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[2023-09-01 17:22:53,090::train::INFO] [train] Iter 00485 | loss 3.2674 | loss(rot) 2.7414 | loss(pos) 0.4847 | loss(seq) 0.0414 | grad 6.9717 | lr 0.0010 | time_forward 1.1270 | time_backward 1.2650 |
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[2023-09-01 17:23:11,383::train::INFO] [train] Iter 00487 | loss 2.3288 | loss(rot) 0.0183 | loss(pos) 2.3106 | loss(seq) 0.0000 | grad 2.8828 | lr 0.0010 | time_forward 4.2350 | time_backward 6.0200 |
[2023-09-01 17:23:19,885::train::INFO] [train] Iter 00488 | loss 3.8540 | loss(rot) 3.4624 | loss(pos) 0.3916 | loss(seq) 0.0000 | grad 4.2234 | lr 0.0010 | time_forward 3.5730 | time_backward 4.9250 |
[2023-09-01 17:23:22,813::train::INFO] [train] Iter 00489 | loss 1.7958 | loss(rot) 0.5563 | loss(pos) 1.2003 | loss(seq) 0.0392 | grad 4.5808 | lr 0.0010 | time_forward 1.3740 | time_backward 1.5510 |
[2023-09-01 17:23:31,438::train::INFO] [train] Iter 00490 | loss 2.4458 | loss(rot) 0.1008 | loss(pos) 2.3330 | loss(seq) 0.0119 | grad 4.7862 | lr 0.0010 | time_forward 3.7280 | time_backward 4.8620 |
[2023-09-01 17:23:39,636::train::INFO] [train] Iter 00491 | loss 2.1252 | loss(rot) 1.1603 | loss(pos) 0.4636 | loss(seq) 0.5013 | grad 4.1394 | lr 0.0010 | time_forward 3.5070 | time_backward 4.6870 |
[2023-09-01 17:23:42,399::train::INFO] [train] Iter 00492 | loss 3.8120 | loss(rot) 3.3380 | loss(pos) 0.4583 | loss(seq) 0.0157 | grad 4.7678 | lr 0.0010 | time_forward 1.3360 | time_backward 1.4230 |
[2023-09-01 17:23:45,942::train::INFO] [train] Iter 00493 | loss 3.1846 | loss(rot) 2.8812 | loss(pos) 0.3033 | loss(seq) 0.0000 | grad 4.1535 | lr 0.0010 | time_forward 1.5800 | time_backward 1.9600 |
[2023-09-01 17:23:56,009::train::INFO] [train] Iter 00494 | loss 3.0832 | loss(rot) 2.1902 | loss(pos) 0.4211 | loss(seq) 0.4720 | grad 4.7040 | lr 0.0010 | time_forward 4.1400 | time_backward 5.9230 |
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