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[2023-09-02 06:56:32,162::train::INFO] [train] Iter 07178 | loss 2.2980 | loss(rot) 0.3777 | loss(pos) 1.9051 | loss(seq) 0.0152 | grad 7.5813 | lr 0.0010 | time_forward 1.2170 | time_backward 1.3080 |
[2023-09-02 06:56:42,440::train::INFO] [train] Iter 07179 | loss 2.6808 | loss(rot) 2.1642 | loss(pos) 0.1088 | loss(seq) 0.4079 | grad 3.7007 | lr 0.0010 | time_forward 4.0380 | time_backward 6.2100 |
[2023-09-02 06:56:45,191::train::INFO] [train] Iter 07180 | loss 2.9608 | loss(rot) 2.6778 | loss(pos) 0.1905 | loss(seq) 0.0926 | grad 4.7035 | lr 0.0010 | time_forward 1.2820 | time_backward 1.4650 |
[2023-09-02 06:56:53,468::train::INFO] [train] Iter 07181 | loss 2.6339 | loss(rot) 2.5105 | loss(pos) 0.0956 | loss(seq) 0.0278 | grad 3.2561 | lr 0.0010 | time_forward 3.4730 | time_backward 4.8010 |
[2023-09-02 06:56:56,985::train::INFO] [train] Iter 07182 | loss 2.9013 | loss(rot) 2.6378 | loss(pos) 0.2569 | loss(seq) 0.0067 | grad 5.0776 | lr 0.0010 | time_forward 2.0610 | time_backward 1.4530 |
[2023-09-02 06:57:07,273::train::INFO] [train] Iter 07183 | loss 2.5768 | loss(rot) 2.4023 | loss(pos) 0.1696 | loss(seq) 0.0050 | grad 3.6784 | lr 0.0010 | time_forward 4.2750 | time_backward 6.0090 |
[2023-09-02 06:57:17,076::train::INFO] [train] Iter 07184 | loss 1.3260 | loss(rot) 0.6107 | loss(pos) 0.6119 | loss(seq) 0.1034 | grad 4.9385 | lr 0.0010 | time_forward 4.0510 | time_backward 5.7480 |
[2023-09-02 06:57:19,498::train::INFO] [train] Iter 07185 | loss 1.9412 | loss(rot) 1.8040 | loss(pos) 0.0940 | loss(seq) 0.0432 | grad 4.5589 | lr 0.0010 | time_forward 1.1650 | time_backward 1.2530 |
[2023-09-02 06:57:28,527::train::INFO] [train] Iter 07186 | loss 1.3321 | loss(rot) 0.6972 | loss(pos) 0.3682 | loss(seq) 0.2667 | grad 5.1817 | lr 0.0010 | time_forward 3.7780 | time_backward 5.2480 |
[2023-09-02 06:57:37,895::train::INFO] [train] Iter 07187 | loss 1.2610 | loss(rot) 0.6755 | loss(pos) 0.3035 | loss(seq) 0.2820 | grad 3.4525 | lr 0.0010 | time_forward 4.0390 | time_backward 5.3250 |
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