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[2023-09-02 05:55:30,635::train::INFO] [train] Iter 06679 | loss 0.6996 | loss(rot) 0.2583 | loss(pos) 0.3298 | loss(seq) 0.1116 | grad 3.9626 | lr 0.0010 | time_forward 1.2660 | time_backward 1.4870 |
[2023-09-02 05:55:38,695::train::INFO] [train] Iter 06680 | loss 1.4526 | loss(rot) 0.0841 | loss(pos) 1.3533 | loss(seq) 0.0152 | grad 5.7661 | lr 0.0010 | time_forward 3.3910 | time_backward 4.6470 |
[2023-09-02 05:55:48,122::train::INFO] [train] Iter 06681 | loss 2.4294 | loss(rot) 2.0786 | loss(pos) 0.1870 | loss(seq) 0.1638 | grad 3.6212 | lr 0.0010 | time_forward 3.9730 | time_backward 5.4510 |
[2023-09-02 05:55:57,612::train::INFO] [train] Iter 06682 | loss 1.9231 | loss(rot) 0.7357 | loss(pos) 0.5187 | loss(seq) 0.6688 | grad 4.4440 | lr 0.0010 | time_forward 3.9480 | time_backward 5.5380 |
[2023-09-02 05:56:05,248::train::INFO] [train] Iter 06683 | loss 1.2800 | loss(rot) 0.3998 | loss(pos) 0.4360 | loss(seq) 0.4442 | grad 3.4831 | lr 0.0010 | time_forward 3.1500 | time_backward 4.4780 |
[2023-09-02 05:56:08,548::train::INFO] [train] Iter 06684 | loss 2.7461 | loss(rot) 2.5666 | loss(pos) 0.1773 | loss(seq) 0.0021 | grad 3.9118 | lr 0.0010 | time_forward 1.4280 | time_backward 1.8700 |
[2023-09-02 05:56:10,751::train::INFO] [train] Iter 06685 | loss 2.3435 | loss(rot) 2.2143 | loss(pos) 0.1260 | loss(seq) 0.0032 | grad 3.7181 | lr 0.0010 | time_forward 1.0090 | time_backward 1.1910 |
[2023-09-02 05:56:13,473::train::INFO] [train] Iter 06686 | loss 1.5467 | loss(rot) 1.2629 | loss(pos) 0.0873 | loss(seq) 0.1965 | grad 4.9061 | lr 0.0010 | time_forward 1.2720 | time_backward 1.4110 |
[2023-09-02 05:56:22,451::train::INFO] [train] Iter 06687 | loss 1.9918 | loss(rot) 1.4515 | loss(pos) 0.1241 | loss(seq) 0.4162 | grad 9.7556 | lr 0.0010 | time_forward 3.6370 | time_backward 5.3370 |
[2023-09-02 05:56:30,805::train::INFO] [train] Iter 06688 | loss 1.9638 | loss(rot) 1.0903 | loss(pos) 0.3691 | loss(seq) 0.5043 | grad 3.4331 | lr 0.0010 | time_forward 3.5580 | time_backward 4.7930 |
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