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[2023-10-23 06:12:06,301::train::INFO] [train] Iter 567285 | loss 0.6651 | loss(rot) 0.3351 | loss(pos) 0.3188 | loss(seq) 0.0112 | grad 4.3065 | lr 0.0000 | time_forward 3.4180 | time_backward 4.7110 |
[2023-10-23 06:12:14,273::train::INFO] [train] Iter 567286 | loss 0.2769 | loss(rot) 0.2575 | loss(pos) 0.0185 | loss(seq) 0.0009 | grad 21.3632 | lr 0.0000 | time_forward 3.3350 | time_backward 4.6340 |
[2023-10-23 06:12:20,653::train::INFO] [train] Iter 567287 | loss 1.1433 | loss(rot) 0.6000 | loss(pos) 0.1327 | loss(seq) 0.4106 | grad 3.5208 | lr 0.0000 | time_forward 2.6810 | time_backward 3.6960 |
[2023-10-23 06:12:27,583::train::INFO] [train] Iter 567288 | loss 1.1146 | loss(rot) 1.0632 | loss(pos) 0.0254 | loss(seq) 0.0259 | grad 3.0922 | lr 0.0000 | time_forward 3.0090 | time_backward 3.9180 |
[2023-10-23 06:12:35,561::train::INFO] [train] Iter 567289 | loss 1.1817 | loss(rot) 0.8441 | loss(pos) 0.1105 | loss(seq) 0.2271 | grad 3.7817 | lr 0.0000 | time_forward 3.3730 | time_backward 4.6030 |
[2023-10-23 06:12:43,646::train::INFO] [train] Iter 567290 | loss 0.5803 | loss(rot) 0.4096 | loss(pos) 0.1480 | loss(seq) 0.0227 | grad 4.9493 | lr 0.0000 | time_forward 3.3230 | time_backward 4.7580 |
[2023-10-23 06:12:51,474::train::INFO] [train] Iter 567291 | loss 0.7211 | loss(rot) 0.6216 | loss(pos) 0.0532 | loss(seq) 0.0463 | grad 4.1871 | lr 0.0000 | time_forward 3.4220 | time_backward 4.4030 |
[2023-10-23 06:12:59,379::train::INFO] [train] Iter 567292 | loss 1.5565 | loss(rot) 1.2407 | loss(pos) 0.0700 | loss(seq) 0.2459 | grad 3.2362 | lr 0.0000 | time_forward 3.4300 | time_backward 4.4710 |
[2023-10-23 06:13:07,551::train::INFO] [train] Iter 567293 | loss 0.5215 | loss(rot) 0.1293 | loss(pos) 0.0221 | loss(seq) 0.3700 | grad 2.4638 | lr 0.0000 | time_forward 3.5860 | time_backward 4.5830 |
[2023-10-23 06:13:12,934::train::INFO] [train] Iter 567294 | loss 1.0084 | loss(rot) 0.9773 | loss(pos) 0.0280 | loss(seq) 0.0031 | grad 3.6737 | lr 0.0000 | time_forward 2.2600 | time_backward 3.1190 |
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