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[2023-10-25 02:01:45,720::train::INFO] [train] Iter 590262 | loss 0.5969 | loss(rot) 0.5691 | loss(pos) 0.0269 | loss(seq) 0.0009 | grad 2.5673 | lr 0.0000 | time_forward 2.7840 | time_backward 3.6700 |
[2023-10-25 02:01:54,687::train::INFO] [train] Iter 590263 | loss 1.1501 | loss(rot) 0.6162 | loss(pos) 0.2751 | loss(seq) 0.2588 | grad 3.5703 | lr 0.0000 | time_forward 3.6690 | time_backward 5.2950 |
[2023-10-25 02:02:03,637::train::INFO] [train] Iter 590264 | loss 0.1844 | loss(rot) 0.1495 | loss(pos) 0.0212 | loss(seq) 0.0137 | grad 2.0170 | lr 0.0000 | time_forward 3.6480 | time_backward 5.2990 |
[2023-10-25 02:02:10,782::train::INFO] [train] Iter 590265 | loss 0.4314 | loss(rot) 0.2119 | loss(pos) 0.0250 | loss(seq) 0.1946 | grad 3.5691 | lr 0.0000 | time_forward 3.0280 | time_backward 4.1130 |
[2023-10-25 02:02:12,516::train::INFO] [train] Iter 590266 | loss 1.1470 | loss(rot) 0.3408 | loss(pos) 0.7961 | loss(seq) 0.0101 | grad 9.6098 | lr 0.0000 | time_forward 0.7760 | time_backward 0.9550 |
[2023-10-25 02:02:15,140::train::INFO] [train] Iter 590267 | loss 0.5541 | loss(rot) 0.0237 | loss(pos) 0.5292 | loss(seq) 0.0012 | grad 7.3069 | lr 0.0000 | time_forward 1.1960 | time_backward 1.4250 |
[2023-10-25 02:02:23,993::train::INFO] [train] Iter 590268 | loss 1.1807 | loss(rot) 1.1521 | loss(pos) 0.0281 | loss(seq) 0.0006 | grad 6.0125 | lr 0.0000 | time_forward 3.6190 | time_backward 5.2050 |
[2023-10-25 02:02:32,910::train::INFO] [train] Iter 590269 | loss 0.4102 | loss(rot) 0.1029 | loss(pos) 0.3020 | loss(seq) 0.0052 | grad 3.9709 | lr 0.0000 | time_forward 3.6450 | time_backward 5.2690 |
[2023-10-25 02:02:40,523::train::INFO] [train] Iter 590270 | loss 0.1274 | loss(rot) 0.0953 | loss(pos) 0.0237 | loss(seq) 0.0084 | grad 1.9941 | lr 0.0000 | time_forward 3.2430 | time_backward 4.3670 |
[2023-10-25 02:02:48,086::train::INFO] [train] Iter 590271 | loss 0.6203 | loss(rot) 0.5164 | loss(pos) 0.0153 | loss(seq) 0.0886 | grad 2.3241 | lr 0.0000 | time_forward 3.2310 | time_backward 4.3280 |
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