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[2023-10-22 15:32:00,485::train::INFO] [train] Iter 559093 | loss 1.2230 | loss(rot) 1.1492 | loss(pos) 0.0121 | loss(seq) 0.0618 | grad 5.2128 | lr 0.0000 | time_forward 3.3540 | time_backward 4.9500 |
[2023-10-22 15:32:10,141::train::INFO] [train] Iter 559094 | loss 0.3574 | loss(rot) 0.1129 | loss(pos) 0.2376 | loss(seq) 0.0069 | grad 4.6453 | lr 0.0000 | time_forward 3.9500 | time_backward 5.7020 |
[2023-10-22 15:32:18,729::train::INFO] [train] Iter 559095 | loss 1.9331 | loss(rot) 1.8489 | loss(pos) 0.0322 | loss(seq) 0.0520 | grad 4.1893 | lr 0.0000 | time_forward 3.5340 | time_backward 5.0520 |
[2023-10-22 15:32:28,137::train::INFO] [train] Iter 559096 | loss 0.3333 | loss(rot) 0.2262 | loss(pos) 0.1006 | loss(seq) 0.0065 | grad 3.5662 | lr 0.0000 | time_forward 3.9710 | time_backward 5.4330 |
[2023-10-22 15:32:38,133::train::INFO] [train] Iter 559097 | loss 1.6761 | loss(rot) 1.6066 | loss(pos) 0.0359 | loss(seq) 0.0336 | grad 27.9848 | lr 0.0000 | time_forward 4.2160 | time_backward 5.7760 |
[2023-10-22 15:32:48,622::train::INFO] [train] Iter 559098 | loss 0.3538 | loss(rot) 0.1107 | loss(pos) 0.1857 | loss(seq) 0.0574 | grad 3.9897 | lr 0.0000 | time_forward 4.5080 | time_backward 5.9770 |
[2023-10-22 15:32:56,640::train::INFO] [train] Iter 559099 | loss 0.4551 | loss(rot) 0.1850 | loss(pos) 0.0375 | loss(seq) 0.2326 | grad 2.9976 | lr 0.0000 | time_forward 3.4510 | time_backward 4.5640 |
[2023-10-22 15:32:59,578::train::INFO] [train] Iter 559100 | loss 1.4054 | loss(rot) 1.1644 | loss(pos) 0.0403 | loss(seq) 0.2007 | grad 3.7954 | lr 0.0000 | time_forward 1.3480 | time_backward 1.5860 |
[2023-10-22 15:33:08,285::train::INFO] [train] Iter 559101 | loss 0.4212 | loss(rot) 0.2586 | loss(pos) 0.0233 | loss(seq) 0.1393 | grad 2.7203 | lr 0.0000 | time_forward 3.8040 | time_backward 4.8990 |
[2023-10-22 15:33:15,972::train::INFO] [train] Iter 559102 | loss 1.6438 | loss(rot) 1.2380 | loss(pos) 0.2437 | loss(seq) 0.1621 | grad 6.9152 | lr 0.0000 | time_forward 3.1930 | time_backward 4.4910 |
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