text stringlengths 56 1.16k |
|---|
[2023-09-03 04:51:39,871::train::INFO] [train] Iter 18177 | loss 1.4914 | loss(rot) 0.7601 | loss(pos) 0.3116 | loss(seq) 0.4198 | grad 6.1263 | lr 0.0010 | time_forward 1.2000 | time_backward 1.4450 |
[2023-09-03 04:51:48,317::train::INFO] [train] Iter 18178 | loss 0.7234 | loss(rot) 0.1516 | loss(pos) 0.5058 | loss(seq) 0.0660 | grad 3.5223 | lr 0.0010 | time_forward 3.4470 | time_backward 4.9950 |
[2023-09-03 04:51:56,478::train::INFO] [train] Iter 18179 | loss 1.0441 | loss(rot) 0.4291 | loss(pos) 0.1817 | loss(seq) 0.4332 | grad 5.0065 | lr 0.0010 | time_forward 3.4140 | time_backward 4.7430 |
[2023-09-03 04:52:03,580::train::INFO] [train] Iter 18180 | loss 2.3498 | loss(rot) 2.1135 | loss(pos) 0.1761 | loss(seq) 0.0602 | grad 3.6536 | lr 0.0010 | time_forward 3.0460 | time_backward 4.0520 |
[2023-09-03 04:52:10,970::train::INFO] [train] Iter 18181 | loss 2.0910 | loss(rot) 1.7337 | loss(pos) 0.1521 | loss(seq) 0.2053 | grad 7.7858 | lr 0.0010 | time_forward 3.0470 | time_backward 4.3400 |
[2023-09-03 04:52:17,543::train::INFO] [train] Iter 18182 | loss 0.6117 | loss(rot) 0.0812 | loss(pos) 0.5186 | loss(seq) 0.0119 | grad 4.8590 | lr 0.0010 | time_forward 2.7380 | time_backward 3.8320 |
[2023-09-03 04:52:20,130::train::INFO] [train] Iter 18183 | loss 0.9384 | loss(rot) 0.3405 | loss(pos) 0.2635 | loss(seq) 0.3344 | grad 4.2142 | lr 0.0010 | time_forward 1.2390 | time_backward 1.3440 |
[2023-09-03 04:52:26,986::train::INFO] [train] Iter 18184 | loss 1.7038 | loss(rot) 0.0157 | loss(pos) 1.6875 | loss(seq) 0.0005 | grad 6.0042 | lr 0.0010 | time_forward 2.9870 | time_backward 3.8670 |
[2023-09-03 04:52:35,210::train::INFO] [train] Iter 18185 | loss 0.6949 | loss(rot) 0.1549 | loss(pos) 0.3725 | loss(seq) 0.1675 | grad 2.6740 | lr 0.0010 | time_forward 3.3700 | time_backward 4.8510 |
[2023-09-03 04:52:41,918::train::INFO] [train] Iter 18186 | loss 1.6066 | loss(rot) 1.1942 | loss(pos) 0.1037 | loss(seq) 0.3086 | grad 8.2722 | lr 0.0010 | time_forward 2.8050 | time_backward 3.8990 |
[2023-09-03 04:52:44,442::train::INFO] [train] Iter 18187 | loss 0.8104 | loss(rot) 0.1919 | loss(pos) 0.3424 | loss(seq) 0.2761 | grad 3.9331 | lr 0.0010 | time_forward 1.1520 | time_backward 1.3690 |
[2023-09-03 04:52:47,014::train::INFO] [train] Iter 18188 | loss 1.3511 | loss(rot) 0.0257 | loss(pos) 1.3222 | loss(seq) 0.0032 | grad 5.9940 | lr 0.0010 | time_forward 1.1940 | time_backward 1.3750 |
[2023-09-03 04:52:54,767::train::INFO] [train] Iter 18189 | loss 1.4557 | loss(rot) 0.2249 | loss(pos) 1.2201 | loss(seq) 0.0108 | grad 4.1598 | lr 0.0010 | time_forward 3.3130 | time_backward 4.4360 |
[2023-09-03 04:53:01,801::train::INFO] [train] Iter 18190 | loss 2.8057 | loss(rot) 1.9866 | loss(pos) 0.3206 | loss(seq) 0.4985 | grad 7.0004 | lr 0.0010 | time_forward 2.8980 | time_backward 4.1320 |
