text stringlengths 56 1.16k |
|---|
[2023-10-24 12:17:33,840::train::INFO] [train] Iter 583079 | loss 0.2661 | loss(rot) 0.0601 | loss(pos) 0.1618 | loss(seq) 0.0442 | grad 5.1072 | lr 0.0000 | time_forward 1.5080 | time_backward 1.5850 |
[2023-10-24 12:17:40,778::train::INFO] [train] Iter 583080 | loss 0.4271 | loss(rot) 0.1117 | loss(pos) 0.2935 | loss(seq) 0.0219 | grad 5.0583 | lr 0.0000 | time_forward 3.0470 | time_backward 3.8880 |
[2023-10-24 12:17:50,291::train::INFO] [train] Iter 583081 | loss 0.1088 | loss(rot) 0.0225 | loss(pos) 0.0745 | loss(seq) 0.0118 | grad 2.7799 | lr 0.0000 | time_forward 3.9330 | time_backward 5.5620 |
[2023-10-24 12:18:01,037::train::INFO] [train] Iter 583082 | loss 0.4401 | loss(rot) 0.0966 | loss(pos) 0.1971 | loss(seq) 0.1465 | grad 2.8976 | lr 0.0000 | time_forward 4.5010 | time_backward 6.2420 |
[2023-10-24 12:18:10,528::train::INFO] [train] Iter 583083 | loss 1.3204 | loss(rot) 1.2921 | loss(pos) 0.0256 | loss(seq) 0.0027 | grad 4.7109 | lr 0.0000 | time_forward 3.9500 | time_backward 5.5370 |
[2023-10-24 12:18:20,092::train::INFO] [train] Iter 583084 | loss 0.7487 | loss(rot) 0.5252 | loss(pos) 0.0723 | loss(seq) 0.1512 | grad 3.6202 | lr 0.0000 | time_forward 3.9160 | time_backward 5.6450 |
[2023-10-24 12:18:23,508::train::INFO] [train] Iter 583085 | loss 0.6916 | loss(rot) 0.6364 | loss(pos) 0.0297 | loss(seq) 0.0255 | grad 3.0142 | lr 0.0000 | time_forward 1.5300 | time_backward 1.8840 |
[2023-10-24 12:18:32,839::train::INFO] [train] Iter 583086 | loss 0.5628 | loss(rot) 0.2424 | loss(pos) 0.0238 | loss(seq) 0.2966 | grad 3.1739 | lr 0.0000 | time_forward 3.9920 | time_backward 5.3170 |
[2023-10-24 12:18:41,001::train::INFO] [train] Iter 583087 | loss 0.5635 | loss(rot) 0.3166 | loss(pos) 0.0532 | loss(seq) 0.1936 | grad 3.4878 | lr 0.0000 | time_forward 3.4980 | time_backward 4.6580 |
[2023-10-24 12:18:49,340::train::INFO] [train] Iter 583088 | loss 1.8189 | loss(rot) 1.7785 | loss(pos) 0.0222 | loss(seq) 0.0182 | grad 4.3771 | lr 0.0000 | time_forward 3.4940 | time_backward 4.8420 |
[2023-10-24 12:18:59,561::train::INFO] [train] Iter 583089 | loss 0.8552 | loss(rot) 0.0320 | loss(pos) 0.8219 | loss(seq) 0.0014 | grad 8.1976 | lr 0.0000 | time_forward 4.1600 | time_backward 6.0570 |
[2023-10-24 12:19:02,455::train::INFO] [train] Iter 583090 | loss 0.3686 | loss(rot) 0.2075 | loss(pos) 0.0410 | loss(seq) 0.1201 | grad 2.4212 | lr 0.0000 | time_forward 1.3130 | time_backward 1.5790 |
[2023-10-24 12:19:10,751::train::INFO] [train] Iter 583091 | loss 0.6588 | loss(rot) 0.6230 | loss(pos) 0.0357 | loss(seq) 0.0000 | grad 1.9627 | lr 0.0000 | time_forward 3.5460 | time_backward 4.7110 |
[2023-10-24 12:19:20,252::train::INFO] [train] Iter 583092 | loss 0.6962 | loss(rot) 0.4311 | loss(pos) 0.1304 | loss(seq) 0.1346 | grad 4.0836 | lr 0.0000 | time_forward 4.0340 | time_backward 5.4650 |
