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[2023-10-23 10:58:57,621::train::INFO] [train] Iter 569983 | loss 0.5187 | loss(rot) 0.1971 | loss(pos) 0.0494 | loss(seq) 0.2722 | grad 3.8943 | lr 0.0000 | time_forward 3.6200 | time_backward 5.2680 |
[2023-10-23 10:59:05,320::train::INFO] [train] Iter 569984 | loss 0.7571 | loss(rot) 0.7255 | loss(pos) 0.0316 | loss(seq) 0.0001 | grad 8.7963 | lr 0.0000 | time_forward 3.2310 | time_backward 4.4650 |
[2023-10-23 10:59:07,855::train::INFO] [train] Iter 569985 | loss 0.3117 | loss(rot) 0.0910 | loss(pos) 0.1189 | loss(seq) 0.1017 | grad 3.8620 | lr 0.0000 | time_forward 1.2400 | time_backward 1.2920 |
[2023-10-23 10:59:15,854::train::INFO] [train] Iter 569986 | loss 0.8704 | loss(rot) 0.5204 | loss(pos) 0.0849 | loss(seq) 0.2651 | grad 3.0956 | lr 0.0000 | time_forward 3.3150 | time_backward 4.6810 |
[2023-10-23 10:59:24,807::train::INFO] [train] Iter 569987 | loss 0.9549 | loss(rot) 0.5195 | loss(pos) 0.1294 | loss(seq) 0.3061 | grad 3.3710 | lr 0.0000 | time_forward 3.8640 | time_backward 5.0860 |
[2023-10-23 10:59:33,651::train::INFO] [train] Iter 569988 | loss 0.3633 | loss(rot) 0.0310 | loss(pos) 0.3234 | loss(seq) 0.0088 | grad 2.9507 | lr 0.0000 | time_forward 3.7540 | time_backward 5.0860 |
[2023-10-23 10:59:36,438::train::INFO] [train] Iter 569989 | loss 0.9733 | loss(rot) 0.3490 | loss(pos) 0.2093 | loss(seq) 0.4151 | grad 3.0625 | lr 0.0000 | time_forward 1.3540 | time_backward 1.4310 |
[2023-10-23 10:59:44,339::train::INFO] [train] Iter 569990 | loss 0.4614 | loss(rot) 0.0933 | loss(pos) 0.3377 | loss(seq) 0.0304 | grad 5.1572 | lr 0.0000 | time_forward 3.3790 | time_backward 4.5180 |
[2023-10-23 10:59:51,172::train::INFO] [train] Iter 569991 | loss 2.4390 | loss(rot) 2.4102 | loss(pos) 0.0254 | loss(seq) 0.0033 | grad 4.5345 | lr 0.0000 | time_forward 2.9800 | time_backward 3.8500 |
[2023-10-23 10:59:58,716::train::INFO] [train] Iter 569992 | loss 1.0757 | loss(rot) 0.4017 | loss(pos) 0.6608 | loss(seq) 0.0132 | grad 9.2887 | lr 0.0000 | time_forward 3.2220 | time_backward 4.3180 |
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