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[2023-10-22 14:03:40,507::train::INFO] [train] Iter 558394 | loss 0.8425 | loss(rot) 0.2862 | loss(pos) 0.5279 | loss(seq) 0.0284 | grad 5.7531 | lr 0.0000 | time_forward 1.3940 | time_backward 1.4510 |
[2023-10-22 14:03:43,389::train::INFO] [train] Iter 558395 | loss 0.1632 | loss(rot) 0.1307 | loss(pos) 0.0198 | loss(seq) 0.0126 | grad 6.3096 | lr 0.0000 | time_forward 1.4020 | time_backward 1.4750 |
[2023-10-22 14:03:51,393::train::INFO] [train] Iter 558396 | loss 1.2194 | loss(rot) 1.1627 | loss(pos) 0.0566 | loss(seq) 0.0001 | grad 9.5446 | lr 0.0000 | time_forward 3.4640 | time_backward 4.5360 |
[2023-10-22 14:03:54,198::train::INFO] [train] Iter 558397 | loss 1.0429 | loss(rot) 0.9963 | loss(pos) 0.0450 | loss(seq) 0.0016 | grad 11.6668 | lr 0.0000 | time_forward 1.3040 | time_backward 1.4980 |
[2023-10-22 14:04:03,816::train::INFO] [train] Iter 558398 | loss 1.2368 | loss(rot) 1.2128 | loss(pos) 0.0230 | loss(seq) 0.0010 | grad 3.2188 | lr 0.0000 | time_forward 3.8890 | time_backward 5.7250 |
[2023-10-22 14:04:13,652::train::INFO] [train] Iter 558399 | loss 0.8461 | loss(rot) 0.3744 | loss(pos) 0.0167 | loss(seq) 0.4550 | grad 3.8545 | lr 0.0000 | time_forward 3.9630 | time_backward 5.8690 |
[2023-10-22 14:04:16,558::train::INFO] [train] Iter 558400 | loss 0.7667 | loss(rot) 0.0489 | loss(pos) 0.7094 | loss(seq) 0.0084 | grad 7.9186 | lr 0.0000 | time_forward 1.3960 | time_backward 1.5060 |
[2023-10-22 14:04:19,628::train::INFO] [train] Iter 558401 | loss 0.1500 | loss(rot) 0.1364 | loss(pos) 0.0131 | loss(seq) 0.0005 | grad 3.6141 | lr 0.0000 | time_forward 1.4280 | time_backward 1.6380 |
[2023-10-22 14:04:22,635::train::INFO] [train] Iter 558402 | loss 0.2860 | loss(rot) 0.0698 | loss(pos) 0.1503 | loss(seq) 0.0659 | grad 4.3764 | lr 0.0000 | time_forward 1.3910 | time_backward 1.6120 |
[2023-10-22 14:04:32,334::train::INFO] [train] Iter 558403 | loss 0.8792 | loss(rot) 0.2968 | loss(pos) 0.3630 | loss(seq) 0.2193 | grad 3.7341 | lr 0.0000 | time_forward 3.9800 | time_backward 5.7150 |
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