text
stringlengths 56
1.16k
|
|---|
[2023-09-02 12:52:05,660::train::INFO] [train] Iter 10085 | loss 1.6639 | loss(rot) 0.7776 | loss(pos) 0.4002 | loss(seq) 0.4861 | grad 4.6580 | lr 0.0010 | time_forward 3.3380 | time_backward 4.7630 |
[2023-09-02 12:52:15,664::train::INFO] [train] Iter 10086 | loss 1.0069 | loss(rot) 0.3008 | loss(pos) 0.5775 | loss(seq) 0.1286 | grad 5.8606 | lr 0.0010 | time_forward 4.0000 | time_backward 6.0010 |
[2023-09-02 12:52:17,944::train::INFO] [train] Iter 10087 | loss 0.8148 | loss(rot) 0.1289 | loss(pos) 0.6499 | loss(seq) 0.0360 | grad 3.5655 | lr 0.0010 | time_forward 1.0540 | time_backward 1.2210 |
[2023-09-02 12:52:26,439::train::INFO] [train] Iter 10088 | loss 1.1424 | loss(rot) 0.1542 | loss(pos) 0.9685 | loss(seq) 0.0197 | grad 6.3502 | lr 0.0010 | time_forward 3.6810 | time_backward 4.7850 |
[2023-09-02 12:52:36,123::train::INFO] [train] Iter 10089 | loss 2.1256 | loss(rot) 0.0368 | loss(pos) 2.0854 | loss(seq) 0.0033 | grad 8.6586 | lr 0.0010 | time_forward 4.0330 | time_backward 5.6470 |
[2023-09-02 12:52:38,795::train::INFO] [train] Iter 10090 | loss 2.8271 | loss(rot) 1.9007 | loss(pos) 0.3347 | loss(seq) 0.5917 | grad 5.6844 | lr 0.0010 | time_forward 1.2700 | time_backward 1.3980 |
[2023-09-02 12:52:46,699::train::INFO] [train] Iter 10091 | loss 2.1682 | loss(rot) 1.9039 | loss(pos) 0.1017 | loss(seq) 0.1626 | grad 9.5753 | lr 0.0010 | time_forward 3.3140 | time_backward 4.5860 |
[2023-09-02 12:52:51,454::train::INFO] [train] Iter 10092 | loss 1.6723 | loss(rot) 1.4758 | loss(pos) 0.1965 | loss(seq) 0.0000 | grad 6.2991 | lr 0.0010 | time_forward 2.0350 | time_backward 2.7160 |
[2023-09-02 12:53:01,431::train::INFO] [train] Iter 10093 | loss 1.1497 | loss(rot) 0.1378 | loss(pos) 0.7760 | loss(seq) 0.2358 | grad 5.0365 | lr 0.0010 | time_forward 3.8870 | time_backward 6.0720 |
[2023-09-02 12:53:09,982::train::INFO] [train] Iter 10094 | loss 2.1878 | loss(rot) 1.3287 | loss(pos) 0.3535 | loss(seq) 0.5056 | grad 6.1503 | lr 0.0010 | time_forward 3.5920 | time_backward 4.9550 |
[2023-09-02 12:53:20,008::train::INFO] [train] Iter 10095 | loss 1.9721 | loss(rot) 1.5775 | loss(pos) 0.2098 | loss(seq) 0.1848 | grad 5.5098 | lr 0.0010 | time_forward 4.0080 | time_backward 6.0140 |
[2023-09-02 12:53:28,801::train::INFO] [train] Iter 10096 | loss 1.5976 | loss(rot) 1.3776 | loss(pos) 0.1769 | loss(seq) 0.0431 | grad 8.2260 | lr 0.0010 | time_forward 3.6500 | time_backward 5.1400 |
[2023-09-02 12:53:31,217::train::INFO] [train] Iter 10097 | loss 1.9297 | loss(rot) 1.3742 | loss(pos) 0.1878 | loss(seq) 0.3676 | grad 5.3846 | lr 0.0010 | time_forward 1.1300 | time_backward 1.2840 |
[2023-09-02 12:53:40,806::train::INFO] [train] Iter 10098 | loss 1.7817 | loss(rot) 1.0860 | loss(pos) 0.2606 | loss(seq) 0.4351 | grad 5.1319 | lr 0.0010 | time_forward 3.9300 | time_backward 5.6550 |
