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[2023-10-25 18:06:23,359::train::INFO] [train] Iter 598754 | loss 0.7582 | loss(rot) 0.2751 | loss(pos) 0.0663 | loss(seq) 0.4168 | grad 4.2187 | lr 0.0000 | time_forward 2.8950 | time_backward 3.8120 |
[2023-10-25 18:06:31,366::train::INFO] [train] Iter 598755 | loss 0.3748 | loss(rot) 0.2852 | loss(pos) 0.0895 | loss(seq) 0.0000 | grad 4.9219 | lr 0.0000 | time_forward 3.4490 | time_backward 4.5540 |
[2023-10-25 18:06:38,835::train::INFO] [train] Iter 598756 | loss 0.3077 | loss(rot) 0.1758 | loss(pos) 0.0599 | loss(seq) 0.0720 | grad 4.1278 | lr 0.0000 | time_forward 3.1710 | time_backward 4.2950 |
[2023-10-25 18:06:41,559::train::INFO] [train] Iter 598757 | loss 0.5064 | loss(rot) 0.3199 | loss(pos) 0.1409 | loss(seq) 0.0456 | grad 2.7147 | lr 0.0000 | time_forward 1.3050 | time_backward 1.4160 |
[2023-10-25 18:06:50,491::train::INFO] [train] Iter 598758 | loss 0.5267 | loss(rot) 0.4984 | loss(pos) 0.0270 | loss(seq) 0.0014 | grad 21.6603 | lr 0.0000 | time_forward 3.6850 | time_backward 5.2440 |
[2023-10-25 18:06:53,296::train::INFO] [train] Iter 598759 | loss 0.3948 | loss(rot) 0.1203 | loss(pos) 0.0274 | loss(seq) 0.2471 | grad 2.0821 | lr 0.0000 | time_forward 1.3470 | time_backward 1.4540 |
[2023-10-25 18:07:01,396::train::INFO] [train] Iter 598760 | loss 0.9623 | loss(rot) 0.1998 | loss(pos) 0.0860 | loss(seq) 0.6766 | grad 4.4260 | lr 0.0000 | time_forward 3.5790 | time_backward 4.5180 |
[2023-10-25 18:07:10,107::train::INFO] [train] Iter 598761 | loss 0.3193 | loss(rot) 0.3019 | loss(pos) 0.0171 | loss(seq) 0.0003 | grad 3.9330 | lr 0.0000 | time_forward 3.6350 | time_backward 5.0730 |
[2023-10-25 18:07:17,630::train::INFO] [train] Iter 598762 | loss 0.2274 | loss(rot) 0.0992 | loss(pos) 0.0883 | loss(seq) 0.0399 | grad 2.7448 | lr 0.0000 | time_forward 3.1820 | time_backward 4.3380 |
[2023-10-25 18:07:25,005::train::INFO] [train] Iter 598763 | loss 0.2795 | loss(rot) 0.1417 | loss(pos) 0.0715 | loss(seq) 0.0663 | grad 3.8361 | lr 0.0000 | time_forward 3.1160 | time_backward 4.2550 |
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