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[2023-10-24 19:34:12,153::train::INFO] [train] Iter 587000 | loss 0.5021 | loss(rot) 0.2609 | loss(pos) 0.1514 | loss(seq) 0.0898 | grad 4.1111 | lr 0.0000 | time_forward 2.5210 | time_backward 3.1550
[2023-10-24 19:34:56,592::train::INFO] [val] Iter 587000 | loss 1.2139 | loss(rot) 0.9290 | loss(pos) 0.1019 | loss(seq) 0.1829
[2023-10-24 19:35:03,967::train::INFO] [train] Iter 587001 | loss 0.4825 | loss(rot) 0.0734 | loss(pos) 0.4062 | loss(seq) 0.0030 | grad 4.9410 | lr 0.0000 | time_forward 2.7580 | time_backward 3.6580
[2023-10-24 19:35:06,939::train::INFO] [train] Iter 587002 | loss 0.5666 | loss(rot) 0.2980 | loss(pos) 0.1430 | loss(seq) 0.1257 | grad 3.2200 | lr 0.0000 | time_forward 1.3620 | time_backward 1.6060
[2023-10-24 19:35:13,965::train::INFO] [train] Iter 587003 | loss 0.3382 | loss(rot) 0.1738 | loss(pos) 0.0128 | loss(seq) 0.1516 | grad 2.0926 | lr 0.0000 | time_forward 3.0510 | time_backward 3.9620
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[2023-10-24 19:35:26,645::train::INFO] [train] Iter 587005 | loss 0.5347 | loss(rot) 0.4960 | loss(pos) 0.0360 | loss(seq) 0.0027 | grad 2.3486 | lr 0.0000 | time_forward 2.5330 | time_backward 3.2170
[2023-10-24 19:35:33,633::train::INFO] [train] Iter 587006 | loss 0.1026 | loss(rot) 0.0895 | loss(pos) 0.0131 | loss(seq) 0.0001 | grad 2.1640 | lr 0.0000 | time_forward 3.0160 | time_backward 3.9700
[2023-10-24 19:35:36,273::train::INFO] [train] Iter 587007 | loss 0.3739 | loss(rot) 0.1062 | loss(pos) 0.0515 | loss(seq) 0.2162 | grad 2.3902 | lr 0.0000 | time_forward 1.2590 | time_backward 1.3770
[2023-10-24 19:35:38,940::train::INFO] [train] Iter 587008 | loss 0.0971 | loss(rot) 0.0777 | loss(pos) 0.0172 | loss(seq) 0.0022 | grad 2.1876 | lr 0.0000 | time_forward 1.3040 | time_backward 1.3610
[2023-10-24 19:35:45,928::train::INFO] [train] Iter 587009 | loss 0.2145 | loss(rot) 0.1548 | loss(pos) 0.0181 | loss(seq) 0.0417 | grad 2.8054 | lr 0.0000 | time_forward 3.1000 | time_backward 3.8840
[2023-10-24 19:35:53,054::train::INFO] [train] Iter 587010 | loss 0.9483 | loss(rot) 0.7096 | loss(pos) 0.0095 | loss(seq) 0.2292 | grad 2.9926 | lr 0.0000 | time_forward 3.1290 | time_backward 3.9950
[2023-10-24 19:36:00,962::train::INFO] [train] Iter 587011 | loss 0.7574 | loss(rot) 0.5738 | loss(pos) 0.0259 | loss(seq) 0.1577 | grad 6.0738 | lr 0.0000 | time_forward 3.2770 | time_backward 4.6280
[2023-10-24 19:36:08,976::train::INFO] [train] Iter 587012 | loss 0.3472 | loss(rot) 0.1317 | loss(pos) 0.2024 | loss(seq) 0.0131 | grad 3.5400 | lr 0.0000 | time_forward 3.3090 | time_backward 4.7020
[2023-10-24 19:36:17,056::train::INFO] [train] Iter 587013 | loss 0.7507 | loss(rot) 0.7053 | loss(pos) 0.0442 | loss(seq) 0.0012 | grad 2.3618 | lr 0.0000 | time_forward 3.3300 | time_backward 4.7470
