text stringlengths 56 1.16k |
|---|
[2023-10-23 07:41:35,063::train::INFO] [train] Iter 568194 | loss 0.8470 | loss(rot) 0.4059 | loss(pos) 0.0343 | loss(seq) 0.4068 | grad 3.0469 | lr 0.0000 | time_forward 3.0870 | time_backward 3.9560 |
[2023-10-23 07:41:41,231::train::INFO] [train] Iter 568195 | loss 0.1490 | loss(rot) 0.1212 | loss(pos) 0.0136 | loss(seq) 0.0141 | grad 2.7071 | lr 0.0000 | time_forward 2.6670 | time_backward 3.4970 |
[2023-10-23 07:41:48,072::train::INFO] [train] Iter 568196 | loss 1.0200 | loss(rot) 0.6477 | loss(pos) 0.0596 | loss(seq) 0.3127 | grad 2.8042 | lr 0.0000 | time_forward 2.9660 | time_backward 3.8720 |
[2023-10-23 07:41:54,842::train::INFO] [train] Iter 568197 | loss 0.1232 | loss(rot) 0.1059 | loss(pos) 0.0137 | loss(seq) 0.0036 | grad 2.1054 | lr 0.0000 | time_forward 2.9070 | time_backward 3.8590 |
[2023-10-23 07:42:02,805::train::INFO] [train] Iter 568198 | loss 0.3587 | loss(rot) 0.3327 | loss(pos) 0.0256 | loss(seq) 0.0004 | grad 4.5403 | lr 0.0000 | time_forward 3.2920 | time_backward 4.6680 |
[2023-10-23 07:42:10,259::train::INFO] [train] Iter 568199 | loss 0.6866 | loss(rot) 0.4763 | loss(pos) 0.1427 | loss(seq) 0.0676 | grad 3.6118 | lr 0.0000 | time_forward 3.3980 | time_backward 4.0520 |
[2023-10-23 07:42:12,927::train::INFO] [train] Iter 568200 | loss 0.4154 | loss(rot) 0.1407 | loss(pos) 0.1125 | loss(seq) 0.1622 | grad 4.2521 | lr 0.0000 | time_forward 1.2490 | time_backward 1.4160 |
[2023-10-23 07:42:17,936::train::INFO] [train] Iter 568201 | loss 0.3799 | loss(rot) 0.0808 | loss(pos) 0.0129 | loss(seq) 0.2862 | grad 2.0973 | lr 0.0000 | time_forward 2.1950 | time_backward 2.8110 |
[2023-10-23 07:42:24,164::train::INFO] [train] Iter 568202 | loss 1.1953 | loss(rot) 1.1391 | loss(pos) 0.0274 | loss(seq) 0.0288 | grad 48.5145 | lr 0.0000 | time_forward 2.6640 | time_backward 3.5490 |
[2023-10-23 07:42:30,385::train::INFO] [train] Iter 568203 | loss 2.0030 | loss(rot) 1.4651 | loss(pos) 0.0800 | loss(seq) 0.4579 | grad 5.4802 | lr 0.0000 | time_forward 2.7280 | time_backward 3.4910 |
[2023-10-23 07:42:33,403::train::INFO] [train] Iter 568204 | loss 1.3971 | loss(rot) 0.9674 | loss(pos) 0.0747 | loss(seq) 0.3551 | grad 4.0728 | lr 0.0000 | time_forward 1.3790 | time_backward 1.6350 |
[2023-10-23 07:42:41,228::train::INFO] [train] Iter 568205 | loss 0.6498 | loss(rot) 0.2272 | loss(pos) 0.1681 | loss(seq) 0.2546 | grad 2.8768 | lr 0.0000 | time_forward 3.3640 | time_backward 4.4500 |
[2023-10-23 07:42:48,045::train::INFO] [train] Iter 568206 | loss 1.7795 | loss(rot) 1.2946 | loss(pos) 0.0715 | loss(seq) 0.4134 | grad 15.5554 | lr 0.0000 | time_forward 3.0100 | time_backward 3.8050 |
[2023-10-23 07:42:55,728::train::INFO] [train] Iter 568207 | loss 0.9364 | loss(rot) 0.3343 | loss(pos) 0.2772 | loss(seq) 0.3248 | grad 6.0691 | lr 0.0000 | time_forward 3.3280 | time_backward 4.3520 |
