 221000 loss: 0.0023 lr: 0.02
iteration: 222000 loss: 0.0024 lr: 0.02
iteration: 223000 loss: 0.0023 lr: 0.02
iteration: 224000 loss: 0.0022 lr: 0.02
iteration: 225000 loss: 0.0022 lr: 0.02
iteration: 226000 loss: 0.0023 lr: 0.02
iteration: 227000 loss: 0.0023 lr: 0.02
iteration: 228000 loss: 0.0022 lr: 0.02
iteration: 229000 loss: 0.0022 lr: 0.02
iteration: 230000 loss: 0.0023 lr: 0.02
iteration: 231000 loss: 0.0022 lr: 0.02
iteration: 232000 loss: 0.0022 lr: 0.02
iteration: 233000 loss: 0.0022 lr: 0.02
iteration: 234000 loss: 0.0022 lr: 0.02
iteration: 235000 loss: 0.0022 lr: 0.02
iteration: 236000 loss: 0.0022 lr: 0.02
iteration: 237000 loss: 0.0022 lr: 0.02
iteration: 238000 loss: 0.0023 lr: 0.02
iteration: 239000 loss: 0.0024 lr: 0.02
iteration: 240000 loss: 0.0022 lr: 0.02
iteration: 241000 loss: 0.0023 lr: 0.02
iteration: 242000 loss: 0.0023 lr: 0.02
iteration: 243000 loss: 0.0022 lr: 0.02
iteration: 244000 loss: 0.0022 lr: 0.02
iteration: 245000 loss: 0.0022 lr: 0.02
iteration: 246000 loss: 0.0022 lr: 0.02
iteration: 247000 loss: 0.0022 lr: 0.02
iteration: 248000 loss: 0.0022 lr: 0.02
iteration: 249000 loss: 0.0022 lr: 0.02
iteration: 250000 loss: 0.0022 lr: 0.02
iteration: 251000 loss: 0.0022 lr: 0.02
iteration: 252000 loss: 0.0022 lr: 0.02
iteration: 253000 loss: 0.0021 lr: 0.02
iteration: 254000 loss: 0.0022 lr: 0.02
iteration: 255000 loss: 0.0023 lr: 0.02
iteration: 256000 loss: 0.0022 lr: 0.02
iteration: 257000 loss: 0.0024 lr: 0.02
iteration: 258000 loss: 0.0022 lr: 0.02
iteration: 259000 loss: 0.0022 lr: 0.02
iteration: 260000 loss: 0.0022 lr: 0.02
iteration: 261000 loss: 0.0022 lr: 0.02
iteration: 262000 loss: 0.0022 lr: 0.02
iteration: 263000 loss: 0.0021 lr: 0.02
iteration: 264000 loss: 0.0022 lr: 0.02
iteration: 265000 loss: 0.0021 lr: 0.02
iteration: 266000 loss: 0.0021 lr: 0.02
iteration: 267000 loss: 0.0022 lr: 0.02
iteration: 268000 loss: 0.0021 lr: 0.02
iteration: 269000 loss: 0.0021 lr: 0.02
iteration: 270000 loss: 0.0021 lr: 0.02
iteration: 271000 loss: 0.0021 lr: 0.02
iteration: 272000 loss: 0.0022 lr: 0.02
iteration: 273000 loss: 0.0021 lr: 0.02
iteration: 274000 loss: 0.0021 lr: 0.02
iteration: 275000 loss: 0.0021 lr: 0.02
iteration: 276000 loss: 0.0021 lr: 0.02
iteration: 277000 loss: 0.0021 lr: 0.02
iteration: 278000 loss: 0.0021 lr: 0.02
iteration: 279000 loss: 0.0021 lr: 0.02
iteration: 280000 loss: 0.0021 lr: 0.02
iteration: 281000 loss: 0.0020 lr: 0.02
iteration: 282000 loss: 0.0021 lr: 0.02
iteration: 283000 loss: 0.0021 lr: 0.02
iteration: 284000 loss: 0.0022 lr: 0.02
iteration: 285000 loss: 0.0021 lr: 0.02
iteration: 286000 loss: 0.0021 lr: 0.02
iteration: 287000 loss: 0.0021 lr: 0.02
iteration: 288000 loss: 0.0021 lr: 0.02
iteration: 289000 loss: 0.0021 lr: 0.02
