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[2023-10-24 16:58:55,044::train::INFO] [train] Iter 585567 | loss 0.4465 | loss(rot) 0.1135 | loss(pos) 0.0490 | loss(seq) 0.2840 | grad 3.3069 | lr 0.0000 | time_forward 2.5290 | time_backward 3.2350 |
[2023-10-24 16:58:57,772::train::INFO] [train] Iter 585568 | loss 0.2071 | loss(rot) 0.0406 | loss(pos) 0.1205 | loss(seq) 0.0461 | grad 3.1427 | lr 0.0000 | time_forward 1.2970 | time_backward 1.4270 |
[2023-10-24 16:59:04,753::train::INFO] [train] Iter 585569 | loss 0.4317 | loss(rot) 0.3748 | loss(pos) 0.0316 | loss(seq) 0.0252 | grad 3.5376 | lr 0.0000 | time_forward 3.0180 | time_backward 3.9610 |
[2023-10-24 16:59:07,578::train::INFO] [train] Iter 585570 | loss 0.6077 | loss(rot) 0.3437 | loss(pos) 0.0323 | loss(seq) 0.2317 | grad 2.9307 | lr 0.0000 | time_forward 1.2850 | time_backward 1.5360 |
[2023-10-24 16:59:14,987::train::INFO] [train] Iter 585571 | loss 0.3560 | loss(rot) 0.1654 | loss(pos) 0.0115 | loss(seq) 0.1791 | grad 1.8768 | lr 0.0000 | time_forward 3.1960 | time_backward 4.2100 |
[2023-10-24 16:59:23,037::train::INFO] [train] Iter 585572 | loss 1.4818 | loss(rot) 1.3587 | loss(pos) 0.0510 | loss(seq) 0.0721 | grad 5.3234 | lr 0.0000 | time_forward 3.3190 | time_backward 4.7270 |
[2023-10-24 16:59:30,249::train::INFO] [train] Iter 585573 | loss 1.5152 | loss(rot) 0.6509 | loss(pos) 0.1702 | loss(seq) 0.6940 | grad 4.0515 | lr 0.0000 | time_forward 3.1150 | time_backward 4.0940 |
[2023-10-24 16:59:37,244::train::INFO] [train] Iter 585574 | loss 0.6339 | loss(rot) 0.2851 | loss(pos) 0.1895 | loss(seq) 0.1593 | grad 3.8953 | lr 0.0000 | time_forward 2.9810 | time_backward 4.0110 |
[2023-10-24 16:59:45,446::train::INFO] [train] Iter 585575 | loss 1.3156 | loss(rot) 0.9355 | loss(pos) 0.0364 | loss(seq) 0.3437 | grad 3.7133 | lr 0.0000 | time_forward 3.4020 | time_backward 4.7970 |
[2023-10-24 16:59:53,633::train::INFO] [train] Iter 585576 | loss 0.9982 | loss(rot) 0.0177 | loss(pos) 0.9579 | loss(seq) 0.0226 | grad 9.5361 | lr 0.0000 | time_forward 3.3940 | time_backward 4.7900 |
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