-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain_20231020-092414.log
1464 lines (1367 loc) · 149 KB
/
train_20231020-092414.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
2023-10-20 09:24:14,209 INFO **********************Start logging**********************
2023-10-20 09:24:14,209 INFO CUDA_VISIBLE_DEVICES=ALL
2023-10-20 09:24:14,209 INFO Training in distributed mode : total_batch_size: 8
2023-10-20 09:24:14,209 INFO cfg_file cfgs/kitti_models/pointpillar_mobilenetv3_onecat_label_3swin_rdiou_3cat.yaml
2023-10-20 09:24:14,209 INFO batch_size 4
2023-10-20 09:24:14,209 INFO epochs 130
2023-10-20 09:24:14,209 INFO workers 4
2023-10-20 09:24:14,209 INFO extra_tag default
2023-10-20 09:24:14,209 INFO ckpt None
2023-10-20 09:24:14,209 INFO pretrained_model None
2023-10-20 09:24:14,209 INFO launcher pytorch
2023-10-20 09:24:14,209 INFO tcp_port 18888
2023-10-20 09:24:14,209 INFO sync_bn False
2023-10-20 09:24:14,209 INFO fix_random_seed False
2023-10-20 09:24:14,209 INFO ckpt_save_interval 1
2023-10-20 09:24:14,209 INFO local_rank 0
2023-10-20 09:24:14,209 INFO max_ckpt_save_num 30
2023-10-20 09:24:14,209 INFO merge_all_iters_to_one_epoch False
2023-10-20 09:24:14,209 INFO set_cfgs None
2023-10-20 09:24:14,209 INFO max_waiting_mins 0
2023-10-20 09:24:14,209 INFO start_epoch 0
2023-10-20 09:24:14,209 INFO num_epochs_to_eval 0
2023-10-20 09:24:14,209 INFO save_to_file False
2023-10-20 09:24:14,209 INFO use_tqdm_to_record False
2023-10-20 09:24:14,209 INFO logger_iter_interval 50
2023-10-20 09:24:14,209 INFO ckpt_save_time_interval 300
2023-10-20 09:24:14,209 INFO wo_gpu_stat False
2023-10-20 09:24:14,209 INFO use_amp False
2023-10-20 09:24:14,209 INFO cfg.ROOT_DIR: /home/kemove/tem/OpenPCDet-master
2023-10-20 09:24:14,209 INFO cfg.LOCAL_RANK: 0
2023-10-20 09:24:14,209 INFO cfg.CLASS_NAMES: ['Car']
2023-10-20 09:24:14,209 INFO ----------- DATA_CONFIG -----------
2023-10-20 09:24:14,209 INFO cfg.DATA_CONFIG.DATASET: KittiDataset
2023-10-20 09:24:14,209 INFO cfg.DATA_CONFIG.DATA_PATH: ../data/kitti
2023-10-20 09:24:14,209 INFO cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [0, -39.68, -3, 69.12, 39.68, 1]
2023-10-20 09:24:14,210 INFO ----------- DATA_SPLIT -----------
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.DATA_SPLIT.train: train
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.DATA_SPLIT.test: val
2023-10-20 09:24:14,210 INFO ----------- INFO_PATH -----------
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.INFO_PATH.train: ['kitti_infos_train.pkl']
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.INFO_PATH.test: ['kitti_infos_val.pkl']
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.GET_ITEM_LIST: ['points']
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.FOV_POINTS_ONLY: True
2023-10-20 09:24:14,210 INFO ----------- DATA_AUGMENTOR -----------
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder']
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'gt_sampling', 'USE_ROAD_PLANE': True, 'DB_INFO_PATH': ['kitti_dbinfos_train.pkl'], 'PREPARE': {'filter_by_min_points': ['Car:5', 'Pedestrian:5', 'Cyclist:5'], 'filter_by_difficulty': [-1]}, 'SAMPLE_GROUPS': ['Car:15', 'Pedestrian:15', 'Cyclist:15'], 'NUM_POINT_FEATURES': 4, 'DATABASE_WITH_FAKELIDAR': False, 'REMOVE_EXTRA_WIDTH': [0.0, 0.0, 0.0], 'LIMIT_WHOLE_SCENE': False}, {'NAME': 'random_world_flip', 'ALONG_AXIS_LIST': ['x']}, {'NAME': 'random_world_rotation', 'WORLD_ROT_ANGLE': [-0.78539816, 0.78539816]}, {'NAME': 'random_world_scaling', 'WORLD_SCALE_RANGE': [0.95, 1.05]}]
2023-10-20 09:24:14,210 INFO ----------- POINT_FEATURE_ENCODING -----------
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'intensity']
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'intensity']
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG.DATA_PROCESSOR: [{'NAME': 'mask_points_and_boxes_outside_range', 'REMOVE_OUTSIDE_BOXES': True}, {'NAME': 'shuffle_points', 'SHUFFLE_ENABLED': {'train': True, 'test': False}}, {'NAME': 'transform_points_to_voxels', 'VOXEL_SIZE': [0.16, 0.16, 4], 'MAX_POINTS_PER_VOXEL': 32, 'MAX_NUMBER_OF_VOXELS': {'train': 16000, 'test': 40000}}]
2023-10-20 09:24:14,210 INFO cfg.DATA_CONFIG._BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml
2023-10-20 09:24:14,210 INFO ----------- MODEL -----------
2023-10-20 09:24:14,210 INFO cfg.MODEL.NAME: PointPillar
2023-10-20 09:24:14,210 INFO ----------- VFE -----------
2023-10-20 09:24:14,210 INFO cfg.MODEL.VFE.NAME: PillarVFE
2023-10-20 09:24:14,210 INFO cfg.MODEL.VFE.WITH_DISTANCE: False
2023-10-20 09:24:14,210 INFO cfg.MODEL.VFE.USE_ABSLOTE_XYZ: True
2023-10-20 09:24:14,210 INFO cfg.MODEL.VFE.USE_NORM: True
2023-10-20 09:24:14,210 INFO cfg.MODEL.VFE.NUM_FILTERS: [64]
2023-10-20 09:24:14,210 INFO ----------- MAP_TO_BEV -----------
2023-10-20 09:24:14,210 INFO cfg.MODEL.MAP_TO_BEV.NAME: PointPillarScatter
2023-10-20 09:24:14,210 INFO cfg.MODEL.MAP_TO_BEV.NUM_BEV_FEATURES: 64
2023-10-20 09:24:14,210 INFO ----------- BACKBONE_2D -----------
2023-10-20 09:24:14,210 INFO cfg.MODEL.BACKBONE_2D.NAME: MobileNetV3_Backbone_onecat_3swin_3cat
2023-10-20 09:24:14,210 INFO cfg.MODEL.BACKBONE_2D.LAYER_NUMS: [3, 5, 5]
2023-10-20 09:24:14,210 INFO cfg.MODEL.BACKBONE_2D.LAYER_STRIDES: [2, 2, 2]
2023-10-20 09:24:14,210 INFO cfg.MODEL.BACKBONE_2D.NUM_FILTERS: [64, 128, 256]
2023-10-20 09:24:14,210 INFO cfg.MODEL.BACKBONE_2D.UPSAMPLE_STRIDES: [1, 2, 4]
2023-10-20 09:24:14,210 INFO cfg.MODEL.BACKBONE_2D.NUM_UPSAMPLE_FILTERS: [128, 128, 128]
2023-10-20 09:24:14,210 INFO ----------- DENSE_HEAD -----------
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.NAME: AnchorHeadRDIoU
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.CLASS_AGNOSTIC: False
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.USE_DIRECTION_CLASSIFIER: True
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.DIR_OFFSET: 0.78539
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.DIR_LIMIT_OFFSET: 0.0
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.NUM_DIR_BINS: 2
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.ANCHOR_GENERATOR_CONFIG: [{'class_name': 'Car', 'anchor_sizes': [[3.9, 1.6, 1.56]], 'anchor_rotations': [0, 1.57], 'anchor_bottom_heights': [-1.78], 'align_center': False, 'feature_map_stride': 2, 'matched_threshold': 0.6, 'unmatched_threshold': 0.45}]
2023-10-20 09:24:14,210 INFO ----------- TARGET_ASSIGNER_CONFIG -----------
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.NAME: AxisAlignedTargetAssigner
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.POS_FRACTION: -1.0
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.SAMPLE_SIZE: 512
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.NORM_BY_NUM_EXAMPLES: False
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.MATCH_HEIGHT: False
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.TARGET_ASSIGNER_CONFIG.BOX_CODER: ResidualCoder
2023-10-20 09:24:14,210 INFO ----------- LOSS_CONFIG -----------
2023-10-20 09:24:14,210 INFO ----------- LOSS_WEIGHTS -----------
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.cls_weight: 1.0
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.loc_weight: 2.0
2023-10-20 09:24:14,210 INFO cfg.MODEL.DENSE_HEAD.LOSS_CONFIG.LOSS_WEIGHTS.dir_weight: 0.2
