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[2023-10-23 10:48:10,589::train::INFO] [train] Iter 569883 | loss 0.3685 | loss(rot) 0.1332 | loss(pos) 0.0326 | loss(seq) 0.2027 | grad 2.6323 | lr 0.0000 | time_forward 1.3350 | time_backward 1.4010 |
[2023-10-23 10:48:13,368::train::INFO] [train] Iter 569884 | loss 1.1681 | loss(rot) 0.7642 | loss(pos) 0.2027 | loss(seq) 0.2012 | grad 3.3285 | lr 0.0000 | time_forward 1.3500 | time_backward 1.4270 |
[2023-10-23 10:48:20,021::train::INFO] [train] Iter 569885 | loss 0.2994 | loss(rot) 0.2575 | loss(pos) 0.0346 | loss(seq) 0.0074 | grad 3.0120 | lr 0.0000 | time_forward 2.8860 | time_backward 3.7630 |
[2023-10-23 10:48:27,972::train::INFO] [train] Iter 569886 | loss 0.4416 | loss(rot) 0.2667 | loss(pos) 0.0769 | loss(seq) 0.0980 | grad 3.7662 | lr 0.0000 | time_forward 3.3990 | time_backward 4.5490 |
[2023-10-23 10:48:35,368::train::INFO] [train] Iter 569887 | loss 1.5315 | loss(rot) 0.9476 | loss(pos) 0.1682 | loss(seq) 0.4157 | grad 5.2002 | lr 0.0000 | time_forward 3.1740 | time_backward 4.2180 |
[2023-10-23 10:48:38,124::train::INFO] [train] Iter 569888 | loss 0.2351 | loss(rot) 0.0462 | loss(pos) 0.0190 | loss(seq) 0.1699 | grad 1.6962 | lr 0.0000 | time_forward 1.3210 | time_backward 1.4330 |
[2023-10-23 10:48:41,004::train::INFO] [train] Iter 569889 | loss 0.1600 | loss(rot) 0.1251 | loss(pos) 0.0269 | loss(seq) 0.0080 | grad 2.4900 | lr 0.0000 | time_forward 1.3210 | time_backward 1.5270 |
[2023-10-23 10:48:49,369::train::INFO] [train] Iter 569890 | loss 0.5043 | loss(rot) 0.4369 | loss(pos) 0.0558 | loss(seq) 0.0116 | grad 3.2374 | lr 0.0000 | time_forward 3.6100 | time_backward 4.7510 |
[2023-10-23 10:48:56,968::train::INFO] [train] Iter 569891 | loss 0.2092 | loss(rot) 0.0809 | loss(pos) 0.0138 | loss(seq) 0.1145 | grad 1.6303 | lr 0.0000 | time_forward 3.2240 | time_backward 4.3720 |
[2023-10-23 10:49:04,478::train::INFO] [train] Iter 569892 | loss 2.2710 | loss(rot) 2.2183 | loss(pos) 0.0462 | loss(seq) 0.0066 | grad 23.0913 | lr 0.0000 | time_forward 3.1180 | time_backward 4.3880 |
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