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[2023-10-22 16:30:48,084::train::INFO] [train] Iter 559593 | loss 0.7864 | loss(rot) 0.2139 | loss(pos) 0.0800 | loss(seq) 0.4924 | grad 3.0774 | lr 0.0000 | time_forward 3.9780 | time_backward 5.6480 |
[2023-10-22 16:30:58,331::train::INFO] [train] Iter 559594 | loss 3.7299 | loss(rot) 0.0019 | loss(pos) 3.7280 | loss(seq) 0.0000 | grad 14.3301 | lr 0.0000 | time_forward 4.4900 | time_backward 5.7530 |
[2023-10-22 16:31:06,398::train::INFO] [train] Iter 559595 | loss 0.6670 | loss(rot) 0.3043 | loss(pos) 0.1272 | loss(seq) 0.2355 | grad 4.5773 | lr 0.0000 | time_forward 3.4540 | time_backward 4.6090 |
[2023-10-22 16:31:16,064::train::INFO] [train] Iter 559596 | loss 0.6191 | loss(rot) 0.1352 | loss(pos) 0.0404 | loss(seq) 0.4435 | grad 2.8944 | lr 0.0000 | time_forward 4.0220 | time_backward 5.6400 |
[2023-10-22 16:31:25,675::train::INFO] [train] Iter 559597 | loss 1.2814 | loss(rot) 0.8168 | loss(pos) 0.1072 | loss(seq) 0.3575 | grad 4.7772 | lr 0.0000 | time_forward 3.9480 | time_backward 5.6610 |
[2023-10-22 16:31:28,457::train::INFO] [train] Iter 559598 | loss 0.1191 | loss(rot) 0.0294 | loss(pos) 0.0304 | loss(seq) 0.0593 | grad 2.1889 | lr 0.0000 | time_forward 1.3460 | time_backward 1.4330 |
[2023-10-22 16:31:38,311::train::INFO] [train] Iter 559599 | loss 0.5355 | loss(rot) 0.3593 | loss(pos) 0.1391 | loss(seq) 0.0371 | grad 3.3432 | lr 0.0000 | time_forward 4.3600 | time_backward 5.4900 |
[2023-10-22 16:31:40,638::train::INFO] [train] Iter 559600 | loss 0.7199 | loss(rot) 0.1420 | loss(pos) 0.0536 | loss(seq) 0.5243 | grad 3.8739 | lr 0.0000 | time_forward 1.0700 | time_backward 1.2540 |
[2023-10-22 16:31:48,466::train::INFO] [train] Iter 559601 | loss 1.0214 | loss(rot) 0.9918 | loss(pos) 0.0190 | loss(seq) 0.0106 | grad 6.3293 | lr 0.0000 | time_forward 3.3410 | time_backward 4.4850 |
[2023-10-22 16:31:57,900::train::INFO] [train] Iter 559602 | loss 0.7950 | loss(rot) 0.3561 | loss(pos) 0.1064 | loss(seq) 0.3326 | grad 2.7254 | lr 0.0000 | time_forward 4.0610 | time_backward 5.3690 |
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