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[2023-10-24 05:42:09,150::train::INFO] [train] Iter 579773 | loss 0.2157 | loss(rot) 0.1870 | loss(pos) 0.0181 | loss(seq) 0.0107 | grad 12.7549 | lr 0.0000 | time_forward 3.7210 | time_backward 5.1850 |
[2023-10-24 05:42:18,792::train::INFO] [train] Iter 579774 | loss 0.4425 | loss(rot) 0.1558 | loss(pos) 0.0583 | loss(seq) 0.2285 | grad 2.2151 | lr 0.0000 | time_forward 3.9300 | time_backward 5.7090 |
[2023-10-24 05:42:21,601::train::INFO] [train] Iter 579775 | loss 1.3283 | loss(rot) 0.0480 | loss(pos) 1.2679 | loss(seq) 0.0124 | grad 4.8493 | lr 0.0000 | time_forward 1.3270 | time_backward 1.4780 |
[2023-10-24 05:42:29,880::train::INFO] [train] Iter 579776 | loss 0.2993 | loss(rot) 0.1287 | loss(pos) 0.0198 | loss(seq) 0.1508 | grad 2.6909 | lr 0.0000 | time_forward 3.5190 | time_backward 4.7570 |
[2023-10-24 05:42:39,583::train::INFO] [train] Iter 579777 | loss 0.9077 | loss(rot) 0.5702 | loss(pos) 0.0519 | loss(seq) 0.2856 | grad 3.5933 | lr 0.0000 | time_forward 4.2300 | time_backward 5.4690 |
[2023-10-24 05:42:49,483::train::INFO] [train] Iter 579778 | loss 0.3434 | loss(rot) 0.1206 | loss(pos) 0.0788 | loss(seq) 0.1440 | grad 3.1342 | lr 0.0000 | time_forward 4.0270 | time_backward 5.8700 |
[2023-10-24 05:42:56,622::train::INFO] [train] Iter 579779 | loss 1.5362 | loss(rot) 1.1361 | loss(pos) 0.0870 | loss(seq) 0.3131 | grad 7.1590 | lr 0.0000 | time_forward 2.9800 | time_backward 4.1560 |
[2023-10-24 05:43:06,279::train::INFO] [train] Iter 579780 | loss 0.8606 | loss(rot) 0.7409 | loss(pos) 0.0434 | loss(seq) 0.0762 | grad 2.6698 | lr 0.0000 | time_forward 3.9960 | time_backward 5.6580 |
[2023-10-24 05:43:14,654::train::INFO] [train] Iter 579781 | loss 1.5726 | loss(rot) 1.3788 | loss(pos) 0.0951 | loss(seq) 0.0987 | grad 4.8709 | lr 0.0000 | time_forward 3.4800 | time_backward 4.8910 |
[2023-10-24 05:43:22,153::train::INFO] [train] Iter 579782 | loss 0.4737 | loss(rot) 0.1438 | loss(pos) 0.2564 | loss(seq) 0.0735 | grad 2.6887 | lr 0.0000 | time_forward 3.1910 | time_backward 4.3040 |
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