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[2023-09-01 23:25:04,884::train::INFO] [train] Iter 03482 | loss 2.1655 | loss(rot) 0.0110 | loss(pos) 2.1539 | loss(seq) 0.0006 | grad 5.6558 | lr 0.0010 | time_forward 3.8470 | time_backward 5.4070 |
[2023-09-01 23:25:14,795::train::INFO] [train] Iter 03483 | loss 1.8885 | loss(rot) 0.0676 | loss(pos) 1.8141 | loss(seq) 0.0069 | grad 5.3561 | lr 0.0010 | time_forward 4.0730 | time_backward 5.8340 |
[2023-09-01 23:25:25,096::train::INFO] [train] Iter 03484 | loss 3.0657 | loss(rot) 2.7711 | loss(pos) 0.2852 | loss(seq) 0.0094 | grad 4.4760 | lr 0.0010 | time_forward 4.0900 | time_backward 6.2080 |
[2023-09-01 23:25:33,165::train::INFO] [train] Iter 03485 | loss 2.7440 | loss(rot) 1.6216 | loss(pos) 0.5611 | loss(seq) 0.5613 | grad 4.1768 | lr 0.0010 | time_forward 3.4350 | time_backward 4.6190 |
[2023-09-01 23:25:41,148::train::INFO] [train] Iter 03486 | loss 2.2867 | loss(rot) 1.8281 | loss(pos) 0.1546 | loss(seq) 0.3039 | grad 5.5898 | lr 0.0010 | time_forward 3.3670 | time_backward 4.6120 |
[2023-09-01 23:25:51,548::train::INFO] [train] Iter 03487 | loss 2.1978 | loss(rot) 1.8165 | loss(pos) 0.3360 | loss(seq) 0.0453 | grad 5.2543 | lr 0.0010 | time_forward 4.3610 | time_backward 6.0330 |
[2023-09-01 23:26:00,291::train::INFO] [train] Iter 03488 | loss 2.0427 | loss(rot) 0.9426 | loss(pos) 0.5213 | loss(seq) 0.5788 | grad 3.1957 | lr 0.0010 | time_forward 3.6290 | time_backward 5.1100 |
[2023-09-01 23:26:03,062::train::INFO] [train] Iter 03489 | loss 2.7386 | loss(rot) 2.0480 | loss(pos) 0.2158 | loss(seq) 0.4748 | grad 3.8282 | lr 0.0010 | time_forward 1.2610 | time_backward 1.5070 |
[2023-09-01 23:26:11,056::train::INFO] [train] Iter 03490 | loss 2.2982 | loss(rot) 1.3308 | loss(pos) 0.4047 | loss(seq) 0.5628 | grad 4.1384 | lr 0.0010 | time_forward 3.3730 | time_backward 4.6170 |
[2023-09-01 23:26:19,857::train::INFO] [train] Iter 03491 | loss 0.9230 | loss(rot) 0.3182 | loss(pos) 0.5095 | loss(seq) 0.0954 | grad 4.1897 | lr 0.0010 | time_forward 3.6830 | time_backward 5.1150 |
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