iteration: 290000 loss: 0.0021 lr: 0.02
iteration: 291000 loss: 0.0022 lr: 0.02
iteration: 292000 loss: 0.0021 lr: 0.02
iteration: 293000 loss: 0.0020 lr: 0.02
iteration: 294000 loss: 0.0020 lr: 0.02
iteration: 295000 loss: 0.0021 lr: 0.02
iteration: 296000 loss: 0.0021 lr: 0.02
iteration: 297000 loss: 0.0022 lr: 0.02
iteration: 298000 loss: 0.0021 lr: 0.02
iteration: 299000 loss: 0.0020 lr: 0.02
iteration: 300000 loss: 0.0021 lr: 0.02
Exception in thread Thread-1:
Traceback (most recent call last):
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/threading.py", line 932, in _bootstrap_inner
self.run()
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py", line 83, in load_and_enqueue
sess.run(enqueue_op, feed_dict=food)
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 967, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1115, in _run
raise RuntimeError('Attempted to use a closed Session.')
RuntimeError: Attempted to use a closed Session.
Config:
{'all_joints': [[0],
[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]],
'all_joints_names': ['L1hip',
'L1knee',
'L1ankle',
'L1toe',
'L2hip',
'L2knee',
'L2ankle',
'L2toe',
'L3hip',
'L3knee',
'L3ankle',
'L3toe',
'R1hip',
'R1knee',
'R1ankle',
'R1toe',
'R2hip',
'R2knee',
'R2ankle',
'R2toe',
'R3hip',
'R3knee',
'R3ankle',
'R3toe',
'Leye',
'Reye',
'nose',
'Lantenna1',
'Lantenna2',
'Lantenna3',
'Lantenna4',
'Rantenna1',
'Rantenna2',
'Rantenna3',
'Rantenna4',
'Lbody',
'Rbody',
'rear',
'centerbody',
'Lshoulder',
'Rshoulder'],
'alpha_r': 0.02,
'apply_prob': 0.5,
'batch_size': 1,
'contrast': {'clahe': True,
'claheratio': 0.1,
'histeq': True,
'histeqratio': 0.1},
'convolution': {'edge': False, 'emboss': False, 'sharpen': False},
'covering': True,
'crop_by': 0.15,
'crop_pad': 0,
'cropratio': 0.4,
'dataset': 'training-datasets/iteration-1/UnaugmentedDataSet_stinkbugsJul15/stinkbugs_DLC80shuffle1.mat',
'dataset_type': 'imgaug',
'decay_steps': 30000,
'deterministic': False,
'display_iters': 1000,
'elastic_transform': True,
'fg_fraction': 0.25,
'gaussian_noise': False,
'global_scale': 0.8,
'grayscale': False,
'init_weights': '/media/data/model-weights/resnet_v1_50.ckpt',
'intermediate_supervision': False,
'intermediate_supervision_layer': 12,
'location_refinement': True,
'locref_huber_loss': True,
'locref_loss_weight': 0.05,
'locref_stdev': 7.2801,
'log_dir': 'log',
'lr_init': 0.0005,
'max_input_size': 1500,
'mean_pixel': [123.68, 116.779, 103.939],
'metadataset': 'training-datasets/iteration-1/UnaugmentedDataSet_stinkbugsJul15/Documentation_data-stinkbugs_80shuffle1.pickle',
'min_input_size': 64,
'mirror': True,
'motion_blur': True,
'motion_blur_params': {'angle': [-90, 90], 'k': 7},
'multi_stage': False,
'multi_step': [[0.005, 10000],
[0.02, 430000],
