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[2023-10-24 02:33:58,972::train::INFO] [train] Iter 578174 | loss 0.7795 | loss(rot) 0.5017 | loss(pos) 0.2169 | loss(seq) 0.0609 | grad 4.4784 | lr 0.0000 | time_forward 3.5750 | time_backward 4.8850 |
[2023-10-24 02:34:06,282::train::INFO] [train] Iter 578175 | loss 0.6074 | loss(rot) 0.5153 | loss(pos) 0.0427 | loss(seq) 0.0494 | grad 4.1961 | lr 0.0000 | time_forward 3.1250 | time_backward 4.1830 |
[2023-10-24 02:34:08,990::train::INFO] [train] Iter 578176 | loss 0.3397 | loss(rot) 0.0669 | loss(pos) 0.0200 | loss(seq) 0.2527 | grad 2.3218 | lr 0.0000 | time_forward 1.2570 | time_backward 1.4480 |
[2023-10-24 02:34:19,014::train::INFO] [train] Iter 578177 | loss 0.9865 | loss(rot) 0.9270 | loss(pos) 0.0537 | loss(seq) 0.0058 | grad 3.8675 | lr 0.0000 | time_forward 3.9940 | time_backward 5.9970 |
[2023-10-24 02:34:29,453::train::INFO] [train] Iter 578178 | loss 1.4190 | loss(rot) 1.1878 | loss(pos) 0.0317 | loss(seq) 0.1994 | grad 3.2045 | lr 0.0000 | time_forward 4.1540 | time_backward 6.2830 |
[2023-10-24 02:34:38,161::train::INFO] [train] Iter 578179 | loss 0.4715 | loss(rot) 0.1693 | loss(pos) 0.0467 | loss(seq) 0.2555 | grad 2.7865 | lr 0.0000 | time_forward 3.7520 | time_backward 4.9530 |
[2023-10-24 02:34:46,430::train::INFO] [train] Iter 578180 | loss 0.4026 | loss(rot) 0.3644 | loss(pos) 0.0250 | loss(seq) 0.0132 | grad 40.5925 | lr 0.0000 | time_forward 3.4820 | time_backward 4.7830 |
[2023-10-24 02:34:56,070::train::INFO] [train] Iter 578181 | loss 0.5225 | loss(rot) 0.2490 | loss(pos) 0.0543 | loss(seq) 0.2192 | grad 3.3272 | lr 0.0000 | time_forward 3.9290 | time_backward 5.7080 |
[2023-10-24 02:35:04,619::train::INFO] [train] Iter 578182 | loss 0.1820 | loss(rot) 0.0878 | loss(pos) 0.0769 | loss(seq) 0.0174 | grad 3.5253 | lr 0.0000 | time_forward 3.6300 | time_backward 4.9160 |
[2023-10-24 02:35:13,488::train::INFO] [train] Iter 578183 | loss 0.3469 | loss(rot) 0.1447 | loss(pos) 0.0379 | loss(seq) 0.1643 | grad 2.6446 | lr 0.0000 | time_forward 3.7780 | time_backward 5.0870 |
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