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[2023-10-25 04:25:55,635::train::INFO] [train] Iter 591561 | loss 0.6581 | loss(rot) 0.6207 | loss(pos) 0.0372 | loss(seq) 0.0002 | grad 60.5259 | lr 0.0000 | time_forward 3.0860 | time_backward 4.1620 |
[2023-10-25 04:26:04,627::train::INFO] [train] Iter 591562 | loss 0.0995 | loss(rot) 0.0773 | loss(pos) 0.0205 | loss(seq) 0.0017 | grad 1.5484 | lr 0.0000 | time_forward 3.7370 | time_backward 5.2520 |
[2023-10-25 04:26:07,448::train::INFO] [train] Iter 591563 | loss 1.6032 | loss(rot) 1.3775 | loss(pos) 0.0431 | loss(seq) 0.1825 | grad 4.1541 | lr 0.0000 | time_forward 1.2940 | time_backward 1.5240 |
[2023-10-25 04:26:15,438::train::INFO] [train] Iter 591564 | loss 1.4851 | loss(rot) 1.0254 | loss(pos) 0.0504 | loss(seq) 0.4094 | grad 5.2308 | lr 0.0000 | time_forward 3.4370 | time_backward 4.5500 |
[2023-10-25 04:26:17,952::train::INFO] [train] Iter 591565 | loss 0.2484 | loss(rot) 0.2245 | loss(pos) 0.0238 | loss(seq) 0.0002 | grad 2.5721 | lr 0.0000 | time_forward 1.1760 | time_backward 1.3340 |
[2023-10-25 04:26:25,368::train::INFO] [train] Iter 591566 | loss 1.6961 | loss(rot) 1.6497 | loss(pos) 0.0465 | loss(seq) 0.0000 | grad 13.5045 | lr 0.0000 | time_forward 3.1610 | time_backward 4.2420 |
[2023-10-25 04:26:33,553::train::INFO] [train] Iter 591567 | loss 1.2471 | loss(rot) 0.4806 | loss(pos) 0.3291 | loss(seq) 0.4375 | grad 3.8684 | lr 0.0000 | time_forward 3.4910 | time_backward 4.6910 |
[2023-10-25 04:26:41,829::train::INFO] [train] Iter 591568 | loss 0.8375 | loss(rot) 0.7818 | loss(pos) 0.0558 | loss(seq) 0.0000 | grad 3.9222 | lr 0.0000 | time_forward 3.4470 | time_backward 4.8270 |
[2023-10-25 04:26:49,942::train::INFO] [train] Iter 591569 | loss 1.2622 | loss(rot) 1.2001 | loss(pos) 0.0550 | loss(seq) 0.0070 | grad 6.9002 | lr 0.0000 | time_forward 3.4900 | time_backward 4.6200 |
[2023-10-25 04:26:58,808::train::INFO] [train] Iter 591570 | loss 1.0642 | loss(rot) 0.3060 | loss(pos) 0.4425 | loss(seq) 0.3158 | grad 4.2253 | lr 0.0000 | time_forward 3.6390 | time_backward 5.2230 |
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