[0.002, 730000],
[0.001, 1030000]],
'net_type': 'resnet_50',
'num_joints': 41,
'optimizer': 'sgd',
'pairwise_huber_loss': False,
'pairwise_predict': False,
'partaffinityfield_predict': False,
'pos_dist_thresh': 17,
'pre_resize': [],
'project_path': '/media/data/stinkbugs-DLC-2022-07-15',
'regularize': False,
'rotation': 25,
'rotratio': 0.4,
'save_iters': 50000,
'scale_jitter_lo': 0.5,
'scale_jitter_up': 1.25,
'scoremap_dir': 'test',
'shuffle': True,
'snapshot_prefix': '/media/data/stinkbugs-DLC-2022-07-15/data_augm_04_mirror/dlc-models/iteration-1/stinkbugsJul15-trainset80shuffle1/train/snapshot',
'stride': 8.0,
'weigh_negatives': False,
'weigh_only_present_joints': False,
'weigh_part_predictions': False,
'weight_decay': 0.0001}
2022-07-19 01:27:31.880427: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22344 MB memory: -> device: 0, name: NVIDIA RTX A5000, pci bus id: 0000:1a:00.0, compute capability: 8.6
iteration: 1000 loss: 0.0289 lr: 0.005
iteration: 2000 loss: 0.0197 lr: 0.005
iteration: 3000 loss: 0.0184 lr: 0.005
iteration: 4000 loss: 0.0171 lr: 0.005
iteration: 5000 loss: 0.0157 lr: 0.005
iteration: 6000 loss: 0.0148 lr: 0.005
iteration: 7000 loss: 0.0137 lr: 0.005
iteration: 8000 loss: 0.0131 lr: 0.005
iteration: 9000 loss: 0.0124 lr: 0.005
iteration: 10000 loss: 0.0126 lr: 0.005
iteration: 11000 loss: 0.0147 lr: 0.02
iteration: 12000 loss: 0.0150 lr: 0.02
iteration: 13000 loss: 0.0117 lr: 0.02
iteration: 14000 loss: 0.0104 lr: 0.02
iteration: 15000 loss: 0.0096 lr: 0.02
iteration: 16000 loss: 0.0090 lr: 0.02
iteration: 17000 loss: 0.0085 lr: 0.02
iteration: 18000 loss: 0.0081 lr: 0.02
iteration: 19000 loss: 0.0078 lr: 0.02
iteration: 20000 loss: 0.0075 lr: 0.02
iteration: 21000 loss: 0.0076 lr: 0.02
iteration: 22000 loss: 0.0072 lr: 0.02
iteration: 23000 loss: 0.0069 lr: 0.02
iteration: 24000 loss: 0.0068 lr: 0.02
iteration: 25000 loss: 0.0070 lr: 0.02
iteration: 26000 loss: 0.0064 lr: 0.02
iteration: 27000 loss: 0.0066 lr: 0.02
iteration: 28000 loss: 0.0063 lr: 0.02
iteration: 29000 loss: 0.0061 lr: 0.02
iteration: 30000 loss: 0.0059 lr: 0.02
iteration: 31000 loss: 0.0059 lr: 0.02
iteration: 32000 loss: 0.0057 lr: 0.02
iteration: 33000 loss: 0.0059 lr: 0.02
iteration: 34000 loss: 0.0056 lr: 0.02
iteration: 35000 loss: 0.0055 lr: 0.02
iteration: 36000 loss: 0.0054 lr: 0.02
iteration: 37000 loss: 0.0053 lr: 0.02
iteration: 38000 loss: 0.0051 lr: 0.02
iteration: 39000 loss: 0.0051 lr: 0.02
iteration: 40000 loss: 0.0050 lr: 0.02
iteration: 41000 loss: 0.0050 lr: 0.02
iteration: 42000 loss: 0.0049 lr: 0.02
iteration: 43000 loss: 0.0053 lr: 0.02
iteration: 44000 loss: 0.0049 lr: 0.02
iteration: 45000 loss: 0.0048 lr: 0.02
iteration: 46000 loss: 0.0048 lr: 0.02
iteration: 47000 loss: 0.0048 lr: 0.02
