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[2023-10-23 17:55:19,406::train::INFO] [train] Iter 573779 | loss 0.2240 | loss(rot) 0.1395 | loss(pos) 0.0339 | loss(seq) 0.0506 | grad 2.6484 | lr 0.0000 | time_forward 3.3610 | time_backward 4.2470 |
[2023-10-23 17:55:27,542::train::INFO] [train] Iter 573780 | loss 0.9838 | loss(rot) 0.6973 | loss(pos) 0.0380 | loss(seq) 0.2484 | grad 3.9717 | lr 0.0000 | time_forward 3.5640 | time_backward 4.5690 |
[2023-10-23 17:55:35,862::train::INFO] [train] Iter 573781 | loss 0.2254 | loss(rot) 0.0978 | loss(pos) 0.1084 | loss(seq) 0.0192 | grad 4.8777 | lr 0.0000 | time_forward 3.7630 | time_backward 4.5530 |
[2023-10-23 17:55:46,101::train::INFO] [train] Iter 573782 | loss 0.9314 | loss(rot) 0.2406 | loss(pos) 0.6076 | loss(seq) 0.0833 | grad 8.6809 | lr 0.0000 | time_forward 4.5740 | time_backward 5.6610 |
[2023-10-23 17:55:48,973::train::INFO] [train] Iter 573783 | loss 0.4092 | loss(rot) 0.0969 | loss(pos) 0.2493 | loss(seq) 0.0630 | grad 4.5292 | lr 0.0000 | time_forward 1.4480 | time_backward 1.4210 |
[2023-10-23 17:55:56,464::train::INFO] [train] Iter 573784 | loss 1.5386 | loss(rot) 0.8464 | loss(pos) 0.1788 | loss(seq) 0.5134 | grad 3.4514 | lr 0.0000 | time_forward 3.3870 | time_backward 4.1020 |
[2023-10-23 17:56:06,562::train::INFO] [train] Iter 573785 | loss 0.2091 | loss(rot) 0.0722 | loss(pos) 0.0349 | loss(seq) 0.1021 | grad 1.9485 | lr 0.0000 | time_forward 4.6860 | time_backward 5.4100 |
[2023-10-23 17:56:09,590::train::INFO] [train] Iter 573786 | loss 0.2356 | loss(rot) 0.1290 | loss(pos) 0.0270 | loss(seq) 0.0796 | grad 1.6255 | lr 0.0000 | time_forward 1.6050 | time_backward 1.4200 |
[2023-10-23 17:56:20,741::train::INFO] [train] Iter 573787 | loss 0.8065 | loss(rot) 0.0103 | loss(pos) 0.7955 | loss(seq) 0.0007 | grad 15.5301 | lr 0.0000 | time_forward 4.6590 | time_backward 6.4890 |
[2023-10-23 17:56:33,520::train::INFO] [train] Iter 573788 | loss 1.8669 | loss(rot) 1.3291 | loss(pos) 0.1564 | loss(seq) 0.3814 | grad 4.3358 | lr 0.0000 | time_forward 5.5120 | time_backward 7.2630 |
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