text stringlengths 56 1.16k |
|---|
[2023-10-23 01:34:32,374::train::INFO] [train] Iter 564498 | loss 1.1722 | loss(rot) 0.7550 | loss(pos) 0.1066 | loss(seq) 0.3106 | grad 4.6545 | lr 0.0000 | time_forward 3.4050 | time_backward 4.4740 |
[2023-10-23 01:34:39,310::train::INFO] [train] Iter 564499 | loss 0.1533 | loss(rot) 0.1097 | loss(pos) 0.0436 | loss(seq) 0.0000 | grad 2.5159 | lr 0.0000 | time_forward 3.0160 | time_backward 3.9160 |
[2023-10-23 01:34:42,345::train::INFO] [train] Iter 564500 | loss 0.7378 | loss(rot) 0.1245 | loss(pos) 0.2448 | loss(seq) 0.3685 | grad 3.7803 | lr 0.0000 | time_forward 1.4030 | time_backward 1.6300 |
[2023-10-23 01:34:47,886::train::INFO] [train] Iter 564501 | loss 0.4092 | loss(rot) 0.1261 | loss(pos) 0.0526 | loss(seq) 0.2306 | grad 3.7216 | lr 0.0000 | time_forward 2.4330 | time_backward 3.0950 |
[2023-10-23 01:34:55,801::train::INFO] [train] Iter 564502 | loss 0.3416 | loss(rot) 0.0536 | loss(pos) 0.1591 | loss(seq) 0.1289 | grad 3.5722 | lr 0.0000 | time_forward 3.2970 | time_backward 4.6150 |
[2023-10-23 01:35:01,723::train::INFO] [train] Iter 564503 | loss 0.2010 | loss(rot) 0.0156 | loss(pos) 0.1818 | loss(seq) 0.0037 | grad 6.2245 | lr 0.0000 | time_forward 2.5750 | time_backward 3.3440 |
[2023-10-23 01:35:08,260::train::INFO] [train] Iter 564504 | loss 0.9058 | loss(rot) 0.8564 | loss(pos) 0.0218 | loss(seq) 0.0276 | grad 8.0646 | lr 0.0000 | time_forward 2.8360 | time_backward 3.6980 |
[2023-10-23 01:35:16,254::train::INFO] [train] Iter 564505 | loss 0.6958 | loss(rot) 0.2939 | loss(pos) 0.0982 | loss(seq) 0.3036 | grad 2.5891 | lr 0.0000 | time_forward 3.2900 | time_backward 4.7010 |
[2023-10-23 01:35:18,895::train::INFO] [train] Iter 564506 | loss 0.4207 | loss(rot) 0.2049 | loss(pos) 0.0273 | loss(seq) 0.1886 | grad 1.8644 | lr 0.0000 | time_forward 1.2690 | time_backward 1.3690 |
[2023-10-23 01:35:25,987::train::INFO] [train] Iter 564507 | loss 0.3808 | loss(rot) 0.0988 | loss(pos) 0.0346 | loss(seq) 0.2474 | grad 2.2878 | lr 0.0000 | time_forward 3.0700 | time_backward 4.0180 |
[2023-10-23 01:35:33,877::train::INFO] [train] Iter 564508 | loss 1.0220 | loss(rot) 0.6617 | loss(pos) 0.0482 | loss(seq) 0.3120 | grad 3.5534 | lr 0.0000 | time_forward 3.2800 | time_backward 4.6060 |
[2023-10-23 01:35:36,593::train::INFO] [train] Iter 564509 | loss 0.4600 | loss(rot) 0.0596 | loss(pos) 0.1892 | loss(seq) 0.2113 | grad 3.3750 | lr 0.0000 | time_forward 1.2910 | time_backward 1.4220 |
[2023-10-23 01:35:44,492::train::INFO] [train] Iter 564510 | loss 0.9848 | loss(rot) 0.6785 | loss(pos) 0.0444 | loss(seq) 0.2619 | grad 8.6597 | lr 0.0000 | time_forward 3.2720 | time_backward 4.6060 |
[2023-10-23 01:35:52,520::train::INFO] [train] Iter 564511 | loss 0.5890 | loss(rot) 0.5352 | loss(pos) 0.0353 | loss(seq) 0.0186 | grad 2.3524 | lr 0.0000 | time_forward 3.4080 | time_backward 4.6160 |
