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[2023-09-02 11:25:46,352::train::INFO] [train] Iter 09376 | loss 2.2781 | loss(rot) 1.7418 | loss(pos) 0.2097 | loss(seq) 0.3266 | grad 8.1365 | lr 0.0010 | time_forward 3.7100 | time_backward 5.0590 |
[2023-09-02 11:25:55,362::train::INFO] [train] Iter 09377 | loss 1.4587 | loss(rot) 0.4514 | loss(pos) 0.6704 | loss(seq) 0.3368 | grad 5.3978 | lr 0.0010 | time_forward 3.9920 | time_backward 5.0140 |
[2023-09-02 11:26:04,796::train::INFO] [train] Iter 09378 | loss 1.4233 | loss(rot) 0.6071 | loss(pos) 0.3260 | loss(seq) 0.4902 | grad 4.1450 | lr 0.0010 | time_forward 4.0020 | time_backward 5.4290 |
[2023-09-02 11:26:07,512::train::INFO] [train] Iter 09379 | loss 1.2080 | loss(rot) 0.2801 | loss(pos) 0.8744 | loss(seq) 0.0535 | grad 5.6575 | lr 0.0010 | time_forward 1.2980 | time_backward 1.4140 |
[2023-09-02 11:26:17,299::train::INFO] [train] Iter 09380 | loss 2.7811 | loss(rot) 2.0716 | loss(pos) 0.2014 | loss(seq) 0.5081 | grad 6.9790 | lr 0.0010 | time_forward 4.0490 | time_backward 5.6990 |
[2023-09-02 11:26:27,463::train::INFO] [train] Iter 09381 | loss 1.1872 | loss(rot) 0.1820 | loss(pos) 0.9695 | loss(seq) 0.0357 | grad 3.5862 | lr 0.0010 | time_forward 4.1270 | time_backward 6.0340 |
[2023-09-02 11:26:30,135::train::INFO] [train] Iter 09382 | loss 2.4400 | loss(rot) 0.2922 | loss(pos) 2.1391 | loss(seq) 0.0087 | grad 7.1537 | lr 0.0010 | time_forward 1.3370 | time_backward 1.3310 |
[2023-09-02 11:26:32,612::train::INFO] [train] Iter 09383 | loss 1.4932 | loss(rot) 1.2637 | loss(pos) 0.1189 | loss(seq) 0.1106 | grad 4.0194 | lr 0.0010 | time_forward 1.1500 | time_backward 1.3240 |
[2023-09-02 11:26:40,221::train::INFO] [train] Iter 09384 | loss 0.8908 | loss(rot) 0.0609 | loss(pos) 0.5651 | loss(seq) 0.2648 | grad 2.6908 | lr 0.0010 | time_forward 3.1690 | time_backward 4.4220 |
[2023-09-02 11:26:43,000::train::INFO] [train] Iter 09385 | loss 2.4684 | loss(rot) 1.9805 | loss(pos) 0.1562 | loss(seq) 0.3317 | grad 4.5169 | lr 0.0010 | time_forward 1.3300 | time_backward 1.4460 |
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