[2023-09-03 04:53:09,018::train::INFO] [train] Iter 18191 | loss 1.5908 | loss(rot) 0.6941 | loss(pos) 0.3228 | loss(seq) 0.5738 | grad 5.5264 | lr 0.0010 | time_forward 3.0280 | time_backward 4.1860 |
[2023-09-03 04:53:15,835::train::INFO] [train] Iter 18192 | loss 1.4506 | loss(rot) 1.3530 | loss(pos) 0.0958 | loss(seq) 0.0018 | grad 7.6945 | lr 0.0010 | time_forward 2.9160 | time_backward 3.8990 |
[2023-09-03 04:53:22,705::train::INFO] [train] Iter 18193 | loss 0.9630 | loss(rot) 0.6116 | loss(pos) 0.2480 | loss(seq) 0.1035 | grad 6.5322 | lr 0.0010 | time_forward 2.9170 | time_backward 3.9490 |
[2023-09-03 04:53:31,028::train::INFO] [train] Iter 18194 | loss 2.1473 | loss(rot) 0.1982 | loss(pos) 1.9376 | loss(seq) 0.0114 | grad 7.0076 | lr 0.0010 | time_forward 3.4320 | time_backward 4.8870 |
[2023-09-03 04:53:35,082::train::INFO] [train] Iter 18195 | loss 1.2395 | loss(rot) 0.0232 | loss(pos) 1.2086 | loss(seq) 0.0078 | grad 8.5314 | lr 0.0010 | time_forward 1.8070 | time_backward 2.2450 |
[2023-09-03 04:53:37,638::train::INFO] [train] Iter 18196 | loss 0.5973 | loss(rot) 0.3463 | loss(pos) 0.2007 | loss(seq) 0.0504 | grad 5.0991 | lr 0.0010 | time_forward 1.1740 | time_backward 1.3480 |
[2023-09-03 04:53:45,860::train::INFO] [train] Iter 18197 | loss 1.1727 | loss(rot) 0.6105 | loss(pos) 0.1571 | loss(seq) 0.4052 | grad 4.3099 | lr 0.0010 | time_forward 3.3400 | time_backward 4.8790 |
[2023-09-03 04:53:53,760::train::INFO] [train] Iter 18198 | loss 1.5980 | loss(rot) 1.4993 | loss(pos) 0.0842 | loss(seq) 0.0145 | grad 10.4291 | lr 0.0010 | time_forward 3.3860 | time_backward 4.5100 |
[2023-09-03 04:54:02,341::train::INFO] [train] Iter 18199 | loss 0.7994 | loss(rot) 0.2707 | loss(pos) 0.3875 | loss(seq) 0.1412 | grad 3.6902 | lr 0.0010 | time_forward 3.4930 | time_backward 5.0840 |
[2023-09-03 04:54:09,722::train::INFO] [train] Iter 18200 | loss 1.4946 | loss(rot) 0.6843 | loss(pos) 0.2614 | loss(seq) 0.5489 | grad 4.5108 | lr 0.0010 | time_forward 3.1640 | time_backward 4.2130 |
[2023-09-03 04:54:17,310::train::INFO] [train] Iter 18201 | loss 1.6672 | loss(rot) 1.3042 | loss(pos) 0.1264 | loss(seq) 0.2366 | grad 3.9702 | lr 0.0010 | time_forward 3.2960 | time_backward 4.2890 |
[2023-09-03 04:54:24,778::train::INFO] [train] Iter 18202 | loss 1.0390 | loss(rot) 0.8232 | loss(pos) 0.0909 | loss(seq) 0.1248 | grad 6.2490 | lr 0.0010 | time_forward 3.1580 | time_backward 4.3060 |
[2023-09-03 04:54:33,331::train::INFO] [train] Iter 18203 | loss 1.5199 | loss(rot) 0.4499 | loss(pos) 0.5862 | loss(seq) 0.4838 | grad 5.4757 | lr 0.0010 | time_forward 3.5630 | time_backward 4.9860 |
[2023-09-03 04:54:41,666::train::INFO] [train] Iter 18204 | loss 0.9543 | loss(rot) 0.0786 | loss(pos) 0.8663 | loss(seq) 0.0094 | grad 6.1307 | lr 0.0010 | time_forward 3.5340 | time_backward 4.7970 |
[2023-09-03 04:54:48,188::train::INFO] [train] Iter 18205 | loss 1.4668 | loss(rot) 0.9656 | loss(pos) 0.1386 | loss(seq) 0.3626 | grad 5.4709 | lr 0.0010 | time_forward 2.7300 | time_backward 3.7880 |