[2023-10-23 01:35:55,254::train::INFO] [train] Iter 564512 | loss 0.1977 | loss(rot) 0.0405 | loss(pos) 0.0294 | loss(seq) 0.1278 | grad 2.0772 | lr 0.0000 | time_forward 1.2940 | time_backward 1.4360 |
[2023-10-23 01:36:01,493::train::INFO] [train] Iter 564513 | loss 0.1200 | loss(rot) 0.0926 | loss(pos) 0.0273 | loss(seq) 0.0000 | grad 1.5706 | lr 0.0000 | time_forward 2.7180 | time_backward 3.5180 |
[2023-10-23 01:36:08,371::train::INFO] [train] Iter 564514 | loss 0.2296 | loss(rot) 0.0697 | loss(pos) 0.1348 | loss(seq) 0.0251 | grad 2.4272 | lr 0.0000 | time_forward 2.8490 | time_backward 4.0260 |
[2023-10-23 01:36:15,457::train::INFO] [train] Iter 564515 | loss 0.6940 | loss(rot) 0.2983 | loss(pos) 0.0364 | loss(seq) 0.3593 | grad 3.8217 | lr 0.0000 | time_forward 2.9930 | time_backward 4.0890 |
[2023-10-23 01:36:22,275::train::INFO] [train] Iter 564516 | loss 0.4766 | loss(rot) 0.1204 | loss(pos) 0.0468 | loss(seq) 0.3095 | grad 3.6250 | lr 0.0000 | time_forward 2.9990 | time_backward 3.8150 |
[2023-10-23 01:36:29,044::train::INFO] [train] Iter 564517 | loss 1.3890 | loss(rot) 1.3652 | loss(pos) 0.0176 | loss(seq) 0.0062 | grad 5.9877 | lr 0.0000 | time_forward 2.8140 | time_backward 3.9520 |
[2023-10-23 01:36:31,720::train::INFO] [train] Iter 564518 | loss 1.3156 | loss(rot) 0.7923 | loss(pos) 0.0698 | loss(seq) 0.4535 | grad 3.5395 | lr 0.0000 | time_forward 1.2490 | time_backward 1.4240 |
[2023-10-23 01:36:34,902::train::INFO] [train] Iter 564519 | loss 0.8338 | loss(rot) 0.7993 | loss(pos) 0.0345 | loss(seq) 0.0000 | grad 2.7812 | lr 0.0000 | time_forward 1.4710 | time_backward 1.7070 |
[2023-10-23 01:36:40,899::train::INFO] [train] Iter 564520 | loss 0.9780 | loss(rot) 0.6014 | loss(pos) 0.3465 | loss(seq) 0.0301 | grad 7.6555 | lr 0.0000 | time_forward 2.6470 | time_backward 3.3470 |
[2023-10-23 01:36:43,577::train::INFO] [train] Iter 564521 | loss 0.9369 | loss(rot) 0.5630 | loss(pos) 0.0808 | loss(seq) 0.2931 | grad 4.6741 | lr 0.0000 | time_forward 1.2520 | time_backward 1.4140 |
[2023-10-23 01:36:51,399::train::INFO] [train] Iter 564522 | loss 1.2141 | loss(rot) 0.6374 | loss(pos) 0.1170 | loss(seq) 0.4597 | grad 4.2776 | lr 0.0000 | time_forward 3.2400 | time_backward 4.5790 |
[2023-10-23 01:36:54,083::train::INFO] [train] Iter 564523 | loss 0.2580 | loss(rot) 0.1898 | loss(pos) 0.0142 | loss(seq) 0.0539 | grad 2.1521 | lr 0.0000 | time_forward 1.2770 | time_backward 1.4040 |
[2023-10-23 01:37:01,335::train::INFO] [train] Iter 564524 | loss 0.4005 | loss(rot) 0.2431 | loss(pos) 0.0316 | loss(seq) 0.1258 | grad 3.0876 | lr 0.0000 | time_forward 3.1260 | time_backward 4.1220 |
[2023-10-23 01:37:07,057::train::INFO] [train] Iter 564525 | loss 0.2418 | loss(rot) 0.2085 | loss(pos) 0.0251 | loss(seq) 0.0082 | grad 2.7811 | lr 0.0000 | time_forward 2.5140 | time_backward 3.2050 |
[2023-10-23 01:37:14,187::train::INFO] [train] Iter 564526 | loss 0.8587 | loss(rot) 0.1309 | loss(pos) 0.2260 | loss(seq) 0.5018 | grad 4.3944 | lr 0.0000 | time_forward 3.1640 | time_backward 3.9630 |
