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[2023-10-24 16:18:23,535::train::INFO] [train] Iter 585167 | loss 0.7173 | loss(rot) 0.4269 | loss(pos) 0.0241 | loss(seq) 0.2662 | grad 3.1743 | lr 0.0000 | time_forward 2.9460 | time_backward 3.8660 |
[2023-10-24 16:18:26,232::train::INFO] [train] Iter 585168 | loss 1.3147 | loss(rot) 1.2478 | loss(pos) 0.0555 | loss(seq) 0.0115 | grad 7.6564 | lr 0.0000 | time_forward 1.2840 | time_backward 1.4110 |
[2023-10-24 16:18:34,165::train::INFO] [train] Iter 585169 | loss 0.7076 | loss(rot) 0.4322 | loss(pos) 0.1977 | loss(seq) 0.0777 | grad 3.9574 | lr 0.0000 | time_forward 3.2850 | time_backward 4.6290 |
[2023-10-24 16:18:41,413::train::INFO] [train] Iter 585170 | loss 0.5777 | loss(rot) 0.0494 | loss(pos) 0.1548 | loss(seq) 0.3734 | grad 5.6622 | lr 0.0000 | time_forward 3.1620 | time_backward 4.0830 |
[2023-10-24 16:18:47,877::train::INFO] [train] Iter 585171 | loss 0.7285 | loss(rot) 0.6928 | loss(pos) 0.0103 | loss(seq) 0.0254 | grad 4.6397 | lr 0.0000 | time_forward 2.7870 | time_backward 3.6740 |
[2023-10-24 16:18:50,654::train::INFO] [train] Iter 585172 | loss 0.2227 | loss(rot) 0.0536 | loss(pos) 0.0370 | loss(seq) 0.1321 | grad 2.7154 | lr 0.0000 | time_forward 1.2880 | time_backward 1.4850 |
[2023-10-24 16:18:58,728::train::INFO] [train] Iter 585173 | loss 0.2611 | loss(rot) 0.1971 | loss(pos) 0.0227 | loss(seq) 0.0414 | grad 2.8660 | lr 0.0000 | time_forward 3.3260 | time_backward 4.7370 |
[2023-10-24 16:19:01,486::train::INFO] [train] Iter 585174 | loss 0.4764 | loss(rot) 0.1098 | loss(pos) 0.0697 | loss(seq) 0.2968 | grad 2.7001 | lr 0.0000 | time_forward 1.3180 | time_backward 1.4360 |
[2023-10-24 16:19:09,418::train::INFO] [train] Iter 585175 | loss 2.2255 | loss(rot) 1.6394 | loss(pos) 0.1744 | loss(seq) 0.4117 | grad 3.0589 | lr 0.0000 | time_forward 3.4680 | time_backward 4.4520 |
[2023-10-24 16:19:12,099::train::INFO] [train] Iter 585176 | loss 1.1054 | loss(rot) 0.9909 | loss(pos) 0.0379 | loss(seq) 0.0766 | grad 10.9203 | lr 0.0000 | time_forward 1.2670 | time_backward 1.4100 |
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