2023-10-20 09:24:14,210 INFO ----------- POST_PROCESSING -----------
2023-10-20 09:24:14,210 INFO cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
2023-10-20 09:24:14,210 INFO cfg.MODEL.POST_PROCESSING.SCORE_THRESH: 0.4
2023-10-20 09:24:14,210 INFO cfg.MODEL.POST_PROCESSING.OUTPUT_RAW_SCORE: False
2023-10-20 09:24:14,210 INFO cfg.MODEL.POST_PROCESSING.EVAL_METRIC: kitti
2023-10-20 09:24:14,210 INFO ----------- NMS_CONFIG -----------
2023-10-20 09:24:14,210 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.MULTI_CLASSES_NMS: False
2023-10-20 09:24:14,210 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_TYPE: nms_gpu
2023-10-20 09:24:14,210 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_THRESH: 0.01
2023-10-20 09:24:14,210 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_PRE_MAXSIZE: 4096
2023-10-20 09:24:14,210 INFO cfg.MODEL.POST_PROCESSING.NMS_CONFIG.NMS_POST_MAXSIZE: 500
2023-10-20 09:24:14,210 INFO ----------- OPTIMIZATION -----------
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 4
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.NUM_EPOCHS: 100
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.OPTIMIZER: adam_onecycle
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.LR: 0.003
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.WEIGHT_DECAY: 0.01
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.MOMENTUM: 0.9
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.MOMS: [0.95, 0.85]
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.PCT_START: 0.4
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.DIV_FACTOR: 10
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.DECAY_STEP_LIST: [35, 45]
2023-10-20 09:24:14,210 INFO cfg.OPTIMIZATION.LR_DECAY: 0.1
2023-10-20 09:24:14,211 INFO cfg.OPTIMIZATION.LR_CLIP: 1e-07
2023-10-20 09:24:14,211 INFO cfg.OPTIMIZATION.LR_WARMUP: False
2023-10-20 09:24:14,211 INFO cfg.OPTIMIZATION.WARMUP_EPOCH: 1
2023-10-20 09:24:14,211 INFO cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10
2023-10-20 09:24:14,211 INFO cfg.TAG: pointpillar_mobilenetv3_onecat_label_3swin_rdiou_3cat
2023-10-20 09:24:14,211 INFO cfg.EXP_GROUP_PATH: kitti_models
2023-10-20 09:24:14,218 INFO ----------- Create dataloader & network & optimizer -----------
2023-10-20 09:24:14,274 INFO Database filter by min points Car: 14357 => 13532
2023-10-20 09:24:14,283 INFO Database filter by difficulty Car: 13532 => 10759
2023-10-20 09:24:14,287 INFO Loading KITTI dataset
2023-10-20 09:24:14,331 INFO Total samples for KITTI dataset: 3712
2023-10-20 09:24:16,431 INFO ==> Loading parameters from checkpoint /home/kemove/tem/OpenPCDet-master/output/kitti_models/pointpillar_mobilenetv3_onecat_label_3swin_rdiou_3cat/default/ckpt/checkpoint_epoch_100.pth to CPU
2023-10-20 09:24:16,483 INFO ==> Loading optimizer parameters from checkpoint /home/kemove/tem/OpenPCDet-master/output/kitti_models/pointpillar_mobilenetv3_onecat_label_3swin_rdiou_3cat/default/ckpt/checkpoint_epoch_100.pth to CPU
2023-10-20 09:24:16,518 INFO ==> Done
2023-10-20 09:24:17,708 INFO ----------- Model PointPillar created, param count: 7767300 -----------
2023-10-20 09:24:17,708 INFO DistributedDataParallel(
(module): PointPillar(
(vfe): PillarVFE(
(pfn_layers): ModuleList(
(0): PFNLayer(
(linear): Linear(in_features=10, out_features=64, bias=False)
(norm): BatchNorm1d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
)
)
)
(backbone_3d): None
(map_to_bev_module): PointPillarScatter()
(pfe): None
(backbone_2d): MobileNetV3_Backbone_onecat_3swin_3cat(
(blocks): ModuleList(
(0): Sequential(
(0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
(1): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), bias=False)
(2): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(3): ReLU()
(4): Block(
(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
(5): Block(
(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
(6): Block(
(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128, bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
)
(1): Sequential(
(0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
(1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), bias=False)
(2): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(3): ReLU()
(4): Block(
(conv1): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
(5): Block(
(conv1): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
(6): Block(
(conv1): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
(7): Block(
(conv1): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
(8): Block(
(conv1): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
)
(2): Sequential(
(0): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
(1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), bias=False)
(2): BatchNorm2d(256, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(3): ReLU()
(4): Block(
(conv1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
(5): Block(
(conv1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
(6): Block(
(conv1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
(7): Block(
(conv1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
(8): Block(
(conv1): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): ReLU(inplace=True)
(se): SeModule(
(se): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(inplace=True)
(4): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): Hardsigmoid()
)
)
(conv3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act3): ReLU(inplace=True)
)
)
)
(deblocks): ModuleList(
(0): Sequential(
(0): ConvTranspose2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(1): Sequential(
(0): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
(2): Sequential(
(0): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(4, 4), bias=False)
(1): BatchNorm2d(128, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU()
)
)
(transformer): ModuleList(
(0): SwinTransformer(
(layers): ModuleList(
(0): BasicLayer(
(blocks): ModuleList(
(0): SwinTransformerBlock(
(norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=64, out_features=192, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=64, out_features=64, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=64, out_features=256, bias=True)
(act): GELU()
(fc2): Linear(in_features=256, out_features=64, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(1): SwinTransformerBlock(
(norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=64, out_features=192, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=64, out_features=64, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): DropPath(drop_prob=0.067)
(norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=64, out_features=256, bias=True)
(act): GELU()
(fc2): Linear(in_features=256, out_features=64, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(2): SwinTransformerBlock(
(norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=64, out_features=192, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=64, out_features=64, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): DropPath(drop_prob=0.133)
(norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=64, out_features=256, bias=True)
(act): GELU()
(fc2): Linear(in_features=256, out_features=64, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(3): SwinTransformerBlock(
(norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=64, out_features=192, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=64, out_features=64, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): DropPath(drop_prob=0.200)
(norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=64, out_features=256, bias=True)
(act): GELU()
(fc2): Linear(in_features=256, out_features=64, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
)
)
)
(norm0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
)
(1): SwinTransformer(
(layers): ModuleList(
(0): BasicLayer(
(blocks): ModuleList(
(0): SwinTransformerBlock(
(norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=128, out_features=384, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=128, out_features=128, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=128, out_features=512, bias=True)
(act): GELU()
(fc2): Linear(in_features=512, out_features=128, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(1): SwinTransformerBlock(
(norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=128, out_features=384, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=128, out_features=128, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): DropPath(drop_prob=0.067)
(norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=128, out_features=512, bias=True)
(act): GELU()
(fc2): Linear(in_features=512, out_features=128, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(2): SwinTransformerBlock(
(norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=128, out_features=384, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=128, out_features=128, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): DropPath(drop_prob=0.133)
(norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=128, out_features=512, bias=True)
(act): GELU()
(fc2): Linear(in_features=512, out_features=128, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(3): SwinTransformerBlock(
(norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=128, out_features=384, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=128, out_features=128, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): DropPath(drop_prob=0.200)
(norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=128, out_features=512, bias=True)
(act): GELU()
(fc2): Linear(in_features=512, out_features=128, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
)
)
)
(norm0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
)
(2): SwinTransformer(
(layers): ModuleList(
(0): BasicLayer(
(blocks): ModuleList(
(0): SwinTransformerBlock(
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=256, out_features=768, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=256, out_features=256, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): Identity()
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=256, out_features=1024, bias=True)
(act): GELU()
(fc2): Linear(in_features=1024, out_features=256, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(1): SwinTransformerBlock(
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=256, out_features=768, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=256, out_features=256, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): DropPath(drop_prob=0.067)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=256, out_features=1024, bias=True)
(act): GELU()
(fc2): Linear(in_features=1024, out_features=256, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(2): SwinTransformerBlock(
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=256, out_features=768, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=256, out_features=256, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): DropPath(drop_prob=0.133)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=256, out_features=1024, bias=True)
(act): GELU()
(fc2): Linear(in_features=1024, out_features=256, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
(3): SwinTransformerBlock(
(norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(attn): WindowAttention(
(qkv): Linear(in_features=256, out_features=768, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=256, out_features=256, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
(softmax): Softmax(dim=-1)
)
(drop_path): DropPath(drop_prob=0.200)
(norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=256, out_features=1024, bias=True)
(act): GELU()
(fc2): Linear(in_features=1024, out_features=256, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
)
)
)
)
(norm0): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
)
)
)
(dense_head): AnchorHeadRDIoU(
(cls_loss_func): SigmoidQualityFocalClassificationLoss()
(dir_loss_func): WeightedCrossEntropyLoss()
(conv_cls): Conv2d(384, 2, kernel_size=(1, 1), stride=(1, 1))
(conv_box): Conv2d(384, 14, kernel_size=(1, 1), stride=(1, 1))
(conv_dir_cls): Conv2d(384, 4, kernel_size=(1, 1), stride=(1, 1))
)
(point_head): None
(roi_head): None
)
)
2023-10-20 09:24:17,713 INFO **********************Start training kitti_models/pointpillar_mobilenetv3_onecat_label_3swin_rdiou_3cat(default)**********************
2023-10-20 09:24:30,837 INFO Train: 101/130 ( 78%) [ 0/464 ( 0%)] Loss: 0.5030 (0.503) LR: 9.681e-04 Time cost: 00:00/05:07 [00:13/2:33:34] Acc_iter 46401 Data time: 0.11(0.11) Forward time: 0.57(0.57) Batch time: 0.68(0.68)
2023-10-20 09:24:55,192 INFO Train: 101/130 ( 78%) [ 49/464 ( 11%)] Loss: 0.4905 (0.502) LR: 9.622e-04 Time cost: 00:25/03:27 [00:37/1:55:40] Acc_iter 46450 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.50)
2023-10-20 09:25:20,054 INFO Train: 101/130 ( 78%) [ 99/464 ( 21%)] Loss: 0.5658 (0.513) LR: 9.561e-04 Time cost: 00:49/03:02 [01:02/1:54:53] Acc_iter 46500 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.50)
2023-10-20 09:25:20,414 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:25:20 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 56°C, 99 % | 18408 / 24576 MB | kemove(18162M) root(168M)
[1] NVIDIA GeForce RTX 3090 | 53°C, 99 % | 18527 / 24576 MB | kemove(18510M) root(4M)
2023-10-20 09:25:45,344 INFO Train: 101/130 ( 78%) [ 149/464 ( 32%)] Loss: 0.5443 (0.516) LR: 9.500e-04 Time cost: 01:15/02:37 [01:27/1:55:00] Acc_iter 46550 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:26:10,278 INFO Train: 101/130 ( 78%) [ 199/464 ( 43%)] Loss: 0.4534 (0.519) LR: 9.440e-04 Time cost: 01:40/02:12 [01:52/1:54:27] Acc_iter 46600 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:26:35,193 INFO Train: 101/130 ( 78%) [ 249/464 ( 54%)] Loss: 0.4588 (0.520) LR: 9.379e-04 Time cost: 02:05/01:47 [02:17/1:53:56] Acc_iter 46650 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:26:35,556 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:26:35 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 62°C, 81 % | 18716 / 24576 MB | kemove(18480M) root(157M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 100 % | 18527 / 24576 MB | kemove(18510M) root(4M)
2023-10-20 09:27:00,478 INFO Train: 101/130 ( 78%) [ 299/464 ( 64%)] Loss: 0.5049 (0.520) LR: 9.319e-04 Time cost: 02:30/01:22 [02:42/1:53:44] Acc_iter 46700 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:27:25,370 INFO Train: 101/130 ( 78%) [ 349/464 ( 75%)] Loss: 0.5761 (0.521) LR: 9.259e-04 Time cost: 02:55/00:57 [03:07/1:53:13] Acc_iter 46750 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.50)
