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[2023-10-22 22:16:19,290::train::INFO] [train] Iter 562590 | loss 0.3558 | loss(rot) 0.1384 | loss(pos) 0.1230 | loss(seq) 0.0944 | grad 4.0769 | lr 0.0000 | time_forward 2.4280 | time_backward 3.2240 |
[2023-10-22 22:16:28,095::train::INFO] [train] Iter 562591 | loss 0.2740 | loss(rot) 0.0385 | loss(pos) 0.2297 | loss(seq) 0.0057 | grad 5.3019 | lr 0.0000 | time_forward 3.8820 | time_backward 4.9190 |
[2023-10-22 22:16:36,424::train::INFO] [train] Iter 562592 | loss 0.2200 | loss(rot) 0.0788 | loss(pos) 0.0357 | loss(seq) 0.1055 | grad 2.5988 | lr 0.0000 | time_forward 3.6170 | time_backward 4.7080 |
[2023-10-22 22:16:45,414::train::INFO] [train] Iter 562593 | loss 0.7682 | loss(rot) 0.2023 | loss(pos) 0.1742 | loss(seq) 0.3918 | grad 5.4629 | lr 0.0000 | time_forward 3.7470 | time_backward 5.2400 |
[2023-10-22 22:16:52,655::train::INFO] [train] Iter 562594 | loss 0.4681 | loss(rot) 0.1988 | loss(pos) 0.0885 | loss(seq) 0.1807 | grad 4.1781 | lr 0.0000 | time_forward 3.0150 | time_backward 4.2220 |
[2023-10-22 22:16:58,159::train::INFO] [train] Iter 562595 | loss 1.6045 | loss(rot) 1.4508 | loss(pos) 0.0276 | loss(seq) 0.1261 | grad 7.3740 | lr 0.0000 | time_forward 2.3330 | time_backward 3.1670 |
[2023-10-22 22:17:01,024::train::INFO] [train] Iter 562596 | loss 0.7578 | loss(rot) 0.7233 | loss(pos) 0.0309 | loss(seq) 0.0036 | grad 2.1863 | lr 0.0000 | time_forward 1.3310 | time_backward 1.5310 |
[2023-10-22 22:17:03,407::train::INFO] [train] Iter 562597 | loss 3.8211 | loss(rot) 0.0876 | loss(pos) 3.7335 | loss(seq) 0.0000 | grad 21.7022 | lr 0.0000 | time_forward 1.0930 | time_backward 1.2090 |
[2023-10-22 22:17:12,354::train::INFO] [train] Iter 562598 | loss 0.8327 | loss(rot) 0.1402 | loss(pos) 0.5796 | loss(seq) 0.1129 | grad 4.4695 | lr 0.0000 | time_forward 3.9340 | time_backward 5.0090 |
[2023-10-22 22:17:21,699::train::INFO] [train] Iter 562599 | loss 1.0677 | loss(rot) 0.8507 | loss(pos) 0.0425 | loss(seq) 0.1744 | grad 5.1586 | lr 0.0000 | time_forward 3.5570 | time_backward 5.7840 |
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