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[2023-10-25 00:35:21,651::train::INFO] [train] Iter 589463 | loss 0.3359 | loss(rot) 0.1240 | loss(pos) 0.0238 | loss(seq) 0.1881 | grad 2.3968 | lr 0.0000 | time_forward 3.7330 | time_backward 5.2690 |
[2023-10-25 00:35:28,927::train::INFO] [train] Iter 589464 | loss 1.7341 | loss(rot) 1.6792 | loss(pos) 0.0490 | loss(seq) 0.0059 | grad 25.2999 | lr 0.0000 | time_forward 3.0680 | time_backward 4.2050 |
[2023-10-25 00:35:36,111::train::INFO] [train] Iter 589465 | loss 2.9784 | loss(rot) 0.0011 | loss(pos) 2.9773 | loss(seq) 0.0000 | grad 12.9379 | lr 0.0000 | time_forward 3.0310 | time_backward 4.1500 |
[2023-10-25 00:35:37,865::train::INFO] [train] Iter 589466 | loss 3.2329 | loss(rot) 0.2118 | loss(pos) 3.0206 | loss(seq) 0.0005 | grad 17.8451 | lr 0.0000 | time_forward 0.8080 | time_backward 0.9430 |
[2023-10-25 00:35:40,107::train::INFO] [train] Iter 589467 | loss 0.8952 | loss(rot) 0.4330 | loss(pos) 0.2121 | loss(seq) 0.2501 | grad 3.2598 | lr 0.0000 | time_forward 1.0190 | time_backward 1.2190 |
[2023-10-25 00:35:47,471::train::INFO] [train] Iter 589468 | loss 0.3683 | loss(rot) 0.1958 | loss(pos) 0.1028 | loss(seq) 0.0697 | grad 4.2480 | lr 0.0000 | time_forward 3.1460 | time_backward 4.2130 |
[2023-10-25 00:35:54,283::train::INFO] [train] Iter 589469 | loss 0.2745 | loss(rot) 0.2083 | loss(pos) 0.0183 | loss(seq) 0.0479 | grad 2.9159 | lr 0.0000 | time_forward 2.9730 | time_backward 3.8360 |
[2023-10-25 00:35:56,999::train::INFO] [train] Iter 589470 | loss 0.2754 | loss(rot) 0.1712 | loss(pos) 0.0306 | loss(seq) 0.0736 | grad 4.6171 | lr 0.0000 | time_forward 1.3060 | time_backward 1.4080 |
[2023-10-25 00:36:04,417::train::INFO] [train] Iter 589471 | loss 0.8767 | loss(rot) 0.0300 | loss(pos) 0.8405 | loss(seq) 0.0061 | grad 6.9217 | lr 0.0000 | time_forward 3.2060 | time_backward 4.2090 |
[2023-10-25 00:36:12,322::train::INFO] [train] Iter 589472 | loss 1.9856 | loss(rot) 0.1270 | loss(pos) 1.8582 | loss(seq) 0.0004 | grad 10.9101 | lr 0.0000 | time_forward 3.3770 | time_backward 4.5240 |
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