iteration: 48000 loss: 0.0047 lr: 0.02
iteration: 49000 loss: 0.0046 lr: 0.02
iteration: 50000 loss: 0.0045 lr: 0.02
iteration: 51000 loss: 0.0044 lr: 0.02
iteration: 52000 loss: 0.0044 lr: 0.02
iteration: 53000 loss: 0.0044 lr: 0.02
iteration: 54000 loss: 0.0043 lr: 0.02
iteration: 55000 loss: 0.0042 lr: 0.02
iteration: 56000 loss: 0.0044 lr: 0.02
iteration: 57000 loss: 0.0042 lr: 0.02
iteration: 58000 loss: 0.0042 lr: 0.02
iteration: 59000 loss: 0.0041 lr: 0.02
iteration: 60000 loss: 0.0041 lr: 0.02
iteration: 61000 loss: 0.0041 lr: 0.02
iteration: 62000 loss: 0.0041 lr: 0.02
iteration: 63000 loss: 0.0041 lr: 0.02
iteration: 64000 loss: 0.0040 lr: 0.02
iteration: 65000 loss: 0.0042 lr: 0.02
iteration: 66000 loss: 0.0040 lr: 0.02
iteration: 67000 loss: 0.0039 lr: 0.02
iteration: 68000 loss: 0.0040 lr: 0.02
iteration: 69000 loss: 0.0039 lr: 0.02
iteration: 70000 loss: 0.0039 lr: 0.02
iteration: 71000 loss: 0.0039 lr: 0.02
iteration: 72000 loss: 0.0040 lr: 0.02
iteration: 73000 loss: 0.0038 lr: 0.02
iteration: 74000 loss: 0.0039 lr: 0.02
iteration: 75000 loss: 0.0038 lr: 0.02
iteration: 76000 loss: 0.0036 lr: 0.02
iteration: 77000 loss: 0.0036 lr: 0.02
iteration: 78000 loss: 0.0038 lr: 0.02
iteration: 79000 loss: 0.0039 lr: 0.02
iteration: 80000 loss: 0.0048 lr: 0.02
iteration: 81000 loss: 0.0038 lr: 0.02
iteration: 82000 loss: 0.0038 lr: 0.02
iteration: 83000 loss: 0.0036 lr: 0.02
iteration: 84000 loss: 0.0036 lr: 0.02
iteration: 85000 loss: 0.0036 lr: 0.02
iteration: 86000 loss: 0.0035 lr: 0.02
iteration: 87000 loss: 0.0035 lr: 0.02
iteration: 88000 loss: 0.0035 lr: 0.02
iteration: 89000 loss: 0.0035 lr: 0.02
iteration: 90000 loss: 0.0035 lr: 0.02
iteration: 91000 loss: 0.0035 lr: 0.02
iteration: 92000 loss: 0.0034 lr: 0.02
iteration: 93000 loss: 0.0034 lr: 0.02
iteration: 94000 loss: 0.0033 lr: 0.02
iteration: 95000 loss: 0.0034 lr: 0.02
iteration: 96000 loss: 0.0033 lr: 0.02
iteration: 97000 loss: 0.0035 lr: 0.02
iteration: 98000 loss: 0.0033 lr: 0.02
iteration: 99000 loss: 0.0033 lr: 0.02
iteration: 100000 loss: 0.0032 lr: 0.02
iteration: 101000 loss: 0.0033 lr: 0.02
iteration: 102000 loss: 0.0032 lr: 0.02
iteration: 103000 loss: 0.0033 lr: 0.02
iteration: 104000 loss: 0.0033 lr: 0.02
iteration: 105000 loss: 0.0032 lr: 0.02
iteration: 106000 loss: 0.0032 lr: 0.02
iteration: 107000 loss: 0.0031 lr: 0.02
iteration: 108000 loss: 0.0032 lr: 0.02
iteration: 109000 loss: 0.0030 lr: 0.02
iteration: 110000 loss: 0.0031 lr: 0.02
iteration: 111000 loss: 0.0032 lr: 0.02
iteration: 112000 loss: 0.0033 lr: 0.02
iteration: 113000 loss: 0.0035 lr: 0.02
iteration: 114000 loss: 0.0031 lr: 0.02
iteration: 115000 loss: 0.0032 lr: 0.02
iteration: 116000 loss: 0.0031 lr: 0.02
iteration: 117000 loss: 0.0030 lr: 0.02
