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[2023-10-25 06:34:16,079::train::INFO] [train] Iter 592760 | loss 1.3068 | loss(rot) 0.6257 | loss(pos) 0.1016 | loss(seq) 0.5795 | grad 3.8479 | lr 0.0000 | time_forward 1.2960 | time_backward 1.3880 |
[2023-10-25 06:34:18,853::train::INFO] [train] Iter 592761 | loss 0.5368 | loss(rot) 0.0821 | loss(pos) 0.0646 | loss(seq) 0.3901 | grad 3.1404 | lr 0.0000 | time_forward 1.3220 | time_backward 1.4160 |
[2023-10-25 06:34:25,384::train::INFO] [train] Iter 592762 | loss 0.0650 | loss(rot) 0.0438 | loss(pos) 0.0136 | loss(seq) 0.0076 | grad 1.2716 | lr 0.0000 | time_forward 2.8930 | time_backward 3.6340 |
[2023-10-25 06:34:33,931::train::INFO] [train] Iter 592763 | loss 0.8805 | loss(rot) 0.4458 | loss(pos) 0.0505 | loss(seq) 0.3843 | grad 20.9356 | lr 0.0000 | time_forward 3.5990 | time_backward 4.9440 |
[2023-10-25 06:34:43,285::train::INFO] [train] Iter 592764 | loss 0.5311 | loss(rot) 0.1863 | loss(pos) 0.1471 | loss(seq) 0.1977 | grad 4.3565 | lr 0.0000 | time_forward 3.8890 | time_backward 5.4620 |
[2023-10-25 06:34:51,238::train::INFO] [train] Iter 592765 | loss 0.3471 | loss(rot) 0.1139 | loss(pos) 0.0845 | loss(seq) 0.1486 | grad 2.5213 | lr 0.0000 | time_forward 3.3750 | time_backward 4.5740 |
[2023-10-25 06:34:59,079::train::INFO] [train] Iter 592766 | loss 0.3654 | loss(rot) 0.2800 | loss(pos) 0.0598 | loss(seq) 0.0257 | grad 5.5924 | lr 0.0000 | time_forward 3.3780 | time_backward 4.4600 |
[2023-10-25 06:35:07,054::train::INFO] [train] Iter 592767 | loss 0.2391 | loss(rot) 0.0564 | loss(pos) 0.1238 | loss(seq) 0.0589 | grad 2.6198 | lr 0.0000 | time_forward 3.3650 | time_backward 4.6070 |
[2023-10-25 06:35:14,557::train::INFO] [train] Iter 592768 | loss 0.1879 | loss(rot) 0.0359 | loss(pos) 0.1318 | loss(seq) 0.0203 | grad 3.5475 | lr 0.0000 | time_forward 3.2320 | time_backward 4.2670 |
[2023-10-25 06:35:23,362::train::INFO] [train] Iter 592769 | loss 0.6859 | loss(rot) 0.6609 | loss(pos) 0.0156 | loss(seq) 0.0093 | grad 5.5504 | lr 0.0000 | time_forward 3.5900 | time_backward 5.2110 |
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