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[2023-09-02 20:02:00,569::train::INFO] [train] Iter 13772 | loss 0.6054 | loss(rot) 0.2066 | loss(pos) 0.3331 | loss(seq) 0.0658 | grad 5.5277 | lr 0.0010 | time_forward 3.8440 | time_backward 4.9830 |
[2023-09-02 20:02:08,862::train::INFO] [train] Iter 13773 | loss 1.4490 | loss(rot) 1.2400 | loss(pos) 0.1996 | loss(seq) 0.0094 | grad 6.9186 | lr 0.0010 | time_forward 3.3780 | time_backward 4.9110 |
[2023-09-02 20:02:11,600::train::INFO] [train] Iter 13774 | loss 1.4787 | loss(rot) 1.3628 | loss(pos) 0.0955 | loss(seq) 0.0204 | grad 5.5659 | lr 0.0010 | time_forward 1.2990 | time_backward 1.4350 |
[2023-09-02 20:02:14,400::train::INFO] [train] Iter 13775 | loss 1.1196 | loss(rot) 0.4761 | loss(pos) 0.4098 | loss(seq) 0.2337 | grad 4.2828 | lr 0.0010 | time_forward 1.3380 | time_backward 1.4580 |
[2023-09-02 20:02:17,612::train::INFO] [train] Iter 13776 | loss 3.1481 | loss(rot) 0.0229 | loss(pos) 3.1252 | loss(seq) 0.0000 | grad 7.6158 | lr 0.0010 | time_forward 1.4040 | time_backward 1.7770 |
[2023-09-02 20:02:27,971::train::INFO] [train] Iter 13777 | loss 1.1477 | loss(rot) 0.2701 | loss(pos) 0.2722 | loss(seq) 0.6054 | grad 3.0230 | lr 0.0010 | time_forward 4.0830 | time_backward 6.2590 |
[2023-09-02 20:02:36,456::train::INFO] [train] Iter 13778 | loss 0.7126 | loss(rot) 0.1968 | loss(pos) 0.3600 | loss(seq) 0.1558 | grad 3.2825 | lr 0.0010 | time_forward 3.3710 | time_backward 5.1110 |
[2023-09-02 20:02:46,653::train::INFO] [train] Iter 13779 | loss 1.1317 | loss(rot) 0.4210 | loss(pos) 0.3402 | loss(seq) 0.3706 | grad 1.8616 | lr 0.0010 | time_forward 4.1510 | time_backward 6.0400 |
[2023-09-02 20:02:49,385::train::INFO] [train] Iter 13780 | loss 2.2612 | loss(rot) 1.6446 | loss(pos) 0.2079 | loss(seq) 0.4087 | grad 4.4773 | lr 0.0010 | time_forward 1.2560 | time_backward 1.4720 |
[2023-09-02 20:02:58,548::train::INFO] [train] Iter 13781 | loss 1.1026 | loss(rot) 0.4538 | loss(pos) 0.2510 | loss(seq) 0.3979 | grad 3.4438 | lr 0.0010 | time_forward 3.8020 | time_backward 5.3430 |
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