iteration: 118000 loss: 0.0031 lr: 0.02
iteration: 119000 loss: 0.0030 lr: 0.02
iteration: 120000 loss: 0.0030 lr: 0.02
iteration: 121000 loss: 0.0030 lr: 0.02
iteration: 122000 loss: 0.0029 lr: 0.02
iteration: 123000 loss: 0.0029 lr: 0.02
iteration: 124000 loss: 0.0030 lr: 0.02
iteration: 125000 loss: 0.0029 lr: 0.02
iteration: 126000 loss: 0.0030 lr: 0.02
iteration: 127000 loss: 0.0029 lr: 0.02
iteration: 128000 loss: 0.0028 lr: 0.02
iteration: 129000 loss: 0.0029 lr: 0.02
iteration: 130000 loss: 0.0029 lr: 0.02
iteration: 131000 loss: 0.0028 lr: 0.02
iteration: 132000 loss: 0.0028 lr: 0.02
iteration: 133000 loss: 0.0029 lr: 0.02
iteration: 134000 loss: 0.0028 lr: 0.02
iteration: 135000 loss: 0.0031 lr: 0.02
iteration: 136000 loss: 0.0029 lr: 0.02
iteration: 137000 loss: 0.0030 lr: 0.02
iteration: 138000 loss: 0.0027 lr: 0.02
iteration: 139000 loss: 0.0028 lr: 0.02
iteration: 140000 loss: 0.0028 lr: 0.02
iteration: 141000 loss: 0.0028 lr: 0.02
iteration: 142000 loss: 0.0027 lr: 0.02
iteration: 143000 loss: 0.0028 lr: 0.02
iteration: 144000 loss: 0.0027 lr: 0.02
iteration: 145000 loss: 0.0027 lr: 0.02
iteration: 146000 loss: 0.0030 lr: 0.02
iteration: 147000 loss: 0.0028 lr: 0.02
iteration: 148000 loss: 0.0027 lr: 0.02
iteration: 149000 loss: 0.0028 lr: 0.02
iteration: 150000 loss: 0.0030 lr: 0.02
iteration: 151000 loss: 0.0028 lr: 0.02
iteration: 152000 loss: 0.0027 lr: 0.02
iteration: 153000 loss: 0.0027 lr: 0.02
iteration: 154000 loss: 0.0027 lr: 0.02
iteration: 155000 loss: 0.0027 lr: 0.02
iteration: 156000 loss: 0.0028 lr: 0.02
iteration: 157000 loss: 0.0027 lr: 0.02
iteration: 158000 loss: 0.0026 lr: 0.02
iteration: 159000 loss: 0.0026 lr: 0.02
iteration: 160000 loss: 0.0026 lr: 0.02
iteration: 161000 loss: 0.0026 lr: 0.02
iteration: 162000 loss: 0.0026 lr: 0.02
iteration: 163000 loss: 0.0027 lr: 0.02
iteration: 164000 loss: 0.0026 lr: 0.02
iteration: 165000 loss: 0.0027 lr: 0.02
iteration: 166000 loss: 0.0029 lr: 0.02
iteration: 167000 loss: 0.0027 lr: 0.02
iteration: 168000 loss: 0.0027 lr: 0.02
iteration: 169000 loss: 0.0030 lr: 0.02
iteration: 170000 loss: 0.0028 lr: 0.02
iteration: 171000 loss: 0.0026 lr: 0.02
iteration: 172000 loss: 0.0025 lr: 0.02
iteration: 173000 loss: 0.0027 lr: 0.02
iteration: 174000 loss: 0.0026 lr: 0.02
iteration: 175000 loss: 0.0026 lr: 0.02
iteration: 176000 loss: 0.0026 lr: 0.02
iteration: 177000 loss: 0.0026 lr: 0.02
iteration: 178000 loss: 0.0032 lr: 0.02
iteration: 179000 loss: 0.0030 lr: 0.02
iteration: 180000 loss: 0.0027 lr: 0.02
iteration: 181000 loss: 0.0027 lr: 0.02
iteration: 182000 loss: 0.0027 lr: 0.02
iteration: 183000 loss: 0.0025 lr: 0.02
iteration: 184000 loss: 0.0026 lr: 0.02
iteration: 185000 loss: 0.0026 lr: 0.02
iteration: 186000 loss: 0.0026 lr: 0.02
