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[2023-10-24 23:31:25,009::train::INFO] [train] Iter 588964 | loss 0.3843 | loss(rot) 0.0466 | loss(pos) 0.1617 | loss(seq) 0.1761 | grad 4.6230 | lr 0.0000 | time_forward 3.6750 | time_backward 5.0690 |
[2023-10-24 23:31:32,471::train::INFO] [train] Iter 588965 | loss 1.9590 | loss(rot) 1.5354 | loss(pos) 0.3611 | loss(seq) 0.0624 | grad 9.4577 | lr 0.0000 | time_forward 3.1880 | time_backward 4.2700 |
[2023-10-24 23:31:34,712::train::INFO] [train] Iter 588966 | loss 0.5610 | loss(rot) 0.0506 | loss(pos) 0.4905 | loss(seq) 0.0198 | grad 6.6933 | lr 0.0000 | time_forward 1.0390 | time_backward 1.1990 |
[2023-10-24 23:31:43,262::train::INFO] [train] Iter 588967 | loss 2.7205 | loss(rot) 2.0817 | loss(pos) 0.2400 | loss(seq) 0.3987 | grad 11.2282 | lr 0.0000 | time_forward 3.6030 | time_backward 4.9200 |
[2023-10-24 23:31:53,468::train::INFO] [train] Iter 588968 | loss 0.4018 | loss(rot) 0.2209 | loss(pos) 0.0743 | loss(seq) 0.1066 | grad 2.9671 | lr 0.0000 | time_forward 4.2200 | time_backward 5.9830 |
[2023-10-24 23:32:03,477::train::INFO] [train] Iter 588969 | loss 0.2453 | loss(rot) 0.0779 | loss(pos) 0.0778 | loss(seq) 0.0896 | grad 2.1165 | lr 0.0000 | time_forward 4.0920 | time_backward 5.9130 |
[2023-10-24 23:32:13,994::train::INFO] [train] Iter 588970 | loss 1.3497 | loss(rot) 1.3081 | loss(pos) 0.0253 | loss(seq) 0.0163 | grad 4.0981 | lr 0.0000 | time_forward 4.1300 | time_backward 6.3830 |
[2023-10-24 23:32:17,221::train::INFO] [train] Iter 588971 | loss 1.2600 | loss(rot) 0.6550 | loss(pos) 0.5940 | loss(seq) 0.0111 | grad 6.8438 | lr 0.0000 | time_forward 1.5730 | time_backward 1.6500 |
[2023-10-24 23:32:19,938::train::INFO] [train] Iter 588972 | loss 0.2596 | loss(rot) 0.1504 | loss(pos) 0.0171 | loss(seq) 0.0922 | grad 1.9285 | lr 0.0000 | time_forward 1.1950 | time_backward 1.4110 |
[2023-10-24 23:32:29,814::train::INFO] [train] Iter 588973 | loss 0.7280 | loss(rot) 0.6698 | loss(pos) 0.0345 | loss(seq) 0.0237 | grad 3.7204 | lr 0.0000 | time_forward 4.0870 | time_backward 5.7660 |
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