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[2023-10-24 04:43:10,165::train::INFO] [train] Iter 579273 | loss 0.4616 | loss(rot) 0.1355 | loss(pos) 0.3045 | loss(seq) 0.0216 | grad 3.5843 | lr 0.0000 | time_forward 1.4990 | time_backward 1.8690 |
[2023-10-24 04:43:13,028::train::INFO] [train] Iter 579274 | loss 0.6597 | loss(rot) 0.2304 | loss(pos) 0.1332 | loss(seq) 0.2960 | grad 4.0537 | lr 0.0000 | time_forward 1.3130 | time_backward 1.5330 |
[2023-10-24 04:43:21,285::train::INFO] [train] Iter 579275 | loss 0.9962 | loss(rot) 0.3587 | loss(pos) 0.4684 | loss(seq) 0.1692 | grad 5.6913 | lr 0.0000 | time_forward 3.5170 | time_backward 4.7090 |
[2023-10-24 04:43:30,132::train::INFO] [train] Iter 579276 | loss 1.1862 | loss(rot) 0.1275 | loss(pos) 1.0576 | loss(seq) 0.0011 | grad 7.0974 | lr 0.0000 | time_forward 3.7360 | time_backward 5.1080 |
[2023-10-24 04:43:38,311::train::INFO] [train] Iter 579277 | loss 0.4321 | loss(rot) 0.0947 | loss(pos) 0.0507 | loss(seq) 0.2867 | grad 2.5685 | lr 0.0000 | time_forward 3.4360 | time_backward 4.7400 |
[2023-10-24 04:43:47,812::train::INFO] [train] Iter 579278 | loss 0.6856 | loss(rot) 0.2543 | loss(pos) 0.0564 | loss(seq) 0.3749 | grad 3.7405 | lr 0.0000 | time_forward 3.9330 | time_backward 5.5640 |
[2023-10-24 04:43:57,336::train::INFO] [train] Iter 579279 | loss 0.3680 | loss(rot) 0.1532 | loss(pos) 0.0608 | loss(seq) 0.1540 | grad 2.5814 | lr 0.0000 | time_forward 3.8430 | time_backward 5.6780 |
[2023-10-24 04:44:05,804::train::INFO] [train] Iter 579280 | loss 0.6201 | loss(rot) 0.2354 | loss(pos) 0.0532 | loss(seq) 0.3314 | grad 2.9795 | lr 0.0000 | time_forward 3.5800 | time_backward 4.8840 |
[2023-10-24 04:44:15,355::train::INFO] [train] Iter 579281 | loss 0.7128 | loss(rot) 0.5238 | loss(pos) 0.0792 | loss(seq) 0.1098 | grad 2.4651 | lr 0.0000 | time_forward 3.8930 | time_backward 5.6560 |
[2023-10-24 04:44:24,321::train::INFO] [train] Iter 579282 | loss 0.3548 | loss(rot) 0.3181 | loss(pos) 0.0331 | loss(seq) 0.0036 | grad 29.3262 | lr 0.0000 | time_forward 3.8970 | time_backward 5.0660 |
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