[2023-10-24 19:36:19,709::train::INFO] [train] Iter 587014 | loss 0.5664 | loss(rot) 0.1005 | loss(pos) 0.2718 | loss(seq) 0.1941 | grad 4.5012 | lr 0.0000 | time_forward 1.2720 | time_backward 1.3770
[2023-10-24 19:36:26,584::train::INFO] [train] Iter 587015 | loss 0.4435 | loss(rot) 0.0889 | loss(pos) 0.0378 | loss(seq) 0.3168 | grad 2.8289 | lr 0.0000 | time_forward 3.0160 | time_backward 3.8560
[2023-10-24 19:36:33,975::train::INFO] [train] Iter 587016 | loss 0.4816 | loss(rot) 0.2417 | loss(pos) 0.0178 | loss(seq) 0.2221 | grad 3.2646 | lr 0.0000 | time_forward 3.2570 | time_backward 4.1310
[2023-10-24 19:36:36,627::train::INFO] [train] Iter 587017 | loss 1.9115 | loss(rot) 1.8895 | loss(pos) 0.0218 | loss(seq) 0.0001 | grad 6.5957 | lr 0.0000 | time_forward 1.2710 | time_backward 1.3780
[2023-10-24 19:36:44,024::train::INFO] [train] Iter 587018 | loss 1.2158 | loss(rot) 1.0019 | loss(pos) 0.0500 | loss(seq) 0.1638 | grad 4.2933 | lr 0.0000 | time_forward 3.2460 | time_backward 4.1310
[2023-10-24 19:36:51,366::train::INFO] [train] Iter 587019 | loss 0.7986 | loss(rot) 0.4672 | loss(pos) 0.0265 | loss(seq) 0.3048 | grad 9.3330 | lr 0.0000 | time_forward 3.2090 | time_backward 4.1300
[2023-10-24 19:36:54,446::train::INFO] [train] Iter 587020 | loss 0.8209 | loss(rot) 0.5143 | loss(pos) 0.1425 | loss(seq) 0.1641 | grad 4.1936 | lr 0.0000 | time_forward 1.4010 | time_backward 1.6770
[2023-10-24 19:36:57,607::train::INFO] [train] Iter 587021 | loss 0.5470 | loss(rot) 0.5080 | loss(pos) 0.0389 | loss(seq) 0.0001 | grad 4.1011 | lr 0.0000 | time_forward 1.4250 | time_backward 1.7250
[2023-10-24 19:37:00,196::train::INFO] [train] Iter 587022 | loss 0.4857 | loss(rot) 0.1581 | loss(pos) 0.0280 | loss(seq) 0.2997 | grad 2.3034 | lr 0.0000 | time_forward 1.1900 | time_backward 1.3950
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[2023-10-24 19:37:22,249::train::INFO] [train] Iter 587025 | loss 0.4197 | loss(rot) 0.1739 | loss(pos) 0.0460 | loss(seq) 0.1998 | grad 2.4073 | lr 0.0000 | time_forward 2.8780 | time_backward 3.7890
[2023-10-24 19:37:28,753::train::INFO] [train] Iter 587026 | loss 1.5929 | loss(rot) 1.0897 | loss(pos) 0.2047 | loss(seq) 0.2984 | grad 6.9077 | lr 0.0000 | time_forward 2.7980 | time_backward 3.7020
[2023-10-24 19:37:36,758::train::INFO] [train] Iter 587027 | loss 0.4037 | loss(rot) 0.3719 | loss(pos) 0.0163 | loss(seq) 0.0155 | grad 2.7733 | lr 0.0000 | time_forward 3.3840 | time_backward 4.6180
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[2023-10-24 19:37:54,452::train::INFO] [train] Iter 587030 | loss 0.3677 | loss(rot) 0.0574 | loss(pos) 0.1431 | loss(seq) 0.1672 | grad 4.2432 | lr 0.0000 | time_forward 3.1950 | time_backward 4.0640
[2023-10-24 19:38:01,499::train::INFO] [train] Iter 587031 | loss 0.2569 | loss(rot) 0.0459 | loss(pos) 0.1431 | loss(seq) 0.0679 | grad 4.4688 | lr 0.0000 | time_forward 3.0700 | time_backward 3.9740
