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[2023-10-22 18:18:17,395::train::INFO] [train] Iter 560492 | loss 0.6528 | loss(rot) 0.3261 | loss(pos) 0.0297 | loss(seq) 0.2971 | grad 5.6240 | lr 0.0000 | time_forward 2.9430 | time_backward 3.7830 |
[2023-10-22 18:18:26,702::train::INFO] [train] Iter 560493 | loss 0.7981 | loss(rot) 0.0594 | loss(pos) 0.6160 | loss(seq) 0.1227 | grad 6.0307 | lr 0.0000 | time_forward 3.8370 | time_backward 5.4660 |
[2023-10-22 18:18:35,104::train::INFO] [train] Iter 560494 | loss 1.5733 | loss(rot) 0.9823 | loss(pos) 0.1609 | loss(seq) 0.4301 | grad 4.3194 | lr 0.0000 | time_forward 3.6090 | time_backward 4.7900 |
[2023-10-22 18:18:42,875::train::INFO] [train] Iter 560495 | loss 1.5383 | loss(rot) 0.0053 | loss(pos) 1.5323 | loss(seq) 0.0007 | grad 14.0189 | lr 0.0000 | time_forward 3.2870 | time_backward 4.4810 |
[2023-10-22 18:18:52,981::train::INFO] [train] Iter 560496 | loss 1.3913 | loss(rot) 0.9763 | loss(pos) 0.1887 | loss(seq) 0.2262 | grad 19.1700 | lr 0.0000 | time_forward 4.2510 | time_backward 5.8530 |
[2023-10-22 18:19:03,018::train::INFO] [train] Iter 560497 | loss 0.9096 | loss(rot) 0.8773 | loss(pos) 0.0232 | loss(seq) 0.0091 | grad 11.5533 | lr 0.0000 | time_forward 4.1370 | time_backward 5.8960 |
[2023-10-22 18:19:13,022::train::INFO] [train] Iter 560498 | loss 1.6119 | loss(rot) 1.3572 | loss(pos) 0.1143 | loss(seq) 0.1404 | grad 2.7171 | lr 0.0000 | time_forward 4.2720 | time_backward 5.7280 |
[2023-10-22 18:19:21,409::train::INFO] [train] Iter 560499 | loss 0.3832 | loss(rot) 0.3483 | loss(pos) 0.0348 | loss(seq) 0.0000 | grad 4.2561 | lr 0.0000 | time_forward 3.6450 | time_backward 4.7400 |
[2023-10-22 18:19:30,565::train::INFO] [train] Iter 560500 | loss 0.4425 | loss(rot) 0.4050 | loss(pos) 0.0375 | loss(seq) 0.0000 | grad 4.4234 | lr 0.0000 | time_forward 3.7300 | time_backward 5.4220 |
[2023-10-22 18:19:38,349::train::INFO] [train] Iter 560501 | loss 2.0282 | loss(rot) 1.5062 | loss(pos) 0.1504 | loss(seq) 0.3715 | grad 11.1806 | lr 0.0000 | time_forward 3.3920 | time_backward 4.3890 |
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