-
Notifications
You must be signed in to change notification settings - Fork 217
/
grounded_hqsam.log
1693 lines (1693 loc) · 143 KB
/
grounded_hqsam.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
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
./data/seginw/Airplane-Parts is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: Airplane . Body . Cockpit . Engine . Wing .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
Accumulating evaluation results...
DONE (t=0.03s).
Accumulating evaluation results...
DONE (t=0.03s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.338
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.458
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.323
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.389
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.445
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.595
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.548
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.548
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.480
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.702
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.376
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.531
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.379
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.384
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.394
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.626
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.445
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.581
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.581
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.500
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.568
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.729
Final results: [0.3756864279270625, 0.5308008595890644, 0.37882019756215113, 0.383993399339934, 0.3939833084795228, 0.6256601374423156, 0.4446666666666667, 0.581, 0.581, 0.5, 0.5683333333333332, 0.7285714285714288]
./data/seginw/Bottles is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: bottle . can . label .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
Accumulating evaluation results...
DONE (t=0.02s).
Accumulating evaluation results...
DONE (t=0.02s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.673
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.742
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.696
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.529
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.854
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.860
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.860
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.663
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.741
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.686
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.663
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.532
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.843
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.854
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.854
Final results: [0.6626787513291967, 0.7410354550908884, 0.6860321238854317, -1.0, -1.0, 0.6629373463152295, 0.5324625566004877, 0.8431905259491467, 0.8538488331591781, -1.0, -1.0, 0.8538488331591781]
./data/seginw/Brain-Tumor is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: tumor .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
Accumulating evaluation results...
DONE (t=0.02s).
Accumulating evaluation results...
DONE (t=0.02s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.125
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.195
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.147
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.310
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.112
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.565
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.697
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.633
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.781
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.120
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.191
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.149
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.312
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.098
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.522
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.643
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.600
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700
Final results: [0.12001396443868577, 0.19069205611404016, 0.1487699392293854, -1.0, 0.3123744228271123, 0.09785765198870178, 0.0, 0.5216216216216216, 0.6432432432432431, -1.0, 0.6, 0.7]
./data/seginw/Chicken is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: chicken .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
Accumulating evaluation results...
DONE (t=0.01s).
Accumulating evaluation results...
DONE (t=0.00s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.753
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.930
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.825
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.771
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.753
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.036
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.340
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.836
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.820
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.840
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.845
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.930
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.930
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.853
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.841
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.040
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.360
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.900
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.900
Final results: [0.8445395907890446, 0.9302970297029702, 0.9302970297029702, -1.0, 0.8527581329561527, 0.8412297096582723, 0.039999999999999994, 0.36, 0.9, -1.0, 0.9, 0.9]
./data/seginw/Cows is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: cow .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
processed 29/60 images. time: 19.67s, ETA: 21.02s
processed 59/60 images. time: 38.95s, ETA: 0.66s
Accumulating evaluation results...
DONE (t=0.04s).
Accumulating evaluation results...
DONE (t=0.03s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.586
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.810
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.700
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.611
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.697
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.164
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.726
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.803
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.792
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.896
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.478
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.804
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.560
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.470
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.799
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.146
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.612
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.658
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.638
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.830
Final results: [0.47811492252895554, 0.8036358041935029, 0.560070607670227, -1.0, 0.4698286333720617, 0.7992425760573034, 0.14638783269961977, 0.612167300380228, 0.6577946768060836, -1.0, 0.638135593220339, 0.8296296296296296]
./data/seginw/Electric-Shaver is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: caorau .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
Accumulating evaluation results...
DONE (t=0.02s).
Accumulating evaluation results...
