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[2023-10-23 00:21:53,469::train::INFO] [train] Iter 563789 | loss 0.4099 | loss(rot) 0.1335 | loss(pos) 0.0367 | loss(seq) 0.2396 | grad 2.9109 | lr 0.0000 | time_forward 3.3450 | time_backward 4.2690 |
[2023-10-23 00:22:00,410::train::INFO] [train] Iter 563790 | loss 0.4593 | loss(rot) 0.2037 | loss(pos) 0.1630 | loss(seq) 0.0925 | grad 3.8603 | lr 0.0000 | time_forward 2.9700 | time_backward 3.9680 |
[2023-10-23 00:22:02,863::train::INFO] [train] Iter 563791 | loss 1.3340 | loss(rot) 1.0389 | loss(pos) 0.0333 | loss(seq) 0.2618 | grad 4.2355 | lr 0.0000 | time_forward 1.2150 | time_backward 1.2340 |
[2023-10-23 00:22:09,733::train::INFO] [train] Iter 563792 | loss 0.5894 | loss(rot) 0.0089 | loss(pos) 0.5802 | loss(seq) 0.0003 | grad 8.8005 | lr 0.0000 | time_forward 2.9040 | time_backward 3.9510 |
[2023-10-23 00:22:16,097::train::INFO] [train] Iter 563793 | loss 0.7529 | loss(rot) 0.3352 | loss(pos) 0.0305 | loss(seq) 0.3872 | grad 3.8948 | lr 0.0000 | time_forward 2.7310 | time_backward 3.6290 |
[2023-10-23 00:22:24,214::train::INFO] [train] Iter 563794 | loss 1.0160 | loss(rot) 0.7714 | loss(pos) 0.0711 | loss(seq) 0.1735 | grad 7.0618 | lr 0.0000 | time_forward 3.5300 | time_backward 4.5840 |
[2023-10-23 00:22:31,901::train::INFO] [train] Iter 563795 | loss 1.4115 | loss(rot) 0.4793 | loss(pos) 0.1585 | loss(seq) 0.7736 | grad 5.3847 | lr 0.0000 | time_forward 3.4280 | time_backward 4.2560 |
[2023-10-23 00:22:38,966::train::INFO] [train] Iter 563796 | loss 0.7009 | loss(rot) 0.3724 | loss(pos) 0.0472 | loss(seq) 0.2813 | grad 3.2955 | lr 0.0000 | time_forward 2.9390 | time_backward 4.1230 |
[2023-10-23 00:22:46,468::train::INFO] [train] Iter 563797 | loss 0.3368 | loss(rot) 0.0325 | loss(pos) 0.3019 | loss(seq) 0.0025 | grad 7.6000 | lr 0.0000 | time_forward 3.2280 | time_backward 4.2710 |
[2023-10-23 00:22:54,589::train::INFO] [train] Iter 563798 | loss 0.5482 | loss(rot) 0.1114 | loss(pos) 0.1152 | loss(seq) 0.3216 | grad 3.0139 | lr 0.0000 | time_forward 3.3210 | time_backward 4.7970 |
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