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[2023-10-22 17:06:45,666::train::INFO] [train] Iter 559893 | loss 1.6485 | loss(rot) 0.9142 | loss(pos) 0.2466 | loss(seq) 0.4877 | grad 6.2020 | lr 0.0000 | time_forward 4.0750 | time_backward 5.1070 |
[2023-10-22 17:06:53,513::train::INFO] [train] Iter 559894 | loss 1.8903 | loss(rot) 1.8106 | loss(pos) 0.0348 | loss(seq) 0.0449 | grad 2.6038 | lr 0.0000 | time_forward 3.2280 | time_backward 4.6150 |
[2023-10-22 17:07:02,603::train::INFO] [train] Iter 559895 | loss 0.3289 | loss(rot) 0.0452 | loss(pos) 0.2782 | loss(seq) 0.0054 | grad 4.2407 | lr 0.0000 | time_forward 3.6680 | time_backward 5.4190 |
[2023-10-22 17:07:09,986::train::INFO] [train] Iter 559896 | loss 0.0620 | loss(rot) 0.0385 | loss(pos) 0.0216 | loss(seq) 0.0019 | grad 1.2748 | lr 0.0000 | time_forward 3.1410 | time_backward 4.2380 |
[2023-10-22 17:07:20,322::train::INFO] [train] Iter 559897 | loss 0.3942 | loss(rot) 0.0378 | loss(pos) 0.3525 | loss(seq) 0.0039 | grad 3.2106 | lr 0.0000 | time_forward 4.6200 | time_backward 5.7130 |
[2023-10-22 17:07:31,415::train::INFO] [train] Iter 559898 | loss 0.2908 | loss(rot) 0.2497 | loss(pos) 0.0321 | loss(seq) 0.0090 | grad 2.5463 | lr 0.0000 | time_forward 5.2060 | time_backward 5.8820 |
[2023-10-22 17:07:33,739::train::INFO] [train] Iter 559899 | loss 0.3656 | loss(rot) 0.3194 | loss(pos) 0.0438 | loss(seq) 0.0024 | grad 3.0251 | lr 0.0000 | time_forward 1.0680 | time_backward 1.2520 |
[2023-10-22 17:07:42,592::train::INFO] [train] Iter 559900 | loss 0.3585 | loss(rot) 0.1520 | loss(pos) 0.0623 | loss(seq) 0.1443 | grad 3.1424 | lr 0.0000 | time_forward 3.6850 | time_backward 5.1650 |
[2023-10-22 17:07:45,449::train::INFO] [train] Iter 559901 | loss 1.5390 | loss(rot) 1.0790 | loss(pos) 0.0802 | loss(seq) 0.3797 | grad 4.0205 | lr 0.0000 | time_forward 1.3450 | time_backward 1.5090 |
[2023-10-22 17:07:55,967::train::INFO] [train] Iter 559902 | loss 0.1951 | loss(rot) 0.1221 | loss(pos) 0.0093 | loss(seq) 0.0637 | grad 1.8541 | lr 0.0000 | time_forward 4.0360 | time_backward 6.4800 |
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