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[2023-09-01 18:33:34,154::train::INFO] [train] Iter 01084 | loss 4.4634 | loss(rot) 3.2708 | loss(pos) 1.1915 | loss(seq) 0.0011 | grad 10.6422 | lr 0.0010 | time_forward 3.3740 | time_backward 4.5210 |
[2023-09-01 18:33:43,902::train::INFO] [train] Iter 01085 | loss 3.1022 | loss(rot) 2.4245 | loss(pos) 0.6637 | loss(seq) 0.0139 | grad 9.7658 | lr 0.0010 | time_forward 4.0350 | time_backward 5.7090 |
[2023-09-01 18:33:53,584::train::INFO] [train] Iter 01086 | loss 1.5000 | loss(rot) 0.0538 | loss(pos) 1.1088 | loss(seq) 0.3374 | grad 4.4134 | lr 0.0010 | time_forward 4.0820 | time_backward 5.5970 |
[2023-09-01 18:33:56,428::train::INFO] [train] Iter 01087 | loss 2.4701 | loss(rot) 0.7306 | loss(pos) 1.2068 | loss(seq) 0.5326 | grad 5.4538 | lr 0.0010 | time_forward 1.3470 | time_backward 1.4920 |
[2023-09-01 18:34:04,832::train::INFO] [train] Iter 01088 | loss 1.4144 | loss(rot) 0.4330 | loss(pos) 0.9108 | loss(seq) 0.0707 | grad 3.4769 | lr 0.0010 | time_forward 3.4520 | time_backward 4.8860 |
[2023-09-01 18:34:07,659::train::INFO] [train] Iter 01089 | loss 3.8155 | loss(rot) 2.8362 | loss(pos) 0.4817 | loss(seq) 0.4977 | grad 4.1166 | lr 0.0010 | time_forward 1.3270 | time_backward 1.4970 |
[2023-09-01 18:34:16,549::train::INFO] [train] Iter 01090 | loss 1.2967 | loss(rot) 0.5409 | loss(pos) 0.6684 | loss(seq) 0.0874 | grad 2.7051 | lr 0.0010 | time_forward 3.5870 | time_backward 5.2990 |
[2023-09-01 18:34:25,345::train::INFO] [train] Iter 01091 | loss 3.8411 | loss(rot) 2.8138 | loss(pos) 0.5816 | loss(seq) 0.4458 | grad 3.6179 | lr 0.0010 | time_forward 3.4650 | time_backward 5.3270 |
[2023-09-01 18:34:34,971::train::INFO] [train] Iter 01092 | loss 2.5964 | loss(rot) 1.0431 | loss(pos) 0.9731 | loss(seq) 0.5801 | grad 6.4331 | lr 0.0010 | time_forward 3.5500 | time_backward 6.0730 |
[2023-09-01 18:34:43,229::train::INFO] [train] Iter 01093 | loss 3.2470 | loss(rot) 2.7756 | loss(pos) 0.2223 | loss(seq) 0.2491 | grad 3.3841 | lr 0.0010 | time_forward 3.2350 | time_backward 5.0200 |
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