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[2023-10-24 06:39:13,263::train::INFO] [train] Iter 580272 | loss 0.5880 | loss(rot) 0.3535 | loss(pos) 0.0835 | loss(seq) 0.1511 | grad 2.6436 | lr 0.0000 | time_forward 1.0340 | time_backward 1.2840 |
[2023-10-24 06:39:21,196::train::INFO] [train] Iter 580273 | loss 0.1398 | loss(rot) 0.0901 | loss(pos) 0.0496 | loss(seq) 0.0000 | grad 2.1999 | lr 0.0000 | time_forward 3.4610 | time_backward 4.4700 |
[2023-10-24 06:39:29,837::train::INFO] [train] Iter 580274 | loss 0.2854 | loss(rot) 0.1837 | loss(pos) 0.0146 | loss(seq) 0.0871 | grad 2.4053 | lr 0.0000 | time_forward 3.6230 | time_backward 5.0140 |
[2023-10-24 06:39:37,051::train::INFO] [train] Iter 580275 | loss 1.5134 | loss(rot) 1.4210 | loss(pos) 0.0505 | loss(seq) 0.0419 | grad 3.8152 | lr 0.0000 | time_forward 3.0430 | time_backward 4.1670 |
[2023-10-24 06:39:45,607::train::INFO] [train] Iter 580276 | loss 0.4743 | loss(rot) 0.2766 | loss(pos) 0.0416 | loss(seq) 0.1561 | grad 3.2115 | lr 0.0000 | time_forward 3.4620 | time_backward 5.0910 |
[2023-10-24 06:39:55,124::train::INFO] [train] Iter 580277 | loss 1.0753 | loss(rot) 0.7330 | loss(pos) 0.1376 | loss(seq) 0.2048 | grad 3.3671 | lr 0.0000 | time_forward 4.1340 | time_backward 5.3790 |
[2023-10-24 06:40:03,818::train::INFO] [train] Iter 580278 | loss 0.3261 | loss(rot) 0.1962 | loss(pos) 0.1199 | loss(seq) 0.0100 | grad 2.7384 | lr 0.0000 | time_forward 3.6980 | time_backward 4.9930 |
[2023-10-24 06:40:12,084::train::INFO] [train] Iter 580279 | loss 0.5305 | loss(rot) 0.0754 | loss(pos) 0.0584 | loss(seq) 0.3968 | grad 2.5201 | lr 0.0000 | time_forward 3.5120 | time_backward 4.7500 |
[2023-10-24 06:40:21,404::train::INFO] [train] Iter 580280 | loss 0.7424 | loss(rot) 0.4114 | loss(pos) 0.0760 | loss(seq) 0.2550 | grad 4.1016 | lr 0.0000 | time_forward 3.8300 | time_backward 5.4860 |
[2023-10-24 06:40:30,605::train::INFO] [train] Iter 580281 | loss 0.9627 | loss(rot) 0.8109 | loss(pos) 0.0352 | loss(seq) 0.1166 | grad 23.1310 | lr 0.0000 | time_forward 3.7690 | time_backward 5.4280 |
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