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[2023-10-22 23:51:18,020::train::INFO] [train] Iter 563489 | loss 1.5039 | loss(rot) 0.9962 | loss(pos) 0.1522 | loss(seq) 0.3556 | grad 7.4138 | lr 0.0000 | time_forward 1.2780 | time_backward 1.4890 |
[2023-10-22 23:51:20,777::train::INFO] [train] Iter 563490 | loss 0.8425 | loss(rot) 0.8044 | loss(pos) 0.0317 | loss(seq) 0.0064 | grad 4.6548 | lr 0.0000 | time_forward 1.2920 | time_backward 1.4390 |
[2023-10-22 23:51:28,386::train::INFO] [train] Iter 563491 | loss 0.2025 | loss(rot) 0.1690 | loss(pos) 0.0310 | loss(seq) 0.0025 | grad 2.4389 | lr 0.0000 | time_forward 3.2700 | time_backward 4.3350 |
[2023-10-22 23:51:31,133::train::INFO] [train] Iter 563492 | loss 1.5885 | loss(rot) 0.6932 | loss(pos) 0.3550 | loss(seq) 0.5404 | grad 4.2542 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4310 |
[2023-10-22 23:51:33,963::train::INFO] [train] Iter 563493 | loss 0.9875 | loss(rot) 0.4769 | loss(pos) 0.1101 | loss(seq) 0.4005 | grad 3.2044 | lr 0.0000 | time_forward 1.3690 | time_backward 1.4580 |
[2023-10-22 23:51:37,310::train::INFO] [train] Iter 563494 | loss 0.8336 | loss(rot) 0.6573 | loss(pos) 0.0445 | loss(seq) 0.1318 | grad 3.5699 | lr 0.0000 | time_forward 1.4960 | time_backward 1.8470 |
[2023-10-22 23:51:45,806::train::INFO] [train] Iter 563495 | loss 0.3576 | loss(rot) 0.2693 | loss(pos) 0.0494 | loss(seq) 0.0388 | grad 2.6711 | lr 0.0000 | time_forward 3.5370 | time_backward 4.9560 |
[2023-10-22 23:51:51,515::train::INFO] [train] Iter 563496 | loss 0.3158 | loss(rot) 0.1415 | loss(pos) 0.0445 | loss(seq) 0.1299 | grad 2.5082 | lr 0.0000 | time_forward 2.5050 | time_backward 3.1980 |
[2023-10-22 23:51:59,024::train::INFO] [train] Iter 563497 | loss 0.6435 | loss(rot) 0.2987 | loss(pos) 0.0779 | loss(seq) 0.2668 | grad 3.9605 | lr 0.0000 | time_forward 3.1290 | time_backward 4.3760 |
[2023-10-22 23:52:07,345::train::INFO] [train] Iter 563498 | loss 1.0210 | loss(rot) 0.9505 | loss(pos) 0.0705 | loss(seq) 0.0000 | grad 26.1922 | lr 0.0000 | time_forward 3.4300 | time_backward 4.8890 |
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