[2023-09-02 12:53:50,620::train::INFO] [train] Iter 10099 | loss 1.2664 | loss(rot) 0.7738 | loss(pos) 0.2680 | loss(seq) 0.2246 | grad 3.9517 | lr 0.0010 | time_forward 3.9350 | time_backward 5.8740 |
[2023-09-02 12:54:00,050::train::INFO] [train] Iter 10100 | loss 0.9174 | loss(rot) 0.1830 | loss(pos) 0.5859 | loss(seq) 0.1485 | grad 2.2402 | lr 0.0010 | time_forward 3.9560 | time_backward 5.4540 |
[2023-09-02 12:54:02,711::train::INFO] [train] Iter 10101 | loss 1.7389 | loss(rot) 0.9036 | loss(pos) 0.3034 | loss(seq) 0.5319 | grad 4.1567 | lr 0.0010 | time_forward 1.2390 | time_backward 1.4170 |
[2023-09-02 12:54:12,549::train::INFO] [train] Iter 10102 | loss 1.6140 | loss(rot) 0.3309 | loss(pos) 0.9815 | loss(seq) 0.3017 | grad 3.4942 | lr 0.0010 | time_forward 3.9630 | time_backward 5.8710 |
[2023-09-02 12:54:22,309::train::INFO] [train] Iter 10103 | loss 0.9475 | loss(rot) 0.4401 | loss(pos) 0.2974 | loss(seq) 0.2100 | grad 3.0286 | lr 0.0010 | time_forward 4.0100 | time_backward 5.7460 |
[2023-09-02 12:54:24,612::train::INFO] [train] Iter 10104 | loss 0.8416 | loss(rot) 0.4831 | loss(pos) 0.2390 | loss(seq) 0.1194 | grad 3.2123 | lr 0.0010 | time_forward 1.0740 | time_backward 1.2260 |
[2023-09-02 12:54:34,693::train::INFO] [train] Iter 10105 | loss 2.4070 | loss(rot) 2.0978 | loss(pos) 0.1867 | loss(seq) 0.1225 | grad 3.9209 | lr 0.0010 | time_forward 3.9700 | time_backward 6.1080 |
[2023-09-02 12:54:44,577::train::INFO] [train] Iter 10106 | loss 2.0097 | loss(rot) 1.1814 | loss(pos) 0.3182 | loss(seq) 0.5101 | grad 3.6789 | lr 0.0010 | time_forward 4.0080 | time_backward 5.8730 |
[2023-09-02 12:54:47,220::train::INFO] [train] Iter 10107 | loss 2.0188 | loss(rot) 1.8365 | loss(pos) 0.1776 | loss(seq) 0.0047 | grad 4.8047 | lr 0.0010 | time_forward 1.2450 | time_backward 1.3940 |
[2023-09-02 12:54:57,234::train::INFO] [train] Iter 10108 | loss 2.5232 | loss(rot) 2.2573 | loss(pos) 0.1634 | loss(seq) 0.1025 | grad 4.2339 | lr 0.0010 | time_forward 4.2070 | time_backward 5.8040 |
[2023-09-02 12:55:05,949::train::INFO] [train] Iter 10109 | loss 1.8431 | loss(rot) 1.4389 | loss(pos) 0.2548 | loss(seq) 0.1494 | grad 6.4758 | lr 0.0010 | time_forward 3.6560 | time_backward 5.0560 |
[2023-09-02 12:55:15,815::train::INFO] [train] Iter 10110 | loss 0.7766 | loss(rot) 0.1443 | loss(pos) 0.4260 | loss(seq) 0.2062 | grad 3.2103 | lr 0.0010 | time_forward 3.9910 | time_backward 5.8710 |
[2023-09-02 12:55:25,875::train::INFO] [train] Iter 10111 | loss 1.2274 | loss(rot) 0.6624 | loss(pos) 0.1944 | loss(seq) 0.3706 | grad 3.3696 | lr 0.0010 | time_forward 4.0600 | time_backward 5.9960 |
[2023-09-02 12:55:35,863::train::INFO] [train] Iter 10112 | loss 1.6392 | loss(rot) 0.6908 | loss(pos) 0.3289 | loss(seq) 0.6196 | grad 4.5687 | lr 0.0010 | time_forward 4.0400 | time_backward 5.9440 |
[2023-09-02 12:55:45,217::train::INFO] [train] Iter 10113 | loss 1.9036 | loss(rot) 1.7395 | loss(pos) 0.1432 | loss(seq) 0.0209 | grad 6.3761 | lr 0.0010 | time_forward 3.9530 | time_backward 5.3970 |
