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[2023-09-02 03:03:26,763::train::INFO] [train] Iter 05280 | loss 1.4099 | loss(rot) 0.7801 | loss(pos) 0.3553 | loss(seq) 0.2745 | grad 3.7338 | lr 0.0010 | time_forward 1.0330 | time_backward 1.1890 |
[2023-09-02 03:03:36,773::train::INFO] [train] Iter 05281 | loss 1.9930 | loss(rot) 1.3266 | loss(pos) 0.3084 | loss(seq) 0.3581 | grad 4.5089 | lr 0.0010 | time_forward 5.5790 | time_backward 4.4200 |
[2023-09-02 03:03:58,543::train::INFO] [train] Iter 05282 | loss 2.4990 | loss(rot) 2.1268 | loss(pos) 0.1791 | loss(seq) 0.1931 | grad 3.3549 | lr 0.0010 | time_forward 17.5820 | time_backward 4.1840 |
[2023-09-02 03:04:14,527::train::INFO] [train] Iter 05283 | loss 1.1656 | loss(rot) 0.1421 | loss(pos) 0.5774 | loss(seq) 0.4460 | grad 5.4523 | lr 0.0010 | time_forward 11.3800 | time_backward 4.6020 |
[2023-09-02 03:04:27,456::train::INFO] [train] Iter 05284 | loss 1.3850 | loss(rot) 0.1510 | loss(pos) 1.1881 | loss(seq) 0.0459 | grad 5.5517 | lr 0.0010 | time_forward 7.1670 | time_backward 5.7580 |
[2023-09-02 03:04:50,341::train::INFO] [train] Iter 05285 | loss 2.0089 | loss(rot) 0.0817 | loss(pos) 1.9213 | loss(seq) 0.0058 | grad 7.9796 | lr 0.0010 | time_forward 7.1580 | time_backward 15.7230 |
[2023-09-02 03:05:18,422::train::INFO] [train] Iter 05286 | loss 1.3969 | loss(rot) 0.2084 | loss(pos) 1.1597 | loss(seq) 0.0288 | grad 8.4747 | lr 0.0010 | time_forward 12.0970 | time_backward 15.9790 |
[2023-09-02 03:05:25,666::train::INFO] [train] Iter 05287 | loss 1.3749 | loss(rot) 0.8761 | loss(pos) 0.1703 | loss(seq) 0.3284 | grad 4.8373 | lr 0.0010 | time_forward 3.3060 | time_backward 3.9340 |
[2023-09-02 03:05:29,030::train::INFO] [train] Iter 05288 | loss 1.9624 | loss(rot) 0.1299 | loss(pos) 1.5841 | loss(seq) 0.2484 | grad 5.4001 | lr 0.0010 | time_forward 1.4950 | time_backward 1.8650 |
[2023-09-02 03:05:38,341::train::INFO] [train] Iter 05289 | loss 1.1381 | loss(rot) 0.4228 | loss(pos) 0.5364 | loss(seq) 0.1790 | grad 5.3083 | lr 0.0010 | time_forward 3.9130 | time_backward 5.3760 |
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