[2023-10-23 07:43:02,349::train::INFO] [train] Iter 568208 | loss 1.1755 | loss(rot) 1.1628 | loss(pos) 0.0116 | loss(seq) 0.0011 | grad 6.0892 | lr 0.0000 | time_forward 2.8340 | time_backward 3.7830 |
[2023-10-23 07:43:09,103::train::INFO] [train] Iter 568209 | loss 0.7470 | loss(rot) 0.0178 | loss(pos) 0.7251 | loss(seq) 0.0041 | grad 6.9234 | lr 0.0000 | time_forward 2.9350 | time_backward 3.8150 |
[2023-10-23 07:43:17,129::train::INFO] [train] Iter 568210 | loss 0.1016 | loss(rot) 0.0563 | loss(pos) 0.0420 | loss(seq) 0.0033 | grad 1.9828 | lr 0.0000 | time_forward 3.2190 | time_backward 4.8040 |
[2023-10-23 07:43:24,956::train::INFO] [train] Iter 568211 | loss 0.8032 | loss(rot) 0.0442 | loss(pos) 0.4662 | loss(seq) 0.2927 | grad 7.3159 | lr 0.0000 | time_forward 3.2420 | time_backward 4.5830 |
[2023-10-23 07:43:28,007::train::INFO] [train] Iter 568212 | loss 0.7025 | loss(rot) 0.6652 | loss(pos) 0.0288 | loss(seq) 0.0086 | grad 5.6447 | lr 0.0000 | time_forward 1.3610 | time_backward 1.6870 |
[2023-10-23 07:43:30,718::train::INFO] [train] Iter 568213 | loss 0.6620 | loss(rot) 0.2661 | loss(pos) 0.2055 | loss(seq) 0.1904 | grad 3.6438 | lr 0.0000 | time_forward 1.2400 | time_backward 1.4570 |
[2023-10-23 07:43:38,483::train::INFO] [train] Iter 568214 | loss 0.3130 | loss(rot) 0.1029 | loss(pos) 0.1592 | loss(seq) 0.0508 | grad 6.1491 | lr 0.0000 | time_forward 3.2070 | time_backward 4.5550 |
[2023-10-23 07:43:46,275::train::INFO] [train] Iter 568215 | loss 0.2600 | loss(rot) 0.2202 | loss(pos) 0.0398 | loss(seq) 0.0000 | grad 2.5763 | lr 0.0000 | time_forward 3.2020 | time_backward 4.5860 |
[2023-10-23 07:43:54,174::train::INFO] [train] Iter 568216 | loss 2.1363 | loss(rot) 1.6170 | loss(pos) 0.1222 | loss(seq) 0.3970 | grad 3.8969 | lr 0.0000 | time_forward 3.3710 | time_backward 4.5260 |
[2023-10-23 07:44:02,421::train::INFO] [train] Iter 568217 | loss 0.7676 | loss(rot) 0.5002 | loss(pos) 0.0274 | loss(seq) 0.2400 | grad 3.1675 | lr 0.0000 | time_forward 3.3810 | time_backward 4.8620 |
[2023-10-23 07:44:09,928::train::INFO] [train] Iter 568218 | loss 0.2064 | loss(rot) 0.0597 | loss(pos) 0.1381 | loss(seq) 0.0086 | grad 2.2748 | lr 0.0000 | time_forward 3.2750 | time_backward 4.2290 |
[2023-10-23 07:44:17,320::train::INFO] [train] Iter 568219 | loss 0.3919 | loss(rot) 0.0280 | loss(pos) 0.3590 | loss(seq) 0.0049 | grad 5.4527 | lr 0.0000 | time_forward 3.2030 | time_backward 4.1860 |
[2023-10-23 07:44:24,282::train::INFO] [train] Iter 568220 | loss 0.2189 | loss(rot) 0.1804 | loss(pos) 0.0247 | loss(seq) 0.0138 | grad 3.8451 | lr 0.0000 | time_forward 3.0870 | time_backward 3.8710 |
[2023-10-23 07:44:31,420::train::INFO] [train] Iter 568221 | loss 0.7758 | loss(rot) 0.5204 | loss(pos) 0.0231 | loss(seq) 0.2323 | grad 2.6437 | lr 0.0000 | time_forward 3.2040 | time_backward 3.9310 |
[2023-10-23 07:44:38,338::train::INFO] [train] Iter 568222 | loss 0.7372 | loss(rot) 0.6948 | loss(pos) 0.0197 | loss(seq) 0.0226 | grad 2.6599 | lr 0.0000 | time_forward 3.0730 | time_backward 3.8420 |