[2023-09-02 12:55:55,473::train::INFO] [train] Iter 10114 | loss 2.5593 | loss(rot) 2.2431 | loss(pos) 0.0742 | loss(seq) 0.2420 | grad 4.4898 | lr 0.0010 | time_forward 4.0850 | time_backward 6.1670 |
[2023-09-02 12:55:58,121::train::INFO] [train] Iter 10115 | loss 1.6060 | loss(rot) 0.5779 | loss(pos) 0.5473 | loss(seq) 0.4809 | grad 5.5020 | lr 0.0010 | time_forward 1.2410 | time_backward 1.4050 |
[2023-09-02 12:56:08,074::train::INFO] [train] Iter 10116 | loss 1.9309 | loss(rot) 1.1707 | loss(pos) 0.2388 | loss(seq) 0.5214 | grad 3.9079 | lr 0.0010 | time_forward 4.0660 | time_backward 5.8830 |
[2023-09-02 12:56:16,772::train::INFO] [train] Iter 10117 | loss 1.7360 | loss(rot) 0.9740 | loss(pos) 0.2503 | loss(seq) 0.5117 | grad 4.4894 | lr 0.0010 | time_forward 3.6630 | time_backward 5.0320 |
[2023-09-02 12:56:26,880::train::INFO] [train] Iter 10118 | loss 1.4805 | loss(rot) 1.3222 | loss(pos) 0.1450 | loss(seq) 0.0133 | grad 4.4554 | lr 0.0010 | time_forward 4.0850 | time_backward 6.0200 |
[2023-09-02 12:56:35,606::train::INFO] [train] Iter 10119 | loss 1.0481 | loss(rot) 0.6722 | loss(pos) 0.1189 | loss(seq) 0.2570 | grad 4.6423 | lr 0.0010 | time_forward 3.6930 | time_backward 5.0300 |
[2023-09-02 12:56:38,316::train::INFO] [train] Iter 10120 | loss 2.3566 | loss(rot) 1.7986 | loss(pos) 0.0758 | loss(seq) 0.4822 | grad 4.9835 | lr 0.0010 | time_forward 1.2370 | time_backward 1.4700 |
[2023-09-02 12:56:47,492::train::INFO] [train] Iter 10121 | loss 1.7339 | loss(rot) 1.5239 | loss(pos) 0.1174 | loss(seq) 0.0927 | grad 6.1905 | lr 0.0010 | time_forward 3.8870 | time_backward 5.2850 |
[2023-09-02 12:56:57,337::train::INFO] [train] Iter 10122 | loss 1.5140 | loss(rot) 1.4145 | loss(pos) 0.0839 | loss(seq) 0.0156 | grad 3.5785 | lr 0.0010 | time_forward 4.0200 | time_backward 5.8220 |
[2023-09-02 12:57:05,944::train::INFO] [train] Iter 10123 | loss 2.5850 | loss(rot) 2.3537 | loss(pos) 0.1567 | loss(seq) 0.0746 | grad 3.2996 | lr 0.0010 | time_forward 3.6100 | time_backward 4.9930 |
[2023-09-02 12:57:14,520::train::INFO] [train] Iter 10124 | loss 0.9279 | loss(rot) 0.6674 | loss(pos) 0.0839 | loss(seq) 0.1766 | grad 6.1634 | lr 0.0010 | time_forward 3.5180 | time_backward 5.0550 |
[2023-09-02 12:57:17,188::train::INFO] [train] Iter 10125 | loss 0.3782 | loss(rot) 0.0767 | loss(pos) 0.2513 | loss(seq) 0.0502 | grad 2.8321 | lr 0.0010 | time_forward 1.2530 | time_backward 1.4120 |
[2023-09-02 12:57:19,935::train::INFO] [train] Iter 10126 | loss 1.8187 | loss(rot) 1.2775 | loss(pos) 0.1378 | loss(seq) 0.4034 | grad 3.6284 | lr 0.0010 | time_forward 1.3050 | time_backward 1.4390 |
[2023-09-02 12:57:29,981::train::INFO] [train] Iter 10127 | loss 1.6335 | loss(rot) 1.2456 | loss(pos) 0.1253 | loss(seq) 0.2626 | grad 3.6036 | lr 0.0010 | time_forward 4.0790 | time_backward 5.9610 |
