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[2023-10-25 14:24:19,529::train::INFO] [train] Iter 596756 | loss 0.8372 | loss(rot) 0.0237 | loss(pos) 0.8128 | loss(seq) 0.0007 | grad 9.1780 | lr 0.0000 | time_forward 3.8130 | time_backward 5.7530 |
[2023-10-25 14:24:22,415::train::INFO] [train] Iter 596757 | loss 0.9471 | loss(rot) 0.6600 | loss(pos) 0.1017 | loss(seq) 0.1854 | grad 4.2507 | lr 0.0000 | time_forward 1.4360 | time_backward 1.4470 |
[2023-10-25 14:24:25,222::train::INFO] [train] Iter 596758 | loss 0.3306 | loss(rot) 0.2934 | loss(pos) 0.0183 | loss(seq) 0.0189 | grad 39.8448 | lr 0.0000 | time_forward 1.3510 | time_backward 1.4290 |
[2023-10-25 14:24:33,137::train::INFO] [train] Iter 596759 | loss 0.3435 | loss(rot) 0.1796 | loss(pos) 0.0374 | loss(seq) 0.1266 | grad 2.8585 | lr 0.0000 | time_forward 3.4010 | time_backward 4.5100 |
[2023-10-25 14:24:41,439::train::INFO] [train] Iter 596760 | loss 1.5738 | loss(rot) 1.5226 | loss(pos) 0.0280 | loss(seq) 0.0233 | grad 21.7579 | lr 0.0000 | time_forward 3.5400 | time_backward 4.7590 |
[2023-10-25 14:24:49,750::train::INFO] [train] Iter 596761 | loss 0.3622 | loss(rot) 0.0692 | loss(pos) 0.1225 | loss(seq) 0.1704 | grad 3.2892 | lr 0.0000 | time_forward 3.5780 | time_backward 4.7300 |
[2023-10-25 14:24:58,400::train::INFO] [train] Iter 596762 | loss 2.0407 | loss(rot) 1.9539 | loss(pos) 0.0778 | loss(seq) 0.0090 | grad 6.2549 | lr 0.0000 | time_forward 3.6010 | time_backward 5.0460 |
[2023-10-25 14:25:08,099::train::INFO] [train] Iter 596763 | loss 0.9522 | loss(rot) 0.4321 | loss(pos) 0.2122 | loss(seq) 0.3079 | grad 3.3372 | lr 0.0000 | time_forward 4.0180 | time_backward 5.6770 |
[2023-10-25 14:25:16,682::train::INFO] [train] Iter 596764 | loss 0.3712 | loss(rot) 0.0966 | loss(pos) 0.0279 | loss(seq) 0.2468 | grad 3.0413 | lr 0.0000 | time_forward 3.6680 | time_backward 4.9110 |
[2023-10-25 14:25:26,163::train::INFO] [train] Iter 596765 | loss 1.3463 | loss(rot) 1.0501 | loss(pos) 0.0457 | loss(seq) 0.2505 | grad 8.9542 | lr 0.0000 | time_forward 3.8090 | time_backward 5.6680 |
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