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[2023-09-01 21:49:31,009::train::INFO] [train] Iter 02683 | loss 1.9071 | loss(rot) 1.0126 | loss(pos) 0.5070 | loss(seq) 0.3875 | grad 4.9500 | lr 0.0010 | time_forward 4.1680 | time_backward 6.0730 |
[2023-09-01 21:49:39,672::train::INFO] [train] Iter 02684 | loss 2.0799 | loss(rot) 0.8094 | loss(pos) 1.1908 | loss(seq) 0.0797 | grad 6.8128 | lr 0.0010 | time_forward 3.7260 | time_backward 4.9310 |
[2023-09-01 21:49:48,726::train::INFO] [train] Iter 02685 | loss 2.0385 | loss(rot) 0.7386 | loss(pos) 0.6740 | loss(seq) 0.6259 | grad 3.7993 | lr 0.0010 | time_forward 3.8570 | time_backward 5.1950 |
[2023-09-01 21:49:57,041::train::INFO] [train] Iter 02686 | loss 1.6016 | loss(rot) 1.0747 | loss(pos) 0.3478 | loss(seq) 0.1791 | grad 3.7893 | lr 0.0010 | time_forward 3.5370 | time_backward 4.7750 |
[2023-09-01 21:50:05,119::train::INFO] [train] Iter 02687 | loss 2.4570 | loss(rot) 1.3362 | loss(pos) 0.6728 | loss(seq) 0.4480 | grad 7.2373 | lr 0.0010 | time_forward 3.2850 | time_backward 4.7900 |
[2023-09-01 21:50:13,705::train::INFO] [train] Iter 02688 | loss 1.1970 | loss(rot) 0.4235 | loss(pos) 0.3109 | loss(seq) 0.4626 | grad 4.4851 | lr 0.0010 | time_forward 3.6310 | time_backward 4.9510 |
[2023-09-01 21:50:16,399::train::INFO] [train] Iter 02689 | loss 3.5633 | loss(rot) 3.0666 | loss(pos) 0.4933 | loss(seq) 0.0034 | grad 6.0984 | lr 0.0010 | time_forward 1.2110 | time_backward 1.4800 |
[2023-09-01 21:50:24,474::train::INFO] [train] Iter 02690 | loss 3.5067 | loss(rot) 2.7993 | loss(pos) 0.4919 | loss(seq) 0.2155 | grad 6.9830 | lr 0.0010 | time_forward 3.3150 | time_backward 4.7570 |
[2023-09-01 21:50:27,178::train::INFO] [train] Iter 02691 | loss 2.1245 | loss(rot) 1.3856 | loss(pos) 0.2815 | loss(seq) 0.4573 | grad 4.1780 | lr 0.0010 | time_forward 1.2710 | time_backward 1.4290 |
[2023-09-01 21:50:30,390::train::INFO] [train] Iter 02692 | loss 1.9359 | loss(rot) 1.3656 | loss(pos) 0.2065 | loss(seq) 0.3638 | grad 4.1142 | lr 0.0010 | time_forward 1.4110 | time_backward 1.7980 |
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