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[2023-10-25 00:46:12,437::train::INFO] [train] Iter 589563 | loss 0.1572 | loss(rot) 0.0413 | loss(pos) 0.0536 | loss(seq) 0.0623 | grad 2.5158 | lr 0.0000 | time_forward 3.6440 | time_backward 5.2530 |
[2023-10-25 00:46:18,917::train::INFO] [train] Iter 589564 | loss 1.6763 | loss(rot) 0.0060 | loss(pos) 1.6698 | loss(seq) 0.0004 | grad 16.3173 | lr 0.0000 | time_forward 2.7900 | time_backward 3.6860 |
[2023-10-25 00:46:21,684::train::INFO] [train] Iter 589565 | loss 0.4584 | loss(rot) 0.0987 | loss(pos) 0.3330 | loss(seq) 0.0266 | grad 3.4240 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4500 |
[2023-10-25 00:46:29,104::train::INFO] [train] Iter 589566 | loss 0.3558 | loss(rot) 0.0677 | loss(pos) 0.0184 | loss(seq) 0.2698 | grad 2.3394 | lr 0.0000 | time_forward 3.1730 | time_backward 4.2450 |
[2023-10-25 00:46:36,935::train::INFO] [train] Iter 589567 | loss 1.0709 | loss(rot) 0.8902 | loss(pos) 0.0780 | loss(seq) 0.1027 | grad 6.7961 | lr 0.0000 | time_forward 3.3670 | time_backward 4.4610 |
[2023-10-25 00:46:44,428::train::INFO] [train] Iter 589568 | loss 1.1215 | loss(rot) 0.4766 | loss(pos) 0.2094 | loss(seq) 0.4355 | grad 2.4875 | lr 0.0000 | time_forward 3.1620 | time_backward 4.3280 |
[2023-10-25 00:46:53,401::train::INFO] [train] Iter 589569 | loss 0.3243 | loss(rot) 0.3001 | loss(pos) 0.0240 | loss(seq) 0.0002 | grad 3.6575 | lr 0.0000 | time_forward 3.6890 | time_backward 5.2800 |
[2023-10-25 00:46:56,106::train::INFO] [train] Iter 589570 | loss 0.1819 | loss(rot) 0.0533 | loss(pos) 0.0379 | loss(seq) 0.0907 | grad 1.9967 | lr 0.0000 | time_forward 1.2390 | time_backward 1.4630 |
[2023-10-25 00:47:04,407::train::INFO] [train] Iter 589571 | loss 0.7859 | loss(rot) 0.1745 | loss(pos) 0.0248 | loss(seq) 0.5866 | grad 2.4785 | lr 0.0000 | time_forward 3.5120 | time_backward 4.7510 |
[2023-10-25 00:47:07,198::train::INFO] [train] Iter 589572 | loss 0.7869 | loss(rot) 0.7207 | loss(pos) 0.0630 | loss(seq) 0.0032 | grad 5.7851 | lr 0.0000 | time_forward 1.3240 | time_backward 1.4640 |
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