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[2023-10-23 18:18:12,021::train::INFO] [train] Iter 573979 | loss 0.5409 | loss(rot) 0.5127 | loss(pos) 0.0280 | loss(seq) 0.0002 | grad 5.7093 | lr 0.0000 | time_forward 3.5070 | time_backward 4.6760 |
[2023-10-23 18:18:14,748::train::INFO] [train] Iter 573980 | loss 0.6604 | loss(rot) 0.3642 | loss(pos) 0.0240 | loss(seq) 0.2722 | grad 5.4767 | lr 0.0000 | time_forward 1.3090 | time_backward 1.4160 |
[2023-10-23 18:18:23,681::train::INFO] [train] Iter 573981 | loss 0.6245 | loss(rot) 0.5526 | loss(pos) 0.0214 | loss(seq) 0.0505 | grad 4.7550 | lr 0.0000 | time_forward 3.6540 | time_backward 5.2750 |
[2023-10-23 18:18:26,913::train::INFO] [train] Iter 573982 | loss 0.9390 | loss(rot) 0.3380 | loss(pos) 0.1421 | loss(seq) 0.4589 | grad 3.3505 | lr 0.0000 | time_forward 1.4600 | time_backward 1.7690 |
[2023-10-23 18:18:35,675::train::INFO] [train] Iter 573983 | loss 1.1096 | loss(rot) 1.0886 | loss(pos) 0.0185 | loss(seq) 0.0025 | grad 4.7766 | lr 0.0000 | time_forward 3.6120 | time_backward 5.1360 |
[2023-10-23 18:18:38,414::train::INFO] [train] Iter 573984 | loss 0.6793 | loss(rot) 0.3040 | loss(pos) 0.3005 | loss(seq) 0.0747 | grad 4.3759 | lr 0.0000 | time_forward 1.2790 | time_backward 1.4560 |
[2023-10-23 18:18:45,867::train::INFO] [train] Iter 573985 | loss 0.1787 | loss(rot) 0.1028 | loss(pos) 0.0447 | loss(seq) 0.0313 | grad 2.6934 | lr 0.0000 | time_forward 3.1740 | time_backward 4.2760 |
[2023-10-23 18:18:52,874::train::INFO] [train] Iter 573986 | loss 1.0169 | loss(rot) 0.9841 | loss(pos) 0.0263 | loss(seq) 0.0065 | grad 5.2671 | lr 0.0000 | time_forward 2.9720 | time_backward 4.0330 |
[2023-10-23 18:19:00,330::train::INFO] [train] Iter 573987 | loss 0.3435 | loss(rot) 0.0859 | loss(pos) 0.0212 | loss(seq) 0.2364 | grad 2.6362 | lr 0.0000 | time_forward 3.1280 | time_backward 4.3240 |
[2023-10-23 18:19:09,246::train::INFO] [train] Iter 573988 | loss 0.2505 | loss(rot) 0.1159 | loss(pos) 0.0260 | loss(seq) 0.1085 | grad 2.1210 | lr 0.0000 | time_forward 3.6790 | time_backward 5.2330 |
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