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[2023-09-02 02:28:20,299::train::INFO] [train] Iter 04981 | loss 1.8346 | loss(rot) 1.1806 | loss(pos) 0.3807 | loss(seq) 0.2733 | grad 4.4864 | lr 0.0010 | time_forward 3.4450 | time_backward 4.6560 |
[2023-09-02 02:28:28,957::train::INFO] [train] Iter 04982 | loss 3.6749 | loss(rot) 0.0475 | loss(pos) 3.6274 | loss(seq) 0.0000 | grad 7.5417 | lr 0.0010 | time_forward 3.7320 | time_backward 4.9220 |
[2023-09-02 02:28:37,405::train::INFO] [train] Iter 04983 | loss 2.1164 | loss(rot) 0.8098 | loss(pos) 0.7111 | loss(seq) 0.5955 | grad 5.5200 | lr 0.0010 | time_forward 3.6040 | time_backward 4.8410 |
[2023-09-02 02:28:45,908::train::INFO] [train] Iter 04984 | loss 2.0050 | loss(rot) 1.5598 | loss(pos) 0.2074 | loss(seq) 0.2378 | grad 5.0443 | lr 0.0010 | time_forward 3.6670 | time_backward 4.8340 |
[2023-09-02 02:28:48,782::train::INFO] [train] Iter 04985 | loss 1.7166 | loss(rot) 1.3228 | loss(pos) 0.3241 | loss(seq) 0.0698 | grad 5.5547 | lr 0.0010 | time_forward 1.3910 | time_backward 1.4790 |
[2023-09-02 02:28:51,520::train::INFO] [train] Iter 04986 | loss 2.3454 | loss(rot) 1.5709 | loss(pos) 0.5206 | loss(seq) 0.2539 | grad 9.2750 | lr 0.0010 | time_forward 1.2650 | time_backward 1.4710 |
[2023-09-02 02:28:59,914::train::INFO] [train] Iter 04987 | loss 1.6042 | loss(rot) 0.3095 | loss(pos) 1.0969 | loss(seq) 0.1979 | grad 3.5480 | lr 0.0010 | time_forward 3.4530 | time_backward 4.9370 |
[2023-09-02 02:29:08,470::train::INFO] [train] Iter 04988 | loss 2.0190 | loss(rot) 1.7838 | loss(pos) 0.2352 | loss(seq) 0.0000 | grad 3.7342 | lr 0.0010 | time_forward 3.6070 | time_backward 4.9450 |
[2023-09-02 02:29:17,786::train::INFO] [train] Iter 04989 | loss 3.0160 | loss(rot) 2.8711 | loss(pos) 0.1442 | loss(seq) 0.0007 | grad 3.7147 | lr 0.0010 | time_forward 3.9180 | time_backward 5.3940 |
[2023-09-02 02:29:20,497::train::INFO] [train] Iter 04990 | loss 2.7564 | loss(rot) 2.4994 | loss(pos) 0.2538 | loss(seq) 0.0032 | grad 4.7079 | lr 0.0010 | time_forward 1.2800 | time_backward 1.4280 |
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