iteration: 187000 loss: 0.0026 lr: 0.02
iteration: 188000 loss: 0.0025 lr: 0.02
iteration: 189000 loss: 0.0025 lr: 0.02
iteration: 190000 loss: 0.0025 lr: 0.02
iteration: 191000 loss: 0.0026 lr: 0.02
iteration: 192000 loss: 0.0025 lr: 0.02
iteration: 193000 loss: 0.0024 lr: 0.02
iteration: 194000 loss: 0.0025 lr: 0.02
iteration: 195000 loss: 0.0025 lr: 0.02
iteration: 196000 loss: 0.0025 lr: 0.02
iteration: 197000 loss: 0.0024 lr: 0.02
iteration: 198000 loss: 0.0025 lr: 0.02
iteration: 199000 loss: 0.0025 lr: 0.02
iteration: 200000 loss: 0.0026 lr: 0.02
iteration: 201000 loss: 0.0024 lr: 0.02
iteration: 202000 loss: 0.0024 lr: 0.02
iteration: 203000 loss: 0.0024 lr: 0.02
iteration: 204000 loss: 0.0024 lr: 0.02
iteration: 205000 loss: 0.0026 lr: 0.02
iteration: 206000 loss: 0.0025 lr: 0.02
iteration: 207000 loss: 0.0024 lr: 0.02
iteration: 208000 loss: 0.0023 lr: 0.02
iteration: 209000 loss: 0.0024 lr: 0.02
iteration: 210000 loss: 0.0025 lr: 0.02
iteration: 211000 loss: 0.0024 lr: 0.02
iteration: 212000 loss: 0.0024 lr: 0.02
iteration: 213000 loss: 0.0024 lr: 0.02
iteration: 214000 loss: 0.0023 lr: 0.02
iteration: 215000 loss: 0.0023 lr: 0.02
iteration: 216000 loss: 0.0023 lr: 0.02
iteration: 217000 loss: 0.0024 lr: 0.02
iteration: 218000 loss: 0.0023 lr: 0.02
iteration: 219000 loss: 0.0024 lr: 0.02
iteration: 220000 loss: 0.0023 lr: 0.02
iteration: 221000 loss: 0.0024 lr: 0.02
iteration: 222000 loss: 0.0023 lr: 0.02
iteration: 223000 loss: 0.0023 lr: 0.02
iteration: 224000 loss: 0.0024 lr: 0.02
iteration: 225000 loss: 0.0023 lr: 0.02
iteration: 226000 loss: 0.0024 lr: 0.02
iteration: 227000 loss: 0.0024 lr: 0.02
iteration: 228000 loss: 0.0023 lr: 0.02
iteration: 229000 loss: 0.0024 lr: 0.02
iteration: 230000 loss: 0.0029 lr: 0.02
iteration: 231000 loss: 0.0024 lr: 0.02
iteration: 232000 loss: 0.0024 lr: 0.02
iteration: 233000 loss: 0.0023 lr: 0.02
iteration: 234000 loss: 0.0023 lr: 0.02
iteration: 235000 loss: 0.0024 lr: 0.02
iteration: 236000 loss: 0.0023 lr: 0.02
iteration: 237000 loss: 0.0023 lr: 0.02
iteration: 238000 loss: 0.0023 lr: 0.02
iteration: 239000 loss: 0.0023 lr: 0.02
iteration: 240000 loss: 0.0024 lr: 0.02
iteration: 241000 loss: 0.0022 lr: 0.02
iteration: 242000 loss: 0.0023 lr: 0.02
iteration: 243000 loss: 0.0023 lr: 0.02
iteration: 244000 loss: 0.0023 lr: 0.02
iteration: 245000 loss: 0.0022 lr: 0.02
iteration: 246000 loss: 0.0023 lr: 0.02
iteration: 247000 loss: 0.0023 lr: 0.02
iteration: 248000 loss: 0.0023 lr: 0.02
iteration: 249000 loss: 0.0023 lr: 0.02
iteration: 250000 loss: 0.0022 lr: 0.02
iteration: 251000 loss: 0.0023 lr: 0.02
iteration: 252000 loss: 0.0022 lr: 0.02
iteration: 253000 loss: 0.0022 lr: 0.02
iteration: 254000 loss: 0.0023 lr: 0.02
iteration: 255000 loss: 0.0022 lr: 0.02
