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[2023-10-23 09:39:47,134::train::INFO] [train] Iter 569284 | loss 0.8731 | loss(rot) 0.5465 | loss(pos) 0.0616 | loss(seq) 0.2650 | grad 9.4912 | lr 0.0000 | time_forward 2.8460 | time_backward 3.7550 |
[2023-10-23 09:39:56,974::train::INFO] [train] Iter 569285 | loss 0.3488 | loss(rot) 0.0749 | loss(pos) 0.0332 | loss(seq) 0.2407 | grad 2.5188 | lr 0.0000 | time_forward 4.0380 | time_backward 5.7980 |
[2023-10-23 09:40:06,065::train::INFO] [train] Iter 569286 | loss 0.6290 | loss(rot) 0.4236 | loss(pos) 0.0227 | loss(seq) 0.1828 | grad 4.0807 | lr 0.0000 | time_forward 3.9080 | time_backward 5.1800 |
[2023-10-23 09:40:15,815::train::INFO] [train] Iter 569287 | loss 0.5561 | loss(rot) 0.0743 | loss(pos) 0.4668 | loss(seq) 0.0150 | grad 5.6013 | lr 0.0000 | time_forward 3.9490 | time_backward 5.7990 |
[2023-10-23 09:40:18,620::train::INFO] [train] Iter 569288 | loss 0.2674 | loss(rot) 0.0976 | loss(pos) 0.0158 | loss(seq) 0.1541 | grad 1.9165 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4750 |
[2023-10-23 09:40:28,560::train::INFO] [train] Iter 569289 | loss 0.6090 | loss(rot) 0.1972 | loss(pos) 0.0908 | loss(seq) 0.3210 | grad 3.1196 | lr 0.0000 | time_forward 4.0630 | time_backward 5.8750 |
[2023-10-23 09:40:36,711::train::INFO] [train] Iter 569290 | loss 0.5261 | loss(rot) 0.1283 | loss(pos) 0.1796 | loss(seq) 0.2182 | grad 4.7308 | lr 0.0000 | time_forward 3.3890 | time_backward 4.7580 |
[2023-10-23 09:40:40,156::train::INFO] [train] Iter 569291 | loss 1.5680 | loss(rot) 1.0286 | loss(pos) 0.1868 | loss(seq) 0.3526 | grad 4.7711 | lr 0.0000 | time_forward 1.5060 | time_backward 1.9360 |
[2023-10-23 09:40:50,242::train::INFO] [train] Iter 569292 | loss 0.7647 | loss(rot) 0.3581 | loss(pos) 0.0607 | loss(seq) 0.3458 | grad 3.3177 | lr 0.0000 | time_forward 4.2470 | time_backward 5.8240 |
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