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[2023-10-23 03:33:07,434::train::INFO] [train] Iter 565687 | loss 0.2859 | loss(rot) 0.0990 | loss(pos) 0.0375 | loss(seq) 0.1494 | grad 2.0908 | lr 0.0000 | time_forward 3.3280 | time_backward 4.6450 |
[2023-10-23 03:33:15,410::train::INFO] [train] Iter 565688 | loss 0.2490 | loss(rot) 0.1144 | loss(pos) 0.0623 | loss(seq) 0.0723 | grad 2.6248 | lr 0.0000 | time_forward 3.2870 | time_backward 4.6870 |
[2023-10-23 03:33:22,896::train::INFO] [train] Iter 565689 | loss 0.8751 | loss(rot) 0.2846 | loss(pos) 0.4056 | loss(seq) 0.1849 | grad 3.4924 | lr 0.0000 | time_forward 3.1980 | time_backward 4.2850 |
[2023-10-23 03:33:29,700::train::INFO] [train] Iter 565690 | loss 0.7757 | loss(rot) 0.2902 | loss(pos) 0.0480 | loss(seq) 0.4375 | grad 4.0315 | lr 0.0000 | time_forward 2.9800 | time_backward 3.8200 |
[2023-10-23 03:33:37,109::train::INFO] [train] Iter 565691 | loss 0.3817 | loss(rot) 0.2400 | loss(pos) 0.0120 | loss(seq) 0.1297 | grad 4.8941 | lr 0.0000 | time_forward 3.2120 | time_backward 4.1930 |
[2023-10-23 03:33:44,349::train::INFO] [train] Iter 565692 | loss 0.5659 | loss(rot) 0.4424 | loss(pos) 0.0424 | loss(seq) 0.0811 | grad 3.9260 | lr 0.0000 | time_forward 3.0920 | time_backward 4.1440 |
[2023-10-23 03:33:51,544::train::INFO] [train] Iter 565693 | loss 0.1560 | loss(rot) 0.1089 | loss(pos) 0.0419 | loss(seq) 0.0052 | grad 1.9924 | lr 0.0000 | time_forward 3.1490 | time_backward 4.0440 |
[2023-10-23 03:33:53,812::train::INFO] [train] Iter 565694 | loss 0.6771 | loss(rot) 0.6318 | loss(pos) 0.0357 | loss(seq) 0.0095 | grad 2.5487 | lr 0.0000 | time_forward 1.0200 | time_backward 1.2450 |
[2023-10-23 03:33:56,455::train::INFO] [train] Iter 565695 | loss 0.3021 | loss(rot) 0.2612 | loss(pos) 0.0351 | loss(seq) 0.0058 | grad 2.3024 | lr 0.0000 | time_forward 1.2630 | time_backward 1.3770 |
[2023-10-23 03:34:02,852::train::INFO] [train] Iter 565696 | loss 0.6377 | loss(rot) 0.4051 | loss(pos) 0.0273 | loss(seq) 0.2053 | grad 4.4437 | lr 0.0000 | time_forward 2.7770 | time_backward 3.6160 |
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