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[2023-10-24 07:38:29,678::train::INFO] [train] Iter 580772 | loss 0.1293 | loss(rot) 0.0577 | loss(pos) 0.0151 | loss(seq) 0.0565 | grad 1.4867 | lr 0.0000 | time_forward 1.3270 | time_backward 1.5270 |
[2023-10-24 07:38:32,478::train::INFO] [train] Iter 580773 | loss 0.4619 | loss(rot) 0.0864 | loss(pos) 0.0174 | loss(seq) 0.3582 | grad 2.0550 | lr 0.0000 | time_forward 1.3360 | time_backward 1.4610 |
[2023-10-24 07:38:39,799::train::INFO] [train] Iter 580774 | loss 0.7385 | loss(rot) 0.5402 | loss(pos) 0.0710 | loss(seq) 0.1272 | grad 17.6744 | lr 0.0000 | time_forward 3.1670 | time_backward 4.1490 |
[2023-10-24 07:38:49,230::train::INFO] [train] Iter 580775 | loss 0.4027 | loss(rot) 0.3417 | loss(pos) 0.0276 | loss(seq) 0.0335 | grad 2.7753 | lr 0.0000 | time_forward 3.8700 | time_backward 5.5570 |
[2023-10-24 07:38:56,968::train::INFO] [train] Iter 580776 | loss 0.4854 | loss(rot) 0.1489 | loss(pos) 0.3322 | loss(seq) 0.0043 | grad 9.1189 | lr 0.0000 | time_forward 3.2090 | time_backward 4.5260 |
[2023-10-24 07:38:59,446::train::INFO] [train] Iter 580777 | loss 0.5011 | loss(rot) 0.1556 | loss(pos) 0.1142 | loss(seq) 0.2313 | grad 4.1406 | lr 0.0000 | time_forward 1.2020 | time_backward 1.2730 |
[2023-10-24 07:39:07,742::train::INFO] [train] Iter 580778 | loss 0.8781 | loss(rot) 0.0143 | loss(pos) 0.4768 | loss(seq) 0.3869 | grad 8.0117 | lr 0.0000 | time_forward 3.4390 | time_backward 4.8410 |
[2023-10-24 07:39:13,303::train::INFO] [train] Iter 580779 | loss 1.4121 | loss(rot) 1.0137 | loss(pos) 0.0661 | loss(seq) 0.3323 | grad 4.9906 | lr 0.0000 | time_forward 2.3790 | time_backward 3.1790 |
[2023-10-24 07:39:16,021::train::INFO] [train] Iter 580780 | loss 0.6042 | loss(rot) 0.3857 | loss(pos) 0.0301 | loss(seq) 0.1883 | grad 3.2358 | lr 0.0000 | time_forward 1.2940 | time_backward 1.4210 |
[2023-10-24 07:39:25,475::train::INFO] [train] Iter 580781 | loss 1.7996 | loss(rot) 1.7126 | loss(pos) 0.0346 | loss(seq) 0.0525 | grad 3.7225 | lr 0.0000 | time_forward 3.8520 | time_backward 5.5980 |
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