[2023-10-23 01:37:16,386::train::INFO] [train] Iter 564527 | loss 0.4616 | loss(rot) 0.0907 | loss(pos) 0.0766 | loss(seq) 0.2942 | grad 4.1349 | lr 0.0000 | time_forward 1.0210 | time_backward 1.1760 |
[2023-10-23 01:37:24,336::train::INFO] [train] Iter 564528 | loss 2.0025 | loss(rot) 1.3770 | loss(pos) 0.1684 | loss(seq) 0.4571 | grad 27.2906 | lr 0.0000 | time_forward 3.3130 | time_backward 4.6250 |
[2023-10-23 01:37:26,530::train::INFO] [train] Iter 564529 | loss 0.1000 | loss(rot) 0.0710 | loss(pos) 0.0251 | loss(seq) 0.0039 | grad 1.6223 | lr 0.0000 | time_forward 1.0230 | time_backward 1.1680 |
[2023-10-23 01:37:34,426::train::INFO] [train] Iter 564530 | loss 1.1079 | loss(rot) 0.5667 | loss(pos) 0.0575 | loss(seq) 0.4838 | grad 28.7214 | lr 0.0000 | time_forward 3.2710 | time_backward 4.6220 |
[2023-10-23 01:37:41,853::train::INFO] [train] Iter 564531 | loss 0.6058 | loss(rot) 0.5700 | loss(pos) 0.0205 | loss(seq) 0.0152 | grad 12.0605 | lr 0.0000 | time_forward 3.1820 | time_backward 4.2420 |
[2023-10-23 01:37:49,202::train::INFO] [train] Iter 564532 | loss 0.1648 | loss(rot) 0.0462 | loss(pos) 0.1087 | loss(seq) 0.0099 | grad 4.2914 | lr 0.0000 | time_forward 3.2240 | time_backward 4.1210 |
[2023-10-23 01:37:55,690::train::INFO] [train] Iter 564533 | loss 1.0758 | loss(rot) 0.6650 | loss(pos) 0.2907 | loss(seq) 0.1201 | grad 6.4411 | lr 0.0000 | time_forward 2.8450 | time_backward 3.6410 |
[2023-10-23 01:38:03,587::train::INFO] [train] Iter 564534 | loss 0.7495 | loss(rot) 0.6376 | loss(pos) 0.0360 | loss(seq) 0.0758 | grad 3.3975 | lr 0.0000 | time_forward 3.2660 | time_backward 4.6270 |
[2023-10-23 01:38:11,378::train::INFO] [train] Iter 564535 | loss 0.5423 | loss(rot) 0.3283 | loss(pos) 0.0476 | loss(seq) 0.1664 | grad 3.3411 | lr 0.0000 | time_forward 3.2410 | time_backward 4.5470 |
[2023-10-23 01:38:19,265::train::INFO] [train] Iter 564536 | loss 0.7191 | loss(rot) 0.5905 | loss(pos) 0.1234 | loss(seq) 0.0052 | grad 4.4766 | lr 0.0000 | time_forward 3.4090 | time_backward 4.4740 |
[2023-10-23 01:38:25,781::train::INFO] [train] Iter 564537 | loss 1.1743 | loss(rot) 1.1426 | loss(pos) 0.0167 | loss(seq) 0.0150 | grad 25.2735 | lr 0.0000 | time_forward 2.8050 | time_backward 3.7080 |
[2023-10-23 01:38:33,753::train::INFO] [train] Iter 564538 | loss 0.4760 | loss(rot) 0.1694 | loss(pos) 0.0792 | loss(seq) 0.2273 | grad 3.1879 | lr 0.0000 | time_forward 3.3050 | time_backward 4.6630 |
[2023-10-23 01:38:41,689::train::INFO] [train] Iter 564539 | loss 1.5774 | loss(rot) 0.1282 | loss(pos) 1.4449 | loss(seq) 0.0043 | grad 8.5898 | lr 0.0000 | time_forward 3.2860 | time_backward 4.6470 |
[2023-10-23 01:38:48,409::train::INFO] [train] Iter 564540 | loss 0.5645 | loss(rot) 0.0840 | loss(pos) 0.0885 | loss(seq) 0.3919 | grad 4.9063 | lr 0.0000 | time_forward 3.0270 | time_backward 3.6900 |
