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[2023-10-24 01:34:15,133::train::INFO] [train] Iter 577675 | loss 0.8332 | loss(rot) 0.5757 | loss(pos) 0.2108 | loss(seq) 0.0467 | grad 6.8948 | lr 0.0000 | time_forward 3.2680 | time_backward 4.3760 |
[2023-10-24 01:34:24,533::train::INFO] [train] Iter 577676 | loss 0.9743 | loss(rot) 0.4526 | loss(pos) 0.1372 | loss(seq) 0.3845 | grad 3.4872 | lr 0.0000 | time_forward 3.9680 | time_backward 5.4280 |
[2023-10-24 01:34:33,943::train::INFO] [train] Iter 577677 | loss 0.7170 | loss(rot) 0.6260 | loss(pos) 0.0359 | loss(seq) 0.0551 | grad 3.9124 | lr 0.0000 | time_forward 3.9990 | time_backward 5.4080 |
[2023-10-24 01:34:43,100::train::INFO] [train] Iter 577678 | loss 0.3319 | loss(rot) 0.2360 | loss(pos) 0.0192 | loss(seq) 0.0767 | grad 3.5700 | lr 0.0000 | time_forward 3.8420 | time_backward 5.3120 |
[2023-10-24 01:34:50,690::train::INFO] [train] Iter 577679 | loss 0.1935 | loss(rot) 0.1338 | loss(pos) 0.0218 | loss(seq) 0.0379 | grad 2.6587 | lr 0.0000 | time_forward 3.2540 | time_backward 4.3330 |
[2023-10-24 01:35:00,742::train::INFO] [train] Iter 577680 | loss 1.2541 | loss(rot) 1.2305 | loss(pos) 0.0234 | loss(seq) 0.0001 | grad 4.5992 | lr 0.0000 | time_forward 4.3050 | time_backward 5.7440 |
[2023-10-24 01:35:03,465::train::INFO] [train] Iter 577681 | loss 0.0994 | loss(rot) 0.0773 | loss(pos) 0.0217 | loss(seq) 0.0004 | grad 1.4602 | lr 0.0000 | time_forward 1.3340 | time_backward 1.3850 |
[2023-10-24 01:35:06,231::train::INFO] [train] Iter 577682 | loss 0.5489 | loss(rot) 0.2557 | loss(pos) 0.0701 | loss(seq) 0.2231 | grad 3.1228 | lr 0.0000 | time_forward 1.3120 | time_backward 1.4210 |
[2023-10-24 01:35:08,577::train::INFO] [train] Iter 577683 | loss 0.4374 | loss(rot) 0.1526 | loss(pos) 0.0216 | loss(seq) 0.2633 | grad 3.0791 | lr 0.0000 | time_forward 1.1160 | time_backward 1.2260 |
[2023-10-24 01:35:11,365::train::INFO] [train] Iter 577684 | loss 0.8751 | loss(rot) 0.5844 | loss(pos) 0.0777 | loss(seq) 0.2131 | grad 4.4161 | lr 0.0000 | time_forward 1.3400 | time_backward 1.4280 |
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