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[2023-09-02 14:28:55,372::train::INFO] [train] Iter 10876 | loss 2.0380 | loss(rot) 1.5153 | loss(pos) 0.1227 | loss(seq) 0.4000 | grad 5.2960 | lr 0.0010 | time_forward 1.2390 | time_backward 1.4440 |
[2023-09-02 14:29:05,462::train::INFO] [train] Iter 10877 | loss 2.6610 | loss(rot) 2.5299 | loss(pos) 0.1185 | loss(seq) 0.0126 | grad 3.3105 | lr 0.0010 | time_forward 4.2670 | time_backward 5.8210 |
[2023-09-02 14:29:08,226::train::INFO] [train] Iter 10878 | loss 1.2206 | loss(rot) 0.3147 | loss(pos) 0.3995 | loss(seq) 0.5064 | grad 4.6139 | lr 0.0010 | time_forward 1.2530 | time_backward 1.5070 |
[2023-09-02 14:29:20,664::train::INFO] [train] Iter 10879 | loss 1.2159 | loss(rot) 0.2426 | loss(pos) 0.5911 | loss(seq) 0.3822 | grad 5.4643 | lr 0.0010 | time_forward 5.6000 | time_backward 6.8350 |
[2023-09-02 14:29:30,938::train::INFO] [train] Iter 10880 | loss 2.1138 | loss(rot) 1.4117 | loss(pos) 0.3080 | loss(seq) 0.3941 | grad 4.2717 | lr 0.0010 | time_forward 4.2540 | time_backward 6.0160 |
[2023-09-02 14:29:33,621::train::INFO] [train] Iter 10881 | loss 1.3752 | loss(rot) 1.2037 | loss(pos) 0.1060 | loss(seq) 0.0655 | grad 4.7733 | lr 0.0010 | time_forward 1.2550 | time_backward 1.4240 |
[2023-09-02 14:29:43,532::train::INFO] [train] Iter 10882 | loss 1.3291 | loss(rot) 0.3666 | loss(pos) 0.6914 | loss(seq) 0.2712 | grad 5.9053 | lr 0.0010 | time_forward 4.0060 | time_backward 5.2470 |
[2023-09-02 14:29:54,051::train::INFO] [train] Iter 10883 | loss 1.4383 | loss(rot) 0.7422 | loss(pos) 0.2154 | loss(seq) 0.4807 | grad 3.2301 | lr 0.0010 | time_forward 4.1650 | time_backward 6.3500 |
[2023-09-02 14:30:01,693::train::INFO] [train] Iter 10884 | loss 1.2037 | loss(rot) 0.3976 | loss(pos) 0.3442 | loss(seq) 0.4619 | grad 3.7641 | lr 0.0010 | time_forward 3.2150 | time_backward 4.4240 |
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