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[2023-10-25 03:42:36,117::train::INFO] [train] Iter 591161 | loss 0.7057 | loss(rot) 0.1364 | loss(pos) 0.5181 | loss(seq) 0.0512 | grad 6.7437 | lr 0.0000 | time_forward 3.1540 | time_backward 4.2390 |
[2023-10-25 03:42:45,185::train::INFO] [train] Iter 591162 | loss 0.6649 | loss(rot) 0.4752 | loss(pos) 0.0386 | loss(seq) 0.1511 | grad 2.3399 | lr 0.0000 | time_forward 3.8150 | time_backward 5.2500 |
[2023-10-25 03:42:51,834::train::INFO] [train] Iter 591163 | loss 0.4716 | loss(rot) 0.2233 | loss(pos) 0.0200 | loss(seq) 0.2283 | grad 3.5002 | lr 0.0000 | time_forward 2.9050 | time_backward 3.7400 |
[2023-10-25 03:43:00,688::train::INFO] [train] Iter 591164 | loss 0.4267 | loss(rot) 0.0427 | loss(pos) 0.3392 | loss(seq) 0.0448 | grad 8.2135 | lr 0.0000 | time_forward 3.6450 | time_backward 5.2060 |
[2023-10-25 03:43:08,137::train::INFO] [train] Iter 591165 | loss 0.3817 | loss(rot) 0.1542 | loss(pos) 0.1930 | loss(seq) 0.0344 | grad 4.5488 | lr 0.0000 | time_forward 3.1910 | time_backward 4.2540 |
[2023-10-25 03:43:16,294::train::INFO] [train] Iter 591166 | loss 0.6535 | loss(rot) 0.3108 | loss(pos) 0.0430 | loss(seq) 0.2996 | grad 3.8653 | lr 0.0000 | time_forward 3.4860 | time_backward 4.6690 |
[2023-10-25 03:43:23,742::train::INFO] [train] Iter 591167 | loss 0.1689 | loss(rot) 0.1294 | loss(pos) 0.0395 | loss(seq) 0.0000 | grad 2.0694 | lr 0.0000 | time_forward 3.2050 | time_backward 4.2400 |
[2023-10-25 03:43:31,475::train::INFO] [train] Iter 591168 | loss 1.6062 | loss(rot) 0.0540 | loss(pos) 1.5516 | loss(seq) 0.0005 | grad 17.9794 | lr 0.0000 | time_forward 3.3510 | time_backward 4.3780 |
[2023-10-25 03:43:37,921::train::INFO] [train] Iter 591169 | loss 0.1351 | loss(rot) 0.0964 | loss(pos) 0.0384 | loss(seq) 0.0003 | grad 1.9435 | lr 0.0000 | time_forward 2.7040 | time_backward 3.7390 |
[2023-10-25 03:43:46,266::train::INFO] [train] Iter 591170 | loss 0.2289 | loss(rot) 0.0860 | loss(pos) 0.1264 | loss(seq) 0.0165 | grad 4.3198 | lr 0.0000 | time_forward 3.5690 | time_backward 4.7730 |
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