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[2023-10-23 18:40:27,956::train::INFO] [train] Iter 574178 | loss 0.4111 | loss(rot) 0.1804 | loss(pos) 0.0319 | loss(seq) 0.1989 | grad 2.3364 | lr 0.0000 | time_forward 1.0100 | time_backward 1.1690 |
[2023-10-23 18:40:34,950::train::INFO] [train] Iter 574179 | loss 0.5207 | loss(rot) 0.2569 | loss(pos) 0.0689 | loss(seq) 0.1948 | grad 3.2245 | lr 0.0000 | time_forward 3.0330 | time_backward 3.9580 |
[2023-10-23 18:40:37,631::train::INFO] [train] Iter 574180 | loss 1.0353 | loss(rot) 0.3963 | loss(pos) 0.2554 | loss(seq) 0.3835 | grad 4.6382 | lr 0.0000 | time_forward 1.2650 | time_backward 1.4130 |
[2023-10-23 18:40:45,284::train::INFO] [train] Iter 574181 | loss 0.1711 | loss(rot) 0.0537 | loss(pos) 0.1114 | loss(seq) 0.0061 | grad 3.7276 | lr 0.0000 | time_forward 3.2560 | time_backward 4.3920 |
[2023-10-23 18:40:47,965::train::INFO] [train] Iter 574182 | loss 0.3688 | loss(rot) 0.0514 | loss(pos) 0.3104 | loss(seq) 0.0070 | grad 4.1104 | lr 0.0000 | time_forward 1.2610 | time_backward 1.4160 |
[2023-10-23 18:40:50,724::train::INFO] [train] Iter 574183 | loss 1.1084 | loss(rot) 0.3226 | loss(pos) 0.7830 | loss(seq) 0.0028 | grad 7.4005 | lr 0.0000 | time_forward 1.2720 | time_backward 1.4840 |
[2023-10-23 18:40:57,848::train::INFO] [train] Iter 574184 | loss 0.2240 | loss(rot) 0.0667 | loss(pos) 0.0205 | loss(seq) 0.1367 | grad 2.3887 | lr 0.0000 | time_forward 3.0540 | time_backward 4.0620 |
[2023-10-23 18:41:04,374::train::INFO] [train] Iter 574185 | loss 0.2383 | loss(rot) 0.1654 | loss(pos) 0.0156 | loss(seq) 0.0573 | grad 2.8703 | lr 0.0000 | time_forward 2.7890 | time_backward 3.7340 |
[2023-10-23 18:41:12,661::train::INFO] [train] Iter 574186 | loss 0.5254 | loss(rot) 0.4887 | loss(pos) 0.0191 | loss(seq) 0.0175 | grad 3.2332 | lr 0.0000 | time_forward 3.5710 | time_backward 4.7130 |
[2023-10-23 18:41:19,920::train::INFO] [train] Iter 574187 | loss 0.3620 | loss(rot) 0.0148 | loss(pos) 0.3440 | loss(seq) 0.0033 | grad 5.6744 | lr 0.0000 | time_forward 3.1340 | time_backward 4.1220 |
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