iteration: 256000 loss: 0.0022 lr: 0.02
iteration: 257000 loss: 0.0023 lr: 0.02
iteration: 258000 loss: 0.0022 lr: 0.02
iteration: 259000 loss: 0.0024 lr: 0.02
iteration: 260000 loss: 0.0022 lr: 0.02
iteration: 261000 loss: 0.0023 lr: 0.02
iteration: 262000 loss: 0.0023 lr: 0.02
iteration: 263000 loss: 0.0021 lr: 0.02
iteration: 264000 loss: 0.0021 lr: 0.02
iteration: 265000 loss: 0.0023 lr: 0.02
iteration: 266000 loss: 0.0023 lr: 0.02
iteration: 267000 loss: 0.0023 lr: 0.02
iteration: 268000 loss: 0.0022 lr: 0.02
iteration: 269000 loss: 0.0022 lr: 0.02
iteration: 270000 loss: 0.0022 lr: 0.02
iteration: 271000 loss: 0.0022 lr: 0.02
iteration: 272000 loss: 0.0022 lr: 0.02
iteration: 273000 loss: 0.0021 lr: 0.02
iteration: 274000 loss: 0.0022 lr: 0.02
iteration: 275000 loss: 0.0021 lr: 0.02
iteration: 276000 loss: 0.0021 lr: 0.02
iteration: 277000 loss: 0.0021 lr: 0.02
iteration: 278000 loss: 0.0022 lr: 0.02
iteration: 279000 loss: 0.0023 lr: 0.02
iteration: 280000 loss: 0.0022 lr: 0.02
iteration: 281000 loss: 0.0022 lr: 0.02
iteration: 282000 loss: 0.0023 lr: 0.02
iteration: 283000 loss: 0.0022 lr: 0.02
iteration: 284000 loss: 0.0021 lr: 0.02
iteration: 285000 loss: 0.0021 lr: 0.02
iteration: 286000 loss: 0.0021 lr: 0.02
iteration: 287000 loss: 0.0021 lr: 0.02
iteration: 288000 loss: 0.0022 lr: 0.02
iteration: 289000 loss: 0.0021 lr: 0.02
iteration: 290000 loss: 0.0023 lr: 0.02
iteration: 291000 loss: 0.0022 lr: 0.02
iteration: 292000 loss: 0.0021 lr: 0.02
iteration: 293000 loss: 0.0021 lr: 0.02
iteration: 294000 loss: 0.0021 lr: 0.02
iteration: 295000 loss: 0.0021 lr: 0.02
iteration: 296000 loss: 0.0021 lr: 0.02
iteration: 297000 loss: 0.0021 lr: 0.02
iteration: 298000 loss: 0.0021 lr: 0.02
iteration: 299000 loss: 0.0020 lr: 0.02
iteration: 300000 loss: 0.0021 lr: 0.02
Exception in thread Thread-2:
Traceback (most recent call last):
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1377, in _do_call
return fn(*args)
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1360, in _run_fn
return self._call_tf_sessionrun(options, feed_dict, fetch_list,
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1453, in _call_tf_sessionrun
return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled
[[{{node fifo_queue_enqueue}}]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/threading.py", line 932, in _bootstrap_inner
self.run()
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py", line 83, in load_and_enqueue
sess.run(enqueue_op, feed_dict=food)
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 967, in run
result = self._run(None, fetches, feed_dict, options_ptr,
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1190, in _run
results = self._do_run(handle, final_targets, final_fetches,
