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[2023-10-24 23:59:03,987::train::INFO] [train] Iter 589163 | loss 0.2533 | loss(rot) 0.1840 | loss(pos) 0.0239 | loss(seq) 0.0454 | grad 2.3691 | lr 0.0000 | time_forward 4.4420 | time_backward 5.8180 |
[2023-10-24 23:59:15,548::train::INFO] [train] Iter 589164 | loss 0.6142 | loss(rot) 0.2591 | loss(pos) 0.1632 | loss(seq) 0.1920 | grad 2.7480 | lr 0.0000 | time_forward 4.9460 | time_backward 6.6120 |
[2023-10-24 23:59:26,529::train::INFO] [train] Iter 589165 | loss 2.2030 | loss(rot) 0.0057 | loss(pos) 2.1969 | loss(seq) 0.0004 | grad 14.3801 | lr 0.0000 | time_forward 4.7000 | time_backward 6.2770 |
[2023-10-24 23:59:36,334::train::INFO] [train] Iter 589166 | loss 1.2791 | loss(rot) 1.0551 | loss(pos) 0.0223 | loss(seq) 0.2016 | grad 1.6841 | lr 0.0000 | time_forward 4.1590 | time_backward 5.6430 |
[2023-10-24 23:59:39,535::train::INFO] [train] Iter 589167 | loss 0.3761 | loss(rot) 0.0942 | loss(pos) 0.1316 | loss(seq) 0.1503 | grad 3.2729 | lr 0.0000 | time_forward 1.4260 | time_backward 1.7720 |
[2023-10-24 23:59:49,156::train::INFO] [train] Iter 589168 | loss 0.4761 | loss(rot) 0.0399 | loss(pos) 0.4234 | loss(seq) 0.0128 | grad 5.3872 | lr 0.0000 | time_forward 4.0620 | time_backward 5.5550 |
[2023-10-25 00:00:00,552::train::INFO] [train] Iter 589169 | loss 0.5459 | loss(rot) 0.3010 | loss(pos) 0.0400 | loss(seq) 0.2049 | grad 3.6501 | lr 0.0000 | time_forward 4.7610 | time_backward 6.6320 |
[2023-10-25 00:00:12,383::train::INFO] [train] Iter 589170 | loss 2.8037 | loss(rot) 0.0023 | loss(pos) 2.8014 | loss(seq) 0.0000 | grad 11.6549 | lr 0.0000 | time_forward 5.1400 | time_backward 6.6880 |
[2023-10-25 00:00:22,838::train::INFO] [train] Iter 589171 | loss 0.2653 | loss(rot) 0.0712 | loss(pos) 0.1913 | loss(seq) 0.0027 | grad 4.7643 | lr 0.0000 | time_forward 4.4560 | time_backward 5.9960 |
[2023-10-25 00:00:33,621::train::INFO] [train] Iter 589172 | loss 0.6872 | loss(rot) 0.5891 | loss(pos) 0.0407 | loss(seq) 0.0574 | grad 3.5315 | lr 0.0000 | time_forward 4.4710 | time_backward 6.3080 |
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