[2023-10-23 01:38:55,013::train::INFO] [train] Iter 564541 | loss 0.3503 | loss(rot) 0.1248 | loss(pos) 0.1642 | loss(seq) 0.0613 | grad 3.7249 | lr 0.0000 | time_forward 2.8580 | time_backward 3.7430 |
[2023-10-23 01:39:02,930::train::INFO] [train] Iter 564542 | loss 2.4745 | loss(rot) 2.0405 | loss(pos) 0.1787 | loss(seq) 0.2554 | grad 5.1867 | lr 0.0000 | time_forward 3.2360 | time_backward 4.6780 |
[2023-10-23 01:39:10,714::train::INFO] [train] Iter 564543 | loss 1.0590 | loss(rot) 1.0157 | loss(pos) 0.0255 | loss(seq) 0.0178 | grad 17.1839 | lr 0.0000 | time_forward 3.2520 | time_backward 4.5280 |
[2023-10-23 01:39:17,079::train::INFO] [train] Iter 564544 | loss 0.4935 | loss(rot) 0.4255 | loss(pos) 0.0672 | loss(seq) 0.0008 | grad 3.0174 | lr 0.0000 | time_forward 2.7100 | time_backward 3.6510 |
[2023-10-23 01:39:20,988::train::INFO] [train] Iter 564545 | loss 1.3854 | loss(rot) 0.9581 | loss(pos) 0.0467 | loss(seq) 0.3806 | grad 43.1659 | lr 0.0000 | time_forward 1.7700 | time_backward 2.1370 |
[2023-10-23 01:39:27,335::train::INFO] [train] Iter 564546 | loss 1.2282 | loss(rot) 1.1900 | loss(pos) 0.0379 | loss(seq) 0.0003 | grad 4.7739 | lr 0.0000 | time_forward 2.6790 | time_backward 3.6640 |
[2023-10-23 01:39:34,221::train::INFO] [train] Iter 564547 | loss 1.8437 | loss(rot) 1.7896 | loss(pos) 0.0535 | loss(seq) 0.0007 | grad 5.9167 | lr 0.0000 | time_forward 2.9350 | time_backward 3.9120 |
[2023-10-23 01:39:42,261::train::INFO] [train] Iter 564548 | loss 0.5599 | loss(rot) 0.2869 | loss(pos) 0.0363 | loss(seq) 0.2367 | grad 2.6287 | lr 0.0000 | time_forward 3.3940 | time_backward 4.6420 |
[2023-10-23 01:39:44,501::train::INFO] [train] Iter 564549 | loss 0.2730 | loss(rot) 0.1060 | loss(pos) 0.0256 | loss(seq) 0.1414 | grad 1.9313 | lr 0.0000 | time_forward 1.0090 | time_backward 1.2290 |
[2023-10-23 01:39:51,894::train::INFO] [train] Iter 564550 | loss 0.1105 | loss(rot) 0.0783 | loss(pos) 0.0169 | loss(seq) 0.0153 | grad 2.0934 | lr 0.0000 | time_forward 3.2010 | time_backward 4.1880 |
[2023-10-23 01:39:59,792::train::INFO] [train] Iter 564551 | loss 0.6621 | loss(rot) 0.5387 | loss(pos) 0.0364 | loss(seq) 0.0870 | grad 2.5022 | lr 0.0000 | time_forward 3.2200 | time_backward 4.6750 |
[2023-10-23 01:40:07,754::train::INFO] [train] Iter 564552 | loss 0.4955 | loss(rot) 0.2894 | loss(pos) 0.1104 | loss(seq) 0.0957 | grad 5.0213 | lr 0.0000 | time_forward 3.2790 | time_backward 4.6810 |
[2023-10-23 01:40:14,144::train::INFO] [train] Iter 564553 | loss 1.1581 | loss(rot) 0.7360 | loss(pos) 0.1484 | loss(seq) 0.2737 | grad 5.1910 | lr 0.0000 | time_forward 2.7680 | time_backward 3.6190 |
[2023-10-23 01:40:16,805::train::INFO] [train] Iter 564554 | loss 1.8876 | loss(rot) 1.3029 | loss(pos) 0.0717 | loss(seq) 0.5130 | grad 5.2285 | lr 0.0000 | time_forward 1.2590 | time_backward 1.3980 |
