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[2023-10-24 20:06:13,416::train::INFO] [train] Iter 587266 | loss 0.3602 | loss(rot) 0.0830 | loss(pos) 0.1387 | loss(seq) 0.1386 | grad 2.7892 | lr 0.0000 | time_forward 1.3120 | time_backward 1.4410 |
[2023-10-24 20:06:16,205::train::INFO] [train] Iter 587267 | loss 0.2862 | loss(rot) 0.2020 | loss(pos) 0.0205 | loss(seq) 0.0638 | grad 2.8282 | lr 0.0000 | time_forward 1.3520 | time_backward 1.4350 |
[2023-10-24 20:06:26,276::train::INFO] [train] Iter 587268 | loss 1.4544 | loss(rot) 1.3349 | loss(pos) 0.0454 | loss(seq) 0.0741 | grad 13.0915 | lr 0.0000 | time_forward 4.1250 | time_backward 5.9420 |
[2023-10-24 20:06:34,624::train::INFO] [train] Iter 587269 | loss 0.7126 | loss(rot) 0.2172 | loss(pos) 0.0488 | loss(seq) 0.4466 | grad 3.2503 | lr 0.0000 | time_forward 3.5070 | time_backward 4.8380 |
[2023-10-24 20:06:44,731::train::INFO] [train] Iter 587270 | loss 0.5470 | loss(rot) 0.2613 | loss(pos) 0.0380 | loss(seq) 0.2477 | grad 2.0249 | lr 0.0000 | time_forward 4.3050 | time_backward 5.8000 |
[2023-10-24 20:06:47,534::train::INFO] [train] Iter 587271 | loss 0.8447 | loss(rot) 0.5781 | loss(pos) 0.0292 | loss(seq) 0.2374 | grad 2.3735 | lr 0.0000 | time_forward 1.3320 | time_backward 1.4670 |
[2023-10-24 20:06:54,268::train::INFO] [train] Iter 587272 | loss 0.3460 | loss(rot) 0.1987 | loss(pos) 0.1045 | loss(seq) 0.0428 | grad 3.2461 | lr 0.0000 | time_forward 2.8970 | time_backward 3.8210 |
[2023-10-24 20:07:03,409::train::INFO] [train] Iter 587273 | loss 0.2569 | loss(rot) 0.1086 | loss(pos) 0.0199 | loss(seq) 0.1283 | grad 1.8901 | lr 0.0000 | time_forward 3.8810 | time_backward 5.2560 |
[2023-10-24 20:07:06,210::train::INFO] [train] Iter 587274 | loss 0.3282 | loss(rot) 0.0493 | loss(pos) 0.2752 | loss(seq) 0.0036 | grad 7.4098 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4720 |
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