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[2023-10-23 21:22:08,552::train::INFO] [train] Iter 575577 | loss 0.8923 | loss(rot) 0.3370 | loss(pos) 0.0289 | loss(seq) 0.5265 | grad 3.4080 | lr 0.0000 | time_forward 3.0250 | time_backward 4.2200 |
[2023-10-23 21:22:16,285::train::INFO] [train] Iter 575578 | loss 0.4195 | loss(rot) 0.1051 | loss(pos) 0.0707 | loss(seq) 0.2437 | grad 3.0464 | lr 0.0000 | time_forward 3.3790 | time_backward 4.3510 |
[2023-10-23 21:22:23,885::train::INFO] [train] Iter 575579 | loss 0.3940 | loss(rot) 0.1209 | loss(pos) 0.0122 | loss(seq) 0.2609 | grad 2.6293 | lr 0.0000 | time_forward 3.3190 | time_backward 4.2770 |
[2023-10-23 21:22:31,935::train::INFO] [train] Iter 575580 | loss 1.1518 | loss(rot) 0.4483 | loss(pos) 0.1887 | loss(seq) 0.5148 | grad 4.1203 | lr 0.0000 | time_forward 3.3780 | time_backward 4.6680 |
[2023-10-23 21:22:38,122::train::INFO] [train] Iter 575581 | loss 0.2961 | loss(rot) 0.1663 | loss(pos) 0.0320 | loss(seq) 0.0977 | grad 2.6180 | lr 0.0000 | time_forward 2.5870 | time_backward 3.5980 |
[2023-10-23 21:22:41,540::train::INFO] [train] Iter 575582 | loss 0.3185 | loss(rot) 0.1031 | loss(pos) 0.0178 | loss(seq) 0.1976 | grad 2.3573 | lr 0.0000 | time_forward 1.6750 | time_backward 1.7390 |
[2023-10-23 21:22:49,891::train::INFO] [train] Iter 575583 | loss 0.2193 | loss(rot) 0.1783 | loss(pos) 0.0166 | loss(seq) 0.0243 | grad 4.0073 | lr 0.0000 | time_forward 3.5910 | time_backward 4.7540 |
[2023-10-23 21:22:56,158::train::INFO] [train] Iter 575584 | loss 0.8678 | loss(rot) 0.1251 | loss(pos) 0.0478 | loss(seq) 0.6949 | grad 3.3658 | lr 0.0000 | time_forward 2.7140 | time_backward 3.5500 |
[2023-10-23 21:23:03,843::train::INFO] [train] Iter 575585 | loss 0.9580 | loss(rot) 0.7560 | loss(pos) 0.0532 | loss(seq) 0.1488 | grad 4.8467 | lr 0.0000 | time_forward 3.1790 | time_backward 4.5030 |
[2023-10-23 21:23:12,590::train::INFO] [train] Iter 575586 | loss 0.8922 | loss(rot) 0.8263 | loss(pos) 0.0307 | loss(seq) 0.0352 | grad 38.7867 | lr 0.0000 | time_forward 3.3560 | time_backward 5.3870 |
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