[2023-10-24 12:19:28,948::train::INFO] [train] Iter 583093 | loss 0.4078 | loss(rot) 0.0437 | loss(pos) 0.1719 | loss(seq) 0.1922 | grad 2.7229 | lr 0.0000 | time_forward 3.6440 | time_backward 5.0490 |
[2023-10-24 12:19:37,787::train::INFO] [train] Iter 583094 | loss 1.8252 | loss(rot) 1.7735 | loss(pos) 0.0517 | loss(seq) 0.0000 | grad 4.2736 | lr 0.0000 | time_forward 3.6920 | time_backward 5.1440 |
[2023-10-24 12:19:40,712::train::INFO] [train] Iter 583095 | loss 0.7070 | loss(rot) 0.2704 | loss(pos) 0.2312 | loss(seq) 0.2053 | grad 3.1775 | lr 0.0000 | time_forward 1.3850 | time_backward 1.5360 |
[2023-10-24 12:19:43,898::train::INFO] [train] Iter 583096 | loss 0.7723 | loss(rot) 0.4219 | loss(pos) 0.0353 | loss(seq) 0.3150 | grad 2.8730 | lr 0.0000 | time_forward 1.6480 | time_backward 1.5360 |
[2023-10-24 12:19:53,291::train::INFO] [train] Iter 583097 | loss 2.2684 | loss(rot) 1.8863 | loss(pos) 0.1756 | loss(seq) 0.2064 | grad 7.4321 | lr 0.0000 | time_forward 4.0510 | time_backward 5.3380 |
[2023-10-24 12:20:03,949::train::INFO] [train] Iter 583098 | loss 0.5248 | loss(rot) 0.1501 | loss(pos) 0.2386 | loss(seq) 0.1361 | grad 3.0731 | lr 0.0000 | time_forward 4.5620 | time_backward 6.0920 |
[2023-10-24 12:20:14,197::train::INFO] [train] Iter 583099 | loss 0.3226 | loss(rot) 0.2214 | loss(pos) 0.0919 | loss(seq) 0.0092 | grad 2.6169 | lr 0.0000 | time_forward 4.1540 | time_backward 6.0910 |
[2023-10-24 12:20:25,315::train::INFO] [train] Iter 583100 | loss 0.2691 | loss(rot) 0.0612 | loss(pos) 0.0534 | loss(seq) 0.1545 | grad 3.0995 | lr 0.0000 | time_forward 5.4420 | time_backward 5.6720 |
[2023-10-24 12:20:34,875::train::INFO] [train] Iter 583101 | loss 0.9078 | loss(rot) 0.8836 | loss(pos) 0.0214 | loss(seq) 0.0028 | grad 4.3800 | lr 0.0000 | time_forward 3.8880 | time_backward 5.6690 |
[2023-10-24 12:20:43,952::train::INFO] [train] Iter 583102 | loss 0.3412 | loss(rot) 0.1063 | loss(pos) 0.0537 | loss(seq) 0.1812 | grad 2.9238 | lr 0.0000 | time_forward 3.7140 | time_backward 5.3600 |
[2023-10-24 12:20:51,116::train::INFO] [train] Iter 583103 | loss 0.6521 | loss(rot) 0.2724 | loss(pos) 0.0307 | loss(seq) 0.3490 | grad 4.9950 | lr 0.0000 | time_forward 3.0400 | time_backward 4.1210 |
[2023-10-24 12:21:00,626::train::INFO] [train] Iter 583104 | loss 0.5043 | loss(rot) 0.4633 | loss(pos) 0.0277 | loss(seq) 0.0133 | grad 2.9507 | lr 0.0000 | time_forward 4.2840 | time_backward 5.2230 |
[2023-10-24 12:21:10,880::train::INFO] [train] Iter 583105 | loss 0.2392 | loss(rot) 0.2156 | loss(pos) 0.0207 | loss(seq) 0.0029 | grad 2.1750 | lr 0.0000 | time_forward 4.3590 | time_backward 5.8920 |
[2023-10-24 12:21:20,142::train::INFO] [train] Iter 583106 | loss 0.7534 | loss(rot) 0.3151 | loss(pos) 0.0477 | loss(seq) 0.3906 | grad 8.1659 | lr 0.0000 | time_forward 3.7190 | time_backward 5.5400 |
[2023-10-24 12:21:27,126::train::INFO] [train] Iter 583107 | loss 0.5603 | loss(rot) 0.1143 | loss(pos) 0.0448 | loss(seq) 0.4012 | grad 3.1917 | lr 0.0000 | time_forward 3.1750 | time_backward 3.8050 |