2023-10-20 09:27:50,327 INFO Train: 101/130 ( 78%) [ 399/464 ( 86%)] Loss: 0.5741 (0.523) LR: 9.199e-04 Time cost: 03:20/00:32 [03:32/1:52:45] Acc_iter 46800 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:27:50,691 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:27:50 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 64°C, 61 % | 18716 / 24576 MB | kemove(18480M) root(157M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 99 % | 18527 / 24576 MB | kemove(18510M) root(4M)
2023-10-20 09:28:15,597 INFO Train: 101/130 ( 78%) [ 449/464 ( 97%)] Loss: 0.5038 (0.522) LR: 9.139e-04 Time cost: 03:45/00:07 [03:57/1:52:28] Acc_iter 46850 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:28:22,565 INFO Train: 101/130 ( 78%) [ 463/464 (100%)] Loss: 0.5467 (0.522) LR: 9.122e-04 Time cost: 03:52/00:00 [04:04/1:52:19] Acc_iter 46864 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:28:29,879 INFO Train: 102/130 ( 78%) [ 0/464 ( 0%)] Loss: 0.5201 (0.520) LR: 9.121e-04 Time cost: 00:00/05:06 [04:12/2:28:21] Acc_iter 46865 Data time: 0.14(0.14) Forward time: 0.52(0.52) Batch time: 0.66(0.66)
2023-10-20 09:28:47,319 INFO Train: 102/130 ( 78%) [ 35/464 ( 8%)] Loss: 0.5009 (0.528) LR: 9.079e-04 Time cost: 00:18/03:35 [04:29/1:52:28] Acc_iter 46900 Data time: 0.00(0.01) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:29:12,285 INFO Train: 102/130 ( 78%) [ 85/464 ( 18%)] Loss: 0.5246 (0.527) LR: 9.019e-04 Time cost: 00:43/03:09 [04:54/1:51:35] Acc_iter 46950 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:29:12,649 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:29:12 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 56 % | 18763 / 24576 MB | kemove(18480M) root(173M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 98 % | 18527 / 24576 MB | kemove(18510M) root(4M)
2023-10-20 09:29:37,791 INFO Train: 102/130 ( 78%) [ 135/464 ( 29%)] Loss: 0.5523 (0.522) LR: 8.959e-04 Time cost: 01:08/02:45 [05:20/1:51:56] Acc_iter 47000 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:30:02,788 INFO Train: 102/130 ( 78%) [ 185/464 ( 40%)] Loss: 0.4232 (0.519) LR: 8.900e-04 Time cost: 01:33/02:20 [05:45/1:51:16] Acc_iter 47050 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:30:27,771 INFO Train: 102/130 ( 78%) [ 235/464 ( 51%)] Loss: 0.5893 (0.520) LR: 8.840e-04 Time cost: 01:58/01:55 [06:10/1:50:41] Acc_iter 47100 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:30:28,135 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:30:28 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 100 % | 18794 / 24576 MB | kemove(18480M) root(179M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 98 % | 18527 / 24576 MB | kemove(18510M) root(4M)
2023-10-20 09:30:53,601 INFO Train: 102/130 ( 78%) [ 285/464 ( 61%)] Loss: 0.4718 (0.521) LR: 8.781e-04 Time cost: 02:24/01:30 [06:35/1:50:49] Acc_iter 47150 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:31:18,498 INFO Train: 102/130 ( 78%) [ 335/464 ( 72%)] Loss: 0.4683 (0.519) LR: 8.722e-04 Time cost: 02:49/01:04 [07:00/1:50:10] Acc_iter 47200 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:31:43,454 INFO Train: 102/130 ( 78%) [ 385/464 ( 83%)] Loss: 0.5919 (0.521) LR: 8.663e-04 Time cost: 03:14/00:39 [07:25/1:49:37] Acc_iter 47250 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:31:43,817 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:31:43 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 66 % | 18796 / 24576 MB | kemove(18480M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 99 % | 18531 / 24576 MB | kemove(18514M) root(4M)
2023-10-20 09:32:08,942 INFO Train: 102/130 ( 78%) [ 435/464 ( 94%)] Loss: 0.5676 (0.520) LR: 8.604e-04 Time cost: 03:39/00:14 [07:51/1:49:21] Acc_iter 47300 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:32:22,908 INFO Train: 102/130 ( 78%) [ 463/464 (100%)] Loss: 0.4576 (0.520) LR: 8.571e-04 Time cost: 03:53/00:00 [08:05/1:49:03] Acc_iter 47328 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:32:30,854 INFO Train: 103/130 ( 79%) [ 0/464 ( 0%)] Loss: 0.5370 (0.537) LR: 8.570e-04 Time cost: 00:00/05:03 [08:13/2:21:28] Acc_iter 47329 Data time: 0.11(0.11) Forward time: 0.53(0.53) Batch time: 0.64(0.64)
2023-10-20 09:32:41,304 INFO Train: 103/130 ( 79%) [ 21/464 ( 5%)] Loss: 0.4794 (0.517) LR: 8.545e-04 Time cost: 00:11/03:43 [08:23/1:49:06] Acc_iter 47350 Data time: 0.00(0.01) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 09:33:06,304 INFO Train: 103/130 ( 79%) [ 71/464 ( 15%)] Loss: 0.5296 (0.517) LR: 8.486e-04 Time cost: 00:36/03:17 [08:48/1:47:59] Acc_iter 47400 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:33:06,670 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:33:06 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 100 % | 18813 / 24576 MB | kemove(18480M) root(197M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 99 % | 18531 / 24576 MB | kemove(18514M) root(4M)
2023-10-20 09:33:31,638 INFO Train: 103/130 ( 79%) [ 121/464 ( 26%)] Loss: 0.5115 (0.518) LR: 8.428e-04 Time cost: 01:01/02:52 [09:13/1:48:01] Acc_iter 47450 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:33:57,702 INFO Train: 103/130 ( 79%) [ 171/464 ( 37%)] Loss: 0.5480 (0.518) LR: 8.369e-04 Time cost: 01:27/02:29 [09:39/1:48:42] Acc_iter 47500 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:34:23,248 INFO Train: 103/130 ( 79%) [ 221/464 ( 48%)] Loss: 0.4617 (0.518) LR: 8.311e-04 Time cost: 01:53/02:03 [10:05/1:48:23] Acc_iter 47550 Data time: 0.00(0.00) Forward time: 0.50(0.51) Batch time: 0.50(0.51)
2023-10-20 09:34:23,615 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:34:23 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 90 % | 18793 / 24576 MB | kemove(18480M) root(177M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 100 % | 18531 / 24576 MB | kemove(18514M) root(4M)
2023-10-20 09:34:50,322 INFO Train: 103/130 ( 79%) [ 271/464 ( 58%)] Loss: 0.4844 (0.518) LR: 8.253e-04 Time cost: 02:20/01:39 [10:32/1:49:13] Acc_iter 47600 Data time: 0.00(0.00) Forward time: 0.50(0.51) Batch time: 0.50(0.52)
2023-10-20 09:35:17,063 INFO Train: 103/130 ( 79%) [ 321/464 ( 69%)] Loss: 0.4858 (0.519) LR: 8.195e-04 Time cost: 02:46/01:14 [10:59/1:49:26] Acc_iter 47650 Data time: 0.00(0.00) Forward time: 0.50(0.51) Batch time: 0.50(0.52)
2023-10-20 09:35:42,349 INFO Train: 103/130 ( 79%) [ 371/464 ( 80%)] Loss: 0.5098 (0.519) LR: 8.137e-04 Time cost: 03:12/00:48 [11:24/1:48:39] Acc_iter 47700 Data time: 0.00(0.00) Forward time: 0.50(0.51) Batch time: 0.50(0.52)
2023-10-20 09:35:42,722 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:35:42 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 87 % | 18803 / 24576 MB | kemove(18480M) root(187M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 98 % | 18531 / 24576 MB | kemove(18514M) root(4M)
2023-10-20 09:36:07,667 INFO Train: 103/130 ( 79%) [ 421/464 ( 91%)] Loss: 0.5245 (0.519) LR: 8.079e-04 Time cost: 03:37/00:22 [11:49/1:47:58] Acc_iter 47750 Data time: 0.00(0.00) Forward time: 0.50(0.51) Batch time: 0.51(0.52)
2023-10-20 09:36:28,626 INFO Train: 103/130 ( 79%) [ 463/464 (100%)] Loss: 0.5075 (0.519) LR: 8.031e-04 Time cost: 03:58/00:00 [12:10/1:47:18] Acc_iter 47792 Data time: 0.00(0.00) Forward time: 0.49(0.51) Batch time: 0.49(0.51)