[2023-10-23 07:44:41,012::train::INFO] [train] Iter 568223 | loss 0.8019 | loss(rot) 0.7093 | loss(pos) 0.0306 | loss(seq) 0.0620 | grad 14.0855 | lr 0.0000 | time_forward 1.2830 | time_backward 1.3880 |
[2023-10-23 07:44:48,473::train::INFO] [train] Iter 568224 | loss 0.9892 | loss(rot) 0.7157 | loss(pos) 0.0216 | loss(seq) 0.2519 | grad 3.6076 | lr 0.0000 | time_forward 3.2630 | time_backward 4.1950 |
[2023-10-23 07:44:56,617::train::INFO] [train] Iter 568225 | loss 0.6985 | loss(rot) 0.5534 | loss(pos) 0.0404 | loss(seq) 0.1047 | grad 3.4882 | lr 0.0000 | time_forward 3.3660 | time_backward 4.7740 |
[2023-10-23 07:45:01,518::train::INFO] [train] Iter 568226 | loss 1.5878 | loss(rot) 1.2192 | loss(pos) 0.0685 | loss(seq) 0.3001 | grad 4.2416 | lr 0.0000 | time_forward 2.1290 | time_backward 2.7700 |
[2023-10-23 07:45:09,448::train::INFO] [train] Iter 568227 | loss 0.3759 | loss(rot) 0.1009 | loss(pos) 0.0361 | loss(seq) 0.2389 | grad 2.3440 | lr 0.0000 | time_forward 3.2990 | time_backward 4.6270 |
[2023-10-23 07:45:16,213::train::INFO] [train] Iter 568228 | loss 0.8175 | loss(rot) 0.0475 | loss(pos) 0.6232 | loss(seq) 0.1468 | grad 5.2626 | lr 0.0000 | time_forward 2.9170 | time_backward 3.8450 |
[2023-10-23 07:45:23,628::train::INFO] [train] Iter 568229 | loss 0.4549 | loss(rot) 0.1188 | loss(pos) 0.0328 | loss(seq) 0.3033 | grad 3.3021 | lr 0.0000 | time_forward 3.2370 | time_backward 4.1750 |
[2023-10-23 07:45:30,467::train::INFO] [train] Iter 568230 | loss 2.0806 | loss(rot) 0.0078 | loss(pos) 2.0723 | loss(seq) 0.0005 | grad 18.7516 | lr 0.0000 | time_forward 2.9540 | time_backward 3.8810 |
[2023-10-23 07:45:33,087::train::INFO] [train] Iter 568231 | loss 0.5191 | loss(rot) 0.1181 | loss(pos) 0.0356 | loss(seq) 0.3654 | grad 2.3132 | lr 0.0000 | time_forward 1.2400 | time_backward 1.3770 |
[2023-10-23 07:45:35,803::train::INFO] [train] Iter 568232 | loss 1.2763 | loss(rot) 1.0446 | loss(pos) 0.0303 | loss(seq) 0.2014 | grad 41.8540 | lr 0.0000 | time_forward 1.3180 | time_backward 1.3940 |
[2023-10-23 07:45:38,482::train::INFO] [train] Iter 568233 | loss 0.1777 | loss(rot) 0.1509 | loss(pos) 0.0242 | loss(seq) 0.0025 | grad 3.2000 | lr 0.0000 | time_forward 1.2820 | time_backward 1.3950 |
[2023-10-23 07:45:46,743::train::INFO] [train] Iter 568234 | loss 0.4691 | loss(rot) 0.1183 | loss(pos) 0.3473 | loss(seq) 0.0035 | grad 5.2315 | lr 0.0000 | time_forward 3.4770 | time_backward 4.7790 |
[2023-10-23 07:45:53,947::train::INFO] [train] Iter 568235 | loss 0.7751 | loss(rot) 0.1751 | loss(pos) 0.1331 | loss(seq) 0.4669 | grad 4.5286 | lr 0.0000 | time_forward 3.1560 | time_backward 4.0440 |
[2023-10-23 07:45:57,045::train::INFO] [train] Iter 568236 | loss 1.0396 | loss(rot) 0.9845 | loss(pos) 0.0221 | loss(seq) 0.0330 | grad 4.0349 | lr 0.0000 | time_forward 1.4260 | time_backward 1.6690 |