[2023-09-03 04:54:55,404::train::INFO] [train] Iter 18206 | loss 0.6478 | loss(rot) 0.4418 | loss(pos) 0.2030 | loss(seq) 0.0031 | grad 4.0167 | lr 0.0010 | time_forward 2.9770 | time_backward 4.2350 |
[2023-09-03 04:55:02,809::train::INFO] [train] Iter 18207 | loss 1.1425 | loss(rot) 0.6867 | loss(pos) 0.2130 | loss(seq) 0.2429 | grad 3.8853 | lr 0.0010 | time_forward 3.0740 | time_backward 4.3280 |
[2023-09-03 04:55:05,430::train::INFO] [train] Iter 18208 | loss 1.1484 | loss(rot) 1.0048 | loss(pos) 0.1047 | loss(seq) 0.0389 | grad 5.8768 | lr 0.0010 | time_forward 1.2390 | time_backward 1.3780 |
[2023-09-03 04:55:08,037::train::INFO] [train] Iter 18209 | loss 1.9521 | loss(rot) 1.5523 | loss(pos) 0.1689 | loss(seq) 0.2309 | grad 4.8311 | lr 0.0010 | time_forward 1.2180 | time_backward 1.3870 |
[2023-09-03 04:55:10,730::train::INFO] [train] Iter 18210 | loss 0.6077 | loss(rot) 0.2231 | loss(pos) 0.3272 | loss(seq) 0.0575 | grad 3.4293 | lr 0.0010 | time_forward 1.2610 | time_backward 1.4280 |
[2023-09-03 04:55:18,029::train::INFO] [train] Iter 18211 | loss 1.7214 | loss(rot) 0.9347 | loss(pos) 0.1979 | loss(seq) 0.5889 | grad 5.1791 | lr 0.0010 | time_forward 3.1390 | time_backward 4.1570 |
[2023-09-03 04:55:26,070::train::INFO] [train] Iter 18212 | loss 1.1535 | loss(rot) 1.0057 | loss(pos) 0.1471 | loss(seq) 0.0006 | grad 3.6659 | lr 0.0010 | time_forward 3.5010 | time_backward 4.5360 |
[2023-09-03 04:55:34,759::train::INFO] [train] Iter 18213 | loss 0.9197 | loss(rot) 0.4462 | loss(pos) 0.2290 | loss(seq) 0.2445 | grad 3.3541 | lr 0.0010 | time_forward 3.5970 | time_backward 5.0880 |
[2023-09-03 04:55:37,292::train::INFO] [train] Iter 18214 | loss 1.2090 | loss(rot) 0.3640 | loss(pos) 0.4263 | loss(seq) 0.4187 | grad 6.5867 | lr 0.0010 | time_forward 1.1660 | time_backward 1.3630 |
[2023-09-03 04:55:45,854::train::INFO] [train] Iter 18215 | loss 0.9568 | loss(rot) 0.8283 | loss(pos) 0.1120 | loss(seq) 0.0165 | grad 6.6812 | lr 0.0010 | time_forward 3.5160 | time_backward 5.0420 |
[2023-09-03 04:55:54,418::train::INFO] [train] Iter 18216 | loss 0.7690 | loss(rot) 0.1180 | loss(pos) 0.6388 | loss(seq) 0.0122 | grad 3.5455 | lr 0.0010 | time_forward 3.5510 | time_backward 5.0110 |
[2023-09-03 04:56:03,173::train::INFO] [train] Iter 18217 | loss 1.5467 | loss(rot) 0.8678 | loss(pos) 0.1763 | loss(seq) 0.5026 | grad 4.0657 | lr 0.0010 | time_forward 3.6170 | time_backward 5.1230 |
[2023-09-03 04:56:10,094::train::INFO] [train] Iter 18218 | loss 1.4942 | loss(rot) 1.3498 | loss(pos) 0.1382 | loss(seq) 0.0062 | grad 6.1529 | lr 0.0010 | time_forward 2.8540 | time_backward 4.0630 |
[2023-09-03 04:56:17,868::train::INFO] [train] Iter 18219 | loss 0.5449 | loss(rot) 0.1302 | loss(pos) 0.3815 | loss(seq) 0.0332 | grad 3.5716 | lr 0.0010 | time_forward 3.3490 | time_backward 4.4220 |
