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[2023-10-23 09:17:29,924::train::INFO] [train] Iter 569084 | loss 0.7363 | loss(rot) 0.0766 | loss(pos) 0.6538 | loss(seq) 0.0059 | grad 7.2366 | lr 0.0000 | time_forward 1.6330 | time_backward 1.9770 |
[2023-10-23 09:17:32,470::train::INFO] [train] Iter 569085 | loss 1.4466 | loss(rot) 1.4128 | loss(pos) 0.0336 | loss(seq) 0.0001 | grad 3.6415 | lr 0.0000 | time_forward 1.2530 | time_backward 1.2900 |
[2023-10-23 09:17:36,061::train::INFO] [train] Iter 569086 | loss 1.6025 | loss(rot) 1.5808 | loss(pos) 0.0216 | loss(seq) 0.0000 | grad 12.9256 | lr 0.0000 | time_forward 1.5910 | time_backward 1.9970 |
[2023-10-23 09:17:39,039::train::INFO] [train] Iter 569087 | loss 0.8614 | loss(rot) 0.4639 | loss(pos) 0.0344 | loss(seq) 0.3631 | grad 1.7904 | lr 0.0000 | time_forward 1.4470 | time_backward 1.5170 |
[2023-10-23 09:17:49,187::train::INFO] [train] Iter 569088 | loss 0.6236 | loss(rot) 0.2718 | loss(pos) 0.0557 | loss(seq) 0.2961 | grad 4.8544 | lr 0.0000 | time_forward 4.8340 | time_backward 5.3110 |
[2023-10-23 09:17:59,328::train::INFO] [train] Iter 569089 | loss 0.5821 | loss(rot) 0.2684 | loss(pos) 0.0654 | loss(seq) 0.2483 | grad 2.7092 | lr 0.0000 | time_forward 4.2840 | time_backward 5.8530 |
[2023-10-23 09:18:10,165::train::INFO] [train] Iter 569090 | loss 0.5479 | loss(rot) 0.1750 | loss(pos) 0.0751 | loss(seq) 0.2978 | grad 2.5851 | lr 0.0000 | time_forward 4.6300 | time_backward 6.2040 |
[2023-10-23 09:18:20,864::train::INFO] [train] Iter 569091 | loss 0.4910 | loss(rot) 0.0123 | loss(pos) 0.4776 | loss(seq) 0.0012 | grad 7.0818 | lr 0.0000 | time_forward 4.5520 | time_backward 6.1430 |
[2023-10-23 09:18:24,065::train::INFO] [train] Iter 569092 | loss 1.4817 | loss(rot) 0.8287 | loss(pos) 0.0866 | loss(seq) 0.5663 | grad 8.2324 | lr 0.0000 | time_forward 1.6610 | time_backward 1.5380 |
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