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1370, in _do_run
return self._do_call(_run_fn, feeds, fetches, targets, options,
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1396, in _do_call
raise type(e)(node_def, op, message) # pylint: disable=no-value-for-parameter
tensorflow.python.framework.errors_impl.CancelledError: Graph execution error:
Detected at node 'fifo_queue_enqueue' defined at (most recent call last):
File "train_augmentation_models.py", line 27, in <module>
train_all_shuffles(config_path, # config.yaml, common to all models
File "/home/jonas2/DLC_files/python_scripts/CaseStudyScripts/train_all_shuffles.py", line 34, in train_all_shuffles
deeplabcut.train_network(config_path, # config.yaml, common to all models
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/training.py", line 207, in train_network
train(
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py", line 168, in train
batch, enqueue_op, placeholders = setup_preloading(batch_spec)
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py", line 69, in setup_preloading
enqueue_op = q.enqueue(placeholders_list)
Node: 'fifo_queue_enqueue'
Enqueue operation was cancelled
[[{{node fifo_queue_enqueue}}]]
Original stack trace for 'fifo_queue_enqueue':
File "train_augmentation_models.py", line 27, in <module>
train_all_shuffles(config_path, # config.yaml, common to all models
File "/home/jonas2/DLC_files/python_scripts/CaseStudyScripts/train_all_shuffles.py", line 34, in train_all_shuffles
deeplabcut.train_network(config_path, # config.yaml, common to all models
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/training.py", line 207, in train_network
train(
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py", line 168, in train
batch, enqueue_op, placeholders = setup_preloading(batch_spec)
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/deeplabcut/pose_estimation_tensorflow/core/train.py", line 69, in setup_preloading
enqueue_op = q.enqueue(placeholders_list)
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/ops/data_flow_ops.py", line 346, in enqueue
return gen_data_flow_ops.queue_enqueue_v2(
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 4063, in queue_enqueue_v2
_, _, _op, _outputs = _op_def_library._apply_op_helper(
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py", line 797, in _apply_op_helper
op = g._create_op_internal(op_type_name, inputs, dtypes=None,
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 3754, in _create_op_internal
ret = Operation(
File "/home/jonas2/miniconda/envs/dlc/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 2133, in __init__
self._traceback = tf_stack.extract_stack_for_node(self._c_op)
Config:
{'all_joints': [[0],
[1],
[2],
[3],