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[2023-09-03 00:55:59,824::train::INFO] [train] Iter 16070 | loss 1.9380 | loss(rot) 0.1168 | loss(pos) 1.6542 | loss(seq) 0.1670 | grad 5.2923 | lr 0.0010 | time_forward 2.7100 | time_backward 4.0170 |
[2023-09-03 00:56:08,735::train::INFO] [train] Iter 16071 | loss 1.1519 | loss(rot) 1.0485 | loss(pos) 0.0640 | loss(seq) 0.0393 | grad 11.5316 | lr 0.0010 | time_forward 3.5100 | time_backward 5.3980 |
[2023-09-03 00:56:20,616::train::INFO] [train] Iter 16072 | loss 1.7705 | loss(rot) 1.0781 | loss(pos) 0.1198 | loss(seq) 0.5726 | grad 2.8230 | lr 0.0010 | time_forward 3.8300 | time_backward 8.0480 |
[2023-09-03 00:56:30,416::train::INFO] [train] Iter 16073 | loss 1.0986 | loss(rot) 0.6458 | loss(pos) 0.0845 | loss(seq) 0.3684 | grad 4.5360 | lr 0.0010 | time_forward 3.8820 | time_backward 5.9040 |
[2023-09-03 00:56:38,508::train::INFO] [train] Iter 16074 | loss 1.8377 | loss(rot) 1.8029 | loss(pos) 0.0347 | loss(seq) 0.0000 | grad 3.7711 | lr 0.0010 | time_forward 3.5020 | time_backward 4.5880 |
[2023-09-03 00:56:48,085::train::INFO] [train] Iter 16075 | loss 2.2892 | loss(rot) 1.5019 | loss(pos) 0.1452 | loss(seq) 0.6422 | grad 2.9124 | lr 0.0010 | time_forward 4.0530 | time_backward 5.5200 |
[2023-09-03 00:56:51,424::train::INFO] [train] Iter 16076 | loss 2.4101 | loss(rot) 1.5368 | loss(pos) 0.3396 | loss(seq) 0.5337 | grad 4.9384 | lr 0.0010 | time_forward 1.4510 | time_backward 1.8840 |
[2023-09-03 00:56:59,820::train::INFO] [train] Iter 16077 | loss 1.4670 | loss(rot) 0.6534 | loss(pos) 0.2966 | loss(seq) 0.5170 | grad 3.7124 | lr 0.0010 | time_forward 3.5830 | time_backward 4.8100 |
[2023-09-03 00:57:02,494::train::INFO] [train] Iter 16078 | loss 2.3250 | loss(rot) 1.8891 | loss(pos) 0.1193 | loss(seq) 0.3166 | grad 3.3956 | lr 0.0010 | time_forward 1.2320 | time_backward 1.4390 |
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