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[2023-09-02 13:15:49,854::train::INFO] [train] Iter 10275 | loss 2.2674 | loss(rot) 0.0141 | loss(pos) 2.2526 | loss(seq) 0.0007 | grad 6.3036 | lr 0.0010 | time_forward 3.8320 | time_backward 5.4830 |
[2023-09-02 13:15:52,637::train::INFO] [train] Iter 10276 | loss 1.8996 | loss(rot) 0.9509 | loss(pos) 0.5157 | loss(seq) 0.4330 | grad 5.4573 | lr 0.0010 | time_forward 1.3370 | time_backward 1.4430 |
[2023-09-02 13:15:55,348::train::INFO] [train] Iter 10277 | loss 2.3122 | loss(rot) 1.3134 | loss(pos) 0.4528 | loss(seq) 0.5460 | grad 4.1271 | lr 0.0010 | time_forward 1.2800 | time_backward 1.4280 |
[2023-09-02 13:15:58,114::train::INFO] [train] Iter 10278 | loss 1.9693 | loss(rot) 1.7542 | loss(pos) 0.1435 | loss(seq) 0.0715 | grad 5.0235 | lr 0.0010 | time_forward 1.3250 | time_backward 1.4370 |
[2023-09-02 13:16:00,828::train::INFO] [train] Iter 10279 | loss 1.9152 | loss(rot) 0.6996 | loss(pos) 0.8619 | loss(seq) 0.3538 | grad 5.5566 | lr 0.0010 | time_forward 1.2760 | time_backward 1.4340 |
[2023-09-02 13:16:09,052::train::INFO] [train] Iter 10280 | loss 1.7745 | loss(rot) 0.0598 | loss(pos) 1.4614 | loss(seq) 0.2533 | grad 7.4674 | lr 0.0010 | time_forward 3.5300 | time_backward 4.6900 |
[2023-09-02 13:16:11,858::train::INFO] [train] Iter 10281 | loss 2.6019 | loss(rot) 2.4794 | loss(pos) 0.1183 | loss(seq) 0.0042 | grad 9.5473 | lr 0.0010 | time_forward 1.3890 | time_backward 1.4130 |
[2023-09-02 13:16:22,077::train::INFO] [train] Iter 10282 | loss 1.1420 | loss(rot) 0.1938 | loss(pos) 0.5697 | loss(seq) 0.3784 | grad 3.7773 | lr 0.0010 | time_forward 4.2040 | time_backward 6.0120 |
[2023-09-02 13:16:32,886::train::INFO] [train] Iter 10283 | loss 2.1732 | loss(rot) 1.9333 | loss(pos) 0.1594 | loss(seq) 0.0806 | grad 4.4173 | lr 0.0010 | time_forward 4.4550 | time_backward 6.3520 |
[2023-09-02 13:16:35,665::train::INFO] [train] Iter 10284 | loss 1.6799 | loss(rot) 0.7198 | loss(pos) 0.4337 | loss(seq) 0.5264 | grad 5.4953 | lr 0.0010 | time_forward 1.3580 | time_backward 1.4170 |
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