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[2023-10-23 15:09:22,647::train::INFO] [train] Iter 572280 | loss 0.8446 | loss(rot) 0.8061 | loss(pos) 0.0277 | loss(seq) 0.0109 | grad 4.2635 | lr 0.0000 | time_forward 3.2030 | time_backward 4.5290 |
[2023-10-23 15:09:30,384::train::INFO] [train] Iter 572281 | loss 0.3496 | loss(rot) 0.1644 | loss(pos) 0.0547 | loss(seq) 0.1306 | grad 2.5641 | lr 0.0000 | time_forward 3.2070 | time_backward 4.5270 |
[2023-10-23 15:09:33,170::train::INFO] [train] Iter 572282 | loss 0.5306 | loss(rot) 0.3821 | loss(pos) 0.0186 | loss(seq) 0.1299 | grad 3.8538 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4590 |
[2023-10-23 15:09:39,954::train::INFO] [train] Iter 572283 | loss 1.8035 | loss(rot) 1.2971 | loss(pos) 0.0574 | loss(seq) 0.4490 | grad 5.6946 | lr 0.0000 | time_forward 2.9740 | time_backward 3.8060 |
[2023-10-23 15:09:46,385::train::INFO] [train] Iter 572284 | loss 2.1823 | loss(rot) 1.6888 | loss(pos) 0.2314 | loss(seq) 0.2620 | grad 3.7099 | lr 0.0000 | time_forward 2.7340 | time_backward 3.6930 |
[2023-10-23 15:09:48,823::train::INFO] [train] Iter 572285 | loss 2.1656 | loss(rot) 1.5107 | loss(pos) 0.1718 | loss(seq) 0.4831 | grad 9.2988 | lr 0.0000 | time_forward 1.1650 | time_backward 1.2710 |
[2023-10-23 15:09:56,236::train::INFO] [train] Iter 572286 | loss 0.2030 | loss(rot) 0.0412 | loss(pos) 0.0320 | loss(seq) 0.1298 | grad 2.0001 | lr 0.0000 | time_forward 3.2360 | time_backward 4.1730 |
[2023-10-23 15:10:03,529::train::INFO] [train] Iter 572287 | loss 0.6321 | loss(rot) 0.4249 | loss(pos) 0.0146 | loss(seq) 0.1925 | grad 1.5752 | lr 0.0000 | time_forward 3.1780 | time_backward 4.1120 |
[2023-10-23 15:10:05,747::train::INFO] [train] Iter 572288 | loss 0.1314 | loss(rot) 0.0948 | loss(pos) 0.0364 | loss(seq) 0.0003 | grad 1.9736 | lr 0.0000 | time_forward 1.0360 | time_backward 1.1790 |
[2023-10-23 15:10:12,587::train::INFO] [train] Iter 572289 | loss 1.3540 | loss(rot) 1.2242 | loss(pos) 0.0281 | loss(seq) 0.1017 | grad 39.1037 | lr 0.0000 | time_forward 2.9600 | time_backward 3.8760 |
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