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[2023-10-25 07:16:58,224::train::INFO] [train] Iter 593159 | loss 0.2481 | loss(rot) 0.2059 | loss(pos) 0.0335 | loss(seq) 0.0088 | grad 2.2389 | lr 0.0000 | time_forward 3.2300 | time_backward 4.3120 |
[2023-10-25 07:17:04,993::train::INFO] [train] Iter 593160 | loss 1.1693 | loss(rot) 0.7165 | loss(pos) 0.0282 | loss(seq) 0.4246 | grad 5.4652 | lr 0.0000 | time_forward 2.9020 | time_backward 3.8640 |
[2023-10-25 07:17:11,078::train::INFO] [train] Iter 593161 | loss 0.3101 | loss(rot) 0.2824 | loss(pos) 0.0265 | loss(seq) 0.0011 | grad 4.0716 | lr 0.0000 | time_forward 2.5650 | time_backward 3.5160 |
[2023-10-25 07:17:18,607::train::INFO] [train] Iter 593162 | loss 0.2672 | loss(rot) 0.1132 | loss(pos) 0.1443 | loss(seq) 0.0098 | grad 4.5919 | lr 0.0000 | time_forward 3.1960 | time_backward 4.3310 |
[2023-10-25 07:17:27,523::train::INFO] [train] Iter 593163 | loss 0.4866 | loss(rot) 0.1301 | loss(pos) 0.0872 | loss(seq) 0.2694 | grad 3.0569 | lr 0.0000 | time_forward 3.6680 | time_backward 5.2450 |
[2023-10-25 07:17:36,373::train::INFO] [train] Iter 593164 | loss 0.2929 | loss(rot) 0.1463 | loss(pos) 0.0232 | loss(seq) 0.1235 | grad 6.8147 | lr 0.0000 | time_forward 3.5570 | time_backward 5.2890 |
[2023-10-25 07:17:45,344::train::INFO] [train] Iter 593165 | loss 0.5505 | loss(rot) 0.0905 | loss(pos) 0.0445 | loss(seq) 0.4155 | grad 2.6581 | lr 0.0000 | time_forward 3.6410 | time_backward 5.3270 |
[2023-10-25 07:17:54,090::train::INFO] [train] Iter 593166 | loss 1.2482 | loss(rot) 1.2184 | loss(pos) 0.0298 | loss(seq) 0.0000 | grad 3.6625 | lr 0.0000 | time_forward 3.7330 | time_backward 5.0090 |
[2023-10-25 07:17:56,863::train::INFO] [train] Iter 593167 | loss 2.2064 | loss(rot) 1.5311 | loss(pos) 0.1320 | loss(seq) 0.5433 | grad 6.8137 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4630 |
[2023-10-25 07:17:59,162::train::INFO] [train] Iter 593168 | loss 0.5069 | loss(rot) 0.3211 | loss(pos) 0.1187 | loss(seq) 0.0671 | grad 4.9955 | lr 0.0000 | time_forward 1.0490 | time_backward 1.2290 |
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