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[2023-10-25 00:13:03,413::train::INFO] [train] Iter 589263 | loss 0.6802 | loss(rot) 0.2702 | loss(pos) 0.3213 | loss(seq) 0.0887 | grad 4.3652 | lr 0.0000 | time_forward 1.3080 | time_backward 1.4310 |
[2023-10-25 00:13:11,342::train::INFO] [train] Iter 589264 | loss 0.8492 | loss(rot) 0.5084 | loss(pos) 0.0195 | loss(seq) 0.3213 | grad 4.6833 | lr 0.0000 | time_forward 3.3610 | time_backward 4.5640 |
[2023-10-25 00:13:13,446::train::INFO] [train] Iter 589265 | loss 0.1572 | loss(rot) 0.0393 | loss(pos) 0.1067 | loss(seq) 0.0112 | grad 5.2076 | lr 0.0000 | time_forward 0.9710 | time_backward 1.1290 |
[2023-10-25 00:13:23,218::train::INFO] [train] Iter 589266 | loss 0.4538 | loss(rot) 0.0702 | loss(pos) 0.3542 | loss(seq) 0.0294 | grad 4.7277 | lr 0.0000 | time_forward 4.0070 | time_backward 5.7620 |
[2023-10-25 00:13:31,117::train::INFO] [train] Iter 589267 | loss 1.3155 | loss(rot) 1.0041 | loss(pos) 0.0278 | loss(seq) 0.2836 | grad 19.9882 | lr 0.0000 | time_forward 3.2120 | time_backward 4.6840 |
[2023-10-25 00:13:39,802::train::INFO] [train] Iter 589268 | loss 1.7553 | loss(rot) 1.3703 | loss(pos) 0.1047 | loss(seq) 0.2803 | grad 5.8204 | lr 0.0000 | time_forward 3.4800 | time_backward 5.2020 |
[2023-10-25 00:13:50,512::train::INFO] [train] Iter 589269 | loss 2.3946 | loss(rot) 0.5162 | loss(pos) 1.8775 | loss(seq) 0.0009 | grad 22.6845 | lr 0.0000 | time_forward 4.2850 | time_backward 6.4210 |
[2023-10-25 00:13:58,855::train::INFO] [train] Iter 589270 | loss 0.4034 | loss(rot) 0.0423 | loss(pos) 0.3484 | loss(seq) 0.0127 | grad 9.1798 | lr 0.0000 | time_forward 3.4840 | time_backward 4.8560 |
[2023-10-25 00:14:09,328::train::INFO] [train] Iter 589271 | loss 0.8001 | loss(rot) 0.0164 | loss(pos) 0.7818 | loss(seq) 0.0019 | grad 5.3003 | lr 0.0000 | time_forward 4.2780 | time_backward 6.1930 |
[2023-10-25 00:14:18,639::train::INFO] [train] Iter 589272 | loss 1.5482 | loss(rot) 0.6305 | loss(pos) 0.5086 | loss(seq) 0.4091 | grad 9.7193 | lr 0.0000 | time_forward 3.9050 | time_backward 5.4030 |
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