[2023-09-03 04:56:26,228::train::INFO] [train] Iter 18220 | loss 1.5379 | loss(rot) 1.4545 | loss(pos) 0.0708 | loss(seq) 0.0127 | grad 3.2184 | lr 0.0010 | time_forward 3.5020 | time_backward 4.8540 |
[2023-09-03 04:56:34,831::train::INFO] [train] Iter 18221 | loss 1.0880 | loss(rot) 0.3264 | loss(pos) 0.5543 | loss(seq) 0.2073 | grad 3.5566 | lr 0.0010 | time_forward 3.4460 | time_backward 5.1540 |
[2023-09-03 04:56:42,074::train::INFO] [train] Iter 18222 | loss 1.0715 | loss(rot) 0.9649 | loss(pos) 0.0704 | loss(seq) 0.0362 | grad 26.7716 | lr 0.0010 | time_forward 3.1360 | time_backward 4.1030 |
[2023-09-03 04:56:44,635::train::INFO] [train] Iter 18223 | loss 1.4334 | loss(rot) 0.0396 | loss(pos) 1.3900 | loss(seq) 0.0038 | grad 8.0186 | lr 0.0010 | time_forward 1.2030 | time_backward 1.3540 |
[2023-09-03 04:56:51,880::train::INFO] [train] Iter 18224 | loss 1.2052 | loss(rot) 0.5302 | loss(pos) 0.1697 | loss(seq) 0.5053 | grad 4.9611 | lr 0.0010 | time_forward 2.9560 | time_backward 4.2850 |
[2023-09-03 04:56:54,419::train::INFO] [train] Iter 18225 | loss 2.5007 | loss(rot) 2.0624 | loss(pos) 0.2786 | loss(seq) 0.1597 | grad 4.9430 | lr 0.0010 | time_forward 1.1780 | time_backward 1.3570 |
[2023-09-03 04:57:02,570::train::INFO] [train] Iter 18226 | loss 1.7420 | loss(rot) 1.5788 | loss(pos) 0.0751 | loss(seq) 0.0881 | grad 7.2770 | lr 0.0010 | time_forward 3.3190 | time_backward 4.8290 |
[2023-09-03 04:57:10,796::train::INFO] [train] Iter 18227 | loss 0.8701 | loss(rot) 0.7371 | loss(pos) 0.1329 | loss(seq) 0.0000 | grad 7.7681 | lr 0.0010 | time_forward 3.3640 | time_backward 4.8530 |
[2023-09-03 04:57:19,247::train::INFO] [train] Iter 18228 | loss 1.3914 | loss(rot) 1.0726 | loss(pos) 0.1119 | loss(seq) 0.2069 | grad 4.9174 | lr 0.0010 | time_forward 3.3980 | time_backward 5.0380 |
[2023-09-03 04:57:26,122::train::INFO] [train] Iter 18229 | loss 1.3879 | loss(rot) 0.6933 | loss(pos) 0.6363 | loss(seq) 0.0584 | grad 4.7494 | lr 0.0010 | time_forward 2.8510 | time_backward 4.0210 |
[2023-09-03 04:57:28,660::train::INFO] [train] Iter 18230 | loss 1.5424 | loss(rot) 1.4505 | loss(pos) 0.0820 | loss(seq) 0.0099 | grad 5.3666 | lr 0.0010 | time_forward 1.1700 | time_backward 1.3650 |
[2023-09-03 04:57:36,850::train::INFO] [train] Iter 18231 | loss 2.1687 | loss(rot) 1.9719 | loss(pos) 0.0840 | loss(seq) 0.1127 | grad 3.1648 | lr 0.0010 | time_forward 3.4250 | time_backward 4.7620 |
[2023-09-03 04:57:39,398::train::INFO] [train] Iter 18232 | loss 1.1898 | loss(rot) 0.5867 | loss(pos) 0.2797 | loss(seq) 0.3234 | grad 3.8020 | lr 0.0010 | time_forward 1.1930 | time_backward 1.3510 |
[2023-09-03 04:57:46,576::train::INFO] [train] Iter 18233 | loss 1.1431 | loss(rot) 0.9489 | loss(pos) 0.1019 | loss(seq) 0.0923 | grad 4.8320 | lr 0.0010 | time_forward 3.0540 | time_backward 4.1210 |
