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[2023-10-22 15:44:39,433::train::INFO] [train] Iter 559194 | loss 0.5832 | loss(rot) 0.4243 | loss(pos) 0.0377 | loss(seq) 0.1212 | grad 2.8375 | lr 0.0000 | time_forward 3.7700 | time_backward 5.0740 |
[2023-10-22 15:44:42,200::train::INFO] [train] Iter 559195 | loss 0.7799 | loss(rot) 0.4360 | loss(pos) 0.0488 | loss(seq) 0.2952 | grad 4.1938 | lr 0.0000 | time_forward 1.3120 | time_backward 1.4510 |
[2023-10-22 15:44:51,294::train::INFO] [train] Iter 559196 | loss 0.6394 | loss(rot) 0.5595 | loss(pos) 0.0339 | loss(seq) 0.0460 | grad 2.2799 | lr 0.0000 | time_forward 3.8180 | time_backward 5.2480 |
[2023-10-22 15:45:00,857::train::INFO] [train] Iter 559197 | loss 1.1197 | loss(rot) 0.5633 | loss(pos) 0.1191 | loss(seq) 0.4373 | grad 3.4215 | lr 0.0000 | time_forward 3.9580 | time_backward 5.6030 |
[2023-10-22 15:45:04,211::train::INFO] [train] Iter 559198 | loss 1.4660 | loss(rot) 1.2390 | loss(pos) 0.0668 | loss(seq) 0.1603 | grad 3.7363 | lr 0.0000 | time_forward 1.4820 | time_backward 1.8690 |
[2023-10-22 15:45:12,397::train::INFO] [train] Iter 559199 | loss 1.7632 | loss(rot) 1.3373 | loss(pos) 0.0550 | loss(seq) 0.3710 | grad 6.2646 | lr 0.0000 | time_forward 3.4810 | time_backward 4.7020 |
[2023-10-22 15:45:14,681::train::INFO] [train] Iter 559200 | loss 0.7781 | loss(rot) 0.4421 | loss(pos) 0.1013 | loss(seq) 0.2347 | grad 4.5240 | lr 0.0000 | time_forward 1.0680 | time_backward 1.2120 |
[2023-10-22 15:45:22,185::train::INFO] [train] Iter 559201 | loss 0.5784 | loss(rot) 0.2473 | loss(pos) 0.1029 | loss(seq) 0.2282 | grad 4.0277 | lr 0.0000 | time_forward 3.2090 | time_backward 4.2780 |
[2023-10-22 15:45:29,717::train::INFO] [train] Iter 559202 | loss 0.7443 | loss(rot) 0.7104 | loss(pos) 0.0324 | loss(seq) 0.0015 | grad 3.6829 | lr 0.0000 | time_forward 3.2080 | time_backward 4.3220 |
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