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[2023-09-03 02:36:45,396::train::INFO] [train] Iter 16969 | loss 1.1010 | loss(rot) 0.7016 | loss(pos) 0.0651 | loss(seq) 0.3343 | grad 4.0778 | lr 0.0010 | time_forward 3.8570 | time_backward 5.1660 |
[2023-09-03 02:36:52,864::train::INFO] [train] Iter 16970 | loss 1.3302 | loss(rot) 0.5136 | loss(pos) 0.4210 | loss(seq) 0.3956 | grad 4.7069 | lr 0.0010 | time_forward 3.1680 | time_backward 4.2970 |
[2023-09-03 02:37:01,569::train::INFO] [train] Iter 16971 | loss 1.8207 | loss(rot) 1.3588 | loss(pos) 0.0536 | loss(seq) 0.4083 | grad 6.2738 | lr 0.0010 | time_forward 3.7410 | time_backward 4.9620 |
[2023-09-03 02:37:09,018::train::INFO] [train] Iter 16972 | loss 2.4340 | loss(rot) 1.4284 | loss(pos) 0.4433 | loss(seq) 0.5623 | grad 5.8526 | lr 0.0010 | time_forward 3.1130 | time_backward 4.3320 |
[2023-09-03 02:37:11,453::train::INFO] [train] Iter 16973 | loss 1.4096 | loss(rot) 1.2932 | loss(pos) 0.1042 | loss(seq) 0.0121 | grad 9.1998 | lr 0.0010 | time_forward 1.1480 | time_backward 1.2830 |
[2023-09-03 02:37:19,454::train::INFO] [train] Iter 16974 | loss 1.1160 | loss(rot) 0.4160 | loss(pos) 0.4581 | loss(seq) 0.2419 | grad 5.3928 | lr 0.0010 | time_forward 3.4150 | time_backward 4.5830 |
[2023-09-03 02:37:29,312::train::INFO] [train] Iter 16975 | loss 1.5024 | loss(rot) 1.1548 | loss(pos) 0.1249 | loss(seq) 0.2227 | grad 3.4657 | lr 0.0010 | time_forward 4.1150 | time_backward 5.7390 |
[2023-09-03 02:37:39,107::train::INFO] [train] Iter 16976 | loss 1.6358 | loss(rot) 1.0009 | loss(pos) 0.1463 | loss(seq) 0.4886 | grad 4.4035 | lr 0.0010 | time_forward 4.0010 | time_backward 5.7900 |
[2023-09-03 02:37:41,962::train::INFO] [train] Iter 16977 | loss 1.5183 | loss(rot) 0.8519 | loss(pos) 0.4006 | loss(seq) 0.2658 | grad 3.5182 | lr 0.0010 | time_forward 1.4200 | time_backward 1.4320 |
[2023-09-03 02:37:51,611::train::INFO] [train] Iter 16978 | loss 1.8482 | loss(rot) 1.6441 | loss(pos) 0.2041 | loss(seq) 0.0000 | grad 3.6688 | lr 0.0010 | time_forward 3.8790 | time_backward 5.7670 |
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