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[2023-10-25 11:10:42,112::train::INFO] [train] Iter 595157 | loss 0.9744 | loss(rot) 0.7124 | loss(pos) 0.0442 | loss(seq) 0.2177 | grad 4.6229 | lr 0.0000 | time_forward 3.2070 | time_backward 4.3440 |
[2023-10-25 11:10:44,909::train::INFO] [train] Iter 595158 | loss 0.3434 | loss(rot) 0.3140 | loss(pos) 0.0294 | loss(seq) 0.0000 | grad 4.5620 | lr 0.0000 | time_forward 1.3420 | time_backward 1.4510 |
[2023-10-25 11:10:54,125::train::INFO] [train] Iter 595159 | loss 0.9941 | loss(rot) 0.3535 | loss(pos) 0.2258 | loss(seq) 0.4148 | grad 2.8235 | lr 0.0000 | time_forward 3.9450 | time_backward 5.2670 |
[2023-10-25 11:11:04,016::train::INFO] [train] Iter 595160 | loss 1.4271 | loss(rot) 0.9173 | loss(pos) 0.1865 | loss(seq) 0.3233 | grad 2.7933 | lr 0.0000 | time_forward 3.9710 | time_backward 5.9170 |
[2023-10-25 11:11:06,789::train::INFO] [train] Iter 595161 | loss 0.8463 | loss(rot) 0.4414 | loss(pos) 0.0594 | loss(seq) 0.3455 | grad 3.3228 | lr 0.0000 | time_forward 1.3320 | time_backward 1.4370 |
[2023-10-25 11:11:16,761::train::INFO] [train] Iter 595162 | loss 0.7927 | loss(rot) 0.3951 | loss(pos) 0.0961 | loss(seq) 0.3015 | grad 3.6654 | lr 0.0000 | time_forward 4.0670 | time_backward 5.9020 |
[2023-10-25 11:11:26,766::train::INFO] [train] Iter 595163 | loss 1.2826 | loss(rot) 0.7138 | loss(pos) 0.1335 | loss(seq) 0.4353 | grad 11.9562 | lr 0.0000 | time_forward 4.0420 | time_backward 5.9600 |
[2023-10-25 11:11:36,876::train::INFO] [train] Iter 595164 | loss 1.8972 | loss(rot) 1.8557 | loss(pos) 0.0409 | loss(seq) 0.0006 | grad 3.8450 | lr 0.0000 | time_forward 4.0050 | time_backward 6.1020 |
[2023-10-25 11:11:44,622::train::INFO] [train] Iter 595165 | loss 0.1955 | loss(rot) 0.1206 | loss(pos) 0.0170 | loss(seq) 0.0579 | grad 2.5668 | lr 0.0000 | time_forward 3.2410 | time_backward 4.5010 |
[2023-10-25 11:11:53,424::train::INFO] [train] Iter 595166 | loss 0.7351 | loss(rot) 0.1669 | loss(pos) 0.4875 | loss(seq) 0.0806 | grad 3.7642 | lr 0.0000 | time_forward 3.7090 | time_backward 5.0900 |
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