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[2023-10-23 11:56:45,416::train::INFO] [train] Iter 570482 | loss 0.6192 | loss(rot) 0.4868 | loss(pos) 0.1014 | loss(seq) 0.0310 | grad 3.3692 | lr 0.0000 | time_forward 1.2410 | time_backward 1.4580 |
[2023-10-23 11:56:53,834::train::INFO] [train] Iter 570483 | loss 0.2691 | loss(rot) 0.2232 | loss(pos) 0.0454 | loss(seq) 0.0005 | grad 13.2825 | lr 0.0000 | time_forward 3.4910 | time_backward 4.9060 |
[2023-10-23 11:56:56,559::train::INFO] [train] Iter 570484 | loss 0.2075 | loss(rot) 0.0768 | loss(pos) 0.0627 | loss(seq) 0.0681 | grad 2.7990 | lr 0.0000 | time_forward 1.3010 | time_backward 1.4200 |
[2023-10-23 11:56:59,470::train::INFO] [train] Iter 570485 | loss 0.3311 | loss(rot) 0.3102 | loss(pos) 0.0204 | loss(seq) 0.0005 | grad 3.9100 | lr 0.0000 | time_forward 1.3410 | time_backward 1.5460 |
[2023-10-23 11:57:06,849::train::INFO] [train] Iter 570486 | loss 0.3901 | loss(rot) 0.1674 | loss(pos) 0.0158 | loss(seq) 0.2069 | grad 3.7226 | lr 0.0000 | time_forward 3.1880 | time_backward 4.1880 |
[2023-10-23 11:57:15,097::train::INFO] [train] Iter 570487 | loss 0.3112 | loss(rot) 0.0657 | loss(pos) 0.2273 | loss(seq) 0.0182 | grad 5.6247 | lr 0.0000 | time_forward 3.4530 | time_backward 4.7930 |
[2023-10-23 11:57:23,488::train::INFO] [train] Iter 570488 | loss 0.7848 | loss(rot) 0.4356 | loss(pos) 0.0866 | loss(seq) 0.2626 | grad 3.2288 | lr 0.0000 | time_forward 3.4900 | time_backward 4.8970 |
[2023-10-23 11:57:30,396::train::INFO] [train] Iter 570489 | loss 0.4000 | loss(rot) 0.1337 | loss(pos) 0.0214 | loss(seq) 0.2450 | grad 2.6035 | lr 0.0000 | time_forward 2.9590 | time_backward 3.9470 |
[2023-10-23 11:57:33,628::train::INFO] [train] Iter 570490 | loss 0.8224 | loss(rot) 0.4574 | loss(pos) 0.3255 | loss(seq) 0.0395 | grad 4.7389 | lr 0.0000 | time_forward 1.4680 | time_backward 1.7600 |
[2023-10-23 11:57:40,692::train::INFO] [train] Iter 570491 | loss 2.8127 | loss(rot) 2.1282 | loss(pos) 0.3295 | loss(seq) 0.3549 | grad 7.0647 | lr 0.0000 | time_forward 3.0230 | time_backward 4.0280 |
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