[2023-10-23 01:40:24,733::train::INFO] [train] Iter 564555 | loss 0.3635 | loss(rot) 0.3259 | loss(pos) 0.0185 | loss(seq) 0.0192 | grad 2.5815 | lr 0.0000 | time_forward 3.2750 | time_backward 4.6500 |
[2023-10-23 01:40:30,964::train::INFO] [train] Iter 564556 | loss 0.4789 | loss(rot) 0.0795 | loss(pos) 0.0627 | loss(seq) 0.3367 | grad 3.0905 | lr 0.0000 | time_forward 2.8050 | time_backward 3.4230 |
[2023-10-23 01:40:38,821::train::INFO] [train] Iter 564557 | loss 2.1397 | loss(rot) 2.0337 | loss(pos) 0.0852 | loss(seq) 0.0208 | grad 10.0088 | lr 0.0000 | time_forward 3.2690 | time_backward 4.5850 |
[2023-10-23 01:40:40,861::train::INFO] [train] Iter 564558 | loss 1.2007 | loss(rot) 0.9249 | loss(pos) 0.0905 | loss(seq) 0.1852 | grad 4.8822 | lr 0.0000 | time_forward 0.9420 | time_backward 1.0960 |
[2023-10-23 01:40:48,649::train::INFO] [train] Iter 564559 | loss 0.2322 | loss(rot) 0.1581 | loss(pos) 0.0231 | loss(seq) 0.0510 | grad 1.5949 | lr 0.0000 | time_forward 3.2480 | time_backward 4.5360 |
[2023-10-23 01:40:55,608::train::INFO] [train] Iter 564560 | loss 0.5009 | loss(rot) 0.1238 | loss(pos) 0.0322 | loss(seq) 0.3448 | grad 2.9488 | lr 0.0000 | time_forward 2.9880 | time_backward 3.9670 |
[2023-10-23 01:41:02,149::train::INFO] [train] Iter 564561 | loss 0.7536 | loss(rot) 0.0304 | loss(pos) 0.7096 | loss(seq) 0.0136 | grad 8.9208 | lr 0.0000 | time_forward 2.8420 | time_backward 3.6960 |
[2023-10-23 01:41:08,857::train::INFO] [train] Iter 564562 | loss 0.2687 | loss(rot) 0.0448 | loss(pos) 0.2071 | loss(seq) 0.0168 | grad 3.3259 | lr 0.0000 | time_forward 2.9270 | time_backward 3.7790 |
[2023-10-23 01:41:11,985::train::INFO] [train] Iter 564563 | loss 0.6128 | loss(rot) 0.0378 | loss(pos) 0.5728 | loss(seq) 0.0022 | grad 7.1587 | lr 0.0000 | time_forward 1.3970 | time_backward 1.7270 |
[2023-10-23 01:41:20,217::train::INFO] [train] Iter 564564 | loss 0.4622 | loss(rot) 0.2529 | loss(pos) 0.0818 | loss(seq) 0.1276 | grad 2.9140 | lr 0.0000 | time_forward 3.3240 | time_backward 4.9050 |
[2023-10-23 01:41:22,429::train::INFO] [train] Iter 564565 | loss 0.1746 | loss(rot) 0.1405 | loss(pos) 0.0315 | loss(seq) 0.0026 | grad 3.7432 | lr 0.0000 | time_forward 1.0180 | time_backward 1.1800 |
[2023-10-23 01:41:29,257::train::INFO] [train] Iter 564566 | loss 0.5529 | loss(rot) 0.1232 | loss(pos) 0.0495 | loss(seq) 0.3802 | grad 3.6317 | lr 0.0000 | time_forward 2.9090 | time_backward 3.9150 |
[2023-10-23 01:41:31,904::train::INFO] [train] Iter 564567 | loss 0.4350 | loss(rot) 0.3926 | loss(pos) 0.0345 | loss(seq) 0.0079 | grad 3.8770 | lr 0.0000 | time_forward 1.2270 | time_backward 1.4170 |
[2023-10-23 01:41:40,077::train::INFO] [train] Iter 564568 | loss 0.7989 | loss(rot) 0.4846 | loss(pos) 0.1407 | loss(seq) 0.1736 | grad 3.8969 | lr 0.0000 | time_forward 3.3990 | time_backward 4.7710 |
[2023-10-23 01:41:48,230::train::INFO] [train] Iter 564569 | loss 0.2936 | loss(rot) 0.1434 | loss(pos) 0.1341 | loss(seq) 0.0161 | grad 3.0276 | lr 0.0000 | time_forward 3.3720 | time_backward 4.7770 |