2023-10-20 09:36:35,991 INFO Train: 104/130 ( 80%) [ 0/464 ( 0%)] Loss: 0.4792 (0.479) LR: 8.029e-04 Time cost: 00:00/04:43 [12:18/2:07:40] Acc_iter 47793 Data time: 0.11(0.11) Forward time: 0.55(0.55) Batch time: 0.66(0.66)
2023-10-20 09:36:39,491 INFO Train: 104/130 ( 80%) [ 7/464 ( 2%)] Loss: 0.4862 (0.506) LR: 8.021e-04 Time cost: 00:04/03:54 [12:21/1:47:13] Acc_iter 47800 Data time: 0.00(0.02) Forward time: 0.50(0.51) Batch time: 0.50(0.52)
2023-10-20 09:37:04,468 INFO Train: 104/130 ( 80%) [ 57/464 ( 12%)] Loss: 0.5285 (0.523) LR: 7.964e-04 Time cost: 00:29/03:24 [12:46/1:44:14] Acc_iter 47850 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:37:04,852 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:37:04 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 64°C, 28 % | 18940 / 24576 MB | kemove(18480M) root(197M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 99 % | 18531 / 24576 MB | kemove(18514M) root(4M)
2023-10-20 09:37:29,819 INFO Train: 104/130 ( 80%) [ 107/464 ( 23%)] Loss: 0.5198 (0.518) LR: 7.906e-04 Time cost: 00:54/02:59 [13:12/1:44:20] Acc_iter 47900 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:37:54,724 INFO Train: 104/130 ( 80%) [ 157/464 ( 34%)] Loss: 0.5228 (0.517) LR: 7.849e-04 Time cost: 01:19/02:34 [13:37/1:43:32] Acc_iter 47950 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:38:19,715 INFO Train: 104/130 ( 80%) [ 207/464 ( 45%)] Loss: 0.4764 (0.517) LR: 7.792e-04 Time cost: 01:44/02:08 [14:02/1:43:00] Acc_iter 48000 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:38:20,081 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:38:20 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 64 % | 18914 / 24576 MB | kemove(18480M) root(197M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 99 % | 18531 / 24576 MB | kemove(18514M) root(4M)
2023-10-20 09:38:45,045 INFO Train: 104/130 ( 80%) [ 257/464 ( 55%)] Loss: 0.5653 (0.517) LR: 7.735e-04 Time cost: 02:09/01:44 [14:27/1:42:47] Acc_iter 48050 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:39:09,984 INFO Train: 104/130 ( 80%) [ 307/464 ( 66%)] Loss: 0.5533 (0.520) LR: 7.678e-04 Time cost: 02:34/01:18 [14:52/1:42:14] Acc_iter 48100 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:39:34,922 INFO Train: 104/130 ( 80%) [ 357/464 ( 77%)] Loss: 0.6156 (0.521) LR: 7.621e-04 Time cost: 02:59/00:53 [15:17/1:41:43] Acc_iter 48150 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 09:39:35,290 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:39:35 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 80 % | 18914 / 24576 MB | kemove(18480M) root(197M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 100 % | 18531 / 24576 MB | kemove(18514M) root(4M)
2023-10-20 09:40:00,241 INFO Train: 104/130 ( 80%) [ 407/464 ( 88%)] Loss: 0.5028 (0.522) LR: 7.565e-04 Time cost: 03:24/00:28 [15:42/1:41:26] Acc_iter 48200 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:40:25,168 INFO Train: 104/130 ( 80%) [ 457/464 ( 98%)] Loss: 0.5743 (0.521) LR: 7.508e-04 Time cost: 03:49/00:03 [16:07/1:40:56] Acc_iter 48250 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:40:28,141 INFO Train: 104/130 ( 80%) [ 463/464 (100%)] Loss: 0.6258 (0.522) LR: 7.501e-04 Time cost: 03:52/00:00 [16:10/1:40:52] Acc_iter 48256 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:40:35,436 INFO Train: 105/130 ( 81%) [ 0/464 ( 0%)] Loss: 0.5152 (0.515) LR: 7.500e-04 Time cost: 00:00/04:57 [16:17/2:08:47] Acc_iter 48257 Data time: 0.11(0.11) Forward time: 0.54(0.54) Batch time: 0.65(0.65)
2023-10-20 09:40:56,989 INFO Train: 105/130 ( 81%) [ 43/464 ( 9%)] Loss: 0.5243 (0.510) LR: 7.452e-04 Time cost: 00:22/03:32 [16:39/1:41:03] Acc_iter 48300 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:40:57,373 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:40:57 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 64°C, 28 % | 18908 / 24576 MB | kemove(18484M) root(187M)
[1] NVIDIA GeForce RTX 3090 | 57°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:41:22,767 INFO Train: 105/130 ( 81%) [ 93/464 ( 20%)] Loss: 0.5513 (0.514) LR: 7.396e-04 Time cost: 00:47/03:09 [17:05/1:41:49] Acc_iter 48350 Data time: 0.00(0.01) Forward time: 0.50(0.51) Batch time: 0.50(0.51)
2023-10-20 09:41:47,925 INFO Train: 105/130 ( 81%) [ 143/464 ( 31%)] Loss: 0.5735 (0.515) LR: 7.340e-04 Time cost: 01:13/02:43 [17:30/1:40:53] Acc_iter 48400 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:42:14,987 INFO Train: 105/130 ( 81%) [ 193/464 ( 42%)] Loss: 0.4781 (0.518) LR: 7.284e-04 Time cost: 01:40/02:19 [17:57/1:42:10] Acc_iter 48450 Data time: 0.00(0.00) Forward time: 0.50(0.51) Batch time: 0.50(0.52)
2023-10-20 09:42:15,353 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:42:15 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 40 % | 18902 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 98 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:42:40,965 INFO Train: 105/130 ( 81%) [ 243/464 ( 52%)] Loss: 0.4489 (0.517) LR: 7.228e-04 Time cost: 02:06/01:54 [18:23/1:41:52] Acc_iter 48500 Data time: 0.00(0.00) Forward time: 0.54(0.51) Batch time: 0.55(0.52)
2023-10-20 09:43:06,655 INFO Train: 105/130 ( 81%) [ 293/464 ( 63%)] Loss: 0.4768 (0.517) LR: 7.172e-04 Time cost: 02:31/01:28 [18:48/1:41:20] Acc_iter 48550 Data time: 0.00(0.00) Forward time: 0.50(0.51) Batch time: 0.50(0.52)
2023-10-20 09:43:31,815 INFO Train: 105/130 ( 81%) [ 343/464 ( 74%)] Loss: 0.5074 (0.516) LR: 7.117e-04 Time cost: 02:57/01:02 [19:14/1:40:31] Acc_iter 48600 Data time: 0.00(0.00) Forward time: 0.59(0.51) Batch time: 0.60(0.51)
2023-10-20 09:43:32,183 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:43:32 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 67 % | 18892 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:43:59,666 INFO Train: 105/130 ( 81%) [ 393/464 ( 85%)] Loss: 0.5203 (0.517) LR: 7.062e-04 Time cost: 03:24/00:36 [19:41/1:41:08] Acc_iter 48650 Data time: 0.00(0.00) Forward time: 0.49(0.52) Batch time: 0.49(0.52)
2023-10-20 09:44:25,426 INFO Train: 105/130 ( 81%) [ 443/464 ( 95%)] Loss: 0.4712 (0.516) LR: 7.006e-04 Time cost: 03:50/00:10 [20:07/1:40:36] Acc_iter 48700 Data time: 0.00(0.00) Forward time: 0.50(0.51) Batch time: 0.50(0.52)
2023-10-20 09:44:35,388 INFO Train: 105/130 ( 81%) [ 463/464 (100%)] Loss: 0.4838 (0.516) LR: 6.984e-04 Time cost: 04:00/00:00 [20:17/1:40:15] Acc_iter 48720 Data time: 0.00(0.00) Forward time: 0.50(0.51) Batch time: 0.50(0.52)
2023-10-20 09:44:42,745 INFO Train: 106/130 ( 82%) [ 0/464 ( 0%)] Loss: 0.5628 (0.563) LR: 6.983e-04 Time cost: 00:00/04:47 [20:25/1:59:39] Acc_iter 48721 Data time: 0.11(0.11) Forward time: 0.54(0.54) Batch time: 0.65(0.65)