[2023-09-03 04:57:49,127::train::INFO] [train] Iter 18234 | loss 0.8689 | loss(rot) 0.3935 | loss(pos) 0.2386 | loss(seq) 0.2368 | grad 3.7811 | lr 0.0010 | time_forward 1.1890 | time_backward 1.3590 |
[2023-09-03 04:57:56,374::train::INFO] [train] Iter 18235 | loss 1.0724 | loss(rot) 0.7571 | loss(pos) 0.2099 | loss(seq) 0.1054 | grad 5.3213 | lr 0.0010 | time_forward 3.0450 | time_backward 4.1990 |
[2023-09-03 04:57:58,970::train::INFO] [train] Iter 18236 | loss 2.1832 | loss(rot) 1.7916 | loss(pos) 0.1147 | loss(seq) 0.2768 | grad 3.0931 | lr 0.0010 | time_forward 1.2110 | time_backward 1.3820 |
[2023-09-03 04:58:01,587::train::INFO] [train] Iter 18237 | loss 1.3269 | loss(rot) 0.1323 | loss(pos) 1.1761 | loss(seq) 0.0185 | grad 5.0308 | lr 0.0010 | time_forward 1.2190 | time_backward 1.3950 |
[2023-09-03 04:58:04,139::train::INFO] [train] Iter 18238 | loss 1.3929 | loss(rot) 0.9309 | loss(pos) 0.1839 | loss(seq) 0.2782 | grad 6.3384 | lr 0.0010 | time_forward 1.2040 | time_backward 1.3440 |
[2023-09-03 04:58:11,732::train::INFO] [train] Iter 18239 | loss 1.1056 | loss(rot) 0.1015 | loss(pos) 1.0002 | loss(seq) 0.0039 | grad 4.5329 | lr 0.0010 | time_forward 3.2170 | time_backward 4.3730 |
[2023-09-03 04:58:19,559::train::INFO] [train] Iter 18240 | loss 1.1144 | loss(rot) 1.0152 | loss(pos) 0.0980 | loss(seq) 0.0013 | grad 8.3011 | lr 0.0010 | time_forward 3.1920 | time_backward 4.6320 |
[2023-09-03 04:58:22,180::train::INFO] [train] Iter 18241 | loss 1.8419 | loss(rot) 0.7553 | loss(pos) 0.5246 | loss(seq) 0.5620 | grad 3.5694 | lr 0.0010 | time_forward 1.2100 | time_backward 1.4080 |
[2023-09-03 04:58:29,444::train::INFO] [train] Iter 18242 | loss 2.4292 | loss(rot) 2.3085 | loss(pos) 0.1084 | loss(seq) 0.0123 | grad 3.1991 | lr 0.0010 | time_forward 2.8830 | time_backward 4.3700 |
[2023-09-03 04:58:32,103::train::INFO] [train] Iter 18243 | loss 0.7251 | loss(rot) 0.6217 | loss(pos) 0.1034 | loss(seq) 0.0000 | grad 14.0332 | lr 0.0010 | time_forward 1.2130 | time_backward 1.4310 |
[2023-09-03 04:58:39,918::train::INFO] [train] Iter 18244 | loss 0.8140 | loss(rot) 0.3951 | loss(pos) 0.2428 | loss(seq) 0.1761 | grad 3.9470 | lr 0.0010 | time_forward 3.3270 | time_backward 4.4840 |
[2023-09-03 04:58:47,398::train::INFO] [train] Iter 18245 | loss 0.8329 | loss(rot) 0.1679 | loss(pos) 0.2699 | loss(seq) 0.3951 | grad 4.8109 | lr 0.0010 | time_forward 3.0150 | time_backward 4.4610 |
[2023-09-03 04:58:49,758::train::INFO] [train] Iter 18246 | loss 1.1710 | loss(rot) 0.5050 | loss(pos) 0.3136 | loss(seq) 0.3524 | grad 3.1867 | lr 0.0010 | time_forward 1.0910 | time_backward 1.2660 |
[2023-09-03 04:58:58,463::train::INFO] [train] Iter 18247 | loss 1.0950 | loss(rot) 0.8894 | loss(pos) 0.2053 | loss(seq) 0.0003 | grad 7.8623 | lr 0.0010 | time_forward 3.5170 | time_backward 5.1730 |
[2023-09-03 04:59:06,782::train::INFO] [train] Iter 18248 | loss 1.4885 | loss(rot) 0.8539 | loss(pos) 0.2489 | loss(seq) 0.3857 | grad 5.4071 | lr 0.0010 | time_forward 3.5250 | time_backward 4.7900 |
