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[2023-10-22 19:05:12,563::train::INFO] [train] Iter 560892 | loss 0.7266 | loss(rot) 0.3369 | loss(pos) 0.3821 | loss(seq) 0.0076 | grad 7.0685 | lr 0.0000 | time_forward 3.8590 | time_backward 5.9980 |
[2023-10-22 19:05:15,563::train::INFO] [train] Iter 560893 | loss 0.8958 | loss(rot) 0.8746 | loss(pos) 0.0202 | loss(seq) 0.0010 | grad 6.2657 | lr 0.0000 | time_forward 1.4240 | time_backward 1.5720 |
[2023-10-22 19:05:25,185::train::INFO] [train] Iter 560894 | loss 0.3914 | loss(rot) 0.1930 | loss(pos) 0.0190 | loss(seq) 0.1794 | grad 2.8654 | lr 0.0000 | time_forward 3.8630 | time_backward 5.7180 |
[2023-10-22 19:05:27,577::train::INFO] [train] Iter 560895 | loss 0.9074 | loss(rot) 0.7505 | loss(pos) 0.0477 | loss(seq) 0.1091 | grad 3.9675 | lr 0.0000 | time_forward 1.0780 | time_backward 1.3110 |
[2023-10-22 19:05:37,109::train::INFO] [train] Iter 560896 | loss 0.6580 | loss(rot) 0.1512 | loss(pos) 0.4453 | loss(seq) 0.0616 | grad 4.5587 | lr 0.0000 | time_forward 3.9880 | time_backward 5.5420 |
[2023-10-22 19:05:46,231::train::INFO] [train] Iter 560897 | loss 0.6113 | loss(rot) 0.5140 | loss(pos) 0.0497 | loss(seq) 0.0477 | grad 19.3391 | lr 0.0000 | time_forward 3.7650 | time_backward 5.3540 |
[2023-10-22 19:05:54,168::train::INFO] [train] Iter 560898 | loss 0.4672 | loss(rot) 0.3765 | loss(pos) 0.0811 | loss(seq) 0.0095 | grad 5.2791 | lr 0.0000 | time_forward 3.3750 | time_backward 4.5580 |
[2023-10-22 19:05:57,474::train::INFO] [train] Iter 560899 | loss 0.4725 | loss(rot) 0.2300 | loss(pos) 0.0954 | loss(seq) 0.1470 | grad 3.5650 | lr 0.0000 | time_forward 1.4800 | time_backward 1.8240 |
[2023-10-22 19:06:05,322::train::INFO] [train] Iter 560900 | loss 0.8063 | loss(rot) 0.1040 | loss(pos) 0.6972 | loss(seq) 0.0051 | grad 5.3199 | lr 0.0000 | time_forward 3.1260 | time_backward 4.7180 |
[2023-10-22 19:06:08,312::train::INFO] [train] Iter 560901 | loss 0.5134 | loss(rot) 0.1222 | loss(pos) 0.2684 | loss(seq) 0.1227 | grad 5.4501 | lr 0.0000 | time_forward 1.4120 | time_backward 1.5740 |
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