2023-10-20 09:44:57,207 INFO Train: 106/130 ( 82%) [ 29/464 ( 6%)] Loss: 0.5005 (0.512) LR: 6.951e-04 Time cost: 00:15/03:38 [20:39/1:36:56] Acc_iter 48750 Data time: 0.00(0.01) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 09:44:57,575 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:44:57 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 63°C, 77 % | 18892 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 57°C, 100 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:45:22,938 INFO Train: 106/130 ( 82%) [ 79/464 ( 17%)] Loss: 0.4925 (0.515) LR: 6.896e-04 Time cost: 00:40/03:16 [21:05/1:37:57] Acc_iter 48800 Data time: 0.00(0.01) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:45:47,998 INFO Train: 106/130 ( 82%) [ 129/464 ( 28%)] Loss: 0.5323 (0.513) LR: 6.842e-04 Time cost: 01:05/02:49 [21:30/1:36:52] Acc_iter 48850 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:46:12,967 INFO Train: 106/130 ( 82%) [ 179/464 ( 39%)] Loss: 0.5411 (0.513) LR: 6.787e-04 Time cost: 01:30/02:23 [21:55/1:36:03] Acc_iter 48900 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:46:13,334 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:46:13 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 66°C, 70 % | 18874 / 24576 MB | kemove(18484M) root(191M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 98 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:46:38,294 INFO Train: 106/130 ( 82%) [ 229/464 ( 49%)] Loss: 0.4911 (0.512) LR: 6.733e-04 Time cost: 01:56/01:58 [22:20/1:35:43] Acc_iter 48950 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:47:03,270 INFO Train: 106/130 ( 82%) [ 279/464 ( 60%)] Loss: 0.3848 (0.512) LR: 6.679e-04 Time cost: 02:21/01:33 [22:45/1:35:06] Acc_iter 49000 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:47:28,264 INFO Train: 106/130 ( 82%) [ 329/464 ( 71%)] Loss: 0.4733 (0.512) LR: 6.625e-04 Time cost: 02:46/01:07 [23:10/1:34:34] Acc_iter 49050 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:47:28,643 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:47:28 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 29 % | 18874 / 24576 MB | kemove(18484M) root(191M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:47:53,824 INFO Train: 106/130 ( 82%) [ 379/464 ( 82%)] Loss: 0.5231 (0.511) LR: 6.571e-04 Time cost: 03:11/00:42 [23:36/1:34:20] Acc_iter 49100 Data time: 0.04(0.00) Forward time: 0.53(0.50) Batch time: 0.57(0.50)
2023-10-20 09:48:20,411 INFO Train: 106/130 ( 82%) [ 429/464 ( 92%)] Loss: 0.5748 (0.513) LR: 6.517e-04 Time cost: 03:38/00:17 [24:02/1:34:30] Acc_iter 49150 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.51)
2023-10-20 09:48:37,379 INFO Train: 106/130 ( 82%) [ 463/464 (100%)] Loss: 0.4720 (0.512) LR: 6.480e-04 Time cost: 03:55/00:00 [24:19/1:34:06] Acc_iter 49184 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:48:44,736 INFO Train: 107/130 ( 82%) [ 0/464 ( 0%)] Loss: 0.5616 (0.562) LR: 6.479e-04 Time cost: 00:00/05:14 [24:27/2:05:46] Acc_iter 49185 Data time: 0.15(0.15) Forward time: 0.53(0.53) Batch time: 0.68(0.68)
2023-10-20 09:48:52,218 INFO Train: 107/130 ( 82%) [ 15/464 ( 3%)] Loss: 0.5959 (0.537) LR: 6.463e-04 Time cost: 00:08/03:48 [24:34/1:34:31] Acc_iter 49200 Data time: 0.00(0.01) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:48:52,586 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:48:52 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 62°C, 100 % | 18865 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 56°C, 98 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:49:17,500 INFO Train: 107/130 ( 82%) [ 65/464 ( 14%)] Loss: 0.5784 (0.517) LR: 6.410e-04 Time cost: 00:33/03:22 [24:59/1:33:29] Acc_iter 49250 Data time: 0.00(0.01) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:49:42,481 INFO Train: 107/130 ( 82%) [ 115/464 ( 25%)] Loss: 0.4962 (0.517) LR: 6.356e-04 Time cost: 00:58/02:55 [25:24/1:32:30] Acc_iter 49300 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:50:07,456 INFO Train: 107/130 ( 82%) [ 165/464 ( 36%)] Loss: 0.5179 (0.519) LR: 6.303e-04 Time cost: 01:23/02:30 [25:49/1:31:51] Acc_iter 49350 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:50:07,822 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:50:07 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 66°C, 53 % | 18776 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:50:32,779 INFO Train: 107/130 ( 82%) [ 215/464 ( 46%)] Loss: 0.5054 (0.519) LR: 6.250e-04 Time cost: 01:48/02:05 [26:15/1:31:36] Acc_iter 49400 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:50:57,729 INFO Train: 107/130 ( 82%) [ 265/464 ( 57%)] Loss: 0.4930 (0.518) LR: 6.198e-04 Time cost: 02:13/01:40 [26:40/1:31:02] Acc_iter 49450 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:51:24,115 INFO Train: 107/130 ( 82%) [ 315/464 ( 68%)] Loss: 0.5191 (0.517) LR: 6.145e-04 Time cost: 02:40/01:15 [27:06/1:31:20] Acc_iter 49500 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:51:24,484 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:51:24 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 95 % | 18776 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:51:49,775 INFO Train: 107/130 ( 82%) [ 365/464 ( 79%)] Loss: 0.4987 (0.516) LR: 6.092e-04 Time cost: 03:05/00:50 [27:32/1:31:05] Acc_iter 49550 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:52:14,726 INFO Train: 107/130 ( 82%) [ 415/464 ( 89%)] Loss: 0.4895 (0.515) LR: 6.040e-04 Time cost: 03:30/00:24 [27:57/1:30:29] Acc_iter 49600 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:52:38,672 INFO Train: 107/130 ( 82%) [ 463/464 (100%)] Loss: 0.4843 (0.514) LR: 5.990e-04 Time cost: 03:54/00:00 [28:20/1:29:56] Acc_iter 49648 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.51)
2023-10-20 09:52:45,992 INFO Train: 108/130 ( 83%) [ 0/464 ( 0%)] Loss: 0.5060 (0.506) LR: 5.989e-04 Time cost: 00:00/04:33 [28:28/1:44:52] Acc_iter 49649 Data time: 0.08(0.08) Forward time: 0.55(0.55) Batch time: 0.63(0.63)
2023-10-20 09:52:46,493 INFO Train: 108/130 ( 83%) [ 1/464 ( 0%)] Loss: 0.5591 (0.533) LR: 5.988e-04 Time cost: 00:01/04:12 [28:28/1:36:54] Acc_iter 49650 Data time: 0.00(0.04) Forward time: 0.50(0.52) Batch time: 0.50(0.57)
2023-10-20 09:52:46,862 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:52:46 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 61°C, 97 % | 18776 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 55°C, 100 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:53:11,779 INFO Train: 108/130 ( 83%) [ 51/464 ( 11%)] Loss: 0.4811 (0.509) LR: 5.936e-04 Time cost: 00:26/03:29 [28:54/1:29:47] Acc_iter 49700 Data time: 0.00(0.01) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 09:53:36,750 INFO Train: 108/130 ( 83%) [ 101/464 ( 22%)] Loss: 0.4841 (0.510) LR: 5.884e-04 Time cost: 00:51/03:02 [29:19/1:28:41] Acc_iter 49750 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:54:01,722 INFO Train: 108/130 ( 83%) [ 151/464 ( 33%)] Loss: 0.4809 (0.508) LR: 5.833e-04 Time cost: 01:16/02:37 [29:44/1:28:02] Acc_iter 49800 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.50)
2023-10-20 09:54:02,090 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:54:02 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 100 % | 18742 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 98 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:54:27,071 INFO Train: 108/130 ( 83%) [ 201/464 ( 43%)] Loss: 0.4994 (0.505) LR: 5.781e-04 Time cost: 01:41/02:12 [30:09/1:27:50] Acc_iter 49850 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.51(0.50)
2023-10-20 09:54:52,034 INFO Train: 108/130 ( 83%) [ 251/464 ( 54%)] Loss: 0.4883 (0.504) LR: 5.730e-04 Time cost: 02:06/01:47 [30:34/1:27:16] Acc_iter 49900 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 09:55:17,027 INFO Train: 108/130 ( 83%) [ 301/464 ( 65%)] Loss: 0.5371 (0.504) LR: 5.679e-04 Time cost: 02:31/01:21 [30:59/1:26:46] Acc_iter 49950 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.50)