[2023-10-24 12:21:35,641::train::INFO] [train] Iter 583108 | loss 0.6720 | loss(rot) 0.3410 | loss(pos) 0.0645 | loss(seq) 0.2665 | grad 5.2197 | lr 0.0000 | time_forward 3.5980 | time_backward 4.9140 |
[2023-10-24 12:21:39,140::train::INFO] [train] Iter 583109 | loss 0.7436 | loss(rot) 0.4471 | loss(pos) 0.0809 | loss(seq) 0.2156 | grad 3.9097 | lr 0.0000 | time_forward 1.4880 | time_backward 2.0070 |
[2023-10-24 12:21:47,305::train::INFO] [train] Iter 583110 | loss 0.5711 | loss(rot) 0.1114 | loss(pos) 0.4217 | loss(seq) 0.0380 | grad 5.0984 | lr 0.0000 | time_forward 3.7640 | time_backward 4.3850 |
[2023-10-24 12:21:57,715::train::INFO] [train] Iter 583111 | loss 0.7126 | loss(rot) 0.2594 | loss(pos) 0.2590 | loss(seq) 0.1942 | grad 3.5888 | lr 0.0000 | time_forward 4.0090 | time_backward 6.3980 |
[2023-10-24 12:22:06,782::train::INFO] [train] Iter 583112 | loss 0.2646 | loss(rot) 0.2523 | loss(pos) 0.0120 | loss(seq) 0.0003 | grad 1.4279 | lr 0.0000 | time_forward 3.9000 | time_backward 5.1630 |
[2023-10-24 12:22:13,368::train::INFO] [train] Iter 583113 | loss 0.2195 | loss(rot) 0.0406 | loss(pos) 0.0201 | loss(seq) 0.1588 | grad 1.9096 | lr 0.0000 | time_forward 2.7630 | time_backward 3.8210 |
[2023-10-24 12:22:20,796::train::INFO] [train] Iter 583114 | loss 1.2444 | loss(rot) 0.8098 | loss(pos) 0.0888 | loss(seq) 0.3458 | grad 4.6316 | lr 0.0000 | time_forward 3.1390 | time_backward 4.2850 |
[2023-10-24 12:22:33,152::train::INFO] [train] Iter 583115 | loss 0.3371 | loss(rot) 0.0879 | loss(pos) 0.1546 | loss(seq) 0.0946 | grad 3.8784 | lr 0.0000 | time_forward 6.0560 | time_backward 6.2980 |
[2023-10-24 12:22:35,977::train::INFO] [train] Iter 583116 | loss 1.0444 | loss(rot) 0.9120 | loss(pos) 0.0243 | loss(seq) 0.1080 | grad 4.9830 | lr 0.0000 | time_forward 1.3130 | time_backward 1.5080 |
[2023-10-24 12:22:38,773::train::INFO] [train] Iter 583117 | loss 0.4151 | loss(rot) 0.3767 | loss(pos) 0.0251 | loss(seq) 0.0133 | grad 1.8683 | lr 0.0000 | time_forward 1.3380 | time_backward 1.4540 |
[2023-10-24 12:22:47,632::train::INFO] [train] Iter 583118 | loss 0.3670 | loss(rot) 0.1682 | loss(pos) 0.0300 | loss(seq) 0.1688 | grad 2.4269 | lr 0.0000 | time_forward 3.4720 | time_backward 5.3820 |
[2023-10-24 12:22:50,897::train::INFO] [train] Iter 583119 | loss 0.3304 | loss(rot) 0.1070 | loss(pos) 0.2074 | loss(seq) 0.0160 | grad 4.9020 | lr 0.0000 | time_forward 1.6080 | time_backward 1.6540 |
[2023-10-24 12:22:59,241::train::INFO] [train] Iter 583120 | loss 0.3526 | loss(rot) 0.1123 | loss(pos) 0.0724 | loss(seq) 0.1679 | grad 2.3697 | lr 0.0000 | time_forward 3.4710 | time_backward 4.8670 |
[2023-10-24 12:23:12,257::train::INFO] [train] Iter 583121 | loss 0.2678 | loss(rot) 0.0868 | loss(pos) 0.0450 | loss(seq) 0.1359 | grad 1.7686 | lr 0.0000 | time_forward 6.8170 | time_backward 6.1960 |
