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[2023-09-02 01:53:06,962::train::INFO] [train] Iter 04681 | loss 2.0990 | loss(rot) 1.1160 | loss(pos) 0.5356 | loss(seq) 0.4474 | grad 3.6017 | lr 0.0010 | time_forward 1.2770 | time_backward 1.4460 |
[2023-09-02 01:53:09,711::train::INFO] [train] Iter 04682 | loss 3.4642 | loss(rot) 2.5365 | loss(pos) 0.5085 | loss(seq) 0.4191 | grad 4.9634 | lr 0.0010 | time_forward 1.2980 | time_backward 1.4480 |
[2023-09-02 01:53:18,060::train::INFO] [train] Iter 04683 | loss 1.8754 | loss(rot) 1.2224 | loss(pos) 0.1843 | loss(seq) 0.4686 | grad 3.8977 | lr 0.0010 | time_forward 3.3720 | time_backward 4.9730 |
[2023-09-02 01:53:27,262::train::INFO] [train] Iter 04684 | loss 2.3999 | loss(rot) 1.4523 | loss(pos) 0.3721 | loss(seq) 0.5755 | grad 4.9375 | lr 0.0010 | time_forward 3.8000 | time_backward 5.3980 |
[2023-09-02 01:53:35,294::train::INFO] [train] Iter 04685 | loss 1.6369 | loss(rot) 1.3387 | loss(pos) 0.1167 | loss(seq) 0.1815 | grad 4.2461 | lr 0.0010 | time_forward 3.3470 | time_backward 4.6810 |
[2023-09-02 01:53:45,574::train::INFO] [train] Iter 04686 | loss 2.0481 | loss(rot) 1.0858 | loss(pos) 0.5090 | loss(seq) 0.4532 | grad 3.9655 | lr 0.0010 | time_forward 4.3270 | time_backward 5.9490 |
[2023-09-02 01:53:53,696::train::INFO] [train] Iter 04687 | loss 2.4559 | loss(rot) 1.8876 | loss(pos) 0.1172 | loss(seq) 0.4510 | grad 3.8119 | lr 0.0010 | time_forward 3.4460 | time_backward 4.6730 |
[2023-09-02 01:53:56,351::train::INFO] [train] Iter 04688 | loss 1.2519 | loss(rot) 0.2976 | loss(pos) 0.7786 | loss(seq) 0.1756 | grad 4.5070 | lr 0.0010 | time_forward 1.2530 | time_backward 1.3980 |
[2023-09-02 01:54:06,207::train::INFO] [train] Iter 04689 | loss 2.2102 | loss(rot) 1.9727 | loss(pos) 0.1491 | loss(seq) 0.0883 | grad 3.4778 | lr 0.0010 | time_forward 4.0240 | time_backward 5.7950 |
[2023-09-02 01:54:08,396::train::INFO] [train] Iter 04690 | loss 2.5928 | loss(rot) 2.2958 | loss(pos) 0.2095 | loss(seq) 0.0875 | grad 5.9776 | lr 0.0010 | time_forward 1.0330 | time_backward 1.1520 |
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