2023-10-20 09:55:17,393 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:55:17 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 89 % | 18742 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 100 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:55:42,329 INFO Train: 108/130 ( 83%) [ 351/464 ( 76%)] Loss: 0.5403 (0.506) LR: 5.628e-04 Time cost: 02:56/00:56 [31:24/1:26:27] Acc_iter 50000 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.50)
2023-10-20 09:56:07,318 INFO Train: 108/130 ( 83%) [ 401/464 ( 86%)] Loss: 0.5008 (0.508) LR: 5.577e-04 Time cost: 03:21/00:31 [31:49/1:25:58] Acc_iter 50050 Data time: 0.00(0.00) Forward time: 0.51(0.50) Batch time: 0.51(0.50)
2023-10-20 09:56:32,272 INFO Train: 108/130 ( 83%) [ 451/464 ( 97%)] Loss: 0.4970 (0.508) LR: 5.527e-04 Time cost: 03:46/00:06 [32:14/1:25:30] Acc_iter 50100 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:56:32,673 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:56:32 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 24 % | 18742 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:56:38,643 INFO Train: 108/130 ( 83%) [ 463/464 (100%)] Loss: 0.6039 (0.508) LR: 5.515e-04 Time cost: 03:53/00:00 [32:20/1:25:31] Acc_iter 50112 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:56:45,966 INFO Train: 109/130 ( 84%) [ 0/464 ( 0%)] Loss: 0.5620 (0.562) LR: 5.514e-04 Time cost: 00:00/04:41 [32:28/1:43:03] Acc_iter 50113 Data time: 0.11(0.11) Forward time: 0.52(0.52) Batch time: 0.63(0.63)
2023-10-20 09:57:04,420 INFO Train: 109/130 ( 84%) [ 37/464 ( 8%)] Loss: 0.4943 (0.503) LR: 5.476e-04 Time cost: 00:19/03:34 [32:46/1:25:01] Acc_iter 50150 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:57:29,404 INFO Train: 109/130 ( 84%) [ 87/464 ( 19%)] Loss: 0.5062 (0.507) LR: 5.426e-04 Time cost: 00:44/03:08 [33:11/1:24:25] Acc_iter 50200 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:57:54,344 INFO Train: 109/130 ( 84%) [ 137/464 ( 30%)] Loss: 0.5903 (0.507) LR: 5.376e-04 Time cost: 01:08/02:43 [33:36/1:23:54] Acc_iter 50250 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:57:54,711 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:57:54 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 66°C, 74 % | 18742 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 98 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:58:19,652 INFO Train: 109/130 ( 84%) [ 187/464 ( 40%)] Loss: 0.4504 (0.507) LR: 5.326e-04 Time cost: 01:34/02:18 [34:01/1:23:46] Acc_iter 50300 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:58:44,584 INFO Train: 109/130 ( 84%) [ 237/464 ( 51%)] Loss: 0.5002 (0.507) LR: 5.277e-04 Time cost: 01:59/01:53 [34:26/1:23:14] Acc_iter 50350 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:59:09,540 INFO Train: 109/130 ( 84%) [ 287/464 ( 62%)] Loss: 0.4479 (0.507) LR: 5.227e-04 Time cost: 02:24/01:28 [34:51/1:22:46] Acc_iter 50400 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:59:09,905 INFO kemove-Z790-D-DDR4 Fri Oct 20 09:59:09 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 66°C, 83 % | 18733 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 100 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 09:59:34,883 INFO Train: 109/130 ( 84%) [ 337/464 ( 73%)] Loss: 0.5408 (0.507) LR: 5.178e-04 Time cost: 02:49/01:03 [35:17/1:22:30] Acc_iter 50450 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 09:59:59,838 INFO Train: 109/130 ( 84%) [ 387/464 ( 83%)] Loss: 0.4672 (0.507) LR: 5.129e-04 Time cost: 03:14/00:38 [35:42/1:22:02] Acc_iter 50500 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 10:00:24,842 INFO Train: 109/130 ( 84%) [ 437/464 ( 94%)] Loss: 0.5052 (0.507) LR: 5.080e-04 Time cost: 03:39/00:13 [36:07/1:21:36] Acc_iter 50550 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:00:25,211 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:00:25 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 47 % | 18733 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 10:00:38,154 INFO Train: 109/130 ( 84%) [ 463/464 (100%)] Loss: 0.4987 (0.506) LR: 5.054e-04 Time cost: 03:52/00:00 [36:20/1:21:29] Acc_iter 50576 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:00:45,503 INFO Train: 110/130 ( 85%) [ 0/464 ( 0%)] Loss: 0.5816 (0.582) LR: 5.053e-04 Time cost: 00:00/04:51 [36:27/1:41:57] Acc_iter 50577 Data time: 0.11(0.11) Forward time: 0.56(0.56) Batch time: 0.67(0.67)
2023-10-20 10:00:56,949 INFO Train: 110/130 ( 85%) [ 23/464 ( 5%)] Loss: 0.5515 (0.497) LR: 5.031e-04 Time cost: 00:12/03:41 [36:39/1:21:30] Acc_iter 50600 Data time: 0.00(0.01) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:01:21,882 INFO Train: 110/130 ( 85%) [ 73/464 ( 16%)] Loss: 0.5244 (0.500) LR: 4.982e-04 Time cost: 00:37/03:15 [37:04/1:20:36] Acc_iter 50650 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 10:01:46,900 INFO Train: 110/130 ( 85%) [ 123/464 ( 27%)] Loss: 0.6647 (0.501) LR: 4.934e-04 Time cost: 01:02/02:50 [37:29/1:20:12] Acc_iter 50700 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:01:47,269 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:01:47 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 66°C, 89 % | 18733 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 100 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 10:02:12,214 INFO Train: 110/130 ( 85%) [ 173/464 ( 37%)] Loss: 0.5318 (0.505) LR: 4.886e-04 Time cost: 01:27/02:26 [37:54/1:20:04] Acc_iter 50750 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:02:37,150 INFO Train: 110/130 ( 85%) [ 223/464 ( 48%)] Loss: 0.5517 (0.503) LR: 4.838e-04 Time cost: 01:52/02:00 [38:19/1:19:32] Acc_iter 50800 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 10:03:02,100 INFO Train: 110/130 ( 85%) [ 273/464 ( 59%)] Loss: 0.5440 (0.504) LR: 4.790e-04 Time cost: 02:17/01:35 [38:44/1:19:03] Acc_iter 50850 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.50)
2023-10-20 10:03:02,470 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:03:02 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 66°C, 99 % | 18733 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 10:03:27,372 INFO Train: 110/130 ( 85%) [ 323/464 ( 70%)] Loss: 0.4587 (0.504) LR: 4.743e-04 Time cost: 02:42/01:10 [39:09/1:18:44] Acc_iter 50900 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:03:52,387 INFO Train: 110/130 ( 85%) [ 373/464 ( 80%)] Loss: 0.5054 (0.505) LR: 4.695e-04 Time cost: 03:07/00:45 [39:34/1:18:18] Acc_iter 50950 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:04:17,374 INFO Train: 110/130 ( 85%) [ 423/464 ( 91%)] Loss: 0.5108 (0.505) LR: 4.648e-04 Time cost: 03:32/00:20 [39:59/1:17:51] Acc_iter 51000 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:04:17,743 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:04:17 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 46 % | 18733 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 10:04:37,721 INFO Train: 110/130 ( 85%) [ 463/464 (100%)] Loss: 0.5672 (0.505) LR: 4.610e-04 Time cost: 03:52/00:00 [40:20/1:17:37] Acc_iter 51040 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:04:45,092 INFO Train: 111/130 ( 85%) [ 0/464 ( 0%)] Loss: 0.4617 (0.462) LR: 4.609e-04 Time cost: 00:00/04:59 [40:27/1:39:47] Acc_iter 51041 Data time: 0.09(0.09) Forward time: 0.54(0.54) Batch time: 0.63(0.63)
