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[2023-10-24 06:06:19,946::train::INFO] [train] Iter 579973 | loss 2.3278 | loss(rot) 2.0340 | loss(pos) 0.1902 | loss(seq) 0.1036 | grad 6.3909 | lr 0.0000 | time_forward 3.9280 | time_backward 5.3810 |
[2023-10-24 06:06:28,306::train::INFO] [train] Iter 579974 | loss 1.4848 | loss(rot) 0.1474 | loss(pos) 1.3161 | loss(seq) 0.0213 | grad 9.8044 | lr 0.0000 | time_forward 3.5280 | time_backward 4.8280 |
[2023-10-24 06:06:32,674::train::INFO] [train] Iter 579975 | loss 5.4937 | loss(rot) 0.0714 | loss(pos) 5.4222 | loss(seq) 0.0000 | grad 17.7671 | lr 0.0000 | time_forward 1.9660 | time_backward 2.3990 |
[2023-10-24 06:06:39,577::train::INFO] [train] Iter 579976 | loss 0.5600 | loss(rot) 0.5241 | loss(pos) 0.0325 | loss(seq) 0.0034 | grad 79.2933 | lr 0.0000 | time_forward 2.9030 | time_backward 3.9960 |
[2023-10-24 06:06:42,376::train::INFO] [train] Iter 579977 | loss 0.3899 | loss(rot) 0.2910 | loss(pos) 0.0089 | loss(seq) 0.0900 | grad 3.6034 | lr 0.0000 | time_forward 1.3610 | time_backward 1.4340 |
[2023-10-24 06:06:50,161::train::INFO] [train] Iter 579978 | loss 1.0506 | loss(rot) 0.0033 | loss(pos) 1.0471 | loss(seq) 0.0002 | grad 12.9896 | lr 0.0000 | time_forward 3.4100 | time_backward 4.3710 |
[2023-10-24 06:06:52,826::train::INFO] [train] Iter 579979 | loss 4.2053 | loss(rot) 0.0023 | loss(pos) 4.2030 | loss(seq) 0.0000 | grad 41.9743 | lr 0.0000 | time_forward 1.2530 | time_backward 1.4090 |
[2023-10-24 06:07:01,385::train::INFO] [train] Iter 579980 | loss 0.1681 | loss(rot) 0.1296 | loss(pos) 0.0271 | loss(seq) 0.0114 | grad 2.7224 | lr 0.0000 | time_forward 3.6240 | time_backward 4.9310 |
[2023-10-24 06:07:07,369::train::INFO] [train] Iter 579981 | loss 0.4801 | loss(rot) 0.1755 | loss(pos) 0.0226 | loss(seq) 0.2819 | grad 2.7742 | lr 0.0000 | time_forward 2.5310 | time_backward 3.4500 |
[2023-10-24 06:07:10,163::train::INFO] [train] Iter 579982 | loss 0.3478 | loss(rot) 0.1186 | loss(pos) 0.0296 | loss(seq) 0.1996 | grad 2.3354 | lr 0.0000 | time_forward 1.3130 | time_backward 1.4780 |
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