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[2023-10-25 02:23:57,852::train::INFO] [train] Iter 590462 | loss 0.7687 | loss(rot) 0.3578 | loss(pos) 0.0803 | loss(seq) 0.3306 | grad 4.4739 | lr 0.0000 | time_forward 3.6590 | time_backward 5.2460 |
[2023-10-25 02:24:06,670::train::INFO] [train] Iter 590463 | loss 0.9566 | loss(rot) 0.8870 | loss(pos) 0.0248 | loss(seq) 0.0448 | grad 4.9203 | lr 0.0000 | time_forward 3.6060 | time_backward 5.2090 |
[2023-10-25 02:24:09,376::train::INFO] [train] Iter 590464 | loss 0.6613 | loss(rot) 0.5565 | loss(pos) 0.0715 | loss(seq) 0.0333 | grad 2.6573 | lr 0.0000 | time_forward 1.2730 | time_backward 1.4300 |
[2023-10-25 02:24:12,186::train::INFO] [train] Iter 590465 | loss 1.6720 | loss(rot) 1.3938 | loss(pos) 0.0538 | loss(seq) 0.2244 | grad 4.1838 | lr 0.0000 | time_forward 1.3370 | time_backward 1.4480 |
[2023-10-25 02:24:19,073::train::INFO] [train] Iter 590466 | loss 0.6751 | loss(rot) 0.6476 | loss(pos) 0.0203 | loss(seq) 0.0071 | grad 8.0989 | lr 0.0000 | time_forward 2.9120 | time_backward 3.9470 |
[2023-10-25 02:24:26,588::train::INFO] [train] Iter 590467 | loss 0.1118 | loss(rot) 0.0794 | loss(pos) 0.0271 | loss(seq) 0.0053 | grad 1.8426 | lr 0.0000 | time_forward 3.2180 | time_backward 4.2940 |
[2023-10-25 02:24:33,759::train::INFO] [train] Iter 590468 | loss 0.4347 | loss(rot) 0.0642 | loss(pos) 0.2871 | loss(seq) 0.0834 | grad 5.5307 | lr 0.0000 | time_forward 3.0230 | time_backward 4.1450 |
[2023-10-25 02:24:40,946::train::INFO] [train] Iter 590469 | loss 0.7152 | loss(rot) 0.6654 | loss(pos) 0.0262 | loss(seq) 0.0236 | grad 32.6784 | lr 0.0000 | time_forward 3.0440 | time_backward 4.1410 |
[2023-10-25 02:24:49,256::train::INFO] [train] Iter 590470 | loss 0.4744 | loss(rot) 0.1203 | loss(pos) 0.3223 | loss(seq) 0.0318 | grad 4.1108 | lr 0.0000 | time_forward 3.4690 | time_backward 4.8370 |
[2023-10-25 02:24:57,117::train::INFO] [train] Iter 590471 | loss 0.3112 | loss(rot) 0.0962 | loss(pos) 0.1544 | loss(seq) 0.0606 | grad 2.1279 | lr 0.0000 | time_forward 3.3510 | time_backward 4.5070 |
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