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[2023-10-23 13:44:49,836::train::INFO] [train] Iter 571481 | loss 1.6396 | loss(rot) 1.5995 | loss(pos) 0.0385 | loss(seq) 0.0016 | grad 6.2339 | lr 0.0000 | time_forward 3.1830 | time_backward 4.1070 |
[2023-10-23 13:44:57,006::train::INFO] [train] Iter 571482 | loss 1.8038 | loss(rot) 1.3693 | loss(pos) 0.0515 | loss(seq) 0.3829 | grad 10.6803 | lr 0.0000 | time_forward 3.0950 | time_backward 4.0710 |
[2023-10-23 13:45:03,713::train::INFO] [train] Iter 571483 | loss 0.2423 | loss(rot) 0.1061 | loss(pos) 0.1316 | loss(seq) 0.0046 | grad 4.3160 | lr 0.0000 | time_forward 2.8710 | time_backward 3.8340 |
[2023-10-23 13:45:12,177::train::INFO] [train] Iter 571484 | loss 1.3313 | loss(rot) 0.6030 | loss(pos) 0.2184 | loss(seq) 0.5100 | grad 4.5965 | lr 0.0000 | time_forward 3.5110 | time_backward 4.9500 |
[2023-10-23 13:45:14,960::train::INFO] [train] Iter 571485 | loss 0.2156 | loss(rot) 0.0494 | loss(pos) 0.0379 | loss(seq) 0.1283 | grad 1.7451 | lr 0.0000 | time_forward 1.3570 | time_backward 1.4220 |
[2023-10-23 13:45:23,745::train::INFO] [train] Iter 571486 | loss 0.2136 | loss(rot) 0.1904 | loss(pos) 0.0229 | loss(seq) 0.0003 | grad 2.2006 | lr 0.0000 | time_forward 3.6440 | time_backward 5.1380 |
[2023-10-23 13:45:30,707::train::INFO] [train] Iter 571487 | loss 0.1838 | loss(rot) 0.1013 | loss(pos) 0.0254 | loss(seq) 0.0571 | grad 1.5424 | lr 0.0000 | time_forward 2.9620 | time_backward 3.9960 |
[2023-10-23 13:45:34,002::train::INFO] [train] Iter 571488 | loss 0.7981 | loss(rot) 0.0715 | loss(pos) 0.6468 | loss(seq) 0.0798 | grad 4.8911 | lr 0.0000 | time_forward 1.4410 | time_backward 1.8520 |
[2023-10-23 13:45:40,271::train::INFO] [train] Iter 571489 | loss 0.4700 | loss(rot) 0.0730 | loss(pos) 0.0642 | loss(seq) 0.3328 | grad 3.3553 | lr 0.0000 | time_forward 2.7220 | time_backward 3.5430 |
[2023-10-23 13:45:43,449::train::INFO] [train] Iter 571490 | loss 0.9337 | loss(rot) 0.0649 | loss(pos) 0.8672 | loss(seq) 0.0017 | grad 5.9953 | lr 0.0000 | time_forward 1.4700 | time_backward 1.7050 |
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