[2023-10-23 07:46:04,746::train::INFO] [train] Iter 568237 | loss 1.4010 | loss(rot) 0.9902 | loss(pos) 0.0881 | loss(seq) 0.3227 | grad 14.1356 | lr 0.0000 | time_forward 3.4150 | time_backward 4.2750 |
[2023-10-23 07:46:12,856::train::INFO] [train] Iter 568238 | loss 1.4608 | loss(rot) 1.3721 | loss(pos) 0.0887 | loss(seq) 0.0000 | grad 8.2229 | lr 0.0000 | time_forward 3.3660 | time_backward 4.7410 |
[2023-10-23 07:46:19,513::train::INFO] [train] Iter 568239 | loss 0.6492 | loss(rot) 0.3250 | loss(pos) 0.0651 | loss(seq) 0.2591 | grad 3.5183 | lr 0.0000 | time_forward 2.8400 | time_backward 3.8130 |
[2023-10-23 07:46:26,772::train::INFO] [train] Iter 568240 | loss 0.4328 | loss(rot) 0.1586 | loss(pos) 0.0292 | loss(seq) 0.2451 | grad 3.2574 | lr 0.0000 | time_forward 3.1690 | time_backward 4.0860 |
[2023-10-23 07:46:34,942::train::INFO] [train] Iter 568241 | loss 0.9981 | loss(rot) 0.4574 | loss(pos) 0.3864 | loss(seq) 0.1543 | grad 4.2687 | lr 0.0000 | time_forward 3.3780 | time_backward 4.7880 |
[2023-10-23 07:46:41,832::train::INFO] [train] Iter 568242 | loss 0.4186 | loss(rot) 0.0649 | loss(pos) 0.0147 | loss(seq) 0.3390 | grad 1.8497 | lr 0.0000 | time_forward 2.9610 | time_backward 3.9250 |
[2023-10-23 07:46:49,911::train::INFO] [train] Iter 568243 | loss 0.0899 | loss(rot) 0.0668 | loss(pos) 0.0213 | loss(seq) 0.0018 | grad 0.8896 | lr 0.0000 | time_forward 3.3580 | time_backward 4.7170 |
[2023-10-23 07:46:57,536::train::INFO] [train] Iter 568244 | loss 0.4475 | loss(rot) 0.0648 | loss(pos) 0.3238 | loss(seq) 0.0589 | grad 4.7233 | lr 0.0000 | time_forward 3.2460 | time_backward 4.3760 |
[2023-10-23 07:47:04,251::train::INFO] [train] Iter 568245 | loss 2.0368 | loss(rot) 1.2169 | loss(pos) 0.2303 | loss(seq) 0.5897 | grad 7.4810 | lr 0.0000 | time_forward 2.9010 | time_backward 3.8120 |
[2023-10-23 07:47:11,878::train::INFO] [train] Iter 568246 | loss 0.8641 | loss(rot) 0.0667 | loss(pos) 0.1848 | loss(seq) 0.6126 | grad 7.1498 | lr 0.0000 | time_forward 3.2820 | time_backward 4.3410 |
[2023-10-23 07:47:18,928::train::INFO] [train] Iter 568247 | loss 0.3657 | loss(rot) 0.1697 | loss(pos) 0.0142 | loss(seq) 0.1818 | grad 2.2923 | lr 0.0000 | time_forward 3.0420 | time_backward 4.0060 |
[2023-10-23 07:47:27,007::train::INFO] [train] Iter 568248 | loss 0.2904 | loss(rot) 0.0801 | loss(pos) 0.1902 | loss(seq) 0.0201 | grad 2.0860 | lr 0.0000 | time_forward 3.4920 | time_backward 4.5840 |
[2023-10-23 07:47:34,487::train::INFO] [train] Iter 568249 | loss 0.3275 | loss(rot) 0.2942 | loss(pos) 0.0291 | loss(seq) 0.0041 | grad 4.9185 | lr 0.0000 | time_forward 3.2440 | time_backward 4.2320 |
[2023-10-23 07:47:37,175::train::INFO] [train] Iter 568250 | loss 1.2127 | loss(rot) 0.7200 | loss(pos) 0.0871 | loss(seq) 0.4056 | grad 4.9763 | lr 0.0000 | time_forward 1.2820 | time_backward 1.4030 |