[2023-09-03 04:59:13,437::train::INFO] [train] Iter 18249 | loss 0.5730 | loss(rot) 0.1610 | loss(pos) 0.3571 | loss(seq) 0.0550 | grad 3.1948 | lr 0.0010 | time_forward 2.8210 | time_backward 3.8300 |
[2023-09-03 04:59:16,018::train::INFO] [train] Iter 18250 | loss 1.5705 | loss(rot) 0.9605 | loss(pos) 0.0871 | loss(seq) 0.5230 | grad 4.3172 | lr 0.0010 | time_forward 1.1620 | time_backward 1.4150 |
[2023-09-03 04:59:23,432::train::INFO] [train] Iter 18251 | loss 1.0428 | loss(rot) 0.8350 | loss(pos) 0.1711 | loss(seq) 0.0367 | grad 10.1466 | lr 0.0010 | time_forward 3.1270 | time_backward 4.2750 |
[2023-09-03 04:59:26,017::train::INFO] [train] Iter 18252 | loss 1.6615 | loss(rot) 1.2491 | loss(pos) 0.1357 | loss(seq) 0.2768 | grad 9.3096 | lr 0.0010 | time_forward 1.1730 | time_backward 1.4100 |
[2023-09-03 04:59:34,186::train::INFO] [train] Iter 18253 | loss 1.2809 | loss(rot) 0.4083 | loss(pos) 0.3500 | loss(seq) 0.5226 | grad 3.8334 | lr 0.0010 | time_forward 3.4880 | time_backward 4.6770 |
[2023-09-03 04:59:42,587::train::INFO] [train] Iter 18254 | loss 0.5488 | loss(rot) 0.1021 | loss(pos) 0.4261 | loss(seq) 0.0205 | grad 3.4646 | lr 0.0010 | time_forward 3.3500 | time_backward 5.0480 |
[2023-09-03 04:59:45,226::train::INFO] [train] Iter 18255 | loss 0.8033 | loss(rot) 0.1934 | loss(pos) 0.2690 | loss(seq) 0.3409 | grad 3.3495 | lr 0.0010 | time_forward 1.2090 | time_backward 1.4190 |
[2023-09-03 04:59:53,766::train::INFO] [train] Iter 18256 | loss 2.1101 | loss(rot) 1.9303 | loss(pos) 0.1754 | loss(seq) 0.0045 | grad 5.2364 | lr 0.0010 | time_forward 3.3180 | time_backward 5.2190 |
[2023-09-03 04:59:55,934::train::INFO] [train] Iter 18257 | loss 1.2922 | loss(rot) 0.6724 | loss(pos) 0.1562 | loss(seq) 0.4636 | grad 3.5823 | lr 0.0010 | time_forward 1.0060 | time_backward 1.1580 |
[2023-09-03 05:00:04,212::train::INFO] [train] Iter 18258 | loss 2.3333 | loss(rot) 2.1385 | loss(pos) 0.1508 | loss(seq) 0.0441 | grad 4.7471 | lr 0.0010 | time_forward 3.5700 | time_backward 4.7050 |
[2023-09-03 05:00:12,504::train::INFO] [train] Iter 18259 | loss 3.5172 | loss(rot) 0.0244 | loss(pos) 3.4927 | loss(seq) 0.0000 | grad 7.6225 | lr 0.0010 | time_forward 3.4180 | time_backward 4.8700 |
[2023-09-03 05:00:15,057::train::INFO] [train] Iter 18260 | loss 0.6513 | loss(rot) 0.0656 | loss(pos) 0.4265 | loss(seq) 0.1592 | grad 2.8656 | lr 0.0010 | time_forward 1.1850 | time_backward 1.3540 |
[2023-09-03 05:00:22,558::train::INFO] [train] Iter 18261 | loss 0.9716 | loss(rot) 0.7704 | loss(pos) 0.2012 | loss(seq) 0.0000 | grad 7.7385 | lr 0.0010 | time_forward 3.1910 | time_backward 4.2910 |
[2023-09-03 05:00:30,047::train::INFO] [train] Iter 18262 | loss 0.7557 | loss(rot) 0.1559 | loss(pos) 0.1620 | loss(seq) 0.4378 | grad 2.3624 | lr 0.0010 | time_forward 3.2350 | time_backward 4.2500 |