[2023-10-24 12:23:24,408::train::INFO] [train] Iter 583122 | loss 2.3752 | loss(rot) 0.3480 | loss(pos) 2.0266 | loss(seq) 0.0006 | grad 22.0934 | lr 0.0000 | time_forward 3.4880 | time_backward 8.6600 |
[2023-10-24 12:23:27,026::train::INFO] [train] Iter 583123 | loss 2.7336 | loss(rot) 1.7346 | loss(pos) 0.4365 | loss(seq) 0.5624 | grad 4.3597 | lr 0.0000 | time_forward 1.3080 | time_backward 1.3060 |
[2023-10-24 12:23:38,524::train::INFO] [train] Iter 583124 | loss 0.3928 | loss(rot) 0.1291 | loss(pos) 0.0386 | loss(seq) 0.2251 | grad 2.2613 | lr 0.0000 | time_forward 6.2820 | time_backward 5.2110 |
[2023-10-24 12:23:46,247::train::INFO] [train] Iter 583125 | loss 0.7640 | loss(rot) 0.4801 | loss(pos) 0.1073 | loss(seq) 0.1766 | grad 3.8090 | lr 0.0000 | time_forward 3.2860 | time_backward 4.4340 |
[2023-10-24 12:23:56,663::train::INFO] [train] Iter 583126 | loss 0.3374 | loss(rot) 0.1189 | loss(pos) 0.0240 | loss(seq) 0.1945 | grad 2.2760 | lr 0.0000 | time_forward 5.7110 | time_backward 4.7030 |
[2023-10-24 12:24:00,022::train::INFO] [train] Iter 583127 | loss 0.1125 | loss(rot) 0.0817 | loss(pos) 0.0295 | loss(seq) 0.0013 | grad 1.8690 | lr 0.0000 | time_forward 1.5230 | time_backward 1.8320 |
[2023-10-24 12:24:08,634::train::INFO] [train] Iter 583128 | loss 0.3113 | loss(rot) 0.0996 | loss(pos) 0.0248 | loss(seq) 0.1869 | grad 1.5112 | lr 0.0000 | time_forward 3.6950 | time_backward 4.9130 |
[2023-10-24 12:24:17,825::train::INFO] [train] Iter 583129 | loss 0.6982 | loss(rot) 0.5320 | loss(pos) 0.0470 | loss(seq) 0.1192 | grad 3.4668 | lr 0.0000 | time_forward 4.0540 | time_backward 5.1340 |
[2023-10-24 12:24:25,977::train::INFO] [train] Iter 583130 | loss 0.2738 | loss(rot) 0.2367 | loss(pos) 0.0216 | loss(seq) 0.0155 | grad 6.2077 | lr 0.0000 | time_forward 3.4400 | time_backward 4.7080 |
[2023-10-24 12:24:35,737::train::INFO] [train] Iter 583131 | loss 0.3224 | loss(rot) 0.1023 | loss(pos) 0.0292 | loss(seq) 0.1909 | grad 2.0408 | lr 0.0000 | time_forward 4.0010 | time_backward 5.7550 |
[2023-10-24 12:24:43,922::train::INFO] [train] Iter 583132 | loss 0.4180 | loss(rot) 0.0717 | loss(pos) 0.2661 | loss(seq) 0.0802 | grad 4.9680 | lr 0.0000 | time_forward 3.4710 | time_backward 4.7120 |
[2023-10-24 12:24:53,949::train::INFO] [train] Iter 583133 | loss 0.4091 | loss(rot) 0.1002 | loss(pos) 0.0546 | loss(seq) 0.2543 | grad 2.3994 | lr 0.0000 | time_forward 4.0170 | time_backward 6.0070 |
[2023-10-24 12:25:04,632::train::INFO] [train] Iter 583134 | loss 0.2844 | loss(rot) 0.2590 | loss(pos) 0.0200 | loss(seq) 0.0054 | grad 2.4417 | lr 0.0000 | time_forward 4.5600 | time_backward 6.1200 |
[2023-10-24 12:25:12,659::train::INFO] [train] Iter 583135 | loss 1.4885 | loss(rot) 0.7884 | loss(pos) 0.2593 | loss(seq) 0.4409 | grad 3.7185 | lr 0.0000 | time_forward 3.4040 | time_backward 4.6200 |