[2023-09-02 12:57:33,234::train::INFO] [train] Iter 10128 | loss 1.7948 | loss(rot) 0.0290 | loss(pos) 1.7640 | loss(seq) 0.0018 | grad 5.6273 | lr 0.0010 | time_forward 1.4130 | time_backward 1.8360 |
[2023-09-02 12:57:41,949::train::INFO] [train] Iter 10129 | loss 1.8468 | loss(rot) 0.9601 | loss(pos) 0.2995 | loss(seq) 0.5871 | grad 3.3880 | lr 0.0010 | time_forward 3.6590 | time_backward 5.0430 |
[2023-09-02 12:57:47,542::train::INFO] [train] Iter 10130 | loss 2.3582 | loss(rot) 1.9477 | loss(pos) 0.0774 | loss(seq) 0.3330 | grad 4.3279 | lr 0.0010 | time_forward 2.3850 | time_backward 3.2030 |
[2023-09-02 12:57:55,781::train::INFO] [train] Iter 10131 | loss 1.9176 | loss(rot) 1.3793 | loss(pos) 0.1757 | loss(seq) 0.3626 | grad 6.1643 | lr 0.0010 | time_forward 3.4970 | time_backward 4.7380 |
[2023-09-02 12:57:58,390::train::INFO] [train] Iter 10132 | loss 2.1228 | loss(rot) 1.3273 | loss(pos) 0.2492 | loss(seq) 0.5463 | grad 5.8326 | lr 0.0010 | time_forward 1.2060 | time_backward 1.4000 |
[2023-09-02 12:58:07,606::train::INFO] [train] Iter 10133 | loss 1.3449 | loss(rot) 1.0237 | loss(pos) 0.0662 | loss(seq) 0.2550 | grad 5.9630 | lr 0.0010 | time_forward 3.8930 | time_backward 5.3200 |
[2023-09-02 12:58:10,276::train::INFO] [train] Iter 10134 | loss 1.6301 | loss(rot) 0.4370 | loss(pos) 0.9325 | loss(seq) 0.2606 | grad 4.3981 | lr 0.0010 | time_forward 1.2350 | time_backward 1.4320 |
[2023-09-02 12:58:18,961::train::INFO] [train] Iter 10135 | loss 1.4377 | loss(rot) 1.1644 | loss(pos) 0.0748 | loss(seq) 0.1984 | grad 3.3151 | lr 0.0010 | time_forward 3.6430 | time_backward 5.0380 |
[2023-09-02 12:58:21,614::train::INFO] [train] Iter 10136 | loss 2.0051 | loss(rot) 1.8028 | loss(pos) 0.0999 | loss(seq) 0.1023 | grad 6.6576 | lr 0.0010 | time_forward 1.2250 | time_backward 1.4240 |
[2023-09-02 12:58:30,371::train::INFO] [train] Iter 10137 | loss 0.6639 | loss(rot) 0.4793 | loss(pos) 0.1411 | loss(seq) 0.0435 | grad 6.7114 | lr 0.0010 | time_forward 3.7750 | time_backward 4.9790 |
[2023-09-02 12:58:40,924::train::INFO] [train] Iter 10138 | loss 1.6927 | loss(rot) 1.5854 | loss(pos) 0.0845 | loss(seq) 0.0228 | grad 6.0867 | lr 0.0010 | time_forward 4.3670 | time_backward 6.1820 |
[2023-09-02 12:58:43,561::train::INFO] [train] Iter 10139 | loss 1.0589 | loss(rot) 0.9261 | loss(pos) 0.0499 | loss(seq) 0.0829 | grad 3.6775 | lr 0.0010 | time_forward 1.2340 | time_backward 1.4000 |
[2023-09-02 12:58:53,415::train::INFO] [train] Iter 10140 | loss 1.3846 | loss(rot) 0.8996 | loss(pos) 0.1166 | loss(seq) 0.3685 | grad 3.2076 | lr 0.0010 | time_forward 4.1160 | time_backward 5.7350 |
[2023-09-02 12:59:03,218::train::INFO] [train] Iter 10141 | loss 1.2186 | loss(rot) 0.9549 | loss(pos) 0.2637 | loss(seq) 0.0000 | grad 5.1737 | lr 0.0010 | time_forward 4.0130 | time_backward 5.7860 |
