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[2023-09-01 18:45:28,895::train::INFO] [train] Iter 01184 | loss 1.7712 | loss(rot) 0.4342 | loss(pos) 1.2809 | loss(seq) 0.0561 | grad 5.3094 | lr 0.0010 | time_forward 3.1440 | time_backward 4.2400 |
[2023-09-01 18:45:38,755::train::INFO] [train] Iter 01185 | loss 3.4573 | loss(rot) 2.7866 | loss(pos) 0.3123 | loss(seq) 0.3584 | grad 3.4474 | lr 0.0010 | time_forward 4.0350 | time_backward 5.8220 |
[2023-09-01 18:45:45,358::train::INFO] [train] Iter 01186 | loss 2.5233 | loss(rot) 1.6956 | loss(pos) 0.2423 | loss(seq) 0.5854 | grad 2.1022 | lr 0.0010 | time_forward 2.8550 | time_backward 3.7390 |
[2023-09-01 18:45:55,501::train::INFO] [train] Iter 01187 | loss 3.4900 | loss(rot) 3.2359 | loss(pos) 0.2541 | loss(seq) 0.0000 | grad 2.8025 | lr 0.0010 | time_forward 4.1900 | time_backward 5.9500 |
[2023-09-01 18:46:04,244::train::INFO] [train] Iter 01188 | loss 2.7851 | loss(rot) 2.2126 | loss(pos) 0.3372 | loss(seq) 0.2352 | grad 3.9497 | lr 0.0010 | time_forward 3.8210 | time_backward 4.9190 |
[2023-09-01 18:46:06,948::train::INFO] [train] Iter 01189 | loss 1.4468 | loss(rot) 0.5068 | loss(pos) 0.6992 | loss(seq) 0.2408 | grad 3.4277 | lr 0.0010 | time_forward 1.2660 | time_backward 1.4340 |
[2023-09-01 18:46:16,243::train::INFO] [train] Iter 01190 | loss 3.5749 | loss(rot) 2.6614 | loss(pos) 0.3746 | loss(seq) 0.5388 | grad 1.9858 | lr 0.0010 | time_forward 3.7520 | time_backward 5.5390 |
[2023-09-01 18:46:24,595::train::INFO] [train] Iter 01191 | loss 3.5733 | loss(rot) 2.7850 | loss(pos) 0.3286 | loss(seq) 0.4597 | grad 3.5422 | lr 0.0010 | time_forward 3.2280 | time_backward 5.1200 |
[2023-09-01 18:46:34,567::train::INFO] [train] Iter 01192 | loss 1.3283 | loss(rot) 0.4013 | loss(pos) 0.5920 | loss(seq) 0.3349 | grad 5.0807 | lr 0.0010 | time_forward 3.9100 | time_backward 5.8270 |
[2023-09-01 18:46:37,206::train::INFO] [train] Iter 01193 | loss 1.8292 | loss(rot) 0.2039 | loss(pos) 1.5995 | loss(seq) 0.0258 | grad 5.4853 | lr 0.0010 | time_forward 1.2340 | time_backward 1.4010 |
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