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[2023-09-02 03:42:44,327::train::INFO] [train] Iter 05580 | loss 2.4480 | loss(rot) 2.2548 | loss(pos) 0.1622 | loss(seq) 0.0309 | grad 5.7519 | lr 0.0010 | time_forward 3.6720 | time_backward 5.0380 |
[2023-09-02 03:42:55,410::train::INFO] [train] Iter 05581 | loss 0.7314 | loss(rot) 0.1198 | loss(pos) 0.4572 | loss(seq) 0.1544 | grad 2.5553 | lr 0.0010 | time_forward 4.2780 | time_backward 6.8010 |
[2023-09-02 03:43:03,542::train::INFO] [train] Iter 05582 | loss 1.9630 | loss(rot) 1.4763 | loss(pos) 0.0683 | loss(seq) 0.4184 | grad 4.0678 | lr 0.0010 | time_forward 3.4580 | time_backward 4.6710 |
[2023-09-02 03:43:13,604::train::INFO] [train] Iter 05583 | loss 2.7596 | loss(rot) 2.2256 | loss(pos) 0.2538 | loss(seq) 0.2801 | grad 2.8143 | lr 0.0010 | time_forward 4.2600 | time_backward 5.7980 |
[2023-09-02 03:43:22,809::train::INFO] [train] Iter 05584 | loss 2.9564 | loss(rot) 2.3288 | loss(pos) 0.1172 | loss(seq) 0.5104 | grad 3.5658 | lr 0.0010 | time_forward 3.8260 | time_backward 5.3760 |
[2023-09-02 03:43:32,163::train::INFO] [train] Iter 05585 | loss 2.7998 | loss(rot) 2.2837 | loss(pos) 0.1805 | loss(seq) 0.3356 | grad 3.4462 | lr 0.0010 | time_forward 3.8950 | time_backward 5.4550 |
[2023-09-02 03:43:42,286::train::INFO] [train] Iter 05586 | loss 1.5177 | loss(rot) 0.2673 | loss(pos) 1.2267 | loss(seq) 0.0237 | grad 4.4920 | lr 0.0010 | time_forward 4.0210 | time_backward 6.0980 |
[2023-09-02 03:43:52,329::train::INFO] [train] Iter 05587 | loss 2.6636 | loss(rot) 2.2085 | loss(pos) 0.0987 | loss(seq) 0.3564 | grad 3.7259 | lr 0.0010 | time_forward 3.9980 | time_backward 6.0420 |
[2023-09-02 03:44:01,057::train::INFO] [train] Iter 05588 | loss 2.5678 | loss(rot) 2.3634 | loss(pos) 0.1342 | loss(seq) 0.0703 | grad 3.1726 | lr 0.0010 | time_forward 3.7180 | time_backward 5.0060 |
[2023-09-02 03:44:08,702::train::INFO] [train] Iter 05589 | loss 2.2930 | loss(rot) 1.7254 | loss(pos) 0.2027 | loss(seq) 0.3650 | grad 5.0466 | lr 0.0010 | time_forward 3.2920 | time_backward 4.3490 |
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