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[2023-10-25 01:51:18,347::train::INFO] [train] Iter 590161 | loss 1.5567 | loss(rot) 1.5335 | loss(pos) 0.0178 | loss(seq) 0.0053 | grad 3.3555 | lr 0.0000 | time_forward 3.4320 | time_backward 4.5900 |
[2023-10-25 01:51:25,703::train::INFO] [train] Iter 590162 | loss 0.9661 | loss(rot) 0.9485 | loss(pos) 0.0119 | loss(seq) 0.0057 | grad 5.6064 | lr 0.0000 | time_forward 3.0390 | time_backward 4.3130 |
[2023-10-25 01:51:27,962::train::INFO] [train] Iter 590163 | loss 0.6746 | loss(rot) 0.2644 | loss(pos) 0.1063 | loss(seq) 0.3040 | grad 4.3751 | lr 0.0000 | time_forward 1.0320 | time_backward 1.2230 |
[2023-10-25 01:51:34,734::train::INFO] [train] Iter 590164 | loss 1.6409 | loss(rot) 0.0260 | loss(pos) 1.6140 | loss(seq) 0.0009 | grad 15.4648 | lr 0.0000 | time_forward 2.9120 | time_backward 3.8580 |
[2023-10-25 01:51:43,628::train::INFO] [train] Iter 590165 | loss 0.5004 | loss(rot) 0.2486 | loss(pos) 0.0526 | loss(seq) 0.1993 | grad 3.4185 | lr 0.0000 | time_forward 3.6530 | time_backward 5.2380 |
[2023-10-25 01:51:46,362::train::INFO] [train] Iter 590166 | loss 1.9495 | loss(rot) 0.9577 | loss(pos) 0.4777 | loss(seq) 0.5140 | grad 5.7147 | lr 0.0000 | time_forward 1.2960 | time_backward 1.4350 |
[2023-10-25 01:51:53,570::train::INFO] [train] Iter 590167 | loss 2.4858 | loss(rot) 0.4999 | loss(pos) 1.9850 | loss(seq) 0.0009 | grad 19.7068 | lr 0.0000 | time_forward 3.0700 | time_backward 4.1160 |
[2023-10-25 01:51:56,282::train::INFO] [train] Iter 590168 | loss 0.5555 | loss(rot) 0.4382 | loss(pos) 0.1074 | loss(seq) 0.0099 | grad 2.8916 | lr 0.0000 | time_forward 1.2780 | time_backward 1.4300 |
[2023-10-25 01:52:03,554::train::INFO] [train] Iter 590169 | loss 0.6047 | loss(rot) 0.5681 | loss(pos) 0.0152 | loss(seq) 0.0214 | grad 3.4229 | lr 0.0000 | time_forward 3.1010 | time_backward 4.1680 |
[2023-10-25 01:52:06,254::train::INFO] [train] Iter 590170 | loss 0.5252 | loss(rot) 0.1347 | loss(pos) 0.0515 | loss(seq) 0.3390 | grad 2.9942 | lr 0.0000 | time_forward 1.3060 | time_backward 1.3920 |
[2023-10-25 01:52:09,055::train::INFO] [train] Iter 590171 | loss 0.0866 | loss(rot) 0.0597 | loss(pos) 0.0182 | loss(seq) 0.0087 | grad 1.1809 | lr 0.0000 | time_forward 1.3540 | time_backward 1.4430 |
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