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[2023-10-23 20:03:26,984::train::INFO] [train] Iter 574878 | loss 0.9927 | loss(rot) 0.6966 | loss(pos) 0.0638 | loss(seq) 0.2323 | grad 7.3342 | lr 0.0000 | time_forward 4.2390 | time_backward 5.8890 |
[2023-10-23 20:03:30,336::train::INFO] [train] Iter 574879 | loss 0.7301 | loss(rot) 0.0647 | loss(pos) 0.6468 | loss(seq) 0.0186 | grad 7.3689 | lr 0.0000 | time_forward 1.5250 | time_backward 1.8240 |
[2023-10-23 20:03:33,139::train::INFO] [train] Iter 574880 | loss 1.0023 | loss(rot) 0.8110 | loss(pos) 0.0413 | loss(seq) 0.1500 | grad 5.1991 | lr 0.0000 | time_forward 1.3280 | time_backward 1.4610 |
[2023-10-23 20:03:43,207::train::INFO] [train] Iter 574881 | loss 1.2548 | loss(rot) 0.8317 | loss(pos) 0.0712 | loss(seq) 0.3519 | grad 4.1085 | lr 0.0000 | time_forward 4.3150 | time_backward 5.7500 |
[2023-10-23 20:03:53,115::train::INFO] [train] Iter 574882 | loss 1.0817 | loss(rot) 0.7254 | loss(pos) 0.0758 | loss(seq) 0.2805 | grad 4.4846 | lr 0.0000 | time_forward 4.0330 | time_backward 5.8710 |
[2023-10-23 20:03:55,920::train::INFO] [train] Iter 574883 | loss 1.1692 | loss(rot) 1.1039 | loss(pos) 0.0142 | loss(seq) 0.0511 | grad 2.5984 | lr 0.0000 | time_forward 1.3390 | time_backward 1.4640 |
[2023-10-23 20:03:58,764::train::INFO] [train] Iter 574884 | loss 0.3642 | loss(rot) 0.3291 | loss(pos) 0.0335 | loss(seq) 0.0016 | grad 4.3141 | lr 0.0000 | time_forward 1.3320 | time_backward 1.4740 |
[2023-10-23 20:04:08,669::train::INFO] [train] Iter 574885 | loss 0.7892 | loss(rot) 0.5578 | loss(pos) 0.0534 | loss(seq) 0.1780 | grad 2.9029 | lr 0.0000 | time_forward 4.0390 | time_backward 5.8620 |
[2023-10-23 20:04:17,702::train::INFO] [train] Iter 574886 | loss 0.3050 | loss(rot) 0.1072 | loss(pos) 0.0512 | loss(seq) 0.1466 | grad 2.9196 | lr 0.0000 | time_forward 3.8110 | time_backward 5.2180 |
[2023-10-23 20:04:20,491::train::INFO] [train] Iter 574887 | loss 2.0426 | loss(rot) 1.4290 | loss(pos) 0.3034 | loss(seq) 0.3103 | grad 7.5223 | lr 0.0000 | time_forward 1.3310 | time_backward 1.4550 |
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