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[2023-10-22 19:27:42,223::train::INFO] [train] Iter 561091 | loss 0.6712 | loss(rot) 0.4014 | loss(pos) 0.2261 | loss(seq) 0.0438 | grad 3.4194 | lr 0.0000 | time_forward 3.4060 | time_backward 5.2020 |
[2023-10-22 19:27:52,099::train::INFO] [train] Iter 561092 | loss 0.5973 | loss(rot) 0.0637 | loss(pos) 0.5099 | loss(seq) 0.0237 | grad 6.0066 | lr 0.0000 | time_forward 4.2490 | time_backward 5.6230 |
[2023-10-22 19:28:01,927::train::INFO] [train] Iter 561093 | loss 0.8578 | loss(rot) 0.4049 | loss(pos) 0.0655 | loss(seq) 0.3875 | grad 4.0683 | lr 0.0000 | time_forward 4.2610 | time_backward 5.5630 |
[2023-10-22 19:28:11,953::train::INFO] [train] Iter 561094 | loss 0.6578 | loss(rot) 0.2652 | loss(pos) 0.1067 | loss(seq) 0.2860 | grad 3.3666 | lr 0.0000 | time_forward 4.4060 | time_backward 5.6150 |
[2023-10-22 19:28:19,369::train::INFO] [train] Iter 561095 | loss 2.3172 | loss(rot) 1.6814 | loss(pos) 0.1101 | loss(seq) 0.5257 | grad 5.5126 | lr 0.0000 | time_forward 3.1850 | time_backward 4.2270 |
[2023-10-22 19:28:28,446::train::INFO] [train] Iter 561096 | loss 2.4311 | loss(rot) 1.5679 | loss(pos) 0.3337 | loss(seq) 0.5295 | grad 6.6478 | lr 0.0000 | time_forward 3.8600 | time_backward 5.2150 |
[2023-10-22 19:28:36,673::train::INFO] [train] Iter 561097 | loss 0.7672 | loss(rot) 0.1968 | loss(pos) 0.5532 | loss(seq) 0.0172 | grad 5.0864 | lr 0.0000 | time_forward 3.3750 | time_backward 4.8490 |
[2023-10-22 19:28:45,819::train::INFO] [train] Iter 561098 | loss 1.9144 | loss(rot) 1.3992 | loss(pos) 0.2112 | loss(seq) 0.3040 | grad 6.6692 | lr 0.0000 | time_forward 3.7670 | time_backward 5.3760 |
[2023-10-22 19:28:55,962::train::INFO] [train] Iter 561099 | loss 1.5018 | loss(rot) 1.0550 | loss(pos) 0.1631 | loss(seq) 0.2837 | grad 3.7175 | lr 0.0000 | time_forward 3.8530 | time_backward 6.2860 |
[2023-10-22 19:29:05,139::train::INFO] [train] Iter 561100 | loss 0.3766 | loss(rot) 0.0540 | loss(pos) 0.0377 | loss(seq) 0.2848 | grad 2.4008 | lr 0.0000 | time_forward 3.8730 | time_backward 5.3010 |
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