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[2023-10-25 05:31:17,311::train::INFO] [train] Iter 592160 | loss 2.8545 | loss(rot) 2.3040 | loss(pos) 0.2163 | loss(seq) 0.3343 | grad 21.6063 | lr 0.0000 | time_forward 3.8550 | time_backward 5.0980 |
[2023-10-25 05:31:19,706::train::INFO] [train] Iter 592161 | loss 0.4488 | loss(rot) 0.2253 | loss(pos) 0.0300 | loss(seq) 0.1934 | grad 2.7457 | lr 0.0000 | time_forward 1.1530 | time_backward 1.2400 |
[2023-10-25 05:31:22,563::train::INFO] [train] Iter 592162 | loss 0.3288 | loss(rot) 0.0691 | loss(pos) 0.0237 | loss(seq) 0.2360 | grad 1.9753 | lr 0.0000 | time_forward 1.3420 | time_backward 1.5110 |
[2023-10-25 05:31:30,562::train::INFO] [train] Iter 592163 | loss 1.4212 | loss(rot) 0.7170 | loss(pos) 0.0648 | loss(seq) 0.6394 | grad 6.2993 | lr 0.0000 | time_forward 3.3670 | time_backward 4.6290 |
[2023-10-25 05:31:33,143::train::INFO] [train] Iter 592164 | loss 0.5153 | loss(rot) 0.2942 | loss(pos) 0.0311 | loss(seq) 0.1900 | grad 3.0463 | lr 0.0000 | time_forward 1.2250 | time_backward 1.3530 |
[2023-10-25 05:31:40,004::train::INFO] [train] Iter 592165 | loss 1.0346 | loss(rot) 0.4144 | loss(pos) 0.0765 | loss(seq) 0.5438 | grad 3.4036 | lr 0.0000 | time_forward 2.9040 | time_backward 3.9500 |
[2023-10-25 05:31:49,063::train::INFO] [train] Iter 592166 | loss 5.0191 | loss(rot) 0.0071 | loss(pos) 5.0120 | loss(seq) 0.0000 | grad 39.1075 | lr 0.0000 | time_forward 3.6770 | time_backward 5.3780 |
[2023-10-25 05:31:57,161::train::INFO] [train] Iter 592167 | loss 0.5643 | loss(rot) 0.3867 | loss(pos) 0.1131 | loss(seq) 0.0645 | grad 3.6073 | lr 0.0000 | time_forward 3.4410 | time_backward 4.6540 |
[2023-10-25 05:32:06,052::train::INFO] [train] Iter 592168 | loss 0.6841 | loss(rot) 0.6450 | loss(pos) 0.0382 | loss(seq) 0.0009 | grad 21.7334 | lr 0.0000 | time_forward 3.7180 | time_backward 5.1700 |
[2023-10-25 05:32:15,037::train::INFO] [train] Iter 592169 | loss 0.6555 | loss(rot) 0.5936 | loss(pos) 0.0119 | loss(seq) 0.0499 | grad 3.9847 | lr 0.0000 | time_forward 3.7130 | time_backward 5.2680 |
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