[2023-09-03 05:00:38,720::train::INFO] [train] Iter 18263 | loss 1.6677 | loss(rot) 1.3493 | loss(pos) 0.1729 | loss(seq) 0.1454 | grad 6.2002 | lr 0.0010 | time_forward 3.4990 | time_backward 5.1710 |
[2023-09-03 05:00:46,614::train::INFO] [train] Iter 18264 | loss 0.6575 | loss(rot) 0.1755 | loss(pos) 0.4110 | loss(seq) 0.0710 | grad 3.4083 | lr 0.0010 | time_forward 3.3700 | time_backward 4.5220 |
[2023-09-03 05:00:53,866::train::INFO] [train] Iter 18265 | loss 1.5044 | loss(rot) 0.7723 | loss(pos) 0.3102 | loss(seq) 0.4219 | grad 4.1559 | lr 0.0010 | time_forward 3.1100 | time_backward 4.1380 |
[2023-09-03 05:01:01,211::train::INFO] [train] Iter 18266 | loss 1.1867 | loss(rot) 0.3816 | loss(pos) 0.5148 | loss(seq) 0.2903 | grad 3.8267 | lr 0.0010 | time_forward 3.1370 | time_backward 4.2050 |
[2023-09-03 05:01:09,646::train::INFO] [train] Iter 18267 | loss 1.4168 | loss(rot) 0.6776 | loss(pos) 0.2089 | loss(seq) 0.5303 | grad 2.9537 | lr 0.0010 | time_forward 3.5120 | time_backward 4.9200 |
[2023-09-03 05:01:12,232::train::INFO] [train] Iter 18268 | loss 1.6515 | loss(rot) 1.4721 | loss(pos) 0.1790 | loss(seq) 0.0003 | grad 11.1281 | lr 0.0010 | time_forward 1.2100 | time_backward 1.3720 |
[2023-09-03 05:01:14,876::train::INFO] [train] Iter 18269 | loss 0.8225 | loss(rot) 0.2864 | loss(pos) 0.4374 | loss(seq) 0.0987 | grad 3.1500 | lr 0.0010 | time_forward 1.2420 | time_backward 1.3980 |
[2023-09-03 05:01:22,372::train::INFO] [train] Iter 18270 | loss 1.4894 | loss(rot) 0.6898 | loss(pos) 0.1742 | loss(seq) 0.6254 | grad 4.3488 | lr 0.0010 | time_forward 3.0910 | time_backward 4.4020 |
[2023-09-03 05:01:24,962::train::INFO] [train] Iter 18271 | loss 1.7208 | loss(rot) 0.9223 | loss(pos) 0.5158 | loss(seq) 0.2827 | grad 4.9271 | lr 0.0010 | time_forward 1.1730 | time_backward 1.4130 |
[2023-09-03 05:01:32,742::train::INFO] [train] Iter 18272 | loss 0.8452 | loss(rot) 0.5717 | loss(pos) 0.1835 | loss(seq) 0.0900 | grad 5.4703 | lr 0.0010 | time_forward 3.1600 | time_backward 4.6180 |
[2023-09-03 05:01:35,056::train::INFO] [train] Iter 18273 | loss 1.8519 | loss(rot) 1.1680 | loss(pos) 0.1604 | loss(seq) 0.5234 | grad 3.6896 | lr 0.0010 | time_forward 1.0850 | time_backward 1.2250 |
[2023-09-03 05:01:43,359::train::INFO] [train] Iter 18274 | loss 1.3353 | loss(rot) 0.5291 | loss(pos) 0.3509 | loss(seq) 0.4552 | grad 3.7269 | lr 0.0010 | time_forward 3.2490 | time_backward 5.0370 |
[2023-09-03 05:01:48,799::train::INFO] [train] Iter 18275 | loss 1.0404 | loss(rot) 0.0951 | loss(pos) 0.9418 | loss(seq) 0.0035 | grad 3.7578 | lr 0.0010 | time_forward 2.2610 | time_backward 3.1770 |
[2023-09-03 05:01:51,361::train::INFO] [train] Iter 18276 | loss 1.1079 | loss(rot) 0.4070 | loss(pos) 0.6675 | loss(seq) 0.0335 | grad 5.7026 | lr 0.0010 | time_forward 1.1700 | time_backward 1.3890 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.