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[2023-10-23 10:37:09,642::train::INFO] [train] Iter 569783 | loss 0.4370 | loss(rot) 0.1621 | loss(pos) 0.0450 | loss(seq) 0.2299 | grad 7.7205 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4750 |
[2023-10-23 10:37:18,751::train::INFO] [train] Iter 569784 | loss 1.0624 | loss(rot) 0.6983 | loss(pos) 0.0969 | loss(seq) 0.2671 | grad 3.3430 | lr 0.0000 | time_forward 3.6830 | time_backward 5.4230 |
[2023-10-23 10:37:27,098::train::INFO] [train] Iter 569785 | loss 1.3439 | loss(rot) 0.2589 | loss(pos) 1.0833 | loss(seq) 0.0018 | grad 7.1246 | lr 0.0000 | time_forward 3.5920 | time_backward 4.7510 |
[2023-10-23 10:37:36,128::train::INFO] [train] Iter 569786 | loss 0.2782 | loss(rot) 0.2127 | loss(pos) 0.0323 | loss(seq) 0.0332 | grad 2.8263 | lr 0.0000 | time_forward 3.6270 | time_backward 5.4000 |
[2023-10-23 10:37:43,728::train::INFO] [train] Iter 569787 | loss 0.7091 | loss(rot) 0.5342 | loss(pos) 0.0175 | loss(seq) 0.1574 | grad 2.0234 | lr 0.0000 | time_forward 3.2790 | time_backward 4.3190 |
[2023-10-23 10:37:50,552::train::INFO] [train] Iter 569788 | loss 0.7510 | loss(rot) 0.1837 | loss(pos) 0.0602 | loss(seq) 0.5071 | grad 4.8400 | lr 0.0000 | time_forward 2.9550 | time_backward 3.8660 |
[2023-10-23 10:37:59,728::train::INFO] [train] Iter 569789 | loss 0.5098 | loss(rot) 0.2196 | loss(pos) 0.1818 | loss(seq) 0.1084 | grad 3.7502 | lr 0.0000 | time_forward 3.7900 | time_backward 5.3830 |
[2023-10-23 10:38:08,721::train::INFO] [train] Iter 569790 | loss 0.6295 | loss(rot) 0.2219 | loss(pos) 0.2452 | loss(seq) 0.1623 | grad 4.6246 | lr 0.0000 | time_forward 3.8470 | time_backward 5.1430 |
[2023-10-23 10:38:17,761::train::INFO] [train] Iter 569791 | loss 0.9069 | loss(rot) 0.4070 | loss(pos) 0.0520 | loss(seq) 0.4478 | grad 3.6254 | lr 0.0000 | time_forward 3.6670 | time_backward 5.3700 |
[2023-10-23 10:38:20,561::train::INFO] [train] Iter 569792 | loss 0.4518 | loss(rot) 0.4258 | loss(pos) 0.0259 | loss(seq) 0.0000 | grad 3.3061 | lr 0.0000 | time_forward 1.3690 | time_backward 1.4260 |
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