2023-10-20 10:04:49,589 INFO Train: 111/130 ( 85%) [ 9/464 ( 2%)] Loss: 0.4583 (0.483) LR: 4.601e-04 Time cost: 00:05/03:53 [40:31/1:19:27] Acc_iter 51050 Data time: 0.00(0.01) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 10:05:14,472 INFO Train: 111/130 ( 85%) [ 59/464 ( 13%)] Loss: 0.4763 (0.489) LR: 4.554e-04 Time cost: 00:30/03:22 [40:56/1:16:54] Acc_iter 51100 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:05:39,384 INFO Train: 111/130 ( 85%) [ 109/464 ( 23%)] Loss: 0.5408 (0.496) LR: 4.508e-04 Time cost: 00:54/02:57 [41:21/1:16:20] Acc_iter 51150 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 10:05:39,751 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:05:39 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 63 % | 18733 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 10:06:04,699 INFO Train: 111/130 ( 85%) [ 159/464 ( 34%)] Loss: 0.4681 (0.499) LR: 4.461e-04 Time cost: 01:20/02:32 [41:46/1:16:14] Acc_iter 51200 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 10:06:29,673 INFO Train: 111/130 ( 85%) [ 209/464 ( 45%)] Loss: 0.4719 (0.501) LR: 4.415e-04 Time cost: 01:45/02:07 [42:11/1:15:45] Acc_iter 51250 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:06:54,649 INFO Train: 111/130 ( 85%) [ 259/464 ( 56%)] Loss: 0.4788 (0.502) LR: 4.369e-04 Time cost: 02:10/01:42 [42:36/1:15:17] Acc_iter 51300 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:06:55,018 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:06:55 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 66°C, 100 % | 18733 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 99 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 10:07:19,972 INFO Train: 111/130 ( 85%) [ 309/464 ( 67%)] Loss: 0.5275 (0.501) LR: 4.323e-04 Time cost: 02:35/01:17 [43:02/1:15:00] Acc_iter 51350 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.50)
2023-10-20 10:07:44,926 INFO Train: 111/130 ( 85%) [ 359/464 ( 77%)] Loss: 0.4280 (0.501) LR: 4.277e-04 Time cost: 03:00/00:52 [43:27/1:14:32] Acc_iter 51400 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:08:09,884 INFO Train: 111/130 ( 85%) [ 409/464 ( 88%)] Loss: 0.4610 (0.502) LR: 4.232e-04 Time cost: 03:25/00:27 [43:52/1:14:04] Acc_iter 51450 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:08:10,256 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:08:10 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 35 % | 18733 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 98 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 10:08:35,241 INFO Train: 111/130 ( 85%) [ 459/464 ( 99%)] Loss: 0.5276 (0.502) LR: 4.187e-04 Time cost: 03:50/00:02 [44:17/1:13:45] Acc_iter 51500 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:08:37,225 INFO Train: 111/130 ( 85%) [ 463/464 (100%)] Loss: 0.4562 (0.501) LR: 4.183e-04 Time cost: 03:52/00:00 [44:19/1:13:43] Acc_iter 51504 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.50)
2023-10-20 10:08:44,599 INFO Train: 112/130 ( 86%) [ 0/464 ( 0%)] Loss: 0.4095 (0.410) LR: 4.182e-04 Time cost: 00:00/04:35 [44:26/1:27:22] Acc_iter 51505 Data time: 0.08(0.08) Forward time: 0.56(0.56) Batch time: 0.65(0.65)
2023-10-20 10:09:07,034 INFO Train: 112/130 ( 86%) [ 45/464 ( 10%)] Loss: 0.4646 (0.504) LR: 4.142e-04 Time cost: 00:23/03:29 [44:49/1:13:11] Acc_iter 51550 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 10:09:31,961 INFO Train: 112/130 ( 86%) [ 95/464 ( 20%)] Loss: 0.5592 (0.501) LR: 4.097e-04 Time cost: 00:47/03:04 [45:14/1:12:36] Acc_iter 51600 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:09:32,336 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:09:32 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 85 % | 18745 / 24576 MB | kemove(18484M) root(181M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 100 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 10:09:57,338 INFO Train: 112/130 ( 86%) [ 145/464 ( 31%)] Loss: 0.4704 (0.500) LR: 4.052e-04 Time cost: 01:13/02:40 [45:39/1:12:35] Acc_iter 51650 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:10:22,506 INFO Train: 112/130 ( 86%) [ 195/464 ( 42%)] Loss: 0.4880 (0.500) LR: 4.008e-04 Time cost: 01:38/02:15 [46:04/1:12:12] Acc_iter 51700 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.49(0.50)
2023-10-20 10:10:47,473 INFO Train: 112/130 ( 86%) [ 245/464 ( 53%)] Loss: 0.4544 (0.501) LR: 3.964e-04 Time cost: 02:03/01:49 [46:29/1:11:41] Acc_iter 51750 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.51(0.50)
2023-10-20 10:10:47,842 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:10:47 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 52 % | 18778 / 24576 MB | kemove(18484M) root(182M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 98 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 10:11:12,832 INFO Train: 112/130 ( 86%) [ 295/464 ( 64%)] Loss: 0.5463 (0.501) LR: 3.920e-04 Time cost: 02:28/01:24 [46:55/1:11:24] Acc_iter 51800 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:11:38,179 INFO Train: 112/130 ( 86%) [ 345/464 ( 74%)] Loss: 0.4924 (0.502) LR: 3.876e-04 Time cost: 02:54/00:59 [47:20/1:11:04] Acc_iter 51850 Data time: 0.00(0.00) Forward time: 0.51(0.50) Batch time: 0.51(0.50)
2023-10-20 10:12:03,183 INFO Train: 112/130 ( 86%) [ 395/464 ( 85%)] Loss: 0.4560 (0.502) LR: 3.832e-04 Time cost: 03:19/00:34 [47:45/1:10:35] Acc_iter 51900 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:12:03,555 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:12:03 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 66°C, 100 % | 20928 / 24576 MB | kemove(18484M) root(222M)
[1] NVIDIA GeForce RTX 3090 | 59°C, 98 % | 18849 / 24576 MB | kemove(18832M) root(4M)
2023-10-20 10:12:28,522 INFO Train: 112/130 ( 86%) [ 445/464 ( 96%)] Loss: 0.5391 (0.502) LR: 3.789e-04 Time cost: 03:44/00:09 [48:10/1:10:13] Acc_iter 51950 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:12:37,506 INFO Train: 112/130 ( 86%) [ 463/464 (100%)] Loss: 0.5659 (0.502) LR: 3.773e-04 Time cost: 03:53/00:00 [48:19/1:10:03] Acc_iter 51968 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.50)
2023-10-20 10:12:44,904 INFO Train: 113/130 ( 87%) [ 0/464 ( 0%)] Loss: 0.4967 (0.497) LR: 3.773e-04 Time cost: 00:00/05:18 [48:27/1:35:36] Acc_iter 51969 Data time: 0.09(0.09) Forward time: 0.56(0.56) Batch time: 0.65(0.65)
2023-10-20 10:13:00,412 INFO Train: 113/130 ( 87%) [ 31/464 ( 7%)] Loss: 0.4689 (0.495) LR: 3.746e-04 Time cost: 00:16/03:39 [48:42/1:10:11] Acc_iter 52000 Data time: 0.00(0.00) Forward time: 0.50(0.50) Batch time: 0.50(0.51)
2023-10-20 10:13:25,440 INFO Train: 113/130 ( 87%) [ 81/464 ( 17%)] Loss: 0.4969 (0.496) LR: 3.703e-04 Time cost: 00:41/03:12 [49:07/1:09:18] Acc_iter 52050 Data time: 0.00(0.00) Forward time: 0.49(0.50) Batch time: 0.50(0.50)
2023-10-20 10:13:25,810 INFO kemove-Z790-D-DDR4 Fri Oct 20 10:13:25 2023 535.54.03
[0] NVIDIA GeForce RTX 3090 | 65°C, 60 % | 21064 / 24576 MB | kemove(18484M) root(261M)
[1] NVIDIA GeForce RTX 3090 | 58°C, 98 % | 18849 / 24576 MB | kemove(18832M) root(4M)