[2023-10-23 01:41:55,111::train::INFO] [train] Iter 564570 | loss 0.7139 | loss(rot) 0.0124 | loss(pos) 0.7001 | loss(seq) 0.0014 | grad 5.8179 | lr 0.0000 | time_forward 2.9750 | time_backward 3.9030 |
[2023-10-23 01:42:01,903::train::INFO] [train] Iter 564571 | loss 1.7038 | loss(rot) 1.3074 | loss(pos) 0.1411 | loss(seq) 0.2553 | grad 4.7834 | lr 0.0000 | time_forward 2.9080 | time_backward 3.8810 |
[2023-10-23 01:42:08,856::train::INFO] [train] Iter 564572 | loss 0.2867 | loss(rot) 0.1498 | loss(pos) 0.0293 | loss(seq) 0.1076 | grad 2.5460 | lr 0.0000 | time_forward 3.0500 | time_backward 3.9010 |
[2023-10-23 01:42:16,064::train::INFO] [train] Iter 564573 | loss 0.1966 | loss(rot) 0.1397 | loss(pos) 0.0299 | loss(seq) 0.0269 | grad 1.8783 | lr 0.0000 | time_forward 3.1370 | time_backward 4.0680 |
[2023-10-23 01:42:18,780::train::INFO] [train] Iter 564574 | loss 0.9524 | loss(rot) 0.8295 | loss(pos) 0.1214 | loss(seq) 0.0014 | grad 13.6617 | lr 0.0000 | time_forward 1.2100 | time_backward 1.5020 |
[2023-10-23 01:42:21,562::train::INFO] [train] Iter 564575 | loss 0.1858 | loss(rot) 0.0461 | loss(pos) 0.1252 | loss(seq) 0.0146 | grad 3.0859 | lr 0.0000 | time_forward 1.2980 | time_backward 1.4770 |
[2023-10-23 01:42:24,334::train::INFO] [train] Iter 564576 | loss 0.2677 | loss(rot) 0.1062 | loss(pos) 0.0234 | loss(seq) 0.1381 | grad 2.8783 | lr 0.0000 | time_forward 1.2920 | time_backward 1.4780 |
[2023-10-23 01:42:29,225::train::INFO] [train] Iter 564577 | loss 0.2888 | loss(rot) 0.0913 | loss(pos) 0.0205 | loss(seq) 0.1769 | grad 1.8885 | lr 0.0000 | time_forward 2.0970 | time_backward 2.7770 |
[2023-10-23 01:42:35,577::train::INFO] [train] Iter 564578 | loss 0.4921 | loss(rot) 0.0898 | loss(pos) 0.3900 | loss(seq) 0.0123 | grad 8.0823 | lr 0.0000 | time_forward 2.7290 | time_backward 3.6190 |
[2023-10-23 01:42:43,532::train::INFO] [train] Iter 564579 | loss 0.8717 | loss(rot) 0.5360 | loss(pos) 0.0273 | loss(seq) 0.3084 | grad 3.8921 | lr 0.0000 | time_forward 3.2290 | time_backward 4.7230 |
[2023-10-23 01:42:46,255::train::INFO] [train] Iter 564580 | loss 0.3152 | loss(rot) 0.0986 | loss(pos) 0.0378 | loss(seq) 0.1788 | grad 2.1819 | lr 0.0000 | time_forward 1.2900 | time_backward 1.4300 |
[2023-10-23 01:42:52,969::train::INFO] [train] Iter 564581 | loss 0.2787 | loss(rot) 0.0652 | loss(pos) 0.0176 | loss(seq) 0.1959 | grad 2.0417 | lr 0.0000 | time_forward 2.9070 | time_backward 3.7840 |
[2023-10-23 01:42:59,701::train::INFO] [train] Iter 564582 | loss 0.3251 | loss(rot) 0.0636 | loss(pos) 0.0156 | loss(seq) 0.2459 | grad 1.8163 | lr 0.0000 | time_forward 2.9320 | time_backward 3.7970 |
[2023-10-23 01:43:06,195::train::INFO] [train] Iter 564583 | loss 1.5434 | loss(rot) 1.3677 | loss(pos) 0.0524 | loss(seq) 0.1233 | grad 3.9610 | lr 0.0000 | time_forward 2.8030 | time_backward 3.6870 |
