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[2023-10-23 15:33:05,602::train::INFO] [train] Iter 572480 | loss 3.1379 | loss(rot) 0.0026 | loss(pos) 3.1353 | loss(seq) 0.0000 | grad 24.3409 | lr 0.0000 | time_forward 2.7620 | time_backward 3.5420 |
[2023-10-23 15:33:08,379::train::INFO] [train] Iter 572481 | loss 1.0151 | loss(rot) 0.4899 | loss(pos) 0.0513 | loss(seq) 0.4740 | grad 3.8450 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4650 |
[2023-10-23 15:33:16,324::train::INFO] [train] Iter 572482 | loss 2.7254 | loss(rot) 0.1487 | loss(pos) 2.5766 | loss(seq) 0.0000 | grad 21.7025 | lr 0.0000 | time_forward 3.2810 | time_backward 4.6600 |
[2023-10-23 15:33:24,367::train::INFO] [train] Iter 572483 | loss 1.3321 | loss(rot) 1.3127 | loss(pos) 0.0157 | loss(seq) 0.0038 | grad 2.7622 | lr 0.0000 | time_forward 3.3050 | time_backward 4.7350 |
[2023-10-23 15:33:30,893::train::INFO] [train] Iter 572484 | loss 0.3037 | loss(rot) 0.2661 | loss(pos) 0.0376 | loss(seq) 0.0000 | grad 2.9959 | lr 0.0000 | time_forward 2.8220 | time_backward 3.7010 |
[2023-10-23 15:33:37,550::train::INFO] [train] Iter 572485 | loss 0.8204 | loss(rot) 0.4756 | loss(pos) 0.0561 | loss(seq) 0.2887 | grad 3.4158 | lr 0.0000 | time_forward 2.8990 | time_backward 3.7550 |
[2023-10-23 15:33:44,755::train::INFO] [train] Iter 572486 | loss 0.4209 | loss(rot) 0.0845 | loss(pos) 0.0642 | loss(seq) 0.2723 | grad 3.1276 | lr 0.0000 | time_forward 3.1340 | time_backward 4.0680 |
[2023-10-23 15:33:52,652::train::INFO] [train] Iter 572487 | loss 0.7336 | loss(rot) 0.2720 | loss(pos) 0.2383 | loss(seq) 0.2232 | grad 3.1852 | lr 0.0000 | time_forward 3.2710 | time_backward 4.6230 |
[2023-10-23 15:33:59,939::train::INFO] [train] Iter 572488 | loss 0.7077 | loss(rot) 0.6392 | loss(pos) 0.0293 | loss(seq) 0.0392 | grad 6.3959 | lr 0.0000 | time_forward 3.2030 | time_backward 4.0810 |
[2023-10-23 15:34:02,722::train::INFO] [train] Iter 572489 | loss 0.4868 | loss(rot) 0.2396 | loss(pos) 0.0360 | loss(seq) 0.2111 | grad 2.5975 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4720 |
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