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[2023-10-23 04:13:03,844::train::INFO] [train] Iter 566086 | loss 1.5570 | loss(rot) 1.0204 | loss(pos) 0.0889 | loss(seq) 0.4477 | grad 15.3644 | lr 0.0000 | time_forward 3.2480 | time_backward 4.5660 |
[2023-10-23 04:13:11,699::train::INFO] [train] Iter 566087 | loss 0.3534 | loss(rot) 0.1440 | loss(pos) 0.0474 | loss(seq) 0.1621 | grad 2.1249 | lr 0.0000 | time_forward 3.3620 | time_backward 4.4900 |
[2023-10-23 04:13:18,302::train::INFO] [train] Iter 566088 | loss 0.3505 | loss(rot) 0.0268 | loss(pos) 0.0570 | loss(seq) 0.2667 | grad 1.8045 | lr 0.0000 | time_forward 2.8870 | time_backward 3.7120 |
[2023-10-23 04:13:20,469::train::INFO] [train] Iter 566089 | loss 0.8095 | loss(rot) 0.5224 | loss(pos) 0.2833 | loss(seq) 0.0037 | grad 7.1692 | lr 0.0000 | time_forward 0.9900 | time_backward 1.1750 |
[2023-10-23 04:13:26,546::train::INFO] [train] Iter 566090 | loss 1.1129 | loss(rot) 0.5374 | loss(pos) 0.2469 | loss(seq) 0.3286 | grad 6.3619 | lr 0.0000 | time_forward 2.6610 | time_backward 3.4100 |
[2023-10-23 04:13:35,088::train::INFO] [train] Iter 566091 | loss 0.4497 | loss(rot) 0.2128 | loss(pos) 0.1583 | loss(seq) 0.0787 | grad 2.5128 | lr 0.0000 | time_forward 4.1010 | time_backward 4.4380 |
[2023-10-23 04:13:42,176::train::INFO] [train] Iter 566092 | loss 0.4645 | loss(rot) 0.1029 | loss(pos) 0.3531 | loss(seq) 0.0085 | grad 7.2526 | lr 0.0000 | time_forward 3.0530 | time_backward 4.0320 |
[2023-10-23 04:13:44,785::train::INFO] [train] Iter 566093 | loss 0.4739 | loss(rot) 0.2591 | loss(pos) 0.0499 | loss(seq) 0.1649 | grad 3.5295 | lr 0.0000 | time_forward 1.2490 | time_backward 1.3570 |
[2023-10-23 04:13:52,620::train::INFO] [train] Iter 566094 | loss 0.7361 | loss(rot) 0.4278 | loss(pos) 0.0724 | loss(seq) 0.2359 | grad 4.3418 | lr 0.0000 | time_forward 3.4120 | time_backward 4.4200 |
[2023-10-23 04:13:59,337::train::INFO] [train] Iter 566095 | loss 0.3370 | loss(rot) 0.1910 | loss(pos) 0.0234 | loss(seq) 0.1225 | grad 2.7355 | lr 0.0000 | time_forward 2.9160 | time_backward 3.7970 |
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