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[2023-09-02 01:30:52,406::train::INFO] [train] Iter 04481 | loss 1.9874 | loss(rot) 0.5669 | loss(pos) 0.6783 | loss(seq) 0.7422 | grad 4.8494 | lr 0.0010 | time_forward 3.4370 | time_backward 4.6730 |
[2023-09-02 01:30:55,108::train::INFO] [train] Iter 04482 | loss 3.2258 | loss(rot) 3.0548 | loss(pos) 0.1710 | loss(seq) 0.0000 | grad 5.5279 | lr 0.0010 | time_forward 1.2950 | time_backward 1.4030 |
[2023-09-02 01:31:04,889::train::INFO] [train] Iter 04483 | loss 2.9498 | loss(rot) 2.5685 | loss(pos) 0.2303 | loss(seq) 0.1511 | grad 4.0016 | lr 0.0010 | time_forward 4.0310 | time_backward 5.7460 |
[2023-09-02 01:31:07,168::train::INFO] [train] Iter 04484 | loss 1.1218 | loss(rot) 0.4348 | loss(pos) 0.6229 | loss(seq) 0.0641 | grad 4.3327 | lr 0.0010 | time_forward 1.0670 | time_backward 1.2090 |
[2023-09-02 01:31:10,639::train::INFO] [train] Iter 04485 | loss 2.8051 | loss(rot) 1.9189 | loss(pos) 0.3854 | loss(seq) 0.5008 | grad 3.4307 | lr 0.0010 | time_forward 1.4970 | time_backward 1.9700 |
[2023-09-02 01:31:19,403::train::INFO] [train] Iter 04486 | loss 1.2770 | loss(rot) 0.4938 | loss(pos) 0.2364 | loss(seq) 0.5468 | grad 4.7130 | lr 0.0010 | time_forward 3.7200 | time_backward 5.0420 |
[2023-09-02 01:31:22,132::train::INFO] [train] Iter 04487 | loss 2.6576 | loss(rot) 2.3459 | loss(pos) 0.2926 | loss(seq) 0.0191 | grad 4.8070 | lr 0.0010 | time_forward 1.2830 | time_backward 1.4410 |
[2023-09-02 01:31:24,879::train::INFO] [train] Iter 04488 | loss 1.3355 | loss(rot) 0.6420 | loss(pos) 0.2821 | loss(seq) 0.4113 | grad 5.4765 | lr 0.0010 | time_forward 1.3120 | time_backward 1.4310 |
[2023-09-02 01:31:27,172::train::INFO] [train] Iter 04489 | loss 2.2305 | loss(rot) 1.5835 | loss(pos) 0.2509 | loss(seq) 0.3960 | grad 3.8191 | lr 0.0010 | time_forward 1.0870 | time_backward 1.2020 |
[2023-09-02 01:31:36,287::train::INFO] [train] Iter 04490 | loss 1.7763 | loss(rot) 0.2349 | loss(pos) 1.5318 | loss(seq) 0.0095 | grad 5.1897 | lr 0.0010 | time_forward 3.8250 | time_backward 5.2860 |
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