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[2023-10-24 09:28:05,055::train::INFO] [train] Iter 581671 | loss 0.1779 | loss(rot) 0.1566 | loss(pos) 0.0144 | loss(seq) 0.0069 | grad 1.7929 | lr 0.0000 | time_forward 4.0290 | time_backward 5.8880 |
[2023-10-24 09:28:07,900::train::INFO] [train] Iter 581672 | loss 0.1380 | loss(rot) 0.1097 | loss(pos) 0.0178 | loss(seq) 0.0105 | grad 2.6746 | lr 0.0000 | time_forward 1.3660 | time_backward 1.4750 |
[2023-10-24 09:28:10,805::train::INFO] [train] Iter 581673 | loss 0.7533 | loss(rot) 0.4132 | loss(pos) 0.1286 | loss(seq) 0.2115 | grad 3.5496 | lr 0.0000 | time_forward 1.3870 | time_backward 1.4800 |
[2023-10-24 09:28:20,849::train::INFO] [train] Iter 581674 | loss 0.5252 | loss(rot) 0.2373 | loss(pos) 0.0237 | loss(seq) 0.2642 | grad 2.7004 | lr 0.0000 | time_forward 4.0810 | time_backward 5.9600 |
[2023-10-24 09:28:23,706::train::INFO] [train] Iter 581675 | loss 0.3140 | loss(rot) 0.1968 | loss(pos) 0.0299 | loss(seq) 0.0872 | grad 2.7779 | lr 0.0000 | time_forward 1.3540 | time_backward 1.5000 |
[2023-10-24 09:28:31,776::train::INFO] [train] Iter 581676 | loss 0.2853 | loss(rot) 0.0434 | loss(pos) 0.2394 | loss(seq) 0.0026 | grad 5.7918 | lr 0.0000 | time_forward 3.3780 | time_backward 4.6520 |
[2023-10-24 09:28:34,595::train::INFO] [train] Iter 581677 | loss 1.0913 | loss(rot) 1.0442 | loss(pos) 0.0202 | loss(seq) 0.0270 | grad 4.8133 | lr 0.0000 | time_forward 1.3310 | time_backward 1.4840 |
[2023-10-24 09:28:44,771::train::INFO] [train] Iter 581678 | loss 1.5546 | loss(rot) 1.0722 | loss(pos) 0.0566 | loss(seq) 0.4258 | grad 3.1818 | lr 0.0000 | time_forward 4.3460 | time_backward 5.7860 |
[2023-10-24 09:28:54,689::train::INFO] [train] Iter 581679 | loss 0.4492 | loss(rot) 0.2159 | loss(pos) 0.0394 | loss(seq) 0.1939 | grad 5.9388 | lr 0.0000 | time_forward 4.0490 | time_backward 5.8660 |
[2023-10-24 09:29:04,489::train::INFO] [train] Iter 581680 | loss 0.2432 | loss(rot) 0.1538 | loss(pos) 0.0257 | loss(seq) 0.0637 | grad 1.9193 | lr 0.0000 | time_forward 3.9720 | time_backward 5.8240 |
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