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[2023-10-24 03:09:20,112::train::INFO] [train] Iter 578474 | loss 2.5123 | loss(rot) 2.4882 | loss(pos) 0.0192 | loss(seq) 0.0049 | grad 10.8303 | lr 0.0000 | time_forward 3.1080 | time_backward 4.1520 |
[2023-10-24 03:09:29,763::train::INFO] [train] Iter 578475 | loss 0.4592 | loss(rot) 0.2490 | loss(pos) 0.0945 | loss(seq) 0.1157 | grad 2.2690 | lr 0.0000 | time_forward 3.9090 | time_backward 5.7380 |
[2023-10-24 03:09:39,423::train::INFO] [train] Iter 578476 | loss 0.8860 | loss(rot) 0.1269 | loss(pos) 0.3674 | loss(seq) 0.3916 | grad 3.9817 | lr 0.0000 | time_forward 4.1020 | time_backward 5.5540 |
[2023-10-24 03:09:42,694::train::INFO] [train] Iter 578477 | loss 1.2937 | loss(rot) 0.8706 | loss(pos) 0.1302 | loss(seq) 0.2929 | grad 19.8262 | lr 0.0000 | time_forward 1.4790 | time_backward 1.7880 |
[2023-10-24 03:09:45,250::train::INFO] [train] Iter 578478 | loss 1.5149 | loss(rot) 0.8964 | loss(pos) 0.4512 | loss(seq) 0.1672 | grad 6.6952 | lr 0.0000 | time_forward 1.2060 | time_backward 1.3350 |
[2023-10-24 03:09:52,560::train::INFO] [train] Iter 578479 | loss 0.3367 | loss(rot) 0.2046 | loss(pos) 0.0226 | loss(seq) 0.1096 | grad 3.0335 | lr 0.0000 | time_forward 2.9810 | time_backward 4.3140 |
[2023-10-24 03:09:54,862::train::INFO] [train] Iter 578480 | loss 0.3310 | loss(rot) 0.1184 | loss(pos) 0.0713 | loss(seq) 0.1414 | grad 2.7262 | lr 0.0000 | time_forward 1.0650 | time_backward 1.2340 |
[2023-10-24 03:10:04,413::train::INFO] [train] Iter 578481 | loss 1.6995 | loss(rot) 0.8945 | loss(pos) 0.2160 | loss(seq) 0.5890 | grad 3.7469 | lr 0.0000 | time_forward 3.9120 | time_backward 5.6350 |
[2023-10-24 03:10:07,707::train::INFO] [train] Iter 578482 | loss 1.3965 | loss(rot) 0.9061 | loss(pos) 0.1429 | loss(seq) 0.3475 | grad 9.3355 | lr 0.0000 | time_forward 1.4650 | time_backward 1.8270 |
[2023-10-24 03:10:11,261::train::INFO] [train] Iter 578483 | loss 0.6797 | loss(rot) 0.3915 | loss(pos) 0.0688 | loss(seq) 0.2193 | grad 4.4515 | lr 0.0000 | time_forward 1.5140 | time_backward 2.0260 |
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