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[2023-10-25 05:52:04,110::train::INFO] [train] Iter 592360 | loss 1.7983 | loss(rot) 1.5987 | loss(pos) 0.0683 | loss(seq) 0.1313 | grad 18.3923 | lr 0.0000 | time_forward 3.6600 | time_backward 5.2300 |
[2023-10-25 05:52:06,820::train::INFO] [train] Iter 592361 | loss 0.2103 | loss(rot) 0.0881 | loss(pos) 0.0112 | loss(seq) 0.1110 | grad 2.1318 | lr 0.0000 | time_forward 1.2990 | time_backward 1.4070 |
[2023-10-25 05:52:14,867::train::INFO] [train] Iter 592362 | loss 0.6487 | loss(rot) 0.4427 | loss(pos) 0.1703 | loss(seq) 0.0358 | grad 5.5940 | lr 0.0000 | time_forward 3.4580 | time_backward 4.5850 |
[2023-10-25 05:52:22,244::train::INFO] [train] Iter 592363 | loss 1.2614 | loss(rot) 1.2380 | loss(pos) 0.0198 | loss(seq) 0.0036 | grad 11.2574 | lr 0.0000 | time_forward 3.1650 | time_backward 4.2090 |
[2023-10-25 05:52:31,888::train::INFO] [train] Iter 592364 | loss 0.7968 | loss(rot) 0.1389 | loss(pos) 0.6524 | loss(seq) 0.0054 | grad 5.0867 | lr 0.0000 | time_forward 3.9250 | time_backward 5.7160 |
[2023-10-25 05:52:41,020::train::INFO] [train] Iter 592365 | loss 0.6838 | loss(rot) 0.2633 | loss(pos) 0.3777 | loss(seq) 0.0429 | grad 5.9608 | lr 0.0000 | time_forward 3.7360 | time_backward 5.3930 |
[2023-10-25 05:52:48,662::train::INFO] [train] Iter 592366 | loss 0.4650 | loss(rot) 0.3649 | loss(pos) 0.0126 | loss(seq) 0.0874 | grad 4.6148 | lr 0.0000 | time_forward 3.2490 | time_backward 4.3890 |
[2023-10-25 05:52:51,338::train::INFO] [train] Iter 592367 | loss 0.2218 | loss(rot) 0.0541 | loss(pos) 0.1520 | loss(seq) 0.0157 | grad 4.3523 | lr 0.0000 | time_forward 1.2740 | time_backward 1.3980 |
[2023-10-25 05:53:00,343::train::INFO] [train] Iter 592368 | loss 0.5015 | loss(rot) 0.0666 | loss(pos) 0.4283 | loss(seq) 0.0066 | grad 12.0692 | lr 0.0000 | time_forward 3.7300 | time_backward 5.2720 |
[2023-10-25 05:53:09,905::train::INFO] [train] Iter 592369 | loss 1.8253 | loss(rot) 1.6651 | loss(pos) 0.0695 | loss(seq) 0.0907 | grad 3.3965 | lr 0.0000 | time_forward 4.0100 | time_backward 5.5490 |
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