[2023-09-02 12:59:13,313::train::INFO] [train] Iter 10142 | loss 2.0816 | loss(rot) 1.8907 | loss(pos) 0.1908 | loss(seq) 0.0000 | grad 3.7744 | lr 0.0010 | time_forward 4.1610 | time_backward 5.9300 |
[2023-09-02 12:59:23,255::train::INFO] [train] Iter 10143 | loss 2.0323 | loss(rot) 1.4275 | loss(pos) 0.1257 | loss(seq) 0.4791 | grad 4.4185 | lr 0.0010 | time_forward 4.1310 | time_backward 5.8080 |
[2023-09-02 12:59:33,234::train::INFO] [train] Iter 10144 | loss 1.8152 | loss(rot) 1.5360 | loss(pos) 0.2771 | loss(seq) 0.0021 | grad 4.9014 | lr 0.0010 | time_forward 4.0470 | time_backward 5.9290 |
[2023-09-02 12:59:42,968::train::INFO] [train] Iter 10145 | loss 2.3428 | loss(rot) 2.2222 | loss(pos) 0.1067 | loss(seq) 0.0139 | grad 4.5155 | lr 0.0010 | time_forward 4.0580 | time_backward 5.6720 |
[2023-09-02 12:59:51,217::train::INFO] [train] Iter 10146 | loss 1.0415 | loss(rot) 0.1073 | loss(pos) 0.5034 | loss(seq) 0.4308 | grad 5.1240 | lr 0.0010 | time_forward 3.3940 | time_backward 4.8510 |
[2023-09-02 13:00:01,229::train::INFO] [train] Iter 10147 | loss 1.9030 | loss(rot) 1.0286 | loss(pos) 0.4531 | loss(seq) 0.4214 | grad 5.3960 | lr 0.0010 | time_forward 4.0170 | time_backward 5.9910 |
[2023-09-02 13:00:11,122::train::INFO] [train] Iter 10148 | loss 2.2439 | loss(rot) 1.8078 | loss(pos) 0.1482 | loss(seq) 0.2880 | grad 3.1747 | lr 0.0010 | time_forward 4.2120 | time_backward 5.6780 |
[2023-09-02 13:00:19,506::train::INFO] [train] Iter 10149 | loss 1.0220 | loss(rot) 0.2715 | loss(pos) 0.1775 | loss(seq) 0.5730 | grad 3.2992 | lr 0.0010 | time_forward 3.5500 | time_backward 4.8300 |
[2023-09-02 13:00:24,194::train::INFO] [train] Iter 10150 | loss 1.9336 | loss(rot) 1.3255 | loss(pos) 0.2407 | loss(seq) 0.3675 | grad 5.6952 | lr 0.0010 | time_forward 2.0830 | time_backward 2.6010 |
[2023-09-02 13:00:34,018::train::INFO] [train] Iter 10151 | loss 1.4714 | loss(rot) 0.6075 | loss(pos) 0.3403 | loss(seq) 0.5236 | grad 4.5382 | lr 0.0010 | time_forward 3.8710 | time_backward 5.9500 |
[2023-09-02 13:00:43,990::train::INFO] [train] Iter 10152 | loss 2.1667 | loss(rot) 1.4355 | loss(pos) 0.3048 | loss(seq) 0.4264 | grad 4.1098 | lr 0.0010 | time_forward 4.1870 | time_backward 5.7600 |
[2023-09-02 13:00:46,622::train::INFO] [train] Iter 10153 | loss 2.0016 | loss(rot) 1.8295 | loss(pos) 0.1720 | loss(seq) 0.0001 | grad 3.9659 | lr 0.0010 | time_forward 1.2230 | time_backward 1.4060 |
[2023-09-02 13:00:55,684::train::INFO] [train] Iter 10154 | loss 1.7083 | loss(rot) 1.5434 | loss(pos) 0.1616 | loss(seq) 0.0033 | grad 4.3587 | lr 0.0010 | time_forward 3.8240 | time_backward 5.2340 |
[2023-09-02 13:01:04,700::train::INFO] [train] Iter 10155 | loss 2.3890 | loss(rot) 2.0165 | loss(pos) 0.0913 | loss(seq) 0.2812 | grad 4.7265 | lr 0.0010 | time_forward 3.8120 | time_backward 5.2020 |
[2023-09-02 13:01:07,384::train::INFO] [train] Iter 10156 | loss 1.9591 | loss(rot) 1.7495 | loss(pos) 0.1222 | loss(seq) 0.0874 | grad 5.9885 | lr 0.0010 | time_forward 1.2530 | time_backward 1.4270 |
