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[2023-09-02 03:17:16,539::train::INFO] [train] Iter 05380 | loss 3.2989 | loss(rot) 3.0735 | loss(pos) 0.1529 | loss(seq) 0.0726 | grad 3.8278 | lr 0.0010 | time_forward 1.3620 | time_backward 1.4830 |
[2023-09-02 03:17:25,280::train::INFO] [train] Iter 05381 | loss 3.3301 | loss(rot) 2.6894 | loss(pos) 0.2928 | loss(seq) 0.3478 | grad 3.0599 | lr 0.0010 | time_forward 3.8280 | time_backward 4.9090 |
[2023-09-02 03:17:34,616::train::INFO] [train] Iter 05382 | loss 1.6937 | loss(rot) 0.7640 | loss(pos) 0.5640 | loss(seq) 0.3657 | grad 4.4364 | lr 0.0010 | time_forward 3.9770 | time_backward 5.3550 |
[2023-09-02 03:17:45,356::train::INFO] [train] Iter 05383 | loss 0.9573 | loss(rot) 0.3323 | loss(pos) 0.5622 | loss(seq) 0.0628 | grad 4.2281 | lr 0.0010 | time_forward 4.3040 | time_backward 6.4330 |
[2023-09-02 03:17:55,766::train::INFO] [train] Iter 05384 | loss 0.8297 | loss(rot) 0.0507 | loss(pos) 0.7670 | loss(seq) 0.0120 | grad 7.7491 | lr 0.0010 | time_forward 4.1270 | time_backward 6.2790 |
[2023-09-02 03:17:58,156::train::INFO] [train] Iter 05385 | loss 1.4443 | loss(rot) 0.5732 | loss(pos) 0.5116 | loss(seq) 0.3594 | grad 4.3522 | lr 0.0010 | time_forward 1.0840 | time_backward 1.3020 |
[2023-09-02 03:18:07,225::train::INFO] [train] Iter 05386 | loss 1.2840 | loss(rot) 0.5828 | loss(pos) 0.6321 | loss(seq) 0.0691 | grad 4.9782 | lr 0.0010 | time_forward 3.8070 | time_backward 5.2590 |
[2023-09-02 03:18:17,705::train::INFO] [train] Iter 05387 | loss 0.8278 | loss(rot) 0.5015 | loss(pos) 0.2082 | loss(seq) 0.1181 | grad 3.4941 | lr 0.0010 | time_forward 4.2190 | time_backward 6.2570 |
[2023-09-02 03:18:28,279::train::INFO] [train] Iter 05388 | loss 2.4724 | loss(rot) 2.1202 | loss(pos) 0.3522 | loss(seq) 0.0000 | grad 4.1706 | lr 0.0010 | time_forward 4.1360 | time_backward 6.4330 |
[2023-09-02 03:18:38,690::train::INFO] [train] Iter 05389 | loss 2.0398 | loss(rot) 0.9611 | loss(pos) 0.5459 | loss(seq) 0.5327 | grad 6.2365 | lr 0.0010 | time_forward 4.2060 | time_backward 6.2010 |
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