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[2023-10-24 19:38:14,969::train::INFO] [train] Iter 587033 | loss 0.1104 | loss(rot) 0.0700 | loss(pos) 0.0399 | loss(seq) 0.0005 | grad 1.7833 | lr 0.0000 | time_forward 3.1690 | time_backward 4.1530
[2023-10-24 19:38:21,664::train::INFO] [train] Iter 587034 | loss 1.6530 | loss(rot) 1.5607 | loss(pos) 0.0406 | loss(seq) 0.0518 | grad 19.0081 | lr 0.0000 | time_forward 2.9250 | time_backward 3.7670
[2023-10-24 19:38:29,681::train::INFO] [train] Iter 587035 | loss 0.6748 | loss(rot) 0.6215 | loss(pos) 0.0531 | loss(seq) 0.0002 | grad 3.9506 | lr 0.0000 | time_forward 3.3160 | time_backward 4.6970
[2023-10-24 19:38:32,287::train::INFO] [train] Iter 587036 | loss 0.4857 | loss(rot) 0.1600 | loss(pos) 0.0901 | loss(seq) 0.2356 | grad 3.3391 | lr 0.0000 | time_forward 1.2180 | time_backward 1.3840
[2023-10-24 19:38:40,347::train::INFO] [train] Iter 587037 | loss 0.7600 | loss(rot) 0.0247 | loss(pos) 0.7265 | loss(seq) 0.0088 | grad 5.9312 | lr 0.0000 | time_forward 3.3940 | time_backward 4.6500
[2023-10-24 19:38:47,430::train::INFO] [train] Iter 587038 | loss 0.2561 | loss(rot) 0.0223 | loss(pos) 0.2302 | loss(seq) 0.0037 | grad 2.6654 | lr 0.0000 | time_forward 3.0450 | time_backward 4.0360
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[2023-10-24 19:39:32,623::train::INFO] [train] Iter 587047 | loss 0.4910 | loss(rot) 0.3554 | loss(pos) 0.0324 | loss(seq) 0.1033 | grad 2.6184 | lr 0.0000 | time_forward 3.3850 | time_backward 4.7410
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[2023-10-24 19:41:25,014::train::INFO] [train] Iter 587067 | loss 1.1198 | loss(rot) 0.8271 | loss(pos) 0.0612 | loss(seq) 0.2314 | grad 2.9058 | lr 0.0000 | time_forward 3.3460 | time_backward 4.7480
[2023-10-24 19:41:33,175::train::INFO] [train] Iter 587068 | loss 0.9084 | loss(rot) 0.4875 | loss(pos) 0.1385 | loss(seq) 0.2824 | grad 2.9762 | lr 0.0000 | time_forward 3.5250 | time_backward 4.6330
[2023-10-24 19:41:41,254::train::INFO] [train] Iter 587069 | loss 0.7265 | loss(rot) 0.6356 | loss(pos) 0.0617 | loss(seq) 0.0291 | grad 4.1390 | lr 0.0000 | time_forward 3.3790 | time_backward 4.6960
[2023-10-24 19:41:43,950::train::INFO] [train] Iter 587070 | loss 0.9892 | loss(rot) 0.9509 | loss(pos) 0.0378 | loss(seq) 0.0005 | grad 4.2225 | lr 0.0000 | time_forward 1.2720 | time_backward 1.4210
[2023-10-24 19:41:51,099::train::INFO] [train] Iter 587071 | loss 0.2402 | loss(rot) 0.2038 | loss(pos) 0.0171 | loss(seq) 0.0193 | grad 2.3434 | lr 0.0000 | time_forward 3.1540 | time_backward 3.9920
[2023-10-24 19:41:53,337::train::INFO] [train] Iter 587072 | loss 1.3487 | loss(rot) 1.2661 | loss(pos) 0.0613 | loss(seq) 0.0213 | grad 4.4776 | lr 0.0000 | time_forward 1.0170 | time_backward 1.2180
[2023-10-24 19:42:01,054::train::INFO] [train] Iter 587073 | loss 0.2631 | loss(rot) 0.2387 | loss(pos) 0.0220 | loss(seq) 0.0023 | grad 2.3375 | lr 0.0000 | time_forward 3.2030 | time_backward 4.5100
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