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[2023-10-25 14:59:41,191::train::INFO] [train] Iter 597055 | loss 0.0901 | loss(rot) 0.0591 | loss(pos) 0.0310 | loss(seq) 0.0000 | grad 1.6697 | lr 0.0000 | time_forward 3.5430 | time_backward 4.9310 |
[2023-10-25 14:59:49,404::train::INFO] [train] Iter 597056 | loss 0.5022 | loss(rot) 0.0539 | loss(pos) 0.0246 | loss(seq) 0.4237 | grad 2.3067 | lr 0.0000 | time_forward 3.4830 | time_backward 4.7270 |
[2023-10-25 14:59:52,163::train::INFO] [train] Iter 597057 | loss 0.8060 | loss(rot) 0.2491 | loss(pos) 0.3270 | loss(seq) 0.2298 | grad 7.4894 | lr 0.0000 | time_forward 1.2970 | time_backward 1.4590 |
[2023-10-25 15:00:01,757::train::INFO] [train] Iter 597058 | loss 0.2687 | loss(rot) 0.2487 | loss(pos) 0.0153 | loss(seq) 0.0047 | grad 2.9702 | lr 0.0000 | time_forward 3.9280 | time_backward 5.6600 |
[2023-10-25 15:00:11,284::train::INFO] [train] Iter 597059 | loss 0.5657 | loss(rot) 0.0340 | loss(pos) 0.5228 | loss(seq) 0.0089 | grad 5.4215 | lr 0.0000 | time_forward 3.8940 | time_backward 5.6310 |
[2023-10-25 15:00:20,777::train::INFO] [train] Iter 597060 | loss 0.5658 | loss(rot) 0.0871 | loss(pos) 0.2257 | loss(seq) 0.2531 | grad 3.5885 | lr 0.0000 | time_forward 3.8920 | time_backward 5.5980 |
[2023-10-25 15:00:30,311::train::INFO] [train] Iter 597061 | loss 1.0910 | loss(rot) 0.7336 | loss(pos) 0.0588 | loss(seq) 0.2986 | grad 4.7588 | lr 0.0000 | time_forward 3.8300 | time_backward 5.7000 |
[2023-10-25 15:00:38,580::train::INFO] [train] Iter 597062 | loss 0.7538 | loss(rot) 0.5539 | loss(pos) 0.0520 | loss(seq) 0.1480 | grad 15.4804 | lr 0.0000 | time_forward 3.4750 | time_backward 4.7920 |
[2023-10-25 15:00:47,392::train::INFO] [train] Iter 597063 | loss 0.6742 | loss(rot) 0.0155 | loss(pos) 0.6545 | loss(seq) 0.0041 | grad 9.7094 | lr 0.0000 | time_forward 3.7560 | time_backward 5.0530 |
[2023-10-25 15:00:56,801::train::INFO] [train] Iter 597064 | loss 0.3359 | loss(rot) 0.1105 | loss(pos) 0.0552 | loss(seq) 0.1703 | grad 2.3701 | lr 0.0000 | time_forward 3.8220 | time_backward 5.5840 |
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