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[2023-10-23 07:30:44,717::train::INFO] [train] Iter 568083 | loss 0.7893 | loss(rot) 0.3257 | loss(pos) 0.4560 | loss(seq) 0.0076 | grad 4.4774 | lr 0.0000 | time_forward 3.2870 | time_backward 4.5750 |
[2023-10-23 07:30:47,149::train::INFO] [train] Iter 568084 | loss 0.4502 | loss(rot) 0.1329 | loss(pos) 0.3059 | loss(seq) 0.0114 | grad 5.7776 | lr 0.0000 | time_forward 1.1450 | time_backward 1.2830 |
[2023-10-23 07:30:55,173::train::INFO] [train] Iter 568085 | loss 0.9502 | loss(rot) 0.6701 | loss(pos) 0.0323 | loss(seq) 0.2478 | grad 2.7214 | lr 0.0000 | time_forward 3.3570 | time_backward 4.6460 |
[2023-10-23 07:31:03,082::train::INFO] [train] Iter 568086 | loss 2.7612 | loss(rot) 0.0205 | loss(pos) 2.7401 | loss(seq) 0.0005 | grad 14.5760 | lr 0.0000 | time_forward 3.3330 | time_backward 4.5740 |
[2023-10-23 07:31:05,699::train::INFO] [train] Iter 568087 | loss 0.5572 | loss(rot) 0.5151 | loss(pos) 0.0421 | loss(seq) 0.0000 | grad 4.7214 | lr 0.0000 | time_forward 1.1890 | time_backward 1.4240 |
[2023-10-23 07:31:11,174::train::INFO] [train] Iter 568088 | loss 0.4917 | loss(rot) 0.2029 | loss(pos) 0.0197 | loss(seq) 0.2691 | grad 4.1622 | lr 0.0000 | time_forward 2.4090 | time_backward 3.0640 |
[2023-10-23 07:31:13,828::train::INFO] [train] Iter 568089 | loss 2.4183 | loss(rot) 2.3244 | loss(pos) 0.0200 | loss(seq) 0.0738 | grad 7.6302 | lr 0.0000 | time_forward 1.2400 | time_backward 1.4110 |
[2023-10-23 07:31:16,298::train::INFO] [train] Iter 568090 | loss 0.6795 | loss(rot) 0.6085 | loss(pos) 0.0177 | loss(seq) 0.0532 | grad 8.7919 | lr 0.0000 | time_forward 1.1730 | time_backward 1.2930 |
[2023-10-23 07:31:18,526::train::INFO] [train] Iter 568091 | loss 0.5938 | loss(rot) 0.1447 | loss(pos) 0.3230 | loss(seq) 0.1262 | grad 9.0042 | lr 0.0000 | time_forward 1.0310 | time_backward 1.1950 |
[2023-10-23 07:31:21,208::train::INFO] [train] Iter 568092 | loss 0.3421 | loss(rot) 0.2662 | loss(pos) 0.0248 | loss(seq) 0.0511 | grad 3.0954 | lr 0.0000 | time_forward 1.2600 | time_backward 1.4180 |
[2023-10-23 07:31:29,102::train::INFO] [train] Iter 568093 | loss 0.9648 | loss(rot) 0.4808 | loss(pos) 0.4621 | loss(seq) 0.0219 | grad 6.1498 | lr 0.0000 | time_forward 3.2980 | time_backward 4.5920 |
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