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[2023-10-25 19:24:23,350::train::INFO] [train] Iter 599453 | loss 0.4247 | loss(rot) 0.2074 | loss(pos) 0.0154 | loss(seq) 0.2019 | grad 1.5883 | lr 0.0000 | time_forward 1.3110 | time_backward 1.4400 |
[2023-10-25 19:24:26,126::train::INFO] [train] Iter 599454 | loss 1.1936 | loss(rot) 0.7604 | loss(pos) 0.1561 | loss(seq) 0.2771 | grad 4.1649 | lr 0.0000 | time_forward 1.3210 | time_backward 1.4130 |
[2023-10-25 19:24:34,533::train::INFO] [train] Iter 599455 | loss 0.3868 | loss(rot) 0.3275 | loss(pos) 0.0563 | loss(seq) 0.0029 | grad 2.5740 | lr 0.0000 | time_forward 3.5450 | time_backward 4.8580 |
[2023-10-25 19:24:44,720::train::INFO] [train] Iter 599456 | loss 0.1732 | loss(rot) 0.1531 | loss(pos) 0.0170 | loss(seq) 0.0030 | grad 2.0983 | lr 0.0000 | time_forward 4.3140 | time_backward 5.8700 |
[2023-10-25 19:24:47,549::train::INFO] [train] Iter 599457 | loss 0.7645 | loss(rot) 0.3404 | loss(pos) 0.0887 | loss(seq) 0.3354 | grad 3.6422 | lr 0.0000 | time_forward 1.3060 | time_backward 1.5200 |
[2023-10-25 19:24:57,656::train::INFO] [train] Iter 599458 | loss 1.3715 | loss(rot) 0.0777 | loss(pos) 1.2919 | loss(seq) 0.0018 | grad 11.4695 | lr 0.0000 | time_forward 3.9790 | time_backward 6.1240 |
[2023-10-25 19:25:07,631::train::INFO] [train] Iter 599459 | loss 0.3464 | loss(rot) 0.2637 | loss(pos) 0.0375 | loss(seq) 0.0452 | grad 71.3808 | lr 0.0000 | time_forward 4.0540 | time_backward 5.9180 |
[2023-10-25 19:25:14,758::train::INFO] [train] Iter 599460 | loss 0.8432 | loss(rot) 0.3246 | loss(pos) 0.1662 | loss(seq) 0.3524 | grad 2.6220 | lr 0.0000 | time_forward 3.0210 | time_backward 4.1020 |
[2023-10-25 19:25:17,567::train::INFO] [train] Iter 599461 | loss 0.2765 | loss(rot) 0.0752 | loss(pos) 0.1797 | loss(seq) 0.0216 | grad 4.4573 | lr 0.0000 | time_forward 1.3680 | time_backward 1.4370 |
[2023-10-25 19:25:25,989::train::INFO] [train] Iter 599462 | loss 0.2749 | loss(rot) 0.0772 | loss(pos) 0.0701 | loss(seq) 0.1276 | grad 3.5043 | lr 0.0000 | time_forward 3.5520 | time_backward 4.8380 |
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