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[2023-10-23 17:10:58,352::train::INFO] [train] Iter 573379 | loss 0.4542 | loss(rot) 0.3171 | loss(pos) 0.0105 | loss(seq) 0.1266 | grad 7.0435 | lr 0.0000 | time_forward 3.6380 | time_backward 5.1720 |
[2023-10-23 17:11:07,056::train::INFO] [train] Iter 573380 | loss 1.7033 | loss(rot) 1.5072 | loss(pos) 0.0255 | loss(seq) 0.1706 | grad 20.5387 | lr 0.0000 | time_forward 4.0930 | time_backward 4.6080 |
[2023-10-23 17:11:15,913::train::INFO] [train] Iter 573381 | loss 0.9514 | loss(rot) 0.5191 | loss(pos) 0.0326 | loss(seq) 0.3998 | grad 6.3248 | lr 0.0000 | time_forward 3.6350 | time_backward 5.2190 |
[2023-10-23 17:11:23,832::train::INFO] [train] Iter 573382 | loss 1.4643 | loss(rot) 0.8029 | loss(pos) 0.1230 | loss(seq) 0.5384 | grad 4.4336 | lr 0.0000 | time_forward 3.3620 | time_backward 4.5530 |
[2023-10-23 17:11:31,251::train::INFO] [train] Iter 573383 | loss 0.2431 | loss(rot) 0.0780 | loss(pos) 0.0274 | loss(seq) 0.1377 | grad 2.0246 | lr 0.0000 | time_forward 3.1940 | time_backward 4.2220 |
[2023-10-23 17:11:39,620::train::INFO] [train] Iter 573384 | loss 1.3833 | loss(rot) 1.1967 | loss(pos) 0.0419 | loss(seq) 0.1447 | grad 5.8619 | lr 0.0000 | time_forward 3.5680 | time_backward 4.7980 |
[2023-10-23 17:11:42,536::train::INFO] [train] Iter 573385 | loss 0.8049 | loss(rot) 0.1561 | loss(pos) 0.6377 | loss(seq) 0.0110 | grad 8.0062 | lr 0.0000 | time_forward 1.4340 | time_backward 1.4800 |
[2023-10-23 17:11:51,363::train::INFO] [train] Iter 573386 | loss 2.0382 | loss(rot) 1.9368 | loss(pos) 0.0337 | loss(seq) 0.0677 | grad 11.3523 | lr 0.0000 | time_forward 3.8350 | time_backward 4.9880 |
[2023-10-23 17:11:54,106::train::INFO] [train] Iter 573387 | loss 0.5502 | loss(rot) 0.4315 | loss(pos) 0.0201 | loss(seq) 0.0987 | grad 5.4641 | lr 0.0000 | time_forward 1.3160 | time_backward 1.4230 |
[2023-10-23 17:12:01,669::train::INFO] [train] Iter 573388 | loss 0.2311 | loss(rot) 0.2005 | loss(pos) 0.0306 | loss(seq) 0.0000 | grad 2.9503 | lr 0.0000 | time_forward 3.3030 | time_backward 4.2560 |
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