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[2023-10-23 05:31:52,970::train::INFO] [train] Iter 566886 | loss 0.3143 | loss(rot) 0.1423 | loss(pos) 0.1668 | loss(seq) 0.0052 | grad 3.0704 | lr 0.0000 | time_forward 3.2130 | time_backward 4.1860 |
[2023-10-23 05:32:01,067::train::INFO] [train] Iter 566887 | loss 0.6909 | loss(rot) 0.6492 | loss(pos) 0.0417 | loss(seq) 0.0000 | grad 6.7330 | lr 0.0000 | time_forward 3.3470 | time_backward 4.7470 |
[2023-10-23 05:32:07,984::train::INFO] [train] Iter 566888 | loss 0.7371 | loss(rot) 0.2552 | loss(pos) 0.0407 | loss(seq) 0.4412 | grad 3.3914 | lr 0.0000 | time_forward 2.9510 | time_backward 3.9620 |
[2023-10-23 05:32:10,618::train::INFO] [train] Iter 566889 | loss 1.5211 | loss(rot) 1.0860 | loss(pos) 0.1398 | loss(seq) 0.2953 | grad 3.5784 | lr 0.0000 | time_forward 1.2510 | time_backward 1.3810 |
[2023-10-23 05:32:12,899::train::INFO] [train] Iter 566890 | loss 0.7544 | loss(rot) 0.4698 | loss(pos) 0.0436 | loss(seq) 0.2409 | grad 2.6295 | lr 0.0000 | time_forward 1.0510 | time_backward 1.2260 |
[2023-10-23 05:32:16,055::train::INFO] [train] Iter 566891 | loss 1.8922 | loss(rot) 1.8492 | loss(pos) 0.0419 | loss(seq) 0.0011 | grad 15.8401 | lr 0.0000 | time_forward 1.4100 | time_backward 1.7430 |
[2023-10-23 05:32:23,273::train::INFO] [train] Iter 566892 | loss 0.8113 | loss(rot) 0.2678 | loss(pos) 0.2636 | loss(seq) 0.2799 | grad 2.7852 | lr 0.0000 | time_forward 3.0440 | time_backward 4.1610 |
[2023-10-23 05:32:25,856::train::INFO] [train] Iter 566893 | loss 0.1632 | loss(rot) 0.1511 | loss(pos) 0.0121 | loss(seq) 0.0000 | grad 26.6129 | lr 0.0000 | time_forward 1.1970 | time_backward 1.3820 |
[2023-10-23 05:32:32,160::train::INFO] [train] Iter 566894 | loss 0.9893 | loss(rot) 0.0468 | loss(pos) 0.9202 | loss(seq) 0.0223 | grad 8.7761 | lr 0.0000 | time_forward 2.7300 | time_backward 3.5520 |
[2023-10-23 05:32:39,271::train::INFO] [train] Iter 566895 | loss 0.9934 | loss(rot) 0.9871 | loss(pos) 0.0061 | loss(seq) 0.0002 | grad 7.4165 | lr 0.0000 | time_forward 3.0970 | time_backward 4.0110 |
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