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[2023-09-02 04:44:16,888::train::INFO] [train] Iter 06079 | loss 2.6700 | loss(rot) 2.5055 | loss(pos) 0.1645 | loss(seq) 0.0000 | grad 4.7629 | lr 0.0010 | time_forward 3.9220 | time_backward 5.2340 |
[2023-09-02 04:44:19,625::train::INFO] [train] Iter 06080 | loss 1.2189 | loss(rot) 0.5103 | loss(pos) 0.4556 | loss(seq) 0.2530 | grad 4.6593 | lr 0.0010 | time_forward 1.2940 | time_backward 1.4390 |
[2023-09-02 04:44:28,335::train::INFO] [train] Iter 06081 | loss 2.6372 | loss(rot) 2.4132 | loss(pos) 0.2240 | loss(seq) 0.0000 | grad 2.6066 | lr 0.0010 | time_forward 3.7300 | time_backward 4.9770 |
[2023-09-02 04:44:37,849::train::INFO] [train] Iter 06082 | loss 0.9344 | loss(rot) 0.5242 | loss(pos) 0.3124 | loss(seq) 0.0979 | grad 3.6725 | lr 0.0010 | time_forward 3.9940 | time_backward 5.5160 |
[2023-09-02 04:44:46,329::train::INFO] [train] Iter 06083 | loss 2.4234 | loss(rot) 2.2193 | loss(pos) 0.1638 | loss(seq) 0.0403 | grad 4.4154 | lr 0.0010 | time_forward 3.5240 | time_backward 4.9530 |
[2023-09-02 04:44:56,352::train::INFO] [train] Iter 06084 | loss 2.6643 | loss(rot) 2.4874 | loss(pos) 0.1736 | loss(seq) 0.0032 | grad 3.5647 | lr 0.0010 | time_forward 4.0800 | time_backward 5.9400 |
[2023-09-02 04:45:06,407::train::INFO] [train] Iter 06085 | loss 2.2158 | loss(rot) 1.3570 | loss(pos) 0.3361 | loss(seq) 0.5227 | grad 3.4175 | lr 0.0010 | time_forward 4.0410 | time_backward 6.0100 |
[2023-09-02 04:45:09,221::train::INFO] [train] Iter 06086 | loss 1.5489 | loss(rot) 0.7966 | loss(pos) 0.3608 | loss(seq) 0.3915 | grad 6.4191 | lr 0.0010 | time_forward 1.2940 | time_backward 1.5180 |
[2023-09-02 04:45:17,622::train::INFO] [train] Iter 06087 | loss 2.1527 | loss(rot) 1.6896 | loss(pos) 0.1823 | loss(seq) 0.2808 | grad 3.2754 | lr 0.0010 | time_forward 3.5610 | time_backward 4.8360 |
[2023-09-02 04:45:27,542::train::INFO] [train] Iter 06088 | loss 2.5229 | loss(rot) 2.0675 | loss(pos) 0.2658 | loss(seq) 0.1897 | grad 2.9451 | lr 0.0010 | time_forward 4.1970 | time_backward 5.7190 |
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