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[2023-09-02 04:31:41,194::train::INFO] [train] Iter 05980 | loss 1.2895 | loss(rot) 0.6587 | loss(pos) 0.3696 | loss(seq) 0.2611 | grad 3.6071 | lr 0.0010 | time_forward 2.6090 | time_backward 3.4470 |
[2023-09-02 04:31:49,797::train::INFO] [train] Iter 05981 | loss 1.2086 | loss(rot) 0.0672 | loss(pos) 1.1305 | loss(seq) 0.0109 | grad 9.0721 | lr 0.0010 | time_forward 3.6440 | time_backward 4.9550 |
[2023-09-02 04:31:53,206::train::INFO] [train] Iter 05982 | loss 1.2285 | loss(rot) 0.2896 | loss(pos) 0.8834 | loss(seq) 0.0555 | grad 5.1463 | lr 0.0010 | time_forward 1.4190 | time_backward 1.9870 |
[2023-09-02 04:32:04,071::train::INFO] [train] Iter 05983 | loss 0.5736 | loss(rot) 0.1551 | loss(pos) 0.3487 | loss(seq) 0.0699 | grad 3.0112 | lr 0.0010 | time_forward 4.4640 | time_backward 6.3970 |
[2023-09-02 04:32:06,824::train::INFO] [train] Iter 05984 | loss 1.0808 | loss(rot) 0.1627 | loss(pos) 0.9091 | loss(seq) 0.0090 | grad 5.1332 | lr 0.0010 | time_forward 1.2510 | time_backward 1.4990 |
[2023-09-02 04:32:15,758::train::INFO] [train] Iter 05985 | loss 2.2264 | loss(rot) 0.0581 | loss(pos) 2.1669 | loss(seq) 0.0014 | grad 9.0204 | lr 0.0010 | time_forward 3.8080 | time_backward 5.1230 |
[2023-09-02 04:32:23,763::train::INFO] [train] Iter 05986 | loss 3.5514 | loss(rot) 0.0044 | loss(pos) 3.5466 | loss(seq) 0.0004 | grad 12.0106 | lr 0.0010 | time_forward 3.3120 | time_backward 4.6890 |
[2023-09-02 04:32:33,604::train::INFO] [train] Iter 05987 | loss 2.2471 | loss(rot) 1.8762 | loss(pos) 0.1868 | loss(seq) 0.1841 | grad 2.9371 | lr 0.0010 | time_forward 4.1460 | time_backward 5.6920 |
[2023-09-02 04:32:43,478::train::INFO] [train] Iter 05988 | loss 2.4177 | loss(rot) 2.3098 | loss(pos) 0.0913 | loss(seq) 0.0166 | grad 5.4805 | lr 0.0010 | time_forward 3.9660 | time_backward 5.9040 |
[2023-09-02 04:32:53,786::train::INFO] [train] Iter 05989 | loss 2.7841 | loss(rot) 1.7042 | loss(pos) 0.4171 | loss(seq) 0.6629 | grad 4.5441 | lr 0.0010 | time_forward 4.3680 | time_backward 5.9370 |
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