[2023-09-02 13:01:13,820::train::INFO] [train] Iter 10157 | loss 2.3096 | loss(rot) 1.7508 | loss(pos) 0.1093 | loss(seq) 0.4495 | grad 4.6340 | lr 0.0010 | time_forward 2.6970 | time_backward 3.7020 |
[2023-09-02 13:01:23,409::train::INFO] [train] Iter 10158 | loss 1.7399 | loss(rot) 1.0415 | loss(pos) 0.3273 | loss(seq) 0.3711 | grad 4.7896 | lr 0.0010 | time_forward 3.9150 | time_backward 5.6600 |
[2023-09-02 13:01:31,376::train::INFO] [train] Iter 10159 | loss 0.7077 | loss(rot) 0.1675 | loss(pos) 0.3144 | loss(seq) 0.2258 | grad 3.9006 | lr 0.0010 | time_forward 3.2930 | time_backward 4.6700 |
[2023-09-02 13:01:41,411::train::INFO] [train] Iter 10160 | loss 0.6259 | loss(rot) 0.0420 | loss(pos) 0.5763 | loss(seq) 0.0076 | grad 3.7069 | lr 0.0010 | time_forward 4.1220 | time_backward 5.8870 |
[2023-09-02 13:01:48,727::train::INFO] [train] Iter 10161 | loss 0.8411 | loss(rot) 0.0786 | loss(pos) 0.7496 | loss(seq) 0.0129 | grad 4.8374 | lr 0.0010 | time_forward 3.0450 | time_backward 4.2670 |
[2023-09-02 13:01:58,770::train::INFO] [train] Iter 10162 | loss 1.6278 | loss(rot) 0.9080 | loss(pos) 0.2338 | loss(seq) 0.4861 | grad 4.2541 | lr 0.0010 | time_forward 4.1650 | time_backward 5.8760 |
[2023-09-02 13:02:07,760::train::INFO] [train] Iter 10163 | loss 2.4354 | loss(rot) 1.6409 | loss(pos) 0.2055 | loss(seq) 0.5890 | grad 3.9648 | lr 0.0010 | time_forward 3.7650 | time_backward 5.2090 |
[2023-09-02 13:02:15,704::train::INFO] [train] Iter 10164 | loss 1.0543 | loss(rot) 0.8347 | loss(pos) 0.1987 | loss(seq) 0.0210 | grad 6.3213 | lr 0.0010 | time_forward 3.3470 | time_backward 4.5930 |
[2023-09-02 13:02:18,367::train::INFO] [train] Iter 10165 | loss 1.2120 | loss(rot) 1.0663 | loss(pos) 0.1457 | loss(seq) 0.0000 | grad 5.9226 | lr 0.0010 | time_forward 1.2390 | time_backward 1.4200 |
[2023-09-02 13:02:27,474::train::INFO] [train] Iter 10166 | loss 1.6121 | loss(rot) 1.4484 | loss(pos) 0.1616 | loss(seq) 0.0021 | grad 3.4697 | lr 0.0010 | time_forward 3.8440 | time_backward 5.2610 |
[2023-09-02 13:02:35,982::train::INFO] [train] Iter 10167 | loss 1.2654 | loss(rot) 0.6499 | loss(pos) 0.1996 | loss(seq) 0.4159 | grad 3.6693 | lr 0.0010 | time_forward 3.4470 | time_backward 5.0570 |
[2023-09-02 13:02:44,120::train::INFO] [train] Iter 10168 | loss 0.6515 | loss(rot) 0.0823 | loss(pos) 0.5525 | loss(seq) 0.0167 | grad 3.3113 | lr 0.0010 | time_forward 3.3610 | time_backward 4.7730 |
[2023-09-02 13:02:53,900::train::INFO] [train] Iter 10169 | loss 2.6104 | loss(rot) 2.3097 | loss(pos) 0.3001 | loss(seq) 0.0007 | grad 4.7493 | lr 0.0010 | time_forward 3.9500 | time_backward 5.8270 |
[2023-09-02 13:03:01,486::train::INFO] [train] Iter 10170 | loss 1.9920 | loss(rot) 1.8899 | loss(pos) 0.0962 | loss(seq) 0.0060 | grad 3.5088 | lr 0.0010 | time_forward 3.2230 | time_backward 4.3590 |