DONE (t=0.01s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.832
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.860
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.856
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.832
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.817
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.917
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.933
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.933
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.721
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.860
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.856
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.721
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.725
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.808
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.829
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.829
Final results: [0.7211879898777362, 0.8601922038699978, 0.855851447213687, -1.0, -1.0, 0.7211984956177734, 0.725, 0.8083333333333332, 0.8291666666666668, -1.0, -1.0, 0.8291666666666668]
./data/seginw/Elephants is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: elephant .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
processed 29/99 images. time: 19.36s, ETA: 46.74s
processed 59/99 images. time: 38.31s, ETA: 25.98s
processed 89/99 images. time: 57.33s, ETA: 6.44s
Accumulating evaluation results...
DONE (t=0.07s).
Accumulating evaluation results...
DONE (t=0.06s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.802
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.930
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.878
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.407
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.676
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.862
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.479
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.867
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.913
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.750
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.889
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.934
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.775
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.925
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.878
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.325
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.618
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.842
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.465
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.831
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.869
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.750
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.837
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.892
Final results: [0.7750377694498445, 0.9254450462620254, 0.8776366940611678, 0.3248982041061249, 0.6175165126446855, 0.8416365886776701, 0.4647668393782383, 0.8310880829015543, 0.8694300518134714, 0.75, 0.8365079365079365, 0.8919354838709678]
./data/seginw/Fruits is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: apple . lemon . orange . pear . strawberry .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
Accumulating evaluation results...
DONE (t=0.03s).
Accumulating evaluation results...
DONE (t=0.02s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.817
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.881
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.881
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.801
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.853
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.883
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.883
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.879
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.823
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.881
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.881
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.840
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.877
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.877
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.877
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.871
Final results: [0.8229372937293729, 0.8811881188118812, 0.8811881188118812, -1.0, 0.9, 0.8395214521452145, 0.8766666666666666, 0.8766666666666666, 0.8766666666666666, -1.0, 0.9, 0.8708333333333332]
./data/seginw/Garbage is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: bin . garbage . pavement . road .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
processed 29/142 images. time: 19.33s, ETA: 75.33s
processed 59/142 images. time: 38.41s, ETA: 54.03s
processed 89/142 images. time: 57.44s, ETA: 34.20s
processed 119/142 images. time: 76.41s, ETA: 14.77s
Accumulating evaluation results...
DONE (t=0.12s).
Accumulating evaluation results...
DONE (t=0.11s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.327
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.381
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.343
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.800
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.016
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.359
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.554
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.838
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.870
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.800
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.416
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.904
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.250
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.336
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.261
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.011
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.284
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.472
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.744
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.775
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.366
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.804
Final results: [0.2500298897198872, 0.3362147086694614, 0.260681702612343, 0.6999999999999998, 0.011260271495086701, 0.2835845960750263, 0.47189496444885853, 0.7435502120829259, 0.7752321310413673, 0.7, 0.36583333333333334, 0.8042297316653935]
./data/seginw/Ginger-Garlic is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: garlic . ginger .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
Accumulating evaluation results...
DONE (t=0.01s).
Accumulating evaluation results...
DONE (t=0.01s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.500
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.587
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.506
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.554
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.198
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.830
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.864
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.860
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.536
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.536
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.833
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.614
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.152
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.820
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.837
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.900
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.832
Final results: [0.4564294032344411, 0.5362198866945519, 0.5362198866945519, -1.0, 0.8333333333333331, 0.6137990674067407, 0.15227272727272728, 0.8204545454545455, 0.8371212121212123, -1.0, 0.9, 0.8316666666666667]
./data/seginw/Hand is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: Hand-Segmentation . hand .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
processed 29/60 images. time: 19.06s, ETA: 20.38s
processed 59/60 images. time: 38.00s, ETA: 0.64s
Accumulating evaluation results...
DONE (t=0.02s).
Accumulating evaluation results...
DONE (t=0.02s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.727
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.964
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.672
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.727
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.677
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.978
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.978
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.978
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.748
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.908
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.712
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.749
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.778
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.965
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.967
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.967
Final results: [0.7482272742844522, 0.9083143257182247, 0.7118459449517528, -1.0, -1.0, 0.7485556061856102, 0.7783333333333334, 0.9650000000000001, 0.9666666666666666, -1.0, -1.0, 0.9666666666666666]
./data/seginw/Hand-Metal is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: hand . metal .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
processed 29/65 images. time: 20.75s, ETA: 25.76s
processed 59/65 images. time: 41.31s, ETA: 4.20s
Accumulating evaluation results...
