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[2023-10-22 13:38:12,865::train::INFO] [train] Iter 558194 | loss 1.5122 | loss(rot) 0.0067 | loss(pos) 1.5051 | loss(seq) 0.0004 | grad 19.3610 | lr 0.0000 | time_forward 1.4070 | time_backward 1.6770 |
[2023-10-22 13:38:23,645::train::INFO] [train] Iter 558195 | loss 0.3436 | loss(rot) 0.0540 | loss(pos) 0.2808 | loss(seq) 0.0088 | grad 4.8625 | lr 0.0000 | time_forward 4.2740 | time_backward 6.4730 |
[2023-10-22 13:38:26,757::train::INFO] [train] Iter 558196 | loss 0.5158 | loss(rot) 0.1687 | loss(pos) 0.0398 | loss(seq) 0.3073 | grad 3.1705 | lr 0.0000 | time_forward 1.5090 | time_backward 1.5990 |
[2023-10-22 13:38:37,126::train::INFO] [train] Iter 558197 | loss 0.2252 | loss(rot) 0.1140 | loss(pos) 0.0900 | loss(seq) 0.0212 | grad 2.5993 | lr 0.0000 | time_forward 4.4400 | time_backward 5.9270 |
[2023-10-22 13:38:40,451::train::INFO] [train] Iter 558198 | loss 1.1469 | loss(rot) 0.4823 | loss(pos) 0.1995 | loss(seq) 0.4650 | grad 3.3556 | lr 0.0000 | time_forward 1.5100 | time_backward 1.8120 |
[2023-10-22 13:38:50,522::train::INFO] [train] Iter 558199 | loss 1.1470 | loss(rot) 0.4286 | loss(pos) 0.2315 | loss(seq) 0.4869 | grad 2.8502 | lr 0.0000 | time_forward 4.6510 | time_backward 5.4030 |
[2023-10-22 13:38:58,913::train::INFO] [train] Iter 558200 | loss 0.2878 | loss(rot) 0.1298 | loss(pos) 0.0209 | loss(seq) 0.1371 | grad 1.9796 | lr 0.0000 | time_forward 3.6730 | time_backward 4.7130 |
[2023-10-22 13:39:08,561::train::INFO] [train] Iter 558201 | loss 0.3681 | loss(rot) 0.1346 | loss(pos) 0.1607 | loss(seq) 0.0728 | grad 3.5014 | lr 0.0000 | time_forward 3.8760 | time_backward 5.7690 |
[2023-10-22 13:39:18,298::train::INFO] [train] Iter 558202 | loss 1.2249 | loss(rot) 0.3589 | loss(pos) 0.8280 | loss(seq) 0.0379 | grad 6.1294 | lr 0.0000 | time_forward 3.9930 | time_backward 5.7400 |
[2023-10-22 13:39:27,951::train::INFO] [train] Iter 558203 | loss 2.1954 | loss(rot) 2.1196 | loss(pos) 0.0746 | loss(seq) 0.0012 | grad 4.0896 | lr 0.0000 | time_forward 4.0840 | time_backward 5.5660 |
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