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[2023-09-02 00:28:06,633::train::INFO] [train] Iter 03982 | loss 2.8049 | loss(rot) 2.4877 | loss(pos) 0.3151 | loss(seq) 0.0020 | grad 3.2938 | lr 0.0010 | time_forward 4.2540 | time_backward 5.8640 |
[2023-09-02 00:28:15,803::train::INFO] [train] Iter 03983 | loss 2.5495 | loss(rot) 2.2126 | loss(pos) 0.2762 | loss(seq) 0.0607 | grad 4.3647 | lr 0.0010 | time_forward 3.9980 | time_backward 5.1680 |
[2023-09-02 00:28:18,048::train::INFO] [train] Iter 03984 | loss 0.8122 | loss(rot) 0.2679 | loss(pos) 0.4719 | loss(seq) 0.0724 | grad 3.4047 | lr 0.0010 | time_forward 0.9920 | time_backward 1.2510 |
[2023-09-02 00:28:28,224::train::INFO] [train] Iter 03985 | loss 0.9836 | loss(rot) 0.2267 | loss(pos) 0.6848 | loss(seq) 0.0720 | grad 3.6289 | lr 0.0010 | time_forward 4.1310 | time_backward 6.0410 |
[2023-09-02 00:28:38,376::train::INFO] [train] Iter 03986 | loss 2.3998 | loss(rot) 1.3962 | loss(pos) 0.3310 | loss(seq) 0.6727 | grad 4.3429 | lr 0.0010 | time_forward 4.1440 | time_backward 6.0040 |
[2023-09-02 00:28:41,125::train::INFO] [train] Iter 03987 | loss 1.1735 | loss(rot) 0.2874 | loss(pos) 0.4695 | loss(seq) 0.4167 | grad 3.9526 | lr 0.0010 | time_forward 1.2910 | time_backward 1.4550 |
[2023-09-02 00:28:50,685::train::INFO] [train] Iter 03988 | loss 1.9547 | loss(rot) 1.5346 | loss(pos) 0.2112 | loss(seq) 0.2089 | grad 4.3966 | lr 0.0010 | time_forward 4.1000 | time_backward 5.4560 |
[2023-09-02 00:28:53,409::train::INFO] [train] Iter 03989 | loss 2.6600 | loss(rot) 1.5411 | loss(pos) 0.5224 | loss(seq) 0.5965 | grad 5.2675 | lr 0.0010 | time_forward 1.2920 | time_backward 1.4280 |
[2023-09-02 00:29:03,276::train::INFO] [train] Iter 03990 | loss 2.7969 | loss(rot) 1.8709 | loss(pos) 0.4344 | loss(seq) 0.4916 | grad 5.0613 | lr 0.0010 | time_forward 4.1100 | time_backward 5.7550 |
[2023-09-02 00:29:12,104::train::INFO] [train] Iter 03991 | loss 2.5288 | loss(rot) 2.3207 | loss(pos) 0.2060 | loss(seq) 0.0021 | grad 5.3051 | lr 0.0010 | time_forward 3.6400 | time_backward 5.1490 |
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