[2023-10-23 07:47:43,778::train::INFO] [train] Iter 568251 | loss 1.5851 | loss(rot) 1.4616 | loss(pos) 0.0411 | loss(seq) 0.0823 | grad 3.6706 | lr 0.0000 | time_forward 2.8500 | time_backward 3.7500 |
[2023-10-23 07:47:51,681::train::INFO] [train] Iter 568252 | loss 0.4107 | loss(rot) 0.1327 | loss(pos) 0.0719 | loss(seq) 0.2061 | grad 2.6033 | lr 0.0000 | time_forward 3.4220 | time_backward 4.4780 |
[2023-10-23 07:47:54,312::train::INFO] [train] Iter 568253 | loss 1.0480 | loss(rot) 0.6131 | loss(pos) 0.0997 | loss(seq) 0.3352 | grad 5.0166 | lr 0.0000 | time_forward 1.2130 | time_backward 1.4150 |
[2023-10-23 07:47:57,053::train::INFO] [train] Iter 568254 | loss 0.3393 | loss(rot) 0.0726 | loss(pos) 0.1446 | loss(seq) 0.1221 | grad 3.8302 | lr 0.0000 | time_forward 1.3060 | time_backward 1.4310 |
[2023-10-23 07:48:03,680::train::INFO] [train] Iter 568255 | loss 0.3354 | loss(rot) 0.1024 | loss(pos) 0.0396 | loss(seq) 0.1934 | grad 3.0454 | lr 0.0000 | time_forward 2.8570 | time_backward 3.7670 |
[2023-10-23 07:48:06,350::train::INFO] [train] Iter 568256 | loss 0.4209 | loss(rot) 0.0634 | loss(pos) 0.0167 | loss(seq) 0.3409 | grad 1.6419 | lr 0.0000 | time_forward 1.2630 | time_backward 1.4040 |
[2023-10-23 07:48:12,968::train::INFO] [train] Iter 568257 | loss 0.0902 | loss(rot) 0.0682 | loss(pos) 0.0191 | loss(seq) 0.0029 | grad 2.0866 | lr 0.0000 | time_forward 2.8440 | time_backward 3.7700 |
[2023-10-23 07:48:19,936::train::INFO] [train] Iter 568258 | loss 0.4069 | loss(rot) 0.0845 | loss(pos) 0.3034 | loss(seq) 0.0191 | grad 4.3698 | lr 0.0000 | time_forward 2.9230 | time_backward 4.0430 |
[2023-10-23 07:48:27,953::train::INFO] [train] Iter 568259 | loss 0.9827 | loss(rot) 0.8223 | loss(pos) 0.0587 | loss(seq) 0.1017 | grad 3.2888 | lr 0.0000 | time_forward 3.4830 | time_backward 4.5310 |
[2023-10-23 07:48:36,447::train::INFO] [train] Iter 568260 | loss 1.6103 | loss(rot) 1.3275 | loss(pos) 0.0827 | loss(seq) 0.2000 | grad 3.5147 | lr 0.0000 | time_forward 3.8970 | time_backward 4.5940 |
[2023-10-23 07:48:38,747::train::INFO] [train] Iter 568261 | loss 1.1062 | loss(rot) 1.0536 | loss(pos) 0.0431 | loss(seq) 0.0095 | grad 9.2490 | lr 0.0000 | time_forward 1.0290 | time_backward 1.2670 |
[2023-10-23 07:48:45,597::train::INFO] [train] Iter 568262 | loss 0.3407 | loss(rot) 0.1762 | loss(pos) 0.0285 | loss(seq) 0.1360 | grad 3.1146 | lr 0.0000 | time_forward 2.9510 | time_backward 3.8950 |
[2023-10-23 07:48:53,814::train::INFO] [train] Iter 568263 | loss 0.8385 | loss(rot) 0.0118 | loss(pos) 0.8259 | loss(seq) 0.0007 | grad 10.9004 | lr 0.0000 | time_forward 3.3480 | time_backward 4.8650 |
[2023-10-23 07:49:01,067::train::INFO] [train] Iter 568264 | loss 0.3086 | loss(rot) 0.1385 | loss(pos) 0.1298 | loss(seq) 0.0403 | grad 3.2012 | lr 0.0000 | time_forward 3.1530 | time_backward 4.0960 |
[2023-10-23 07:49:09,294::train::INFO] [train] Iter 568265 | loss 0.3377 | loss(rot) 0.1403 | loss(pos) 0.0124 | loss(seq) 0.1850 | grad 2.8805 | lr 0.0000 | time_forward 3.3910 | time_backward 4.8330 |
