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[2023-10-24 22:52:58,698::train::INFO] [train] Iter 588664 | loss 0.1688 | loss(rot) 0.1090 | loss(pos) 0.0308 | loss(seq) 0.0290 | grad 2.0144 | lr 0.0000 | time_forward 3.8510 | time_backward 5.3480 |
[2023-10-24 22:53:06,788::train::INFO] [train] Iter 588665 | loss 0.4677 | loss(rot) 0.1021 | loss(pos) 0.0402 | loss(seq) 0.3254 | grad 2.3031 | lr 0.0000 | time_forward 3.3730 | time_backward 4.7120 |
[2023-10-24 22:53:25,159::train::INFO] [train] Iter 588666 | loss 1.7115 | loss(rot) 1.2118 | loss(pos) 0.1397 | loss(seq) 0.3600 | grad 3.4607 | lr 0.0000 | time_forward 12.3050 | time_backward 6.0620 |
[2023-10-24 22:53:35,407::train::INFO] [train] Iter 588667 | loss 2.0472 | loss(rot) 1.5421 | loss(pos) 0.2476 | loss(seq) 0.2575 | grad 3.5161 | lr 0.0000 | time_forward 4.2960 | time_backward 5.9480 |
[2023-10-24 22:53:45,627::train::INFO] [train] Iter 588668 | loss 0.5727 | loss(rot) 0.1766 | loss(pos) 0.0359 | loss(seq) 0.3602 | grad 3.0270 | lr 0.0000 | time_forward 4.1410 | time_backward 6.0750 |
[2023-10-24 22:53:49,440::train::INFO] [train] Iter 588669 | loss 2.2434 | loss(rot) 1.8620 | loss(pos) 0.2699 | loss(seq) 0.1115 | grad 9.4542 | lr 0.0000 | time_forward 1.7290 | time_backward 2.0810 |
[2023-10-24 22:53:58,165::train::INFO] [train] Iter 588670 | loss 1.5041 | loss(rot) 0.4748 | loss(pos) 0.8566 | loss(seq) 0.1727 | grad 10.8799 | lr 0.0000 | time_forward 3.6860 | time_backward 5.0260 |
[2023-10-24 22:54:08,371::train::INFO] [train] Iter 588671 | loss 1.4880 | loss(rot) 1.4626 | loss(pos) 0.0243 | loss(seq) 0.0011 | grad 4.7390 | lr 0.0000 | time_forward 4.1960 | time_backward 5.9810 |
[2023-10-24 22:54:11,227::train::INFO] [train] Iter 588672 | loss 1.1256 | loss(rot) 0.9089 | loss(pos) 0.0617 | loss(seq) 0.1550 | grad 3.4431 | lr 0.0000 | time_forward 1.3480 | time_backward 1.5050 |
[2023-10-24 22:54:14,067::train::INFO] [train] Iter 588673 | loss 0.3222 | loss(rot) 0.1301 | loss(pos) 0.1431 | loss(seq) 0.0490 | grad 5.3206 | lr 0.0000 | time_forward 1.4020 | time_backward 1.4340 |
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