[2023-10-23 01:43:14,028::train::INFO] [train] Iter 564584 | loss 0.7227 | loss(rot) 0.5970 | loss(pos) 0.0551 | loss(seq) 0.0707 | grad 4.1501 | lr 0.0000 | time_forward 3.4190 | time_backward 4.4110 |
[2023-10-23 01:43:21,817::train::INFO] [train] Iter 564585 | loss 2.0759 | loss(rot) 1.4612 | loss(pos) 0.2119 | loss(seq) 0.4028 | grad 4.0493 | lr 0.0000 | time_forward 3.4140 | time_backward 4.3720 |
[2023-10-23 01:43:29,005::train::INFO] [train] Iter 564586 | loss 1.6606 | loss(rot) 1.1118 | loss(pos) 0.0920 | loss(seq) 0.4569 | grad 15.6330 | lr 0.0000 | time_forward 3.1190 | time_backward 4.0660 |
[2023-10-23 01:43:35,327::train::INFO] [train] Iter 564587 | loss 0.2957 | loss(rot) 0.1153 | loss(pos) 0.0426 | loss(seq) 0.1377 | grad 2.5749 | lr 0.0000 | time_forward 2.6480 | time_backward 3.6700 |
[2023-10-23 01:43:38,092::train::INFO] [train] Iter 564588 | loss 0.5051 | loss(rot) 0.0744 | loss(pos) 0.2292 | loss(seq) 0.2015 | grad 3.7635 | lr 0.0000 | time_forward 1.3010 | time_backward 1.4610 |
[2023-10-23 01:43:44,951::train::INFO] [train] Iter 564589 | loss 0.2805 | loss(rot) 0.0662 | loss(pos) 0.1671 | loss(seq) 0.0472 | grad 6.0255 | lr 0.0000 | time_forward 2.9190 | time_backward 3.9360 |
[2023-10-23 01:43:47,596::train::INFO] [train] Iter 564590 | loss 2.5292 | loss(rot) 2.2713 | loss(pos) 0.0717 | loss(seq) 0.1862 | grad 5.5566 | lr 0.0000 | time_forward 1.2650 | time_backward 1.3770 |
[2023-10-23 01:43:55,931::train::INFO] [train] Iter 564591 | loss 0.4662 | loss(rot) 0.1420 | loss(pos) 0.1883 | loss(seq) 0.1359 | grad 3.3186 | lr 0.0000 | time_forward 3.2800 | time_backward 5.0510 |
[2023-10-23 01:43:59,068::train::INFO] [train] Iter 564592 | loss 1.2514 | loss(rot) 1.0657 | loss(pos) 0.0715 | loss(seq) 0.1143 | grad 3.8187 | lr 0.0000 | time_forward 1.4220 | time_backward 1.7130 |
[2023-10-23 01:44:01,681::train::INFO] [train] Iter 564593 | loss 0.2072 | loss(rot) 0.0788 | loss(pos) 0.0276 | loss(seq) 0.1008 | grad 2.1277 | lr 0.0000 | time_forward 1.2420 | time_backward 1.3670 |
[2023-10-23 01:44:04,132::train::INFO] [train] Iter 564594 | loss 3.0248 | loss(rot) 0.0023 | loss(pos) 3.0225 | loss(seq) 0.0000 | grad 20.7505 | lr 0.0000 | time_forward 1.2020 | time_backward 1.2470 |
[2023-10-23 01:44:06,845::train::INFO] [train] Iter 564595 | loss 0.6876 | loss(rot) 0.4730 | loss(pos) 0.0726 | loss(seq) 0.1420 | grad 3.1525 | lr 0.0000 | time_forward 1.2970 | time_backward 1.4130 |
[2023-10-23 01:44:09,522::train::INFO] [train] Iter 564596 | loss 0.1206 | loss(rot) 0.0409 | loss(pos) 0.0203 | loss(seq) 0.0593 | grad 1.8733 | lr 0.0000 | time_forward 1.2660 | time_backward 1.3910 |
[2023-10-23 01:44:16,388::train::INFO] [train] Iter 564597 | loss 0.8849 | loss(rot) 0.5548 | loss(pos) 0.0709 | loss(seq) 0.2593 | grad 2.9515 | lr 0.0000 | time_forward 2.8470 | time_backward 4.0160 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.