[2023-09-02 13:03:10,283::train::INFO] [train] Iter 10171 | loss 1.3496 | loss(rot) 0.9409 | loss(pos) 0.0980 | loss(seq) 0.3108 | grad 4.5327 | lr 0.0010 | time_forward 3.7290 | time_backward 5.0640 |
[2023-09-02 13:03:19,383::train::INFO] [train] Iter 10172 | loss 1.2604 | loss(rot) 0.5725 | loss(pos) 0.1870 | loss(seq) 0.5010 | grad 4.3648 | lr 0.0010 | time_forward 3.8950 | time_backward 5.2010 |
[2023-09-02 13:03:29,224::train::INFO] [train] Iter 10173 | loss 1.5129 | loss(rot) 0.0366 | loss(pos) 1.2447 | loss(seq) 0.2317 | grad 4.9208 | lr 0.0010 | time_forward 3.9270 | time_backward 5.9110 |
[2023-09-02 13:03:31,934::train::INFO] [train] Iter 10174 | loss 1.1640 | loss(rot) 0.9688 | loss(pos) 0.1511 | loss(seq) 0.0442 | grad 3.8237 | lr 0.0010 | time_forward 1.2700 | time_backward 1.4360 |
[2023-09-02 13:03:39,498::train::INFO] [train] Iter 10175 | loss 1.6911 | loss(rot) 1.3361 | loss(pos) 0.0591 | loss(seq) 0.2959 | grad 4.3113 | lr 0.0010 | time_forward 3.1990 | time_backward 4.3610 |
[2023-09-02 13:03:45,469::train::INFO] [train] Iter 10176 | loss 2.0676 | loss(rot) 1.6204 | loss(pos) 0.1402 | loss(seq) 0.3070 | grad 4.8061 | lr 0.0010 | time_forward 2.5190 | time_backward 3.4490 |
[2023-09-02 13:03:53,973::train::INFO] [train] Iter 10177 | loss 2.8239 | loss(rot) 2.0694 | loss(pos) 0.2386 | loss(seq) 0.5159 | grad 6.6582 | lr 0.0010 | time_forward 3.5250 | time_backward 4.9750 |
[2023-09-02 13:04:04,232::train::INFO] [train] Iter 10178 | loss 1.1657 | loss(rot) 0.1959 | loss(pos) 0.8198 | loss(seq) 0.1500 | grad 3.8011 | lr 0.0010 | time_forward 4.2680 | time_backward 5.9880 |
[2023-09-02 13:04:06,932::train::INFO] [train] Iter 10179 | loss 1.7114 | loss(rot) 0.9761 | loss(pos) 0.2467 | loss(seq) 0.4885 | grad 4.6442 | lr 0.0010 | time_forward 1.2310 | time_backward 1.4640 |
[2023-09-02 13:04:15,704::train::INFO] [train] Iter 10180 | loss 0.6973 | loss(rot) 0.0289 | loss(pos) 0.6643 | loss(seq) 0.0041 | grad 2.9553 | lr 0.0010 | time_forward 3.7120 | time_backward 5.0570 |
[2023-09-02 13:04:21,647::train::INFO] [train] Iter 10181 | loss 0.9779 | loss(rot) 0.1196 | loss(pos) 0.8357 | loss(seq) 0.0226 | grad 6.8091 | lr 0.0010 | time_forward 2.5190 | time_backward 3.4200 |
[2023-09-02 13:04:30,996::train::INFO] [train] Iter 10182 | loss 0.9911 | loss(rot) 0.3342 | loss(pos) 0.1940 | loss(seq) 0.4629 | grad 3.2027 | lr 0.0010 | time_forward 3.8420 | time_backward 5.5040 |
[2023-09-02 13:04:34,412::train::INFO] [train] Iter 10183 | loss 1.6412 | loss(rot) 0.6538 | loss(pos) 0.7793 | loss(seq) 0.2081 | grad 4.0303 | lr 0.0010 | time_forward 1.4280 | time_backward 1.9840 |
[2023-09-02 13:04:43,270::train::INFO] [train] Iter 10184 | loss 2.5708 | loss(rot) 1.7162 | loss(pos) 0.3222 | loss(seq) 0.5325 | grad 6.4073 | lr 0.0010 | time_forward 3.6780 | time_backward 5.1770 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.