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[2023-10-22 14:28:22,324::train::INFO] [train] Iter 558594 | loss 1.0539 | loss(rot) 0.6192 | loss(pos) 0.1375 | loss(seq) 0.2971 | grad 3.3186 | lr 0.0000 | time_forward 4.1830 | time_backward 6.5520 |
[2023-10-22 14:28:33,634::train::INFO] [train] Iter 558595 | loss 0.2220 | loss(rot) 0.1572 | loss(pos) 0.0590 | loss(seq) 0.0058 | grad 2.2193 | lr 0.0000 | time_forward 4.6390 | time_backward 6.6670 |
[2023-10-22 14:28:43,501::train::INFO] [train] Iter 558596 | loss 1.6375 | loss(rot) 1.4964 | loss(pos) 0.0453 | loss(seq) 0.0958 | grad 4.1562 | lr 0.0000 | time_forward 4.2390 | time_backward 5.6250 |
[2023-10-22 14:28:50,832::train::INFO] [train] Iter 558597 | loss 1.1964 | loss(rot) 1.1710 | loss(pos) 0.0180 | loss(seq) 0.0073 | grad 4.3874 | lr 0.0000 | time_forward 3.2580 | time_backward 4.0700 |
[2023-10-22 14:28:57,399::train::INFO] [train] Iter 558598 | loss 0.4157 | loss(rot) 0.1195 | loss(pos) 0.0490 | loss(seq) 0.2472 | grad 2.0023 | lr 0.0000 | time_forward 2.7410 | time_backward 3.8220 |
[2023-10-22 14:29:04,869::train::INFO] [train] Iter 558599 | loss 1.4827 | loss(rot) 0.8057 | loss(pos) 0.2697 | loss(seq) 0.4073 | grad 4.9753 | lr 0.0000 | time_forward 3.1070 | time_backward 4.3470 |
[2023-10-22 14:29:08,049::train::INFO] [train] Iter 558600 | loss 1.7033 | loss(rot) 0.0061 | loss(pos) 1.6967 | loss(seq) 0.0005 | grad 10.4498 | lr 0.0000 | time_forward 1.5440 | time_backward 1.6300 |
[2023-10-22 14:29:18,035::train::INFO] [train] Iter 558601 | loss 0.2428 | loss(rot) 0.0440 | loss(pos) 0.0334 | loss(seq) 0.1654 | grad 1.7647 | lr 0.0000 | time_forward 4.1890 | time_backward 5.7940 |
[2023-10-22 14:29:28,943::train::INFO] [train] Iter 558602 | loss 0.5528 | loss(rot) 0.1772 | loss(pos) 0.0726 | loss(seq) 0.3031 | grad 3.9858 | lr 0.0000 | time_forward 4.5420 | time_backward 6.3630 |
[2023-10-22 14:29:39,071::train::INFO] [train] Iter 558603 | loss 0.8653 | loss(rot) 0.3845 | loss(pos) 0.0887 | loss(seq) 0.3920 | grad 2.3819 | lr 0.0000 | time_forward 4.4230 | time_backward 5.7020 |
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