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[2023-10-24 04:20:01,390::train::INFO] [train] Iter 579073 | loss 1.3208 | loss(rot) 1.3019 | loss(pos) 0.0173 | loss(seq) 0.0017 | grad 6.2240 | lr 0.0000 | time_forward 1.5220 | time_backward 1.9350 |
[2023-10-24 04:20:04,455::train::INFO] [train] Iter 579074 | loss 0.1955 | loss(rot) 0.1729 | loss(pos) 0.0222 | loss(seq) 0.0004 | grad 3.1488 | lr 0.0000 | time_forward 1.4140 | time_backward 1.6480 |
[2023-10-24 04:20:07,145::train::INFO] [train] Iter 579075 | loss 0.8714 | loss(rot) 0.1640 | loss(pos) 0.2161 | loss(seq) 0.4912 | grad 4.3663 | lr 0.0000 | time_forward 1.2170 | time_backward 1.4340 |
[2023-10-24 04:20:15,194::train::INFO] [train] Iter 579076 | loss 0.3827 | loss(rot) 0.1534 | loss(pos) 0.0265 | loss(seq) 0.2029 | grad 2.1958 | lr 0.0000 | time_forward 3.3940 | time_backward 4.6530 |
[2023-10-24 04:20:25,596::train::INFO] [train] Iter 579077 | loss 0.2556 | loss(rot) 0.1363 | loss(pos) 0.0979 | loss(seq) 0.0214 | grad 3.1420 | lr 0.0000 | time_forward 3.9830 | time_backward 6.4150 |
[2023-10-24 04:20:35,182::train::INFO] [train] Iter 579078 | loss 1.7362 | loss(rot) 1.2756 | loss(pos) 0.0641 | loss(seq) 0.3966 | grad 3.7908 | lr 0.0000 | time_forward 3.9580 | time_backward 5.6260 |
[2023-10-24 04:20:38,238::train::INFO] [train] Iter 579079 | loss 0.7498 | loss(rot) 0.7292 | loss(pos) 0.0205 | loss(seq) 0.0001 | grad 50.7565 | lr 0.0000 | time_forward 1.5220 | time_backward 1.5310 |
[2023-10-24 04:20:41,136::train::INFO] [train] Iter 579080 | loss 0.3917 | loss(rot) 0.2096 | loss(pos) 0.0721 | loss(seq) 0.1100 | grad 2.8645 | lr 0.0000 | time_forward 1.4090 | time_backward 1.4860 |
[2023-10-24 04:20:45,953::train::INFO] [train] Iter 579081 | loss 1.7969 | loss(rot) 1.7809 | loss(pos) 0.0123 | loss(seq) 0.0038 | grad 3.9054 | lr 0.0000 | time_forward 2.2690 | time_backward 2.5440 |
[2023-10-24 04:20:53,704::train::INFO] [train] Iter 579082 | loss 0.9888 | loss(rot) 0.2794 | loss(pos) 0.6880 | loss(seq) 0.0213 | grad 5.0933 | lr 0.0000 | time_forward 3.2770 | time_backward 4.4690 |
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