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[2023-09-01 22:37:39,320::train::INFO] [train] Iter 03082 | loss 1.0957 | loss(rot) 0.1996 | loss(pos) 0.8486 | loss(seq) 0.0476 | grad 4.4443 | lr 0.0010 | time_forward 3.3420 | time_backward 5.1390 |
[2023-09-01 22:37:47,851::train::INFO] [train] Iter 03083 | loss 2.6629 | loss(rot) 2.0980 | loss(pos) 0.5484 | loss(seq) 0.0165 | grad 6.5595 | lr 0.0010 | time_forward 3.5750 | time_backward 4.9520 |
[2023-09-01 22:37:50,539::train::INFO] [train] Iter 03084 | loss 1.1692 | loss(rot) 0.5572 | loss(pos) 0.4311 | loss(seq) 0.1810 | grad 5.1566 | lr 0.0010 | time_forward 1.2280 | time_backward 1.4560 |
[2023-09-01 22:37:55,298::train::INFO] [train] Iter 03085 | loss 0.7126 | loss(rot) 0.2655 | loss(pos) 0.3735 | loss(seq) 0.0736 | grad 3.4286 | lr 0.0010 | time_forward 2.0410 | time_backward 2.7140 |
[2023-09-01 22:37:57,866::train::INFO] [train] Iter 03086 | loss 2.2716 | loss(rot) 1.7548 | loss(pos) 0.1426 | loss(seq) 0.3743 | grad 4.8493 | lr 0.0010 | time_forward 1.1470 | time_backward 1.4180 |
[2023-09-01 22:38:05,021::train::INFO] [train] Iter 03087 | loss 1.6666 | loss(rot) 0.4754 | loss(pos) 0.7832 | loss(seq) 0.4080 | grad 4.1572 | lr 0.0010 | time_forward 3.0480 | time_backward 4.0640 |
[2023-09-01 22:38:12,581::train::INFO] [train] Iter 03088 | loss 1.0799 | loss(rot) 0.3839 | loss(pos) 0.2603 | loss(seq) 0.4357 | grad 3.5514 | lr 0.0010 | time_forward 3.1800 | time_backward 4.3770 |
[2023-09-01 22:38:14,841::train::INFO] [train] Iter 03089 | loss 2.3021 | loss(rot) 1.4315 | loss(pos) 0.3530 | loss(seq) 0.5176 | grad 5.1659 | lr 0.0010 | time_forward 1.0180 | time_backward 1.2380 |
[2023-09-01 22:38:23,271::train::INFO] [train] Iter 03090 | loss 2.4918 | loss(rot) 1.8641 | loss(pos) 0.3379 | loss(seq) 0.2897 | grad 5.6406 | lr 0.0010 | time_forward 3.5490 | time_backward 4.8780 |
[2023-09-01 22:38:25,568::train::INFO] [train] Iter 03091 | loss 2.2722 | loss(rot) 1.2158 | loss(pos) 0.5578 | loss(seq) 0.4986 | grad 6.2628 | lr 0.0010 | time_forward 1.0450 | time_backward 1.2500 |
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