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[2023-10-24 02:57:24,389::train::INFO] [train] Iter 578374 | loss 1.8805 | loss(rot) 1.7040 | loss(pos) 0.0391 | loss(seq) 0.1374 | grad 4.1715 | lr 0.0000 | time_forward 3.5630 | time_backward 4.8490 |
[2023-10-24 02:57:34,007::train::INFO] [train] Iter 578375 | loss 0.7484 | loss(rot) 0.2464 | loss(pos) 0.4286 | loss(seq) 0.0733 | grad 5.0656 | lr 0.0000 | time_forward 3.8970 | time_backward 5.7190 |
[2023-10-24 02:57:42,228::train::INFO] [train] Iter 578376 | loss 0.6392 | loss(rot) 0.6110 | loss(pos) 0.0282 | loss(seq) 0.0000 | grad 12.5887 | lr 0.0000 | time_forward 3.4890 | time_backward 4.7290 |
[2023-10-24 02:57:52,069::train::INFO] [train] Iter 578377 | loss 0.3558 | loss(rot) 0.0300 | loss(pos) 0.0640 | loss(seq) 0.2617 | grad 2.6479 | lr 0.0000 | time_forward 4.0410 | time_backward 5.7960 |
[2023-10-24 02:57:54,766::train::INFO] [train] Iter 578378 | loss 1.8620 | loss(rot) 1.5362 | loss(pos) 0.0501 | loss(seq) 0.2756 | grad 3.7215 | lr 0.0000 | time_forward 1.2700 | time_backward 1.4230 |
[2023-10-24 02:58:03,837::train::INFO] [train] Iter 578379 | loss 0.3129 | loss(rot) 0.1828 | loss(pos) 0.0936 | loss(seq) 0.0365 | grad 3.1760 | lr 0.0000 | time_forward 3.8580 | time_backward 5.2100 |
[2023-10-24 02:58:13,570::train::INFO] [train] Iter 578380 | loss 1.0597 | loss(rot) 0.8958 | loss(pos) 0.0627 | loss(seq) 0.1012 | grad 3.0860 | lr 0.0000 | time_forward 4.0020 | time_backward 5.7270 |
[2023-10-24 02:58:16,350::train::INFO] [train] Iter 578381 | loss 0.9494 | loss(rot) 0.8016 | loss(pos) 0.0458 | loss(seq) 0.1020 | grad 4.0195 | lr 0.0000 | time_forward 1.3360 | time_backward 1.4410 |
[2023-10-24 02:58:19,148::train::INFO] [train] Iter 578382 | loss 0.8403 | loss(rot) 0.4523 | loss(pos) 0.0321 | loss(seq) 0.3559 | grad 2.7785 | lr 0.0000 | time_forward 1.3690 | time_backward 1.4270 |
[2023-10-24 02:58:25,764::train::INFO] [train] Iter 578383 | loss 0.5472 | loss(rot) 0.2715 | loss(pos) 0.0372 | loss(seq) 0.2385 | grad 4.0938 | lr 0.0000 | time_forward 2.8910 | time_backward 3.7200 |
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