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[2023-10-22 19:39:17,448::train::INFO] [train] Iter 561191 | loss 0.6571 | loss(rot) 0.1621 | loss(pos) 0.0710 | loss(seq) 0.4240 | grad 2.7645 | lr 0.0000 | time_forward 3.3210 | time_backward 4.4730 |
[2023-10-22 19:39:20,437::train::INFO] [train] Iter 561192 | loss 0.3715 | loss(rot) 0.0469 | loss(pos) 0.0268 | loss(seq) 0.2977 | grad 1.9717 | lr 0.0000 | time_forward 1.3690 | time_backward 1.6160 |
[2023-10-22 19:39:29,831::train::INFO] [train] Iter 561193 | loss 1.8671 | loss(rot) 1.4080 | loss(pos) 0.1147 | loss(seq) 0.3444 | grad 11.7493 | lr 0.0000 | time_forward 3.9550 | time_backward 5.4340 |
[2023-10-22 19:39:38,284::train::INFO] [train] Iter 561194 | loss 0.3094 | loss(rot) 0.0539 | loss(pos) 0.0105 | loss(seq) 0.2450 | grad 2.1520 | lr 0.0000 | time_forward 3.5230 | time_backward 4.9270 |
[2023-10-22 19:39:41,324::train::INFO] [train] Iter 561195 | loss 0.1955 | loss(rot) 0.1556 | loss(pos) 0.0387 | loss(seq) 0.0012 | grad 2.2892 | lr 0.0000 | time_forward 1.4740 | time_backward 1.5630 |
[2023-10-22 19:39:44,154::train::INFO] [train] Iter 561196 | loss 1.6457 | loss(rot) 1.0565 | loss(pos) 0.0580 | loss(seq) 0.5312 | grad 5.4503 | lr 0.0000 | time_forward 1.3690 | time_backward 1.4570 |
[2023-10-22 19:39:46,942::train::INFO] [train] Iter 561197 | loss 0.9451 | loss(rot) 0.5681 | loss(pos) 0.0719 | loss(seq) 0.3051 | grad 8.0063 | lr 0.0000 | time_forward 1.3670 | time_backward 1.4190 |
[2023-10-22 19:39:56,857::train::INFO] [train] Iter 561198 | loss 0.4052 | loss(rot) 0.3873 | loss(pos) 0.0172 | loss(seq) 0.0007 | grad 4.0676 | lr 0.0000 | time_forward 4.2680 | time_backward 5.6430 |
[2023-10-22 19:40:06,045::train::INFO] [train] Iter 561199 | loss 0.2328 | loss(rot) 0.2077 | loss(pos) 0.0201 | loss(seq) 0.0050 | grad 2.0601 | lr 0.0000 | time_forward 3.9710 | time_backward 5.2140 |
[2023-10-22 19:40:13,229::train::INFO] [train] Iter 561200 | loss 1.5450 | loss(rot) 0.2022 | loss(pos) 1.3419 | loss(seq) 0.0009 | grad 10.4351 | lr 0.0000 | time_forward 3.0800 | time_backward 4.1010 |
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