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[2023-10-24 03:32:07,639::train::INFO] [train] Iter 578674 | loss 1.4794 | loss(rot) 1.0082 | loss(pos) 0.0922 | loss(seq) 0.3790 | grad 4.5022 | lr 0.0000 | time_forward 3.9810 | time_backward 5.7190 |
[2023-10-24 03:32:16,483::train::INFO] [train] Iter 578675 | loss 0.8696 | loss(rot) 0.3782 | loss(pos) 0.0971 | loss(seq) 0.3943 | grad 5.9421 | lr 0.0000 | time_forward 3.7820 | time_backward 5.0590 |
[2023-10-24 03:32:25,427::train::INFO] [train] Iter 578676 | loss 0.3816 | loss(rot) 0.1105 | loss(pos) 0.0939 | loss(seq) 0.1772 | grad 3.2249 | lr 0.0000 | time_forward 3.7680 | time_backward 5.1730 |
[2023-10-24 03:32:33,591::train::INFO] [train] Iter 578677 | loss 2.3368 | loss(rot) 1.8202 | loss(pos) 0.2341 | loss(seq) 0.2826 | grad 6.0522 | lr 0.0000 | time_forward 3.4810 | time_backward 4.6790 |
[2023-10-24 03:32:41,662::train::INFO] [train] Iter 578678 | loss 1.8865 | loss(rot) 0.0064 | loss(pos) 1.8801 | loss(seq) 0.0000 | grad 23.1602 | lr 0.0000 | time_forward 3.2740 | time_backward 4.7940 |
[2023-10-24 03:32:51,662::train::INFO] [train] Iter 578679 | loss 0.5370 | loss(rot) 0.1739 | loss(pos) 0.1710 | loss(seq) 0.1921 | grad 3.1299 | lr 0.0000 | time_forward 4.0540 | time_backward 5.9420 |
[2023-10-24 03:33:01,530::train::INFO] [train] Iter 578680 | loss 2.0581 | loss(rot) 1.5311 | loss(pos) 0.1419 | loss(seq) 0.3851 | grad 3.4036 | lr 0.0000 | time_forward 3.9580 | time_backward 5.9060 |
[2023-10-24 03:33:10,154::train::INFO] [train] Iter 578681 | loss 0.3943 | loss(rot) 0.1543 | loss(pos) 0.1093 | loss(seq) 0.1307 | grad 3.9332 | lr 0.0000 | time_forward 3.6910 | time_backward 4.9300 |
[2023-10-24 03:33:12,740::train::INFO] [train] Iter 578682 | loss 1.0582 | loss(rot) 0.4211 | loss(pos) 0.2253 | loss(seq) 0.4118 | grad 5.4068 | lr 0.0000 | time_forward 1.2120 | time_backward 1.3720 |
[2023-10-24 03:33:15,538::train::INFO] [train] Iter 578683 | loss 0.2661 | loss(rot) 0.2460 | loss(pos) 0.0148 | loss(seq) 0.0053 | grad 4.8513 | lr 0.0000 | time_forward 1.3310 | time_backward 1.4450 |
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