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[2023-10-24 14:30:36,442::train::INFO] [train] Iter 584168 | loss 0.4429 | loss(rot) 0.3783 | loss(pos) 0.0505 | loss(seq) 0.0142 | grad 5.6513 | lr 0.0000 | time_forward 3.7320 | time_backward 4.8370 |
[2023-10-24 14:30:44,840::train::INFO] [train] Iter 584169 | loss 0.4251 | loss(rot) 0.1413 | loss(pos) 0.0314 | loss(seq) 0.2524 | grad 3.0067 | lr 0.0000 | time_forward 3.5450 | time_backward 4.8500 |
[2023-10-24 14:30:50,391::train::INFO] [train] Iter 584170 | loss 0.5181 | loss(rot) 0.0987 | loss(pos) 0.0568 | loss(seq) 0.3625 | grad 2.7617 | lr 0.0000 | time_forward 2.5130 | time_backward 3.0350 |
[2023-10-24 14:30:53,875::train::INFO] [train] Iter 584171 | loss 1.6973 | loss(rot) 0.7957 | loss(pos) 0.4275 | loss(seq) 0.4741 | grad 4.0739 | lr 0.0000 | time_forward 1.6140 | time_backward 1.8670 |
[2023-10-24 14:30:56,825::train::INFO] [train] Iter 584172 | loss 1.4424 | loss(rot) 1.3282 | loss(pos) 0.0649 | loss(seq) 0.0493 | grad 3.6308 | lr 0.0000 | time_forward 1.3880 | time_backward 1.5480 |
[2023-10-24 14:30:59,702::train::INFO] [train] Iter 584173 | loss 0.4526 | loss(rot) 0.1336 | loss(pos) 0.0862 | loss(seq) 0.2327 | grad 5.0910 | lr 0.0000 | time_forward 1.4610 | time_backward 1.4120 |
[2023-10-24 14:31:07,103::train::INFO] [train] Iter 584174 | loss 1.2372 | loss(rot) 0.3925 | loss(pos) 0.8436 | loss(seq) 0.0011 | grad 10.6781 | lr 0.0000 | time_forward 3.5900 | time_backward 3.7870 |
[2023-10-24 14:31:15,362::train::INFO] [train] Iter 584175 | loss 0.4044 | loss(rot) 0.0884 | loss(pos) 0.0204 | loss(seq) 0.2956 | grad 2.3168 | lr 0.0000 | time_forward 3.6470 | time_backward 4.6100 |
[2023-10-24 14:31:22,817::train::INFO] [train] Iter 584176 | loss 1.1187 | loss(rot) 0.4076 | loss(pos) 0.2108 | loss(seq) 0.5003 | grad 3.1269 | lr 0.0000 | time_forward 3.2000 | time_backward 4.2520 |
[2023-10-24 14:31:25,747::train::INFO] [train] Iter 584177 | loss 0.2079 | loss(rot) 0.0975 | loss(pos) 0.0232 | loss(seq) 0.0873 | grad 5.3054 | lr 0.0000 | time_forward 1.5100 | time_backward 1.4160 |
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