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[2023-10-24 05:30:59,508::train::INFO] [train] Iter 579673 | loss 0.5557 | loss(rot) 0.0240 | loss(pos) 0.5233 | loss(seq) 0.0084 | grad 7.1902 | lr 0.0000 | time_forward 3.3750 | time_backward 4.5540 |
[2023-10-24 05:31:07,530::train::INFO] [train] Iter 579674 | loss 0.5309 | loss(rot) 0.5056 | loss(pos) 0.0223 | loss(seq) 0.0030 | grad 2.5167 | lr 0.0000 | time_forward 3.3130 | time_backward 4.7050 |
[2023-10-24 05:31:15,210::train::INFO] [train] Iter 579675 | loss 0.6820 | loss(rot) 0.6570 | loss(pos) 0.0247 | loss(seq) 0.0003 | grad 71.9073 | lr 0.0000 | time_forward 3.1660 | time_backward 4.5100 |
[2023-10-24 05:31:17,950::train::INFO] [train] Iter 579676 | loss 1.8580 | loss(rot) 1.0323 | loss(pos) 0.1881 | loss(seq) 0.6375 | grad 5.7446 | lr 0.0000 | time_forward 1.3280 | time_backward 1.4080 |
[2023-10-24 05:31:20,710::train::INFO] [train] Iter 579677 | loss 0.3755 | loss(rot) 0.2926 | loss(pos) 0.0282 | loss(seq) 0.0548 | grad 4.5720 | lr 0.0000 | time_forward 1.3090 | time_backward 1.4070 |
[2023-10-24 05:31:28,959::train::INFO] [train] Iter 579678 | loss 1.8756 | loss(rot) 1.8232 | loss(pos) 0.0356 | loss(seq) 0.0169 | grad 5.5721 | lr 0.0000 | time_forward 3.2770 | time_backward 4.9450 |
[2023-10-24 05:31:31,842::train::INFO] [train] Iter 579679 | loss 0.5319 | loss(rot) 0.2027 | loss(pos) 0.1281 | loss(seq) 0.2012 | grad 5.3101 | lr 0.0000 | time_forward 1.3510 | time_backward 1.5280 |
[2023-10-24 05:31:34,211::train::INFO] [train] Iter 579680 | loss 0.5764 | loss(rot) 0.1901 | loss(pos) 0.1284 | loss(seq) 0.2578 | grad 3.7491 | lr 0.0000 | time_forward 1.0800 | time_backward 1.2850 |
[2023-10-24 05:31:41,205::train::INFO] [train] Iter 579681 | loss 0.7088 | loss(rot) 0.2574 | loss(pos) 0.1774 | loss(seq) 0.2740 | grad 3.9961 | lr 0.0000 | time_forward 2.9740 | time_backward 4.0170 |
[2023-10-24 05:31:50,579::train::INFO] [train] Iter 579682 | loss 0.8266 | loss(rot) 0.7929 | loss(pos) 0.0334 | loss(seq) 0.0003 | grad 3.9197 | lr 0.0000 | time_forward 3.7400 | time_backward 5.6300 |
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