DONE (t=0.05s).
Accumulating evaluation results...
DONE (t=0.04s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.809
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.907
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.878
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.335
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.842
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.638
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.916
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.936
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.925
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.936
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.812
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.903
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.839
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.401
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.841
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.650
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.899
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.920
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.950
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.917
Final results: [0.811581650403562, 0.9030538252178439, 0.8385466522615975, -1.0, 0.4008052668329053, 0.8407598492563809, 0.6503327417923691, 0.8985137533274179, 0.9201197870452527, -1.0, 0.95, 0.9168439716312058]
./data/seginw/House-Parts is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: aluminium door . aluminium window . cellar window . mint cond roof . plaster . plastic door . plastic window . plate fascade . wooden door . wooden fascade . wooden window . worn cond roof .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
processed 29/201 images. time: 19.37s, ETA: 114.89s
processed 59/201 images. time: 38.42s, ETA: 92.48s
processed 89/201 images. time: 57.74s, ETA: 72.67s
processed 119/201 images. time: 76.84s, ETA: 52.95s
processed 149/201 images. time: 96.10s, ETA: 33.54s
processed 179/201 images. time: 115.44s, ETA: 14.19s
Accumulating evaluation results...
DONE (t=0.32s).
Accumulating evaluation results...
DONE (t=0.31s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.100
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.146
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.109
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.045
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.093
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.202
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.250
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.424
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.444
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.261
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.472
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.623
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.085
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.131
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.091
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.035
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.095
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.243
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.215
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.402
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.291
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.454
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.536
Final results: [0.08521419957390203, 0.1305643715731481, 0.09122843553279428, 0.03481824173099917, 0.09464966751895551, 0.24305259622829994, 0.215129607795673, 0.3822629848065605, 0.4016862913674572, 0.2914742653369344, 0.45406752713313425, 0.5360874195595035]
./data/seginw/HouseHold-Items is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: bottle . mouse . perfume . phone .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
Accumulating evaluation results...
DONE (t=0.01s).
Accumulating evaluation results...
DONE (t=0.01s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.601
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.626
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.626
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.700
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.700
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.700
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.601
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.626
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.626
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.700
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.700
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.700
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700
Final results: [0.600990099009901, 0.6262376237623762, 0.6262376237623762, -1.0, -1.0, 0.600990099009901, 0.7, 0.7, 0.7, -1.0, -1.0, 0.7]
./data/seginw/Nutterfly-Squireel is data path !
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
final text_encoder_type: bert-base-uncased
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
final text_encoder_type: bert-base-uncased
Input text prompt: butterfly . squirrel .
<All keys matched successfully>
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using `tokenizers` before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
/scratch/work/mingqiao/Anaconda/anaconda/envs/prompt_sam/lib/python3.8/site-packages/transformers/modeling_utils.py:884: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
warnings.warn(
test_ap_on_seginw.py:119: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
topk_boxes = topk_indexes // prob.shape[2]
processed 29/237 images. time: 19.57s, ETA: 140.38s
processed 59/237 images. time: 38.56s, ETA: 116.33s
processed 89/237 images. time: 57.58s, ETA: 95.75s
processed 119/237 images. time: 76.67s, ETA: 76.02s
processed 149/237 images. time: 96.05s, ETA: 56.73s
processed 179/237 images. time: 115.22s, ETA: 37.33s
processed 209/237 images. time: 134.30s, ETA: 17.99s
Accumulating evaluation results...
DONE (t=0.14s).
Accumulating evaluation results...
DONE (t=0.14s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.811
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.981
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.890
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.571
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.817
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.808
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.903
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.914
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.767
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.918
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.771
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.966
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.837
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.800
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.783
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.765
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.838
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.854
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.800
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.861