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[2023-09-02 01:06:49,798::train::INFO] [train] Iter 04281 | loss 3.3243 | loss(rot) 2.6867 | loss(pos) 0.2477 | loss(seq) 0.3899 | grad 4.9113 | lr 0.0010 | time_forward 3.4530 | time_backward 5.0060 |
[2023-09-02 01:06:59,105::train::INFO] [train] Iter 04282 | loss 2.0872 | loss(rot) 1.4038 | loss(pos) 0.2982 | loss(seq) 0.3852 | grad 5.1422 | lr 0.0010 | time_forward 3.9150 | time_backward 5.3880 |
[2023-09-02 01:07:08,743::train::INFO] [train] Iter 04283 | loss 1.3375 | loss(rot) 0.5756 | loss(pos) 0.7058 | loss(seq) 0.0560 | grad 4.5845 | lr 0.0010 | time_forward 4.0270 | time_backward 5.6070 |
[2023-09-02 01:07:18,519::train::INFO] [train] Iter 04284 | loss 1.5449 | loss(rot) 0.6821 | loss(pos) 0.4821 | loss(seq) 0.3807 | grad 5.2949 | lr 0.0010 | time_forward 4.0390 | time_backward 5.7350 |
[2023-09-02 01:07:28,624::train::INFO] [train] Iter 04285 | loss 2.6766 | loss(rot) 2.4256 | loss(pos) 0.2497 | loss(seq) 0.0012 | grad 4.1696 | lr 0.0010 | time_forward 4.1730 | time_backward 5.9270 |
[2023-09-02 01:07:39,326::train::INFO] [train] Iter 04286 | loss 2.3806 | loss(rot) 2.1632 | loss(pos) 0.2174 | loss(seq) 0.0000 | grad 4.7387 | lr 0.0010 | time_forward 4.2310 | time_backward 6.4670 |
[2023-09-02 01:07:42,172::train::INFO] [train] Iter 04287 | loss 3.1452 | loss(rot) 2.8841 | loss(pos) 0.2547 | loss(seq) 0.0064 | grad 4.9509 | lr 0.0010 | time_forward 1.3410 | time_backward 1.5020 |
[2023-09-02 01:07:50,719::train::INFO] [train] Iter 04288 | loss 2.6316 | loss(rot) 2.3011 | loss(pos) 0.3078 | loss(seq) 0.0227 | grad 7.0512 | lr 0.0010 | time_forward 3.6500 | time_backward 4.8920 |
[2023-09-02 01:07:53,560::train::INFO] [train] Iter 04289 | loss 0.6067 | loss(rot) 0.2701 | loss(pos) 0.2966 | loss(seq) 0.0401 | grad 3.6255 | lr 0.0010 | time_forward 1.3430 | time_backward 1.4930 |
[2023-09-02 01:08:02,244::train::INFO] [train] Iter 04290 | loss 1.9051 | loss(rot) 1.3332 | loss(pos) 0.1375 | loss(seq) 0.4343 | grad 3.5630 | lr 0.0010 | time_forward 3.6350 | time_backward 5.0470 |
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