[2023-10-24 12:25:22,651::train::INFO] [train] Iter 583136 | loss 0.2571 | loss(rot) 0.0581 | loss(pos) 0.1848 | loss(seq) 0.0142 | grad 3.4073 | lr 0.0000 | time_forward 4.0520 | time_backward 5.9360 |
[2023-10-24 12:25:31,375::train::INFO] [train] Iter 583137 | loss 0.3414 | loss(rot) 0.0914 | loss(pos) 0.0166 | loss(seq) 0.2333 | grad 1.9936 | lr 0.0000 | time_forward 3.6850 | time_backward 5.0360 |
[2023-10-24 12:25:34,728::train::INFO] [train] Iter 583138 | loss 1.6538 | loss(rot) 1.5768 | loss(pos) 0.0670 | loss(seq) 0.0100 | grad 2.5528 | lr 0.0000 | time_forward 1.4930 | time_backward 1.8570 |
[2023-10-24 12:25:44,856::train::INFO] [train] Iter 583139 | loss 0.8020 | loss(rot) 0.4341 | loss(pos) 0.2001 | loss(seq) 0.1678 | grad 3.8611 | lr 0.0000 | time_forward 4.3020 | time_backward 5.8220 |
[2023-10-24 12:25:54,922::train::INFO] [train] Iter 583140 | loss 0.9541 | loss(rot) 0.5533 | loss(pos) 0.0354 | loss(seq) 0.3654 | grad 8.8967 | lr 0.0000 | time_forward 4.0880 | time_backward 5.9740 |
[2023-10-24 12:26:04,976::train::INFO] [train] Iter 583141 | loss 0.5183 | loss(rot) 0.4527 | loss(pos) 0.0367 | loss(seq) 0.0289 | grad 3.3882 | lr 0.0000 | time_forward 4.0840 | time_backward 5.9670 |
[2023-10-24 12:26:13,241::train::INFO] [train] Iter 583142 | loss 1.3209 | loss(rot) 0.5321 | loss(pos) 0.2966 | loss(seq) 0.4921 | grad 3.7592 | lr 0.0000 | time_forward 3.5180 | time_backward 4.7430 |
[2023-10-24 12:26:15,919::train::INFO] [train] Iter 583143 | loss 0.1460 | loss(rot) 0.0582 | loss(pos) 0.0223 | loss(seq) 0.0655 | grad 1.5475 | lr 0.0000 | time_forward 1.2460 | time_backward 1.4290 |
[2023-10-24 12:26:18,709::train::INFO] [train] Iter 583144 | loss 0.4397 | loss(rot) 0.4003 | loss(pos) 0.0394 | loss(seq) 0.0000 | grad 21.0899 | lr 0.0000 | time_forward 1.3790 | time_backward 1.4080 |
[2023-10-24 12:26:27,051::train::INFO] [train] Iter 583145 | loss 1.4746 | loss(rot) 1.4242 | loss(pos) 0.0504 | loss(seq) 0.0000 | grad 4.4333 | lr 0.0000 | time_forward 3.5630 | time_backward 4.7750 |
[2023-10-24 12:26:36,890::train::INFO] [train] Iter 583146 | loss 0.3226 | loss(rot) 0.1784 | loss(pos) 0.0261 | loss(seq) 0.1181 | grad 1.8923 | lr 0.0000 | time_forward 3.9540 | time_backward 5.8820 |
[2023-10-24 12:26:47,153::train::INFO] [train] Iter 583147 | loss 1.2923 | loss(rot) 0.9796 | loss(pos) 0.0701 | loss(seq) 0.2426 | grad 20.8057 | lr 0.0000 | time_forward 4.1190 | time_backward 6.1400 |
[2023-10-24 12:26:49,968::train::INFO] [train] Iter 583148 | loss 0.7401 | loss(rot) 0.6668 | loss(pos) 0.0643 | loss(seq) 0.0090 | grad 3.2600 | lr 0.0000 | time_forward 1.3580 | time_backward 1.4530 |
[2023-10-24 12:26:59,865::train::INFO] [train] Iter 583149 | loss 0.2001 | loss(rot) 0.1516 | loss(pos) 0.0181 | loss(seq) 0.0305 | grad 5.2551 | lr 0.0000 | time_forward 4.0610 | time_backward 5.8090 |
[2023-10-24 12:27:09,631::train::INFO] [train] Iter 583150 | loss 0.5679 | loss(rot) 0.3247 | loss(pos) 0.1249 | loss(seq) 0.1183 | grad 2.5693 | lr 0.0000 | time_forward 3.9860 | time_backward 5.7760 |
