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[2023-09-02 05:19:54,399::train::INFO] [train] Iter 06379 | loss 2.5690 | loss(rot) 2.1631 | loss(pos) 0.1812 | loss(seq) 0.2247 | grad 4.1108 | lr 0.0010 | time_forward 1.2700 | time_backward 1.4130 |
[2023-09-02 05:20:03,200::train::INFO] [train] Iter 06380 | loss 2.0891 | loss(rot) 1.9310 | loss(pos) 0.0625 | loss(seq) 0.0955 | grad 5.5698 | lr 0.0010 | time_forward 3.7070 | time_backward 5.0910 |
[2023-09-02 05:20:11,225::train::INFO] [train] Iter 06381 | loss 2.3138 | loss(rot) 2.1381 | loss(pos) 0.1071 | loss(seq) 0.0686 | grad 5.2086 | lr 0.0010 | time_forward 3.3550 | time_backward 4.6660 |
[2023-09-02 05:20:21,109::train::INFO] [train] Iter 06382 | loss 2.3216 | loss(rot) 1.6016 | loss(pos) 0.4538 | loss(seq) 0.2663 | grad 4.9615 | lr 0.0010 | time_forward 4.0350 | time_backward 5.8450 |
[2023-09-02 05:20:29,920::train::INFO] [train] Iter 06383 | loss 1.9666 | loss(rot) 1.6878 | loss(pos) 0.1693 | loss(seq) 0.1096 | grad 4.3451 | lr 0.0010 | time_forward 3.7420 | time_backward 5.0650 |
[2023-09-02 05:20:38,725::train::INFO] [train] Iter 06384 | loss 1.3621 | loss(rot) 0.0820 | loss(pos) 1.2512 | loss(seq) 0.0288 | grad 7.4864 | lr 0.0010 | time_forward 3.7970 | time_backward 5.0050 |
[2023-09-02 05:20:41,414::train::INFO] [train] Iter 06385 | loss 1.6754 | loss(rot) 1.4282 | loss(pos) 0.0883 | loss(seq) 0.1589 | grad 3.8384 | lr 0.0010 | time_forward 1.2360 | time_backward 1.4500 |
[2023-09-02 05:20:50,676::train::INFO] [train] Iter 06386 | loss 2.0433 | loss(rot) 1.1203 | loss(pos) 0.4623 | loss(seq) 0.4607 | grad 3.4009 | lr 0.0010 | time_forward 3.9590 | time_backward 5.2820 |
[2023-09-02 05:21:00,680::train::INFO] [train] Iter 06387 | loss 1.6985 | loss(rot) 0.1640 | loss(pos) 1.5327 | loss(seq) 0.0018 | grad 5.8383 | lr 0.0010 | time_forward 4.1090 | time_backward 5.8910 |
[2023-09-02 05:21:08,985::train::INFO] [train] Iter 06388 | loss 2.1159 | loss(rot) 0.0200 | loss(pos) 2.0934 | loss(seq) 0.0025 | grad 10.7440 | lr 0.0010 | time_forward 3.4930 | time_backward 4.8090 |
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