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[2023-10-24 06:17:26,673::train::INFO] [train] Iter 580072 | loss 0.8852 | loss(rot) 0.1368 | loss(pos) 0.4035 | loss(seq) 0.3449 | grad 3.6697 | lr 0.0000 | time_forward 3.5180 | time_backward 4.9540 |
[2023-10-24 06:17:35,963::train::INFO] [train] Iter 580073 | loss 1.3974 | loss(rot) 0.5035 | loss(pos) 0.6062 | loss(seq) 0.2877 | grad 4.3844 | lr 0.0000 | time_forward 3.8650 | time_backward 5.4220 |
[2023-10-24 06:17:42,569::train::INFO] [train] Iter 580074 | loss 0.4561 | loss(rot) 0.2931 | loss(pos) 0.0226 | loss(seq) 0.1404 | grad 3.7912 | lr 0.0000 | time_forward 2.8060 | time_backward 3.7960 |
[2023-10-24 06:17:44,845::train::INFO] [train] Iter 580075 | loss 0.4013 | loss(rot) 0.1385 | loss(pos) 0.0155 | loss(seq) 0.2473 | grad 2.3451 | lr 0.0000 | time_forward 1.0500 | time_backward 1.2220 |
[2023-10-24 06:17:54,005::train::INFO] [train] Iter 580076 | loss 0.7529 | loss(rot) 0.4636 | loss(pos) 0.0388 | loss(seq) 0.2505 | grad 4.8083 | lr 0.0000 | time_forward 3.8520 | time_backward 5.3050 |
[2023-10-24 06:18:02,454::train::INFO] [train] Iter 580077 | loss 0.3594 | loss(rot) 0.0931 | loss(pos) 0.2284 | loss(seq) 0.0379 | grad 4.3059 | lr 0.0000 | time_forward 3.5590 | time_backward 4.8870 |
[2023-10-24 06:18:05,165::train::INFO] [train] Iter 580078 | loss 1.0685 | loss(rot) 0.8465 | loss(pos) 0.0534 | loss(seq) 0.1686 | grad 15.8553 | lr 0.0000 | time_forward 1.2960 | time_backward 1.4130 |
[2023-10-24 06:18:12,867::train::INFO] [train] Iter 580079 | loss 2.0840 | loss(rot) 0.9868 | loss(pos) 0.5996 | loss(seq) 0.4976 | grad 4.2389 | lr 0.0000 | time_forward 3.2040 | time_backward 4.4610 |
[2023-10-24 06:18:15,699::train::INFO] [train] Iter 580080 | loss 1.0283 | loss(rot) 0.4742 | loss(pos) 0.0640 | loss(seq) 0.4901 | grad 3.9683 | lr 0.0000 | time_forward 1.3350 | time_backward 1.4940 |
[2023-10-24 06:18:24,301::train::INFO] [train] Iter 580081 | loss 0.2687 | loss(rot) 0.0819 | loss(pos) 0.0223 | loss(seq) 0.1645 | grad 1.8294 | lr 0.0000 | time_forward 3.6730 | time_backward 4.9250 |
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