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[2023-10-25 04:15:11,603::train::INFO] [train] Iter 591461 | loss 0.3610 | loss(rot) 0.3448 | loss(pos) 0.0151 | loss(seq) 0.0011 | grad 46.2426 | lr 0.0000 | time_forward 2.8210 | time_backward 3.7710 |
[2023-10-25 04:15:14,329::train::INFO] [train] Iter 591462 | loss 0.9996 | loss(rot) 0.4491 | loss(pos) 0.1867 | loss(seq) 0.3638 | grad 4.3524 | lr 0.0000 | time_forward 1.2760 | time_backward 1.4470 |
[2023-10-25 04:15:23,118::train::INFO] [train] Iter 591463 | loss 0.5054 | loss(rot) 0.0450 | loss(pos) 0.4421 | loss(seq) 0.0183 | grad 4.9669 | lr 0.0000 | time_forward 3.6250 | time_backward 5.1610 |
[2023-10-25 04:15:30,840::train::INFO] [train] Iter 591464 | loss 2.2093 | loss(rot) 1.8745 | loss(pos) 0.2671 | loss(seq) 0.0677 | grad 4.6473 | lr 0.0000 | time_forward 3.2660 | time_backward 4.4530 |
[2023-10-25 04:15:39,628::train::INFO] [train] Iter 591465 | loss 1.0325 | loss(rot) 0.8532 | loss(pos) 0.0279 | loss(seq) 0.1513 | grad 8.9992 | lr 0.0000 | time_forward 3.6080 | time_backward 5.1760 |
[2023-10-25 04:15:48,415::train::INFO] [train] Iter 591466 | loss 0.4900 | loss(rot) 0.4445 | loss(pos) 0.0455 | loss(seq) 0.0001 | grad 1.8832 | lr 0.0000 | time_forward 3.5900 | time_backward 5.1940 |
[2023-10-25 04:15:52,535::train::INFO] [train] Iter 591467 | loss 0.8844 | loss(rot) 0.4740 | loss(pos) 0.0953 | loss(seq) 0.3152 | grad 3.5728 | lr 0.0000 | time_forward 1.8950 | time_backward 2.2220 |
[2023-10-25 04:16:00,140::train::INFO] [train] Iter 591468 | loss 0.8714 | loss(rot) 0.5613 | loss(pos) 0.1404 | loss(seq) 0.1697 | grad 3.5148 | lr 0.0000 | time_forward 3.2070 | time_backward 4.3930 |
[2023-10-25 04:16:02,996::train::INFO] [train] Iter 591469 | loss 0.3003 | loss(rot) 0.0445 | loss(pos) 0.0214 | loss(seq) 0.2344 | grad 1.5418 | lr 0.0000 | time_forward 1.3020 | time_backward 1.5050 |
[2023-10-25 04:16:11,110::train::INFO] [train] Iter 591470 | loss 0.4185 | loss(rot) 0.0745 | loss(pos) 0.0942 | loss(seq) 0.2498 | grad 2.4918 | lr 0.0000 | time_forward 3.4810 | time_backward 4.6200 |
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