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[2023-10-23 09:50:43,304::train::INFO] [train] Iter 569384 | loss 1.9141 | loss(rot) 1.8570 | loss(pos) 0.0551 | loss(seq) 0.0020 | grad 17.6913 | lr 0.0000 | time_forward 3.5410 | time_backward 4.8260 |
[2023-10-23 09:50:53,234::train::INFO] [train] Iter 569385 | loss 0.7840 | loss(rot) 0.5868 | loss(pos) 0.0428 | loss(seq) 0.1544 | grad 23.8781 | lr 0.0000 | time_forward 4.0530 | time_backward 5.8730 |
[2023-10-23 09:50:56,111::train::INFO] [train] Iter 569386 | loss 0.4509 | loss(rot) 0.3613 | loss(pos) 0.0248 | loss(seq) 0.0648 | grad 2.3627 | lr 0.0000 | time_forward 1.3360 | time_backward 1.5390 |
[2023-10-23 09:50:58,773::train::INFO] [train] Iter 569387 | loss 0.9028 | loss(rot) 0.3483 | loss(pos) 0.0945 | loss(seq) 0.4600 | grad 5.3554 | lr 0.0000 | time_forward 1.2480 | time_backward 1.3920 |
[2023-10-23 09:51:01,120::train::INFO] [train] Iter 569388 | loss 0.8547 | loss(rot) 0.3912 | loss(pos) 0.2102 | loss(seq) 0.2534 | grad 3.5642 | lr 0.0000 | time_forward 1.0800 | time_backward 1.2620 |
[2023-10-23 09:51:10,041::train::INFO] [train] Iter 569389 | loss 0.7530 | loss(rot) 0.7229 | loss(pos) 0.0297 | loss(seq) 0.0004 | grad 5.8724 | lr 0.0000 | time_forward 3.8570 | time_backward 5.0610 |
[2023-10-23 09:51:12,814::train::INFO] [train] Iter 569390 | loss 1.4176 | loss(rot) 1.2029 | loss(pos) 0.0395 | loss(seq) 0.1752 | grad 4.2446 | lr 0.0000 | time_forward 1.3180 | time_backward 1.4520 |
[2023-10-23 09:51:15,745::train::INFO] [train] Iter 569391 | loss 1.6711 | loss(rot) 1.1547 | loss(pos) 0.1097 | loss(seq) 0.4067 | grad 5.5773 | lr 0.0000 | time_forward 1.3510 | time_backward 1.5410 |
[2023-10-23 09:51:19,351::train::INFO] [train] Iter 569392 | loss 0.5907 | loss(rot) 0.3627 | loss(pos) 0.0350 | loss(seq) 0.1930 | grad 3.2408 | lr 0.0000 | time_forward 1.6330 | time_backward 1.9700 |
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