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[2023-10-25 01:18:28,698::train::INFO] [train] Iter 589863 | loss 0.6238 | loss(rot) 0.2549 | loss(pos) 0.0577 | loss(seq) 0.3112 | grad 2.9700 | lr 0.0000 | time_forward 1.3030 | time_backward 1.5120 |
[2023-10-25 01:18:36,312::train::INFO] [train] Iter 589864 | loss 1.9002 | loss(rot) 1.0531 | loss(pos) 0.2429 | loss(seq) 0.6042 | grad 4.8289 | lr 0.0000 | time_forward 3.2230 | time_backward 4.3880 |
[2023-10-25 01:18:39,181::train::INFO] [train] Iter 589865 | loss 0.4772 | loss(rot) 0.4553 | loss(pos) 0.0157 | loss(seq) 0.0062 | grad 1.2705 | lr 0.0000 | time_forward 1.3490 | time_backward 1.5170 |
[2023-10-25 01:18:46,519::train::INFO] [train] Iter 589866 | loss 1.3601 | loss(rot) 0.8083 | loss(pos) 0.0483 | loss(seq) 0.5036 | grad 5.4830 | lr 0.0000 | time_forward 3.1030 | time_backward 4.2310 |
[2023-10-25 01:18:54,472::train::INFO] [train] Iter 589867 | loss 0.5060 | loss(rot) 0.1433 | loss(pos) 0.0601 | loss(seq) 0.3026 | grad 3.7010 | lr 0.0000 | time_forward 3.3590 | time_backward 4.5920 |
[2023-10-25 01:19:01,449::train::INFO] [train] Iter 589868 | loss 1.4604 | loss(rot) 1.4210 | loss(pos) 0.0245 | loss(seq) 0.0149 | grad 15.6636 | lr 0.0000 | time_forward 2.9720 | time_backward 4.0010 |
[2023-10-25 01:19:10,683::train::INFO] [train] Iter 589869 | loss 0.5295 | loss(rot) 0.3504 | loss(pos) 0.0393 | loss(seq) 0.1398 | grad 3.2459 | lr 0.0000 | time_forward 3.7840 | time_backward 5.4470 |
[2023-10-25 01:19:13,457::train::INFO] [train] Iter 589870 | loss 0.1770 | loss(rot) 0.0326 | loss(pos) 0.1206 | loss(seq) 0.0238 | grad 3.7681 | lr 0.0000 | time_forward 1.3310 | time_backward 1.4400 |
[2023-10-25 01:19:22,707::train::INFO] [train] Iter 589871 | loss 1.1068 | loss(rot) 0.5818 | loss(pos) 0.0860 | loss(seq) 0.4389 | grad 5.9086 | lr 0.0000 | time_forward 3.8440 | time_backward 5.4020 |
[2023-10-25 01:19:30,270::train::INFO] [train] Iter 589872 | loss 1.2030 | loss(rot) 1.1149 | loss(pos) 0.0438 | loss(seq) 0.0443 | grad 9.1336 | lr 0.0000 | time_forward 3.3050 | time_backward 4.2550 |
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