[2023-10-23 07:49:11,968::train::INFO] [train] Iter 568266 | loss 0.3877 | loss(rot) 0.1532 | loss(pos) 0.0236 | loss(seq) 0.2108 | grad 2.8221 | lr 0.0000 | time_forward 1.2760 | time_backward 1.3960 |
[2023-10-23 07:49:19,559::train::INFO] [train] Iter 568267 | loss 1.5056 | loss(rot) 1.4858 | loss(pos) 0.0181 | loss(seq) 0.0017 | grad 70.4530 | lr 0.0000 | time_forward 3.2260 | time_backward 4.3600 |
[2023-10-23 07:49:26,973::train::INFO] [train] Iter 568268 | loss 1.4262 | loss(rot) 1.2766 | loss(pos) 0.0516 | loss(seq) 0.0981 | grad 3.3915 | lr 0.0000 | time_forward 3.3400 | time_backward 4.0700 |
[2023-10-23 07:49:29,607::train::INFO] [train] Iter 568269 | loss 1.4552 | loss(rot) 0.9053 | loss(pos) 0.0968 | loss(seq) 0.4531 | grad 4.1143 | lr 0.0000 | time_forward 1.2550 | time_backward 1.3770 |
[2023-10-23 07:49:36,498::train::INFO] [train] Iter 568270 | loss 1.4817 | loss(rot) 0.9205 | loss(pos) 0.1467 | loss(seq) 0.4146 | grad 6.2220 | lr 0.0000 | time_forward 3.0980 | time_backward 3.7900 |
[2023-10-23 07:49:43,399::train::INFO] [train] Iter 568271 | loss 0.4943 | loss(rot) 0.1760 | loss(pos) 0.0486 | loss(seq) 0.2697 | grad 3.4376 | lr 0.0000 | time_forward 2.9710 | time_backward 3.9260 |
[2023-10-23 07:49:45,810::train::INFO] [train] Iter 568272 | loss 0.2115 | loss(rot) 0.1521 | loss(pos) 0.0255 | loss(seq) 0.0340 | grad 2.3065 | lr 0.0000 | time_forward 1.1740 | time_backward 1.2330 |
[2023-10-23 07:49:53,991::train::INFO] [train] Iter 568273 | loss 1.2043 | loss(rot) 1.1489 | loss(pos) 0.0318 | loss(seq) 0.0235 | grad 3.5784 | lr 0.0000 | time_forward 3.4210 | time_backward 4.7580 |
[2023-10-23 07:50:02,077::train::INFO] [train] Iter 568274 | loss 0.3731 | loss(rot) 0.1483 | loss(pos) 0.2034 | loss(seq) 0.0214 | grad 2.9389 | lr 0.0000 | time_forward 3.3550 | time_backward 4.7280 |
[2023-10-23 07:50:09,064::train::INFO] [train] Iter 568275 | loss 1.3434 | loss(rot) 0.5818 | loss(pos) 0.6043 | loss(seq) 0.1573 | grad 4.0546 | lr 0.0000 | time_forward 2.9680 | time_backward 4.0150 |
[2023-10-23 07:50:16,249::train::INFO] [train] Iter 568276 | loss 0.1320 | loss(rot) 0.0515 | loss(pos) 0.0529 | loss(seq) 0.0277 | grad 3.6322 | lr 0.0000 | time_forward 3.1430 | time_backward 4.0380 |
[2023-10-23 07:50:19,009::train::INFO] [train] Iter 568277 | loss 0.1907 | loss(rot) 0.0915 | loss(pos) 0.0209 | loss(seq) 0.0783 | grad 1.6720 | lr 0.0000 | time_forward 1.2400 | time_backward 1.5160 |
[2023-10-23 07:50:27,086::train::INFO] [train] Iter 568278 | loss 0.8834 | loss(rot) 0.4807 | loss(pos) 0.0693 | loss(seq) 0.3333 | grad 4.1455 | lr 0.0000 | time_forward 3.3130 | time_backward 4.7600 |
[2023-10-23 07:50:33,989::train::INFO] [train] Iter 568279 | loss 0.5814 | loss(rot) 0.2907 | loss(pos) 0.1294 | loss(seq) 0.1613 | grad 4.1761 | lr 0.0000 | time_forward 2.9570 | time_backward 3.9420 |
