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[2023-10-25 07:28:17,545::train::INFO] [train] Iter 593259 | loss 0.6461 | loss(rot) 0.5253 | loss(pos) 0.0221 | loss(seq) 0.0987 | grad 3.7637 | lr 0.0000 | time_forward 3.6410 | time_backward 5.3310 |
[2023-10-25 07:28:24,740::train::INFO] [train] Iter 593260 | loss 2.1379 | loss(rot) 2.1069 | loss(pos) 0.0309 | loss(seq) 0.0001 | grad 4.0141 | lr 0.0000 | time_forward 3.0640 | time_backward 4.1290 |
[2023-10-25 07:28:32,958::train::INFO] [train] Iter 593261 | loss 0.3540 | loss(rot) 0.1296 | loss(pos) 0.0526 | loss(seq) 0.1718 | grad 3.1279 | lr 0.0000 | time_forward 3.5310 | time_backward 4.6830 |
[2023-10-25 07:28:40,841::train::INFO] [train] Iter 593262 | loss 0.4696 | loss(rot) 0.0915 | loss(pos) 0.0956 | loss(seq) 0.2825 | grad 2.8397 | lr 0.0000 | time_forward 3.3560 | time_backward 4.5240 |
[2023-10-25 07:28:43,553::train::INFO] [train] Iter 593263 | loss 1.7001 | loss(rot) 1.2466 | loss(pos) 0.0355 | loss(seq) 0.4180 | grad 4.7492 | lr 0.0000 | time_forward 1.2910 | time_backward 1.4170 |
[2023-10-25 07:28:46,269::train::INFO] [train] Iter 593264 | loss 0.4181 | loss(rot) 0.3256 | loss(pos) 0.0164 | loss(seq) 0.0762 | grad 2.9072 | lr 0.0000 | time_forward 1.3040 | time_backward 1.3860 |
[2023-10-25 07:28:53,722::train::INFO] [train] Iter 593265 | loss 2.5329 | loss(rot) 2.0066 | loss(pos) 0.1305 | loss(seq) 0.3958 | grad 5.5691 | lr 0.0000 | time_forward 3.1520 | time_backward 4.2720 |
[2023-10-25 07:28:56,599::train::INFO] [train] Iter 593266 | loss 1.7612 | loss(rot) 1.5704 | loss(pos) 0.0667 | loss(seq) 0.1242 | grad 8.5978 | lr 0.0000 | time_forward 1.3010 | time_backward 1.5740 |
[2023-10-25 07:29:03,904::train::INFO] [train] Iter 593267 | loss 1.5695 | loss(rot) 1.4816 | loss(pos) 0.0215 | loss(seq) 0.0663 | grad 9.8723 | lr 0.0000 | time_forward 3.0700 | time_backward 4.2320 |
[2023-10-25 07:29:06,650::train::INFO] [train] Iter 593268 | loss 0.5353 | loss(rot) 0.1056 | loss(pos) 0.1062 | loss(seq) 0.3235 | grad 3.3490 | lr 0.0000 | time_forward 1.3010 | time_backward 1.4420 |
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