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[2023-10-23 20:49:02,726::train::INFO] [train] Iter 575277 | loss 1.6608 | loss(rot) 1.6363 | loss(pos) 0.0243 | loss(seq) 0.0001 | grad 3.9391 | lr 0.0000 | time_forward 3.4470 | time_backward 4.6240 |
[2023-10-23 20:49:10,409::train::INFO] [train] Iter 575278 | loss 0.5019 | loss(rot) 0.3304 | loss(pos) 0.0181 | loss(seq) 0.1534 | grad 2.3791 | lr 0.0000 | time_forward 3.2410 | time_backward 4.4230 |
[2023-10-23 20:49:16,911::train::INFO] [train] Iter 575279 | loss 0.2053 | loss(rot) 0.1964 | loss(pos) 0.0080 | loss(seq) 0.0009 | grad 2.8022 | lr 0.0000 | time_forward 2.7960 | time_backward 3.7040 |
[2023-10-23 20:49:25,004::train::INFO] [train] Iter 575280 | loss 0.4630 | loss(rot) 0.2230 | loss(pos) 0.0413 | loss(seq) 0.1988 | grad 2.8525 | lr 0.0000 | time_forward 3.4730 | time_backward 4.6160 |
[2023-10-23 20:49:32,916::train::INFO] [train] Iter 575281 | loss 0.0876 | loss(rot) 0.0721 | loss(pos) 0.0153 | loss(seq) 0.0002 | grad 2.0035 | lr 0.0000 | time_forward 3.3760 | time_backward 4.5330 |
[2023-10-23 20:49:36,340::train::INFO] [train] Iter 575282 | loss 0.2242 | loss(rot) 0.0566 | loss(pos) 0.0726 | loss(seq) 0.0950 | grad 2.2667 | lr 0.0000 | time_forward 1.5430 | time_backward 1.8770 |
[2023-10-23 20:49:39,044::train::INFO] [train] Iter 575283 | loss 1.0291 | loss(rot) 0.6828 | loss(pos) 0.0641 | loss(seq) 0.2821 | grad 3.8682 | lr 0.0000 | time_forward 1.2700 | time_backward 1.4300 |
[2023-10-23 20:49:47,091::train::INFO] [train] Iter 575284 | loss 2.6695 | loss(rot) 2.3062 | loss(pos) 0.0859 | loss(seq) 0.2774 | grad 3.6176 | lr 0.0000 | time_forward 3.4490 | time_backward 4.5960 |
[2023-10-23 20:49:49,832::train::INFO] [train] Iter 575285 | loss 0.1229 | loss(rot) 0.0860 | loss(pos) 0.0364 | loss(seq) 0.0005 | grad 1.7656 | lr 0.0000 | time_forward 1.2750 | time_backward 1.4620 |
[2023-10-23 20:49:55,723::train::INFO] [train] Iter 575286 | loss 3.1264 | loss(rot) 0.0047 | loss(pos) 3.1217 | loss(seq) 0.0000 | grad 28.1637 | lr 0.0000 | time_forward 2.5500 | time_backward 3.3380 |
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