[2023-10-23 07:50:41,411::train::INFO] [train] Iter 568280 | loss 1.1485 | loss(rot) 0.6345 | loss(pos) 0.0701 | loss(seq) 0.4439 | grad 5.1180 | lr 0.0000 | time_forward 3.2160 | time_backward 4.2020 |
[2023-10-23 07:50:43,861::train::INFO] [train] Iter 568281 | loss 1.7797 | loss(rot) 1.2835 | loss(pos) 0.1094 | loss(seq) 0.3868 | grad 8.2969 | lr 0.0000 | time_forward 1.1370 | time_backward 1.3090 |
[2023-10-23 07:50:46,476::train::INFO] [train] Iter 568282 | loss 0.4630 | loss(rot) 0.1095 | loss(pos) 0.0532 | loss(seq) 0.3002 | grad 2.7552 | lr 0.0000 | time_forward 1.2260 | time_backward 1.3860 |
[2023-10-23 07:50:53,643::train::INFO] [train] Iter 568283 | loss 0.4874 | loss(rot) 0.1457 | loss(pos) 0.1857 | loss(seq) 0.1560 | grad 4.3479 | lr 0.0000 | time_forward 3.0830 | time_backward 4.0800 |
[2023-10-23 07:50:59,264::train::INFO] [train] Iter 568284 | loss 0.4650 | loss(rot) 0.0504 | loss(pos) 0.4016 | loss(seq) 0.0130 | grad 9.0534 | lr 0.0000 | time_forward 2.4720 | time_backward 3.1450 |
[2023-10-23 07:51:07,287::train::INFO] [train] Iter 568285 | loss 1.7997 | loss(rot) 1.7260 | loss(pos) 0.0468 | loss(seq) 0.0268 | grad 3.7400 | lr 0.0000 | time_forward 3.3500 | time_backward 4.6700 |
[2023-10-23 07:51:15,364::train::INFO] [train] Iter 568286 | loss 0.2571 | loss(rot) 0.1404 | loss(pos) 0.0260 | loss(seq) 0.0907 | grad 1.7800 | lr 0.0000 | time_forward 3.2810 | time_backward 4.7920 |
[2023-10-23 07:51:17,632::train::INFO] [train] Iter 568287 | loss 0.6018 | loss(rot) 0.3822 | loss(pos) 0.0500 | loss(seq) 0.1695 | grad 2.2858 | lr 0.0000 | time_forward 1.0440 | time_backward 1.2200 |
[2023-10-23 07:51:25,684::train::INFO] [train] Iter 568288 | loss 2.9580 | loss(rot) 0.0228 | loss(pos) 2.9349 | loss(seq) 0.0002 | grad 19.5181 | lr 0.0000 | time_forward 3.3130 | time_backward 4.7350 |
[2023-10-23 07:51:33,700::train::INFO] [train] Iter 568289 | loss 0.8685 | loss(rot) 0.3603 | loss(pos) 0.3723 | loss(seq) 0.1359 | grad 3.3952 | lr 0.0000 | time_forward 3.3410 | time_backward 4.6710 |
[2023-10-23 07:51:36,440::train::INFO] [train] Iter 568290 | loss 0.7524 | loss(rot) 0.2744 | loss(pos) 0.0402 | loss(seq) 0.4378 | grad 2.7820 | lr 0.0000 | time_forward 1.3080 | time_backward 1.4280 |
[2023-10-23 07:51:43,632::train::INFO] [train] Iter 568291 | loss 1.0941 | loss(rot) 0.6604 | loss(pos) 0.0542 | loss(seq) 0.3795 | grad 10.0637 | lr 0.0000 | time_forward 3.1120 | time_backward 4.0770 |
[2023-10-23 07:51:46,353::train::INFO] [train] Iter 568292 | loss 0.6094 | loss(rot) 0.2231 | loss(pos) 0.2021 | loss(seq) 0.1842 | grad 3.6988 | lr 0.0000 | time_forward 1.2860 | time_backward 1.4330 |
[2023-10-23 07:51:52,295::train::INFO] [train] Iter 568293 | loss 0.8870 | loss(rot) 0.5540 | loss(pos) 0.0770 | loss(seq) 0.2560 | grad 6.9137 | lr 0.0000 | time_forward 2.5880 | time_backward 3.3500 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.