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[2023-09-03 02:48:42,958::train::INFO] [train] Iter 17069 | loss 1.8495 | loss(rot) 1.7571 | loss(pos) 0.0798 | loss(seq) 0.0127 | grad 3.5601 | lr 0.0010 | time_forward 3.4510 | time_backward 4.6050 |
[2023-09-03 02:48:52,565::train::INFO] [train] Iter 17070 | loss 1.7576 | loss(rot) 0.8490 | loss(pos) 0.2797 | loss(seq) 0.6288 | grad 3.4831 | lr 0.0010 | time_forward 4.2090 | time_backward 5.3940 |
[2023-09-03 02:49:01,311::train::INFO] [train] Iter 17071 | loss 1.2294 | loss(rot) 0.3420 | loss(pos) 0.3423 | loss(seq) 0.5451 | grad 3.3473 | lr 0.0010 | time_forward 3.4580 | time_backward 5.2850 |
[2023-09-03 02:49:09,867::train::INFO] [train] Iter 17072 | loss 1.7718 | loss(rot) 0.9775 | loss(pos) 0.2521 | loss(seq) 0.5422 | grad 3.4984 | lr 0.0010 | time_forward 3.3300 | time_backward 5.2220 |
[2023-09-03 02:49:18,025::train::INFO] [train] Iter 17073 | loss 0.7842 | loss(rot) 0.1862 | loss(pos) 0.3256 | loss(seq) 0.2724 | grad 3.3831 | lr 0.0010 | time_forward 3.2660 | time_backward 4.8880 |
[2023-09-03 02:49:21,178::train::INFO] [train] Iter 17074 | loss 0.9762 | loss(rot) 0.0299 | loss(pos) 0.9400 | loss(seq) 0.0063 | grad 5.3234 | lr 0.0010 | time_forward 1.3250 | time_backward 1.8250 |
[2023-09-03 02:49:23,768::train::INFO] [train] Iter 17075 | loss 0.7219 | loss(rot) 0.2276 | loss(pos) 0.2158 | loss(seq) 0.2785 | grad 2.7216 | lr 0.0010 | time_forward 1.1870 | time_backward 1.3990 |
[2023-09-03 02:49:33,206::train::INFO] [train] Iter 17076 | loss 2.8548 | loss(rot) 2.7090 | loss(pos) 0.1426 | loss(seq) 0.0032 | grad 3.0266 | lr 0.0010 | time_forward 3.9470 | time_backward 5.4880 |
[2023-09-03 02:49:41,276::train::INFO] [train] Iter 17077 | loss 2.2188 | loss(rot) 1.5545 | loss(pos) 0.1704 | loss(seq) 0.4939 | grad 3.9673 | lr 0.0010 | time_forward 3.4180 | time_backward 4.6480 |
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