[2023-10-24 12:27:14,466::train::INFO] [train] Iter 583151 | loss 1.6041 | loss(rot) 1.4490 | loss(pos) 0.0616 | loss(seq) 0.0935 | grad 4.7786 | lr 0.0000 | time_forward 2.1620 | time_backward 2.6700 |
[2023-10-24 12:27:21,955::train::INFO] [train] Iter 583152 | loss 0.8858 | loss(rot) 0.3010 | loss(pos) 0.4569 | loss(seq) 0.1279 | grad 3.1000 | lr 0.0000 | time_forward 3.0320 | time_backward 4.4540 |
[2023-10-24 12:27:31,960::train::INFO] [train] Iter 583153 | loss 0.1640 | loss(rot) 0.1363 | loss(pos) 0.0207 | loss(seq) 0.0069 | grad 1.8403 | lr 0.0000 | time_forward 4.1150 | time_backward 5.8860 |
[2023-10-24 12:27:41,073::train::INFO] [train] Iter 583154 | loss 0.3108 | loss(rot) 0.0522 | loss(pos) 0.2568 | loss(seq) 0.0019 | grad 7.4958 | lr 0.0000 | time_forward 3.8370 | time_backward 5.2740 |
[2023-10-24 12:27:49,749::train::INFO] [train] Iter 583155 | loss 2.2473 | loss(rot) 2.1903 | loss(pos) 0.0449 | loss(seq) 0.0121 | grad 3.6429 | lr 0.0000 | time_forward 3.6570 | time_backward 5.0150 |
[2023-10-24 12:27:58,654::train::INFO] [train] Iter 583156 | loss 0.5267 | loss(rot) 0.0889 | loss(pos) 0.0445 | loss(seq) 0.3933 | grad 2.4157 | lr 0.0000 | time_forward 3.7240 | time_backward 5.1790 |
[2023-10-24 12:28:06,882::train::INFO] [train] Iter 583157 | loss 0.7068 | loss(rot) 0.3336 | loss(pos) 0.1895 | loss(seq) 0.1838 | grad 3.1665 | lr 0.0000 | time_forward 3.4800 | time_backward 4.7440 |
[2023-10-24 12:28:15,867::train::INFO] [train] Iter 583158 | loss 0.2323 | loss(rot) 0.0579 | loss(pos) 0.0222 | loss(seq) 0.1521 | grad 1.6120 | lr 0.0000 | time_forward 3.8200 | time_backward 5.1630 |
[2023-10-24 12:28:23,661::train::INFO] [train] Iter 583159 | loss 0.9694 | loss(rot) 0.2540 | loss(pos) 0.7085 | loss(seq) 0.0070 | grad 10.9079 | lr 0.0000 | time_forward 3.3180 | time_backward 4.4730 |
[2023-10-24 12:28:26,171::train::INFO] [train] Iter 583160 | loss 0.2178 | loss(rot) 0.0521 | loss(pos) 0.0727 | loss(seq) 0.0930 | grad 2.9453 | lr 0.0000 | time_forward 1.1880 | time_backward 1.3180 |
[2023-10-24 12:28:35,887::train::INFO] [train] Iter 583161 | loss 0.9051 | loss(rot) 0.2147 | loss(pos) 0.6841 | loss(seq) 0.0063 | grad 8.1512 | lr 0.0000 | time_forward 3.9370 | time_backward 5.7760 |
[2023-10-24 12:28:42,913::train::INFO] [train] Iter 583162 | loss 0.6899 | loss(rot) 0.0267 | loss(pos) 0.6611 | loss(seq) 0.0021 | grad 10.8006 | lr 0.0000 | time_forward 2.9260 | time_backward 4.0980 |
[2023-10-24 12:28:51,162::train::INFO] [train] Iter 583163 | loss 0.5499 | loss(rot) 0.3938 | loss(pos) 0.0399 | loss(seq) 0.1161 | grad 3.7036 | lr 0.0000 | time_forward 3.5010 | time_backward 4.7440 |
[2023-10-24 12:28:59,446::train::INFO] [train] Iter 583164 | loss 0.7825 | loss(rot) 0.7606 | loss(pos) 0.0197 | loss(seq) 0.0022 | grad 3.7048 | lr 0.0000 | time_forward 3.5000 | time_backward 4.7820 |
