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[2023-10-23 08:22:06,022::train::INFO] [train] Iter 568583 | loss 0.4314 | loss(rot) 0.3458 | loss(pos) 0.0316 | loss(seq) 0.0540 | grad 50.6367 | lr 0.0000 | time_forward 3.2600 | time_backward 4.2430 |
[2023-10-23 08:22:12,962::train::INFO] [train] Iter 568584 | loss 1.5971 | loss(rot) 1.5711 | loss(pos) 0.0174 | loss(seq) 0.0087 | grad 4.0339 | lr 0.0000 | time_forward 3.0090 | time_backward 3.9280 |
[2023-10-23 08:22:21,091::train::INFO] [train] Iter 568585 | loss 0.6652 | loss(rot) 0.3100 | loss(pos) 0.0773 | loss(seq) 0.2779 | grad 3.7971 | lr 0.0000 | time_forward 3.3390 | time_backward 4.7860 |
[2023-10-23 08:22:23,776::train::INFO] [train] Iter 568586 | loss 1.9647 | loss(rot) 1.4523 | loss(pos) 0.2740 | loss(seq) 0.2384 | grad 7.4389 | lr 0.0000 | time_forward 1.2680 | time_backward 1.4140 |
[2023-10-23 08:22:30,895::train::INFO] [train] Iter 568587 | loss 1.0335 | loss(rot) 0.5509 | loss(pos) 0.1430 | loss(seq) 0.3396 | grad 3.2835 | lr 0.0000 | time_forward 3.0750 | time_backward 4.0410 |
[2023-10-23 08:22:39,195::train::INFO] [train] Iter 568588 | loss 0.5235 | loss(rot) 0.0946 | loss(pos) 0.0562 | loss(seq) 0.3727 | grad 2.6580 | lr 0.0000 | time_forward 3.5320 | time_backward 4.7650 |
[2023-10-23 08:22:41,729::train::INFO] [train] Iter 568589 | loss 0.2077 | loss(rot) 0.0822 | loss(pos) 0.0727 | loss(seq) 0.0528 | grad 2.7913 | lr 0.0000 | time_forward 1.2550 | time_backward 1.2760 |
[2023-10-23 08:22:49,951::train::INFO] [train] Iter 568590 | loss 0.6019 | loss(rot) 0.3621 | loss(pos) 0.0366 | loss(seq) 0.2032 | grad 3.3556 | lr 0.0000 | time_forward 3.3970 | time_backward 4.8220 |
[2023-10-23 08:22:53,038::train::INFO] [train] Iter 568591 | loss 1.1117 | loss(rot) 1.0734 | loss(pos) 0.0382 | loss(seq) 0.0000 | grad 7.3833 | lr 0.0000 | time_forward 1.3960 | time_backward 1.6880 |
[2023-10-23 08:22:55,702::train::INFO] [train] Iter 568592 | loss 1.4180 | loss(rot) 0.0888 | loss(pos) 1.3286 | loss(seq) 0.0006 | grad 25.6246 | lr 0.0000 | time_forward 1.2170 | time_backward 1.4350 |
[2023-10-23 08:22:58,490::train::INFO] [train] Iter 568593 | loss 1.0585 | loss(rot) 1.0425 | loss(pos) 0.0144 | loss(seq) 0.0017 | grad 3.4857 | lr 0.0000 | time_forward 1.2930 | time_backward 1.4780 |
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