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[2023-09-03 00:31:56,445::train::INFO] [train] Iter 15871 | loss 1.7452 | loss(rot) 0.5334 | loss(pos) 0.9063 | loss(seq) 0.3056 | grad 6.7479 | lr 0.0010 | time_forward 4.1040 | time_backward 5.3480 |
[2023-09-03 00:32:04,880::train::INFO] [train] Iter 15872 | loss 2.4472 | loss(rot) 1.4941 | loss(pos) 0.3808 | loss(seq) 0.5723 | grad 4.1961 | lr 0.0010 | time_forward 3.5860 | time_backward 4.8440 |
[2023-09-03 00:32:13,173::train::INFO] [train] Iter 15873 | loss 1.7533 | loss(rot) 1.6452 | loss(pos) 0.1068 | loss(seq) 0.0012 | grad 6.8230 | lr 0.0010 | time_forward 3.2910 | time_backward 4.9980 |
[2023-09-03 00:32:21,812::train::INFO] [train] Iter 15874 | loss 2.0257 | loss(rot) 1.0489 | loss(pos) 0.4826 | loss(seq) 0.4942 | grad 6.2371 | lr 0.0010 | time_forward 3.5580 | time_backward 5.0780 |
[2023-09-03 00:32:31,867::train::INFO] [train] Iter 15875 | loss 1.1109 | loss(rot) 0.2547 | loss(pos) 0.7711 | loss(seq) 0.0851 | grad 7.0186 | lr 0.0010 | time_forward 4.1600 | time_backward 5.8920 |
[2023-09-03 00:32:39,618::train::INFO] [train] Iter 15876 | loss 1.7132 | loss(rot) 0.7399 | loss(pos) 0.6125 | loss(seq) 0.3608 | grad 7.9616 | lr 0.0010 | time_forward 3.2390 | time_backward 4.5060 |
[2023-09-03 00:32:49,262::train::INFO] [train] Iter 15877 | loss 0.5182 | loss(rot) 0.1836 | loss(pos) 0.2944 | loss(seq) 0.0403 | grad 3.1459 | lr 0.0010 | time_forward 4.2620 | time_backward 5.3780 |
[2023-09-03 00:32:58,564::train::INFO] [train] Iter 15878 | loss 0.9082 | loss(rot) 0.2508 | loss(pos) 0.2588 | loss(seq) 0.3985 | grad 4.3663 | lr 0.0010 | time_forward 3.8850 | time_backward 5.4130 |
[2023-09-03 00:33:08,676::train::INFO] [train] Iter 15879 | loss 1.6294 | loss(rot) 0.3909 | loss(pos) 0.7417 | loss(seq) 0.4968 | grad 6.1904 | lr 0.0010 | time_forward 3.8040 | time_backward 6.3010 |
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