[2023-10-24 12:29:09,306::train::INFO] [train] Iter 583165 | loss 0.5885 | loss(rot) 0.0957 | loss(pos) 0.4744 | loss(seq) 0.0185 | grad 4.8572 | lr 0.0000 | time_forward 4.0000 | time_backward 5.8560 |
[2023-10-24 12:29:12,071::train::INFO] [train] Iter 583166 | loss 0.6538 | loss(rot) 0.4475 | loss(pos) 0.0290 | loss(seq) 0.1773 | grad 2.5783 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4490 |
[2023-10-24 12:29:21,841::train::INFO] [train] Iter 583167 | loss 1.1421 | loss(rot) 0.1457 | loss(pos) 0.7895 | loss(seq) 0.2069 | grad 5.7868 | lr 0.0000 | time_forward 4.0010 | time_backward 5.7390 |
[2023-10-24 12:29:24,543::train::INFO] [train] Iter 583168 | loss 0.5105 | loss(rot) 0.2227 | loss(pos) 0.0679 | loss(seq) 0.2199 | grad 2.9695 | lr 0.0000 | time_forward 1.3020 | time_backward 1.3970 |
[2023-10-24 12:29:27,916::train::INFO] [train] Iter 583169 | loss 0.9827 | loss(rot) 0.0340 | loss(pos) 0.9453 | loss(seq) 0.0034 | grad 11.8738 | lr 0.0000 | time_forward 1.5150 | time_backward 1.8540 |
[2023-10-24 12:29:36,902::train::INFO] [train] Iter 583170 | loss 1.4377 | loss(rot) 0.8195 | loss(pos) 0.1141 | loss(seq) 0.5041 | grad 5.3255 | lr 0.0000 | time_forward 3.7870 | time_backward 5.1960 |
[2023-10-24 12:29:46,838::train::INFO] [train] Iter 583171 | loss 0.7346 | loss(rot) 0.0179 | loss(pos) 0.7148 | loss(seq) 0.0019 | grad 7.9834 | lr 0.0000 | time_forward 4.1900 | time_backward 5.7280 |
[2023-10-24 12:29:49,575::train::INFO] [train] Iter 583172 | loss 1.6387 | loss(rot) 1.0188 | loss(pos) 0.2618 | loss(seq) 0.3582 | grad 6.5249 | lr 0.0000 | time_forward 1.3230 | time_backward 1.4110 |
[2023-10-24 12:29:57,656::train::INFO] [train] Iter 583173 | loss 1.8221 | loss(rot) 1.3514 | loss(pos) 0.0449 | loss(seq) 0.4259 | grad 3.8176 | lr 0.0000 | time_forward 3.4240 | time_backward 4.6530 |
[2023-10-24 12:30:05,682::train::INFO] [train] Iter 583174 | loss 4.2415 | loss(rot) 0.0016 | loss(pos) 4.2399 | loss(seq) 0.0000 | grad 31.7169 | lr 0.0000 | time_forward 3.4010 | time_backward 4.6230 |
[2023-10-24 12:30:14,847::train::INFO] [train] Iter 583175 | loss 0.3235 | loss(rot) 0.0803 | loss(pos) 0.0149 | loss(seq) 0.2283 | grad 1.7823 | lr 0.0000 | time_forward 3.8760 | time_backward 5.2860 |
[2023-10-24 12:30:23,489::train::INFO] [train] Iter 583176 | loss 0.7081 | loss(rot) 0.1390 | loss(pos) 0.4386 | loss(seq) 0.1305 | grad 4.5877 | lr 0.0000 | time_forward 3.6140 | time_backward 5.0240 |
[2023-10-24 12:30:33,294::train::INFO] [train] Iter 583177 | loss 0.5190 | loss(rot) 0.2566 | loss(pos) 0.0670 | loss(seq) 0.1955 | grad 2.8050 | lr 0.0000 | time_forward 3.9740 | time_backward 5.8280 |
[2023-10-24 12:30:42,382::train::INFO] [train] Iter 583178 | loss 1.8496 | loss(rot) 1.8167 | loss(pos) 0.0318 | loss(seq) 0.0011 | grad 3.9573 | lr 0.0000 | time_forward 3.8370 | time_backward 5.2470 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.