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[2023-10-25 11:58:32,928::train::INFO] [train] Iter 595557 | loss 0.4998 | loss(rot) 0.0625 | loss(pos) 0.3207 | loss(seq) 0.1166 | grad 6.2154 | lr 0.0000 | time_forward 3.9650 | time_backward 5.2800 |
[2023-10-25 11:58:41,236::train::INFO] [train] Iter 595558 | loss 1.2518 | loss(rot) 1.1937 | loss(pos) 0.0353 | loss(seq) 0.0228 | grad 12.9930 | lr 0.0000 | time_forward 3.5030 | time_backward 4.8010 |
[2023-10-25 11:58:44,076::train::INFO] [train] Iter 595559 | loss 1.5244 | loss(rot) 1.1706 | loss(pos) 0.0573 | loss(seq) 0.2965 | grad 4.1966 | lr 0.0000 | time_forward 1.3070 | time_backward 1.5300 |
[2023-10-25 11:58:54,048::train::INFO] [train] Iter 595560 | loss 0.6521 | loss(rot) 0.5825 | loss(pos) 0.0232 | loss(seq) 0.0464 | grad 2.2423 | lr 0.0000 | time_forward 4.1120 | time_backward 5.8280 |
[2023-10-25 11:58:56,782::train::INFO] [train] Iter 595561 | loss 0.2931 | loss(rot) 0.0898 | loss(pos) 0.1410 | loss(seq) 0.0622 | grad 3.4708 | lr 0.0000 | time_forward 1.3120 | time_backward 1.4190 |
[2023-10-25 11:59:06,618::train::INFO] [train] Iter 595562 | loss 0.4700 | loss(rot) 0.2011 | loss(pos) 0.2458 | loss(seq) 0.0232 | grad 3.9286 | lr 0.0000 | time_forward 4.0340 | time_backward 5.7980 |
[2023-10-25 11:59:15,655::train::INFO] [train] Iter 595563 | loss 0.4260 | loss(rot) 0.1537 | loss(pos) 0.0598 | loss(seq) 0.2126 | grad 3.2861 | lr 0.0000 | time_forward 3.7400 | time_backward 5.2940 |
[2023-10-25 11:59:24,764::train::INFO] [train] Iter 595564 | loss 0.5217 | loss(rot) 0.0524 | loss(pos) 0.4025 | loss(seq) 0.0668 | grad 6.5630 | lr 0.0000 | time_forward 3.8250 | time_backward 5.2810 |
[2023-10-25 11:59:32,941::train::INFO] [train] Iter 595565 | loss 0.7851 | loss(rot) 0.5165 | loss(pos) 0.0300 | loss(seq) 0.2386 | grad 2.8328 | lr 0.0000 | time_forward 3.4640 | time_backward 4.7090 |
[2023-10-25 11:59:43,323::train::INFO] [train] Iter 595566 | loss 1.0314 | loss(rot) 0.3280 | loss(pos) 0.4935 | loss(seq) 0.2100 | grad 3.8478 | lr 0.0000 | time_forward 4.3560 | time_backward 6.0230 |
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