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[2023-10-23 00:33:32,153::train::INFO] [train] Iter 563889 | loss 1.6421 | loss(rot) 1.4885 | loss(pos) 0.0611 | loss(seq) 0.0925 | grad 10.1663 | lr 0.0000 | time_forward 2.7830 | time_backward 3.6290 |
[2023-10-23 00:33:39,371::train::INFO] [train] Iter 563890 | loss 1.3222 | loss(rot) 1.2291 | loss(pos) 0.0757 | loss(seq) 0.0174 | grad 3.7227 | lr 0.0000 | time_forward 3.2140 | time_backward 4.0010 |
[2023-10-23 00:33:46,149::train::INFO] [train] Iter 563891 | loss 2.1995 | loss(rot) 1.6913 | loss(pos) 0.0947 | loss(seq) 0.4135 | grad 5.0754 | lr 0.0000 | time_forward 2.9430 | time_backward 3.8310 |
[2023-10-23 00:33:53,837::train::INFO] [train] Iter 563892 | loss 0.1670 | loss(rot) 0.1135 | loss(pos) 0.0425 | loss(seq) 0.0110 | grad 4.4265 | lr 0.0000 | time_forward 3.2060 | time_backward 4.4790 |
[2023-10-23 00:34:00,622::train::INFO] [train] Iter 563893 | loss 0.1811 | loss(rot) 0.0685 | loss(pos) 0.0168 | loss(seq) 0.0958 | grad 1.5270 | lr 0.0000 | time_forward 2.9560 | time_backward 3.8270 |
[2023-10-23 00:34:08,454::train::INFO] [train] Iter 563894 | loss 0.6231 | loss(rot) 0.5084 | loss(pos) 0.0523 | loss(seq) 0.0624 | grad 3.0825 | lr 0.0000 | time_forward 3.5380 | time_backward 4.2900 |
[2023-10-23 00:34:16,341::train::INFO] [train] Iter 563895 | loss 1.1228 | loss(rot) 0.8072 | loss(pos) 0.0885 | loss(seq) 0.2271 | grad 3.3688 | lr 0.0000 | time_forward 3.5570 | time_backward 4.3260 |
[2023-10-23 00:34:18,988::train::INFO] [train] Iter 563896 | loss 0.9077 | loss(rot) 0.7156 | loss(pos) 0.0381 | loss(seq) 0.1539 | grad 3.8463 | lr 0.0000 | time_forward 1.2430 | time_backward 1.4010 |
[2023-10-23 00:34:21,718::train::INFO] [train] Iter 563897 | loss 1.6376 | loss(rot) 1.5511 | loss(pos) 0.0495 | loss(seq) 0.0370 | grad 3.4696 | lr 0.0000 | time_forward 1.2320 | time_backward 1.4820 |
[2023-10-23 00:34:28,372::train::INFO] [train] Iter 563898 | loss 0.5892 | loss(rot) 0.2815 | loss(pos) 0.1033 | loss(seq) 0.2043 | grad 4.5893 | lr 0.0000 | time_forward 2.9390 | time_backward 3.7110 |
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