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[2023-09-01 18:58:40,081::train::INFO] [train] Iter 01284 | loss 3.4307 | loss(rot) 2.9920 | loss(pos) 0.4386 | loss(seq) 0.0001 | grad 3.8174 | lr 0.0010 | time_forward 4.1830 | time_backward 6.1100 |
[2023-09-01 18:58:50,309::train::INFO] [train] Iter 01285 | loss 3.1350 | loss(rot) 1.9762 | loss(pos) 0.5859 | loss(seq) 0.5730 | grad 3.4847 | lr 0.0010 | time_forward 4.2400 | time_backward 5.9830 |
[2023-09-01 18:59:00,488::train::INFO] [train] Iter 01286 | loss 3.8468 | loss(rot) 2.5904 | loss(pos) 0.7503 | loss(seq) 0.5061 | grad 3.4267 | lr 0.0010 | time_forward 4.1140 | time_backward 6.0620 |
[2023-09-01 18:59:10,507::train::INFO] [train] Iter 01287 | loss 1.5386 | loss(rot) 0.6076 | loss(pos) 0.5966 | loss(seq) 0.3344 | grad 3.2633 | lr 0.0010 | time_forward 4.0150 | time_backward 6.0010 |
[2023-09-01 18:59:13,223::train::INFO] [train] Iter 01288 | loss 3.3806 | loss(rot) 3.0094 | loss(pos) 0.1934 | loss(seq) 0.1778 | grad 3.3548 | lr 0.0010 | time_forward 1.2570 | time_backward 1.4440 |
[2023-09-01 18:59:15,921::train::INFO] [train] Iter 01289 | loss 3.1470 | loss(rot) 3.0327 | loss(pos) 0.1112 | loss(seq) 0.0030 | grad 2.3628 | lr 0.0010 | time_forward 1.2740 | time_backward 1.4210 |
[2023-09-01 18:59:24,925::train::INFO] [train] Iter 01290 | loss 2.1931 | loss(rot) 0.8659 | loss(pos) 0.8688 | loss(seq) 0.4584 | grad 5.5162 | lr 0.0010 | time_forward 3.7600 | time_backward 5.2400 |
[2023-09-01 18:59:33,504::train::INFO] [train] Iter 01291 | loss 1.7441 | loss(rot) 0.2458 | loss(pos) 1.4467 | loss(seq) 0.0516 | grad 4.0043 | lr 0.0010 | time_forward 3.5690 | time_backward 5.0070 |
[2023-09-01 18:59:44,333::train::INFO] [train] Iter 01292 | loss 1.9481 | loss(rot) 0.7455 | loss(pos) 0.8222 | loss(seq) 0.3804 | grad 4.1559 | lr 0.0010 | time_forward 4.0100 | time_backward 6.8150 |
[2023-09-01 18:59:47,029::train::INFO] [train] Iter 01293 | loss 2.6900 | loss(rot) 2.5008 | loss(pos) 0.1347 | loss(seq) 0.0546 | grad 2.3302 | lr 0.0010 | time_forward 1.2850 | time_backward 1.4080 |
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