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[2023-10-22 13:25:30,935::train::INFO] [train] Iter 558094 | loss 0.5756 | loss(rot) 0.0592 | loss(pos) 0.0298 | loss(seq) 0.4865 | grad 2.0177 | lr 0.0000 | time_forward 18.4810 | time_backward 10.2500 |
[2023-10-22 13:25:33,782::train::INFO] [train] Iter 558095 | loss 0.1740 | loss(rot) 0.1417 | loss(pos) 0.0289 | loss(seq) 0.0034 | grad 2.4649 | lr 0.0000 | time_forward 1.3480 | time_backward 1.4960 |
[2023-10-22 13:25:43,874::train::INFO] [train] Iter 558096 | loss 0.9008 | loss(rot) 0.3911 | loss(pos) 0.0964 | loss(seq) 0.4133 | grad 2.6240 | lr 0.0000 | time_forward 4.1620 | time_backward 5.9270 |
[2023-10-22 13:25:53,450::train::INFO] [train] Iter 558097 | loss 0.9480 | loss(rot) 0.3297 | loss(pos) 0.0785 | loss(seq) 0.5398 | grad 4.6048 | lr 0.0000 | time_forward 3.8960 | time_backward 5.6760 |
[2023-10-22 13:26:04,625::train::INFO] [train] Iter 558098 | loss 0.7356 | loss(rot) 0.2521 | loss(pos) 0.0705 | loss(seq) 0.4130 | grad 4.6149 | lr 0.0000 | time_forward 4.4180 | time_backward 6.7550 |
[2023-10-22 13:26:14,241::train::INFO] [train] Iter 558099 | loss 1.4041 | loss(rot) 0.0221 | loss(pos) 1.3797 | loss(seq) 0.0023 | grad 10.8306 | lr 0.0000 | time_forward 3.8980 | time_backward 5.7130 |
[2023-10-22 13:26:17,309::train::INFO] [train] Iter 558100 | loss 0.9227 | loss(rot) 0.8610 | loss(pos) 0.0512 | loss(seq) 0.0105 | grad 4.6909 | lr 0.0000 | time_forward 1.4570 | time_backward 1.6080 |
[2023-10-22 13:26:27,709::train::INFO] [train] Iter 558101 | loss 2.0282 | loss(rot) 1.5938 | loss(pos) 0.0523 | loss(seq) 0.3821 | grad 10.1762 | lr 0.0000 | time_forward 4.3490 | time_backward 6.0480 |
[2023-10-22 13:26:30,566::train::INFO] [train] Iter 558102 | loss 0.4823 | loss(rot) 0.0668 | loss(pos) 0.3959 | loss(seq) 0.0196 | grad 5.5554 | lr 0.0000 | time_forward 1.3580 | time_backward 1.4950 |
[2023-10-22 13:26:39,501::train::INFO] [train] Iter 558103 | loss 0.4242 | loss(rot) 0.0921 | loss(pos) 0.0551 | loss(seq) 0.2771 | grad 2.7363 | lr 0.0000 | time_forward 3.7980 | time_backward 5.1340 |
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